Socio-demographic Determinants of Economic ...

Socio-demographic Determinants of Economic Growth: Age-Structure, Preindustrial Heritage and Sociolinguistic Integration Edward Crenshaw, Ohio State University Kristopher Robison, Northern Illinois University This study establishes a socio-demographic theory of international development derived from selected classical and contemporary sociological theories. Four hypotheses are tested: (1. population growth’s effect on development depends on age-structure; (2. historic population density (used here as an indicator of preindustrial social complexity) boosts contemporary economic performance; (3. ethnic polarization impairs economic growth; and (4. a nation’s degree of sociolinguistic integration positively influences economic performance. Investigating annual changes in real gross domestic product per capita from 1970 to 2000, our pooled time-series analyses of 101 developed and developing countries generally support these hypotheses net of common alternative explanations, suggesting that the etiology of economic growth could benefit from the reintroduction of classic and contemporary sociological theories.

Introduction Economic growth and attendant changes in social organization have been central themes of analysis since the earliest days of social science. As an independent variable, economic development has been linked to urbanization, fertility, mortality, democratization, income inequality and many other social phenomena, predictive power that justifies a sociological interest in the etiology of economic growth and social development. In this article we propose a demographically-driven, ecological-evolutionary model of economic growth. Following logic common to several ecological, evolutionary and functionalist accounts of social change, we investigate the influences of age-dependency, historical population density, ethnic/racial polarization, and sociolinguistic integration on economic growth over three decades (1970 to 2000), bringing these dimensions together in a theoretically and empirically integrated study for the first time. In the following analyses of all nations for which data exist, these ecological and evolutionary characteristics play important roles in economic development controlling for more frequently considered alternative explanations.

The authors would like to acknowledge helpful comments from William Form, Joan Huber, Social Forces Editor François Nielsen and the reviewers, and support from the National Science Founda-

tion (SES-0218367). Direct correspondence to Edward Crenshaw, Department of Sociology, Ohio State University, 1885 Neil Ave., Columbus, OH 43210. E-mail: [email protected]. © The University of North Carolina Press

Social Forces 88(5) 2217–2240, July 2010

Figure 2218 • Social Forces 88(5) 1 Figure 1. The Socio-Demographic Engine of Change in Early Social Science

Population Pressures

Competition-Induced Differentiation & Interdependence

Economic Growth

Population and Development Although population dynamics were the fulcrum of early sociological accounts of economic change (Spencer 1852; Durkheim 1893), this demographic focus in comparative studies of national development was lost for most of the post-WWII era. Despite a series of empirical studies during the 1990s that linked population change to development, there has been little investigation of the influence of other socio-demographic dimensions on economic change and no attempt at all to link empirical findings to sociological theory. While some studies have investigated 2 the role ofFigure age structure on economic growth (Mankiw et al. 1992; Barlow 1994; Brander and Dowrick 1994; Kelley and Schmidt 1995; Crenshaw et al. 1997; Sachs and Warner 1997; Bloom and Williamson 1998), only a handful have considered either population density’s influence on economic growth (Simon and Gobin 1980; Kelley and Schmidt 1995; Crenshaw and Oakey 1998; Burkett et al. 1999; Acemoglu et al.Age 2002) or the role of ethnicity in development (Easterly Structure and Levine 1997; Brukett et al. 1999; Putterman 2000). Therefore, while we are beginning to appreciate socio-demographic influences on development, a more comprehensive theory-driven account of population’s impact on economic growth Degree of Social would be useful. Preindustrial Social Differentiation & given Complexity This tardiness in scrutinizing population’s role in development is ironic Interdependency (Demographic the longstanding pro-natalist tradition in Western thought. Many social com(Complexity and Inheritance) mentators of antiquity and the medieval period (e.g., Cicero, Plato, Machiavelli) Adaptability) as well as many of the first social scientists considered increases in population an indicator of successful, expansionary social systems. Adam (i.e., Smith (1776) noted Structural Ethnicthe Dualism/ that market size limits complexity of the division of Conduciveness labor. Spencerto(1852) Polarization elaborated this principle by explicitly linking increased population sizeGrowth) and density Economic to economic change via innovation, which Durkheim (1893) later attributed to competition. As individuals and groups find themselves struggling for scarce resources in denselySociolinguistic populated environments, they tend to differentiate their Integration economic activities (to reduce direct competition) and to innovate to increase (i.e., Linguistic organizational specialization and technological surplus (see Figure 1). Essentially, Dominance) development are partial responses to population pressures, and while intensely competitive environments may harm human welfare in the short-term, the emergent property springing from such competition is a population used to hard work,

Socio-demographic Determinants of Economic Growth • 2219

long hours and self-sacrifice nested in a complex division of labor, attributes which may lead in time to superior economic performance at the macro level.

Population Growth, Age-Structure and Economic Development The modern study of population growth’s influence on economic growth began with Malthus’ (1798) famous dictum that agricultural productivity increases arithmetically while population increases geometrically. That is, in the absence of “preventive checks,” populations overshoot their abilities to secure subsistence and “positive checks” (i.e., mortality) restore the equilibrium. Coale and Hoover (1958) elaborated on this theory by stressing family economics and capital formation. According to their perspective, rapid population growth forces families to consume savings, adversely affecting capital formation and national savings rates. In addition, high youth dependency ratios force nations to invest scarce capital in a game of catch-up to provide education, jobs and infrastructure for a rapidly expanding labor force. Allocating capital to less-productive segments of the population (e.g., educational expenditures) forces nations to undercapitalize those already in the labor force (Bloom and Freeman 1988), resulting in sub-optimal investment patterns and subsequent under-performance in economic growth. On the other hand, many economists assert that abundant labor has been and remains a prerequisite for economic take-off, even going so far as to attribute much of the “Asian miracle” to its huge, inexpensive labor reserves (Williamson 1998). One argument asserts that as competition for jobs depresses wages, profitability increases for owners of capital and encourages greater domestic investment (Lewis 1954, 1958). Labor force effects are therefore mediated by investment rates. Related arguments involve scale effects and demand effects. A growing labor force encourages scale effects such as expanding domestic markets, growing complexity in divisions of labor, greater volumes and superior mixes of diffused information, technology and skills, and declining per capita costs associated with public infrastructure due to heavy use (Simon 1981). On the demand side, an increase in population may lead to a rise in consumer demand for durable goods and real estate (Easterlin 1967). Regardless, older studies linking population growth and economic growth failed to differentiate labor force and fertility effects, leading to weak and equivocal findings (Kuznets 1967; Easterlin 1967; Simon and Gobin 1980; Firebaugh 1983; McNicoll 1984; Chesnais 1987). This dearth of robust statistical evidence linking population and economic growth was due to the inappropriate specification of models (Crenshaw et al. 1997). While various combinations of birth and death rates may produce the identical rate of population growth, age structures may differ substantially. To correct this, population growth must be disaggregated into its productive (i.e., competitive and adaptive) and non-productive segments to determine the true relationships between demographic change and economic change. Indeed, cross-national evidence increasingly points out that youth dependency (i.e., the ratio of children to productive adults) does retard economic

2220 • Social Forces 88(5)

growth, but that adult population growth (i.e., labor force growth) has the opposite influence (Bloom and Freeman 1988; Barlow 1994; Brander and Dowrick 1994; Crenshaw et al. 1997). Thus, some types of population growth contribute to competition and adaptability more directly than others, and so not all forms of population growth contribute equally to social change. Clearly, the increase of productive adults should be the operative variable in economic progress. As the major actors in any economy, adults compete for scare resources, differentiate themselves to avoid head-on competition, apply diffused information to production, and engage in major consumer activity. Population growth among children and the elderly, however, has far fewer beneficial multipliers and, even when children and retired persons are common in the labor force, the contributions of these age segments to ecological processes such as differentiation and diffusion are minor relative to adult contributions.

Population Density, Proto-Modernity and Economic Development Early ideas about the latent functions of intense social competition suggest that socio-demographic dynamics may also have long-run influences on social development. Contemporary theoretical statements concerning the long-run effects of population growth by Boserup (1965, 1981), Lenski (1966, 2005) and Simon (1981) are compatible with early population-driven models of social change. Some early sociological theories posited that the wellspring of organizational and technological innovation and subsequent social development is competitive specialization. Historical differences in climates, disease regimes, geography and social environment culminate in differential demographic inheritances among contemporary countries, differences that allow us to place contemporary nations along a continuum that runs from pre-modernity to modernity. Some developing countries are proto-modern societies, or those preindustrial societies where historical population pressures forced populations to experience social evolution in terms of advanced agrarianism, institutional development, and socio-spatial complexities/efficiencies well before the modern era. Essentially, historic population density serves as a convenient marker of the starting line in the contemporary race for economic growth. This perspective is bolstered by noting that every industrialized country in the world has a history of advanced agrarianism, either as a consequence of long-term social evolution or, in the case of settler colonies (e.g., the United States), direct transplantation of advanced agrarianism to new territories. As Lenski and Nolan (1984) point out, this is no accident given that plow agriculture produces the economic surplus that facilitates a large, densely-settled population, the creation of written language, the need for money to replace barter, and other hallmarks of modernity. The “developed world” is comprised of these kinds of nations. Other nations in this advanced agrarian group, whose technological evolution may have been impaired by a variety of factors (e.g., the parasitic Mongol regimes of China and India) might now be expected to catch up at an accelerated rate.


Specifically, proto-modern societies exhibit social and spatial properties directly related to their demographic inheritances that predispose them to rapid development upon exposure to modern technology and external capital. First, protomodern societies tend to produce higher levels of agricultural surplus. Population density is inversely related to farm size in traditional agriculture, consistent with the need to subdivide land holdings generation after generation. Over time, many advanced agrarian systems revert to small-holding societies, and this dependence on land fosters an intensification in land use as more and more people derive subsistence from less and less arable land (Boserup 1965). There is thus a tendency for small farms to use land much more productively (Berry and Cline 1979). Contemporary evidence suggests that higher levels of aggregate surplus, when combined with intense competition for land, most often result in renewed agricultural innovation, greater occupational specialization and a concomitant increase in nonfarm employment in rural areas (Clark 1967; World Bank 1978; Boserup 1990:86). Second, historically-dense societies are generally less burdened by severe economic inequalities and other social/demographic fault lines. Unlike formerly horticultural societies, where low population densities and correspondingly low levels of political/ economic development allowed colonial conquest and subsequent inequalities in land and capital (i.e., Africa and Latin America), proto-modern societies have generally experienced shortages of land and capital but an abundance of labor. As labor becomes the only flexible source of wealth, the problem of hoarding labor power forced elites to distribute subsistence more evenly. Moreover, elites subdivided their lands among their children over the generations, resulting in more even distributions of income or subsistence (Nolan and Lenski 1985; Crenshaw 1992, 1993). As Diamond and Bellwood (2003) note, when farming populations spread across the world’s continents, their technologies gave them the wherewithal to displace, assimilate or wipe out non-agrarian competitors. Today, therefore, we would expect proto-modern societies to be more ethnically and linguistically homogeneous, and to have been more resistant to settlement by other agrarian groups (i.e., colonialism). According to Lenski (2005), we see the repetition of this pattern in the modern world, with industrial and post-industrial populations exerting tremendous selection pressures on human language and (material and non-material) culture worldwide. Third, proto-modern societies are more spatially articulated, leading to improvements in scale effects and the early penetration of markets into rural areas. Such societies usually exhibit articulated networks of villages, towns and cities prior to industrialization (Boserup 1981). Dense, permanent settlements encourage complex trade and communications networks, attributes that promote early commercial development in rural and semi-rural districts as well as the spread of literacy and education. Moreover, dense settlement usually supports infrastructure such as roads, canals and ports (Glover and Simon 1975; Boserup 1981). Thus, proto-modern societies have enjoyed a higher level of spatial articulation due to more advanced infrastructure and, in turn, the advanced political development


that resulted from activities such as irrigation and road building (Chanda and Putterman 2007). These complex settlement patterns and linking infrastructure also encourage the spread of commercial agriculture and cottage industries – important precursors to industrialization (Kriedte et al. 1981). Finally, proto-modern societies are the cradles of modern social psychology. Private property, contracts, wage labor and inheritance law evolved well before industrialism in response to the breakdown of communal land tenure and production systems under the onslaught of population pressures (Popkin 1979; Boserup 1990). Moreover, the “possessive individualism” of the West (MacPherson 1962) is in fact not unique to the West; as Simmel notes (1971[1908]), true individualism becomes possible only with the increasing structural-functional differentiation of populations. Growing population density forces many individuals to specialize to reduce competition, and this specialization necessarily increases the number of unique social transactions, accelerates the process of role segmentation, and gradually weakens the primordial group ties within a population. In short, the density-induced transition from “mechanical” to “organic” solidarity (Durkheim 1893) begins prior to industrialism. Our sociological approach anticipates a positive coefficient between demographic inheritance (i.e., historic population density) and contemporary economic growth per capita. On the other hand, Acemoglu et al. (2002) argue that the history of European colonialism and selective investment bred institutional configurations (i.e., governmental constraints on property rights) that “reversed” the fortunes of the old agrarian empires. Their proof consists of a negative correlation between estimates of population density in A.D. 1500 and the level of economic development in 1995 (GDP/c). While we might concede that such a temporary historical effect is possible, the historical record linking high density and high civilization (e.g., Egypt, China, India, Japan, southern Europe) suggests that contemporary growth among such societies should be relatively rapid – an accelerated “recovery,” perhaps (Chanda and Putterman 2007). We contend that whatever the advantages or disadvantages of colonialism, the traditional advantages related to population and social complexity should be ascendant in the post-colonial world.

The Ecology of Ethnic Competition Although ethnic, racial and linguistic diversity can be a social benefit, history clearly demonstrates that ethnic/racial competition can also challenge social cohesion rather than reinforce it. Recent conflicts such as those in Yugoslavia, India, Indonesia, Rwanda and Iraq demonstrate that ethnic, racial, linguistic or religious cleavages are extremely potent social forces that shape national histories. Nevertheless, many have noted the usefulness of ethnicity as a social tool; ethnic, racial or linguistic markers promote efficient intra-group relations. Recent sociological research highlights the economic benefits of ethnicity-based social capital, (i.e., Aldrich and Waldinger 1990; Waldinger 1994), generally because the use


of ascribed statuses as primary axes of organization can provide the phenotypic markers, tight social interdependencies, and common cultural/social warp and woof that make for effective social organization and social control (Hechter 1987). As Portes (1998) points out, however, the downside of ethnic solidarity is out-grouping. While ethnic competition models are common in the social sciences, social scientists rarely explain exactly why phenotypic or linguistic differences should so often lead to competition and conflict. An ecological perspective helps to fill this gap by noting that, while ethnicity provides a fairly efficient organizational basis for many large unions, the ability of such unions to forge beneficial ties across groups is sharply circumscribed precisely because ascribed status is so effective for the in-group. The human ecological notion of symbiotic and commensal unions explains why (Hawley 1968). Symbiotic unions are exchange relationships between parties with complementary differences. Mutually dependent manufacturers in a production chain or the gender/age division of labor within families are good examples of symbiotic unions. Commensal unions, on the other hand, are based on the similarity of members or units, such as labor unions or professional associations. Commensal unions are therefore made up of like members who cooperate in drawing sustenance from a common resource base. Hawley (1968:332) states that, “The symbiotic union enhances the efficiency of production or creative effort; the commensal union, since its parts are homogeneous, can only react and is suited, therefore, only to protective or conservative actions.” Although commensal unions work to conserve the positions and powers of their members, they still play a large role in social change. A predominance of commensal unions attempting to fill similar niches leads to competitive pressures which set the stage for organizational and technological innovation and social change (complementary differentiation). Where complementary specialization is relatively easy, commensalism will yield to symbiosis and competition to cooperation and exchange (i.e., structural-functional differentiation). Where such complementary change is impossible, difficult or profitless, competition intensifies. In the language of classical theory, sociocultural diversity sometimes interferes with social differentiation and interdependency (see also Blau 1977). The very thing that makes human “races” or “ethnicities” strong axes for social organization and solidarity necessarily devalues weaker ties with out-groups. This is because, unlike biological specialization among the social insects, ethnic or racial differences are relatively superficial and may be partially or wholly socially constructed. Racial or ethnic differences serve as convenient markers to promote beneficial externalities (i.e., social capital) within in-groups but, unlike bees, they create no natural division of labor facilitating economic and social cooperation across groups. Thus, social organization dependent on race or ethnicity boosts competition between groups while interfering with the conversion of this competition into cooperative specialization and productive social change. The great irony is that the strength and efficacy of ethnic-based social organization makes complementary


adaptation between groups difficult and sometimes impossible. Unfortunately, given that in-group solidarity is generally heightened by external threats (Simmel 1955), sharp ethnic, racial and/or linguistic polarization may serve as a cohesive factor by protecting group identities from the corrosive influences of modernity. For these reasons, interracial or inter-ethnic commensalism may damage intergroup relations in what becomes a self-perpetuating cycle. Suspicions or hostility between ascribed groups may also impair cultural diffusion processes critical to modernization. As Rogers (1983) notes, the principle of homophily, or the degree to which interacting actors resemble one another, is important in any process of social change. Extending this principle to economies, the more closely economic actors are matched in terms of cultural or social background the greater their likelihood of successfully exchanging goods and information. Therefore, in a racially- or ethnically-segmented society transmission of values, technologies and information may be hampered, lines of communication may be truncated, and national markets may be slow to form. Easterly and Levine (1997) also posit that ethnic diversity harms economic performance via political processes, encouraging a diverse interest structure that can lead to conflicts in public policy and implementation (see also Alesina et al. 1999). If a society is sufficiently polarized in this way, political deadlock and corruption, civil conflict, and non-optimal public spending may lead to delays in creating necessary political and economic infrastructure (e.g., roads, central banks) and institutional complexities (e.g., property law, contract law). To restate Easterly and Levine’s account in the language of our theoretical framework, collective goods in a racially- or ethnically-diverse setting may be viewed from the prism of the “inclusive fitness” of competing groups (Hamilton 1964; Nielsen 1994; Williams 1994), thereby damaging the consensus on institutional change. Nevertheless, ethnicity is not necessarily destiny. Some studies have found no strong correlation between social diversity and economic growth (e.g., Lian and Oneal 1997). Also, if a nation can manage to instill a sense of “being in this together” among diverse elements of a population, it is possible that ethnic/racial differences may fade over time. As linguistic barriers crumble and a common history and sense of nationhood prevail, ethnicity may play a more neutral or even a positive role in development. As Anderson and Paskeviciute (2006) note, diversity and linguistic diversity are not the same; many countries, such as the United States and Brazil, have ethnically/racially diverse populations but a national language that dominates the society. Because linguistic homogeneity promotes increased communication, diffusion, cross-cutting economic and political ties, and intermarriage between distinctive groups, some empirical evidence suggests that linguistic homogeneity is positively associated with economic development (Nettle 2000). Therefore, we propose that “sociolinguistic integration” (i.e., the linguistic unification of an otherwise ethnically- or racially-diverse national territory) will have a positive influence on a nation’s rate of economic growth.


Theoretical Synopsis

Sociolinguistic Integration (i.e., Linguistic Dominance)

(i.e., Structural Conduciveness to Economic Growth) Ethnic Dualism/ Polarization

Degree of Social Differentiation & Interdependency (Complexity and Adaptability) Preindustrial Social Complexity (Demographic Inheritance)

Age Structure

Figure 2. Proposed Sociodemographic Model of Economic Development

Figure 2

Contemporary Economic Growth

Although a growing literature linking population to economic development now exists, such studies have yet to establish socio-demographic dynamics as central to theoretical accounts of economic and social change. Population growth’s influence on economic growth depends on the relative growth of population segments. Although the multiplication of working adults should enhance economic performance and social complexity, the proliferation of non-working children and elders could not produce the same effect and might in fact retard economic growth (see Figure 2).


Historical population density is a salient manifestation of the degree of structuralfunctional differentiation and interdependency (i.e., social complexity) a society has achieved through the competition-avoidance involving specialization and innovation. It is a convenient marker of a nation’s starting point in the race for contemporary economic growth. The ethnic/racial/linguistic composition of the population should matter for economic growth because social organization based on ascribed status may not lend itself to adaptive differentiation and interdependency. Alternatively, if a dominant language can unite an otherwise diverse population then the pernicious influences of diversity might be partially overcome.

Design and Variables We apply pooled time-series cross-section analysis to an annualized (unbalanced) panel of data covering much of the globe (i.e., 101 nations) for the years 19702000.1 The main advantage of this method is the larger sample size acquired by combining a cross-section and time-series design into a country-year database. This methodology also allows us to analyze subtle changes over time in the dependent variable. A pooled analysis will also permit observation of variation over both time and space simultaneously, with the additional advantage of allowing robustness checks in the form of temporal and regional fixed effects. The major disadvantage is that the error structure is complicated by the inclusion of cases that can have non-random variation over space and time. Pooling data with an improper model specification may also lead to the conclusion that the error terms are heteroscedastic and autocorrelated when in fact they are not. Attempting to correct these potential problems, we follow Beck and Katz (1995,1998) and use an ordinary least squares model with panel-corrected standard errors and a lagged dependent variable. Moreover, given that standard country-dummies (i.e., fixed effects) eliminate all cross-sectional variation in the sample, we are constrained to using regional and temporal fixed effects (i.e., the inclusion of region dummies and decadal growth averages). The generic model can be specified as follows: Yi,t = α+ β Yi,t–1 + β Xi,t–1 + εi,t where Yi,t is the dependent variable for country i at time t, and Yi,t–1 is the same variable lagged one year. Xi,t–1 is a vector of important covariates each lagged one year to better capture causality and to rule out reciprocal causation. Dependent Variable Our dependent variable is an annual change score of real gross domestic product per capita (Heston et al. 2002). Real gross domestic product per capita is the best indicator of national wealth currently available given its standardization in 1996 international dollars, its adjustment for actual buying power, and its exclu-


sion of factor income from abroad. We opted to predict annual change-scores over annual rates because the change-score is better behaved in our sample (CV = 2.7 compared to CV = 3.9 for the annualized rate of economic growth) and substantially decreases the “spikiness” of the dependent variable. We also enter a lagged dependent variable (the change-score for the year prior), which is essential to compensate for autocorrelation (Beck and Katz 1995). We incorporate a time-series of secondary school enrollments to control for human capital (World Bank 2002). Given the important role modernization theorists assign to schooling, some going so far as to term mass education as the “handmaiden of industrialism” (Kerr et al. 1960), it is not surprising that human capital indicators (typically measured as school enrollment ratios) have become standard features of cross-national analyses of economic growth (e.g., Barro 1991; Firebaugh and Beck 1994). Focal Predictors Our models incorporate four focal variables: (1. the age-dependency ratio; (2. an estimate of a country’s population density circa A.D. 1500; (3. the ratio of the second largest ethnic group to the largest group; and (4. the difference between linguistic fractionalization subtracted from ethnic fractionalization. The first indicator is simply the measurement of how burdened working populations are by those who do not work (population above and below 15-65/population 15-65); such dependency ratios are fairly common in cross-national assessments of economic growth. Population and age-structure variables have been adapted from the World Bank (World Bank 2002).2 The proto-modern effect is represented by an estimate of population density in A.D. 1500, adapted from McEvedy and Jones (1978), who supply demographic estimates of historical populations for contemporary national-state equivalents.3 We chose persons per square mile in A.D. 1500 because it maximizes our samples and, more importantly, precludes the run-up in population densities since the Industrial Revolution began. Thus, in keeping with the theory’s focus on preindustrial social complexity, our use of population density in A.D. 1500 prevents any confusion between modern and pre-modern social complexity. Our ethnic dualism ratio (operationalized as the ratio of the second-largest ethnic group’s percentage of the population over the majority group’s percentage, circa 1980) comes from the World Christian Encyclopedia, a survey of world religion that incorporates official statistics. Following Blau’s (1977) argument that extreme diversity increases intergroup affiliation while the existence of only a few large groups decreases social interaction and entrenches in-group solidarities, we attempt to tap the most salient ethnic divisions in each population with this indicator. Although strong ethnic pluralism may damage a nation’s ability to unify its economic and political landscape, demography isn’t necessarily destiny. If linguistic fractionalization is substantially lower than ethnic fractionalization, it


suggests a process of cultural assimilation. A common language for a growing proportion of a nation’s citizenry eases political and economic exclusion, decreases internal colonialism/regional ghettoization, and lowers barriers to information and technology. Using variables from Fearon (2003), we calculate this difference; the greater the difference between ethnic and cultural/linguistic fractionalization, the greater a nation’s sociolinguistic integration. We predict such consolidation partially mitigates other negative influences.

Variables Suggested by Alternative Theories (Controls) An obvious barrier to economic expansion is civil war, a generally ruinous event that ravages a national economy. Ethnic competition and rapid population growth have been linked to a higher incidence of internal warfare (Goldstone 1991), so to guarantee that our focal effects are not spurious due to internal violence we create an annualized dummy variable coded 1 if a given nation experienced a civil war in that year (PRIO 2004). We define civil war as any conflict between state actors and non-state actors that results in at least 1,000 battle-related deaths in a given year. On the other hand, political-economists generally emphasize external causes of economic growth such as colonialism, foreign trade and investment. One of the more innovative approaches to studying the effects of past imperial policies on developing nations belongs to Acemoglu et al. (2002). They suggest that where prior social development (measured by population density in 1500) precluded actual large-scale European settlements, the colonial powers relied on extractive institutions that “reversed” the fortunes of these colonies, essentially impoverishing them by the middle of the 20th century. As our study period cannot address the influences of colonialism prior to 1970, our goal here is simply to demonstrate that the broad parameters of social evolution that influence economic development can be found net of any lingering institutional influences of colonialism. We accomplish this by introducing three control variables: (1. a dummy variable for European settler colonies (e.g., USA, Canada) (SETTLER COLONIES), (2. a dummy variable coded 1 if a country was ever colonized (COLONIAL STATUS), and (3. an interaction term between COLONIAL STATUS and POPULATION DENSITY IN A.D. 1500.4 This interaction term applies Acemoglu et al.’s theory to economic growth since 1970 (i.e., the influence of colonial institutions on modern economic growth varies by the degree of preindustrial social development, or historic population density). The influence of trade on economic growth is a point of contention between dependency/world-systems theorists and modernization/neo-classical economic theorists. Dependency theorists suggest exports as a proportion of GDP is an important control variable (Bornschier and Chase-Dunn 1985), although many consider it “dependent development” or “distorted development.”(Evans 1979) Some suggest that such dependent development does not provide the same economic multipliers as indigenous industrialization, so as export dominance grows we would expect to see anemic economic growth. On the other hand, neo-classical economists view ex-


port activities as particularly beneficial to economic growth because they are thought to expand access to external markets, convert idle resources into foreign exchange, and prevent stagnation due to internal market saturation. Combining these ideas, we control for merchandise exports as a percentage of GDP (World Bank 2002). As for domestic and foreign investments, modernization theory views both domestic and direct private foreign investment as assets in both developed and developing economies, while critical political-economic theories typically consider foreign investment as a prime culprit in many Third World social ills. For this reason, we model a time-series of both direct private foreign investment as a percentage of GDP and gross domestic investment as a percentage of GDP (World Bank 2002). Because some theories posit that the influence of demographic and other macrosocial structural characteristics on economic growth is mediated by investment, our last model, which looks for our focal effects net of investment, constitutes an extremely conservative test of the proposed demographic hypotheses. We were not able to employ country fixed effects even though doing so has become fairly common in cross-national pooled time-series analysis. Given that three of our four focal variables are cross-sectional in nature (a fact dictated by data availability and/or theoretical concerns), and that country dummies eliminate cross-sectional variation, we have opted to follow Beck and Katz’s (2001) advice to avoid the use of country fixed effects. In lieu of country- and temporal-dummies, all reported models include regional controls (essentially, regionally-fixed effects) as well as decadal dummies for average economic growth among our sample of nations for the 1980s and 1990s (with the 1970s averages as the reference category).

Results Table 1 reports the initial tests of our hypotheses. Model 1 demonstrates the important influences of our lagged dependent variable, the prior level of development and human capital formation (secondary school enrollments), consistent with many cross-national assessments of economic growth. Turning to our focal variables, Model 2 investigates the impact of age structure on economic development. The age dependency ratio is statistically significant, demonstrating that, on average over the study period, a 1 unit rise in the dependency ratio led to a decrease of approximately $1.01 in economic growth per annum. The R2 statistics are relatively low, averaging around .3 for these models. While no one has explained the tendency of big-N studies to produce lower-than-expected R2 statistics, the tendency clearly exists (see Reisinger’s 1997 meta-analysis for confirmation). Short of using fixed effects (to eliminate cross-sectional variation), we found no alternative combination of theoretically-relevant variables that would produce higher R2 values over the three decades of our study period. Model 3 introduces ethnic dualism. All else constant, ethnic polarization discourages rapid economic development; for every 1 percent increase of a nation’s second largest ethnic group in relation to its majority group, average change

Table 1: Pooled Cross-Section Time Series Results for Yearly Change in Per Capita Real Gross Domestic Product regressed on Socio-demographic Model 1 2 3 4 5 6 RGDP/c (t-t1) .274*** .273*** .271*** .266*** .265*** .264*** [6.11] [6.12] [6.11] [6.04] [5.79] [5.80] RGDP/c (t1) .011*** .010** .009** .008** .009** .009** [2.91] [2.55] [2.47] [2.40] [2.55] [2.53] Secondary school % gross (t-t1) 1.369*** 1.160** 1.231** 1.175** 1.089** 1.150** [2.61] [2.34] [2.52] [2.40] [2.19] [2.29] Age dependency ratio (t-t1) -1.011** -1.013** -0.874* -0.940** -0.863* [2.09] [2.09] [1.80] [1.97] [1.89] Ethnic dualism -.394*** -.242*** -.178*** -.158*** [3.15] [3.40] [3.51] [3.40] Population density circa 1500 1.159** 1.729** 1.719** [2.45] [2.28] [2.28] Assimilation -.33 -2.211** [1.48] [2.41] Assimilation (squared) .033** [2.49] 1990s -42.322** -45.226** -46.376** -41.973** -42.034* -42.516* [2.34] [2.41] [2.43] [2.27] [1.94] [1.95] 1980s -51.339*** -52.411*** -53.094*** -51.609*** -54.541*** -54.867*** [2.93] [2.97] [2.97] [2.91] [2.61] [2.62] Middle East -14.9 -8.181 -7.903 -20.825 .431 5.652 [.37] [.20] [.20] [.58] [.01] [.12] Asia 41.232 34.469 38.051 5.045 10.507 9.849 [.71] [.61] [.66] [.11] [.19] [.18] Africa -17.017 -8.258 3.289 -17.981 6.928 17.105 [.40] [.19] [.07] [.46] [.14] [.32] East Europe -69.112* -83.970** -92.180** -98.136*** -85.465* -91.402** [1.72] [2.09] [2.42] [2.76] [1.96] [2.13]


in RGDP/c declines by $.39 per annum. Model 4 confirms these effects and introduces population density circa A.D. 1500. On average, for every additional person per square mile half a millennium ago, national economies experienced an average annual increase in RGDP per capita of $1.16 (which is to say, about $35 of RGDP/c over 30 years for every additional person per square mile in the year 1500). Thus, this indicator of preindustrial social complexity suggests that nations do not begin the race for economic development on the same starting line, but in fact came into the modern era with unequal cultural and geophysical endowments.


Notes: z statistics in brackets *significant at 10%, **significant at 5%, ***significant at 1%

Observations Number of U.N. country code R-square

Constant

Latin America

Oceania

-70.789** -73.780** -68.936** -68.613** -66.567** -74.956*** [2.21] [2.28] [2.07] [2.06] [2.25] [2.70] -56.86 -53.949 -49.802 -64.942 -33.727 -28.12 [1.25] [1.22] [1.10] [1.62] [.65] [.53] 15.565 108.123* 117.915** 113.117** 96.903 98.166 [.36] [1.91] [2.12] [2.02] [1.43] [1.45] 2,950 2,950 2,950 2,950 2,710 2,710 110 110 110 110 101 101 .28 .28 .28 .28 .29 .29

This supports the theories of Spencer and Durkheim (cf., Malthus) and ecologicalevolutionary theory’s view of techno-ecological heritage effects (Crenshaw 1992). Models 5 and 6 demonstrate a somewhat countervailing influence of cultural assimilation, however. The introduction of assimilation decreases the magnitude of ethnic dualism (from b = -.24 in Model 4 to b = -.16 in Model 6), which indicates that nations with a high degree of national integration have partially overcome the economic disadvantages of ethnic polarization. And, indeed, sociolinguistic integration itself is an important determinant of economic growth; assimilation first decreases economic dynamism, given the social disorganization attended on early assimilation pressures, but then turns positive at a relatively high level of sociolinguistic assimilation (the turning point is around 33, which is in the higher range of the variable). This establishes the plausibility of the structural/ ecological theory, even controlling for regional and temporal effects. Table 2 reports the results of pitting our sociological account against more commonly tested explanations. Model 1 demonstrates that, indeed, experiencing a civil war in a given year leads to a sharp drop in RGDP per capita the following year – on average, about $64 per person. However, this only negligibly influences our focal effects. The coefficient and z-ratio for age-dependency both decline to a small degree, but the effect is still significant at the .1 level. On the other hand, the coefficients for historical population density, ethnic dualism and the second-degree polynomial for sociolinguistic integration are relatively unchanged by the introduction of internal warfare. Somewhat surprisingly, we find no statistically significant effect for merchandize exporting in Model 2, although its inclusion improves the effect of age dependency. Model 2, therefore, allows us to dismiss the notion that the relationships between our focal variables and economic growth are simply spurious due to mutual correlations with international trade. Models 3 through 5 investigate the role of colonial heritage on contemporary economic

Table 2: Pooled Cross-Section Time Series Results for Yearly Change in Per Capita Real Gross Domestic Product Regressed on Socio-demographic Model and Select Controls 1 2 3 4 5 6 RGDP/ c (t-t1) .263*** .261*** .262*** .256*** .256*** .237*** [5.80] [5.38] [5.82] [5.80] [5.83] [4.33] RGDP/ c (t1) .009** .010*** .008** .008** .008** .010** [2.50] [2.62] [2.16] [2.16] [2.35] [2.57] Secondary school % gross (t-t1) 1.153** 1.046** 1.230** 1.222** 1.258** 1.263** [2.28] [2.05] [2.46] [2.48] [2.43] [2.53] Age dependency ratio (t-t1) -.822* -1.210*** -.672 -.999** -.953** -1.377* [1.80] [2.67] [1.47] [2.03] [2.03] [1.89] Ethnic dualism -.158*** -.170*** -.070* -.213*** -.208*** -.163 [3.46] [3.87] [1.79] [4.70] [4.85] [0.74] Population density circa 1500 1.704** 1.695** 2.075** 1.894** 1.011 1.854*** [2.26] [2.11] [2.33] [2.19] [1.35] [3.39] Assimilation -2.164** -2.110** -3.046** -3.367*** -3.328*** -2.556** [2.33] [2.35] [2.50] [2.67] [2.71] [2.33] Assimilation (squared) .033** .032** .045** .049*** .049*** .038** [2.43] [2.55] [2.57] [2.70] [2.74] [2.31] Civil War (1 = yes) -63.947** [2.46] Merchandise exports/ GDP (t-t1) -114.896 [.59] Selected settler colonies (1 = yes) 67.366** 36.06 35.632 [2.30] [1.32] [1.32] FDI net flows/ GDP (t-t1) -1.011 [.40] GDI/GDP (t-t1) 10.438*** [7.45] Colonial status (1 = former colony) 70.224*** 56.357*** [6.08] [4.17]


growth. In Model 3 we see that settler colonies have had an advantage over other nations; on average, the economies of such colonies have grown $67 more per annum. On the other hand, restricting the reference category to never-colonized nations, Model 4 demonstrates that settler colonies have experienced no statistically significant advantage in economic performance over historically-independent nations. Surprisingly, former (non-settler) colonies have enjoyed some economic advantage over independent countries – on average, about $70 more per annum, ceteris paribus. While it is important to be cautious when working with dummy

-41.824* -45.215** -40.127* -42.778** [1.96] [2.20] [1.87] [1.97] -54.604*** -60.191*** -53.788** -55.684*** [2.65] [3.18] [2.56] [2.64] 5.587 13.308 5.704 -33.893 [.12] [.30] [.12] [.78] 11.71 17.087 1.122 -29.913 [.22] [.33] [.02] [.59] 15.541 31.612 12.468 -31.32 [.29] [.61] [.24] [.63] -91.615** -94.143** -90.587** -132.146*** [2.12] [2.28] [2.11] [3.02] -75.282*** -72.925*** -112.011*** -152.884*** [2.71] [2.66] [6.56] [8.15] -28.879 -15.463 -37.106 -85.034* [.54] [.30] [.74] [1.78] 95.916 122.140* 88.84 109.358 [1.42] [1.82] [1.29] [1.58] 2,710 2,595 2,710 2,710 101 100 101 101 .29 .30 .29 .29

Notes: z statistics in brackets *significant at 10%, **significant at 5%, ***significant at 1%

Observations Number of U.N. country code R-square

Constant

Latin America

Oceania

East Europe

Africa

Asia

Middle East

1980s

1990s

Colony * Density 1500

1.058 [.72] -43.695** -47.890* [1.98] [1.92] -56.174*** -53.898** [2.66] [2.12] -35.344 17.263 [.81] [.35] -29.841 17.511 [.59] [.33] -29.613 46.479 [.59] [.97] -136.557*** -115.856** [3.04] [2.41] -153.962*** -73.369 [7.98] [1.39] -83.746* -10.283 [1.74] [.20] 114.918 125.322 [1.62] [1.37] 2,710 2,491 101 99 .30 .31


variables (in that such group averages can be strongly biased by extreme cases), the pros and cons of colonialism are not central to our arguments here. Rather, Model 5 indicates that there is no apparent contemporary economic penalty associated with being both a former colony and a dense (i.e., pre-industrially complex) society, contrary to Acemoglu et al. (2002) (cf., Chadra and Putterman 2007). Were that the case, our interaction term Colony*Density 1500 should be negative and statistically significant. We conclude that whatever institutional benefits and/or infirmities may have been conferred on colonies by their metropole nations, our


statistical test cannot confirm the idea that more densely-settled (former) colonies have fared worse than other places in more recent times. Model 6 reports our most conservative analysis, where we control for both domestic and foreign investment as a percentage of the economy. Although investment dynamics have dominated the sociological literature on development, it is important to note that many development theories assume that structural influences on economic performance, such as age-structure or ethnic polarization, are partially or completed mediated by investment rates. If this is the case, we would expect to see such structural effects fade to statistical non-significance in the presence of investment controls, so our focus here is not on how investment influences economic performance but rather on how controlling for investment influences our focal relationships. Model 6 demonstrates the dominance of gross domestic investment on economic performance; on average, a 1 percent increase in domestic investment over total GDP results in a $10 increase in RGDP/c per annum. Interestingly, net of domestic investment, foreign investment plays no generalizable role in economic performance. The coefficient is weak and statistically nonsignificant. More importantly, however, investment rates do mediate some of our focal effects. While both age dependency and historic population density remain virtually unchanged, apparently ethnic dualism’s effect on economic growth is wholly mediated by investment, and sociolinguistic integration is also weakened by controlling investment levels (although it remains significant at the 5 percent level). As suggested by Easterly and Levine (1997), ethnic polarization leads to political stalemate, state failure and slow business expansion due to uncertainty and high risk. In nations where sharp ethnic rivalries remain unmitigated by a strong sense of nationhood, it stands to reason that economic performance might languish as important (generally state-provided) requisites fail to materialize.

Discussion This study demonstrates that a sociological account of economic growth is plausible and empirically sustainable even when considering competing models. Although we have confirmed that age-dependency retards economic growth in the short-term, the effect of historical population density makes it relatively clear that, in keeping with classical sociological theory, the long-run consequences of population growth for societal advancement have been positive rather than negative. Historical population density clearly exerts a robust, positive effect on contemporary economic growth rates net of war, colonial status, export regime and investment patterns. By most interpretations of Malthusian economic theory, high population density should be associated with adversity, poor labor absorption, lagging capital formation and poor macroeconomic performance, and perhaps it has been under some historically-unique circumstances. Nevertheless, our analyses demonstrate quite the opposite is generally true. As a form of societal investment, long-run population increase forces adaptive differentiation and interdependency on societies, complexi-


ties that lead to higher surpluses and socio-spatial characteristics that predispose these societies to accelerated integration into the modern world. Another element of our model answers the perplexing question of why a reduction in fertility might be beneficial in the short-term but disastrous over time. Rapid growth in the dependent population may retard economic growth, but it probably does not actually impoverish a society (i.e., it will not zero-out growth, but only slow it down). If a society can hold its own economically during its baby booms, then it stands a good chance of ratcheting up its economic activity when these children finally enter the labor force – a demographic windfall and ratchet effect (Crenshaw et al. 1997). The discussion of population growth’s influence on economic growth should therefore revolve around age-specific population growth and attained population size/density. Such dynamics will dramatically increase in importance as East Asia, North America and Europe exhaust their adult population growth potential and face swelling ranks of the elderly, the flip-side of the typical Third World situation. From a strictly sociological point of view, the robust relationships between ethnic dualism, sociolinguistic integration/assimilation and economic growth may hold greater interest. While postmodern views of race and ethnic relations run the gamut from condemnation of race as a social construct to glorification of racial consciousness and identity, gesellschaft simply may not be as good as gemeinschaft in encouraging social cohesion and cooperation. It stands to reason that the course of rapid social change might be easier for those countries that retain vestiges of primordial solidarities (i.e., common cultural understanding, a sense of common destiny, and/or phenotypic homophily). If social trust, cooperation, mutual understanding and “fellow-feeling” are important to economic change, it is logical to view ethnic and linguistic variation as a potential liability rather than as an asset in a nation’s quest for economic success. Our results also suggest that a unifying or dominant language can partially bridge the problems associated with heterogeneity. Obviously, the removal of language barriers will generally improve a nation’s business climate and market efficiencies. However, we find that this benefit accrues only to nations with an overwhelmingly dominate language. These results also shed light on modernization theory and, to a lesser extent, neoclassical economics. Modernization theory has accumulated decades of anomalies it has difficulty explaining. Why have Pacific Rim countries made such great economic strides while many of the more developed parts of Latin America have languished? How can countries such as Taiwan combine rapid economic growth with low levels of income inequality? In our view, these anomalies may spring from improperly specifying the “starting line” and from overlooking socio-demographic dynamics. Rostow (1960) notes that a society’s initial economic conditions govern its future industrialization, but most modernization theorists have poorly specified these initial conditions. Understanding a country’s demographic and technological history allows us to do so, at least in a rough fashion.


This research anchors socio-demographic effects in classical and contemporary macro-sociological theory. Over the years, studies of economic growth and demographic change have become disconnected from earlier traditions in social science. While focusing on the “proximate determinants” of economic growth such as investment or technological innovation may be helpful for public policy, pursuing such explanations leads to neglect of more fundamental dynamics that give rise to investment and innovation – how social environments shape human motivations. Rooting demographic-ecological variables in abstract evolutionary theory helps to reset the focus on the master processes of differentiation and interdependency which are, at their most basic level, humanity’s adaptation to environmental challenges.

Notes 1. Country data used in the sample: Albania, Algeria, Angola, Argentina, Australia, Austria, Bangladesh, Barbados, Belgium, Bolivia, Botswana, Brazil, Belize, Bulgaria, Burundi, Cambodia, Cameroon, Canada, Cape Verde, Central African Republic, Sri Lanka, Chad, Chile, China, Colombia, Comoros, Republic of the Congo, Congo Democratic Republic, Costa Rica, Cuba, Cyprus, Benin, Denmark, Dominican Republic, Ecuador, El Salvador, Ethiopia, Fiji, Finland, France, the Gambia, Ghana, Greece, Guatemala, Guinea, Guyana, Haiti, Honduras, Hungary, Iceland, India, Indonesia, Iran, Ireland, Israel, Cote d`Ivoire, Jamaica, Japan, Jordan, Kenya, Republic of Korea, Lebanon, Lesotho, Madagascar, Malawi, Malaysia, Mali, Mauritania, Mexico, Morocco, Mozambique, Namibia, Nepal, Netherlands, New Zealand, Nicaragua, Niger, Nigeria, Pakistan, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Portugal, Guinea-Bissau, Romania, Rwanda, Senegal, Sierra Leone, Singapore, Vietnam, South Africa, Zimbabwe, Spain, Sweden, Syria, Thailand, Togo, Trinidad &Tobago, Tunisia, Turkey, Uganda, Egypt, United Kingdom, Tanzania, United States, Burkina Faso, Venezuela and Zambia. 2. Given that no nation conducts a registration system on an annual basis, yearly population and age-structure values are estimates. Personal communication with the World Bank determined that these estimates are generated using fertility and mortality data applied to a baseline population figure frequently derived from household surveys, suggesting that the estimates are well-grounded in extant empirical information. More importantly, GDP or other non-demographic variables (that might compromise our multivariate analyses) have not been used in the creation of these estimates (Bos 2005). 3. Estimation of historic populations are inherently problematic. While some have severely criticized the intricate and often opaque methodologies used to create such estimates (e.g., Durand 1977), most scholars concede that current estimates are probably as close as we can come to the “truth” given the dearth of historical data (Caldwell and Schindlmayr 2002). Regardless, we were able to roughly replicate our findings using population density measured in 1965. The major difference is that significance levels are weaker (.1 or less), which is to be expected given the temporal lags needed between density and development suggested by ecological theory. 4. Settler Colonies include Argentina, Australia, Canada, Chile, Costa Rica, Ireland, New Zealand, South Africa and the United States. All colonies are colonies of Western Europe and include: Argentina, Australia, Canada, Chile, Costa Rica, Ireland, New Zealand, South Africa, United States Of America, Bangladesh, Cambodia, Sri Lanka,


India, Indonesia, South Korea, Malaysia, Philippines, Singapore, Vietnam, Algeria, Angola, Bolivia, Botswana, Brazil, Burundi, Cameroon, Central African Republic, Chad, Colombia, Congo, Zaire, Cuba, Cyprus, Benin, Dominican Republic, Ecuador, El Salvador, Fiji, Gambia, The, Ghana, Greece, Guatemala, Guinea, Guyana, Haiti, Honduras, Hungary, Israel, Cote D’Ivoirie, Jamaica, Jordan, Kenya, Lebanon, Lesotho, Madagascar, Malawi, Mali, Mauritania, Mexico, Morocco, Namibia, Nicaragua, Niger, Nigeria, Pakistan, Paraguay, Peru, Poland, Guinea Bissau, Rwanda, Senegal, Sierra Leone, Zimbabwe, Syria, Togo, Trinidad & Tobago, Tunisia, Uganda, Egypt, Tanzania, Burkina Faso/Upper Volta and Zambia. The countries in the reference category (colonizers or non-colonies) are: China People’s Republic, Japan, Nepal, Thailand, Albania, Austria, Belgium, Bulgaria, Denmark, Finland, France, Iran, Netherlands, Portugal, Romania, Spain, Sweden, Turkey and the United Kingdom.

References Acemoglu, Daron, Simon Johnson and James A. Robinson. 2002. “Reversal of Fortune: Geography and Institutions in the Making of the Modern World Income Distribution.” The Quarterly Journal of Economics 117(4):1231-94. Aldrich, Howard, and Robert Waldinger. 1990. “Ethnicity and Entrepreneurship.” Annual Review of Sociology 16:111-35. Alesina, Alberto, Reza Baqir and William Easterly 1999. “Public Goods and Ethnic Divisions.” The Quarterly Journal of Economics 114(4):1243-84. Anderson, Christopher J., and Aida Paskeviciute. 2006. “How Ethnic and Linguistic Heterogeneity Influence the Prospects for Civil Society: A Comparative Study of Citizenship Behavior.” Journal of Politics 68(4):783-802. Barlow, Robin. 1994. “Population Growth and Economic Growth: Some More Correlations.” Population and Development Review 20(1):153-65. Barrett, David B. 1982. World Christian Encyclopedia: A Comparative Study of Churches and Religions in the Modern World, A.D. 1900-2000. Oxford University Press. Barro, Robert J. 1991. “Economic Growth in a Cross-Section of Countries.” The Quarterly Journal of Economics 106(2):407-43. Beck, Nathan, and Jonathan N. Katz. 1995, “What To Do (and Not To Do) with TimeSeries Cross-Section Data.” American Political Science Review 89(2):634-47. ______. 1998. “Taking Time Seriously: Time-Series-Cross-Section Analysis with a Binary Dependent Variable” American Journal of Political Sciences 42(4):1260-88. ______. 2001. “Throwing Out the Baby with the Bath Water: A Comment on Green, Kim, and Yoon.” International Organization 55(2):487-95. Berry, R. Albert, and William R. Cline. 1979. Agrarian Structure and Productivity in Developing Countries. John Hopkins University Press. Blau, Peter M. 1977. Inequality and Heterogeneity: A Primitive Theory of Social Structure.Free Press. Bloom, David E., and Richard B. Freeman. 1988. “Economic Development and the Timing and Components of Population Growth.” Journal of Policy Modeling 10(1):57-81. Bloom, David E., and Jeffrey G. Williamson. 1998. “Demographic Transitions and Economic Miracles in Emerging Asia.” World Bank Economic Review 12(3):419‑55. Bornschier, Volker, and Christopher Chase‑Dunn. 1985.Transnational Corporations and Underdevelopment. Praeger. Bos, Edward. 2005. World Bank Data Development Group. Personal Communication April 15, 2005.


Boserup, Ester. 1965. The Conditions of Agricultural Growth: The Economics of Agrarian Change under Population Pressure. Aldine. ______. 1981. Population and Technological Change: A Study of Long-Term Trends. University of Chicago Press. ______. 1990. Economic and Demographic Relationships in Development. Johns Hopkins University Press. Brander, James A., and Steve Dowrick. 1994. “The Role of Fertility and Population in Economic Growth – Empirical Results from Aggregate Cross‑National Data.” Journal of Population Economics 7(1):1‑25. Burkett, John P., Catherin Humblet and Louis Putterman. 1999. “Preindustrial and Postwar Economic Development: Is There a Link?” Economic Development and Cultural Change 47(3):471-95. Caldwell, John C., and Thomas Schindlmayr. 2002. “Historical Population Estimates: Unraveling the Consensus.” Population and Development Review 20(2):183-204. Chanda, Areendam, and Louis Putterman. 2007. “Early Starts, Reversals and Catch-up in the Process of Economic Development.” Scandinavian Journal of Economics 109(2):387-413. Chesnais, Jean-Claude. 1987. “Population Growth and Development: An Unexplained Boom.” Population Bulletin of the United Nations 21-22:17-25. Clark, Colin. 1967. Population Growth and Land Use. St. Martin’s Press. Coale, Ansley J., and Edgar M. Hoover. 1958. Population Growth and Economic Development in Low Income Countries. Princeton University Press. Crenshaw, Edward M. 1992. “Cross-National Determinants of Income Inequality: A Replication And Extension Using Ecological-Evolutionary Theory.” Social Forces 71(2):339-63. ______. 1993. “Polity, Economy and Technoecology: Alternative Explanations for Income Inequality.” Social Forces 71(3):807-16. Crenshaw, Edward M., Ansari Ameen and Matthew Christenson. 1997. “Population Dynamics and Economic Development: The Differential Effects of Age-Specific Population Growth Rates on Per Capita Economic Growth in Developing Countries, 1965 to 1990.” American Sociological Review 62(6):974-84. Crenshaw, Edward M., and Doyle Ray Oakey. 1998. “Jump-Starting Development: Hyperurbanization as a Long-Term Economic Investment.” Sociological Focus 31(3):321-40. Diamond, Jared, and Peter Bellwood. 2003. “Farmers and Their Languages: the First Expansions.” Science 300(5619):597-603. Durand, John D. 1977. “Historical Estimates of World Population: An Evalution.” Population and Development Review 3(3):253-96. Durkheim, Emile. 1964[1893]. The Division of Labor in Society. Free Press. Easterlin, Richard A. 1967. “The Effects of Population Growth on the Economic Development of Developing Countries.” The Annals of the American Academy of Political and Social Science 369(1):98-108. Easterly, William, and Ross Levine. 1997. “Africa’s Growth Tragedy: Policies and Ethnic Divisions.” The Quarterly Journal of Economics 112(4):1203-50. Evans, Peter. 1979. Dependent Development. Princeton University Press. Fearon, James D. 2003. “Ethnic and Cultural Diversity by Country.” Journal of Economic Growth 8(2):195-222. Firebaugh, Glenn. 1983. “Scale Economy or Scale Entropy? Country Size and Rate of Economic Growth, 1950-1977.” American Sociological Review 48(2):257-69.


Firebaugh, Glenn, and Frank D. Beck. 1994. “Does Economic Growth Benefit the Masses? Growth, Dependence, and Welfare in the Third World.” American Sociological Review 59(5):631-53. Glover, Donald R., and Julian L. Simon. 1975. “The Effect of Population Density on Infrastructure: The Case of Road Building.” Economic Development and Cultural Change 23(3):453-68. Goldstone, Jack A. 1991. Revolution and Rebellion in the Early Modern World. University of California Press. Hamilton, W.D. 1964. “The Genetic Evolution of Social Behavior.” (Parts I and II) Journal of Theoretical Biology 7(1):1-16, 17-52. Hawley, Amos. 1968. “Ecology.” Pp. 328-37. The International Encyclopedia of the Social Sciences, Volume 4. Macmillan. Hechter, Michael. 1987. Principles of Social Solidarity. University of California Press. Heston, Alan, Robert Summers and Bettina Aten. 2002. Penn World Table Version 6.1, Center for International Comparisons at the University of Pennsylvania. Available at: http://pwt.econ.upenn.edu/php_site/pwt_index.php. Kelley, Allen C., and Robert M. Schmidt. 1995. “Aggregate Population and Economic Growth Correlations: The Role of the Components of Demographic Change.” Demography 32(4):543-55. Kerr, Clark, John T. Dunlop, Frederick Harbison and Charles A. Myers. 1960. Industrialism and Industrial Man: The Problems of Labor and Management in Economic Growth. Oxford University Press. Kriedte, Peter, Hans Medick and Jurgen Schlumbohm. 1981. Industrialization before Industrialization: Rural Industry in the Genesis of Capitalism. Cambridge University Press. Kuznets, Simon. 1967. “Population and Economic Growth.” Proceedings of the American Philosophical Society 111(3):170-93. Lenski, Gerhard. 1966. Power and Privilege: a Theory of Social Stratification. McGraw‑Hill. Lenski, Gerhard, and Patrick Nolan. 1984. “Trajectories of Development: a Test of Ecological‑Evolutionary Theory.” Social Forces 63(1):1‑23 Lenski, Gerhard. 2005. Ecological-Evolutionary Theory: Principles and Applications. Paradigm Publishers. Lewis, W. Arthur. 1954. “Economic Development with Unlimited Supplies of Labour.” Manchester School 22(2):137-91. ______. 1958. “Unlimited Labour: Further Notes.” Manchester School 26(1):1-32. Lian, Brad, and John R. Oneal. 1997. “Cultural Diversity and Economic Development: A Cross-National Study of 98 Countries, 1960-1985.” Econonomic Development & Cultural Change 46(1):61-77. MacPherson, Crawford Brough. 1962. The Political Theory of Possessive Individualism. Oxford University Press. Malthus, Thomas R. 1798. An Essay on the Principle of Population, as it Affects the Future Improvement of Society. J. Johnson. Mankiw, N. Gregory, David Romer and David N. Weil. 1992. “A Contribution to the Empirics of Economic Growth.” The Quarterly Journal of Economics 107(2):407-37. McEvedy, Colin, and Richard Jones. 1978. Atlas of World Population History. New York: Facts on File.


McNicoll, Geoffrey. 1984. “Consequences of Rapid Population Growth: an Overview and Assessment.” Population and Development Review 10(2):177-240. Nettle, Daniel. 2000. “Linguistic Fragmentation and the Wealth of Nations: The FishmanPool Hypothesis Reexamined.” Economic Development and Cultural Change 48(2):335-48. Nielsen, François. 1994. “Sociobiology and Sociology.” Annual Review of Sociology 20:267-303. Nolan, Patrick, and Gerhard Lenski. 1985. “Technoeconomic Heritage, Patterns of Development, and the Advantage of Backwardness.” Social Forces 64(2):341‑58. Popkin, Samuel L. 1979. The Rational Peasant: The Political Economy of Rural Society in Vietnam. University of California Press. Portes, Alejandro. 1998. “Social Capital: Its Origin and Applications in Modern Sociology.” Annual Review of Sociology 24:1-24. PRIO. 2004. International Peace Research Institute, Oslo. Armed Conflict Dataset, 19462002. Available at: http://www.prio.no/cwp/ArmedConflict/. Putterman, Louis. 2000. “Can an Evolutionary Approach to Development Predict Post‑War Economic Growth?” Journal of Development Studies 36(1):1‑30. Reisinger, Heribert. 1997. “The Impact of Research Designs on R2 in Linear Regression Models: An Explanatory Meta-Analysis.” Journal of Empirical Generalisations in Marketing Science 2(1):1-12. Rogers, Everett M. 1983. Diffusion of Innovations, 3rd Edition. Free Press. Rostow, Walt W. 1960. The Stages of Economic Growth: A Non-Communist Manifesto. Cambridge University Press. Sachs, Jeffery D., and Andrew M. Warner. 1997. “Fundamental Sources of Long‑Run Growth.” American Economic Review 87(1):184‑88. Simmel, Georg. 1971[1908]. “Group Expansion and the Development of Individuality.” Pp. 251-93. Georg Simmel: On Individuality and Social Forms. D.N. Levine, editor. University of Chicago Press. ______. 1955. Conflict and the Web of Group Affiliations. Reinhard Bendix, translator. Free Press. Simon, Julian L. 1981. The Ultimate Resource. Princeton University Press. Simon, Julian L., and Roy Gobin. 1980. “The Relationship between Population and Economic Growth in LDCs.” Pp. 215-34. Research in Population Economics, Volume 2. Julian.L. Simon, editor. JAI Press. Smith, Adam. 1965[1776]. An Inquiry into the Nature and Causes of the Wealth of Nations. The Modern Library. Spencer, Herbert. 1972[1852]. “Population and Progress.” Pp. 33-37. Herbert Spencer: On Social Evolution. J.D.Y. Peel, editor. University of Chicago Press. Waldinger, Robert. 1994. “The Making of an Immigrant Niche.” International Migration Review 28:3-30. Williamson, Jeffrey G. 1998. “Growth, Distribution, and Demography: Some Lessons from History.” Explorations in Economic History 35(3):241-71. World Bank. 1978. Rural Employment and Nonfarm Employment. Washington, DC: World Bank. ______. 2002. World Development Indicators, 2002 (CD-ROM). Washington, DC: World Bank.