GROWTH, GIRLS’ EDUCATION,AND FEMALE LABOR:

A LONGITUDINAL ANALYSIS

Jane Arnold Lincove University of Texas at Austin, USA*

ABSTRACT

The link between economic growth and labor market participation is complex and elusive. Investments in female education are expected to increase women’s productivity at home, but the relationship to labor force participation is less clear. Research has identified a U-shaped relationship where women leave the labor market at early stages of economic development and return when a white-collar sector develops (Sinha, 1967; Durand, 1975; Pampel & Tanaka, 1986; Goldin, 1995; Horton, 1996; Mammen & Paxson, 2000; Juhn & Ureta, 2003). This study replicates previous models using time-series analysis and consideration of large increases in female schooling over the past 30 years. The results suggest that investments in female education can overcome potential reductions in female participation due to increases in wealth, and policies to invest in girls’ education appear to have benefits for labor markets, as well as family production.

JEL Classifications: J16, J24, 017, I28Keywords: Female Labor, Girls’ Education, Human Capital Corresponding Author’s Email Address: lincove@mail.utexas.edu

INTRODUCTION

Economic growth is expected to increase human capital in developing countries through investments in education and expanded labor markets. Investments in female education are particularly important for transforming economic growth into positive development outcomes as female literacy is linked to fertility, child health, and opportunities for women (Summers, 1994). While economic growth and female education have contributed to reduced fertility and improved child health, they have not consistently resulted in expansion of labor market opportunities for women.

International comparisons illustrate that female labor force participation is high in low-income countries and highly-developed countries, and relatively low in middle-income countries, creating a U-shaped relationship between national income and female participation. Theorists have attributed this relationship to changes in labor market structure (Sinha, 1967; Durand, 1975; Juhn & Ureta, 2003), social norms regarding the nature of women’s work (Goldin, 1995), and cultural factors such as religion, social mobility, and family structure (Youssef, 1974; Semyonov, 1980; Horton, 1996). This study focuses on the important intervening effect of female education in the relationship between economic growth and female labor force participation. Theoretically, female education can increase productivity of women at home (Becker, 1975; 1985) or in the workplace (T.W. Schultz, 1960). In the past thirty years, developing countries have invested significant resources to expand female schooling (Hill & King, 1993; Herz & Sperling, 2004). Since girls’ education is a preferred policy strategy to promote development (Summers, 1994; T.P. Schultz, 2002; Herz & Sperling, 2004), it is important to examine how these investments influence labor markets. If female

46

education results in greater productivity at home, investments may accelerate the decline in female participation. Alternatively, if female education increases labor market opportunities for women, investments may counteract the negative effects of initial economic growth. Previous cross-sectional studies have controlled for female education, but longitudinal analysis has not given adequate consideration to the effect of changes in female schooling over time.

The relationship between education, women’s work, and economic growth has profound implications for development policy. If the U-shaped curve is a typical development path, periods of economic growth may lead unexpectedly to reductions in female labor force participation. More interestingly, investments in girls’ education may lead to even more dramatic reductions in the size of the labor force if the benefits of education accrue mostly in the home. It is still unclear how households and labor markets respond to girls’ education. Longitudinal analysis is needed determine if reductions in female labor force participation are a typical response to increases in female schooling.

This study adds to understanding of the role of women in developing countries over time by integrating theories of education, labor, and growth in longitudinal analysis. First, previous cross-sectional and longitudinal models are replicated with added consideration of the time-lagged relationship between female education and workforce participation. Next, fixed-effects models are tested to determine if the relationships identified in cross-sectional results reflect a development path over time. The results support previous conclusions about the U-shaped relationship between wealth and female labor force participation. However, controlling for country-specific fixed effects, the negative effect of initial economic growth is small, and female schooling is positively related to female labor force participation over time. If expansion of female education accompanies economic growth, developing countries will most likely see little or no decline in female labor force participation as growth progresses. Simulations suggest that low-income countries are likely to see increases in female participation when economic growth is accompanied by investments in girls’ education.

THEORETICAL BACKGROUND

The U-Shaped Female Participation Curve

The U-shaped female participation curve was first suggested by Sinha (1967) who observed that very poor countries have high female participation, which tends to fall during early stages of development. Middle-income countries have relatively low female participation. At the other extreme, highly-developed countries also have high levels of female participation. Sinha attributed the downward portion of the U-shaped curve to a decrease in female agricultural work during shifts to urbanization and non-agricultural production. The upward portion of the U-shaped curve occurs when women return to the labor force at advanced stages of development to fill service sector jobs. Durand (1975) also attributes international differences in female participation to the gendered structure of labor markets, arguing that female participation can rise or fall during development depending on whether industries that employ women expand or contract. Citing Boserup’s (1970) theory of women in development, Pampel and Tanaka (1986) argue that women are excluded from early industrial jobs due to physical limitations, gender discrimination, and the domestic demands of large families. Early stages of mechanized farming and industrial expansion cause women to leave the formal economic sector to focus on domestic production until white collar jobs emerge that reward education. Importantly, women’s access to these jobs depends on their access to education during the intervening period of development (Boserup, 1970).

47

Goldin (1995) applied the U-shaped curve to family production, arguing that a social stigma excludes women from participating in industrial jobs. In Goldin’s model, industrialization causes a husband’s wages to increase. This income effect enables the wife to substitute home production for labor market participation. At later stages of development, the wife can access white-collar jobs that do not require heavy labor and offer wages sufficient to surpass both the stigma against female work and the benefits of her productivity at home. A shortcoming of this approach is the assumption that the social stigma is a global norm transcending cultural differences. Galor & Weil (1996) offer an alternative explanation, suggesting that women are excluded from industrial labor because of inferior physical endowments rather than social norms.

Although the U-shaped curve was originally observed in cross-sectional data from multiple countries, authors speculate that this is a development path traveled by individual countries over time (Sinha, 1967; Pampel & Tanaka, 1986; Goldin, 1995). The U-shape consistently appears in multi-country data from the 1950s and 1960s (Sinha, 1967; Durand, 1975; Pampel & Tanaka, 1986) through the 1990s (Goldin, 1995; Mammen & Paxson, 2000; Juhn & Ureta, 2003). This indicates a persistent pattern, but does not prove that countries travel through the U-shape over time. Country-level longitudinal studies have observed several individual countries experiencing declines or increases in female participation in relation to labor market changes (Sinha, 1967; Durand, 1975; Horton, 1996; Juhn & Ureta, 2003). The most convincing evidence of a longitudinal path comes from Mammen and Paxson (2000), who use fixed-effects time-series modeling to illustrate that the U-shaped curve describes female participation over time in multi-country analysis, although the effect size does diminish compared to OLS regression models due to country-specific effects that are stable across time.

The Role of Female Education

Theories of the U-shaped curve rely implicitly or explicitly on a correlations between economic growth and female education. Literacy is a prerequisite for the upward slope of the U-shaped curve where women gain entry into modern, white-collar jobs that reward education. Boserup (1970) argues that access to schooling is crucial to women’s capacity to benefit from modernization and gain access to rewarding jobs. Thus, the connection between education and labor market participation is central to whether development contributes to economic freedom for women.

Education has not been the focus of previous studies of female participation. Pampel and Tanaka (1986) and Goldin (1995) both control for female education in regression models. However, these authors use current female enrollment levels as a proxy for the education level of women in the labor market. If female enrollments are growing quickly, current enrollment rates should vary significantly across generations. Mammen and Paxson (2000) are able to correct for this problem using cross-sectional household-level data. In India and Thailand, they find that a woman’s educational attainment is associated with the probability of white collar employment, suggesting that education may play a role in the upward portion of the U-shaped curve. Longitudinal and multi-country studies have yet to deal adequately with the effect of growth in female schooling over time.

The effects of female schooling are particularly important for policy-makers because promoting girls’ education is a central development strategy (T.P. Schultz, 2002). Female education is promoted as a policy to increase household productivity by reducing fertility and improving child health, as well as a strategy to build the labor force (Summers, 1994). For many countries, female education has changed dramatically during periods of economic growth (Hill & King, 1993). Female primary school enrollment rates in developing countries have increased from 52 percent thirty years ago, to 88 percent today.1 Growth in individual countries from all regions has been very impressive. To cite a few examples, Algeria increased girls’ secondary enrollment from

48

6 percent in 1970, to 73 percent in 2000; Brazil increased from 26 percent in 1970, to full enrollment in 2000; South Korea increased from 32 percent in 1970, to 94 percent in 2000.2 Importantly, increases such as these are often the result of explicit policies to increase girls’ education. For example, Mexico’s PROGRESA program, which provides stipends for school attendance, is credited with increasing girls’ primary completion by 15 percent; a scholarship program for girls in Bangladesh is credited with almost doubling female enrollment; and Indonesia has reached 90 percent enrollment for girls through a program with a focus on building new schools that meet the specific educational needs of girls (Herz & Sperling, 2004). Countries that have not invested in girls’ education have experienced limited growth. Girls’ secondary enrollment remains below 20 percent in Pakistan, Cambodia, and many sub-Saharan African countries.3

Theories of Female Education and Female Labor

From a human capital perspective, gains in female schooling should be related to labor market opportunities for women, because employment opportunities create an incentive for parents to invest in daughters (T.W. Schultz, 1960). Actual economic growth appears to be associated with expanded educational opportunities for girls, regardless of female labor force participation. Despite gains in access to schooling, gender segmentation and wage discrimination continue to limit women’s opportunities in many countries (Celle de Bowman, 2000; Tzannotos, 2003). If female schooling increases during economic growth but female labor force participation decreases or remains very low, an alternative to the human capital perspective is needed. Theories of education and development offer three explanations that are independent of labor market opportunities. First, education for both sons and daughters may be a normal good, with investments increasing as wealth increases, regardless of future returns to schooling (Goldin, 1995; Galor & Weil, 1996). Second, countries may invest broadly in education for girls and boys to maintain political stability and support democratic institutions (T. W. Schultz, 1960; Wise & Darling-Hammond, 1983). Third, home production, rather than labor market productivity, may be the target of female schooling in some countries.

Theory and empirical work support the third hypothesis. Case studies reveal that school curricula and instructional techniques often reinforce women’s domestic role and inferiority to men (Smock, 1981; Rose & Tembon, 1999; Wynd, 1999). At the same time, schooling promotes literacy, analytical thinking, knowledge of hygiene, and other basic skills that have been shown to improve home productivity (Jejeebhoy, 1995). Lam and Duryea (1999) argue that with initial levels of female schooling, families get greater benefits from the production of high-quality children than from women’s wages. With secondary schooling, women’s market wages exceed the value of home production, and women enter the labor market. This theory implies that the relationship between female schooling and labor force participation is also U-shaped. With initial increases in primary schooling, women in less-developed countries leave the labor market to specialize in home production. After a generation of girls accesses secondary schooling, women reenter the labor market when wages reward educational investments. Additional support for this approach comes from demographic research, which demonstrates that primary education for mothers can improve child health, but only secondary education creates labor market opportunities (Gille, 1985; Mason, 1993; Jejeebhoy, 1995). This theoretical approach differs from theories of the U-shaped curve by attributing decreases in participation to changes in women’s home productivity rather than structural changes in the labor market.

Lam and Duryea (1999) find support for this hypothesis using household-level data in Brazil, where women with primary schooling have dramatically lower birthrates than uneducated women, but do not participate in labor markets at high rates, despite increasing wages. With a secondary education, Brazilian women display both low fertility and high labor force participation. Behrman et al. (1999) found that education in

49

India increased for both males and females during the Green Revolution, a time when labor market returns to human capital increased significantly for men but not at all for women. These authors speculate that women’s home productivity in educating sons was valued to such an extent that female education became an important component of economic growth, despite women’s exclusion from labor markets. A similar study of human capital returns in Pakistan also found that women’s education can accrue as benefits to male relatives, even if women are excluded from the labor force (Fafchamps & Quisumbing, 1999).

If the primary function of female schooling is to increase the home productivity of women, expansion of female education may contribute to the downward portion of the U-shaped curve. Alternatively, schooling may also counteract the downward effects of economic growth if educated women reenter the labor market. As educational opportunities expand, the effects of female schooling may also be growing over time. Thus a complete model of the U-shaped curve integrates the effects of both schooling and growth.

Other Theoretical Influences on Female Participation

A final group of theories argues that social, cultural, and political factors influence female paticipation as much as economic factors. These theories focus on non-economic obstacles to female participation, which may vary with each country’s cultural and political institutions (Youssef, 1974; Semyonov, 1980; Horton, 1996). Both Islam and Catholicism are associated in theory with lower female labor force participation or delegation of women to specific employment sectors (Youssef, 1974; Smock, 1981; Nassar, 2003). Women’s economic freedom may also be associated with political rights. Political freedom is linked to economic rights and the ability to freely participate in labor markets (Sen, 1996).

METHODOLOGY

Data & Variables

Based on the theories outlined above, an integrated model of female participation should include the effects of economic growth, education, labor market structure, and cultural variables. Data were compiled from the World Bank’s World Development Indicators (WDI) (World Bank, 2002), United Nations Women’s Indicator Statistics (WISTAT) (UN, 1999), and the International Labour Organization’s (ILO) employment statistics (ILO, 2000). Full data are available for 141 developed and developing countries from 1970 to 2000. Because of the intervals at which the ILO published employment data, longitudinal observations are available for the years 1990, 1995, and 2000. This is not a sufficient time-frame to see an individual country transition from low- to high-income, but individual countries at different stages of development should experience the decreases and increases in female labor force participation described by theory.

Female labor force participation is measured using the ILO’s definition of economic participation. An individual is considered economically active if she is employed or seeking employment in the formal economy. This measure can be particularly problematic for women because they may not be counted as employed if they participate in family agricultural production or small family enterprises (Pampel & Tanaka, 1986; Heward, 1999). The shortcomings of ILO data are balanced by the benefit of the survey’s international coverage. This study uses ILO measures of female economic participation with the caveat that women’s labor force participation may be underreported, particularly in countries with a large informal sector.

50

Selecting the appropriate group of women for analysis is important. Women’s labor force participation is interrupted by pregnancy and child care, so reductions in participation are expected among women of childbearing age (Becker, 1975). These issues are discussed in detail by Goldin (1994), who identifies labor force participation of women after their childbearing years as the most appropriate measure of long-term change. Wherever possible, this study uses disaggregated female economic participation for ages 45 to 59 as the primary dependent variable. Longitudinal analysis uses overall female economic participation for ages 18 to 59, because there is not sufficient lagged data on the educational access of women in the older age group. Ideally, this data would be disaggregated to include only married women who are more likely to face a choice between domestic work and labor market participation. Disaggregated data by marital status are not available for most developing countries, so this study uses participation rates for all women.4

The first independent variable is economic growth. This study uses the natural log of GDP per capita to measure wealth. GDP per capita was taken from the WDI and is measured in constant U.S. dollars to facilitate interpretations of international comparisons across time.5 The second independent variable is female education. Based on theories of home productivity and labor market wages, female labor force participation is expected to decrease with primary schooling and increase with secondary schooling (Lam & Duryea, 1999). There is no way to identify increased secondary enrollment independent of increased primary enrollment, because access to secondary school is dependent on completion of primary school. This study uses gross female enrollment in secondary school as the single measure of female education.6 Importantly, it is the education of women currently in the workforce that should be related to female labor market prospects. With rapidly expanding female education, access for women who are currently working can be very different from current enrollment. Unlike previous studies, this study uses lagged female secondary enrollment to reflect educational access during the time when the target age group of women would have been in secondary school. For women aged 45 to 59 enrollment is lagged 30 years, and for women aged 18 to 59 enrollment is lagged 15 years.

Theories identify two structural changes in the labor market as the force behind the U-shaped curve. The first structural change is a rise in industrial labor. This is measured by the percent of the labor force employed in industrial jobs. The second change is a rise in white collar labor. This is measured by the percent of the labor force employed in the service sector. Both labor market variables are taken from the WDI, which uses the ILO’s definitions of employment sectors.

Measures of cultural obstacles to female participation include religion and political rights. Religious norms are expected to discourage female labor force participation most strongly in Islamic countries, where female participation in economic markets may be discouraged, and to a lesser extent in Catholic countries, where women’s domestic activity and high fertility are valued (Youssef, 1974; Smock, 1981; Pillai & Wang, 1999). The effect of religion is tested with two dummy variables indicating whether a country has a majority Islamic or Catholic citizens. These data are from the Adherents Organization, which documents international religious membership and participation (Adherents Organization, 2004). Data concerning political rights come from the Freedom in the World Survey for 2000 (Freedom House, 2004). Freedom House gathers data on political rights and civil liberties from multiple sources including news reports, non-governmental organizations, academic studies, and site visits. These data include a ranking of civil liberties on a scale from one to seven. Freedom House assigns a ranking of one to the “most-free” countries and seven to the “least-free” countries. To facilitate interpretation of regression coefficients, these rankings were reversed to assign higher values to countries with greater freedom. 7

51

Cross-Sectional Models

The first set of estimations uses ordinary least squares (OLS) regression to estimate the relationship between growth, education, and female economic participation using cross-sectional data from the year 2000. This strategy replicates Goldin’s methodological approach (Goldin, 1995). The first model tests female economic participation (FEP) as a function of GDP per capita and female gross enrollment rates (GER) in secondary school. The dependent variable is the economic participation rate of women ages 45 to 59. Secondary gross enrollment rates are lagged 30 years to reflect access to education for this group of women. A U-shaped relationship for both GDP per capita and female secondary enrollment is tested by including quadratic terms in the regression. The U-shape would be confirmed by a negative coefficient for the linear term and a positive coefficient for the quadratic term. The full model tests the effects of wealth and education, while controlling for labor market structure and cultural variables. Control variables include dummy variables for majority Islamic and Catholic, the index of civil liberties, and two measures of labor market structure.

FEP(45-59) = + 1 ln(GDP per capita) + 2 ln(GDP per capita)2 + 3 GER(t-30) (1) + 4 GER2

(t-30) +

FEP(45-59) = + 1 ln(GDP per capita) + 2 ln(GDP per capita)2 + 3 GER(t-30) (2) + 4 GER2

(t-30) + 5 industry + 6 service + 7 Islamic + 8 Catholic + 9 civil liberties +

Longitudinal Models

The cross-sectional models provide a snapshot of the relationship between variables. Longitudinal models provide insight into how female participation has changed over time. Longitudinal data are not available for a sufficient period to use the economic participation rates of older women and 30-year lagged education variables. Instead, female economic participation for ages 18 to 59 is the dependent variable, and education is lagged 15 years to reflect average educational access for this group of women. Religion variables are excluded from the longitudinal models because they do not vary across time. First, OLS models were run using pooled, longitudinal data. This replicates the strategy of Pampel and Tanaka (1986) with updated data. The reduced and full models are:

FEP = + 1 ln(GDP per capita) + 2 ln(GDP per capita)2 + 3 GER(t-15) (3) + 4 GER2

(t-15) +

FEP = + 1 ln(GDP per capita) + 2 ln(GDP per capita)2 + 3 GER(t-15) (4) + 4 GER2

(t-15) + 5 industry + 6 service + 7 civil liberties +

OLS estimation of pooled longitudinal data introduces potential sources of bias. First, including multiple observations from countries across time introduces bias from country-specific effects that do not vary across time. It is highly likely that unmeasured country-specific effects, such as cultural norms, religion, and attitudes towards women influence economic growth and female access to schooling, as well as female economic participation. The second problem is that pooled longitudinal data introduces autocorrelation if errors are correlated across years.

52

The first problem is solved by using a fixed-effects model, which controls for country specific effects by estimating:

yit = i + ’(xit) + it (5)

Where i identifies an individual country and i is the unobservable fixed effects for the ith county. The fixed-effects model (5) can be rewritten in terms of deviations from the group mean as:

yit - yi· = ’(xit – xi·) + it - i· (6)

Or in terms of the group mean as:

yi· = i + ’xi· + i· (7)

Where i· is the mean within i. Thus, the intercept in the fixed-effects model reflects the unobservable country-specific effects, while the coefficients measure the effect of changes in the group means across time. In this case, the fixed-effects models that mirror the OLS models in equations 3 and 4 are:

FEPi· = i + 1 ln(GDP per capita)i· + 2 ln(GDP per capita)2i·+ 3 GERi· (8)

+ 4 GER2i· + i

FEPi· = i + 1 ln(GDP per capita)i· + 2 ln(GDP per capita)2i·]+ 3 GERi· (9)

+ 4 GER2i· + 5 industryi·+ 6 servicei·+ 7 civil libertiesi·

+ i

To correct for autocorrelation, the fixed-effects model is estimated using a generalized least squares estimation. This estimation reduces bias by adjusting for correlated errors within groups with a maximum likelihood estimator (Greene, 1997). This approach replicates the method of Mammen & Paxson (2003), adding the effects of education and cultural influences.

RESULTS

Summary Statistics

Table 1 lists the 141 countries included in longitudinal analysis, and Table 2 lists means and standard errors for all variables. Countries are identified as low-, middle-, or high-income based on the World Bank’s categorizations in 2000, with income expressed in constant dollars based on the 1995 U.S. dollar. Low income countries have a mean GDP per capita of $404, middle income countries have a mean GDP per capita of $2,864,

53

TABLE 1 COUNTRIES IN LONGITUDINAL REGRESSION ANALYSIS

Sub Saharan Africa (n=39)

South America (n=11)

Europe(n=35)

East Asia (n=25)

Angola Argentina Albania Australia Benin Bolivia Austria Bangladesh* Botswana Brazil Azerbaijan* Brunei* Burkina Faso Chile Belgium Cambodia Burundi Colombia Bulgaria China Cameroon Ecuador Croatia* Fiji Cape Verde Guyana Czech Republic* Hong Kong Cen. African Rep. Paraguay Denmark India Chad Peru Estonia* Indonesia Comoros Uruguay Finland Japan D.R. Congo Venezuela France Republic of Korea Rep. of Congo Georgia* Lao PDR Cote D’Ivoire Middle East/ Greece Malaysia Ethiopia North Africa Hungary Maldives* The Gambia (n=15) Iceland Mongolia Ghana Algeria Ireland Nepal Guinea Bahrain Italy New Zealand Guinea-Bissau Cyprus* Kazakhstan* Pakistan Kenya Egypt Kyrgyz Republic* Papua New Guinea Lesotho Iran Luxembourg Philippines Liberia Israel FYR Macedonia* Singapore Madagascar Jordan Malta Solomon Islands* Malawi Kuwait Moldova* Sri Lanka Mali Lebanon Netherlands Thailand Mauritania Morocco Norway Vietnam* Mauritius Oman Poland Mozambique Saudi Arabia Portugal North America/ Niger Syria Romania Central America Nigeria Tunisia Russian Federation* (n=16) Rwanda United Arab Spain The Bahamas* Senegal Emirates Sweden Barbados Sierra Leone Switzerland Belize* Sudan Turkey Canada Swaziland United Kingdom Costa Rica Tanzania Uzbekistan* Dominican Republic* Togo El Salvador Uganda Guatemala Zambia Haiti* Zimbabwe Honduras Jamaica Mexico Nicaragua

Panama Trinidad and Tobago

United States* * Countries are excluded from OLS regression Models 1 and 2 due to missing secondary school enrollment data for 1970.

54

TABLE 2 VARIABLE MEANS BY NATIONAL INCOME (STANDARD ERRORS IN PARENTHESES)

Variable Low Income n=53

Middle Income n=54

High Income n=34

Female economic participation (% of total female population)

ages 45-59 (WISTAT) 69.1 (1.81) 45.1 (2.44) 53.8 (3.41)

all women (WDI) 40.3 (1.05) 31.7 (1.53) 39.1 (1.53)

Female secondary school enrollment (% of school-aged population)

1970 6.4 (1.89) 28.1 (2.71) 63.5 (3.70)

1985 24.4 (3.97) 53.3 (3.44) 88.4 (2.45)

2000 29.5 (3.09) 74.3 (2.41) 108.3 (4.43)

Labor market structure (% of total employment)

% agriculture 62.3 (3.05) 19.7 (1.78) 4.93 (0.67)

% industry 11.1 (1.12) 26.6 (0.94) 27.9 (1.54)

% service 24.9 (2.08) 53.0 (1.82) 66.9 (1.62)

Wealth (US $) GDP per capita $404 (28) $2,864 (240) $24,840 (1968) Political Freedom civil liberties 3.4 (0.15) 4.5 (0.20) 6.0 (0.26)

Sources: World Bank (2002), ILO (2000), UN (1999), Freedom House (2004)

and high-income countries have a mean GDP per capita of $24,840. Mean values for female economic participation variables support the hypothesis of the U-shaped relationship between wealth and female economic participation. For women ages 45 to 59, low-income countries have a mean economic participation rate of 69.1 percent, middle-income countries have a mean rate of 45.1 percent, and high-income countries have a mean rate of 53.8 percent. For women ages 18 to 59 the effect remains but is less pronounced.

The mean values of female secondary enrollment indicate that female access to education is much greater in wealthier countries. In 1970, female enrollment in secondary school averaged only 6.4 percent in low-income countries. Educational access improved to 24.4 percent in 1985, and 29.5 in 2000, but this remains low compared to wealthier countries. Female secondary enrollment in middle income countries has increased more substantially from a mean of 28.1 percent in 1970, to 53.3

55

percent in 1985, and 72.9 percent in 2000. Secondary enrollment in upper-income countries increased from a mean of 63.5 percent in 1970, to 88.4 percent in 1985, and to full enrollment by 2000.

Table 3 displays variable means by the level of female education. The first group includes countries with less than 50 percent female gross enrollment in secondary school, the second group includes countries with 50 to 80 percent enrollment, and the third group includes countries with greater than 80 percent enrollment. For women ages 45 to 59, countries with low female enrollment have a mean female economic participation rate of 66.7 percent. Mean participation falls to 42.7 percent in countries in the middle, and increases to 56.6 percent in countries high female schooling. A similar but less dramatic effect occurs for women ages 18 to 59. Countries with high female secondary enrollment in 2000 have also experienced the most dramatic growth in female enrollments since 1970.

TABLE 3 VARIABLE MEANS BY GIRLS’ SECONDARY SCHOOL ENROLLMENT (STANDARD ERRORS IN PARENTHESES)

Variable < 50% n=50

50% to 80% n=38

> 80% n=53

Female economic participation (% of total female population)

ages 45-59 (WISTAT) 66.7 (2.35) 42.7 (3.00) 56.6 (2.48) all women (WDI) 38.4 (1.26) 30.8 (1.82) 39.4 (1.22) Female secondary school enrollment (gross %)

1970 5.4 (0.68) 27.0 (3.22) 58.3 (3.54) 1985 15.3 (1.73) 51.0 (3.57) 84.4 (2.79) 2000 23.2 (1.81) 69.1 (1.33) 103.2 (2.95) Labor market structure (% of total employment)

% agriculture 60.6 (3.57) 23.2 (2.89) 11.7 (1.75) % industry 12.4 (1.35) 23.9 (1.19) 27.3 (1.28) % service 26.5 (2.34) 51.3 (2.84) 60.1 (1.91) Wealth (US $) GDP per capita $522 (73) $4,276 (1022) $15,700 (1970) political freedom civil liberties 3.4 (0.16) 4.2 (0.24) 5.7 (0.21)

Sources: World Bank (2002), ILO (2000), UN (1999), Freedom House (2004)

Time Series Graphs

The time-series graphs in Figure 1 illustrate the changing relationship between female labor force participation and economic growth over time. The fitted regression lines reflect an estimated quadratic relationship from a simple reduced-form OLS regression for each year. In 1970 and 1980, the U-shaped curve is rather pronounced. By 1990 and 2000 the U-shaped begins to weaken, and by 2000, appears to be flattening out. Figure 2 illustrates a similar change taking place in the relationship between female

56

labor force participation and female education. Figures 1 and 2 provide initial evidence that the cross-sectional relationship between growth, education, and female labor force participation is changing over time and does not reflect a stable longitudinal path. The disintegration of the U-shaped curves is an initial indication that there is not one constant path through which development influences female economic participation over time.

FIGURE 1 FEMALE ECONOMIC PARTICIPATION AND NATIONAL WEALTH

Sources: World Bank (2002); International Labor Organization (2000)

Cross-Sectional Regression Results

The cross-sectional regression models were tested using data from the year 2000. The results are displayed in Table 4. The dependent variable is the rate of economic participation of women ages 45 to 59. Because lagged school enrollment data are not available for all countries, the cross-sectional regressions include a subset of 119 countries (see Table 1). The results for Model 1 confirm the U-shaped relationship for GDP per capita, as in Goldin (1995), and an independent U-shaped relationship for secondary school enrollment. The coefficients on both linear terms are negative and significant, and coefficients on both quadratic terms are positive and significant. Model 2 adds controls for labor market structure, religion, and political freedom. In the full

57

model, the linear coefficient for GDP per capita is -19.557 (p<0.05), and the quadratic coefficient is 0.989 (not significant). The linear coefficient for school enrollment is -0.500 (p<0.05), and the quadratic coefficient is 0.008 (p<0.01). The relative size of the large linear coefficients compared with the small quadratic coefficients suggests that both wealth and education must reach high levels before the U-shaped curve begins to slope upwards. As predicted by theory, the percent of employment in the industrial sector has a negative effect on female economic participation (p<0.10). Contrary to theoretical predictions, the coefficient on the percent of employment in the service sector is also negative, but this effect is not significant.

The variables reflecting social and cultural context are all significant. The excluded group in Model 2 is countries that are neither majority Islamic nor majority Catholic. Negative coefficients on the religion dummy variables indicate that female labor force participation is approximately 9.1 percent lower in Islamic countries (p<0.05) and 8.6 percent lower in Catholic countries (p<0.01) compared with countries with other or no majority religion. An increase in civil liberties by one point on the Freedom House scale is associated with an increase of approximately 3.5 percent in female economic participation (p<0.01).

Based on cross-sectional analysis, female economic participation appears to decrease with increases in both income and female schooling, and to increase slightly when income and female schooling reach higher levels. The significance of female schooling variables suggests that education has as independent effect on women’s economic participation, and it is not simply the correlation between wealth and education that influences the home productivity effect described by Lam and Duryea (1999). Expansion of industry is associated with lower female employment, but the expansion of the service sector is not associated with higher female employment. What appears to be more important is the cultural context. Catholic or Islamic norms appear to lower female labor force participation. It is notable that the addition of these control variables has a greater influence on the coefficients on wealth variables than the coefficients on female school enrollment variables. Youssef (1974) argues that religious obstacles to women’s participation in economic markets are more difficult to overcome than religious obstacles to girls’ education. Finally, these regressions confirm that political freedom is positively associated with women’s access to economic markets through employment.

58

TABLE 4 EFFECTS OF WEALTH & EDUCATION ON FEMALE ECONOMIC PARTICIPATION

CROSS-SECTIONAL REGRESSIONS

Model 1 Model 2 coeff. t-ratio coeff. t-ratio

Constant 208.417 5.63*** 147.752 4.17*** Wealth log (GDP per capita) -34.824 -3.49*** -19.557 -2.01** [log (GDP per capita)]2 1.824 2.89*** 0.989 1.65 Female Education secondary enrollment (t-30) -0.494 -2.01** -0.500 -2.06** (secondary enrollment)2

(t-30) 0.009 3.45*** 0.008 2.99*** Labor Market Structure % industrial sector -0.314 -1.76* % service sector -0.146 -1.38 Religion majority Islamic -9.102 -2.18**

majority Catholic -8.635 -2.34***

Political Freedom civil liberties 3.519 2.83*** R-squared 0.423 0.556 Adjusted R-squared 0.402 0.519 No. of Observations 119 119

*p<0.10, **p<0.05, ***p<0.01

Longitudinal Regression Results

Correctly specified longitudinal models can help determine if this cross-sectional relationship also describes a development path over time. For the longitudinal estimations, the dependent variable is female economic participation for ages 18 to 59, with secondary school enrollment lagged 15 years. The data set includes 141 countries, listed in Table 1, with a mean of 2.8 observations per country. Tables 5 and 6 display the results for the cross-sectional, pooled longitudinal OLS, and fixed-effects models. Two statistical tests were conducted to determine whether pooled longitudinal OLS or fixed-effect estimation is more appropriate, with results displayed in Tables 5 and 6. First, the Woolridge test of autocorrelation in panel data was performed on the pooled longitudinal OLS models (Woolridge, 2002; Drukker, 2003). This test generates an F-statistic for the null hypothesis that there is no autocorrelation within countries across years. The second procedure is performed on the fixed-effect models and produces an F-statistic for the null hypothesis that fixed effects are equal to zero. For both the reduced and full models, the F-statistics are not significant, leading to rejection of the null hypotheses that autocorrelation and fixed effects are zero. These results suggest that the autocorrelation

59

and country-specific effects bias OLS estimation, and fixed-effects estimators are appropriate for this data. The existence of significant country-specific effects is also supported by the significance of religion variables in the cross-sectional OLS models above.

Results for the reduced-form model are displayed in Table 5. First, the cross-sectional models are replicated using economic participation of women ages 18 to 59 as the dependent variable. These results closely resemble the results for women ages 45 to 59. The U-shaped relationship between GDP per capita and female economic participation is confirmed, as well as the U-shaped relationship between secondary enrollment and female economic participation. Next, the OLS model is replicated with pooled data from 1990, 1995, and 2000. These results also support U-shaped relationships for both GDP per capita and female secondary enrollment. When the longitudinal model is estimated as a fixed-effects model, the reduced-form estimation confirms the predicted U-shaped relationship between GDP per capita and female economic participation. The size of the coefficients on GDP per capita varies across the three models. The coefficient on the linear term is -12.525 (p<0.05) in the cross-sectional model, and rises to -18.696 (p<0.01) in the pooled OLS model, which is similar to the results of Pampel and Tanaka’s pooled OLS estimations (Pampel & Tanaka, 1986). The linear coefficient on GDP per capita falls to only -5.009 (p<0.01) in the fixed-effects model. Similar to Mammen and Paxson (2000), the fixed-effects model appears to correct for overestimation of the negative effect of economic growth in OLS estimations.

More importantly, the U-shaped relationship between female secondary enrollment and female economic participation disappears in the fixed-effects models. In the reduced form cross-sectional model the linear coefficient on secondary enrollment is -0.327 (p<0.01), falling to -0.252 (p<0.01) in the pooled longitudinal OLS model. The cross-sectional and longitudinal OLS models also have small, positive quadratic coefficients of 0.004 (p<0.01) and 0.003 (p<0.01), respectively. In the fixed effects model, the direction of the coefficients is reversed. The linear coefficient is 0.146 (p<0.01), and the quadratic term is -0.0004 (p<0.05). This suggests that the true effect of female education on participation is positive, with small diminishing returns. This relationship remains in the fixed-effects estimation of the full model that controls for labor market structure and civil liberties. These results indicate that the negative relationship between female schooling and female economic participation in OLS models is due to bias in the estimation and does not reflect the true effect within countries over time. Instead, female schooling appears to have a positive effect on female labor force participation.

The fixed-effects estimation of the full model, displayed in Table 6, also differs from the OLS models in the effects of labor market structure. In the OLS models, both industrial employment and service employment have negative coefficients, although only the coefficient on service employment is statistically significant. This runs contrary to theories of the U-shaped curve that associate industry with decreased female employment and growth of the service sector with increased female employment. In the fixed-effects model, the results for labor market structure partially support the predictions of theory. Industrial employment is not a significant predictor of participation. The coefficient on service employment is -0.180 (p<0.01) in the longitudinal OLS model, and 0.083 (p<0.01) in the fixed-effects model. Thus the fixed-effects results support the assertion that service employment increases female participation, but not the assertion that industrial labor reduces participation.

60

TABLE 5 EFFECT OF WEALTH & EDUCATION ON FEMALE ECONOMIC PARTICIPATION

CROSS-SECTIONAL AND LONGITUDINAL REGRESSIONS

OLScross-sectional

OLSlongitudinal Fixed-Effects

coeff. t-ratio coeff. t-ratio coeff. t-ratio Constant 90.353 4.57*** 111.231 9.23*** 33.237 4.94*** Wealth ln (GDP per capita) -12.525 -2.32** -18.696 -5.37*** -5.009 -2.36*** ln (GDP per capita)2 0.727 2.18** 1.096 5.37*** 0.578 4.11*** Female Education secondary enrollment -0.327 -2.88*** -0.252 -3.77*** 0.146 6.35*** secondary enrollment2 0.004 3.94*** 0.003 5.90*** -0.0004 -2.29**

R-squared+ 0.271 0.297 0.478 Adjusted R-Squared 0.249 0.289 n/a Woolridge Test of autocorrelation

F = 128.84Prob>F = 0.00

F-Test of Ho: No fixed effects

F = 159.09Prob>F = 0.00

No. of Observations++ 141 389 389

No. of Countries 141 141 141 *p<0.10, **p<0.05, ***p<0.01 + R-squared for fixed-effects models is within country variation. ++ Mean observations per country is 2.8.

61

TABLE 6 EFFECT OF WEALTH & EDUCATION ON FEMALE ECONOMIC PARTICIPATION

CROSS-SECTIONAL AND LONGITUDINAL REGRESSION FULL MODEL

OLScross-sectional

OLSlongitudinal Fixed-Effects

coeff. t-ratio coeff. t-ratio coeff. t-ratio Constant 77.43 3.98*** 94.268 7.96*** 29.362 4.28*** Wealth ln (GDP per capita) -9.974 -1.88* -14.654 -4.53*** -3.848 -2.04** ln (GDP per capita)2 0.589 1.81* 0.890 4.49*** 0.463 3.28*** Female Education secondary enrollment -0.228 -2.00** -0.166 -2.53** 0.148 6.46*** secondary enrollment2 0.003 3.04*** 0.003 4.61*** -0.0006 -2.85*** Labor Market Structure

% industrial sector -0.075 -0.86 -0.071 -1.31 -0.042 -1.02 % service sector -0.142 -2.48** -0.180 -5.11*** 0.083 2.63*** Political Rights civil liberties 1.832 3.19*** 1.624 4.81*** -0.102 -0.85 R-squared 0.350 0.382 0.509 Adjusted R-squared 0.316 0.371 n/a Woolridge Test autocorrelation

252.23 Prob>F = 0.00

F-Test of Ho: No fixed effects

146.54 Prob>F = 0.00

No. of Observations++ 141 389 389 No. of Countries 141 141 141

*p<0.10, **p<0.05, ***p<0.01 + R-squared for fixed-effects models is within country variation ++ Mean observations per country is 2.8

The coefficients on civil liberties are positive and significant in the OLS models and negative and not significant in the fixed-effects model. The positive effect of civil liberties on female economic participation may be a component of country-specific effects. The civil liberties index does not directly measure access to economic markets as much as political access through free press, transparent government, and civil society. It is likely that women’s access to economic markets is correlated with a political ideology that promotes civil liberties, but an increase in the civil liberties index does not necessary mean that labor markets are more open to women.

To put these results in perspective, Table 7 displays the estimated effects of economic growth and increases in girls’ education on three fictional countries. The first simulation takes on the mean values for all low-income countries. The second simulation takes on the mean values for sub-Saharan Africa, where girls’ education remains very low. The third simulation takes on the mean values for a middle-income country. Table 7 displays the estimated level of female participation from the cross-sectional OLS,

62

pooled longitudinal OLS, and fixed-effects models. The effect of a 10 percent increase in GDP per capita is displayed, followed by the effect of an increase of 20 percentage points in female secondary enrollment. The net effect is the predicted change in the female participation rate due to combined gains in GDP per capita and female enrollment, expressed in percentage points. For example, if a country begins with 30 percent female participation and the net effect is -5.0, this would mean participation is predicted to fall to 25 percent as a result of increases in GDP per capita and female enrollment.

The comparison illustrates the problem of misspecification in the OLS models. In the low-income example, the simulated country begins with a GDP per capita of $404 and female secondary enrollment at 24.4%. An increase in GDP per capita to $444 results in a predicted decrease in female participation of 0.4 percentage points in the cross-sectional OLS model and 0.5 percentage points in the longitudinal OLS model. The fixed-effects model predicts that female participation will increase by 0.2 percentage points. The difference between models is more dramatic if economic growth is accompanied by an increase in girls’ education. When female secondary rates are increased to 44.4%, female participation falls by 1.0 percentage point in the cross-sectional OLS model and 0.9 percentage points in the longitudinal OLS model. The fixed-effects model predicts that female participation will increase by 2.4 percentage points. Thus, the net effect of both changes is a 1.4 percentage point drop in female participation in the OLS models, and a 2.6 percentage point increase in the fixed-effects models.

A similar difference occurs in the simulation of a sub-Saharan African country. This country begins with a GDP per capita of $604 and female enrollment of only 14.8 percent. An increase in GDP per capita to $664 results in a predicted 0.3 percentage point decrease in participation in the cross-sectional OLS model, and a 0.5 percentage point decrease in the longitudinal model. The fixed-effects model predicts a 0.3 percentage point increase in participation. The simulated increase in enrollment up to 34.8 percent results in predicted decreases of 2.6 percentage points in the cross-sectional OLS model, and a 2.0 percentage points in the longitudinal OLS model. In the fixed-effects model the predicted effect is an increase of 2.5 percentage points. Like the simulated low-income country, the predicted net effects of GDP growth and female education in a sub-Saharan African country are negative in both OLS models and positive in the fixed-effects model.

The third simulation for a middle-income country has less variation across models. This country begins with a GDP per capita of $2,864 and female enrollment of 53.3 percent. The OLS models predict an increase in GDP per capita will result in a 0.1 percentage point decrease in participation. The fixed effects model predicts a 0.4 percentage point increase. The effects of education are positive in all three models, and the net effects are positive for all three models. Thus, misspecification of the U-shaped curve appears to be a bigger problem for low-income countries and countries with low female education. The fixed-effects model predicts that female participation in these countries will grow as a result of both economic growth and female education. This evidence runs contrary to previous estimations of the U-shaped curve using OLS methods.

63

TABLE 7 SIMULATIONS OF FEMALE ECONOMIC PARTICIPATION

Low-income Country GDP per capita: $404 Lagged female GER: 24.4%

OLScross-sectional

OLSlongitudinal

Fixed-Effects

Estimated FEP 35.8 34.1 27.3 GDP per cap. increases to $454+ -0.4 -0.5 +0.2 Female GER increases to 44.4%+ -1.0 -0.9 +2.4 Net Effect+ -1.4 -1.4 +2.6

Sub-Saharan African Country GDP per capita: $604 Lagged female GER: 14.8%

OLScross-sectional

OLSlongitudinal

Fixed-Effects

Estimated FEP 36.0 33.4 26.9 GDP per cap. increases to $664+ -0.3 -0.5 +0.3 Female GER increases to 34.8%+ -2.6 -2.0 +2.5 Net Effect+ -2.9 -2.5 +2.8

Middle-income Country GDP per capita: $2,864 Lagged female GER: 53.3%

OLScross-sectional

OLSlongitudinal Fixed-Effects

Estimated FEP 30.7 26.9 36.6 GDP per cap. increases to $3,151+ -0.1 -0.1 +0.4 Female GER increases to 73.3%+ +3.6 +2.6 +2.9 Net Effect+ +3.5 +2.5 +3.3 + Changes are expressed as percentage point gains or losses.

64

DISCUSSION

From previous cross-sectional research, we might expect female workforce participation to fall during initial periods of economic growth. The cross-sectional results in this study support this expectation, as well as the expectation that women may leave the labor force as a result of early increases in access to schooling. Cross-sectional analysis can lead to the misleading conclusion that early stages of economic development lead women to focus on home production, while men specialize in industrial labor market production. This is close to the expectation of Becker’s (1975; 1985) human capital approach, which predicts that an efficient household division of labor is one where men specialize in production in the labor market, while women specialize in home production.

However, longitudinal analysis with fixed-effects models suggests that while a U-shaped female labor force participation curve exists in a snapshot, developing countries do not necessarily experience a decline in female labor force participation as national incomes increase. One reason is that most previous models do not capture the effects of country-specific characteristics that determine how women participate in labor markets. A second reason in that economic growth is often accompanied by planned expansions in girls’ education. Although the U-shaped relationship between economic growth and female economic participation remains in the fixed-effects models, the magnitude of the decline in female economic participation as incomes rise is much smaller. At the same time, female access to schooling has only positive effects on female economic participation without an initial period where women leave the labor market to focus on domestic production. This means that investments in girls’ education benefit development in terms of both home productivity and workforce development. Division of benefits between the home and the workforce may depend on country-specific variables such as religion, social norms, family structure, and attitudes towards women’s work. Countries where women have historically specialized in domestic production may see more educated women out of the labor market compared to other countries, but this does not mean that increases in schooling will result in fewer women in the labor market overall. With the addition of investments in female education, many countries may see little or no drop in female economic participation as wealth increases.

The results of this study also provide new insight into the relationship between labor market structure and female labor participation. Industrial employment does not have a statistically significant negative effect on female economic participation in any of the longitudinal models. This conflicts with the assumption that industrial employment is too physically demanding for women or is stigmatized in most societies. Labor researchers observe that women do play an integral role in industrial employment and export production in developing countries, particularly in fast-growing regions like Central America, where female industrial labor is fueling economic growth (Standing, 1989; Raynolds, 1998). The second labor market shift to white collar service jobs does bring women into the labor market, as predicted by Sinha (1967), Boserup (1970), and Goldin (1995).

CONCLUSION

Historically, men and women have not had equal access to education. A positive result of economic growth has been increased access to schooling for girls, with complex implications for women, families, and markets. Researchers have had difficulty empirically linking educational investments in men with labor market productivity and economic growth (Pritchett, 1996; Griliches, 1997; Gundlach, 2001). The issue is even more complicated for women, as they divide their time between childcare and work throughout their lives. In addition, women’s access to labor markets can be limited by cultural constraints, which further complicate expectations about changing labor market

65

behavior during periods of growth. Historically, girls’ education has been promoted as a policy that improves home productivity. The results of this study suggest that girls’ education will also contribute to labor market productivity.

Theories of economic growth have recognized the importance of the initial endowment of physical capital and human capital in determining the rate of economic growth (Barro, 1991; Mankiw, Romer & Weil, 1992). The longitudinal results here indicate that both initial endowments of human capital for women and the initial level of female access to labor markets may be relevant in how growth influences women’s contributions to the labor market. Endowment effects concerning female education may be a function of many factors including labor market structure, cultural variables, and family economics.

The response of women to increases in wealth and education is an important factor in public policy for developing countries. Particularly, it is important to understand how female education has external benefits for families – such as reduced fertility and improved child health – and labor markets – through expansion of the skilled workforce. The results of this study suggest that initial economic growth is accompanied by a decline in female economic participation. However, longitudinal models that correct for country-specific effects and autocorrelation suggest that this decline is not as big as previously thought. In addition, longitudinal analysis shows that female schooling can increase female economic participation. These results suggest that countries with different initial levels of female schooling and female labor force participation will travel different paths to development. The U-shaped curve of female labor force participation may disappear over time as more and more countries take a non-traditional path.

ENDNOTES

* The author thanks Gary Painter, Elizabeth Graddy, Dolores Conway, and anonymous reviewers for many helpful comments, as well as participants in the 2005 conference of the Institute for Women’s Policy Research in Washington, D.C. 1 Data from www.worldbank.org. 2 Data from the World Development Indicators. 3 Data from the World Development Indicators. 4 There is some evidence that measures of marital status may be misleading regarding domestic arrangements. In regions such as Latin America cohabitation of non-married partners is common (Juhn & Ureta, 2003). 5 Another option is to use purchasing power parity (PPP) per capita, which measures the value of wealth in relation to product prices. PPP is more problematic than GDP in multi-country analysis due to variations in product consumption across countries. The estimations in this study were replicated using PPP per capita in place of GDP per capita. The direction and significance were the same, and there was no effect on the influence of other variables. PPP per capita resulted in larger coefficients on wealth variables all around. 6 Pampel & Tanaka (1986) argue that the ratio of females to males in secondary is the correct measure for estimation because it measures female skills relative to males. However, high female enrollment compared to males can result from civil wars and other domestic problems that take boys out of school, so absolute female enrollment is preferred for this study. 7 This strategy is also used by McMahon (1999) who tests the influence of education on social outcomes, including the spread of democracy.

66

REFERENCES

Adherents Organization, Majority Religions, http://www.adherents.com, 2004 Barro, R. J., “Economic growth in a cross section of countries”, The Quarterly

Journal of Economics, 1991, Vol. 106, pp. 407-443. Becker, G., Human Capital, 1975, National Bureau of Economic Research. Becker, G., “Human capital, effort, and the sexual division of labor”, Journal of

Labor Economics, 1985, Vol. 3, pp. S33-S58. Behrman, J. R., Foster, A. D., Rosenzweig, M. R., & Vashishtha, P, “Women's

schooling, home teaching, and economic growth”, The Journal of Political Economy,1991, Vol. 107, pp. 682-714.

Boserup, E., Women's Role in Economic Development, 1970, New York: St. Martin's Press.

Celle de Bowman, O., “Peruvian female industrialists and the globalization project: Deindustrialization and women's independence”, Gender and Society, 2000, Vol. 14, pp. 540-559.

Drukker, D.M., “Testing for serial correlation in linear panel-data models”, Stata Journal, Vol. 3, pp. 168-177.

Durand, J.D., The Labor Force in Economic Development, 1975, Princeton University Press.

Fafchamps, M., & Quisumbing, A. R., “Human capital, productivity, and labor allocation in rural Pakistan”, The Journal of Human Resources, 1999, Vol. 34, pp. 369-406.

Freedom House, Freedom in the World, http://www.freedomhouse.org, 2004. Galor, O., & Weil, D. N., “The gender gap, fertility, and growth”, The American

Economic Review, Vol. 86, pp. 374-387. Gille, H., “Policy Implications” in J. Cleland & J. Hobcraft (eds.), Reproductive

Change in Developing Countries: Evidence from the World Fertility Survey, 1985, Oxford University Press.

Goldin, C., “The U-shaped Female Labor Force Function in Economic Development and Economic History”, NBER Working Papers (No. 4707), 1994, National Bureau of Economic Research.

Goldin, C. “The U-shaped Female Labor Force Function in Economic Development and Economic History” in T. P. Schultz (ed.), Investment in Women's Human Capital, 1995, University of Chicago Press.

Greene, W. H., Econometric Analysis (3rd ed.), 1997, Prentice-Hall, Inc. Griliches, Z., “Education, human capital, and growth: A personal perspective”,

Journal of Labor Economics, 1997, Vol. 15, pp. S330-334. Gundlach, E., “Education and economic development: An empirical

perspective”, Journal of Economic Development, 2001, Vol. 26, pp. 37-59. Herz, B., & Sperling, G.B., “What Works in Girls’ Education: Evidence from

the Developing World”, 2004, Council on Foreign Relations.Heward, C. “Closing the Gender Gap? The Informal Sector in Pakistan” in C.

Heward & S. Bunwaree (eds.), Gender, Education & Development, 1999, Zed Books Ltd., pp. 203-217.

Hill, M. A., & King, E. M., “Women's Education in Developing Countries: An Overview” in E. M. King & M. A. Hill (eds.), Women's Education in Developing Countries, 1999, The Johns Hopkins University Press.

Horton, S. “Women and Industrialization in Asia: Overview,” in S. Horton (ed.) Women and Industrialization in Asia, 1999, Routledge, pp. 1-42.

International Labour Organisation, Yearbook of Labour Statistics, 2000, International Labour Office.

Jejeebhoy, S. R., Women's Education, Autonomy, and Reproductive Behavior: Experience from Developing Countries, 1995, Clarendon Press.

67

Juhn, C. & Ureta, M, “Employment of Married Women and Economic Development: Evidence from Latin American Countries”, paper presented to the Society of Labor Economists Meeting, Toronto, 2003.

Lam, D., & Duryea, S., “Effects of schooling on fertility, labor supply, and investments in children, with evidence from Brazil”, The Journal of Human Resources,1999, Vol. 34, pp. 160-192.

Mammen, K. & Paxson, C., “Women’s work and economic development”, The Journal of Economic Perspectives, 2000, Vol. 14, pp. 141-164.

Mankiw, N. G., Romer, D., & Weil, D. N., “A contribution to the empirics of economic growth”, The Quarterly Journal of Economics, 1992, Vol. 107, 407-437.

Mason, K. O., “The Impact of Women's Position on Demographic Change During the Course of Development” in N. Federici, K. O. Mason & S. Sogner (eds.) Women's Position and Demographic Change, 1992, Clarendon Press. McMahon, W. , Education and Development: Measuring the Social Benefits, 1999,

Oxford University Press. Nassar, H. “Egypt: Structural Adjustment and Women's Employment” in E. A.

Doumato & M. P. Posusney (eds.), Women and Globalization in the Arab Middle East,2003, Lynne Rienner Publishers, Inc.

Pampel, F. C., & Tanaka, K., “Economic development and female labor force participation: A reconsideration”, Social Forces, 1986, Vol. 64(3), pp. 599-619.

Pillai, V. K., & Wang, G.Z., Women's Reproductive Rights in Developing Countries, 1999, Ashgate Publishing Company.

Pritchett, L. “Where Has All the Education Gone?”, World Bank Policy Research Working Papers (No. 1581), 1996, The Work Bank.

Raynolds, L. T., “Harnessing women's work: Restructuring agricultural and industrial labor forces in the Dominican Republic”, Economic Geography, 1998, Vol. 74, pp. 149-169.

Rose, P., & Tembon, M., “Girls and Schooling in Ethiopia” in C. Heward & S. Bunwaree (eds.), Gender, Education & Development, 1999, Zed Books Ltd., pp. 85-100.

Schultz, T. W. , “Capital formation by education”, The Journal of Political Economy, 1960, Vol. 68, pp. 571-583.

Schultz, T. P., “Why governments should invest more to educate girls”, WorldDevelopment, 2002, Vol. 30, pp. 207-225.

Semyonov, M., “The social context of women's labor force participation: A comparative analysis”, The American Journal of Sociology, 1980, Vol. 86, pp. 534-550.

Sen, G., Gender, “Markets and states: A selective review and research agenda”, World Development, 1996, Vol. 24, pp. 821-829.

Sinha, J.N., “Dynamics of Female Participation in Economic Activity”, Proceedings of the World Population Conference, 1967, Belgrade, Vol. 4, pp. 336-337.

Smock, A. C., Women's Education in Developing Countries: Opportunities and Outcomes, 1981, Praeger Publishers.

Standing, G., “Global feminization through flexible labor”, World Development,1989, Vol. 17, pp. 1077-1095.

Summers, L., “Investing in All the People: Educating Women in Developing Countries”, EDI Working Papers (No. 45), 1994, The World Bank.

Tzannatos, Z., & Kaur, I., “Women in the MENA Labor Market” in E. A. Doumato & M. P. Posusney (eds.), Women and Globalization in the Arab Middle East,2003, Lynne Rienner Publishers, Inc.

United Nations, WISTAT: Women's Indicators Database, 1999, United Nations Publications.

Wise, A. E. and Darling-Hammond, L., “Educational vouchers: Regulating their efficiency and effectiveness, Educational Researcher, 1983, Vol. 12, pp. 9-12 & 18.

68

Woolridge, J.M., Econometric Analysis of Cross Section and Panel Data,2002, MIT Press.

World Bank, World Development Indicators, 2002, World Bank. Wynd, S. , “Education, Schooling, and Fertility in Niger” in C. Heward & S.

Bunwaree (eds.), Gender, Education & Development, 1999, Zed Books Ltd., pp. 101-116.

Youssef, N. H., Women and Work in Developing Societies, 1974, Greenwood Press.