Language acquisition – Part 2 of 3 Genetic Typology of Human Natural Language. Hidden Colors Theory

INTERNATIONAL JOURNAL OF RESEARCH & METHODOLOGY IN SOCIAL SCIENCE VOL. 4, NO. 3

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TABLE OF CONTENT

Language acquisition – Part 2 of 3 Genetic Typology of Human Natural Language. Hidden Colors Theory Salatino, D. R.

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Using pValue to Measure Significance Level of Continuous Data ………………..

Louangrath, P.I. Mandatory Disclosures and Investment Decisions in Consumer Goods Sector of Nigerian Economy ……………………….

Siyanbola, T. T., Enyi, E. P., Adegbie, F. F., and Nwaobia, A. N. Residents' Perception of Seafood as a Tourism Product Maliva, Nelly S., Wilbard, Juliana G., Mbilinyi, Bahati D., Massawe, Donatus P. and Mkwizu, Kezia H.

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ASEAN Economic Community: A Case Study of Failed Economic Integration ….

Louangrath, P.I. and Sutanapong, Chanoknath

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28-48

49-67

68-76

77-116

International Journal of Research & Methodology in Social Science Vol. 4, No. 3, p.1 (Jul. – Sep. 2018). ISSN 2415-0371 (online)

Language Acquisition Part 2 of 3 Genetic Typology of Natural Human Language Hidden Colors Theory Dante Roberto Salatino

About the author Dante Roberto Salatino is a researcher of the Institute of Philosophy and of the Institute of Linguistics - Lecturer in the General Psychology Department – Lecturer in the Philosophical Aspects of Physical-Mathematical Science Department - Faculty of Philosophy and Letters Teacher and Researcher in Artificial Intelligence in the Mechatronics Career - Faculty of Engineering - National University of Cuyo - Email for correspondence: dantesalatino@gmail.com

ABSTRACT In this second part, we will highlight the relationship between the theory of the evolution of species and linguistics. The preceding will serve as antecedents to develop a Genetic Typology, following the guidelines established by non-orthodox evolutionary theory that makes explicit the "genetic composition" of all languages. To achieve these objectives, we will invoke the Theory of Hidden Colors, by analogy. A tool used in biology to explain, through the study of mutations (in plants and animals), the genetic mechanism that determines the development of an embryo or a new plant. In this part of the work, we propose to demonstrate that the origin of a new language can be explained similarly as today, it is known that a new species emerges. The research carried out suggests that Protolanguages (the precursors of natural human language) are genetic (social) strategies for communicating the reality apprehended, and therefore, it is not relative because it reflects the vision we have of this reality. The Urlingua (the universal language), on the other hand, is relative because it determines how we interpret the world and our reality in it.

Keyword: Natural language, language acquisition, Psycholinguistics, Transcurssive Logic.

CITATION: Salatino, D. R. (2018). “Language acquisition – Part 2 of 3. Genetic Typology of Human Natural Language. Hidden Colors Theory.” Inter. J. Res. Methodol. Soc. Sci., Vol., 4, No. 3: pp. 1-27. (Jul. – Sep. 2018); ISSN: 2415-0371.

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1.0 INTRODUCTION Linguistic taxonomy proposes different ways to classify the languages of the world. The best known are typological and genetic classification. The first is based on the grammatical similarities that define a language. While the second, taking as reference the phylogenetic relationships between the languages that derive from a common language, characterizes a specific linguistic family.

The genetic classification is based on the evolution of languages. Understanding by evolution, in linguistics, the changes and diversification suffered by different languages, with the passage of time. This type of classification is distinguished from any other by the criterion with which the similarities that determine a family are approached, the method used to investigate them and the justification of their use, considering it as if it was the paradigm in the research in this field of knowledge. The latter, according to Greenberg, derives from the fact that the term "genetic" is not a mere metaphor, but a biological parallel with the "evolutionary taxonomy" (Greenberg, 2001).

However, the same author points out that despite the similarity between biological and linguistic evolution, in biology nothing is comparable, for example, to the law of regular phonetic change that allows the reconstruction of ancestral languages (Greenberg, 2005).

Due to the previous inconsistencies, as we anticipated in the first part of this work, the symbiotic analysis called Genetic Typology was decided, with the firm purpose of using the already established sociobiological homology in a coherent and fertile way.

In this second part, we will first address, the possible origin of a new language and the linguistic variability it exhibits, taking as a guide the evolutionary biological process of speciation.

Secondly, we will outline a probable genetic origin of natural human language, considering its essential social component as a kind of "cellular composition," whose parallel with biology can be evidenced by the Theory of Hidden Colors.

The proposed approach supports the central hypothesis of this research: "In natural human language the subjective (evident) aspects can be approached from the logic of the proper sense of the universal language of the living beings." From this basic assumption and by an alternative theory of evolution, a secondary hypothesis arises: "In the natural human language the different languages are the product of a genetic evolution manifested through an evolutionary variability."

2.0 GENETIC TYPOLOGY OF NATURAL HUMAN LANGUAGE In the first place, a parallel between speciation (see Appendix A) and the origin of a new language, can be established by using the theory of punctuated equilibrium (Eldredge & Gould, 1972, Dixon, 2001, p.67). This theory addresses the origin and deployment of species in geological time and proposes that the appearance of a new species is the result of a genotypic (hidden) gradualism that promotes abruptly, phenotypic jumps without intermediate elements. This is opposed to the phenotypic gradualism (see Appendix A) with intermediate evolutionary stages (the missing link) proposed by Darwin (1859). Darwin used to support his theory the analogy between linguistic families that came from a common ancestor and species, as well as the historical linguistics of Schleicher (1853) (see Appendix B). This evolutionary alternative is also very similar to what is usually found in different languages, whose changes are not gradual. (Figure 1 - left) In the second place, we can propose a simile between evolutionary biological novelty and linguistic variability. Two processes explain the evolutionary novelty. One accepted by the majority is the splicing of RNA (Figure 1 - center). In this figure, we can see that the DNA molecule that occupies a gene has a particular disposition.

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Fig. 1. Specification and evolutionary novelty

In the genes are the exons that contain the information to produce a given protein. Each exon encodes a specific portion of the entire protein. In eukaryotic cells, (see Appendix A) the exons are separated by extended regions of DNA called introns which have no apparent share in the protein code. The figure shows the different stages through which the complete process of protein-coding must pass. It highlights the presence of a kind of "machine" consisting of a hundred proteins and half a dozen small molecules of RNA, which is responsible for sectioning the introns, discarding them, and cause the "splicing" of the exons to form the mature RNA. This will form a certain protein, and a "folding" process will give it its specific functionality (Sampedro, 2002, p.49).

The other biological mechanism that we will invoke to try to explain the linguistic variability is the mixture of exons (Figure 1 - right). The exon is a functional unit, not of the gene (see Appendix A), which is like a "text," but of the protein that the gene gives meaning (Doolittle & Gilbert, 1978). Linguistically, the exon, as a grammatical particle, would be a "discursive marker."

Impromptu unions of exons of neighboring genes are assumed in a single gene, thus grafting, novelty and evolutionary variability without altering the usual "reading" of the "text." True discursive markers that, without syntactic participation in the base text, serve to establish relationships between functional modules and, therefore, give rise to a new function, different from those that were encoded in the genomic DNA (or original text). Promotion of an evolutionary novelty that, in the case of natural language (preserving analogies), could entirely explain the emergence of new languages.

It is interesting to know that bacteria have introns in some of their genes and use RNA to get rid of them. The only difference is that in this case, the intron's RNA is the one that makes the cut and the splice. The chemical reactions that are carried out in this process are the same as during the operation of the eukaryotic machine. Therefore, it is possible that this bacterial RNA, when evolving, has been transformed into the small RNA molecules of the "splicing machine" that in eukaryotes ends up being the very center of coordination of all the "machines" that intervene in the complex reading of gene expression. This mechanism is in everything equivalent to the PAU (universal autonomous pattern) that links, as a universal language that we analyzed in the first part of this work, all the real systems (see Appendix A) and gives support to the origin of all languages.

The biological (theoretical) mechanism suggested is useful because it, on the one hand, supports a genetic typology of natural human language. On the other side, it will serve us later, when we try to explain why the different grammatical structures keep a specific order, and therefore, they fulfill a particular function in each language. The latter is essential because it makes vernacular expression something that may or may not share structures and/or meanings with another, no matter how close it may be (For example: Spanish and Basque). That leads to the typology based on the syntax, used habitually, lose transcendence.

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3.0 GENETICS OF NATURAL HUMAN LANGUAGE Having the elements provided in the previous point, we will try to outline a possible origin of the natural language of man. We start from the assumption that the subjectivon or the "constructor of subjectivity" (Salatino, 2018) is the equivalent of a eukaryotic cell, with its evident (superficial) nucleus and one or more 'organelles' (deep). This would allow distributing the natural language according to the domain (nucleus) and a series of modalities (organelles) that would fulfill a typification function.

The sense that we assign to this taxonomic possibility is based on the fact that, by taking the theory of evolution as a support, according to an alternative view, it is possible to answer as to why there are as many languages as there are and why they are the way they are. In this way, we eliminated in some way the difficulties that the pseudo-evolutionary linguistic approaches of which are not lacking, such as the genetic one, which is determinedly seeking a common origin or an original language, or the one that proposes the influence of the "cultural invasion."

The proposal presented here, although it also seeks a common origin, does not do so from a single origin, but from a universal origin. This is the only way we see of sustaining a "homogenous relational pattern." To the current linguistic-genetic classification, by not getting a common ancestor, it is impossible to explain the pseudo-evolutionary anomalies that are presented step-bystep.

The transcurssive logic that animates our real systems offers the possibility of considering the social as a 'cellular composition' that would sustain, at that level, the culture, as a psychic level to cognition and a biological level to life. In this way, it would be manifested through the sublime expressions of subjectivity: living, knowing and having a symbolic language, thus proving the central hypothesis of this work, displayed in the introduction (Salatino, 2009).

To test the secondary hypothesis about linguistic variability, we will invoke, with due distance, the stages of the genetic program that directs the development, the structuring and the differentiation of the different organs of an embryo, of any species. In this program, there are only two types of genes: those that define the location and those that determine the type of organ. Before different cells begin to specialize, a body scheme is put into operation that indicates the position of the main regions (head, trunk, tail, etc.).

A "homeotic" gene (see Appendix A) is one that indicates, within the development program, the location of the organs along the anteroposterior axis [syntactic axis] of the embryo. E. B. Lewis (1978) showed that the homeotic genes encoded specific functions of the development pattern by assigning the respective segmental identities. The grouping of these genes could also be determined evolutionarily. That is subtle variants of one another allowed it to acquire new functions that would explain the segmental variability that the corporal plan manifests in different species.

In 1984, W. McGinnis discovers the "homeobox" (Hox - or homeotic box) which are homeotic genes that share a common structure of 180 nucleotides (see Appendix A), belong to the same structural family and have, in the evolutionary history, a common ancestor. The proteins that encode these genes have a variable region whose amino acid sequence (see Appendix A) is very different from one species to another, and a region formed by 60 amino acids, called "homeodomain." These last sequences are located in the same order in the chromosomes of very different animal groups, already present in the ancestors of vertebrates and insects, before the Cambrian (Dessain & McGinnis, 1993) [Universals?]. In all animals they fulfill the same mission in embryonic development. That is, the sequences responsible for the development of different parts of the body (legs, eyes, urogenital system, etc.), both in invertebrates and in amphibians, reptiles, birds, and mammals.

Antonio GarcĂ­a-Bellido, a Spanish geneticist, was able to demonstrate that the Hox genes specify the development of the different embryonic organs, giving them their "identity." For example, the Hox genes that determine the development of a mouse eye, introduced in a Drosophila Melanogaster (fruit fly), in the different segments that produce its legs, wings, antennae, etc., induce the appearance of "eyes of a fly" in these locations. Therefore, there is a group of genes, which are 4

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the same in all species, that define or identify what an "eye" is, and thus with all other organs and body parts of an embryo.

The analysis of the genes carried out by García-Bellido showed the existence of integrated genetic operations, which specify a specific morphology in a combinatorial way. Each process is defined by specific molecular interactions between genes, constituting a "syntagma," considering that genes are like "words." And the words, both in biology and in grammar, are organized in syntagmas (García-Bellido, 1986).

García-Bellido's research showed that Hox genes control the watertight compartments (anterior and posterior) in which the embryo body of the Drosophila divides its development. This control is done by few Hox genes in an additive way, modulating many other genes. The "syntagma" of García-Bellido defines the following sequence: a "selector gene" determines what and when to locate the different elements. This information, once adequate for the "regulatory genes" that establish the identity of each element in the anteroposterior and superficial-deep axes, respectively, is transferred to an "encoding gene." Then, it is transmitted to the "realisator genes" that indicate how and where to locate those elements (García-Bellido, 1975).

We will take as a model the genetic discoveries outlined to explain how the control and management of "identities" could arise as structural units of the reality posed by the Transcurssive Logic.

An essential consequence of accepting the previous mechanism as a possible regulator of the enormous diversity in all human subjective manifestations, primarily and fundamentally of language, is that such difference then represents only minor adjustments to something that, evolutionarily, was "invented," several million years ago.

The urlingua suggested in the first part of this work, in a similar way to urbilaterian (see Appendix A) the theoretical ancestor of all modern animals, including man, would already have in the bud the three proposed necessary components of reality, arranged on two levels, one superficial and one deep. As we said, this provision only admits that it is addressed from its syntax, since the only thing that matters here is that everything is in its proper place, just like in the Hox gene, and not the function that each part or element can meet later. For this reason, the six orders that admit the real individual components (S, V, O) (Salatino, 2018) give us, in there chaining, a syntactic way of superficially 'seeing' reality. However, this syntactic disposition is twofold; on the one hand, from the surface it takes a body in the spatial, on the other hand, from the deep, it is imbued in the temporal.

In an attempt to systematize and if we continue to accept the suggested concept of social DNA (Ibidem, p.6) we can then suggest a more detailed organization of this structural complex. In living beings, DNA is packaged mainly in the cell nucleus in their respective chromosomes. The genes that are housed there promote the ordering of a precise sequence of future events that will make the development and growth of that living being. In our proposal, we will also propose a similar structural and, of course, functional integration (Figure 2).

Fig. 2. Suggested genetic composition of “social chromosome”

As the previous figure shows, a so-called "social genome" would be made up of "chromosomes" whose "genes" are divided into two levels: a) a coding level of location and direct superficial expression, and b) a regulatory level of a deep location, and indirect superficial expression. The latter would be responsible for the "great evolutionary leaps" between the different language families. Variations made possible through cumulative "internal changes" that, after a short time compared to the total evolutionary time, arise (become superficial) as something new and 5

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different or evolved, without leaving intermediate traces (genotypic gradualism). This mechanism recalls the evolutionary process suggested by Eldredge and Gould (1972) of the punctuated equilibrium, which we presented previously. This point of view is opposed to the "phenotypic gradualism" lexico-grammatical predominant even today that tries to explain the evolution of languages through 'leafy hierarchical trees.'

The regulatory gene, the equivalent of biological Hox, "does not know" to build verbs, or nouns, or prepositions; only "knows" to temporarily orient each element, whatever it may be, according to the respective grammatical convention and its correspondence or not, with some aspect or a real phenomenon.

The encoder gene to receive the indication of the regulatory gene of what is the position of the verb, for example, provides to the performer genes that 'say' how and where to use the verb.

When in biology, two Hox genes are artificially activated in the same area of the body, the "logic" of the posterior in the row is imposed. To this universal phenomenon, GinĂŠs Morata (Morata & Lawrence, 1975) called it phenotypic suppression. This phenomenon could explain, in our case, linguistic aspects that are appearing in two "contiguous" languages, will end up predominating only in one of them. It is important to note that the phenotypic suppression is telling us that the producer genes are the same for all Hox genes and that the essential difference between a Hox protein and another is the relative affinity for these producing genes. The more posterior a Hox is, the more affinity its protein has for the shared battery of producing genes. These biological phenomena give a possible clue as to why successive duplications of the Hox genes that gave rise to urbilaterian were so easily able to generate a functionally distinct Hox gene family. Almost any small modification, for example, a "letter" of the DNA that exchanged a single amino acid of the duplicated Hox protein would be enough to significantly alter its affinity for the realisator genes.

In our approach, a similar mechanism could help us to explain the differences, sometimes abysmal, that is found between two languages that although very close, geographically or culturally, neither the linguistic classification nor the supposed cultural invasion, can explain their differences.

It is proposed that the changes that were imposed in each duplication that gave rise to the urlingua had as an effect an increase in the affinity (both spatial and temporal) concerning the previous situation. In other words, it depends on the "position" in the row that the degree of affinity grows exponentially, as we will have an opportunity to see. Going in the opposite direction, with decreasing degrees of affinity, would give rise to unfeasible "mutations." Dead languages?

It is evident that the "structures" deployed on the superficial axis have evolved separately in each "linguistic lineage," but the logical foundation of the system must have been the same in all of them: the row of regulatory genes invented by the urlingua. To give a theoretical example: it is possible that the logical foundation of the origin of the verb had arisen from the urlingua and that later, the lexico-grammarian gradualism, would have contributed the sophisticated verbal giblets that we observed in each existing living language.

In an abstract sense then, superficial structures, which science considers to typify languages, and linguistic particularities, as well as characterizing processes through the verb are similar to urlingua, that is, they handle the same logic, the PAU, thus becoming the most subtle example of non-gradual evolution.

As in this work, in biology, the concept of homology is fundamental (Sampedro, 2002, p.128). For example, the wings are not homologous since both crows and bats have them and yet both have a common ancestor a reptile without wings. Are homologous as appendices, since they share the majority of the genetic system that organizes the cells within each segment. For this reason, they are called segmental polarity genes.

The homology is stable as an abstract operation. In the linguistic field, we propose the existence of these segmental polarity genes that, despite their name, would "know" nothing of syntax, but of abstract designs that are capable of generating pure forms or homeomorphic geometric patterns. Once these genes have raised the outline, other specific genes would give body to that pure form through the contribution of syntactic chords structures. One way to explain the

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similarities between the different languages, which as you can guess is not superficial, is to accept a universal mechanism equivalent to that described, in all languages.

It is not likely that a language has "invented" a syntactic structure from scratch and as a product of gradual evolution. On the other hand, there could have been an instantiation mechanism (see Appendix A) that allowed to reuse and recombine functional units much more complex than the "orthodox forms." Two concrete examples of this would be, on the one hand, the origin of the subjectivon and on the other, the deciphering of the row of the regulatory gene present in each social chromosome.

Each subjectivon is formed with three complete and previously functional 'genetic elements': S, V, and O. Orthodox gradualism only edits them, hierarchizes them, but it is the polycontexture (see Appendix A), proposed by the Transcurssive Logic that generates the noble Heterarchic mechanism (see Appendix A) of interrelations between simultaneous functional instances (Salatino, 2017).

If we imagined for a moment what a system would be like to organize the syntax of a language, we could say that it would be a single regulatory gene (Hox) with a battery attached to the realisator genes. An integrated and previously functional genetic system that would be used by all languages to organize and regenerate their few differentiated structures. The simple serial and divergent duplication of the initial regulatory gene would generate, essentially from within the social genome, a syntactic body organized by more than half a dozen divisions of the original genetic model. Gradualism would thus have the enormous work of polishing and elaborating this system, but not of being its origin, at least in the superficial. It is probable that a mechanism of gradual (continuous) development has been operative in the depth of the "genome", explaining in this way, why only those regulatory genes that could increase their affinity, in successive divisions, with their performing genes would succeed.

Summarizing what has been considered here, we could say that the evolution of languages may not have been gradual. But through models supported in identities that have remained in a state of stable imbalance, product of affinities, and evidenced a particular behavior in situations that may have been internal or external; appreciation of the latter, which, as we have seen, has a solid logical basis.

The row of the regulatory gene (Hox) would be formed by a 'sum', but this sum (logic) is not the one proposed by the hierarchical gradualism (monocontextural - see Appendix A), that is, it is not binary (inclusive), but the generated by a genetic duplication that considered one element at a time but simultaneously. An exclusive logical sum (XOR) that would allow us to deliver a heterarchical structure by enabling each integrating factor to work in a particular context. (see Appendix A) The duplication of regulatory genes is not distinct because it is serial, but because the original gene already has an integrated network of genes under its control, then the duplication of that gene results in the 'conceptual duplication' of the original model. In other words, the realisator genes do not physically duplicate but go from having a regulator to having six or more, arranged in a single row (Figure 3).

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Fig. 3. Genetic aspects of development

Reference: GR: regulator gene Figure 3 shows the findings in the Drosophila Melanogaster and its schematization in a supposed "social chromosome." In this way, we could say that the origin of natural human language is the product of a limited number of instances and its evolution depends on specific mutations that would occur in this mechanism, through time.

Figure 4 proposes one of many ways of coding a row of a regulator gene.

Fig. 4. Theoretical disposition of a row of a regulator gene

References: MSB: most significant bit - LSB: least significant bit The scheme reveals what is known in computer science as a logical word. A unit of measurement of the amount of information that a programmable electronic device can handle as a semantic entity and which is called a byte (see Appendix A). In turn, the byte is formed by a unitary element: the bit (see Appendix A), arranged in linear succession in groups of eight.

The power of this, in appearance so simple arrangement, lies in that it encloses itself and in so little space, a whole numbering system that allows from storing information or encode orders to comply.

Respecting the original layout, we have highlighted in the figure some elements that in our case, will also be relevant, among them, point out the least significant bit (LSB), and the most significant bit (MSB), as the extreme unit elements in this word ' Pattern. The degree of significance is given by the exponent (n) that shows the base of the system (2n) (see Appendix A), which grows geometrically from right to left. The degree of significance has great importance in this type of notation because, while allowing us to 'count' (from 0 to ∞), it allows too the calculation of the position in the row of a given element (bit).

On the other hand, and this is very useful for our purpose, if we assign conventionally for example, to each box, the 'control' of a specific syntactic structure, it is enough with the indication 8

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of its presence or not in a given language. A particular arrangement of 1s and 0s will provide us with a quick and accurate overview of the syntactic structure of the language in question.

Finally, we have specified in the figure, to approximate the scheme to the biological "Hox row," a division by halves that aims to establish the management of more general structural "locations," mimicking the distribution in compartments of the different segments of the body of a living being.

The structuring of the different levels (superficial and deep) that at first sight may seem disparate and even opposite, actually responds to the same logic. The duplication of the original regulatory gene is not a significant complication, but the attached genes cannot be improvised to give structure to a new level; necessarily in each division, the new regulatory gene 'inherits' the old, at least initially, and thus becomes a 'model of models.' This meta-control is based on at least three fundamental aspects: a) Symbiosis, b) Duplication of genes that regulate other genes, and c) Sequential polarity, which does not serve to define, for example, a certain syntactic structure, but specifies the abstract operation necessary to generate functional geometric patterns of pure forms that can be used for different purposes.

Sequential polarity genes do not need to be contiguous in the "genome" to act in a block.

The simple activation of a gene, which can be indicated by a '1' in a row box, can chain all others.

The place and time in which a specific gene is activated depending on the zones of the "regulatory DNA," adjacent to the "text" of the gene itself.

These structures are susceptible to mutations (small or large) and a change of this type in the regulatory area of the gene, can act on its own so that this model of segmental polarity genes is reused.

4. HIDDEN COLORS In all the works produced on Transcurssive Logic, has been used a specific color code. This was not done with an aesthetic sense, but with a practical and methodological one.

The basic contextures (see Appendix A) were assigned respective colors to be able to handle intuitively the logical calculations that arise when operating with them. The choice of colors is not capricious. Instead, it responds in a whole to the physical theory of the colors of light, with its additive and subtractive elements. It follows the RGB code that uses a Cartesian coordinate system in which a space contained in a cube on the side equal to the unit is defined (see Appendix A).

Fig. 5. Binary codes and relations between colors

Figure 6 establishes the relationship that has been considered between the colors initially assigned, and those that acquire the contextures and the identities that depend on them according to Figure 5.

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Fig. 6. Allocation of colors according to color theory

The initial assignment of colors, that is, blue (001) to the subject (S), green (010) to the object (O) and black (000) to ∇, responds to the fact that the binary (010) each represents the decimal of its valence (see Appendix A), which is 1, 2 and 0, respectively. An apparent exception is the contexture V, which houses the objective or visible change (VO) since with valence 3, it should correspond to the color cyan (011) since it has the corresponding binary. However, it is assigned red (100).

If there is no valence 0, as happens in the universe designed by Peirce, (where only exist the primary, the secondary and the tertiary) we could not find an explanation to the appearances, that is, we could never understand the subjective. This tries to represent in the subjective reality, the red color assigned to the change, with a decimal value of 4 (100 binary), instead of the value 3 that would correspond to its valence. Red is complementary of cyan, we saw it before, and this means that both summed give white, that is, unity. The change referred to by valence 3 is the immanent integration of an apparent change and a hidden change. When in the course of the evolution of subjective reality, both changes are differentiated, the hidden takes the 3 of the valence (cyan or 011), and the superficial or apparent it's complementary. Stay with the 4 or the 100 assigned to the red color, in compliance with the laws that govern the theory of colors and thus respecting, strictly, the basic principles of Transcurssive Logic over complex identities. That is, those that are constituted by elements that have the triple relationship of opposition, complementarity, and concurrence, as do the colors of light.

As we can see, are proposing that everything is arranged as if it were "made with colors".

Obviously, we are not referring to the colors we perceive as characterizers of what we can scrutinize with our eyes, but to those who, following the same laws that these are not obvious to us, that is, that being hidden serves as a structural metaphor for everything that is contained in the reality we are analyzing. Of these last colors is what we are going to talk about and we are going to do it from a practical case that reflects, with the least possible level of doubt, where is the distinction between two different languages. To reach this demonstration, we will necessarily have to introduce ourselves, in some detail, into the germinal antecedents of the different languages.

4.1 Where are the hidden colors originated?

When we propose, in the first part of this work (Salatino, 2018, p.12), the way in which the process of morphogenesis was carried out in the universal language in the variety of meiotic (sexed) reproduction, we saw the participation of two gametes (50% each) in the generation of a new universal mother tongue (UMT). The equal involvement of the "parents" only is corroborated in the "core" of the new element, where are "stored" the hereditary characteristics, both "paternal" and "maternal."

We can follow the sequence of alternatives that give rise to the hidden colors in Figure 7.

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Fig. 7. Hidden colors

Outside the nucleus, that is, in the cytoplasm of the new "descendant," things are not so even. In this sector of the "social cell," the greatest contribution corresponds to REM (or real fact) that acts as "mother." In other words, by joining two compatible and complementary social cells, who represents the "father" contributes, outside of 50% of the nucleus, with one of the "cellular poles": the black (first hidden color), that is, gives the ' total absence of colors', so we will characterize it with a '0'. This last contribution certifies the 'sexual union' and thus from a single element, a dimensionless black point (0D) is passed to a line that extends from the black pole (0) to the white pole (1) through a continuous grayscale that gives rise to the first dimension (1D = what) of this cellular reality.

From the initial process described, cell development continues on its own and is observed in the evolution that each cell pole shows separately.

The maternal pole unfolds in its basic components: red, green, and blue, since the white is the additive synthesis of these three primary sources of color, while the paternal pole remains unchanged. In this way, the second dimension originates from the geometric-functional (2D = when). This means that a plane is formed in whose vertexes there are four possible combinations between the two original colors: black and white (see in the figure). These combinations are: 00 (0) = black, 01 (1) = blue, 10 (2) = green and 11 (3) = red (we saw why there is neither the cyan nor the white). In one of the diagonals of this plane that joins the opposite ends (black - red (which replaces white) is preserved the information in the grayscale.

In a further step towards 'cellular adulthood,' an important evolution of the paternal pole and a minimum of the maternal pole is observed. Thus, the paternal pole, which cannot be differentiated in its components because it has none, is limited to absorbing and reflecting alternatively, the colors emitted by the maternal pole. This process is put into operation by combining, in pairs, the maternal colors reflected so that the color obtained is complementary and opposed to the color absorbed or missing in a particular situation. The previous figure shows a detail of this mechanism and how the appearance of complementary or secondary colors occurs: yellow, cyan, and magenta that show the subtractive behavior of the paternal pole. Since, although the sum of these 'pairs' shows the reflected maternal colors, their difference (algebraic sum) shows us the absorbed colors, which added, give the black (000 (0)), as shown in the figure.

As the previous figure is also shown, the third dimension (3D = how) arises from the geometrical-functional when introducing each color in a vertex of a cube; a disposition that now, allows assigning to each color the respective complete codes.

The cubic spatial distribution establishes, in the REM, the emergence of two well-detailed levels. On the one hand, the superficial or evident, represented by the primary colors or "emitted" by the maternal pole and that summed to give the white. On the other hand, the deep or hidden level 11

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that represents the secondary or complementary colors, or the "absorbed/reflected" by the paternal pole.

In the larger diagonal of this cube, which joins the black with the white, has been 'saved' the information provided by the 'mother' on the grayscale. That means nothing other than the guide of the sequence of a location of the different colors (primary and secondary) ordered by a continuous and increasing scale of "wavelengths," thus adjusting to the metaphor of the grayscale. Figure 8 summarizes the above.

4.2. What are the relationships between hidden colors and suggested genetics for current languages?

If we adhere entirely to the genetic metaphor to explain the origin of languages, we must think of a set of pre-established instructions that say how a given language should be born and developed. A kind of "user manual," to use an everyday expression.

Fig. 8. Evolution of hidden colors in REM

The ability to interpret this manual is independent of the manual itself, and therefore, it is essential to learn the language in which it is written to follow its guidelines. This language that should be learned is neither more nor less than the universal mother tongue. Learning this language involves the learner and also their environment. This relationship will be responsible for the emergence of the "sense" of what is learned by doing so that their "interpreter" becomes independent of the instructions, that is, of the language itself.

Another derivative of accepting the genetic metaphor is to admit the existence of fertilization, whose direct consequence is the social, genetic exchange between the participants.

Having made it clear that our real systems are linked by the same fundamental logical element (PAU), naturally allows us to state that they are, themselves, structured by a single universal mechanism: the genetic one.

The genetic constitution of the sociocultural system is sustained, as we saw, in the social DNA, and the way between this DNA and a specific "adult" language is transformed in this way, into a question of interpretation.

To clarify the previous statement, we will assume the existence of a biological cell that has the ability to 'translate' messages from its genetic language to our mother tongue. Let us also suppose that on the outer side of its cellular membrane, accessible to us through a microscope, there is an area that functions as if it were a computer monitor, that is, that it has the possibility of coding 12

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a message through the lighting or turn off several luminous spots. This means of communication has the only limitation of being able to work only with two colors: black and white. The message that the cell will make evident on this screen will look very much like a text written in black ink on white paper. The message you will send us will be I'M ALIVE. The above is everything concerning the interface between the cell and us.

Inside the cell, things are very different. There are possibilities to handle all the colors of the spectrum except white and black. Following the suppositions let's say that to be able to arm a message in English, the cell has to be organized in some way. This organization responds to a color scheme, the only material available for these purposes. There is at its core a grid of limited size, which shows a profusion of small squares of different colors distributed, apparently, in a random way. Particular cellular elements are responsible for following an "instruction manual" that tells each one what to do to communicate with other cellular elements, but that is outside the nucleus. To be more precise, they are on the outer surface of the cell membrane, on the supposed screen, and "communicate" that they "dress" in black or white, depending on the occasion. The aforementioned "instruction manual" has only two sheets. Sheet No. 1 has instructions for writing the first two word (s) in English I'M. While the sheet No. 2, to write the third word ALIVE, so that, once read and strictly followed the instructions, you can read, on the external screen, the message that will be displayed in two parts.

Each "instruction manual" has its cover of a particular color. This color tells its bearer, in what small square color of the grid, should be placed. Before a general order, all the cellular elements already located in their respective small squares have to read the first page of their manual, and there they will find only a white or black square and consequently it will be what they will have to communicate to the surface element to show us. Thus, it appears before our amazed eyes, the word I'M. A new general order, intracellular, says: read the second page and again, only there is a white or black square that will be communicated to the surface as appropriate. The word ALIVE now appears on the screen. It is very easy for any of us to mentally compose the complete message and understand what we were meant to say even though we know absolutely nothing of the existence of the color pattern that served as a guide to issue such a particular message.

Our lack of knowledge about this pattern is based on the fact that we cannot see it. Inside the cell, on the other hand, the opposite happens, none of the elements that intervened to produce this amazing fact, "knows" what I help to build and much less knows the message in question.

As a conclusion then we can say that, knowing something of the intimacy of this particular cell, the pattern of hidden colors served as a frame of reference to make a particular aspect evident.

In the real biological cell, the apparent aspects are promoted by the genes. These genes are evident in their action, as long as they are active and their activity or not ultimately depends on a pattern of hidden colors that act as "keys" for a certain "lock," "open" or " they close "the activity of a given gene. A true binary mechanism, as demonstrated by GarcĂ­a-Bellido (GarcĂ­a-Bellido et al., 1979, p.101). The metaphor of colors is very appropriate in biological genetics because it allows us to understand that a gradual pattern of activations, a "scale of grays "for example, arrange the correct place in which a specific part of the body should be developed in an adult organism that started from a simple cell formed by the conjunction of two gametes.

In the different languages, following the initial proposal meticulously, we can say that the structuring of the expressions in each of them obeys a hidden color pattern that makes it possible to arrange each expressive element in its correct place. This, without having a relationship with whoever interprets this expression, constitutes the "language in which that language is written," a faithful reflection of its universal mother tongue and that has nothing to do with the language it communicates on the surface. In this way, it is established that the universal mother tongue is something different from the natural mother tongue.

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4.3. Basic principles of the functioning of hidden colors One way to understand the genetic functioning of biology is to analyze mutations. Since a mutation, in general, is the product of inactivation of one or more genes, the superficial expression of this 'anomaly' can guide where it originated and explained the transformations that become evident.

A simple way of understanding what we mean by the term mutation is to imagine what would happen if, in the cell of the previous point, there was an error of interpretation. Half of the cellular nuclear elements, correctly located each in its respective small square of color, read sheet No. 2 of its instruction manual, instead of No. 1 like the rest, to generate the first part of the message. The result, on the surface, would be a message that, at least for us, would be incomprehensible since something like this would appear on the screen: I'MIVE. Similarly, we propose, in the study of languages, the analysis of the variations that exist between them, but addressing them from the genetic point of view, as we understand the term in this study.

The inactivation of a gene in biology means that either an organ or a group of them will not develop or that a specific function, in a developed organ, will not be carried out.

Before arriving at the comparative analysis between two languages taken as an example, we will do a theoretical exercise to highlight the method to be used in this comparison.

Let's assume that S, V, and O, the superficial components of REM are mutant elements that derive from hidden colors. The white and that ∇, the black, is the one that fuses the deep components that are a direct consequence of the combination, of the pairs, of the superficial elements and of course, of the deep colors that derive from it. Figure 9 details the proposed scheme.

Fig. 9. Pattern of hidden colors of REM

The chromatic formula of an evolved REM, as deduced from the previous figure, would be S, sv, V, vo, O, os (blue, magenta, red, yellow, green, cyan). The blue and green colors of the surface, in appearance, are opposite and complementary, this means that, for example, if S were lost it would be replaced by O and vice versa.

We propose that in all languages something similar happens to what was stated above and that such alternatives have their roots in the identity of the different elements.

This way of approaching the study will allow us to predict what kind of structure we will have in a language when a distinctive 'regional' characteristic represented by a specific color is "lost" or "is not expressed" in an apparent way. The different surface elements (S, V, O) of a REM are responsible for "producing the hidden colors" and, therefore, also granting different identities, as we have already seen.

Using a scheme as the one proposed, we can suggest that two languages, however different they may seem, may have a similar "hidden disposition." This means that the change that takes 14

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place in the appearance is due to only two circumstances: on the one hand, an alteration, not of the code, but of its interpretation and on the other, to an alteration of the element that grants a certain identity. The change occurs due to a variation in the production of hidden colors. In any of the above cases, the hidden colors represent a frame of reference and not a "set of instructions" of how to "manufacture" a verb, a noun or an adjective or any part of a habitual linguistic expression.

The languages we will use in the exercise will be: Spanish and Hopi. The method we will use to establish differences and similarities will then be based on two fundamental premises: 1) Each element of the REM that gives rise to the universal mother tongue from which the considered languages derive, is necessary for a particular hidden color and the combination of these colors will determine a specific segmental identity in a given expression. 2) The habitual role of these constituent elements will be to generate the hidden colors in their respective expressive segments. A variation that "inactivates or removes" one or more of these identity elements will result in the "loss or modification" of color and, therefore, in the modification of any of the identities.

When at a previous point we spoke of a "row of regulatory genes," we were referring precisely to this scheme that we have just raised through the hidden colors and their producing elements.

As the whole mechanism has a binary basis, let's not forget that it operates in the monocontexture of our evident world, the expression of an individual gene can merely be made manifest by means of a '1' placed in the respective "box", as well as its inactivation, by a '0' in that same place.

The above considerations will help us establish a pattern of expressive activity by "tracing" genes that are activated in a given time or that are not activated and remain hidden. The problem then is restricted to how do we distinguish, for example, a specific syntactic structure?

In biology, it is used, to identify if a gene is active or not, the so-called molecular probes (see Appendix A). In our case, we are going to use, metaphorically, a similar procedure. These "probes", which we could call here "syntactic", since the purpose is to find out the correspondence or not between the structures of two expressions, will be of two types: i) those that can recognize a subjectivon, that is, the scale of gray that regulates the structural disposition and ii) those that can recognize a complete expression. The probes of the first type make their recognition in the products that emerged in the transcription stage of a language, that is: very little from its origin, and that has to do with the syntax. While those of the second group, allow tracing elements that appeared in the translation stage, which represents the stage before the definitive linguistic functionalization that appears in the 'mature' language and which gives it semantic handling.

Whorf (1978, p.51) teaches us that, referred to verbs, the Hopi formalizes in different ways the contrast between point and extension in the location of a phenomenon, making it independent of time, space or both. Hopi verbs also have three verb tenses as in Spanish, but grouped in another way: 1) Positive (present - past) 2) Future 3) Generalized (usitative).

The verbs belong to several different conjugations but in the most important of them, in magnitude, is the segmental aspect. That consists of a final reduplication of the CVCV root (consonant, vowel, consonant, vowel), which is in the third person of the singular of the intransitive voice, plus the durative suffix -ta, which produces a modification in the meaning of the simple word.

The change would manifest itself through a phenomenon that, indicated by the root and shown in its precise aspect in the form of a manifestation on a point, becomes the externalization of a series of repeated and interconnected segments of a greater phenomenon and a wider segmental character. Its extension is made in a dimension that remains indifferent to space, time, or both simultaneously.

We will take as the only example the one shown in Figure 10.

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Fig. 10. Characterization of the Hopi verb

(Example is taken from Whorf, 1978, p.52) As the previous figure shows, the generic form (chromatic formula) of the Hopi action would be the punctual and its extension as duration. That is, from our functional geometry would be an event that manifests itself successively as a point (the precise and dimensionless), a line (reduplication, the one-dimensional) and a plane (the extension, the two-dimensional). In this way, the "what" and the "when" are affirmed simultaneously without granting any preponderance to any of them. (Kakectaca, 1978, p.68) Although given its fundamental characteristics that are: I. Not require an external agent.

II. Manifest recursively After an inapparent change that accumulates; it is triggered by a specific internal situation.

III. Not have an external duration but internal.

IV. Show activity and result at the same time without specifying where it comes from. ... in fact, it can be assimilated to the dynamics of our ∇. The above is possible since it is equivalent to a reorganization process, that is, to a non-apparent change that is chromatically summarized in the BLACK and that, as we saw above, is derived from the secondary colors reflected from the surface: MAGENTA, YELLOW, and CIANO. The Hopi belongs to the domain SOV (Ethnologue, 2018) and according to the transcurssive logic, to the levorotatory variant, contrary to the Spanish that integrates the SVO domain and therefore it is a dextrorotatory language. Figure 11 shows the chromatic formula of both languages.

Fig. 11. Chromatic formulas of Hopi and Spanish

Applying the method of the syntactic probe, in this case, we will see what a Hopi verb becomes when we pass it to Spanish. (Figure 12)

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Fig. 12. Genetic variation of a Hopi verb

References: : Nucleus -

: Gamete - ↻ and ↺: Direction of rotation - ⊕: XOR

As shown in Figure 12, if we paired (an XOR is applied) to the core (SOV) or superficial (white) of the Hopi expression, with a "probe" that searches for the superficial of the Spanish, that in this case is the "gamete" of the Spanish expression (O∇S) (from the same levorotatory variant as the Hopi nucleus). We obtain an intermediate product that is neutral. That is, with no direction of rotation whatsoever and which we have identified as a signic splicing, since by mimicking one of the evolutionary stages of natural language (see Appendix A). It tells us, on the one hand, of the common basic logic that have these two languages and, on the other hand, gives meaning to the use in Hopi, of the positive verb tense (present - past).

If the signic splicing obtained we paired (we apply an XOR) with the Hopi gamete (OS∇), the representative of the black in this language, which is clockwise, we get the white or superficial of Spanish, which is what we were looking. In this way, we have proven that the syntactic probe was useful because it managed to extract from the Hopi structure its complementary: SVO. It has been demonstrated then, that the expression Hopi that by different sociocultural circumstances had an "inactive gene" in its structure was present when activated, also by the same conditions, in an expression in Spanish. But the concrete and transcendent are that the gene was always present from the very origin in both languages, which, as suggested by what we have just seen, is a common origin.

This analysis, in appearance, is very limited because we have done with a single verb, nevertheless serves to strengthen several aspects of our approach.

First, we demonstrate the operation of the method used. Secondly, look from another perspective of the relationships that link different languages and try some explanation of their differences. Third, by deepening in some way, in the vision of the reality that the Hopi people have, to realize that the real systems (those that structure the subjective reality) are the same for everyone, regardless of geographical or temporal distribution. This confirms the non-provenance of Whorfian relativism since languages seem to be organized according to experience and not vice-versa. The fact that the Hopi language does not contain any reference, neither explicit nor implicit, to the time (Whorf, 1978, p.58), as we understand it and express it poorly in our language, does not mean that this language cannot entirely explain every observable phenomenon in the universe. The latter depends on a question of interpretation and not on a deficient or primitive code as Whorf claims to have discovered.

I disagree with Whorf (Op. Cit.) when he says that time in the Hopi 'disappears.' This contradicts the more than two dozen lexemes that refer to time, which appear in his Hopi-English working Dictionary (unpublished manuscript). (Malotki, 1983, p. 15) I think that it is interpreted differently, and I do not mean that we do it mathematically from the hand of Einsteinian relativity, and they do it from a simple linguistic expression, but that a unique deep chromatic pattern of another way despite having a common origin. We firmly support that in our case, the social inheritance that we will develop in the third part of this work was in charge of modifying the conception of time and making it a slave of the Judeo-Christian heritage. One that consolidated the tripartite linear time that characterizes the Indo-European languages, in spite of deriving from the Latin and the Greek, that had more than three ways to deal with time.

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Finally, the simple example presented serves to realize that the reign of the subject-predicate structure that has been and continues to be considered essential to 'think' and 'communicate,' loses sustenance when we learn to "look at it from within." That is, from the polycontexture. Proving that the grammar with a functional center in preaching does not correspond to a conventional universal language but rather, to the subject - action - object scheme (SVO) that we have proposed from the universal language.

It must be considered that the aforementioned scheme only appears to be universal in the languages that derive from Greek. In this language, there are independent things, substances that are denominated with nouns that show properties or attributes, which are designated with adjectives.

There are also changes or events in the world that relate such things and are reflected in the verbs.

This gives support to the 'doctrine of the kinds of words' in a particular ontology of being. This, which a priori seems to provide an advantage by proposing a suitably uniform structure, gives all these languages a marked ambiguity, as we can see in the following expression: “Puedo ver la llegada desde el cerro” (I can see the arrival from the hill).

Here, the term “llegada” (arrival) represents a noun (the signal that indicates the end of the competition); an adjective (the goal of a competition); or a verb (the action and result of the arrival of the competitors)?

In the Hopi, not "contaminated" by Western culture, one can "see" inside the polycontexture. Something evident in the analysis the speaker makes of reality regarding "events" instead of things, substances or materials, justifying in this way that a noun in Spanish “fleco” (fringe) is a verb “flecar” (Aguilar Criado, 1999, p. 115) (make fringes) in Hopi.

Perhaps we can find a more grammatical explanation to what we have presented as an example if we consider that these Hopi verbs, as we said, arise from a root that is in an intransitive voice. The intransitive verbs (Nebrija's neutrals or absolutes) are those that express a complete preaching, that is, they express an idea without the action falling on any person or thing. Given this characteristic, they exclude, de facto, the direct complement since there is no thing independent of the subject that participates in the expression. Among these intransitive verbs are state verbs that serve to express a more or less fixed and stable situation of the subject and whose prototype in Spanish is the verb "estar" (to be) (although there are many others, such as: "llegar" (to arrive)). In these verbs, the intervention of the subject is more than restricted since it is not the speaker who produces the action, but instead, it is that which manifests itself in him. Grammatically speaking, these verbs occupy an intermediate place between active and passive verbs.

Is not this the border, the Rubicon, that Greco-dependent language crossed, pouncing on ambiguity? Alea iacta est! In other words, should the word “llegada” (arrival), in Spanish, not having to have been a verb and nothing else? It is thus demonstrated, on the one hand, the ambiguity of the code or everyday language, and on the other, the operative logic of a mechanism of acquisition of natural human language.

5. CONCLUSION We will recount of the heretofore considered through a couple of definitions: 1) The Protolanguages, that is to say, those that encompass globally the natural human language, are genetic (social) strategies to communicate the reality apprehended and insofar as they are not relative since they do not determine the vision one has of that reality, on the contrary, they reflect it. The urlingua (the universal language), on the other hand, is relative to determine how we interpret the world and our reality in it.

2) The selector gene is the strategy used by how many languages have evolved on the planet, an evolution that, although it seems overwhelming, only responds to a few innovative principles. This selector gene should comply with the following properties: a. Activate just in one area of the expression (occupy a bit). b. Define always the identity of the zone that is active, determining the weight of the byte, that is, the square occupied by a '1'.

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c. Do not 'know' about verbs, adjectives or nouns, limit yourself to selecting one structure or another from a repertoire of available forms in each domain, which is different from another. The "decision" of each gene is binary (García-Bellido et al., 1979) and acts in a combinatorial way. In a nutshell, they integrate a byte. The selection function is combined through the "sum" of the powers of 2 (2n) active, which have a '1' in their box and for this reason (their franc binarism) can be explained from the monocontexture, which is strictly binary. d. Always act by regulating complex and coherent networks of other genes (realisator genes). A direct consequence of this fact is that the unit of action (operative) of the selector gene is the "social cell" or REM. When a selector gene is inactivated in a certain "social cell" and activated in a different one, in that single cell, a determined element is as if it were in another part of the structure. e. Although the scope of operation of the selector gene is REM, they give it a series of secondary properties that make coherence significantly affect its interrelation with other REMS. For this reason, the REMS that have the same combination of selector genes active tend to remain together, languages of a particular family, for example, and tend to 'reject' other REMS that exhibit different combinations.

The progressive activation throughout the development, which could happen, for example, in the child, is dividing and subdividing the primitive expression in specific areas, according to the combinatorial code of the selector genes. The latter is a principle that we propose as universal in the development of natural human language, considering it an essential mechanism in its acquisition.

Each selector gene and its network of producing genes constitute a "model" that allows evolutionary novelty. The activation of a selector gene in an unusual site until then or in a duplication where a slight mutation occurs will cause the preexisting model to be used in different times and places, generating true morphological and functional novelties brusquely and efficiently. It is in this way that we propose that human languages evolve.

There is a selector gene that gives each segment the property of posterior, as opposed to the previous one. This constitutes an index that determines which is the most significant bit and which is the least significant bit (or which is the first and the last in the sequence) and that also shows how to "read" the sequence. If, for example, we deactivate the selector gene that points to the posterior compartment of the segment, the previous compartment will be duplicated and vice versa, disappearing, in each case, the inactivated compartment. In this way, the reading sequence is broken. That is, instead of being 20 → 27, it becomes 20 → 23 or 24 → 27 twice, respectively. This geometrical-functional disposition is transcendental because that is how we propose that the indications of the four dimensions that we have given to the subjective reality posed in this study are "managed" from the different languages: S (what), O (how), V (when) and ∇ (why). The appearance of a "skeleton" of linguistic expressions in the Urlingua has few possibilities, and one of them is instantiation (as we saw).

The generation of 'models' from other models 'molds,' arises from the progressive increase in the powers of 20 → 27, which are represented in the dimensional possibilities of a byte. There is a byte that when governing these dimensions says how many models to use and at the same time, when and where to use them. The proposed ancestor of all languages (urlingua) is likely to have been built by genetic models, and its subsequent evolution has mostly been based on instantiating, reusing and then recombining these practically intact models.

Evolution is the archetype of the subjective and above all of the human subjectivity. On the one hand, because our brain is the result of this evolution, but on the other side and even more critical, because the subjective aspects that characterize the man beyond his life (knowledge and symbolic language) are the exclusive product of the emerging psycho-bio-sociocultural, all aspects, purely subjective.

From the evolutionary achievements of man, we can say everything and without fear of making mistakes, mentioning only one of them: the symbolic language, since this implies, in the first place being alive and therefore, grasping, understanding, elaborating (thinking) and communicating. The symbolic natural language is our indelible evolutionary mark, and the urlingua 19

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is a proposal for a complex and universal biological solution, which occurred only once in the history of the earth, which would explain why this evolution has left no evidence of a gradual transition.

In the third and last part of this paper series, we will analyze the general aspects of the acquisition of natural language and the relevance of linguistic universals, lexical contextures, and sociocultural heritage.

REFERENCES Aguilar Criado, E. (1999). Las bordadoras de mantones de manila de Sevilla. Trabajo y género en la producción doméstica. Sevilla, España, Universidad de Sevilla.

Bateson, W. (1894). Materials for the Study of Variation. Treated with Especial Regard to Discontinuity in the Origin of Species. London, Macmillan and Co. Darwin, C. (1859). On the origin of species by means of natural Selection. London, John Murray.

Darwin, C. (1871). The Descent of Man, and Selection in Relation to Sex. London, John Murray.

Dessain, S.; McGinnis, W. (1993). Drosophila Homeobox Genes. In Advances in Developmental Biochemistry. Paul M. Wassarman, Editor. Connecticut, USA, JAI PRESS INC. Dixon, R. M. W. (2001). The Rise and Fall of Languages. London, Cambridge University Press.

Doolittle, W. F. (1978). Genes in pieces: were they ever together? Nature, 272:267.

Eldredge, N.; Gould, S. J. (1972). Punctuated Equilibria: An Alternative to Phyletic Gradualism. In Models in paleobiology (pp. 82-115), Schopf, T.J.M. (Ed.) - San Francisco, Freeman, Cooper & Co. Ethnologue. (2018) Word Order in the World’s Languages. https://www.ethnologue.com/.

Consulted 16/07/2018.

García-Bellido, A. (1975). Genetic Control of Wing Disc Development in Drosophila. Cell Patterning. Ciba Foundation Symposium 29. New York, American Elsevier.

García-Bellido, A.; Lawrence, P. A.; Morata, G. (1979). Compartments in Animal Development.

Scientific American, Vol. 241, Nº 1, pp. 102-111.

García-Bellido, A. (1986). Genetic Analysis of Morphogenesis. In Genetics, Development, and Evolution. 17th Stadler Genetic Symposium. Edited by J. Perry Gustafson et al. New York, PLENUM PRESS. Gilbert, W. (1978). Why genes in pieces? Nature, 271:501.

Greenberg, J. H. (2001). The Methods and Purposes of Linguistic Genetic Classification. Language and Linguistics, 2.2, pp. 11-135.

Greenberg, J. H. (2005). Genetic Linguistics. Essays on Theory and Method. New York, Oxford University Press.

Guillén, I. et al. (1995). The Function of engrailed and the specification of Drosophila wing pattern. Development, 121, pp. 3447-3456.

Günther, G. (1979). Beiträge zur Grundlegung einer operationsfähigen Dialektik. (T. II) Hamburg, Felix Meiner Verlag.

Kakectaca, M. (1978). Lessons in Hopi. Tucson, The University of Arizona Press.

Lewis, E. B. (1978). A gene complex controlling segmentation in Drosophila. Nature, Vol. 276, 7, pp. 565-570.

Malotki, E. (1983). Hopi Time. A linguistic Analysis of the Temporal Concepts in the Hopi Language. Berlin, Walter de Gruyter.

McGinnis, W. et al. (1984). A conserved DNA sequence in homeotic genes of the Drosophila Melanogaster Antennapedia and bthorax complexes. Nature, Vol. 308, pp. 428-433.

Morata, G. and Lawrence, P. A. (1975). Control of compartment development by the engrailed gene in Drosophila. Nature 255, pp. 614-617.

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Salatino, D. R. (2009). Semiótica de los sistemas reales – Tesis Doctoral en Letras especialidad Psicolingüística por la Facultad de Filosofía y Letras de la Universidad Nacional de Cuyo, Mendoza, Argentina – Director: Dra. Liliana Cubo de Severino.

Salatino, D. R. (2017). Tratado de Lógica Transcursiva. El origen evolutivo del sentido en la realidad subjetiva. Mendoza, Argentina, Primera autoedición. ISBN: 978-987-42-5099-5.

Salatino, D. R. (2018). “Language acquisition – Part 1 of 3.” Inter. J. Res. Methodol. Soc. Sci., Vol., 4, No. 2: pp. 1-14. (Apr. – Jun. 2018); ISSN: 2415-0371.

Sampedro, J. (2002). Deconstruyendo a Darwin. Los enigmas de la evolución a la luz de la nueva genética – Barcelona, Crítica.

Sharp, Phillip (1993). Split Genes and RNA splicing. Nobel Lecture.

Schleicher, A. (1853). Die ersten Spaltungen des indogermanischen Urvolkes. Allgemeine Monatsschrift für Wissenschaft und Literatur (September). 786-787.

Schleicher, A. (1860). Die Deutsche Sprache. Stuttgart, Cotta.

Schleicher, A. (1863-2014). Darwin’s Theory and Linguistics: An Open Letter to Dr. Ernst Haeckel. In Revista argentina de historiografía, VI, 2, pp. 123-134.

Schleicher, A. (1864-1869). Darwinism Tested by the Science of Language. London, John Camden Hotten.

Whorf, B. L. (1978). Language, Thought, and Reality. Selected Writings. John B. Carroll (Editor).

Massachusetts, The MIT Press.

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APPENDIX A DNA and RNA: are the nucleic acids that form the basis of our genome or set of chromosomes of a cell. (Author's Note) Amino acid: an organic acid that forms the basis of proteins. (Author's Note) The base of the system: the base of the binary system is 2, as well as that of the decimal system, is 10.

Bit: denomination that derives from the apocopation of the English expression: binary information digit. (Author's Note) Byte: the term coined in 1957 by W. Buchholz to indicate a continuous sequence of 8 bits.

The eukaryotic cell: is the cell that has a cytoplasm surrounded by a membrane (cell membrane) and also, unlike prokaryotic cells, has an organized cell nucleus where the hereditary material including DNA, which is the base of the cell, is housed heritage.

Speciation: It is known as speciation or cladogenesis to the process by which a population, reproductively isolated from its mother species, gives rise to another or other species. (Author's Note) Evolutionary stages of natural language: the following table shows the three stages through which our natural language passed through evolutionary history (Salatino, 2009). In each stage, it can be seen from its "genetic composition" to its degree of integration.

References: FAP (Fixed Action Pattern) - S: subject - O: object - V: action Exon: a segment of a gene and its corresponding transcript that remains in a mature messenger RNA, after the elimination of the introns. (Author's Note) Phenotype: a set of visible characters that shows an individual as a result of the interaction of its genotype and its immediate environment. (Author's Note) Gen: is the ordered sequence of nucleotides in the DNA molecule that contains the information to make other larger molecules that fulfill specific organic functions; e.g., proteins. (Author's Note) 22

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Genotype: a set of genes that resides in the nucleus of all the cells of a living being. There the genetic information is lodged in the form of DNA that determines the phenotype of an individual.

Heterarchy: a term introduced by McCulloch in 1945 to refer to the situation of interdependence that must exist between different levels or subsystems in which different processes are developed simultaneously. It is considered, as a relational structure, complementary to the hierarchy.

Homeosis: a concept developed by the English biologist and geneticist William Bateson (18611926). Through it he defined the acquisition by an organ of the characteristics of another organ: ... the essential phenomenon is not that there has been a change, but that something has been changed into the likeness of something else. (Bateson, 1894, p.85) Bateson described in this way, the defective patterns in segmented animals, in which the structure of a segment was replaced by a different structure, typical of another segment. The principle elaborated by Bateson has the merit of suggesting the presence of natural serial repetitions and an organization of the complex based on discontinuous units.

Instance: Computer term derived from a concrete manifestation of an abstraction that turns out to be a model of concrete or prototypical things of the real world. (Author's Note) Monocontexture: it is called contexture (GĂźnther, 1979) to a domain in which there are only two logical values (binary logical domain), and the postulates of classical logic are respected. The monocontexture can be seen as a universe where reality is strictly hierarchical, that is, where causality is linear (cause, then effect). In it there is only place for an ontological niche (continent), be it that of an object, of a subject or of the transformation that binds them, and their respective contents. (Salatino, 2009) Nucleotide: is the organic molecule formed by a nitrogenous base (adenine, guanine, thymine, cytosine or uracil), a sugar (ribose (RNA) or deoxyribose (DNA), and one to three molecules of phosphoric acid (AMP, ADP, and ATP) is the elementary component of nucleic acids: DNA and RNA (Author's Note) Generic universal autonomous pattern: (PAU) fundamental logical core of subjective reality. An ontological-relational pattern that shows a complex structure, that is, its structural elements have a triple relationship of opposition, complementarity, and concurrency (simultaneity).

Policontexture: the domain where multiple contextures interrelate. Relationships are between continents, not between contents. Thus, the context or ontological niche of the object is related to the contexture of the subject, and this relationship defines the subjectivity that is lodged in a different contexture. Transcurssive logic determines four interrelated essential contextures. The contexture of the subject (S), the contexture of the object (O), the contexture of the transformation (V) that links them, apparently, in a direct way, and the contexture of the non-evident transformation (∇) that indirectly relates them. In this last contexture lies subjectivity.

Protein: chemical substance formed by amino acids (essential organic acids), and fundamental material of plant and animal cells. (Author's Note) Real systems: aspects in which the subjective reality has been arbitrarily divided with the object of its study. There are three: psycho-internal, bio-external and sociocultural.

RGB system: each primary color is arranged in one of the three Cartesian axes of a cube, and each assumes an arbitrary value between 0 and 255. The figure shows a "diffuse cube" with all possible 23

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combinations between the colors primary. The color of the sample arises from the "weight" of each color.

Molecular probes: they are molecules of different degree of complexity (RNA or proteins) that, being complementary to the molecules that are searched or tracked, adhere to them if they exist in the organism in which they are sought. Using a biochemical contraption, the complex: molecule sought + molecular probe, stains whit a specific color, so it is easy to locate in the ocular observation, the place or places where that gene studied is expressed. (Guillén et al., 1995) Urbilateria: first bilateral animal in the evolutionary history.

Valence: in Transcurssive Logic represents the "relational force" between the basic textures. (figure): S (subject) - O (object) - V (apparent transformation, evident or quantitative change) - ∇ (non-apparent transformation, hidden or qualitative change)

References: →, ⇒, ⇛: valences

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APPENDIX B Darwin's theory and linguistics August Schleicher, in 1853, published his "genealogical tree theory," to establish the kinship between a mother tongue and the Indo-European languages known inspired by the Systema Naturae of Linnaeus. (Figure A1) Fig. A1. Representation of the Schleicher Stammbaumtheorie

(Excerpted from Schleicher, 1853, page 787) But the concrete thing is that Schleicher strongly adheres to the idea of evolutionism, after reading the publication of Darwin, in 1860, as he leaves it recorded in a letter sent to Ernst Haeckel (1863), the great defender of Darwinism in Germany. In Die Deutsche Sprache (1860) on page 28, we can see an arborescent scheme that tries to explain the origin of the different linguistic families from an original language (Urspache) (Figure A3). Scheme very similar to that published by Darwin in 1859 (Figure A2), and according to the correlation that establishes in Chapter XIII, between languages and biological species: “It may be worthwhile to illustrate this view of classification, by taking the case of languages. If we possessed a perfect pedigree of mankind, a genealogical arrangement of the races of man would afford the best classification of the various languages now spoken throughout the world; and if all extinct Languages, and all intermediate and slowly changing dialects, had to be included, such an arrangement would, I think, be the only possible one. Yeti t might be that some very ancient language had altered Little, and had given rise to few new languages, whilst others (owing to the spreading and subsequent isolation and states of civilization of the several races, descended from a common race) had altered much, and had given rise to many new Languages and dialects. The various degrees of difference in the languages from the same stock, would have to be expressed by groups subordinate to groups; but the proper or even only possible arrangement would Still be genealogical; and this would be strictly natural, as it would connect together all Languages, extinct and modern, by the closest affinities, and would give the filiation and origin of each tongue.” (Darwin, 1859, p. 422)

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Fig. A2. Arborescent strcture of Darwin

(Excerpted from Darwin, 1859, p.128)

Figure A3. A common ancestor

(Excerpted from Die Deutsche Sprache, 1860, p.28) We can also see schemes like the previous one, in other works by Schleicher. For example, in Darwinism Tested by the Science of Language (1863, p.70). (Figure A4)

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Fig. A4. Indo-German Family

(Excerpted from Darwinism Tested by the Science of Language, 1864, p.70) In fact, Darwin, sought support for his theory in linguistics, after he knew the 1864 work of Schleicher, as shown in the following paragraph: "With respect to the origin of articulate language, after having read the highly interesting work of Prof. Schleicher, entitled" Darwinism Tested by the Science of Language ", I cannot doubt that language owes its origin to the imitation and modification, aided by signs and gestures, of various natural sounds, the voices of other animals, and man's own instinctive cries "(Darwin, 1871, p.56)

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Using pValue to Measure Significance Level of Continuous Data Louangrath, P.I.

About the Author Louanglath, P.I. is an Assistant Professor in Business Administration at Bangkok University, Thailand. For correspondence, Email: Lecturepedia@gmail.com

ABSTRACT In this paper, we provide a practical method on how to calculate pValue for continuous data. In later paper, we will cover pValue for discrete data. For continuous distribution in part 1, we defined pValue as 1 - CDF; for discrete distribution in part 2, we define pValue as 1 - MDF. The purpose of this paper is to provide a practical guide on how to calculate the pValue if the sample size, and critical value for T or Z are known. The methodology has practical value to researchers in both natural and social science. All quantitative research requires the reporting of statistical significance.

The T and Z values are known in the field. We claim new discovery for the method on how to calculate pValue through the TZ conversion equation. We proposed three steps in calculating pValue: (i) converting chi square or F to T, (ii) with known T, obtain Z by using the TZ conversion equation Z = (1.15T – 1.64) + 1.40, and (iii) with known Z, determine F(Z). The pValue is simply 1 – F(Z). This procedure is a contribution to the field because it serves as an effective tool for editorial and peer reviews in research publication. We present three tables for converting test statistics T, chi square, and F to pValue.

Keyword: error level, pValue, significance level

CITATION: Louangrath, P.I. (2018). “Using pValue to Measure Significance Level of Continuous Data” Inter.

J. Res. Methodol. Soc. Sci., Vol., 4, No. 3, pp. 28-48. (Jul. – Sep. 2018); ISSN: 2415-0371.

1.0 INTRODUCTION In research publications, pValue is generated by computer software. It is never reported as a result of a manual calculation. Thus, in peer or editorial review, the reported pValue is taken at face value.

The purpose of this paper is to provide a manual calculation method of how to determine pValue.

Despite suggestions made by some writers to use other methods for reporting significance level, pValue remains a common indicator to report.

In finding the significance level for hypothesis testing, the researcher begins with a threshold against which the observed value is compared. For example, if the researcher uses 95% confidence interval, the error level is 5%. pValue is the measurement of the error level. It answers 28

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the question of “what is the error level?” In answering this question, the standard decision rule is that the finding is not significant if the error level is larger than 5%, i.e. p > 0.05. In such a case, the null hypothesis cannot be rejected. If the error level is equal to or less than 5% or p ≤ 0.05, the null hypothesis is rejected. In sum, the significance level is the pValue; the pValue is the error level.

When reporting pValue, authors tend to describe whether the value is for one or two tails. In this paper, we do not explain pValue in terms of the “tails.” The significance level or the alpha region of the distribution curve comes from a unit circle; that circle is divided into two regions, namely 95% confidence interval and the remaining 5% random error regions all round the edge of the circle. In essence, the circle has no tail. Thus, it is an error to assert that for a 5% error level, the upper and lower tail has 2.5% error level each. If 2.5% error level is used, the confidence interval becomes 97.5%, not 95%. Fig. 1 helps illustrate this point.

Fig. 1. Transforming 3D elliptical sphere to a unit circle and half circle

In Fig. 1, the data cloud is represented by the sphere. The black dots inside the sphere represent data points (Picture A). To facilitate easy comprehension, the 3D model is simplified into 2D model of a unit circle (Picture B). This 2D model, the circle is divided into two regions: 95% confidence non-sificance region (white) and 5% error region or significance level (shaded). This 2D model is further simplified by removing one half of the circle (Picture C). What remains is the half circle with two regions divided evenly all round maintaining the distance of 95% and 5%. Thus, the concept of “upper tail” and “lower tail” is an inaccurate description of the half-circle which we call the distribution curve. In this paper, when we refer to the 5% error (alpha region) level, it is 5% not the so-called 2.5% “one-tail.”

The purpose of this paper is to provide a meaningful and practical guide for manually calculating the pValue. Although statistical software programs provide pValue as an automatic calculation, it is worth knowing how that value is calculated. For editorial or peer review purpose, knowing the method of calculating the pValue helps facilitate the evaluation of research findings.

What is the pValue? pValue is the significance level. It is the error level determined by 1 – F(Z) where F(Z) is the cumulative distribution function (CDF) or the area under the distribution curve. In this paper, we assume the normal distribution curve as a reference curve. The reading of the pValue is the reading of the random error region on the distribution curve. If the error level or the result of 1 – F(Z) is less than the predetermined error level, i.e. 5% in a 95% confidence interval then the finding is statistically significant. If the observed error level (1 – F(Z)) is greater than 5% then the finding is statistically not significant.

The alternative reading is the critical value (Z) or the percentage probability of the event or F(Z). In the pValue reading, the error level is set as the threshold level; however, in the alternative reading, the critical score or the percentage probability is used by comparing the observed to the threshold value. For instance, in a 95% confidence interval, the null hypothesis of non-significance is rejected if the observed F(Z) is equal to or greater than 95% or F ( Z ) ≥ 0.95 . If the critical value 29

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is used, then the value of the observed test statistic Z is used. For 95% confidence interval, the threshold value is 1.65. Note that for 95% confidence interval, the critical value for Z is not 1.97; at Z = 1.97, the percentage probability is 97.5%, not 95%. As shown in Fig. 1, the 5% error is all round the edge of the circle; there is no upper or lower tail. The error level is 5% equi-distance all round the edge of the circle.

2.0 LITERATURE pValue is used in many fields of research (Bhaskar and DeSale, 2002), both in the natural and social sciences (Wetzels et al., 2001). Depending on the field of research, the pValue may range from 1% to 5% (Nuzzo, 2014). In social science, a 5% level is generally used. In the natural science, using 1% pValue is common. However, a pValue reading of 1% or less may be a false alarm reading (Goodman, 2001). Thus, it is important to know how to verify the calculation.

There had been evidence of the misuse and misunderstanding of pValue. It has been misunderstood that it means the null hypothesis cannot be rejected (Colquhoun, 2014). There had been attempts to propose alternative means to reject the null hypothesis other than by using the pValue. One proposed alternative is the use of confidence interval (Lee, 2017; Ranstam, 2012).

However, confidence interval reading is not much difference than pValue because confidence interval is F(Z) and the pValue is 1 - F(Z). The confidence interval is not an alternative to pValue; it is one step less in the calculation, but not an alternative. It has also been suggested that a fixed pValue be removed and the pValue be used as a continuous indices to indicate the strength of evidence against the null hypothesis (Arhrein et al., 2017; Arhein and Greenland, 2017). Arhein's suggestion is an alternative reading of pValue, not an alternative method to pValue. pValue is not the final criterion for rejecting the null hypothesis, there must also be other supporting evidence (Goodman, 1999; Wasserstein and Lazar, 2016). For instance, effect size (Cohen, 1988; Cohen, 1992). Standardized means and odds ratio may be used to measure effect size (Hedges and Olkin, 1985; p. 369). Correlation coefficient is a common measure for effect size (Britten, 1996; Moller and Thornhill, 1998; Reed and Frankham, 2001; Koricheva, 2002; Rosenthal and Rubin, 1978).

Effect size is the measure of the magnitude of the phenomenon (Kelley and Preacher, 2012).

It considered a good research practice to report the effect size (Wilkinson; 1999; Nakagawa and Cuthill, 2007). The APA Task Force on Statistical Inference urged that:

“Always present effect sizes for primary outcomes...If the units of measurement are meaningful on a practical level (e.g., number of cigarettes smoked per day), then we usually prefer an unstandardized measure (regression coefficient or mean difference) to a standardized measure (r or d).”—L. Wilkinson and APA Task Force on Statistical Inference (1999, p. 599).

The problem with the effect size measurement is that it gives the reading in a critical value form, not on the probability space such as the confidence interval (F(Z)) or the pValue (1 – F(Z)).

For instance, the effect size under the Cohen d method, the result of the test statistic is not readily readable in percentage probability format. The Cohen’s d is given by: d=

X 1 − X 2 µ1 − µ2 = S S

(1)

( n1 − 1) S12 + (n2 − 1) S22 (Cohen, 1998, p. 67); other authors suggest removing -2 from n1 + n2 − 2 the denominator of the pool variance (McGrath and Meyer, 2006; Hartung, 2008, p.14). The evaluation of the value of d was provided by Sawilowsky (Sawilowsky, 2009; pp.467-474) as a range from 0.01 (very small), 0.20 (small), 0.50 (medium), 0.80 (large), 1.20 (very large, and 2.0

where S =

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(huge). These adjectives describing the magnitude of the effect are not meaningful because the Cohen’s d actually mimics the standard score equation: z = (xi − x ) / s where the percentage probability F(Z) may be obtained, likewise the Cohen’s d is a functional equivalence of reading the z score without going another step of finding F(Z) from which the pValue may be calculated by: 1 – F(Z).

Although the effect size is recommended for reporting the findings, nevertheless, pValue remains the most commonly used indicators for rejecting the null hypothesis because the pValue speaks in percentage probability of the error term or the error level measured in percentage form.

Since pValue is generated by software program and there is no easy manual method for calculating the pValue unless, the three steps process we introduced in this paper fills the gap in the literature.

3.0 DATA FOR ILLUSTRATION 3.1 Data used For purposes of illustration, we use the percentage change of GDP and percentage change of export volume of 10 countries in ASEAN for a period of 5 years: 2010 – 2014. This secondary data was obtained from the IMF’s annual report of World Economic Outlook 2017. In the example, we assume that export contributes to the GDP and will test this assumption by a linear equation: Ygdp = a + bX exp .

Table 1. Percentage change in GDP* of 10 countries in ASEAN from 2008 to 2017 Country 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 1* 0.18 (0.26) 0.15 0.35 0.03 (0.05) (0.05) (0.24) (0.12) 0.12 0.10 0.09 0.11 0.10 2 0.20 0.08 0.14 0.10 0.09 0.19 0.03 0.31 0.18 0.03 0.03) 0.03) 0.08 0.09 3 4 0.25 0.08 0.17 0.19 0.14 0.17 0.11 0.08 0.11 0.07 5 0.19 0.12) 0.22 0.17 0.06 0.03 0.05 (0.12) 0.06 6 0.48 0.10 0.30 0.21 0.01 0.09 (0.09) 0.06 0.05 0.05 0.03 0.04 0.03 7 0.16 0.03) 0.18 0.12 0.12 0.09 0.02 (0.02) 0.02 0.05 8 0.07 0.23 0.17 0.05 0.05 9 0.11 0.03) 0.21 0.09 0.07 0.06 0.03) (0.01) 0.03 0.11 10 0.27 0.03 0.11 0.19 0.16 0.10 0.09 0.03 0.05 0.09 *The 10 countries are: Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, Philippines, Singapore, Thailand, and Vietnam.

Source: https://www.imf.org/external/pubs/ft/weo/2017/02/weodata/index.aspx

The GDP patterns are presented in Fig. 1. Among the 10 ASEAN countries, Myanmar and Cambodia show the highest level of percentage change in annual GDP while the remaining 8 countries are in the lower level. We will later test whether this apparent group difference is significant by calculating the pValue of their difference. The test statistic for the difference will be obtained by: T=

( X1 − X 2 ) − ( µ1 − µ2 )

(2)

1 1 S + n1 n2

where S is the pool standard deviation determined by: S pool =

( n1 − 1) S12 + (n2 − 1) S22 n1 + n2 − 2

(3)

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Once we obtained T in (2), we will follow equations (9) and (10) to obtain the pValue to verify whether the two groups of countries are significantly different in their GDP. Fig. 2. Patterns of percentage change of GDP for ASEAN countries: 2008 – 2017 0.60 0.50 0.40 0.30 0.20 0.10 (0.10)

1

2

3

4

5

6

7

8

9

10

(0.20) (0.30) Brunei Myanmar

Cambodia Philippines

Indonesia Singapore

Laos Thailand

Malaysia Vietnam

The percentage change of annual GDP is defined as the dependent variable (DV). The independent variable (IV) is percentage change of annual export. The export data for 10 countries over a period of 10 years (2008 – 2017) is presented in Table 2.

Table 2. Percentage change of export of 10 countries in ASEAN from 2008 to 2017 2013 2014 2015 2016 2017 Country 2008 2009 2010 2011 2012 1 2 3 4 5 6 7 8 9 10

(7.05) 2.18 (2.95) 3.66 7.30) 0.01 2.21) 4.84 6.26 2.13

(6.66) (4.96) 5.61 4.88 10.47) 12.89 (5.87) (7.66) 12.14) 3.74

11.71 33.85 3.43 16.73 3.62 10.30 19.86 17.44 14.22 6.58

(4.58) 5.85 6.69 1.67 6.26 9.63 0.69) 6.67 9.51 4.13

(2.80) 5.98 1.85 6.69 6.49) 4.86 5.97 1.70 4.88 5.59

(9.28) 6.35 3.05 0.62 0.07 5.36 3.72 5.95 2.72 6.94

4.32 (13.77) (1.89) (1.98) 13.85 12.64 4.51 0.83 1.63 0.08 0.69) 5.56 22.08 (4.80) 0.53 6.10 6.50 4.05 3.63 8.22 9.49 (0.11) 3.44 7.18 11.52 0.44 4.99 11.38 3.36 4.74 1.11 4.10 0.26 1.57 2.79 6.59 10.98 9.77 0.74 15.85

Source: https://www.imf.org/external/pubs/ft/weo/2017/02/weodata/index.aspx In Fig. 1, we saw patterns of the percentage change in GDP for the 10 countries over the 10 years period. That patterns show the 10 countries moving in the same direction in a tighter formation. In contrast, the patterns of the percentage export change in Fig. 2, the pattern is less uniform. Export growth patterns among the countries are more diverse. The issue of pattern recognition and trend testing is beyond the scope of this paper. This cursory visual observation points to a possible research issue worth exploring.

Fig. 3. Patterns of percentage change in export for ASEAN countries: 2008 - 2017

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2

3

4

5

6

7

8

9

10

(10.00) (20.00) Brunei Myanmar

Cambodia Philippines

Indonesia Singapore

Laos Thailand

Malaysia Vietnam

The hypothesis we wanted to test is whether export significantly contributes to the GDP? There are three steps in this verification. Firstly, there is a relationship between export and the GDP or Ygdp = a + bX exp ; in this first step, it is necessary to prove that the relationship between X and Y exists by showing that b ≠ 0 . Second, determining the intensity of that relationship by calculating the correlation coefficient r by r = b( S x / S y ) ; and third, obtain the test statistic under the T equation: Tr = A / B where A = r n − 2 and B = 1 − r 2 . With known T, we then used equations 9 and 10 to determined the pValue.

4.0 METHODOLOGY 4.1 Converting chi square to T There are two steps in converting chi square to T. First, chi square is expressed in terms of correlation coefficient. Second, the correlation coefficient is extracted from the T equation in the test statistic for correlation coefficient. These two steps are illustrated below.

r=

χ12

(4)

n

where r = correlation coefficient; χ12 = chi square with df = 1, and n = sample size. To write the equation in terms of chi square, we obtain: χ12 = r 2 ( n ) . In the second step, we obtain the T value by: Tr =

r n−2

(5)

1 − r2

After chi square is converted to T, use equations 9 and 10 to convert T to pValue.

Chi square may be converted to correlation coefficient by taking the square root of the quotient of chi square (df = 1) divided by the sample size (Cohen, 1965). Using chi square with

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degree of freedom 1 is the most simplest form, larger degree of freedom makes the process more complicated (Rosenthal et al., 2000).

In case where we deal with the observed chi square that has large degree of freedom, we proposed the following procedure: (i) covert the chi square observed to chi square with df = 1, and (ii) used equation 4 to process chi square to r as in equation (4). The chi square conversion may be obtained by:

(

)

 2 2 2 χ − χ   o e 1 χ2 =   n1 + n2 − 2  χe2    

(6)

where χ e2 is the chi square value at df = 1 and χo2 is the observed chi squared with df > 1. Put the result of equation (6) into equation (4) to determine the value of r.

4.2 Converting F to T Where there are more than one chi square distributed data, the test statistic is the F test. The F test verifies the ratio of the variance of the two data sets. In order to convert the F statistic to pValue, it is necessary to convert F to T first. The T equation is given by:

r X−X T= S/ n

(7)

The F test is the ratio of the variance; thus, the equivalence of F and T is given by: X−X F =  S/ n

2

(8)

This ratio works out to be F = T 2 . To express T in terms of F then T = F . After F is converted to T, use equations 9 and 10 to convert T to pValue.

As F statistic increases linearly, the pValue decreases in a begative log pattern. As F becomes larger, pValue approaches zero. This behavior is similar to our description of the pValue in chi square (Fig. 4). Recall that F statistic is a test statistic for two samples that are chi square distributed. Thus, the graphic representation of the F statistic vis-à-vis pValue is similar to that observed in the chi square case.

In multiple regression ANOVA where F = MSR ÷ MSE , the equivalabce between F and T

by T = F is also used. Note that generally in multiple regression ANOVA, the strength of the proposed model is measured by R squared. R squared measures the explanatory power of the proposed model, this measure is not the same as pValue. pValue measures the significance or the probability of not occurring, not how many percent can the proposed model explain the data.

4.3 Determining the pValue in three steps We propose the conversion of T to pValue in three steps. First, with known T a bridge of T to Z must be constructed. This is accomplished by:

Z = (1.15T − 1.64) + 1.40

(9)

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where Z = critical value; T = Student T score, and n = sample size. Equation (9) is a new discovery in T-to-Z conversion method introduced by this paper for the first time. In the second step, with known Z critical value, the percentage probability may be determined by: F (Z ) =

( (

1

1 + exp − π β1Z 5 + β 2 Z 3 + β3Z

(10)

))

where β1 = 0.0004406, β 2 =0.0418198, and β 3 = 0.90000000. In the third step, the pValue is obtained by 1 – F(Z) which is the percentage error or the significance level or alpha level ( α ) in the distribution curve. For negative Z value, the pValue is obtaine by: 1 – (1 – (F(Z)).

5.0 FINDINGS AND DISCUSSION We present our findings in two parts. In 5.1, we present findings of pValue for nonparametric case.

In 5.2, we present findings pValue in parametric case where the linear regression for the percentage change in GDP (Y) and export (X) are used as variables.

5.1 Non-parametric testing for pValue using the standard score method In Figs. 1 and 2 we present the raw data for percentage change in the annual GDP and percentage change in export volume from 2008 to 2017. By visual impression, Fig. 1 shows that the percentage change of the GDP has a predictable pattern. This pattern is less obvious in the percentage change of export volume shown in Fig. 2. We test both data sets for their pValue by using the standard score equation and presented their results in Tables 3 and 4.

Table 3. pValue of percentage change in GDP under non-parametric testing Country Country ASEAN ASEAN Country Country Mean Mean SD Z F(Z) Brunei (0.02) 0.08 0.07 (1.41) 0.0780 Cambodia 0.07 0.08 0.07 (0.11) 0.4545 Indonesia 0.06 0.08 0.07 (0.36) 0.3606 Laos 0.08 0.08 0.07 0.03 0.5114 Malaysia 0.03 0.08 0.07 (0.77) 0.2202 Myanmar 0.05 0.08 0.07 (0.41) 0.3394 Philippines 0.05 0.08 0.07 (0.50) 0.3086 Singapore 0.03 0.08 0.07 (0.67) 0.2510 Thailand 0.04 0.08 0.07 (0.57) 0.2839 Vietnam 0.06 0.08 0.07 (0.26) 0.3986

pValue 1 – F(Z) 0.92 0.55 0.64 0.49 0.78 0.66 0.69 0.75 0.72 0.60

There is no significant growth of the GDP over the 10 years period for each country using the ASEAN group as a reference. The pValue for all countries’ percentage change in GDP are: 0.60 < p < 0.92.

We also examined the percentage change of export for the 10 countries over a period of 10 years using ASEAN group mean and standard deviation as reference points. The pValue range is 0.13 < p < 0.88. No country experience significant change in export volume growth.

Table 4. pValue of percentage change in export under non-parametric testing Country Country ASEAN ASEAN Country Country pValue Mean Mean SD Z F(Z) 1 – F(Z) Brunei (0.03) 0.06 0.07 (1.19) 0.1159 0.88 Cambodia 0.14 0.06 0.07 1.14 0.8723 0.13

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Indonesia Laos Malaysia Myanmar Philippines Singapore Thailand Vietnam

0.02 0.11 0.01 0.08 0.04 0.04 0.04 0.10

0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06

0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07

(0.44) 0.69 (0.65) 0.36 (0.19) (0.19) (0.27) 0.54

0.3315 0.7559 0.2565 0.6388 0.4232 0.4232 0.3940 0.7039

0.67 0.24 0.74 0.36 0.58 0.58 0.61 0.30

5.2 Parametric testing for pValue using linear regression For parametric testing, we examined the relationship between export and GDP and calculated the pValue for the relationship. The results show that there are 3 countries where the percentage change in export significantly contributed to the GDP; these countries include: Philippines, Singapore and Thailand. There are 7 countries where the percentage change in export does not significantly contribute to the growth of the GDP; these countries include: Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, and Vietnam.

Table 5. Relationship between GDP and export Country Linear Eq. r T Y = -0.42 + 0.59X Brunei 0.43 1.34 Cambodia Y = -0.16 + 0.13X 0.43 1.36 Y = -0.17 + 0.39X 0.36 1.09 Indonesia Y = 7.83 + 0.03X 0.22 0.63 Laos Malaysia Y = -0.08 + 0.12X 0.13 0.36 Myanmar Y = 1.59 + 0.42X 0.49 1.59 0.91 6.13 Philippines Y = 3.53 + 0.17X Singapore Y = 0.38 + 0.69X 0.81 3.92 Thailand Y = 2.71 + 0.35X 0.59 2.08 Vietnam Y = 5.89 + 0.03X 0.10 0.28 *Significant p ≤ 0.05.

Z 1.30 1.32 1.01 0.48 0.18 1.59 6.81 4.27 2.15 0.09

F(Z) 0.9033 0.9070 0.8437 0.6842 0.5707 0.9448 1.0000 1.0000 0.9854 0.5344

pValue 0.0967 0.0930 0.1563 0.3158 0.4293 0.0552 -* 0.0000* 0.0146* 0.4656

For purposes of procedural illustration, we select one country with significant finding for export growth contributing to the GDP: Philippines. Philippines has a predictive function for export to GDP as: Y = 3.53 + 0.17X. From this equation the slope of the linear regression line is b = 0.17.

The standard deviation for export growth was Sx = 8.44 and the standard deviation for the GDP growth was Sy = 1.58. The correlation coefficient is calculated by r = b(Sx / Sy) = 0.17(8.44 / 1.58) = 0.91. Using the T equation for the test statistic: T = A ÷ B where A = r n − 2 and B = 1 − r 2 ; the calculation is A = 0.91 10 − 2 = 2.57 and B = 1 − 0.912 = 0.41 . Thus, T = 2.57 ÷ 0.41 = 6.21 .

The value of T is converted to Z by Z = (1.15T − 1.64) + 1.40 or Z = ((1.15)1.36) + 1.40 or Z = 6.81. With a critical value of Z = 6.81, the percentage probability is obtained by using equation 10, the result is F(Z) = 10000. The pValue is obtained by 1 – 1.0000 = 0.0000. The threshold level for error is 0.05; since 0.0000 is less than 0.05, the pValue is statistically significant.

We illustrate a non-significant case by using Vietnam. The regression equation for Vietnam is Y = 5.89 + 0.03X. The slope is b = 0.03; the standard deviations are Sx = 5.39 and Sy = 1.62. The correlation coefficient is r = b(Sx / Sy) = 0.03(5.39/1.62) = 0.10. The test statistic is obtained by T = A ÷ B where A = 0.10 10 − 2 = 0.28 and B = 1 − 0.102 = 0.99 ; thus, T = 0.28 ÷ 0.99 = 0.28 .

The value of T is converted to Z by Z = (1.15(0.28)) – 1.64) + 1.40 = 0.09. Use equation (9) to obtain the percentage probability, the result is F(Z) = 0.5344 and pValue for Vietnam is 1 – 0.5344 = 0.4656. Since 0.4656 is larger that 0.05, Vietnam’s percentage change in export does not significantly contribute to the percentage change in the GDP.

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As a group, ASEAN’s general prediction function for the relationship between percentage change in export and percentage change in GDP is: Y = 0.04 + 0.003X. The slope of the line is 0.003 and the standard deviations are: Sx = 5.02 and Sy 0.02. The correlation coefficient r = b(Sx/Sy) is 0.71. The test statistic is T = 2.81. using equations (9), we convert T to Z by Z = ((1.15(2.81) – 1.64) + 1.40; the result is Z = 3.00. using equation (10), we determine the percentage probability to be F(Z) = 0.9990 thus, the pValue is 1 – 0.9990 or 0.0010. We conclude that the percentage change in export significant contribute to the percentage change in the annual GDP. Recall that table 5 shows 3 out of 10 countries had significant export contributed to the GDP; it may be inferred that the volume of export of these three economies in ASEAN may have been substantial to have contributed to a condition of “export-led” GDP growth for the ASEAN group.

6.0 CONCLUSION The intended contribution of this paper is the practical guide for determining pValue. All quantitative research publications are required to report the significance level. pValue is the significance or error level. Throughout this paper, the pValue or error level is fixed at 5% in a 95% confidence interval. We introduce the conversion process in three simple steps: (i) converting chi square or F to T, (ii) with known T, used the bridging equation to convert T to Z by Z = (1.15T – 1.64) + 1.40, and (iii) with known Z, determine F(Z). The pValue is simply 1 – F(Z). These three steps may be accomplished with manual calculation without any aid of computer software. In addition, these three easy steps will help facilitate editorial or peer reviews in verifying the pValue in research any article in natural or social science. We have tabulated four tables for pValued based on the T, Z, chi square and F statistics. These tables are presented inn Appendices 1, 2, 3, and 4.

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APPENDIX 1 pValue from T Statistic

Assume that T = 1.15X then Z = (1.15T – 1.64) + 1.40. In the second step, find f(Z0 by 1 F (Z ) = where β1 = 0.0004406, β 2 =0.0418198, and β 3 = 5 3 1 + exp − π β1Z + β 2 Z + β3Z

( (

))

0.90000000. Obtain pValue by 1 – F(Z). pValue of T Statistic 4.50

0.7000

4.00

0.6000

3.50 0.5000

3.00 2.50

0.4000

2.00

0.3000

1.50

0.2000

1.00 0.1000

0.50 -

1

3

5

7

9

11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 T

T 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 1.10 1.20 1.30 1.40 1.50 1.64 1.70 1.80 1.90 2.00 2.10

Z (0.24) (0.13) (0.01) 0.11 0.22 0.34 0.45 0.57 0.68 0.80 0.91 1.03 1.14 1.26 1.37 1.49 1.65 1.72 1.83 1.95 2.06 2.18

1 - F(Z)

F(Z) 0.4052 0.4503 0.4960 0.5418 0.5870 0.6311 0.6736 0.7139 0.7517 0.7867 0.8187 0.8475 0.8732 0.8957 0.9152 0.9319 0.9510 0.9577 0.9673 0.9751 0.9813 0.9862 40

pValue = 1 – F(Z) 0.5948 0.5497 0.5040 0.4582 0.4130 0.3689 0.3264 0.2861 0.2483 0.2133 0.1813 0.1525 0.1268 0.1043 0.0848 0.0681 0.0490 0.0423 0.0327 0.0249 0.0187 0.0138

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2.20 2.30 2.40 2.50 2.60 2.70 2.80 2.90 3.00 3.10 3.20 3.30 3.40 3.50 3.60 3.70 3.80 3.90 4.00

2.29 2.41 2.52 2.64 2.75 2.87 2.98 3.10 3.21 3.33 3.44 3.56 3.67 3.79 3.90 4.02 4.13 4.25 4.36

0.9900 0.9928 0.9950 0.9965 0.9976 0.9984 0.9990 0.9994 0.9996 0.9998 0.9999 0.9999 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000

41

0.0100 0.0072 0.0050 0.0035 0.0024 0.0016 0.0010 0.0006 0.0004 0.0002 0.0001 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

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APPENDIX 2 pValue from Z Statistic

With known +Z, obtain F(Z) by F ( Z ) =

( (

1

1 + exp − π β1Z 5 + β 2 Z 3 + β3Z

))

where β1 =

0.0004406, β 2 =0.0418198, and β 3 = 0.90000000. Obtain pValue by 1 – F(Z) where the value of Z ranges from 0 to +4. With known -Z, obtain F(Z) by (1 - (1- F(Z)) where the value of Z ranges from 0 to -4: 1 where β1 = 0.0004406, β 2 =0.0418198, and β 3 = F (Z ) = 1 + exp − π β1Z 5 + β 2 Z 3 + β3Z

( (

))

0.90000000.

pValue of Negative & Positive Z Statistic 5.00

0.6000

4.00 0.5000

3.00 2.00

0.4000

1.00 0.3000

1

3

5

7

9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55

(1.00) 0.2000

(2.00) (3.00)

0.1000

(4.00) (5.00)

-

Z

Z (4.00) (3.85) (3.70) (3.55) (3.40) (3.25) (3.10) (2.95) (2.80) (2.65) (2.50) (2.35) (2.20) (2.05) (1.90) (1.75)

1 - F(Z)

F(Z) 1.0000 0.0000 0.0000 0.0001 0.0002 0.0003 0.0006 0.0011 0.0020 0.0033 0.0054 0.0084 0.0129 0.0192 0.0277 0.0391

pValue = 1 – F(Z) 0.0000 1.0000 1.0000 0.9999 0.9998 0.9997 0.9994 0.9989 0.9980 0.9967 0.9946 0.9916 0.9871 0.9808 0.9723 0.9609 42

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(1.60) (1.45) (1.30) (1.15) (1.00) (0.85) (0.70) (0.55) (0.40) (0.25) (0.10) 0.05 0.20 0.35 0.50 0.65 0.80 0.95 1.10 1.25 1.40 1.55 1.70 1.85 2.00 2.15 2.30 2.45 2.60 2.75 2.90 3.05 3.20 3.35 3.50 3.65 3.80 3.95 4.00

0.0540 0.0729 0.0963 0.1247 0.1585 0.1976 0.2420 0.2912 0.3446 0.4013 0.4602 0.5000 0.5199 0.5792 0.6368 0.6914 0.7421 0.7882 0.8291 0.8646 0.8948 0.9198 0.9402 0.9563 0.9688 0.9783 0.9852 0.9902 0.9937 0.9961 0.9976 0.9986 0.9992 0.9996 0.9998 0.9999 1.0000 1.0000 1.0000 1.0000

0.9460 0.9271 0.9037 0.8753 0.8415 0.8024 0.7580 0.7088 0.6554 0.5987 0.5398 0.5000 0.4801 0.4208 0.3632 0.3086 0.2579 0.2118 0.1709 0.1354 0.1052 0.0802 0.0598 0.0437 0.0312 0.0217 0.0148 0.0098 0.0063 0.0039 0.0024 0.0014 0.0008 0.0004 0.0002 0.0001 0.0000 0.0000 0.0000 0.0000

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APPENDIX 3 pValue from Chi Square Statistic

χ12

First convert chi square to r by r =

then obtain T by Tr =

n

r n−2 1 − r2

. With known T, convert T

to Z by T = 1.15X then Z = (1.15T – 1.64) + 1.40. With known Z, obtain F(Z) by 1 F (Z ) = where β1 = 0.0004406, β 2 =0.0418198, and β 3 = 1 + exp − π β1Z 5 + β 2 Z 3 + β3Z

( (

))

0.90000000. Obtain pValue by 1 – F(Z). pValue of Chi Square Statistic 16.00

0.1800

14.00

0.1600

12.00

0.1400 0.1200

10.00

0.1000 8.00 0.0800 6.00

0.0600

4.00

0.0400

2.00

0.0200

X^2

Chi square 1.00 1.20 1.30 1.40 1.50 1.64 1.76 1.88 2.00 2.12 2.24 2.36 2.48 2.60 2.72 2.84 2.96 3.08 3.20

Z 1.00 1.10 1.14 1.18 1.22 1.28 1.33 1.37 1.41 1.46 1.50 1.54 1.57 1.61 1.65 1.69 1.72 1.75 1.79

111

106

96

101

91

86

81

76

71

66

61

56

51

46

41

36

31

26

21

16

11

6

1

-

1 - F(Z)

F(Z) 0.8415 0.8636 0.8732 0.8820 0.8901 0.9003 0.9082 0.9154 0.9220 0.9280 0.9335 0.9385 0.9431 0.9474 0.9513 0.9549 0.9582 0.9613 0.9641 44

pValue = 1 – F(Z) 0.1585 0.1364 0.1268 0.1180 0.1099 0.0997 0.0918 0.0846 0.0780 0.0720 0.0665 0.0615 0.0569 0.0526 0.0487 0.0451 0.0418 0.0387 0.0359

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3.32 3.44 3.56 3.68 3.80 3.92 4.04 4.16 4.28 4.40 4.52 4.64 4.76 4.88 5.00 5.12 5.24 5.36 5.48 5.60 5.72 5.84 5.96 6.08 6.20 6.32 6.44 6.56 6.68 6.80 6.92 7.04 7.16 7.28 7.40 7.52 7.64 7.76 7.88 8.00 8.12 8.24 8.36 8.48 8.60 8.72 8.84 8.96 9.08 9.20 9.32 9.44

1.82 1.85 1.89 1.92 1.95 1.98 2.01 2.04 2.07 2.10 2.13 2.15 2.18 2.21 2.24 2.26 2.29 2.32 2.34 2.37 2.39 2.42 2.44 2.47 2.49 2.51 2.54 2.56 2.58 2.61 2.63 2.65 2.68 2.70 2.72 2.74 2.76 2.79 2.81 2.83 2.85 2.87 2.89 2.91 2.93 2.95 2.97 2.99 3.01 3.03 3.05 3.07

0.9667 0.9692 0.9714 0.9735 0.9754 0.9772 0.9788 0.9803 0.9817 0.9831 0.9843 0.9854 0.9864 0.9874 0.9883 0.9892 0.9899 0.9907 0.9913 0.9919 0.9925 0.9931 0.9936 0.9940 0.9945 0.9949 0.9952 0.9956 0.9959 0.9962 0.9965 0.9967 0.9970 0.9972 0.9974 0.9976 0.9978 0.9979 0.9981 0.9982 0.9984 0.9985 0.9986 0.9987 0.9988 0.9989 0.9990 0.9990 0.9991 0.9992 0.9992 0.9993 45

0.0333 0.0308 0.0286 0.0265 0.0246 0.0228 0.0212 0.0197 0.0183 0.0169 0.0157 0.0146 0.0136 0.0126 0.0117 0.0108 0.0101 0.0093 0.0087 0.0081 0.0075 0.0069 0.0064 0.0060 0.0055 0.0051 0.0048 0.0044 0.0041 0.0038 0.0035 0.0033 0.0030 0.0028 0.0026 0.0024 0.0022 0.0021 0.0019 0.0018 0.0016 0.0015 0.0014 0.0013 0.0012 0.0011 0.0010 0.0010 0.0009 0.0008 0.0008 0.0007

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9.56 9.68 9.80 9.92 10.04 10.16 10.28 10.40 10.52 10.64 10.76 10.88 11.00 11.12 11.24 11.36 11.48 11.60 11.72 11.84 11.96 12.08 12.20 12.32 12.44 12.56 12.68 12.80 12.92 13.04 13.16 13.28 13.40 13.52 13.64 13.76 13.88 14.00

3.09 3.11 3.13 3.15 3.17 3.19 3.21 3.22 3.24 3.26 3.28 3.30 3.32 3.33 3.35 3.37 3.39 3.41 3.42 3.44 3.46 3.48 3.49 3.51 3.53 3.54 3.56 3.58 3.59 3.61 3.63 3.64 3.66 3.68 3.69 3.71 3.73 3.74

0.9994 0.9994 0.9994 0.9995 0.9995 0.9996 0.9996 0.9996 0.9997 0.9997 0.9997 0.9997 0.9998 0.9998 0.9998 0.9998 0.9998 0.9998 0.9998 0.9999 0.9999 0.9999 0.9999 0.9999 0.9999 0.9999 0.9999 0.9999 0.9999 0.9999 0.9999 0.9999 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000

46

0.0006 0.0006 0.0006 0.0005 0.0005 0.0004 0.0004 0.0004 0.0003 0.0003 0.0003 0.0003 0.0002 0.0002 0.0002 0.0002 0.0002 0.0002 0.0002 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

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APPENDIX 4 pValue from F Statistic

First convert F to T by F = T 2 . To express T in terms of F then T = F . With known T, covert T to Z by T = 1.15X then Z = (1.15T – 1.64) + 1.40. In the second step, find f(Z0 by 1 F (Z ) = where β1 = 0.0004406, β 2 =0.0418198, and β 3 = 1 + exp − π β1Z 5 + β 2 Z 3 + β3Z

( (

))

0.90000000. Obtain pValue by 1 – F(Z). pValue of F Statistic 6.00

0.2000 0.1800

5.00

0.1600 0.1400

4.00

0.1200 3.00

0.1000 0.0800

2.00

0.0600 0.0400

1.00

0.0200 -

1

3

5

7

9

11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 F

F 1.00 1.10 1.20 1.30 1.40 1.50 1.60 1.70 1.80 1.90 2.00 2.10 2.20 2.30 2.40 2.50 2.60 2.70 2.80 2.90 3.00 3.10

T 1.00 1.05 1.10 1.14 1.18 1.22 1.26 1.30 1.34 1.38 1.41 1.45 1.48 1.52 1.55 1.58 1.61 1.64 1.67 1.70 1.73 1.76

Z 0.91 0.97 1.02 1.07 1.12 1.17 1.21 1.26 1.30 1.35 1.39 1.43 1.47 1.50 1.54 1.58 1.61 1.65 1.68 1.72 1.75 1.78

1 - F(Z)

F(Z) 0.8187 0.8332 0.8463 0.8582 0.8691 0.8790 0.8882 0.8965 0.9042 0.9113 0.9178 0.9238 0.9293 0.9344 0.9392 0.9435 0.9476 0.9513 0.9548 0.9580 0.9610 0.9638 47

pValue = 1 – F(Z) 0.1813 0.1668 0.1537 0.1418 0.1309 0.1210 0.1118 0.1035 0.0958 0.0887 0.0822 0.0762 0.0707 0.0656 0.0608 0.0565 0.0524 0.0487 0.0452 0.0420 0.0390 0.0362

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3.20 3.30 3.40 3.50 3.60 3.70 3.80 3.90 4.00 4.10 4.20 4.30 4.40 4.50 4.60 4.70 4.80 4.90 5.00

1.79 1.82 1.84 1.87 1.90 1.92 1.95 1.97 2.00 2.02 2.05 2.07 2.10 2.12 2.14 2.17 2.19 2.21 2.24

1.82 1.85 1.88 1.91 1.94 1.97 2.00 2.03 2.06 2.09 2.12 2.14 2.17 2.20 2.23 2.25 2.28 2.31 2.33

0.9664 0.9688 0.9710 0.9730 0.9749 0.9767 0.9784 0.9799 0.9813 0.9827 0.9839 0.9850 0.9861 0.9871 0.9880 0.9889 0.9897 0.9904 0.9911

48

0.0336 0.0312 0.0290 0.0270 0.0251 0.0233 0.0216 0.0201 0.0187 0.0173 0.0161 0.0150 0.0139 0.0129 0.0120 0.0111 0.0103 0.0096 0.0089

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Mandatory Disclosures and Investment Decisions in Consumer Goods Sector of Nigerian Economy Siyanbola, T. T.,

Enyi, E. P.,

Adegbie, F. F.,

& Nwaobia, A. N.

ABSTRACT Recent financial crises and global corporate failures have been attributed to inadequate provision of information to investors. This study determined the effect of mandatory disclosures of accounting information on investment decisions in consumer goods sector of the Nigerian economy. Positive Accounting Theory and Portfolio Theory were reviewed as theoretical foundation for this study.

Data were collected from audited financial reports of 8 out of 22 firms in consumer goods sector listed on Nigerian Stock Exchange as of 2016 for 10 years period from 2007 to 2016, using purposive judgment sampling. Pre-estimation and post estimation tests were conducted on the series and the final regression estimate was made. The Hausman test results favor the adoption of random effect models without control variables, while models with control variables indicated the appropriateness of fixed effect using Breusch-Pagan Lagrangrian Multiplier test. Both models confirmed the presence of heteroscedasticity and robust standard errors were used to estimate them to avoid estimation bias. The probability value for Wooldridge tests for autocorrelation at 0.67 and 0.07, for volume of share traded and market value of shares respectively, confirmed absence of first order autocorrelation. Final regression analysis shows that both IFRSDI and RinDI at 146.75(p = 0.00) and 825.53(p = 0.00) for volume of shares traded and market value of shares respectively, have positive and significant relationship with investment decisions in consumer goods sector of Nigerian economy. The study concluded that accounting information disclosures are among determinants of investment decisions in consumer good sectors in Nigeria. It was recommended that investors should always demand adequate disclosure of accounting information by corporate entities in Nigeria before investing in those companies.

Key words: Accounting disclosure, International financial reporting standard disclosure, Investment decisions, Mandatory disclosure, Regulatory induced disclosure.

JEL Code: G01 CITATION: Siyanbola, T. T., Enyi, E. P., Adegbie, F. F., and Nwaobia, A. N. (2018). “Mandatory Disclosures and Investment Decisions in Consumer Goods Sector of Nigerian Economy.” Inter. J. Res. Methodol. Soc. Sci., Vol., 4, No. 3: pp. 49-67. (Jul. – Sep. 2018); ISSN: 2415-0371.

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1.0 BACKGROUND INFORMATION The introduction of International Financial Reporting Standards (IFRS) by the International Accounting Standards Board (IASB) replaced the International Accounting Standards (IAS) and created the need for the harmonisation or convergence of accounting standards of different countries across the globe in order to conform to international best practice. One of the merits of this uniformity in accounting standards is the seamless capital market trading among nations without the need to re-adjust their financial reports before international capital market activities are conducted. By 2016, 149 countries had converged to IFRS either by adapting, adopting or harmonising their local standards to international standards (IASB, 2016). The fact that the whole world has become a global village, without any hindrance resulting from differences in financial interpretation from one country to another, has led to increased demand for quality financial reporting by discerning investors. Quality financial reporting can only be possible when all material facts are contained in a firm’s annual report. According to Popova et al. (2013), disclosure is one of the tools used by a firm’s management to communicate information to investors and all other stakeholders. Wallace and Nassir (1995) regard disclosure as a way of communicating economic information concerning a firm’s financial performance and position. Disclosure can take two forms: mandatory and voluntary. While mandatory disclosures are compelled by statutes and accounting standards, voluntary disclosures are at the discretion of a firm’s management (Umoren, 2009). Both mandatory and voluntary disclosures are needed by investors for the purpose of investment decisions. The focus of this study is on mandatory disclosures.

Authors are divided on what is to be regarded as mandatory disclosures. While authors such as Akhtaruddin (2005), Owusu-Ansah (1998) and Sejjaaka (2004) regard mandatory disclosures as those imposed by law and other regulations within a country; others like Umoren and Okougbu (2011) regard mandatory as only those required by international standards. Eintwistle (1997) was more specific when he asserted that mandatory disclosures occur when companies are compelled by the regulatory authorities to disclose certain amount of information in corporate reports, but he failed to mention that corporate entities are also compelled to comply with the disclosure requirements imposed by accounting standards set and recognised by the country. This study, therefore, considered the two classes of mandatory disclosures namely: mandatory IFRS disclosures and mandatory Regulatory-induced disclosures.

Akintoye (2016) classified the functions of financial management into five decisions: investment, financing, profit, dividend and retention, and liquidity. For Akintoye, investment decision takes the center stage as it involves commitment of resources towards the acquisition of assets for a longer period. One of the criteria on which an investor bases his investment is access to information on that investment. Non-availability of vital information is tantamount to information asymmetry which may result in serious agency problems and wrong investment decisions. Herbei et al. (2015) believe that the increasing need for information as an important tool in investment decision guidance which creates close tie between information and investment decisions making process; hence, information is vital when it comes to investment decisions.

Under the Nigerian government’s support, studies were conducted by World Bank on the compliance of Nigerian firms with international best practices. Aside from the World Bank reports (2004) and (2011) numerous other authors including Adeyemi (2006); Adeyemi and Asaolu (2013); Bala (2013); Feyintimi (2014); Ibrahim and Jafar (2013); Saidu and Dauda (2014); Siyanbola et al., (2014); Umoren and Okougbo (2011); Wallace (1988); Zango et al., (2015) have also investigated disclosure practices in Nigeria. They observed weak corporate reporting practices aside from the fact that the level of disclosure compliance by listed companies in Nigeria lag behind what is obtainable in other markets. This study examined the effect of mandatory disclosures on investment decision in firms operating in the consumer goods sector in Nigeria economy during and post IFRS adoption periods. Twenty five generally applicable IFRSs and thirteen SAS were selected for this study as IFRS mandatory disclosure requirements and disclosure requirements mandated by Companies and Allied Matters Act (CAMA), and the Nigerian Stock Exchange were regarded as

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Regulatory induced mandatory disclosures. Investment decisions were represented by volume of shares traded and market value of shares of the selected firms.

Many studies such as Ailwan et al., (2013); Al-jabali and Ata (2014); Dutta and Nezlobin (2016); Klinskhon and Ussahawanitchakit (2016); Wen (2007) were carried out in developed countries on the impact of accounting information disclosures on investment decisions, but in Nigeria only few related studies are in place, such as Adeyemi and Ogundele (2003); Agboola et al., (2013) and Kantude (2005). These studies provided no significant validity of existing empirical evidence of the impact of accounting information disclosures on investment decisions in Nigerian firms. This study intends to fill this gap in the literature.

Onoh et al., (2017) examined trading volume and market turnover in the Nigerian Capital Market: Implication to stock market returns using data sourced on daily statistics from trading at the stock market from 2001 to 2015. The study found the all share index increased the volume of the transaction ratio by 6%. It also found that volume of the transactions having a positive correlation with turnover ratio as a 1% increase in volume of shares traded increases turnover ratio by 24%.

Low trading volume stocks also out-performed the high trading volume stocks due to illiquidity.

The true position of the model validity would have been revealed by the result of robustness test which was not carried out. Our study addressed this oversight.

The study by Angahar and Malizu (2015) adopted an ex-post facto research design with population of 198 companies listed in the Nigerian Stock Exchange as of 2011, using a sample of 40 companies reveals a significant relationship between earnings and stock returns, but nonsignificant relationship between change in earnings and stock returns. The study was affected by the period of the study, which was 5 years; this was considered too short for this type of study. The recommendation that listed companies should work toward increasing their earnings at the end of the year, since stock returns are related to earning pose some dangers because companies would go extra miles, including manipulation of accounting figures in order to meet this objective. This is tantamount to fraud and the gap in the period of study had been guarded against in this study by using 10 years period between 2007 and 2016. This is long enough to bring out generalizable conclusion.

Most prior studies traced variations in disclosure compliance to different firm attributes including profitability, age, liquidity, leverage, size, industry type and audit quality (Glaum and Street, 2002; Street and Bryant, 2000; Street and Gray, 2001). Few other studies, such as Ailwan, Katrib and Samara (2013); Al-jabali and Ata (2014) and Dutta and Nezlobin (2016) were related to investment decision concentrated on share price as the proxy for investment decision. Since share price can be affected by many factors including inflation and state of the economy, we, therefore, used both the volume of share traded and the market value of shares that can be directly attributed to the level of disclosure by companies in this study. We only controlled for profitability, size, leverage and liquidity to bring out the impact of these variables.

Two approaches of deriving the disclosure indices are Dichotomous (Cooke, 1992) and Partial Compliance (Al-Shaib, 2003). Dichotomous is unweighted as each item in the standard is treated equally; the only fault in it is that standards with higher number of items to be disclosed would have a higher score than those with smaller ones. Partial compliance approach, on the other hand, treats all standards equally no matter what the number of items is required to be disclosed by a standard. Since full disclosure is required to help investors in their investment decisions, this study was based on Dichotomous approach.

The remaining sections of this paper are organized as follows: Section 2 discussed statement of the problem including the objective of the study; Section 3 reviewed the relevant literature including empirical review of mandatory disclosure and investment decisions, theoretical framework and hypotheses development. Methodology and variable measurement are contained in Section 4; detailed analysis and discussion of results are reflected in Section 5; and the conclusion and recommendations are presented in Section 6.

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2.0 STATEMENT OF THE PROBLEM For a financial report to be considered appropriate for investment decision, certain qualities must be present. Such qualities include verifiability, relevance, completeness, neutrality, timeliness, understandability and reliability (IASB, 2010). Since investors depend on financial reports in making decision, they are always eager to believe that all the qualities mentioned are present in the report. However, problems relating to the quality of financial information contained in financial reports have been an area of debate in accounting theory and practice. In theory, the problem of relevance of financial information to investment decision had been directed at the failure of traditional accounting to recognize and measure intangible assets (Barth et al., 2008). Of all the 4ms (man, machine, money and material) of production, it is only human resource (men), which is the coordinating aspect of all the other 3ms, that is treated as expenses (in the Statement of Profit or Loss) while others are capitalized in the Statement of Financial Position. In practice, the effect of Enron, WorldCom and other international financial scandals had called to question the quality of financial reporting. This led to the enactment of Sarbanes-Oxley Act by the US government in 2002 (Klinskhon & Ussahawanitchakit, 2016). Nigeria also had similar scandal in Cadbury Plc. due to over-inflated financial statement, which was estimated to be between N13bn and N15bn (Ajayi, 2006). It led to the adjustment of the company’s account, thus, leading to an operating loss of N4.665bn in 2006 from the previous year’s profit of N2.711bn (Cadbury Plc. 2006). This led to loss of value to investors as their stock prices slid in the market. The above problems occurred in spite of the local accounting standards (SAS) and other regulations before the adoption of IFRS and the monitoring through Financial Reporting Council of Nigeria in 2011, a consequent of the recommendation of World Bank Report (2011). It is not certain if compliance with the mandatory IFRS, and other reviewed regulations, had helped to resolve the transparency problems. This paper was directed at determining the effect of mandatory disclosures on investment decisions in listed firms within the consumer goods sector of Nigerian economy in pre and post IFRS periods.

3.0 LITERATURE REVIEW 3.1 Mandatory Disclosures To assist investors in forming opinion about a company, all necessary information must be provided in the annual report. Karim and Ahmed (2005) regarded disclosure as the appearance of an item of information in the annual report of an entity, but Khodadadi et al. (2010) were more specific as to the type of information involved by defining it as the transferring and presentation of economic information (financial/non-financial and quantitative/qualitative) relating to the status and operations of the firm. Alberto (2010) categorized disclosure practice into institutional or mandatory and voluntary or firm specific. Information are said to be mandatory, if they are required by accounting standards (IFRS disclosures). They are also mandatory if required by law, regulations and widely used in business practices; this is called regulatory induced disclosure. Each institution is mandated to disclose certain information in line with the statute guiding it, aside from the accounting standard (Sejjaaka, 2004). To achieve uniformity, this study only considered the requirements of CAMA and NSE as mandatory regulatory disclosures. CAMA CAP. C20 LFN 2004 is presently the major statute governing financial reporting of private and public companies in Nigeria (Umoren, 2009). Matters relating to financial reporting in CAMA can be obtained from Part XI – Financial Statement and Audit. Specifically Ss331 – 334 are concerned with accounting records; form and content of company individual and group financial statements are contained in Ss335 - 341. Section 19 of amendment to the listing rules released by SEC in May 2014 (NSE, 2014) detailed the disclosure requirements in the financial report of listed companies in Nigeria.

3.2 Accounting Standards Accounting practices are guided by rules and regulations which are compiled into accounting standards (Izedonmi & Ola, 2001). Before the convergence to International Financial Reporting Standards (IFRS), Nigeria regulated its accounting practices through the application of both the local and international standards. The local standards were Statement of Accounting Standards

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(SAS) set by the Nigerian Accounting Standards Board (NASB). The international standards, on the other hand, International Accounting Standards (IASs), were set by International Accounting Standards Committee (IASC). Due to low compliance level with SAS by Nigerian firms and based on the recommendations of the World Bank (2011), Nigeria dropped its local standards and started to adopt the International Financial Reporting Standards (IFRS). Based on the recommendation and in conformity with the world best practice, Financial Reporting Council of Nigeria was also established to take over the functions of the Nigerian Accounting Standards Board.

3.3 Investment Decisions The process involved in investment decision includes the giving up of consumption of resources in exchange for security acquisition. Once the investor decided to utilise funds to acquire securities, the next move is to allocate the funds among available securities in the market. Anao et al. (1993) recognised investment environments to be legal and financial. While the legal environment takes care of the various statutes establishing an organization, which include such issue as its investment windows, the financial environment is made up of money market and capital market. The money market exists for sourcing short term finance with maturity ranging from one to two years. The capital market is in place for sourcing long term funds, such as corporate stocks, preference shares, debentures and bonds. They also recognised other types of investments such as: investment in real estate; investment in chattels; insurance policy (life and non-life); pension funds; investment trust and unit trust. The most common investment open to individual investor is investment in stock/shares of entities which form the basis of our study.

Portfolio theory can be used to explain the type of financial information that an individual requires when investing or divesting in a company. Therefore, individual investor can split investment between a company that provides inadequate information and the one with full and adequate information, with a view to reducing the impact of risk by engaging in what is termed as portfolio diversification (Markowitz, 1959). There are three methods of measuring investment decisions: volume of shares traded; market value of shares and volume of new issues within the period. Out of these three, volume of shares seems to be more appealing as it can be directly attributed to level of disclosure by a company. Market value of shares is also good but the price involved in the computation can be affected by many factors including inflation and state of the economy. Volume of new issue within the period is less popular in the sense that not all the listed companies embark on new issue of share in the period under investigation. To avoid such gaps, we dropped this last method and adopted the first two measurements to represent our dependent variables in this study. Volume of share is the quantity or number of shares traded daily in Nigerian Stock Exchange. The growth of any capital market is attributable to the volume of shares traded in that market as captured by Oluwatoyin & Gbadebo (2009). Market value of shares, sometimes regarded as market capitalization, is defined as the product of share price and number of outstanding shares of listed domestic companies. It is one of the indicators used in determining the growth of national economy; hence, it is normally compared with the countryâ&#x20AC;&#x2122;s GDP from one period to another. According to statistics from World Federation of Exchange data base (2016), the total market capitalization of listed domestic companies in Nigeria in 2015 was $49,973,880,000.

The value had fluctuated over the past 22 years with the highest growth reported at 51.00% in 2007 and the lowest at 4.02% in 2002.

3.4 Empirical Review Various scholars discussed in this study had studied the effect of mandatory disclosures on investment decisions in firms. Onoh et al. (2017) investigated the effect of trade volume and market turnover on daily stock returns of NSE (using stata statistical package to regress the relationship) found trade volume having negative but significant effect on stock returns attributable to possible anticipation of higher market illiquidity by investors. This was consistent with the positive cross-sectional relationship between stock returns and illiquidity. Biddle and Hillary (2006), in their study on the relationship between accounting information quality and

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investment efficiency, found accounting information quality negatively associated with both firm under-investment and over-investment. Firms with higher quality information deviate less from anticipated investment levels and also are insensitive to macro-economic conditions. This relation between accounting information quality and investment efficiency was supported more directly by Cheng et al., (2013) in their examination of investment behavior of sampled firms that disclosed internal control weaknesses under the Sarbanes Oxley Act. They found that prior to the disclosure, firms under-invest when they are financially constrained and over-invest when they are financially unconstrained. They also found that after the disclosure, the firms’ investment efficiency improves significantly.

Ren (2016) also found the improvement of the accounting information quality to be a solution to under or over investment of a firm, which can lead to improvement in efficiency of capital allocation. Several studies such as those by Daske et al. (2008); Li (2010); and Soderstrom and Sun (2008) emphasized the importance of enforcement mechanisms in investment decisions resulting from IFRS adoption. This implies that convergence to IFRS may not be effective unless there is enforcement mechanism which includes the activities of regulatory bodies and local statutes, such as the Financial Reporting Council of Nigeria regulations, the Companies and Allied Matters Act and Nigerian Stock Exchange Rules. Since IFRS are principle based accounting standards, firms need to follow general principles rather than the detailed standards and adapt these general principles to specific situation (Ball, 2009). Therefore, the legal system or what we regard as other regulatory induced activities are important in determining accounting quality needed for investment decisions in firms. Various studies were industry specific: Bala (2013) wrote on effect of IFRS adoptions on financial report of Nigerian listed companies – case study of Oil and Gas Co.; Siyanbola et al., (2014) assessed compliance with disclosure requirements of IAS 16 by listed agricultural firms in Nigeria; Amaoko and Asante (2012) were on six of the eight listed banks in Ghana; Adekunle and Asaolu (2013), Bshayreh et al. (2014), Saidu and Dauda (2014), Sejaaaka (2014), Zango et al. (2015) were all based on the banking subsector of financial service sector.

From empirical review, we observed the paucity of literature relating mandatory disclosure to investment in firms in consumer goods sector in Nigeria.

3.5 Theoretical Consideration and Hypothesis Development Both positive accounting theory and portfolio theory form the basis on which the hypotheses of this paper were developed. The relevance of positive accounting theory, used in several similar studies

by Adeyemi, 2006; Ali, Ahmed and Henry, 2004 and Umoren, 2009 is believed to have direct linkage with four other theories namely: agency, shareholders, stakeholders and information asymmetry. As the theory emphasised, the manager acts as the agent (agency theory), in his selfish way of withholding information (asymmetric information theory) from the owners (shareholders theory) and others including creditors (stakeholders’ theory), by not disclosing fully all material information deemed useful for the decision making by other parties.

Portfolio theory as developed by Markowitz (1959) is premised on risk and returns accruable to an average investor; all subsequent theories of finance and investment borrow from this theory. Therefore, every investor requires adequate information relating to the entities to which he wants to put his money. This paper makes the following testable hypotheses:

(i)

IFRS and other regulatory induced disclosures have no significant relationship with volume of shares of listed consumer good firms in Nigeria; and

(ii)

IFRS and other regulatory induced disclosures do not have any significant relationship with market value of shares of listed consumer good companies in Nigeria.

4.0 DATA AND METHODOLOGY This study adopted an ex-post facto research design. Secondary data were extracted from annual reports and accounting information of eight companies for a period of ten years (2007 – 2016).

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Purposive sampling method was used to select the sampled firms from the total population of one hundred and seventy four (174) firms listed on Nigerian Stock Exchange (NSE, 2017). The criterion for selection is the availability of data. Three variables were identified: dependent variable represented by investment decisions with two proxies of volume of shares traded and market value of shares; independent variable represented by mandatory disclosure indices with two proxies of IFRS Disclosure Index (IFRSDI) and Regulatory induced Disclosure Index (RinDI) and the control variables of firm characteristics proxies of profitability (ROE), firm size, leverage (LEV) and liquidity (LIQ). The measurement procedures of these variables are as discussed below.

4.1 Investment Decisions Several studies in the past, for example, Ailwan et al., (2013); Al-jabali and Ata (2014) and Dutta and Nezlobin (2016) showed that accounting disclosure relates to investment decisions; they concentrated on share prices as proxy for investment decision. Since share price can be affected by many factors including inflation and state of the economy, we used both volume of shares traded and market value of shares that can be directly traced to the level of disclosure by companies.

Volume of shares (VS) and market value of shares (MV) are defined as: VSit = Volume of Shares of individual firm obtained from NSE and MVit = Share price multiplied of the Volume of shares traded.

4.2 Mandatory Disclosures The Dichotomous approach (Cooke, 1992) was adopted to determine the disclosure indices using 25 commonly applicable IFRS and 13 SAS for the period prior to IFRS adoption. ‘Yes’ was indicated when disclosed and ‘No’ for non-disclosure. The same approach was adopted for RinDI using disclosure requirements in CAMA and NSE. The study examined 23 of such items, assigning ‘Yes’ for disclosure and ‘No’ for non-disclosure.

4.3 Firms’ Characteristics Profitability can be measured using Return on Equity (ROE) (ratio of net income to shareholders’ equity). It can also be measured by using Return on Assets (ROA) (ratio of net income to total assets). Other variants of profitability used in the past are: Return on Capital Employed (ROCE) used by Agyei-Mensah (2013) and Profit margin adopted by Akman (2011), Feyitimi (2014), Gongon and Gongon (2014) and Mbetu et al., (2014). Scholars who had earlier used ROE as proxy for profitability include: Agyei-Mensah (2012); Alanezi et al., (2016); Atanasovki et al., (2015); Demir and Bahadir (2014); Kareem and Ahmed (2005); Pounce et al., (2016) and Santos et al.

(2014). Those that had used ROA in the past include: Abid and Sahiq (2015) and Adeyemi & Asaolu (2013). We defined profitability as ROE, computed as Profit after Tax/Total Equity.

Studies of the association of disclosure compliance with company size have attributed higher disclosure of information by larger firms than smaller firms (Owusu-Ansah and Yeoh, 2005; Wallace et al. (1994). Scholars have also used several concepts to define company size in their studies. Some of these concepts include: book value of equity; total assets; log of market capitalization; net assets; log of total assets; total sales; number of employees; log of value traded; assets-in-place; asset size. This study adopted Demir and Bahadir (2014) concept of total assets to proxy company size, that is, the size of a firm is measured by the magnitude of its balance sheet position at the end of the period.

Some studies used different proxies for leverage in a firm using either debt/equity ratio (Agyei-Mensah, 2013; Bothelo et al., 2015; Kang and Grey, 2011; Yiadom and Atsunyo, 2014), or debt/total asset ratio (Abid and Shaiq, 2015; Demir and Bahadir, 2014; Santos et al., 2014), or total liabilities/total assets ratio (Alturki, 2014), or log of total debt/equity (Alanezi et al., 2016). For the purpose of this study, we defined leverage as the ratio of total debt on equity.

Wallace and Nasir (1995) found that firms with lower liquidity tends to provide more detailed information in their annual report than a firm with higher liquidity, hence they found a significant negative association between disclosure levels and liquidity ratios. This contradicts the

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findings of Adeyemi and Asaolu (2013) and Agyei-Mensha (2012) which showed positive relationship between disclosure levels and liquidity ratios. In this study we defined liquidity as Liquid Asset/Current Liability.

4.4 Modelling The models used in this study to test the hypotheses are: Model 1 (without control variables) Log (VS) it = β0 + β1IFRS DIit + β2R in DIit + µit Model 1 (with Control Variables) Log (VS) it = β0 + β1IFRS DIit + β2R in DIit + β4ROEit + Β5Sizeit + β6LEVit + β7LIQit + µit Model 2 (without control variables) Log (MV) it = α0 + α1IFRS DIit + α2R in DIit + Ɛit Model 2 (with control variables) Log (MV) it =

α0 + α1IFRS DIit + α2R in DIit + α4ROEit + α5Sizeit + α6LEVit + α7LIQit + Ɛit

We expected accounting information disclosure variants to be significant and positively related to investment decisions because the more the information disclosed by a firm in its annual reports, the more responsive the investors to its shares in the market.

All-together we used 80-firm year observations for our models. Data obtained from volume of shares traded in model 1 and market value of shares in model 2 are discussed and analysed.

5.0 FINDINGS AND DISCUSSIONS 5.1 Descriptive Analysis We provide an overview of the data set including description of main attributes of the data. The descriptive analysis of the panel data was done by numerical representation shown in Table 1. Table 1 shows the mean, maximum, minimum and standard deviation of all variables of mandatory disclosure measured by IFRS (IFRSDI) and other regulatory induced mandatory disclosures (RinDI); Investment Decisions measured by log of volume of share traded (Ln(VS)) and Log of market value of shares (Ln(MV)); and the control variables of Return on Equity (ROE), firms’ Size (SIZE), Leverage (LEV) and Liquidity (LIQ).

The mean values for RinDI and IFRSDI were 0.6379 and 0.3424 respectively, while the mean values of Ln(VS), Ln(MV), ROE, SIZE, LEV, and LIQ were 17.808, 22.043, 0.0244, 24.632, 11.175, and 0.6914, respectively. There is also evidence of variation in the minimum and maximum values of all variables for the period of study. The variable with a higher degree of dispersion from the mean is the LEV, indicating variations within the data set over the years under study for quoted firms under the consumer goods sector in Nigeria.

Table 1. Descriptive Statistics Variables Mean

RDI IFRSDI LN(VS) LN(MV) ROE SIZE

0.6379 0.3424 17.8080 22.0430 0.0244 24.6320

Std. deviation

0.0725 0.1606 2.2111 2.5971 2.3786 1.4736

Minimum

0.13 0.02 9.91 13.33 -20.88 20.74 56

Maximum

0.76 0.67 20.54 25.41 0.93 26.90

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LEV 11.1750 LIQ 0.6914 Source: Researcher’s Study, 2018

82.8920 0.3211

-8.93 0.21

743.12 1.69 *Observations: 80

5.2 Empirical Analysis 5.2.1 Correlation Analysis The empirical analysis began by establishing the nature of the relationship between Mandatory disclosure (measured by IFRS (IFRSDI) and other regulatory induced mandatory disclosures (RinDI)) and Investment Decisions (measured by log of volume of share traded (Ln(VS)) and Log of market value of shares (Ln(MV))) and the control variables (Return on Equity (ROE), firms’ Size (SIZE), Leverage (LEV), and Liquidity (LIQ)). The panel data of all variables under study were correlated and the results obtained are shown in Table 2.

Table 2. Pearson’s Correlation Result RinDI IFRSDI ROE SIZE RDI 1

IFRSDI .472*** 1 p-val. .000 ROE .036 .077 1 p-val. .754 .499 * SIZE .219 .429*** .058 p-val. .051 .000 .609 LEV -.048 -.110 -.99*** p-val. .673 .330 .000 * LIQ .247 -.059 .137 p-val. .027 .603 .226 *** * VS .289 .212 -.037 p-val. .009 .059 .747 ** ** MV .238 .255 .024 p-val. .034 .022 .832 *,**,***Significant at 10%, 5%, 1% Source: Researcher’s Study, 2018

LEV

LIQ

VS

MV

1 -.057 .617 -.35*** .001 .847*** .000 .873*** .000

1 -.160 .157 .042 .713 .004 .972

1 -.168 .136 -.263** .018

1 .915*** .000

1

The correlation analysis in Table 2 reveals a significant positive association between RinDI and SIZE, LIQ, and each of the surrogates of Investment Decision of VS and MV, with an R- value of +0.219, +0.247, +0.289, and +0.238 respectively. IFRSDI also has a significant positive association with SIZE and each of the surrogates of Investment Decision of VS and MV with an Rvalue of +0.429, +0.212, and +0.255 respectively. Although RinDI has a significant positive correlation with LIQ, IFRSDI has an insignificant negative association with LIQ of consumer goods companies for the period under study. This implies that while RinDI has a direct and positive association with liquidity, IFRSDI has inverse and little connection with liquidity of consumer good companies in Nigeria. The regression analysis in the next section shows the extent and direction of the relationship of our variables.

5.2.2 Trends of Annualized Average Investment Decision We show the trends of annualized average investment decision measures of volume of share traded (VS) and market value of shares (MV) for the period under study. Specifically, the data obtained for VS and MV for all the consumer goods companies were aggregated and their mean computed for each year under study as shown in Figure 1 indicating the patterns of the annualized average VS

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and MV over the period of study. This was performed in order to examine the pattern of the volume of shares and the market value of shares of listed consumer good firms in Nigeria in pre and post IFRS periods.

Fig. 1. Trends of annualized average VS and MV 220 Post-IFRS

Pre-IFRS

200 180

140 25,000

120

20,000

100

In Naira Million

In Naira Million

160 30,000

15,000 10,000 5,000 2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

Year Average VS

Average MV

Source: Researcher’s Study, 2018 Figure 1 shows the “trend lines” of annualized average VS and MV for the period of 2007 to 2016. Specifically, it shows that both VS and MV have been fluctuating for the periods before and after the adoption of IFRS. While average annualized VS experienced a sharp decline in the period after the adoption of IFRS, there seems to be a general fluctuation in the periods of pre and post IFRS. However, the patterns in the average annualized MV showed an upward movement for the period immediately after the adoption of IFRS which subsequently had a downward movement in 2014 to 2016. This implies that improvement in the “trend line” was short-lived post adoption period, as the downward pressure in the movement of MV between 2014 and 2016 and in the movement of VS between 2015 and 2016 evidence non-significance of IFRS adoption in consumer goods sector after the first 2 years of convergence to IFRS by Nigeria.

5.2.3 Test of Hypothesis One (Ho1) Research Hypothesis 1 (Ho1): IFRS and other regulatory induced disclosures have no significant effect on volume of shares of listed consumer good firms in Nigeria Table 3. Regression Analysis, n = 80 Dependent Variable: Ln(VS) Model 1a Variables Coeff t-stat C 15.35 15.62 IFRSDI 0.889 1.63 RDI 3.597 2.33 ROE SIZE LEV -

58

Prob 0.00* 0.10*** 0.02** -

Model 1b Coeff t-stat 14.39 1.66 - 0.23 -0.38 0.23 0.21 0.99 1.48 0.11 0.32 0.03 1.52

Prob 0.14 0.72 0.84 0.18 0.76 0.17

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LIQ Adjusted R-squared F-Statistics Diagnostic Tests Hausman test Rho Statistics Lagrangrian multiplier test Heteroskedasticity test Autocorrelation test Cross sectional indep. Test *,**,***Significant at 10%, 5%, 1% Source: Researcher’s Study, 2018

-

-

-

0.07 10.06(0.01**) Statistics Prob.

4.29 0.12 250 0.000*** 6.07 0.001*** 0.05 0.83 0.84 0.42

0.41

1.11 0.30 0.79 146.75(0.00***) Statistics Prob.

13.15 0.04** 0.9 0.000*** 545.71 0.000*** 0.19 0.67 0.14 0.89

The result of the diagnostic tests is shown in Table 3. The Hausman test showed the probability values of 0.12 and 0.04 for models 1a and 1b respectively, implying that the null hypothesis to estimate random effect was accepted for model 1a and it was not accepted for model 1b; thus, model 1a was tested for the appropriateness for random effect using Breusch-Pagan Lagrangrian multiplier test. Model 1b was tested for appropriateness for fixed effect using the rho statistic. The significance of the Lagrangrian multiplier test at 1% for model 1a shows that random effect is appropriate; the probability value of 0.001 for Rho statistic for model 1b indicates that fixed effect is appropriate. In addition, the probability values of Breusch-Pagan heteroskedasticity test for both models 1a and 1b were significant at 1%, implying that the null hypothesis of constant variance was not accepted and there is presence of heteroskedasticity. As such, if predictions are based on their regression estimates it will be biased and inconsistent.

Furthermore, the probability values for both Wooldridge test for autocorrelation and Pesaran's test of cross sectional independence are insignificant at 10% for both models, indicating that there is absence of first-order autocorrelation and residuals are not cross sectionally correlated.

Thus, in line with Hoechle (2007) the presence of heteroskedasticity indicates that there is a need to estimate the models using robust standard errors (to estimate the models) to avoid estimation bias.

We present the empirical models in 1a and 1b, thus: Model 1a Ln(VS)it = α1 + β1IFRSDIit + β2RDIit + µ1 Ln(VS) it = 15.35 + 0.889IFRSDIit + 3.597RDIit + µ1 Model 1b Ln(VS)it = α2 + β3IFRSDIit + β4RDIit + β5ROEit + β6SIZEit+ β7LEVit + β8LIQit + µ2 Ln(VS)it = 14.39 – 0.23IFRSDIit + 0.23RDIit + 0.99ROEit + 0.11SIZEit+ 0.03LEVit + 0.41LIQit

The result of the regression analysis in Table 3 shows that mandatory disclosure measured

by IFRS and other regulatory induced mandatory disclosures (RinDI) individually have significant positive effects on the log of volume of share traded (Ln(VS)). This is indicated by the signs and the probability of the t-statistics of the coefficients, that is β1 = +0.889>0 (0.10*); β2 = +3.591>0 (0.02**). However, when the control variables of Return on Equity (ROE), firms’ Size (SIZE), Leverage (LEV), and Liquidity (LIQ) were introduced, both IFRSDI and RinDI have insignificant negative and positive effects on Ln(VS) respectively. All the control variables also have insignificant positive effects on Ln(VS). This is indicated by the signs and the probability of the tstatistics of the coefficients: β3 = -0.23<0 (0.72); β4 = +0.23>0 (0.84); β5 = +0.99>0 (0.18); β6 = +0.11>0 (0.76); β7 = +0.03>0 (0.17); β8 = +0.41>0 (0.30).

Additionally, the adjusted R-squared of model 1a showed that about 7% variations in Ln(VS) can be attributed to IFRSDI and RinDI, while the remaining 93% variations in Ln(VS) are caused by other factors not included in this model. The adjusted R-squared of model 1b showed that 59

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about 79% variations in Ln(VS) can be attributed to IFRSDI, RinDI and control variables of ROE, SIZE, LEV, and LIQ while the remaining 21% variations in Ln(VS) are caused by other factors not included in this model. This shows that while model 1a has a weak explanatory power, model 1b has a stronger explanatory power. The probability values of the F-statistic for both models at 10% level of significance shows that the regression results are statistically significant.

The null hypothesis asserting that “IFRS and other regulatory induced disclosures have no significant effect on volume of shares of listed consumer good firms in Nigeria” is rejected. IFRS and other regulatory induced disclosures have significant effect on volume of shares of listed consumer good firms in Nigeria.

5.2.4 Test of Hypothesis Two (Ho2) Research Hypothesis 2 (Ho2): IFRS and other regulatory induced disclosures have no significant effect on market value of shares of listed consumer good companies in Nigeria Table 4. Regression Analysis , n = 80

Model 2a t-stat Prob 42.01 0.00*** 6.12 0.00*** 6.38 0.00*** -

Model 2b t-stat -9.59 -0.88 1.15 5.00 16.58 5.09 2.74

Variables Coeff Coeff C 18.28 -20.89 IFRSDI 2.80 -1.09 RDI 4.40 2.38 ROE 3.09 SIZE 1.63 LEV 0.09 LIQ 0.93 Adjusted R-squared 0.0591 0.8477 F-Statistics 98.53(0.00***) 825.53(0.00***) Diagnostic Tests Statistics Prob.

Statistics Hausman test 2.90 0.23 74.5 Rho Statistics 0.9 Lagrangrian multiplier test 248 0.00*** Heteroskedasticity test 2.16 0.14 786.37 Autocorrelation test 3.59 0.10* 1.81 Cross sectional independence test 1.42 0.27 0.67 Dependent Variable: Ln(MV); *,**,***Significant at 10%, 5%, 1% Source: Researcher’s Study, 2018

Prob 0.00*** 0.38 0.25 0.00*** 0.00*** 0.00*** 0.00***

Prob.

0.00*** 0.00*** 0.00*** 0.07** 0.50

The result of the diagnostic tests in Table 4 showed the various tests performed on the models to determine their appropriateness. Specifically, the Hausman test shows the probability values of 0.23 and p = 0.00 for models 2a and 2b respectively, implying that the null hypothesis to estimate random effect was accepted for model 2a while it was not accepted for model 2b; thus, model 2a was tested for the appropriateness for random effect using Breusch-Pagan Lagrangrian multiplier test and model 2b was tested for appropriateness for fixed effect using the rho statistics.

The significance of the Lagrangrian multiplier test at 1% for model 2a shows that random effect is appropriate, p = 0.00 for Rho statistics for model 2b indicates that fixed effect is appropriate. In addition, the probability values of Breusch-Pagan heteroskedasticity test for both models 1a and 1b were insignificant and significant at 10% respectively, implying that the null hypothesis of constant variance was accepted for model 2a and there is absence of heteroskedasticity. While it was not accepted for model 2b, there is presence of heteroskedasticity.

Furthermore, the probability values for Wooldridge test for autocorrelation are significant at 10% which implies that there is presence of first-order autocorrelation in both model 2a and model 2b. This indicates that the residuals are correlated over time. The Pesaran's test of cross sectional 60

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independence are insignificant at 10% for both models, indicating that there is absence of first-order autocorrelation and residuals are not cross-sectionally correlated. Thus, in line with Hoechle (2007), the presence of autocorrelation in model 2a and the presence of both heteroskedasticity and autocorrelation in model 2b indicate that there is a need to estimate the models using robust standard errors to estimate the models to avoid estimation bias. We present the empirical models 2a and 2b as: Model 2a LN(MV)it = α3 + β9IFRSDIit + β10RDIit + µ3 LN(MV) it = 18.28 + 2.80IFRSDIit + 4.40RDIit Model 2b LN(MV)it = α4 + β11IFRSDIit + β12RDIit + β13ROEit + β14SIZEit+ β16LEVit + β17LIQit + µ4 LN(MV)it = -20.89 – 1.09IFRSDIit + 2.38RDIit + 3.09ROEit + 1.63SIZEit+ 0.09LEVit + 0.93LIQit

The result of the regression analysis in Table 4 shows that Mandatory disclosure measured by IFRS and other regulatory induced mandatory disclosures (RinDI) individually have significant positive effects on the log of market value of share (Ln(MV)). This is indicated by the signs and the probability of the t-statistics for the coefficients, that is β9 = +2.801>0 (0.00***); β10 = +4.40>0 (0.00***). However, when the control variables of Return on Equity (ROE), firms’ Size (SIZE), Leverage (LEV) and Liquidity (LIQ) were introduced, both IFRSDI and RinDI have insignificant negative and positive effects on Ln(MV) respectively. All control variables also have significant positive effects on Ln(MV). This is indicated by the signs and the probability of the t-statistics for the coefficients, that is β11 = -1.09<0 (0.38); β12= +2.38>0 (0.25); β13 = +3.09>0 (p = 0.00***); β14 = +1.63>0 (p = 0.00***); β15 = +0.09>0 (p = 0.00***); β16= +0.93>0 (p = 0.00***).

Additionally, the adjusted R-squared of model 2a showed that about 6% variations in Ln(MV) can be attributed to IFRSDI and RinDI, while the remaining 94% variations in Ln(MV) are caused by other factors not included in this model. The adjusted R-squared of model 2b showed that about 85% variations in Ln(MV) can be attributed to IFRSDI, RinDI and control variables of ROE, SIZE, LEV, and LIQ while the remaining 15% variations in Ln(MV) are caused by other factors not included in this model. This shows that while model 2a has a weak explanatory power, model 2b has stronger explanatory power. However, the probability values of the F-statistic for both models at 10% level of significance show that the regression results are statistically significant. Thus, the null hypothesis 2 that “IFRS and other regulatory induced disclosures have no significant effect on the market value of shares of listed consumer good companies in Nigeria” is rejected. Therefore, IFRS and other regulatory induced disclosures have significant effect on market value of shares of listed consumer good companies in Nigeria.

The result signifies that the combined effects of mandatory disclosures have positive and significant relationship with investment decisions (both volume of shares traded and market value of shares). This suggests that both IFRSDI and RinDI are positively related to both the volume of shares traded and market value of shares and that the effects are also positive. This finding is consistent with our a priori expectations that mandatory disclosures contribute positively and significantly to investment decisions.

Some studies, for example, Al-Sawalqa (2012); DeZoyasa and Rudkin (2010); Kumar (2011); Obamuyi (2013) and Onoh et al. (2017) support this assertion by finding volume of shares traded are influenced by actions of investors with different level of information, most especially mandatory information. Studies by Abiodun (2012); Dung (2010); Loureiro and Taboada (2012) and Oyerinde (2011) also found significant relationship between disclosed information and share values. Nevertheless, studies by Anderson and Epstein (1995); Baker and Haslam (1973; Farj et al.

(2016) found information disclosed in the annual reports as a basis for building investment decisions to be moderately but of no significant effect. In the same vein Cortijo and Yezegel (2005)

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and Saeedi and Ebrahim (2010) also found declining relationship between accounting information disclosed and stock value.

The ex-control variable (model 1a) adjusted R² of 0.07 indicates a weak explanatory power of this model, which implies that the model explains only about 7% of the variation in volume of shares traded. Nevertheless, when control variables were introduced, the explanatory power improved to 0.79; hence, the role of firm characteristics is much in affecting the relationship between mandatory disclosures and volume of shares traded. Similarly, the ex-control variables (model 2a) adjusted R² of 0.0591 is also comparable to 0.07 found under the volume of shares. This also indicates weak explanatory power as the model explained only about 5.91% of variations in market value of shares. However, when control variables were introduced, the explanatory power improved to 84.77% compared to 79% reported under the volume of shares traded. Similar to our conclusion under the volume of shares traded, the role of firm characteristics is high in affecting the relationship between mandatory disclosures and market value of shares.

This study is of importance to investors, regulators and other stakeholders as it provides them with empirical evidence on the relationship between mandatory disclosures and investment decisions undertaken by individual investors. Our study is limited to consumer goods sector of Nigerian economy. It also provides a benchmark for measuring level of compliance by each entity and, thus, serves as corrective measures on subsequent transactions between investors and firms.

6.0 CONCLUSION AND RECOMMENDATION This study focused on the effect of accounting information disclosure on investment decisions in consumer goods sector in Nigeria. This was achieved in three stages: firstly an association was established between and among the variables and it was established that while RinDI has a direct and positive association with liquidity, IFRSDI has inverse and little connection with liquidity.

Secondly, the pattern of the movement in dependent variable was reviewed and it was observed that improvement in the trend was short-lived post adoption period, as the downward trend in the movement of MV between 2014 and 2016 and in the movement of VS between 2015 and 2016 evidence non-significance of IFRS adoption in consumer goods sector after the first 2 years of convergence to IFRS by Nigeria. Thirdly, the result of regression analysis shows that both IFRSDI and RinDI have positive and significant relationship with investment decisions in consumer goods sector in Nigeria. It was concluded that accounting information disclosures determine investment decisions in consumer good sectors in Nigeria. We recommend that investors should always demand adequate disclosure of accounting information by corporate entities before investing in those companies.

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Residents' Perception of Seafood as a Tourism Product Nelly S. Maliva, Juliana G. Wilbard, Bahati D. Mbilinyi, Donatus P. Massawe & Kezia H. Mkwizu About the Authors Nelly S. Maliva, University of Dar es Salaam Business School, Email: nmaliva@yahoo.co.uk Juliana G. Wilbard, Recca Investment, Email: julswil2004@yahoo.com Bahati D. Mbilinyi, The Open University of Tanzania, Email: bmbilinyi@yahoo.com Donatus P. Massawe, University of Dar es Salaam Business School, Email: donatuspeter@gmail.com Kezia H. Mkwizu, correponding author, Open University of Tanzania, Email: kmkwizu@hotmail.com ABSTRACT Tourism product is one of the concepts that have attracted attention of scholars in tourism studies.

Although tourism is growing, the development of tourism products is a challenge for various destinations including coastal areas which widely depend on coastal tourism. One of the abundant products in coastal areas is seafood. Studies related to coastal areas in tourism have concentrated on food in general such as dining experiences while others in relation to tourists’ perception of services quality in the hospitality industry but very few have researched on residents’ perception of seafood as a tourism product within destinations. Those studies conducted on seafood mainly focused on tourists’ perception on seafood offered and scantly address issues related to residents’ perception of seafood as a tourism product. The approach of this study was qualitative and captured in-depth information on whether or not residents perceive seafood as a tourism product. The study was carried out in Dar es Salaam City in Tanzania. Interviews were conducted with respondents who were selected through convenience sampling at the Kivukoni fish market. The result of this study revealed that awareness and participation were the core factors in residents’ perception of seafood as a tourism product. The outcome of this study can contribute to addressing economic growth which is one of the Sustainable Development Goals (SDGs) by linking seafood and tourism. Also at the national level, the tourism sector can use the information of this study to build awareness of seafood as tourism product in coastal areas of Tanzania.

Keyword: residents’ perception, seafood, tourism product JEL Code: M39 CIATATION: Maliva, Nelly S., Wilbard, Juliana G., Mbilinyi, Bahati D., Massawe, Donatus P. and Mkwizu, Kezia H. (2018). “Is Location a competitive advantage on Retail Convenience shopping?” Inter. J. Res. Methodol. Soc. Sci., Vol., 4, No. 3, pp. 68-76. (Jul. – Sep. 2018); ISSN: 2415-0371.

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1. INTRODUCTION Tourism plays an important role globally and contributes significantly to the Gross Domestic Product (GDP) of many countries. WTTC (2017) showed that total contribution of tourism and travel to GDP was USD 7,613.3 billion worldwide in 2016. Although tourism is growing, a study

by Ardabili, Rasouli, Daryani, Molaie and Sharegi (2011) stated that little attention is been given to food in tourism and hence food is treated as secondary and minor role in the tourism industry.

Another study by Galvez, Guzman, Buiza and Viruel (2017) found that foreign tourists in Peru exhibit dimensions of new food experience and socialization as motivation for food satisfaction.

Generally there are scarce studies on seafood as a tourism product.

Tourism product is one of the concepts that have attracted attention of scholars in tourism studies. Despite the growth in tourism, the development of tourism products is a challenge for various destinations including coastal areas which widely depend on coastal tourism. One of the abundant products in coastal areas is seafood. Studies related to coastal areas in tourism have concentrated on food in general including dining experiences (Adabili et al., 2011; Horng & Tsai, 2012; Galvez et al., 2017; Mbilinyi, 2017). Further studies by Mas (2015) in Spain, and Su, Wall and Wang (2017) in China looked at seafood in relation to island development and consumption and not particularly the residents’ perception of seafood as a tourism product within destinations.

On the other hand, Ukwayi, Eja and Unawanede (2012) assessed tourist perception on service quality delivery in the hospitality industry at Cross River State in Nigeria. Generally, studies conducted on seafood mainly focused on tourists’ perception on seafood offered and scantly address issues related to residents’ perception of seafood as a tourism product since they are one of the stakeholders of coastal tourism.

Tanzania is endowed with world class coastal attractions (Tanzania Coastal Management Partnership, 2001). However, the diversification to coastal attractions has not received much attention from the perspective of giving attention to seafood as a tourism product. A number of recent studies in Tanzania have examined various issues on coastal tourism in locations such as Bagamoyo, Dar es Salaam, Kilwa, Tanga and Zanzibar with research related to leakages, festivals, women in tourism, customer relationship, food, airline customers satisfaction and heritage (Anderson, 2013; Mgonja, Backman & Backman, 2014; Jani, 2016; Maliva, 2016; Maliva & Jani, 2017; Wilbard & Jani, 2017; Mbilinyi, 2017; Jani & Naji, 2017; Singa’mbi & Lwoga, 2017) but there is limited literature on seafood as a tourism product. A recent study by Mbilinyi (2017) conducted in Tanzania was interested in the dining experiences of tourists food consumption whereby the finding showed that determinants such as food quality and food services were identified as significant in the total dining experience of tourists.

As more destinations still need to increase the number of tourists visiting their attractions, it is important for studies to consider diversification of research in various areas including food in tourism. In order for Tanzania to embrace the abundant tourism resources, this paper therefore, aim to explore residents’ perception of seafood as a tourism product. Specifically, the study identified core factors of residents’ perception of seafood as a tourism product in terms of awareness and participation. This study is crucial in efforts to boost coastal tourism and contribute to diversification from wildlife-based attractions to seafood attractions in tourism.

2. LITERATURE REVIEW 2.1 Concepts Definition 2.1.1 Perception The concept of perception has been defined in many studies (Swarbrooke & Horner, 1999; Solomon, 2011; Robbins, 2005; Stevenson & Waite, 2012; Araria, Carmelo & Leon, 2013; Lamb, Hair & McDaniel, 2014). The study by Swarbrooke and Horner (1999) defined perception as the subjective interpretation by individuals of the data available to them to provide opinions of and attitudes towards products, places and organizations. Stevenson and Waite (2012) referred perception as the ability to see, hear and become aware of something through senses. On the other hand, Lamb et al. (2014) described perception as a process by which people select, organize and

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interpret stimuli into a meaningful and coherent picture. In this paper, perception is defined as the awareness of residents and their participation in relation to seafood as a tourism product.

Furthermore, in this study the residents are “fish vendors”.

2.1.2 Tourism Product Various studies have provided the definition of tourism product (Xu, 2010; Candela & Figini, 2012; Gortan-Carlin & Krajnovic, 2016; Kozhevnikova, 2016). Tourism product can consist of cultural products that are heritage, fashion and also be referred to as music (Carlin & Krajnovic, 2016).

Another scholar defined tourism product as a bundle of goods and services in tourism (Candela & Figini, 2012). Tourism product has also been defined from the marketing perspective both tangible and intangible because it caters for tourism needs and promoted in the marketplace (Xu, 2010).

Another study in Nepal found that Chitwan National Park is a unique product in tourism (Kafle, 2014). In Tanzania, various studies have been done on national parks such as Kitulo, Udzungwa as well as Saadani National Park which is also a unique national park since it is the only national park in Tanzania which borders the ocean (Mkwizu, 2016, 2017a, 2017b, 2017c, 2018; Quintero, 2018).

This paper defines tourism product as seafood including various species of fish and fish products.

2.2 Theoretical Literature Review Theoretical framework for this study is grounded on the theory of planned behaviour which was developed by Ajzen (1991). Theory of planned behaviour assumes individual’s behaviour is underpinned by three constructs which are intention, subjective norms and perceived behavioral control (Ajzen, 1991; Schultz, 2014, Meng, Zhang & Pan, 2015). The intentions to behave or attitude are predicted through application (Hus, 2014). Subjective norms refer to social pressure or influence from family, friends and significant others (Ajzen, 1991; Hsu, 2014; Schultz, 2014).

Perceived behaviour is referred to ease of difficulty of performing the behaviour due to past experiences or expected impediments and obstacles (Hsu, 2014). Since this study assumes that individual residents’ attitude towards seafood as a tourist product, and their beliefs on the potential of seafood and participation in seafood activities in developing seafood as a tourism product would ultimately result into an intention to attract tourists into seafood tourism as well as involving more residents to participate into seafood activities.

Studies have used theory of planned behaviour to explain some food related behaviours. The study by Hsu (2014) used theory of planned behaviour to study food tourism by examining food behaviour and argued that personal traits such as food neophobia and sensation-seeking influenced food tourism in relation to people’s food choice behaviour. Hsu (2014) found that new experience, good reputation, affordable price and cultural experience were the major reasons associated with purchase of Taiwanese traditional food. Similarly Schultz (2014) utilized theory of planned behaviour to research not just food but within the field of tourism by focusing on participation in sustainable tourism certification programs, and found that attitudes played a major role as to why Bed and Breakfast (B&B) operators engage in sustainable tourism certification programs.

Hence this study adopts theory of planned behaviour to guide in exploring perception of seafood as a tourism product by residents. Furthermore, this paper argues that residents’ perception of seafood as a tourism product is related to awareness and participation as the perceived ability of seafood as a tourism product. Using theory of planned behaviour enables this study to explore whether residents perceive seafood as a tourism product or not. In theory of planned behaviour, the perceived behaviour is how easy or hard it is for a person to perform a behaviour or rather a person’s ability to perform a behaviour (Conner & Armitage, 1998; Schultz, 2014).

2.3 Empirical Literature Review Previous studies on perception and food tourism exist including Xu (2010), Ardabili et al (2011), Kumar, Sakthivel and Rananathan (2013), Galvez et al (2017), and Mbilinyi (2017). Xu (2010) focused on the concept of perception by using Smith’s framework and found that tangible physical plant was considered to be the core of tourism product in China. Kumar et al (2013) conducted a

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research in India to understand local residents’ perception and attitude towards model tourism village. The study by Kumar et al (2013) used principal component analysis and rotated component matrix to analyse data. The results showed that those residents benefitting from tourism do support tourism compare to those residents who do not benefit from tourism.

Another study conducted in India by Rajesh (2013) developed a theoretical model on the impact of tourist perceptions, destination image and tourists satisfaction on destination loyalty.

Rajesh (2013) found that tourist perception was influenced by factors such as historical and cultural attractions, destination affordability, travel environment, infrastructure, entertainment and natural attractions. Again the study by Rejesh (2013) focuses on tourist perception while this is interested on residents’ perception. Perception in tourism has also been studied by other scholars such as Dolnicar and Huybers (2010). In the study by Dolnicar and Huybers (2010) done in Australia, shows that the research concern was on tourists and their perception on different cities like Sydney and Canberra. The application of non-parametric technique called the Perception Based Market Segmentation (PBMS) enabled the study to identify perceptions on different cities by tourists and these perceptions include statements such as “it is vibrant”, “has a pleasant climate” and “is friendly” (Dolnicar & Huybers, 2010).

A similar study by Gnanapala (2015) also investigated tourists’ perception and the focus was on destination management. Hence most studies in tourism have conducted research on perception with majority on destination management, cities and destination loyalty. Few studies like Xu (2010) in China and Grudda (2010) in New Zealand investigated perception on tourism product and seafood. Although Robinson and Hite (2015) did a study in the US on Gulf Coast seafood, the interest was on tourists’ preference and perception in relation to safety of seafood. The use of factor analysis and bivariate probit estimation showed that travellers to the Gulf Coast value fresh seafood for reasons of health, and perceive the seafood to be safe (Robinson & Hite, 2015). Therefore, labeling of seafood was a major issue in seafood for consumers (Robinson & Hite, 2015).

Su et al (2017) did a study in China and used a qualitative approach to investigate residents’ perception on fishing tourism and island development. Su et al (2017) analysed the test and photographic data using thematic analysis and found that majority of respondents had high school and university education. Further results indicated that the residents provide free fishing gear and barbecue appliances to visitors to have fun in fishing and barbecue.

In Tanzania, Mbilinyi (2017) identified determinants of the tourists’ food dining experiences and revealed quality dining to be an important factor. The main suggestion of Mbilinyi (2017) study was diversification of tourism products by including food tourism and seafood inclusive as tourism product. Therefore, to add knowledge in tourism studies, this study explored residents’ perception of seafood as a tourism product.

3. DATA & METHODOLOGY A cross sectional design is adopted in this study. The paradigm approach of this study was qualitative and therefore captured in-depth information on awareness and participation to explore residents’ perception of seafood as a tourism product. The study was conducted in Dar es Salaam City at Kivukoni fish market in Tanzania. The Kivukoni fish market was selected as the study area for this paper because it is the largest fish market in Dar es Salaam. The unit of analysis is fish vendors as residents. Interviews were face to face and done on one to one basis not as a group in order to obtain rich information.

As the population of fish vendors is not known, the respondents were selected using convenience sampling and a saturation level of 6 respondents was achieved. The concept of perception is adopted from the studies by Ajzen (1991), Schutlz (2014) and Meng et al (2015) in order to explore residents’ perception of seafood as a tourism product. The respondents were asked on their perception in terms of their awareness of seafood as a tourism product, and their participation in seafood as a tourism product. The qualitative data collected was subjected to content analysis. The data was recorded on a note book and then content analysis involved coding the answers in order to generate themes on awareness and participation.

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4. FINDINGS & DISCUSSION

Findings on the profile of the six respondents are shown on Table 1.

Table 1. Profile of Respondents No. Age Gender 1 Above 50 Male 2 Above 45 Male 3 25-35 Male 4 Above 35 Male 5 Above 25 Female 6 Above 35 Female Source: Field data, 2018

Income per day Above 100,000 TZS Above 100,000 TZS Above 45,000 TZS Above 50,000 TZS Above 45,000 TZS Above 65,000 TZS

Education secondary secondary primary secondary primary primary

The above profile suggests that most of the respondents were male, from 35 years to above 50 years, earn income above 100,000 TZS and have a secondary education. The findings differ from Su et al (2017) which shows that majority of the interviewed respondents were high school and university graduates.

Interview findings presented in Table 2 showed subthemes that emerged from the interview.

Table 2. Awareness and Participation Respondents Sub-themes for Awareness

Sub-themes for Participation

1st respondent

New face in the fish market means a tourist, I show fish to tourists, smile and say “karibu”

2nd respondent

I show fish to tourists. Being friendly

3rd respondent 4th respondent

Charge more to tourists. A welcome smile Friendly to customers

5th respondent

Friendly to customers

6th respondent

Customers like fresh fish not overstayed fish

Fish tasting, trust, convincing, fish education, persuasion, discounts, livelihood Discounts, fish tasting, convincing, livelihood Discounts Convincing the fish is fresh and good, to make ends meet Selling whole/retail price, fry fish to add value, livelihood Discount, sell only fresh fish, livelihood

Source: Field data, 2018 The findings in Table 2 show that for awareness, the subtheme “friendly to customers” was dominant followed by subthemes of “I show fish to tourists”, “smile”, “new face in the fish market means a tourist”, “customers like fresh fish”, and “charge more to tourists”. This suggests that residents are aware about seafood as a tourism product when they are able to be friendly to tourists when they sell fish and show the fish to tourists. The findings further suggests that residents are aware of seafood as a tourism product when they are able to smile to tourists as they sell fish and another respondent uses “karibu” in Kiswahili language which means welcome. The ability to identify new faces in the fish market means that the new faces are tourists, and therefore feel that they are able to sell to tourists. The interview findings also suggest that awareness of seafood as a tourism product is when the fish vendors have the ability to know that customers like fresh fish which means that tourists go to visit the fish market because they like fresh fish. Other fish vendors charge more to tourists, and thus contribute earnings which are crucial to their livelihood.

Table 2 also show findings of participation for the interviewed respondents who stated that they participate in seafood as a tourism product mainly for “livelihood” through “giving discounts”, 72

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“convincing” tourists to buy their fish which is “fresh and good”, and also offer “fish tasting to tourists”. Another respondent also stated that he “educates” tourists about fish while another respondent participates in seafood as a tourism product by selling “fried fish to add value” as well as sell fish on whole and retails prices.

In addition, one of the respondents pointed out that “it will be good if there are fish festivals so that fish eating can be promoted as a tourism product”. This implies that the subthemes identified in this study for participation by residents on seafood as a tourism product are “livelihood”, “giving discounts”, “convincing”, “fresh and good”, “fish tasting”, “educate”, “fried fish to add value”, and “sell fish on whole and retails prices”. The findings of this study differ from a study conducted in China by Su et al (2017) which showed that residents perceive and participate in fishing tourism by providing free fishing gear and barbecue appliances to tourists while in this study the residents mostly give discounts and provide fish tasting to tourists to buy the fish. The variation of results suggests that residents use various tactics to lure tourists to buy fish and each style is unique to the particular location and response of tourists. The findings of this study are different from Robinson and Hite (2015) in the USA which put emphasis on labeling of seafood.

The variation in the results is due to the fact that in this study fish sellers use fish tasting and trust as a way of assuring the tourists that they sell fish which is fresh and good.

The findings of this study reveal that the awareness and participation behaviours of residents in their perception of seafood as a tourism product is contributed by many factors such as ability to identify tourists by face, being friendly, give discounts, build trust, livelihood and fish tasting.

Therefore, these findings support TPB by Ajzen (1991, 1988) in that the behaviour of residents’ perception of seafood as a tourism product is influenced by awareness and participation.

5. CONCLUSION This study concludes that awareness and participation are the core factors of residents’ perception of seafood as a tourism product in Dar es Salaam. In awareness, the subthemes that emerged from the interviewed respondents were “friendly to customers”, “I show fish to tourists”, “smile”, “new face in the fish market means a tourist”, “customers like fresh fish”, and “charge more to tourists”.

For the core factor of participation, the subthemes that were highlighted by the respondents were “livelihood”, “giving discounts”, “convincing”, “fresh and good”, “fish tasting”, “educate”, “fried fish to add value”, and “sell fish on whole and retail prices”. Awareness and participation identified in this study as core factors of residents’ perception of seafood as a tourism product support the theory of planned behaviour which assumes that individual’s behaviour is underpinned by intention, subjective norms and perceived behaviour.

The outcome of this study can contribute to addressing economic growth which is one of the Sustainable Development Goals (SDGs) by linking seafood and tourism. Also at the national level, the tourism sector can use the information of this study to promote seafood as a tourism product in coastal areas of Tanzania. This study also provides insights on suggestions by one of the interviewee recommending fish festivals as a way of promoting seafood as a tourism product. The proposed recommendation can also assist to attract more residents to participate in seafood activities.

Limitations of the study is that this is a cross sectional design paper and therefore, future researchers can explore the possibility of conducting a quantitative study on the identified core factors of awareness and participation to analyze residents’ perception of seafood as a tourism product.

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ASEAN Economic Community (AEC) A Case Study of Failed Economic Integration Louangrath, P.I.

& Sutanapong, Chanoknath

About the Author Louanglath, P.I. is an Assistant professor in Business Administration at Bangkok University, Thailand. For correspondence, Email: Lecturepedia@gmail.com Chanoknath Sutanapong is an independent researcher. She may be reached by email at: chanoknath.sutanapong@gmail.com

ABSTRACT In 2015, the ASEAN 10 countries launched their economic integration effort called the ASEAN Economic Community (AEC). The purpose of this paper is to assess the effect of that integration four years after its implementation. We used the percentage change of the GDP and percentage change of export volume from 2005 to 2018 as the data. The study divided the data into two periods; ten years before integration and 4 years after integration. In the post-integration period, we are limited to four years because only four years had passed since the AEC integration in 2005. The secondary data was extracted from the IMF’s World Economic Outlook Report 2018. The paper employed series of tests to verify the significance of changes in GDP and export growth. The T test for pre- and post-integration shows that there was no significant difference in the GDP growth (p = 0.7090), but there was significant reduction in export growth (p = 0.0047). For the ASEAN countries, there was a net loss of -0.88 ± 1.36 percentage points in the GDP growth and -1.70 ± 4.33 percentage point in export growth. We concluded that the AEC integration created a net loss for the ASEAN countries. We identified two countries who sustained significant loss in the annual GDP growth: Malaysia (T = 1.98; p = 0.0198), and Singapore (T = -2.98; p = 0.0004). No country experienced significant gain in the GDP growth. Statistical test for export growth showed that Malaysia had significant gain in export growth while four countries had significant loss: Brunei (p = 0.0335 ), Laos (p = 0.0000 ), Myanmar (p = 0.0000 ), and Singapore (p = 0.0065).

Keyword: AEC, ASEAN, economic integration, economic performance index JEL Code: D72, F15, F55, E10, L16, O31, O40 CITATION: Louangrath, P.I. and Sutanapong, Chanoknath (2018). “ASEAN Economic Community: A Case Study of Failed Economic Integration.” Inter. J. Res. Methodol. Soc. Sci., Vol. 4, No. 3; pp. 77-116.

(Jul. – Sep. 2018).

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1.0 INTRODUCTION The purpose of this paper is to examine economic growth in pre- and post-AEC integration. We confine our scope of economic growth to the percentage change in the GDP and export. ASEAN Economic Community (AEC) was launched in 2015 with the purpose of integrating the 10 economies into a common economic community. ASEAN has ten countries; these countries include: Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, Philippines, Singapore, Thailand, and Vietnam. The research issue of this paper is to answer the questions of whether after four years of integration, had there been significant evidence of benefits?

The ASEAN countries had traditionally been divided into two groups. The original ASEAN member nations include Brunei, Indonesia, Malaysia, Philippines, Singapore, and Thailand. The new members include Cambodia, Laos, Myanmar, and Vietnam. The original members had long been known to be more developed than new members. A successful economic integration should eliminate this intra-ASEAN distinction. If the motto: “One Vision, One Identity, One Community” is to mean any thing, the differentiation between the original 6 and new 4 members countries must be eliminated. If one economic community is to be meaningful, the AEC must show some evidence of integration. Economic integration is defined as the creation of a common market where a group of countries unify their separate economies into one common market for purposes of greater economic power and growth. Integration allows member countries greater interaction on the basis of economic, security, political, or social and cultural issues. Economic integration in this sense is synonymous with regional integration. Economic integration is akin to regionalism, such that of NAFTA, to which the AEC may be equated. It is the creation of a new form of organization that coexists with the old forms (De Lombaerde and Van Langenhove, 2007). The AEC is an integration of 10 economies without losing the individual country's sovereignty.

Existing literature speaks of economic integration in the most favorable light. Prointegration literature cited the following benefits of economic integration: growth, increase openness and competition, innovation, increase trade, technology transfer, increase investment, and labor mobility. However, the experience of the AEC integration among the 10 ASEAN countries has been nothing but a net loss to all members. Although there is an evidence of “integration,” this evidence leans towards uniform loss of growth. Before the integration, the distinction of the original members and new members had been maintained; however, after integration, all countries shared the same fate of slow growth in export and GDP. Since the integration of the 10 ASEAN countries in 2015, research on the effect of the integration has not been forthcoming. This paper may be one of early assessment attempt of the integration. Note that in Fig. 1, there was a semblance of diversity among the 10 countries from 2005 to 2014. However, from 2015 to 2018, we saw a decline in the GDP growth pattern of all 10 countries.

This research employed 14 years of data. The data is divided into two periods. Before the integration, the data were collected from 2005 to 2014 (Fig. 1, year 1 – 10). The post integration period runs from 2015 to 2018 (Fig. 1, year 11 – 14). The data was sourced from the IMF’s annual report on World Economic Outlook 2018 edition. To put the issue of integration in perspective, we also used the competitiveness ranking from the Global Competitiveness Index (GCI) as another source of macroeconomic data.

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Fig. 1. Percentage change in GDP during pre- and –post AEC: 2005 – 2018*

*AEC-2015 is at year 11; year 1 = 2005 and year 14 = 2018.

What we observed in the GDP patterns from 2005 to 2018, we also observed in the export growth pattern in the sample period. Although the pattern is less obvious than in the GDP case, percentage change in export volume showed that there is a marked decrease after the integration in 2015. Prior to 2015, the year-to-year percentage change in export growth were high; however, after the integration, this pattern becomes uniform and maintained a consistently lower levels for all ten countries.

The main aim of this paper is to measure the effect of the AEC integration. In so doing, we reviewed five approaches to measuring the effect of economic integration, namely (i) Consumer utility and firm’s demand as indicator for integration effect by Peretto (2003), (ii) Degree of openness and connection as indication of integration by Arribas et al., 2006; (iii) Cobb-Douglas production approach to measure the effect of integration by Badinger (2001), (iv) Schumpetrian growth model, and (v) unified growth model.

Our review is not exhaustive, but these three approaches are adequate to put the AEC economic integration into perspective. After reviewing these three approaches, we offer a more simplified approach to measuring the effect of economic integration through the use of statistical tests of pre- and post-changes in macroeconomic indicators.

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Fig. 2. Percentage change in export in pre-AEC: 2005 – 2014

This paper is organized into seven sections. In section 1, we introduced the fact that after four years of the AEC integration since 2015, the ASEAN ten countries had not seen positive economic benefits, despite some evidence of group integration. Section 2 provides the theoretical background in economic growth models under the perspective of development economics. Section 3 presents economic integration as one aspect of economic development model. Section 4 presents the data used in this paper. Section 5 presents the findings follows by discussion in section 6. In section 6 we outlined the mutual loss among the ASEAN countries. We offer an index of the effect of economic integration based on the percentage growth of the GDP and export. Lastly, in section 7, we conclude that the AEC integration thus far (2015 – 2018) had produced a net loss for ASEAN. 2.0 LITERATURE REVIEW 2.1 Economic development theory There are several theories providing growth models. These theories include (i) Harod-Domar model, (ii) Solow-Swan or exogenous growth model, (iii) endogenous growth theory, (iv) Schumpterian growth theory, (v) the big push theory, and (vi) unified growth theory. This list is not exhaustive but we will review them in order to put the AEC integration in context of growth theory.

We commence our review with the classical model of Harod-Domar.

According to the Keynesian classical theory, economic growth depends on savings and the productivity of capital. The model was called Horod-Domar model (Harod, 1939l Domarm 1946).

Similar idea was introduced in 1924 (Cassel, 1967). It is the precursor of exogenous growth model (Hagerman, 2009). The solution to the model was unstable (Scarfe, 1977) and it was later replaced by the Solow-Swan model (Sato, 1964; Solow, 1994). (Appendix 1).

2.1.1 Exogenous growth theory (Solow-Swan Model) The Solow-Swan growth was also known as exogenous growth theory. According to Solow-Swan, economic growth has been explained by growth theories. The Solow-Swan growth model, for instance, asserts that there is a diminishing return on capital and labor. Labor and capital is depreciated over the years. The amount of new investment is to replace these depreciated valued.

This net effect is called a “steady state.” The level of work productivity throughout the world should converge to the same level (Lucas, 1990). The Solow-Swan growth model is called exogenous growth theory. (Appendix 2).

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2.1.2 Endogenous growth theory The endogenous growth theory is another theory used to explain economic growth. Lucas and Romer introduced the endogenous growth theory. According to endogenous growth theory human capital, skills and knowledge contribute to economic growth (Mankiw et al., 1992; Sala-i-Martin et al., 2004; Romer, 1994, 1990). For instance, knowledge in mathematics and science are considered indicative of human capital which may contribute to economic growth (Hanushek and Kimko, 2000). At one time, researchers argued that there is a positive correlation between student mathematic and scientific knowledge and the country's economic growth in less developed countries (Hanusheck and Woessmann, 2000, 2001). In more developed economies, there is a lack of correlation between student's test score and economic growth (Breton, 2015). It is cognitive skills, not test score that correlates to economic growth (Hanusheck and Woesmann, 2015).

(Appendix 3).

2.1.3 Schumpeter’s model of creative destruction Another explanation of economic growth comes from Schumpeter. According to Schumpter, the creation of new goods and service is a sign of economic growth (Gordon, 2016). Economic growth in Asia had traditionally been contributed to capital investment (Krugman, 1994). The Schumpeterian growth theory asserts that growth resulted from innovation (Landreth, 1976), Growth comes as a result of creative destruction (Aghion; 2002; Carlin and Soskie, 2006), also called the Aghion-Howitt model. Schumpeter's creative destruction is described as:

“The fundamental impulse that sets and keeps the capitalist engine in motion comes from the new consumer’s goods, the new methods of production or transportation the new markets,.... [This process] incessantly revolutionizes the economic structure from within, incessantly destroying the old one, incessantly creating a new one. This process of Creative Destruction is the essential fact about capitalism.” - Schumpeter (1942; p.

83).

Innovation has the effect of reducing cost (Schleifer, 1986; Corriveau, 1988). Cost reduction increases demand. Increase demand produces growth.

Using Schumpeter's argument as the basis, Aghion and Howitt asserted that “individual innovations are sufficiently important to affect the entire economy.” (Aghion and Howitt, 1992, p.

324). The result of innovation is the destruction existing technology, and creation of new ones.

There are two effects that come from innovation: (i) creative destruction, and (ii) general equilibrium, resulted from new level of skilled labor from the innovation. Under this theory, economic growth will happen so long as there are continuing research to create new innovation.

When there is an oscillation of research effort in deterministic fashion, i.e. going up and down, not continually increasing, the economic will stop growing due to the “no-growth trap” (id., 325).

Growth depends on continuing research and development; R&D efforts in the next period must be higher than the previous one. As soon as the efforts become stationary or less than the prior period, the growth effect will be adversely affected.

This type of growth dependency may not work for all countries. For instance, if a country sees that R&D is more efficient in another country then it will wait for the spillover benefits from another country's innovation instead of engaging in a more costly R&D on its own. Thus, growth in this fashion sometimes is less than optimal (Romer, 1990). However, sometimes instead of the spillover effect, there might be a “business-stealing” effect (Tirole, 1988; p. 399) where the business in one country waits to steal the technology from the true creator. This business-stealing effect hinders innovation in the country. (Appendix 4).

2.1.4 The Big Push Theory (Rosenstein-Rodan Model)

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Economic growth is explained by the Big Push Theory. According to the Big Push Theory, a country can move from one developmental stage to the next by large investment in the infrastructure (Murphy et al., 1989). A group of industries acting in concert and move in the same direction could bring a push needed to help the country graduate to the next stage of development (Nath, 1962). This “big push” is made possible by the increased in scale of operation (Mead, 1952).

The goal of the Big Push is to achieve industrialization. Rosenstein-Rodan explained: “Industrialization meant (and still means today) urbanization. What are towns compared with rural zones? They are areas of relatively higher wages. Industrialization proceeded by concentrating in areas of high wages (towns), not in the rural areas. The rich countries were the urban zones and the poorer countries the rural zones of the world economy. That was the reason for the widening gap between developed and underdeveloped countries. The market mechanism alone will not lead to the creation of social overhead capital, which normally accounts for 30 to 35 percent of total investment. That must be sponsored, planned, or programmed (usually by public investment). To take advantage of external economies (due to indivisibilities) required an “optimum” size of enterprises to be brought about by a simultaneous planning of several complementary industries. In the process of development, pecuniary external economies play the same role as technological ones.” (Rosenstein-Rodan, 1984, p. 209; see also Scitovsky, 1954).

In describing how the Big Push works, Rosenstein-Rodan wrote: “There is a minimum level of resources that must be devoted to . .. a development program if it is to have any chance for success. Launching a country into self-sustaining growth is a little like getting an airplane off the ground. There is a critical ground speed which must be passed before the craft can become airborne.” (Rosenstein, 1961: “Notes on the Theory of the ‘Big Push’”).

There are three mechanisms that are used for the Big Push: (i) simultaneous investment in increasing returns in multiple sectors, (ii) multiple equilibria in the economy to switch the economy from cottage equilibria to industrial equilibria, and (iii) shared cost among multiple sectors in investing in the infrastructure (Murphy et al., 1989). The strategy of the Big Push Theory is through: “[t]he movement of machinery and capital towards labor, instead of moving labor towards capital, is the process of industrialization which, together with agrarian improvement, is the most important aspect of the economic development of the depressed areas.” (Rosenstein, 1944; p. 161).

Success of the Big Push theory depends on skilled labor force (Misra and Puri, 2010). In undeveloped economies, price failed to act as signal due to the lack of competition (Scitovsky, 1954). Ideally, if in a perfectly competitive market, if industries acted in concerted, the application of technology in the industry will result in cost reduction (Myint, 1969). This reduction in cost will spill over throughout the industry to lower prices of goods and services and, thus, increase demands (Agarwala and Singh, 1969). As the result, the economy grows. (Appendix 5).

2.1.5 Unified theory of economic growth Lastly, a more recent development is the unified growth theory. The unified theory of growth is a response to the inconsistency or inadequacy of the exogenous and endogenous growth models. The unified growth theory suggests that:

“… the transition from stagnation to growth is an inevitable outcome of the process of development. The inherent Malthusian interaction between the level of technology and the size and the composition of the population accelerated the pace of technological 82

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progress, and ultimately raised the importance of human capital in the production process. The rise in the demand for human capital in the second phase of industrialization, and its impact on the formation of human capital as well as on the onset of the demographic transition, brought about significant technological advancements along with a reduction in fertility rates and population growth, enabling economies to convert a larger share of the fruits of factor accumulation and technological progress into growth of income per capita, and paving the way for the emergence of sustained economic growth.” (Galor, 173).

UGT is an attempt to explain economic growth in a historical perspective by tracing economic development in the modern state through series of developmental stages, Thus, UGT described economic development as: “...in early stages of development economies were in the proximity of a stable Malthusian equilibrium. Technology advanced rather slowly, and generated proportional increases in output and population. The inherent positive interaction between population and technology in this epoch, however, gradually increased the pace of technological progress, and due to the delayed adjustment of population, output per capita advanced at a miniscule rate. The slow pace of technological progress in the Malthusian epoch provided a limited scope for human capital in the production process and parents, therefore, had no incentive to reallocate resources towards human capital formation of their offspring.” (Galor, p. 235).

The building blocks of UGT are: (i) Malthusian elements, (ii) forces behind technological progress in the process of development, (iii) origin of human capital formation, and (iv) determination of paternal decisions regarding the quantity and quality of their offspring. The formal argument of UGT is based on the Cobb-Douglas production function. Thus, UGT did not add new things into the literature on development economics, except describing the same issue in a different perspective; in this case, historical perspective. (Appendix 6).

It is against the backdrop of these various growth models that we examine and assess the effect of the AEC integration. Our objective is not to verify which growth theory explains the AEC integration, but we attempt to answer a basic question: “Has the AEC integration produce tangible benefits?” We will measure these benefits in terms of economic growth. In so doing, we use the percentage change in the GDP and export as our unit of analysis.

3.0 ECONOMIC INTEGRATION AS ENGINE FOR GROWTH As we assess economic integration as an alternative path for economic growth, we are reminded that not all economic development models produced success. We cite one case of a failed economic development theory used in India during that country’s second 5-year plan. That economic development model focuses on domestic consumer market (Feldman, 1964). It was known as Feldman-Mahanalobis Model (Mahanalobis, 1953). The model assumes (i) closed economy; (ii) the economy consists of two sectors: consumption goods sector C and capital goods sector K; (iii) capital goods are non-shiftable; (iv) full capacity production; (v) investment is determined by supply of capital goods; (vi) no changes in prices; (vii) capital is the only scarce factor; and (viii) production of capital goods is independent of the production of consumer good This model was used in India’s second 5-years plan, and was a subject of great discussion (Bronfenbrenner, 2003). The full capacity output equation for the model was presented by Ghatak:

[

]

  λ β + λc β c (1 + λk β k )t − 1  Yt = Y0 1 + α 0 k k λk β k  

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where λk is the share of investment in capital goods, λc is the share in consumer goods (Ghatak, 2003). According to the model, long run growth depends on long-term investment in capital goods.

Since the model emphasizes growth from domestic consumption, problems from increased money supply and inflation caused the Indian government to abandon the model in 1961 (Sen, 1991). It is not the failure of the model that we should appreciate, but the foresight of the Indian government to recognize the failure and has the ability and willingness to turn away from it. Similar foresight should be considered by the ASEAN countries in light of the current predicament of the AEC integration.

3.1 Economic integration as a factor for economic growth Economic growth due to increase efficiency is called intensive growth. Growth that resulted from increase in input is called extensive growth (Bjork, 1999). The development of new goods and services contributes to economic growth (Gordon (2016). About 80% of growth comes from technology and 20% comes from investment (Krugman, 1994). Much of the benefits of growth came from increase in productivity. Improved productivity had contributed to the decrease in cost.

In the 20th century, the real price of goods fell by 90% due to increase in productivity (Rosenberg).

That rise in productivity came from technological innovation (Lucas, 1988). With the introduction of technology, the work-week hours were reduced and resulted in the improvement of working condition among workers (Whaples, 1991). Institutional change also contributes to economic growth (Acemoglu and Robinson, 2012). Pro-business law, for instance, is conducive to economic growth (Landes, 1969). Such institutional changes help the states to better manage the nations (Johnson and Koyama, 2017). No one could refute that the rule of law contributes to the betterment of life and economy of the nation (De Soto, 2000).

In this paper we are examining one factor that is a potential contributor to economic growth: economic integration. It is considered an institutional change in line with the growth proposition urged by Acemoglu and Robinson, Johnson and Koyama, and De Soto.

There are two aspects of integration, namely political and economic integrations. In the literature, integration generally includes both. For instance, the working of GATT and its culmination into the creation of the European Community is an example of political and economic integrations. The EU is both a political and economic community. The ASEAN Economic Community is an attempt to have economic integration without political integration. Due to its intra-group diversity in political institutions, infrastructure and historical development, it seemed logical that an attempt to affect political integration, like that found in the EU, would inevitably fail; thus, only economic integration may be possible. The AEC’s model is akin to the North American Free Trade Agreement (NAFTA) where political integration is not the aim. In NAFTA, the US, Canada and Mexico integrated their markets, but each country retains its own political sovereignty.

3.2 Combined political and economic integration Integration generally entails both political and economic integration. The argument in favor of political integration is that it contributes to competition and innovation in the integrated economy.

Political integration has a positive market size effect, but a negative on trade openness (Spolaore and Wacziarg, 2005). Economic integration has positive effect on growth and increasing competition in economic markets (Peretto, 2003; Grossman and Helpman, 1991). By accepting these two arguments, integrationists urged both political and economic integrations. The support for growth through integration is openness. The effects of country size on growth are less important as economies become more open (Alesina et al., 2000; 2005); country size would not be an issue if there is an accompanied political integration. For instance, Brou and Ruta (2007) wrote that:

“Political integration makes the competition for transfers more intense. Firms must increase their rent seeking effort in order to maintain their share of government transfers. As profits from the political market fall, each firm must rely more on the economic market for profits. This makes competition in the economic market more 84

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intense and increases the incentive to innovate. At the same time, higher costs of rent seeking drive some local firms out of the market. This has the effect of reducing the number of firms competing in the economic market, which reduces the incentive to innovate. Overall, the effect of political integration on innovation, growth and welfare is ambiguous.” – Brou and Ruta, 2007; p. 3.

However, in the case of AEC integration, only economic integration is targeted.

Traditionally, the ASEAN countries followed a policy of political independence and the principle of noninterference; each member country refrained from interfering in another country’s internal politics. Thus, the obvious question is “can regional integration succeed if only economic integration is implemented, without political integration?”

3.3 Economic integration without political integration The AEC is not an exceptional case in economic integration without political integration. NAFTA, for instance, is an integration that dispenses with political integration. The AEC is an attempt to create economic and cultural communities without political integration. The common rationale for economic integration is the idea of economies of scale derived from economic integration (O'Sullivan and Sheffrin, 2003). The argument is that one country alone could do less efficiently than a group of nations acting in consonant. This is called economies of scale of public goods by means of economic integration.

Although efficiency in external trade may be achieved, it comes at the cost of losing some degrees of political sovereignty. Adherents of advocacy of economic integration include: Milanovic (1996), Alesina and Spolaore (1997), Bolton and Roland (1997), Casella and Feinstein (2002) and Alesina et al., (2005).

Economic integration is presented in 5 stages: (i) free exchange area, (ii) customs union, (iii) common market, (iv) economic union, (v) economic and monetary union (Balassa, 1961). Viner’s concept of custom unions as one of a number of arrangements for reducing tariff barriers between political units while maintaining barriers against imports from outside regions (Viner, 1937). The effect of customs union is trade diversion and trade creation. Haebler described trade union as:

“A customs union must be especially advantageous for small States, since these are particularly injured if they exclude one another's goods. We must emphasise that the economic advantages of a customs union can be proved only by exact Free Trade reasoning as to the international division of labour and the Theory of Comparative Cost, and not by any reference to racial, cultural, and other relations. From an economic standpoint a general removal of duties by the States would be better than a removal of duties only between themselves retaining their duties against other countries.” Gottfried von Haeberler (1936), The Theory of International Trade, pp.390.

Haebler’s description includes free-trade area, a customs union, a common market, an economic union and complete economic integration (Belassa, p. 176). The question is what is the effect of the integration? (Belassa, 1961; p. 177).

The economic effect of integration may include “integration-induced technology-led growth” and “integration-induced investment-led growth” (Baldwin and Seghezza, 1996). The economic integration intends to break down barriers. Barriers are hinderances to rade. These hindrances include language, colonial past, military or political relations, currencies, or trade agreements on trade tariffs (Brahmbhatt, 1998; Frankel, 2000; Knetter and Slaughter, 1999).

The indicators of the effect of integration include: (i) openness. (ii) balanced relationship, (iii) indirect relationship, and (iv) sizes. In this paper, we will not measure these five factors. In an attempt to answer the question of what had been the effect of the AEC integration in the past four years, we look at two indicators: annual percentage change in the GDP and export volume.

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3.4 Modeling the effect of economic integration 3.4.1 Consumer utility and firm’s demand as indicator for integration effect In this part of the literature review, we examined the formal argument presented by Peretto (2003), Brou and Ruta (2007) and their progeny. At the end of this review of the formal arguments and models, we will reject them as impracticable and not reflective of all cases of economic integration.

In place of these formal arguments, we will present a more simplified measure of the effect of economic integration by introducing two factors as indicators of the effect of economic integration: percentage change in the GDP and export volume. We are mindful of the fact that Perrett, Brou and Ruta, and other analyzed the effect of economic integration in the context of the more developed economies in the West. Even the latest experiment of NAFTA, as a recent example of economic integration without political union, may be generalized under Peretto’s argument; however, the case of ASEAN Economic Community (AEC) does not have the equivalent basic developmental preconditions. The ten economies of ASEAN are mixed bag of developed, developing, and undeveloped economies. Thus, the experience of the AEC may not follow the predicted patterns of “benefits” foresaw by Peretto ad subsequent studies in the same line of argument. Nevertheless, we present their formal arguments and models in our literature review so that the AEC integration may be seen in its rightful theoretical perspective. It is in such light and context that we could better evaluate the AEC four years after its implementation (from 2015 to 2018).

Perettor presented the argument and model for the effect of economic integration in series of elegant mathematical models. We summarized Perettor’s formal argument and models in the following statements (1) – (8). Peretto, like many economists writing on economic integration, tend to focus on consumers and firm as the starting point. This bottom-up approach started in a predictable and logical pattern of presentation. Consumer’s utility and firm’s demand prior to the integration are contrasted with those after the integration under the condition of with tariff (prior to integration) and without tariff (after integration). We will trace these steps.

Consumer index is given by:

C=

[∑ Ci∈−1 /∈]∈/∈−1

(1)

where ∈> 1 is the elasticity of product substitution, C i is consumer purchase of different goods, N is the number of goods.

As the economies integrate, more technology will be made available in one common market.

As the result, labor productivity will rise. Labor productivity rises with the rise of accumulated technology: Z& t = LZ zi K i K = Zi +

N

γ

∑ 1 + δ ( N + 1) j ≠i

(2)

Zj

where 0 ≤ γ ≤ , Z i is the new knowledge generated in the time interval t after integration dt by allocating labor LZi units to R&D, γ is the fraction of knowledge that becomes public, and δ is the speed of congestion of increasing return.

With the above preliminaries, Peretto then presented trade model with and without tariffs.

The trade model with tariffs, i.e. prior to integration, has the following defined terms: c = countries,

N k = domestic firms in k countries; M =

c

∑ Nk

is the global number of firms, and τ = tariffs.

k =1

Now, the new consumer index and demand may be presented:

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∈/∈−1

c Ns  Nk  ∈−1 /∈ ∈−1 /∈ + ∑ ∑ Cis Ck =  ∑ Cik  i =1  s ≠ k i =1  

(3)

Ck implies that in an integrated economy, a typical consumer prefers all goods regardless of whether the goods are offered by domestic or foreign firms. The demand for the firm in the integrated economy is: K ik = Zik +

Nk

γ

c Ns

γ

∑ 1 + δ ( N − 1) Z jk + ∑ ∑ 1 + δ ( N + 1) Z j j ≠i

(4)

s = k j =1

K ik implies that in an integrated economy, the demand for domestic firm is a spillover from the global population of firms.

In country k, consumers maximize Ck subject to: 1 / 1−∈

Nk  c Ns Ek  ∑ Pik Cik + ∑ ∑ (1 + τ )1−∈ P1js−∈   s ≠ k j =1  i =1 

(5)

The consumer demand for i product from domestic firm is: P −∈ D Cik = Ek ik Pk1−∈

(6)

The demand for i products imported from another country. Thus, the demand by firm i in country k is: Pik−∈ (1 + τ ) Pik−∈ D F + LE s X ik = X ik + X ik = LEk Pk1−∈ Ps1−∈

(7)

D F where X ik and X ik are firm’s domestic and foreign sales, respectively. The superscript D stands for domestic and F stands for foreign. The term Ps is the price index for consumer goods in country s. The price elasticity of (7) is given by:

ξ =∈ −(∈ −1)

1 + (1 + τ )1− 2∈(c − 1)

1

c

1 + (1 + τ )∈(c − 1) 1 + (c − 1)(1 + τ )1−∈ cN

(8)

Equations (1) to (8) serve as the foundational materials for the argument that trade in integrated and nonintegrated markets are unequal. Thus far, the argument is logical, but what follow does not reflect the real world as the model begins to contrasts autarky and free trade. Autarky is an extreme case. Integration analysis is not contrasting integrated economy with extreme case of nontrade of autarky for such condition does not exist in normal circumstances. The ASEAN, for instance, instead of acting in consonant as in AEC, had each country pursued its own independent trade policy. Firms in such countries succeed through their own efforts. However, with integration,

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firms’ efforts may be more organized or targeted. Marketing cost may be reduced as a benefit of integration. Integration analysis is about comparing independent trade policies in un-integrated economies with the unified policy in integrated economies.

Peretto outlined autarky condition by looking at the number of firms and cost reduction effect. In autarky, the number of firms is given by:

φ=

L [1 − θ (ξ − 1)] + ρ ξN α

( NN autarky )

The cost structure is: g =θ

ξ − 1 θ φθ (ξ − 1)α − ρ  1 1 log N + log +  ρ ξ − 1 ξ ρ 1 − θ (ξ − 1) 

where price elasticity of demand is defined by ξ = given by: α =

GGautarky ∈ ( N − 1) + 1 and the productivity of R&D is N

1 + (γ + δ )( N − 1) (Peretto, p. 190). The welfare under autarky is given by 1 + δ ( N − 1)

(UU autarky ) :

U=

1

ρ

log N + log

ξ − 1 θ φθ (ξ − 1)α − ρ + ξ ρ 1 − θ (ξ − 1)

(UU autarky )

In contrast, Peretto presented the free trade condition as:

φ=

L 1 − θ [ξ (cN ) − 1] ρ + N ξ (cN ) α (cN )

( NNtrade )

NNtrade represents the number of firms in each country at general equilibrium of global economy where the global industry is in systematic Nash equilibrium with free entry and exit. R&D 1 + (γ + δ )(cN − 1) productivity measure is: α = . The rate of cost reduction as the result of open 1 + δ (cN − 1) trade is given by: g =θ

φθ (ξ − 1)α − ρ 1 − θ (ξ − 1)

(GGtrade )

The welfare under open trade is given by: U=

 ξ −1 1  g  1 ∈/∈−1  +  + log log(cN ) ρ  ξ cN  ρ 

By substituting (GGtrade ) , the general welfare is obtained by: U=

ξ − 1 θ φθ (ξ − 1)α − ρ  1 1 log(cN ) + log  ρ ∈ −1 ξ ρ 1 − θ (ξ − 1) 

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(UU trade )

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The analysis is to compare (GGtrade ) and (UU trade ) to GGautarky and (UU autarky ) , the net effect is that the number of firms in free trade is larger than that in autarky, and the utility level in free trade is also higher than that in autarky.

At this point, we pose a simple question: What if the number of firms under free trade increases, but the welfare does not? This is the case of the AEC integration. If the number of firms increases, but the welfare of the people (consumers), as measured by the rate of growth of the GDP, does not follow at the same rate, we must also for alternative model to help explain the AEC’s experience.

3.4.2 Degree of openness and connection as indication of integration One approach offered the measure of integration by looking at the following factors: openness, balanced relationship, indirect relationship, and size (Arribas et al., 2006, p. 4). Arribas et al. provided a formal argument for the degree of integration as follows:

DIi = DOi ( DTCi )

where DOi =

∑ DOij =

j∈N

(9)

∑ j∈N X ij Yˆi

and DTCi =

∑ j∈N αij∞ βij∞ ∑

( )

∞2 α ij j∈N

∑

( )

∞2 β ij j∈N

. Other elements

are defined as: N = set of nodes of economies; g = number of elements in N , i.e. countries; X ij = flow from economy I to j; Yi = activity volume or size of economy, i.e. GDP; Yˆi = Yi − α iYi is the Yi production destined for export; αi = is the relative weight of economy I to the world ∑ j∈N Y j economy (Arribas et al., pp. 4 – 9). This argument asserts that the degree of integration is determined by the degree of openness (DO) and the degree of total connection (DTC).

We find this approach not effective as a measure of the effect of economic integration. The degrees of openness and connection between economies do not tell us the level of change after the integration. For instance, this approach cannot answer the question of how has consumer utility, firm demand or net trade volume or percentage change in the GDP been affected by the economic integration? Thus, the question of “degree of integration” or the index of integration is not a useful measure. It may have an academic value, but lacks the practical value. One main issue in analyzing economic integration is “what are the benefits from integration?”

3.4.3 Cobb-Douglas production function approach to measure the effect of integration We present another approach to explain for the anticipated benefits from economic integration by Badinger (2001). Badinger’s approach relied on the endogenous growth factor of the Cobb-Douglas production function (Cobb and Douglas, 1928) with constant return to scale: Y = AK α L1−α

(Silberberg and Suen, 2001) or simply Y = AK α . This approach is classified as microeconomic approach (Filipe and Adams, 2005). It is written in a log-difference format, Badinger’s argument follows: ∆ ln Yt = ∆ ln At + α∆ ln Kt

(10)

where y = Y / L is the GDP per employee; A is the total factor of production; and K = K / L is the physical capital per employee. For trans-log conversion of the Cobb-Douglas function, see (Berndt and Christensen, 1973; Wynn and Holden, 1974). The Cobb-Douglass approach uses the measure of 89

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productivity as the unit of analysis (Douglas, 1976). According to Badinger, growth comes from two sources stemming from integration: investment and technology. For integration investment-led growth, there are two competing hypotheses: ∆ ln At = δ1A + ϕ1A INTt ∆ ln At = δ 2 A + ϕ 2 A∆INTt

(11a) (12b)

For integration technology-led growth, there are also two competing hypotheses: ∆ ln Kt = δ1k + ϕ1k INTt ∆ ln Kt = δ 2k + ϕ 2k ∆INTt

(13a) (13b)

The argument from (10) to (14b) (Badinger, pp. 6-14) are the clarification of what Baldwin and Seghezza (1996) described the permanent effect that led to the steeper slope of the growth path.

Both Badinger and Baldwin had accurately described the experience of Europe’s integration effort under GATT and its later day of the EU. In this paper, we asked whether the so-called permanent effect of integration investment-led and integration technology-led growths could result in the AEC integration among the 10 ASEAN countries? The empirical evidence in Figs. 1 and 2 suggests that the EU experience was not repeated in the AEC. Both Arribas et al. and Badinger presented valid arguments in favor of economic integration. However, neither could explain the experience of the AEC integration since 2015.

Where Badinger hypothesized that there would be technology-led and investment-led growth from integration, the AEC produced neither. Where Arribas et al. expected to see the economies becoming more integrated, there is an evidence of integration in a slow growth direction.

In this paper, we offer a more simplified view of the degree of integration in the context of the AEC. Using the ASEAN Economic Community (AEC) as the proxy to illustrate our proposed measurement of integration, we employ the percentage growth in the GDP and the percentage growth in export volume as the indicators for integration. To which we offered the following propositions: (i) prior to integration, countries would manifest diversity in GDP and export growth rates, (ii) after integration, the diversity among member countries’ export and GDP growth rates will be narrower; and (iii) there are three possible effects of integration, namely no change, loss, or gain.

Using the ASEAN countries as a group proxy to explain the rationale for the above three propositions, we assert that (a) the diversity in proposition 1 may be attributed to the diversity in economic, political and historical development or factor endowment of the countries; (b) the diversity is expected to be narrower after integration due to common policy affecting trade, i.e. removal of trade barriers and common trade policy; and (c) the effects of integration depends on how the economic integration is implemented. If the removal of tariffs is not accompanied by increase in trade volume, the government’s loss in revenue could not be adequately compensated, or if the increase in trade volume is due to free trade arrangement with larger trading partners where trade terms are unequal with net imports contributed to declining trade term, then the net effect of integration may be negative and vice versa or no change depending on the trade term condition subsequent to the integration. We relied on empirical evidence in Fig. 1 and Fig. 2 to support our three propositions and explanations.

4.0 DATA AND METHODOLOGY This paper used secondary data. The data was extracted from the IMF’s annual report of World Economic Outlook 2017. ASEAN’s common market called ASEAN Economic Community (AEC) was launched in 2015. We tracked the group’s GDP and export patterns 10 years before the integration: 2005 – 2014, and compared them to those from 2015 to 2018.

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Ten years prior to the AEC integration (2005 – 2014), the percentage change of the GDP for the ten ASEAN countries ranged from the low of 0.38 (Brunei) to the high of 8.21 (Myanmar). As a group, the 10 years mean for the GDP percentage change was 5.68 ± 2.32. The country GDP change from 2005 to 2014 is presented in Table 1.

Table 1. Percentage change in GDP during pre-AEC: 2005 – 2014 Year 1* 2 3 4 5 6 7 8 9 10 2005 0.39 13.25 5.69 6.94 3.27 13.57 4.78 7.49 4.19 7.55 2006 4.41 4.97 6.98 0.77 5.50 8.95 4.70 3.08 5.24 8.86 5.44 7.13 2007 0.12 0.21 6.35 7.85 9.60 1.99 6.62 9.11 2008 (1.98) 6.69 7.44 7.82 7.64 3.60 4.15 1.79 1.73 5.66 2009 (1.82) 0.09 4.70 7.37 8.33 5.14 1.15 (0.60) (0.69) 5.40 2010 2.65 5.96 6.38 8.02 6.87 5.35 7.63 15.24 7.51 6.42 2011 3.74 0.84 6.24 7.18 6.17 7.99 4.85 5.59 3.66 6.35 7.24 5.25 2012 0.91 7.26 6.03 7.81 1.89 7.33 6.68 4.08 2013 2.13) 7.44 5.56 8.03 5.20 8.43 7.06 5.11 2.69 5.42 2014 (2.51) 7.07 5.01 7.61 5.70 7.99 6.15 3.88 0.98 5.98 Mean 0.38 7.59 5.88 7.84 5.81 8.21 5.31 6.13 3.49 6.20 n 10 10 10 10 10 10 10 10 10 10 2.54 SD 3.49 0.78 0.52 2.35 3.55 1.96 4.42 2.81 0.80 T 1.83 1.83 1.83 1.83 1.83 1.83 1.83 1.83 1.83 1.83 (1.09) 5.58 5.43 7.54 4.45 6.15 4.18 3.57 1.86 5.74 µ 2.82 3.87 0.86 0.57 2.60 3.93 2.17 4.90 3.12 0.89 σ pValue** 0.01 0.21 0.47 0.18 0.48 0.14 0.56 0.42 0.83 0.41 *ASEAN 10 countries: 1. Brunei, 2. Cambodia, 3. Indonesia, 4. Laos, 5. Malaysia, 6. Myanmar, 7.

Philippines, 8. Singapore, 9. Thailand, and 10. Vietnam. **pValue in relations to group: 5.68 ± 2.32.

The integration occurred in 2015, four years after integration had shown a decrease in the growth of the ASEAN’s GDP. The argument in favor of integration asserts that integration will result in the increase of the GDP, i.e. through investment-led or technology-led growth; the percentage change in the GDP with show a positive increase. If we run the time series in the linear form Y = a + bX, the slope of the line would be positive: b > 0. Brunei changed from 0.38 to -0.33 and Myanmar changed from 8.21 to 6.62. The overall picture for the group does not look promising.

The mean for the GDP change in ASEAN after AEC integration for the period 2015 – 2018 is 4.80 ± 2.45; there has been a loss of about one percentage point (5.68 – 4.80 = 0.88).

Table 2. Percentage change in GDP during post-AEC: 2015 – 2018 Year 1* 2 3 4 5 6 7 8 2015 (0.41) 7.20 4.88 7.27 2.95 6.99 6.07 2.24 2016 (2.47) 7.04 5.03 7.02 2.27 5.87 6.92 2.40 2017 0.55 6.95 5.07 6.83 4.00 6.72 6.67 3.62 2018 0.99 6.90 5.30 6.81 3.50 6.90 6.67 2.94 Mean (0.33) 7.02 5.07 6.98 3.18 6.62 6.58 2.80 n 4 4 4 4 4 4 4 4 SD 1.54 0.13 0.18 0.21 0.74 0.51 0.36 0.62 T 2.35 2.35 2.35 2.35 2.35 2.35 2.35 2.35 (2.14) 6.87 4.86 6.73 2.31 6.02 6.16 2.07 µ 2.19 0.19 0.25 0.30 1.06 0.73 0.52 0.89 σ pValue** 0.02 0.18 0.46 0.19 0.25 0.23 0.23 0.21

91

9 3.02 3.28 3.90 3.87 3.52 4 0.44 2.35 3.00 0.62 0.30

10 6.68 6.21 6.81 6.60 6.58 4 0.26 2.35 6.27 0.37 0.23

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*ASEAN 10 countries: 1. Brunei, 2. Cambodia, 3. Indonesia, 4. Laos, 5. Malaysia, 6. Myanmar, 7.

Philippines, 8. Singapore, 9. Thailand, and 10. Vietnam. **pValue in relations to group: 4.80 ± 2.45.

Export is used as an indicator of benefits or loss in accessing the AEC integration. The argument in favor of economic integration asserts that integration will produce more trade. Trade consists of import and export; in this paper, we used net trade data as the indicator for the effect of integration on trade. If the net trade increases, it means that integration produces an increase in export. If net trade decrease, it may mean that import increases. An increase in import may explain a general decrease in net trade. The general picture of export earning for the ten countries in ASEAN, measured by the percentage change, over the ten years period (2004 – 2015) prior to the AEC integration is presented in Fig. 2.

The raw data for the pre-AEC integration is presented in Table 3. Brunei has the lowest percentage change in export (-1.47) and Myanmar was the highest export earner as measured by percentage change (13.35). The average for the ASEAN group in this pre-AEC integration period was 6.88 ± 5.29.

Table 3. Percentage change in export during pre-AEC: 2005 – 2014 Year 1* 2 3 4 5 6 7 8 9 10 12.50 7.76 2005 (1.26) 8.77 11.89 11.73 5.78 15.62 0.99 5.95 2006 4.42 18.77 4.52 14.42 6.91 39.45 16.81 11.15 10.79 14.30 8.60 8.89 2007 (9.1) (3.06) (3.18) 7.56 (3.85) 15.84 7.00 13.31 (2.95) 13.66 (7.30) 0.01 (2.21) 4.84 6.26 2008 (7.05 2.18 2.13 2009 (6.66) (4.96) 5.61 4.88 (10.47) 12.89 (5.87) (7.66) (12.14) 3.74 2010 11.71 33.85 3.43 16.73 3.62 10.30 19.86 17.44 14.22 6.58 (4.58) 15.85 6.69 21.67 6.26 9.63 (0.69) 6.67 2011 9.51 4.13 2012 (2.80) 25.98 1.85 6.69 (6.49) 4.86 15.97 1.70 4.88 15.59 2013 (9.28) 16.35 3.05 10.62 0.07 15.36 3.72 5.95 2.72 16.94 2014 4.32 13.85 1.63 22.08 6.50 9.49 11.52 3.36 0.26 10.98 Mean (1.47) 12.76 3.25 13.00 0.10 13.35 6.71 6.46 5.32 9.37 n 10 10 10 10 10 10 10 10 10 10 7.07 SD 12.34 4.45 5.93 6.63 10.46 8.93 6.82 7.33 5.47 1.83 1.83 1.83 1.83 1.83 T 1.83 1.83 1.83 1.83 1.83 (5.56) 5.61 0.68 9.57 (3.73) 7.29 1.54 2.51 1.07 6.20 µ 7.84 13.69 4.93 6.58 7.35 11.60 9.91 7.56 8.13 6.07 σ pValue** 0.06 0.13 0.25 0.12 0.10 0.49 0.47 0.38 0.38 0.32 *ASEAN 10 countries: 1. Brunei, 2. Cambodia, 3. Indonesia, 4. Laos, 5. Malaysia, 6. Myanmar, 7.

Philippines, 8. Singapore, 9. Thailand, and 10. Vietnam. **pValue in relations to group: 6.89 ± 5.29

Similar to the pre-integration GDP, the percentage change in export in the post-integration period (2005 – 2018) showed that Brunei went from -1.47 to -3.91 and the ASEAN export champion, Myanmar, went from 13.35 to 5.77 or a loss of 7.58 percentage points in export growth.

For the ASEAN group, the average annual percentage growth of export was 6.89 ± 5.29 prior to integration; this value dropped to 5.18 ± 4.73 after integration.

Table 4. Percentage change in export during post-AEC: 2015 – 2018 Year 1* 2 3 4 5 6 7 2015 (13.77) 12.64 0.08 (4.80) 4.05 (0.11) 0.44 2016 (1.89) 14.51 (0.69) 10.53 3.63 3.44 4.99 2017 (1.98) 10.83 5.56 17.18 11.38 6.10 8.22 2018 2.02 2.57 11.20 6.22 2.28 3.88 10.41 Mean (3.91) 12.30 2.79 3.53 4.95 5.77 6.81

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8 4.74 1.11 4.10 3.96 3.48

9 1.57 2.79 6.59 3.99 3.74

10 9.77 10.74 15.85 13.10 12.37

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n 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 SD 6.84 1.67 3.60 6.50 2.19 7.76 5.09 1.61 2.14 2.71 T 2.35 2.35 2.35 2.35 2.35 2.35 2.35 2.35 2.35 2.35 11.94) 0.33 1.44) (4.10) 2.37 (3.34) 0.82 1.58 1.22 9.18 µ 9.74 2.38 5.13 9.25 3.12 11.05 7.25 2.30 3.05 3.86 σ pValue** 0.26 0.07 0.31 0.36 0.48 0.45 0.37 0.36 0.38 0.06 *ASEAN 10 countries: 1. Brunei, 2. Cambodia, 3. Indonesia, 4. Laos, 5. Malaysia, 6. Myanmar, 7.

Philippines, 8. Singapore, 9. Thailand, and 10. Vietnam. **pValue in relations to group: 5.18 ± 4.73.

5.0 FINDINGS 5.1 Selecting appropriate testing protocol We examined the net gain of GDP and export growth in a year-to-year basis during pre- and postintegration. This examination was accomplished in two steps. First, we look at country-by-country change and then we looked at the ASEAN as a group in the pre- and post-integration period.

We note that the group’s effect size under the Cohen’s d measure is low. The effect size for the percentage change in GDP is 0.33 and 0.37 for export. However, despite low group effect size, per country effect size showed that five countries had positive large effect size on their GDP; these countries include: Indonesia (1.20), Laos (1.86), Malaysia (1.27), and Singapore (0.87). For export, two countries showed large effect size: Laos (1.56), and Malaysia (-0.83).

The use of effect size had been advocated by many researchers; the APA, for instance, insisted that:

“Always present effect sizes for primary outcomes...If the units of measurement are meaningful on a practical level (e.g., number of cigarettes smoked per day), then we usually prefer an unstandardized measure (regression coefficient or mean difference) to a standardized measure (r or d).” — L. Wilkinson and APA Task Force on Statistical Inference (1999, p. 599).

Although there are several effect size measurement methods, i.e. correlation (Cohen, 1992; Olejnik, 2003; and Steiger, 2004) and difference families (Hedges and Olkin, 1985; Hedges, 1981) of effect size, we found them inadequate. The Cohen’s d employs the same method as the standard score equation, but differed in using the pool standard deviation instead of sample standard deviation. Even so, the result is an incomplete calculation since a standard score calculation must be followed by the reading of the percentage probability: F(Z) from which the pValue may be calculated as 1 – F(Z). If these later steps are followed, the evaluation scale of Cohen d reading would break down. According to the Cohen’s and Sawilowsky’s table on effect size, 0.80 is considered large (Cohen, 1988; p. 67; Sawilowsky, 2009); however, under the Z score equation 0.80 would produce F(Z) = 0.7882 and pValue = 0.2118. Due to such weakness, we opted for the T test to measure the pre- and post-integration by: T=

(X1 − X 2 ) − (µ1 − µ2 )

(14)

1 1 S + n1 n2

where S =

(n1 − 1) SE12 + (n2 − 1)SE22 n1 + n2 − 2

.

5.2 Net change of percentage GDP growth from AEC integration In measuring the effect of economic integration, we divide the countries into two categorizes: losers and winners. Winner countries are those that made significant gain in percentage change in the

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GDP; losers are those who made significant loss in the percentage change in the GDP. Under the 95% confidence interval, no country had made significant gain from the integration. If the confidence interval is lowered to 90%, two countries (Cambodia (p = 0.0636) and Singapore (p = 0.0903)) made significant gain in percentage change in the GDP growth.

Note that the significance measurement obtained through the T test is more informing than the Cohen’s d effect size measurement. Under the effect size measurement, four countries experience “large” effect in the percentage change in the GDP as the result of the AEC integration.

However, when the significance is used with well defined confidence interval, only two countries had significant change under 90% CI, but not significant at 95%.

Table 5: Before and After AEC Integration effect on GDP and export % growth 1* 2 3 4 5 6 7 8 9 10 %∆ GDP 0.38 7.59 5.88 7.84 5.81 8.21 5.31 6.13 3.49 6.20 Pre GDP (0.33) 7.02 5.07 6.98 3.18 6.62 6.58 2.80 3.52 6.58 Post GDP (0.71) (0.57) (0.81) (0.86) (2.63) (1.59) 1.27 (3.33) 0.03 0.38 Net Effect 0.31 0.19 1.20 1.86 1.27 0.51 (0.75) 0.87 (0.01) (0.53) size: d T test (0.27) 1.53 0.28 0.06 0.42 1.15 0.64 1.37 0.93 0.18 pValue 0.2910 0.0636 0.4673 0.4322 0.4041 0.1392 0.3100 0.0903 0.2033 0.4846 *ASEAN 10 countries: 1. Brunei, 2. Cambodia, 3. Indonesia, 4. Laos, 5. Malaysia, 6. Myanmar, 7.

Philippines, 8. Singapore, 9. Thailand, and 10. Vietnam.

The net change in the percentage change in the GDP is illustrated pictorially by Fig. 3. Note that only three countries experienced a net gain (Philippines (+1.27), Thailand (+0.03) and Vietnam (+0.38)). These values are obtained by using the ten years mean of pre-integration period less the four years mean of the post-integration. Eight countries had their means GDP growth dropped after the AEC integration.

At a glance, the AEC integration does not deliver what it had promised. As an economic community that is committed to regulatory harmonization, removal of tariffs, labor migration, and free flow of capital, one expects that the post-integration GDP for all members would change in a positive direction. However, empirical evidence points to an opposite direction. The evidence from the past four years showed that the AEC has failed.

If the GDP is any indication of the standard of living, a decrease in the percentage growth of the GDP presents a poor prospect for economic prosperity. The GDP measures presented in Table 5 and Fig. 3 are percentage change from year to year, not the actual GDP growth. The percentage change is used as the unit of analysis because it provides a clearer picture than the actual per capita GDP amount.

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Fig. 3. Net GDP gain as annual percentage growth comparing 10 years prior to 4 years after AEC integration (2005 – 2018) 2.00 1.27

1.00 0.38 (0.71)

(0.57)

(0.81)

(0.86)

0.03

V ie tn am

Th ai la nd

Si ng ap or e

(1.59)

(2.00)

(3.00)

Ph ili pp in es

M ya nm ar

M al ay sia

La os

di a

In do ne sia

(1.00)

Ca m bo

B ru ne i

-

(2.63) (3.33)

(4.00)

5.3 Net change of percentage export growth from AEC integration Export had been seen as the engine for growth among the ASEAN countries CITE. We examined the export growth pattern for 10 years prior to the AEC integration and compared it to the fours years post-integration. There are four countries which show significant decrease in export: Brunei, Indonesia, Laos, and Myanmar. Only one country experience significant net gain in percentage export growth: Cambodia. The remaining five countries (Malaysia, Philippines, Singapore, Thailand, and Vietnam showed some gains, but those gains are not significant (Table 6).

Table 6: Before and After AEC Integration effect on export and export % growth 1* 2 3 4 5 6 7 8 9 10 %∆ Export (1.47) 2.76 3.25 13.00 0.10 13.35 6.71 6.46 5.32 9.37 Pre Export (3.91) 12.30 2.79 3.53 4.95 5.77 6.81 3.48 3.74 12.37 Post Export (2.44) (0.46) (0.46) (9.47) 4.85 (7.58) 0.10 (2.98) (1.58) 3.00 Net Effect 0.35 0.04 0.11 1.56 (0.83) 0.77 (0.01) 0.50 0.25 (0.61) size: d T test (2.42) 3.01 (1.16) (2.68) 0.83 (1.73) (0.48) 1.36 1.11 (0.01) pValue 0.0009 0.0004 0.0057 0.0002 0.2375 0.0119 0.2141 0.0922 0.1498 0.4008 *ASEAN 10 countries: 1. Brunei, 2. Cambodia, 3. Indonesia, 4. Laos, 5. Malaysia, 6. Myanmar, 7.

Philippines, 8. Singapore, 9. Thailand, and 10. Vietnam

What the test static lacks, the visual effect for the net change of the integration provides the shocking picture in Fig. 4. Only two countries showed gains in percentage change in the export growth. The remaining eight countries lost their export growth momentum after the AEC integration. The scope of this paper is to measure the change and the net gain or loss of the integration, the reasons or causation for the loss of the export growth is another sub-issue worth pursuing.

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Fig. 4. Net export gain as annual percentage growth comparing 10 years prior to 4 years after AEC integration (2005 – 2018) 6.00

4.85

4.00

3.00

2.00

V ie tn am

Th ai la nd

Ph ili pp in es

M ya nm ar

M al ay sia

La os

Si ng ap or e

0.10

(0.46)

In do ne si a

Br un ei

(2.00)

(0.46)

Ca m bo di a

(2.44)

-

(1.58)

(2.98)

(4.00) (6.00) (8.00)

(7.58)

(10.00)

(9.47)

(12.00)

6.0 DISCUSSION 6.1 AEC integration did not help optimize utility The empirical findings of this study contradict the general argument in favor of economic integration which asserts that:

“…integration raises growth and welfare because exit of domestic firms is more than compensated by entry of foreign firms. In other words, the fact that domestic consumers and producers gain access to foreign goods and knowledge means that integration generates a larger, more competitive market where firms have access to a more diverse body of technological spillovers that supports faster growth. The growth effect is larger the less competitive the economy is before integration, while it is negligible for economies that are very competitive to begin with. The effects of a gradual reduction of barriers to trade.” (Peretto, 2003, p. 178).

We accept Peretto’s argument that a “typical consumer maximizes life time utility” by: ∞

U (t ) = ∫ e − ρ (τ −t ) log C (τ )dτ

(15)

t

where ρ > 0 subject to budget constraint and initial wealth: ∞

∞

−A −A ∫ e E (τ )dτ ≤ ∫ e W (τ )dτ + B(t ) t

(16)

t

τ where A = ∫ r ( s)ds , ρ = individual discount rate; E = per capita consumption expenditure, B = t

individual assets, W ≡ 1 is wage rate and C is consumption defined by: 96

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∈/∈−1

N  C =  ∑ C∈−1 /∈ i =1 

,∈> 1

(17)

However, the problem with the AEC integration is that maximized or optimized U (t ) was not achieved. We examined the percentage change in the GDP and export before and after the integration, the value before was greater than the value after the integration. The ideal condition is U (t ) pre < U (t ) post ; however, four years after the integration, empirical evidence shows that U (t ) pre > U (t ) post .

Peretto also presented the argument for technology and its effect after integration. Each firm produced with technology level given as: X t = X tθ ( Lxi − φ )

(18)

Provided that φ > 0,0 < θ < 1 where X t is the firm’s output and Lxi is the firm’s labor in production, φ is the overhead cost, and Z is the firm’s technology stock. The term Z θ argues that i

labor productivity rise with accumulated stock of technology.

At equilibrium, the firm maximizes profit at: ∞

Vi (t ) = ∫ e − A ∏ i (τ )dτ

(19)

t

τ where A = ∫ r ( s)ds . The instantaneous profits are: t

∏ i = Pi X i − Lxi − LZi

(20)

where Lxi is the total production cost, LZi is R&D expenditures, and Vi is the value of the firm.

We do not have the data for technology transfer before or after the AEC integration. However, by inference from the Global Competitiveness Index, we note that the growth of knowledge-base in the ASEAN countries from 2015 to 2018 was not significant.

However, according to empirical evidence in this study, if the effect of the AEC integration continues to be what it has been for the past four years, the life time utility U (t ) would be less than what it could or should be because the condition after the integration is less than what it had been before the integration.

The basis of the argument of free trade “within the group” under economic integration is that the removal of tariffs would create more trades. At the same time the removal of tariffs will result in the loss of one source of government revenue. The gain from trade must compensate for that loss. In case of the AEC, the experience for the past four years after integration we saw a reduction in both the percentage growth of the GDP and export. To verify if the removal of tariff contributed to the loss of government revenue, we examined government revenue data in ten countries in the AEC prior to and after the integration.

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6.2 The integration result in the loss of government revenue Why the removal of tariffs did not contribute to the increase in trade? The main argument for economic integration is the expected increase in net trade that would result from the removal of tariffs. Tariffs are seen as trade barriers.

By removing trade barriers, trade volume should increase. If so, we would expect to see that the percentage of export volume will increase in a year-by-year basis. However, the pattern of percentage export for the AEC between 2015 and 2018 had steadily decreased resulting in the loss from 6.88 ± 5.29 to 5.18 ± 4.73. The removal of trade barrier in the ASEAN countries did not contribute to the increase in net export growth.

Table 7. Government revenue during pre-AEC: 2005-2014 Year 2005 2006 2007 2008 2009 2010 Brunei 45.22 47.67 32.43 63.09 38.45 43.79 Cambodia 11.95 12.78 15.13 15.89 15.81 17.05 Indonesia 17.86 18.87 17.78 19.45 15.38 15.64 Laos 12.39 13.09 13.89 14.16 14.98 20.10 Malaysia 22.01 23.30 23.65 23.85 24.81 22.46 Myanmar 10.49 10.99 10.66 10.05 9.28 9.11 Philippines 17.84 19.05 18.69 18.66 17.40 16.80 Singapore 19.91 19.76 23.79 24.00 17.38 21.08 Thailand 20.97 20.78 20.22 20.03 19.51 20.73 24.98 26.33 26.10 26.58 25.59 27.26 Vietnam Mean 20.36 21.26 20.23 23.58 19.86 21.40 n 10 10 10 10 10 10 SD 9.93 10.46 6.44 14.74 8.08 9.21 T 1.83 1.83 1.83 1.83 1.83 1.83 14.62 15.21 16.51 15.05 15.18 16.07 µ 11.01 11.60 7.15 16.35 8.96 10.21 σ

2011 55.34 15.58 17.01 20.00 23.85 9.85 17.60 23.14 21.11 25.88 22.94 10 12.30 1.83 15.82 13.64

2012 46.72 16.93 17.25 21.41 25.04 19.03 18.61 22.21 21.30 22.60 23.11 10 8.68 1.83 18.09 9.62

2013 46.57 18.52 16.86 21.13 24.12 20.10 18.85 21.39 22.15 23.08 23.28 10 8.47 1.83 18.37 9.40

2014 37.66 19.85 16.46 20.80 23.66 21.97 18.96 21.19 21.39 22.24 22.42 10 5.71 1.83 19.11 6.33

The mean value for government revenue in ASEAN as a percentage of the GDP for the 10 years period prior to the AEC integration was 21.84 ± 9.13. After the integration, the government revenue as a percentage of the GDP was reduced to 19.69 ± 2.51. Although the reduction in the government revenue is not statistically significant (p = 0.3278), it was enough to generate multiplicative effect to reverberate throughout the economies in the ten countries.

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Fig. 5. Government revenue as percentage of the GDP for the 10 ASEAN countries.

70

60

50

40

30

20

10

0 2005

2006

2007

Brunei Philippines

2008

2009

Cambodia Singapore

2010

2011

Indonesia Thailand

2012

2013

Laos Vietnam

Table 8. Government revenue during post-AEC: 2015-2018 Country 2015 2016 2017 Brunei 24.15 17.61 19.53 Cambodia 18.78 19.85 19.50 Indonesia 14.88 14.33 13.98 Laos 21.13 16.23 17.02 Malaysia 22.53 20.71 19.62 Myanmar 18.73 18.82 18.23 Philippines 19.38 19.11 19.60 Singapore 21.42 20.98 23.34 Thailand 22.31 21.95 21.10 Vietnam 23.76 23.75 23.50 Mean 20.71 19.33 19.54 n 4.00 4.00 4.00 SD 2.35 2.35 2.35 T 1.83 1.83 1.83 18.56 17.18 17.39 µ 2.61 2.61 2.61 σ

2014

2015

Malaysia

2016

2017

2018

Myanmar

2018 19.46 19.54 14.18 17.31 19.03 17.44 19.81 20.78 21.36 22.95 19.19 4.00 2.35 1.83 17.04 2.61

6.3 Index of change in the GDP and export Does the AEC integration mean mutual loss? During ten years prior to the integration, ASEAN experienced greater GDP and export growth, see Figs. 1 & 2. From 2005 to 2014, the trend of GDP growth rates for the ten countries had a group average of 5.68 ± 2.32 compared to four years after integration, the group’s average percentage growth rate was 4.80 ± 2.45. It has been a mutual loss as the result of the integration.

What is the implication of the loss in percentage growth in the GDP and export growth?

Empirical evidence in this paper is antithetical to theoretical expectation of economic integration.

Could it be possible that during the post-integration period, there was a downward trend of government revenue? If so, it may explain the reduction in the GDP growth. However, how can the reduction in export growth rate be explained in light of the removal of the removal of tariffs?

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To provide a clearer picture of the performance evaluation of the AEC integration, we constructed indices for the GDP and export growth rate change. Using the year 2014 as the base year, we constructed an index to illustrate the performance line for the percentage change in the GDP and export growth. Both the mean percentage value of GDP and export growth rate from 2005 through 2014 was designated as 100, i.e. using the ten years mean prior to integration as the base value ending in 2014. Subsequent years: 2015, 2016, 2017 and 2018 were compared to this base year. Fig. 5 tracks the changes year-by-year from 2015 through 2018. Success means that the value should exceed 100; failure means the value is less than 100. Since the AEC integration in 2015, there has been consistent failure in percentage growth in the GDP and export volume.

Fig. 5. AEC index* of percentage GDP and export growth using ten years average prior to the integration ending in 2014 as the base value 120.00 100 100

100.00

100 100

100 100

100 100 86.63

82.57 89.96

80.00 76.76

78.20

88.91

71.51

60.00

40.00 21.22

20.00

FY2015

FY2016 GDP

FY2017

Export

Base GDP

FY2018

Base Export

*In constructing the idex, the following steps were followed: (i) determine the ten years average from 2005 – 2014 by: x = (1 / n)∑ xi , and (ii) use the observed percentage change in each year ( xi ) to track the change in comparison to a fixed base-year ( x ) by I = ((( xi − x ) / x )100) + 100 .

7.0 CONCLUSION Prior to the AEC integration, the ASEAN had diverse economic identity. Some countries enjoyed larger and faster growth, and some may be growing at a lower and slower rate. After the AEC integration, this diversity has almost been eradicated. All countries are growing uniformly at a lower and slower rate. In this assessment after four years after the integration, the ASEAN Economic Community has proved to be a failure. Unless the ASEAN states reassessed the economic effect of the AEC integration and re-aligned their policy to re-establish growth rate and speed at the pre-integration period, the entire region will suffer.

Evidence of economic integration from the OECD showed that there is a net gain from the integration. However, four years after the AEC integration, the ten countries in ASEAN experience a net loss. In this paper we examined 10 years before integration and 4 years after integration; the indicators used for the measurement were percentage change in GDP and percentage change in export. The result shows that four years after integration, there has been a net loss to the group. The 10 years pre-integration saw a mean percentage GDP growth of 5.68 ± 2.32 in the pre-integration period and 4.80 ± 2.45 in the post integration. A country-by-country examination showed that Singapore posted the greatest loss in GDP growth (-3.33); for export, Myanmar experience the greatest loss (-7.58) in percentage points. The over all significance for net gain of GDP for ASEAN is -0.27 (p = 0.7090 or not significant), and the group’s significance for net export gain is -2.42 (p = 0.0047). There is no significant loss in GDP growth, but there is significant loss of export growth.

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The implication of this finding is that the GDP growth is less sensitive to negative effect of the integration, but the percentage growth of export is more sensitive.

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APPENDIX 1 Harod-Domar Model – Classical Growth Model

Output under Harod-Domar model depends on capital stock: Y = f (K ) . The marginal product of dY dY Y capital is constant and exhibits constant rate to scale: =c⇒ = . Capital is a necessary dK dK K condition; without capital, output cannot be produced: f (0) = 0 . These assumptions create the following condition of equal rates: Y = ck ⇒ log(Y ) = log(c) + log( K ) d log(Y ) d log( K ) Y& K& = ⇒ = dt dt Y K

(A1.2)

Further assumptions of the Harod-Domar include the assertion that savings rate and output equals investment: sY = S = I . Thus, the change in capital stock equals the change in investment: ∆K = I − δK . The relationship between savings, capital, and investment may be summarized as: K& I Y = −δ = S −δ K K K

(A1.2)

The relationship between output, savings and capital is: Y& = sc − δ Y

(A1.3)

The derivation of growth rate of the output follows: dY Yt +1 − Yt = dK Kt + sYt − dKt − Kt Yt +1 − Yt c= dK sYt − δ Yt dY δdK   c sYt −  = Yt +1 − Yt dY   dK Yt +1 − Yt cs − cδ = dY Yt c=

s

(A1.4)

dY dY dK Yt +1 − Yt −δ = dK dK dY Yt

sc − δ =

∆Y Y

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APPENDIX 2 Solow-Swan Growth Model – Neo-Classical Exogenous Growth Model

The Solow-Swan model is two-factors production model: L(labor) and K(capital) where elasticity if substitution is asymptotically equal to one (Barelli and Pessôa, 2003; Litina and Palivos, 2008). The output equation is given by: Y (t ) = K (t ) a ( A(t ) L(t ))1− a

(A2.1)

where t is time period and the elasticity of output is 0 < a < 1 , and Y (t ) is the total production. The term A(t ) is the labor-augmented technology or knowledge. The initial conditions are A(0), K (0) and L(0) . Labor grows at a rate of n and capital grows at g: L(t ) = L(0)e nt

(A2.2)

A(t ) = A(0)e gt

(A2.3)

The effective unit of labor: A(t ) L(t ) grows at rates of (n + g ) and δ is the depreciation rate of the capital stock. The consumption amount is given by: c = cY (t ) with 0, c < 1 with savings rate of dK (t ) . The production function Y ( K , A, L) s = 1 − c . What is saved is used for investment: K& (t ) = dt in per unit of labor is given by: y (t ) =

Y (t ) = k (t ) a A(t ) L(t )

(A2.4)

The general equation for the Solow-Sawn model is given by: k&(t ) = sk (t )a − (n + g + δ )k (t )

(A2.5)

where sk (t ) a = sy (t ) is investment per unit of effective labor and (n + g + δ )k (t ) is the break even point of investment (Romer, 2011). The investment at time t, k (t ) converges to steady-state is: 1 1− a

 s  k * =   n + g +δ 

(A2.6)

The capital stock K and labor AL are growing at a rate of (n + g ) .

The equilibrium k * produces: K (t ) s = Y (t ) n + g + δ where

(A2.7)

K (t ) = k (t )1− a with the marginal rate of production (MRP) as: Y (t )

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∂Y aA1− a MPK = = ∂K ( K / L)1− a The ratio of income that has been appropriate for capital accumulation is a and one can equate the rate of capital accumulation to the rate of return by:

a=

K

∂Y ∂K = rK Y Y

(A2.8)

REFERENCES Barelli, Paulo; Pessôa, Samuel de Abreu (2003). “Inada conditions imply that production function must be asymptotically Cobb–Douglas.” Economics Letters. 81(3): 361–363. doi:10.1016/S0165-1765(03)00218-0.

Litina, Anastasia; Palivos, Theodore (2008). “Do Inada conditions imply that production function must be asymptotically Cobb–Douglas? A comment.” Economics Letters. 99(3): 498–499. doi:10.1016/j.econlet.2007.09.035.

Romer, David (2011). “The Solow Growth Model.” Advanced Macroeconomics (Fourth ed.). New York: McGraw-Hill. pp. 6–48. ISBN 978-0-07-351137-5.

Caselli, F.; Feyrer, J. (2007). “The Marginal Product of Capital.” The Quarterly Journal of Economics. 122(2): 535–68. doi:10.1162/qjec.122.2.535.

Lucas, Robert (1990). “Why doesn't Capital Flow from Rich to Poor Countries?” American Economic Review. 80(2): 92–96

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APPENDIX 3 Endogenous Growth Model

Economic growth is a consequence of endogenous factor (Romer, 1994). This assertion is opposite of the exogenous growth model. According to endogenous growth model, growth comes from investment inhuman capital, innovation, and knowledge. In this presentation of the endogenous model, we extract the argument from the work of Uzawa (1965).

The aggregate production function is given by: Y (t ) = F[ K (t ), A(t ), L(t )]

(A3.1)

Technological knowledge at time t is A(t ) . The rate of improvement in labor efficiency: A& (t ) / A(t ) depends on the labor force, L(t ) : A& (t ) = φ [ LE (t ) / L(t )] A(t )

(A3.2)

The larger the improvement in labor efficiency, the higher the proportion of labor force in education sector with non increasing marginal returns:

φ ' ( s) ≥ 0  for all 0 ≤ s ≤ 1 φ ' ' ( s ) ≤ 0

(A3.3)

The total labor force L(t ) at time t grows at constant rate n to be inelastically supplied: L p (t ) + LE (t ) ≤ L(t )

(A3.4)

L; (t ) / L(t ) = n

Let I (t ) be investment, c(t ) be consumption, then I (t ) + c (t ) ≤ Y (t ) where I (t ), c (t ) ≥ 0 and the rate of capital accumulation is: K& (t ) = I (t ) − µK (t ) K (t )

(A3.5)

where µ is the rate of capital depreciation of capital.

Let per capita output by y = Y / L relate to capital-labor ratio: k = K / L by the function y = f (k ) where f (k ) = F ( K , L) / L = F (k , l ) . The function f (k ) is twice-differentiated: f (k ) > 0, f ' (k ) > 0, f ' ' (k ) < 0 for all k > 0 : y (t ) =

Y (t ) L (t )

=

output per capita

(A3.6)

k (t ) =

K (t ) L (t )

=

aggregate capital-labor ratio

(A3.7)

=

labor allocated to productive sector

(A3.8)

u (t ) =

L p (t ) L (t )

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s (t ) =

z (t ) Y (t )

=

investment ratio

(A3.9)

According to the model, the objective is to maximize: ∞

∫ (1 − s(t ) y(t )c

− st

(A3.10)

dt

0

Subject to the following constraints: k&(t ) = s (t ) y (t ) − λk (t )

(A3.11)

A& (t ) = A(t )φ (1 − u (t ))

(A3.12)

 K (t )   under the condition that 0 ≤ s(t ), u (t ) ≤ 1 y (t ) = A(t )u (t ) f   A(t )u (t )  K (0) δ , λ = n + µ , k (0) = , A(0) are given constants; u (t ) and s (t ) are continuous.

L(0)

where

where

REFERENCES Uzawa, Hirofumi (1965). “Optimum Technical Change in An Aggregative Model of Economic Growth.” International Economic Review, Vol. 6, No. 1. (Jan., 1965), pp. 18-31. http://kisi.deu.edu.tr/yesim.kustepeli/uzawa1965.pdf http://links.jstor.org/sici?sici=00206598%28196501%296%3A1%3C18%3AOTCIAA%3E2.0.CO%3B2-Y

Romer, P. M. (1994). “The Origins of Endogenous Growth.” The Journal of Economic Perspectives. 8(1): 3–22. doi:10.1257/jep.8.1.3. JSTOR 2138148.

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APPENDIX 4 Schumpeterian Creative Destruction Growth Model

The following summary is extracted from Aghion and Howitt (1992). The Schumpeterian growth model is also called the Aghion-Howitt Model. The model argues that a monopolist has no incentive to innovate. The key to economic growth is innovation. However, for a monopolist, there is no incentive to engage in research and development to produce innovation because research requires capital investment, and the returns from such investment is less than optimum. However, in a competitive market, competition produces innovation and innovation leads to growth. Our summary of the Schumpeterian model begins with the categorization of labor.

There are three types of labor in the market, namely unskilled laborers (M) who are employed to produce consumer goods, skilled laborers (N) who are employed to produce intermediate goods, and specialized labor who are employed in research and development to produce innovation. The production function for M is given by: y = AF (x)

(A4.1)

where F ' > 0 ; F ' ' < 0 ; and y is the flow output of consumption, x is the flow of intermediate input, and A s the production function of intermediate inputs.

According to this model, intermediate goods used skilled labor to produce under linear technology function: X = L where L is the flow of skilled labor in intermediate good sector.

Innovation is a random event that arrived as Poisson event: λφ (n, R) where n is the flow of skilled labor in research and development, λ is a constant parameter and φ is the technology used in research and development. A condition where φ (0, R) = 0 means if the economy allocates zero skilled labor to research and development then the economy will have zero growth.

Let time be continuous τ ≥ 0 where t = 0,1,... and the production function is given by: At = A0γ t

(A4.2)

where A0 is the initial value, A is productivity, and the factor of productivity γ > 1 . Let xt be the flow of intermediate good produced by a monopolist during the interval t, xt equal to the labor employed in the manufacturing sector. The price charged by ht monopolist is: pt = At F ' ( xt )

(A4.3)

The objective is to maximize [ At F ' ( x) − wt ]xt given At and the wage rate wt of skilled labor. The productivity-adjusted wage is wt ≡ wt / At ; the marginal revenue function is ~ ω ( x ) = F ' ( x) + xF ' ' ( x) . If revenue is downward sloping and satisfies Inada conditions then assume ~ ( x ) = ∞ , lim ω ~ ( x) = ∞ for any output w and output x . The that ω~ ' ( x ) < 0 for all x > 0 , lim ω t t x →0

x →∞

condition for production is: wt = ω~( xt ) or xt = ~ x (wt ) where ~ x is the function ω~ −1 . The monopolist profit is given by:

π t = Atπ~ (wt )

(A4.4)

where π~ (w) ≡ −( ~ x ( w)) 2 F ' ' ( ~ x ( w)) . According to the Cobb-Douglas production function, the consumption-good technology is F ( x ) = x a , 0 < a < 1 yielding the price equation for a monopolist: 110

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1  w  (a −1) w 1− a  pt = t , Monopolist profit: π t =  . The  wt xt ; and the monopolist output: xt =  2t  a  a  a 

expected profit from research is given by:

λφ ( z, s)Vt +1 − wt z − wts s

(A4.5)

where Vt +1 is innovation at time t+0 and wts is the wage rate of specialized labor. Thus, innovation is given by:

Vt +1 =

π t +1 r + λϕ (n + 1)

(A4.6)

A monopolist will not do research because Vt +1 − Vt < Vt +1 . This condition is the building block o Schumpeterian creative destruction. The next objective is to answer the question of how to allocate skilled labor N to manufacture and research:

ω~( N − nt ) γπ~ (ω~ ( N − nt ) ≥ λϕ ' (nt ) r + λϕ (nt +1)

,

n≥0

(A4.7)

Research employment at t is a function of research employment at t+0: nt = ψ (nt +1) where ψ [0, N ) → R+ . The marginal benefit of research is given by:

ω~ ( N − nt ) c(nt ) ≡ λϕ ' (nt ) b(nt +1) ≡

(A4.8)

γπ~ (ω~ ( N − nt +1) r + λϕ (nt +1)

(A4.9)

where c(nt ) → ∞ as nt → N . The stationary equilibrium is defined as:

ω~( N − nˆ ) γπ~ (ω~ ( N − nˆ ) = λϕ ' (nˆ ) r + λϕ (n)

(A4.10)

Finally, the Average Growth Rate (AGR) and the Variance Growth Rate (VGR) are given as:

AVR = λϕ (nˆ ) ln γ

(A4.11)

VGR = λϕ (nˆ )(ln γ )2

(A4.12)

In words, the argument of the model is summarized, thus: “…the model assumes, following Schumpeter, that individual innovations are sufficiently important to affect the entire economy. A period is the time between two successive innovations. The length of each period is random, because of the stochastic nature of the innovation process, but the relationship between the amount of research in

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two successive periods can be modelled as deterministic. The amount of research this period depends negatively upon the expected amount next period, through two effects.

The first effect is that of creative destruction. The payoff from research this period is the prospect of monopoly rents next period. Those rents will last only until the next innovation occurs, at which time the knowledge underlying the rents will be rendered obsolete. Thus the expected present value of the rents depends negatively upon the Poisson arrival rate of the next innovation. The expectation of more research next period will increase that arrival rate, and hence will discourage research this period.

The second effect is a general equilibrium effect working through the wage of skilled labor, which can be used either in research or in manufacturing. In order to be consistent with the conditions for labor-market equilibrium, the expectation of more research next period must correspond to an expectation of higher demand for skilled labor in research next period, which implies the expectation of a higher real wage of skilled labor. Higher wages next period will reduce the monopoly rents that can be gained by exclusive knowledge of how to produce the best products. Thus the expectation of more research next period will discourage research this period by reducing the flow of rents expected to accrue to a successful innovator.” – (Aghion and Howitt, 1992, p. 324).

REFERENCES Aghion, Philippe, and Peter Howitt (1992). “A Model of Growth Through Creative Destruction.”

Econometrica, 60, no. 2: 323-351. doi:10.3386/w3223 http://nrs.harvard.edu/urn-3:HUL.InstRepos:12490578

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APPENDIX 5 The Big Push Theory or Rosenstein-Rodan Growth Model

Rosenstein-Rodan illustrated the concept of the Big Push Theory by presenting two factual scenario of production and spending behavior of consumers and their effect on the economy and, thus, vis-àvis the development of the economy: “Let us assume that 20,000 unemployed workers [...] are taken from the land and put into a large shoe factory. They receive wages substantially higher than their previous meager income in natura. [...] If these workers spent all their wages on shoes, a market for the products of their enterprise would arise [...]. The trouble is that the workers will not spend all their wages on shoes.” ...

“If, instead, one million unemployed workers were taken from the land and put, not into one industry, but into a whole series of industries which produce the bulk of the goods on which the workers would spend their wages, what was not true in the case of one shoe factory would become true in the case of a whole system of industries: it would create its own additional market, thus realising an expansion of world output with the minimum disturbance of the world markets.” - Rosenstein-Rodan, P. N. (1943).

“Problems of Industrialisation of Eastern and South-Eastern Europe,” The Economic Journal, 53, 202–11.

The basic condition is to consider a close economy with a continuum sectors z ∈ [0,1] and the

1   Cobb-Douglas utility function: U [ x( z)] = exp ∫ ln x ( z )dz  where x( z) is consumption of goods z .   0  The economy has labor L > 0 and wage bill of w = 1 and each firm has equal access to technology:

yT ( z ) = l T ( z ) , each access to technology has increasing return of M in production function:

{

Y M ( z ) = max 0,[l M ( z ) − F ] / γ

}

(A6.1)

where γ (0,1) . Since every firm has access to techmology, there is a tendency to create multiple equilibria at: 1 − γv > d >

1 − γv v

(A6.2) 1

In order to escape this equilibria, firms needs to upgrade technology U [ x( z )]∫ u[ z ( z )]dz with 0

1 u[ x ( z )] = α ( z ) − β ( z )2 ; thus, the linear demand system may be constructed as: 2

α − βx( z ) λ α − λp ( z ) x( z ) = and β

(A6.3)

p( z )

(A6.4)

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1

α ∫ p ( z )dz − βγ where λ = 0

1

∫ p( z)

(A6.5) 2 dz

0

The following definition applied: x(z ) = sectoral demand; p(z ) = price, and Y = aggregate demand.

In explaining the Big Push Theory, Kreikermeir and Wrona summarized it in three propositions: m , there are 2[m + 2m(1 + m) three equilibria exists: no industrialization, complete industrialization, and incomplete industrialization. For L < L (m) , there is an addition a parameter range leading to multiple equilibria in which the possibilities of complete and incomplete industrialization co-exist, p. 10.

“Proposition 1: Labor condition L ≥ L (m) where Lm ≡

Proposition 2: Under sufficient condition ( L ∈ ( L(m), L (m)) full industrialization is welfare superior to partial industrialization and, therefore, every regime featuring multiple equilibria constitutes a poverty trap, p.13.

1  z    P M ( z )dz + ( z )dz − Y ( z )  ∫ ∫    1   0 z  ~ U ( z ) = 1 −  z 1 2 2  M T 2  ∫ P ( z ) + ∫ P ( z ) dz    0 z Proposition 3: An economy cannot escape from poverty tram by opening up to free international trade with more partner countries.” p. 14

[

] [

]

REFERENCE Kreickemeier, Udo, and Wrona, Jens (2017). “Industrialisation and the Big Push in a Global Economy.” Discusion Papwr No. 249. düsseldorf university press (dup) on behalf of Heinrich‐ Heine‐Universität Düsseldorf, Faculty of Economics, Düsseldorf Institute for Competition Economics (DICE), Universitätsstraße 1, 40225 Düsseldorf, Germany. ISSN 2190‐9938 (online) – ISBN 978‐3‐86304‐248‐6 Wrona, Jens; Kreickemeier, Udo (2016): “Industrialisation and the Big Push in a Global Economy.”

Beiträge zur Jahrestagung des Vereins für Socialpolitik 2016: Demographischer Wandel Session: International Trade and Development, No. A19-V3, ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften, Leibniz-Informationszentrum Wirtschaft, Kiel und Hamburg https://www.econstor.eu/bitstream/10419/145707/1/VfS_2016_pid_6698.pdf http://www.dice.hhu.de/fileadmin/redaktion/Fakultaeten/Wirtschaftswissenschaftliche_Fakultae t/DICE/Discussion_Paper/249_Kreickemeier_Wrona.pdf

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APPENDIX 6 Unified Theory of Growth

The change in the labor force in any given time may be represented by: Lt +1 = nt Lt

(A6.1)

where nt = gross population growth rate. The quasi-linear utility function is given gy; ut = mt + γ ln(nt )

(A6.2)

where γ is the weight put on children with the individual budget constraint: wt = pt nt + mt

(A6.2)

where wt is the individual income measured by manufactured goods. The optimization problem is put by the demand for children: nt =

γ

(A6.3)

Pt

Output and new technology in industry sectors are produced according to following production function: Yt A = µAt∈( LtA )α = At +1 − At ,

0 < α ,∈< 1

YtM = δM tφ LM t = M t +1 − M t ,

0 < φ <1

(A6.4)

where At is factor productivity in agriculture and M t is the productivity in manufacture.

The net growth rate of a variable Z between two periods is given as: Z − Zt gtz = t +1 Zt

(A6.5)

The productivity growth in agriculture sector is: gtA ≡ µAtt −1( LtA )α

(A6.6)

For the manufacturing sector: gtA ≡ µM tφ −1( LM t )

(A6.7)

At equilibrium, the total labor is given as: LtA + LM t = Lt

(A6.8)

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For food market equilibrium: LA  n L1−α θt = t =  t t Lt  µAt∈ 

1/ α

   

(A6.9)

The relative price of food: Pt =

(δM tφ )(γLt )1−α

(A6.10)

µAt∈

A constant growth in the agricultural sector implies that: 1 + g A = (1 + g L )α /(1−∈)

(A6.11)

For the manufacturing sector: 1 + g M = (1 + g L )1 /( L −φ )

(A6.12)

The population grows at a rate of: 1 + gtL+1 = (1 + gtL )η

,

η ≡α +

α∈

φα 1− ∈ 1 − φ −

(A6.13)

In the long run, the population will grow at a constant rate: gtL+1 = gtL = g L .

Finally, the balanced growth path is given by: ∈<∈crit ≡

1 − φ − α − 2αφ 1 − φ + αφ

(A6.14)

REFERENCE Strulik, Holger, and Weisdorf, Jacob (2008). “The Simplest Unified Growth Theory.” Leibniz Universitat Hannover, Discussion Paper No. 375. ISSN 0949-9962 also Strulik, H., & Weisdorf, J. L. (2007). The Simplest Unified Growth Theory. Department of Economics, University of Copenhagen. https://curis.ku.dk/ws/files/23346699/0721.pdf

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