Employer-Provided Supplementary Insurance

and the Consumption in

Irina Zainullina

Ph.D. student

Department of Applied Economics

University of Minnesota – Twin Cities

September 2016 1. Introduction

Despite the fact that the Russian health care system offers a tax financed, mandatory universal insurance, called OMS, and, therefore, many medical services are supposed to be affordable to most of the population and improve the nation’s health, statistics shows quite the opposite. The life expectancy is 76 years for Russian women and 65 years for

Russian men1. This is on average 5-6 years less than in other OECD countries. Since the collapse of the in 1991, the mortality among Russian men rose by 60%, four times higher than the European average. According to the Russian Federal State Statistics Service (RFSSS),

48.4% of all deaths in Russia in 2015 were caused by cardiovascular . Neoplasms were the second leading cause accounting for 15.6% of deaths while external causes like suicides, car , and homicides accounted for 8.4% of all deaths2.

According to the 2015 report of the RFSSS, the primary factors contributing to the lower life expectancy in Russia are widespread alcohol and drug abuse, smoking, environmental pollution, low standards of living and difficulties with accessing healthcare in rural regions. As it was mentioned before, cardiovascular diseases and cancer together account for about 65% of all deaths in Russia. According to Evans (1937), Wardle et al. (2015), survival could be improved, and mortality can be decreased if the is diagnosed at an early stage when treatment is more effective. However, a famous Russians proverb can describe people’s behavior – “Time heals”. That is, according to Russia Longitudinal Monitoring Survey, as many as 72% of people having health issues would not visit a doctor regarding those and would wait for an illness to go away, but in return, this can lead to the more severe symptoms.

There are few possible explanations for this behavior, and one of those is a cultural factor.

While Europeans believe that good health is the result of their efforts on control and prevention

1 The World Bank Data, 2015 2 Federal State Statistic Service: http://www.gks.ru/

2 of diseases, most Russians, according to a recent survey, believe that good or bad health is determined by nature, and it is hard to do anything about it (Danton, 2007). Also, many

Russians say that they get stressed just thinking about visiting a doctor at a local public clinic and spending hours waiting in the line to a general practitioner. Moreover, as shown by Gordeev et al. (2015), in practice Russian patients have to pay a lot informally to get access to some services or to get treatment through universal insurance but avoiding the long wait-lists.

The private in Russia is called DMS and is represented by voluntary supplementary medical insurance. It provides preferential access to treatments that are also available free of charge by the universal coverage, but with some waiting time, and covers some out-of-pocket payments for health care services that are generally excluded from the universal health care system. DMS also has a broader list of private and public health care providers since the insured is no longer required to visit a clinic assigned to his local area.

The share of DMS in Russia is growing quickly but is still considered to be less than in other developed countries (about 6% of the working population in 2014, compared to 11% in the U.K., 12% in Israel and 16% in Spain). The DMS insurance in Russia is mostly sponsored by an employer, and the share of individually purchased private medical insurance remains very small – only about 5% of all private health insurance.

The primary goal of this research is to identify whether there is an impact of having employer-provided supplementary health insurance on health services utilization. First, the theory behind this includes the direct price effect. That is health care becomes relatively cheaper for the insured people, and if a health care is considered a normal good, then reduction of its price will make the insured use more health care (de Meza, 1983). Second, the privately insured person knows that he has a wider choice of clinics and hospitals available to him and, therefore, can see a GP or a specialist within shorter wait times. Since he would not need to spend hours in

3 the line, he can decide to have a check-up appointment. These two points may lead to a hypothesis that having a DMS policy increases the number of doctor visits, which, on the other hand, may result in possible early diagnostics of the diseases at the early stage when the chances to cure those are higher.

To my knowledge, no studies on the effects of the supplementary health insurance on health care utilization in Russia have been conducted. However, there exist studies of the influence of complementary health insurance on outpatient care and hospitalization in Ireland.

Bolhaar et al. (2008) estimate dynamic panel data model for the decision to purchase a supplementary private health insurance at the household level and utilization of health care, where both the decision to buy the insurance and the utilization of the medical services depend on the values in a previous period. The results show that there is no significant effect of the supplementary insurance on the health care consumption. Schokkaert et al. (2010) use count model to evaluate the effect of supplemental health insurance on the usage of hospital care, on visits to a GP, drugs consumption and visits to a specialist in Belgium. They also find no evidence of adverse selection in the coverage of supplemental health insurance, but strong effects of socio- economic background.

Albouy and Crepon (2007) find no influence of complementary health insurance on hospital care consumption in Benin using the simultaneous equations model. And Buchmueller

(2004) estimates insurance coverage and utilization jointly using a bivariate probit model to find that individuals with supplemental coverage in France have substantially more physician visits than those without it.

I use the data from Russia Longitudinal Monitoring Survey (RLMS)3 . The RLMS-HSE

3 “Russia Longitudinal Monitoring Survey, RLMS-HSE”, conducted by Higher School of Economics and ZAO “Demoscope” together with Carolina Population Center, University of North Carolina at Chapel Hill and the Institute of Sociology RAS. (RLMS-HSE sites: http://www.cpc.unc.edu/projects/rlms-hse, http://www.hse.ru/org/hse/rlms)

4 obtains information on health, health care, reproduction, income, assets, expenditures, , time use, and from members of a nationally representative sample of

Russian households and from the individuals themselves. It was collected annually from 1992.

For the purpose of this paper, I focus on rounds 21-23, which represent years 2012-2014, since these rounds are based on the latest version of the questionnaire for the respondents.

I use three measures of the health services utilization: visiting a doctor in the past 30 days when an individual has a health concern as opposed to the self-treatment, visiting a doctor for a routine check-up in the past 30 days, and having a hospital stay during the past 12 months. I examine the link between having a supplementary insurance and the utilization of health care services using a linear probability model (LPM) and the propensity score matching.

My results show that there are positive effects of having employer-provided supplementary health insurance on health care services utilization, such as general practitioner visits in response to the health concern, routine check-ups, and inpatient stay. Such positive effects are statistically significant for men, but not significant for women. Also, more educated men as well as those with a better health tend to treat their health as one of their assets and are more likely to use the health care services when they do not have health concerns.

The main issue here is distinguishing a causal effect of the supplementary health insurance on the health care services utilization from the effect of the adverse selection, caused by the unobserved heterogeneity when there also exist the unmeasured characteristics which vary among the individuals and affect their level of the health services utilization.

The paper is structured as follows: Section 2 describes the health care system in Russia,

Section 3 provides the dataset description, Section 4 discusses the econometric models, Section 5 presents the results, and the final section concludes.

5 2. Healthcare system in Russia

Health financing in the Russian Federation is a comparatively even mix of financing from public sources (general taxation and payroll contributions for OMS) and the out-of-pocket payments (Popovich et al., 2011). Out-of-pocket payments include direct fees for services and medications as well as informal payments. There is no formal cost-sharing through user prices for services covered in the primary package of guaranteed services. In 2013, total health spending in

Russia was 5.84 % of GDP according to the most recent WHO estimates in the European Health for All database4, which is significantly low in comparison with other countries of the WHO

European Region.

A significant part of health care is funded by public sources of payment for medical care. It accounts for 62.37% of total health expenditure on average. Financing of the state health system is based on the “Program of state guarantees of free medical care to citizens of the Russian

Federation”. Over the past years, the cost of the program averaged for 82.27% of public spending on health care. Still, these costs are not enough to guarantee full coverage of the state's responsibilities to protect the health of every resident.

Such lack of the public health system funding affects the quality and availability of public medical care. Dissatisfaction with the poor quality of medical services provided with public health facilities is reflected in numerous annual surveys across the country. The average level of patients who were happy with received medical care they received does not reach 40% according to the population surveys directed by the Ministry of Regional Development of Russian

Federation. In 2009 this level was at 34.7%, in 2010 – 34%, in 2011 – 35.8% (Tompson, 2007).

Similarly, according to the All-Russian Public Opinion Research Center poll, only 44% of respondents gave a positive grade to the current state of the Russian healthcare system in 2014.

4 The WHO Regional Office for Europe: http://www.euro.who.int/

6 Thus, the majority of the population is not satisfied with the medical care they receive and are forced to find alternative and more effective ways of receiving it.

Officially, just a few health services provided in state and municipal medical facilities should be subject to direct full payment. However, in reality, many state and local facilities also provide fee-paying services, and this is poorly regulated. For example, “services-for-charge” enable patients to access treatment without being on a wait list or to stay in a more comfortable room during inpatient treatment.

The basic OMS package covers the usual health needs of the residents, while the budget package covers specialized and high-technology medical care, outpatient pharmaceutical costs for specific groups as well as emergency care. There is no volume restriction for care included in the OMS, but notable exceptions are outpatient prescription drugs, which must be purchased out-of-pocket.

The OMS package currently covers outpatient and inpatient care provided to patients with infectious and parasitic diseases (excluding venereal diseases, tuberculosis or HIV/AIDS). It also covers cancer, endocrine system and skin diseases, nutrition abnormalities, neurological and blood diseases, cardiovascular diseases, eye, ear and respiratory diseases, digestive system pathology, all types of injuries and poisonings, pregnancy, childbirth, postpartum and abortion, certain conditions originating in children in the perinatal period, emergency care.

The voluntary private health insurance, named DMS, is a limited aspect of health financing in Russia as, from coverage and financial points of view, it is still mostly confined to the major cities, and it still covers quite a small part of the population – about 6% in 2015

(Popovich et al., 2011). The volume of DMS contributions increased almost six times from 2000 to 2009. However, in spite of such substantial growth, DMS financing still makes up just less than half the amount spent on paid services (Kucheryavenko, 2014).

7 The DMS policies are mainly provided by employers and very rarely by the individuals. In the original OMS health insurance legislation, DMS coverage was supposed to be complementary

– covering only items on the “negative” list, that is which are not covered by the OMS. However,

DMS has developed far beyond this boundary and has acted as a supplementary insurance, overlapping the OMS benefits package. The main difference is that DMS grants access to medical facilities formerly belonging to the "closed" health systems, which are equipped with higher quality staff, and are not dependent on a patient’s place of residence or occupation. It should be mentioned, however, that DMS does not cover any costs related to the treatment of most expensive diseases.

Employer contributions under the DMS policy are tax deductible if the length of the contract between the employer and the insurance company is at least one year. If the employer has a contract for DMS with any insurance company, he should offer this voluntary health benefit to all prospective employees when hiring. Employees do not need to pay any part of the costs of the DMS insurance unless they want to add discounted coverage for family members

(spouse and children).

3. Data

The data used for this research comes from Russia Longitudinal Monitoring Survey –

Higher Schools of Economics (RLMS-HSE). The RLMS is a household-based survey designed to measure the effects of Russian reforms on the economic well-being of households and individuals. These effects are estimated by a variety of means: detailed monitoring of individuals' health status and dietary intake, measurement of household-level expenditures and service utilization, and collection of related community-level data, containing region-specific prices and community infrastructure data. Data have been collected 23 times since 1992. Of these, 19

8 rounds represent the RLMS Phase II, which has been run together with the Carolina Population

Center at the University of North Carolina at Chapel Hill. Today RLMS represents one of the few nationally representative sources of household and individual data for Russia that is increasingly drawn upon by scholars and students for the national and cross-national studies. This data now allows researchers to answer policy-relevant questions concerning the design and impact of programs and policies affecting a broad range of social sector outcomes (Kozyreva et al., 2015).

The survey was designed to allow various modules of questions to be included from round to round.

This was designed as an annual survey. In both urban and rural substratas, interviewers were expected to visit each selected dwelling up to three times to secure the interviews. They were not allowed to make replacements of any sort. After round VII in 1996, all individuals and households were followed when they moved out of the household units – this created the current longitudinal cohort. The data enumerator team also attempted to find households who moved in the 1994-96 period. There were also several replenishments by regions as well as the nationwide in 2000, 2003, 2006 and 2010, following an identical sample selection approach, to maintain the representativeness on the national level (Kozyreva et al., 2016).

Over half of the individuals participated in eight rounds of RLMS-HSE. This constitutes a good basis for longitudinal analysis, however after the Round 20 questions on the supplementary health insurance were changed, that is why for the purpose of this paper I will focus on the last three rounds of the survey since the health questionnaires in those years were the same. 22,534 individuals were interviewed in 2012 which represents Round 21, 21,753 – in 2013 (Round 22), and in 2014 Round 23 had 18,373 respondents.

I will restrict my working sample to the employed people of the working age as defined by the federal law: 16-54 years for women and 16-59 for men. However, I also exclude those

9 individuals who are younger than 18 years, as they are unlikely to have a strong connection to the labor market (Meer and Rosen, 2004). Therefore, working sample for Round 21 has 9,382 individuals, 9,023 individuals for Round 22, and 7,499 – for Round 21. About 6.5% of those individuals have a private health insurance, which corresponds with the nationwide statistics; most of those insurance policies are employer-provided DMS.

Descriptives of the demographic and socioeconomic characteristics for the full sample as well as for the employed respondents of working age that have employer-sponsored DMS and employed individuals that do not have an employer-sponsored private health insurance are presented in Table 1 of the Appendix.

We can see that those who have an employer-sponsored DMS policy tend to live in urban areas and have a higher monthly wage. Those who have an employer-sponsored health insurance are also more educated and are more likely to be married. There is also a gender difference; men are more likely to have a DMS policy than women.

Amongst working age individuals there is a little variation in having employer-provided supplementary insurance by age categories – for all five age groups the number of DMS-insured individuals represents 5.5-6.8% of the total sample.

As for the variables of interest for this paper, the first one represents individual’s behavior that when facing health problems. Respondents were to answer two questions: "Have you had any health concerns in the last 30 days?". And if the answer was positive, the respondent was asked: "What did you do to solve those health problems." The two possible responses were either visiting a doctor for professional help or self-treatment at home.

The second indicator of the health care services utilization I am interested in is the occurrence of inpatient stays. The respondent was asked to answer the following question: "Have spent at least one night in hospital in the past three months?".

10 And the third measurement of the health care services utilization I am looking at is the routine check-up visits. The respondent was asked whether he visited a GP for a routine check- up, not because he had any health concerns.

Table 2 in the Appendix provides descriptive statistics on these characteristics of health care services consumption as well as other health characteristics: how often does a respondent visit a GP or specialist on average during a year; whether there were any sick leave days within the 12-month period and how many of those; self-reported health status; body mass index

(BMI); whether the responder smokes or not. I also include information on whether the individual has any mental health concerns, chronic illnesses or disabilities. However, the last two are grouped in three categories as offered by Bolhaar et al. (2008). Health conditions that are expected to be more sensitive to price variations fall under the "Chronic illness (type 1)" category while those conditions that are expected to be less sensitive to the price of care are labeled as

"Chronic disease (type 2)." More detailed classification is provided in Table 3 of the Appendix.

We can see that those who have DMS insurance are more likely to visit a GP or a specialist if they had any health concerns in the past 30 days. The proportion of respondents who have mentioned a recent health concern and chose to see a doctor instead of self-treatment is about ten percentage points higher than among those who do not have a DMS. They also tend to have more doctor visits within a year in comparison to the full sample and the sample of those who are not holding a supplementary health insurance. The descriptives also show that those with private health insurance have more sick leave days.

4. Theoretical framework

First, I want to consider what may impact the employer’s decision to offer the health benefits to the employees. The first reason to offer DMS insurance is the increased motivation for

11 prospective employees to stay with this particular firm: having good health benefits within the firm decreases the mobility of the insured worker (Madrian, 2004). Second, employer- provided health insurance gives the firm an opportunity to have tax deductibles. Third, firm that offers the DMS policy looks more competitive among other potential employers. However, there may exist potential employees who are not interested in this kind of health benefits. Previous works by Feldman (1993), Gruber (1994) have shown that employers who provide health benefits, tend to shift costs to their workers by lowering wages. Thus, young and relatively health workers may decide to decline the job offer from the firm that offers DMS insurance and accept the one from the employer without such benefit, but who offers higher wage or salary.

Next, I want to estimate how a variety of medical services utilization measures depends on having employer-provided supplementary health insurance (DMS) and other covariates. In all of my models the dependent variable, Y takes a value of 1, if the individual used this specific health care service during the period mentioned in the questionnaire, and 0 otherwise. I would also run separate models for men and women, to decrease the possible adverse selection into DMS.

The independent variables in my models include an indicator of having DMS policy, marital status, age category, education category (incomplete secondary education, secondary education, professional or technical education, higher education), indicator of living in the urban area, monthly salary, smoking indicator, self-assessed health status, BMI, chronic disease (type

1), chronic disease (type 2), mental disease.

Even though the survey represents a cross-sectional study, there are about 7,300 individuals of working age that participated in all three rounds, so that could be a good sample for the time-series analysis. However, since the working sample contains data from only three rounds (2012-2014), there were not enough individuals who had changes in their DMS status, which does not allow me to use a fixed effects model, for example.

12 That is why I focus on two different methods for each of the measure of interest: linear probability model and propensity score matching.

The main issue is that the DMS status may be endogenous to utilization decisions. Current health is the result of past behavior and health investments, those and insurance decisions as well as future health investments are significantly affected by individual preferences and health risks. We cannot observe those characteristics, and this leads to the endogeneity problems. It is also necessary to distinguish the moral effect from adverse selection, since both can be sources of inefficiency. Moral hazard happens when the insured person changes his behavior so that he becomes more exposed to the excessive risk. The adverse selection represents the reverse relationship – the individuals, who are more exposed to the risks are likely to seek for a better insurance coverage. This is possible because of the asymmetric information and the fact that the insurer cannot distinguish between the low-risk and high-risk individuals. Separating effects from these two issues is difficult since the two problems are observationally similar: bad outcomes tend to be more prevalent among the insured as compared to the uninsured (Abbring et al.,

2003). The variety of health-related individual characteristics in the RLMS dataset provides a good test for reducing the possibility of adverse selection, so does the propensity score matching method.

There can also be a bias due to the possible measurement errors in the estimates. One of the reasons is that the sample of those with a DMS in the survey is small and also because of inconsistencies in the individuals’ responses. There were observations when an individual has

DMS provided through employment, but his occupation was listed as “unemployed”. Such observations were clearly excluded from the analysis, but there is still a possibility that this kind of measurement error remains in the data and may lead to an underestimate of the effects of

DMS on health care services utilization.

13 i. Linear probability model

The linear probability model expresses the binary dependent variable as a linear function of the explanatory variables. It can deliver biased, but still consistent estimates even if some variables are omitted. Therefore, despite the known limitation of the linear probability model of predicting values outside the unit interval, I decide to use it for my analysis.

The general regression model then would be:

Yi = α0 +α1DMSi +α2 Xi +α3DMS ⋅ Age +α4DMS ⋅ChronicDecease +εi (1)

where Yi is a measure of a health care utilization (visiting doctor while having a health concern,

having an inpatient stay, having a routine check-up visit); DMSi is an indicator that the

individual has employer-provided supplementary private health insurance; Xi is the vector of

individual exogenous covariates, and εi is the error term. I also include interactions between the

DMS-status and the age category, as well as between DMS-status and having a chronic disease of type 1 or 2. I also use standard errors that are robust to the heteroscedasticity.

ii. Propensity score matching

Propensity score matching (PSM) is a good method to correct for the selection bias. It involves estimating of a propensity score using probit or logit models, and then weighting or matching observations with similar predicted propensities. This makes it possible to condition on one dimension (propensity score) instead of conditioning on many covariates.

The idea behind the PSM method is that conditional on observed baseline covariates (X), selection bias would disappear, as there will be created a control group, similar to the treated group.

There are few methods to do the matching, including nearest neighbor, caliper and radius, stratification, and kernel matching. It is good to try all and check for the sensitivity. In this

14 analysis the results were robust. The results from the nearest-neighbor matching based on the propensity score, estimated using probit-model are presented in the next section of the paper.

The propensity score for this analysis would be estimated the following way:

e(Xi ) = P(DMSi =1| Xi ) = E(DMSi | Xi ) (3)

After each observation is assigned the propensity score, the distribution of observed baseline covariates will be the same for those with the same propensity score, no matter what group (control or treated) they are in, so the propensity score is a balancing score (Rosenbaum and Rubin, 1985).

Still, we can only balance for the observable characteristics using the PSM method and correct for sample selection bias due to observable differences between those who have DMS policy and not. While using propensity score models, we can perform matching based only on the observable characteristics, and thus there may exist a possible omitted variable bias if there is a systematic difference in unobserved characteristics between the treatment and the control groups.

5. Results

In this section, I present the results obtained from the linear probability models and the matching models. As mentioned in Section 4, I run separate models for men and women. I am also mainly interested in the results related to those who have an employer-provided DMS coverage, but I will also discuss the effects of the other covariates.

i. Doctor visits in response to health concern

To begin, I estimate the four models for analyzing individual’s behavior when having any health concerns. Table 4 of the Appendix contains the marginal effects for the probit model when estimating the probability of having the DMS insurance (propensity score). The results of the

15 linear probability models and also the mean difference from the matching are presented in Table

5 of the Appendix. The columns 1 and 3 contain the marginal effects of the covariates included in the linear probability model while Columns 2 and 4 represent the comparison of means among the groups with and without the DMS insurance using matching. Again, matching methods mean comparing DMS-insured individuals to non DMS-insured a very similar distribution of other covariates. We can see that the estimated marginal effects are close to the matching results both for women and men. For men, the DMS-status causes a significant increase of about 15% on the probability of seeking a professional medical care when facing a health concern. However, the positive estimates for the female sample are insignificant for both LPM and PSM.

Other results from the linear probability models show that both male and female smokers are less likely to visit the doctor when facing any health problem that occurred in last 30 days.

This is an interesting finding, but since we do not have a more detailed data on the nature of recent health concerns, it is not possible to say whether those problems were related to the smoking behavior or not.

The results also indicate that men with some college education are more likely to visit a doctor. This may be a sign of the differences in the value assigned to good health, that is the less educated individuals treat their health only as something needed to be able to work, and those visit doctor only when they face a more advanced illness, compared with the better-educated individuals, who regard their health as one of their assets. This can also explain the fact that both men and women with better health are more likely to seek a professional medical help when they face any new health problem.

ii. Routine check-up visits

DMS-status also has an insignificant effect on the probability of a routine check-up visits

16 for women when estimated using the linear probability model. However, Propensity Score

Matching method shows that there is a significant positive effect of having DMS insurance both for men and women. Linear Probability model for men provides similar result. That is those women and men with an employer-sponsored DMS insurance are more likely to have at least one routine check-up doctor visit in past three months. Both the linear probability model and the matching report that having a DMS insurance leads to a 15% increase in the probability of a check-up visit for men. However, I cannot conclude how exactly does the DMS affect this probability. It could be possible, that the employer that provides supplementary private health insurance also requires his employees to attend more of the routine medical examinations throughout the year. In that case, the increased probability and the number of the check-ups is not a decision made by the insured employee, but a direct instruction provided by the firm.

The results from the LPM show that both men and women living in the urban area are significantly less likely to have a routine check-up comparing to those who live in the urban area.

This result may seem confusing, but one of the possible reasons can be that the full-time employed individuals who live in the urban areas have more opportunities to spend their leisure time, and thus not so much free time left for a doctor visit when not having any health concerns.

Having any mental disease also turned up to have a positive and significant effect. It increases the probability of a routine doctor visit for 6% for women and 8% for men.

iii. Inpatient stays

DMS-insurance also has a statistically significant effect on the hospital usage among men.

When an individual has an employer-provided supplementary health insurance, then the probability of overnight stays increases by 4.6% according to the LMP and by 2.4% according to the matching methods.

The self-assessed health status is also affecting the probability of the inpatient stay in the

17 past three months. Both men and women have higher chances to spend at least one night at a hospital the worse their health is. The same is for men older than 55 years.

Both men and women also are more likely to have an inpatient stay if they have a chronic disease that is less sensitive to the price of care or a mental illness. As for women, BMI also has a significant positive relation with this measure of health care services utilization.

6. Conclusions

Using the RLMS data, I was able to estimate the effect of having an employer-provided supplementary health insurance (DMS) on the of health care services utilization using the linear probability models and the matching methods. However, as previous researches have pointed out, a supplementary insurance status is likely to be an endogenous variable in this context.

Therefore, a causal interpretation of the statistical relationship can be problematic.

The RLMS data has shown that those who have an employer-sponsored DMS policy tend to live in urban areas and have a higher monthly wage. This can be likely explained by the fact that with the current lack of private insurance provision, an employer provides it as a benefit to the more qualified employees and mainly in urban areas. The results have also shown that those with private health insurance have more sick leave days. This can be surprising at some point, but can be explained by more frequent visits to a doctor. Since a DMS policyholder has an easily accessible medical care with private insurance, he might go to a doctor with the flu or a cold, that he used to treat at home, and get prescriptions as well as the official doctor statement for a paid sick leave. Another possible reason could be the possible increased number of routine exams during a year due to having a supplementary health insurance – at one of those regular check- ups an individual may be diagnosed with an illness he was not aware of before as it was at the early stage, and get a sick leave while it is being treated.

18 My results show that there is a statistically significant positive effect of having an employer-provided supplementary health insurance on health care services utilization, such as general practitioner visits in response to the health concern, routine check-ups and inpatient stay for men.

Also, more educated men as well as those with a better health are more likely to have a routine check-up exam or visit a doctor when they have a new health problem. This means that those individuals behave in a more preventive way, treating their health as one of their assets, comparing to the less educated ones.

However, DMS-status has an insignificant effect on the measures of the health care services use for women. One of the possible reasons can be a comparably small sample size of women who actually have a DMS policy, which affects the efficiency of the estimators, and limited conclusions can be made. The finding can also be due to the well-known fact that women tend to seek medical care more often comparing to the men in any case, and DMS policy will not have a significant impact on the change in their behavior.

There also exists another limitation relating to the data available in the RLMS survey.

Since it is not possible to know what is the nature of the individual’s recent health problems, we know that other omitted unobservables alter the decision to visit a healthcare professional or have a self-treatment at home. Again, an incidence of the adverse selection may lead to the fact that the DMS-insured people would be less healthy than those without the DMS health insurance, and would be more motivated to seek care. This will lead to the upward bias for the estimated coefficient for the DMS insurance status. It is also not possible to conclude whether individuals consult an educated professional for the minor illness or more serious problems.

My analyses did not include user fees or informal under the counter payments. As mentioned in the paper, both of these types of payments are common and important in Russia

19 and can potentially influence the utilization of the health care services. Including this information could be relevant and improve the results for the future research. The RLMS health questionnaire has few questions on the health expenditures, but the responses may not be trustworthy, as it may be hard for some individuals to differentiate between the two types of payments.

It is also desirable to obtain results from panel data by looking at the results across individuals over the longer period of time. That analysis would help to account for the individual unobservable characteristics that would reduce the issues arising from heterogeneity. This could be the next step for future research.

To conclude, even though I find that having an employer-provided supplementary health insurance leads to a greater utilization of health care services, it still is not guaranteed that having a DMS insurance leads to better health (Levy and Meltzer, 2004). While the increase in the probability of preventative and routine examinations may increase the chances to diagnose and treat diseases, examining of the relation between the DMS and the health status can be done in future research.

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23

Table 1. Sample demographic characteristics of the employed working age population in Russia in 2012-2014 21 round (2012) 22 round (2013) 23 round (2014)

Full sample* DMSe ° No DMS Full sample* DMSe ° No DMS Full sample* DMSe ° No DMS Female 51.1% 46.9% 51.2% 50.4% 45.6% 50.7% 50.1% 42.9% 50.5% Age (in years) 37.36 37.41 37.34 37.65 38.44 37.61 37.56 37.78 37.55 Married 59.7% 65.1% 59.3% 59.4% 67.8% 58.9% 60.0% 69.2% 59.5% Number of children 0.93 0.96 0.93 0.95 0.90 0.95 0.99 0.99 0.98 under 18 years old

Education level: 0-6 years of school 0.1% - 0.1% 0.1% - 0.1% 0.1% - 0.1% 7-8 years of school 2.4% 0.8% 2.5% 2.1% 2% 2.1% 2.0% 0.5% 2.1% Incomplete high 14.3% 10.7% 14.7% 7.4% 3.9% 7.6% 7.7% 4.3% 8.0% school 26.8% 18.6% 27.4% 33.2% 24.8% 33.8% 32.2% 26.0% 32.7% High school 24.7% 20.4% 25.0% 25.5% 22.0% 25.7% 24.5% 17.6% 25.1% Professional 31.1% 49.1% 29.7% 31.8% 47.3% 30.8% 33.3% 51.7% 32.0% education Higher education Urban 71.7% 83.1% 70.7% 71.2% 85.2% 70.2% 70.9% 88.2% 71.7% Rural 28.3% 16.9% 29.3% 28.8% 14.8% 29.8% 29.1% 11.8% 28.3%

Monthly wage 19,089 30,471 18,163 20,794 32,055 20,005 23,197 39,201 21,607 DMS 7.6% - - 6.3% - - 6.3% - -

DMSe 6.4% - - 5.4% - - 5.6% - -

Observations 9,382 597 8,647 9,023 491 8,446 7,499 422 7,012

* Individuals of working age (18 to 54 years for women, 18 to 59 years for men) ° Individuals that have an employer-sponsored supplementary private health insurance (DMS)

24 Table 2. Descriptive statistics on health and characteristics of the employed working age population in Russia in 2012-2014

21 round (2012) 22 round (2013) 23 round (2014)

Full sample DMSe No DMS Full sample DMSe No DMS Full sample DMSe No DMS Health concerns (past 30 days) 27.4% 29.7% 27.1% 23.6% 23.0% 23.5% 25.2% 28.7% 24.9% In response to those: Visited a doctor 29.0% 36.7% 28.2% 27.5% 33.6% 26.9% 28.2% 43.9% 26.3% Did not visit a doctor 71.0% 63.3% 71.8% 72.5% 66.5% 73.1% 72.3% 56.1% 73.7%

Hospital stay (last 3 months) 3.6% 4.9% 3.5% 3.2% 3.5% 3.1% 3.6% 6.2% 3.5% Number of nights in hospital 13.48 15.93 13.29 14.01 14.41 14.12 13.31 14.62 13.06

Visit GP or a specialist: Several times a month 2.0% 2.7% 1.4% 1.7% 3.1% 1.6% 1.7% 2.6% 1.5% Once a month 3.7% 8.5% 3.0% 3.7% 7.2% 3.4% 3.6% 5.7% 3.4% 2-3 times a year 29.8% 34.7% 28.5% 30.9% 41.2% 29.7% 30.1% 44.8% 29.2% Once a year 29.3% 27.8% 29.3% 29.9% 27.8% 29.6% 29.3% 24.2% 29.7% Less than once a year 35.3% 25.5% 36.4% 33.7% 20.8% 34.0% 35.4% 22.7% 36.3%

Had sick leave days 17.4% 27.9% 16.6% 17.4% 27.6% 16.8% 18.8% 27.0% 18.2% Number of sick leave days 18.00 19.75 17.75 16.09 18.71 15.80 16.42 19.59 16.14

Self-rated health status: Very good 2.2% 1.5% 2.2% 2.0% 1.2% 2.0% 2.3% 1.9% 2.3% Good 44.4% 43.2% 44.5% 43.8% 40.5% 44.0% 46.3% 46.7% 46.3% Average 50.5% 51.8% 50.1% 50.8% 54.9% 50.6% 48.6% 48.3% 48.6% Poor 2.8% 3.4% 2.8% 3.3% 3.3% 3.4% 2.6% 2.8% 2.7% Bad 0.1% 0.2% 0.1% 0.1% - 0.1% 0.1% 0.2% 0.1%

BMI 25.87 26.26 25.84 24.78 26.21 26.04 25.96 25.69 25.97 Smoking 39.5% 38.2% 39.7% 40.0% 35.2% 40.4% 38.2% 37.2% 38.4%

Mental concerns 8.9% 7.1% 8.9% 8.9% 6.8% 8.9% 8.6% 7.1% 8.7% Chronic illness (type 1) 19.2% 21.3% 18.8% 19.3% 27.8% 18.7% 17.4% 21.3% 17.6% Chronic illness (type 2) 38.7% 42% 38.3% 45.2% 47.8% 44.8% 45.1% 48.3% 44.5%

Observations 9,382 597 8,647 9,023 491 8,446 7,499 422 7,012

25

Table 3. Categories of health diseases and disabilities

Category Illness

Mental disease Mental disorders

Depression

Chronic illness (type 1) Skin disease

Musculoskeletal diseases

Chronic illness (type 2) Cancer

Blood disease

Diseases of the nervous system

Diseases of the

Diseases of the digestive system

Diseases of the genitourinary system

Diseases of the

Endocrine diseases

Disability (categories I, II or III)

26

Table 4. Marginal effects for propensity score estimation

Female Male Married 0.016 * 0.021 * (0.006) (0.007) Urban area 0.039 * 0.034 * (0.008) (0.008) Age category: 18-25 years - - 26-35 years 0.007 -0.004 (0.100) (0.013) 36-45 years -0.000 -0.007 (0.009) (0.013) 46-55 years 0.021 *** -0.011 (0.011) (0.014) > 55 years - -0.031* (0.016) Smoking 0.000 -0.014 * (0.007) (0.007) Health status: Bad - - Poor - 0.379 (0.400) Fair -0.006 0.003 (0.029) (0.029) Good 0.005 0.004 (0.026) (0.019) Very good -0.000 0.004 (0.026) (0.019) Chronic disease (I) 0.012 0.019 * (0.007) (0.008) Chronic disease (II) 0.023 * 0.018 * (0.006) (0.007) Mental illness -0.003 -0.023 (0.009) (0.015) BMI -0.002 * 0.000 (0.001) (0.001) N 5998 6011

Note: Probit regression. The dependent variable is 1 if the individual has the employer-provided DMS health insurance, and 0 otherwise. *,**,*** denote significance at the 1, 5 and 10% level, respectively.

27 Table 5. Estimation results for the decision to visit a doctor when having a health concern that happened in the last 30 days

Female Male LPM PSM LPM PSM Married -0.004 -0.037 (0.020) (0.028) Urban area 0.014 0.019 (0.020) (0.020) DMS insurance 0.049 0.051 0.159 ** 0.151 ** (0.059) (0.065) (0.054) (0.049) Age category: 18-25 years - - 26-35 years 0.054 -0.031 (0.103) (0.084) 36-45 years 0.059 -0.137 (0.102) (0.086) 46-55 years 0.025 -0.037 (0.099) (0.097) > 55 years - 0.107 (0.115) Education: Incomplete secondary - - Complete secondary -0.004 -0.001 (0.047) (0.041) Some college -0.039 0.095 ** (0.047) (0.045) Higher 0.001 -0.009 (0.048) (0.045) Smoking -0.055 ** -0.051 ** (0.025) (0.025) Health status: Bad - - Poor 0.250 0.379 (0.210) (0.400) Fair 0.367 ** 0.222 *** (0.094) (0.118) Good 0.219 ** 0.135 (0.084) (0.109) Very good 0.164 *** 0.164 *** (0.085) (0.085) Chronic disease (I) 0.021 0.026 (0.046) (0.048) Chronic disease (II) 0.061 0.005 (0.049) (0.046) Mental illness 0.011 0.042 (0.027) (0.040) BMI -0.001 -0.002 (0.002) (0.003) N 2217 2217 1492 1491

Standard errors are clustered at the individual level and shown in parentheses. *,**,*** denote significance at the 1, 5 and 10% level, respectively. 28 Table 6. Estimation results for the probabilities of routine check-up visits

Female Male LPM PSM LPM PSM Married -0.005 0.016 (0.013) (0.012) Urban area -0.035 ** - 0.038 * (0.015) (0.013) DMS insurance 0.001 0.078 * 0.215 * 0.213 * (0.033) (0.035) (0.031) (0.030) Age category: 18-25 years - - 26-35 years - 0.039 -0.067 (0.023) (0.019) 36-45 years - 0.014 -0.061 (0.023) (0.021) 46-55 years - 0.028 -0.067 (0.025) (0.022) > 55 years - 0.077 (0.028) Education: Incomplete secondary - - Complete secondary 0.002 0.022 (0.028) (0.041 Some college 0.016 0.025 (0.027) (0.019) Higher - 0.041 0.024 (0.048) (0.020) Smoking - 0.044 * 0.001 (0.016) (0.011) Health status: Bad - - Poor 0.127 - 0.192 * (0.198) (0.041) Fair - 0.029 0.016 (0.094) (0.050) Good - 0.036 0.002 (0.055) (0.033) Very good - 0.034 -0.008 (0.055) (0.032) Chronic disease (I) 0.164 0.059 *** (0.033) (0.031) Chronic disease (II) - 0.002 - 0.014 (0.029) (0.028) Mental illness 0.061 * 0.081 * (0.021) (0.023) BMI 0.002 0.002 (0.001) (0.001) N 6006 2217 1492 1491

Standard errors are clustered at the individual level and shown in parentheses. *,**, *** denote significance at the 1, 5 and 10% level, respectively.

29 Table 7. Estimation results for the probabilities of inpatient stay

Female Male LPM PSM LPM PSM Married 0.002 0.002 (0.005) (0.004) Urban area 0.003 0.003 (0.005) (0.004) DMS insurance 0.019 0.006 0.046 * 0.022 ** (0.033) (0.018) (0.021) (0.010) Age category: 18-25 years - - 26-35 years 0.002 0.018 (0.022) (0.019) 36-45 years - 0.001 0.010 (0.024) (0.006) 46-55 years - 0.043 0.009 (0.021) (0.010) > 55 years - 0.106 ** (0.043) Education: Incomplete secondary - - Complete secondary - 0.001 0.007 (0.011) (0.005) Some college 0.001 0.009 (0.011) (0.006) Higher - 0.006 0.005 (0.011) (0.006) Smoking - 0.009 * - 0.001 (0.006) (0.004) Health status: Bad - - Poor 0.351 * - 0.025 (0.188) (0.012) Fair 0.115 * 0.091 * (0.024) (0.026) Good 0.033* 0.015 * (0.005) (0.004) Very good 0.018* 0.005 * (0.004) (0.002) Chronic disease (I) 0.011 0.009 (0.019) (0.013) Chronic disease (II) 0.035 ** 0.026 ** (0.015) (0.012) Mental illness 0.024 ** 0.014 * (0.010) (0.010) BMI 0.001 ** - 0.001 (0.001) (0.001) N 6006 6006 6021 6021

Standard errors are clustered at the individual level and shown in parentheses. *,**, *** denote significance at the 1, 5 and 10% level, respectively.

30