nations of social inequalities than either dependency/world-systems theory or urban .... whether total foreign capital penetration or its sectoral counterparts were.
Population Research and Policy Review 12: 297-313, 1993. © 1993 KIuwer Academic Publishers. Printed in the Netherlands.
Dimensions o f social inequality in the Third World
A cross-national analysis of income inequality and mortality decline EDWARD CRENSHAW & ANSARI AMEEN The Ohio State University, Columbus, Ohio, USA
Abstract. This cross-national assessment of the empirical determinants of income inequality and infant mortality employs policy-relevant variables suggested by the major macrosocial theories of development and stratification. Findings based on sample sizes ranging from 34 to 61 LDCs indicate that modernization and ecological-evolutionary theories provide more consistent explanations of social inequalities than either dependency/world-systems theory or urban bias theory. Our analyses point to economic growth and the development of rural infrastructure and social complexity as the most expedient methods for facilitating mortality reduction and income equalization, We conclude that simplistic policy-orientations stressing such phenomena as urban bias or population growth should be replaced by more complex perspectives that include an emphasis on rural social organization. Key words: Infant mortality, Income inequality, Less-developed countries, Rural development
Introduction Although few would suggest that absolute equality is likely in the real world, social inequality is important because it has been linked to important policy questions such as poverty, political conflict and differentials in fertility and mortality (Wood & Magno de Carvalho 1988: 184; Murdoch 1980: 68-69; Pampel & Williamson 1989; Boswell & Dixon 1990). Social inequality is therefore one of the more pressing questions posed by development studies. This cross-national assessment of the structure of social inequality investigates the empirical determinants of income inequality and, as a further step, the average annual percentage change in infant mortality from 1965 to 1985, the latter an attempt at a dynamic view of how policy-relevant variables influence the distribution of resources.
Public policy and theories of stratification Several theories of national development and stratification have shaped our perceptions of income distribution. Modernization, ecological-evolutionary, dependency/world-systems, and political theories all suggest dynamics that are at once policy-relevant and academically interesting. Modernization theorists anticipate higher levels of social inequalities in developing countries relative to industrialized nations, a relationship best described by an inverted
298 U-shaped function. Modernization theory suggests that a concentration of wealth by those with entrepreneurial skills is the most efficient method of using scarce capital during early industrialization. Accelerated investment rates depend upon the growth of modern sectors, while social overhead capital is typically acquired from extractive and agricultural sectors. Thus, modernization theorists expect increases in the relative inequality nations exhibit during intermediate stages of development, followed by subsequent declines as the need for capital concentration becomes less urgent, notions that explain why most modernization theorists embrace the assumption that economic growth stimulates decreases in inequalities by virtue of 'trickle down' effects. Economic growth, while initially exacerbating inequality, eventually benefits entire populations as class mobility occurs in response to greater employment opportunities, higher wages won through labor specialization, and the cheaper commodity prices that result from mass production (Deane 1979). Therefore, if the 'logic of industrialism' thesis is correct then public policy should favor rapid economic growth over issues of distribution and allocation. The best long-term policy would be one that maximizes the efficiency and profitability of the private sector in order to shorten the transitional 'high-inequality' phase of development. Ecological-evolutionary theory also contributes to our understanding of variations in social inequality across nations. Apparently the technological heritage of nations has lasting effects on their stratification systems. The adoption of plow agriculture at a relatively early point in world history allowed for the production of far higher surpluses, which today translate into larger, more densely-settled populations, urban communities, greater degrees of economic specialization, and more complex administrative bureaucracies (Lenski & Nolan 1984; Nolan & Lenski 1985; Crenshaw 1992). More specifically, such societies historically experienced labor abundance but, due to population pressures, relative scarcities of land and capital. As labor replaced other factors of production as the principal source of elite wealth, the problem of hoarding labor power forced more equitable distributions of scarce resources including income or its equivalent. Moreover, growing scarcities of land over generations compelled elites to continuously subdivide their assets. The consequences of these patterns are lower levels of land inequality and flatter income distributions. From the standpoint of social policy, the insights gained through the ecological-evolutionary perspective go well beyond this 'heritage' effect. Given that income distribution and poverty in the developing world are largely driven by the rural/urban divide, one important contribution of ecological-evolutionary theory to policy debates is its focus on the social carrying capacity of rural areas. As historical populations rose to the level of subsistence, population densities rose, creating complex divisions of labor in the form of highly-articulated systems of villages and towns and the rural infrastructure (e.g., roads and canals) that supported densely-settled environments (Boserup 1990: 71). In essence, old agrarian societies that exhibit
299 densely-settled rural areas are more articulated, and for this reason enjoy higher degrees of economic specialization, more non-farm employment opportunities in rural areas, and higher rates of land productivity (World Bank 1978; Berry & Cline 1979: 34-39). Even more important from a policy standpoint, delivery of government services and other interventions (e.g., medical services, education, development programs) is more efficient in highdensity environments, and the accessibility of such programs/services to targeted populations will of course be higher (for specific examples in the Third World see Leinbach 1984; McNulty, Ayeni, Filani & Olaore 1984). The importance of population density to both income redistribution and differential access to scarce resources is therefore likely to be great. Dependency/world systems perspectives posit that social inequality is inherent in capitalist development. Specifically, foreign investment is viewed as a primary mechanism through which relative social inequalities are intensified in the following ways: (1) capital-intensive foreign investment leads to gross sectoral disparities, the development of labor aristocracies, and the tendency toward the underabsorption of labor; (2) multinationals monopolize local credit and at the same time opt to return profits to home countries rather than re-investing locally, resulting in both lagging growth of national income and wealth concentration; and (3) governments excessively control labor in the effort to attract and retain foreign investment, a practice which fosters social class rigidity and limits upward mobility (Evans & Timberlake 1980; Timberlake 1985). These effects are also thought to vary by sector. The capital intensity of foreign agribusiness leads to both labor shedding and land enclosure, resulting in landtessness, poverty, and income inequality. Foreign investment in extractives, by contrast, creates a small but well-paid elite and thereby increases sectoral and personal income inequality. Foreign investment in manufacturing differs in that a low-wage labor pool and severe unemployment and underemployment are conditions under which investment in manufacturing may be most lucrative. Manufacturing for local markets may boost inequality since manufacturing firms tend to produce items aimed at the wealthier segments of Third World populations. The demand for TNC-produced goods is therefore predicated on high levels of income inequality. Given that large scale redistributions of wealth are improbable and unlikely to enable poorer segments of national populations to purchase TNC-produced goods, manufacturing TNCs have stakes in supporting government reinforcement of existing stratification systems. This emphasis on foreign-owned manufacturing firms has become known as the 'dependent development' model within the dependency perspective (Cardoso & Faletto 1979; Evans 1979; Bornschier & Chase-Dunn 1985). Of course, should this model of income distribution prove correct, the obvious policy conclusion would be greater national controls on transnational investment and/or avoidance of such investment. Political theories such as Olson's distributive coalitions theory and Lipton's urban bias theory focus on political rather than economic elites (Olson 1982;
300 Lipton 1977), and are therefore more policy oriented than other macrosocial theories of stratification. According to Lipton, the most powerful interest groups of Third World nations rely primarily on urban power bases, and for this reason most economic public policy initiatives encourage the concentration of resources in urban rather than rural areas. In addition, Lipton notes that severe discrimination against rural areas exists in both market and command economies, thereby challenging the notion that urban bias is a unique product of capitalist economic orders. Although limited, some support for the effects of urban bias has been found (Lipton 1984; Bradshaw 1987; London & Smith 1988; Nolan & White 1984). Public policy can be pulled in divergent directions depending on which of these broad theories influences policy-makers. Given that each has enjoyed some degree of empirical support in the past, new research that models competing theories in single tests of validity and strength is necessary. Moreover, because most of these approaches are used primarily to explain inequalities in the developing world, this new research should initially be restricted to developing countries in order to avoid confusion in research findings. Finally, given the paucity and relatively poor quality of data on income distributions in the Third World, the use of a variety of data sources and dimensions of inequality has become common. Effects consistent across specifications and/or data sources are thought to be more robust and reliable, necessities when engaged in policy research.
Design and variables In keeping with our goals, only developing countries are included in these samples. Because 'developing nation' status may confer political as well as economic meanings, we exclude all nations designated as 'core' by Snyder and Kick (1979) as modified by Bollen (1983). This excluded all developed countries, leaving only middle- and low-income countries around the globe. Sample sizes range from 34 to 61; Appendix A lists the nations used for each set of equations. Ordinary least-squares regression was selected as the method of analysis. Three dependent variables are considered: the percent of national income held by the poorest 40 and richest 20 percent of national populations, and the average annual percentage change in the infant mortality rate between 1965 and 1985. Each of these dependent variables was regressed on a base model that alternatively contained as few as six or as many as eleven predictor variables, depending on which dependent variable was considered and whether total foreign capital penetration or its sectoral counterparts were used. y1-2 = f{GP,GS,TF,FE,FM,FA,AD,PG,LA,UB} y 3 = f{GP,GS,TF,FE,FM,FA,AD,PG,LA,UB,IF,IC}
301 where y~: Income share of the bottom 40% - 1970 y2: Income share of the top 20% - 1970 y3: Average annual percentage change in Infant Mortality Rates 1965-85 GP: Log Gross National Product per capita 1965 GS: Quadratic of GP TF: Log Total Foreign Capital Penetration 1967 FE: Foreign Capital Penetration, Extractives 1967 FM: Foreign Capital Penetration, Manufacturing 1967 FA: Foreign Capital Penetration, Agriculture 1967 AD: Log Agricultural Density 1960 PG: Average Annual Population Growth 1960-65 LA: Percent Labor Force in Agriculture 1970 UB: Log Urban Bias 1965 IF: Infant Mortality Rate 1965 IC: Infant Mortality Rate 1965/Child Mortality Rate 1965. Data from Hoover (1989; and in unreported analyses of Gini coefficients from Muller (1988) have been selected because of greater standardization. In these data, all observations are restricted to the years between 1965 and 1975, and Hoover attempts to adjust the data for the unit of observation (i.e., whether individuals or households were the subjects of the surveys), Although problems remain with these data, they represent more refined information than has been available heretofore. Net of development, infant mortality has been used as an important proxy for income inequality in past research. The use of the annual average percentage change in infant mortality between 1965 and 1985 therefore links this study with that body of literature concerning the provision of basic human needs (Hicks & Streeten 1979; Moon & Dixon 1992). Since infant mortality is usually highest among the poor, the use of this variable net of the level of development is considered a legitimate method of assessing the influence of social structure on change in social inequalities. The infant mortality rate in 1965 is added to these equations to control for initial levels of mortality. Also, because no nation in this sample experienced increases in infant mortality, negative coefficients indicate improvements among the poorest segments of national populations, while positive coefficients indicate lagging mortality decline (UN 1987). This is not to suggest that infant mortality rates across nations and over time are more reliable than data on income distribution. Indeed, many scholars have questioned the coverage of infant mortality statistics in developing nations (Pampel & Williamson 1989: 155-156; Flegg 1982), and some have empirically demonstrated this undercount. On the other hand, age at death is a critical variable in this undercount (Lumbiganon et al. 1990), suggesting that vital statistics for older children may be more reliable. We devise a
302 quality-check variable based on this assumption: the ratio of the infant mortality rate (deaths age 0 to I per thousand) to the child mortality rate (deaths age 1 to 5 per thousand), both measured in 1965. Clearly, this ratio should usually be greater than 1, and somewhat higher than 1 if a national registration system is capturing the true differential. Controlling for this ratio is a crude but (in our view) effective means of assessing the impact of undercount on change in the infant mortality rate. Data for child mortality are adopted from Ross et al. (1988). Gross national product per capita in 1965 and its quadratic are adopted from Bornschier and Chase-Dunn (1985). Both variables are suggested by modernization theory, and while some might assume that the exclusion of developed countries from the samples might eliminate the need to model the squared tenn, this is not the case. This variable is logged to correct for skewness. The total foreign capital penetration indicator (PEN) was constructed following conventional practice: the book-value of total foreign-owned stock in 1967 was divided by the square root of the value of domestic stock in 1967 multiplied by the total population in 1967 (Ballmer-Cao & Scheidegger 1979). This variable was logged to improve its distribution. Sectoral penetration indicators in extractives, manufacturing and agriculture were constructed in a similar manner. These were not logged, however, because transforming them did not consistently improve their distributions in these samples. Population density in agricultural areas was constructed by taking the total labor force in agriculture in 1960 and dividing by square kilometers of arable land in 1960. This variable is also logged. Although this indicator has been used as a measure of agricultural adversity in some previous research (Firebaugh 1979; Chan 1989), densely-settled agricultural areas should in fact foster income equalization, at least from the ecological-evolutionary perspective. While it is conceivable that agricultural density does tap adversity to some degree, reliance on this measure for that purpose ignores the social 'scaffolding' required to maintain high densities: more intricate divisions Of labor, higher agrarian technologies, greater levels of articulation among villages and towns, and higher levels of social organization within microeconomic environments. The data were obtained from Taylor and Hudson (1972) and the World Bank (1986). Because the poorest segments of national populations tend to exhibit the highest fertility, it is important to control for average annual population growth, in this case between the years 1960 and 1965. Rapid population growth results in higher dependency ratios among the poorest segments of populations, thereby reducing the power of poor families to earn and save. Annual average population growth statistics have been obtained from the World Bank (1986). It should be noted that Ahluwalia (1976), Bollen and Jackman (1985a), and Simpson (1990) have controlled for fertility and rapid population growth in past investigations of income inequality.
303 Urban bias in 1965 is defined as the ratio of non-agricultural productivity to agricultural productivity. The data were obtained from the World Bank (1986). The indicator is constructed by dividing non-agricultural productivity in 1965 (non-agrarian GDP divided by labor force in non-agrarian sectors) by agrarian productivity in 1965 (agrarian GDP divided by labor force in agriculture). Although Lipton considers this indicator a fair proxy for an urban bias in national policy, the indicator is imperfect because of its inability to differentiate truly urban from rural production. Nonetheless, it probably does reflect wage and investment differentials in a rough fashion, although whether or not these differentials are generated by political manipulation is debatable. Given this concern, it is also necessary to determine whether or not the 'urban bias' effect is actually related to the productivity disparity or simply to dualism, or the relative size of the rural economy. For this reason, this study adds another important control: the percentage of the labor force in agriculture in 1965. The source of these data was the World Bank (1986). Adding this variable should allow us to differentiate the effects of biasinduced productivity differentials from those of simple rural-urban size differences on social inequalities.
Analysis Table 1 reports the zero-order correlations between the variables used in these models. The indicators of income inequality are highly related to one another, as one would suspect. The link between average annual change in infant mortality and income inequality appears more tenuous, however, which can be explained by the breadth of the two types of indicator. While income inequality is a fairly narrow assessment of social inequality, infant mortality rates and their change over time encompass a far wider set of social disparities, including income inequality, differentials in access to public institutions, and the spatial distributions of national populations. Moreover, differentials in international development complicate these zero-order effects (development effects have not been partialled out of these correlations for infant mortality). The correlations also provide evidence of the deleterious effects of total foreign capital penetration and its sectoral component equivalent in manufacturing on income distributions. In addition, the effect of agricultural density clearly supports an ecological-evolutionary approach, while more limited SUl~port is found for urban bias theory. Finally, the influence of population growth on income distribution is demonstrated. Rapid population growth exacerbates income inequality. Correlations between variables in the models determining changes in infant mortality suggest a different picture. While both agricultural density and urban bias effects are evidenced, total foreign capital penetration alone among the foreign investment variables is significantly related to changes in
0.44 0.01 0.27 0.11 0.51 0.01 0.18 0.30 0.36 0.03 0.21 0.22 -0.31 0.07 0.55 0.01 0.14 0.41 0.42 0.01
-0.11 0.54 -0.21 0.21 -0.52 0.01 -0.55 0.01 -0.31 0.07 -0.48 0.01 -0.21 0.23 0.45 0.01 -0.46 0.01 0.i1 0.49 -0.21 0.21
3. Infant mortality rate
4.
5. Log GNP/c 1965
6.
7. Foreign capital penetration 1967
8. Foreign capital penetration manufacturing 1967
9.
10. Log agricultural density 1960
11. Annual average population growth 1960-1965
12. Percent labor force in agriculture 1960
13. Log urban bias 1965
Foreign capital penetration agriculture 1967
Log foreign capital penetration 1967
Average annual change in infant mortality, 1965-1985
0.32 0.06
-0.88 0.01
2
2. Income - top 20%
1. Income - bottom 40%
1
0.64 0.01
0.38 0.01
0.69 0.01
-0.11 0.42
-0.08 0.54
-0.14 0.26
-0.47 0.01
-0.08 0.51
-0.27 0.04
-0.60 0.01
3
0.32 0.01
0.65 0.01
0.05 0.72
-0.22 0.08
-0.18 0.14
-0.19 0.13
-0.15 0.23
-0.25 0.05
-0.64 0.01
4
0.01 0.96
0.60 0.01
0.38 0.03
0.58 0.01
-0.24 0.15
-0.79 0.01
0.01 0.98
-0.45 0.01
5
0.49 0.01
0.66 0.01
0.65 0.01
0.20 0.24
-0.32 0.05
0.36 0.03
-0.39 0.02
6
0.10 0.57
0.27 0.12
0,27 0.12
-0.19 0.29
0,21 0.22
-0.35 0.04
7
0.28 0.11
0.03 0.88
-0.44 0.01
0.17 0.33
-0.42 0.01
8
Table 1. Zero-order correlations and p-values between variables determining income inequality (sample sizes vary)
9
0.01 0,94
0.10 0.57
0.24 0.16
0.04 0.83
-0.07 0.68
0.17 0.31
0.26 0.13
10
0.37 0.03
0.11 0.53
11
0.62 0.01
12
13
305 Table 2. Standardized regression coefficients of income shares of bottom 40% and top 20% on base models (Hoover Dataset) Income shares of top 20 and bottom 40 percent of national populations
Log Gross National Product/c 1965 Gross National Product/c2 Log foreign capital penetration 1967 Foreign capital penetration - Extractives 1967 Foreign capital penetration - Manufacturing 1967 Foreign capital penetration - Agriculture 1967 Log agricultural density 1960 Annual average population growth 1960-1965 Percent labor force in agriculture 1970 Log urban bias 1965 N
a2
R2-Adjusted
Bottom 4O%
Bottom 40%
Top 20%
Top 20%
-9.48** 8.80** 0.04
-9.67"* 9.03** -
8.95** -8,46** 0.09
8 44** -7.97** -
-
0,01
-
-
0.10
-
0.02 0.32** 0.29** -0.43** -0.42** -0,40" -0.48* 0.07 0,11 36.0 35.00 0,81 0.79 0.77 0.72
.0.22** 0.41"* 0.46* 0.04 36.00 0.74 0.68
0.12
0.12 0.01 -0.27* 0.46** 0.46 0.09 35.00 0.73 0.64
* p ~< 0.10, two-tailed test. ** p ~ 0.05, two-tailed test.
infant mortality, and the direction of the coefficient (r = - 0.25, p ~< 0.05) indicates that greater investment dependency accelerates decline in infant mortality, contrary to dependency theory. Also, while average annual population growth shares no bivariate relationship to change in infant mortality, larger percentages of the labor force in agriculture retard mortality declines. The results reported below have been subjected to rigorous regression diagnostics. Influential outliers have been identified using partial regression plots, studentized residuals, hat matrix, and DFITS diagnostics (Bollen & Jackman 1985b), Tests for heteroscedasticity include Spearman rank order correlation and the Breuseh-Pagan global test. Tests for multicollinearity include variance inflation factors and beta correlations (Johnson, Johnson & Buse 1987). In particular, the introduction of the second-degree polynomial for development generates severe collinearity. Nonetheless, the effects reported below were statistically significant and fairly stable across a variety of specifications and samples. Table 2 contains the standardized regression coefficients produced when the income shares of the poorest 40 and richest 20 percent of national populations are regressed on the base models. All four equations reveal consistent relationships. A clear modernization effect is demonstrated by the statistical significance of gross national product per capita and its quadratic. It appears that the income shares of the poorest strata decrease as industrialization proceeds until a threshold is reached, after which some income equalization occurs. The reverse occurs to the income shares of the top 20 percent of national populations, a finding consistent with the works of Kuz-
306 nets and others. Neither dependency effects nor urban bias effects are evidenced here. Considering the relationship between foreign capital penetration and urban bias demonstrated by London and Smith (1988), it might be considered illegitimate to control for both in the same equation. Dropping urban bias from these equations does not change the substantive conclusions of this analysis, however. On the other hand, agricultural density is related to income shares as posited by ecological-evolutionary theory, suggesting that the rural areas of densely-settled nations exhibit better economic and social organization and less severe social inequalities. Finally, both population growth and the percentage of the labor force in agriculture exacerbate income inequality. Moreover, reiterating these equations using Muller's formulation of the GINI coefficient produces similar results, although foreign capital penetration in manufacturing attains significance, a difference we attribute to differing formulations of the dependent variable rather than to sample composition. Table 3 reports the results of regressing the average annual percentage change in infant mortality between 1965 and 1985 on the full sample (Equations 1 and 4), the full sample with a control for data quality (Equations 2 and 5), and a sample cleansed of outlying cases (Equations 3 and 6). The first three equations model the log of total foreign capital penetration, while the last three incorporate its sectoral counterparts in extractive industries, manufacturing and agriculture. The level of infant mortality in 1965 is also incorporated to assess the impact of level on the rate of change. All six equations exhibit a statistically-significant curvilinear effect of national wealth on mortality decline. These coefficients suggest that mortality decline is rapid from low to intermediate levels of development, but this tapers off to a more modest decline at higher levels of national wealth. While this apparently contradicts the pattern found in the first part of the analysis (where inequality increased with development to a point but decreased thereafter), this finding bears out demographic transition theory and is quite compatible with modernization concepts. Mortality-reducing technologies (e.g., pesticides, public health measures) are easily diffused throughout most developing nations, and these technologies bring on rapid mortality decline that may outstrip socioeconomic development. At later stages of development, however, incremental improvements in infant mortality depend on better diets, medical facilities, and a modicum of individual or family income, requirements that force mortality declines to moderate until socieconomic development catches up. Therefore, while these results appear to reverse the familiar U-shaped hypothesis, they do not in fact contradict modernization theory. Another consistency across equations is the significant, negative influence of the log of agricultural population density on change in infant mortality. Although a more conventional view of population density would clearly suggest otherwise, here we see that densely-settled nations hold an advantage when it comes to rapid mortality reduction, perhaps through superiority in
a b * *
Equation Equation p ~< 0.10, p ~< 0.05,
3 excludes Chile and Costa Rica. 6 excludes Chile and Costa Rica. two-tailed test. two-tailed test,
R2-Adjusted
Infant mortality rate 1965 Log Gross National Product/c 1965 Gross National Produet/c 2 Log foreign capital penetration 1967 Foreign capital penetration - Extractives 1967 Foreign capital penetration - Manufacturing 1967 Foreign capital penetration - Agriculture 1967 Log agricultural density 1960 A n n u a l average population growth 1960-1965 Percent labor force in agriculture 1970 Log urban bias 1965 I M R / C M R ratio N R2 0,07 -9,26* 9,18"* ,0.09 -
-
-0.30** --0.03 0.26 -0.08 -0.38** 61.00 0.65 0.59
-
-
-0.27** 0.06 0.31 -0.06 61.00 0.61 0.55
2
0.24* -7.84** 7.60** ,0.04
1
~).36"* 0.07 0.22** -0.09 -0.15 59.00 0.68 0.63
-
-
-
0,22 -9.70** 9.47** ,0.04
3~
0.29** -0.17 -0.24** 0.06 0.41" -0.11 61.00 0.66 0.59
-0.05
0,26** -5.23** 4.95* -
4
A n n u a l average change in infant mortality, 1965-1985
0.30** ,0.16 -0,26** -0.04 0.35* -0.13 ,0.37** 61.00 0.70 0.63
,0.09
0.11 -6.76** 6.62** -
5
Table 3, Standardized regression coefficients of average annual change in infant mortality (1965-1985) regressed on base models
0.22* -0.11 -0.34** 0.05 0.27 -0.10 --0.18 59.00 0.71 0.65
-0.12
-
0,24* -8,36** 8.14"*
6b
ta~ --,,I
308 terms of social organization, institutional heritage, higher accessibility to public programs, and of course more even distributions of scarce resources. Other consistencies involve total foreign capital penetration and its sectoral equivalents. Equations 1 through 3 of Table 3 demonstrate that the log of total foreign capital penetration does not exert any significant influence on change in the infant mortality rate, and even if it did the signs of the coefficients contradict dependency theory (negative rather than positive), which would lead us to believe that foreign investment aids a nation in reducing infant death. Equations 4 through 6 clarify these results. Apparently foreign investment dominance in extractives and agriculture exert no significant influence, while foreign capital penetration in manufacturing retards mortality decline, perhaps in the manner suggested by dependent-development theory. How does undercounting infant deaths across nations influence these results? It is possible that the curvilinear effect of national wealth on change in infant mortality is a statistical artifact; we might expect better reporting (i.e., more advanced registration systems) at advanced levels of national wealth, ergo the decay in the rate of infant mortality change at upper levels of income. Simply put, this decay may reflect improved registration (and therefore higher numbers of recorded deaths per thousand) rather than any actual relative decay across nations as a function of GNP/c. To aid in diagnosing any potential problem, we include an infant/child mortality rate ratio for 1965 in Equations 2, 3, 5 and 6. To reiterate, we assume that high ratios (or high IMR compared to CMR) are indicative of superior registration systems because underreporting of infant mortality should decrease this ratio. If we accept this interpretation, then Equations 2 and 5 clearly demonstrate a sharp improvement in infant mortality in response to the ratio; the higher the infant mortality in relation to child mortality, the faster the decline in infant mortality between 1965 and 1985. It is important to note that the zero-order correlation between the level of infant mortality in 1965 and this IMR/CMR ratio is -0.69 for this sample, suggesting nations that enjoyed low-mortality regimes in 1965 exhibited high ratios, which accords well with our interpretation of this ratio as a proxy for the quality of national registration systems. Two things stand out in our diagnosis of data problems. First, the analyses point to Chile and Costa Rica as outlying cases. Once these nations are excluded (Equations 3 and 6), we can see the IMR/CMR ratio fails to attain significance. This may indicate that the superiority of the national registration systems of Chile and Costa Rica account for their status as outliers. Second, neither controlling for potential data problems nor the exclusion of outlying cases seriously changes the substantive conclusions of the analysis. Moreover, regressing the annual average percentage change in child mortality confirms these findings (analysis not shown). Therefore, using Equations 4 and 6 from Table 3 as a guide, apparently the initial level of mortality, national wealth, foreign capital penetration in manufacturing and agricultural density all in-
309 fluence changes in infant mortality, variables that account for at least 66 percent of the variance in the dependent variable. Given that violations of OLS were either nonexistent or had negligible consequences, we conclude that the results are robust.
Discussion
In summary, the weight of evidence suggests that modernization theory and ecological-evolutionary theory provide stronger, more consistent explanations of social inequality than do either dependency/world-systems or urban bias theories. Of the ten equations estimated here, all supported modernization or modernization-compatible effects as well as ecological-evolutionary theory. On the other hand, dependency effects were found only in the analysis of changes in infant mortality, and urban bias effects not at all. Perhaps the most important conclusion that can be drawn from this research is that social inequality is driven by a variety of macrosocial processes and simplistic policy-orientations (e.g., redistributive government, trickle down) may be both ineffective and dangerous. Substantively, we can be fairly certain that the 'logic' of industrialism operates in the developing world. Inequality is partially a function of the need to concentrate meager capital, and this inevitably breeds income and social inequalities during the early stages of development As development continues and more capital becomes available, however, a significant deconcentration of income occurs. In accordance with the views of orthodox economists, our research lends support to previous studies that point to increases in the social well-being of national populations as development increases. Our research also indicates that the manner in which development is pursued does in fact make significant alterations in the pace of social improvements. More specifically, the organization of the agricultural sector is an important determinant of income equalization. Nations that enjoy high rural carrying capacities in terms of arable land, economic infrastructure, and social organization experience more even distributions of social resources. Therefore, policy-makers should concentrate on economic growth and, perhaps as importantly, on agricultural development and infrastructural investment in rural areas, particularly in those nations where a large proportion of the national population languishes in thinly-settled and economically-isolated regions. Second, the relationship between income or social inequality and foreign investment is more complex than dependency theory suggests. Indeed, neither total foreign capital penetration nor penetration in extractives or agriculture exert any strong influence on inequality in this analysis. On the other hand, the negative influence of foreign capital penetration in manufacturing provides prima facie evidence in support of dependent development theory, although this support is partial. According to dependent development theory, three of the major mediating factors involved here would be: (1) economic
310 growth, (2) sectoral distortion, and (3) government strength/intervention. Given the gravity of the policy question, in a separate analysis (not shown) we have modeled numerous variables that roughly represent most of these mediating forces. While we found weak evidence that the influence of foreign investment in manufacturing on infant mortality decline is mediated by income inequality (one of four tests using the Hoover & Muller datasets indicates this), our modeling of theoretically important intervening variabIes failed to account for foreign manufactnring's effects on either income inequality or infant mortality declines. Although we modeled economic growth, the GINI coefficient of sectoral inequality, social insurance history, several proxies of state strength, public health expenditures, commodity concentration, long-term external debt in 1970, and overurbanization as possible variables that mediate the influence of foreign manufacturing penetration on income inequality and infant mortality, none of these variables appear to mediate these relationships. Therefore, while the correlation between foreign investment in manufacturing and social inequality seems valid, we are less sanguine about the causal mechanisms that produce this correlation. Alternative hypotheses exist. For instance, if transnational manufacturers are drawn to large consumer markets in developing countries (as suggested by Bornschier and Chase-Dunn), it is possible that patterns of social inequality that created these markets pre-existed foreign investment in manufacturing and are in fact responsible for this correlation. Also, much foreign investment in manufacturing occurred under the umbrella of import-substitution, a set of national policies that penalize rural areas through price twists and the overvaluation of national currencies. A plethora of correlates with import-substitution, such as external debt, state-owned enterprises, nationalization and political corruption, were not captured by our modest efforts to control for intervening variables. Nonetheless, if import-substitution does create this pattern, it raises the question of whether it is foreign manufacturing investment per se or the set of policies that induced that investment that is to blame for lagging mortality decline or social inequality. Future research should also investigate the linkages between modernization and ecological-evolutionary effects, public policy and social inequality. What aspects of economic development encourage its curvilinear relationship with social inequality? Does it occur due to sectoral redistribution of resources, as suggested by classical modernization theory, or does development engender political reforms that redistribute income and public resources in later stages of development, as suggested by several theorists? In regard to ecological-evolutionary theory, what are the social characteristics engendered by agricultural density? Is agriculture a more vital segment of these national economies? Are medical infrastructure and social insurance programs correlated with technoeconomic heritage? Is state-building faster or more efficient in older agrarian societies? Many such questions exist because ecological evolutionary theory has not enjoyed the same level of
311 theoretical and empirical elaboration as have modernization and dependency theories. Nonetheless, our investigation suggests that students of international development should seriously consider the contributions of ecologicalevolutionary theory in their empirical work. We found no evidence for an urban bias effect, suggesting that simple dualism in national economies rather than government-induced productivity differentials may be responsible for much of the income inequality in Third World nations. On the other hand, the evidence points to the importance of rural areas to systems of stratification in the developing world, so urban bias remains one plausible explanation for inequality. Unfortunately, we will be unable to separate bias in macroeconomic policy from structural problems J~n Third World agriculture until a detailed, cross-national dataset on such policies becomes available.
Appendix: Sample composition Hoover dataset (n = 36) Mexico, Honduras, Costa Rica, Panama, Columbia, Venezuela, Ecuador, Peru, Brazil, Bolivia, Chile, Argentina, Uruguay, Spain, Senegal, Sierra Leone, Kenya, Tanzania, Zambia, Zimbabwe, Malawi, Tunisia, Sudan, Iran, Turkey, Egypt, Hong Kong, South Korea, India, Pakistan, Burma*, Sri Lanka, Thailand, Malaysia, Philippines, Indonesia. Muller dataset (n = 34) Mexico, Honduras, E1 Salvador, Nicaragua, Costa Rica, Panama, Columbia, Venezuela, Peru, Brazil, Bolivia, Chile, Argentina, Uruguay, Spain, Senegal, Sierra Leone, Kenya, Tanzania, Zimbabwe, Malawi, South Africa, Tunisia, Sudan, Iran, Turkey, Egypt, South Korea, India, Sri Lanka, Thailand, Malaysia, Philippines, Indonesia. Infant mortality equations (n = 61) Dominican Republic, Jamaica, Honduras, Mexico, Trinidad and Tobago, Guatemala, E1 Salvador, Nicaragua, Costa Rica, Panama, Columbia, Venezuela, Ecuador, Peru, Paraguay, Argentina, Chile, Uruguay, Senegal, Benin, Mauritania, Liberia, Sierra Leone, Ghana, Togo, Cameroon, Nigeria, Central African Republic, Chad, Zaire, Kenya, Tanzania, Burundi, Rwanda, Ethiopia, Zambia, Zimbabwe, Malawi, South Africa, Morocco, Algeria, Tunisia, Sudan, Afghanistan, Iran, Turkey, Iraq, Egypt, Jordan, Syria, Hong Kong, South Korea, India, Pakistan, Sri Lanka, Thailand, Malaysia, Philippines, Indonesia, Papua New Guinea. * Because no data have been reportedfor Burmaon sectoralforeigncapital measures,Burma was automaticallyexcludedfromequationsconsideringthe effectsof sectoralforeigncapitalon the dependentvariables.
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Address for correspondence: Edward Crenshaw, Department of Sociology, 190 North Oval Mall, 300 Bricker Hall, The Ohio State University, Columbus, OH 43210-1353, USA Phone: (614) 292-6681
