Chapter 5. Empowering Women Can Diversify the Economy

Kalpana Kochhar, Sonali Jain-Chandra, and Monique Newiak
Published Date:
February 2017
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Romina Kazandjian, Lisa Kolovich, Kalpana Kochhar and Monique Newiak 

Gender inequality decreases the variety of goods countries produce and export, particularly for low-income and developing economies. This happens through at least two channels. First, gender gaps in opportunity, such as lower educational enrollment rates for girls than for boys, harm diversification by constraining the potential pool of human capital available in an economy. Second, gender gaps in the labor market impede the development of new ideas by decreasing the efficiency of the labor force. Our empirical estimates provide evidence that gender-friendly policies could help countries diversify their economies.


The sharp decline in commodity prices in 2014–15 is a powerful reminder for countries—especially those rich in resources—of the need to diversify their output and export bases. The drop in oil prices since the end of 2014 and in other commodity prices thereafter has put substantial pressure on many resource-intensive countries, with marked declines in export and fiscal revenues that have slowed growth and created a need for significant macroeconomic adjustment in many of these countries (IMF 2016). Oil prices have increased somewhat from their low of under $30 a barrel in early 2016, but they remain significantly lower than at their peak in 2013. Prices for other commodities are expected to remain at only a fraction of their high levels in the medium term. As a result, reforms to stimulate product and export diversification have gained renewed importance on policymakers’ agendas, particularly in resource-intensive economies.

A long-held tenet of international trade, Ricardo’s theory of comparative advantage, proposes that countries should specialize in the production of goods and services with lower relative opportunity costs. Historically, many low-income countries have relied on relatively few trading partners and specialized in commodity and primary products, mainly due to their resource endowments, as might be predicted by the Heckscher-Ohlin model. Yet, a lack of diversification is associated with both lower economic growth and higher volatility.

It is now well established in the literature that diversification and structural transformation—continued dynamic reallocation of resources to more productive sectors and activities—are associated with economic growth, especially at the early stages of development (Papageorgiou and Spatafora 2012).1Figure 5.1, panel 1, shows that greater export product diversification is associated with higher economic growth, although the relationship is heterogenous. Panel 2 highlights the positive association between diversification—that is, more variety in exports—and less volatile growth, particularly at lower levels of development. (Annex 5.1 describes in detail the measures of diversification used in this chapter.)

Figure 5.1.The Benefits of Diversification

Source: IMF 2014a.

A well-educated workforce is a key driver of diversification and structural transformation (Elborgh-Woytek and others 2013; IMF 2014a). Human capital accumulation can foster economic diversification by promoting the development of skill-intensive industries and new technologies and by facilitating technological diffusion between firms (Bal-Gunduz, Dabla-Norris, and Intal forthcoming). Whereas primary and secondary education can enable a country to imitate frontier technology, tertiary education can increase its possibility of innovating (Aghion and Howitt 2006). IMF (2014a) finds that human capital accumulation is not only a determinant of diversification, but it is also strongly associated with quality upgrading, which also stimulates growth.

Building on this literature, we introduce gender equality as an additional determinant of economic diversification with two main hypotheses. First, gender gaps in opportunity, such as in education, harm diversification directly by limiting the potential pool of human capital. In particular, the unequal allocation of educational investment leads to suboptimal female human capital accumulation and, as a result, slower technology adoption and innovation (the human capital channel). Second, a gender gap in the labor market shrinks the talent pool from which employers can choose, limits the number of female entrepreneurs (Cuberes and Teignier 2014a; Esteve-Volart 2004; Christiansen and others 2016a; Christiansen and others 2016b), and can impede a country’s ability to diversify (the resource allocation channel).

In fact, the data show that gender inequality and economic diversification appear to be interlinked phenomena (Figure 5.2). High levels of gender inequality, as measured by an extended version of the United Nations’ Gender Inequality Index (GII), are associated with lower levels of export diversification (a combined measure of export product variety and equality in export shares) (Figure 5.2, panel 1). And they are negatively associated with output diversification (a measure of equality in contribution of sectors to real output, including services) mainly in low-income and developing economies (Figure 5.2, panel 2).

Figure 5.2.The Linkage between Gender Inequality and Economic Diversification

Sources: World Bank, World Development Indicators database; United Nations; IMF 2014a; and IMF staff calculations.

This analysis contributes to the literature in three ways. First, we demonstrate empirically that gender inequality is negatively associated with both output and export diversification in low-income and developing economies. Second, our results suggest that both inequality of opportunities and lower female labor force participation are associated with lower economic diversification. Third, we provide evidence on causality.

Gender gaps in both opportunity and outcomes are found to be negatively associated with diversification, particularly in low-income and developing economies. This effect is above and beyond the standard drivers of diversification identified in the literature. The negative relationship between inequality of opportunity and diversification supports the hypothesis of the human capital channel, whereas the association between female labor force participation and diversification supports the premise of the resource allocation channel, which reduces the creation of ideas and development of sectors. Finally, because gender inequality and diversification are interlinked, and because diversification may also affect gender inequality, the chapter addresses endogeneity concerns in its empirical specification by introducing novel instruments in the instrumental variable general method of moments (IV-GMM) regressions. This treatment isolates the causal effect of the country-specific degree of gender inequality on output and export diversification.

A Brief Literature Review

Diversification, development, and growth are closely interlinked, particularly in low-income countries. Despite significant cross-country variation, greater diversification has been associated with improved macroeconomic performance: higher growth, reduced volatility, and increased resilience to external shocks (Koren and Tenreyro 2007; Cadot, Carrere, and Strauss-Kahn 2011). Singer (1950) demonstrates that a country’s initial level of diversification is positively correlated with economic growth. Using an instrumental variable Bayesian model averaging approach to move beyond correlation, IMF 2014a finds that for low-income countries, extensive diversification (introducing new product lines), intensive diversification (creating a more balanced mix of existing products), and the broader process of output diversification are indeed drivers of economic growth. Diversification also involves shifting resources from sectors with high volatility, such as mining and agriculture, to less volatile sectors, such as manufacturing, resulting in greater stability. Countries with more diversified production structures tend to have less volatile output, consumption, and investment (Moore and Walkes 2010; Mobarak 2005). There is a nonlinear, U-shaped relationship between diversification and development (Imbs and Wacziarg 2003). As countries develop, they diversify until they reach a critical point beyond which they start specializing in low-volatility sectors (Imbs and Wacziarg 2003; Koren and Tenreyro 2007; Cadot, Carrere, and Strauss-Kahn 2011).

Another strand of the literature documents a strong negative link between growth in real GDP per capita and gender inequality. On a macro level, the relationship between gender inequality and economic growth has been a topic of increasing interest in the academic and policy literature in recent decades. Dating back to the early 1990s, a special issue of World Development was dedicated to introducing a gender lens to macroeconomics (Cagatay, Elson, and Grown 1995). Since then, abundant scholarship has developed on the topic of gender inequality and its connection to economic development and growth (see, for example, World Bank 2012).

Economic development has been shown to decrease gender inequality, whereas persistent discrimination against women can adversely affect development (Goldin 1995; Hill and King 1995; Dollar and Gatti 1999; Tzannatos 1999; Stotsky 2006; Cuberes and Teignier 2014b). This analysis focuses on the latter direction of causality, but many other studies have explored the former (for example, Galor and Weil 1996; Fernandez 2007; Alesina, Giuliano, and Nunn 2011; Duflo 2012 for both directions). The following results demonstrate some of the channels through which gender inequality can negatively affect macroeconomic performance:

  • Education—A number of studies confirm the negative effect of gender disparity in education on growth (Hill and King 1995; Engelbrecht 1997; Forbes 1998; Dollar and Gatti 1999; Klasen 1999; Knowles, Lorgelly, and Owen 2002; Klasen and Lamanna 2009; Seguino 2010). Dollar and Gatti (1999) find that gender inequality in education negatively affects growth in countries where female education is high. Klasen (1999) demonstrates that the negative effect is present in all economies.2Berge and Wood (1994) support the hypothesis that an educated female labor force is a determinant of manufacturing exports growth. Using measures of gender inequality beyond education gaps, a recent study by Amin, Kuntchev, and Schmidt (2015) confirms its strong negative impact on economic growth, but only in poor countries. We hypothesize that these negative effects of gender inequality in educational opportunities affect growth at least in part by obstructing the economic diversification process.

  • Occupation—Occupational choice models are based on the assumption that men and women have the same distribution of talent (Cuberes and Teignier 2012; Esteve-Volart 2004). Gender gaps in entrepreneurship distort the efficient allocation of talent and access to educational opportunities (Cuberes and Teignier 2012). Because a certain percentage of women are prevented from becoming entrepreneurs, they are forced to work as employees, which increases the labor supply, causing equilibrium wages and aggregate productivity to fall. Gender gaps in labor force participation are modeled as preventing a fraction of women from supplying labor to the market, hence decreasing income per capita.3Esteve-Volart (2004) makes explicit the negative endogenous effect of gender gaps in education on growth: the suboptimal allocation of managerial talent explicitly leads to lower female human capital accumulation and thus slower technology adoption and innovation, which reduces aggregate output and obstructs economic growth. The negative effects of gender discrimination in managerial talent allocation are more serious for sectors where high-level skills are needed, such as the nonagricultural sector, whereas restricted female labor force participation in general affects all sectors, including agriculture. We explore whether the channels posited in these models affect growth via their effects on the dynamic process of diversification and structural transformation of the economy.

  • Aggregate measures of gender inequality and growth—Recent empirical evidence, using an extended version of the UN’s GII shows that several dimensions of gender inequality are strongly associated with lower growth, particularly in low-income countries (Gonzales and others 2015b; Hakura and others 2016). In this chapter, we test whether measures of gender inequality are also related to lower export and output diversification.

  • Gender wage inequality—It has had a positive effect on export-led growth in semi-industrialized, export-oriented economies (Seguino 2000), but it has had a negative effect in low-income agricultural countries (Seguino 2010). On the other hand, accounting for the different productivity of male and female workers, Schober and Winter-Ebmer (2011) do not find support for the hypothesis that increased gender inequality contributes to growth, but argue that it may indeed hamper it. Finally, using a model of endogenous savings, fertility, and labor market participation, Cavalcanti and Tavares (2016) show that an increase of 50 percent in the gender wage gap could lead to a decrease in income per capita by 35 percent. Given the lack of extensive and reliable data on wage inequality, this chapter focuses instead on gender inequality in education, reproductive health, women’s empowerment, and labor market participation, the subcomponents of the multidimensional GII.

Structural transformation has been shown to coincide with episodes of decreases in gender inequality, particularly in the services sector. Several studies examine the relationship between women’s economic participation and structural transformation. These studies focus predominantly on the influence of the services sector (Akbulut 2011; Olivetti and Petrongolo 2014; Ngai and Petrongolo 2014; Rendall 2013). Rendall (2013) finds that structural transformation has been important in reducing gender inequality by decreasing the labor demand for physical attributes (“brawn”). Economies with lower brawn requirements offer better labor market opportunities because they allow women to take advantage of their comparative advantage in less physical (“brain”) attributes. Cavalcanti and Tavares (2007) link increases in female labor force participation to increases in government expenditures, leading to higher demand for services provided by the government. This in turn further encourages female labor force participation, especially when the public sector typically employs more women. These studies emphasize the direction of causation from the structural transformation of the economy to women’s economic participation. The novelty of this analysis is to explore the reverse relationship, namely whether greater gender equality can enhance and support the process of structural transformation.

Empirical Strategy and Results

There are no theoretical studies on the impact of gender inequality in opportunities and outcomes on output and export diversification. Most theoretical studies of gender inequality and growth examine the causal channels of fertility and the education of children (Galor and Weil 1996; Lagerlöf 2003; Cavalcanti and Tavares 2007; Doepke and Tertilt 2008; Agénor, Canuto, and da Silva 2010). The empirical investigation in this study is therefore broadly based on the theoretical occupational choice models of Cuberes and Teignier (2012) and Esteve-Volart (2004), which examine the effects of gender discrimination on aggregate output and economic growth. We explore whether the channels posited in these models are similarly at play in the dynamic process of economic diversification. To test for the effect, we include gender inequality as a determinant of diversification, along with other potential drivers of diversification previously highlighted in the literature. Annex 5.1 gives an overview of the data, and Annex 5.2 presents technical details on the empirical strategy.

To determine the direction of causality between gender inequality on diversification, we use a large data set of legal restrictions on women’s economic activity as instruments for gender inequality. Restrictions on women’s economic participation have been shown to limit women’s access to finance (Demirgüç-Kunt, Klapper, and Singer 2013), employment (Amin and Islam 2014), labor force participation (Gonzales and others 2015a), asset ownership and wealth (Deere and others 2013), property rights (Razavi 2003), and adoption of new technologies (Quisumbing and Pandolfelli 2010). Specifically, using extensive panel data of gender-related legal restrictions, a recent study by Gonzales and others (2015a) demonstrates that restrictions on women’s rights to inheritance and property, as well as legal impediments to economic activity, such as the right to open a bank account or to freely pursue a profession, significantly exacerbate gender gaps in labor force participation. This analysis uses the results from this stream of the literature to argue that gender-based legal restrictions are valid instruments to address endogeneity concerns in the analysis of the impact of gender inequality on diversification: legal restrictions exacerbate gender inequality, which, in turn, impedes output and export diversification. (As noted, Annex 5.2 lays out the details of the empirical strategy.)

Results on Export and Output Diversification

Gender inequality is strongly and negatively associated with export diversification in low-income and developing economies, even after accounting for the other standard drivers of diversification, discussed in Annex 5.1. Table 5.1 presents the baseline estimation, which includes time and country fixed effects, along with a large set of structural country characteristics, policies, and cyclical factors. In particular, we find the following:

  • Gender inequality, as measured by the extended version of the UN’s GII, is strongly associated with export diversification. In particular, moving from a situation of absolute gender inequality to perfect gender equality (as measured by the index) could decrease the Theil index of export diversification (that is, increase export diversification in low-income and developing economies), by 0.6 to 2 units. The magnitude of this effect is equivalent to up to about two standard deviations of the index across low-income and developing economies. The results also show that higher levels of gender inequality are significantly associated with lower levels of export diversification across all levels of development.

  • The effect of gender inequality comes on top of structural characteristics previously highlighted in the literature. Our results confirm the U-shaped relationship between export diversification and development (Dabla-Norris and others 2013), in which countries diversify until they reach a certain level of development but reconcentrate thereafter. A higher share of mining in output is associated with a less diversified export base. In line with a larger pool of talent, population size (in most of our specifications) and human capital (in some specifications) are associated with higher export diversification.

  • The impact of gender inequality remains when controlling for policies associated with export diversification. In particular, institutions—including creating a better business environment, as measured for example by the Frasier Summary Index of Institutions, or legal systems and property rights—are significantly and positively associated with higher levels of diversification. A higher degree of openness in international trade expands the possible pool of trading partners and demand for exports, and our results confirm a positive and significant relationship with export diversification. Better infrastructure is also strongly associated with higher degrees of export diversification.

  • Finally, macroeconomic factors also appear to play a role. Real exchange rate appreciation and terms-of-trade improvement are associated with lower degrees of export diversification, possibly reflecting the effect of lower price competitiveness in the short term and higher quantities of exports of the main sectors when their prices are high.4

Table 5.1.Explaining Export Diversification
Gender Inequality
Gender Inequality Index0.703**0.776***1.381***0.665**
in low-income developing1.014**1.113**0.1200.630
Structural Factors
Lag Human capital index0.04600.07430.03090.0887
Log(Real GDP per capita)−1.838***−1.712***−0.970***−0.971***
Mining as share of GDP0.00937**0.0119***0.0119***0.0236***
1. Institutions
Fraser Institute Sum. Index−0.116***−0.0700***
2. Openness
Freedom to trade−0.0646***−0.0219*
3. Infrastructure
Log(landlines/1000 workers)−0.129***−0.110***
4. Macro/Cyclical Factors
Terms of Trade0.00427***
Country fixed effectsYESYESYESYES
Time fixed effectsYESYESYESYES
Adjusted R20.1200.1130.07420.213
Sources: Barro and Lee 2013; Gonzales and others 2015b; IMF 2014b; Penn World Table 8.1; Stosky and others 2016; World Bank, Women, Business and the Law database; and IMF staff calculations.Note: Positive values indicate negative association with diversification. Standard errors in parentheses, * p < 0.1; ** p < 0.05; *** p < 0.01. All specifications include country and time fixed effects. REER = real effective exchange rate.

Gender inequality is negatively associated with output diversification in low-income and developing economies. To capture the role that the services sector may play in the economy, we examine output diversification in a similar empirical setup. The results for structural characteristics and policies are broadly comparable to the ones on export diversification described elsewhere in this chapter. Gender inequality in low-income and developing economies is negatively associated with output diversification in all our specifications.5 However, we find mixed results on gender inequality for the remainder of countries. There is a significant and positive association of gender inequality and output diversification in some of the regressions for these countries, likely reflecting the fact that low gender inequality may result in greater participation of women in the services sector, in which countries tend to reconcentrate production as they develop.

In addition, our results provide evidence on two main channels through which gender inequality inhibits economic diversification, the human capital channel and the resource allocation channel. To test for the contribution of different dimensions of gender inequality, we include female labor force participation, gender gaps in education, female representation in parliament, and indicators of female health (maternal mortality and adolescent fertility) simultaneously into our regressions. The results in Table 5.2 highlight that there is some evidence for the human capital channel—a higher female-to-male enrollment ratio is significantly and positively related to export diversification, particularly in low-income and developing economies. In addition, there is evidence for the resource allocation channel, as higher female labor force participation rates are associated with higher export diversification levels in low-income and developing economies. The results also provide some evidence that better health outcomes, in terms of lower maternal mortality ratios and adolescent fertility rates, are positively associated with export diversification. The results are broadly similar for output diversification, where higher female labor force participation and higher educational enrollment ratios for girls relative to boys in low-income and developing economies are associated with higher output diversification when controlling for policies and institutions (see Kazandjian and others 2016).

Table 5.2.Explaining Export Diversification: Dimensions of Gender Inequality
Gender Inequality
Female labor force0.4730.7580.859*−0.0324
participation rate(0.472)(0.468)(0.462)(0.423)
in LIDCs−2.748***−2.935***−3.111***−2.092**
Secondary enrollment ratio−0.006030.0444−0.3280.316
in LIDCs−0.986**−1.034**−0.167−1.590***
Women in parliament−0.00265−0.00271−0.002920.00444
in LIDCs0.006910.006060.00800*0.00578
Maternal mortality ratio0.00142**0.00151**0.001040.00169***
in LIDCs−0.000415−0.000411−1.73e-05−0.00111
Adolescent fertility rate0.000586−0.0009660.002310.00341
in LIDCs−0.00143−0.0008210.003930.0122**
Structural Factors
Lag Human capital index−0.358**−0.392**−0.288*−0.387***
Log(Real GDP per capita)−2.059***−2.051***−1.626***−0.848
Mining as share of GDP0.0114**0.0151**0.0151***0.0390***
1. Institutions
Fraser Institute Sum.−0.115***−0.124***
2. Openness
Freedom to trade−0.0516*** (0.0149)−0.00345 (0.0168)
3. Infrastructure
Log(landlines) per−0.0499*−0.0532**
1000 workers(0.0271)(0.0261)
4. Macro/Cyclical factors
Terms of Trade0.00485***
Sources: Barro and Lee 2013; Gonzales and others 2015b; IMF 2014b; Penn World Table 8.1; Stosky and others 2016; World Bank, Women, Business and the Law database; and IMF staff calculations.Note: Positive values indicate negative association with diversification. Standard errors in parentheses, * p < 0.1, ** p < 0.05, *** p < 0.01. All specifications include country and time fixed effects. + = negatively associated with diversification. LIDCs = low-income developing countries; REER = real effective exchange rate.

Finally, we also find evidence for causality in the specifications by instrumenting gender inequality with legal rights. Table 5.3 highlights gender inequality as a significant determinant of export diversification,6 even after including legal rights for women, such the right to be the head of a household or full community marital property rights, as instruments for gender inequality in generalized method of moments (GMM) regressions. The instruments we use pass standard econometric and rule-of-thumb tests. Each of the instruments is individually significant in the first-stage regressions and the F-statistics of the IV regressions are well above the rule-of-thumb threshold value of 10, providing evidence that the instruments are not weak. In addition, in specifications with two or more instruments, the p-values of the Hansen J statistic do not allow us to reject the joint null hypothesis that the instruments are uncorrelated with the error term, supporting our hypothesis that the excluded instruments are indeed correctly excluded from the estimated equation, that is, that they are exogenous. These results suggest that gender inequality may be indeed a cause of lower economic diversification.

Table 5.3.Explaining Export Diversification: Instrumental Variable GMM
Gender Inequality Index5.785***3.534**
Lag Human capital index0.02510.420***
Log(GDP per capita)−1.307***−0.666*
Mining as share of GDP0.0318***0.0105
Fraser Institute Sum.−0.0498
Freedom to trade−0.0405***
Log(landlines) per−0.0919***
1000 workers(0.0281)
Terms of Trade0.00427***
p-value of Hansen0.2960.248
J statistic
Instrument F-test13.2712.85
Source: IMF staff calculations.Notes: Positive values indicate negative association with diversification. Standard errors in parentheses, * p < 0.1, ** p < 0.05, *** p < 0.01. All specifications include country and time fixed effects. REER = real effective exchange rate.


This analysis presents, to the best of our knowledge, the first empirical evidence that gender inequality impacts both export and output diversification. Using a multidimensional index to capture gender inequality, as well as individual indicators of gender inequality, we show that gender inequality, both in outcomes and in opportunities, negatively impacts export and output diversification in low-income and developing economies. This analysis provides evidence that both gender equity in opportunities as well as outcomes matter for economic diversification. In particular, we show that both gender inequality in opportunities, such as education, and lower female labor force participation, are negatively associated with diversification. The former supports the hypothesis of inequality constraining the level of human capital, which limits diversification—and could be tested along generalized inequality of opportunity in future research. The latter supports the theory of an inefficient allocation of resources leading to suboptimal creation of ideas and development of sectors.

Our empirical work provides support for causality between gender inequality and diversification. The effect of gender inequality on diversification can be separated from the effect of diversification on gender inequality thanks to our empirical estimation strategy, which uses country-specific laws and regulations as instruments for gender inequality. These legal restrictions, such as a woman’s inability to receive equal inheritance compared with men, to be the head of a household, or to have joint titling of property, skew the efficient allocation of resources by impeding women’s economic participation and preventing households from giving the same opportunities to daughters and sons.

By linking gender inequality to lower economic diversification—which is widely acknowledged to be a source of sustainable growth—we highlight a new channel through which gender equality boosts growth.


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Annex 5.1. Data

Export Product Diversification

We use the Theil index of export diversification from IMF (2014b) which follows Cadot, Carrere, and Strauss-Kahn (2011). The index can be decomposed into a “between” and a “within” subindex:

in which i is the product index and N the total number of products. The “between” Theil index captures the extensive margin of diversification, that is, the number of products, while the “within” Theil index captures the intensive margin (product shares). Lower values of the Theil index indicate higher levels of export product diversification. The index is available for 188 countries from 1962–2010.

Output Diversification

As services are not included in the calculation of export product diversification, we additionally use the output diversification Theil index in our regressions to account for the impact of changes in the services sector. Following the methodology used for the export Theil index described above, the output diversification index was constructed for the real subsectors from the UN’s sectoral database in IMF 2014b. The index covers 188 countries from 1970 to 2010.

Gender Inequality Index

The gender inequality index (GII) is the extended version of the United Nations Gender Inequality Index (Gonzales and others 2015b; Stotsky and others 2016), which captures gender inequality across areas of health (maternal mortality ratios and adolescent fertility rates), empowerment (share of parliamentary seats and education attainment at the secondary level for both males and females), and labor force participation (rates by sex). While the GII has drawbacks (such as a complicated functional form and a combination of indicators that compare men and women with indicators that pertain only to women), it is preferable to alternatives such as the United Nations Development Program’s related Development Index (GDI, in which one of the main components is not observed and is imputed). The index spans values between 0 and 1, with higher values indicating higher gender inequality. The index is available for 141 countries from 1990–2013.


The vector of controls includes the log of expenditure-side real GDP at chained purchasing power parity (in millions of 2005 U.S. dollars) and its square, the log of population (in millions), and an index of overall human capital accumulation per person based on years of schooling and returns to education (five-year lag, from Barro and Lee 2013), all from Penn World Table 8.1. We control for measures of institutions including legal systems and property rights (from the Fraser Institute); globalization (from the KOF Index of Globalization); infrastructure, including the share of paved roads and length of landlines (from the Calderon-Serven database); financial development (an index of financial reform, interest rate controls, and private sector credit to GDP as robustness checks); and the scale of investment in the economy (investment in percent of GDP and per worker). In addition, we test whether being resource-rich exhibits a negative effect on diversification by introducing the share of mining in GDP or the share of fuel exports into the regressions.

Legal Restrictions as Instruments

We use the World Bank/International Finance Corporation Women, Business, and the Law database, which tracks various legal restrictions on women’s economic rights in 100 countries from 1960 to 2010 for our instrumentation strategy. See Kazandjian and others 2016 for a complete set of summary statistics.

Countries Included in the Sample

Low-Income and Developing Countries

Bangladesh, Benin, Bolivia, Burundi, Cambodia, Cameroon, Central African Republic, Democratic Republic of Congo, Republic of Congo, Côte d’Ivoire, Ghana, Honduras, Kenya, Kyrgyz Republic, Lao People’s Democratic Republic, Lesotho,* Liberia, Malawi, Mali, Mauritania, Moldova, Mongolia, Mozambique, Nepal, Niger, Rwanda, Senegal, Sierra Leone, Sudan, Tajikistan, Tanzania, Togo, Uganda, Republic of Yemen, Zambia, Zimbabwe (* denotes data available for output diversification only).

Other Countries

Albania, Argentina, Armenia, Australia, Austria, Belgium, Brazil, Bulgaria, Canada, Chile, China, Colombia, Costa Rica, Croatia, Denmark, Dominican Republic, Ecuador, Arab Republic of Egypt, El Salvador, Estonia, Finland, France, Germany, Greece, Guatemala, Hungary, India, Indonesia, Islamic Republic of Iran, Iraq, Ireland, Israel, Italy, Jamaica, Japan, Jordan, Kazakhstan, Latvia, Lithuania, Malaysia, Mexico, Morocco, Namibia,* Netherlands, New Zealand, Norway, Pakistan, Panama, Paraguay, Peru, Philippines, Poland, Portugal, Romania, Saudi Arabia, Singapore, Slovak Republic, Slovenia, South Africa, Spain, Sri Lanka, Sweden, Switzerland, Syrian Arab Republic, Thailand, Tunisia, Turkey, Ukraine, United Kingdom, United States, Uruguay, Venezuela (* denotes data available for output diversification only).

Annex 5.2. Empirical Specification

We analyze the effect of gender inequality on diversification together with determinants previously highlighted in the literature. To obtain unbiased estimates, we control for unobservable variables that differ across countries, as well as common effects over time in the following relationship for the period 1990–2010 in our baseline estimations:

in which

  • Diversificationit represents the measure of either export or output diversification as defined in Annex 5.1 for country i at time t.

  • The main contribution of our paper is to test whether gender inequality exhibits a significant effect on diversification. Gender Inequalityit tests for this effect at two levels: first, to account for the combined effect of several dimensions of gender inequality, we use the extended version of the United Nations Gender Inequality Index, that is, a combination of gaps in labor force participation, education, and reproductive health, as well as female seats in parliaments as described in Annex 5.1. In a second step, to test for the effect of individual measures of gender inequality, the index is replaced by the female-to-male gross enrollment ratio in secondary school, the female labor force participation rate, the share of female seats in parliament, the adolescent fertility rate, and the maternal mortality ratio. As the relationship between diversification and gender inequality may vary across levels of development, we include a low-income and developing country interaction term (LIDC) in our main regressions.

  • Structural Characteristicsit may significantly impact a country’s ability to diversify. We therefore include real GDP per capita and its square in the regression to account for the overall level of development, as well as the turning point after which countries reconcentrate their export or output structure (IMF 2014b; Dabla-Norris and others 2013). The baseline regressions also include population size to capture the pool of workers potentially able to produce different products in a country, along with an index of human capital to account for a country’s ability to generate and implement new ideas. In addition, we test whether being resource-rich exhibits a negative effect on diversification by introducing the share of mining in GDP or the share of fuel exports into the regressions.

  • Institutionsit shape the environment in which businesses operate and the ease of entering a market to implement an idea or to produce a new product. To account for this impact, our regressions use both general institutional quality (for example, the Frasier Institute Summary Index), as well as specific dimensions of the regulatory environment (for example, legal systems and property rights).

  • Cyclical Factorsit may boost or compress a certain sector in the short term, therefore impacting diversification over time. We therefore introduce macroeconomic variables, such as terms of trade, real effective exchange rates, and real GDP growth into our regressions, in addition to time fixed effects.

  • Policiesit may foster economic diversification. Here, we test for several policy dimensions, such as more openness to trade (through an index of globalization, the degree of freedom to trade internationally, and average tariff rates), financial development (an index of financial reform, interest rate controls, and private sector credit to GDP as robustness checks), the scale of investment in the economy (investment in percent of GDP and per worker), and infrastructure development (density of landlines and length of road network).

  • To capture other factors over time and by country we include μi and θt, that is, country fixed effects and time fixed effects into our baseline regressions. εit represents the error term.

In addition to the fixed effects specifications, we address the endogenous relationship between economic diversification and gender inequality by using the instrumental variable generalized method of moments (IV-GMM) technique.7 Gender inequality in outcomes and opportunities may cause lower levels of export and output diversification, but lower levels of diversification may lead to larger gender inequalities in outcomes and opportunities. Therefore, to determine the direction of causality, we use IV-GMM in addition to the fixed effects specifications as highlighted elsewhere in this chapter.8 In particular, the instrumental variables approach isolates the causal effect of the country-specific degree of gender inequality, as measured by the GII, on export and output diversification.

We introduce legal rights for women as instruments into our specifications. To be valid, an instrument needs to fulfill two criteria: (1) not have a direct impact on export and output diversification (be uncorrelated with the error term of the regression), and (2) be highly correlated with gender inequality, the endogenous regressor of interest. Similar to the institutions and growth literature, we draw from a large dataset of legal restrictions on women’s economic activity. We argue that gender-based legal restrictions—the mere existence of laws on the books of a country—do not exert a direct impact on export and output diversification, thus fulfilling the first condition of exogeneity, which we confirm with the Hansen statistical test. Legal rights have been shown to have a direct and strong impact on gender inequality, supported by various strands of the literature, which makes them good candidates to fulfill the second condition of relevance of the instrument in theory and which we also confirm in the empirical results.

A version of the chapter was previously published as Kazandjian and others 2016.

The process of structural transformation is characterized by two dimensions: horizontal (across sectors) and vertical (within a sector). Diversification into new higher-value-added sectors is the horizontal dimension. Quality upgrading is the vertical dimension and focuses on producing higher-quality (and generally higher-priced) products within existing sectors (IMF 2014a).

Earlier studies show somewhat different results: Barro and Lee (1994) and Barro and Sala-i-Martin (1995) find that female secondary education has a negative impact on growth, as low female educational attainment signifies “backwardness” and hence higher growth potential. Klasen (1999) and Lorgelly and Owen (1999), however, suggest that the finding may reflect multicollinearity problems resulting from the inclusion of both female and male education variables in the regression analysis and the disproportionate influence of a few outlier countries.

Cuberes and Teignier (2014a) present an updated version of the model in which women have the choice to become self-employed, in addition to being entrepreneurs and workers. In this version of the model, women face two additional exogenous restrictions: only a fraction can become self-employed, and those who become workers receive lower wages than men. The main results are not qualitatively different.

The results hold when real GDP per capita growth is used as an alternative to capture cyclical effects. Several measures of income inequality were included in the regressions but did not yield significant results.

For the complete set of results, see Kazandjian and others 2016.

The results are similar for output diversification; see Kazandjian and others 2016.

See Bandiera and Natraj 2013 for a discussion of panel regressions and the endogenous relationship between gender inequality and growth.

All regressions are estimated using heteroskedasticity-robust Huber-White standard errors.

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