Seguino.00.Gender.inequality.and.Economic.growth.pdf

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Gender Inequality and Economic Growth:

A Cross-Country Analysis

STEPHANIE SEGUINO *University of Vermont, Burlington, USA

Summary. This paper investigates empirically the determinants of economic growth for a set ofsemi-industrialized export-oriented economies in which women provide the bulk of labor in theexport sector. The primary hypothesis tested is that gender inequality which contributes to womensrelatively lower wages was a stimulus to growth via the eect on exports during 197595. Empiricalanalysis shows that GDP growth is positively related to gender wage inequality in contrast to recentwork which suggests that income inequality slows growth. Evidence also indicates that part of theimpact of gender wage inequality on growth is transmitted through its positive eect on investmentas a share of GDP. 2000 Elsevier Science Ltd. All rights reserved.

Key words gender, inequality, economic growth, semi-industrialized economies, export-led

growth

1. INTRODUCTION

In recent years, there has been increasedinterest in the eect of shifts in income distri-

bution on economic growth.1

Furthering thateort, economists who incorporate feministperspectives in their work are beginning tobuild a body of work that extends analysis toconsider the ways in which the distribution ofincome by gender can inuence short- andlong-term macroeconomic outcomes. 2

The relevance of gender as a macroeconomicvariable has not yet been widely embraced bythe economics profession, however. As a result,research emanating from the recently renewedacademic interest in the determinants of

economic growth is virtually devoid of agendered perspective (Barro & Sala-i-Martin,1991; Grossman & Helpman, 1991; Kim & Lau,1996). Nor, on the empirical side, does the spateof cross-sectional growth accounting studiesthat followed on the heels of new growth theoryconsider gender a signicant explanatory vari-able. 3 The research presented here is an eortto ll the latter lacuna. In particular, this studyinvestigates empirically the impact of genderinequality on economic growth.

A basic premise of this research is that the

eects of gender on growth are likely to dependon the structure of the economy. The focus ofthe research presented here is therefore limited

to a set of semi-industrialized countries withdata covering 197595. The countries in thesample are similar in that they have adopted, tovarying degrees, an export orientation with a

large share of exports produced in female-dominated manufacturing industries. Througha growth accounting strategy, we examine therelevance of gender inequality for explainingGDP growth rates, using panel data and avariety of well-established conditioning vari-ables.

This task is humbly embarked on, owing tothe data diculties and conceptual problemsassociated with doing cross-country analyses.The very modest goal is to determine whetherthere are any empirical regularities in the data

that link gender inequality, through its eectson gender wage dierentials and education,with economic growth over time and acrosscountries. The main hypothesis tested is thatgender inequality which works to lower

World Development Vol. 28, No. 7, pp. 12111230, 2000 2000 Elsevier Science Ltd. All rights reserved

Printed in Great Britain0305-750X/00/$ - see front matter

PII: S0305-750X(00)00018-8www.elsevier.com/locate/worlddev

*Excellent research assistance was provided by Chris

Cichoski. The study was made possible by funding from

the University of Vermonts Deans Fund and University

Committee on Research and Scholarship. Helpful

comments were provided by Ric McIntyre, Elaine

McCrate, two anonymous referees, and members of

the Engendering Macroeconomics and International

Economics Working Group.

1211


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women's wages relative to mens is a stimulus togrowth in export-oriented economies. We alsoinvestigate the possibility that the growth eectof gender wage dierentials is transmitted viathe stimulus to investment, serving as a signal

of protability. Empirical results support bothof these hypotheses.

These ndings contrast sharply with recentwork which suggests that income inequality,measured at the household level (where femaleand male income is pooled, thereby obscuringincome inequality along gender lines) impedesgrowth because it produces social conict. Theeconomic eects of conict, it is argued in thatwork, are transmitted through the negativeimpact on investment and macroeconomicpolicy. These diering results may be reconciled

by recognizing that inequality is less likely toproduce social conict if the burden is born bywomen, a group traditionally socialized toaccept gender inequality as a socially accept-able outcome.

This research is presented as follows. Section2 discusses the mainstream literature on thetheory and empirics of economic growth. Thetheoretical underpinnings of the role of genderas a macro variable are then taken up. Section 3lays out the growth accounting approach usedin this paper and presents descriptive data.

Section 4 presents the regression results.Section 5 summarizes and suggests avenues forfuture research.

2. GROWTH THEORY, GROWTHACCOUNTING, AND GENDER

(a) Recent developments in growth theory

The brief review of developments in thegrowth theory literature presented here is not

meant to be exhaustive, but rather, highlightsthose areas of thought and empirical workrelevant to an investigation of the relationshipbetween growth and gender. Earlier economicgrowth models focused attention on the posi-tive role that accumulation of capital and labor,and technical progress play in increasing percapita output. Since the 1980s, economists haveturned attention to the determinants of tech-nical progress which had previously beenmodelled as exogenous.

Numerous researchers have emphasized the

importance of trade and trade policies ininuencing technical progress and thereby therate of economic growth. There are several

avenues by which trade is thought to stimulategrowth. Exports provide the foreign exchangeto license technology or gain access to importsof intermediate and capital goods that embodynew technology, thereby raising aggregate

productivity (Grossman & Helpman, 1991;Romer, 1991). 4 In addition, exports permiteconomies of scale and specialization (assuminga limited home market) that raise productivityand therefore output. Macro policies thatpromote openness to trade are also hypothe-sized to play a positive role. For example,economic openness is argued to contribute to acompetitive economic environment, promotingallocative eciency, and thus enhancing outputper worker (World Bank, 1991). 5

The results of empirical tests on the rela-

tionship between trade and growth are mixed,however. While some research nds a robustrelationship between exports and growth, tradepolicy measures are less consistently signi-cantly related to growth (e.g., Levine & Renelt,1992; Harrison, 1995; Rodriguez & Rodrik,1999). The empirical results suggest that if thereis a positive relationship between trade andgrowth, that nexus is related to the ability ofexports (not economic openness per se) to helpcountries overcome balance-of-paymentsconstraints and to obtain the foreign exchange

to purchase best practice technology (e.g.,Esfahani, 1991).

Some evidence indicates that exports aloneare insucient to promote growth and thatgrowth depends on a variety of additionalfactors. For example, some studies nd growthto be positively aected by exports in semi-in-dustrialized economies only when the lattervariable is interacted with a measure of humancapital (Edwards, 1992; Levin & Raut, 1997).This nding suggests that exports are associ-ated with gains in output primarily for those

countries with sucient human capital toabsorb new technologies.

In addition, economic structure appears toinuence the way and degree to which tradeaects growth. For example, some studies ndthat the ratio of manufactured exports to GDPhas a positive eect on growth whereas primarycommodity exports to GDP do not (Levin &Raut, 1997; Sachs & Warner, 1997). This mightbe explained by the fact that the intermediateand capital goods imports required to producemanufactured goods in a semi-industrialized

economy embody technology, producing anexport 3 import 3 growth link. Countriesspecializing in primary commodity exports,

WORLD DEVELOPMENT1212


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however, import mainly nished manufacturedgoods which have few spillover eects onproductivity and output growth. 6

Finally, some authors note that the spectac-ular growth of exports in some Asian econo-

mies may not have been the primary causalfactor in stimulating growth. For instance,according to Rodrik (1994, 1995), rapid exportgrowth in Korea and Taiwan was stimulated byan investment boom. The surge in investmentwas itself precipitated, it is argued, by a varietyof state-level policies and domestic economicconditions that raised the protability ofinvestment, and provided the savings topurchase imported capital goods (Amsden,1989; Wade, 1990). It is therefore dicult todisentangle the role of exports in stimulating

growth since while they provide the foreignexchange to purchase imported goods, thedemand for those goods is driven by investmentwhich requires a climate that promotes positiveprot expectations.

In addition to the the recent focus on trade,mainstream growth theory has begun toconsider the causal eect of income distributionon growth. Of particular interest are severalpapers which present models and empiricsindicating that income inequality can producesocial conict that may retard economic growth

(Alesina & Rodrik, 1994; Persson & Tabellini,1994; Larran & Vergara, 1998). Two avenuesby which income distribution aects growth areadvanced in that work. The negative growtheects of income inequality may be transmittedthrough the eect on investment where thepotential social conict that inequality signalscan create uncertainty that dampens investment(Larran & Vergara, 1998). Further, incomeinequality may produce political con ict thatpolicy makers attempt to the placate withgrowth-inhibiting macro policies.

The empirical studies of the relationshipbetween income distribution and growth relyon household data, and therefore do notcapture income inequality along gender lines ina number of countries. 7 In particular, whilehousehold data indicate relative equality inrapidly-growing Asia, the degree of genderinequality is among the highest in the world(ILO, various years). In order to make deni-tive statements about the causal relationshipbetween income distribution and growth then itwould appear necessary to account for the

eect of gender inequality. The role of gender instimulating growth is taken up in more detail inthe next section.

(b) Gender and growth

Feminist scholars maintain that gender is animportant macroeconomic variable and thatgender relations can aect economic develop-

ment and growth. The state of gender relations,which frequently results in divergent outcomesby gender, is readily observable in severaleconomic arenas: (i) job segregation within thepaid labor market, (ii) the division of laborbetween paid and unpaid labor, (iii) the distri-bution of income and resources within thehousehold, (iv) access to the redistributions bythe state, such as access to education and socialsafety net programs, and (v) credit in nancialmarkets. The eect of gendered economicopportunities is that women and men on aver-

age occupy dierent class positions, withwomen more likely to the be poor, malnour-ished, less educated, and overworked relative tothe men (Davis, 1981; Benera & Roldan, 1987;Deere, 1990; Wright, 1996). 8

Gender inequality is neither constant overtime nor across countries. Institutions changeas a result of collective action, and the eectsare observable (though not unambiguously so)on a number of measures such as gender wagedierentials and employment rates, hours ofpaid and unpaid work, rates of unemployment,

educational attainment, and other moreconcrete measures of well-being such as lifeexpectancy rates and the ratio of females tomales in the population.

What are the implications of genderinequality for economic growth? A growingbody of research has laid the foundation at themacro level for understanding how gendermight aect the pace of economic growth. 9 Inthis paper, we focus primarily on the causalmechanisms linked to job segregation in thepaid labor market, wage dierentials, and,

subsidiarily, to education.Job segregation by gender is a pervasive

characteristic of most economies (Jacobsen,1997), but the distribution of jobs by gendercan only in part be explained by correlationsbetween gender and education. Women tend tobe ``crowded'' into lower paying jobs, explain-ing a portion of the widely observable genderdierential wage rates by job and industry. 10

If women are crowded into industries thatproduce price elastic goods, this practice mayhave implications for trade patterns and

economic growth (Blecker & Seguino, 1999).For example, in the case considered here, jobsegregation that crowds women into export

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industries where price elasticities of demand arerelatively high may articially lower womenswages, due to their restricted bargaining power.The resulting gender wage dierentials (takingthe male wage as a benchmark) may be a

stimulus to export expansion.Insofar as growth empirics have accurately

detected a relationship between exports, tech-nical progress, and growth, we can hypothesizethat gender inequality has a positive eect ontechnical progress and growth whereby exportearnings provide the resources to purchasesophisticated technologies. The linkagebetween gender inequality and economicgrowth then can be summarized as: genderinequality 3 export expansion 3 technicalchange 3 economic growth. It should be

emphasized that the hypothesized eect of thegender wage gap on growth is likely to beinuenced by the structure of the economy.Here the gender inequality hypothesis pertainsto semi-industrialized countries as compared tocommodity-dependent exporters or industrial-ized countries which have access to domesti-cally-generated technical knowledge.

Low wages (for women, in our case) thatstimulate exports may not be sucient topromote growth. At least in the case of somelate industrializing countries, this competitive

advantage has been accompanied by a varietyof state policies and institutions that promotelearning to enable workers to integrate newimported technologies (Amsden, 1989). That is,in addition to export competitiveness, growthmay also be dependent upon the existence of askilled labor force that is able to competentlyadopt new technologies. We may take accountof this by linking the level of educationalattainment with a measure of gender inequalityas a determinant of growth.

An additional link between gender and

growth is through the eect of gender wagedierentials on the rate of capital accumula-tion. In Kaleckian approaches, investment is afunction of protability as well as output (theaccelerator eect), with protability signaled bythe share of income going to workers. Ingeneral, a low share of income going to workers(implying a high prot share) is thought to be astimulus to investment. 11 You (1991) haspointed out that rm responsiveness to prot-ability may dier by sector, with investment byrms producing nontradeables or for a

protected domestic market less sensitive toshifts in protability than rms producing forexport. Given womens segregation in export

industries where price elasticities of demand arehigh and the so-called protability eect large,capital accumulation may be stimulated by adrop in womens relative wages (a widening ofthe gender wage gap).

This point is consistent with that made bynumerous scholars who have investigated therelationship between gender and export-ori-ented growth. For example, Erturk andCa!gatay (1995) argue that feminization of thelabor force, associated with lower unit laborcosts, stimulates investment. Standing (1989,1999) notes that in recent years, competitivepressures resulting from globalization (i.e.,greater economic openness) have inducedemployers to substitute female workers formale workers, resulting in a feminization of the

labor force. In sum, gender inequality, asmeasured by gender wage dierentials, couldhave a positive eect on growth via the eect oninvestment under some structural conditions.We therefore also test for the eect of genderinequality on investment.

Further, the state of gender relations inu-ences educational outcomes. For example,gender relations may aect access to educa-tional and training opportunities, inuencingboth the distribution of human capital betweenwomen and men, as well as the total quantity

insofar as gender norms inhibit or promoteeducational attainment by sex.

Several cross-sectional analyses nd thatfemale educational attainment has a positiveeect on economic growth (Benavot, 1989; Hill& King, 1995). The purported link betweenwomens education and growth diers,however. While Barro and Lee (1996) nd thatthe growth impact of female education istransmitted through its negative eect onfertility, female educational investments mayalso be linked to the productivity of unpaid or

reproductive labor (Hill & King, 1995). In paidlabor markets, female educational attainment islikely to be positively related to aggregateproductivity growth. The positive eect offemale education may fall below its potential,however, if women are sequestered in low skill

jobs despite their qualications for more skilledpositions. This practice may also constrain thepositive eect of women's education on growth.

The net eect of gendered educationaloutcomes on growth is therefore a prioriunclear, at least for semi-industrialized econo-

mies, and therefore no hypotheses are advancedas to the possible sign on these variables in theanalysis that follows. We simply note here that



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it is relevant to disaggregate human capitalvariables by gender to take account of dier-ences in types of labor which women and menperform, and the social constraints on thedistribution of their labor.

3. CORRELATIONS BETWEEN GENDERINEQUALITY AND GROWTH: SAMPLE,

METHODOLOGY, AND DATA

Of the multiple potential eects of gender ongrowth, this paper focuses primarily on theexport-technical progress and investment links,using a growth accounting methodology. Moreprecisely, gender wage dierentials are includedas an explanatory variable that is assumed to

promote technological advance and therebygrowth. We also check to see if gender wageinequality is a stimulus to investment. Theserelationships are likely to be valid only forsemi-industrialized economies, as the previoussection suggests, and in particular, those thatare export-oriented and for which womenprovide the bulk of labor in export industries.For this reason, the sample used in this analysis(described in greater detail below) is restrictedto a group of semi-industrialized export-ori-ented economies. In the econometric analysis, a

very typical set of conditioning variables isused, with attention focused on the relationshipbetween growth and gender dierentials inwages and education.

(a) The sample

The sample is comprised of a set of semi-in-dustrialized countries that rely on exports as asignicant component of aggregate demandand for which gender-disaggregated wage dataare available. The sample is drawn from lower-

and middle-income countries as dened by theWorld Development Report 1995 (World Bank,1995). To select the sample, a semi-industrial-ized export orientation (SIEO) index wasdeveloped to permit the ranking of countries.The index was created as follows: its value isthe sum of the natural logarithm of the share ofexports in GDP, the ratio of machinery andtransport goods to non-oil primary commodi-ties in exports, and the share of manufacturingin output. 12 The rst of these variables is anindicator of export orientation, while the

second and third reect the degree to which acountry has attained a semi-industrializedstatus. Countries with SIEO values above a

threshold value of 1.0 were included in thesample. An additional criterion, noted above, isthe availability of gender-disaggregated wagedata. Data gaps here led a number of otherwisequalied countries to be dropped from the

sample.A look at the data for this reduced sample in

Figure 1 shows that the higher the value of acountrys SIEO index, the greater its corre-sponding average annual per capita growth ratefor 198093. 13 Further, as the data in Table 1show, the major export industries in selectedsample countries are female-dominated.

(b) The growth accounting methodology

For consistency with previous studies, theempirical specication of this paper is derivedfrom a neoclassical production functionframework, with output a function of thecapital stock, skilled-adjusted female and malelabor supply measured as human capital, andtechnological progress. 14 The underlyingproduction function can be specied as:

Yit AitFKYHKFYHKMitY 1

where Y is output, A is technological change,K is the capital stock, HKF and HKM are

female and male human capital, respectively, iis the country index, and t is time. 15

The determinants of technological changecan be decomposed into: (i) country-specicxed eects, (ii) a time eect, common acrossall countries (used to pick up factors in theglobal economic environment such as the oilprice shock that may inuence output), and (iii)the eect of specic and changing countryconditions which inuence the growth rate ofexports. In the latter category, we focus onfemale/male wage dierentials. More formally,

technical change can be described as

Ait Ci1 /terWGAPtY 2

where Ci is the country-specic time-invarianteect, / measures the eect of external factorsover time that aect growth not otherwiseincluded in the model, WGAP is the genderwage gap, and r is the eect of gender wagedierentials on growth. The gender variablethen captures the impact of inequality ongrowth after controlling for changes in resourceuse.

Substituting (2) into (1), taking natural logs,dierentiating with respect to time, and usingthe fact that log1 d % d, yields



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dlogYit / Rki a1WGAPit

a2dlogKit a3HKFit

a4HKMit itY 3

where d is the dierence operator, / is the

growth rate of technological change whenvariables are measured at the mean, Rki arexed eects, WGAP is the gender wage gap,HKF and HKM are female and male humancapital, respectively, and it is the error term,assumed to be normal. From (2), the coecienta1 is equal to r.

The regressions are carried out using ve-year averages and period averages for 197595.Use of period averages precludes the xedeects model and, of course, eliminates timeeects so this constrained model is less satisfy-

ing, but it is useful for comparison to otherresearch on the determinants of growth as wellas to the ve-year average regression results.

(c) The data

Data cover 197595. GDP is measured in1985 prices and from this, growth rates arecalculated for the sample countries. The growthrate of the capital stock is proxied as thegrowth rate of gross domestic xed capitalformation. 16

Several measures of human capital are used.These include the percentage of females andmales 15 and over who have completed

secondary education and the growth rate ofsecondary school attainment by sex. Averageyears of total and secondary education perfemale and male are variously included torepresent the stock of human capital. (Regres-sions were also run with these variables

measured in natural logs but were not signi-cant and so are not discussed here.)

Three measures of the gender wage gap areused. One is a basic wage gap variable,WGAP1, measured as LogWM LogWFwhere WM and WF are male and femaleearnings, respectively. Earnings data arecorrected for hours worked where possible (inmost cases). 17 Table 2 provides a summary ofthe gender wage gap data, measured both asratios (for ease of interpretation) and logdierences, and years of country coverage.

A second measure attempts to rene the wagegap variable by correcting gender earnings foreducational attainment. This correction gener-ates what might be called an ``eciency''gender wage gap because it takes into accountdierences in women's and mens secondaryeducational attainment. Dubbed WGAP2, it ismeasured as follows:

WGAP2 LogWM

SYRM

Log

WF

SYRF

Y 4

where SYRMand SYRFare average number ofyears of secondary education per male andfemale 15 and over, respectively. A wide gender

Figure 1. SIEO index and growth rates of per captia GDP, 198093.



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wage gap, coupled with a relatively high edu-cational attainment for women should exert apositive eect on exports (via the eect on unit

labor costs) and thus technological change andeconomic growth. In the sense that educationreects productivity, this correction may bevalid. But from another perspective, educationdierentials between women and men are

themselves indicative of gender discrimination.This discrimination, which can contribute towomens crowding into labor-intensive jobs,aects wage dierentials (and of course exportcosts). Furthermore, education may not accu-rately reect productivity if factors other thanskills determine job access. Thus it is useful toevaluate the eects of both measures of thegender wage gap on economic growth.

Taking account of evidence of an interactionbetween export growth and human capital,another variation of the wage gap (WGAP3) is

used, and in this case, it is simply the interac-tion of WGAP2 and average educationalattainment in the economy. The usefulness ofthis measure can be understood as follows. 18

The eect of gender earnings dierentials thatraise foreign exchange earnings and access tonew technologies on growth depend on the levelof education, since skilled labor is moreconducive to the eective absorption of newtechnologies (Nelson & Phelps, 1966).

It should be noted that the wage data are theweak link in this analysis in that they are not

widely available over a long period of time andthere are dierences in coverage. 19 In somecases where a single data point was available,that number was used as a proxy for the ve-year average in which it fell, under theassumption that gender wage gaps changerelatively slowly. In that way, some of ourinitial sample could be retained. Table 6 inAppendix A gives summary data of the vari-ables used in the econometric analysis, aver-aged for 197595. Table 7, also in Appendix A,lists data sources.

4. REGRESSION RESULTS

(a) Period averages

Regression results are presented rst forcross-country regressions using period aver-ages. Because of the limited number of obser-vations, the main constraint is degrees offreedom. For that reason, a limited set ofindependent variables is experimented with and

attention is focused primarily on the eect ofincorporating measures of the gender wage gap.Given the heteroskedasticity problems

Table 1. Womens share of jobs in major exportindustries, selected countries 197790a

(a)Textiles

(b)Cloth-

ing

(c)Elec-

tronics

Total(a)(c)

Colombia1977 33.0% 80.0% NA 49.9%1984 34.3 79.8 NA 55.91990 NA

Cyprus1977 1984 66.5 83.2 45.8 78.81990 72.3 86.5 33.5 81.8

Hong Kong1977 48.7 70.3 NA 62.71984 47.1 69.1 NA 62.41990 42.2 68.3 NA 60.0

Korea1977 69.0 73.0 55.3 66.91984 65.7 76.7 52.0 64.31990 57.3 72.0 48.7 56.9

Malaysia1977 1984 63.7 89.4 73.7 75.21990 57.8 85.3 75.3 75.3

Philippines1977 1988 46.6 80.0 63.8 66.91990 48.4 79.6 64.9 67.9

Singapore1977 1984 66.8 88.2 75.0 77.61990 58.4 87.1 71.0 73.3

Sri Lanka1977 52.6 82.8 56.0 56.01984 57.5 89.1 72.8 72.81990 50.8 89.4 76.3 76.3

Taiwan1977 69.3 81.4 62.5 69.11984 64.7 80.2 66.8 68.41990 64.7 80.2 54.6 58.7

Thailand1977 NA 1984 75.0 93.0 NA 81.31988 75.6 81.9 NA 92.4

a Source: Data are from ILO (various years) Yearbook ofLabour Statistics and DGBAS (1993) for Taiwan.* NA indicates that employment in that sector is rela-tively low and the sector is not a major exporter.** () indicates that data are unavailable for that year.



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frequently encountered with cross-sectionaldata, the standard errors have been obtainedwith White's variance-covariance matrix in all

regressions.Table 3 summarizes the results obtained

using the period average data. Eqn. (1) consti-tutes a basic growth accounting equation, withGDP growth regressed on growth of the capitalstock (dlog K) and a measure of the skill levelof the labor force (HK), measured as the aver-age years of secondary education per person 15and over. The constant represents the rate oftechnological change or other factors otherwiseunaccounted for. 20 Both independent variablecoecients are positive and signicant, as

expected.Eqn. (2)(4) show the results of incorporat-

ing various measures of the gender wage gap.Eqn. (2) adds the basic wage gap variable(WGAP1). The coecient is positive andsignicant, and the remaining independentvariables are stable, although note that with theinclusion of the gender variable, the size of theconstant term falls. The value of the coecienton WGAP1 (0.015) indicates that a 0.10increase in the gender wage gap leads to a 0.15percentage point increase in GDP growth. This

eect is not insignicant. It implies, for exam-ple, that the dierence in GDP growth ratesattributable to gender wage dierentials for two

countries in our sample, Korea (8.0%) andChile (5.3%), is 1.2 percentage points peryear. 21 By contrast, the eect of increasing

average human capital attainment by one yearis to raise the GDP growth rate 0.5 percentagepoints. 22

Figure 2 shows the partial correlationbetween the gender wage gap and growth, afternetting out the eect of the remaining inde-pendent variables. The results suggest that insemi-industrialized countries with an exportorientation where women are crowded intoexport industries, ceteris paribus, a widergender earnings gap leads to higher rates ofeconomic growth.

The education-adjusted gender wage gapvariable (WGAP2) is substituted in Eqn. (3).The coecient on this variable is also positiveand signicant and indicates that a 0.10 pointincrease in the gap between female and malereturns per year of secondary education raisesGDP growth by 0.10 percentage points. 23 Eqn.(4) uses WGAP3 which captures the comple-mentarity between an export orientation andhuman capital. This variable is positive andsignicant, suggesting that gender wage dier-entials coupled with a high average educational

attainment in the economy promote economicgrowth. The human capital variable becomesinsignicant, however, and this may be due to

Table 2. Mean value of gender wage gapa

Country Period Gender wage gap measured as

WFaWM LogWM LogWF

Brazil 198894 0.533 0.878Chile 1987 0.773 0.294

Colombia 1988 0.846 0.182Costa Rica 197885 0.715 0.401Cyprus 197595 0.584 0.724El Salvador 197594 0.868 0.157Greece 197595 0.748 0.343Hong Kong 198295 0.684 0.348Indonesia 198891 0.649 0.542Korea 197595 0.482 1.071Malaysia 198394 0.505 0.989Mexico 198492 0.795 0.278Paraguay 198394 0.869 0.198Philippines 1993 0.870 0.150Portugal 198995 0.718 0.398

Singapore 198395 0.544 0.840Sri Lanka 198095 0.796 0.272Taiwan 198195 0.646 0.563Thailand 198994 0.669 0.497Turkey 198895 0.865 0.166

a Source: Data are from ILO (various years) Yearbook of Labour Statistics with several exceptions. Data for Indo-nesia are from SAKERNAS (various years). For Mexico, see Alarcon and McKinley (1997) and Taiwan, DGBAS(various years) Yearbook of Labor Statistics.



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the fact that WGAP3 incorporates a measure ofschooling that is collinear with HK.

In sum, the gender wage gap variableperforms relatively robustly in these equations.Given the likelihood of random measurementerror of the wage gap variable leading to adownward bias on the size of the coecient on

this variable, the implications for the positiveeect of gender inequality are likely to be evenstronger than implied by these results.

(b) Five-year averages

Most cross-country research has been limitedto analysis using data on long period averages,but this tends to obscure variations across timein countries. Moreover, institutional dierenceswithin countries cannot easily be captured in

this modeling framework. For that reason, theregressions were done with panel data tocapture the eect of changes in variables within

Figure 2. Partial correlation between gender wage gap and GDP growth.

Table 3. Determinants of GDP growth: period averages, 197595a

Variable Eqn. (1) Eqn. (2) Eqn. (3) Eqn. (4)

Constant 0.011 0.008 0.009 0.016(4.12) (2.25) (4.19) (8.69)

dlog K 0.556 0.511 0.540 0.567

(16.60) (12.01) (22.11) (13.39)HK 0.005 0.005 0.006 )0.001

(2.44) (1.99) (2.56) ()0.45)WGAP1 0.015

(1.95)

WGAP2 0.010(1.78)

WGAP3 0.010(1.77)

Adjusted R2 0.824 0.881 0.875 0.847F-statistic 30.84 47.86 45.13 36.25

N 20

a Numbers in parentheses are t-statistics.*

p` 0X01.**p` 0X05.***p` 0X10.



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countries over time, using ve-year averagesand a maximum of four observations for eachcountry. In this way, time-varying, country-specic eects could be accounted for.

The regressions are estimated using a two-way error components model. The basic model

can be summarized as:

Yit aXitb mitY

where the error term mit has three components:

mit li kt itX

Here li captures the country specic-eectswhile kt represents time-varying eects. Theestimation can be done with a least squaresdummy variable model (LSDV) with dummyvariables used to capture country and time

eects. Country (xed) eects control forunobserved time-invariant dierences thatmight aect growth, such as initial conditions,technological policies that aect innovation,and other institutional factors. In cases withlimited degrees of freedom, however, it ispreferable to use the ``within'' estimator. Forthis procedure, variables are transformed intodeviations from the time and country mean.For example, a variable Y is transformed asfollows:

Yit

Yit

"Yi

"Yt

"YY

where the denotes the transformed variable,and dots indicate averages of observations over

t and/or i. For the purposes of this study, the``within'' estimator is more useful, due to thesmall size of the data set. With the ``within''estimator, the constant term, xed eects, andtime eects can then be recovered from theregression estimates, but in presenting the

results here, these are omitted from thediscussion, given our primary focus on the roleof gender.

The results obtained from these regressionsare shown in Table 4. Eqn. (1) includes asexplanatory variables the growth rate of thecapital stock and economy-wide human capital,measured as the growth rate of the educationalattainment of persons 15 and over. 24 Each issignicant and coecients are positive. Eqn. (2)disaggregates the human capital variable. Thecoecient on the female variable is larger than

that for males and is signicant while the latteris not, for reasons that are not obvious. Eqn.(3) includes WGAP1. The coecient on thewage gap variable in that equation indicatesthat a 0.10 increase in the gender wage gapleads to a 0.60 percentage point increase in theGDP growth rate.

As an illustration of the strength of the eectof this variable, the results imply that for 198589, 4.0 percentage points of the gap betweenKoreas GDP growth (9.2%) and Costa Ricas(3.9%) can be explained by the gender wage

gap. Human capital variables perform poorlyin this regression insofar as coecients are notstable, and, in the case of the male variable, the

Table 4. Determinants of growth: ve-year averages, 197595a

Variable Eqn. (1) Eqn. (2) Eqn. (3) Eqn. (4) Eqn. (5)

dlog K 0.675 0.649 0.328 0.521 0.476(11.45) (11.64) (6.63) (7.59) (8.16)

HK 0.017

(2.12)HKF 0.025 0.008 0.002 0.006

(2.42) (1.90) (2.13) (0.93)HKM 0.001 )0.007 0.001 )0.001

(0.01) ()3.17) (0.09) ()0.45)WGAP1 0.060

(10.18)

WGAP2 0.104(3.41)

WGAP3 0.051(5.69)

DW 1.903 1.812 1.999 1.920 1.885

a R2s are not reported since this statistic is invalid when equations are estimated without a constant term. N 55.

Numbers in parentheses are t-statistics.*p` 0X01.**p` 0X05.

***p` 0X10.



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sign changes. Interestingly, the size of thecoecient on the capital accumulation variablealso declines with the addition of the genderwage gap.

Eqn. (4) uses WGAP2 and the coecient on

that variable is positive and signicant, indi-cating that even after correcting the genderwage variable for education dierences by sex,the larger the ``eciency''wage gap, the higherthe rate of growth. The female human capitalvariable is positive and signicant, while thatfor males is smaller and insignicant. The sizeof the coecient on the capital accumulationvariable is again smaller than in Eqns. (1) and(2).

In Eqn. (5), WGAP3 is used to capture theeect of gender wage dierences on growth. In

this specication, the wage gap variable is againpositive and signicant. The female educationvariable is, however, insignicant along withthe male human capital variable. The size of thecoecient on the capital accumulation variableis again smaller than in Eqns. (1) and (2),suggesting the possibility that a portion of theeect of investment on growth may transmittedthrough the eect of gender wage inequality.

We also want to consider whether we arejustied in adopting a model with country-specic and time eects as compared to a model

in which the coecients are assumed to be thesame for all countries over time. Tests of poo-lability of data were conducted on these esti-mates using F-tests which determine whetherusing a ``within''estimator to control for xedand time eects signicantly reduced variancein the estimated models. For Eqns. (1)(5), theF-tests rejected the null hypothesis (at the 1%level) that xed and time eects were notimportant and therefore the ``within'' estimatorresults are preferred to a pooled model.

Broadly, the two sets of regressions presented

herethe period and ve-year averagespro-vide fairly robust results as to the positive roleof gender wage dierentials in stimulatingeconomic growth. This relationship continuesto hold when the gender wage gap data arecorrected for education, and are interacted withthe level of education in the economy. 25 Thelatter nding provides some support for theview that there are complementarities betweenexports, education, and structure of produc-tion.

Gender-disaggregated measures of human

capital performed less robustly in the ve-yearaverage models, however, and little can beinferred from these results about the role of

education by gender in inuencing economicgrowth, at least for the sample of countriesconsidered here. While some authors havefound a positive link between education andgrowth, those analyses use a broad cross-sec-

tion of countries. The contrasting ndingspresented here may indicate that the role ofeducation by gender also varies by economicstructure. Further research is needed to moredenitively unravel that relationship.

(c) Investment and gender wage dierentials

It might be argued that part of the macro-economic eect of gender wage dierentials ison investment. The decrease in the size of thecoecient on the capital accumulation variable

with the addition of the gender variables inEqns. (3)(5) in Table 4 is consistent with thatargument. Taking the male wage as a bench-mark, wider gender wage dierentials may be asignal of the protability of investment. This isbecause gender wage dierentials may signalweaker bargaining power on the part of femaleworkers, leading to low unit labor costs, and anassumption on the part of employers of thereduced cost of extracting labor eort fromfemale workers and limited resistance to poorworking conditions. Further, the protability

of investment may be aected by the fact thatwomens relatively low wages generate foreignexchange to purchase high-tech capital goods.We test this hypothesis by regressing invest-ment as a share of GDP on gender wagedierentials. 26 Following Larran and Vergara(1998) and others, we also include two variablesthat measure macroeconomic stabilitytherate of ination and the variance of real GDPgrowth. These regressions use averages for197595. The results are presented in Table 5.

The results of estimating Eqn. (1) indicate

that ination and variance of GDP growthhave negative and signicant eects on invest-ment as a share of GDP, presumably because oftheir contribution to economic uncertainty. Thewage gap (WGAP1) has a positive and signi-cant eect on the investment share. The coe-cient (0.138) indicates that a 0.10 increase in thegender wage gap leads to a 1.38 percentagepoint increase in investment as a share of GDP.Thus the greater income inequality in Singa-pore relative to Costa Rica during 197595explains 6.1 percentage points of the dierence

in investment as a share of GDP (38.3% inSingapore compared to 21.2% in Costa Rica).The alternative measures of the gender wage



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gap similarly indicate a positive eect of genderwage inequality on investment, as shown inEqns. (2) and (3). Figures 3 and 4 show theeect of gender wage inequality on investmentas a share of GDP after netting out the eect ofother independent variables, using WGAP1 and

WGAP2, respectively.27

These results suggest that the macroeco-nomic eects of gender inequality are trans-mitted via numerous pathways. The genderwage gap is a stimulus to investment, but alsohas an inuence on economic growth beyondthe eect on investment (as evidenced by the

regression results in Tables 3 and 4), suggestingthat this variable aects the productivity ofinvestment. These results are consistent withthe ndings of authors who link gender wagegaps to investment (Erturk & Ca!gatay, 1995),

and the feminization of the labor force inexport-oriented economies (cf., Ca!gatay &Olzer, 1995; Standing, 1999). They also supportthe argument that gender inequality that stim-ulates exports, providing the foreign exchangeto purchase ``upscale'' technologies, contributesto productivity growth.

The results contrast, however, with recentempirical work that indicates income inequalityslows economic growth and investment. Thosestudies base their argument on the view thatincome inequality leads to political conict that

creates a climate of uncertainty and instability,either overtly or through the eect on macro-economic policy, thus slowing growth.

The results presented here suggest that justwho experiences the inequality matters.Inequality born by women appears to have apositive eect because this condition stimulatesexports and raises prot expectations. Thesetogether stimulate investment and productivitygrowth, without apparently generating thenegative political repercussions that wouldundermine the gains in measured GDP growth.

Why then so little growth-retarding conictin response to gender-based inequality? The keyseems to lie in the socialization of women whoare less inclined than men, at least in the sampleof countries used in this study, to protestincome inequality suciently to slow invest-ment and growth. 28 Following this reasoning,

Figure 3. Partial correlation between gender wage gap and investment.

Table 5. Determinants of investment: period averages,197595a

Variable Eqn. (1) Eqn. (2) Eqn. (3)

Constant 0.197 0.249 0.234(12.46) (19.41) (18.90)

WGAP1 0.138(4.43)

WGAP2 0.084(1.93)

WGAP3 0.072(3.01)

Ination )0.078 )0.068 )0.052()5.13) ()2.76) ()2.46)

VarGDP )1.057 )3.097 )3.499()2.05) ()3.15) ()3.57)

Adjusted R2 0.580 0.195 0.313F-statistic 9.734 2.534 3.888

N 20

a

Numbers in parentheses are t-statistics.*p` 0X01.**p` 0X05.***p` 0X10.



13/20

we might hypothesize that gender wageinequality is likely to produce more positiveeects on growth in countries, the more patri-archal the gender system. This is likely to occurbecause patriarchies produce institutions thatreinforce internalization of social norms thatfavor men, reducing political resistance andtherefore the costliness of gender inequality.

5. SUMMARY AND CONCLUSIONS

This paper investigates whether genderedoutcomes in labor markets and education havemacroeconomic eects and, in particular,whether gender inequality aects the rate ofeconomic growth. The link between gender andgrowth is bound to dier historically, and ineconomies with diering economic structuresand gender systems. The question investigated

here narrowly denes this link along thetrajectory of the eects of discriminatorily lowwages for women on: (a) exports, and thereforetechnological change and productivity growth,and (b) investment. Based on a data set ofmiddle income semi-industrialized economieswith varying degrees of export orientation, thendings reported here indicate that acrosscountries, and over time within countries, thereis a positive link between gender wageinequality and growth via both channels. Inparticular, the evidence is consistent with the

argument that gender inequality stimulatesinvestment, but also enhances the productivityof investment, possibly through the eect that

low wages for women has on exports andtherefore technology imports.

While cross-country regressions can highlightsome useful factors that are statistically corre-lated with economic growth, a richer under-standing of the dynamics of growth at thecountry level is needed and requires a thoroughinvestigation of institutional structures.Nevertheless, these results mark a useful

departure for and are complementary to morein-depth country analyses, adding support tothe view that gendered outcomes are signicantnot only at the micro level but also at the macrolevel.

There are other links between gender andgrowth that could usefully be explored inextensions of this paper. For example, in viewof womens disproportionate performance ofreproductive (unpaid) labor in most economies,it would be useful to assess the macro impact offeminization of the paid labor force. This could

have notable negative macro-level eects ifwomen reduce the amount of time spentworking in the reproductive sector of theeconomy or subsistence production. Or,womens access to paid jobs may increase theircontrol over family income, inducing positiveeects on family well-being.

Further, we test here only for a very speciccase, but future research could usefully bedirected at understanding how growth andgender interact in economies with dieringeconomic structures and gender relations. For

example, it is not clear what the relationshipbetween gender wage inequality and growthmight be in an industrialized economy, or one

Figure 4. Partial correlation between wage gaps and investment.



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that is largely agricultural. It would also beuseful to investigate whether gender inequalityconstrains growth in countries in which patri-archy is less rmly rooted, if indeed inequalitydoes lead to disruptive political conict.

The policy implications of the resultspresented here bear on the question of whichdevelopment and growth strategies are mostcompatible with gender equity. This is indeedthe question at the heart of feminist research onthe interrelationship between gender andmacroeconomics. The export-oriented growthand industrialization strategy has been promo-ted by some as a means to improve womenswell-being. Yet evidence presented here suggeststhat gender inequality is a casual factor ininvestment and economic growth for the semi-

industrialized countries in the sample used here.Moreover, there is little evidence that gender

inequality has dissipated to any marked degreeeven in the most successful of these countries(Seguino, 1997). We might then questionwhether supporting this strategy in its currentform, based as it is on gender inequality, cansuccessfully lead to a more equitable distribu-

tion of labor and resources by gender over time.If we doubt that, our work is to dene strate-gies that make it possible to promote botheconomic growth and gender equality.

In particular, future research might consider

alternative macroeconomic strategies and poli-cies that can raise productivity and materialwell-being. Are there means, for example, togenerate eciency wage eects from raisingwomens relative wages that oset the negativeeect on exports in SIEO countries? Whatalternative approaches might help semi-indus-trialized economies to overcome foreignexchange constraints so that they are not sodependent on cheap labor? Can restrictions onphysical capital mobility make investment thatrelies on low-cost female labor less sensitive to

changes in protability? These are national-level considerations. But international factorsclearly play an inuential role as well,suggesting that the scope of analysis must alsobe enlarged to consider how the global tradingsystem and its institutions might be trans-formed to make gender equity more achievableduring the process of growth.

NOTES

1. Marxist and Neo-Kaleckian analyses, for example,

have examined the relationship between class inequality

and growth (cf., Dutt, 1984, 1990; Blecker, 1989;

Bhaduri & Marglin, 1990; Taylor, 1983, 1991). Another

political economy approach has been to relate the degree

of income inequality (measured as the household distri-

bution of income) to the level of political conict, and to

analyze the resulting eects on growth (Larran &

Vergara, 1998; Alesina & Rodrik, 1994; Persson &

Tabellini, 1994).

2. See C

a!gatay (1998) for a review of this literature.

3. See, for example, Barro (1991), Collins and

Bosworth (1996), Harrison (1995), Levine and Renelt

(1992), Mankiw, Romer and Weil (1992), Nehru and

Dhareshwar (1994), Sachs and Warner (1997), and

Young (1995).

4. Nelson and Pack (1998) make this argument in an

indirect way. They view the spectacular growth of East

Asian economies to be the result of assimilation of

technologies adopted from advanced countries.

5. For a survey of the literature on the relationship

between trade and productivity, see Pack (1989).

6. This is also consistent with Yaghmaians (1994)

results that report little evidence of a positive eect of

exports on African growth, a region that specializes in

commodity exports.

7. Also in contrast to these claims, Neo-Kaleckians

have emphasized the relationship between class distri-

bution of income and the rate of capital accumula-

tion, with inequality exerting a positive impact on

investment and thereby growth in prot-led econo-

mies. See, for example, Bhaduri and Marglin (1990)

and further reference to this literature in Section 2(b)below.

8. See McCrate (1999) for an excellent synopsis of

economists understandings of the intersection of gender,

class, and race.

9. Much of this research was stimulated by the

structural adjustment experience of the 1980s, with

researchers considering the gender eects of adjustment

policies and investigating feed-back eects. See, for

example, Ashfar and Dennis (1991), Benera and

Feldman (1992), Bakker (1994), and Elson (1995).For a discussion of the theoretical issues, see Walters

(1995).



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10. Country studies that assess the portion of gender

wage dierentials due to discrimination are too numer-

ous to be exhaustively inventoried here. For some

examples, see Birdsall and Behrman (1991), Behrman

and Zhang (1995), Horton (1996), and Psacharopoulos

and Tzannatos (1992). The latter two studies nd thatthe bulk of gender wage dierentials (55% in Asia and

75% in Latin America) are explained by factors other

than human capital dierences.

11. At least two arguments for this claim can be made:

(a) prots are the raison d9etre of capitalism, and (b) they

enhance the rms ability to internally nance invest-

ment.

12. The basis of the index is a production function

framework where several variables interact to promote

growth.

13. The R2 for the trend line in Figure 1 is 0.469 and

the t-statistic on the SIEO index coecient is 3.98. Most

of those countries with a threshold SIEO index value

below 1.0 also lacked gender wage data and thus an even

lower threshold would not have not altered the sample

signicantly.

14. This approach is also adopted because it provides a

useful organizational framework and a means to decom-

pose the growth of output into the contributions of avariety of variables. Nevertheless, it should be recalled

that there have been a number of criticisms of this

approach, particularly in the so-called total factor

productivity debates on the sources of Asian growth

with regard to its ability to dierentiate between the

eects of technical change and capital accumulation

(Nelson & Pack, 1998). Some claim that the growth

accounting methodology underestimates the contribu-

tion of technical change because technology is frequently

embodied in capital equipment. Thus the size of the

coecient on the capital accumulation variable is

generally overestimated. Further, others have noted thatthe constant is an inaccurate measure of technical

progress since it also captures the eects of other

variables not controlled for. The approach adopted here

does not fully address these concerns but, rather,

partitions the factors that aect technical progress into

gender wage inequality and other factors. The latter are

either captured by the constant or reected in the time

trend, with the remainder showing up in the error term.

15. Following the practice of a number of studies, we

rely on human capital to measure labor's contribution to

growth (e.g., Collins & Bosworth, 1996). While laborsupply (measured as labor force participation or popu-

lation growth) was not included as an independent

variable in the results presented here, it was included in

separate regressions but was found in most cases to be

insignicant (results available from author on request).

Not including a labor force variable in the regression

results presented here can be justied on two accounts.

First, use of labor force participation rates presents anendogenity/identication problem, since labor force data

for women tend to move pro-cyclically. This might be

remedied by use of population growth rates but this

measure is also inaccurate insofar as it fails to dieren-

tiate between the proportion of labor spent in paid

versus unpaid labor.

16. Capital stock data that cover a sucient number of

years are dicult to come by. The weakness with the

proxy used for capital stock in this data set is that it does

not account for depreciation. It that way, it overstates

the importance of capital. Many studies use insteadinvestment as a share of GDP, but that measure suers

from the same mismeasurement problem. The implica-

tions for this study of overstating the growth rate of the

capital stock is that the impact of the gender wage gap,

via the eect on technical change, may be understated.

As a result, the estimated impact of gender wage

dierentials on growth is likely on the conservative side.

17. Lack of hourly wage data for some countries

introduces a degree of measurement error, with womens

hourly wages potentially understated. In other instances,

however, womens wages may be overstated. For exam-

ple, Korean survey data cover only establishments with

ve or more workers. Women are disproportionately

concentrated in unsurveyed establishments where wages

are lower than in large rms and thus data likely

overstate womens earnings relative to mens. In other

cases, data are collected only on urban workers, and the

direction of bias is not known. Insofar as the errors are

random (more likely in the period average estimations),

the coecient estimates on the gender wage gap

variables are underestimated.

18. Several variants on WGAP3 were tried and each

provided similar results. The variations were on the

measure of human capital selected, and included average

years of secondary education for the total population

age 15 and over, and for women. Results are available

on request.

19. Much of these data are from the International

Labour Organization, and details on dierences in

coverage can be found in Yearbook of Labour Statistics

(various years).

20. A good part of recent growth research has been

focused on the convergence debate. For completeness,



16/20

this model was estimated with initial GDP in 1960, but it

was insignicant in all regressions. Results are available

from the author.

21. The gender wage gap in South Korea is 1.071 and

in Chile is 0.294. The dierence in gender wage gapsbetween the two countries (0.777) is multiplied by 0.15 to

obtain the percentage point dierence in country growth

rates attributable to gender wage dierentials.

22. To test for nonlinearities, the regressions were also

run with squared terms of the gender gap. These proved

to be insignicant, however.

23. Using again the example of South Korea and

Chile, the coecient on WGAP2 indicates that roughly

0.8 percentage points of the dierence in GDP growth

rates between the two countries can be attributed togender inequality.

24. Alternative measures of human capital were exper-

imented with and yielded similar results. This measure of

human capital performs consistently as gender wage

variables are entered, and that may be because it reduces

the problem of collinearity with education-adjusted

wage gap variables. In any case, the various measure-

ments of human capital tried had little eect on gender

wage variables.

25. Some might also be concerned about the direction

of causality between gender inequality and growth.

While it is not likely that, in an export-oriented economy

with women segregated in the export sector, rapid

growth will widen the wage gap (it should narrow),

Granger-causality tests might be used to explore this

relationship further. To do this, two sets of Granger

causality tests were run on annual data. In one set the

causality between GDP growth and WGAP1 was

assessed using two- and four-period lags, and in the

second case, WGAP2 was used as the gender wagevariable. In both cases, the evidence does not support the

claim that GDP Granger-causes the gender wage gap.

Some support is found for the alternative hypothesis,

that gender wage inequality Granger-causes economic

growth, with the results stronger in the case of WGAP1.

26. Insucient data on private investment as a share of

GDP precluded use of that variable in place of total

investment.

27. The R2 on the trend line shown in Figure 3 is 0.59

and the t-statistic on WGAP1 is 5.08. For Figure 4, theR2 is signicantly lower (0.19) and the t-statistic on

WGAP2 is 2.09.

28. On gender socialization in some Asian economies,

see Greenhalgh (1985), Brinton (1988), Lee (1993), and

Hsiung (1996). These studies emphasize the ways that

women are socialized to accept a hierarchial male-

dominated social order, and note the role the state has

played in perpetuating a patriarchal social formation.

The authors do not suggest that women have remained

passive, just that they are more so than men, given their

socialization. It should be noted that in recent years,

women in a number of Asian economies have shown

increased willingness to the protest harsh work condi-

tions, underscoring the fact that gender identities are not

static.

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(For Appendix see opposite)



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Table6.Periodaverages(197595)ofselectedvariables

Country

Female

shareof

manu-

factur-

ingjobs

(%)

Manu-

factur-

ing

shareof

output

(%)

Growthratesof

Averageyears

secondaryeduca-

tionper

Wagegapmeasures

Investmentas%

GD

P

(%

)

GFKF

(%)

Female

Lsup-

ply(%)

Male

L

supp

ly

(%)

Female

Male

WGA

-

P1a

WGA-

P2b

WGA-

P3c

GDP(%)

Ina-

tion(%)

Brazil

25.5

3.0

2.4

4.8

2.3

0.825

0.899

0.878

0.526

1.850

21.2

138.0

Chile

26.5

20.3

5.3

5.7

3.9

2.0

1.866

1.780

0.294

0.305

1.970

19.5

28.0

Colom

bia

40.8

21.3

4.1

5.4

5.5

2.9

1.550

1.242

0.254

0.389

1.780

17.1

22.0

Costa

Rica

31.3

20.0

3.6

2.7

5.7

2.8

1.164

1.109

0.401

0.375

2.020

21.2

19.0

Cyprus

45.3

15.5

7.0

6.8

1.9

0.8

1.980

2.560

0.724

0.247

1.710

25.6

7.0

ElSalva-

dor

41.6

18.4

)0.8

)4.0

2.2

0.7

0.461

0.532

0.157

)0.012

)0.040

15.9

5.9

Greece

30.8

25.7

2.1

1.2

2.6

0.3

1.559

2.379

0.343

)0.176

)1.300

20.0

15.3

Hong

Kong

48.5

18.5

7.3

8.8

2.4

2.0

3.160

3.801

0.372

0.158

1.340

26.2

8.0

Indonesia

46.4

16.3

6.3

8.7

3.7

2.0

0.550

0.682

0.542

0.217

0.890

24.3

10.7

Korea

39.2

28.8

8.0

9.9

3.1

2.0

2.861

3.911

1.071

0.361

3.200

31.2

9.4

Malaysia

47.3

21.8

7.0

9.1

3.6

2.6

1.270

1.796

0.989

0.326

1.810

30.8

4.0

Mexico

30.7

22.2

2.6

1.1

5.1

2.8

1.391

1.665

0.278

0.123

0.700

20.3

33.0

Paragu

ay

38.8

16.4

4.7

5.4

4.4

2.9

1.508

1.045

0.198

0.553

2.730

23.6

17.8

Philip-

pines

46.7

25.0

3.2

2.7

3.1

2.6

1.387

1.424

0.150

0.113

0.750

23.3

11.0

Portug

al

39.5

29.3

3.0

2.6

2.6

0.1

1.141

1.312

0.398

0.189

0.720

28.2

15.0

Singap

ore

45.0

26.7

7.5

7.2

3.4

1.9

1.735

2.031

0.840

0.439

2.530

38.3

3.0

SriLanka

41.6

16.5

4.5

7.5

3.8

1.5

1.852

2.185

0.272

0.028

0.160

23.4

12.0

Taiwan

47.1

34.5

8.0

6.4

3.2

2.0

1.948

3.056

0.563

0.007

0.050

26.0

4.0

Thailand

42.7

23.9

7.7

10.9

2.4

2.6

0.634

0.781

0.504

0.196

0.990

31.1

5.4

Turkey

16.7

17.6

4.0

6.1

2.1

2.3

0.553

1.121

0.166

)0.623

)2.170

19.4

42.6

a

WGAP1isLogWM

LogWF

.

b

WGAP2isLogWM

aSYRM15

LogWF

aSYRF15whereSYRM15andSYRF15areaverageyearssecondaryeducationpermaleandfemale15y

earsandolder,

respectiv

ely.

cWGAP3is(WGAP2*TYR15)whereTYR15isaverageyearsofeducationfortotalpopulationover15.

APPENDIXA



20/20

Table 7. Data sources

Female and male manufacturing earnings See Table 2 for sourcesGDP in 1985 prices World Bank World Development Indicators 1997

CD-ROMInation World Bank World Development Indicators 1997

CD-ROM.Investment share of GDP (%) Penn World Tables, 5.6Labor force growth, total, female and male (annual %) World Bank World Development Indicators 1997

CD-ROM.Macro data for Taiwan DGBAS (1997) National Income in Taiwan Area

of the Republic of ChinaMerchandise exports, constant 1985 $US World Bank World Development Indicators 1997

CD-ROMPercentage of primary, secondary, and high schoolcompleted in the female, male and total population, age15 and over

Barro and Lee (1996)

Population data (male, female, total and over 15) Penn World Tables, 5.6, and for Taiwan, DGBAS(various years), Statistical Yearbook of Republic of

China

Real GDP per capita in constant dollars, chain index,expressed in international prices, base 1985

Penn World Tables, 5.6


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