Demographic change in the former Soviet Union during the transition period

Wurkl I)cvelopmertr

Pergamon

Vol. 26, No. I I, pp. 1957-1975.

lYY8

0 IYYXElsevier Science Ltd All rights reserved.

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SO305750X(98)00113-2

Demographic Change in the Former Soviet Union During the Transition Period CHARLES M. BECKER University of Colorado, Boulder and Denver CO, U.S.A. and

DAVID D. HEMLEY” UniversiQ of Colorado, Boulder and Denver CO, U.S.A. Summary. -

This paper examines patterns of mortality and other demographic changes across the former Soviet Union. Using regional data from the early lY9Os, a simultaneous equations model of fertility, marriage, divorce. infant mortality and abortion is estimated as a function of economic and social variables. The paper then looks at determinants of life expectancy and specific causes of death. Demographic scenarios are then forecast on the basis of specific economic environments: these forecasts in turn are used to forecast life expectancies in the coming decades. In plausible environments, there is littlc reason to anticipate a rapid recovery in male or female life expectancies. while furthe] declines in fertility appear imminent. 0 1498 Elsevier Science Ltd. All rights reserved. 1. INTRODUCTION There is much debate over the extent of real income declines in the former Soviet Union, but little doubt that many people are far worse off in many dimensions today than they were at the onset of the economic decline a decade ago. Furthermore, whereas one may debate the extent and distribution of income declines, there has been a less-noticed but more clearly documented set of demographic events. In parts of the former Soviet Union, these events are virtually catastrophic, whereas elsewhere the trends are less negative. Even in these latter regions, however, increasing data errors may be the main explanation for the relatively modern declines observed. The patterns occurring in the former Soviet Union are not unique, but are normally associated with war, famine, or enormous economic failure, such as the Great Depression, and usually are of far smaller magnitude. The declines observed probably are in large part due to the breakup of the Soviet Union, and the resulting economic and social chaos, although environmental neglect and lifestyles may be equally or more important. The first signs of demographic decay were noticed decades ago, and date from the mid-1960s. This historic declne is discussed at length elsewhere (Shkolnikov et al., 1998), and

has received considerable attention from Soviet and Western epidemiologists and demographers (Shkolnikov et al., 1996a; Shkolnikov MeslC and Vallin, 1997; Notzon et al., 1998). The long-term gradual decline in life expectancy temporarily reversed during 1985-87, and then resumed at an accelerating pace through the mid-1990s. Rising mortality was accompanied by other symptoms of demographic distress as well, including an accelerating decline in birth rates, declining incidence of marriage, and rising divorce rates. This paper focuses on two aspects of the demographic crisis that have received lesser attention. First, WC address the extent to which interregional patterns of life expectancy and specific causes of mortality can be attributed to economic and social phenomena. Second, we develop and estimate a model of demographic behaviors. Sections 2 and 3, respectively, briefly chart mortality and demographic patterns in the *We are grateful to Muzaffar Hasan, Scott Thomas, Sergei Strokov, Sergei Scherbov, and Umit Ukaeva for providing valuable data used in this project, and to Margarita Ibragimova and Dina Urzhumova for superb research assistance. Valuable comments were provided by to participants in conferences at the University of Colorado and Harvard University for assistance and valuable comments. We are also grateful to USAID for support via the CAER II project. All errors and dubious interpretations remain our own.

1957

1958

WORLD

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former Soviet Union. Crossregional estimates of the determinants of mortality appears in section 4, whereas section 5 presents a simultaneous approach to other equations estimating demographic events. Demographic forecasts are then presented in section 6, and are contrasted with forecasts based on purely demographic models.

2. SYSTEM FAILURE: DECLINING LIFE EXPECTANCIES AND RISING MORTALITY RATES Our focus is primarily on the Russion Federation, mainly because of its breadth and the high quality of demographic data: current Russion data are comparable to those for the United States. Comparable data are available for the Baltic states, Belarus and Ukraine; data of more questionable quality exist for Central Asia and the Transcaucasus.’ As Shkolnikov et al. (1998) detail, Russian male life expectancies peaked in 1987 at 64.9 years, declined by 1.3 years to 63.6 in 1991, and then fell an astonishing 6.1 years to 57.5 in 1994. Female life expectancy actually peaked at 74.4 years in 1991, and then declined to 71.1 by 1994. Since then, some recovery has taken place, with 1996 life expectancies recovering to 59.7 and 72.5 years for men and women, respectively. In Russia and elsewhere, though, the post-1987 declines have resulted in life expectancies far below the levels before the 1985-1987 increases, and, hence, imply values similar to those in the 1950s. This life expectancy collapse is not unique to Russia. 1987-94 male life expectancy declines were recorded in all former Soviet republics with credible statistics, including Latvia ( - 7.2 years), Estonia (-5.1) Lithuania (-4.8) Ukraine ( -4.2) Kazakstan (-4.2) and Belarus (-3.8).’ For women, the declines are smaller, but still substantial. To put these figures in perspective, U.S. White and Black male life expectancies rose 1.1 and 0.2 years between 1987-94, respectively, and roughly 5.6 and 5.1 years since 196S.3 The life expectancy trends are nearly universal in Russia: there was no broad region with falling mortality, and only one ohlast, comparing 1993 or 1992 with 1980 or 1985. Declining male life expectancies from 1989-94 were greatest in Northern Russia (-0.8 years), Eastern Siberia (-7.8) and Northwestern Russia (-7.5); the declines were smallest in the Central black-earth region (-4.3) and in the Northern Caucasus (-4.4), where data are questionable, in part because of the Chechen conflict. By oblast, 1989-94 declines were most dramatic in Chita in

East Siberia ( ~ 8.7) in East Siberia’s Tyva Republic (-8.3) which also had the lowest male life expectancy (49.0) and in Murmansk ( -8.3) in the far northwest. Other large declines occurred in Kemerovo in West Siberia (-7.8) and Moscow city ( - 7.7). The differences in life expectancy between Russian men and women is perhaps the largest in the world, and can be attributed to exceptionally high male levels of accidents, injuries and poisoning, cardiovascular disease and neoplasms (Vassin and Costello, 1997). While part of this difference may reflect differential errors in birthdate estimates, the main reason almost certainly is the unhealthy lifestyle of Russian men. The catastrophic impact of WWII is no longer reflected in life expectancies, since life expectancy estimates do not reflect past deaths, and extra deaths due to weaknesses brought on by the trauma have already taken place. Rather, what appears to be unprecedented is middle-aged male mortality in the former USSR. Table 1 presents age-specific male mortality rates for young children and middle-age groups. The glaring feature is that of explosive growth of middle-aged mortality from an already extremely high base, though the exact patterns vary from one country to another. With the exception of Armenia and Uzbekistan, though, the probability of a 35year-old man dying before age 60 appears to have reached 30-40s or more throughout the former Soviet Union. In Russia, mortality rates for men aged 30-44 more than doubled during 1987-93, and nearly doubled for 45-54 year olds. Further but less exceptional mortality increases occurred during 1993-95. A similar situation obtains in Ukraine: if anything, the increases are more dramatic. More moderate rises are recorded elsewhere, but even in the Central Asian republics of Kazakstan and Kyrgyzstan there is a near doubling of the 40-44 cohort’s mortality. Startling increases are also recorded in the Baltic states and, to a somewhat lesser extent, Belarus.’ These increases in mortality account for the overwhelming majority of the decline in life expectancy. Shkolnikov et al.‘s (1997, p. 50) decomposition analysis for Russia finds that rising male mortality in each 5-year age group from 30-34 through 50-54 from 1987-92 causes a decrease of roughly 0.4 years, dwarfing all other increases - indeed, changing mortality for males under 15 and over 60 has virtually no impact at all.’ A similar pattern exists for rising mortality in 1992-93, though the effects on life expectancy are dominated by the 40-44, 45-49 and SO-54 age groups.

DEMOGRAPHIC

In their analysis of excess (EYLL) in 1990 by age group

years and

CHANGE

IN THE FORMER

of life lost region

in the

formerly socialist countries, Murray and Bobadilla (1997) find a sharp distinction between Central Asia (plus Azerbaijan); a “southern” region comprised of Georgia, Armenia and the Balkans; and a “northern” region, which includes

SOVIET

the rest of the USSR.

lY5Y

UNION

As

usual,

Kazakstan

fits

in this categorization. Central Asia is characterized by high child mortality and moderate adult mortality; excess years of life lost arc overwhelmingly due to excess mortality among both boys and girls under 5 years old (80.4% of EYLL); by

behveen

Central

Asia

and

the northern

USSR

Age group Country

and date

Russia

Armenia

Belarus

Estonia

Kazakstan

Kyrgystan

Latvia

Lithuania

Ukraine

Uzbekistan

United

States

1978-79 1987 1990 1993 1995 197X-79 19x7 I900 1993 19X0-81 1987 1991 1994 19X0-81 1987 1YY4 197X-79 1987 1996 197X-79 1987 1990 1991 1994 lYXO-Xl 1987 1994 1980-81 19x7 lY94 197X-79 1987 1993 197X-79 1985-86 19x7 19X9-90 1994 1985 (Black) lY85 (White) 1994 (Black) 1994 (White)

&q,, is the probability

25-2’)

30-34

4.3 2.6 3.3 5.1 5.4

5.2

1.3 I.1 1.6 2.2 2.X 2.0 2.7 3.4 3.3 1.‘) 4.0 3.7 2.6 4.2 3.8 2.3 2.9 2.x 3.2 3.8 1.9 4.6 3.7 2.3 3.X 2.9 2.2 5 2.6 1.9 1.X 1.9 2.1 2.9 1.5 3.7

1.s

3.3 4.3 7.0 7.4

1.9 1.2 2.1 2.x 3.x 2.7 3.5 4.3 4.0 2.5 5.7 4.6 3.0 5.5 5.0 2.9 3.8 4.0 4.6 4.x 2.5 6.6 4.6 2.6 5.1 3.x 2.9 8 3.3 2.5 2.1 2.5 2.6 4.1 1.6 5.0 2.0

35-31) 7.7 4.4 5.6 9.3

10.0 2.6 2.2 2.Y 3.X 5.9 3.7 4.8

6.1 5.9 4.0 8.5 6.6 4.0 7.4 5.‘) 3.x 4.6 5.0 6.4 7.1 3.Y 9.6 6.3 4.0 7.5 5.X 4.0 10 4.5 3.6 3.0 3.3 3.4 5.5 2.0 6.3 2.6

40-44 Y.5 6.2 7.6 13.3 14.1 4.0 2.8 4.5 5.1 7.5 6.1 7.1 8.X 7.4 5.7 12.8 8.5 5.8 10.6 7.8 5.2 7.3 6.X 10.0 8.7 6.7 14.2 7.7 6.2 Il.0 7.2 5.7 14 6.2 5.1 4.2 5.2 5.3 7.7 2.9 X.6 3.3

45-39 13.4 Y.Y 11.7 17.8 lY.3 5.4 4.9 6.X 7.1 IO.6 9.2 10.8 13.1 II.2 9.3 15.7 12.3 Y.4 14.1 10.6 8.6 9.0 10.1 13.9 12.2 Y.0 20.3 10.2 X.3 15.3 10.4 9.2 20 x.2 7.2 7.0 7.6 9.4 IO.5 4.6 10.9 4.3

SO-54 17.5 14.1 16.1 25.3 27.3 X.9 7.7 Y.9

10.0 13.5 12.8 16.0 19.3 15.7 12.3 22.0 16.8 13.3 22.0 15.0 10.7 14.0 13.6 20.0 IS.0 13.0 26.6 13.6 13.1 19.9 14.2 12.8 25 12.0 11.3 10.8 12.0 Il.0 15.3 7.6 14.9 6.6

55-59

O-5

24.6 21.9 23.4 31.3 34.0 14.7 12.1 16.1 17.4 18.1 18.5 21.1 26.0 22.1 20.7 30.6 22.x 21.1 29.6 19.2 17.5 18.9 19.5 25.6 21.9 20.5 34.7 18.7 18.8 26.X 20.1 20.1 36 16.1 16.2 16.1 17.2 20.0 21.3 12.7 20.2 10.6

6.7 5.7 4.4 4.5 5.1 X.3 6.0 6.‘) x.2 4.x 3.7 3.2 3.1 5.1 4.4 3.6 IO.9 x.0 7.X 16.2 7.7 0.6 9.2 9.1 4.5 3.0 4.0 4.4 3.4 3.8 5.5 4.2 4 15.8 16.8 16.3 11.2 10.4 4.5 25 4.2 1.x

:jqx, (‘;) x1.7 24.x 27.7 3X.X 41.1

16.4 13.9 1X.3 IO.6 24.4 22.4 26.0 31.0 26.‘) 23.0 36.4 28.7 23.7 34.5 25.5 20.6 23.7 24.2 31.8 27.Y 23.5 41.4 24.8 22.4 33.4 25.2 23.0 41.2 21.0 lY.6 18.7 20.4 22.3 26.2 13.9 26.4 12.x

of someone who survives to age 45 dying by age 60. Sources: Goskomstat SSSR (1990) and Goskomstat SSSR (19X9), Goskomstat RF (l9Y4) and Goskomstat RF (1996a), Republic of Armenia State Statistical Committee‘(l9Y~), US Bureau of ;he census (19X7) and US Bureau of the Census (1997), Naskomstat Krygyzstan (1995a), UNDP (lYYS), Minzdrav Uzbekistan (lYYS), NSA Kazakstan (lY96), Minstat Belarus (1995), Lithuania Stat (1996), Lithuania Stat et ul. (1996).

1960

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DEVELOPMENT

cause, respiratory infections (56.6%), diarrhea1 diseases (17.0%) and perinatal conditions (10.8%) dominate. Among the northern countries, excess mortality of males 15-59 accounts for 45% of EYLL, some three times as great as EYLL for all children under 5. Not surprisingly, infectious and contagious diseases play only tiny roles in adult male mortality, and hence are not important overall in northern ex-socialist countries’ excess mortality patterns. The data in Table 1 suggest that these distinctive patterns have become blurred. Most recent data for middle-aged mortality in Central Asia suggests patterns such as those for Russia, Ukraine, Belarus and Lithuania during 1987-90. Infant and young child mortality patterns there also appear not to follow the rise in adult mortality. There is certainly not a clear pattern of increasing child mortality anywhere save Armenia: moderate increases in infant mortality in Russia and elsewhere seem to have been offset by continued declines in older child mortality. Since adult men are among the least frequent users of formal health care systems, and since a collapse in hospitals and medical services would normally lead to great increases in child and elderly mortality, it seems implausible to attribute the surge in adult mortality to health system collapse. Several alternative hypotheses have been advanced, some of which are examined in the econometric work below. Nor is it likely that environmental factors arc responsible for the increase in adult mortality, since most such forces operate gradually and with long lags; they also affect the elderly, and are unlikely to have such a pronounced male bias. Immediate environmental factors are almost certainly of reduced importance in recent years as well, since industrial production has fallen dramatically. Given the extent of the mortality crisis, surprisingly little is known about its causes. Largely because the 1985-87 surge in life expcctancy coincided with President Gorbachev’s antialcohol campaign, there is strong belief that rising alcohol consumption (and. in particular, binge drinking) is a central factor. But there are problems both in data and attribution. (See Shkolnikov and Nemstov (1997) and Treml (1997) for a sceptical view). Serious econometric problems exist as well: studies linking alcohol to mortality do not control for smoking or other risky behaviors, because of data limitations. Thus, Lopez (1997) argues that increased smbking plays an important role in rising NIS mortality, though surely it could not account for more than a fraction of the sudden, sharp rise,

especially among young men. Moreover. South Korean men also have high levels of binge alcohol consumption and a high incidence of smoking and have enjoyed a rise in life expectancy from 58 years (lY72) to 67 (1992) and have a gap of only 8 years relative to Korean women (World Bank, lYY4). Other important factors include a diet intensive in animal fats and chemical additives, unhealthy lifestyles, and the psychological stress of the economic transition. Prime-age males have found suddenly themselves economically marginal, barely able to support their families. and without the skills needed for a very different world. The important role of rising incidence of cardiovascular-based mortality (see Puska, 1997 for a comparison with Finland et al., 1997 and Shkolnikov et d., 1997 for causal decompositions) is almost certainly linked to diet, lifestyle and stress, though precise attributions are inherently difficult. If stress and social breakdown are important, then family and social stability characteristic of Central Asia and the Transcaucasus should restrict rising mortality. This appears to be the case to some extent, though substantially inferior data and likely deterioration of quality hinder interpretation. There is considerable evidence that infant mortality rates in Central Asia are vastly understated (see Kingkade and Arriaga, 1997 and Becker et al., 1998) and hence life expectancies overstated, while Murray and Bobadilla (1997) suggest that roughly 20% of all deaths in the region arc unregistered. In short, the combination of data weakness and apparent growth in Central Asian adult malt mortality, especially in Kazakstan, together prevent us from straightforward drawing crosscountry conclusions. Data on the growth of male mortality by (select) cause of death appear in Table 2. The age-specific incidence of cancer mortality is stable, suggesting that the data as a whole arc credible, and do not reflect selective migration or quality changes (Leon et al., 1997); otherwise, Russian mortality exhibits dramatic growth during 1990-9.5. The dominant features, however, are explosive growth of accidents, injuries and poisoning (AIP), and in circulatory system diseases, again from extremely high bases. AIP annual mortalty surpassed 0.5% among adult Russian men by 1995, and in some regions approached or exceeded 1%. AIP mortality is highest in northern Russia, Siberia and the Far East, but between 1990-95 it more than doubled in central Russia and the Urals. The rise in cardiovascular disease is especially surprising

DEMOGRAPHIC

CHANGE

IN THE FORMER

Table 2. Male mortalir?, (deathslhundred

thousarld)

Country and date

Northwest

region

region

Central region

Volga-Vyatsk

region

Central-Black

Earth region

Volga region

Northern

Caucasus

Urals region

West Siberia region

East Siberia region

Far East region

Belarua (all males)

Estonia (all males) Kyrgystan (all males) Latvia (all males) Lithuania Uzhcklstan

UNION

1961

by select cause of death”

Causes

Russia (working age males)

Northern

SOVIET

(all males) (all males)

USA (all males) White Black

I990 1991 1YY2 1993 I Y95 1990 1993 I YY5 1990 1YY3 1995 1990 1993 1YY.s 1YYO 1993 1YYO

1993 lYY5 I900 1993 1995 1YYO lY93 1995 lY90 1993 1905 1YYO 1Y93 lYY5 1990 I993 1995 1990 1993 1995 1980 I985 I9YO 1992 1994 1987 1994 19X7 lYY4 19x7 I994 1987 1994 1987 1994 1994 lYY4

Infectious and parasitic diseases (I)

Neoplasma (II)

Diseases of the circulatory ,y\tem (VII)

Diseases of the respiratoty system (VIII)

19.0

142.7 141.7 142.2 145.1 140.0 126.3 133.5 126.5 163.4 164.3 153.4 166.3 166.Y 150.3 147.5 140.0 137.4 163.4 105.7 161.8 141.7 147.2 143.7 110.3 I2X.Y 125.9 131.x 135.3 132.2 120.0 134.2 12Y.6 127.9 132.6 127.X 1 IX.8 125.6 127.6 145.3 175.3 210.4 228.3 232.7 217.2 254.5 87.8 77. I 235.8 264. I 205.7 241.7 67.3 SO.8 22X.Y 212.1

120.6

30.9 20.1 35.2 55.4 64.2 23.6 57.7 67.7 25.7 73.2 69. I il.6 65.1 77.3 40.7 57.6 70.3 43.0 59.‘) 62.7 25.1 39.2 46.4 26.Y 40.6 44.0 33.6 57.0 66.7 31.2 57.2 67. I 30.6 5X.2 77.3 21.6 4x.9 5Y.0 153.6 121.7 86.7 76.2 88.6 39.6 6Y.0 178.1 149.7 52.8 Xl.4 71.6 70.0 161.5 124.5 71.2 50.1

20. I 23.7 32.6 41.7 7.6 14.5 20.7 18.3 43.3 43.4 16.7 31.8 40. I 14.4 23.6 32.2 17.X 2X.6 31.1 IY.0 2X.2 3X.7 24.8 41.1 51.8 15.6 24.X 31.5 24.3 38.‘) 53. I 26.4 45.3 60.7 23.2 36.7 so.3 4x. 1 41.1 36.0 40.5 42.3 1X.3 _ 44.5 33.7 23.5 _ 43.5

222.3 251.5 329.0 301.0 216.‘) 380.1 434.9 270.7 -171.5 463.6 75X.4 366.5 424.0 213.5 269.9 34X.6 248.3 332. I 356.X 206.9 290.6 320.7 178.1 2x4.1 313.7 20 I .h 304.3 331.3 1YO.l 2YX.7 330.9 lY2.6 320.2 33X.X 199.6 339.5 353.9 4X6.3 545.0 403.5 512.4 60 I .9 630.3 766.7 249.8 325.6 666.9 91Y.O 54x.3 635.Y 227.3 288.1 300.1 240.4

Accidents. injuries and poisoning (XVII) 283.7 303.6 377.0 4XY.X 505.4 262.2 55Y.6 577.3 261.3 559.5 50X.6 260.6 505.3 542.x 28X.4 480.2 512.6 285.2 360.6 3X7.3 262.5 405.5 435.1 200.5 304.6 33.5.5 2XI.3 S31l.X 563.5 309.2 545.4 524.1 37X.7 622.3 62X.2 330.4 596.4 5x3.3 167.0 150.0 166.5 201.7 234.5 155.6 37X.7 100.6 14x.x 15O.h 392.5 153.3 312.0 7X.0 66.6 76.7 134.5

“Groups correspond to 9th revision of the International Statistical Classification of Diseases. Injuries and Causes of Death. For US. circulatory system deaths include chronic obstructive pulmonary diseases, pneumonia and flu; heart disease is defined as circulatory system disease. Sources: Goskomstat SSSR (19X9). Goskomstat RF (lYY4), Goskomstat RF (19Yha), US Bureau of the Census (lYY7). Natskomstat Kyrbystan (1995), Minzdrav Uzbekistan (1995), Minstat Belarus (1YYS) and Lithuania Stat et al. (lYY6).

l’)h2

WORLD

DEVELOPMENT

given the widespread, large decline in the consumption of animal fat in the former USSR (Notzon et al., 1998; for data. see Goskomstat RF, 1996b). The incidence of AIP also doubled in the Baltic states. while growth in Belarus and Kyrgyzstan has been around 50%. Of the countries for which we have data. only Uzbekistan appears to have a “normal” incidence, slightly below US levels - which, of course, are inflated by automobile accidents and, to a lesser extent, homicides. Circulatory system mortality is exceptionally high in the Baltic states, Belarus and Russia, ranging from 0.6-1.0% per annum, with the most dramatic growth occurring in Latvia.”

3. REACTION TO SHOCK: DECLINING FERTILITY AND FAMILIES IN STRESS Rising mortality has not been the only indicator of social and economic chaos in the former Soviet Union. Crude marriage rates have fallen universally - in Slavic Russia (1987-95: - 26%) and Belarus (1987-94: -28%); in the Baltic states of Lithuania (1987-94: -34%) and Estonia (1987-94: -43%); and in the Central Asian republics of Kazakstan (1987-95: -29%) and Uzbekistan (1990-94: -25%:). The most stunning declines of all have occurred in KyrgyzStan (1990-94: -41%) and Latvia (1987-94: -53%‘). It is difficult to comprehend the forces that would give rise to such a broad collapse. The declines in high fertility in Central Asia are staggering, and surely unprecedented in peacetime. The virtual halving of marriage rates in Latvia and Estonia is similarly breathtaking, especially given the latter’s bright economic prospects. Other social behaviors also have changed. Divorce rose in much of the former USSR with Independence, though it has fallen in many parts. In part, this reflects declining marriage rates (i.e., there are also fewer bad marriages). Overall, Russia’s crude divorce rate rose 18% during 1990-95, with the greatest increase occurring in the northern region (+39%:). Divorce rates also rose in Belarus ( +26%). and have been stable or (in some cases, markedly) declining elsewhere. With declining marriage rates, it is hardly surprising that the incidence of birth out of wedlock has risen ubiquitously. In heavily Moslem and socially conservative countries, the increases have been moderate (1987-94 rises from 11.1% to 15.6% in Kazakstan, 10.4% to 16.8% in Kyrgyzstan, 7.5% to 12.1% in Belarus,

and 7.1% to 10.8% in Lithuania). The increases in Latvia (15.5% to 26.4%) and Russia (12.7% to 21.1% in 1995) are more pronounced, while Estonia’s 19X7-94 share of births in unregistered marriages soared from 27.1% to 40.9%. Whether this pattern is temporary reflects social disruption, or reflects movement toward a northern European marital pattern remains to be seen. Within Russia. the proportion of infants born out of wedlock was highest in northern Russia, Siberia, and the Far East - among the regions of greatest social disruption. Abortion data are less comprehensive. Abortions per woman have fallen sharply. Abortions per live birth have fallen in Central Asia, risen but stabilized in Russia, apparently risen in the Baltic states, and doubled during 1987-94 in Belarus. Overall, the number of abortions a woman can expect in her lifetime (TFR multiplied by abortions/birth) in Russia fell from 3.9 in 1990 to 2.7 in 1995; in Belarus, lifetime expected abortions rose from a temporary low of 2.0 in 1987 to 3.5 in 1990 before declining to 2.8 in 1994. While below Soviet era peaks, these means remain very high by world standards (Haub, 1994). Together with marriage, the most striking demographic change has been in fertility behavior. Total fertility rates - the number of children a woman will have if, throughout the course of her fertile life, she experiences the age-specific birth rates prevailing at any instant - have declined universally. In Central Asia, 1987-94 TFRs fell by about one child, in KazakStan (3.19 to 2.34, and then 2.13 in 1995) Kyrgyzstan (4.22 to 3.12), and Uzbekistan (4.57 to 3.58). In Uzbekistan, this decline is partially accounted for by an ongoing demographic transition; however, throughout the region former USSR, 1987 was also a peak fertility year. Nonetheless, it is apparent that the precipitous declines since 1990 (and the extremely low TFR in cities like Bishkek) do not simply reflect reversion to a norm. Belarus and the Baltic states all were characterized by stable fertility, roughly at replacement levels, in the 1980s. Dramatic 1990-94 TFR decreases to unprecedentedly low levels were then recorded: by 23% in Belarus and Lithuania, by 31% in Latvia, and by 33% in Estonia, Russia’s TFR declined by 28% in 1995 relative to 1990, and by 39% relative to 1987. Only limited regional differentiation exists within Russia: IYYO-95 TFR declines ranged from 29% to 34% everywhere save the northern Caucasus (-26%) which, together with East Siberia, was the only region with a TFR above 1.5 in 1995.

DEMOGRAPHIC

CHANGE

IN THE FORMER

It also can be seen from Table 3 that fertility did not decline uniformly across ages or parity. Higher order births declined nearly everwhere, but especially in Russia, where the proportion of children who were first-borns rose from 42% in 1987 to 60% in 1995. This pattern is also reflected in the age structure of fertility. Teen fertility declined least of all - in fact, birth rates for girls under 18 have risen in many places. In contrast, fertility among women aged 30-34 and 35-39 has declined most precipitously, often by 40-50%. Even in Uzbekistan, where 20-24 cohort fertility stayed virtually constant from 1987-94, 30-34 year fertility fell 38% and 35-3’3 fertility declined 52%. In Russia, 1987-95 teenage (15-19) fertility declined 6%, 20-24 year fertility fell 33%, 30-34 year fertility fell 56%, and 35-39 year fertility fell 62%. In short, the fertility collapse is especially pronounced in established families. Women are marrying less often; when they marry, they are less likely to have a first child; when they do, they are far less likely to have a second child. This post-Soviet fertility decline of one-quarter to one-third is not driven by an aging population, since the TFR measure controls for age structure. Nor does it reflect the late 1980s baby boom, which had disappeared by 1990. One hint that the social effects of the transition from communism may be more important than simply reflecting economic deterioration comes from the former German Democratic Republic (Witte and Wagner, 1995). Unlike the CIS, living standards in eastern Germany have risen rather than fallen in the past 5 years. Nevertheless, eastern Germany’s TFR precipitously declined from about 1.5 in 1989 and 1990 to 0.8 toward the end of 1991, and appears to have stabilized at that extraordinarily low level. The nature of the fertility decline is the subject of heated debate. Optimists such as Zakharov and Ivanova (1996) point out that the pronatalist politics of the mid-1980s caused a temporary surge in fertility, characterized by shorter birth spacing and a shift to younger nuptiality and first births. That Russian women should compensate by reducing fertility as they age comes as no surprise; nor, in their view, does temporary postponement of fertility in difficult times. But these explanations seem excessively optimistic. From Table 3, we see that 1995 birth rates for women aged 20-24 (who would have aged from 8-12 to 12-16 during 1983-87, the pronatalist era and peak fertility years, and hence unafffected by pronatalist policies) are 28% below 1980 rates in Russia and 22% lower in &izakstan - despite the marked shift to younger age fertility.

1963

SOVIET UNION

Nor is the optimistic view consistent with surveys (cited by Haub, 1994) which find that the “ideal” number of children declined from a mean of 2.1 in 1991 to 1.5 just 1 year later. The mortality decline is more riveting than the collaspse of marriage and births. In fact, however, these processes are likely to be linked, and indeed, to be tied to one or several underlying variables, which is turn ultimately reflect the social chaos associated with the collapse of the Soviet Union. We now turn to this topic, examining crossregional data from the Russian Federation.

4. DETERMINANTS REGIONAL MORTALITY

OF RUSSIAN DIFFERENTIALS

Variation in life expectancy across Russia will depend on economic, demographic and health factors. Formal analysis is restricted by available data, which are problematic for both economic and health variables. Our strategy in this section is to examine, via regression analysis, the determinants first of a summary statistic, life expectancy, and then turn to an analysis of individual causes of male mortality. A detailed analysis of regional patterns appears in Vassin and Costello (1997). Obvious include explanatory variables measures of personal health (smoking, alcohol consumption, diet), environmental conditions and quality of health care.’ Alcohol consumption data are available (for discussions of quality, however, see Treml, 1997 and Shkolnikov and Nemstov, 1997), as are food consumption data. Smoking data are not availble for all regions; as such, given high collinearity, the C’ODK4 variable captures the effects both of drinking and smoking. No good measures of environmental quality exist, to our knowledge, and we are limited to using an aggregate measure of environmental degradation, the total quantity of air pollutants emitted.” Consumption of animal fats is problematic in a different way: the effects may vary greatly from one social setting to Excess consumption is clearly another. deleterious to health, but in poor societies there are positive effects associated with greater in part by reducing infant protein intake, mortality. Not surprisingly, there is no clear measure of health care quality. Quantitative data on hospital beds and physicians abound, but the causality is likely to be reversed. Given the absence of a national link between infant mortality and adult mortality or life expectancy, we hypothesize that the regional infant mortality rate IMR reflects

WORLD

1964

Table 3. So&l

DEVELOPMENT

demographic

indicutorc-”

out

of

wxllock t YXO lYX7 IYYO t Y93 I Y9.i

RU\hiil

Northern

Iwo I‘N5

region

Northwest Central

Central-Black

Earth

I YYO I945 I YYO I YY5 I 900 I YYS IYYO I 94s

Volga Northern

Caucasus

Ural\ Wrct East

Siberia Siberia

I YYO I YVS 1990 1YY5 IYYO I YYJ I YYO I YY5 I YYO

IYE Far East Belarus

Kazakstan

Kyrgyr

Bishkek Latvia

Lithuania

Uzbekistan

Republic

IYYO IYYS IYXO 19x7 IYYO 1 w-1 1 YXO I YX7 19YO I YYJ IYXO I Y87 I ‘)‘)(I tYY4 I YY5 I YXO 14X7 IYYO I YY4 I YY4 I 005 I YXO 19X7 IYYO t 9YJ IOXO IYX7 IYYU IYYJ I ‘IX0 lYX7 1 YYO 1YY4

t0.h Y.H S.Y

7.5 1.3 x.5 h.X Y.5 1.7 X.7 7.6 s.3 h.6 8.6 7.X Y.0 7.1 Y.2 7.‘) X.7 6.Y Y.0 7.3 X.7 f>.X 10.1 7.1 to.1 IO. I Y.7 7.3 8.X 8.h 7.5 4.9 1O.h Y.X Y.X 7.3 7.0 10.7 ‘1.6 9.‘) 5.X h.3 5.8 Y.8 Y.6 8.S 4,s Y.2 9.6 Y.X h.3 10.9 9.X 10.6 7.9

I .YO 2,lY t .XY 1.3x I.34 I.41

J3.h

5x.2 50.5

t 2.7 I4.h

4s.s

40.3 51.6

21.1

45.6 il.h 3Y.Y 40.0 311.7 44.5 3X.4 4X.3 40.5 SY.5 5.3.7 53.0 45.x 56.4 52.X s4.0 45.0 60.5 a.2 h7.X 55.6 0V.9 5O.Y 32.2 34.3 41.6 42.5 44.6 45.0 53.0 3X.h 38.‘) 41.5 51.7 53. I 4X.0 41.0 39.5

I.26 3X.4 I .hO 43.3 I.117 I .hh I.lH l.YS 1.2’) I .X8 I.32 I .YY 1.35 2.27 l.hY 2.06 I.37 I .9f> 1.32 2.26 I.51 2.08 I .40 2.01 7.08 I.‘)6 I.51 2.02 7.23 2.05 1.37 2.YO 3.11) 2.70 2.34 2.13 4.07 4.22 3.70 3.12 I.31 I .YO 2.16 2.02 I .3Y 2.00 2.17 2.00 I.54 4.x1 4.51 4.OY 3.5x

‘Ol10 IS-19

32.Y 44.7 34.2 4Y.h 3X.3 4X. t 3x. t 4Y.3 3Y.J 50.3

49.3 5 I .5 40.7 51.‘) 39.6 Sfl.0

44.5 s2.5 42.‘) 47.h 53.0 4X.X 43.2 50.6 56.5 53.7 4Y.Y

11.4 24.0 1l.h 23.0 12.4 1Y.7 10.3 IS.0 IO.6 14.4 I I.2 I 0.x IS.9 IY.X 14.x 21.5

16.5 23.I 22.3 30.4 IX.4 29.0 h.4 7.5 x.5 12.1

51.0

h6.2 5Y.4 5f3.X 57.3

22.1 27.1 40.‘) 10.3 II.1 13.2 14.5 IS.6

71.5

10.4

65.3 43.2

If>.8

55.1

26.7

iO.fl

47.2 s5.7 52.4 49.1 SO.6 52.8 52.1 49.7 73.6 -

20-24

25-2s

157.6 t 70.0 Ih1.l

102.0 122.0 OX.0

2.11

67.2 03.‘) h3.4 X4.3 5h.X s.i.7

29.7 51). I 27.1) 14.X 25.2 44.3 26.5 4Y.X 20.4 45.1 25.7 s I .2 70.3 h3.8 43.1 53.Y 30.3 49.2 26.2 01.3 32. I 56.0 31.5 57.0 5X.Y 47.4 32.4 53.9 63.2 557 35.0 96. I

t 13.5 162.5 110.3 120.3 X9.Y 137.8 YY.‘s 167.7 l13.Y I60.2 II33 167.6 114.1) 180.4 135.0 171.X I to.1 163.6 t 13.7 l8l.u 175.5 lh3.5 I IS.2 172.0 173.0 17X.6 137.2 170.4 179. t t 64.4 t 09.4 206.4 227.9 217.1 17Y.4 160.2 270.0

173.5 75.7

108.4

104.4 119.2 Ill3.4 77.5 116.8 132.4 III.3 X4.5 272.5 2x..<

53.1 65.3 5h.3 36.7 63.7 65.1 5h.8 39.4 IY4.6 177.4

22. I 28.8 23. I 15.7 30.4 2Y.7 22.2 16.6 122.4 Y5.1

193.3

109.6

4.5.4

26.4

33.7 2X.0 24.5 41.h 41.0 32.3 37.X 64.X

2XX.K

3,s

10.7 I Y.X X.X 17.3 0.4 I h.5 0.3 1’1.1 X.X t 7.0 0.2 1Y.7 IO.2 26.2 16.3 22. t ICI.4 20.3 Y.4 2X.4 12.X 25.0 12.‘) 20.X 22,s 17.1 l1.h 21.1 2h.Y 23.3 14.1 56.1 5 I.3 37. I 1X.7 26.5 YY.5 84.7

222.2 85.X 154.x 106.4 165.4 I IO.0 157.3 172.9 16X.1 123.0 273.6 2Y4.6

7.1 7.0 IO.8

1S.X 27.x 20.7

2h3.0

44.4 so.0

t03.0

67.X 51.5

3,s3’1

75.7 lOY.2 t24.‘) 106.2 73.6 15x.‘) lX4.Y 142.4 13l.O 121.1 212.2 201.1

15.5 th.Y

39.9

5Y.S

101.2 05.0 YO.4 54. I 101.9 67.5 I 14.5 X7.6 104.4 70.0 Y3.0 h2.7 IOh..’ 72.6 95.X 65.h 117.7 122.4

.x-34

t05.5 Xl.h 6X.X 63.4 140.6 152.X

44.h

49.0 20.7

births

1Ohh

2os9 ?.ZSU 7020

1307 Y70 I7Y2 187X I3Y5 1583 707

542 447 2617

1305 1341 640 x73 312 277 17’)

‘1990 TFK for Russian re#on\ are tor 19X9-Y@ data for 19X7 and earher typically are for the given and preccdmg year m all countries. TFRF for Uzhcklstan l9YO and 19Y4. and Kazakstan 1994 and 1995 are computed from data. Sources: Goskomstat SSSR (1989). Goskom5tat RF (lYY4). Goakomstat RF (lYY6a). US Bureau of the Cenws (lYY7). Natskomstat Kyrgyrstan (lY95a). Natskomstat Kyrgyzstan (l!JYSh), Natskomstat Kyrgyztan (1995~). Minzdrav Uzbekistan (IYYS), Goskomprognozstat (1992). NSA Kazakstan (l’JY7). NSA ( IYYha). Goskomstat KSSK (IYY 1). Mmatat Bclarus (I YYi), and Llrhunnia Stat ev 111. (1996).

DEMOGRAPHIC

CHANGE

IN THE FORMER

SOVIET UNION

1965

caution (Table 4 and Table 5) as its value is doubtless inflated (in absolute value) by selectivity bias. Because of the extremely high correlation beween marriage and divorce, it is practical to include only one in the mortality regressions. Our choice is to include divorce, since it is clearly the more disruptive event.

underlying health care quality, public health and personal health care practices. Some 75-85% of the effects of the IMR coefficient in Table 4 are indirect, rather than simply being an accounting identity.” Other demographic variables may also matter. Divorce places stress on individuals, making them more susceptible to disease. Perhaps more importantly, divorce is also a major determinant of real living standards, especially among women, and poverty is associated with premature mortality. Furthermore, divorce may make people more susceptible to dysfunctional behavior, which in turn leads to early death. Because divorce is selective rather than random, however, the coefficient must bc treated with

The remaining demographic variable included is the total fertility rate, TFR, which we regard as a proxy for the strength of the family structure. In Table 5 (but not Table 4) TFR is also associated with a younger aggregate age structure, and this will bias the coefficient toward increasingly large, negative values. Finally, two economic measures are indicated. Mean household per capita income (for 1990, in

Table 4. Cross-ohlust Ru.vsiart life expectancy regression (absolute value t-statistics in parentheses)

VZirlable

VODKA MEAT MILK

(x IO ‘)

FISH VEGGIES

( x IO ‘)

INCOME

( x IO ~)

DIVORCE AUTO

( x IO

‘)

IMR N. Cauca\u\

dummy

Siheria,‘Far Ea\t North:Northwest

dummy

R’ F No ohs

(1)

(2)

(3)

Ml?”

Me11

Men

68.027 (21.9X) ~ 0.07’) (0.18) 1.533 (1.51) -U.U41 (0.69) -

66.403 (lb.85) ~ 0.425 (0.43) 1.955 (1.83) -U.OlO (0.16) -0.078 (3.00) 9.lb5 (I.bi)

(4) Women

b5.890 82.202 (13.05) (34.46) -. 0.5 I4 0.118 (1.08) (0.35) 1.82b 0.199 (1.45) (0.26) -0.003 0.020 (0.04) (0.45) -0.078 (2.96) x.570 (1%) -0.031 (0.5 1) 0.987 (0.88) - 2.244 - O.b34 - 0.060 1.8Y7 (1.73) (0.43) (0.04) (l.YU) -0.620 -0.608 -0.59X pU.663 (1.66) (1.56) (1.3X) (2.30) U.XUX 1.276 I.214 0.104 (0.59) (0.X’)) (0.79) (0.10) -0.451 -0.372 -0.35Y -0.412 (5.62) (4.7b) (4.46) (b.hb) 1.872 (I .OO) -II.697 (0.34) 0.142 (0.07) 0.44 0.47 0.50 0.55 7.77 6.71 4.36 I l.bX 70 77 77 76

(5)

(6)

Women

Women

X0.842 (2b.57) -0.180 (0.55) 0.558 (0.68) 0.047 (0.99) -0.067 (3.34) 7.805 (1.80) -

79.525 (20.73) -0.251 (0.69) 0.526 (0.55) 0.058 (1.14) -0.067 (3.32) 7.842 (1.56) -0.027 (0.59) 1.298 (1.52) -0.024 (0.02) -0.619 (1.88) -0.554 (0.4X) -0.328 (5.37) I.049 (0.73) -0.934 (0.60) -0.481 (0.30) 0.58 6.21 77

-0.512 (0.45) -0.654 (2.18) -0.510 (0.46) -0.344 (5.71)

0.55 Y.lb 77

(7)

(8)

(9)

(10)

(11)

Men

Women

Men

Women

Me”

-5.807 -1.54Y -0.145 (3.12) (1.33) (0.22) -0.520 -0.229 O.Y25 (l.YY) (1.40) (1.10) I.‘)38 0.656 5.351 (3.18) (1.72) (3.74) 0.047 0.036 0.055 (1.32) (1.59) (1.22) -0.011 (0.72) X.26!, (2.10) -O.OOY (0.24) -0.377 (0.47) -1.687 -0.165 -1.393 (2.16) (0.34) (1.86) -0.066 -0.171 - 1.347 (0.29) (1.21) (3.40) 0.830 0.429 -5.412 (1.00) (0.82) (2.23) -0.106 -0.098 -0.072 (2.20) (3.22) (1.00) -0.535 (0.54) 2.631 (2.35) _ -0.645 (0.60) 0.43 0.30 0.63 7.27 4.16 b.Y4 76 76 73

(12) Women

-0.465 -6.445 ~ 2.087 (1.0X) (2.29) ( I .U3) 0.620 .-~0.735 -U.lY2 (1.12) (2.13) (0.7X) 2.556 0.0020 0.0009 (2.72) (2.00) (1.25) 0.073 0.017 0.02 1 (2.46) (0.36) (0.62) -O.UIh -

(1.53) 0.2114 (0.79) O.OlY (0.79) -0.3X’) (0.74) -0.368 (0.75) -0.566 (2.1X) -2.990 (1.87) -0.021 (0.45) -U.ljY (0.24) l.lb7 (1.5X) 0.150 (0.21) 0.49 4.02 73

-

-1.713 (1.55) -0.097 (0.30) I.270 (1.24) -0.068 (1.26) -0.03X (0.03) -5.658 (3.X6) -0.028 (0.02) 0.47 5.Yl 77

- 1.377 (1.74) ~ 0.221 (0.97) 1.246 (1.6Y) 0.062 (1.63) 0.085 (0.09) -X.424 (8.00) 0.288 (0.2X) 0.63 I I .4X 77

“Equations (l)-(O) are I993 lift expectancies on 1992 levels of independent variables. Equations (7) and (8) arc 1993-89 changes in life exoectancv on lYY2 levels. Eauationa 191 and (IO) are differences on 1992-90 differences las thew wetc the vears for which we have the hest data, even from these, four oha&ations are missing), and equations (I 1) and (12) are 1995-89 differences on 1992 levels for pollution and consumption, and I995 levels for demographic variables, and income (x IO ‘, not 10 ‘). Equations (l), (4), (7) and (8) exclude Chechnya-Ingushetia: sane other equations control for the region with a North Caucasus dummy. POLLUTION is an aggregate measure of emission of harmful materials into the atmosphere, in kg. TFR IS the total fertility rate: VODKA is per capita consumption of commercially produced alcoholic products; INCOME is mea” income per household in rubles; DIVORCE is the crude divorce rate (divorces per thousand people): AUTO is the incidence of private automobile ownership per thousand people; IMR is the infant mortality rate; MEXT is per capita meat consumption: MILK IS per capita consumption of dairy products; FISH is per capita consumption of fish: VEGGIES is per capita consumption of vegetables and vine crops (squash, pumpkins and melons). 1

.

\

/

1966

WORLD

DEVELOPMENT

rubles) serves as a measure of that portion of real income received in cash. Because it was Soviet (and, still to a large extent, post-Soviet) policy to use high cash incomes to compensate for particularly dreadful living conditions and social amenities, greater nominal income may well be associated with lower “real” total income. It is clearly associated with a higher share of private goods and lower share of public amenities in total consumption. Second, we used the incidence of private automobile ownership, which we take as a measure of private wealth across oblasts. Let us now turn to the regression results. Life expectancy regressions appear in Table 4. The first six equations provide regressions of levels on levels; to control for possible fixed effects, we also regressed 1989-93 life expectancy changes on explanatory variable levels in equations (7) and (8) and finally related changes to changes in (9) and (10). We emphasize that the results presented in Table 4 and Table 5 are merely illustrative of a much vaster set of regressions:

given collinearity problems, questionable data, likely changes over time in underlying relationships, interest in but difficulty in interpreting regional dummy variables, and uncertainty as to exact relationships and plausible functional forms, we tried a wide range of alternatives. Nor are the regressions presented the best in terms of statistical fit; indeed, many of the most interesting results concern insignificant coefficients. Infant mortality turns out to be the most significant variable, both for predicting levels and changes in life expectancy. The coefficients are remarkably similar for men and women, and changing samples or adding regional dummies has virtually no impact. With the exception of regressions (7) and (8) the divorce rate is also highly significant, both for men and women. Regressions (l)-(6) imply that a divorce will cost Russian men 4.3 years and women 4.5 years of expected life.“’ Per capita income is a frequently but inconsistently significant variable: higher levels and more

Table 5. Cross-oblast working-axe male mortal@ regressions, Russia 1993 (absolute value t-stat&tics in purentheses) Variable

(3) (9 (6) (X) (1) (2) (4) (7) Infectious/ Cancer Circulatory RespiratoryDigestiveAccidents,Infectwus/ Cancer diseases mjuries paracltic p‘?IaSitiC system \y\tem diseases dlaeascs and diseases disease\ poisoning

Constant POLLUTION TFR VODKA MEAT MILK FISH VkGGIES INCOME DIVORCE AUTO IMR N. Caucawc dumm) Sihcria:Fer East dummy North/Northwest dummy R’ I;

113.259 42.546 176.634 503.Yh7 (4.11, (3.56) (125) (3X2) (153X 1.732 -4.4hl - 19.149 (1.41) (0.49) (0.X& -3.760 -42.484 122.382 -33.430 (4.77) (3.39) (3.65) (0 43) -0.34h -0.440 1.221 1.643 (11.X8) (0.91, (1.74) (0.8X) 0.2X7 0.306 0514 0.20h (I (1.29) (1.46) (0.68) 0.05 ~0.144 0.1149 -0.378 (2.21)) (1.42) (3.24) (0.75) ~0.811 --(1.515 ~0.954 -(l.YhX (2 Sh) (1.55) (0.59) (1.32) ~0.1177 23.038 0.134 (I.003 (0.04) (l).'J3) (1.92) (0 42) 0.257 0.424 O.?hi ~OS122 (2.24) (2.56) (i.04) (U.24) ll.hSi 2 190 x.174 I8216 (O.26) (0.70) (ISI) (181) -0. IS:; ,,.I82 0. (1.45) (1.19) (0 82) I.113 I.602 0.7X3 I.460 (0.63) (2.h')) (0.91) (2.31) IO.701 3666 0.887 17.044 (0.26) (0.04) (0.20) (1.51) -85.YY4 5.846 ml6.674 -6.125 ((I 2X) (1.11) (1.48) (0.48)

OX)

(1hl)

0 39 3.10

I

195 --,,Ihl

0.56 6.27

O.hl) 7.13

0.42 3.46

(Y) (I") (11) (12) Circulatory RrspiraroryDige?ti\eAccldcnti. ryrtcm \y,trm disraw mjur~es dncasep dlrcarc~ and pi,l*i,"l"g

113.h58 72.X25 ~-13.671 (0.25) (0.60) (202) ?Y.hlH I.O62 IO.416 (0 35) (1.36) (0.99) -69.964 7.XY4 ~25.804 (142) (0.94) (1.08) I.844 I.421 -0.472 (I 10) (1.73) (0.35) 5.265 (3.15) -0.316 (0.X3) ';:;;I 1.x1') (0.50) (2.215 ~0.317 ~O.OlY (0.453 (I1 30) 0.4x3 3.h03 0.28X 0132 (050) (0.3X) (1.24) (1.50) I.017 1I 675 1.71(1 24 hY5 (0.93) (0 72) (0.37) (I 69) ~OOSh 0.(,7X -1.44+ ,I,1X (1.W) (0.57) (0.32) (1.36) 1x.509 1.194 2.199 0.490 (3.66) (222) (1.64) (1.07) 2.252 ~180.037 -13.213 -108.033 (1.52) (1.05) (3.43) (0.20) 113.381 9.104 13.392 0.754 (0.89) (0.40) (0.07) (0.68) 3.646 35.262 (0.28) (1.08) 45.x01 (1.75) --0.055 (0.02) ~S.YlJ7 (ILXY) 0.100 (0.27) (108X (0.58) ~W7h

0.28 1.X6

0.38 2.Yh

0.25 2.IY

0.48 (1.16

lhh.27X (2.3h) 1 l.SYO (1.22) Il.935 (0.53) 0.272 (0.20)

X.564 173.035 (1.77) (0.36) lJ.?XY 30.721 (U.0’)) (2.32) ll.YJI -43.6X2 (2Yh) (1.56) ~-0.lY7 44) (0.57)

(I.YIJ (1.17) 76321 13.301 -3.02; (3.6X) 5.377 (1.20) -94.271 (0.90) 300.x7, (2.70) -34.212 (0.31)

0.424 (I.Jh) 1?.'ki0 (1.62) ~(I.282 (0.92) 1.2'17

il. IYh 0 hJ I (15X) ,I.Y9) I.434 I 50x (0.4')) (0.13) 0 OZh K3OY (0.25) (0.72) 4 207 0.365 (Oh5) (1.X1) 12.608 -222.hX6 (4.07) (0.95) 39.435 410.433 (2.81)) (7.08) 5.33Y 4l.hll (0.39) (0.74)

0.62 10.89

0.20 1.63

745.63X (395) 19.027 (0.74) I lh.OYh (1.91) 09x0 (0.2X)

(07X) i5qy-i (1.141) 31.601 (0.76) 19.852 (U.4Y)

(0

II.31 2.94

I OS,

Oh4 11.50

"Equations(l)-(h) are 19Y3 adult(Ih-hO year old) mortality rate\rcgrrssedon 1992 levels of mdependrnt vanables.Equations(7)-(I?)are lYY3S90 changesin mrrrtality regressedon I992 Icvcls. Equation\(13)-(1X)are 1995-93 differences regressedon 1992 consumption and I(195demographic and income data;equations(l9)-(24)arc dafferrncrs regressedon 1992-90 differences (asthesewere the yearsforwhich have the bestdata).Regrewons (I)-(12)have 77 observations; there are 71)for (l3)-(IQ and 73 for regressions (lY)-(24). Income coeftiaent is mulhpliedby 10'm (l)-(6)and IOW theraftcr: pollution coeftictcnt i*mulbphrd hy IO00 and vcgrtablcconsumptmn cocfficienl iamultiplied by 100.Variablesare as delincdm Table 4.

DEMOGRAPHIC

lY67

CHANGE IN THE FORMER SOVIET UNION Table 5. Continued

Variable

ClNl%lant

(13) Infectious/ parasitic diseasrr

(15) C~rculatoly ystem dneases

(14) Cancer

(19) Infectious; paralrlc diseases

(I.hU) -2279 (2.lb)

2b.Y93 (1.37) 1.700 (0.66) 0.014 (1.93) - O.65S (1.82)

1x5.742 (2.01) 20X25 (I.711 II.066 (1.87) 0.392 (0 23)

-2Y26 (0.20) I.073 (0.11) U.Oli (XL)) - I.578 (2.49)

53.236 (2.19) pU.678 (0.1 I) 0.216 (1.08) 0.681 (U.hh) -38 hY8 (1.26) 32.554 (1.00) 46.924 (1.47)

lhO.Ub? (1.94) - 10.949 (0.51) - 1.22x (1.80) - I.935 (0.55) 47.925 (0.46) 3Y2.197 (3 5h) I8 395 (0.17)

34 629 (1 16) 17.084 (2.20) O.SO9 (2.06) 1.S75 ( 1.24) -22 427 (0 5’)) 12758 (0.32) 13.724 ((1.35)

15.166 (1.83) 1.954 (0 91) - 0 067 (0.97) ~O.lYU (0.54) 5.160 (0.49) 45.765 (4.14) 704 (1.08)

-hU271 (1.54) Y.756 (0.96) 0. I 19 ((1.37) -2.401 (1.44) 45.903 (0 Y?) 168.929 (5.15) 3Y.790 (0.7K)

I.831 (0 45) 2.050 (0.77) 0.177 ((1.58) -0.334 (0.65) 13.5hY

0.23 I.99

0.45 5.56

0.35 3 65

0.32 3.76

INCOME

IO 3%

DIVORCE

(1 70) 3.214

VODKA

(18) Acadcnts, injuries .md poisomnp

179.625 (2.54) 7.668 (0.82) -11.17 (U.b.3) 0.349 (0.27)

184.337 (3.20) 4.bXO (O.b2)

POLLUTION

(17) Digestive diseases

1093.967 (5 5X) IU.331 (U.40) -0.195 (2.61) -- 1.003 (11.28)

-23 767 (I 6) mu.131 (0.07) 0.013 (2.40) - 0.225 (0.85)

TFR

(16) Resp~rarory system diseaws

0.035

(20) Cancer

liX.?OS (4.23) -21.779 (0.90) -0 03Y (2.41) 7.054 (4.47)

(21) C~rculatcry sytcm diseases

YX3.2US (6.46) 3 I .hYX

-

(I511 --0.22u (3.31) -57x’)

(22) Rerpratary rytem dkcaaes

126.732 (2.52) -8.hOh (U.26) O.Ul)Y (0.40)

~-?.lml

(O.YO)

(O.Yi)

-13.134 (1.28) 7O.YUJ (0.78) 2.732 (U.X?I

IS376 (I I?) 14.994

(23) D!gestive disease\

25.505 (1.47) ,J.hSh (0.42) 0 OUX

(24) Accidents. injuries and tmiwmne XY 125 (I.151 -69.9X2 40) OS)41

(I

(I.02) (I 22)

pU.J’)X (U.68)

-4264 (1 30)

-6.753 (1.44) 2.937 (11.96) -0.205 (0.54) -0.31x (0.52)

~- 10.82s (0.51) 21.x77 (1.59) 2.177 (I 2X) 0.719 (0.26)

41.Y84 (2.67)

329. I4? 13.07)

MEAT MILKFISHVEGGIES

AUTO IMR N.Caucasus dummy Sibcna:Far East dummy NorthlNorthweat dummy R? F

(2 04) -0213 (4.23) (I 504 (1.94) 8.238 (0.6X) 14.599 (1 80) -0313 (0 041 0.53 7 h2

0.38 4.1

I

0.43 5.11

II

rapid growth (actually, less rapid decline) is associated with lower and falling life expectancies, especially for men. A 10% increase in per capita income (about 4200 1992 rubles) is associated with a decline in male life expectancy of about 0.1 years, and a slightly smaller decline for women. For a large difference (say, between Moscow’s 71,970 and the Dagestan Republic’s 21,640 ruble per capita income), the implied life expectancy differences are 1.13 years for males and 0.95 years for women. Rising fertility is generally associated with a rise in male life expectancy, especially when we look at increments. This positive relationship also exists for females when we examine changes in life expectancy, but not levels. These findings are somewhat sample and dummy sensitive, in part because fertility rates cluster strongly by region. Looking across regions, the 1990-95 decline in Central Russia’s TFR by 0.48 causes a predicted life expectancy decline of 2.57 years for men and 1.23 years for women; for East Siberia, which experienced a TFR decline of 0.75, the corresponding predicted declines are 4.0 and 1.9 years, respectively. Diet turns out to matter considerably. Meat consumption is strongly linked to lower life

X569 (2.bl) 9.882

(0 50) 101.05x

(1 14) -

(1 w 0.100 (0.1(l) -0.477 (0.28) -b5.843 (I hS)

2UR 595 (1.51)

0 47 8.88

0.46 6.83

0.15

I 10

0.26 2.86

0.52 8.b4

expectancies for both men and women. In constrast, consumption of dairy products works in the opposite direction. Fish consumption appears not to have an aggregate effect, while consumption of vegetables is associated with higher life expectancies across oblasts for women (stereotypically, the main consumers of vegetables), but not men. The effects here are large: a high dairy and medium meat consumption oblast such as Pskov has a predicted male life expectancy 8.4years greater than the country’s leading meat (and medium dairy) production center, on a per capita basis, the Kalmyk Republic.” Based on regression (6) the difference in vegetable consumption between Murmansk in the far north and Astrakhan and the Volga delta accounts for just under 0.5 years of expected life for women. Other variables were not consistently signiticant. The impact of air pollution was negative when significant; no obvious pattern holds for ownership. Nor did alcohol automobile consumption have a demonstrated negative effect on life expectancies: when significant or near significant, the effect is positive. This result is somewhat disconcerting (especially as it is borne out by the cause of death regressions) in view of the strong attribution of the Russian

1968

WORLD DEVELOPMENT

male mortality crisis to alcohol by epidemiologists and medical researchers (see National Research Council, 1997 and No&on ct ul.. lY98), and the national time-series evidence. While mismeasurement may be the cause of our failure to find an association, the statistical fits as a whole were quite good, and the signs of other variables highly consistent with expectations. At this point, we can only conclude that the evidence does not support the hypothesis that the level of drinking matters across regions; the distribution of consumption as well as the style (“binge” drinking), of course, may yet prove critical. Statistical fits for specific causes of death (Table 5) ranged more widely, with R’ values for levels (regressions l-h) ranging from 0.3X-0.62.” But efforts to explain the increments to digestive, respiratory, and infectious and parasitic diseases were less successful. Regional dummies tended to be important, though in the case of the Northern Caucasus, it is unclear whether the large negative signs reflect real differences or systematic measurement error. While we were pleasantly surprised to find that inclusion or exclusion of regional dummies and other variables almost never changed other variables’ signs, significance levels wcrc affected. As with life expectancies, the pollution measure had little impact; where it did, the sign was often contrary to expectation. Automobile ownership was similarly inconsistent. Alcohol consumption was not widely significant. but, when it was in the first differences’ regressions, the sign was unexpectedly negative. Possibly, the rise in recorded alcohol consumption largely reflects substitution from snnzogon to (presumably higher quality) commercially produced drink. Alcohol consumption also may be correlated with the age distribution, though this would not explaip the life expectancy results.” Nor are income effects consistent: the 1990-92 first differences regressions and lYY3 levels’ runs generally have expected signs, but the 1903-05 differences on levels regressions do not. Diet and demographic variables fare much better. Meat consumption is associated with higher specific mortality - though, oddly, not of circulatory system disease. Dairy and fish consumption are linked to lower mortality rates. Vegetable consumption is not generally significant, other than having an unexpectedly positive association with cancer incidence. Fertility is generally associated with lower levels of specific mortality, especially in the levcllevel regressions. While these patterns may somewhat reflect a compositional bias (higher

7FR ohirsrs may have more young and fcwcr middle-aged adults), the strong associations of higher fertility in (12) with higher rates of increase in accidents. injuries and poisoning (AIP) is harder to explain directly. rather than in a social context. Infant mortality levels arc strongly positively linked to higher and more rapidly increasing mortality especially in the case of AIP. The rate of change of infant mortality, however, does not appear to affect changes in causes of death. Overall, however, 1MI-Z appears to bc a good proxy for health care (and self-care) quality. Finally. higher and more rapidly rising divorce rates arc strongly associated with higher lcvcls and more rapidly rising rates of mortality (with the anomalous exception of a decline in the growth rate of respiratory mortality being associated with higher divorce rates). For divorce, the coefficients on AIP and circulatory system mortality (CSM) are by far the greatest. Turning to regional effects, we find that the Northern Caucasus regions had far lower specific mortality, especially of AIP, controlling for other forces. The opposite is typically true for Siberia and the Far East. cspccially for AIP and CSM. While lYY3 levels of mortality for CSM and AIP are not abnormal in Siberia and the Far East, the increments both for lYYO-02 and 199%YS are astounding. Clearly, something has occurred in this region which conventional variables do not capture. Taken as a whole, these cause-specific regressions are sobering, for they clearly delineate scvcral areas of uncertainty. Restricting attention to the levels/levels regression, in which we have the grcatcst confidence, dietary and demographic (and hence, health system) variables behave as anticipated. But we remain far from understanding the enormous growth in mortality, and especially the critical increases in accidents and circulatory mortality. It is difficult to avoid ascribing the residuals, and Siberian/Far East dummy and infant mortality cffccts, to underlying social decay.

5. WHAT DRIVES RUSSIAN DEMOGRAPHIC BEHAVIOR? A SIMULTANEOUS EQUATIONS APPROACH We have seen that Russian mortality is heavily influenced by consumption, health care, economic and demographic variables. It also has been shown that other demographic forces have changed dramatically as well. The next step is to model these forces. These equations can then be used to provide forecasts of both mortality and

DEMOGRAPHIC

CHANGE

IN

demographic change in the coming decade. under various scenarios. Our underlying model of demographic behavior is based on constrained optimizing behavior. Our discussion is brief, as the framework is conventional. Because demographic phenomena are interdependent, we simultaneously estimate equations describing marriage, divorce, infant mortality and abortion patterns, using IYY2 cross-ohlust data. Actual variables are. of course, constrained by data availability, leading to some ambiguity in interpretation. The model posits five simultaneous relationships; definitions appear in Table 6. with years noted: MARRIAGE 92= m(DIVORCE 92: out of wedlock 92, age 16-92. university 92. auto 90) TFR Y2 = f(MARRIAGE 92, DIVORCE 92, IMR 92, ABORW 92; university 92, auto 92, income 90. apt space 90) DIVORCE 92 = d(IMR 92: income 90. university 92, tfr 90, marriage 90) IMR 92 = i(TFR Y2; out of wedlock 92. physiciansihosp bed Y2, pollution 92. meat cons 92190) ABORW 92 = a(MARRIAGE 92, TFR 92: age 16-92, age 16-92’. pollution Y2, hospbeds 92. income 90. auto 92, imr YO) Marriage is seen as depending on the divorce rate, since this affects the supply of potential spouses. Age structure plays two roles: a high youth dependency rate signals a general prefercnce of children. and hence marriage. and it is also correlated with a high young adult population share, again raising marriage rates. The incidence of university students reflects social structure: we anticipate that marriage will be more prevalent in higher socioeconomic groups. For similar treasons, marriage is expected to rise with the wealth proxy, private automobile ownership. Finally, marriage incidence should decline with the social acceptability of having children out of wedlock. Fertility is posited to depend on marriage, divorce and abortion rates, as well as on education levels, income and wealth. The only unusual variable is urban living space per person: relaxation of this constraint should increase fertility. Given the Soviet system’s controls on apartment construction, it also seems reasonable to regard causality as flowing from dwelling space to fertility, rather than vice versa. Fertility should increase with marriage, infant mortality (for replacement reasons) and income; it should fall with the value of women’s time (proxied by education) and the abortion rate. Fertility will rise with auto ownership for income effect reasons, but may be negatively related if

THE FORMER SOVIET UNION improved work opportunities the labor force and parents

1969

pull women into substitute “quality” for ‘*quantity” of children - or if cars and children arc, in effect, substitutes. The remaining equations also are straightforward. Divorce is anticipated to depend on the infant mortality rate, since child deaths are a tremendous stress on family relationships, on income and education levels, on lagged marriages (one must first marry in order to divorce) and on lagged fertility, since the presence of children makes it psychologically and financially costlier to divorce. Infant mortality is seen as depending on the fertility rate, in turn. both for standard quality-quantity reasons associated with Gary Becker‘s models, and because absence of knowledge about and access to birth control is associated with poor knowledge of child care, which is of course associated with child mortality. Infant mortality is also seen as depending on health care and environmental quality, on the social acceptability of abortion (in the event of anticipated birth defects) and on maternal health, proxied for by the change in meat consumption. Finally, the abortion rate is seen as depending on variables that affect demand for children, such as the marriage rate, income and wealth, on lagged fertility (a proxy for desired family size), on medical care, on the anticipated likelihood of birth defects, proxied for by the pollution index, on a quadratic age structure expression, and on lagged infant mortality. Estimations are shown in Table 6. Regressions (l)-(S) are estimated simultaneously across ohlmts. We tried three measures of fertility: TFR, the incidence of higher order births (KU?). and the crude birth rate of women aged 25-2’) (CBR2.529). Model fits were extremely good, especially for divorce and fertility. No substantive changes occurred when HOI3 or CUR2529 replaced TFR, so full 2SLS runs for those models are not reported. Turning first to fertility, we find few surprises. Birth rates increase with the incidence of marriage, which is linked to the demand for children (and social acceptability of pregnancy). For opposite reasons, a higher divorce rate is associated with lower fertility. Each 100 infant deaths result in about IO “replacement” births. This phenomenon exists throughout the world; the Russian replacement rate is actually toward the bottom of such estimates, which typically range from about 10 to 45 replacements per 100 infant deaths. Higher abortion rates are associated with lower fertility. If abortion is widely practiced,

1970

WORLD

Table 6. Cross-ohlust

2SLS Russian demographic

(1) Variable

per ‘000 divorces

Constant

1.120 (1.51)

DEVELOPMENT

behavioral

(2)

regressions (absolute

(3)

per ‘000 marriages

ABORW abortions

(4)

Infant mortality

value t-statistics

in parentheses)”

(6)

TFR fertility

Higher order births

(7) Births/O00 women 25-29 years old

- 1.934 (5.33) - 0.230 (7.32) 0.423 (10.16) _

~ 1.8208 (8.69) PO.1399 (6.77) 0.2701 (11.19) _

_ 123.033 (5.18) - 17.130 (7.770) 27.686 (10.28) _

_

_

_

_

(5)

-

DIVORCE MARRIAGE MARRIAGE

lagged

(1990)

TFR TFR lagged IMR

INCOME

lagged

( x 10

‘) (1990)

- 0.034 (5.18) 0.498 (4.76)

OUT OF WEDLOCK

16) squared

POLLUTION

_

( x 10 -‘)

_

HOSPBEDS ( x 10 ‘)

1.270 (3.04) _

MEAT

_

APT SPACE R’ F No obs

0.012 (1.63) _ -0.075 (6.06) 0.099 (4.58) _

AGE 16

0.87 90.8 I 71

- 300.500 (3.60) _ 13.739 (2.75) _ - 0.459 (fl.004) _ _

_

UNVERSITY

AUTO

_

_

(1990)

ABORW

(AGE

_

2.699 (9.22) 0.109 (2.82)

(1990)

IMR lagged

0.661 (7.54) _

3.669 (6.10) 0.312 (5.38) _

0.52 14.54 71

1.543 (1.89) _

9.016 (4.00)

_ _

(4.71) 4.387 _ _ _ _

_

_

-8.616 (2.14) _

_

16.558 (3.04) - 0.334 (3.05) 7.841 (2.30) 6.889 (5.84) - 6.761 (0.570) _

0.244 (4.75) _ _ 0.042 (0.09) 10.403 (2.39) _

_

-0.078 (1.43) _

0.48 6.40 71

0.57 17.00 69

-

0.010 (10.86) _

0.0693 (12.63) _

5.137 (8.46)

- 0.002 (1.56) - 0.006 (2.35) 0.034 (0.75) -

- 0.0034 (4.45) ~0.0015 (0.95) 0.0343 (1.14) _

-0.258 (3.20) ~ 0.222 (1.40) 4.113 (1.36) _

_

_

_

_

-

_

_

-

_

_

- 0.266 (2.61) -

~ 0.2067 (3.56) _

17.819 (2.75) _

0.148 (1.21) 0.90 71.47 69

0.2082 (2.89) 0.94 111.08 69

14.105 (1.89) 0.91 79.30 69

Equations (l)-(5) are estimated simultaneously. 2SLS estimates corresponding to (l)-(4) are not included when alternative fertility measures are used in (6) and (7), as there are few substantive differences. POLLUTION is an aggregate measure of emission of harmful materials into the atmosphere, in kg. TFR is the total fertility rate; UNIVERSITY is the number of university students per 10,000 people; VODKA is per capita consumptiuon of commercially produced alcoholic products; INCOME is mean income per household in rubles; DIVORCE is the crude divorce rate (divorces per thousand people); MARRIAGE is the crude marraige rate; ABORW is the number of abortions per women of child-bearing age; AUTO is the incidence of private automobile ownership per thousand people; IMR is the infant mortality rate; OUT OF WEDLOCK is the proportion of children born outside registered marriage; AGE 16 is the proportion of the population below 16years old; HOSPBEDS is the number of hospital beds/thousand, except for in (4) where it equals hospital beds per physician; MEAT is 1990-92 percentage change in per capita meat consumption; APT SPACE is 1990 urban dwelling space per capita.

DEMOGRAPHIC

CHANGE

IN THE FORMER

indicating access and social acceptance, then unwanted pregnancies are terminated rather than becoming unwanted births. Fertility is not significantly affected by income, but declines with our proxy for the value of maternal time (unverincidence), sity student and with private automobile ownership. Urban apartment space is a critical determinant of higher order fertility, though it is only weakly related to total fertility. At mean values a 10% increase in urban apartment space per capita (about 1.6 m’) will lead to a roughly 25% increase in CBRZSZY, an extremely high elasticity for older mothers. Higher order fertility and births to older women are less influenced by marriage, divorce, infant mortality, or university education. In this last case, student status is no longer a barrier to fertility for older women. In effect, space constraints take over from demographic and life-cycle forces. Modeling marriage and divorce also turns out to be straightforward. Marriage rates are positively influenced by the current divorce rate. This is hardly surprising, since recently divorced people are a group highly eligible for remarriage. Marriage further increases with the incidence of education, and with a region’s wealth. As elsewhere, marriage appears to be a normal and possibly a luxury good, and its incidence rises with socioeconomic status. Consistent with this finding is the result that marriage rates decline with the share of children born out of wedlock. Finally, and not surprisingly, regions in which the preference for children is high (as reflected by the age structure) will have much higher marriage rates than elsewhere. Divorce, in turn, is a highly positive function of lagged marriage. As elsewhere, the risk of divorce is highest in the years immediately following marriage; therefore, relatively shortterm (2year) lags are likely to be most important. At the same time, having children raises the psychological cost of divorce, and lagged fertility rates exert an extremely strong incentive to stay married. Russia’s divorce rate rises strongly with income, a finding consistent with findings from Europe and North America. In Russia, because the high income regions are predominantly ethnically Russian, while low income regions have disproportionate shares of minorities, the variable also may reflect cultural differences. Divorce rates also rise with infant mortality, which is consistent with the hypothesis that a child’s death creates great stress in a marriage. Finally, divorce rates fall with a region’s incidence of higher education. This may reflect a

SOVIET UNION

1971

true education effect (more thoughtful reflection in spousal choice), delayed marriage (divorce rates are higher among teenagers and those in their early 20s than in other age groups), lower alcoholism rates, or a higher opportunity cost of divorce, in terms of foregone claims to the spouse’s future income and perquisites. Explaining abortion incidence is slightly more difficult. The incidence per woman rises with marriage (reflecting an increase in sexual contact), and has a quadratic but mostly positive relationship with the age structure. Abortion rates decline strongly with regional incomes and wealth, a finding consistent with the US experience, and increase with access to medical care as measured by hospital beds per person. Abortion appears to rise with environmental degradation, possibly reflecting fears of birth defects; it rises further with past infant mortality rates. Finally, infant mortality declines with proxies for maternal health, but does not vary with our environmental variable. Not surprisingly, the infant mortality rate is strongly negatively associated with physician density. Infant mortality is also higher in regions with high rates of fertility and out-of-wedlock birth. High fertility and births out-of-wedlock tend also to be associated with earlier pregnancies, which have higher infant mortality rates. These regions further tend to have lower female education, and hence poorer health practices. In short, there is little mystery in Russia’s regional demographic patterns, while understanding them is of great social importance. Infant mortality and other deaths are far more tangible evidence of a government’s success or failure than trends in GDP. Similarly, abnormally declining marriage and birth rates indicate social instability which must be addressed.

6. FORECASTING DEMOGRAPHIC

RUSSIAN CHANGE

The final step is to link economic and demographic forces into medium-run forecasts. We do this by first making a set of assumptions on likely economic and social behavior (Table 7) in order to drive the simultaneous demographic model in the preceding section. These conditional forecasts can then be used in turn to forecast life expectancies from the equations in section 6, which can then be contrasted with pure demographic forecasts. Use of crosssectional data involves the implicit assumption of long-term equilibrium, which is also implied in demographic forecasting, enhancing thus comparability. We are uncomfortable making

1972

WORLD

DEVELOPMENT

long-term assumptions, though, and therefore restrict our horizon to the IO-year period from the end of our data, 1996-2005. Table 7 also provides our forecasts for the post-1995 decade, based on equations (l)-(5) of Table 6 and equations (2) and (6) from Table 4.” In both a baseline and a faily optimistic economic and social environment, life expectancies are surprisingly stable in the coming years.” In OPTIMISTIC, male life expectancy is essentially constant, and female life expectancy is forecast to fall by 1 year in the coming decade. In the baseline projections, male life expectancy falls 1.4years, and female life expectancy falls 2.4 years. These projected declines are driven in large part by continued declines in fertility, and rising divorce rates. Slight increases in the infant mortality rate are projected in BASELINE, further depressing life expectancy, while IMR is essentially constant in OPTIMISTIC. In the baseline scenario, falling milk consumption and eventually rising meat consumption are also negative forces; in the optimistic projections,

benefical effects from rising milk and vegetable consumption are largely offset by the negative effects of increased meat intake. Purely demographic projections offer a vast range of potential outcomes in Russia’s unstable environment. Andrccv et ul. (1998) provide scenarios that range from an increase of 4 years to a decline of 2 years for male life expectancy from 1995-2005. Their medium forecasts are quite close to ours for males, but none of their scenarios envision a closing of the female/male gap, as our projections do. The Russian and other demographic forecasts surveyed by Vishnevsky (1996) do in some cases have a closing gap, but none forecast declining female life expectancy. In short, the distinguishing features between our model and all demographic forecasts of which we are awarc are that our projections are less favorable for women, and are slightly less favorable overall. Our life expectancy results arc driven by trends in consumption and in other demographic

Table 7. Economic-based

Optimistic

Life expectancy, male (years) Lift expectancy, female Total fertility rate Crude marriage rate (1000) Crude divorce rate (1000) Infant mortality rate (/OOO) Abortions/O00 women

demographic fowcasrf

scenario

Baseline

1996

2000

2005

lY96

2000

2005

58.92 70.45 1.41 8.57 4.16 17.39 68.76

58.90 70.04

58.79 69.48 1.16 9.99 7.83 17.51 67.95

61.28 70.63 1.48 6.59 4.40 17.46 71.34

60.56 69.50 1.08 6.40 6.18 18.23 45.46

59.88 68.23 0.85 6.52 7.81 lY.13 30.40

1.25 8.83 6.16 17.45 58.65

Assumed Optimistic

“1995-2000 and growth

baseline scenario growth of meat consumption.

exogenous

variable

growth

scenario

1995-2000 Private automobile ownership University student enrollment Proportion of population below 16 Out-of-wedlock births as share of total Real per capita income Urban per capita living space Air pollution emissions Meat consumption growth rate Milk consumption growth rate Vegetable consumption growth rate Hospital beds Physicians/hospitaI beds Alcohol consumption/capita

scenario

Baseline

I YY5-2000

2000-05

scenario 2000-05

3

5% 0

-2 -2

-1 4

;1 -1 4 3 2.5

8 1.5 2

3 -4

-9

-5 0

2 3 3 0 2 0 rates are set equal to 1993-95

rates

3 0.5 25

--L

1.5 -1

-I 1 5

actual

growth

0 3

rates save for auto ownership

DEMOGRAPHIC

CHANGE

IN THE FORMER

variables, which are themselves forecast. Of these, the infant mortality rate is stable in OPTIMISTIC, and rises slightly under the BASELINE assumptions. Broadly speaking, declining fertility plus rising protein intake offset rising nonmarital births. In contrast, marriage rates are stable in BASELINE and substantially rising in OPTIMISTIC, with increases from increased wealth and divorce more than counteracting an aging population structure and increasing acceptance of nonmarital fertility. Divorce rates continue to surge in both scenarios. driven in OPTIMISTIC by rising incomes and marriage, and by declining fertility. These forces in turn create strong downward pressure on fertility, which continues to decline, with the TFR ending up at 1.16 under OPTIMISTIC and 0.85 - the East German level - under BASELINE. Part of this decline reflects a vicious cycle: declining fertility weakens family ties, which in turn means lower fertility. Growing wealth - or Westernization - captured by the auto ownership variable further depresses fertility, as does in the medium scenario the continued erosion of the higher education system. Decreasing abortion rates and growth in

SOVIET

UNION

1973

replacement fertility in BASELINE hardly counteract these other forces. These 2005 fertility rates are far below those forecast by the UN (1.60 medium variant) or US Census Bureau (l.SO), as reported by Vishnevsky (1996), which seems wildly optimistic. Even our optimistic TFR is at the bottom end of the various projections of Andreev et al. (1998) but our forecasts are only modestly lower than those of the Russian Government’s Center for Economic Conjuncture. The social and economic environment in the coming decade is unlikely to be one that bodes well for recovering fertility or increasing life cxpcctancies. Economic growth alone is of little value. especially if associated with rising meat consumption, purchases of durable consumer goods, and, broadly, “Westernization”. Improved social infrastructure is critical to sustained improvements - gains in health care (reflected in improved infant mortality rates), reinvigorated public education, improved supplies of healthy foods (and a campaign to alter diets) are the keys to unlocking a virtuous cycle. This is not the same as economic recovery but in a wise policy environment, they will mutually reinforce each other.

NOTES 1. A detailed discussion of data quality is given in Anderson and Silver (1997).

and problems

2. Life expectancy in Ukraine fell another 1.3 years in 1995, while it declined 0.6 years in Belarus (Shakhotjko, 1996). Belarussion male life expectancy also never recovered to its 1995 peak of 68.Y years, so that 1995 life expectancy was a full 0.6 years below the 1995 level (Minstat Belarus, 1995). For Karakstan, male life expectancy was also greater in 1965 (66.6). so that the total decline to 60.7 is 5.Y years (Minzdrav Kazakstan. 1996). 3. The 1985-94 rise in U.S. male mortality for the 25-44year age groups largely reflects the spread of AIDS. 4. Brainerd (199X) argues that mortality rates appear to be negatively related to measures of economic reform. This finding may be largely driven, however, by the presence of low-mortality. high-reform Eastern European nations. The sample here does not suggest such linkages: high reform countries such as Estonia and low reform countries such as Ukraine seem to suffer alike. 5. Shkolnikov et al. (lYY6b) also provided a detailed comparative analysis of Russian mortality relative to that in France, and England and Wales. Differences are driven by exceptional rates and growth of cardio-

vascular disease and accidents. injuries and poisoning in among younger adults and the middle-aged Russian population. For comparisons of Russian mortality with Hungary and the United States, see Shkolnikov and MeslC (lYY6). These papers also discuss data comparability by disease type. and problems related to misrcporting of infant mortality, as well as the apparently complete coverage in Russia of elderly mortality. 6. Russian data only distinguish by gender for working age populations. 1YY5 total mortality (all age groups, both genders) from circulatory system diseases was 0.79% (Goskomstat, IYYha). 7. The regressions in Tahlc 4 and Table 5 represent but a small sample of those actually run, since we have virtually no restrictions on functional form, in many cases are uncertain about the quality of data or the best proxy for an ideal variable. and elsewhere encounter collinearity problems. Full sets of health and demographic (Table 6) rcgrcssions can be obtained from the authors. In general (except where noted), the results presented here are highly robust. 8. Obviously, the dcnaity and content of such pollution is extremely important, but such information is unavailable. Data are available for a considerable but incomplete group of Russian cities. Comparable water and soil pollution data are not readily available.

1974

WORLD

DEVELOPMENT

9. The direct effect of a rise in infant mortality by one unit (one additional death per thousand births) will be to lower male life expectancy by about 0.06 years, and female life expectancy by 0.07years. Thus, only a small fraction of the coefficients in Table 4 reflect these direct, accounting effects. 10. These figures are averages from regressions (l)-(6) for the level-level equations, calculated by setting the divorce rate equal to the marriage rate. 11. Data reported are actually for production rather than consumption. It is apparent, however, that production refers to final processing, and hence mostly exclusively) necessarily regional (though not consumption. 12. We do not have regional age-standardized specific cause mortality rates, causing potential bias to some

coefficients. For trends in national mortality by cause of death, standardized for age, see Notzon et al. (1998). 13. Furthermore, presented in Leon age-specific variation.

age-specific adult mortality et al. (1997) suggests little

14. Pessimistic scenarios (not reported) suggest collapsing life expectancies (to below 50 for men) and fertility, as model stability deteriorates in an extremely adverse environment. In such a situation, estimated coefficients almost certainly themselves would be unstable, and hence the projections invalid - however, the implicit warning of ensuing chaos in the face of further dramatic economic and social collapse may well be valid. 15. The trends in these simulations are more telling than the levels, since we did not calibrate to force 1995 or 1996 estimates to equal actual values.

REFERENCES Anderson, B. A. and Silver, B. D. (1997) Issues of data quality in assessing mortality trends and levels in the new independent states. In Premature Death in the New Independent States, ed. National Research Council. National Academy Press, Washington DC. Andreev, E., Scherbov, S. and Willekens, F. (1998) Population of Russia: what can we expect in the future? World Development 26(1 I), this issue. Becker, C. M., Bibosunova, D. I., Holmes, G. E. and Ibragimova, M. M. (1998) Maternal care vs economic wealth and the health of newborns: Bishkek, Kyrgyz Republic and Kansas City, USA. World Development 26( 1 l), this issue. Brainerd, E. (1998) Market reform and mortality in transition economies. World Development 26(1 l), this issue. Gosudarstvennyi Komitet Kazakhshoi SSR po Statistike i Analizu [Goskomstat KSSR] (1991) Demograficheskoi Ezhegodnik Kazakhskoi SSR. Goskomstat KSSR, Alma-Ata. Gosudarstvennyi Komitet po Prognizirovaniiu i Statistike Respubliki Uzbekistan [Goskomprognozstat] (1992) Chislennostj, Sostav i Dvizheniia Naseleniia Respubliki Uzbekistan. Goskomprognozstat, Tashkent. Gosudarstvennyi Komitet Rossiskoi Federatsii po Statisiki [Goskomstat RF] (1994) Demografcheskii Ezhegodnik Rossikoi Federatsii 1993. Goskomstat RF, Moscow. Goskomstat RF (1996a) Demogra$cheskii Ezhegodnik Rossikoi Federatsii 1996. Goskomstat RF, Moscow. Goskomstat RF (1996b) Sotsialjnaia Sfera Rossii. Goskomstat RF, Moscow. Goskomstat SSSR (1989) Naseleniie SSSR 1988. Goskomstat SSSR, Moscow. Gqskomstat SSSR (1990) Demograjicheskii Ezhegodnik SSSR 1990. Goskomstat SSSR, Moscow. Haub, C. (1994) Population change in the former Soviet Republics. Population Bulletin 49(4).

Kingkade, W. W. and Arriaga, E. E. (1997) Mortality in the new independent states: patterns and impacts. In Premature Death in the New Independent States, ed. National Research Council. National Academy Press, Washington DC. Lee, H. and Bobadilla, J. L. (1994) Health Statistics for the Americas. World Bank Technical Paper No. 262, World Bank, Washington DC. Lee, D., Chenet, L., Shkolnikov, V. M., Zakharov, S., Shapiro, J., Rakhmanova, G., Vassin, S. and McKee, M. (1997) Huge variation in Russian mortality rates 1984-94: artefact, alcohol, or what? Lancet 350 (August 9). Lithanian Department of Statistics [Lithuania Stat] (1996) Health and Social Werfnre. Stat Lithuania, Vilnius. Lithuanian Department of Statistics, Statistical Office of Estonia, and Central Statistical Bureau of Latvia. (1996) Demographic Statistics in the Baltic Countries. Statistical Office of Estonia, Tallinn. Lopez, A. (1997) Mortality from tobacco in the new independent states. In Premature Death in the New Independent Stutes, ed. National Research Council. National Academy Press, Washington DC. Ministerstvo Statistiki i Analiza Respubliki Belarusj [Minstat Belarus] (1995) Naseieniie Respubliki Belurusj. Minstat Belarus, Minsk. Ministerstvo Zdravookhraneniya Respubliki Kazakstan [Minzdrav Kazakstan] (1996) Zdorovje Nuseleniia Respubliki Kazakstan i Deiateljnostj Uchrezhdenii Zdravokhraneniia v 1995 Godu. Minzdrav Kazakstan, Almaty. Ministerstvo Zdravookhraneniya Respubliki Uzbekistan [Minzdrav Uzbekistan] (1995) Health of Population and Health Protection in Republic of Uzbekistan in 1994. Minzdrav Uzbekistan, Tashkent. Murray, C. J. L. and Bobadilla, J. L. (1997) Epidemiological transitions in the former socialist economies: divergent patterns of mortality and causes of death. In Premaiure Death in the New Independent States,

DEMOGRAPHIC

CHANGE

IN THE FORMER

ed. National Research Council. National Academy Press, Washington DC. National Research Council (ed.) (1997) Premature Death in the New Independent States. National Academy Press, Washington DC. NSA Kazakstan (1996a) Unpublished data (20.06.96: Chislennostj nasclcniia po polu na nachalo 1996 goda). NSA Kazakstan. Almatv. N$A Kazakstan (then ‘.Goskom&at Respubliki KazakStan”)/\ (1996b) Demow&heskii Ezhewdnik Kuzuksian. NSA Kazakstan, Almaty. Natsionaljnoe Statisticheskoe Agenstvo Respubliki Kazakstan [NSA Kazakstan] (1997) Demograficheskii Ezhegodnik Katukstunu. NSA Kazakstan, Almaty. Natsionaljnoe Statisticheskii Komitet [Natskomstat Kyrgyzstan] (19Y5) Kyrgyzstun 1: i?ifrukh. Natskomstat, Bishkek. Natskomstat Kyrgyzstan (19YSa) Unpublished data (order 18.081768). Natskomstat Kyrgyzstan (1995b) Dcmogruficheskii Ezhegodnik KyrDzskoi Respuhliki 1993. Natskomstat Kyrgyzstan, Bishkek. Natskomstat Kyrgyzstan (1095~) Statisticheskii Biullettenj 12. NSA Kyrgyzstan, Bishkek, December. Notzon, F. G., Komarov. Y. M., Ermakov, S. P., Sempos. C. T.. Marks, J. S. and Sempos, E. V. (1998) Causes of declining life expectancy in Russia. Journal ofthe American Medical Association 279( 10). Puska, P. (1997) Chronic disease prevention in the new economic states: Finnish experiences. In Premature Deuth in the New Independent States. ed. National Research Council. National Academy Press. Washington DC. Republic of Armenia State Statistical Committee (1995) Unpublished data. Shakhotjko. L. P. (1996) Naseleniie Respuhliki Belarusj 1’ Kontse XX Veka. Nauchno-issledovateljskii Institut Statistiki pri Minstate Respubliki Belarusj. Minsk. Shkolnikov, V. M., Cornia, G. A.. Leon. D. A. and Mesl6, F. (1998) Causes of the Russian mortality: evidence and interpretations. World Del,elopment 26( 11) this issue. Shkolnikov, V. M. and Mesle. F. (1996a) The Russian epidemiological crisis as mirrored by mortality trends. In Russia’s Demographic “Criris”. ed. J. DaVanzo. RAND, Santa Monica, CA. Shkolnikov, V. M., Mesle. F. and Vallin, J. (lYY6b) Health crisis in Russia. Population 8, 123-154 (Part I) and 155-190 (Part II). I

”

”

SOVIET

UNION

1915

Shkolnikov, V. M.. MeslC, F. and Vallin, J. (lYY7) Recent trends in life expectancy and causes of death in Russia. In Premature Deuth in the Nebv Independent Stater. ed. National Research Council. National Academy Press, Washington DC. Shkolnikov, V. M. and Nemstov, A. (lY97) The antivariations alcohol campaign and in Russian mortality. In Premature Deuth in the New In&pendent States. rd. National Research Council. National Academy Pres\, Washington DC. Treml. V. G. (19Y7) Soviet and Russian statistics on alcohol consumption and abuse. In Premuture Deuth in the New Independent States, cd. National Research Council. National Academy Press, Washington DC. UNDP (1945) Ukraine Humtm Del,elopmen/ Rcpon 1995. UNDP, Kyiv. US Bureau of the Census (1987) Statistical Abstract of the United States 1988. Bureau of the Census, Washington DC. US Bureau of the Census (1997) Statistical Abstract of the United States lYY7. Bureau of the Census, Washington DC. US Department of Health and Human Services (1993) Health United States 19Y2. HHS, Hyattesville, MD. Vassin. S. A. and Costello, C. A. (1997) Spatial, age, and cause-of-death patterns of mortality in Russia, 1998-89. In Premature Death in the New Independent States, cd. National Research Council. National Academy Press. Washington DC. Vishnevsky, A. G. (1996) Family. fertility and demographic dynamics in Russia: analysis and forecast. In Russiclts Demogruphic “Crisis “. ed. J. DaVanzo. RAND. Santa Monica, CA. Willekens, F. and Scherbov, S. (1995) Demographic trends in Russia. In Population and Famil,v in the Low Countries lY9-f. eds H. van den Brckcl and F. Deven. Kluwer Academic, Amsterdam. Witte, H. and W,ngner, G. (1995) Declining fertility in East Germany after unification: a demographic response to socioeconomic change. Populution und Der,elopment Review 21(Z). World Bank (lYY4) World Development Report 19Y-l. Oxford University Press, New York. Zakharov. S. V. and Ivanova, E. I. (1996) Fertility decline and recent changes in Russia: on the threshold of the second demographic transition. In Russiu ‘7 Demographic “Crisis “. ed. J. DaVanzo. RAND. Santa Monica, CA.