Local Human Development Index - Concept by Piotr Arak

Local Human Development Index Conceptual foundations, methodology m of measurement and policy application

UNDP PO in Poland

Warsaw, 2012

National Human Development Report team with cooperation of the Warsaw School of Economics: Piotr Arak (UNDP), Agnieszka Chłoń-Domińczak (WSE), Andrey Ivanov (UNDP), Irena E. Kotowska (WSE), Tomasz Panek (WSE), Mihail Peleah (UNDP), Adam Płoszaj (UNDP), Kamil Rakocy (UNDP), Jakub Rok (UNDP)

With contributions, advice and support from: Agnieszka Haber, Iwona Borkowska, Joanna Bitkowska, Elena Danilova-Cross, Jan Herbst, Eva Jespersen, Radomir Matczak, Ben Slay, Paulina Pietrasik, Rafał Trzciński, Bogdan Wojtyniak, Kamil Wyszkowski and National Statistical Office in Poland

Cooperation: Warsaw School of Economics, MojaPolis.pl and the Human Development Report Office

Project carried out on behalf of the Ministry of Regional Development under the indicator component of the project “Strategic development management – enhancing the quality of governance in Poland” financed by the European Union under the Operational Programme Human Capital.

United Nations Development Programme (UNDP) Project Office in Warsaw Szpitalna 6/23 00-031 Warsaw, Poland ISBN-978-83-933274-6-1

Table of Contents 0. Foreword ......................................................................................................................... 4 1. The Concept of Human Development .......................................................................... 6 1.A Alternative measures of development to the Gross Domestic Product ........................... 8 1.1A Complementary to GDP measures of development: ........................................................................ 9 1.A.2 Well-being indicators:.................................................................................................................................. 9 1.A.3 Synthetic and inter-connected measures: ......................................................................................... 10 1.B Indicator initiatives at the local and regional level.......................................................... 11

2. The Local Human Development Index as an aggregate estimate of development outcomes........................................................................................................................... 18 2.A Dimensions of development – the construction of the Local Human Development Index .......................................................................................................................................... 21 2.A.1 Living Standard Index................................................................................................................................ 23 2.A.2 Health Index .................................................................................................................................................. 26 2.A.3 Education Index ........................................................................................................................................... 30 2.C. Putting the HDI in context – additional missing dimensions to be considered through contextual indicators................................................................................................................ 32 2.C.1 Environmental protection ........................................................................................................................ 33 2.C.2 Poverty ............................................................................................................................................................ 34 2.C.3 Labour market .............................................................................................................................................. 35 2.C.4 Civic activity .................................................................................................................................................. 36 2.C.5 Digital engagement ................................................................................................................................... 36 2.C.6 Women empowerment............................................................................................................................. 37 2.D. Tracking Human Development on the local level – current status and trends over time .................................................................................................................................................... 38

3. Linking LHDI to development policies ....................................................................... 39 3.A Policy evaluation approach ............................................................................................... 40 3.B Problems in linking policy with data................................................................................. 42 3.C Work schedule .................................................................................................................... 44

5. References: .................................................................................................................... 48 Annex 1. Table of used indicators ................................................................................... 52

0. Foreword Since the publication of the first Human Development Report in 1990, an exciting debate emerged world-wide on issues of human development. Needles to say Human Development Reports published annually by UNDP, have made a significant contribution to the debate on inequality, sustainable development, well-being and measuring of development. The Human Development Index is a powerful tool for increasing awareness of problems a country may face and even mobilizing public support. The analytical value of the index however is often questioned. The argument boils down to the question: “What does a ranking among different countries tell us?” HDI should be used with a caution and always related to the context. Despite the criticism, HDI could make sense, and not just as advocacy but also as a policy tool. Over the years, many distinguished experts have contributed to the central ideas in the Human Development Reports but also to the methodology of the Human Development Index. Currently it is time to prepare a new replicable measure of regional and local development in link with policy. Since the beginning the HDI was disaggregated but no methodology and quality of data guaranteed a success of such a project in connection with the policy decisions. Together with the Polish Ministry of Regional Development the UNDP Project Office in Poland is starting a project on a new operational measure of socio-economic development, which methodology will be based on the Human Development Index. National level indicators are only useful for international comparisons. They can indicate the specific problems and priorities of a country. But computation on a local level is important to reveal the kind of intra-national distribution that is critical for domestic policy-making. The global HDI attributes one average value of a composite index for all individuals living within a country. It indicates the “representative” individual. The reality is that individuals within a country are not all identical, especially taking into account spatial distribution. In order to become a policy tool, HDI should suggest different policy options. For that purpose computation on a local level is necessary. National level HDI gives an idea where a country stands vis-à-vis other countries, which is informatively curious but practically rather useless. However, computation of HDI at sub-national level or for different groups could show how (and why) different administrative units or groups within a country stand vis-à-vis each other, what is the goal of development policy in a long-term (2020) perspective, what are the strengths and weaknesses and hence, what Central and Local governments’ priorities could be. From this perspective HDI disaggregation is not about the ranking of municipalities or groups but about the way each of it has achieved its human development level (e.g. good economic performance at the expense of health or good educational opportunities offsetting delays in other areas). The outcomes will contribute to making the regional policy evaluation smart and evidence-based. Especially, taking into account Polish membership in EU and support from the EU structural funds and Cohesion Fund which primary objective is provision of assistance in reducing the development disparities between regions and districts. In order to strengthen the economic and

social cohesion, the Local HDI (LHDI) will serve as a main tool for better planning, distributing, monitoring and controlling spending these funds and project implementation until 2020, as a part of the strategic monitoring system. The paper is proposing a “three-dimensional” HDI, like the global HDI, with possibly different component-indicators. It also suggests a number of contextual indicators for other dimensions of HD not accounted for by the proposed Local HDI. An attempt should be also made to carry out a retrospective analysis of the LHDI, going back 5-10 years in the National Human Development Report. This longitudinal approach would make possible not just monitoring trends over time but also linking the human development outcomes to major political developments, decisions taken, changes in the external economic environment, etc. One particular advantage of this approach is that we would be able to say something about the changes happening after accession to the European Union – in link with EU funding – and a positioning measure can be used to see the changes faster. Also this approach could be described as an ex-post evaluation - an assessment of the relevance, effectiveness and impact of policies carried out some time after their completion. It is undertaken long after completion of some of the EU projects but the intention is to identify the factors of success or failure, to assess the sustainability of results and impacts, and to draw conclusions that may inform other projects and programmes conducted by the Ministry of Regional Development. In that regard, major challenge is related to the availability and quality of data. Especially that building and monitoring a dynamic (and not a positioning) index requires comparable, up-to-date, longitudinal data sets on all aspects of human development process – its outcomes as well as inputs. Both the incomplete availability of high-quality data at the local level and the delay associated with long-term process of collecting it are external to the Human Development Report team (HDR team)1. But, one of the first tasks under this project would be to map existing data sources and determine the feasibility of such comprehensive analysis. Also, we have to deal with the trade-off inherent in every measurement initiative, i.e. balance the comprehensiveness and indepthness of the proposed index on the one hand, with the principles of clarity and meaningfulness for the target group on the other. This document forms a methodological approach to the concept of disaggregation of the HDI in Poland using an “input” – “outcome” approach in link with the contextual measures. This approach is a basis for conducting research using the proposed measures.

Human Development Report team – experts and specialists from UNDP and cooperating with UNDP on the Local Human Development Index Project. 5

1. The Concept of Human Development The first Human Development Report in 1990 opened with the simply stated premise that has guided all subsequent Reports: “People are the real wealth of a nation.” By backing up this assertion with an abundance of empirical data and a new way of thinking about and measuring development, the Human Development Report has had a profound impact on policies around the world. Human development is the expansion of people’s freedoms and capabilities to lead lives that they value and have reason to value. It is about expanding choices. Freedoms and capabilities are a more expansive notion than basic needs, since transition from the latter to the former entailed the emergence of a concept of agency. Basic needs approach encapsulate the pre-defined (in a topdown manner) set of human needs, that is considered to fill in the universal notion of the human well-being. Conversely, capabilities approach stresses the freedom of choice, including the ability to define a personal set of values and objectives, that need not be on par with commonly understood principles of well-being. This transition indicates a move from a rather passive (“what can be done for the person?”) to more autonomous perspective (adding “what can the person do?”) (Alkire 2006). Many ends are necessary for a “good life,” ends that can be intrinsically as well as instrumentally valuable – we may value biodiversity, for example, or natural beauty, independently of its contribution to our living standards (UNDP 2011). As Anand and Sen (1994) put it: “human beings are the real end of all activities, and development must be centered on enhancing their achievements, freedoms, and capabilities. It is the lives they lead that is of importance, not the commodities or income that they happen to possess.” Quality of life is not by itself constituted by income and wealth. Income does not say, whether a person is presently healthy or is she equipped with knowledge capable of changing the person’s position (Anand, Sen 1994). Amartya Sen (1993) wrote that human development has two sides: the formation of human capabilities – such as improved health, knowledge and skills – and the use people make of their acquired capabilities – for leisure, productive purposes or being active in cultural, social and political affairs. Thus, capability should be understood as a various combinations of functionalities (i.e. states and activities that constitutes a personal view of a “good life”) that a person is able to achieve (Sen 1992). Stressing the ability to achieve, and not simply achieved outcomes highlights the utmost importance of the freedom of choice. This theory – known as the capabilities approach – became a dominant paradigm in the human development area, and is reflected in Human Development Reports. Motivations to focus directly on the lives that people lead could be found answering some simple questions. Do they have the capability to live long and healthy lives?? Can they avoid mortality during infancy and childhood? Does their level of education provide opportunities for well-paid job? Are they free from poverty and discrimination? Are they empowered to change their local and national political surrounding? Do they have control over their lives? Are they willing to take the responsibility for their lives in their hands – and not rely only on the government? Anand and Sen

(1994) describe these as basic features of well-being which derive from looking at people as the centre of all development activity. Operationalization of the theory of human development poses a considerable challenge. Embedded in it is a question of listing human ends that are of particular value for the quality of life. There were several attempts to create such sets. Many of these efforts were built upon the capability approach, even though Sen himself refrained from specifying a list of basic capabilities (Alkire 2002). Human Development Index, which lies at the core of every Human Development Report since its first edition, is by far the most recognizable operationalization of Sen’s theory. It supplements a traditional income measure (GNI with capability indicators from the area of health and education. As its author, Mahbub ul Haq, recalls, there were several principles guiding the search for a new index: (1) to find a measure that goes beyond income while retaining methodological soundness, (2) to limit the number of variables to ensure simplicity and manageability, (3) to construct a composite index rather than an extensive set of indicators, and finally (4) to merge social and economic indicators (ul Haq 2003). Since 1990 the HDI has been gradually refined, but main principles remain unchanged. There were also other attempts to specify a list of dimensions of development – oriented towards advancing the human development concept, rather than towards creating a tool for measurement. Particularly, philosopher Martha Nussbaum has proposed a set of 10 central, universal and intrinsically valuable human capabilities. It ranges from being able to live to the end of a human life of a normal length, through being able to have attachments to other persons and things, to having political and material ability to control one’s environment (Nussbaum 2000). Sabina Alkire (2008) points to five widespread, often overlapping, methods of selecting development dimensions, driven by: (1) existing data or convention, (2) assumptions, (3) public consensus, (4) deliberative participatory process, and (5) evidence regarding people’s values. Undoubtedly, happiness, social inclusion and expanding people's human development opportunities is the ultimate objective of economic development, both at national and local level. Specific local conditions often determine such development opportunities. These conditions include economic parameters (employment opportunities, impact of the economic crisis), state of environment, as well as political aspects (history of local civic participation). All these parameters of the local context can be quantified and reflected in the process of (and policies targeting) integrated local and regional development. There are a plethora of variables that might be used to indicate human development and its context. The main distinction is between objective and subjective measurement. HDI falls into the former category, as it reflects people’s objective circumstances, basing on observable, quantitative statistics (Diener, Suh 1997). Conversely, subjective indicators are based on the individual’s perception of his/her status (e.g. level of happiness). The word “happiness” is often used in a general way. It does help to focus thinking and look for measures that count what matters in human life, which is not available in the vast datasets of statistical offices and governmental agencies. Although there is an interest in finding out how happy people are, such subjective measures will be of little help unless they can be combined with sufficient other information to build an understanding of what makes for better lives (Helliwell, Layard and Sachs 2012). Both approaches have their strengths and weaknesses, and should be complementing each other, but incorporation of subjective measures on the local level remain a considerable challenge. Thus, 7

proposed LHDI relies on objective measures used in the Human Development Index and some other that are contextual but also objective. Nonetheless, a follow-up to this project is planned, targeted specifically at the measurement of subjective well-being, since it captures best how people rate the quality of their lives. Finally, embedded in the human development approach is the issue of distribution. Traditional measures of economic performance (such as GNI per capita) tend to be aggregative indicators that are based on averaging the individual circumstances. This inevitably involves the loss of some valuable information. A situation in which, say, three people have respectively income levels (7, 4, 10) looks much like one in which the three respectively have (7, 7, 7), even though the two social situations can scarcely be seen as equivalent in terms of our concerns and values. There is an understandable demand to see whether the actual distribution could not be used instead of the usual aggregate indicators based on simple averages (see Anand, Sen 1994). Other approach calls for using median value instead of arithmetic mean, as it better accounts for social inequalities (Stiglitz, Sen and Fitoussi 2009). Against this background, included in this project is a calculation of the Gender Inequality Index, which captures one of the most important dimensions of inequality in the contemporary society.

1.A Alternative measures of development to the Gross Domestic Product No single measure, or even a limited set of measures, can provide all the information required to assess and manage an economy or to be used by governments (Stiglitz, Sen, Fitoussi 2009). GDP shortcomings, as an index for measuring socio-economic progress, feature again prominently in the public debate, following years of being neglected. Such criticisms are almost as old as the concept itself and specialists have repeatedly warned about limitations of GDP as a welfare indicator. At the end of the day, it is essentially a measure of economic activity, and more specifically of economic activities leading to monetary transactions. These criticisms first culminated during the mid-seventies with worries about ecological limits to growth and an increasing concern over the relative weights to be given to economic and social aspects of human progress, for developed as well as for developing countries. Some early initiatives took place at that time, in particular the attempt by Nordhaus and Tobin (1973) to develop a measure of economic welfare (MEW), based on GDP, but correcting GDP for its most evident limitations. Following these early moves, according to Stiglitz, Sen and Fitoussi (2009), interest in alternatives approaches to GDP temporarily fell, with other pressing but more traditional problems taking centre stage, such as stagflation or rapid increase in unemployment rates and the GDP-targeted policies needed to address them. The Nordhaus-Tobin experiment itself provided some arguments in favour of maintaining GDP primacy, since its conclusion was that, despite its limits, it remained a good indicator of the overall direction of socio-economic progress. Nonetheless, interest in alternatives or complements to GDP resumed progressively during the 90s. Emblematic of this new trend was the creation of the Human Development Index (HDI) that combines GDP with measures of health and educational achievement. This very simple index only synthesizes a limited amount of information. It is also more relevant for comparisons of developing countries than for comparisons of more advanced countries but it remains one of the few indexes that are regularly compiled and widely disseminated by international organizations to

allow systematic cross-country comparisons. It also played a large role in raising the profile of important non-economic dimensions of the quality of life. In the same vein, the 1992 UN Summit in Rio de Janeiro brought the notion of Sustainable Development into the policy debate (“Agenda 21”), with positive consequences for the promotion of sustainable development indicators. This was later followed by a number of more local or country-specific initiatives, often stemming from individual researchers. The number of synthetic indicators of social progress was equal to 2 in 1990 (the HDI and the “kids count index”), climbed to about ten in 1990 and to about thirty in 2001-2002 (Afsa et al. 2008). This growing interest may reflect a combination of objective as well as societal factors. A first one probably lies with the increasing visibility of some of the adverse consequences of economic activity on the environment i.e. climatic change. In such a context, we present an overview of the main tools that have been proposed until now to better measure socio-economic progress or well-being. The last attempt to move towards a more subjective measure of development was made by the British Office for National Statistics (2012) and we are to see the effects of work of the “Beyond GDP” initiative of the European Commission. 1.1A Complementary to GDP measures of development: Genuine Progress Indicator (GPI) is a variant of the Index of Sustainable Economic Welfare (ISEW) first proposed by Daly and Cobb in 1989. Both the GPI and ISEW use the same personal consumption data as GDP but make deductions to account for income inequality and costs of crime, environmental degradation, and loss of leisure and additions to account for the services from consumer durables and public infrastructure as well as the benefits of volunteering and housework. By differentiating between economic activity that diminishes both natural and social capital and activity that enhances such capital, the GPI and its variants are designed to measure sustainable economic welfare rather than economic activity alone (see more Talberth, Cobb, Slattery 2006). Adjusted net saving, (also known as genuine saving), is a sustainability indicator building on the concepts of green national accounts. Adjusted net savings measure the true rate of savings in an economy after taking into account investments in human capital, depletion of natural resources and damage caused by pollution (see more World Bank 2012). 1.A.2 Well-being indicators: Ecological footprint is a measure of human demand on the Earth's ecosystems. It is a standardized measure of demand for natural capital that may be contrasted with the planet's ecological capacity to regenerate. It represents the amount of biologically productive land and sea area necessary to supply the resources a human population consumes, and to assimilate associated waste. Using this assessment, it is possible to estimate how much of the Earth (or how many planet Earths) it would take to support humanity if everybody followed a given lifestyle. The ecological footprint concept and calculation method was developed by Mathis Wackernagel, under William Rees' supervision at the University of British Columbia in 1992. Since 2006, a first set of ecological footprint standards exist that detail both communication and calculation procedures.

They are available at www.footprintstandards.org and were developed in a public process facilitated by Global Footprint Network (2012). Subjective well-being (SWB) and its variants as happiness, life satisfaction etc. are measures obtained through self-reports: people are asked to evaluate their lives as a whole or some aspect of it. The questions can be relatively straightforward and a widely used one simply asks: ‘Taking all things together, would you say you are: very happy, quite happy, not very happy or not at all happy’. More elaborate measures use multiple items to target a specific part of SWB and consequently render more reliable results single-item measures do (see more: Helliwell, Layard, Sachs 2012, Van Hoorn, Andre 2007 and Office of National Statistics 2012). Gross national happiness (GNH) was designed in an attempt to define an indicator that measures quality of life or social progress in more holistic and psychological terms than only the economic. Bhutan’s GNH Index is a multidimensional measure built from data drawn from periodic surveys that are representative by district, gender, age, rural-urban residence, etc. The GNH Index provides an overview of performance across 9 domains of GNH (psychological wellbeing, time use, community vitality, cultural diversity, ecological resilience, living standard, health, education, good governance). The index is aggregated out of 33 clustered (grouped) indicators. Each clustered indicator is further composed of several variables. When unpacked, the 33 clustered indicators have 124 variables. The term "gross national happiness" was coined in 1972 by Bhutan's fourth King, Jigme Singye Wangchuck (see more Ura, Alkire, Zangmo, Wangdi 2012).

1.A.3 Synthetic and inter-connected measures: Happy Planet Index (HPI) is an index of human well-being and environmental impact that was introduced by the New Economics Foundation (NEF) in 2006. The index is an efficiency measure, it ranks countries on how many long and happy lives they produce per unit of environmental input. Each country’s HPI value is a function of its average subjective life satisfaction, life expectancy at birth, and ecological footprint per capita. The 2012 HPI report ranks 151 countries and is the third time the index has been published (see more: New Economics Foundation 2012). Living Planet Index (LPI) is an indicator of the state of global biological diversity, based on trends in vertebrate populations of species from around the world. The LPI provides the general public, scientists and policy-makers with information on trends in the abundance of the world’s vertebrates and offers insights into which habitats or ecosystems have species that are declining most rapidly. The Living Planet Index was originally developed by World Wide Fund for Nature (WWF) in collaboration with UNEP-WCMC, the biodiversity assessment and policy implementation arm of the United Nations Environment Programme. The LPI is calculated using a database maintained by the Zoological Society of London (ZSL), which contains over 10,000 population trends for more than 2,500 species of fish, amphibians, reptiles, birds and mammals (see more: WWF 2012). The Multidimensional Poverty Index (MPI) was developed in 2010 by Oxford Poverty & Human Development Initiative and the United Nations Development Programme and uses different factors to determine poverty beyond income-based lists. It replaced the previous Human Poverty

Index. The index uses the same three dimensions as the Human Development Index: health, education, and standard of living. These are measured using ten indicators (see more UNDP 2010). OECD Your Better Life Index is an interactive tool available at the OECD website that allows you to see how countries perform according to the importance you give to each of 11 topics – community, education, environment, civic, engagement, health, housing, income, jobs, life satisfaction, safety, work-life balance– that contribute to well-being. Your Better Life Index currently profiles the 34 OECD member countries as well as key partners Brazil and Russia across the 11 topics of well-being chosen by the OECD. The Index contains an overall description of the quality of life in each country, followed by its performance across the 24 individual indicators (see OECD 2012). The last one, which is one of the most popular measures of social development and progress is the Human Development Index (HDI) covered widely in this paper. Most measures of social progress are the results of initiatives of academics and non-government organisations. Some are popular enough to be presented in the list above and have been agents of differently understood development. The most popular indices are from inter-governmental agencies, which have undertaken in recent years a number of initiatives bearing on this issue. The initiatives covered in this part of the paper differ in terms of scope and objective. In only four instances (the Human Development Index developed by the UNDP, and the Genuine Savings measure pioneered by the World Bank, the Multidimensional Poverty Index, Living Planet Index and Happy Planet Index) these initiatives have led to the development of a synthetic indicator that purports to provide a comprehensive measure of social conditions in various countries. In a few other cases we relate to one-off initiatives related to the broad agenda of measuring progress or well-being (the OECD and British National Statistical Office initiatives) or to on-going projects that have not yet delivered concrete results as “Beyond GDP” by the European Commission. Beside the covered measures there are many approaches to development covered in the literature as Ian Morris (2010) social development of societies theory and many other as the European Union Social Indicators, Sustainable Development Indicators or the European Foundation for the Improvement of Living and Working Conditions Indicators of Living and Working Conditions. Many other more theoretical approaches were described by the Commission on the Measurement of Economic Performance and Social Progress (Stiglitz, Sen, Fitoussi 2009).

1.B Indicator initiatives at the local and regional level Apart from national measurement initiatives, there is a plethora of alternative development indices at the local and regional level. Following the call to measure what really matters, the local sustainable development, quality of life or human development measurement initiatives are flourishing. We focus on reviewing attempts to disaggregate HDI for local or regional level but include also some insights from the quality-of-life strand, as it is useful in complementing the picture of measuring social progress. Then we move on to briefly overview Polish experiences in this area. Those wishing to delve deeper into local and regional sustainable development

measures could use one of the following reviews (Tanguay et al., 2010; Sing et al. 2009; and for Polish perspective: Borys, 2005; Rok, 2012). HDIs disaggregated at sub-national level differ according to the approach to applying the “original” HDR methodology. Straight application of this methodology is suitable for countries where sub-national units are big enough to avoid the problems related to small populations or unequal distribution of economic potential, such as China and Russia (Ivanov, Peleah, 2011). For instance, Russian Federation NHDR for 2006/2007 provides HDI disaggregated for 79 regions, based on regional values of life expectancy at birth, enrolment in education for people aged 7-24, and GDP per capita (PPP). Both in Russia and China NHDRs education component is based on enrolment rates for all three educational tiers, which is acceptable given the presence of higher education institutions in the regional centres. Second group follows the basic HDR methodology but includes different variables under the three dimensions of HDI. It is either due to problem of scale, data availability or adapting the measure to meet specific local needs. Third approach goes beyond the “original” methodology and rebuilds the framework for measuring human development. Usually, such initiatives extend the number of dimensions of development taken into account. Analysis of 10 recent distinguished examples of second and third approach is presented in a table below. The most interesting ones are then discussed in detail.

Table 1. Recent developments in measuring HDI on the local and regional level Country/ year of publication Bulgaria 2003

Dimensions of disaggregatio n Municipality

Year of data

Data used Components of HDI

Health

Knowledge

Decent life Real GDP for final consumption; own calculations based on data from the national nonfinancial accounts by institutional sectors â&#x20AC;&#x201C; households, central and local government, and enterprises. Estimated per capita net Panchayat Samiti domestic product; calculations based on data provided by Directorate of Economics and Statistics in Jaipur; (see Hemlata, 2008, p.12 for details)

2002

Follows HDR methodology

Life expectancy calculated using abridged life tables; data for municipalities smoothed with the district average

Combined educational index, including net enrolment rate for people aged 7-18 (1/3 weight), and literacy rate among people aged >15 (2/3 weight; based on 2001 census data)

India: Rajasthan 2008

Panchayat Samiti (small rural districts), gender

2001

Follows HDR methodology

Health Status Index, including access to safe drinking water, access to medical, educational and power amenities, sex ratio, access to improved road

Total literacy rate (2/3 weight), and Female literacy rate (1/3 weight)

Mexico 2010

Municipality, Household and individual level

2006, 2008

Two additional dimensions described: being free from local crime and the absence of violence against women.

Municipality mortality rate

Municipality level: school attendance rate and adult literacy rate

level:

infant

Household level: life expectancy for the age, gender and state of residence

Household level: literacy (age >6), school attendance (age =6), and schooling rate (age >6)

Municipality level: estimation of per capita average annual household income; combining census data (observable characteristics of each household) and national income surveys.

Notes http://hdr.undp.org/en/report s/nationalreports/europetheci s/bulgaria/name,3261,en.html

http://books.google.pl/books?i d=e3FUqVUjJgC&printsec=frontcover&dq= human+development+index+ rajasthan&source=bl&ots=bmJ WTCQkXJ&sig=c0HmPZriICscG 5F2OyQNmseprWI&hl=en&sa= X&ei=yV8UMKkF8_ptQa0t4CYCw&v ed=0CC0Q6AEwAA#v=onepag e&q&f=false http://hdr.undp.org/en/report s/global/hdr2010/papers/HDR P_2010_23.pdf

Household level: per capita household total yearly current income (PPP)

Country/ year of publication Moldova 2007

Dimensions of disaggregatio n Ethnic group (Roma)

Year of data

Data used Components of HDI

Knowledge

Decent life

Literacy rate for Roma was obtained from survey. Gross enrolment rates (GER) for Roma and non-Roma were estimated from survey basing on enrolment rates for each grade and weighted by share of children belonging to each age group. High school graduate ratio, measured as the percentage of at least high school graduates among those aged >18 (2/3 weight), and gross enrolment rates of primary and high schools (1/3 weight)

GDP for Roma was estimated at the level of 60% of national, basing on ratio of Roma/NonRoma incomes from survey.

http://www.undp.md/publicat ions/roma%20_report/UNDP, %20Romii%20in%20Republica %20Moldova%20(Chisinau,%2 02007).pdf

Income per capita figures obtained from the Family Income and Expenditures Survey (FIES). Adjusted to ensure temporal and spatial comparability: (1) nominal income is adjusted to 1997 price levels using regional consumer price indices; (2) income estimates are adjusted using provincial cost-of-living indices

http://hdn.org.ph/wpcontent/uploads/2009/05/cha pter-3-provinces-and-humandevelopment.pdf

Enrolment rates based on Educational Areas’ reports of students and teachers 2000, Education Information Centre, Office of Permanent Secretary, Ministry of Education.

HDI uses GDP per capita (provided by National Account Division, Office of the National Economic and Social Development Board) without PPP adjustment, as used for intra-country comparison HAI uses incomes from Household Survey for «Income index» component

http://hdr.undp.org/en/report s/nationalreports/asiathepacifi c/thailand/thailand_2003_en.p df

20052006

Follows HDR methodology

Life expectancy at birth estimated basing on findings of survey on child mortality among Roma population.

Philippines 2009

Province

2006

Follows HDR methodology; HDI-1 (described here) is used for interprovincial comparison, while HDI-2 is a straight application of HDI methodology, to allow for international comparisons of chosen provinces

Life expectancy; an interpolation using actual 1995 and 2000 estimates was done to obtain values for 2006

Thailand 2003

Province

2001, various years

Introduce Human Achievement Index (HAI), composite index, using 40 indicators that cover eight aspects of human development2

Life expectancy provided by Development Evaluation Division, National Economic and Social Development Board with reference to civil registration from Registration Administration Bureau Department, Ministry of Interior.

Notes

Health

Health, education, employment, income, housing and living environment, family and community life, transport and communication, and participation. 14

Country/ year of publication Turkey 2004

Dimensions of disaggregatio n Region, Province

Year of data

Data used Components of HDI

2000

Follows HDR methodology

Ukraine 2003

Region

2001

Composite index with components 3 is used

United States of America since 2008

State, Congressional district (~ 650,000 people each), Top10 metropolitan areas, race, gender Region

20082009 and 20102011

Follows HDR methodology

2005

Follows HDR methodology

Uzbekistan 2008

Notes

Health

Knowledge

Decent life

PhD Thesis Life expectancy data for 1997.

Adult literacy rate from Population Census 2000; Combined enrolment rate, provided by State Institute of Statistics

http://hdr.undp.org/en/report s/nationalreports/europetheci s/turkey/turkey_2004_en.pdf

Life expectancy, provided by Council for Studying Productive Forces of Ukraine

Adult literacy rate and Combined enrolment rate, provided by Council for Studying Productive Forces of Ukraine

GDP per capita by provinces, provided by State Institute of Statistics (Gross National Domestic Product (GNDP) by province is calculated by production starting from 1987) Estimated GDP per capita by regions, provided by Council for Studying Productive Forces of Ukraine

Life expectancy at birth, calculated from mortality data from the Centres for Disease Control and Prevention, National Centre for Health Statistics, and population data from the CDC WONDER database. Life expectancy, provided by State Committee on Statistics

School enrolment for the population age 3 and older (1/3 weight), and educational degree attainment for the population >25 years (2/3 weight), provided by the American Community Survey, U.S. Census Bureau. Education attainment level, provided by State Committee on Statistics

Median personal earnings of all full- and part-time workers >16 years, provided by the American Community Survey, U.S. Census Bureau.

http://measureofamerica.org/ human-development/

GDP per capita by regions, provided by State Committee on Statistics

http://hdr.undp.org/en/report s/nationalreports/europetheci s/uzbekistan/Uzbekistan_2007 _nhdr_en.pdf

http://hdr.undp.org/en/report s/nationalreports/europetheci s/ukraine/Ukraine_2003_en.pd f

Source: Own research based on: Andrey Ivanov, Mihail Peleah (2011), â&#x20AC;&#x153;Disaggregation of Human Development Index. Opportunities and challenges for local level policy-makingâ&#x20AC;?, Bratislava: UNDP BRC.

Demographic development, Labour market development, Material wellbeing of population, Living conditions, Education level, State of the health-care system, Social environment, Funding of human development, Environmental situation. 15

The most advanced effort in disaggregating HDI seems to be put forward in Mexican NHDR. Using the income expenditure survey authors attempt to build an HDI for household and individual level, thus being able to report the results by gender, age, ethnicity or almost any other grouping. To this end, the following variables are computed (de la Torre, Moreno 2010): • Health: income-adjusted life expectancy for a given individual considering his/her age, gender and state, which is then imputed to every individual in the survey sample and related to international thresholds •

Knowledge: enrolment for people aged 6 (the age at which children are supposed to be enrolled at school); literacy rate for individuals >6 years old; schooling for people aged 624

•

Decent life: available resources index - household annual income based on monetary and nonmonetary income. The former includes benefits from employment, lending of assets and public and private transfers. The latter consist of received gifts and services provided from within the household, such as rental value of owner occupied dwelling or self consumption

There are a few more important contributions of the Mexican NHDR, such as adjustment for oil revenues, accounting for migration effect or inequality. In the former case, the oil revenues transferred to central government are deducted, and accounted for further redistribution of these funds according to reallocation formulas. It is especially useful in case of countries, where some regional economies rely on extraction of natural resources, owned or heavily taxed by central government. Accounting for migration effect enables answering a question how would states’ HDI look like if migrants were to stay in their states of origin. Finally, accounting for inequality among both dimensions and entities is possible thanks to the use of generalized mean with “inequality aversion” parameter (de la Torre, Moreno 2010). The approach that departs the most from the “original” HDI methodology is employed by Thailand NHDR. Human Achievement Index (HAI) consists of 40 indicators grouped in 21 components and than in 8 areas of human development (see: UNDP 2007A). Components include inter alia family life, civil society participation, employment and basic appliances. HAI attempts to provide an overall assessment of the human development situation at the provincial level. It uses goal posts for each indicator, following the methodology of HDI, and uses equal weights to aggregate variables within dimensions and dimensions into composite index. Temporal analyses (there were two editions of HAI – in 2003 and 2006) are hampered due to change of a list of variables included in the index. The insightful approach to disaggregate HDI from Central Eastern Europe was undertaken in Bulgarian 2003 NHDR. HDI was calculated for the LAU-1 level, covering 262 entities. Worth noting is an attempt to put local HDI in context, to identify the main features that differentiate one cluster of municipalities from another. To that end, 36 indicators were selected, basing on explorative factor analysis, and then structured into a more general dimensions. Assessing municipalities against these dimensions allowed for constructing seven typologies underlying regional

disparities, such as urban versus rural areas, Turkish versus Bulgarian type of municipality, or type of demographic outlook (UNDP, 2004). Finally, worth noting is the Measure of America initiative, undertaken by Social Science Research Council. It shows how HDI might be adjusted to pertain to a highly developed country, e.g. in the field of education (starting with 3 years old children, and accounting for educational degree attainment). But it also paves the way for innovative, user-friendly and regularly updated visualization tools, such as interactive maps and charts available at the website (measureofamerica.org/human-development). It is an example of how to reach out and create a platform for effective advocacy and growing citizens’ involvement. Quality of life measurement initiatives might be divided into the universal and specific ones. While the latter is created – often in a bottom-up and participative process – to fit the specific needs of a given community, the former attempts to employ a more general-purpose approach. One of the most prominent studies analyzing local quality of life is Urban Audit conducted at the initiative of the Directorate-General for Regional Policy at the European Commission. Last edition of this study took place in 2009 and involved almost 400 European cities (including Polish cities as well). Dataset used in this study consisted of 329 variables, grouped in the following domains: demography, social aspects, economics aspects, civic involvement, training and training provision, environment, travel and transport, information society, and culture and recreation. Apart from collecting the objective figures, a parallel perception survey was undertaken to assess the subjective quality of life (for data see: epp.eurostat.ec.europa.eu/portal/page/portal/region_cities/city_urban). While wide-ranging measurement initiatives with local level data are scarce, there are plenty of various local and regional initiatives undertaken locally. One example worth noting is Community Indicators Victoria (Australia), which provides a community wellbeing indicator framework with local level data. Approximately 80 variables are grouped into five broad categories, namely: healthy, safe and inclusive communities, dynamic resilient local economies, sustainable built and natural environments, culturally rich and vibrant communities, democratic and engaged communities. This set is intended as a starting point for local communities willing to adapt such measure. Then, the participative process should lead to identifying further issues – important from the viewpoint of the community members. Data sources include both existing administrative databases, as well as the dedicated survey developed to fill in the gaps in existing datasets (for data see: communityindicators.net.au). Among Polish measurement initiatives aiming at assessing the quality of life, one of the most comprehensive is definitely the Social Diagnosis (Diagnoza Społeczna). It is a bi-annual, panel research with sample size exceeding 40,000 individuals. Thanks to the size of the sample, results of the study are representative on a regional level. Growing importance of quality-of-life studies is reflected in the decision of GUS to start in 2011 a new research program called Quality of life and social cohesion (Jakość życia i spójność społeczna). Another wide-ranging tool for measuring local quality of life is the System for Local Government Analyses (System Analiz Samorządowych). While it doesn’t offer unique data, its role is rather to facilitate utilization of structured datasets by Polish local governments (see: www.sas24.org). In Poland, there are also several local initiatives aimed at measuring quality of life, undertaken usually by local governments (see: Borys, Rogala, 2008 for 17

examples). Recent example of such initiative is a representative survey conducted in Łódź, which aimed at providing evidence of citizens’ needs and their opinions about public service performance (uml.lodz.pl/miasto/strategia/badanie_jakosci_zycia). Coming back to Human Development Index, first attempt to calculate disaggregated HDI in Poland was taken in 1993 (Mijakowska 1993, in: Akder, 1994). Following the HDR methodology and using data for 1990, HDI was computed for 49 voivodeships. International goal posts were replaced with “domestic” minimum and maximum values, which allowed for increased differentiation among regions. Results of this study showed that Warsaw and Cracow agglomerations are top performers, while worst performers are found mostly in north-eastern Masovia region (Ostrołęckie, Siedleckie, and Ciechanowskie). Since the early 1990s – to our best knowledge – there was no other attempt to disaggregate HDI for sub-national level in Poland.

2. The Local Human Development Index as an aggregate estimate of development outcomes Within each country, there are significant disparities, gaps: among regions, between the sexes, between urban and rural areas. Operationalizing the Human Development requires some analysis of the distribution of Human Development within a individual country at a sub-national level. Human Development Index (HDI) on the local level can become a useful tool to understand the underlying sources of and potential causes of present and future problems. Studies in disaggregated HDI have been initiated in a number of countries (see Akder 1994, Ivanov & Peleah 2011). This proposal goes further – its purpose is not a simple disaggregation of the HDI on the regional level but a policy tool that would reflect the policy ‘outcomes’ and ‘inputs’ and allow for temporal and spatial analysis in Poland. It will propose in-depth analysis of the very process that has led to the current status (and if data permits – to HDI improvement over time). Such analysis needs to account for the factors influencing the changes in its individual components and how those factors relate to policies. A truly “policy operationalized HDI” needs to take into consideration the entire results-oriented chain and account for the human development outcomes against the inputs into the process (financial, human, natural resources etc.) and why is this allocation of different resources done. The case for a policy tool measure must not be seen simply in terms of having "more" information, but rather in terms of using "more relevant" information. (see Anand, Sen 1994). Computation of the HDI on the local level allows for more extensive international comparisons. Two countries may have the same HDI, but the distribution likely varies within each (Ivanov, Peleah 2011). Furthermore, calculating LHDI for the last 5-10 years allows for confronting the trend with events and factors that might have influenced it. A comparison using sufficient amount of data can for instance illustrate the influence of different regional policies. HDI computed at sub-national level or for different groups could show how (and why) different administrative units within a country stand vis-à-vis each other, what are the strengths and weaknesses and hence what central and local governments priorities could be (Ivanov, Peleah 2011). From this perspective, Local HDI is not a ranking of municipalities or provinces – it is about

the way each of it has achieved its HDI value (good fiscal policy and reasonable investments in the crucial development fields of health and education). Its purpose is to help local governments focus on adequate policy measures. Spatial dimension is the most important aspect of disaggregation, when feeding into regional and local policy is the goal (see Ivanov, Peleah 2011 and UNDP 2006). Countries of the Europe and CIS region show great variety of territorial structures of different nature and legacy. In some cases these are big areas (like województwo in Poland), with population of millions of people. In many other cases these are much smaller provinces, with population of hundreds of thousands or below. On the lowest level are municipalities, with population varying between hundreds and thousands inhabitants (small rural settlements) and up to the hundreds of thousands (cities). In general, the bigger the territorial unit, the more data are available. The key challenges are health data due to the number of administrational problems and possible data retention. GNI or income data on lower administrative levels can be successfully replaced with taxpayer’s income. At the same time, most of education data are available, at least on regional and provincial levels European Union uses NUTS nomenclature which was created and developed according to the principles of institutional breakdowns: administrative units and an approximate and comparable internationally number of inhabitants4. Table 2. Territorial structures considered for computation of the Local HDI in Poland Level

General characteristics

NUTS 2 - Voivodeships (Województwa)

Large territorial formations, with population Administrative units.

NUTS 3 – Subregions (Podregiony)

Subregional non-administrative units used in Eurostat analyses.

LAU* 1 - Districts and city districts (Powiaty i miasta na prawach powiatu)

Smaller territorial formations, with a large variation of population (from small districts to large cities as the capital city of Warsaw). Administrative units.

LAU 2 - Municipalities (Gminy)

Lowest level territorial formations, administrative units.

Known data issues in Poland Income (GDP data) are available Education data are available Health data are available. Income (GDP data) are available Education data are available Health data are available. GDP not available but computation is possible using taxpayers income data Education data are available Health data are limited. Limited data availability HDI computation difficult or almost impossible.

* At a more detailed level, there are the districts and municipalities. These are called "Local Administrative Units" (LAU), Source: Own research based on: Andrey Ivanov, Mihail Peleah (2011), “Disaggregation of Human Development Index. Opportunities and challenges for local level policy-making”, Bratislava: UNDP BRC.

Bearing in mind limited availability of appropriate data, we propose that in order to built reliable and valid LHDI, the primary unit of analysis for this study is LAU 1. This choice is further justified by activities pursued at the district level in Polish administrative system – 4

The EU NUTS nomenclature will be used in this paper from here on. 19

including inter alia secondary education, and provision of health care (more important than at the LAU 2 level). Placing our analysis on the level of NUTS 2 wouldn’t allow for a thorough examination of a local policy processes. Limited availability of data at the LAU 2 level impedes reliability of an indicator constructed at this level. Nevertheless, this proposal includes measures proposed for NUTS 2 and LAU 2 levels. Computation of these indicators are described in the text only when it differs from a measure proposed for the LAU 1 level. One should bear in mind that effort to calculate LHDI at the municipality level is rather a heuristic attempt, than a full-fledged indicator of human development. It is not sufficient to have good indicators and relevant statistics to feed them on the regional level. From decision-making perspective it is even more important to distinguish what stage of the process particular indicator should be reflecting and monitoring. Modifying the logic outlined by Ivanov and Peleah (2011) and adjusting it to the specific Polish reality of data availability at the province level, two stages should be distinguished in that regard: 1. Defining and populating input indicators – the quantitative estimate of the personal, financial, physical and other resources (time is often being disregarded allocated for the process). 2. Defining and populating outcome indicators – the quantitative estimate of the change in the immediate area of intervention (improved access to and use of). Apart from the outcomes, impact also could be monitored and attempts are often being made in that regard assessing positive (or negative) externalities and sustainability. Externalities are the impact of a “second range”, the broader implication of the interventions that can be linked to it but cannot be statistically correlated in sufficiently robust manner due to wider range of possible factors that may have contributed and the complex causality links. But given the multiple factors involved in a “human development outcome”, the question of attribution (“whose success – or failure – is this or that outcome?”) is close-to-impossible to solve. This is why for assessing such externalities usually qualitative data are being used to provide broader contextual information about the particular intervention. Whereas ‘sustainability’ is the “longevity of the impact” – does it fade away immediately or shortly after the intervention or lasts on (Ivanov, Peleah 2011). Each type of indicator requires different type of data. Also, what is even more important, each stage of the process requires different indicators. Using input indicators for measuring (and reporting) outcomes is a commonly committed mistake and leads to flawed policy conclusions. The traditional HDI is a mixture of inputs and outcome indicators. From human development perspective, GDP is clearly an input indicator. Only literacy rate and life expectancy can be considered as outcomes. This mixture is a major problem and a source of claims against the robustness of HDI as a measure of development (Ivanov, Peleah 2011). The key is to find sufficient measures enabling computation outcome indicators at the local level, in order to estimate policy effects in the spatial dimension. Improvement of the quality of life is the long-term goal of every government – in the case of Poland see the “Long-term Development Strategy. Poland 2030. Third Wave of Modernity” – Ministerstwo Administracji i Cyfryzacji (2012) or

the Human Capital Development Strategy (Ministerstwo Pracy i Polityki Społecznej 2012), In order to achieve such an important aim measures of progress and policy evaluation is needed in adequate dimensions.

2.A Dimensions of development – the construction of the Local Human Development Index Human development is a process of enlarging people's choices. In principle, these choices can be infinite and change over time. But at all levels of development, the three essential ones are for people to lead a long and healthy life, to acquire knowledge and to have access to resources needed for a decent standard of living. But human development does not end there. We have already described attempts to create a list of most important human needs or capabilities. Additional choices, highly valued by many people, range from political, economic and social freedom to opportunities for being creative and productive also in the digital sphere, and enjoying personal self-respect and guaranteed human rights (see UNDP 1990). According to this concept of human development, income is clearly only one of the options that people would like to have, albeit an important one. It is not the total sum of their lives. Development must, therefore, be more than just the expansion of income and wealth. Its focus must be people. There are many systems for measuring and monitoring human development; the ideal would be to include many variables, to obtain as comprehensive a picture as possible. But the current lack of relevant comparable statistics precludes that. Nor is such comprehensiveness entirely desirable. Too many indicators could produce a perplexing picture perhaps distracting policymakers from the main overall trends. The crucial issue therefore is of emphasis and policy output evaluation. The original HDI methodology suggests that the measurement of human development should focus on the three essential elements of human life: longevity (health), knowledge (education) and decent living standards (represented by income levels). Because of the differences in the indices used in the LHDI, we propose to use the revised computation method from the Human Development Report from 2010. In 2010 were introduced two major changes in HDI - way of aggregation and indicators. First, aggregation of individual domains has been changed from arithmetic to geometric mean, to reflect limited substitutability between dimensions of Human Development. Second, indicators of education changed from literacy and enrolment to mean and expected years of schooling. Geometric mean works better to capture unbalanced development (it pays more attention to lowest values, comparing to arithmetic mean; see Zambrano 2011). However, new indicators are not always available, and thus for HDR2012 trends team used so-called "hybrid" index--new aggregation method, old indicators. There are no strong arguments against the new method of HDI computation. For the purpose of the LHDI development this methodology should be used in order to compute the “input” and “outcome” index.

Construction of the Local Human Development Index is simple, and using the last methodology (geometric means) the final formula (both of the "input" and "outcome" LHDI) should present as follows:

Where “LSI” stands for Living Standards Index “HI” for Health Index and “EI” for Education Index. All indexes computed on the basis of geometrical means. Special effort must go into developing a simple quantitative measure to capture the many aspects of human life with sufficient data available at the local level (LAU1). Hence the lack of data on the LAU2 level (schooling, income and health) the computation of the index is going to be done at the LAU1 level using some of the data from the lower territorial level. Additional computation is to be done using the LAU1 results of the LHDI in order to perform an analysis of the NUTS2 regions. Basically using the available data two levels of territorial analysis are applicable based on the indices from the LAU1 level: (1) LAU1 and (2) NUTS2. But Local HDI will also depend on the performance over time of the worst and the best performers in the country for each variable. Whereas it is not a problem for interregional comparisons at a given point in time, for the purposes of comparing a given district’s performance over time, the “goalposts” for each variable must held constant. In this way changes in HDI over time for the LAU 1 will depend only on change over time and not on how the worst and best-performing LAU 1 units are doing. Granted that the “goalposts” need to be fixed if the Local HDI is to be comparable over time, we need to ask how the goalposts should be determined (see Anand, Sen 1994). The HDI introduced in 2010 has a form of geometric mean of dimension indices obtained from the indicators by normalization based on minima and maxima observed over the period for which the HDI has been computed – in our case it will probably be 2007-2010, given the data availability (see more Frequently Asked Questions (FAQs) about the Human Development Index at the Human Development Office website). Table 3. Dimensions and indicators of the “outcome” Local Human Development Index (LAU1 level) Dimensions of development

Economical

Educational

Health

Share of children enrolled in pre-school education (3-4)

Life expectancy

Human Development Indicators

Income per capita (total taxpayers income plus agricultural income based on comparative fiscal hectares) Total transfers from social assistance per capita

Average result of the secondary school exam (only the mathematic-natural science part)

Age-standardized mortality rates for cancer and for cardiovascular diseases per 100,000

The more subtle part, taking into consideration the construction of the Local HDI “outcome” indicators, are the political “inputs” in sense of policy measures affecting the lives of people living in different regions. Table 4. Dimensions and indicators of the “input” Local Human Development Index (LAU1 level) Dimensions of development

Human Development Indicators

Economical

Educational

Health

Total public expenditures on the LAU1 level per capita without EU funds (sum of municipalities and districts expenditures)

Student-teacher ratio

Number of full-time equivalent primary care physicians per 1,000 people

EU funds in local budget per capita

Local government expenditures on education per student

Number of full-time equivalent nurses and midwives per 1,000 people

The local governments are not autarkies and are influenced by central governmental planning and wealth distribution but they have a say about the distribution of services and public spending in their region. That is why the input-outcome decision chain is applicable. 2.A.1 Living Standard Index While the pioneers of measurement of national output and income stressed the importance of social concerns, economic growth became the main focus after the Second World War. Growth in the capital stock was seen as the means of achieving development, and the growth rate of per capita GDP became the sole measure of development. Income was first developed as a way of measuring welfare and well-being by Pigou, who described economic welfare as the measurable part of human welfare. Simon Kuznets, the constructor of the GDP measure, warned that “the welfare of a nation can scarcely be inferred from a measurement of national income” (Kuznets 1962). Income is useful, convenient proxy but this convenience comes at the price of high level of crudeness. It is a means, not an end. Well-being of a society depends on the uses to which the income is put, the quality and quantity of governmental redistribution, not the level of income itself. Present income of a person or of a municipality may offer little guidance to future development prospects. If regional authorities have already invested in its people, its potential income may be much higher that what its current income level shows, and vice versa. Taking theory into practice it may be proven that high income levels, by themselves, are no guarantee of human progress. The simple truth is that there is no automatic link between income growth (GNI – the measure of living standards in the HDI on the national level) growth and human progress. As Mahbub ul Haq put it “any measure that values a gun several hundred times more than a bottle of milk is bound to raise serious questions about its relevance to human progress” (ul Haq 2003). This statement appears to refer to the paradox of value, that has been already formulated by Aristotle as diamond–water paradox. It goes as follows: why water which is essential for our life has lower price than diamonds

which are not a necessity? The paradox has been solved by introduction of the principle of diminishing marginal utility. Mark Skousen put it simply: “If there is a large amount of water available everywhere, an additional glass of water will be relatively cheap. On the other hand, if a community lives in the Arabian desert, where water is fairly scarce, the community will highly prize each additional unit of water. The same principle applies to diamonds. If diamonds are abundant, the price of diamonds falls. If diamonds are scarce, the price goes up” (2009, p. 174). This explains well the difference in price and the economic value. At the first glance, previously mentioned ul Haq’s statement would seem to run afoul of the value paradox. But it is not. It simply says that the economic value is not always relevant as a measure of human progress. There is also a bulk of evidence confirming that after a certain level of GDP per capita is achieved, there is no link between GDP growth and advancing human well-being (Easterlin 2010). Questioning the assumption of a direct conversion of income into human well-being has recently gained prominence in public sphere. Thus, going Beyond GDP5 is of utmost importance. One particular case of such effort is a report to President Sarkozy by Commission on the Measurement of Economic Performance and Social Progress (Stiglitz, Sen, Fitoussi 2009), which developed an extensive set of recommendations. The first key component of human development – command over resources needed for a decent living – is perhaps the most difficult measure simply. The HDI uses the Gross National Income (GNI) per capita figures, which provide approximations of the relative power to buy commodities and to gain command over resources for a descent living standard. The actual measure of the economical dimension is a logarithm of income. But given the scarce data of many of the variables, we must for the time being make the best use of an income indicator and financial allocation connected with local governments budget and European Union's funds. GNI per capita is a tool and a proxy itself. At national level it is a proxy of economic opportunities, of the potential capabilities of the country and its population to devote resources for improvement of people’s lives. At sub-national level however it’s meaning is completely different. Disaggregated GNI tells us something about the value added generated on a certain territory. Due to the redistributive function of the central government it has little to do with the resources devoted (or available) for development purposes at sub-national levels. The state taxes its citizens and economic entities, and after that redistributes the resources accumulated. The trick is that municipalities’ budgets are part of the state budget. Even more important is the asymmetry of the relationship: better-off entities (both legal and individual) contribute more and receive relatively less as public services. This is part of “social solidarity” principle (as described in Ivanov, Peleah 2011). This is the effect of the redistributive policies of the central government (value added generated in one area redistributed to poorer ones according to central government’s regional development strategies). Its objective is to decrease the gaps in development levels and enforce the economic, social and political cohesion of the country. Without such redistributive functions some areas (important from certain perspective, like social security) may become totally depopulated or rising 5

Beyond GDP is the name of the initiative started in 2007 by European Commission, European Parliament, Club of Rome, WWF and OECD (see: www.beyond-gdp.eu). 24

inequalities may lead to social unrests. But from the human development perspective the important point is that within the redistributive process disparities between sub-national units are decreasing and different spatial entities are converging (being brought closer to the national average). Of course some redistribution takes the form of transfer payments from the national budget that go directly to individuals, rather than to sub-national governments. As such, percapita GNI indicators would capture these payments. These are the reasons why using disaggregated GNI for the purposes of HDI calculation is meaningless, unless the level of disaggregation is high enough and the country – big enough – Polish districts are certainly not that case. We would receive the pre-redistribution picture, which is even more remote from the human development reality than the one reflected in “GDP only”. For the purposes of HDI disaggregation we need an estimation of the “disposable income” or the standard of living devoted to development issues at the respective administrative level (Ivanov, Peleah 2011). Household income (or expenditures) data, provide relevant picture of the individual’s well being (including shadow economy involvement). It has however a major constrain: data on household incomes does not reflect “public services” consumption (direct consumption of social services like health and education or indirect improvement of living conditions through regional development and infrastructure projects financed from central sources to improve the situation of underdeveloped areas of the country). Then we can confront household income with a measure of policy input, i.e. yearly budget of the local government and EU funds allocation to the region. But to have that data comparable between different spatial entities we need them given per one inhabitant. Appropriately weighted sum of all components of the comparable income of residents of the LAU1 unit should be their total disposable income (per capita). Extension of the list of indicators on which the valuation of obtained free of charge services (education, health, social policy), which, although not directly increase the disposable income should be made possible if such data would be available. The final Income Index should be as close as possible to the disposable income of the inhabitants of the LAU2 unit. Some distortions will probably occur, but the following indicators are as close as possible to capture the real income of from different sources as work, agricultural income and social care. Other indicators are unfortunately unreliable and unavailable at this administrative level. There should be a balance of different measures of the “input” LHDI in the Living Standard Index and logic of financial and structural measures is desirable as in other sub indexes (Health Index and Education Index). Unfortunately because of complications with dividing the financial flows from the local governments budgets and no economic structural measures available this logic is not sustained. All in all, the final outcome measures used in the computation of economic dimension (Living standard Index) of Local Human Development Index could be the following6:

Aggregate measures would be calculated per capita. 25

•

Income per capita based on total taxpayers income – data available through Ministry of Finance (LAU2) and agricultural income based on comparative fiscal hectares7 - data available through the National Statistical Office8 LAU2), Total transfers from social assistance per capita – data available through the Ministry of Finance (LAU1), this indicator includes all social transfers from social assistance in the region per capita.

Input measures for the economic dimension could be the following: • Total public expenditures of the LAU1 unit per capita (sum of municipalities and districts expenditures). Assessing the wealth of the district the expenditures of the LAU2 level government (gmina) that are not components of the LAU2 (powiat) units budget – available through GUS. The EU founded budget lines should be excluded. • EU funds in local budget per capita (LAU1+LAU2, provided by GUS). When analysing the “input” measures of public policies we should also remember that these policies do not have a universal character. Some regional policies and the financial inputs are allocated in specific parts of the country i.e. eastern Poland and to specific LAU2 units. Comparing spatial units that did receive more support with those that did not would be helpful in assessing the effectiveness of the types of public policies. It should be borne in mind, of course, the problems arising from the complexity of the load selection and various types of interactions at the macro level, however, it seems that it is worth to undertake the effort to use the LHDI indicator to identify cause-and-effect relationship between the value and the policies pursued by the state. The second best choice for an outcome indicator would be average monthly gross wages (which includes only economic entities employing more than 9 people – as such the net wage would be better but it is not available through public statistics), compiled with social benefits and income from agriculture. Agricultural employment – a crucial dimension for rural areas’ development – would not be covered in such a methodology, because of a different social and tax system for that occupational group. The second best choice should only be used if the taxpayer’s data are unavailable. 2.A.2 Health Index One of the three dimensions of the Human Development Index is a long and healthy life. The capability to live long and healthy life lies at the core of the human development approach (Anand, Sen 1994). It is both a policy goal and an intrinsic value. Having in mind the logic of construction of the LHDI sub indexes a measure of financial funding of the health care should be used. Unfortunately the National Health Fund does not gather information on health funding on the LAU1 and LAU2 level. Only data on the NUTS2 level are available from the regional National Health Fund centres.

Unit of ground surface imputed, which is equal to 1 hectare of land class, which constitute the basis for farm tax on agricultural land; comparative fiscal hectare value is determined on the basis of surface area, type and class of farmland based on the land register as well as additions to district of taxes. 8 National Statistical Office – Główny Urząd Statystyczny, later on the abbreviation GUS is used. 26

Since the first Human Development Report in 1990, this dimension of HDI is measured by life expectancy at birth (LE), i.e. the number of years a newborn infant would live if prevailing patterns of age-specific mortality rates at the time of birth were to stay the same throughout the child’s life (UNDP 2007B). This outcome measure might be used also at the local and regional level, but the disaggregation faces some challenges. First of all, the smaller the territorial unit for which we would like to have disaggregated HDI, the higher the influence of random death cases (Ivanov, Peleah 2011). In the case of Poland, the main concern is data availability. Calculations of LE provided by GUS are characterized below: • Spatial level: NUTS 3 and above • Time coverage: 1995 onwards for NUTS 2; 2007 onwards for NUTS 3 • Dimensions of disaggregation: gender, urban/rural area, LE for different age cohorts However, LE calculations are based on the population and human mortality data, which is available at the LAU 2 level. Thus, it is possible to make own calculations of LE at the local level but it raises serious methodological concerns that require careful consideration. The main restriction is a limited number of deaths in younger age cohorts, which increases random variation. Taking this into account, there are several possible solutions to overcome the data availability problem (UNDP 2007B; Ivanov, Peleah 2011; Rok 2012): • estimate local LE values using data from higher level of aggregation • use proxies reflecting the phenomenon of long and healthy life, like mortality rates • substitute LE with a highly correlated variable, like infant mortality rate • make own calculations of LE at the local level, but: o use abridged life tables that uses deaths and population in 5-year age groups, and o calculate LE for higher age cohort (e.g. LE for people aged 15), and/or o adjust individual entities’ LE values using regional averages, or o combine multiple years of data In order to provide reliable and extensive outcome measure of health we considered three additional indicators, namely infant mortality rate, low birth weight rate and mortality rates for major cancer and cardiovascular diseases. Infant mortality rate is defined as the ratio of the number of deaths of children less than one year of age to the number of live births in the reference year (Eurostat 2012). It is a widely accepted outcome measure of health care quality, which is e.g. used for the monitoring progress of the Millennium Development Goals (Goal 4: Reduce Child Mortality) or in Agenda 21 (Chapter 6: Protecting and Promoting Human Health). But infant mortality rate is particularly well-suited for developing countries, as the measured phenomenon is highly correlated with the general level of development. Poland has recently recorded a substantial improvement in this regard, so this indicator is not robust for comparisons at the local level anymore. Alternative indicator considered here is low birth weight rate, which assesses the number of low birth weight infants (usually, lower than 2500 g) per 100 births. However, this indicator has a serious caveat – it is unclear whether it measures access to and quality of healthcare in a given region, or rather the general level of public health.. Third of the proposed additional outcome indicators is an age-standardized mortality rates for cancer and for cardiovascular diseases per 100,000. It measures a number of deaths attributed to two major causes of deaths in Poland against the age-standardized population. It covers both the

accessibility and quality of the specialist healthcare provision, as well as the impact of environment and lifestyles on public health. It is better suited for the current level of development of Poland than infant mortality rate, and less ambiguous than low weight birth rate. Data required for calculation of this indicator are the following: • Deaths due to: cancer and cardiovascular diseases (available from GUS, LAU-1, 2002 onwards), and • Population data (available from GUS, LAU-2, 1995 onwards). We measure health outcomes by combining life expectancy and age-standardized mortality rates for cancer and for cardiovascular diseases. In case of Poland, these two variables are not highly correlated, thus combining them into one measure should provide a more comprehensive picture of health outcomes. e The proposed measures of a long and healthy life for different aggregation levels are the following: Regional level (NUTS 2): 1. life expectancy at birth (available from 1995 onwards, provided by GUS) for men and women a. combining the data for both sexes, using weighted average 2. age-standardized mortality rates for cancer and for cardiovascular diseases a. combining data on deaths due to cancer and cardiovascular diseases (2002 onwards, GUS) b. own calculation of the age-standardized population basing on population data from GUS (1995 onwards) District level (LAU 1): 1. data on population and deaths (using abridged life tables9, GUS) a. combining the data for both sexes b. combining multiple (2 or 3) years of data10 - as LE doesn’t change much from year to year combining multiple years of data helps to reduce the impact of random variation c. switching to LE for 15 years old – there are not enough deaths in lower age cohorts to calculate LE at birth d. using the Reed-Merrell transformation to derive the conditional probability of death (see Shryock, Siegel et al. 1980) 2. age-standardized mortality rates for cancer and for cardiovascular diseases a. combining data on deaths due to cancer and cardiovascular diseases (2002 onwards, GUS) b. own calculation of the age-standardized population basing on population data from GUS (1995 onwards) Municipality level (LAU 2) requires estimating LE value, basing on the following data: 1. data on life expectancy at birth for rural and urban areas (2007 onwards, NUTS 3, GUS and 2002-2006, NUTS 3, own calculation using GUS methodology) 9

Age specific mortality rates are grouped into five-year bands. Number of deaths in a given geographic area should exceed 700 to ensure reliability of results.

2. data on life expectancy for 15 years old (2002 onwards, own calculation – as described above, LAU 1) Then, the next steps are the following: 3. combining the NUTS 3 data for both sexes, using weighted average 4. estimate the LE for LAU 2 using NUTS 3 data: adjusted for the rural or urban character of a given LAU 2 5. calibration of the estimation: using data on LE for 15 years old, for LAU 1 As it is noted above, proposed LE indicator uses data combined for both sexes. It is argued that the simple average of LE is fairer than the use of the distribution-corrected variable would be (for details see: Anand, Sen 1994). The attempt to provide a health indicator at the LAU is an example of the complex multilevel modelling and borrowing information from the higher level in a hierarchy. This kind of modelling, if not done well, may result in estimates which may be difficult to verify or justify, and as such – may fire back and ruin the entire undertaking and should be performed with caution. Second best choice outcome measure could be linked to National Institute of Public Health data on health service-based morbidity indicators, e.g. for cardiovascular diseases. Given the wide coverage, this indicator seems as good choice for measuring health outcomes. But, according to the National Institute of Public Health, it is impossible to include this indicator in the proposed measure due to time constraints of this project. Yet another possibility is to use a number of people diagnosed with cancer, an indicator also provided by the National Institute of Public Health. Relevant data is available at the LAU-1 level, but the limited number of cases reported annually is a major constraint to use this indicator as a reliable health measure. The National Institute of Hygiene performs research referring to the monitoring of biological, chemical and physical risk factors in food, water and air as well as diseases and infections control. Unfortunately these studies are not conducted on a yearly basis, as the last report is from 2008 (Wojtyniak, Goryński 2008). Indicators described above refer to the quality and availability of the specialized health care, morbidity as well as to issues of healthy lifestyles, early diagnosis and prevention. As it was noted before, finding a reliable outcome measure for LAU 2 level is a considerable challenge. Second best choice – or a complement indicator – for this level could be a number of deaths recorded per 1000 inhabitants aged 70 or more. Life expectancy, mortality or morbidity rates are outcome measures of health. On the input side, one should think about quantifying resources devoted to the aim of ensuring health and longevity. In Poland, financial resources for health care are distributed mainly through National Health Fund (NHF). Local governments’ spending accounts for approximately 1% of funds devoted to health care. To our best knowledge, there is no data on funding from NHF available at the local level. However, in the end that is the quality and availability of human resources and infrastructure that value the most for the patient, and not the amount of funds behind it. Also the National Health Fund and the Ministry of Health is reorganizing its statistical structure making more data on health infrastructure available, but these new indicators are not available at the moment when this paper is being prepared. Typically, the measures connected with health are defined in relation to the population as a variable relative to the population size (e.g., number of primary care physicians 29

per 1,000 people). When it comes to diagnostics and specialist care, the access to cardiologist, oncologist, internist (internal medicine specialist), and a paediatrician an important factor. In addition to the indicators relating to medical specialists an indicator on support staff (e.g., number of nurses per 1,000 people) should be used. Another indicator describing the functioning of the health care system is the number of given medical advice (including expert advice) per capita, unfortunately the last indicator is negatively correlated with life expectancy. Thus, in the computation of the input measure of health we use the following indicators: • Number of full-time equivalent primary care physicians per 1,000 people (available at LAU2 level, provided by NHF, 2011 onwards); for historical analyses this indicator will be replaced by a number of primary care physicians and dentists per 1,000 people (available at LAU 1, provided by GUS, 2006 onwards), • Number of full-time equivalent primary care nurses and midwives per 1,000 people (available at LAU-2 level, provided by NHF, 2011 onwards); for historical analyses this indicator will be replaced by a number of nurses and midwives per 1,000 people (LAU 1; provided by GUS, 2006 onwards), These two indices measure the human resources engaged in the provision of health care. While number of primary care physicians captures general availability of health care, the number of nurses and midvies is an important feature of the medical facilities in the given region. Finding an input measure at the municipality level pose a serious challenge, as medical facilities are located usually in districts’ centres. Thus, number of health establishments per 10 000 persons (data provided by GUS at LAU 2 level) might be considered as a (rather poor) proxy measure of availability of health care infrastructure at the municipality level. The human resources aspect might be accounted for using the same data as for LAU-1 level, but with particular caution due to profound discrepancy between healthcare institutional setting and territorial structure of the LAU2 level.

2.A.3 Education Index Up to 2011 in HDI reports Education Index consisted of Adult Literacy Index (ALI) and Gross Enrolment Index (GEI). From 2011 the new methodology has been used. ALI and GEI have been replaced by Expected Years of Schooling Index (EYSI) and Mean Years of Schooling Index (MYSI). EYSI measures number of years that a 5-year-old child will spend with his education in his whole life, while MYSI indicates number of years that a 25-year-old person or older has spent in schools (UNDP 2010). Unfortunately, direct use of these indicators on local level is challenging. All four indexes are difficult to be applied at the local level in the case of Poland. Problems with the use of them are twofold. First, the data available at regional level are limited. Measurement of MYSI and EYSI on local level is only possible based of national census data, which are collected every 10 years approximately. It means that the indexes can only be calculated for selected years, and not on regular year-by-year basis. Moreover, census data gives information on achieved level of education, instead of years of education. However number of years of education can be roughly estimated on that basis. The problem with data accessibility is more important in the case of Adult Literacy Index – in Poland there are no such data on local level at all.

Secondly, some available data are not sufficiently meaningful. This is the case of Gross Enrolment Index. In Poland school enrolment – taking into account compulsory education – is very high and does not differentiate local units. The same situation would be with adult literacy if the data were available. Higher education is vital for human development because it is an important factor of career success and consequently high income (see: Strawiński 2007; OECD 2010), while – on a more general level – it builds human capital of a country (see: Herbst 2012) and widens individual’s capabilities. For this reason the data are important for LHDI but are available only for census years, which makes them unsuitable for time-series-analysis as in this study. We find pre-school education as a sub-indicator of education and its link to success in adult life and a good economic growth policy. While early childhood education has the potential to make children better prepared as students in other levels of education, it is all contingent upon the quality of teaching and learning. In recent years, the so-called value-added scores have often been considered as much better indicators of school effectiveness for policy purposes (OECD 2008). In national assessments, a school’s mean score is rarely taken as a measure of the school’s efforts because it largely depends on the socio-economic characteristics of students and only partly on how well the teachers work. Thus, value-added scores, which take into account prior student scores or look at growth trajectories and the impact schools or teachers have on them, are more often preferred when assessing schools and teachers. In 2005 a group of experts has started developing a methodological and statistical background for Polish version of EVA models designed for school evaluation. After 2012 these new measures could be part of the Local HDI but they do not allow historic comparison. For above-mentioned reasons alternative approaches have to be applied. Looking for possible education indexes on local level in the Polish case one can consider following indexes: • •

Share of children enrolled in pre-school education Average result of the primary and secondary school exam

The above indicators can be interpreted as outcome indicators. As for input indicators one might consider: • •

Total local government expenditures (municipalities +district) on education per student (pre-school, primary, secondary and upper-secondary) Student-teacher ratio

The indicators are described in greater details one-by-one in the following part of the chapter. Share of children enrolled in pre-school education measures percentage of children aged 3-4 attending kindergartens. Pre-school education is seen as an important factor of pupils' success in

adult life, as well as good tool for economic growth policy (see: Rolnick, Grunewald 2007). Thus, it can constitute important part of local HDI. • Data availability: o Spatial level: LAU 2 (municipalities) o Time coverage: 2003 onwards Average result of the secondary school exam (only the mathematic-natural science part) can be interpreted as a measure of quality of education. The data are highly reliable and comparable across the country (all of these exams are standardized). Two data sets could be be useful. Firstly, primary school exit examinations (sprawdzian szóstoklasisty) and secondly secondary school exit examinations (sprawdzian gimnazjalny). In order to have comparable data we propose to use only the secondary’s school exams mathematic-natural science part, as it is the most important part of the knowledge-driven society in the long-term perspective (Ministerstwo Administracji i Cyfryzacji 2012). The dynamics can be measured by the average for each year, i.e. the change in the relative position of the LAU1 unit to the country average. •

Data availability: o Spatial level: LAU 2 (municipalities) o Time coverage: 2002 onwards

Total local government expenditures on education per student (pre-school, primary, secondary and upper-secondary) can be seen as a main input index in terms of education. Sum of the LAU1 and LAU2 units expenditures. • Data availability: o Spatial level: LAU 2 (municipalities) o Time coverage: 1995 onwards Student-teacher ratio (primary and secondary schools) is an alternative input measure in the field of education. Lower number of students per teacher could be interpreted as a sign of a better education quality. In the context of LHDI the student-teacher ratio could be calculated for primary schools (szkoły podstawowe) and secondary schools (gimnazja). This index could not be used in the current exercise due to the lack of teacher data at the local level (number of teacher at LAU 1 level is available for 2011 onwards) (number of teachers is not available for previous period). • Data availability: o Spatial level: LAU 2 (municipalities) o Time coverage: 2011 (number of teachers is not available for previous period).

2.C. Putting the HDI in context – additional missing dimensions to be considered through contextual indicators Since its launch, the HDI has been a useful analytical tool for governments, the media and civil society, allowing for comparing human development achievements across nations over time. There are two possible approaches to using the HDI: as a self-sufficient, statistically pure and 32

scientifically robust measure, or as a useful and reliable basis for a broader, more in-depth measure, especially relevant for policy-makers. We argue that the latter approach â&#x20AC;&#x201C; using more complex and subtle indicators in connection with the regular HDI and its dimensions â&#x20AC;&#x201C; is crucial for the purpose of this study. Fukuda-Parr (2003) and Pinenda (2012) state that from the HDIâ&#x20AC;&#x2122;s inception, it was explicitly recognized that the concept of human development is larger than what can be measured by the index. This creates certain policy challenges, since there may be situations where human progress may mask deterioration in other key aspects not covered in the index. For example, civic activity, environment pollution, sustainability of development, social cohesion, labour market conditions and digital engagement could be worsening at the same time as the HDI moves upward. This means that the UNDP must regularly update its methodologies and indicators, as well as try out different indices to better capture certain aspects of human development (Pineda 2012). The welcomed revision of the HDI in 2010 gave new indicators such as the education index, because the formerly used literacy indicator is losing meaning, since many countries have reached the upper limit. The HDI fails to capture important aspects of human development and the focus of the Human Development Report Office11 in recent years has been on refining the measurement of existing indicators, rather than on the inclusion of new dimensions. But in case of Poland we are going to take into account additional contextual measures, useful in exploring the concept of human development. In order to create a useful policy tool, we should go beyond the traditional dimensions of the HDI, which focus on mobilizing public opinion and creating momentum for a change. Building evidence base for policy-makers requires a more comprehensive approach, though principles of clarity and being communicative have to be sustained. Drawing on the analysis of the development processes taking place in Poland and internationally, we propose to include information on five interrelated aspects of human development: sustainable development, poverty and social exclusion, labour market, civic activity and digital engagement.

2.C.1 Environmental protection Among the main challenges facing regions in long-term development is climate change and its impact, environmental degradation, biodiversity loss and unsustainable use of natural resources. Being the year of the Rio+20 Conference12, which gathered world leaders, along with thousands of participants from governments, the private sector, NGOs and other groups, to come together to shape how we can reduce poverty, advance social equity and ensure environmental protection the HDR team also needs to look at sustainable development indices.

Human Development Report Office is part of the UNDP responsible for providing yearly global Human Development Reports. 12 The United Nations Conference on Sustainable Development (UNCSD) was organized in pursuance of General Assembly Resolution 64/236 (A/RES/64/236), and took place in Brazil on 20-22 June 2012 to mark the 20th anniversary of the 1992 United Nations Conference on Environment and Development (UNCED), in Rio de Janeiro. 33

We understand ‘sustainability’ in two ways. One is the sector-specific and the other is processspecific. The sector-specific understanding of sustainability is reflected in the dimensions of the Local HDI with the ‘income’, ‘health’ and ‘education’ dimensions. This approach follows the logic of the various attempts of ‘greening’ the HDI undertaken since 1994. But the process-specific dimension of sustainability is equally important – and has been neglected so far. It is defined as ‘ability to sustain’ the achievements (the status) in each dimension. The status reached in each of the three pillars can be achieved in various ways that usually boil down to ‘borrowing from the future generations’ saddling them with debt (monetary or environmental). Without this ‘ability to sustain’ angle, the Local HDI could lose its informative and policy prioritization power (UNDP Armenia and UNDP BRC 2012). Currently, insufficient data are available in order to perform the process specific computation of the Local HDI. Sustainable development is a concept that overlaps with human development, as understood in this report. The former boils down to a simple question – how much of the natural resources (our ultimate ends) we have to use to attain a desired level of quality of life (our ultimate goal). While the latter attempts to measure and operationalize the ambiguous concept of human well-being, or human quality of life. Having this in mind, we decide to narrow our focus, to include only “environmental” part of the sustainable development. We propose a following contextual measure of sustainable development: non-separately collected household waste, per person (data available from GUS, 2005 onwards, LAU-2) The purpose of development is to create an environment in which all people can expand their capabilities, and opportunities can be enlarged for both present and future generations (UNDP 1994). To secure such possibilities, a special effort should be taken to preserve the ultimate ends, which we all depend on. Producing a growing amount of waste is a symbol of squandering resources and a non-sustainable lifestyle. Collecting waste non-separately adds to it, by making recycling almost impossible. Given the scarcity of resources, the focus on minimising the amount of produced waste and recycling is of foremost importance. Amount of household waste produced reflects the level of environmental consciousness, as it relies on personal choices and attitudes. Additionally, municipal waste management is one of the responsibilities of local governments (LAU-2), justifying the use of this indicator for the purpose of LHDI and allowing people to advocate locally for a more environmentally sustainable solutions.

2.C.2 Poverty Social inclusion policies, both at national and subnational levels, tend to focus on specific groups of disadvantaged and vulnerable people (such as single mothers, elderly people living alone, homeless people and people with disabilities). Oxford Poverty & Human Development Initiative has identified five ‘Missing Dimensions’ of poverty that deprived people cite as important in their experiences of poverty. To call attention to these ‘missing dimensions’, and to use them as a guide to policy and promote collection and analysis of data on five ‘missing dimensions’ of poverty i.e. quality of work, empowerment, physical safety, ability to go about without shame and psychological wellbeing. They are recommended for international comparisons, but some apply for local conditions and we also share this approach. One of the Millennium Development Goals is

to eradicate extreme poverty and hunger, also one of the Europe 2020 headline targets is to lift at least 20 million people out of the risk of poverty and exclusion in Europe. Such a focus tends not to have a spatial dimension, measures being directed at helping those concerned wherever they live. Poverty understood as material social exclusion in a key problem facing many countries. There is a growing awareness, however, of the concentration of social exclusion in particular places, particularly in inner city areas and deprived neighbourhoods. Such concentrations also occur in rural areas, where economic activity is limited and few employment opportunities outside subsistence farming exist (European Commission 2010a). The need of taking into account the poverty and social exclusion in formulating a local human development measure is substantial. It is increasingly recognised, therefore, that the nature of disadvantage affecting people in situations of poverty and social exclusion is influenced by the area where they live. The link between individual circumstances and local situations runs both ways. A concentration of disadvantaged people in certain neighbourhoods results in increased pressure on public services reduced economic activity and private investment, the emergence of ghetto situations and an erosion of social capital. At the same time, living in deprived areas means reduced access to jobs, often inadequate public services, stigmatization and discrimination. The concentration of disadvantage also appears to be a persistent phenomenon, which can spread from one generation to the next. Social policies are an area of human development and, therefore, need to tackle the territorial aspects of disadvantage if they are to succeed in helping people in the places where they live and to encompass the regeneration of deprived areas as well as support to the people concerned themselves. A contextual measure of poverty and social exclusion proposed in this paper is: the share of beneficiaries of social assistance in population13 at LAU 1 level.

2.C.3 Labour market Condition of the local labour market and employment policy represent a central means of tackling issues of poverty and social exclusion, since unemployment, or inactivity, is a major cause of both. Greater participation of the young, women, older people and the low-skilled can make an important contribution to economical but also to an overall improvement of well-being. Most of the governmental programs are national rather than regional, even if it is most relevant in areas of high unemployment and its success is judged inevitably in terms of reducing disparities in employment and unemployment rates within, as well as between, the regions.

Social assistance benefits are granted in the form of cash or non-cash. The first forms include permanent benefit, temporary allowance, deliberate allowance deliberate, purposeful special allowance, benefits and loans for economic independence, support for foster families, aid for independence and to continue learning for certain types of people leaving institutions care centres, nursing homes for children and youth with intellectual disabilities, homes for mothers with small children and pregnant women, juvenile correctional facilities, school and special education centres, youth centres, educational and foster families, benefits and expenses related of learning the Polish language for refugees. Non-cash benefits include mainly support in the form of food, shelter or clothing. 35

Well performing labour markets are key to increasing employment and advancing human development, but these need to be accompanied by measures to support people should they lose their jobs. A contextual measure of sustainable development proposed in this paper is: employment but as the data are gathered only during census the second best choice is the unemployment rate at LAU 1 level.

2.C.4 Civic activity Local HDI could achieve its goal of providing a summary measure that captures a broader conception of regional development. It lacks, however, any information about the political and civic environment where the material capabilities included into the Local HDI can be exercised. This has been noted from the very beginning and correcting it is the purpose of the current effort. With a contextual measure of civic participation we seek to contribute to it by evaluating existing measures of social capital, political involvement and civil society as potential candidates to be used in the extension of the Local HDI. We will argue that however the extension is implemented, it should focus on social capital that affect individualsâ&#x20AC;&#x2122; opportunities to pursue their goals, that is, that affect their capabilities (Cheibub 2010). Social capital is trust that leads to cooperation. People who trust others are more likely to donate time and money to helping other people. Trusting people also tend to believe in a common culture, share the same values i.e. that people should be treated with respect and tolerance. They are also supportive of the legal order. Trust affects the wellbeing of individuals, as well as the well-being of civic society. High levels of trust mean that people feel secure, they have less to worry about; they see others as co-operative rather than competitive. This is also connected with local crime rates (Wilkinson, Pickett 2010). But because of difficulties in capturing a few indicators that are intuitive, clear and sufficiently encompassing to indicate the political and civil environment within which individuals must pursue their goals on the local level we must accept to use only one measure: voter turnout in recent local elections (data available at the LAU 2 level). This indicator enables us to modify the reading of LAU1 based on existing Local HDI much in the same way that the Local HDI modified the reading of LAU1 based on income indicators. Social capital is becoming an important factor in policy measures and thus should also be calculated on the regional level.

2.C.5 Digital engagement Digital engagement is a phrase often used quite loosely, sometimes to mean any use of ICT and especially social media by a corporate or governmental organization. A more specific definition corresponds to how public sector organisations promote participation in policymaking. An attempted definition, might say that digital engagement uses digital tools and techniques to find, listen to and mobilise a community around an issue. Therefore, an objective of the government is to maximize the social and economic potential of ICT, most notably the internet. Moreover the internet is a vital medium of economic and societal activity: for doing business, working, playing, communicating and expressing ourselves freely. Successful delivery of ICT to every citizen will spur

innovation, economic growth and improvements in daily life for both citizens and businesses (European Commission 2010b). Wider deployment and more effective use of digital technologies will thus enable the government to address its key challenges and will provide citizens with a better quality of life through, for example, better health care, safer and more efficient transport solutions, cleaner environment, new media opportunities and easier access to public services and cultural content the vast described in the Human Development Index. There is, therefore, a demand to use an indicator that could describe the capability of citizens in a given region to use the internet. Given that there is also a problem of digital competences (digital exclusion) within the society we would propose to use ‘Internet use in households and by individuals’ but no regional data are available; also ‘internet penetration’ would apply, but quality of data gathered by the Office of Electronic Communication is insufficient. Another measure could be the share of tax declarations with an internet tax relief in every LAU 1 but the tax relief is ending in this accounting year. Hence, the best measure is the ‘share of tax declarations sent by internet’ in every LAU 1. Additionally, it measurers the digital competences, which are in the midst of digital engagement. The only problem, with the proposed indicator for digital engagement, is that it could be correlated with the age-structure of the local population.

2.C.6 Women empowerment The disadvantaged position of women and girls in a society is a major source of gender inequality. One such example is a lower average income, especially appealing when compared to better education outcomes of women. We introduce an additional measure of these inequalities drawing on the same capability approach as the Gender Inequality Index in order to expose differences in the distribution of achievements between women and men in the civic sphere. Preparing a new subnational gender inequality index is preferable but because of the lack of sufficient data we only propose contextual measures linked with women civic involvement in local governments. Empowerment is the process of increasing the capacity of women to make choices and to transform those choices into desired actions and outcomes. Central to this process are actions that both build individual and collective assets, and improve the efficiency and fairness of the organizational and institutional context which govern the use of these assets. Gender inequality varies tremendously across countries and we can expect it to vary also on the regional level of a country. There is not enough evidence on the local and regional level, but unequal distribution of human development on the country level is correlated with high inequality between women and men (UNDP 2011). The World Economic Forum (2011) proves that countries and businesses can thrive if women are educated and engaged as fundamental pillars of the economy, and also proves that diverse – also in a gender perspective – leadership is most likely to find innovative solutions to tackle the current economic challenges and to ensure equitable and sustainable development. Since 1995, the narrowing gap between male and female employment has accounted for almost a quarter of Europe’s annual GDP growth (Economist Intelligence Unit 2012). Such gender gap is still observed in Poland, indicating that women poised to have a similar impact – if they are properly empowered.

The Gender Inequality Index (GII), which is a new indicator by the UNDP, is used since 2010 side by side the HDI. GII reflects disadvantages against women in three dimensions - reproductive health, empowerment and the labour market. The Gender Inequality Index is designed to reveal the extent to which national achievements in these aspects of human development are eroded by gender inequality, and to provide empirical foundations for policy analysis and advocacy efforts. In the case of gender inequality in Poland it would also be appropriate to look at the issue of worklife balance, and the problems i.e. access to nurseries, kindergartens, flexible forms of employment / work. Inability to reconcile raising a child with work is often the primary factor in the lower employment rate among women, but since there are problems in gathering sufficient reliable data they can be used in an analytical framework in forthcoming reports. Permanyer (2011) notes that the GII is a pioneering index, in that it is the first index to include reproductive health indicators as a measurement for gender inequality. Unfortunately these measures are not applicable to developed countries as Poland and as such should be excluded from the analysis. Due to data limitations women's labour force participation and unpaid work are not represented in the labour market dimension of the GII. But there are income data based on tax information from the Ministry of Finance and labour force participation dimension with the use of taxpayer’s income data giving suitable information for economic aspects of gender inequality. We propose two indicators in order to measure empowerment: share of seats held by women at the district (Rada Powiatu – LAU1) and municipality (Rada Gminy – LAU2) councils in the last election. Despite enduring advocacy in support of the strengthening women's representation in the parliament, position of women is still disadvantaged in elected bodies, both at the level of local governments and parliament.

2.D. Tracking Human Development on the local level – current status and trends over time While the first two Human Development Reports have been careful to avoid temporal comparisons of HDI for a given country, there have been a lot of studies - including historical – using United Nations data that enable to see HDI development since 1960. Thus we should try to build such a framework that would enable future HDR teams to further develop this measure. In order to track human development we should enquire whether Local HDI as defined above can satisfactorily measure progress in local human development over time. In taking the time derivative of HDI it is clear from the definition that HDI of the LAU 1 unit will depend on the changes in attainment by it along each of the three dimensions of local human development. As part of the on-going Human Development Report cycle, HDR team in Poland should monitor the impacts of current and previous HDR advocacy campaigns and follow up. Through these efforts, HDR teams should look at results made possible by the use of human development data. As monitoring identifies new results and policy responses, continuing advocacy and related follow-up work can be revised accordingly, eventually feeding into the next HDR process.

Many human development initiatives are helping to expand people’s choices and capabilities. Regional, national and local strategies and policies are being revised to involve and better reflect the needs of the poor and excluded. Budget priorities are being shifted, with corresponding changes in allocation and redistribution systems, and legislation. Prominent media coverage and civil society campaigns are advocating for policy changes as well as changes in thinking (UNDP 2007B). We hope that this also will happen with the outcomes of the studies conducted using the methodology described in this paper.

3. Linking LHDI to development policies Each society functions with a set of economic and political rules created and enforced by the state and its citizens collectively. Economic institutions shape economic incentives: the incentives to become educated, to save and invest, to innovate and adopt new technologies, and so on. It is the political process that determines which economic institutions people live under, and it is the political institutions that determine how this process works. For example, it is political institutions of a nation that determine how politicians behave. This - in turn - determines whether politicians are “agents of the citizens”, albeit imperfect, or are able to abuse the power entrusted to them, or that they have usurped, to amass their own fortunes and to pursue their own agendasdetrimental to those of the citizens (Acemoglu, Robinson 2012). Political institutions include, but are not limited to, written law and to whether the society is democratic. They include the power and capacity of the state to regulate and govern society. It is also necessary to consider more broadly factors that determine how political power is distributed in the society, particularly the ability of different groups to act collectively to pursue their objectives or to stop other people from pursuing theirs. As institutions influence behaviour and incentives in the lives of individuals , they also forge the success or failure of nations. Individual capabilities matter at every level of society, but they need an institutional framework to transform them into a positive force. Acemoglu and Robinson (2012) argue that while economic institutions are crucial for determining whether a country is poor or prosperous, it is politics, policy and political institutions that determine what economic institutions a country has and the level of well-being the citizens have. Braudel’s concept of longue durée reaches deep into the history of nations and territories to explain the contemporary phenomena. Poland is an interesting example of such long-lasting impact of historical circumstances on development trajectories. It is the XIXth century border of partition that often serves as an explanation of distribution of various socio-economical and political phenomena in Poland (see e.g. Kowalski 2000). It overlaps with the widespread concept of a relative under-development of the Eastern Poland, which is believed to date back to the territorial reach of the middle-ages’ wave of modernization. Another important feature of Polish space that influences current socio-economical trends is the distinction between rural and urban areas, which – in the wake of the knowledge-based economy – is giving place to a more sophisticated opposition of metropolitan and non-metropolitan areas (Smętkowski, Gorzelak and Jałowiecki 2009). Lack of evidence of the human development distribution on the local level in

Poland precludes verification of the applicability of the abovementioned suppositions to the human development concept. Simultaneously, filling this gap is a vital purpose of this study. Given the nature of HDI dimension – health and education - the HDI is rather slow index capturing the changes in HDI in a longer time window – investments done in health and education today may be harvested in 5 years or longer. So caution should be exercised when planning to use the HDI for assessment of policy impacts on year-to-year changes in the HDI, but in connection with strategic goals and long-term policy evaluation, it can provide sufficient information on policies effectiveness.

3.A Policy evaluation approach It has been explained in many ways what evaluation functions are. The career behind this term seems to be inseparable from two phenomena; first of all – from the expansion of the public sphere and the growing significance of administration liable for the implementation of specific tasks. Second – from the disappointment by the unsatisfying efficiency of these tasks implementation by administration and the search for new organisational forms for the public administration for a more effective and efficient task execution (Zalewski 2009). The increased interest in evaluation research in Poland is strongly related to the Polish accession to the European Union. The reason for it is simple and relates to the obligatory requirement to evaluate public programmes financed by the EU and to the enforcement of evidence-based policy. Policy evaluation is also significant when the Human Development approach is mentioned because - as discussed in this paper - the HDI can be a policy tool when disaggregated. This approach is exemplified in the index and if one thinks about the LHDI as an evaluation tool it can be seen as an ex post analysis tool using desk research or - more precisely - legacy data analysis as many of the data sources are not available to general public and need to be prepared before using. This measure is combined with an expert panel of statisticians and specialists in different fields for the development of the Local Human Development Index methodology. The expert panellists were affiliated with the Polish Agency for Enterprise Development, Marshal Office of the Pomorskie Voivodship, Human Development Report Office, MojaPolis.pl, National Institute of Public Health, National Statistical Office, Ministry of Regional Development and the Warsaw School of Economics. Also, the remarks by the self-government representatives at a methodological conference organized by the Ministry of Regional Development on September 7th 2012, were crucial for the methodology. UNDP Project Office Poland has signed a mutual agreement with the Warsaw School of Economics on cooperation in developing the new measure. Ex-post evaluation is an evaluation study conducted after the policy intervention (programme, project etc.). In European Commissions practice ex-post evaluation is conducted no later than 3 years after the finished intervention, however in human development practice, it evaluates policies conducted earlier. Ex-post evaluation is one of the typical evaluations conducted depending on the time of the research (see more European Commission 1999). If evaluations are well conducted, and if the results of evaluations are used by decision-makers, they can contribute to improved public programmes, as well as to increased transparency, accountability and costeffectiveness, what is especially important for EU funded policies. Evaluation is to deliver criteria,

methods and measures to assess rationality of public activities. The role of the traditionally understood evaluation would be answering the question whether public programmes executed are effective in the economic sense. In our opinion the human development dimension is equally essential. Defining and tracking the meaning of progress is an old challenge. From time immemorial people have been aware that money is merely a means – and not a goal of its own. Quality of life, happiness and human development are the crucial values that this evaluation approach presents. There is the reason to believe that expectations regarding evaluation are too high. If so, on the one hand, what are the possibilities and on the other hand, what are the limitations in using evaluation outcomes in administrative practice? The analysis in the National Human Development Report will concentrate upon external limitations in the possibilities to use evaluation outcomes despite the awareness that its applicable value depends equally, if not more, on the evaluation process’ internal factors (appropriateness of methodological assumptions, observance of research standards, researcher’s competence etc.). Such selection has been dictated by popularly formulated (often contradictory) expectations towards evaluation research from various societies, in order for their outcomes to contribute to the increased efficiency of the public programmes executed. For example, the evaluation of programmes for the activation of the unemployed, particularly popular in the EU and new to the Polish labour market policy, does not usually include information about who benefits from them and who loses, since it is assumed that increasing the number of jobs is in everybody’s interest, therefore it is justified to include the costs of such training courses in citizen taxation costs. Evaluation has all features of applied research and thereby it does not differ in principle from other research undertakings in terms of designing techniques of collecting data and methods of analysis. The present paper is precisely a description of evaluation as a research process, aiming at obtaining objective, independent assessment based on reliable empirical data obtained according to the principles of selected methods, a process which requires a careful consideration, design and implementation in order to be completed. In relation to ex post evaluation we can say that it is a systematic and objective assessment of a completed project, programme or policy – in the context of their planning, implementation and obtained results. Its objective is the determination of real effects and justification of intervention in a particular form (Haber 2009). In other words, its tasks are, for example to identify strong and weak points of all policies conducted at the local level and to indicate directions of development and modification of future interventions in order to improve the level of human development. Specifying the need to conduct evaluation, the objective for which it is to be implemented and the key functions it has to perform, is the first step both in the process of searching for information and conceptualisation and operationalization of the evaluation study (Haber 2009), which this paper is about. This National Human Development Report is to be an impact assessment and ex-post evaluation of the policies (including EU funded projects) in a comparable time-series on human development. The Local Human Development Index is to be used by the Ministry of Regional Development as a regional policy evaluation tool in the 2020 perspective. The purpose of all UN member states is to

improve human development by improving public services on every level. The purpose of the final report is to assess whether there has been a relation between policy inputs and policy outcomes.

3.B Problems in linking policy with data The idea of using evidence to inform policy is not new. It was Aristotle, who first put forward the notion that different kinds of knowledge should inform rulemaking. What is new, however, is the evidence-based Policy (EBP) approach seen in the last years in developed countries. This new trend in policymaking is getting momentum thanks to the growing availability of statistical data and in order to meet the demands of more and more involved citizens. The current debates originated from the medical sector in the UK in the early 1990s. EBP helps people make wellinformed decisions about policies, programmes and projects by putting the best available evidence from research at the heart of policy development and implementation. It advocates a more rational, rigorous and systematic approach, and moves beyond traditional notions of academic research. In most discussions, the approach has also come to incorporate evidencebased practices (Sutcliffe, Court 2005). Currently, every government's reforming and modernising agenda highlights the need to shift away from ideologically driven politics and towards rational decision making. Policies that are forward looking and shaped by the evidence rather than a response to short-term pressures tackle causes not symptoms (Sutcliffe, Court 2005). The Local Human Development Index tries to be an exemplification of this approach, helping to evaluate policy inputs and outcomes. However, some key problems need to be addressed in the field of statistical data availability in the near future in order to improve the quality of computation of the LHDI but also hopefully of the government policies. Here we would like to emphasize some of the fields in which data availability stands out as a problem in evidence based-policy. UNDP proposes these recommendations: Firstly, opening and starting using all public knowledge repositories by the government. That means redesigning the way of thinking about public statistics, making them available to as many people as possible. All ministries should have access to income data, social security, etc. in order to assess the impact of policies correctly. Secondly, making the administrative data available for general public. Information which UNDP is privileged to obtain should be made public, e.g., income data from the Ministry of Finance, social benefits data from Ministry of Labour and Social Policy and social insurance or statistical data gathered with public funding as the Labour force survey, Household budgets survey or other very good research projects from the National Statistical Office should have datasets available for every citizen. Thirdly, gathering information and data and converging them as useful knowledge in one place. Public policy needs to be data driven in order to perform merit-based evaluation. Different information systems need to be unified or even built from the beginning. Many health indicators exemplify this notion. Some of them are gathered in surveys conducted by the National Statistical Office but they are very basic as the number of beds in hospitals or physicians, or not robust

enough (e.g. number of physicians is not transformed into full-time equivalents). Other data is gathered by the National Institute of Public Health - National Institute of Hygiene but the quality of its datasets can sometimes be questioned (e.g. data on morbidity). In order to perform evidence-based policy in the three dimensions of human development, e.g., standard of living, health and education UNDP points out these deficiencies in statistical information. They could be easily improved if the above three recommendations were met. Standard of living – income data During the search for sufficient information the HDR team encountered several problems with GNI computation and income data overall. Most of the research conducted in the National Statistical Office is comparable at the NUTS2 level and as that is useless when having in mind disaggregating policy goals at the sub-regional level. First of it is a problem of the research population in the survey studies (from 8 to 40 thou. people), and the other is that the tax and social insurance repositories are in different public entities, i.e., Social Insurance (Zakład Ubezpieczeń Społecznych) and the Ministry of Finance. Information about the territorial distribution of welfare could be shared by the Social Insurance if it would share its datasets. The basis for this is the public information status of that information. UNDP managed to access the Ministry’s of Finance data. Theoretically every institution could get access to these data but it takes the Social Insurance and Ministry of Finance a lot of time to produce complete datasets. The main information you could gather from this source are information about money earned from employment, income from businesses, most pensions, interest on savings, rental income, income from a trust and information about every agreement that was taxed or contributed to the social insurance. Health indicators In many places of this paper the problems with health data, its relevance and quality are discussed. The National Statistical Office gathers basic infrastructural indicators as number of beds; number of physician or nurses. Information about the quality of health and physical well being are represented by such variables as life expectancy (NUTS2) and deaths due to different causes (LAU1). Particularly, cancer and cardiovascular diseases are important as they account for the majority of deaths in Poland, and thus are used in the Local Human Development Index. Other possible source of data is National Institute of Public Health with data on health service-based morbidity indicators, e.g., for cardiovascular diseases or number of cancer diagnoses). In case of the former, relevant data is available at the LAU1 level, but the limited number of cases reported annually is a major constraint to use this indicator as a reliable health measure. Morbidity data would require a timely effort to transform them into a measure usable in the HDI project, and as such are a prospective source of valuable healthcare indicators. The National Institute of Hygiene performs research referring to the monitoring of biological, chemical and physical risk factors in food, water and air as well as diseases and infections control. Unfortunately these studies are not conducted on a yearly basis. The datasets and the quality of data will improve with a new data storage system, which is to be finished in 2014. On the policy input side, one should think about quantifying resources devoted to the aim of ensuring health and longevity. In Poland, financial resources for health care are distributed mainly through National Health Fund (NHF). Local governments’ spending accounts for approximately 1% of funds devoted to health care. To our best knowledge, there is no data on funding from NHF available at the local level. The National Health Fund and the Ministry of Health recognises its statistical structure making more data on health infrastructure

available, but these new indicators are not available at the moment. When it comes to diagnostics and specialist care, the access to cardiologist, oncologist, internist (internal medicine specialist), and a paediatrician is an important factor. Ministry of Health gathers from the Voivodship local government information on number of medical rescue teams on LAU1 level and although they are available at the regional websites they are hardly usable because of the “*.pdf” format. The important measure of the accessibility to healthcare is % of medical rescue team trips that exceeded the maximum amount of time specified by law. However, this indicator is collected and aggregated for medical areas – units that differ significantly from LAU-1 system. Such information should be analysed and gathered on the LAU1 level because they give important feedback about the local health infrastructure. Education indicators During our work we find education as the most important from the policy point of view. There are many data sources, and most of them are available through the National Statistical Office (on the level of education attained) but also the Education Information System (System Informacji Oświatowej), which gathers information about every school (the difficulty is the linkage of this system with territorial units as LAU2). As for scientific education research, the Education Research Institute works on many of these datasets and provides analysis on the subject. However, all indices are difficult to be used at the local level. The problems with the use of them are twofold. First, the data available at regional level is limited. The measurement of mean years of schooling on local level is only possible based of national census data, which are collected every 10 years approximately. Moreover, census data gives information on achieved level of education, instead of years of education. However number of years of education can be roughly estimated on that basis. The problem with data accessibility is more important in the case of adult literacy – in Poland there are no such data on local level at all. While formal illiteracy is not an important obstacle in Poland, various studies suggest that actual reading and writing skills might be of a serious concern. In Poland school enrolment – taking into account compulsory education – is very high and does not differentiate local units. The same situation would be with adult literacy if the data were available. In recent years, the so-called value-added scores have often been considered as much better indicators of school effectiveness for policy purposes (OECD 2008). In national assessments, a school’s mean score is rarely taken as a measure of the school’s efforts because it largely depends on the socio-economic characteristics of students and only partly on how well the teachers work. In 2005 a group of experts has started developing a methodological and statistical background for Polish version of EVA models designed for school evaluation. Since 2009 such data are available and after 2012 these new measures could be part of the Local HDI but they do not allow historic comparison.

3.C Work schedule The Local Human Development Index and the project conducted in Poland consist of separate phases: (1) structuring, which is conducted in this paper, (2) observation – research conducted on the data available, (3) analysis and evaluation based on the developed methodology, (4) the National Human Development Report Poland 2012. The results of the analytical work will be presented at an international conference summarizing the project.

Phase 1. Structuring Analysis of the logic of the Human Development Index at the local level. The HDR team did a detailed analysis of institutional context and other tools developed in the UN system but also outside it in academia and in other countries. Identification and establishing connections. The HDR team did a detailed analysis of potential partners for the project i.e. the Warsaw School of Economics, the National Statistical Office and some of potentially interested in the findings public institutions. UNDP included the wide range of national and international experts in consultation of the later developed methodology. Development of a detailed methodology. Based on the diagnosis and the conclusions from the first two processes the HDR team in cooperation with experts from the partner institutions developed a methodology of which this paper is an exemplification. Contents of this material include also detailed techniques of computation and the sources of statistical information available in Poland at the current point. The techniques used in development of the methodological packet included: review of different national and subnational development measures, brain storming with specialist from different fields virtually and directly as well as internal expert panels. The course of the methodology development was based on a constant dialogue with the Ministry of Regional Development. Consultations. Presenting the preliminary methodology of the Local Human Development Index by the UNDP Project Office Poland and discussing in with experts from local governments and the ministries. This was conducted during a conference held at the Ministry of Regional Development September 7, 2012. The outcome of the first phase is the detailed methodology of research including list of measures and sources of statistical data available. Phase 2. Observation The aim of this phase is gathering of all sufficient data essential for the methodology and computation of the Local HDI. As it is essential for the methodology, this phase was conducted simultaneously with phase 1. Gathering of public statistical information available from the National Statistical Office. In partnership with the NSO the HDR team discussed all the data that are relevant for the project and the schedule of making it available to UNDP. Gathering of statistical information from administrative sources as the Ministry of Finance, Ministry of Labour and Social Policy, Ministry of Regional Development, Ministry of Health, National Health Fund, State Elections Commission, National Institute of Public Health, Central Examination Commission and the Office of Electronic Communications.

Evaluation of the relevance of the data provided. Some sources of information as the National Health Fund, Ministry of Health and the Office of Electronic Communications have unreliable data on the LAU1 level. Some sources also have insufficient data about every LAU1 unit making them useless from the HDR team perspective. The outcome of this phase will be a complete set of gathered and processed data. Phase 3. Analysis and evaluation The aim of this phase is to provide information about the LAU1 units based on the gathered data and using the proposed methodology. The actions of this phase will include data analysis of the processed data sets, verification of the logical model of the proposed Local HDI and evaluation of the ‘input’ - ‘outcome’ correlation between 2007-2010. Verification of the socio-economic analysis based on the human development approach. Verification of the allocation of public resources based on the ‘input’-‘outcome’ methodology. Review of the strategy and it’s comprehensiveness. Review of the results and synthetic indices used in comparison with other indices. The outcome of the third Phase will serve as an answer to the proposed methodology and review of its comprehensiveness. Phase 4. National Human Development Report The report on HDI at the local level as a planning and monitoring tool for municipalities including final outcomes is the final product of the study. The Polish report will include information on disaggregated HDI at the local level and good practices of the project. Also, it will describe the methodology, and the approach based on the methodological paper will be replicable in other countries. Prior the launch, the document will be consulted in its preliminary version with other national and international experts who did not directly participate in the project. At this point, after finishing the consultation process, the final changes to the report will be done, so that it can be published.. According to the strategy of the distribution of the projects outcomes, UNDP jointly with the Ministry of Regional Development, will use different channels of public informing and promotion of the project, e.g., e-mailing the summary of the report to important journalists. Presence of UNDP and Ministry of Regional Development experts in media is a must at the final phase. The report will have an electronic version (*.pdf) and it will be translated into English, accessible on Internet and tailored to the perception of all stakeholders. The summary will be a separate product made for the general public. The other outcome of the project will be an international conference organized jointly by the UNDP Project Office Poland and the Ministry of Regional Development to present the outcomes of the project.

All in all, the project will strengthen competitive potential of municipalities, increase territorial cohesion and eliminate inequalities. The major problem areas that will be addressed in the framework of the project are: 1. Lack of tool/methodology and indices to quantify the state of human development and sustainability of the Polish municipalities 2. Missing link between development and policy 3. Lack of robust methodologies for quantifying basic human development indicators at low administrative levels 4. Lack of clarity on how to link inputs into and outcomes from projects under implemented EU funds and their human development and social inclusion implications 5. Insufficient planning and monitoring system of projects devoted to territorial development under EU funds 6. Low knowledge on possible improvements of the planning and assessing of the project devoted to strengthening municipalities and inequalities (recommendations, best practices): sustainable urban/rural planning, equal and adequate distribution and monitoring of structural funds on territorial cohesion and human development As similar challenges exist in all Europe and Commonwealth of Independent States countries, thus the lessons learnt from this project can provide important inputs and clues for addressing those challenges elsewhere. The experience of the Project will be codified into best practices and replicable knowledge kits suitable for scaling-up in RBEC country contexts. All the implementation steps of the project will be thoroughly documented (incl. using multimedia tools) and a number of case studies illustrating the lessons learnt will be produced. A separate outcome of the project will be a â&#x20AC;&#x153;replication tool-kitâ&#x20AC;? for countries in RBEC region based on the methodological paper. In connection with core value of UNDP and its goals, this project provides an opportunity for crosspractice collaboration in the areas of poverty reduction, MDGs, democratic governance (local governance), knowledge management and capacity development. Given the HDI at the local level tool, the involvement of UNDP experts on measuring and monitoring human development and social inclusion will be also sought. This focus on benchmarking of the municipalities and accelerating its inclusive growth will be promoted via knowledge networks in order to include good practice for development in RBEC countries' development programmes and policies. Having finished the first National Human Development Report, UNDP plans to track human development in Poland in cooperation with crucial stakeholders i.e. Ministry of Regional Development, National Statistical Office and the Warsaw School of Economics in the year 2020 perspective.

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Annex 1. Table of used indicators

Dimension of development

Type of indicator

Indicator definition

Economical

outcome

Income per capita

Economical

outcome

Total transfers from social benefits per capita

Educational

outcome

Educational

outcome

Share of children enrolled in preschool education (34) Average result of the secondary school exam (only the mathematic-natural science part)

Data availability – spatial level Method of calculation at LAU 1 NUTS NUTS LAU LAU 2 3 1 2 Local Human Development Index – Outcome Total taxpayers income summed (PIT36, PIT-36L, PIT-37) plus total agricultural income by comparative fiscal hectares (municipalities tax revenue from agricultural tax divided by tax value for one comparative fiscal hectare multiplied by average income from activity in private farms in x x x x agriculture from 1 ha fiscal, which constitute the basis for farm tax on agricultural land; comparative fiscal hectare value is determined on the basis of surface area, type and class of farmland based on the land register as well as additions to district of taxes) divided by population Sum of expenditures on social care and other tasks of social policy: sum of x x x x benefits for individual (LAU2 + LAU1) divided by population

Data availability – time

Data source

2004 ondwards

Ministry of Finance and National Statistical Office (agricultural income based on conversion hectares)

2004 onwards

Ministry of Finance

Sum of children aged 3-4 enrolled in pre-school education divided by sum of children aged 3-4

2003 onwards

National Statistical Office

Average result of the secondary school exam (only the mathematic-natural science part) in a given LAU 1

2002 onwards

Central Examination Commission

Dimension of development

Health

Type of indicator

Indicator definition

outcome

Life expectancy

outcome

Age-standardized mortality rates for cancer and for cardiovascular diseases per 100,000

Economical

input

Total public expenditures on the LAU1 level per capita without EU funds

Economical

input

EU funds in local budget per capita

Educational

input

Student-teacher ratio (primary and middle schools)

Health

Method of calculation at LAU 1

Data availability – spatial level NUTS NUTS LAU LAU 2 3 1 2

Own calculation based on data on population and deaths (using abridged life tables) a. combining the data for both sexes and for multiple (i.e. 2) years x b. switching to LE for 15 years old (not enough deaths recorded in lower age cohorts) c. using the Reed-Merrell transformation to derive the conditional probability of death Sum of crude mortality rates for cancer and cardiovascular diseases divided by age-standardized population of LAU-1 (direct standardization; reference x population - Poland; age were categorized into five-years bands); indicator calculated for 100,000 people Local Human Development Index – Input Sum of municipalities (LAU2) and districts (LAU1) expenditures from local governments budgets without EU x funds budget lines divided by population Sum of EU funds budget lines of local self-governments both LAU1 and LAU2 x divided by population Sum of primary and middle school students divided by sum of primary x and middle school teachers in a given LAU 1

Data availability – time

Data source

National Statistical Office

1995 onwards for NUTS 2; 2007 onwards for NUTS 3

2002 onwards

National Statistical Office

2006 onwards

National Statistical Office

2006 onwards

National Statistical Office

2011 onwards

National Statistical Office

Dimension of development

Educational

Type of indicator

Indicator definition

Method of calculation at LAU 1

input

Local government expenditures on education per student (pre-school, primary, secondary and uppersecondary)

Sum of local government expenditures (municipalities and districts) on education divided by number of pupils/students (pre-school, primary, secondary and upper-secondary) in a given LAU 1

input

Number of full-time equivalent primary care physicians per 1,000 people

Health

input

Number of full-time equivalent primary care nurses and midwives per 1,000 people

Environmental protection

contextual

Non-separately collected household waste, per person

Health

Number of primary care physicians employed in health establishments, providing service financed from public healthcare funds. Full-time equivalent calculated according to hours spend in providing such services, regardless the type of contract (FTE = 40 hours). Territorial code according to the location of the provider. All specialties included Sum of primary care nurses and midwives employed in health establishments, providing service financed from public healthcare funds. Full-time equivalent calculated according to hours spend in providing such services, regardless the type of contract (FTE = 40 hours). Territorial code according to the location of the provider. Contextual measures Amount of non-separately collected household waste (i.e. part of municipal waste collected directly from households) per person living in the given district

Data availability â&#x20AC;&#x201C; spatial level NUTS NUTS LAU LAU 2 3 1 2

Data availability â&#x20AC;&#x201C; time

Data source

1995 onwards

National Statistical Office

2011 onwards

National Health Fund

2011 onwards

National Health Fund

2005 onwards

National Statistical Office

Dimension of development

Poverty

Labour market

Civic activity

Data availability – spatial level NUTS NUTS LAU LAU 2 3 1 2

Data availability – time

Data source

2008 onwards

National Statistical Office

2002 onwards

Ministry of Labour and Social Policy

Only for election years – data used from last (2010) selfgovernment elections

National Electoral Commission

Type of indicator

Indicator definition

Method of calculation at LAU 1

contextual

Share of people in households benefiting from the social assistance environment in the total population

People in households benefiting from the social assistance environment divided by population

Registered unemployment rate at LAU 1 level

The ratio of the number of registered unemployed persons to the economically active civilian population, i.e., excluding persons in active military service, as well as employees of budgetary entities conducting activity within the scope of national defence and public safety. The unemployment rate is given including persons working on private farms in agriculture (comprising a part of the economically active civilian population) estimated on the basis of the results of censuses. The unemployed are localized according to their place of residence or stay, while the employed — according to the address of their place of work.

Voter turnout in local self-government elections

Sum total of all votes cast in last elections to the municipal council on the LAU2 level (Rada Gminy) divided by sum of all people entitled to vote on the LAU1 level

contextual

Dimension of development

Digital engagement

Women empowerment

Type of indicator

Indicator definition

Method of calculation at LAU 1

contextual

Share of tax declarations submitted by internet

Sum of all tax declarations i.e. PIT-36, PIT-37, PIT-37L submitted by internet (by the e-Deklaracje system) divided by sum of all PIT-36, PIT-37 and PIT-37L tax declarations submitted from the LAU1 unit

Seats in local selfgovernment held by women

Sum of seats held by women at the district (Rada Powiatu – LAU1) and municipality (Rada Gminy – LAU2) selfgovernment councils divided by sum of all seats at those councils.

contextual

Data availability – spatial level NUTS NUTS LAU LAU 2 3 1 2

Data availability – time

Data source

2009 onwards

Ministry of Finance

Only for election years - data used from last (2010) selfgovernment elections

National Electoral Commission

The table describes all the indices and methods to calculate them that will be part of the LHDI indexes and additional measures it however does not cover the methods of computation at the NUTS2 level, which would be an weight average of all values of the individual measure divided by population.