Revisiting the Modernization Hypothesis: Longevity and Democracy

World Development Vol. 67, pp. 174–185, 2015 0305-750X/Ó 2014 Elsevier Ltd. All rights reserved. www.elsevier.com/locate/worlddev

http://dx.doi.org/10.1016/j.worlddev.2014.10.003

Revisiting the Modernization Hypothesis: Longevity and Democracy JOANNES JACOBSEN* University of the Faroe Islands, Faroe Islands Summary. — Modernization theory claims that changes in economic fundamentals, like e.g., income per capita and education levels, affect the political structure of a country in a causal way. As an important marker of development, life expectancy can also be conjectured to have a direct effect on political structures. In this paper, we estimate the impact of improvements in life expectancy on democracy. For the purpose of identification we use data from the international epidemiological transition to construct an instrument for life expectancy. We find a statistically and economically significant positive causal effect of improvements in life expectancy on democracy. Ó 2014 Elsevier Ltd. All rights reserved. Key words — epidemiological transition, life expectancy, democratization

1. INTRODUCTION

Besides the question about causality, the positive correlation between development indicies and democracy leaves open the question of which component of these indices is most important for development, theoretically as well as empirically. Both the “physical quality of life” index and the HDI implicitly assume that the marginal contributions of all included components, i.e., life expectancy, education, and income, to development are equal. But from a theoretical perspective, Anand and Sen (2000) argue with respect to the HDI that while “we have to avoid the. . .danger of taking survival and basic education to be all in judging the progress of quality of life. . .having an income is not. . .comparable with being educated or living long, which are valued for their own sake.” This argument points to health and education being more satisfactory markers of economic development than income per capita. In light of modernization theory it therefore seems natural to examine each marker of economic development for its potential to affect political governance separately rather than as part of a broad cluster of economic variables. While both income per capita and education have received ample attention in the modernization literature (for example Acemoglu, Johnson, Robinson, & Yared, 2005, 2008), health in its own right has received virtually no attention. This paper aims to remedy this omission. It examines the causal effect of a general improvement in the health status— as proxied by life expectancy—of a country’s population on the country’s political governance. The mechanisms that undergird the hypothesis that better health can lead to better political outcomes can be established by linking health status to various strands of modernization theory. One hypothesis in the “political culture” view of modernization theory is that there is a direct effect of better health and increased life expectancy on the demand for better political governance. Along these lines Inglehart and Welzel (2005) argue that more “existential security” (e.g., better health and increased life expectancy) changes peoples’ outlook on life and through this channel creates a demand for better political governance (see further discussion of this in the next section). In addition to this mechanism there is also a plausible indirect effect working through the effect of increased life expectancy on other parts of the socioeconomic structure of societies, like e.g., education, industrialization etc (see e.g.,

The basic idea of “modernization theory” is that income per capita, urbanization rates and other fundamental measures of the socioeconomic development of a society can have a causal effect on its political governance. Seymour Lipset’s version of the modernization hypothesis (Lipset, 1959) is that as societies develop economically they will as a consequence of this become more democratic. Lipset’s influential paper, which started the modern analysis of modernization theory, proposed income per capita, urbanization and industrialization rates, and education levels as markers of economic development. Since then empirical investigations of the modernization hypothesis have mainly been couched in terms of these variables or proxies for them like for example energy use (Bollen, 1979; Jackman, 1973). Other measures of economic development have received scant attention. However, there is little reason to view Lipset’s initial list of economic determinants of democracy as definitive. In fact, in later joint work with Larry Diamond and Juan Linz (Diamond, Lipset, & Linz, 1987), Lipset himself recognized that The most powerful predictor of political and civil liberties is not any of the conventional measures of national wealth or industrialization, but rather the physical quality of life, as measured by [an index composed of] infant mortality, life expectancy at age one, and adult literacy [Diamond et al., 1987, p. 10]

Diamond (1992) argues that Lipset’s version of the modernization hypothesis should be slightly reformulated to capture the “physical quality of life” as a marker of development. He proposes including broader measures of economic development that capture direct measures of well-being in empirical work on modernization theory. The UN’s Human Development Index (HDI), which is composed of subindices for life expectancy, education levels, and income per capita, is just such a measure and Diamond goes on to present evidence of a strong positive correlation between the HDI and democracy. But positive correlations do not imply a causal effect going from economic development to democracy. A study that explicitly tries to account for omitted variable bias, general time trends, and reverse causality in their empirical analysis and they conclude that there is no evidence for a causal effect of income per capita on democracy (Acemoglu, Johnson, Robinson, & Yared, 2008).

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Final revision accepted: October 2, 2014.

REVISITING THE MODERNIZATION HYPOTHESIS: LONGEVITY AND DEMOCRACY

Rueschemeyer, Stephens, & Stephens, 1992; Kalemli-Ozcan, Ryder, & Weil, 2000). Existing versions of modernization theory in turn imply that socioeconomic development along these dimensions has a causal effect on democracy. While there is no study that tries to get at a causal estimate of the effect of improvements in health on democracy, the opposite causal channel, namely from democracy to health, has received some attention recently (see e.g., Przeworski, 2003; Besley & Kudamatsu, 2006; Kudamatsu, 2012). These researchers argue that more democracy leads to better public health provision which in turn leads to better health and they present empirical evidence consistent with this idea. The recognition that there likely is a causal effect of democracy on life expectancy which implies that the impact of life expectancy on democracy cannot be estimated consistently with ordinary least squares as any estimate will be contaminated by reverse causality is the starting point for this paper. The main contribution of this paper is therefore to devise an empirical strategy that can isolate that part of the two-way relationship between life expectancy and democracy that stems from the independent effect of life expectancy on democracy. We do this by employing an empirical specification and an instrumental variable identification strategy which gives us exogenous variation in life expectancy such that any found relationship between life expectancy and democracy in our regressions can plausibly be interpreted as that part of the relationship between life expectancy and democracy that stems from the causal effect of life expectancy on democracy. This has to the best of our knowledge not been done before. For the purpose of identification of exogenous variation in life expectancy that can be used to trace out the causal effect of life expectancy on democracy we draw on Acemoglu and Johnson (2007). They argue that starting in the 1940s international health interventions such as more effective public health measures introduced through WHO campaigns and the introduction of new and cheap chemicals and drugs led to large declines in mortality from infectious diseases and therefore to large improvements in life expectancy around the world, especially among people in poor countries and—crucially— that these international health interventions were unrelated to democratic conditions in any given country. Acemoglu and Johnson’s idea for identification is then to utilize this “international epidemiological transition” as a quasi-natural experiment. They propose a “predicted mortality” variable which captures the effects of the international epidemiological transition on world-wide declines in mortality and which they propose as an instrumental variable for increases in life expectancy. We argue below that this variable is qualified as an instrumental variable. For the purpose of this paper we construct a panel dataset covering 51 countries over the period 1940–2000, including a “predicted mortality” variable, that we use to provide estimates of the causal effect of improvements in health—measured as life expectancy—on democracy. We establish a statistically and economically significant positive effect of improvements in health on democracy, thereby lending support to the predictions of modernization theory. In fact, we find that there is a significant initial impact and that this impact increases over time so that the effect of life expectancy on democracy 30 years down the line is two and a half times larger than the initial effect. The rest of the paper is organized as follows. The next section reviews the various theoretical arguments for why better health promotes democracy. We give particular attention to the parts of modernization theory that address the ways in which improvements in health can increase either the likelihood of democratization or the consolidation of existing

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democracy, Section 3 introduces the data used in the paper, Section 4 outlines the econometric methodology, Section 5 presents the main empirical results and proceeds to establish some further results and Section 6 concludes. Finally, Appendix A gives details on the construction of some of the data series used in the regressions. 2. MODERNIZATION THEORY The basic “modernization hypothesis” is Lipset (1959) proposition that “the more well-to-do a nation, the greater the chances that it will sustain democracy.” As measures of “well-to-do” he proposed “wealth, industrialization, urbanization and education.” A problem though with the Lipset paper is that it is very vague with respect to the specific channels through which these aspects of economic development influence the democratization process. Later researchers have built on Lipset’s initial effort and proposed some specific channels linking changes in the economic structure of a society to changes in democracy. For our purposes, the important thing is that two main strands of modernization theory—the political culture view and the political economy view—provide mechanisms that can explain how improvements in health can impinge on democracy. Proponents of the political culture view of modernization theory postulate a causal effect of changes in health and life expectancy on the economic structure and political governance of a given society. In this line of work life expectancy is considered a marker of social development on a par with other social characteristics, such as occupational specialization, urbanization, educational levels, and income levels (Inglehart & Welzel, 2009). The effect of life expectancy on democratic governance is then postulated to work by changing peoples’ outlook on life and political culture (Inglehart & Welzel, 2005, 2009). The basic claim is that political governance only becomes an issue in people’s life when they no longer have to fight for their daily survival. When life is under constant threat from disease and hunger the daily fight for survival is the main priority. But better health and increased life expectancy changes people’s political attitudes as “a growing sense of existential security. . .gives rise to an emancipative ethos based on self-expression values.” These “self-expression values” in turn “are conducive to the emergence of and strengthening of effective democracy” (Inglehart & Welzel, 2005, p. 138 ). As the absence of constant threats to physical survival is the most fundamental aspect of “existential security,” health is almost by definition a core component of “existential security.” Therefore changes in the general health level of the population will have a direct effect on democracy in this scheme. Political economy-oriented scholars of modernization theory argue that changes in the economic structure affect democracy through changes in the power structure of society (Acemoglu & Robinson, 2005; Rueschemeyer et al., 1992). Most relevant here are the indirect effects of life expectancy on democracy working through changes in other markers of the economic structure of society, such as industrialization rates, urbanization rates, and education levels. A standard argument from economic theory to the effect that improved health has an effect on these other indicators of socioeconomic development is that better health increases the time horizon over which returns to investments can be appropriated (see for example Kalemli-Ozcan et al. (2000)). Thus longer expected lifespan will lead to greater investment in physical and human capital. This will in turn lead to higher incomes, more industrialization and higher urbanization rates.

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WORLD DEVELOPMENT

Standard arguments from economic theory therefore point to a link from health to other socioeconomic variables. On the assumption that higher life expectancy has an effect on for example industrialization rates, political economy researchers then point to links from these socioeconomic indicators to democracy. Rueschemeyer et al. (1992), for example, argue that one effect of changes in the economic structure of society such as increased industrialization and urbanization is to increase the power of the working class. Industrialization, urbanization, and higher education levels are considered to attenuate the collective action problem that the working class faces. This increases the power of the working class and enhances its ability to obtain concessions for better democratic governance from the political elite. Changing health levels will therefore also have an indirect effect on democracy through changes in these other aspects of the economic structure. A more recent example of this kind of reasoning is Glaeser and Ponzetto (2007), who provide a mechanism linking education to democracy. They build a model in which education raises the benefits of civic engagement and so raises support of a broad-based regime, i.e., democracy. Ever since Lipset’s paper appeared, the empirical relationships between each of the markers of economic development proposed in that paper and democracy have been extensively examined in the political science literature (see the survey essay of Diamond (1992), and the references therein). This literature has produced very well-documented strong positive correlations between levels of different indicators of socioeconomic development and democracy using different time periods, different countries, different definitions of democracy and so forth (Burkhart & Lewis-Beck (1994) and Muller (1994) are examples of this type of work). Many researchers have interpreted these positive correlations as the result of a causal effect going from economic development to democracy, but this may not be warranted. Acemoglu et al. (2008) is the only study that explicitly tries to account for omitted variable bias, general time trends, and reverse causality in their empirical analysis and they conclude that there is no evidence for a causal effect of income per capita on democracy. Similarly, Acemoglu et al. (2005) find no evidence of a correlation between a country’s education and democracy levels once all time-invariant country characteristics and general time trends in education and democracy are controlled for. The empirical evidence presented until now for an effect of life expectancy on income, industrialization, and so forth is also not conclusive. A study that explicitly tries to account for omitted variable bias and reverse causality in order to find the causal effect of life expectancy on income per capita concludes that there is no evidence for a positive causal effect (Acemoglu & Johnson, 2007). This together with the aforementioned paper of Acemoglu et al. (2008) seems to indicate that higher income per capita can be ruled out as a mediating effect between life expectancy and democracy. As for the specific relationship under scrutiny in this paper, Barro (1999) is the only study we know of that explicitly considers health as one of the determinants of democracy. He includes life expectancy in one of his regressions in his search for “determinants of democracy” and finds a marginally statistically significant positive relationship. But this is done only as a robustness check and no mechanism is proposed as an explanation for the found relationship. Further, the same issues of omitted variable bias and reverse causality as noted above with respect to the relationship between income per capita and democracy imply that the result cannot be interpreted as measuring a causal effect of life expectancy on democracy.

3. DATA AND DESCRIPTIVE STATISTICS In order to construct an instrumental variable for our regressions we follow Acemoglu and Johnson (2007) and define a “predicted mortality” variable as X M dt M Iit ¼ M di1940 ð1Þ d M d1940 where M dt is the average mortality from disease d at time t across the countries in the sample, M d1940 is the average mortality from disease d in 1940 across the countries in the sample, and M di1940 is mortality from disease d in country i in 1940. This variable is the “global mortality” version of the “predicted mortality” variable in Acemoglu and Johnson (2007). The idea behind using this variable to identify exogenous variation in life expectancy is that the driver of predicted mortality in a given country is the interaction between initial mortality from infectious diseases in that country and the change in the world-wide average mortality from those diseases over time. The change in the world-wide average mortality in turn is driven by the introduction of new and cheap chemicals and drugs. Finally, the introduction of new and cheap chemicals and drugs depends on advances in the production technology for chemicals and drugs which is unrelated to the state of democracy in any given country. This ensures that the instrumental variable is unrelated to the state of democracy in any given country and hence provides us with exogenous variation in life expectancy. An important feature of the data is that as the world-wide average mortality from a disease falls relative to its 1940 benchmark the country-specific mortality from this disease is predicted to fall proportionally. Because average mortality from all diseases that we have data for falls over time countries are predicted to converge in their mortality levels. Acemoglu and Johnson do not use this “global mortality” version of their “predicted mortality” variable as they estimate their empirical model by first-difference estimating equations. For that purpose they construct a different version of the “predicted mortality” variable which only requires data for 1940 (and in some cases 1950) which is the “pre-intervention” period and assume a predicted mortality of 0 in either 1980 or 2000 which are the “post-intervention” periods in their firstdifference estimations. We collect—from scratch—data on mortality by disease for 51 countries around the world at ten-year intervals from 1940–2000 in order to construct a full panel dataset of the Acemoglu and Johnson “predicted mortality” variable. The 51 countries in our sample are the same 47 countries as Acemoglu and Johnson use plus Egypt, Japan, Mauritius and The Dominican Republic (see the full list of countries in Table 6). The construction of the mortality variable has turned out to be a daunting task as the necessary data on disease-specific death rates are not conveniently located in a single source or two. Instead, it has been necessary to locate and consult a variety of difficult to obtain sources to get a reasonably complete coverage of the data. We have been able to collect data on mortality from 8 diseases over the period 1940–2000: tuberculosis, malaria, whooping cough, typhoid, measles, pneumonia, influenza, and smallpox. In the end it has only been possible to obtain complete data for 1940. For later years there are gaps in the data, but because of the way it is defined it has still been possible to construct a “predicted mortality” variable that covers all countries and all time periods. Details on the collection of data for the construction of the predicted mortality variable can be found in the Appendix A.

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4.4

9

4.3

8 7

4.2

5

4

4

3.9

3 2

3.8

Polity2 Index

6

4.1

1

3.7

0 -1

3.6 1940

1950

1960

1970

1980

1990

2000

Year life exp.

polity

0.3

9 8

0.25

7

0.2

6 5

0.15

4

Polity2 Index

log life expectancy

We use data for five diseases in the construction of the “predicted mortality” variable: tuberculosis, malaria, whooping cough, typhoid, and measles. Including data for pneumonia, influenza, and smallpox does not enhance the predictive power of the “predicted mortality” variable. On the contrary, including them in the construction of our instrument actually lowers the value of the F-statistic in the first-stage regressions of life expectancy on predicted mortality. Even though we only use data for five diseases whereas Acemoglu and Johnson (2007) have data for 15 diseases, we actually get a better predictor of life expectancy in the sense of higher F-statistics in the first-stage regressions. Our main democracy measure is the Polity2 Index which is the standard measure used in the political science literature. It is taken from the Polity IV database (Marshall & Jaggers, 2007). This measure is composed from two indices, Polity’s Democracy Index and Polity’s Autocracy Index. The Polity Democracy Index ranges from 0 to 10 and is derived as a weighted average of a number of sub-indices: “competitiveness of executive recruitment,” “openness of executive recruitment,” constraint of chief executive” and “competitiveness of political participation.” The Polity Autocracy Index is constructed in a similar way. The composite Polity2 Index is then constructed as the difference between the Democracy Index and the Autocracy Index. Following Acemoglu et al. (2008) countries that are still under colonial rule and hence do not figure as independent countries in the Polity data are assigned the lowest possible score of 10. We use 5-year averages to account for possible erratic behavior in the Polity data. For example, for the year 1960 we use the average of the Polity2 Index over the period 1958–62. Our main measure of the general level of health is life expectancy at birth. Data for life expectancy at birth for 1940 are obtained from Federal Security Agency (1947) and World Health Organization (1951). Data for later years are obtained from the UN’s online data base “World Population Prospects” (United Nations, 2006). A potential concern with this measure is that what matters for democracy is presumably years of adult life. If changes in life expectancy are primarily driven by changes in child mortality any found relationship between life expectancy and democracy will be difficult to interpret as caused by the mechanisms postulated by modernization theory. We will therefore provide results from robustness checks that use life expectancy at the age of 15 as the explanatory variable rather than life expectancy at birth. As was the case for the mortality data, gathering data on life expectancy at age 15 for all 51 countries and 7 time periods has been a large undertaking because a variety of sources had to be located and consulted in order to stitch together a full data series. Details on the construction of this variable can be found in the Appendix A. Figure 1 reveals a pattern that is consistent with a positive causal effect going from life expectancy to democracy. The evolution of the means of log life expectancy and the Polity score over time both follow an upward trend although the evolution of the Polity score is more volatile with a large dip in the 1970s and 1980s relative to the 1950s and 1960s. This temporary dip in the average of the Polity scores in the 1950s and 1960s is partly an artifact of the experience of many of the Asian countries in the sample. In the late 1940s and 1950s these countries gained independence from their colonizers and implemented Western style democratic governance which is reflected in their high Polity scores in the years immediately after independence. These democratic reforms were in almost all cases reversed after a few years, with either the military or other strongmen mounting coups.

log life expectancy

REVISITING THE MODERNIZATION HYPOTHESIS: LONGEVITY AND DEMOCRACY

3

0.1

2

0.05

1 0

0 1940

1950

1960

1970

1980

1990

2000

Year life exp.

polity

Figure 1. Decadal Means of Life Expectancy and Democracy. Decadal Std. dev. of Life Expectancy and Democracy.

Moreover, the standard deviation of the Polity scores across countries shows that over the sample period the democracy scores have converged somewhat even though there is divergence in the 70s and 80s. Therefore, over the entire sample period life expectancy and the democracy scores show a converging trend. This convergence pattern is consistent with the hypothesis that life expectancy has an important effect on democratization. Figure 2 shows the relationship between changes in life expectancy over the period 1940–2000 and changes in the Polity2 Index over the same period. The reason for using changes rather than levels of the variables is to control for

Figure 2. Life expectancy and Polity score.

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WORLD DEVELOPMENT Table 1. Summary statistics

Variable 1940 Democracy (log) Life expectancy (log) Life expectancy Predicted mortality 2000 Democracy (log) Life expectancy (log) Life expectancy Predicted mortality

Observations

Mean

Standard deviation

Minimum

Maximum

at birth at age 15

50 51 51 51

0.82 3.85 3.81 266.43

7.79 0.26 0.15 192.97

10 3.40 3.48 39.20

10 4.22 4.03 802.90

at birth at age 15

51 51 51 51

7.44 4.30 4.10 9.05

4.29 0.08 0.07 6.40

7 4.09 3.51 2.07

10 4.39 4.20 30.38

“Democracy” is the Polity2 Index. “Predicted mortality” is defined in Section 3 of the paper. See the Appendix A for data sources.

omitted time-invariant variables that might be driving both health and democracy (see discussion below). The regression line shown in the figure represents the result of an OLS regression of the change in democracy on changes in life expectancy. As for the levels regression in the literature there is a clear positive relationship. Even though it is unlikely to represent a causal relationship it suggests that the issue warrants further investigation. Table 1 provides summary statistics of the variables used in the main regression specifications. The table shows summary statistics for two years, 1940 and 2000. The main thing to note is the general trend toward better outcomes. Average life expectancy in the sample has increased by 55%, from 47 years to 73.7 years, while the average level of the democracy index has increased by more than eight points on a scale from 10 to 10 over the period 1940–2000. Predicted mortality from the five diseases that we include in our instrumental variable has almost vanished. It has decreased from 266 deaths per 100 thousand people per year in 1940 to only 9 deaths per 100 thousand people per year in 2000. All in all therefore, the data point to significant “socioeconomic modernization” of the countries in the sample over the second half of the 20th century. 4. ESTIMATING FRAMEWORK We now discuss the two issues of omitted variable bias and reverse causality and how to handle them empirically so that the obtained estimates can be interpreted as causal effects. (a) Omitted variables Robinson (2006) notes that “many aspects of the institutions and organization (maybe even the culture and geography) of a society will help to determine its prosperity and its level of democracy. Yet many of these factors will be unobserved and thus omitted from the equations that we estimate.” Therefore cross-section analyses of the correlation between economic development and democracy that do not take account of unobserved factors correlated with the socioeconomic variables used as explanatory variables will give inconsistent estimates. These studies can therefore not be relied upon to give convincing answers to the question at hand. Acemoglu et al. (2008) argue that, “income per capita and democracy are correlated because the same features of a society simultaneously determine how prosperous and how democratic it is,” that is “different constellations of these [initial] conditions lead different societies onto different development paths.” They accordingly conclude that the positive correlation between levels of GDP per capita and levels of measures

of democracy “is generated purely by a cross-sectional relationship.” An often invoked example of an historical “fixed effect” that drives both economic development and democracy is religious legacies. In “The Protestant Ethic and The Spirit of Capitalism” Weber (1930) notes with respect to England: “Is it not possible that their commercial superiority and their adaptation to free political institutions are connected in some way with that record of piety which Montesquieu ascribes to them?” Hence Weber argued that a religious sentiment, namely “the Protestant Ethic,” explained both democracy and “commercial superiority” in England. A relevant paper here is Becker and Woessmann (2009), which suggests that the mechanism that links “piety” with “commercial superiority” is education. Their thesis is based on the idea that “Luther’s demand that all Christians should be able to read the Gospel by themselves led to increased literacy among Protestants that, incidentally, could then also be used in economic activities” (Becker & Woessmann, 2009). This idea can be readily extended to imply a connection between Protestantism and democracy working through the intermediate channel of education. Thus, the adoption of Protestantism in this view was a critical juncture that set the country on a particular development path toward higher income per capita, a better educated citizenry and more democratic polity than countries that did not adopt Protestantism. This speaks directly to the need to control for time-invariant country characteristics when investigating the relationship between e.g., education and democracy. Along the same lines Islam is often invoked as being detrimental to democratization (e.g., Huntington, 1991). Huntington sees the adoption of Islam in a country is a critical juncture that set the country on an unfavorable development path that has locked the country in a state of economic backwardness and political totalitarianism even many centuries after the initial historical event. It is therefore essential to control for the possibility that the correlation between health and democracy is driven by fixed factors like religion or geography. As our main interest is in the effect of life expectancy on democracy and not in the effect of any particular fixed country characteristic we include fixed effects in our specification in order to control for all possible such time-invariant factors rather than trying to identify specific such factors and run the risk of omitting others. In addition to country fixed effects we include time effects in the regressions. As noted above there has been a significant increase in the average value of the democracy index over time. General upward shifts in the democracy index can be caused by a variety of factors unrelated to improvements in health conditions, for example the general shift in ideology toward democracy caused by the collapse of communism. If we do not include time effects the general upward shifts in both

REVISITING THE MODERNIZATION HYPOTHESIS: LONGEVITY AND DEMOCRACY

the democracy index and in life expectancy can cause us to find a spurious relationship between these variables. (b) Reverse causality Even though fixed effect estimation makes sure that only within-country variation is utilized in forming the regression estimates the results do not necessarily imply a causal effect of changes in socioeconomic variables on democracy. There still is a potential problem with reverse causality as an unexpectedly high value of the democracy variable in one period might cause a higher value of the socioeconomic variable in future periods. As already noted there is a recent literature on the effect of democracy on health, so for example a positive shock to democracy in one period caused by some time-varying omitted factor might cause an increase in life expectancy in future periods. Therefore to be able to claim causality running from changes in the general health level of the population to changes in democracy we need exogenous variation in the health-level variable. We create the predicted mortality variable capturing the effect of the international epidemiological transition in order to provide this exogenous variation with respect to democracy. As the instrumental variable is the variable for which we are able to obtain the fewest countries in our dataset this is our constraining variable with respect to temporal and spatial coverage. There are no sub-Saharan African countries in the sample and the African continent is only represented by Egypt and Mauritius. Because of this, there may be an issue with sample selection as the countries for which we have data are not a random sample from the full population of all countries in the world with respect to economic development or democracy levels. On the other hand, the absence of Sub-Saharan countries ensures that the effect of the AIDS epidemic on health and social variables does not cloud our results. (c) Specification In order to control for omitted variable bias and reverse causality we follow the estimation framework of Acemoglu and Johnson (2007). Using a specification with country fixed effects, time dummies and a “predicted mortality” variable created according to the recipe of Acemoglu and Johnson as an instrumental variable for life expectancy the estimating system of equations becomes gitþk ¼ ck xit þ lt þ di þ uit xit ¼ pM Iit þ lt þ di þ vit

ð2Þ ð3Þ

where g is the Polity score variable, x is (log) life expectancy, M Iit is the predicted mortality variable, lt is a full set of time dummies and di is a full set of country fixed effects. c is the parameter of interest. The crucial assumption that allows us to interpret the results of our empirical analysis as the causal effect of life expectancy on democracy is that covðM Iit ; uis Þ ¼ 0, for any t; s. This identifying assumption is justified by the reasoning above that the evolution of the “predicted mortality” variable is driven by the world-wide average change in disease-specific mortality which in turn is driven by world-wide advances in medical science. This implies that the change in predicted mortality in any given country is not related to political or economic events in that country but by factors exogenous to that specific country. The system of Eqns. (2) and (3) together with the identifying assumption covðM Iit ; uis Þ ¼ 0, for any t; s thus embodies our attempt to isolate the causal effect of life expectancy on democracy from the overall relationship between life expectancy and democracy.

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By 1940 many of the richer countries had already been able to reduce mortality from infectious diseases through expensive methods not available to poorer countries. This implied that these countries did not experience the same large shocks to life expectancy as poorer countries did. For our empirical investigation that is a good thing. Eqns. (2) and (3) reveal why it is important for our empirical investigation that different countries experienced different sizes of shocks to life expectancy from the epidemiological transition. If all countries had experienced the same shock to predicted mortality and hence to life expectancy, there would have been a general shift in life expectancy across all countries. But this would mean that it would be impossible to separate the effect from the increase in life expectancy on democracy from for example the effect of a general shift in ideology away from authoritarianism toward democracy. Empirically, this would imply that the effect of the shock to life expectancy would have been absorbed by the time dummies. The widely different experiences with the epidemiological transition across countries implies large variation in x and is therefore exactly the reason why we can obtain relatively precise estimates of the effect of changes in life expectancy on democracy. It is possible that the initial effect does not coincide with the full effect. It may for example take some time before a change in life expectancy induces large changes in people’s “selfexpression” values and hence leads to an increased demand for democracy. Or it may take some time for changes in life expectancy to lead to greater demand for democracy through a better educated citizenry as it takes time to elevate the general education level of the population. In order to estimate the effects of changes in life expectancy on democracy for example 10 or 20 years after the initial change in life expectancy, we allow for differential timing on the dependent and the explanatory variables. Thus, k indexes how many 10-year time periods we lead the dependent variable. The coefficient on life expectancy, ck , then shows the partial effect of a shock to life expectancy at any point t in time on democracy k 10-year periods down the line. To estimate the long-run effect we need to relate life expectancy at the initial date to the democracy score sufficiently far ahead in the future that ck has reached its new long-run value. That is, as k becomes sufficiently large, ck will converge toward the long-run value c1 , and this will then be the long-run effect of life expectation on democracy. How large “sufficiently large” is for ck to be the “long-run” effect, is an empirical question. What we can do is to estimate the parameter of interest in Eqn. (2) for k ¼ 0; 1; 2 . . . and so forth as far ahead as our data will allow. As the time periods go by we expect the coefficients on life expectancy to change from the short-run value to the long-run value. If the coefficients seem to converge to some value we can interpret this value as the long-run effect. But even if the estimated coefficients do not settle down within the time periods allowed by our data the estimates for k ¼ 0; 1; 2 . . . are valuable as they show the transitional dynamics of democracy from the initial effect toward the new long-run equilibrium as a result of an initial shock to life expectancy. 5. RESULTS (a) Pre-existing trends Before we turn to the main empirical analysis we discuss a potential concern with the identification strategy. It is possible to argue that preexisting trends in democracy are causing the

The dependent variables in columns (1)–(3) are current and forwarded values of Predicted Mortality. The dependent variables in columns (4)–(9) are current and forwarded values of Life Expectancy. “Lead” refers to the dependent variable so that e.g., “10 year lead” means that the dependent variable is dated 10 years after the explanatory variable. The explanatory variable in all columns is the current state of democracy as measured by the Polity2 Index. Instrument used in columns (7)–(9) is Polity2 Index lagged one period. Robust standard errors are reported in parentheses. Full set of country fixed effects and time dummies included in all columns. ** Refers to statistical significance at the 5% level. *** Refers to statistical significance at the 1% level.

0.011 (0.10) 51 233 0.00 0.02 0.002 (0.011) 51 284 0.18 1.37

Life expectancy

0.005 (0.005) 51 335 0.39 12.25 0.000 (0.001) 51 234 0.20 0.001 (0.001) 51 285 0.32

Life expectancy

0.004*** (0.001) 51 336 0.49 0.08 (0.25) 51 234 0.34 Predicted

0.49 (0.44) 51 285 0.49 1.78** (0.84) 51 336 0.54 Polity2 Index Countries Country-year observations R-squared First-stage F-value

(8) IV 10 year lead (7) IV No lead (6) OLS 20 year lead (5) OLS 10 year lead (4) OLS No lead (3) OLS 20 year lead (2) OLS 10 year lead (1) OLS No lead Dependent variable

changes in mortality and life expectancy. If this were correct it would invalidate the assumption of the exogeneity of the mortality variable. We therefore investigate the possibility of preexisting trends in democracy being correlated with the changes in life expectancy and predicted mortality. For the purpose of these regressions we extend the data set backward to include values for the Polity Index of democracy for the years 1920 and 1930, so we can check for trends before the international epidemiological transition (these regressions do not include data points covering colonial experiences). Columns (1)–(3) of Table 2 display the results of regressing predicted mortality on current and lagged values of the democracy measure. The logic behind the use of the predicted mortality variable leads us to expect a negative contemporaneous correlation between predicted mortality and democracy but no correlation between future values of predicted mortality and the current value of the democracy variable. Columns (4)–(6) of Table 2 display the results of regressing life expectancy on current and lagged values of the democracy measure. For the same reason as for the predicted mortality variable we expect to see a positive contemporaneous correlation between life expectancy and democracy but no correlation between future life expectancy and current values of the democracy variable. Columns (1) and (4) of Table 2 show that there is a statistically significant negative contemporaneous correlation between predicted mortality and democracy and a strongly significant positive correlation between life expectancy and democracy, respectively. These results are thus as expected. Just as importantly, there is no evidence that preexisting trends in democracy are driving predicted mortality. Columns (2) and (3) show that the correlation between current democracy and predicted mortality 10 or 20 years hence is statistically insignificant and economically small. Similarly, columns (5) and (6) of the Table show that there is no correlation between current values of the democracy variable and life expectancy 10 or 20 years later. As a further check on the issue of reverse causality, we try to perform IV regressions of contemporaneous and future life expectancy on contemporaneous democracy using lagged values of democracy as instruments. The results are displayed in columns (7)–(9) in Table 2. We find that as for the OLS regressions there is no effect of current democracy on life expectancy 10 or 20 years later—even the contemporaneous correlation between life expectancy and democracy from the OLS regression disappears. There is thus no evidence of the state of democracy in a given country on a particular date having an effect on life expectancy in that country 10 or 20 years later. Figures 3 and 4 investigate the issue of preexisting trends further. Figure 3 shows that there is no correlation between changes in predicted mortality over the period 1940–2000 and changes in democracy over the period 1920–40 (dropping Guatemala from the sample and redoing the analysis does not change the conclusions. In fact, the coefficient estimate becomes even more insignificant as the t-statistic falls from 1.13 to 0.90). In a similar fashion, Figure 4 shows that there is no correlation between changes in life expectancy over the period 1940–2000 and changes in democracy over the period 1920–1940. These results give us confidence that the changes in the predicted mortality variable are exogenous with respect to democracy and in particular that it was not changes in democracy before 1940 that drove changes in life expectancy either through the international epidemiological transition or otherwise.

(9) IV 20 year lead

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Table 2. Falsification test, 1920–2000

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Figure 3. Predicted mortality and Polity score.

Figure 4. Life expectancy and Polity score.

(b) Main results We now turn to our main results. Table 3 displays the results of regressing the Polity2 Index on life expectancy. Columns (1) and (2) estimate equation (2) by OLS, while columns (3) and (4) estimate the effect of life expectancy on democracy by 2SLS, i.e., by estimating the system of Eqns. (2) and (3). Including time periods where countries go from being colonies to being sovereign countries will make interpretation of the estimates more difficult. We want to estimate the effect of health on democracy within a sovereign country. Therefore the regressions in column (1) and (3) where we include the

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datapoints where a country either still is a colony or when the country gained independence less than 10 years ago are only included in order to see whether there is a material difference in the estimates when we include these periods and when we do not. For interpretations of the results we will rely on the estimates obtained when these datapoints are excluded. The OLS estimates in columns (1) and (2) show a statistically and economically significant positive effect of life expectancy on democracy. The point estimates with and without colonies are very similar at 10.76 and 11.14 respectively. The 2SLS estimate in column (3) with colonies included is somewhat higher at 14.85 but when we exclude colonial experiences in column (4) we obtain an estimate that at 10.73 is very similar to the OLS estimates. The estimate of 10.73 in column (4) implies that a change in average life expectancy from 47 years to 73—i.e., the change over the period 1940–2000—would lead to an increase in the average score on the Polity2 Index of 4.83 points or 24% if the range of the index. This corresponds roughly to the experience of Ecuador from 1950 to 2000 where life expectancy changed from 48 to 74 and the Polity2 Index changed from 2 to 7.2. In columns (3) and (4) we also see that the instrumental variable is a very good predictor of life expectancy. The first-stage F-statistic is over 200 in both columns. To the extent that we have confidence in the exclusion restriction, this implies that the predicted mortality variable is an excellent instrumental variable for life expectancy. In our main specification we only examine whether an exogenous change in life expectancy has a contemporaneous effect on democracy. If the full effect of life expectancy on democracy manifests itself in the initial effect then we can also interpret the initial effect as the long-run effect. But it is plausible that the effect of a shock to life expectancy on democracy changes over time. A positive shock to life expectancy may for example lead to higher education levels but only with a time lag because it takes time to accumulate human capital. If higher education levels in turn have an effect on democracy through any of the channels discussed above any initial effect of a shock to life expectancy on democracy through an enhancement of “self-expression values” may be amplified over time as the effect from higher education levels kicks in. As a robustness checks we therefore try leading the democracy variable to investigate whether there are delayed effects such that the positive contemporaneous effect established in the previous section gets amplified over time or fades out. With respect to Eqn. (2) this means that we set k equal to 1, 2 and 3 10-year periods respectively, which is as far as the data will allow us to investigate the dynamic effects of life expectancy. Table 4 presents the short-run dynamic effects of increased life expectancy on democracy. As we go across the table from

Table 3. Effect of life expectancy on democracy, 1940–2000 (1) With colony-year observations

(2) Without colony-year observations

(3) With colony-year observations

OLS Dependent variable is the Polity2 Index Life expectancy 10.76*** (3.27) Countries 51 Country-year observations 356 R-squared 0.35 First-stage F-value

(4) Without colony-year observations 2SLS

11.14*** (3.00) 51 336 0.34

14.85*** (4.52) 51 356 0.39 279.89

10.73** (4.96) 51 336 0.34 206.31

The dependent variable is the Polity2 Index. The explanatory variable is life expectancy at birth. Instrument used is predicted mortality. Standard errors are reported in parentheses. Full set of country fixed effects and time dummies included in all columns. ** Refers to statistical significance at the 5% level. *** Refers to statistical significance at the 1% level.

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column (1) which displays the initial effect to column (4) which displays the effect of an increase in life expectancy at a given point in time on democracy 30 years later we see that the effect increases monotonically over time. The point estimate for the initial effect is 10.73 (as we also saw in column (4) of Table 2), the point estimate increases slightly to 11.06 after 10 years, jumps to 20.45 after 20 years and increases further to 26.51 after 30 years. The point estimate in column (1) is statistically significant at the 5% level, while the other estimates are statistically significant at the 1% level. The estimates imply that the effect of a one standard deviation increase in life expectancy increases over time, from a contemporaneous effect of 2.8 points on the Polity2 Index to 6.9 points after 30 years. These are sizeable effects. Whether the effect after 30 years is close to the “steady-state” effect is unfortunately impossible to say. Data limitations prevent us from leading the dependent variable further periods so we cannot investigate what the effect is 40 or more years after a shock to life expectancy. Table 5 presents the results of the same setup but where we substitute life expectancy at age 15 for life expectancy at birth as the dependent variable. The estimated coefficients on life expectancy at age 15 are considerably larger than the estimated coefficients for life expectancy at birth in Table 4. The point estimate of the contemporaneous effect of 21.38 is about twice the size of the estimated coefficient of 10.73 for life expectancy at birth. These estimates imply that the effect of a one standard deviation increase in adult life expectancy in 1940 is a 3.2 point increase in the democracy score, which is a slightly larger contemporaneous impact on democracy than the 2.8 points increase implied by a one standard deviation increase in life expectancy at birth. The dynamic path of democracy in response to a change in life expectancy at age 15 seems to be very similar to the

response to a change in life expectancy at birth. The effect after 10 years is estimated to be about the same as the contemporaneous effect but then there is an upward shift in the effect after 20 years and a further upward shift after 30 years. This means that the point estimates on the dynamic effects when we use life expectancy at age 15 as the independent variable are also about twice as large as when we use life expectancy at birth as the independent variable. After 10 years the point estimate is 21.22 compared to 11.06 when we use life expectancy at birth, after 20 years it is 37.58 compared to 20.45 and after 30 years it is 48.64 compared to 26.51. Because of the worse fit in the firststage equation—the value of the F-statistic is about half that in Table 4—the standard errors on the estimated coefficients in the second stage are about twice as large as in Table 4, so that the statistical significance levels are about the same both for the contemporaneous effect and the dynamic effects. These two features, namely a similar effect on democracy of a one standard deviation increase in life expectancy at birth and at age 15 respectively and a similar dynamic path, suggest that it does not make much of a difference whether we use life expectancy at birth or life expectancy at age 15 as our measure of health. 6. CONCLUDING COMMENTS The basic tenet of modernization theory is the idea that the socioeconomic development of a country has a causal effect on its political governance. Since Lipset (1959) initiated the empirical literature on socioeconomic determinants of democratization, a large number of papers have documented a positive cross country correlation between various measures of socioeconomic development and democracy. One important marker of socioeconomic development that is largely missing in this literature is health. Although there

Table 4. Dynamic effects of life expectancy on democracy, 1940–2000

Dependent variable is the Polity2 Index Life expectancy Countries Country-year observations R-squared First-stage F-value

(1) No lead

(2) 10 year lead

(3) 20 year lead

(4) 30 year lead

10.73** (4.96) 51 336 0.34 206.21

11.06** (5.25) 51 286 0.37 157.25

20.45*** (6.30) 51 235 0.40 114.06

26.51*** (8.13) 50 184 0.34 68.72

2SLS estimation in all columns. The dependent variable is the Polity2 Index. The explanatory variable is life expectancy at birth. “Lead” refers to the dependent variable so that e.g., “10 year lead” means that the dependent variable is dated 10 years after the explanatory variable. Instrument used is predicted mortality. Standard errors are reported in parentheses. Full set of country fixed effects and time dummies included in all columns. ** Refers to statistical significance at the 5% level. *** Refers to statistical significance at the 1% level.

Table 5. Dynamic effects of life expectancy on democracy, 1940–2000

Dependent variable is democracy Life expectancy at age 15 Countries Country-year observations R-squared First-stage F-value

(1) No lead

(2) 10 year lead

(3) 20 year lead

(4) 30 year lead

21.38** (9.99) 51 336 0.35 108.16

21.22** (10.25) 51 286 0.36 81.36

37.58*** (11.90) 51 235 0.38 58.83

48.64*** (15.99) 50 184 0.21 33.87

2SLS estimation in all columns. The dependent variable is the Polity2 Index. The explanatory variable is life expectancy at age 15. “Lead” refers to the dependent variable so that e.g., “10 year lead” means that the dependent variable is dated 10 years after the explanatory variable. Instrument used is predicted mortality. Standard errors are reported in parentheses. Full set of country fixed effects and time dummies included in all columns. ** Refers to statistical significance at the 5% level. *** Refers to statistical significance at the 1% level.

REVISITING THE MODERNIZATION HYPOTHESIS: LONGEVITY AND DEMOCRACY

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Table 6. Data coverage Country Argentina Australia Austria Bangladesh Belgium Brazil Canada Chile China Columbia Costa Rica Denmark Dominican Rep. Ecuador Egypt El Salvador Finland France Germany Greece Guatemala Honduras India Indonesia Ireland Italy Japan South Korea Malaysia Mauritius Mexico Myanmar Netherlands New Zealand Nicaragua Norway Pakistan Panama Paraguay Peru Philippines Portugal Spain Sri Lanka Sweden Switzerland Thailand UK USA Uruguay Venezuela

Predicted mortality

Life expectancy

Democracy

1940–2000 1940–2000 1940–2000 1940–2000 1940–2000 1940–2000 1940–2000 1940–2000 1940–2000 1940–2000 1940–2000 1940–2000 1940–2000 1940–2000 1940–2000 1940–2000 1940–2000 1940–2000 1940–2000 1940–2000 1940–2000 1940–2000 1940–2000 1940–2000 1940–2000 1940–2000 1940–2000 1940–2000 1940–2000 1940–2000 1940–2000 1940–2000 1940–2000 1940–2000 1940–2000 1940–2000 1940–2000 1940–2000 1940–2000 1940–2000 1940–2000 1940–2000 1940–2000 1940–2000 1940–2000 1940–2000 1940–2000 1940–2000 1940–2000 1940–2000 1940–2000

1930–2000 1930–2000 1930–2000 1930–2000 1930–2000 1930–2000 1930–2000 1930–2000 1930–2000 1930–2000 1930–2000 1930–2000 1940–2000 1930–2000 1940–2000 1930–2000 1930–2000 1930–2000 1930–2000 1930–2000 1930–2000 1930–2000 1930–2000 1940–2000 1930–2000 1930–2000 1930–2000 1940–2000 1940–2000 1940–2000 1930–2000 1930–2000 1930–2000 1940–2000 1940–2000 1930–2000 1930–2000 1940–2000 1920, 1940–2000 1940–2000 1930–2000 1940–2000 1930–2000 1930–2000 1930–2000 1930–2000 1940–2000 1930–2000 1930–2000 1930–2000 1940–2000

1920–2000 1920–2000 1920–2000 1920–2000 1920–2000 1920–2000 1920–2000 1920–2000 1920–1930, 1950–2000 1920–2000 1920–2000 1920–2000 1930–2000 1920–2000 1920–2000 1920–2000 1920–2000 1920–2000 1920–2000 1920–2000 1920–2000 1920–2000 1920–2000 1920–2000 1920–2000 1920–2000 1920–2000 1920–2000 1920–2000 1920–2000 1920–2000 1920–2000 1920–2000 1920–2000 1920–2000 1920–2000 1920–2000 1920–2000 1920–2000 1920–2000 1920–2000 1920–2000 1920–2000 1920–2000 1920–2000 1920–2000 1920–2000 1920–2000 1920–2000 1920–2000 1920–2000

are versions of modernization theory that predict a causal effect of better health on democracy there have been no attempts to identify the causal effect of human well-being in the form of health or life expectancy on political governance. This is an important omission given that from a theoretical perspective, health (together with education) may be the most important marker of development. This paper tries to fill this gap in the literature. We test whether improvements in the general health level of the population of a society causes its political governance to improve.

Colony

1920, 1930, 1940

1920

1920, 1930, 1940 1920, 1930, 1940 1920

1920, 1930, 1940 1920, 1930, 1940 1920–1960 1920, 1930, 1940

1920, 1930, 1940

1920, 1930

1920, 1930, 1940

Several theories can explain correlations between levels of socioeconomic variables and democracy. In order to isolate the causal effect of life expectancy on democracy one therefore needs to control for these other plausible mechanisms that can generate the observed positive correlations between life expectancy and democracy. In this paper we implement an identification strategy that takes care to avoid the deficiencies of earlier work. We adopt a panel data setting which allows us to introduce fixed effects into our empirical specification. This makes it possible to con-

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trol for the possible effect of various “critical junctures” that lead to unobserved country heterogeneity. We exploit the “quasi-natural” experiment of the international epidemiological transition that started in the 1940s to create a “predicted mortality” variable that captures the dramatic decrease in mortality rates from various infectious diseases that followed from the worldwide introduction of cheap vaccines and pesticides. We argue that this variable can be employed as an instrumental variable to give exogenous variation in life expec-

tancy which can be used to estimate a causal effect of life expectancy on democracy. We establish a statistically and economically significant positive effect of improvements in life expectancy on democracy. Our results indicate that a positive shock to life expectancy of one standard deviation of (log) life expectancy in 1940, results in an initial increase in the Polity Index of 2.8 points on a 20 points scale. The positive effect increases monotonically thereafter reaching 7 points after 30 years. This is by any measure a sizeable effect.

REFERENCES Acemoglu, D., & Robinson, J. (2005). Economic origins of dictatorship and democracy. Cambridge University Press. Acemoglu, D., Johnson, S., Robinson, J. A., & Yared, P. (2005). From education to democracy?. American Economic Association, Papers and Proceedings, 95(2), 44–49. Acemoglu, D., & Johnson, S. (2007). Disease and development: The effect of life expectancy on economic growth. Journal of Political Economy, 115, 925–985. Acemoglu, D., Johnson, S., Robinson, J. A., & Yared, P. (2008). Income and democracy. American Economic Review, 98(3), 808–842. Acemoglu, D., & Johnson, S. (2007). Disease and development: The effect of life expectancy on economic growth. Journal of Political Economy, 115, 925–985. Anand, S., & Sen, A. (2000). The income component of the human development index. Journal of Human Development, 1(1), 83–106. Arriaga, E. (1968). New life tables for Latin American populations in the nineteenth and twentieth centuries. Population monograph series, no. 3. Berkeley: Institute of International Studies, University of California. Barro, R. J. (1999). Determinants of democracy. Journal of Political Economy, 107, 158–183. Becker, S. O., & Woessmann, L. (2009). Was weber wrong? A human capital theory of protestant economic history. The Quarterly Journal of Economics, 124(2), 531–596. Besley, T., & Kudamatsu, M. (2006). Health and democracy. American Economic Review Papers and Proceedings, 96(2), 313–318. Bollen, K. (1979). Political democracy and the timing of development. American Sociological Review, 44, 572–587. Burkhart, R., & Lewis-Beck, M. (1994). Comparative democracy: The economic development thesis. American Political Science Review, 88(4), 903–910. Central Statistics Office, Ireland. 2004. Irish Life Tables No.14 Dublin: Central Statistics Office, Ireland. Diamond, L. (1992). Economic development and democracy reconsidered. American Behavioral Scientist, 35(4/5), 450–499. Diamond, L., Lipset, S. M., & Linz, J. (1987). Building and Sustaining Democratic Government in Developing Countries: Some Tentative Findings. World Affairs, 150(1), 5–19. Federal Security Agency (1947). Summary of International Vital Statistics, 1937–1944. U.S. Public Health Service, National Office of Vital Statistics. Glaeser, E. L., & Ponzetto, G. A. M. (2007). Why does democracy need education? Journal of Economic Growth, 12, 77–99. Huntington, S. P. (1991). The third wave: Democratization in the late twentieth century. Norman, OK: Oklahoma University Press. Inglehart, R., & Welzel, C. (2005). Modernization, cultural change and democracy: The human development sequence. Cambridge: Cambridge University Press. Inglehart, R. & Welzel, C. (2009). How Development leads to Democracy, Foreign Affairs, March/April. Jackman, R. W. (1973). On the relation of economic development to democratic performance. American Journal of Political Science, 17(3), 611–621. Kalemli-Ozcan, S., Ryder, H. E., & Weil, D. N. (2000). Mortality decline, human capital investment, and economic growth. Journal of Development Economics, 62, 1–23. Kudamatsu, M. (2012). Has democratization reduced infant mortality in sub-Saharan Africa? Evidence from micro data. Journal of the European Economic Association, 10(6), 1294–1317.

Lipset, S. M. (1959). Some social requisites of democracy: Economic development and political legitimacy. American Political Science Review, 53, 69–105. Marshall, M.G., & Jaggers, K. (2007). Polity IV project: Dataset user’s manual. Muller, E. (1994). Economic determinants of democracy. American Sociological Review, 60, 966–982. Przeworski, A. (2003). Democracy and economic development. In E. D. Mansfield, & R. Sisson (Eds.), Political science and the public interest. Columbus: Ohio State University Press. Robinson, J. A. (2006). Economic development and democracy. Annual Review of Political Science, 9, 503–527. Rueschemeyer, D., Stephens, E. H., & Stephens, J. D. (1992). Capitalist development and democracy. University of Chicago Press. United Nations (1982). Model life tables for developing countries. http:// www.un.org/esa/population/publications/Model_Life_Tables/Model_ Life_Tables.htm. United Nations (2006). World population prospects: The 2006 revision. http://www.data.un.org/Data.aspx?d=PopDiv&f=variableID%3a68. Weber, M. (1930). The protestant ethic and the spirit of capitalism. London: Allen&Unwin. World Health Organization (1951). Annual Epidemiological and Vital Statistics, 1939–1946. Geneva: World Health Organization.

APPENDIX A Data on life expectancy for 1940 is taken from various UN Demographic Yearbooks, particularly the 1948 and the 1949 versions. We calculate the unweighted average of male and female life expectancy. Life expectancy at birth for 1950 onwards is downloaded from the online UN demographic database “World Population Prospects: The 2006 Revision.” The data are presented in 5 year intervals, so we use 1950– 55 for 1950 on so forth. Life expectancy at age 15 are from the Human Mortality Database at Berkeley (available at www.mortality.org), the Human Life-Table Database (available at www.lifetable.de), Central Statistics Office, Ireland (2004), Arriaga (1968), and various issues of UN Demographic Yearbooks. The remaining gaps were filled by combing data on life expectancy at birth from UN Demographic Yearbooks with model life tables for developing countries from United Nations (1982). We calculated the unweighted average of male and female life expectancy. Data on democracy are the polity2 series from the Polity IV database available at www.systemicpeace.org/inscr.htm. Acemoglu and Johnson (2007) provide a lengthy discussion of choice of diseases to include in the construction of the predicted mortality variable. We follow their lead and gather data on death rates by disease for as many of the diseases from their list of 15 diseases as we can. We have been able to collect data death rates by disease on 8 of the 15 diseases for the period

REVISITING THE MODERNIZATION HYPOTHESIS: LONGEVITY AND DEMOCRACY

1940–2000 for 51 countries. The 51 countries are the same as in the “base sample” of Acemoglu and Johnson, plus The Dominican Republic, Egypt, Japan, and Mauritius. The eight diseases we have data for are: tuberculosis, malaria, pneumonia, influenza, smallpox, whooping cough, typhoid, and measles. The main sources of data for death rates by disease in 1940 are Summary of International Vital Statistics, 1937–44, published by the Federal Security Agency (1947) of the U.S. government, and World Health Organization (1951). For a couple of countries we have had to substitute death rates from neighboring countries or areas: death rates for China are from Hong Kong, death rates for Indonesia are from Singapore and death rates for South Korea are from Japan. For some diseases we have also substituted death rates from Puerto Rico for death rates for The Dominican Republic. For some countries death rates for 1940 are only available for specific cities. For Bangladesh, India and Pakistan, we use the death rates reported for Calcutta, New Delhi and Karachi respectively.

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Even when these substitutions are made, these two sources leave some gaps, especially for tuberculosis and malaria. The gaps have been filled from a wide variety of sources. For death rates from tuberculosis in Asian countries we have found data in various issues of Tubercle. The death rate from tuberculosis in China is from the homepage of the Tuberculosis and Chest Service, Department of Health, The Government of the Hong Kong Special Administrative Region. Gaps for death rates for malaria have been filled by consulting various documents obtained from the “Books and Documents” homepage of the Office of Medical History of the Office of the Surgeon General of The U.S. Army which is available on the internet, especially from Volume VI of the Clinical Series about the Medical Department of the United States Army in World War II. Death rates by disease from 1950 to 1980 are from various issues of UN Demographic Yearbooks. Death rates for 1990 and 2000 are from the online WHO Mortality Database, and the online WHO Causes of death database.

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