Health and labour force participation: the case-study of russian working age population

Effect of health on labour force participation. The relationship between health and labour force participation of the Russian population. Positive influence health lag proved on labour force participation. Effect of labour force participation on health.

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FEDERAL STATE AUTHONOMOUS

EDUCATIONAL INSTITUTION OF HIGHER EDUCATION

NATIONAL RESEARCH UNIVERSITY

“HIGHER SCHOOL OF ECONOMICS”

Saint-Petersburg School of Economics and Management

Department of Economics

HEALTH AND LABOUR FORCE PARTICIPATION: THE CASE-STUDY OF RUSSIAN WORKING AGE POPULATION

BACHELOR'S THESIS

38.03.01 “Economics”

The student of the group No. BEC144

The programme “Economics”

Solonin Maxim Aleksandrovich

Saint-Petersburg 2018

ABSTRACT

The effect of health on labour force participation is undisputed. This research is assuming the existence of endogeneity of health, which results from the reverse effect of labour force participation on health, justification bias and unobserved factors.

The relationship between health and labour force participation of the Russian population is analysed using simultaneous equation modelling. Data from Russia Longitudinal Monitoring Survey - Higher School of Economics (RLMS-HSE) is applied for 2004 - 2016.

Models are estimated separately for males and females. A significant positive effect of health on labour force participation is confirmed. Health lag proved to be significant and has a positive influence on labour force participation. The reverse effect of labour force participation on health is also found. It demonstrates positive influence for males and negative for females.

health participation labour relationship

INTRODUCTION

Health is considered as a crucial factor for labour supply decision. Apart from being a form of human capital (Grossman, 1972), health also may influence personal preferences between work and leisure. For instance, decline in health may result in increased value of time spent on leisure. Reduction in labour market activity caused by health issues imposes costs on the economy.

Although extensive literature exists based on data from developed countries showing strong relationship between health and labour force participation, only a few recent papers have focused on Russia. Therefore, it is not fully clear, whether Russian case demonstrates similar results. Answering this question will help to understand the costs of health limitations to the economy, estimate the possible effects of governmental reforms and opportunities to stimulate labour force participation through different mechanisms.

The Russian case is particularly of interest, because despite the worldwide trend of increased life expectancy and health conditions in developed countries, Russian population does not demonstrate such improvements, while the government still tends to increase the retirement age in order to stimulate labour activity among older people. However, the effect of such increase is quite ambiguous, because health status might prevent elderly population from continuing labour participation.

The key objective of the present research is to examine the relationship between labour force status and individual health status of Russian working age population using panel data simultaneous equation modelling. To avoid confusion, by working age population we mean individuals aged from 23 to 60. It is expected that most people will get their full-time job after finishing colleges and universities. The upper boundary is the male official retirement age.

Examining the key relationship, the following hypotheses are tested:

1. Health is a significant factor in the labour force participation decision-making.

2. The reverse effect of labour force participation on health can be observed.

A simultaneous equations approach is chosen to model labour force participation and health in order to account for possible endogeneity (Bakhtin, 2018; Cai, 2010; Stern, 1989). Data from Russian Longitudinal Monitoring Survey for years 2004 - 2016 is used, because it allows to better control for heterogeneity. Thus, the relationship between health and labour force participation is being examined thoroughly.

The main hypotheses have been confirmed. Health positively affects labour force participation. The reverse effect of labour participation on health is also observed, but it has opposite signs for males and females. Health lag also confirmed as a significant factor, showing that individuals make decisions about labour participation taking into account their health history.

The remainder of the paper proceeds as follows. Section 2 describes the theoretical framework. Section 3 presents literature review with studies on the similar topic. Sections 4&5 demonstrate data gathered and methodology used in this paper, respectively. Finally, sections 6&7 provide information about the results of the research and discuss and compare them with international researches.

Theoretical Framework

The health care system plays a significant role in the economy of any country. The quality of the health care system has a direct (prevention and treatment) and indirect (through the enhancement of human capital and labour market function) influence on economic development, and it is almost unanimously agreed that contributing to the enhancement of public health, through the health care system, is one of the key goals of any government (Solonin & Gerry, 2015).

Considering health impacts on labour market participation a lot of research has been carried out recently. Before analysing the main papers in this field, it would be useful to review the conceptual, pioneer paper, which is the standard framework in economics for explaining health choices over the life-course Grossman model (Grossman, 1972). Although it emphasises different points, the logic of this model, regarding health as an investment good and part of human capital, extends naturally into the domain of the retirement decision. According to this approach, an individual's health yields both direct utility, stemming from the pleasure of being healthy, and indirect utility, from the improved quality of work and leisure time that good health facilitates.

In terms of retirement, therefore, Grossman model predicts a negative relationship between health and the decision to quit the labour market. First, better health increases the number of healthy days that a person can devote to his activities and remain in the labour force; and second, the better health leads to higher productivity, which means that an individual may receive higher income, which is an extra incentive for prolonging labour participation. Considering the effect of retirement on health, this model's suggestions are rather ambiguous. On the one hand, unemployed people have more time to devote to health production, so they might be more able to increase their health level; but on the other hand, they usually have lower incomes and, consequently, face constraints in their health investments, while also struggling with increasing depreciation rate.

This paper (Grossman, 1972) was mentioned in order to illustrate the standpoint from which health and its relationship with labour force are being considered. Following the logic of this conceptual approach, we carefully examine the health-labour participation relationship using an econometric specification which allows us to take into account the endogeneity issues.

LITERATURE REVIEW

International experience

A lot of research considering the relationship between health and labour force participation decisions has been carried out recently. Thorough investigation of the problem has continued for several decades, but Russian research field still has blank areas, which require contribution. Justifying our interest in health as a multidimensional factor influencing labour participation decision, health was declared the most powerful factor in several papers (Dwyer & Mitchell, 1999; Jones, Rice, & Roberts, 2010).

Bound, Schoenbaum, Stinebrickner, and Waidmann (1999) explore the relationship in dynamics, including individual health level for several periods and estimating the influence of prior health shocks. The results show that the earlier shocks occur, the less likely it is to affect decision about labour participation. Considering lagged health level, it was found significant in individual labour market behaviour. Retirement decisions can also be analysed in a household context in order to control for possible spouses influence. For instance, when one family member has a high salary, another one may decide to quit the labour market in order to devote more time to family. Coile (2004) found that when women experience serious health problems, their husbands tended to increase their labour supply.

Considering endogeneity, which should be taken into consideration in order to get accurate results, there is no actual agreement about its existence. Although many researches claim that there is no direct effect of labour force participation on health (Sickles & Taubman, 1986; Stern, 1989), more recently, Cai (2010) proved a significant effect of labour force status on health, which demonstrates opposite results for males and females. Cai has used simultaneous equation modelling, which is also applied in the present research.

There are two key sources of endogeneity that should be mentioned. The first one is true endogeneity, meaning that there are unobserved factors that affect both health and labour participation (Bound et al., 1999; Cai & Kalb, 2007; Kalwij & Vermeulen, 2008). Assuming that such factors exist, analysing two equations independently leads to an underestimation of the health influence on labour market behaviour (Cai & Kalb, 2007). At the same time, endogeneity might arise from reporting bias, when for social, psychological or economic reasons unemployed people are prone to overstate their health problems as a way of explaining their non-participation. There is evidence both in favour of (Au, Crossley, & Schellhorn, 2005; Jones et al., 2010) and against (Dwyer & Mitchell, 1999) the presence of justification bias.

Russian experience

It should be noted that the Russian research experience in this field is quite scarce, so it is hard to realise, whether the situation is similar to that in developed countries. For example, it can be expected that health is more important in less developed countries (Currie & Madrian, 1999).

Substantial influence of health on retirement is mainly found for the Russian elderly (Lyashok & Roshchin, 2015; Goryakin & Suhrcke, 2017). The influence of health problems accumulates over time and the longer the individual has problems, the higher the probability of quitting the labour market (Lyashok & Roshchin, 2015). This result has already been found for the US (Bound et al., 1999).

Goryakin and Suhrcke (2017) have found a substantial effect of illnesses on labour participation, however, the reverse feedback of labour decisions on health has not been taken into account, that is why we are going to implement simultaneous equation modelling, which will be described in the next section.

The most recent article regarding health - labour participation relationship among the Russian elderly confirmed a strong effect of health on labour force participation. (Bakhtin & Aleksandrova, 2018) The evidence about reverse effect of labour force participation on health is rather mixed. It is strongly significant for females in most specifications, but is insignificant in all the other ones.

Considering the Russian context, despite the recent advancements research experience in this field is still not as rich as in developed countries. Comparing international and Russian research, it can be seen that there is still a lot to be done in order to get the accurate picture of the state of art in the field.

Methodology

As long as endogeneity of health is expected, it is crucial to choose an appropriate method for modelling. Potential problems, which were mentioned in previous part of the paper, are the reverse effect of labour force participation on health, reporting bias and unobserved factors, which influence the dependent variables. In order to receive the results, which could be trusted, it is essential to pay attention to these features.

This research estimates a system of simultaneous equations and follows the model outlined in (Cai, 2010; Bakhtin & Aleksandrova, 2018). Although it is rather difficult to implement, it relies on weaker assumptions and allows for the effects between health and labour participation to run in both directions. The conceptual model consists of three equations.

The true health equation is specified as:

, (1)

where, is the latent true health at time t, is the latent value of being in the labour force and is a set of exogenous variables.

The second equation describes labour force participation:

, (2)

where is a set of exogenous variables, which differs from the set in equation (1). Variables exclusion restrictions will be discussed in the data section.

As long as true health is not observed reported health equation is introduced:

, (3)

where is the reported health measure.

The dependence of reported health measure on labour force participation expresses the justification bias mentioned earlier.

Substituting equation (1) into (3) and (3) into (2) results in the following system:

,

where , , .

Labour force participation is defined as a dummy variable, which equals 1 when individual is in the labour force and 0 - otherwise. Self - assessed health variable is an ordinal health measure and has the scale from 1 to 5 (from very poor to excellent). Therefore, the system is a simultaneous ordered - probit - probit system.

This system is estimated using a full information maximum likelihood approach (FIML). It allows for correlation between error terms in the equations and gives estimates that are both consistent and efficient (Cai, 2010).

DATA DESCRIPTION AND MODEL SPECIFICATION

Data

For the purpose of analysing the relationship between health and labour force participation decisions, the dataset including health characteristics and different demographical, labour and socio-economic parameters is required. This paper uses Russian Longitudinal Monitoring Survey conducted by Higher School of Economics for the years 2004-2016 (RLMS-HSE, 2018). This survey is a series of nationally representative surveys designed to monitor the effects of Russian reforms on the health and economic welfare of households and individuals in the Russian Federation.

This database provides huge amount of information about every aspect of individual's life, including health, employment and numerous demographic and socio-economic characteristics, which are of our interest. Despite being a powerful tool for the analysis of the targeted relationship, several limitations may be faced. First of all, the data itself is interviewing the same people each year, so it is quite hard to get the needed individuals in the sample with all the stated characteristics in the equation. What is more, our choice of dependent variables measuring health is very limited. As long as optimal subjective measure of health is still an open question, it is assumed that RLMS data is consistent and will not distort the results.

Descriptive statistics

Before presenting model specifications and discussing the results, it is important to provide information about the data, by the means of descriptive statistics.

The main variables are presented in Table 1. Men represent 47.6 % of the sample. Slightly above 70% of the population are in the labour force and the average health level is close to moderate. The age of individuals in the sample ranges from 23 to 60 with mean value around 40. Individuals below 23 are excluded from the sample, because they would not complete their studies. Considering education level, on average individuals confirm having full secondary or higher education. As to health characteristics, more than 75% men in the sample smoke or ever smoked, while only 30% of women have the same habit. At the same time, women report existence of health problems in the last 30 days and chronical diseases more frequently. Variable, reflecting alcohol consumption, equals 1 if an individual drinks alcohol more than once a week. Only 13% of the sample have a level of alcohol consumption like that.

Figure 1: Descriptive statistics

Mean Value

N

Female

Male

All

Dependent variables

Labour force status

95934

0.684

0.796

0.737

Self-assessed health

95934

3.258

3.404

3.327

Variables appearing in both equations

Age

95934

40.751

39.714

40.257

Education (EDUC)

95934

17.39

16.299

16.87

Variables appearing in health equation

Smoking

95934

0.322

0.789

0.544

Drinker

95934

0.057

0.23

0.139

Health Problem (AnyhlthPrb_lm)

95749

0.356

0.238

0.3

Chronic (chron)

95870

0.521

0.414

0.47

Variables appearing in labour equation

Children

95934

1.446

1.282

1.368

Tables 2 & 3 tabulate labour force status against self-reported health status by gender. It is clear that there is a strong relationship between health measures and labour status. Those individuals, who are in the labour force, are prone to report higher health levels, while those out of the labour force more often report lower levels of health. Regarding gender differences, men are more likely to change their labour status when their health level changes. Thus, the positive relationship between health status and labour force participation exists for both men and women. That means, the higher the health level, the more likely an individual is in the labour force.

Figure 2: Labour status by health level. Females

Currently employed\SAH

very bad

bad

average

good

very good

Total

% not in labour force

74.32

53.05

29.4

29.72

38.06

31.62

% in labour force

25.68

46.95

70.6

70.28

61.94

68.38

Figure 3: Labour status by health level. Males

Currently employed\SAH

very bad

bad

average

good

very good

Total

not in labour force

82.17

57.82

20.01

15.63

18.58

20.41

in labour force

17.83

42.18

79.99

84.37

81.42

79.59

Model specification

The system investigated consists of two equations: labour force participation equation and self-assessed health equation. The sample is divided by gender and the models are estimated separately for males and females.

The dependent variable in labour equation is a dummy, which equals 1 when individual is in the labour force and 0 - otherwise. The health equation dependent variable is an ordinal health measure ranging from a “very bad” to a “very good” status.

The self-assessed health variable was modified three times in order to conduct sensitivity analysis. It was decided to delete the moderate health level and turn the variable into a dummy, because it is likely that individuals may choose moderate health option, when they just do not want to answer. Another two modifications were created in order to assign the moderate health level either to a “poor status” group or a “good status” group, because the moderate health status is rather ambiguous.

Several demographic and socio-economic variables are included both in health and labour equations. They are age, education, marital status and regional and year dummies. These variables are quite common at least in labour market, but still their inclusion should be interpreted. Age is a significant health factor, because it is assumed that health deteriorates with age. (Grossman, 1972) Education also plays an important role in health equation, as long as educated people can influence health through a larger amount of health-related knowledge (Cai, 2010). Education often demonstrates a substantial effect on health production (Grossman, 1999). Several studies also proved a close relationship between health and marital status. (Wilson and Oswald, 2005)

Finally, year and regional dummies are used in order to control for time outliers and trends and regional peculiarities, which are expected due to Russian unique features.

Regarding the variables, which are present only in health equation, they reflect bad habits (smoking, drinking), health problems in the last 30 days and chronic diseases. These variables are not expected to influence individual labour market behaviour directly rather than indirectly through health status.

A number of children variable is excluded from health equation. It is expected that a child creates obstacles for labour force participation, especially for women, because of childcare responsibility, however this variable should not influence health of an individual substantially.

Summing up, having analysed the relationship between the health level and the labour market status, and having described the data and model specifications we are approaching presentation of the results.

Results

In the present research we have made an attempt to shed light on the relationship between an individual's health status and his (her) labour force status. The procedures mentioned above are aimed at testing the following hypotheses:

1. Health is a significant factor in the labour participation decision.

2. Labour force participation affects health.

Both main hypotheses are confirmed. Appendix 2 contains the tables with all models and coefficients estimated. A positive and strongly significant effect of health on labour force participation is found for both males and females. It means that better health increases the probability of being in the labour force. Considering the reverse effect of labour force participation on health, it is also found significant in most of the models.

Figure 4: Health - labour force participation (LFP) relationship

OrPr23-60

OrPr23-40

OrPr40-60

OrPr23-60lag

Male

Female

Male

Female

Male

Female

Male

Female

LFP on health

0.751***

-0.251**

0.531***

-0.12**

-0.903**

-0.35***

0.501*

-0.178**

(0.116)

(0.045)

(0.139)

(0.029)

(0.015)

(0.104)

(0.216)

(0.062)

Health on LFP

0.296***

0.138***

0.169**

0.05***

1.319***

0.208***

0.243***

0.146***

(0.044)

(0.011)

(0.024)

(0.016)

(0.195)

(0.023)

(0.027)

(0.01)

Table 4 demonstrates the results of ordered-probit - probit models with an age threshold at 40. Such a threshold is chosen in order to check the consistency of results for different age groups. Part of the Russian population starts to receive preferential pensions around 40 (e.g. after military service), and also after this threshold people are more vulnerable to different diseases. What is more, the average employment age in Russia is also around 40.

Reverse effect of labour force participation on health is opposite for males and females in the whole sample and under 40: men report lower health level, when they are not in the labour force, while women state higher health, when not employed. The reason for such a result could be that men are under social pressure and try to justify their non-participation by poor health. At the same time, women tend to evaluate their health worse than men at the same age group. As to the elderly (40+), the reverse effect of labour participation on health for males is negative.

A possible explanation is that for older people, working conditions may be harder and more dangerous than for younger age groups. The last column in Table 4 presents the system, which regards health in dynamics. Health lag is found significant and has a positive influence on labour force participation.

Self-assessed health measure is quite specific and can be perceived differently by individuals. The point of our interest is moderate health level, which equals 3. It could be regarded as close to bad or good condition or even as absence of an answer. In order to understand whether any problems arise from this feature, it is decided to conduct sensitivity analysis and treat moderate health as close to bad, good and exclude it from the model.

Appendix 1 contains tables 5, 6 and 7, which present the health - labour effects calculated from probit - probit models. The first model assigns 0 to very bad and bad health levels and 1 to moderate, good and very good health levels. The second probit - probit system defines the health level as 0, when self - assessed health is very bad, bad or moderate and 1 when health is good or very good. The results are consistent with ordered-probit - probit models discussed above.

The last model excludes the moderate health level from the sample and distinguishes the health level as either bad or good. This model differs from the basic one in the elderly group.

The male reverse effect of labour force participation on health is positive.

The moderate health level excluded helped to precisely distinguish people with good and bad health. Therefore, employed men have a better access to medical care, and consequently a higher health status, while men out of the labour force may experience a health drop due to continuous absence of labour activity.

Regarding independent factors, most of them are significant and with expected signs. Bad habits (smoking, drinking), chronic diseases and recent health problems have negative influence on health in most model specifications. Age influence is also negative both in health and labour equation. Education influences positively both health and labour participation.

The variable relating to the number of children has negative influence for female and positive for male, because women usually take care of children whereas men support family by earning money.

Discussion

Main hypotheses have been confirmed. Health is a very important factor in terms of labour force participation decisions. Russian context explains the importance of health as a labour participation decision-making factor. In Russia unemployment benefits are relatively low compared to those in developed countries, that is why to live on them is hardly possible. The same case is with pensions, so the Russian elderly continue their employment after becoming eligible for pensions. (Bakhtin & Aleksandrova, 2018)

In one of the models health was considered in dynamics with a lagged health status. This variable is significant and has a positive influence on labour participation. Therefore, health history also plays a role in labour market decisions. This result is in line with Bound (1999) who proved that the lagged health level is significant in the decision to continue employment.

Health as an important factor is commonly acknowledged. However, the evidence of reverse effect of labour force participation on health is rather ambiguous. Our research has found that reverse effect is consistently present in most of the model specifications. It partly coincides with the results in Cai (2010) and Bakhtin and Aleksandrova (2018), however, the results contradict other papers published earlier (Stern, 1989; Sickles & Taubman, 1986; Leung & Wong, 2002). Despite the significance, the existence of reverse effect still cannot be claimed. It might be possible that men justify their non-participation by poor health and this reporting bias may outweigh the opposite effect of labour participation. What is more, Cai (2010) mentions a surprising result that the females model demonstrates justification bias, which is explained through women' specific self-selection in the labour force. At the same time, our paper presents the opposite results showing that justification bias exists only among males, which is in line with common views.

Considering independent factors, our analysis has shown that marital status also influences labour status. Its influence is the same in most of the models and consistent with other papers' results (Bakhtin & Aleksandrova, 2018; Cai, 2010). If an individual has a spouse (married), it increases the probability of being in the labour force for males and decreases for females. As to the age, for the younger age group (23 -40) the probability of employment for males decreases with age increasing, while females are more likely to get a job with age. A reason for that could be that employers are more interested in younger employees, while females start to enter the labour market after their children have grown up. Considering the elderly, age negative influence is quite common, because individuals approach retirement age and their skill level and health depreciate. Although most factors demonstrated expected behaviour, several model specifications suggested that education has a negative influence on health for males and positive for females. As a possible explanation, it can be proposed that while getting higher level of education both males and females become more aware of their health level and potential or existing problems. Therefore, their health level is expected to be evaluated more precisely (usually lower). However, with education individuals also learn to take care of their health, how to sustain and appreciate it. As long as it is expected that females monitor their health better and tend to ignore health problems less than males, it can be claimed that gender differences in education influence are quite reasonable. As was mentioned above, most factors' significance and direction of influence are consistent with other studies. Nevertheless, it should be mentioned that the fact that the family has to bring up children was found to negatively influence males' labour participation (Cai, 2010), while in our research the influence is positive. It is consistent with our logic that men should earn money in order to keep the family with a child.

CONCLUSION

This research has focused on the relationship between health and labour force participation of the Russian population. We used RLMS-HSE database for 2004-2016 to estimate a system of simultaneous equations that describes health and labour force participation. Estimation was conducted with a full-information maximum likelihood approach. The results demonstrate that health is crucial when decisions about labour participation are made: people with a higher subjective health level stay in the labour force with higher probability. The reverse effect of labour participation on health is also found significant, however justification bias still puts some doubt on its significance.

Estimations conducted in this paper provided more insights into the relationship between health and labour market behaviour. What is more, taking the advantage of panel data it was possible to control for heterogeneity.

Summing up, the results received can assist decision-makers in policy-making. As long as health is a strong factor influencing labour market behaviour, it is reasonable to adopt socio-economic policy measures, which could improve public health and consequently, increase labour force participation.

REFERENCES

1. Lyashok, V., & Roshchin, S. (2015). Effect of health on labor supply of elderly. Applied Econometrics, 40 (4), 6-27. (in Russian)

2. Au, D. W. H., Crossley, T. F., & Schellhorn, M. (2005). The effect of health changes and long-term health on the work activity of older Canadians. Health Economics, 14 (10), 999-1018.

3. Bakhtin, M., & Aleksandrova, E. (2018). Health and labor force participation of elderly Russians. Applied Econometrics, 49, 5-29.

4. Bound, J., Schoenbaum, M., Stinebrickner, T. R., & Waidmann, T. (1999). The dynamic effects of health on the labor force transitions of older workers. Labour Economics, 6 (2), 179-202.

5. Cai, L. (2010). The relationship between health and labour force participation: Evidence from a panel data simultaneous equation model. Labour Economics, 17 (1), 77-90.

6. Cai, L., & Kalb, G. (2007). Health status and labour force status of older working-age Australian men. Australian Journal of Labour Economics, 10 (4), 227.

7. Coile, C. C. (2004). Health shocks and couples' labor supply decisions (Tech. Rep.). National Bureau of Economic Research.

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19. Russian Longitudinal Monitoring Survey - Higher School of Economics. (2015). Retrieved January 9, 2018, from https://www.hse.ru/rlms/

Appendix 1: Sensitivity analysis

Figure 5: SAH(1,2) = 0, SAH(3,4,5) = 1

PrPr23-60

PrPr23-40

Male

Female

Male

Female

LFP on health

0.745**

-0.264**

0.477**

-0.11**

(0.118)

(0.055)

(0.154)

(0.032)

Health on LFP

0.33***

0.139***

0.174***

0.05**

(0.055)

(0.011)

(0.024)

(0.016)

Figure 6: SAH(1,2,3) = 0, SAH(4,5) = 1

PrPr23-60

PrPr23-40

PrPr40-60

Male

Female

Male

Female

Male

Female

LFP on health

0.958***

-0.208**

1.039***

-0.241***

-0.083

-0.179

(0.101)

(0.072)

(0.018)

(0.059)

(0.546)

(0.157)

Health on LFP

0.276***

0.124***

0.274**

0.045**

0.295**

0.175***

(0.069)

(0.011)

(0.084)

(0.015)

(0.096)

(0.021)

Figure 7: SAH(1,2) = 0, SAH(4,5) = 1

PrPr23-60

PrPr23-40

PrPr40-60

Male

Female

Male

Female

Male

Female

LFP on health

0.974***

-0.411***

1.054***

-0.328***

0.784**

-0.751***

(0.101)

(0.079)

(0.019)

(0.067)

(0.257)

(0.164)

Health on LFP

0.307***

0.2***

0.396*

0.085***

0.304***

0.5

(0.075)

(0.019)

(0.181)

(0.016)

(0.046)

(0.322)

Appendix 2: Coefficients estimates

Figure 8: Ordered - probit systems

...

OrPr23-60

OrPr23-40

OrPr40-60

OrPr23-60

Male

Female

Male

Female

Male

Female

Male

Female

Health equation

lSAH

0.723***

0.974***

(5.14)

(55.42)

Single

0

0

0

0

0

0

0

0

(.)

(.)

(.)

(.)

(.)

(.)

(.)

(.)

Married

-0.479***

-0.0526*

-0.228

-0.0461

1.115***

-0.0744

-0.274

-0.0464

(-3.90)

(-2.46)

(-1.95)

(-1.77)

-21.87

(-1.90)

(-1.40)

(-1.62)

Divorced

-0.167***

0.0456

-0.0717

0.0316

0.510***

0.0138

-0.100

0.00583

(-3.54)

-1.73

(-1.48)

-0.95

-8.28

-0.28

(-1.42)

(0.16)

Widow

-0.276*

-0.112***

-0.172

-0.000467

0.839***

-0.0933*

-0.259

-0.0711

(-2.52)

(-3.75)

(-0.82)

(-0.01)

-9.73

(-2.11)

(-1.28)

(-1.84)

AGE

-0.00539

-0.0301***

-0.0189***

-0.0242***

-0.0472***

-0.0525***

-0.00821

-0.0215***

(-0.82)

(-42.32)

(-4.24)

(-11.72)

(-27.16)

(-9.59)

(-1.07)

(-26.28)

EDUC

-0.0297**

0.0450***

-0.0211

0.0321***

0.0436***

0.0583***

-0.0178

0.0352***

(-3.23)

-14.49

(-1.84)

-11.8

-15.81

-7.97

(-1.22)

(7.96)

smoking

-0.0716***

-0.0811***

-0.0640***

-0.117***

-0.0857***

-0.0466*

-0.0496**

-0.0394*

(-3.90)

(-6.60)

(-3.63)

(-7.05)

(-7.55)

(-2.51)

(-2.79)

(-2.40)

drinker

-0.0734***

-0.0471

-0.0890***

-0.0689*

-0.0658***

-0.0201

-0.0839***

-0.0548

(-3.96)

(-1.95)

(-4.74)

(-2.13)

(-6.00)

(-0.57)

(-4.09)

(-1.73)

AnyHltPrb_lm

-0.411***

-0.601***

-0.498***

-0.559***

-0.361***

-0.623***

-0.472***

-0.515***

(-4.45)

(-42.02)

(-8.74)

(-30.74)

(-14.23)

(-19.62)

(-5.88)

(-29.05)

chron

-0.580***

-0.865***

-0.785***

-0.867***

-0.459***

-0.844***

-0.634***

-0.703***

(-4.46)

(-55.48)

(-8.97)

(-50.32)

(-15.27)

(-21.06)

(-5.94)

(-38.69)

Labour equation

lSAH

0.0616*

0.0550*

(2.25)

(2.13)

Single

0

0

0

0

0

0

0

0

(.)

(.)

(.)

(.)

(.)

(.)

(.)

(.)

Married

0.771***

-0.0631**

0.654***

-0.129***

1.125***

-0.00627

0.752***

-0.0146

-31.44

(-2.83)

-22.69

(-4.70)

-19.36

(-0.14)

(23.86)

(-0.50)

Divorced

0.229***

0.312***

0.111*

0.303***

0.534***

0.278***

0.217***

0.346***

-6.63

-11.28

-2.25

-7.41

-8.03

-5.53

(4.94)

(9.62)

Widow

0.475***

-0.0524

0.393

0.0463

0.836***

0.136**

0.697***

0.0453

-6.43

(-1.64)

-1.83

-0.53

-8.87

-2.66

(7.24)

(1.10)

AGE

-0.0198***

0.00474***

-0.0132***

0.0641***

-0.0352***

-0.0623***

-0.0215***

0.000962

(-20.77)

-5.76

(-5.55)

-30.43

(-16.41)

(-32.80)

(-18.60)

(0.93)

EDUC

0.0556***

0.0607***

0.0698***

0.0453***

0.0374***

0.0767***

0.0575***

0.0623***

-29.7

-35.26

-27.8

-18.95

-12.98

-28.32

(24.65)

(28.50)

children

0.0288**

-0.135***

0.0612***

-0.344***

-0.0152**

-0.0749***

0.0311**

-0.136***

-3.19

(-18.71)

-4.06

(-27.90)

(-3.28)

(-7.34)

(2.79)

(-14.84)

_cons

0.423***

-0.159**

-0.0676

-1.560***

3.478***

3.173***

-0.218*

-0.589***

-7.17

(-2.91)

(-0.81)

(-18.49)

-5.58

-21.47

(-2.53)

(-6.65)

gamma1_2

_cons

0.752***

-0.251***

0.530***

-0.120***

-0.904***

-0.348***

0.504*

-0.178**

-6.5

(-5.53)

-3.83

(-4.20)

(-60.66)

(-3.34)

(2.30)

(-2.88)

gamma2_1

_cons

0.296***

0.138***

0.169***

0.0496**

1.319***

0.208***

0.205***

0.113***

-6.76

-12.06

-7.17

-3.09

-6.78

-9.22

(6.99)

(6.19)

cut_1_1

_cons

-3.274***

-4.131***

-3.957***

-4.021***

-2.490***

-5.021***

-1.793***

-1.315***

(-5.72)

(-41.57)

(-13.77)

(-36.67)

(-47.30)

(-44.21)

(-12.45)

(-12.35)

cut_1_2

_cons

-2.253***

-2.837***

-2.883***

-2.770***

-2.196***

-3.803***

-0.399***

0.264**

(-5.24)

(-36.09)

(-13.91)

(-29.31)

(-30.60)

(-24.19)

(-4.26)

(2.85)

cut_1_3

_cons

-0.437*

-0.558***

-0.950***

-0.494***

-1.659***

-1.618***

2.032***

2.928***

(-2.40)

(-10.50)

(-10.69)

(-5.74)

(-12.28)

(-5.73)

(6.75)

(32.42)

cut_1_4

_cons

1.343***

1.584***

1.134***

1.842***

-1.182***

0.146

4.426***

5.459***

-14.8

-28.79

-8.25

-22.16

(-5.96)

-0.37

(8.11)

(48.17)

atanhrho_12

_cons

-1.067***

0.234***

-0.627**

0.120***

1.349***

0.372**

-0.617*

0.159*

(-3.76)

-4.69

(-3.18)

-3.47

-18.52

-3.07

(-2.03)

(2.37)

N

45584

50012

25004

25449

20580

24563

29997

31291

Year FE

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

District FE

Yes

Yes


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