The countries of Central and Eastern Europe (CEE) have gone through immense political and socioeconomic restructuring after the collapse of communism around 1990. Such transition has affected the lives of populations in these countries in many significant respects. A key aspect of life and wellbeing in any society is that of population health. This paper traces the transitions in population health—life expectancies and mortality rates for both males and females—in seven of the CEE countries during the two decades after the fall of communism. We estimate a series of panel data models to identify some of the common factors that would explain health transitions in these countries, while allowing for country-specific variability. Our findings indicate that the health transitions are strongly country specific. Moreover, income per capita and trade openness are statistically significant common contributors to health transitions.
Two decades have passed since the countries of Central and Eastern Europe (CEE) like countries in the former Soviet Union went through immense political and socioeconomic restructuring that began around 1990 with the collapse of communism. Since then, they have embarked on a transition from closed, totalitarian, and centrally planned economies towards open, democratic, and market-based economies. Such transition has affected the lives of populations in these countries in many significant respects. Many people in these countries have had renewed hopes for improved living conditions and great expectations for a free and prosperous future comparable to those enjoyed by many in Western and Northern Europe for many years.
It is understood that the transition from communism to democratic capitalism has provided natural experiments that allow one to examine the evolution of socioeconomic wellbeing of people in the CEE countries as they restructure their socioeconomic and political institutions away from those of the past to those modeled after Western European institutions. A key aspect of wellbeing in any society is that of population health. So, it is important to examine the evolution of health outcomes in those countries as a result of such historical restructuring over the past two decades. This examination is embedded in the
Across the CEE countries, transition has had an immediate and largely adverse impact on health as noted by McKee [
In spite of improvements in health in these transitional countries, their health attainments fall significantly short of those in the Western and Northern Europe. Researchers have identified a range of factors that could explain the poorer health outcomes in transitional countries. Prominent among these factors are the risk factors linked to cardiovascular diseases, premature mortality, and morbidity as suggested by Figueras et al. [
Beyond the life style factors, poor health outcomes in the CEE countries are related to poorly organized and inefficient health care systems. The latter have been typically oversupplied with doctors and hospital beds, but unequipped with modern technology, and are said to be ill-prepared to engage in health promotion or behavior change [
At the societal level, Kickbusch [
This paper traces the transitions in population health in connection with a few broad macroeconomic and political covariates over the 1990–2009 time period. While it is true that the CEE countries all went through restructuring at about the same time and, generally speaking, they have all moved towards greater political freedom and market-based economy, there have been significant variations in the experiences of individual countries as related to their historical, sociocultural, ethnic, and other distinguishing backgrounds. To allow for such variability, we use a series of panel data models to capture unobserved heterogeneity across these countries. The intent is to identify some of the common factors that would explain health transitions in these countries, while allowing for country-specific variability.
The rest of the paper is organized as follows. Section
Before doing a formal statistical analysis, a description of health transitions overtime would be helpful. To give a sense of the health outcomes before the fall of communism for comparison to those over the period after the fall, health data for the extended period 1985–2009 are considered, although the focus of the study is in the post-communist period 1990–2009. Our focus in this study is on seven countries of CEE, namely, Bulgaria, Czech Republic, Hungary, Poland, Romania, Slovakia, and Slovenia. With the exception of Czech Republic and Slovakia (both part of the former Czechoslovakia till 1993), these countries were independent states before the collapse of communism and thus have retained much of their national identities.
We examine transitions in infant mortality (IM), child (less than 5 years old) mortality (CM), standardized all-cause mortality for all ages, (AM), and suicide rates (SR). As well, we describe the trends in life expectancies at birth (LEB), at age 45, (LE45), and at age 65 (LE65) as general indicators of population health. These data were taken from the World Health Organization (WHO) databases. Mortality rates are shown in Figures
Male infant mortality rate (per 1000 live births).
Male child mortality rate (per 1000 live births).
Male death rate (all cause, all age, per 100,000).
Male suicide rate (per 100,000).
Female infant mortality rate (per 1000 live births).
Female child mortality rate (per 1000 live births).
Female death rate (all cause, all age, per 100,000).
Female suicide rate (per 100,000).
Male life expectancy at birth (years).
Male life expectancy at age 45 (years).
Male life expectancy at age 65 (years).
Female life expectancy at births (years).
Female life expectancy at age 45 (years).
Female life expectancy at age 65 (years).
It can be seen from Figures
Figures
Separate data for male and female suicide rates were not available prior to 1990. Therefore, the data cover the period 1990 onward. Figures
Notwithstanding the idiosyncrasies across the individual countries, declining mortality rates showed an overall improvement in the health of populations in this region two decades after the fall of communism.
This “healthy transition” in the CEE countries can also be examined by looking at life expectancies, a measure closely related to mortalities. Figures
As expected, thanks to declining mortality rates, life expectancies generally rose over that same period. Like mortality rates, life expectancies started at different levels across the countries. However, life expectancies showed either no trend or a slightly upward trend during 1985–1990. After 1990, they rose for some countries and declined for others in the early years of the transition but continued to rise consistently thereafter for all the countries. Slovenia consistently led the group in terms of LEB, LE45, and LE65 for both males and females followed by the Czech Republic for males and Poland or Czech Republic for females. Romania and Bulgaria lagged behind the rest of the group for female life expectancies throughout the period, while sharing such status with Hungary for male life expectancies. What is remarkable is the persistence of divergence among the seven countries so much so that by 2009 there was a gap of 6 years in LEB (between Slovenia and Romania), compared to a gap of 5 years in 1990. The divergence grew wider for LE45 and LE65. A gap of almost 3 years at the beginning of the period grew to over 5 years towards the end of the period for LE45 and from almost 2 years to over 3 years for LE65. Such divergence clearly implies that life expectancies followed different transitions across these countries. Some like Slovenia are almost at par with the countries in Western Europe, whereas others like Bulgaria are still behind.
The trends in mortality rates and life expectancies over the period 1990–2009 as shown in the above figures portray a picture of overall improvement in the health of populations in the CEE countries, with marked variations in the experiences of individual countries.
Comprehensive data are not available for the CEE countries. In particular, data on life style factors such as tobacco and alcohol consumption, health expenditures, and resources are sketchy and not consistently available for the seven countries considered in this study. In the interest of covering the post-communist period to the extent possible for these countries, the study uses a parsimonious set of macrovariables to capture the societal influences on health transitions. More specifically, we use real GDP per capita as a measure of overall material wellbeing, trade openness as a measure of integration to the world economy, and democracy scores for capturing governance quality. Data for mortality rates (except for suicide rates) and life expectancies along with real GDP per capita at constant purchasing parity prices were obtained from the European Health for All Database [
We used both fixed- and random-effect panel regression models for each of the four mortality rates (IM, CM, AM, and SR) and each of the three life expectancies (LEB, LE45, and LE65). The models were estimated for males and females separately. Lack of reliable data (especially for macroeconomic variables) prior to 1990 prevented the estimation of the models for the extended period 1985–2009. Moreover, the focus of this study is on the post-communist era, that is, 1990–2009. The length of this period and the number of cross-sections (seven countries) would have ideally given us a balanced panel of 140 observations. However, missing observations on UDS for some years led to unbalanced panel estimations of regressions with less than the full size of the panel. The fixed and random cross-section effect approaches were used to capture country-specific heterogeneity arising from historical, cultural, and other structural idiosyncrasies of the individual countries. We could not simultaneously consider random time-period effects since our panel was unbalanced. Instead, we have considered country-specific (fixed) time trends.
In light of the above, our typical regression models would thus have the following generic specification:
The models were estimated by the software Eviews Version 5.1. To account for cross-sectional heteroskedasticity and temporal autocorrelation, a panel-corrected standard error (PCSE) methodology as suggested by Beck and Katz [
The first set of results reported in Table
Estimation results for random cross-section effect models.
Dependent variables—mortality rates | ||||||||||||||||
IM | CM | AM | SR | |||||||||||||
Independent variables | Male | Female | Male | Female | Male | Female | Male | Female | ||||||||
B | ||||||||||||||||
Constant | 81.61 | 0.000 | 60.86 | 0.000 | 97.77 | 0.000 | 75.93 | 0.000 | 3195 | 0.000 | 2306 | 0.000 | 48.40 | 0.002 | 14.21 | 0.002 |
LRGDP | −7.215 | 0.000 | −5.309 | 0.000 | −8.677 | 0.000 | −6.726 | 0.000 | −185.3 | 0.000 | −158.2 | 0.000 | −2.022 | 0.262 | −0.564 | 0.302 |
OPI | −0.013 | 0.134 | −0.014 | 0.067 | −0.012 | 0.281 | −0.011 | 0.263 | −1.957 | 0.000 | −0.703 | 0.000 | −0.096 | 0.000 | −0.040 | 0.000 |
UDS | −2.137 | 0.002 | −1.275 | 0.030 | −2.630 | 0.002 | −1.681 | 0.023 | 27.07 | 0.304 | −0.480 | 0.972 | 4.734 | 0.001 | 0.629 | 0.154 |
0.839 | 0.796 | 0.819 | 0.777 | 0.817 | 0872 | 0.495 | 0.635 | |||||||||
132 | 132 | 132 | 132 | 132 | 132 | 126 | 126 | |||||||||
Dependent variables—life expectancies | ||||||||||||||||
LEB | LE45 | LE65 | ||||||||||||||
Independent variables | Male | Female | Male | Female | Male | Female | ||||||||||
B | B | B | ||||||||||||||
Constant | 42.26 | 0.000 | 50.19 | 0.000 | 12.26 | 0.000 | 14.23 | 0.000 | 2.875 | 0.088 | −0.002 | 0.999 | ||||
LRGDP | 2.745 | 0.000 | 2.780 | 0.000 | 1.553 | 0.000 | 2.062 | 0.000 | 1.064 | 0.000 | 1.774 | 0.000 | ||||
OPI | 0.020 | 0.000 | 0.012 | 0.000 | 0.014 | 0.000 | 0.008 | 0.001 | 0.009 | 0.000 | 0.006 | 0.008 | ||||
UDS | −0.310 | 0.268 | −0.017 | 0.934 | −0.325 | 0.194 | −0.076 | 0.682 | −0.170 | 0.327 | −0.076 | 0.645 | ||||
0.868 | 0.894 | 0.753 | 0.851 | 0.737 | 0.833 | |||||||||||
132 | 132 | 132 | 132 | 132 | 132 |
Estimation results for fixed cross-section effect models.
Dependent variables—mortality rates | ||||||||||||||||
IM | CM | AM | SR | |||||||||||||
Independent variables | Male | Female | Male | Female | Male | Female | Male | Female | ||||||||
B | B | |||||||||||||||
BUL | 80.87 | 0.000 | 60.05 | 0.000 | 97.12 | 0.000 | 75.10 | 0.000 | 3163 | 0.000 | 2320 | 0.000 | 44.19 | 0.002 | 14.79 | 0.001 |
CZE | 78.84 | 0.000 | 57.67 | 0.000 | 94.44 | 0.000 | 72.30 | 0.000 | 3103 | 0.000 | 2278 | 0.000 | 47.39 | 0.002 | 14.33 | 0.002 |
HUN | 80.04 | 0.000 | 59.90 | 0.000 | 96.72 | 0.000 | 74.46 | 0.000 | 3336 | 0.000 | 2343 | 0.000 | 65.97 | 0.000 | 19.38 | 0.000 |
POL | 78.69 | 0.000 | 58.15 | 0.000 | 93.81 | 0.000 | 72.31 | 0.000 | 3044 | 0.000 | 2171 | 0.000 | 45.09 | 0.003 | 11.14 | 0.017 |
ROM | 85.78 | 0.000 | 63.93 | 0.000 | 103.1 | 0.000 | 79.81 | 0.000 | 3079 | 0.000 | 2297 | 0.000 | 39.60 | 0.008 | 10.55 | 0.019 |
SLK | 79.51 | 0.000 | 58.79 | 0.000 | 95.01 | 0.000 | 73.24 | 0.000 | 3214 | 0.000 | 2266 | 0.000 | 48.67 | 0.001 | 13.02 | 0.003 |
SLN | 78.87 | 0.000 | 57.78 | 0.000 | 94.54 | 0.000 | 72.29 | 0.000 | 3038 | 0.000 | 2195 | 0.000 | 63.20 | 0.000 | 18.99 | 0.000 |
LRGDP | −7.097 | 0.000 | −5.155 | 0.000 | −8.526 | 0.000 | −6.537 | 0.000 | −178.2 | 0.000 | −153.5 | 0.000 | −2.252 | 0.211 | −0.593 | 0.279 |
OPI | −0.014 | 0.098 | −0.015 | 0.031 | −0.013 | 0.180 | −0.012 | 0.137 | −2.036 | 0.000 | −0.753 | 0.000 | −0.094 | 0.000 | −0.040 | 0.000 |
UDS | −2.066 | 0.004 | −1.193 | 0.044 | −2.544 | 0.005 | −1.581 | 0.040 | 24.15 | 0.382 | −1.237 | 0.931 | 4.493 | 0.000 | 0.502 | 0.205 |
0.950 | 0.940 | 0.947 | 0.936 | 0.909 | 0.951 | 0.916 | 0.923 | |||||||||
132 | 132 | 132 | 132 | 132 | 132 | 126 | 126 | |||||||||
Dependent variables—life expectancies | ||||||||||||||||
LEB | LE45 | LE65 | ||||||||||||||
Independent variables | Male | Female | Male | Female | Male | Female | ||||||||||
B | B | B | ||||||||||||||
BUL | 42.69 | 0.000 | 50.09 | 0.000 | 12.59 | 0.000 | 14.24 | 0.000 | 3.204 | 0.052 | −0.090 | 0.955 | ||||
CZE | 43.55 | 0.000 | 50.63 | 0.000 | 13.06 | 0.000 | 14.33 | 0.000 | 3.112 | 0.083 | −0.039 | 0.982 | ||||
HUN | 40.21 | 0.000 | 49.14 | 0.000 | 10.53 | 0.000 | 13.38 | 0.000 | 2.626 | 0.131 | −0.031 | 0.985 | ||||
POL | 43.43 | 0.000 | 52.06 | 0.000 | 13.18 | 0.000 | 15.70 | 0.000 | 3.875 | 0.031 | 1.403 | 0.420 | ||||
ROM | 42.57 | 0.000 | 49.87 | 0.000 | 13.07 | 0.000 | 14.40 | 0.000 | 3.920 | 0.025 | 0.347 | 0.837 | ||||
SLK | 41.87 | 0.000 | 50.55 | 0.000 | 11.73 | 0.000 | 14.40 | 0.000 | 2.708 | 0.114 | 0.179 | 0.915 | ||||
SLN | 43.91 | 0.000 | 51.88 | 0.000 | 13.82 | 0.000 | 15.66 | 0.000 | 3.751 | 0.037 | 1.233 | 0.482 | ||||
LRGDP | 2.702 | 0.000 | 2.730 | 0.000 | 1.513 | 0.000 | 2.018 | 0.000 | 1.006 | 0.000 | 1.721 | 0.000 | ||||
OPI | 0.021 | 0.000 | 0.012 | 0.000 | 0.015 | 0.000 | 0.008 | 0.000 | 0.010 | 0.000 | 0.006 | 0.002 | ||||
UDS | −0.297 | 0.289 | −0.010 | 0.962 | −0.309 | 0.210 | −0.064 | 0.721 | −0.139 | 0.434 | −0.069 | 0.674 | ||||
0.942 | 0.960 | 0.898 | 0.940 | 0.834 | 0.928 | |||||||||||
132 | 132 | 132 | 132 | 132 | 132 |
The fixed cross-sectional results for mortality rates (IM, CM, AM, and SR) are given in the top portion of Table
As the findings in Table
Trade openness also appears to have contributed to reductions in mortality rates. It has a statistically significant impact on female IM, AM (both males and females), and SR (both males and females). Its negative impacts on male IM and CM (males and females) are not statistically significant however. Here, too, the magnitude of the impact is higher for males than females, although such magnitudes are very small compared to those of income. For example, a 10 percentage point increase in the openness index reduces AM by almost 2 (per 100,000) for males but 0.75 for females. In the absence of other information, it is difficult to interpret the differential impact of openness on mortalities. But previous research by Dolea et al. [
The relationship between democracy score and mortality rates is found to be mixed. While democracy is negatively related to IM and CM, it does not have a statistically significant relationship with AM or female SR. Interestingly, it has a positive relationship with the male SR. A partial explanation for such results may be the fact that all these CEE countries moved to democratize within the first five years after the fall of communism with no marked further improvement thereafter. So, there has not been a great deal of variation in their democracy scores over a significant portion of the study’s time period. It may also be that correlation between democracy and openness makes it hard to disentangle the separate effects of the two as indicated by McKee and Nolte [
Turning to the estimation results for life expectancy transitions (bottom portion of Table
Despite their limited number of regressors, the estimated models fit the data quite well as indicated by the R2s given in Tables
Population health outcomes in the transitional countries of CEE have been drastically affected by the major restructuring of these countries towards open, promarket, and democratic societies. While the transition from communism to capitalism has generally improved the health of populations in these countries, the experience of individual countries has not been the same. There have been some setbacks in the health of people in certain countries, especially in the immediate aftermath of the collapse of communism. Using panel data model, our study identifies some of the common determinants of mortality rates and life expectancies in seven transitional countries of CEE over the period 1990–2009. Of particular note is the significant role of country-specific effects on population health. Our findings consistently show that higher per capita income has been negatively associated with mortality rates and positively with life expectancies. Openness has also been positively associated with life expectancies and negatively associated with mortalities with the exception of male IM and CM for both genders. The findings reveal no significant role for democracy in life expectancies and mixed roles for mortality rates.
Data restrictions have prevented the examination of other socioeconomic covariates of health (e.g., poverty, income inequality) as well as risky behaviors (e.g., alcohol and tobacco consumption) in our study. It is likely that such omitted covariates along with the nonobservable characteristics of the countries have been captured in the country-specific effects. We hope that, with greater availability of data in the above-mentioned domains in the future, some of the country-specific effects could be accounted for by more specific determinants of health in these countries.
The author wishes to thank the editor of the journal for insightful technical comments and an anonymous referee for pointing out some of the weaknesses of an earlier draft of this paper. The current version of the paper has significantly benefited from their comments.