Population health and income inequality: new evidence from Israeli time-series analysis

Amir Shmueli

The Hebrew University and the Gertner Institute, Jerusalem, Israel

Correspondence: Amir Shmueli, Department of Health Management, The Hebrew University School of Public Health, POB 12272 Jerusalem 91120, Israel. E-mail: ashmueli{at}md2.huji.ac.il


    Abstract
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 Abstract
 Methods
 Results
 Discussion
 References
 
Background and objectives The relationship between population health and inequality in income distribution has attracted much attention during the last two decades. The purpose of this paper is to examine that relationship using Israeli time-series data, and considering three types of income: economic, pre-tax, and disposable.

Methods Israeli time series (1979–2000) on life expectancy of men and women at birth and at ages 5 and 65, as well as infant mortality, were related to Gini coefficients measuring inequality in economic, pre-tax (after transfers) and disposable (after taxes) incomes, controlling for gross domestic product (GDP) per capita. This design allows for the estimation of the effects on population health of changes in income inequalities over time as well as of contemporaneous reduction in inequality due to transfers and taxes.

Results None of the three income inequality measures by itself had an effect over time on population health. However, larger contemporaneous reductions in inequality, mainly through the transfers system, were associated with better population health, in particular with lower infant mortality.

Conclusions A significant part of the temporal improvement in the health of the Israeli population has been due to the increasing effort to reduce inequality in economic income by increasing transfer payments. The results are generally inconsistent with the argument of adverse psychosocial effects of inequality on health, and are consistent with inequality being related to other harmful public goods affecting health and with Rodgers' argument.


Keywords Population health, income inequality, Gini, income transfers, Israel, time series

Accepted 23 September 2003

After an era where it was believed that population health is largely determined by economic development (measured e.g. by gross domestic product [GDP] per capita), an increasing body of documentation over the last two decades indicates (though the evidence is mixed) that not only the average income matters, but also how income is distributed among the members of society, or income inequality (measured e.g. by the Gini coefficient).

The ways income inequality affects population (or community) health are still an unsolved issue. Three broad approaches emerge in the debate.1–7 The first takes the individual's health as a pure private good, ‘produced’ by the individual's (absolute) income. If income exhibits diminishing marginal health returns, namely, a given increase in income improves health more in lower incomes than in higher incomes, holding average income constant, lower inequality will be associated with higher average health in the population.8–10 The second approach takes income inequality as a psycho-socially harmful public good. As with many public goods, some individuals consume more of the good or benefit than others. Poor individuals suffer greater distress and unhappiness than the rich from increasing inequality, and their health deteriorates, leading to lower average health. The result is that greater inequality is associated with poorer population health. Under the second approach, the income inequality—health negative connection is stronger than in the first approach, since income inequality serves as a shifter to the income—health relationship at the individual level.11,12 Variants of this approach focus on the importance of relative income, deprivation, or relative socioeconomic status. The third approach sees income inequality as a social indicator which is positively correlated with other harmful (sometimes local) public goods, some of them tangible—such as crime, violence, low expenditure on health services, corruption, and unprotective environment, and some are intangible—such as distrust, low social cohesion, and low investment in social capital.7,13–16

Most of the empirical evidence to date has come from cross-section analysis of developed countries, states, or communities. While such a design keeps standard of living and medical technology largely constant, it suffers from problems of unobserved variation in national cultures, social customs, health systems, and (in)comparability of inequality data. Following several studies (e.g. Judge17 and Deaton18), the present analysis focuses on a single country—Israel—over the last two decades. Over that period, the Israeli population has experienced substantial changes in health, economic growth, and income distribution.

The second contribution of the present analysis is the distinction made among different types of individual's income, and hence, different types of inequality. Discussions on poverty and income inequality distinguish among three types of household's income. Economic income is the income received from labour and from ownership of capital and financial assets. Pre-tax income is the economic income supplemented by transfer income. Transfers are a source of income received not for an economic activity, and include social security allowances and private support received from institutions or individuals. Disposable income, or after-tax income, is the pre-tax income minus direct taxes (income tax and social security payments). The income inequality for each type of income is measured by a separate Gini coefficient.

Considering the three types of income inequality, it is possible to explore the effects of variations over time in any of the above inequality measures, as well as the effects of contemporaneous reductions in inequality by the transfer and tax system. In fact, the main conclusion from the present analysis is that it is these contemporaneous reductions in inequality rather than the levels of the Gini coefficients over time, which affect the health of the population.

The main analysis is done under the assumption that population health at time t is determined by current standard of living and level of inequality (this is the assumption underlying the common cross-sectional analysis as well). This is naturally a simplifying assumption since in reality current health is determined by the entire history of income levels and distributions experienced by the population in previous periods. This conceptually and technically complex issue deserves further analysis; however, an introductory discussion is presented.


    Methods
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 Abstract
 Methods
 Results
 Discussion
 References
 
Data
The data consists of Israeli aggregate time series over the period 1979–2000 (n = 22).

Variables
Health
Population health is indicated by seven measures: life expectancy at birth and at ages 5 and 65 for men and for women, and infant mortality per 1000 live births. The data source is the Central Bureau of Statistics.

Standard of living
The GDP per capita serves as the measure of standard of living. It is measured in constant prices (1995), and the data source is the Central Bureau of Statistics (CBS). Over time, GDP per capita is highly correlated with the level of education in the population (its correlation with the percentage of the population with post-high school education is 0.993), with national expenditure on health per capita (r = 0.986), and with a linear trend, which is commonly used to reflect medical-technological changes over time (r = 0.983). For these reasons, GDP per capita alone represents the standard of living in the analysis that follows, but it should be viewed as reflecting also associated changes in education, lifestyle, prevention, and technical progress, which affect population health over time.

Income inequality
Income inequality is measured by the Gini coefficient. Its range is zero (complete equality) to one (complete inequality). For convenience in the interpretation of the results, the Gini coefficient was multiplied by 100 (namely, ranging from zero to 100). Changes and differences are thus in percentage points terms. The three types of income inequality are measured by Gini economic income, Gini pre-tax income, and Gini disposable income respectively. These coefficients indicate inequality among households, where the households are ranked by the relevant income per standardized (size-adjusted) adult. The National Insurance Institute is the source of the data, which is routinely computed from the CBS' Households' Income Surveys.

Three contemporaneous reductions in inequality are defined by the three Gini coefficients: reduction in inequality due to transfers and direct taxation (Gini economic income – Gini disposable income), reduction in inequality due to transfers only (Gini economic income – Gini pre-tax income), and reduction due to direct taxes only (Gini pre-tax income – Gini disposable income).

Statistical strategy
Prior experimentations showed that multiple linear regression models were superior to logarithmic functional forms. The estimated parameters are interpreted as the marginal effects of the corresponding explanatory variables. Basic statistical inference on the models is based on t-tests. t-value >2.09 means rejection of the null hypothesis at 5% significance level, and t-value > 1.73, at the 10% level. Alternatively, P-value > 0.05 means that the null hypothesis cannot be rejected at 5% significance level.

A major concern in time-series analysis is the possibility of trend-related spurious correlations underlying the results. To minimize such a possibility, the models were estimated with all variables measured as first-differences. Suppose the original model is ht = a + bxt + cgt + ut, where h is a population health indicator, x is GDP per capita, g is Gini coefficient, and u is a random error, t = 1, ..., n. This is the model, which is commonly estimated in cross sections. For period t – 1, the model is ht–1 = a + bxt–1 + cgt–1 + ut–1. The model in first-differences has the same slopes but no constant term, and is estimated by n – 1 observations:

(1)

Using first-differences, the trend is removed, and temporal changes in h are explained by temporal changes in x and in g. However, the joint distribution of the errors (ut – ut–1) is no longer as in the original model. Assuming constant variance (w2) for the u's, and first-order serial correlation (r) between subsequent u's, var(ut – ut–1) = 2(w2 – r) and cov[(ut – ut–1), (ut–1 – ut–2)] = 2r – w2. First-order serial correlation between subsequent errors is a common phenomenon in time-series analysis, which might bias the estimates. Such a correlation is often caused by unmeasured general developments along time. Using first-differences, serial correlation arises even if there is no serial correlation among the u's. Correction of the ordinary least squares for serial correlation was done using iterative (Prais-Winston) generalized least squares.

Now suppose the model includes Gini coefficients for two income types, g1t and g2t: ht = a + bxt + cg1t + dg2t + ut. In first differences, the model is

(2)

If c + d = 0, or c = – d (=e), then the model becomes

(3)
The term in square brackets is the (first difference of the) contemporaneous reduction in inequality, which replaces the two individual Gini's. Consequently, if the restriction c + d = 0 is not rejected (using a simple t-test), the correct specification is the one where the two individual Gini's are replaced by the difference between them.

The models discussed above do not take into account possible cumulative effects of past exposure to standard of living or to inequalities, on present health. Suppose now that health at period t is determined by all prior values of standard of living x and income inequality g:

(4)
Without further structuring of the lagged effects it is impossible to estimate the model since the number of regressors is infinite. Several questions need to be answered first, e.g. whether recent values have stronger or weaker effects than remote past values on present health. A complete analysis of the model in equation (4) is a conceptual and technical challenge that deserves further research. For the present exposition it is assumed that the effects are constant over time, namely b0 = b1 = b2 = ..., and c0 = c1 = c2 = .... Under such assumption, ht = a + b0xt + b0xt–1 + b0xt–2 + ··· + c0gt + c0gt–1 + c0gt–2 + ··· + utt, and ht–1 = a + b0xt–1 + b0xt–2 + ... + c0gt–1 + c0gt–2 + ··· + ut–1, so that the first-difference model becomes

(5)

The extension to two Gini's and the introduction of the contemporaneous reduction in inequality is similar to the analysis discussed above. In parallel to model (2) we have:

(6)
and in parallel to model (3), model (6) becomes

(7)
Models (6)(7) can be estimated using the data, and the results can be compared with those obtained by the estimation of models (2)(3) to learn about the importance of lagged exposure to standard of living and income inequalities to current population health.


    Results
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 Abstract
 Methods
 Results
 Discussion
 References
 
Table 1 presents the descriptive statistics of the variables used in the analysis. The means and standard deviations (SD) are presented, as well as the values in 1979 and in 2000. The health of the Israeli population clearly improved on all measures over the last two decades. GDP per capita increased from 1995 IS 35 864 (USD PPP [purchase power parity] 11 955) in 1979 to 1995 IS 54 323 (USD PPP 17 813) in 2000. The economic growth was accompanied by increased inequality in economic income as measured by the Gini coefficient, from 43.2 in 1979 to 50.9 in 2000. Income transfers have moderated the increase in income inequality, and pre-tax income inequality rose from 36.6 in 1979 to only 39.6 in 2000. Further moderation of the increase in income inequality was achieved through direct taxation, where inequality in disposable income rose from 31.8 in 1979 to 33.4 in 2000. Notice that the main contemporaneous reduction in inequality was achieved by transfers (with a mean of 47.7 – 38.2 = 9.5 percentage points). Taxes reduced inequality by 38.2 – 32.6 = 5.6 percentage points on average, and transfers and taxes together achieved a 15.1 percentage point decrease in inequality on average.


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Table 1 Variables' definitions and descriptive information

 
Table 2 presents the empirical examination of the effect of income inequality on the seven population health indicators. All variables are measured in first-differences. For each indicator, the estimated models are presented in four panels. Panel A presents the effect over time of the level of inequality (Gini) in the three types of income separately, controlling for GDP per capita (model (1) above). Panel B focuses first on the net effect of the Gini's of disposable and pre-tax incomes, controlling for GDP per capita (sub panel (i), model (2) above). Then (sub panel (ii)), the hypothesized restriction that the sum of the net effects is zero is tested using t-test, and is not rejected (P > 0.05) for all health indicators. Consequently, in sub panel (iii), the model is re-estimated with the difference between the Gini's considered—the contemporaneous reduction in inequality by taxes—replacing the two individual Gini's. In other words, the model is estimated under the restriction that the sum of the net effects is zero (model (3) above). Panel C does the same for the Gini's in economic and pre-tax incomes, and Panel D for Gini's in economic and disposable incomes.


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Table 2 The effect of income inequality on population health

 
From Panel A it is clear that none of the measures of inequality had an effect on population health over the two last decades, controlling for GDP per capita. GDP per capita has a positive effect on most of the health indicators. Each additional 1000 IS (1995 prices) of GDP per capita is associated, on average, with 1.5–1.7 months increase in life expectancy at birth or at age 5, with 0.3 drop in infant mortality, and with 1.3–1.4 months increase in life expectancy at age 65. The effect of GDP per capita is generally stable across the types of inequality considered.

The results in Panel B indicate that the contemporaneous reduction in income inequality by the tax system, namely, the difference between Gini pre-tax and Gini disposable incomes (GINI_P – GINI_D) has no effect on the population health. Both GINI_P and GINI_D have insignificant effects both in (i) and in (ii).

Panel C shows that the contemporaneous reduction in income inequality by the transfer system, namely, the difference between Gini economic and Gini pre-tax incomes (GINI_E – GINI_P), has a significant benevolent effect on population health. However, this favourable effect is restricted to a drop in infant mortality (and increase in life expectancy at birth). A one-percentage point increase in the difference (GINI_E – GINI_P) is related to a drop of 0.4 infants' death per 1000 live births. Contemporaneous reduction in income inequality by the transfer system has no effect on life expectancy at ages 5 and 65. Life expectancy at birth increases, however, with the reduction in inequality due to transfers; with one-percentage point higher reduction being related to 2–2.7 months increase.

As expected, similar results are obtained with relation to the contemporaneous reduction in income inequality by both the transfers and the tax system, namely, the difference between Gini economic and Gini disposable incomes (GINI_E – GINI_D) (Panel D). Here, however, increases (significant at 10%) in women's life expectancy at ages 5 and 65 followed as well.

We turn now to the question whether the effect of lagged exposure to GDP per capita and income inequality (and its reduction by transfers and taxes) on current population health can be detected in the data. Estimating models (6)(7), we found that for the six measures based on life expectancy, the results were similar to the ones discussed above, and the models (7) and (3) performed similarly in terms of the parameters' estimates and the residuals' sums of squares (s.s.) (it is assumed that the error u in the two models has the same variance-covariance structure). For infant mortality, some indication of the superiority of the model with lagged effects is found. For the reduction in economic income inequality by both tax and transfers (compare with Table 2 Panel D), the residuals s.s. was 4.706, which is significantly smaller ({chi}2 test, d.f. = 19) than s.s. = 7.530 found for the original model. The effect of GDP per capita was significant –0.458 (–0.268 in Table 2), and the effect of the reduction in inequality was significant –0.162 (–0.393 in Table 2). In other words, allowing for lagged effects on current population health tends to increase the importance of standard of living while reducing that of income inequality.


    Discussion
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 Abstract
 Methods
 Results
 Discussion
 References
 
Using Israeli aggregate time series, the findings provide several insights into the population health—income inequality connection and debate. First, controlling for GDP per capita, inequality in income per se does not affect population health over time. Population health was not affected by the increase in inequality in either type of income, economic, pre-tax, or disposable, experienced by the Israeli population over the last 20 years.

Second, for given labour and capital markets conditions, which generally determine the level of economic income inequality, higher levels of inequality in pre-tax (post-transfers) and disposable incomes are associated with worse population health. Similarly, for given levels of inequality in pre-tax or disposable incomes, higher inequality in economic income is associated with better population health (see below).

Third, and this is the main conclusion of this study, it is the contemporaneous reduction in inequality in income achieved by transfers and direct taxes rather than the level of income inequality, which is important to the health of the population. Sharper reductions in inequality, caused by either higher level of mean transfer or higher progressivity, are associated with better population health. Note that a sharper reduction in inequality due to transfers and taxes usually results in a lower level of disposable (or pre-tax) income inequality, but it is not that lower level that counts—it is the higher reduction that matters. It is interesting to note that replacing the absolute reduction in inequality by the relative reduction (a reduction in the Gini coefficient, e.g. from 45 to 30 constitutes a 15 percentage points absolute reduction and 33% relative reduction) did not change the main conclusions.

Fourth, it is mainly infant mortality that is sensitive to these changes. Lynch et al.7,19 also found that inequality mostly affects infant mortality. The reason for that is probably the higher sensitivity of infant mortality to GDP per capita. Analysis (not reported for brevity) showed that at the means point, a 10% rise in GDP per capita is associated with approximately 2% rise in life expectancy, and with 3% drop in infant mortality. In 1979, the corresponding rates were 2.5% and 27% respectively. Consequently, infant mortality is more sensitive to changes in income implied by the reduction in levels of inequality. Life expectancy at birth for both men and women are positively affected by reduction in inequality due to transfers. Life expectancy at ages 5 and 65 are less sensitive to income inequalities, in particular among men.

The results indicate that among women, the effect of the reduction in inequality by transfers and taxes persists to life expectancy at age 5 and age 65, while among men this effect is significant only in life expectancy at birth. It seems that adult women's health has been more favourably affected by the increasing transfers system. The reason might be that at any point in time, women constitute the majority of the elderly, of single-parent families' heads, and of the unemployed. These population groups have had the highest prevalence of poverty (disposable income lower than 50% of the median disposable income, adjusted for household's size) during the last two decades, and have enjoyed old age, income-maintenance, and unemployment allowances respectively, which largely reduced poverty rates in these populations.20 It might be also the case that healthier mothers give birth to healthier babies, thus reducing infant mortality.

The findings also shed some light on the way income inequality affects population health. The largest effect on infant mortality and life expectancy at birth is achieved by the reduction in inequality due to income transfers (Gini economic income – Gini pre-tax income). Transfers (mainly social security's children, old age, unemployment, and income-maintenance allowances) raise the incomes of the poorest segments of the population (large families, elderly, unemployed, and single mothers), with no effect on the rich individuals' income. The reduction in inequality due to taxes (Gini pre-tax income – Gini disposable income) largely originates from higher tax payments made by the rich (depending on the progressivity of the taxation), and has no effect on population health. The two effects are as expected, if income has diminishing marginal health returns. The estimated effects are inconsistent with the hypothesis that inequality is a harmful psychosocial public good.

The findings suggest also that the reduction in inequality by transfer payments, which has a favourable health effect, might be related to other beneficial public goods such as social solidarity. Looking at the share of GDP spent on public transfers to households, it amounted to 7.6% in 1980, increasing gradually over time and reaching 12.2% in 2000.21 That increase reflects the higher reduction over time in income inequality by transfers. A first-differences regression of the seven health indicators on GDP per capita and on the share of transfers in GDP reveals that only infant mortality is affected by the latter, with one percentage point increase in the share reducing, on average, infant mortality by 0.8 deaths per 1000 live births. Similar relationships were found in a cross-section of nations.22

One may question the appropriateness of controlling for GDP per capita when estimating the effects of inequality in the three types of income. GDP per capita may remain constant, while mean pre-tax and disposable incomes may change due to changes in transfers and/or tax system. However, controlling for private disposable income per capita instead did not change the main conclusions.

The Israeli population over the last two decades has experienced a rising inequality in economic income. That rise has been caused mainly by an increase in the economic income of the middle-upper classes (rather than by increased unemployment or a decrease in the earnings of the poor). It is associated with economic growth, with increased returns on education, management, and entrepreneurship. The high-tech boom of the last decade provides an example. However, the increase in economic income inequality has been much more dramatic than the increase in inequality in pre-tax or disposable incomes. In other words, the reduction in inequality has increased. It was 11.37 percentage points in 1979, reaching 17.44 percentage points in 2000. Such reduction has been achieved by mainly increasing the incomes of the poor through progressive (and increasing) transfers and not by decreasing the incomes of the rich through higher marginal tax rates. An illustration of this effect on infant mortality is shown in Figure 1. Figure 1 presents the actual and the predicted (from the model presented in Table 2 Panel D (iii)) paths of infant mortality in Israel over the last two decades. Presented also is the simulated path (the dotted line), predicted from the same model, with the actual GDP per capita but with the level of reduction in inequality due to transfers and taxes (GINI_E – GINI_D) as in 1979. The gap between the simulated and the predicted paths represents the gain in infant mortality due to the actual larger reductions in inequality over time. The gain reaches 3 infants' lives per 1000 births in the late 1990s.



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Figure 1 Infant mortality gain due to increasing contemporaneous reduction in income inequality (economic versus disposable)

 
Preliminary results on the effect of lagged exposures to income levels and inequalities on present population health indicate that accounting for past exposures tends to increase the effect of income level and to reduce the importance of income inequality, in particular with respect to infant mortality. This issue deserves further research.

In conclusion, a significant part of the temporal improvement in infant mortality and life expectancy at birth of the Israeli population might be related to the increasing effort to reduce inequality in economic income by increasing transfer payments to the poor, but not to the level of inequality per se. The results are generally inconsistent with the argument of adverse psychosocial effects of inequality on health, and are consistent with inequality being related to other harmful public goods affecting health and with Rodgers' argument.


KEY MESSAGES

  • Using a 20 years time series on population health and inequalities in economic, pre-tax, and disposable incomes in Israel, none of the three income inequality measures by itself had an effect over time on population health.
  • However, larger contemporaneous reductions in inequality, mainly through the transfers system, were associated with better population health, in particular with lower infant mortality.
  • The results are generally inconsistent with the argument of adverse psychosocial effects of inequality on health, and are consistent with inequality being related to other harmful public goods affecting health and with Rodgers' argument.

 


    Acknowledgments
 
I wish to thank Jeremy Kark, Orly Manor and the participants of the Epidemiological Unit's seminar for their comments. Two referees provided very useful comments on an earlier draft. I bear sole responsibility for any shortcoming.


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 Abstract
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 Discussion
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