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Engendering Trade

Working paper by Do Quy-Toan, Levchenko Andrei, Raddatz Claudio, 2012

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Inspired by the third Millennium Development Goal ("Promote gender equality and empower women"), this study discusses both theoretically and empirically how female empowerment is a source of comparative advantage that shapes a country's response to trade opening. Reciprocally, it shows that as countries integrate into the world economy, the costs and benefits of gender discrimination shift. The theory goes beyond a potential aggregate wealth effect associated with trade opening, and emphasizes the heterogeneity of impacts. On the one hand, countries in which women are empowered--measured by fertility rates, female labor force participation or female schooling--experience an expansion of industries that use female labor relatively more intensively. On the other hand, the gender gap is smaller in countries that export more in relatively femalelabor intensive sectors. In conclusion, the road to gender equality is paradoxically very specific to each country's productive structure and exposure to world markets.

 
 


 
WORLD DEVELOPMENT REPORT 2012 
GENDER EQUALITY AND DEVELOPMENT 
 
BACKGROUND PAPER 
 
 
 


ENGENDERING TRADE 
 
Do, Quy‐Toan, Andrei Levchenko, and Claudio Raddatz 
 
2011 
 
 
 
 
 
 
The findings, interpretations, and conclusions expressed in this paper are entirely those of the 
authors. They do not necessarily represent the views of the World Development Report 2012 team, 
the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank 
or the governments they represent




 
 


 




Policy Research Working Paper 5777


Engendering Trade
Quy-Toan Do


Andrei A. Levchenko
Claudio Raddatz


The World Bank
Development Research Group
Macroeconomics and Growth Team
August 2011


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Produced by the Research Support Team


Abstract


The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development
issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the
names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those
of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and
its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.


Policy Research Working Paper 5777


The authors analyze the interaction between a country’s
world market integration and its attitude towards gender
roles. They discuss both theoretically and empirically
how female empowerment is a source of comparative
advantage that shapes a country’s response to trade
opening. Reciprocally, the authors show that as countries
integrate into the world economy, the costs and benefits
of gender discrimination shift. Their theory goes beyond
a potential aggregate wealth effect associated with trade
opening, and emphasizes the heterogeneity of impacts.


This paper is a product of the Macroeconomics and Growth Team, Development Research Group. It is part of a larger
effort by the World Bank to provide open access to its research and make a contribution to development policy discussions
around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors
may be contacted through pflewitt@worldbank.org.


On the one hand, countries in which women are
empowered—measured by fertility rates, female labor
force participation or female schooling—experience an
expansion of industries that use female labor relatively
more intensively. On the other hand, the gender gap is
smaller in countries that export more in relatively female-
labor intensive sectors. In an increasingly globalized
economy, the road to gender equality is paradoxically
very specific to each country’s productive structure and
exposure to world markets.




Engendering Trade∗


Quy-Toan Do, Andrei A. Levchenko and Claudio Raddatz†


Keywords: Gender gap, female discrimination, woman empowerment, trade integration, factor


endowments, comparative advantage.


JEL Codes: F43, J16, O11


∗This paper is a background paper for the World Development Report 2012 on Gender Equality and Development.
We thank Francisco Ferreira, Elisa Gamberoni, Gene Grossman, Carolina Sanchez-Paramo and seminar participants
at the World Bank and Georgetown University for helpful comments. Çağatay Bircan provided outstanding research
assistance. The views expressed in the paper are those of the authors and need not represent the views of the World
Bank, its Executive Directors, or the countries they represent.
†World Bank, University of Michigan and NBER, and World Bank, respectively.


1




Introduction


The third Millennium Development Goal is to “Promote gender equality and empower women.” In


this paper, we look at this objective in the context of an increasingly integrated world. Two related


questions come to mind when faced with the issue of globalization and the gender gap. First, how


does discrimination against women influence the way countries integrate into world markets? And


second, how does globalization in turn change countries’ incentives to “promote gender equality and


empower women”?


To address these questions, we take the view of globalization as the expansion of trade in goods


and services, with changes in relative factor prices as a primary consequence. We therefore abstract


from other important socio-economic phenomena associated with globalization, such as cross-border


movements of capital and labor, or the worldwide spread of information, technology, culture, and


social norms, among other things. Our analysis of the interplay between globalization and gender


inequality instead emphasizes the distortionary effects of discrimination on trade patterns, and


reciprocally, the role that trade has in affecting the extent to which women are being discriminated


against.


To guide our discussion, we consider a two-sector specific factors model of international trade


(Jones, 1971; Mussa, 1974), combined with a simplified occupational choice decision along the lines


of Roy (1951). The first sector combines capital with female labor (henceforth the brain sector),


and the second combines capital with male labor (henceforth the brawn sector). Capital flows freely


across sectors, so the marginal products of capital equalize, and this determines the equilibrium


of the economy in autarky. To investigate the effect of world market integration, we consider two


otherwise similar countries that differ along two dimensions: (i) the relative productivity of the


brain versus brawn sectors, and (ii) the severity of gender discrimination, which is modeled as the


extent to which women are restricted from participating in the labor market. The former source of


heterogeneity reflects Ricardian technology differences, while the latter leads to Heckscher-Ohlin-


type differences in female labor endowments. Under trade, capital flows to the sector in which


the country has a comparative advantage. Moreover, gender discrimination, by affecting relative


factor endowments, distorts trade patterns by either exacerbating or mitigating inherent Ricardian


comparative advantage.


The second stage of our theoretical discussion endogenizes the gender gap, and analyzes several


mechanisms through which trade opening will affect discrimination outcomes. When women’s


empowerment is expressed through higher female labor force participation or lower fertility, we


examine how world market integration changes the trade-off between formal labor wages and the


marginal product of home production.1 Similarly, when looking at education, we analyze how trade


opening might affect the returns to girls’ schooling. Finally, we propose a political economy model of


gender discrimination in which relative incomes within a household determine bargaining power and


men choose female labor force participation; men thus face the trade-off between increasing total


1In this paper, we view fertility solely as an indication of women’s opportunity cost of formal labor.


1




household income and a higher bargaining power of female household members as female earnings


increase. In all these cases, by affecting relative factor prices, trade alters the costs and benefits


of restricting female participation in the labor force; countries that exhibit a larger comparative


advantage in the brain sector will tend to discriminate less against women, since such practice


comes at a larger cost.


We take the two main theoretical predictions of the model to the data. First, all else equal, countries


where gender discrimination is less severe have a comparative advantage in exporting goods that


are more intensive in the use of female labor. Second, under trade, countries with comparative


advantage in female intensive sectors will reduce the severity of gender discrimination. To test


these predictions, we use industry-level export data for 61 manufacturing sectors in 146 developed


and developing countries over 45 years. We measure a sector’s female-labor intensity by its average


ratio of female to total employment among countries with full data coverage, and test whether,


consistent with the first prediction, countries where women are more empowered tend to export


relatively more of the female-labor intensive goods. Next we follow the methodology of Almeida and


Wolfenzon (2005), and use a country’s export shares in different sectors to aggregate the industry-


level measures of female labor intensity into a country-level measure of female-labor needs of exports


that captures a country’s comparative advantage in female-labor intensive sectors. We use this


measure to test the second prediction: that gender discrimination is less severe in countries with a


comparative advantage in female-labor intensive sectors. In testing both predictions, we recognize


the possibility of endogeneity of our explanatory variable that comes from the model itself and


address it using instrumentation strategies. In the first case, since the share of a country’s exports


in a sector that is female-labor intensive may affect its attitude towards gender, we instrument


the attitude towards gender by a measure of the distribution of religions in the population. In


the second case, since the comparative advantage of a country in female-labor intensive sectors


depend on its attitude towards gender, we follow the methodology of Do and Levchenko (2007),


and instrument for the female-labor needs of exports by a measure of a country’s exogenously


determined comparative advantage in these sectors using export weights predicted by sector-level


gravity equations. These instrumentation strategies are described and justified in more detail in


section 2 below.


The results support the main empirical predictions of the model. Countries with higher degrees of


women’s empowerment, whether it is measured by higher female labor force participation, lower


fertility, or higher female educational attainment, have a significantly larger export shares in sectors


that are intensive in the use of female labor, controlling for industry and country fixed effects. This


finding is specially strong in two-stage least squares (2SLS) regressions that instrument for the


possible endogeneity of gender attitudes with respect to export composition. Moving from the


25th to the 75 percentile in a gender gap variable increases the export share in a sector at the


75th percentile of female intensity by between 0.7 and 1.89 percentage points more compared to a


sector at the 25th percentile of female intensity, depending on the gender gap measure. Similarly,


countries that have a comparative advantage in the production of female-labor intensive goods tend


2




to exhibit relatively greater female empowerment, especially in terms of a lower fertility rate, and


to a lesser extent in terms of educational attainment and labor force participation. For instance,


moving from the 25th to the 75th percentile in the distribution of the female-labor needs of exports


lowers fertility by as much as 0.21 births per woman, or about 0.37 standard deviations of average


fertility across countries.


Our paper builds on the assumption that male and female labor are not perfect substitutes. This


assumption has also been the cornerstone of recent empirical analyzes by Galor and Weil (1996),


Black and Juhn (2000), Qian (2008), Alesina et al. (2011), and Pitt et al. (2010), among others.


By examining the effect of trade on various measures of the gender gap, the analysis conducted in


this paper relates to an emerging literature on the effect of trade liberalization on female outcomes


(Rendall, 2010; Oostendorp, 2009; Aguayo-Tellez et al., 2010). Finally, our paper belongs to the


broader “Institutions and Trade” literature, which examines both how institutions become a source


of comparative advantage (e.g. Beck, 2003; Levchenko, 2007), as well as how trade in turn influences


institutions (Acemoglu et al., 2005; Braun and Raddatz, 2008; Do and Levchenko, 2007, 2009;


Segura-Cayuela, 2006).


The rest of the paper is organized as follows. Section 1 presents a simple two-country two-sector


model of trade with gender discrimination. It then proposes and analyzes mechanisms to endogenize


the gender gap. Section 2 lays out our empirical strategy to test the predictions of the model.


Section 3 describes the data, while section 4 presents estimation results. Section 5 concludes.


1 A Model of Trade with Gender Discrimination


1.1 The Environment


We consider a two-country, two-sector model. Countries are indexed by i ∈ {X,Y } and sectors are
labeled F and M . Consumers have Cobb-Douglas preferences over the two goods:


u
(
CiF , C


i
M


)
=
(
CiF
)η (


CiM
)1−η


.


Instead of considering sectors of relative brain versus brawn intensities, we restrict attention to a


specific-factors model of production:


Y iF (KF , LF ) = F
iKαFL


1−α
F


Y iM (KM , LM ) = M
iKαML


1−α
M ,


where LF and LM are, respectively, the amount of female labor and male labor employed in


production, and KF and KM are the amounts of capital employed in each sector. Thus, men and


women are assumed not to be substitutes of each other; we take the arguably simplistic view that


3




men supply brawn-only labor, while women supply brain-only labor.2


Countries are characterized by endowments of female labor L̄iF , male labor L̄
i
M and capital K̄


i.


Capital can move freely between sectors, and the market clearing condition for capital implied that


KiF + K
i
M = K̄


i. To capture the notion of gender discrimination, we set L̄iM = 1 for i ∈ {X,Y } ,
while female labor supply is given by L̄iF = 1 − δi in country i. The parameters δi capture the
extent to which female labor supply is being restricted in country i. We can either think of δi as


actual restrictions on women’s participation in the labor force (discrimination, social norms, etc.),


or female schooling restrictions so that
(
1− δi) measures “effective” female labor supply. In this


model, trade will be driven by both Ricardian (relative productivity of sectors A and B will differ


across countries) and Heckscher-Ohlin (countries will differ by their effective endowment of female


labor) motives.


In country i, capital is rented out at rate ri and female and male workers are paid wages wiF and w
i
M ,


respectively. The price of the M -good is set to be numeraire, and the price of F goods is denoted pi.


Given the prevailing extent of discrimination against women (δi), all the goods and factor markets


are perfectly competitive. An equilibrium in this economy is a set of prices
{
pi, ri, wiF , w


i
M


}
i∈{X,Y },


and factor allocations
{
KiM


}
i∈{X,Y }, such that (i) consumers maximize utility; (ii) firms maximize


profits; (iii) all goods and factor markets clear. In the rest of the section, we will first solve the


equilibrium of the economy when countries are in autarky, and compare these outcomes to the case


in which goods can be traded freely across countries so that the law of one price holds. We will


then propose several mechanisms to endogenize δi, the extent of gender imbalance in country i.


1.2 Autarky


To characterize the autarky equilibrium, we look at (i) the first order conditions for optimizing


firms and consumers, and (ii) market clearing conditions. For convenience, we will express all the


unknown parameters of the economy (prices and quantities) as functions of f = K̄KM , which is a


measure of the size of the female-labor intensive sector. To simplify notation, this section drops


the country superscripts.


Firms’ optimization


In each of the two sectors, firms rent capital and hire labor to maximize profits. In other words,


sector M firms solve the following program:


max
K,L


MKαL1−α − rK − wML


2In the rest of the paper, we will use female-labor (resp. male-labor) or brain (resp. brawn) intensive sectors
interchangeably.


4




The necessary and sufficient first-order conditions with respect to KM and LM yield, respectively:


r = αM


(
1


KM


)1−α
=
αM



K̄αf1−α (1)


wM = (1− α)MKαM = (1− α)M
(


f



. (2)


Similarly, sector F firms choose capital and demand labor to maximize


max
K,L


pFKαL1−α − rK − wFL.


The necessary and sufficient first-order conditions with respect to K yield r = αpF
(


1−δ
KF


)1−α
and


since returns to capital equalize across sectors, the expression for r given by equation (1) pins down


relative goods prices:


p =
F


M


(
f − 1
1− δ


)1−α
. (3)


Finally, the first-order conditions with respect to L determine female wages:


wF = (1− α) pF
(
KF


1− δ


=
1


1− δ (1− α)M
(


f



(f − 1) . (4)


Consumers’ optimization and market clearing conditions


Cobb-Douglas utility implies constant expenditure shares on the two goods:


pCF = ηE


CM = (1− η)E,


where expenditure is equal to aggregate income, which is derived from wages paid to labor and


rental of capital:


E = rK̄ + wF (1− δ) + wM .


Therefore, aggregate consumption of good F is


CF = η
rK̄ + wF (1− δ) + wM


p
. (5)


The market clearing condition for good F (equivalently for good M , since Walras’ law holds)


equalizes consumption and production:


η
rK̄ + wF (1− δ) + wM


p
= FKαF (1− δ)1−α .


Substituting for goods and factor prices from (4), (2), and (3) and rearranging gives us the following


5




expression for equilibrium capital allocation:


(1− η) f = 1. (6)


Equilibrium characterization


The autarky equilibrium is thus characterized by the following allocation of resources:LM = 1LF = 1− δ and
KM = (1− η) K̄KF = ηK̄ .


The allocation of capital across sectors does not depend on δ, the extent of gender discrimination.


Any restriction in labor supply is compensated by an increase in female wages, with unit elasticity


of substitution (Cobb-Douglas), so that the factor rewards are independent of δ. To see this, let’s


look at equilibrium wages: wF = 11−δ
η


1−η (1− α) (1− η)αMK̄α
wM = (1− α) (1− η)αMK̄α


, (7)


so that total labor incomes for women and men are given bywFLF =
η


1−η (1− α) (1− η)αMK̄α
wMLM = (1− α) (1− η)αMK̄α


,


which means that relative total labor income of women to men is entirely determined by the relative


weight of female to male goods in the utility function:


wFLF
wMLM


=
η


1− η . (8)


Since capital allocation is independent of the extent of gender discrimination, neither are interest


rates:


r =
α (1− η)αM (K̄)α


(1− η) K̄ .


Finally, prices of female-produced goods are (negatively) responsive to female labor supply:


p =
1


(1− δ)1−α
M


F


(
η


1− η
)1−α


(9)


6




and consumption levels are thus CF = (1− δ)
1−α ηαFK̄α


CM = (1− η)αMK̄α
, (10)


which implies that autarky equilibrium welfare is equal to


U = F ηM1−η
[
ηη (1− η)1−η K̄



(1− δ)(1−α)η . (11)


The main lesson from the autarky case is that general equilibrium forces put a natural limit on


the “effectiveness” of gender discrimination: restricting the female labor supply bids up the price


of the goods produced by women, and therefore women’s wage. Though under the Cobb-Douglas


assumption, the general equilibrium force perfectly offsets discrimination – in the sense that the


total female labor income in the economy is independent of δ – this force is of course much more


general, and will still operate under non-unitary elasticities of substitution.


1.3 Trade


Now suppose countries can freely trade goods with each other. A superscript i ∈ {X,Y } indexes
the countries. We follow the same approach as in the autarky case to solve for the equilibrium


allocation of resources. The only differences lie in the goods market clearing condition that now


aggregates consumption and production from both countries, and prices of good F that equalize


across countries.


Firms’ and consumers’ optimization


Following the same steps as in autarky, we can obtain expressions for equilibrium prices in each


country i ∈ {X,Y } that we express as functions of f i = K̄i
KiM


.





ri = αM
i


K̄i


(
K̄i
)α (


f i
)1−α


pi = M
i


F i


(
f i−1
1−δi


)1−α
wiM = (1− α)M i


(
K̄i
)α ( 1


f i



wiF =


1
1−δi (1− α)M i


(
K̄i
)α (


f i − 1) ( 1
f i



. (12)


On the consumption side, we similarly havepiCiF = η
[
riK̄i + wiF


(
1− δi)+ wiM]


CiM = (1− η)
[
riK̄i + wiF


(
1− δi)+ wiM] .


7




Market clearing conditions and law of one price


In sector F , world consumption and production equalize, so that



i


piF i
(
K̄i −KiM


)α (
1− δi)1−α = η [∑


i


riK̄i +
(
1− δi)wiF + wiM


]
,


which simplifies to ∑
i


M i
(
K̄i
)α( 1


f i


)α [
1− (1− η) f i] = 0. (13)


With frictionless trade across countries, pi = p−i or


M i


F i


(
f i − 1
1− δi


)1−α
=
M−i


F−i


(
f−i − 1
1− δ−i


)1−α
, (14)


where the notation “−i” denotes “not country i”.


Equilibrium characterization


We define:


ρi =


(
F i


M i
M−i


F−i


) 1
1−α 1− δi


1− δ−i .


A value ρ > 1 indicates that country i has a comparative advantage in good F, i.e. the female-labor


intensive good. The comparative advantage can be decomposed into a technological or Ricardian


component
(
F i


M i
M−i
F−i


) 1
1−α


and an institutional or Hecksher-Ohlin component 1−δ
i


1−δ−i , which can ex-
acerbate or attenuate technological differences. We also define the constant


θi =
M−i


M i


(
K̄−i


K̄i



and rewrite the two equations (13) and (14) as a system of two equations with two unknowns(
f i, f−i


)
: 


(
f i
)−α [


1− (1− η) f i]+ θi (f−i)−α [1− (1− η) f−i] = 0
ρi f


−i−1
f i−1 = 1


. (15)


The first equation of (15) is the world market clearing condition for good F. It consists of two terms


that add up to zero. Thus, we have the first implication: for i ∈ {X,Y }
[
(1− η) f i ≥ 1]⇔ [(1− η) f−i ≤ 1] .


In words, and since (1− η) f = 1 in autarky, it means that the female sector shrinks in country
i as a consequence of trade if and only if it expands in i′s trade partner. Intuitively, this first
result means that countries will specialize when they open to trade. If we now examine the law


8




of one price condition (second equation in (15)), we have
[
f i ≥ f−i]⇔ [ρi ≥ 1] : the country with


comparative advantage in the female-labor intensive good ends up having a larger brain sector.


These two results together imply the following equivalence:


[
(1− η) f i > 1]⇔ [ρi > 1] . (16)


In summary, we have the following result:


Proposition 1: Autarky vs. trade outcomes As a result of trade opening, capital flows into


the sector for which the country has a comparative advantage in production.


Proof: Follows immediately as an interpretation of equivalence (16).


Take i, the country with comparative advantage in the female sector. Since capital will flow into


the female sector, interest rates will increase accordingly as capital is now put to more productive


use ( ∂r
i


∂f i
> 0). At the same time, the price of the brain-intensive good will increase, driven by


demand from country −i : ∂p
∂f i


> 0. Consequently, nominal wages will increase for women, and


decrease for men (
∂wiF
∂f i


> 0 and
∂wiM
∂f i


< 0), so that the wage gap (defined as male to female wage


bill ratio) decreases and takes the form:


wiM
(1− δi)wiF


=
1


f i − 1 .


Furthermore, since the price of theA good is higher in i under trade than it was in autarky, consump-


tion of good A will decrease by a factor


[
(1−η)f i


1+
(1−η)fi−1


η


]1−α
< 1, while consumption of good B will


increase by a factor
[
(1− η) f i]1−α > 1.Overall, welfare increases by a factor [(1−η)f i]1−α[


1+
(1−η)fi−1


η


]η(1−α) > 1.


Proposition 2: Comparative statics If comparative advantage in the female-intensive sector


is accentuated (ρi increases), then in the trade equilibrium more capital will flow into that sector:


df i


dρi
(
ρi
)
> 0.




Proof: See Mathematical Appendix.


This section formalizes the intuition that trade opening induces countries to specialize in their


comparative advantage industry, with the associated implications for capital allocation and female


9




wages (Proposition 1). Comparative advantage in our model is a combination of both techno-


logical and institutional differences. An exacerbation of these differences induces an even larger


inflow of capital in the female-labor intensive sector, with the corresponding female wage increase


(Proposition 2). We thus have the first prediction of the model:


Prediction 1: Gender discrimination is a source of Heckscher-Ohlin comparative disadvantage:


countries that discriminate more against women are less likely to export female-labor intensive


goods.


1.4 Endogenizing the Gender Gap


The previous subsection laid out the basic model of trade in which countries differ in both technology


and supply of female labor. In this subsection, we introduce several mechanisms that endogenize the


gender discrimination parameter of the model, namely δi. Since comparative advantage in female-


intensive industries implies higher wages for women, the“returns to gender discrimination” decrease


with the extent of female intensity in production.


1.4.1 Fertility and the gender gap in labor force participation


The first extension of this model is to endogenize 1 − δ, viewed as the measure of female labor
force participation or fertility. We therefore consider a continuum of couples (husband, wife) of


measure one, and investigate their time allocation decisions. To that end, we assume that men


always supply one unit of labor, while women choose δ, the amount of time they spend at home


in home production; child rearing would be a natural way to think of home activities. Households


also own K̄i units of capital invested in production. Finally, we assume that home production is


increasing and concave in female labor and brings benefits v (δ) = γ
(
δ − 12δ2


)
that are measured in


utility terms.3 Households take the vector of prices as given and make their investment decisions


accordingly; in other words, they maximize their indirect utility


maxδ
(η)η (1− η)1−η



[
rK̄i + wiF


(
1− δi)+ wiM]− 12γ (1− δ)2 ,


in which goods consumption decisions have been maximized out. Consequently, the optimal choice


of labor force participation is given by


δi ∈ argmaxδ
(
η


p



(1− η)1−η wiF (1− δ)−


1


2
γ (1− δ)2


which gives


1− δi = 1
γ
ηη (1− η)1−η w


i
F



.


3Note that v (δ) = − 1
2
γ (1− δ)2 up to a constant.


10




In natural logs this becomes:


ln
(
1− δi) = Λsupply + ln(wiF




)
, (17)


with Λsupply = ln
[


1
γ η


η (1− η)1−η
]
. Equation (17) defines the labor supply curve. As expected,


labor force participation (resp. fertility) is an increasing (resp. decreasing) function of the prevailing


female wage, as picture in Figure 1, panel A. We now close the model in autarky and trade,


respectively.


Labor force participation in autarky Plugging in the autarky expressions for wF and p from


equations (7) and (3) and taking natural logs, we get the expression for the equilibrium real female


wage:


ln
wiF


= Λiautarky − [1− η (1− α)] ln
(
1− δi) , (18)


with Λiautarky = ln


[(
η


1−η
)1−η(1−α)


(1− η)α (1− α) (F i)β (M i)1−β (K̄i)α] . Equation (18) defines
the labor demand curve as pictured in Figure 1 panel A, and reflects the fact that female labor


force participation exerts a downward pressure on female wages.


The equilibrium female labor force participation in autarky is therefore the unique intersection


between supply and demand curves and is equal to


ln
(
1− δiautarky


)
=


Λisupply + Λ
i
autarky


2− η (1− α) .


Labor force participation under trade Using the expression for wF and p under trade from


equations (12), the labor demand curve can be written in logs as:


ln
wiF


= Λiautarky + ln g


[
(1− η) f i − 1


η


]
− [1− η (1− α)] ln (1− δi) ,


where g (y) = (1 + y)1−η(1−α)
(


1
1+ηy



. We can write


ln
(
1− δitrade


)
=


Λisupply + Λ
i
autarky


2− η (1− α) +
1


2− η (1− α) ln
{
g


[
(1− η) f i − 1


η


]}
= ln


(
1− δiautarky


)
+


1


2− η (1− α) ln
{
g


[
(1− η) f i − 1


η


]}
.


Consequently,


δiautarky > δ
i
trade


11




if and only if


g


[
(1− η) f i − 1


η


]
> 1


It is easy to verify that g (y) is positive increasing for every y and g (0) = 1. Thus, g
[


(1−η)f i−1
η


]
> 1


if and only if (1− η) f i > 1, and given (16), we conclude that


δiautarky > δ
i
trade


if and only if


ρi > 1.


The labor demand curve therefore shifts up (resp. down) when the country has a comparative


advantage in the female-labor (resp. male-labor) intensive good. Thus, the country that has a


comparative advantage in the female-intensive sector will increase female labor force participation


as a consequence of trade opening.


Prediction 2a: Countries that have a comparative advantage in the female intensive good have


higher female labor force participation and lower fertility once they open to trade. The reverse


holds for countries with a comparative dis-advantage in brain intensive goods.


1.4.2 Trade and the gender gap in education


Another pathway through which trade opening can impact countries differentially is through in-


vestments in education. By affecting the relative returns to male versus female labor, trade might


alter the nature of gender-biased parental investments in education. To capture the notion of edu-


cation in our model, we assume that (1− δ) is the supply of “effective labor,” given that education
increases the productivity of labor. We also consider a dynastic model whereby parents are born at


the beginning of a period t with endowment K̄i of capital; the mother has education (1− δt) that
allows her to supply (1− δt) units of effective labor. Parents produce and make their consumption
decisions. They have two children, one boy and one girl and choose to invest et in educating their


girl at cost 12λe
2
t , which is measured in utility terms. Once again, for the sake of simplicity, we


abstract from boys’ education. The investment sets the next generation’s education following the


law of motion 1− δt+1 = f (1− δt, et) . f (.) is assumed to have the standard regularity properties.
Parents’ optimization program takes future wages as given and maximizes:


Vt (1− δt) = max
e


[(
η


pt



(1− η)1−η wiF t (1− δt)−


1


2
λe2 + βVt+1 (1− δt+1)


]
subject to


1− δt+1 = f (1− δt, e) .


12




The first-order condition gives


λet = βfe (1− δt, et)V ′t+1 (1− δt+1) ,


while the envelope theorem yields:


V ′t (1− δt) =
(
η


pt



(1− η)1−η wiF t.


We consequently have the following Euler equation, which defines the demand for education:


et =
βηη (1− η)1−η


λ
fe (1− δt, et)


wiF t+1
pηt+1


. (19)


To simplify, we assume that f (1− δ, e) = e so that Euler equation (19) fully defines the labor
supply curve:


1− δt = βη
η (1− η)1−η


λ


wiF t
pηt


.


Taking logs,


ln (1− δt) = ln βη
η (1− η)1−η


λ
+ ln


wiF t
pηt


,


This equation is identical to (17) up to a constant, and thus all of the derivations regarding the


impact of trade opening on δ carry over from the previous case. Note that, given the simplification


assumption made for the law of motion of education, the economy converges to its steady state


immediately and the analysis of the properties of the steady state is identical to the labor-force-


participation/fertility case.


Prediction 2b: Countries that have a comparative advantage in the brain-intensive good reduce


the gender gap in education when they open to trade. The reverse holds for countries with a


comparative dis-advantage in brain intensive goods.


1.4.3 The political economy of gender discrimination


Finally, to model the endogenous choice of δi in a political economy setting, we depart from the


unitary household, and assume that husbands and wives bargain over aggregate income so that


their individual utilities end up beinguiF
(
δi
)


=
[
1− ωi (δi)]U i (δi)


uiM
(
δi
)


= ωi
(
δi
)
U i
(
δi
) ,


13




where U i
(
δi
)


is the total indirect utility of the household, U i
(
δi
)


= η
η(1−η)1−η




[
riK̄i + wiF


(
1− δi)+ wiM]


and


ωi
(
δi|wiM , wiF , p


)
= ω


[
wiM


;
wiF
(
1− δi)


]
is the husband’s bargaining weight. The bargaining weight is increasing in the husband’s real income


(ω1 (.) > 0) while decreasing in the wife’s (ω2 (.) < 0). Furthermore, if we assume that bargaining


power is unchanged if both incomes are equally inflated, we can express ωi
(
δi|wiM , wiF , p


)
as a


function of income ratios only:


ωi
(
δi|wiM , wiF , p


)
= Ω


(
wiF
(
1− δi)
wiM


)
.


Finally, in this political economy model, men choose the level of gender discrimination δi to max-


imize their own indirect utility ωi
(
δi
)
U i
(
δi
)
. In doing so, we assume that they internalize the


general equilibrium effects of their policy decision δ.


Gender discrimination in autarky Plugging in autarky equilibrium wages from (7) and drop-


ping country superscripts, bargaining power in autarky is given by


ω (δ) = Ω


(
η


1− η
)
,


which is independent of δ. The bargaining power of husbands is unaffected when they restrict


female labor force participation since any restriction will induce an increase in wages that will


keep payments to female labor constant. In our model, any partial equilibrium effect associated


with a restriction on female labor supply (higher δ) is fully offset by general equilibrium effects.


Although the extreme result is driven by the unit elasticity of substitution specific to Cobb-Douglas


specifications for both preferences and technology, the mechanism is still robust to alternative


functional forms and works as follows: a reduction in female effective labor supply decreases output


in the A sector that induces prices of A goods to go up. This is captured in the expression (9) for


prices. In the Cobb-Douglas case, the price response is exactly equal to output shortage, so that the


allocation of capital across sectors is invariant to changes in δ. As a consequence of the price hike,


female labor becomes relatively more productive and this translates into higher wages for women.


Unit elasticity of substitution implies that total labor income is unaffected by δ (cf. equation 8).


Therefore, any restriction in female supply only affects consumption levels through lower production


(cf. equation 10); ultimately, welfare is adversely affected without any change in men’s bargaining


power (cf. equation 11). The autarky gender discrimination level is therefore minimal: δ = 0.


Gender discrimination under trade Under the trade regime, bargaining power becomes


ωi
(
δi
)


= Ω
(
f i − 1)


14




so that


ωi
(
δi
)
U i
(
δi
) ∝ Ω (f i − 1)× [f i (1− δi)η


(f i − 1)η
]1−α


.


The optimal choice for men in country i is then determined by the first-order condition with respect


to δ :


d ln
[
ωi
(
δi
)
U i
(
δi
)]


d ln (1− δi) =
d ln f


d ln ρ


d ln Ω
(
f i − 1)


d ln f
+ (1− α) d ln f


i


d ln ρ


[
1− η f


i


f i − 1
]


+ η (1− α) (20)
= 0


The right-hand side of (20) consists of three terms. The first term is the change in husbands’


bargaining power. An increase in female labor force participation results in an increase in the


country’s comparative advantage in female good production, inducing an increase of the relative


income of women. This in turn reduces husbands’ bargaining power, since Ω (.) is decreasing in f .


The second term is the allocative effect: an increase in female labor supply will induce the male


sector to shrink, pushing female wages up but also price of the female good up. The net effect on


real income is therefore ambiguous and is positive if and only if 1 − η ff−1 > 0, i.e. if and only if
country i has a comparative advantage in the female-labor intensive good; when a reallocation of


capital across sectors decreases the gains from trade (i.e. when capital flows into the female-labor


[resp. male-labor] intensive sector in the country with comparative advantage in the male-labor


[resp. female-labor] intensive good), the effect on real income is negative. On the contrary, when


capital movements make countries more different, the gains from trade increase. Finally, the third


term is the real income effect of an increased aggregate supply of the female-labor intensive good,


which pushes prices down.


Prediction 2c: Both countries increase discrimination against women when they open to trade.


However the increase in discrimination is more pronounced when the country has a comparative


dis-advantage in the female-labor intensive sector.


2 Empirical Strategy


The model of international trade and gender inequality developed above has two main predictions.


When female participation in labor markets
(
1− δi) is taken as given, countries with lower par-


ticipation will have a comparative disadvantage in the production of female-labor intensive goods,


and will export relatively less of these goods. This first prediction comes from the Hecksher-Ohlin


aspects of the model. A lower female participation, resulting either from cultural or economic


forces, effectively makes the country relatively less abundant in female labor and reduces the force


to export these goods that arises purely from differences in factor endowments.


15




The model also recognizes that in the longer run, a country’s comparative advantage has an impact


on women’s wages and thus their incentives to participate in the formal labor markets. If a country


has a comparative advantage in the goods that are produced by women, female wages will rise and


women will have a greater incentive to participate in the formal labor markets and invest in the


types of human capital that will be valued by the formal economy. By contrast, when a country has


a comparative advantage in the goods produced primarily by males, women’s incentives to invest


in human capital and participate in the formal labor markets will decrease with trade openness.


This is the second empirical prediction that we test below. Both of these predictions suggest that


what matters most for how globalization affects the relative status of women in the society is not


simply the level of overall trade openness, but the country’s comparative advantage. Conversely,


the status of women should affect not just overall trade volumes, but also trade patterns in the


shorter-run.


To test these predictions empirically, we measure an industry’s female-labor intensity FLi as the


share of female workers in the total employment in sector i. We take this measure as a technolog-


ically determined industry characteristic that does not vary across countries. Using this measure,


we first estimate the following regression in a cross-section of industries across countries:


SHAREic = νFLi ×GENDERc + γc + γi + ic, (21)


where SHAREic is the average share of good i in country c total exports, GENDERc is a measure


of women’s participation in the labor force or human capital investment in country c, and γc and


γi are country and industry fixed effects, respectively. This specification allows us to test the first


prediction about the relation between female participation and comparative advantage. If women’s


labor force participation is a source of comparative advantage in sectors that are brain intensive,


the coefficient ν would be significantly positive for measures of female participation and negative


for measures of exclusion. To address the endogeneity of GENDERc predicted by our model,


we instrument it by the composition of religions in a country. Specifically, for each country, we


construct two variables that are the proportions of the population that is Muslim and Christian,


respectively. The interactions FLi × MUSLIMc and FLi × CHRISTIANc are then used to
instrument for the interaction FLi × GENDERc.4 It is important to note that we do not claim
that religion variables are valid instruments for the variable GENDERc, since country fixed effects


are not excluded from the first stage. Rather, the identification assumption is valid if, conditional


on country and industry fixed effects, the interaction between religious composition of country c


and the female-labor intensive character of industry i affects the share of good i in country c total


exports only through its effect on the interaction between gender discrimination in country c and


the female-labor intensive character of industry i.


Starting from the same measure of an industry’s female-labor intensity FLi, we can also test the


second prediction that comparative advantage shapes female labor participation and exclusion. To


4Our results are similar if we use FLi ×MUSLIMc as the only instrument for FLi ×GENDERc.


16




this end, we first measure the “gender content” of each country’s comparative advantage. In order


to do this, we follow Almeida and Wolfenzon (2005) and construct for each country and time period,


a measure of the “female-labor needs of exports”:


FLNXct =
I∑
i=1


ωXictFLi, (22)


where i indexes sectors, c countries, and t time periods. In this expression, ωict is the share of sector


i exports in country c’s total exports to the rest of the world in time period t. Thus, FLNXct


in effect measures the gender composition of exports in country c. This measure will be high if a


country exports mostly in sectors with a large female share of employment, and vice versa.


Using this variable, we would like to estimate the following equation in the cross-section of countries:


GENDERc = α+ βFLNXc + γZc + εc. (23)


The left-hand side variable, GENDERc, is a measure of women’s participation in the labor force


or human capital investment, and Zc is a vector of controls. The main hypothesis is that the effect


of comparative advantage in brain-intensive sectors, FLNX, on women’s labor market outcomes


is positive (β > 0). To deal with reverse causality, we implement an instrumentation strategy


that follows Do and Levchenko (2007), and exploits exogenous geographical characteristics of coun-


tries, along with how those exogenous characteristics affect international trade in different sectors


differentially. The construction of the instrument is described in Appendix B.


We also exploit the time variation in the variables to estimate a panel specification of the type


GENDERct = α+ βFLNXct + γZct + γc + γt + εct, (24)


where country and time fixed effects are denoted by γc and γt respectively. The advantage of the


panel specification is that the use of fixed effects allows us to control for a wide range of omitted


variables, and identify the coefficient purely from the time variation in comparative advantage


and women’s outcomes within a country over time. The panel estimation is carried out on non-


overlapping 5-year averages, to sweep out any variation at the business cycle frequencies.


Our main GENDERc measure is fertility, measured by the total number of births per woman.


Since women typically bear the primary responsibility for caring for their children, a greater num-


ber of children will effectively reduce a woman’s capacity to supply labor to the formal labor market.


The key advantage of a variable like fertility is that unlike other indicators of female labor supply,


the number of births per woman is likely to be measured quite precisely in all countries and at all


levels of development. We also check whether results are robust to two additional measures: female


labor force participation and female educational attainment. Female labor force participation is


perhaps the most direct indicator of the outcome of interest for this study, but it is also likely to


be measured with greater error, especially in poorer countries with large informal sectors. Female


17




educational attainment is measured by the average years of schooling of females over 15 years of


age. This variable measures women’s investment in human capital, which can be interpreted as


making women more suitable for formal sector employment. Note, however, that this variable’s


relationship to female labor supply is probably less straightforward, since staying in school longer


actually reduces one’s labor supply in the short to medium run. To summarize, these three out-


come variables are intended to test the prediction of the model that when trade expands women’s


employment opportunities, they will respond by raising their labor supply and investing in human


capital (predictions 2a, 2b and 2c).


The controls include PPP-adjusted per capita income, overall trade openness, and, in the case of


cross-sectional regressions, regional dummies. Our cross-sectional specifications are estimates on


long-run averages for the period 1962-2007, while in the panel specifications the unit of time is a


5-year period, so all the variables are 5-year averages. The data span 1962 to 2007 in the best of


cases, though not all variables are available for all time periods.


3 Data Sources and Summary Statistics


The key indicator required for the analysis is the share of female workers in the total employment


in each sector. We obtain this information from the UNIDO Industrial Statistics Database (IND-


STAT4 2009). This database contains information on the total employment and female employment


in each manufacturing sector for a large number of countries, starting in the late 1990s. The data


are available at the 3-digit ISIC Revision 3 classification (61 distinct sectors). In order to construct


the share of female workers in total employment in sector i, FLi, we take the mean of this value


across the countries for which these data are available and relatively complete. The resulting sam-


ple includes eleven countries in each of the developed and developing sub-samples: Austria, Malta,


Slovak Republic, Cyprus, Lithuania, Japan, United Kingdom, New Zealand, Korea, Italy, Ireland;


and Indonesia, Turkey, Azerbaijan, Jordan, India, Philippines, Malaysia, Chile, Morocco, Egypt,


Thailand. Table 1 reports the values of FLi in our sample of sectors. It is clear that there is wide


variation in the share of women in sectoral employment. While the mean is 27 percent, these values


range from the high of 71 percent in Wearing Apparel and 62 percent in Knitted and Crocheted


Fabrics to the low of 8 or 9 percent in Motor Vehicles, Bodies of Motor Vehicles, Building and


Repairing of Ships, and Railway Locomotives.5


The export shares ωXict are calculated based on the COMTRADE database, which contains bilateral


trade data starting in 1962 in the SITC revision 1 and 2 classification. The trade data are then


aggregated up to the ISIC Revision 3 classification using a concordance developed by the authors.


5One may be concerned that these values are very different across countries in general, and across developed and
developing countries in particular. However, it turns out that the rankings of sectors are remarkably similar across
countries. The values of FLi computed on the OECD and non-OECD samples have a correlation of 0.89. Pooling
all the countries together, the first principal component explains 77 percent of the cross-sectoral variation across
countries, suggesting that rankings are very similar. We also experimented with taking alternative averages: medians
instead of means across countries; and dropping outlier values of female shares in individual sectors. The results were
very similar.


18




Data on female labor force participation and fertility are sourced from the World Bank’s World


Development indicators, while information on female educational attainment comes from the Barro-


Lee database. Controls – PPP-adjusted per capita income and overall trade openness – come from


the Penn World Tables 6.3 (Heston et al., 2002).


Table 2 reports some summary statistics for the female content of exports for the OECD and non-


OECD country groups. We can see that for the OECD, these averages are slightly lower, at about


0.25, and stable across decades. For the non-OECD countries, the female content of exports is


higher, between 0.27 and 0.30, and, if anything, rising over time. Notably, the dispersion among


the non-OECD countries is both much larger than among the OECD, and increasing over time.


While the OECD sample, the standard deviation is stable at 0.03-0.04, for the non-OECD is rises


monotonically from 0.08 to 0.12 between the 1960s and the 2000s.


Tables 3a to 3c report, for the different time periods, the countries with highest and lowest FLNX


values. Typically, countries with the highest values of female content of exports are those that


export mostly textiles and wearing apparel, while countries with the lowest FLNX are natural


resource exporters. Equally important for our empirical strategy are changes over time. Tables 4a


and 4b report the countries with the largest positive and negative changes in FLNX between the


1960s and today. We can see that relative to the cross-sectional variation, the time variation is also


considerable. Table 5 presents the summary statistics of the country-level variables of interest and


controls. Our final dataset contains country-level variables on up to 146 countries.


4 Regression Results


Table 6 reports the results of estimating the impact of the gender gap on comparative advantage


in trade, i.e. eq. (21). The top panel presents the OLS results, the bottom panel the two-stage


least squares results in which the interaction of FLi with GENDERc is instrumented with the


interactions of FLi with shares of Muslims and Christians in the population. All columns control


include country and sector dummies. Columns 2, 4, and 6 in addition include the interaction terms


means to capture Heckscher-Ohlin specialization forces, following the empirical model of Romalis


(2004): capital intensity of sector i is interacted with capital abundance of country c, and skill


intensity of sector i is interacted with skill abundance of country c.6 We can see that the gender


gap has a clear impact on trade flows, especially when the gender gap measures are instrumented


with religious composition. The bottom of Panel B reports the first-stage results. The first-stage


coefficients are highly statistically significant, and the instruments are comfortably strong in the


econometric sense. The estimates are economically significant: moving from the 25th to the 75


percentile in a gender gap variable increases the export share in a sector at the 75th percentile


6Capital intensity is computed as the share of value added not going to labor compensation, and is built using
the UNIDO database. Skill intensity is computed as the fraction of the total wage bill corresponding to workers
with at least some college education in the US (originally obtained from Autor et al., 1998; and translated from the
US Census Industrial Classification (CIC) into ISIC Rev. 3 industries using a correspondence built by the authors).
Country-level skill and capital abundance are sourced from Hall and Jones (1999).


19




of female intensity by between 0.7 and 1.89 percentage points more than in a sector at the 25th


percentile of female intensity, depending on the GENDERc measure.


We next present the results of testing for the opposite effect: the role of comparative advantage in


determining attitudes towards gender. Table 7 reports the results of estimating the cross-sectional


specification in equation (23), focusing on fertility as the outcome variable. All of the specifications


control for income per capita and overall openness, and columns 2-4 and 6-7 also include population


as an additional control. Both left-hand side and the right-hand side variables are in natural logs.


Columns 1 and 2 report the simple OLS results for fertility. There is a highly significant negative


relationship: greater female content of exports is associated with lower fertility, exactly as theory


would predict. Columns 3 and 4 explore the impact of comparative advantage on the other gender


gap measures: female labor force participation and female educational attainment. We can see that


the OLS coefficients are marginally significant, and with the expected sign. Columns 5 through 8


report the 2SLS results, in which the instrument is the female content of exports as predicted based


purely on geographical characteristics, described in Appendix B. For the main outcome variable of


interest, fertility, the 2SLS coefficient is significant at the one-percent level. For the other outcome


variables, the coefficient retains the correct sign but is not robustly significant.7 The bottom panel


of columns 4 through 6 report the first-stage results. The first stage is quite strong: the instrument


is highly significant as a predictor of actual female content of exports. The partial F-statistics


indicate that the instrument is strong in the econometric sense. The estimates are economically


significant. Moving from the 25th to the 75th percentile in the distribution of the female content


of exports lowers fertility by as much as 0.21 births per woman, or about 0.37 standard deviations


of average fertility across countries.


Table 8 presents the panel results. All of the specifications control for per capita income, openness,


and population. The first column presents the pooled estimates on fertility without any fixed


effects. The second column adds country effects, while column 3, both country and time effects.


The impact of FNLX on fertility is of the expected sign and significant at 1% level throughout.


This is a powerful result: country effects control for all the unobservable features of countries that


do not vary over time, such as geography and climate, ethnic and religious composition, and so


on. Thus, the coefficients in column 2 and 3 are identified from changes in the variables within


a country over time. Columns 4 and 5 present the pooled estimates and the estimates with both


country and time effects for female labor force participation, while columns 6 and 7 do the same


for female educational attainment. As was the case in the cross-section, the results for these two


outcome variables are not robust. For both outcome variables, the inclusion of time effects renders


the coefficient estimate not significant, and of the “wrong” sign. It appears that over the past 40


years, there has been a common time trend in both FLNX and these outcome variables, and thus


7When the adopted measure of attitude toward gender is either female labor force participation or education
attainment, the results are not robust to the inclusion of region fixed effects. The finding is not surprising given the
high spatial correlation of our variables of interest. We indeed expect differences in attitude towards gender to be
higher between rather than within geographical regions. The fertility results are fully robust to the inclusion of region
dummies.


20




controlling for this trend leaves no variation that can be exploited to identify these coefficients.8


Our main industry-level female intensity variable (FL) is computed as the average share of female


workers in each sector in a sample of some 20 developed and developing countries. We favor this


measure because it is most representative of the female intensity of sectors in an average country.


However, a drawback of our main measure is that it does not include non-manufacturing sectors. To


check robustness of our results, we implement an alternative strategy, of constructing a measure of


FL based on data for a single country – the U.S.. We thus use the Labor Force Statistics database


of the U.S. Bureau of Labor Statistics (BLS). Using data from the Current Population Survey,


the BLS publishes “Women in the Labor Force: A Databook” on an annual basis since 2005. It


contains information on total employment and the female share of employment in each industry


covered by the Census. The data are available at the 4-digit U.S. Census 2007 classification (262


distinct sectors, including both manufacturing and non-manufacturing). In order to construct the


share of female workers in total employment in sector i, FLi, we take the mean of this value across


the years for which the data on the female share of employment are available (2004-2009). The


resulting sample includes 78 manufacturing and 15 non-manufacturing sectors. Appendix Table A1


reports the values of FLi for the top and bottom 5 sectors according to U.S. data. These sectors


are similar to what we find in our baseline measure: the least female-intensive sectors tend to be


in heavy machinery, while the most female-intensive sectors in textiles and apparel. Appendix


Table A1 presents the basic summary statistics of the U.S.-based FL measure, breaking up the


manufacturing and the non-manufacturing sectors.


While the U.S.-based alternative FL measure has the advantage of extending the set of sectors


to agriculture and mining, it has two important drawbacks. First, the data are compiled based


on individual-level surveys rather than firm- or plant-level data, and thus relies on workers self-


reporting their industry of occupation. Thus, if the number of individuals in the survey who report


working in a particular sector is small, or if workers make mistakes in reporting their industry of


employment, the data will be measured with error. And second, the U.S. is only one, very special


country, and thus its values of of FL may not be representative of the average country’s experience.9


Both of these considerations will raise the amount of measurement error on the right-hand side,


leading to attenuation bias in the coefficients.


With these caveats in mind, Appendix Tables A3 through A5 replicate all of the regression estimates


in the paper using the U.S.-based FL indicator instead. We can see that by and large, the results


using our main measure of gender gap – fertility – are robust. However, the other two outcome


variables, that are marginally significant in our main results, are less so here.


8For these two outcome variables, adding country effects but no time effects produces marginally significant
coefficients with the right sign, implying that the time effects are responsible for changing the sign of the coefficient.


9For our UNIDO-based measure, averaging of the share of female workers across a couple of dozen countries helps
alleviate both of these problems.


21




5 Conclusion


We have analyzed both theoretically and empirically the interplay between trade and discrimination


against women. The main findings suggest that gender equality is a source of comparative advantage


when a country integrates into world markets. Reciprocally, trade is found to affect societies’


attitudes towards gender.


Our results go beyond positing an unequivocal relationship between overall trade openness and


gender inequality. Instead, we emphasize the heterogeneity of the effects of trade on countries’


industrial structures and attitudes towards women. On the one hand, industries that rely relatively


more on female labor will expand more in countries where women are empowered. On the other


hand, we find a lower gender gap in countries that export more goods that require female labor to


be produced.


From a policy perspective, these results indicate that countries with technologically-based compara-


tive advantage in male-labor intensive goods will have to undertake a larger effort to counterbalance


the economic forces, leading to a slower pace of women’s empowerment compared to countries with


a comparative advantage in female-labor intensive goods. Nonetheless, these same efforts will re-


duce the impact of comparative advantage on the incentives for female labor force participation,


and further feed the conditions to empower women. In an increasingly integrated world market, the


road to female empowerment is paradoxically very specific to each country’s productive structure


and exposure to world markets.


A Mathematical Appendix


From equation (??), let’s try to characterize the behavior of f when the patterns of comparative


advantage ρ are changing.


Dropping the country reference and substituting for fS , f (ρ) is implicitly defined by:


[
1


ρ
(f − 1) + 1



[1− (1− η) f ] + θfα


[
η − 1


ρ
(1− η) (f − 1)


]
= 0


that is denoted x (f, ρ) = 0.


∂x (f, ρ)


∂ρ
= − 1


ρ2
α (f − 1)


1
ρ (f − 1) + 1


[
1


ρ
(f − 1) + 1



[1− (1− η) f ]


+
1


ρ2
(1− η) (f − 1) θfα


and since x (f, ρ) = 0 implies[
1


ρ
(f − 1) + 1



[1− (1− η) f ] = −θfα


[
η − 1


ρ
(1− η) (f − 1)


]
,


22




we have


∂x (f, ρ)


∂ρ
=


1


ρ2
α (f − 1)


1
ρ (f − 1) + 1


θfα
[
η − 1


ρ
(1− η) (f − 1)


]
+


1


ρ2
(1− η) (f − 1) θfα


=
1


ρ2
θfα (f − 1)


1
ρ (f − 1) + 1


{
α


[
η − 1


ρ
(1− η) (f − 1)


]
+ (1− η)


[
1


ρ
(f − 1) + 1


]}
=


1


ρ2
θxα (f − 1)


1
ρ (f − 1) + 1


{
αη + (1− η) + (1− α) 1


ρ
(1− η) (f − 1)


}
On the other hand,


∂x (f, ρ)


∂f
=


1


ρ


α
1
ρ (f − 1) + 1


[
1


ρ
(f − 1) + 1



[1− (1− η) f ]


− (1− η)
[


1


ρ
(f − 1) + 1



+


αθ


f

[
η − 1


ρ
(1− η) (f − 1)


]
− 1


ρ
(1− η) θfα


After substitution


∂x (f, ρ)


∂f
= − θfα 1


ρ


α
1
ρ (f − 1) + 1


[
η − 1


ρ
(1− η) (f − 1)


]


+ θfα (1− η)
[
η − 1ρ(1− η) (f − 1)


]
[1− (1− η) f ]


+ θfα
α


f


[
η − 1


ρ
(1− η) (f − 1)


]
− θfα 1


ρ
(1− η)


taking terms 1 and 3, and 2 and 4 together, we simplify to:


∂x (f, ρ)


∂f
= θfα


[
η − 1


ρ
(1− η) (f − 1)


]
ρ− 1
ρ


 αf [1ρ (f − 1) + 1]



+ θfα
[
η − 1


ρ
(1− η) (f − 1)


]
ρ− 1
ρ


 η (1− η)[1− (1− η) f ] [η − 1ρ(1− η) (f − 1)]



23




We can now compute the local derivative of f with respect to ρ :


f ′ (ρ) = −
∂x(f,ρ)
∂ρ


∂x(f,ρ)
∂f


= − 1
ρ2


θfα(f−1)
1
ρ


(f−1)+1
{
αη + (1− η) + (1− α) 1ρ (1− η) (f − 1)


}
θfα


[
η − 1ρ(1− η) (f − 1)


]
ρ−1
ρ


{
α[1−(1−η)f ]


[
η− 1


ρ
(1− η)(f−1)]+η(1−η)f[ 1


ρ
(f−1)+1


]
f
[
1
ρ


(f−1)+1
]
[1−(1−η)f ]


[
η− 1


ρ
(1− η)(f−1)]


}


= − 1
ρ2


1− (1− η) f
ρ− 1


ρ (f − 1) f [αηρ+ (1− η) ρ+ (1− α) (1− η) (f − 1)]
α [1− (1− η) f ] [ρη − (1− η) (f − 1)] + η (1− η) f [(f − 1) + ρ]


=
(1− η) f − 1


ρ− 1
(f − 1) f


ρ


αηρ+ (1− η) ρ+ (1− α) (1− η) (f − 1)
ηρ [α+ (1− α) (1− η) f ] + (1− η) (f − 1) [α (f − 1) + (1− α) ηf ]


The second and third terms of the equation are always positive, since f > 1. And by virtue of (16),


the first term (1−η)f−1ρ−1 > 0. We thus have


f ′ (ρ) > 0


Q.E.D.


B Instrumentation Strategy


This Appendix describes the steps necessary to build the instrument for the female content of


exports. The construction of the instrument follows Do and Levchenko (2007), and exploits exoge-


nous geographic characteristics of countries together with the empirically observed regularity that


trade responds differentially to the standard gravity forces across sectors. For each industry i, we


estimate the Frankel and Romer (1999) gravity specification, which relates observed trade flows to


exogenous geographic variables:


LogXicd = αi + η
1
i ldistcd + η


2
i lpopc + η


3
i lareac + η


4
i lpopd + η


5
i laread + (25)


η6i landlockedcd + η
7
i bordercd + η


8
i bordercd×ldistcd +


η9i bordercd × popc + η10i bordercd×areac + η11i bordercd×popd +
η12i bordercd×aread + η13i bordercd×landlockedcd + icd,


where LogXicd is the log of exports as a share of GDP in industry i, from country c to country d.


The right-hand side consists of the geographical variables. In particular, ldistcd is the log of distance


between the two countries, defined as distance between the major cities in the two countries, lpopc


is the log of population of country c, lareac log of land area, landlockedcd takes the value of 0, 1,


or 2 depending on whether none, one, or both of the trading countries are landlocked, and bordercd


is the dummy variable for common border. The right-hand side of the specification is identical


to the one Frankel and Romer (1999) use. We use bilateral trade flows from the COMTRADE


database, converted to the 3-digit ISIC Revision 3 classification. To estimate the gravity equation,


the bilateral trade flows Xicd are averaged over the period 1980-2007. This allows to smooth out


24




any short-run variation in trade shares across sectors, and reduce the impact of zero observations.


Having estimated equation (25) for each industry, we then obtain the predicted logarithm of industry


i exports to GDP from country c to each of its trading partners indexed by d, L̂ogXicd. In order to


construct the predicted overall industry i exports as a share of GDP from country c, we then take


the exponential of the predicted bilateral log of trade, and sum over the trading partner countries


d = 1, ..., C, exactly as in Frankel and Romer (1999):


X̂ic =
C∑


d = 1


d 6= c


eL̂ogXicd .


That is, predicted total trade as a share of GDP for each industry and country is the sum of the


predicted bilateral trade to GDP over all trading partners. This exercise extends and modifies the


Frankel and Romer (1999) methodology in two respects. First, and most importantly, it constructs


the Frankel and Romer (1999) predicted trade measures by industry. And second, rather than


looking at total trade, it looks solely at exports.


Do and Levchenko (2007) discuss and justify this strategy at length. As mentioned above, the


objective is to predict trade patterns, not trade volumes. How can this procedure yield different


predictions for X̂ic across sectors if all of the geographical characteristics on the right-hand side


of equation (25) do not vary by sector? Note that the procedure estimates an individual gravity


equation for each sector. Thus, crucially for this strategy, if the vector of estimated gravity coef-


ficients hi differs across sectors, so will the predicted total exports X̂ic across sectors i within the


same country. Indeed, Do and Levchenko (2007) show that the variation in these coefficients across


sectors is substantial, generating variation in predicted trade patterns across countries.


There is another potentially important issue, namely the zero trade observations. In our gravity


sample, only about two-thirds of the possible exporter-importer pairs record positive exports, in


any sector. At the level of individual industry, on average only a third of possible country-pairs


have strictly positive exports, in spite of the coarse level of aggregation.10 We follow the Do and


Levchenko (2007) procedure, and deal with zero observations in two ways. First, following the large


majority of gravity studies, we take logs of trade values, and thus their baseline gravity estimation


procedure ignores zeros. However, instead of predicting in-sample, we use the estimated gravity


model to predict out-of-sample. Thus, for those observations that are zero or missing and are not


used in the actual estimation, we still predict trade.11 In the second approach, we instead estimate


the gravity regression in levels using the Poisson pseudo-maximum likelihood estimator suggested


by (Santos Silva and Tenreyro, 2006). The advantage of this procedure is that it actually includes


zero observations in the estimation, and can predict both zero and non- zero trade values in-sample


10These two calculations make the common assumption that missing trade observations represent zeros (see Help-
man et al., 2008).


11More precisely, for a given exporter-importer pair, we predict bilateral exports out-of-sample for all 61 sectors as
long as there is any bilateral exports for that country pair in at least one of the 61 sectors.


25




from the same estimated equation. Its disadvantage is that it assumes a particular likelihood


function, and is not (yet) a standard way of estimating gravity equations found in the literature.


It turns out that the two are quite close to each other, an indication that the zeros problem is not


an important one for this empirical strategy. This paper only reports the results of implementing


the first approach. The results of using the second one are available upon request.


Armed with a working model for predicting exports to GDP in each industry i, it is straightforward


to construct the instrument for the female content of exports, based on predicted export patterns


rather than actual ones. That is, our instrument will be, in a manner identical to equation (22):


F̂LNXc =


I∑
i=1


ω̂XicFLi.


Here, the predicted share of total exports in industry i in country c, ω̂Xic , is constructed from the


predicted export ratios X̂ic in a straightforward manner:


ω̂Xic =
X̂ic∑I
i=1 X̂ic


.


Note that even though X̂ic is exports in industry i normalized by a country’s GDP, every sector is


normalized by the same GDP, and thus they cancel out when we take the predicted export share.


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28




Table 1: Female Labor Dependence of Sectors
ISIC Code Sector Name Dependence


151 Meat, fish, fruit, vegetables, oils and fats 0.36
152 Dairy products 0.25
153 Grain mill, starch products, and prepared animal feeds 0.20
154 Other food products 0.39
155 Beverages 0.23
160 Tobacco products 0.33
171 Spinning, weaving and finishing of textiles 0.37
172 Other textiles 0.47
173 Knitted and crocheted fabrics and articles 0.62
181 Wearing apparel, except fur apparel 0.71
182 Fur and articles of fur 0.41
191 Leather and leather products 0.43
192 Footwear 0.49
201 Sawmilling and planing of wood 0.16
202 Products of wood, cork, straw and plaiting materials 0.18
210 Paper and paper products 0.23
221 Publishing 0.33
222 Printing and service activities related to printing 0.29
223 Reproduction of recorded media 0.35
231 Coke oven products 0.14
232 Refined petroleum products 0.13
233 Nuclear fuel 0.11
241 Basic chemicals 0.15
242 Other chemical products 0.36
243 Man-made fibres 0.22
251 Rubber products 0.23
252 Plastics products 0.27
261 Glass and glass products 0.19
269 Non-metallic mineral products n.e.c. 0.16
271 Basic iron and steel 0.10
272 Basic precious and non-ferrous metals 0.13
273 Casting of metals 0.12
281 Structural metal products, tanks, reservoirs, steam generators 0.12
289 Other fabricated metal products 0.19
291 General purpose machinery 0.16
292 Special purpose machinery 0.14
293 Domestic appliances n.e.c. 0.28


29




Table 1 (continued): Female Labor Dependence of Sectors
ISIC Code Sector Name Dependence


300 Office, accounting and computing machinery 0.34
311 Electric motors, generators and transformers 0.32
312 Electricity distribution and control apparatus 0.30
313 Insulated wire and cable 0.32
314 Accumulators, primary cells and primary batteries 0.26
315 Electric lamps and lighting equipment 0.34
319 Other electrical equipment n.e.c. 0.42
321 Electronic valves and tubes and other electronic components 0.46
322 TV and radio transmitters; telephony and telegraphy apparatus 0.38
323 TV and radio receivers, sound or video apparatus 0.43
331 Medical appliances and instruments 0.38
332 Optical instruments and photographic equipment 0.45
333 Watches and clocks 0.42
341 Motor vehicles 0.09
342 Bodies for motor vehicles; trailers and semi-trailers 0.08
343 Parts and accessories for motor vehicles and their engines 0.21
351 Building and repairing of ships and boats 0.09
352 Railway and tramway locomotives and rolling stock 0.08
353 Aircraft and spacecraft 0.15
359 Transport equipment n.e.c. 0.21
361 Furniture 0.20
369 Manufacturing n.e.c. 0.38
371 Recycling of metal waste and scrap 0.17
372 Recycling of non-metal waste and scrap 0.25


Mean 0.274
Min 0.08
Max 0.71


Table 2: Summary Statistics for Female Labor Need of Exports


OECD Non-OECD


Mean St. Dev. Countries Mean St. Dev. Countries
1960s .251 .043 20 .271 .077 100
1970s .239 .034 20 .260 .078 103
1980s .244 .043 20 .269 .094 103
1990s .262 .043 20 .302 .111 123
2000s .256 .033 21 .293 .124 127


30




Table 3a: Female Labor Need of Exports: Top 10 and Bottom 10 Countries,
1962-2007.


Highest Female Labor Need of Exports Lowest Female Labor Need of Exports


Lesotho 0.657 Nigeria 0.149
Bangladesh 0.531 Iran 0.148
Haiti 0.510 Algeria 0.148
Mauritius 0.479 Kazakhstan 0.141
Mongolia 0.446 Venezuela, RB 0.138
Cambodia 0.441 Gabon 0.138
Sri Lanka 0.433 Kuwait 0.137
Nepal 0.432 Saudi Arabia 0.137
Dominican Republic 0.425 Iraq 0.134
Pakistan 0.421 Libya 0.133


Table 3b: Female Labor Need of Exports: Top 10 and Bottom 10 Countries, 1960s.


Highest Female Labor Need of Exports Lowest Female Labor Need of Exports


Afghanistan 0.391 Iran 0.154
Mauritius 0.385 Gabon 0.149
Haiti 0.382 Chile 0.144
Pakistan 0.382 Zambia 0.136
Timor-Leste 0.379 Oman 0.135
Dominican Rep. 0.379 Venezuela 0.134
Cuba 0.378 Iraq 0.134
Sierra Leone 0.378 Libya 0.134
Mongolia 0.374 Kuwait 0.133
Hong Kong 0.369 Saudi Arabia 0.133


Table 3c: Female Labor Need of Exports: Top 10 and Bottom 10 Countries, 2000s.


Highest Female Labor Need of Exports Lowest Female Labor Need of Exports


Cambodia 0.660 Saudi Arabia 0.140
Haiti 0.658 Algeria 0.139
Lesotho 0.649 Gabon 0.139
Bangladesh 0.640 Venezuela 0.138
Honduras 0.576 Kuwait 0.138
Sri Lanka 0.550 Nigeria 0.138
Madagascar 0.535 Kazakhstan 0.136
Mongolia 0.533 Iraq 0.135
Mauritius 0.525 Libya 0.134
El Salvador 0.522 Liberia 0.125


31




Table 4a: Female Labor Need of Exports: Top 10 and Bottom 10 Changers since
1960s.


Largest Increase in Female Labor Need of Exports Largest Decrease in Female Labor Need of Exports


Cambodia 0.423 Mozambique -0.094
Honduras 0.326 Liberia -0.095
Haiti 0.276 Sudan -0.114
Albania 0.233 Rwanda -0.115
Sri Lanka 0.226 Ecuador -0.131
Tunisia 0.223 Congo -0.136
Morocco 0.205 Chad -0.154
El Salvador 0.199 Niger -0.161
Madagascar 0.178 Yemen -0.168
Nicaragua 0.166 Angola -0.179


Note: Change is calculated as the difference between the average Female Labor Need of Exports in
2000 and that in 1960.


Table 4b: Female Labor Need of Exports: Top 10 and Bottom 10 Changers since
1980s.


Largest Increase in Female Labor Need of Exports Largest Decrease in Female Labor Need of Exports


Cambodia 0.352 Uruguay -0.056
Honduras 0.238 Yemen -0.057
Albania 0.210 Burundi -0.059
Bangladesh 0.209 Liberia -0.062
Haiti 0.205 Papua New Guinea -0.068
Madagascar 0.194 Guinea-Bissau -0.081
Nicaragua 0.189 Rwanda -0.096
Sri Lanka 0.183 Afghanistan -0.129
El Salvador 0.164 Sudan -0.138
Tunisia 0.158 Chad -0.139


Note: Change is calculated as the difference between the average Female Labor Need of Exports in
2000 and that in 1980.


Table 5: Summary Statistics
Observations Mean Std. Dev.


(Log) Female Labor Need of Exports 146 3.29 0.35
(Log) Real GDP per capita 146 8.57 1.14
(Log) Openness 146 4.19 0.56
(Log) Female Years of Schooling 146 1.63 0.69
(Log) Fertility 146 1.15 0.55
(Log) Female Labor Force Participation 146 3.87 0.39
(Log) Population 146 16.05 1.38
(Log) Predicted Female Labor Need of Exports 146 -1.43 0.06


32




Table 6: Sectoral Export Shares and Female Attainment Measures


(1) (2) (3) (4) (5) (6)


Panel A: OLS


Dep. Var.: Sectoral Share of Exports


Female Labor Intensity *
Fertility


0.08
(0.259)


-0.39
(0.322)


Female Labor Intensity *
Female Labor Force
Participation


0.09**
(0.036)


0.08*
(0.043)


Female Labor Intensity *
Ratio of Female to Total
Education


0.29
(4.122)


3.97
(5.487)


Capital Intensity * Capital
Abundance


1.72**
(0.791)


1.67**
(0.784)


1.53**
(0.751)


Skill Intensity * Skill
Abundance


-1.53
(1.583)


-1.42
(1.546)


-0.44
(1.532)


Country and Sector
Dummies


Yes Yes Yes Yes Yes Yes


R2 0.245 0.253 0.246 0.254 0.227 0.239
Observations 8,533 6,190 8,533 6,190 7,361 5,732


Panel B: 2SLS


Dep. Var.: Sectoral Share of Exports


Female Labor Intensity *
Fertility


-2.27***
(0.761)


-1.59*
(0.941)


Female Labor Intensity *
Female Labor Force
Participation


0.23***
(0.072)


0.19**
(0.096)


Female Labor Intensity *
Ratio of Female to Total
Education


11.07*
(6.450)


4.51
(7.125)


Capital Intensity * Capital
Abundance


1.96***
(0.815)


1.80**
(0.784)


1.90**
(0.789)


Skill Intensity * Skill
Abundance


-1.69
(1.619)


-1.283
(1.536)


-0.57
(1.588)


Country and Sector
Dummies


Yes Yes Yes Yes Yes Yes


Observations 8,416 6,075 8,416 6,075 7,244 5,501
First Stage


Dep. Var.: FLi*
Fertility


FLi*
Fertility


FLi*FLFP FLi*FLFP FLi*Ratio
of Female
to Total


Education


FLi*Ratio
of Female
to Total


Education
Female Labor Intensity *
Muslim Share of
Population


2.71***
(0.111)


2.54***
(0.156)


-30.53***
(1.151)


-29.67***
(1.775)


-0.23***
(0.012)


-0.20***
(0.014)


Female Labor Intensity *
Christian Share of
Population


0.68***
(0.098)


0.37***
(0.138)


-12.61***
(0.883)


-11.30***
(1.499)


0.08***
(0.010)


0.12***
(0.013)


F-test 367.44 252.18 359.32 180.25 604.16 740.47
Partial R2 0.911 0.920 0.947 0.942 0.977 0.981


Notes: Robust standard errors in parentheses; * significant at 10%; ** significant at 5%; *** significant at


1%. Female Labor Intensity (FLi) is the average fraction of female employees in a sector at the ISIC Rev.


3 three-digit level; Fertility Rate is total births per woman; Female Labor Force Participation (FLFP) is


female labor participation rate (% of female population ages 15+); Ratio of Female to Total Education is the


ratio of the Barro-Lee measures of female to total educational attainment. All of the variables are averages


over the period 1962-2007, except for Female Labor Force Participation, which is averaged over the period


1980-2007. Variable definitions and sources are described in detail in the text.


33




Table 7: Cross-Country Regression Results, 1962-2007


OLS 2SLS
(1) (2) (3) (4) (5) (6) (7) (8)


Dependent Variable: Fertility
Rate


Fertility
Rate


Female
Labor


Force Par-
ticipation


Female Ed-
ucational


Attainment


Fertility
Rate


Fertility
Rate


Female
Labor


Force Par-
ticipation


Female Ed-
ucational


Attainment


(Log) Female Labor
Need of Exports
(FLNX)


-0.30***
(0.082)


-0.31***
(0.080)


0.19*
(0.112)


0.24*
(0.138)


-0.49***
(0.136)


-0.51***
(0.126)


0.30*
(0.160)


0.25
(0.190)


(Log) Openness -0.00
(0.04)


-0.07
(0.051)


-0.06
(0.059)


0.10
(0.098)


-0.01
(0.038)


-0.08
(0.052)


-0.06
(0.057)


0.10
(0.096)


(Log) GDP per capita -0.40***
(0.020)


-0.39***
(0.021)


-0.07***
(0.028)


0.48***
(0.045)


-0.41***
(0.020)


-0.40***
(0.020)


-0.06**
(0.028)


0.48***
(0.046)


(Log) Population -0.05**
(0.023)


-0.04
(0.027)


-0.00
(0.032)


-0.06**
(0.022)


-0.04
(0.026)


-0.00
(0.031)


R2 0.636 0.649 0.116 0.592
First Stage


Dep. Var.: (Log)
FLNX
(Log) Predicted FLNX 3.14***


(0.326)
3.14***


(0.320)
3.14***


(0.320)
3.08***


(0.330)
F-test 45.38 34.70 34.70 29.73
R2 0.385 0.387 0.387 0.397
Observations 146 146 146 126 146 146 146 126


Notes: Robust standard errors in parentheses; * significant at 10%; ** significant at 5%; *** significant at 1%. Fertility Rate is (log) total births per


woman; Female Labor Force Participation is (log) female labor participation rate (% of female population ages 15+); Female Educational Attainment is


the (log of) Barro-Lee measure of female educational attainment (average years of schooling among female population ages 15+ at five-year intervals).


All of the variables are averages over the period 1962-2007, except for Female Labor Force Participation, which is averaged over the period 1980-2007.


Variable definitions and sources are described in detail in the text.


34




Table 8: Panel Regression Results, 1962-2007


(1) (2) (3) (4) (5) (6) (7)
Dependent Variable: Fertility


Rate
Fertility


Rate
Fertility


Rate
Female


Labor
Force Par-


ticipation


Female
Labor


Force Par-
ticipation


Female Ed-
ucational


Attainment


Female Ed-
ucational


Attainment


(Log) Female Labor
Need of Exports
(FLNX)


-0.39***
(0.031)


-0.27***
(0.030)


-0.23***
(0.028)


0.20***
(0.045)


-0.02
(0.026)


0.39***
(0.067)


-0.05
(0.060)


(Log) Openness -0.08***
(0.017)


-0.10***
(0.017)


-0.02
(0.017)


-0.04**
(0.021)


-0.04**
(0.015)


0.16***
(0.037)


-0.07**
(0.032)


(Log) GDP per capita -0.37***
(0.009)


-0.26***
(0.020)


-0.19***
(0.022)


-0.07***
(0.011)


0.01
(0.018)


0.62***
(0.021)


-0.06
(0.035)


(Log) Population -0.08***
(0.008)


-0.43***
(0.021)


-0.08**
(0.036)


-0.04***
(0.011)


0.20***
(0.049)


0.09***
(0.016)


0.99***
(0.087)


Country Fixed Effects No Yes Yes No Yes No Yes
Year Fixed Effects No No Yes No Yes No Yes
R2 0.609 0.929 0.938 0.113 0.968 0.508 0.949
Observations 1,247 1,247 1,247 819 819 1,102 1,102


Notes: Robust standard errors in parentheses; * significant at 10%; ** significant at 5%; *** significant at 1%. Fertility Rate is (log) total births per


woman; Female Labor Force Participation is (log) female labor participation rate (% of female population ages 15+); Female Educational Attainment is


the (log of) Barro-Lee measure of female educational attainment (average years of schooling among female population ages 15+ at five-year intervals).


All of the variables are five-year averages over the period 1962-2007, except for Female Labor Force Participation, which is averaged over the period


1980-2007. Variable definitions and sources are described in detail in the text.


35




Table A1: Least and Most Female-Intensive Sectors, U.S. Data
Least Female-Intensive FLi Most Female-Intensive FLi


Logging 5.4 Other apparel and accessories 56.3


Coal Mining 5.7 Leather tanning and finishing 56.3


Cement, concrete, lime, and


gypsum


10.3 Retail bakeries 58


Sawmills and wood


preservation


11.3 Specialized design services 58


Nonmetallic mineral mining


and quarrying


11.5 Cut and sew apparel 66.1


Appendix Table A2: Summary Statistics, U.S.-based measure of FL
Mean Min Max SD N


Manufacturing 29.7 10.3 66.1 13.6 78


Non-manufacturing 25.8 5.4 58 15.6 15


36




Appendix Table A3: Sectoral Export Shares and Female Attainment Measures


(1) (2) (3) (4) (5) (6)


Panel A: OLS


Dep. Var.: Sectoral Share of Exports


Female Labor Intensity *
Fertility


-0.01***
(0.001)


-0.00
(0.001)


Female Labor Intensity *
Female Labor Force
Participation


-0.00
(0.000)


-0.00
(0.000)


Female Labor Intensity *
Ratio of Female to Total
Education


0.03*
(0.020)


-0.00
(0.027)


Capital Intensity * Capital
Abundance


0.38***
(0.166)


0.38***
(0.168)


0.45***
(0.200)


Skill Intensity * Skill
Abundance


9.63***
(2.112)


9.60***
(2.091)


10.42***
(2.251)


Country and Sector
Dummies


Yes Yes Yes Yes Yes Yes


R2 0.229 0.131 0.229 0.131 0.212 0.134
Observations 13,269 8,055 13,269 8,055 11,468 7,309


Panel B: 2SLS


Dep. Var.: Sectoral Share of Exports


Female Labor Intensity *
Fertility


-0.01***
(0.004)


0.00
(0.003)


Female Labor Intensity *
Female Labor Force
Participation


0.01***
(0.000)


-0.00
(0.000)


Female Labor Intensity *
Ratio of Female to Total
Education


0.08***
(0.031)


-0.03
(0.028)


Capital Intensity * Capital
Abundance


0.36**
(0.165)


0.38**
(0.167)


0.43**
(0.196)


Skill Intensity * Skill
Abundance


9.23***
(2.149)


9.64***
(2.074)


10.01***
(2.180)


Country and Sector
Dummies


Yes Yes Yes Yes Yes Yes


Observations 13,269 8,055 13,269 8,055 11,468 7,309
First Stage


Dep. Var.: FLi*
Fertility


FLi*
Fertility


FLi*FLFP FLi*FLFP FLi*Ratio
of Female
to Total


Education


FLi*Ratio
of Female
to Total


Education
Female Labor Intensity *
Muslim Share of
Population


0.02***
(0.001)


0.03***
(0.001)


-0.27***
(0.007)


-0.28***
(0.010)


-0.01***
(0.000)


-0.01***
(0.000)


Female Labor Intensity *
Christian Share of
Population


0.01***
(0.001)


0.01***
(0.001)


-0.15***
(0.005)


-0.15***
(0.007)


0.01***
(0.000)


0.01***
(0.000)


F-test 599.40 442.34 852.97 444.94 898.03 1053.12
Partial R2 0.913 0.927 0.950 0.949 0.976 0.981


Notes: Robust standard errors in parentheses; * significant at 10%; ** significant at 5%; *** significant


at 1%. Female Labor Intensity (FLi) is the average fraction of female employees in a sector at the four-


digit level of the US Census classification; Fertility Rate is total births per woman; Female Labor Force


Participation (FLFP) is female labor participation rate (% of female population ages 15+); Ratio of Female


to Total Education is the ratio of the Barro-Lee measures of female to total educational attainment. All of


the variables are averages over the period 1962-2007, except for Female Labor Force Participation, which is


averaged over the period 1980-2007. Variable definitions and sources are described in detail in the text.


37




Appendix Table A4: Cross-Country Regression Results, 1962-2007


OLS 2SLS
(1) (2) (3) (4) (5) (6) (7) (8)


Dependent Variable: Fertility
Rate


Fertility
Rate


Female
Labor


Force Par-
ticipation


Female Ed-
ucational


Attainment


Fertility
Rate


Fertility
Rate


Female
Labor


Force Par-
ticipation


Female Ed-
ucational


Attainment


(Log) Female Labor
Need of Exports
(FLNX)


-0.57***
(0.132)


-0.56***
(0.127)


0.10
(0.159)


0.36**
(0.181)


-1.03***
(0.233)


-0.94***
(0.222)


0.21
(0.266)


0.19
(0.294)


(Log) Openness -0.00
(0.038)


-0.06
(0.050)


-0.07
(0.063)


0.10
(0.096)


-0.01
(0.042)


-0.06
(0.051)


-0.07
(0.061)


0.10
(0.093)


(Log) GDP per capita -0.37***
(0.021)


-0.36***
(0.022)


-0.08***
(0.028)


0.47***
(0.042)


-0.37***
(0.021)


-0.36***
(0.021)


-0.08***
(0.028)


0.47***
(0.042)


(Log) Population -0.04*
(0.023)


-0.05*
(0.028)


-0.01
(0.032)


-0.04*
(0.023)


-0.05*
(0.027)


-0.01
(0.032)


R2 0.651 0.659 0.091 0.591
First Stage


Dep. Var.: (Log)
FLNX
(Log) Predicted FLNX 1.548***


(0.212)
1.62***


(0.219)
1.62***


(0.219)
1.64***


(0.237)
F-test 20.19 15.95 15.95 14.01
R2 0.328 0.340 0.340 0.359
Observations 146 146 146 126 146 146 146 126


Notes: Robust standard errors in parentheses; * significant at 10%; ** significant at 5%; *** significant at 1%. Fertility Rate is (log) total births per


woman; Female Labor Force Participation is (log) female labor participation rate (% of female population ages 15+); Female Educational Attainment is


the (log of) Barro-Lee measure of female educational attainment (average years of schooling among female population ages 15+ at five-year intervals).


All of the variables are averages over the period 1962-2007, except for Female Labor Force Participation, which is averaged over the period 1980-2007.


Variable definitions and sources are described in detail in the text.


38




Appendix Table A5: Panel Regression Results, 1962-2007


(1) (2) (3) (4) (5) (6) (7)
Dependent Variable: Fertility


Rate
Fertility


Rate
Fertility


Rate
Female


Labor
Force Par-


ticipation


Female
Labor


Force Par-
ticipation


Female Ed-
ucational


Attainment


Female Ed-
ucational


Attainment


(Log) Female Labor
Need of Exports
(FLNX)


-0.56***
(0.043)


-0.28***
(0.047)


-0.24***
(0.042)


0.03
(0.059)


-0.05
(0.030)


0.62***
(0.080)


0.30***
(0.079)


(Log) Openness -0.06***
(0.016)


-0.09***
(0.018)


-0.02
(0.017)


-0.05**
(0.022)


-0.03**
(0.015)


0.14***
(0.037)


-0.09***
(0.033)


(Log) GDP per capita -0.35***
(0.009)


-0.27***
(0.020)


-0.19***
(0.022)


-0.09***
(0.012)


0.01
(0.018)


0.59***
(0.021)


-0.06*
(0.036)


(Log) Population -0.06***
(0.008)


-0.42***
(0.021)


-0.07*
(0.038)


-0.04***
(0.011)


0.20***
(0.049)


0.08***
(0.016)


0.97***
(0.087)


Country Fixed Effects No Yes Yes No Yes No Yes
Year Fixed Effects No No Yes No Yes No Yes
R2 0.610 0.927 0.936 0.083 0.968 0.512 0.950
Observations 1,247 1,247 1,247 819 819 1,102 1,102


Notes: Robust standard errors in parentheses; * significant at 10%; ** significant at 5%; *** significant at 1%. Fertility Rate is (log) total births per


woman; Female Labor Force Participation is (log) female labor participation rate (% of female population ages 15+); Female Educational Attainment is


the (log of) Barro-Lee measure of female educational attainment (average years of schooling among female population ages 15+ at five-year intervals).


All of the variables are five-year averages over the period 1962-2007, except for Female Labor Force Participation, which is averaged over the period


1980-2007. Variable definitions and sources are described in detail in the text.


39




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