The effect of labor market shocks on health: The case of the Russian transition

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The effect of labor market shocks on health: The case of the Russian transition Olga Lazareva National Research University Higher School of Economics, Pokrovsky Blvd. 11, 109028, Moscow, Russia

A R T I C L E I N F O

A B S T R A C T

Article history: Received 14 December 2018 Received in revised form 13 September 2019 Accepted 17 September 2019 Available online xxx

During the first years of the transition to the market economy in Russia, many people experienced the whole range of stressful labor market events, including job loss, wage cuts and nonpayments; some people had to change occupations or take on additional work. These events were caused externally by the unprecedented structural shifts in the economy. This natural experiment provides an opportunity to estimate the causal effect of various labor market shocks on individual health and health-related behaviors. Propensity score matching and difference-in-difference estimates using household survey data show that labor market shocks during the early transition had long-term negative effects on individual health. I also find an increased incidence of smoking and alcohol consumption as well as a higher risk of certain types of chronic health problems for the people affected by labor market shocks. © 2019 Elsevier B.V. All rights reserved.

JEL classification: J62 J24 I10 Keywords: Labor market Health Smoking Alcohol Transition

1. Introduction A number of studies have shown that negative labor market events may affect people’s health. Mostly job loss and unemployment have been shown to have a negative effect on individual health. During periods of deep economic crises or economy-wide structural shifts, such as the transition from a planned to market economy, people experience a wide range of external labor market shocks that are not limited to job loss. The case of the Russian transition to the market economy provides a unique natural experiment setting that allows for the estimation of the causal effect of labor market shocks on individual health, which is the purpose of this paper. This study is not limited to job loss: I estimate the effect of several negative labor market events. Previous literature has examined the effect of economic changes on health and mortality indicators in transition mostly at the aggregate or regional levels (Walberg et al., 1998; Stillman, 2006). I use unique data on individual-level labor market shocks. In addition, previous studies mostly examined short-term effects while I estimate the long-term health effects that are observed 15 years after the start of transition. Another distinguishing

E-mail address: [email protected] (O. Lazareva).

feature of the paper is the use of a wide range of health indicators and health-related outcomes: self-assessed health (SAH) and the EQ-5D measure, the indicators of the occurrence of chronic diseases and cardiovascular problems, the incidence of smoking and alcohol consumption. During the first years of transition in Russia, economic decline was dramatic. Gross domestic product declined by 40% during the first half of the 1990s. Unemployment increased from nonexistent to almost 10% in 1995. The structural shifts in the economy have led to a large-scale labor reallocation across sectors. Employment in the industry declined, while employment in the new sector of the economy – market services – surged. Approximately 42% of employed people permanently changed occupations between 1991 and 1998 (Sabirianova, 2002). Another specific feature of economic transition in Russia was the large-scale nonpayment of wages by enterprises to their workers (Earle and Sabirianova, 2009) and various forms of underemployment (reduced working time and unpaid leaves; see Gimpelson and Kapelyushnikov 2013), which forced many people to obtain additional jobs. Consequently, many employed people in Russia experienced negative labor market events caused by the turmoil of transition and fundamental structural changes in the economy. In this paper, I test the effects of four events: job loss due to plant closure or downsizing, occupational downshifting, the performance of additional work and salary cuts.

http://dx.doi.org/10.1016/j.ehb.2019.100823 1570-677X/© 2019 Elsevier B.V. All rights reserved.

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To estimate the health effects of labor market shocks, I use the data from the Russian Longitudinal Monitoring Survey, which is a household survey. In the main part of my analysis, I use data from the 2006 round of the survey. In the 2006 round of the survey, people were asked a number of questions concerning their labor market experiences during the transition, i.e., beginning in 1991. These data allow us to use a matching method to estimate the effect of labor market shocks. I assume that, conditional on observable individual characteristics at the start of transition, the treatment, i.e., labor market shock, was exogenous. The probability of the shock was related to the severity of economic decline in the sector of individual employment. In a supplementary analysis, I also use a subset of the panel data for 1994–2000 to estimate the difference-in-difference model. Matching estimates are performed separately for men and women. The results show that labor market shocks had a negative long-term impact on overall individual health (both self-assessed health and the EQ-5D measure). In particular, job loss was more harmful for women’s health, additional work was more harmful for men’s health, while occupational downshifting was negatively related to the health of both groups. I find that certain labor market shocks increased the risk of cardiovascular diseases, heart attack and stroke, and chronic problems of the kidneys, gastrointestinal system and spine. Finally, labor market shocks are associated with higher rates of smoking and alcohol consumption for both men and women. Importantly, the negative health effects found in his study are likely to be underestimated due to the fact that we do not observe people who did not survive until 2006 in our sample. Two potential channels through which labor market shocks may affect individual health are income decline and psychological stress. Job loss and wage cuts or nonpayments result in a decline in income, which translates into worse health status through poorer nutrition and fewer resources for medical treatment. Using individual-level survey data, Stillman and Thomas (2008) showed that gross energy intake in Russia did not change much in response to household income fluctuations during the 1990s. However, the diet composition was affected by the income decline. There are a number of ways in which labor market shocks in transition may induce psychological stress. In the planned economy, people had very stable employment, while the market transition has dramatically increased the level of uncertainty with respect to their labor market position and future earnings. Many people had to move to a less qualified job or into completely different occupations, which induced psychological stress due to the loss of human capital, in particular for older people1 . Job loss and occupational downshifting also resulted in a decline in social status and a loss of social networks for many people. Indeed, my analysis shows that people who experienced labor market shocks, in particular, job loss, report the decline of their position in relative income and power distributions. Medical literature shows a strong link between chronic psychological stress and cardiovascular diseases as well as increased smoking and alcohol consumption. In addition to providing evidence on the social cost of economic transformations in terms of the decline in health of the working population, this paper contributes to the understanding of Russia’s mortality crisis. There was a sharp rise in both male and female mortality rates at the beginning of the economic transition in the early 1990s (Vichnevski, 1999). Life expectancy for men dropped from 65 years in 1988 to 58 in 1994; for women, it dropped from 75 to 72. The mortality increase was highest among the working-age

1 Guriev and Zhuravskaya (2009) estimate that one of the reasons for the abnormally low levels of life satisfaction in transition economies is human capital depreciation due to the mismatch of skills demanded in a planned versus market economy.

population over 40, with the main medical cause of death being cardiovascular disease. This rise in mortality is still not fully understood. Brainerd and Cutler (2005) empirically test a wide range of possible explanations and suggest two main explanations: broadly defined psychosocial distress from the transition (stress from increased uncertainty; higher risk of negative outcomes in the absence of a social security net) and an increase in alcohol consumption, which paralleled the rise in mortality. Denisova (2010), in her study of the causes of adult mortality during the transition, showed that both heavy drinking and smoking increased the risk of mortality by almost 60%. The role of labor market transformations during the transition in the mortality crisis is underexplored, although some studies indicate its importance2 . Massive labor reallocation, which is shown in this paper to have negative health effects and increased individual levels of smoking and alcohol consumption, is likely to have contributed to rising mortality in Russia in the early 1990s. The rest of the paper is organized as follows. Section 2 summarizes the main findings in the literature concerning the effect of various work-related events on health and the mechanisms behind these effects. Section 3 provides a discussion of labor reallocation during the transition and a description of data on individual labor market changes and indicators of health and health-related behaviors. Section 4 presents empirical strategy and estimation results. Section 5 concludes the paper. 2. Labor market shocks and health There are a number of studies estimating the effect of job loss and unemployment on health and mortality. Since there is a potential reverse causality problem between individual health and losing a job, several papers study the effect of an exogenous job loss due to plant closures. In one of those studies, Hamilton et al. (1990) show that job insecurity (anticipation of job loss) and job loss itself have negative health effects. Catalano et al. (1993) finds that job loss increases the risk of alcohol abuse. Using propensity score matching with Swedish data, Eliason and Storrie (2009) show that job loss significantly increases mortality risk for men. Black et al. (2015) find negative health effects of job displacement on the health of both men and women in Norway; much of this effect is driven by the increase in smoking behavior. With respect to unemployment, a number of studies show that unemployed people have a lower health status than employed people do (for surveys, see Jin et al., 1995; Dooley et al., 1996; Björklund and Eriksson, 1998). Several studies use individual-level panel data to estimate the causal effect of unemployment on health. While Bjorklund (1985) finds no significant relationship, Kessler et al. (1987) find a negative effect of unemployment on subjective health. Mayer et al. (1991) show that the risk of the deterioration of mental health is greater among unemployed people, and Gerdtham and Johannesson (2003) find that unemployment raises the mortality risk3 . The mechanisms behind the negative health effects of job loss and unemployment costs discussed in the literature are twofold. One reason is a sharp decline in income following job loss, which leads to worse nutrition and fewer financial resources to obtain

2 Walberg et al. (1998) show that the mortality increase in Russia was higher in the urban regions with higher labor turnover. 3 While these studies estimate negative health effects and increased risk of mortality for the individuals experiencing job loss and unemployment, the literature on the aggregate health effects of economic fluctuations typically finds that economic downturns and even severe recessions have positive effects on physical health and reduce mortality risk in the population. This effect is usually attributed to the healthier lifestyles of people during recessions (see Ruhm, 2016).

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Fig. 1. Dynamics of sectoral employment, 100% in 1990. Source: Russian statistical agency data

treatment in the case of health problems. Another reason is psychological distress due to the loss of social status and social connections and declining self-esteem. The health effects of forced occupational change caused by economic restructuring are much less understood. There are a number of studies in the sociological and medical literature examining how various aspects of occupational stress affect health. In particular, these studies show that occupational stress factors, such as low job satisfaction, lack of control, work overload and effort-reward imbalance, are negatively related to mental health and cardiovascular diseases, leading to greater smoking and alcohol consumption (Conway et al., 1981; Bosma et al., 1998; Marmot et al., 1997; Bobak et al., 2005; Greenberg and Grunberg, 1995). One must be careful when interpreting the results of these studies, as most of them do not address endogeneity or the reverse causality problem4 . In the economics literature, Fischer and SousaPoza (2009) provide panel data evidence that higher job satisfaction has a positive effect on workers’ health. These studies suggest some ideas concerning psychological and physiological mechanisms through which occupational change may affect health. Leaving a profession in which one was successful and having to switch to an occupation that one does not prefer is stressful in itself. Such people may feel that their skills are underutilized in their new occupation, which has been shown to have a negative effect on job satisfaction (Allen and Velden, 2001). Additional stress may come from the fact that the occupational switch may result in a loss of social status if the status (prestige) of the new occupation or the person’s status in this occupation is lower than that of the previous occupation (Marmot and Wilkinson, 1999). Guriev and Zhuravskaya (2009) show that people in transition economies who received their education before the start of the transition have lower life satisfaction levels. This can be due both to the declining status of an old occupation and to the forced occupational change and resulting skill mismatch. Work overload arising from the need to acquire new skills over a short period of time may also negatively affect physical and mental health.

The medical literature established a strong link between psychological stress and cardiovascular diseases (Sterling and Eyer, 1981; Henry, 1982; Nicholson et al., 2005). It has been shown that stressful life events negatively affect health (Lantz et al., 2005), and distress leads to more negative health perceptions (Farmer and Ferraro, 1997). Moreover, stress is conducive to increased levels of smoking and alcohol consumption (Pearlin and Radabaugh, 1976; Castro et al., 1987). It is well established by now that smoking negatively affects long-term health, as it is a leading cause of lung cancer and other lung diseases and a major cause of heart disease and stroke (Chaloupka, 2000). Negative health effects of alcohol consumption are due to both short-term consequences of intoxication (increased probability of accidents and violence) and long-term effects of chronic heavy drinking (cirrhosis, coronary heart disease5 ) (Cook and Moore, 2000).

4 Using Russian data, Tekin (2004) shows that unobserved heterogeneity plays an important role in the relationship between alcohol consumption and labor market behavior.

5 While moderate alcohol consumption is sometimes shown to have a positive effect in terms of reducing the risk of coronary heart disease, heavy drinking or binge drinking has an unambiguously negative effect on health.

3. Data In this section, I will discuss the data that I use in the empirical analysis, in particular, the indicators of labor market shocks and the measures of health and health-related behaviors. 3.1. Data on individual labor market shocks The structure of the Russian economy changed dramatically during the transition period. After price and trade liberalization in the early 1990s, different sectors of the formerly planned and mostly closed economy experienced differential demand shocks depending on the degree of their technological backwardness and the competitiveness of their products with imports. The decline in total GDP amounted to almost 60% between 1990 and 1996. This decline was not accompanied by a rise in unemployment to the same extent. Instead, labor market adjustments largely resulted in declining real wages, wage arrears and various forms of underemployment (Gimpelson and Kapeliushnikov, 2013; Gimpelson and Lippold, 2001; World Bank, 2003).

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Fig. 2. Dynamics of industrial output by sector, 100% in 1990. Source: Russian statistical agency data

At the same time, economic restructuring was accompanied by major labor flows across sectors and occupations. The aggregate reallocation of labor across major sectors in the economy is illustrated in Fig. 1. Employment in the industrial and construction sectors declined by 40% from 1990 to 1998. Employment in agriculture also started to fall after 1994 and declined by 20% from 1990 to 2002. In contrast, employment in the market services sector, which was low in the Soviet economy, increased by 40% by 2002, while employment in nonmarket services (mainly education, medicine and state governance) remained virtually unchanged. Thus, labor was reallocated from industry and agriculture to the market services sector. Within the industrial sector, which comprised 30% of total employment in 1990, there were also different trends in output and employment, as some industries suffered more severe demand shocks than others. Fig. 2 illustrates the extent of the output decline by sector. The output declined the least in the energy and fuel sector (between 20% and 40% of the 1990 level), while the deepest decline was observed in the textile industry (almost 90% decline by the end of the 1990s). Such a decline in the industrial sectors and the massive shift of labor into the service sectors affected the labor market position of the large share of the working population. To analyze these changes, I use individual-level data from the Russian Longitudinal Monitoring Survey (RLMS6 ), which provides information on individual labor market histories. This is a panel household survey that is conducted annually starting in 1994 (with the exception of 1997 and 1999). The survey collects a wealth of information on various characteristics of individuals and families, including data on a person’s work, education and health. The RLMS sample is constructed as a repeated representative sample with a split panel. In each survey year, the sample is representative of the country’s population in that year. The sample is based on the sample of residential addresses and families residing there are included in the survey. This means that the majority of respondents are surveyed for several years, and they

form the panel part of the sample. Nevertheless, in each round, there is some sample attrition (approximately 10–15% per year), and new respondents are added to preserve the representativeness of the sample. In the main part of my analysis, I use the data from the 2006 survey round. This is a representative sample of the Russian population in that year; respondents in this sample were asked about their labor market histories throughout the transition. For one of the health indicators (the EQ-5D), I use data from 2005 where it was measured, and I match it with labor market histories obtained from 2006 round. In the supplementary analysis, I use panel data for 1994–2000 matched with labor market histories from 2006. Due to the sample attrition, this analysis is performed for a subsample that survived in the survey until 2006 (40–50% of the sample). In the 2006 round of the RLMS, survey respondents were asked a number of retrospective questions about their labor market history since 19917 . In particular, the following questions were asked: Tell me, please, from 1991 until now has it happened that you:

6 The description of the RLMS survey and the actual data can be found here: https://www.hse.ru/en/rlms/.

7 These questions were asked only of people who were born before 1978, e.g., those who were of working age in the beginning of the 1990s.

 Lost a job because the enterprise where you had been working either closed or conducted sudden layoffs of staff members  You had to change a place of work for another permanent job that did not correspond to your qualifications and that you did not like  You had to agree to additional work that did not correspond to your qualifications and that you did not like  Your salary decreased substantially Respondents were asked in which years these events happened to them. Fig. 3 shows the incidence of these events in the 2006 sample by year. It shows that during the early, most severe years of transition, the incidence of negative labor market events was the highest. The worst year by almost all indicators was 1993. Note that the questions above are formulated in such a way that they try to capture exogenous labor market changes. In particular,

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Fig. 3. Percent of respondents in 2006 sample who faced labor market shocks since 1991, by type and year of shock. Source: RLMS data

job loss only due to plant closures or mass layoffs is measured. Occupational change is measured as a forced shift to a less skilled occupation. Using these questions, I construct five indicators of negative labor market events: loss of a job due to plant closure or downsizing, occupational downshifting, additional work, salary cut, and an indicator for whether any of these events occurred. Each of these indicators is equal to one if the event happened to a person at least once during 1991–1995. I choose this period because it was the time of the deepest economic decline and the most profound structural shifts in the economy. The individual changes in the labor market position during this period were most likely caused by exogenous factors. Table 1 shows the incidence of labor market shocks experienced during the early transition in the 2006 sample; these figures are shown separately for men and women. Eleven percent of the total sample lost jobs due to plant closure or downsizing in 1991–1995, almost 8% had to downgrade to a less qualified job, and 12.5% faced substantial salary cuts. Men and women were equally affected by the economic transformation: for men, the incidence of economic shocks was only slightly higher than for women.

Importantly, there is significant overlap between these labor market shocks. This means that a person could experience several shocks during 1991–1995. Out of 11% who lost jobs due to plant closure or downsizing in that period, some people had only this shock, while others experienced some other shocks as well. Moreover, while job losses or salary cuts can be plausibly considered exogenous shocks, changing to a lower-skilled occupation or taking on additional work requires individuals to take some action that could be a reaction to the exogenous shocks, which is why it is important to observe how the shocks are distributed at the individual level. The second part of Table 1 provides statistics on the combinations of shocks. Out of all people who had at least one labor market shock during 1991–1995, approximately 20% experienced only job loss. Approximately 30% had only salary cut. At the same time, approximately one-third of people experienced job losses or salary cuts in combination with either occupational downshifting or additional work. Only in 10% of the cases do we observe occupational downshifting or additional work without corresponding job loss or salary cut. These statistics are consistent with the interpretation of these two shocks as

Table 1 Incidence of the negative labor market events in 1991–1995.

Did not work after 1990 Of those who worked after 1990: Lost job Occupational downshifting Additional work Salary cut Any of the above Number of observations Combinations of shocks (percent of those who experienced any labor market shock) Only job loss Only salary cut Job loss/salary cut with occ.down./add.work Occ.down/add.work without job loss/salary cut

Women

Men

14,9%

5,4%

12,4% 8,4%

12,6% 9,0%

4,3% 13,7% 23,7% 5,254

4,0% 14,4% 24,1% 3,603

21,7% 29,1% 32,2% 10,8%

19,3% 29,9% 32,7% 10,4%

Note: data for the sample surveyed in 2006.

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Fig. 4. Average self-assessed health scores. Note: self-assessed health is measured on a scale from 1 (worst) to 5 (best) Source: RLMS data

individual-level coping strategies after the experience of exogenous shocks. 3.2. Data on health, smoking and alcohol consumption To measure health and health-related behaviors, I use the same data set. The RLMS questionnaire has a section on health in which a number of questions are asked about different aspects of a person’s health and health-related behavior. The main measure of the level of individual health that I use is self-assessed health: respondents were asked to rate their health on a scale from 1 (best) to 5 (worst). This measure is widely used in health studies. Although it is a subjective measure of health, it has been shown to be highly correlated with objective health measures, such as mortality (Idler and Benyamini, 1997). Thus, it has the benefit of universality and comparability to other studies, while a potential drawback is that it is subjective and may be affected by unobserved individual characteristics. The question on self-assessed health was asked in every round of the RLMS. I transform the variable so that the value 1 corresponds to the worst health and the value 5 corresponds to the best health. Fig. 4 shows the dynamics of the average health scores for the Russian population, separately for men and women. On average, women rate their health lower than men do, which is a typical finding in the data for other countries as well (Strauss et al., 1993; Case and Deaton, 2003). The average health scores of men and women improved slightly over the observation period, that is, since 1995. This finding corresponds to the trend in mortality rates, in which mortality started to decline after it reached a peak in 1994. Another health measure that is used in health studies is the EQ5D index. It is based on five standard questions concerning different aspects of individual health: mobility, self-care, usual activity, pain and anxiety8 . Researchers developed scores to

8 The exact questions asked are as follows: Do you have any problems with mobility? Do you have any problems taking care of yourself? To what extent does your health allow you to carry out your routine chores and duties? Do you feel any pain? Do you feel any anxiety or depression? Answers are on a scale from one to three.

transform individual answers for these five questions into a single continuous health measure, namely, the EQ-5D index (Dolan, 1997). A value of 1 corresponds to full health, while 0 corresponds to death. For some combinations of answers, the EQ-5D can have negative values, which are interpreted as conditions worse than death (implying very serious illness)9 . Since the EQ-5D index is continuous, it is easier to use in empirical estimations than the categorical self-assessed health measure. In addition, it is more informative, since it is based on information that is more detailed and differentiates between many more health states than only the five states derived from the self-assessed health measure. In the RLMS data, the EQ-5D can be constructed only for 2005, when the five questions were asked. I also estimate the effect of labor market shocks on healthrelated behaviors such as smoking and alcohol consumption. In the RLMS, a number of questions about smoking and alcohol consumption are asked. In each round, people are asked whether they smoke and how many cigarettes per day they usually smoke. Fig. 5 shows the dynamics of both the incidence of smoking and the average number of cigarettes per day smoked by men and women for 1995-2006. Almost 60% of Russian men smoked during the observation period, while the share of women smoking rose from 10% to 15% between 1995 and 2006. The average number of cigarettes smoked per day among smokers is 1.5 times higher for men than for women. In the estimation of econometric models, the dependent variable for smoking is the dummy variable for whether a person smoked in the 2006 round of the survey. As for alcohol consumption, people were asked about the frequency of drinking alcohol during the month before the interview as well as the types and quantities of alcohol consumed. All this information is combined into a single measure: the amount of alcohol consumed per day, measured in grams of ethanol. Fig. 6 shows the incidence of alcohol consumption as well as the amount of alcohol per day for drinkers, separately for men and women. More than 60% of men and between 40% and 50% of women report

9 In health economics literature, the EQ-5D and analogous indexes are used to obtain weights for the calculation of QALYs – quality-adjusted life years. For example, if the EQ-5D is equal to 0.5, then a year of life in the corresponding state of health is equivalent to half a year in full health.

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Fig. 5. Incidence and average amount of smoking. Source: RLMS data

Fig. 6. Incidence and average amount of alcohol consumption. Source: RLMS data

some alcohol consumption during the month before the survey. Among drinkers, men drink more than three times as much as women do. In the estimation of econometric models, I use the variable of the logarithm of the average daily alcohol consumption during the last month before the interview in 2006. I take the logarithm to reduce the influence of outliers – people with extremely high levels of alcohol consumption. To avoid the exclusion of nondrinkers from the estimation, I assign to them very low levels of alcohol consumption – 0.01 g of pure ethanol per day (the lowest level observed for drinkers is 0.05 g per day)10 . Finally, respondents in the RLMS were asked about their history of chronic illnesses (heart, lung, kidney, liver, gastrointestinal and

10 Alternatively, one can use dummy variable for whether a person was consuming alcohol in the previous month. Although this variable does not distinguish between different degrees of alcohol consumption, the estimation results are qualitatively similar.

Table 2 The rates of the onset of chronic illnesses, strokes and heart attacks in.1996–2006.

Heart attack Stroke Chronic heart problem Chronic kidney problem Chronic gastrointestinal problem Chronic spine problem

Women

Men

1,6% 1,6% 9,3% 4,8% 8,0% 7,4%

1,9% 1,7% 6,5% 2,2% 6,2% 5,8%

Note: data for the sample surveyed in 2006.

spinal). We know the year when a person was diagnosed with each chronic condition. There is also information about whether and when a person has a heart attack or a stroke. Using these data, I can test whether the labor market shocks experienced by a person during 1991–1995 were related to the onset of chronic illnesses or the incidence of a heart attack or a stroke after 1995. Table 2 shows the rates of the onset of chronic illnesses, strokes and heart attacks

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Table 3 Summary statistics for covariates in matching model. Standardized differences

Age Age squared Years of education Urban dummy

Treatment (any shock)

Control (no shock)

Raw

Matched

49,71 2604,05 12,25 0,81

47,46 2429,52 11,95 0,74

0,18 0,13 0,09 0,15

0,04 0,04 0,07 0,03

Table 4 The effect of labor market shocks on health indicators, matching estimates. (1) Women

(2)

EQ-5D 2005 SAH 2006 Job loss

0.072*** (0.028) Occupation downshifting 0.057* (0.034) Additional work 0.036 (0.045) Salary cut 0.002 (0.016) Any labor market shock 0.038** (0.018) N obs. 3199

0.065 (0.040) 0.106* (0.060) 0.053 (0.068) 0.031 (0.039) 0.061* (0.034) 4425

(3) Men

(4)

EQ-5D 2005 SAH 2006 0.027 (0.025) 0.090*** (0.031) 0.099** (0.047) 0.014 (0.015) 0.039*** (0.015) 2394

0.088 (0.062) 0.145*** (0.050) 0.229*** (0.069) 0.062 (0.038) 0.072** (0.033) 3366

Note: Propensity score matching estimates reported. Each cell represents a separate propensity score matching estimate. Matching variables: age, age squared, years of education, urban/rural dummy. Those who did not work after 1990 are excluded from the sample. Standard errors in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01.

from 1996 to 2006 for the sample surveyed in 2006, separately for men and women. Summary statistics for the variables used in the following analysis are provided in Table A1 in the Appendix. 4. Empirical analysis 4.1. Estimation method The choice of estimation method depends on whether individual labor market shocks used in this study can be viewed as exogenous events that are uncorrelated with unobserved characteristics of the individuals. If this assumption is satisfied, one can use either OLS or matching methods: both of these methods rely on the selection-on-observables assumption to obtain unbiased estimates. In my data, job loss shock is most plausibly exogenous: based on the formulation of the question in the survey, job loss is due to plant closures or mass layoffs. Such events are typically considered to be exogenous, that is, not related to individual unobserved characteristics. Thus, the estimated effect of job loss can be interpreted as a causal effect. Significant salary cuts during the early 1990s are also likely to be related to worsening economic conditions and hyperinflation than to individual performance. It is more difficult to argue that other labor market shocks are completely exogenous. The decision to change an occupation or find an additional job, even if forced by external economic shock, may be partly correlated with unobserved individual characteristics. In the main part of the estimation (Section 4.2), I rely on the selection-on-observables assumption, but the results should be interpreted with caution. In Section 4.3, I conduct some robustness checks, including difference-in-difference estimation. To evaluate the health effects of labor market shocks, I estimate average treatment effects using the propensity score matching method. I use a matching estimator rather than the OLS method, as it is more flexible, more robust and less dependent on functional

form assumptions than the OLS estimator is (Imbens, 2015)11 . The treatment group includes individuals who received a labor market shock in 1991–1995. These people are matched to similar individuals who did not receive a shock during that period. Matching is performed using propensity scores. The estimator of propensity score is based on a logistic regression estimated by the maximum likelihood. Covariates included in propensity score estimation are age, age squared, number of years of education and an indicator of urban or rural place of residence. These variables are not affected by the treatment, that is, by the labor market shocks. Table 3 shows summary statistics for covariates by treatment and control group for cases in which the treatment is any type of labor market shock. The means of variables for treatment and control groups are not very different, and standardized differences for the raw and matched samples are close to zero. This means that the overlap condition is satisfied. I also assume that the unconfoundedness assumption required for the matching estimator is satisfied, as labor market shocks were caused by exogenous factors – economy-wide shocks – and not related to the individual characteristics. 4.2. Estimation results Propensity score matching estimates are performed separately for men and women. Table 4 presents the results of matching estimation of the effects of labor market shocks on self-assessed health and the EQ-5D. The results show that job loss had a negative and significant effect on the health of women, as measured by the EQ-5D. The effect on self-assessed health is not significant for both men and women. Occupational downshifting had a negative effect on the health of men and women for both health measures. This effect appears to be stronger for men. Men who had to take on additional work in the early 1990s experienced negative health effects, while the effect for women was not significant. Decreased salary did not have a significant effect on the health of men or women. The last line of estimates in Table 4 shows that women and men who experienced at least one of these labor market shocks had lower health levels; the effects are significant for both health indicators. Note that these are long-term effects: we observe negative health effects in 2005–2006 due to the labor market shocks that occurred in 1991–1995. We know from the discussion in section 3 that approximately half of the people who experienced labor market shocks had more than one shock during 1991–1995. Table 4a shows estimates for the combinations of the shocks. Experiencing only job loss during that period had a strong negative effect on the health of women. At the same time, for those who experienced only salary cuts, the health effects are insignificant. Job losses or salary cuts in combination with one of the reaction shocks – e.g., occupational downshifting or additional work – had a negative effect on the health of both men and women, as measured by the EQ-5D (but not self-assessed health). Finally, experiencing occupational down-

11

Results obtained by estimating OLS model are qualitatively similar.

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Table 4a The effect of labor market shock combinations on health indicators, matching estimates.

Only job loss Only salary cut Job loss/salary cut with occ.down./add.work Occ.down/add.work without job loss/salary cut N obs.

(1) Women

(2)

(3) Men

(4)

EQ-5D 2005

SAH 2006

EQ-5D 2005

SAH 2006

0.063** (0.027) 0.027 (0.023) 0.051** (0.023) 0.012 (0.030) 3199

0.134** (0.055) 0.097 (0.061) 0.022 (0.049) 0.085 (0.078) 4425

0.031 (0.022) 0.001 (0.021) 0.082** (0.036) 0.040 (0.031) 2394

0.014 (0.044) 0.071 (0.057) 0.102 (0.068) 0.186* (0.109) 3366

Note: Propensity score matching estimates reported. Each cell represents a separate propensity score matching estimate. Matching variables: age, age squared, years of education, urban/rural dummy. Those who did not work after 1990 are excluded from the sample. Standard errors in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01.

Table 5 The effect of labor market shocks on health-related behaviors, matching estimates.

Job loss Occupation downshifting Additional work Salary cut Any labor market shock N obs.

(1) Women

(2)

(3) Men

(4)

Smoking

Alcohol consumption

Smoking

Alcohol consumption

0.019 (0.020) 0.033 (0.026) 0.061 (0.058) 0.016 (0.016) 0.048** (0.021) 4437

0.329** (0.150) 0.303 (0.203) 0.264 (0.390) 0.206 (0.175) 0.365*** (0.131) 4367

0.093*** (0.028) 0.032 (0.041) 0.002 (0.072) 0.021 (0.027) 0.065*** (0.022) 3375

0.524** (0.210) 0.096 (0.272) 0.181 (0.403) 0.458** (0.191) 0.349** (0.177) 3345

Note: Propensity score matching estimates reported. Each cell represents a separate propensity score matching estimate. Matching variables: age, age squared, years of education, urban/rural dummy. Alcohol consumption – amount of alcohol consumed per day, in logarithm. Standard errors in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01.

shifting or additional work without job loss or salary cuts have only a weak negative effect on the self-assessed health of men. Table 5 shows the results of the estimation of the matching model for health-related behaviors: smoking and alcohol consumption. Having experienced a job loss during the early transition has a negative effect on the incidence of smoking for men and on the level of alcohol consumption for both men and women. Other

types of shocks mostly do not have separate significant effects on health-related behaviors. However, as the last line of estimates shows, people who faced any of these shocks in the early 1990s have a higher probability of smoking and consume more alcohol in 2006. Again, these are the long-term effects. The stress of adverse labor market events during the early transition leads to increased smoking and alcohol consumption later in life. Table 5a shows the results of the estimation of the same model for combinations of shocks. We observe higher rates of smoking for men who experienced only a job loss or job loss/salary cut in combination with occupational downshifting or additional work. Experiencing only a salary cut increases alcohol consumption among men, while occupational downshifting or additional work in addition to a salary cut seems to have a mitigating effect. Finally, I estimate the effect of the labor market shocks in 1991– 1995 on the onset of some chronic conditions after 1995. The results of matching estimates are presented in Table 6 for women and in Table 7 for men. They show a more nuanced picture of the effects of labor market shocks. For women, the most severe shock is occupational downshifting: it elevates the risk of a heart attack and chronic spinal and gastrointestinal problems. The need to take on additional work during the early years of transition also increased the risk of chronic spinal and gastrointestinal problems. Experiencing any of the four labor market shocks increased the risk of chronic kidney condition for women. For men, job loss increased the risk of chronic kidney condition but actually decreased the risk of stroke or chronic spinal problems. Occupational downshifting increased the risk of chronic kidney condition. The need to take on additional work leads to an

Table 5a The effect of labor market shock combinations on health-related behaviors, matching estimates.

Only job loss Only salary cut Job loss/salary cut with occ.down./add.work Occ.down/add.work without job loss/salary cut N obs.

(1) Women

(2)

(3) Men

(4)

Smoking

Alcohol consumption

Smoking

Alcohol consumption

0.023 (0.031) 0.015 (0.022) 0.001 (0.022) 0.065 (0.040) 4437

0.019 (0.287) 0.178 (0.212) 0.471* (0.244) 0.101 (0.326) 4367

0.124** (0.053) 0.027 (0.029) 0.093*** (0.033) 0.006 (0.092) 3375

0.034 (0.413) 0.616** (0.260) 0.276 (0.298) 0.241 (0.462) 3345

Note: Propensity score matching estimates reported. Each cell represents a separate propensity score matching estimate. Matching variables: age, age squared, years of education, urban/rural dummy. Alcohol consumption – amount of alcohol consumed per day, in logarithm. Standard errors in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01.

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Table 6 The effect of labor market shocks on chronic diseases and heart problems occurring after 1995, matching estimates for women.

Job loss Occupation downshifting Additional work Salary cut Any labor market shock N obs.

(1) Heart attack

(2) Kidney

(3) Gastro

(4) Spine

0.002 (0.007) 0.019** (0.008) 0.005 (0.009) 0.001 (0.005) 0.005 (0.004) 4441

0.005 (0.020) 0.019 (0.027) 0.019 (0.013) 0.026 (0.016) 0.024** (0.011) 4441

0.007 (0.014) 0.043* (0.023) 0.045* (0.027) 0.004 (0.015) 0.026* (0.015) 4441

0.015 (0.020) 0.076*** (0.028) 0.051* (0.031) 0.005 (0.012) 0.017 (0.012) 4441

Table 7 The effect of labor market shocks on chronic diseases and heart problems occurring after 1995, matching estimates for men.

Occupation downshifting Additional work Salary cut Any labor market shock N obs.

(1) Women

(2)

(3) Men

(4)

EQ-5D 2005 SAH 2006 EQ-5D 2005 SAH 2006

Note: Propensity score matching estimates reported. Each cell represents a separate propensity score matching estimate. Matching variables: age, age squared, years of education, urban/rural dummy. Standard errors in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01.

Job loss

Table 8 The effect of labor market shocks on health indicators, controlling for occupation in 1990.

(1) Stroke

(2) Chronic heart

(3) Kidney

(4) Spine

0.019** (0.008) 0.005 (0.024) 0.009 (0.017) 0.010 (0.006) 0.012* (0.006) 3376

0.009 (0.018) 0.006 (0.030) 0.091** (0.044) 0.028** (0.014) 0.014 (0.012) 3376

0.020* (0.011) 0.048* (0.028) 0.140*** (0.049) 0.000 (0.008) 0.012 (0.008) 3376

0.029*** (0.010) 0.007 (0.016) 0.042 (0.035) 0.029* (0.017) 0.010 (0.012) 3376

Note: Propensity score matching estimates reported. Each cell represents a separate propensity score matching estimate. Matching variables: age, age squared, years of education, urban/rural dummy. Standard errors in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01.

elevated risk of chronic heart and kidney problems. A large salary decline that did not show a significant effect on overall health increased the risk of chronic heart and spinal problems for men. 4.3. Robustness checks and supplementary analysis As I discussed in Section 4.1, previous estimation using matching methods rests on the selection-on-observables assumption. Ideally, I would like to include in the model a number of individual characteristics measured before the start of transition, in particular, initial health level. Unfortunately, the survey starts in 1994, and I do not observe health levels before that. Failure to control for pretransition health or other characteristics correlated with health may cause problems. One potential problem with the previous estimation results for health effects is that people who experienced labor market shocks during the transition period in Russia might have previously worked predominantly in occupations that are relatively more harmful to a person’s health (such as low-skilled manual occupations12 ). That is, labor market shocks may be correlated with initial (pretransition) occupations, which in turn are correlated with initial health levels. This would

12 Case and Deaton (2003) and Gueorguieva et al. (2009) show that people employed in manual occupations are in poorer health and that their health is declining more rapidly.

Job loss

0.042* (0.023) Occupation downshifting 0.022 (0.033) 0.002 Additional work (0.041) Salary cut 0.026 (0.030) Any labor market shock 0.043** (0.022) N obs. 1249

0.057 (0.056) 0.169*** (0.064) 0.165*** (0.045) 0.046 (0.052) 0.093** (0.041) 1306

0.033 (0.041) 0.078** (0.039) 0.036 (0.037) 0.042 (0.032) 0.039* (0.022) 994

0.128** (0.064) 0.114 (0.090) 0.105 (0.120) 0.093 (0.076) 0.120** (0.059) 1033

Note: Propensity score matching estimates reported. Each cell represents a separate propensity score matching estimate. Matching variables: age, age squared, years of education, occupational group, urban/rural dummy. The data are from the 2006 round of the survey, but the information on pretransition occupation is taken from the 2000 round of the survey. Those who did not work after 1990 are excluded from the sample. Standard errors in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01.

introduce bias into the estimation of health effects. To remove the bias, I would need to include either initial health levels or occupations at the start of the transition into matching variables. While I do not have data on pretransition health levels, there is fortunately some information on pretransition occupations. Controlling for occupation, I can at least partly account for pretransition health. The RLMS started in 1994, but in 2000, the respondents were asked about their occupation in 1990. Thus, for a smaller subset of my estimation sample in the 2006 round (approximately one-third of observations), there is information about the occupation in which the person worked in 1990. These data show that all occupational groups faced labor market shocks in early transition, but the frequency of job loss and occupational downshifting was somewhat higher for those in low-skilled occupations. Thus, as an initial robustness check, I include occupation before the start of transition into matching variables so that persons within the same occupational group are compared. I include indicators for the four broad occupational groups13 . The results of the estimation of health effects are presented in Table 8. Due to the much lower number of observations, the accuracy of estimates with occupational categories is reduced, but the main results are still significant. Labor market shocks during the early transition had a negative long-term effect on the health of both men and women. Another factor correlated with health is the level of individual income, both absolute and relative to others. In the 2006 round of the survey, respondents were asked both about their current position in the income distribution (described as a 9-step ladder) and about their position in the income distribution before the start of reforms in 1991. Subjective health levels in 2006 were highly correlated with contemporaneous relative income positions. At the same time, the positions of the survey respondents in income distribution in 2006 and 1991 are only weakly correlated. This finding is not surprising given the very different principles of income distribution in the Soviet economy. Thus, in my matching estimation, I include relative income position before the reforms as an additional control to at least partly capture the effect of initial health. The results are reported in Table 9. The main results

13 These are managers, professionals, skilled blue-collar workers, and unskilled blue-collar workers.

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O. Lazareva / Economics and Human Biology xxx (2019) 100823 Table 9 The effect of labor market shocks on health indicators, controlling for relative income position in 1991. (1) Women

(2)

(3) Men

(4)

EQ-5D 2005 SAH 2006 EQ-5D 2005 SAH 2006 Job loss

0.103*** (0.019) Occupation downshifting 0.088*** (0.029) 0.076 Additional work (0.057) Salary cut 0.058** (0.026) Any labor market shock 0.050*** (0.018) N obs. 3057

0.116*** (0.038) 0.080 (0.081) 0.083 (0.059) 0.014 (0.041) 0.073** (0.033) 4237

0.021 (0.019) 0.064** (0.030) 0.069 (0.042) 0.003 (0.019) 0.013 (0.016) 2265

0.058 (0.047) 0.062 (0.052) 0.235*** (0.058) 0.057 (0.045) 0.064** (0.031) 3187

Note: Propensity score matching estimates reported. Each cell represents a separate propensity score matching estimate. Matching variables: age, age squared, years of education, relative income position in 1991 before transition, urban/rural dummy. Those who did not work after 1990 are excluded from the sample. Standard errors in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01.

11

hold – there is a significant negative relation between labor market shocks and health. All previous estimations were performed on a cross-section of data. As an additional robustness check, I conduct difference-indifference estimation for a subset of panel data for the years 1994– 2000. I evaluate the effect of job loss (as a more exogenous labor market shock) observed in 1996–1998. In this period, the rate of job loss due to plant closures or downsizing declined compared to those in 1991–1995 but remained significant (see Fig. 3). The transitional economy of Russia continued to decline after 1995, albeit at a slower rate than in the early 1990s, and it was hit by an additional shock of the 1998 economic crisis. For people who faced job loss due to plant closures in 1996–1998, I observe preshock health levels and other characteristics in 1994–1995. Thus, the treatment group in difference-in-difference estimation includes people surveyed in 2006 who reported their first job loss in 1996–1998 and who were observed in the sample during those years. The control group includes people who did not experience job loss in 1991-1998. Thus, people who lost jobs in 1991–1995 are excluded from the estimation. I estimate the following model: SAHit=α+ β*JLi*Yt+g*Xit+ ut+eit

Table 10 The effect of job loss in 1996–1998 on health: difference-in-difference estimation.

Job loss in 96-98*Year of job loss Age Age squared Male Years of education Urban Constant N

(1) SAH

(2) SAH, men

(3) SAH, women

0.068* (0.037) 0.027*** (0.004) 0.000*** (0.000) 0.253*** (0.018) 0.005* (0.003) 0.046** (0.021) 2.910*** (0.106) 10,444

0.019 (0.059) 0.026*** (0.006) 0.000* (0.000)

0.112** (0.047) 0.027*** (0.006) 0.000** (0.000)

0.001 (0.005) 0.031 (0.032) 3.247*** (0.152) 4467

0.010** (0.004) 0.062** (0.028) 2.850*** (0.147) 5977

Robust standard errors in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01. Estimation period: 1994–2000. The model includes year fixed effects, sector and occupation fixed effects.

SAHit – self-assessed health of an individual i in year t JLi – indicator for job loss in 1996–1998 Yt – indicator for the period after job loss Xi – individual control variables (age, gender, education, sector of employment, occupation, urban/rural); ut - year fixed effects The model includes the same control variables as in the matching estimation, and in addition, I control for individuals’ occupations and sectors of employment. An additional set of controls should provide a more accurate estimation of the treatment effect. The results are reported in Table 10, showing a significant negative effect of job loss on self-assessed health. Consistent with matching estimation, the effect is mostly significant for women. Note that this is a short-term effect compared to the matching estimation for 2006. Finally, using a difference-in-difference approach, I can test some factors that potentially mediate the effect of labor market shocks on health. As I discussed in Section 2, the negative health effect may be related to declining income levels as well as to psychological problems following the loss of one’s social position after job loss. In RLMS, every round contains questions on the relative position of the individual in the income distribution and a

Table 11 The effect of job loss in 1996–1998 on subjective relative income and power position: difference-in-difference estimation.

Job loss in 96-97*Year of job loss Age Age squared Male Years of education Urban Constant N

(1) Relative income position

(2) Relative power position

0.153* (0.080) 0.050*** (0.011) 0.000*** (0.000) 0.184*** (0.045) 0.026*** (0.008) 0.096* (0.050) 4.745*** (0.260) 10,359

0.225** (0.093) 0.042*** (0.012) 0.000** (0.000) 0.311*** (0.050) 0.040*** (0.008) 0.186*** (0.054) 3.811*** (0.281) 10,264

Robust standard errors in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01. Estimation period: 1994–2000. The model includes year fixed effects, sector and occupation fixed effects.

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question on the relative position of the individual in the power ladder in society (for both questions the scale is from 1 (lowest step) to 9 (highest step)). I test the effect of job loss on these two subjective evaluations using a difference-in-difference estimator with the same set of controls. The results are reported in Table 11. They show that job loss significantly affects a person’s perception of her position in society with respect to income and power, which may in turn have a negative effect on her health. Overall, my estimation shows that negative labor market events that resulted from the drastic economic decline in the early years of the transition in Russia are significantly related to the risks of certain health conditions, rates of smoking and alcohol consumption and had long-term negative health effects on the affected population. Moreover, negative health effects are likely to be underestimated because in the data, I do not observe people who did not survive until 2006.

of labor market shocks. Second, the study shows that in addition to job loss, other labor market changes, such as forced change of occupation resulting in a loss of human capital, have a negative effect on health. Third, this study contributes to the discussion of the Russian mortality crisis during the 1990s. While I do not study mortality directly, the negative effects of labor market shocks on overall health, as well as the increased probability of smoking, alcohol consumption and certain chronic health conditions, show that labor market shocks may be one of the causes of the increased mortality rates during the transition. The implications of this study are not limited to transition economies. These findings show that it is important to take into account potential effects on employees’ health when evaluating the consequences of the major labor market transformations caused by external shocks related to international trade or technological innovations.

5. Conclusion

Appendix A

Understanding the health effects of labor market shocks is important, as it helps to evaluate the social costs of economic crises and downturns. Transition economies in the early 1990s went through unprecedented economic transformation, and Russia experienced one of the worst economic declines. In just a few years, the GDP fell by 40%, and in some industries, production collapsed by 90%. There was massive reallocation of labor from the industrial to services sectors. During this period, a significant share of the working-age population experienced labor market shocks. Importantly, these shocks were not limited to job loss. A significant number of people stayed in their jobs, but their real wages declined dramatically. Many of them had to take additional jobs to sustain their incomes. Others had to switch to new jobs in lower-skilled occupations. Thus, they experienced a depreciation of the human capital that they had accumulated during their previous career. The purpose of this paper is to estimate the effects of such labor market shocks on the level of health, health conditions and healthrelated behaviors of the Russian population. I use individual-level data from the RLMS, which contains retrospective data on respondents’ labor market histories starting in 1991. Using a matching estimation method, I test the effects of several types of labor market shocks – job loss due to plant closure or downsizing, occupational downshifting or having to take on an additional job, and salary cuts. These events were caused by the severe recession and rapid economic transformation during the early years of the transition. Approximately one-quarter of the sample reports that at least one of these events occurred in 1991–1995. The estimation results show that labor market shocks during the early transition years had long-term negative effects on overall individual health; these effects were observed 15 years after the start of the transition. Several robustness checks, including difference-in-difference estimation, confirm this result. Occupational downshifting and taking on additional work are associated with a higher probability of chronic gastrointestinal and spinal conditions for women and chronic kidney conditions for men. At the same time, job loss reduced the risk of stroke and chronic spinal problems for men. Finally, labor market shocks, and in particular, job loss, significantly increased the level of smoking and alcohol consumption both for men and women. This may be one of the channels of the negative effects of labor market shocks on health. Another channel supported by the results of the supplementary analysis is the loss of one’s relative position in society in terms of income and power and the stress induced by this loss. This study contributes to the literature in several ways. First, it provides individual-level evidence on the long-term health effects

Table A1 Summary statistics for the variables for the estimation sample in 2006. Variable

Mean

SD

EQ-5D* Self-assessed health Incidence of smoking Log alcohol consumption Age Age squared Male Years of education Urban

0.73 2.06 0.36 .88 49.16 2592.18 0.43 12.18 0.77

0.25 0.65 0.48 3.19 13.23 1385.07 0.49 3.20 0.41

Min 0.429 0 0 4.60 29 841 0 0 0

Max

N

1 4 1 6.23 100 10000 1 26 1

5640 7846 7868 7768 7873 7873 7873 7817 7873

* Statistics for the EQ-5D are reported for the 2005 survey sample, as this is the year when the questions used to construct this measure were asked.

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Please cite this article in press as: O. Lazareva, The effect of labor market shocks on health: The case of the Russian transition, Econ. Hum. Biol. (2019), https://doi.org/10.1016/j.ehb.2019.100823