“… individuals, besides, may sometimes ruin their fortunes by an excessive consumption of fermented liquor …” Adam Smith, The Wealth of Nations
Introduction The relationship of health and its perceived impact on productivity has had a long history, perhaps even longer than formal economics itself. While most of the effects of health on productivity have mostly been subjective in nature and based on perceived empirical evidence, the advent of modern statistical methods as well as technological advances have enabled economists to quantify and explain the details of health and productivity’s relationship. One of the more popular issues in health economics is the consumption of alcohol. Indeed, there is long-standing perception that point to alcohol consumption as a corrupting and unproductive act. One only has to look at history for evidence of such a perception. For example, laws such as the Prohibition were enacted because of the concern that alcohol consumption was a burden to worker efficiency in American industries, [Berry (1977)]. However, with the growing information and studies of health in both the medical and economic aspect, this belief is slowly being challenged. Indeed, there are studies which purport a u-shaped interaction between productivity and alcohol consumption in two fronts: health-wise and socially. Specifically, there have been studies which show that light alcohol consumption reduces the chance of coronary problems but heavy alcohol consumption or “alcohol abuse” or “alcoholism” tends to increase chances of such diseases like cirrhosis [Cook and Moore (2000)]. There are also studies that show alcohol as a “social lubricant”, [Tekin (2004)] easing interactions amongst workers/employees and thus increasing productivity. But then again, this applies only up to a certain extent of consumption. In the Philippine setting, most studies on alcohol consumption have been done in the medical realm, such as the effects of alcohol to health (beneficial or otherwise), diseases related to consumption and reasons for alcohol consumption. There have also been statistical measurements on consumption, such as number of drinkers as a percent regionally, as well as
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the average number of drinks a person has. Finally, there have also been studies on the alcohol industry such as the effects of the sin tax and the elasticity values of the industry. In the international realm, such studies are also conducted. However, in addition to these, there are studies which have attempted to directly measure the effect of alcohol consumption and alcoholism. Their results and methodologies differ. Some use cross-sectional data while others rely on time-series. There are studies which absorb the addictive nature of alcohol consumption, while there are others which assume that alcohol is a pure choice. Finally, there are also some studies that treat alcohol as a disease. The purpose of our study is to apply these methodologies in the Philippine situation, thereby determining the impact of alcohol consumption and, to an extent, alcoholism in our county. First, we attempt to discover the individual background and status which may lead a person to becoming an alcohol consumer. Second, we will show the general effect of alcohol (controlling for other health variables) to individual productivity by using different subsets of the population, so we may discover the effects of alcohol consumption in different geographic scenarios and lifestyle situations.
Review of Related Literature As part of any academic paper, it is prudent that before we begin with the nitty-gritty of our own data sets, methodologies and results, we must first glimpse at other studies which have been done before our own. More specifically, we take a look at those which shape and influence the scope and scheme of our study. Our discussion throughout this paper is motivated by literature that links heavy drinking and alcoholism to the physical, psychological, and cognitive impairments which would be detrimental to an individuals’ labor market productivity [Tekin (2004)]. Alcohol’s depressant effects on labor market productivity is due to the fact that excessive drinking leads to disability, higher risk to certain diseases (such as cirrhosis), and wide array of social costs [Culyer and Newhouse (2000)]. Economists have thus tried to suppress alcohol consumption through certain policies and tax implementations.
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There have also been studies contrary to this, such as the u-shaped relationship between alcohol consumption and cardiovascular disease [Tekin (2004)]. This implies that alcohol consumption, at moderate levels, may be beneficial in relieving stress and reducing the risk of heart disease. Studies [Tekin (2004)] also point out that alcohol functions as a “social lubricant”. It is said that it creates a positive effect on the sociability of individuals as it plays a networking role if it is consumed with colleagues. Other literatures have tried to incorporate the unique aspects of alcohol. First, alcohol is an intoxicant. If it is consumed excessively, it impairs an individual physically and mentally. Second, alcohol consumption has direct intertemporal consequences [Culyer and Newhouse (2000)]. Past consumption of alcohol affects future consumption since alcohol consumption is habit-forming. Also, chronic alcohol use affects an individual’s physical and mental health over time. A research [Culyer and Newhouse (2000)] on such assumptions has derived a model of drinking and its consequences. Basically, the model relates previous alcohol consumption patterns to current patterns and health status, which in turn is attributed to an individual’s productivity. An individual’s productivity is measured through his earnings. There have also been many attempts to measure the effects of alcohol on health and labor market productivity. One study [Tekin (2004)] discusses the relationship between employment and alcohol consumption. The model used is a neoclassical framework of utility maximization in which individuals allocate their time and money among consumption of leisure, alcohol and a composite good. Another model incorporates alcohol consumption to wage rates by using a human capital framework, wherein wage rates are equated to health components of human capital, non-health components (ex. schooling, experience) and all unobservable determinants of wage rates. More extensive approaches in measuring the income effects of alcoholism takes into account the habitforming nature[Culyer and Newhouse (2000)]. It assumes a myopic view of consumers, that they are unable to realize the habit-forming nature of alcohol as well as its future consequences. One more methodology to note is the life-cycle accounting, wherein only the middle-aged employed are counted because of the observed 'positive' effect of
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alcoholism to very young and very old employed [Mullahy and Sindelar (1991)]. It takes a look on the effect of alcoholism not only on the perceived individual earnings and income decrease, but by also its macroeconomic effect on the labor participation rate. Problems in measuring, however, do not begin with finding the relationship between alcohol and productivity but from the classification of the type of consumption of alcohol. Many literatures suggest different measures of light, moderate, heavy and abusive drinkers, [NCCDP (2005) and UNDCP (2004)]. Other literatures base such classification in a more subjective manner, [Flavin (1991)], using physician and psychiatric analysis. There are also some which focus on the indirect effects of alcohol consumption, especially that of alcohol abuse and binge drinking. Lastly, there are also several studies which criticize the inadequacy of the measurement of the societal cost of alcohol because of the neglect to incorporate events which occur under the influence of alcohol [Eberwine (2005) and Tai, Saunders and Celermajer (1998)]. Such events include drunk driving, domestic violence, assault, etc. In the Philippine setting, most studies have focused on the study of alcohol as a lifestyle related disease, studying its epidemiological characteristics. These same studies also tend to focus on particular sectors of the population such as the youth [Baquilod, Baltazar and Tilgao (2003)]. Meanwhile, other literatures focus more on the detection, prevention and possible effects of alcohol abuse in a particular sector [UNDCP (1994)].
Methodology and Data Description We now head into our own study by first describing the methodologies involved, both from previous studies and the one we will use. Afterwards, the paper will be introducing the data set involved and its details: from which institution it came from, how it was gathered and for a slight background, its intended purpose. Lastly, the variables used in the analysis will be discussed in greater detail.
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Literature Methodology The first methodology from [Tekin (2004)], has two frameworks of note, that of relationship between employment-alcohol consumption and the relationship of wage rate-alcohol consumption. First, we find the relationship of employment and alcohol consumption. It posits that the relationship of alcohol consumption and employment can be measured through utility maximization of allocating both time and money with the consumption of leisure, alcohol and a composite good. This being so, the necessary equations would be the labor participation rate (labor supply) and alcohol demand. The functions would be A = A (PA, W, X, Z)
(1)
L = L (PA, W, X, Z)
(2)
where A is alcohol consumption, PA is the price of alcohol, W is the wage rate, X is the vector of observable factors, L is work in number of hours and Z is a variable which represents all unobservable factors. Labor participation is assumed to have the same determinant of labor supply, such that the equation for labor participation is E
= E (PA, W, X, Z)
(3)
where E is a binary indicator (working/not working) of employment. Labor participation (3) is then made conditional to alcohol demand. E
= E (A, X, Z)
(4)
In the same line of thinking alcohol demand is also made conditional to employment. A
= A (E. X, Z)
(5)
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Assuming the linear nature of the conditional employment equation, we express the fourth equation as E = B1X + B2A + Z
(6)
where the B’s are the parameters to be estimated. Next, we try to find the relationship between wages and alcohol consumption. We use the human capital framework as a means of creating a wage equation, splitting human capital into two important components: health, which is represented by H, and non-health, which is represented by N. The equation then is W = W (H, N, X, V)
(7)
where V represents the unobservable factors. Alcohol consumption is then entered as a determinant of health being H = H (A, K), where K represents the other non-alcohol consumption determinants of health. Assuming a linear situation in equation seven and substituting the determinants of H into the equation, we have W = A1Q + A2A + V
(8)
where Q denotes all other factors observed (N, X, and K) while A’s are the parameters for estimation. There are, of course, difficulties arising from the equation due to biases and correlations between several factors. There might be a correlation between the unobserved factors and alcohol consumption. Selectivity bias is also an issue. It can be assumed that alcohol consumption is judged according to the preference of the individuals who have different time preferences (e.g. having higher preference for present utility over future utility with possible future disutility from the consumption of alcohol).
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In order to address these issues (specifically that of unobserved individual heterogeneity), the article suggests using longitudinal data. With longitudinal data we use the individual means as observations. Thus we find E = B1X* + B2A* + C
(9)
W = A1Q* + A2A* + C
(10)
where asterisks (*) represent the individual means as observations and the C’s represent the new unobserved terms assumed uncorrelated with the observed. The second methodology our thesis relies heavily upon is from [Culyer and Newhouse (2000)]. This model takes into account the habit-forming nature of alcoholism. However, it also assumes a myopic nature of consumers, meaning that they are unable to realize the habitforming nature of alcohol as well as its other future consequences. Lastly, it utilizes aggregate data for the model, and in addition, it also uses lifetime utility and income as optimization constraints. The methodology begins by creating a one-period utility function that is U = U (H, X, A, AT-1) where: H is the person’s current health stock; X, represents other goods; A, represents the alcohol consumption of the person now; and A with subscript (t-1) represents the alcohol consumption in the last period. We thus find the lifetime utility, V: ∞
V = ∑ B t −1U t t =1
where B represents
(1)
1 , r is the discount rate. Therefore, lifetime utility is the discounted (1 + r )
equivalent of period utility until time t. Alcohol consumption comes in as an input of health, which is a determinant of utility. We get the function for health, H, as H = H (M, A, HT-1, Z)
(2)
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where Ht-1 represents a person’s current health stock, M is the consumption of medical/health care and Z represents unobservable factors. There is an assumption that Ht-1 and M has a positive effect on health. Alcohol consumption, on the other hand, is deemed to have a concave-like effect on health, where it is marginally positive but decreasing up to the point where it increases but is marginally negative. Next is a simple time utilization model, where L is the time spent working and D is the time spent in leisure. L+R=D
(3)
The final constraint used is the intertemporal budget constraint: Y = LW + I
(4)
where w is the wage rate, I is the non-labor income and Y is the total personal income of that period. Using (1), (2), (3) and (4) as constraints, the literature derives linear equations for demand of medical care, alcohol and labor supply. It is important to note that these equations come with the assumption that (1) and (2) are quadratic in nature and the marginal utility of wealth is held constant. M = α 0 + α 1 At +1 + α 2 PA + α 3 At −1 + α 4W + α 5 PM + α 6 Z + ε M
(5)
A = δ 0 + δ 1 At +1 + δ 2 PA + δ 3 At −1 + δ 4W + δ 5 PM + δ 6 Z + ε A
(6)
L = γ 0 + γ 1 At +1 + γ 2 PA + γ 3 At −1 + γ 4W + γ 5 PM + γ 6 Z + ε L
PA
(7)
and PM represents the price of alcohol and medical care respectively, and α , δ and γ are
the values to be estimated. The literature further discusses typical procedures and assumptions done and made by previous studies on the topic. The literature also discusses the several measures of health used:
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mortality, morbidity and physical-mental health indicators. Finally, it gives the typical health production function as H=ϖ
0
+ ϖ 1A + ϖ 2M + ϖ 3Y + ϖ 4Z + ε
(8)
The third source of our methodological framework was from [Mullahy and Sindelar (1991)]. Similar to [Culyer and Newhouse (1991)] it seeks to find the effects of alcohol consumption in a broader approach. However, instead of focusing on the lifespan utility of drinkers, it focuses more on the life-cycle effects of drinking, particularly on how it looks at the extreme end of the working population where alcoholism “seems to have counter-intuitive effects”. The literature posits that alcoholism not only affects individual earnings and income, but the labor participation rate. It uses an exogenous assumption of alcohol consumption and readily admits the possible effects on and from other variables (e.g. schooling, family background etc.). It uses this assumption because of the nature of the data available for analysis. Their methodology begins with a basic earnings function, Y = Y (H, K, X) +
ε
(1)
where y is a measure of earnings, h is a vector of health components, k is a vector of nonhealth components and x is the vector of all other covariates. Also, H is H = H (A, S), where A is a measure of alcoholism and S is the measure of all other health outcomes. Given (1), in similar notation to the literature, we have its linear counterpart, yt = ztα + ε t
(2)
where z t summarizes all exogenous values, α represents the values to be determined for each variable and εt is the error term. The paper uses a similar equation for full-time labor force participation I, I t = zt β + ηt
(3)
where I is a binary variable.
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Basic Framework and Thesis Methodology To begin our analysis, we first start with our basic framework which is based on a general productivity model similar to that of [Mullahy and Sindelar (1991)], P = P( H , K , X )
(1)
where P is a measure of productivity being assumed dependent of H, health capital, K is a vector of an individual’s non-health capital (e.g. educational attainment) and X is a vector representing other covariates (e.g. marital status, sex, age, etc.). We assume K and X to be exogenous in nature while H, health, is said to be a function of A,
alcohol consumption, and Z, non-alcohol health variables, such that H = H ( A, Z )
(2)
In our actual methodology, we assumed a linear relationship with most all of the variables (because of the difficulty in using a scatter graph with such a large sample size). We filled K, X, and Z with their counterpart variables from the data set. The respective coefficients and significance per variable were then calculated using EViews. (3)
dd = c + β 1 hhs + β 2 mstat + β 3 age + β 4 sex + β 5 phh + β 6 mhh + β 7 educ + β 8 dpo + β 9 hfac
This was the general equation we used in the overall data set as well as the subsequent subsets. Subsets were used to focus on the effects on alcohol consumption on certain strata of the population. In our analysis, we also conducted a binary choice model using the probit and logit functions in order to discover the possible reasons and backgrounds of alcohol users. In equation form, it is D = D( K , X )
(4)
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where D is the choice to drink or not. It is influenced by non-health human capital, K, and other individual covariates, X. drk = c + β 1 hhs + β 2 mstat + β 3 ageβ 4 sex + β 5 educ + β 6 smk
(5)
Also for our analysis, we adopted a ten percent confidence interval as our significance level.
Data Description Our data set comes from the Department of Health, from the Baseline Behavioral Risk Factor Survey it conducted with the University of the Philippines’ College of Public Health in 2000 for their program, Prevention and Control of Lifestyle Related Diseases. The cross-sectional data collected from the survey were intended to help in the analysis of lifestyle-related diseases, to identify the level of awareness of such diseases, and finally, to determine their impact on health. A 3-stage stratified cluster sampling design was utilized in conducting the survey wherein the primary, secondary and tertiary sampling units, corresponding to provinces, towns and barangays, were randomly selected from each of the 16 health regions. Overall, 32 provinces, 64 towns and 128 barangays were included in the sample. From each of the barangays, 80 households were chosen, in which a randomly chosen adult member (15 years old and above) was interviewed. In total, the number of respondents was 10,240 people. The survey form consists of basic information, such as the socio-demographic background of each respondent. The other sections of the survey generally examine lifestyle-related choices, disease prevention and health awareness. The survey is also structured to yield discrete variable responses, thus, they used questions answerable with yes/no and fixed value responses. In addition, the survey touches on the general health status, disease prevention and lifestyle choices such as drinking and smoking. A notable block of data is that of the
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alcohol lifestyle preferences of the surveyed population. Prevalence of alcohol drinking, age of initiation and the perceived effects of drinking alcohol were thoroughly discussed on a per region basis. Additional information on the type of alcohol drinks consumed, reasons for drinking and type of alcohol drinkers according to number of drinks were also included. We also restructured our data set for the sake of simplicity. Specifically, we omitted respondents whose answers were either unknown or unavailable. We readily admit that such omission might cause bias in the results, but if not done, it would cause problems in our regression. Also we had to redefine several of our variables in order to become binary in form. The variables transformed were the marriage status, educational attainment, and health facility most commonly used used. A more in-depth explanation of our variables shall be tackled in the following section.
Definition and Measurement of Variables This is a list of the variables (raw data or derived) that we used and manipulated. It is important to note that not all variables were considered for the reported estimation. The rationale behind these is explained in the notes section of the paper. Variable
Description
DD
The variable for days debilitated is our main dependent variable and the general measure for productivity losses due to poor health. This was measured in the number of days where an individual was unable to perform his activities due to poor health.
AGE
This is the age of the individual.
SEX
This variable is used to denote the gender of the respondent. It is binary in form where in the value of (1) indicates male and (0), female.
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HHS
This variable is used to denote the household status, which is the number of people living with the respondent.
EDUC
EDUC
or education originally had ranked discrete values to represent the
highest educational attainment of the individual (i.e. none, elementary, high school, college, postgraduate or vocational). However, this variable, for the purposes of the thesis, was transformed into a binary one. Vocational, high school and no schooling were given (0) values while the college and postgraduate responses were treated with a value of (1). MHH
This variable denotes the number of days in the past month the individual suffered from poor mental health.
PHH
Similar to MHH, PHH is also a health status indicator. It denotes the number of days in the past month the individual suffered from poor physical health.
MSTAT
It is the variable for the respondent marriage status. Similar to EDUC, this variable was also transformed into a binary one. Married and live-in couples were given a value of (1) while the rest were given a (0) value.
HFAC
This is the term for health facility used by respondent. It was also transformed to become a binary variable, wherein respondents who selfmedicated, visited a barangay or rural health unit and those who did not use any health facility at all were given a value of (0) and those who consulted a family physician and who visited a provincial, district, or private hospital were assigned a value of (1).
DRK
This is a binary variable that represents whether the respondent consumes alcohol. Yes answers are (1) and no answers are (0).
SMK
This is a binary variable for smoking. Smokers were assigned a (1) value while non-smoker were given a (0) value.
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DPO
This variable denotes the amount of alcohol the respondent drank per occasion within the past month. We chose this as our variable for alcohol consumption because according to most alcohol-related literature, alcoholism symptoms are described as being more on the intensity rather than regularity. For reference, one alcohol unit is considered as either a single can of beer, a single glass of wine, a shot of liquor or one cocktail.
AGEQ
It is the log-linearized AGE variable.
DPOQ
It is the log linearized DPO variable.
HSTAT
It is another form of measuring individual health status, a dummy variable for qualitative answers of 1 to 5, best to poorest respectively.
Estimation Results For the purposes of organization, our estimation results shall be subdivided into different subsections, namely, the determinants of alcohol consumption and primary, initial, regional, and stratified data sets. For each subsection, there shall be a short explanation and commentary on the results. Meanwhile, complete copies of EViews outputs, as well as graphs, shall be compiled in the appendix.
Determinants of Alcohol Consumption and Primary Data Set Results We used both the logit and probit binary methods to discover the probable background of individuals who chose to become alcohol consumers as represented by the (1) value in the DRK variable. What our results show is that there at least 4 variables with significant effects: household size, education, gender and being a smoker. All of them have a positive coefficient which seems irregular for education, as there is often a perception that the higher the education, the less the likelihood of drinking. Another important thing to note is that gender and smoking seem to have the most impact as variables. It suggests that males are more likely to be drinkers than females, and that smokers are
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often drinkers as well. Whether these are causes or highly correlated lifestyle choices (for smoking) remains to be discovered. With respect to the regression with dependent variable DD, the significant variables seem to be age, gender, physical/mental poor health, health facility used and drinks per occasion. While the first five variables seem to have results and coefficients which follow convention, drinks per occasion, DPO, surprisingly has a negative coefficient. Does this mean that drinking actual decreases days debilitated, or perhaps it is a proof of the u-shaped relationship of alcohol towards a person’s health and thus productivity? As was mentioned in the introduction, there are studies which point to the significant increase of health capital from casual drinking, such as protection from coronary diseases, while heavy drinking decreases health capital as well as increasing the risk of certain diseases. These findings, coupled with the fact that only a mere 99 of the 3089 drinkers of the data set (around 3 percent) are heavy drinkers (meaning 2990 are casual drinkers), might be the reason for the negative coefficient of DPO.
Per Region Data Sets Results Unlike the tables in the section of the determinants of alcohol consumption and the primary data set, the regional table will focus mostly on the DPO results of each region. Copies of the EViews regression outputs of each region are available at the appendix of this paper as well as the region codes. The results of the variable DPO per region are, to say the least, mixed. Only two of the regions show any significance: region 12 and 14. Even then, their coefficients are conflicting, as one is negative and one is positive. A possible explanation for such result can be attributed to the specific regional culture. For instance, region 14 (the Cordillera Administrative Region) uses alcohol as an important tool for social interaction thus explain its positive effect. On the other hand, the negative coefficient found in the results from region 12, may be due to the fact that that most of the population of the region 12 are Muslims.
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Stratified: Heavy vs. Casual Drinkers Data Sets Results Finally, we also decided to capture certain data subsets which might be of interest and/or of use to any further analysis. For example, we tested the data subset of heavy drinkers in order to isolate the impact of heavy drinking to days debilitated. For if the u-shaped relationship of alcohol consumption and production truly does occur, the positive effects of alcohol, as well as its negative effects, might be lowered in the result. We thus separated the heavy drinkers from the casual drinkers. There are of course differences in variable significance as well as coefficients. It is prudent to point out the difference in the DPO variable. First, it can be seen that the heavy drinkers had a positive coefficient, meaning a contribution to days debilitated and thus lost productivity. On the other hand, casual drinkers had a negative coefficient, denoting an opposite effect. Second, and maybe more importantly, we note the significance of the variable for each subset. In the heavy drinkers’ subset, there is a definite significance while among casual drinkers there is a definite insignificance. It is also wise to point out that the coefficient of HFAC seems to go against convention amongst heavy drinkers, even though it is (barely) insignificant.
Conclusion and Final Words After viewing our results and analyzing them, we have to state that is quite possible that in the Philippines, the u-shaped relationship of alcohol consumption and productivity certainly does exist. Notable of course are the results of the stratified alcohol consumption data subsets. It seems casual drinkers have little to worry in terms of alcohol affecting their productivity. Conversely, heavy drinkers run a significantly higher risk of their productivity being affected by their consumption of alcohol. We have also discovered that both the significance and impact of alcohol consumption, in a per regional scale, are conflicting at best. However, on a national scale, the effect of alcohol consumption seems significantly positive with respect to productivity. However,
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this must be tempered with the fact that among alcohol drinkers, those most probably suffering from alcoholism (the heavy drinkers) are only a mere 3 percent. It would be negligent to surmise that the effect of alcohol ends there, for as discussed in the introduction, the measurement issues on health are very rarely simple. Indeed, certain studies purport that occasional binge drinking, even among the most casual of drinkers, can have rippling social impacts. Such behavior points as the cause for many social ills: road accidents, marital or familial strife, outbreaks of violence; all factors which would be difficult to compute, let alone incorporate in any model.
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