Trade Liberalization, Gender Segmentation, and Wage Discrimination: Evidence from Egypt. Working Paper 414. June 2008

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2 Trade Liberalization, Gender Segmentation, and Wage Discrimination: Evidence rom Egypt Fatma El-Hamidi Working Paper 414 June 2008 This work beneited rom a inancial grant rom the Economic Research Forum (ERF). The contents and recommendations do not necessarily relect the views o the ERF. Department o Economics &GSPIA, University o Pittsburgh, USA atma@pitt.edu

3 Abstract This study evaluates the impact o trade liberalization and reductions in trade barriers on gender wage inequality in Egypt by using recent Egypt Labor Market Panel Survey data (ELMPS 06) and comparing two years representing an early stage (1998) and an advanced stage in trade liberalization (2006). The analysis ocuses on private sector workers, and compares workers in tradable sectors (sectors in direct competition with the oreign trade) with workers in non-tradable sectors (not in direct competition with oreign goods). Not only is wage discrimination observed regardless o sector o employment, but deterioration is also detected. Results also indicate that tradable sectors experience proportionately higher levels o wage dierences between men and women than non-tradable sectors. ملخص تقي م هذه الدراسة ا ثر تحرير التجارة وتخفيض القيود التجارية على التفاوت في الا جور بين الجنسين في مصر وذلك باستخدام الدراسة الحديثة لسوق العمل في مصر التي ا جراها فريق عمل عام 2006 وا جراء مقارنة بين عام 1998 التي يمثل مرحلة مبكرة من عملية تحرير التجارة وعام 2006 الذي يمثل مرحلة متقدمة من هذه العملية. كما ركز هذا التحليل على عمال القطاع الخاص ونقارن العمال في القطاعات ذات الصفة التجارية (وهي القطاعات التي تتنافس مباشرة مع السلع الا جنبية) مع نظراي هم من العمال في القطاعات ذات الصفة غير التجارية (وهي القطاعات التي لا تتنافس مباشرة مع السلع الا جنبية). ولم يالحظ التمييز في الا جور بين الجنسين فحسب بغض النظر عن مجالات العمل بل لوحظ تدهور ا جور الا ناث. وا ظهرت نتاي ج البحث ا ن هناك فروقا في الا جور بين الذكور والا ناث في القطاعات ذات الصفة التجارية ا كثر من القطاعات ذات الصفة غير التجارية. 1

4 I. Introduction: The 1990s were a period o accelerated structural adustment and trade liberalization in Egypt. Despite considerable debate regarding these reorms, little empirical work has investigated their wage eects. This paper is among the irst attempts to ill this gap through an econometric examination o the link between trade liberalization and industry wage premia. There is a growing body o evidence relating trade liberalization to economic growth. In act, one o the maor challenges o globalization is the distributive consequences o liberalization o trade. On the one hand, trade liberalization changes the conditions o industry, wages, proits, and alters income distribution. On the other hand, removing trade barriers provides incentives to employ the services o those resources in relative abundance (such as labor) in more productive outlets. The link between inequity and ree trade has been developed in Becker s theory o employer taste or discrimination (1957). The theory posits that in a noncompetitive market, excess proits allow employers to purchase or practice discrimination. Thereore, the least discriminatory irm will hire cheaper, unskilled labor. Free trade brings in competitiveness and exposes irms to a wider market. In this environment, Becker predicts market competition to orce out discriminating employers. In addition, increased competition to cut costs lowers discrimination, and eventually leads to greater wage equality. One the other hand, Berik et al (2004), suggest that an increase in trade can in act increase gender wage premia in regions where emale workers may have lower bargaining power and /or are segregated into lower-paying, lower-status obs. As wages are considered one o the most important determinants o the economic well being or women, economists have long been interested in researching causes o gendered labor market outcomes, in particular gender wage dierentials. Such interest has been partly due to the act that gender wage inequality induces labor market rigidity, eiciency losses and an inept use o actors o production. Various theories not necessarily mutually exclusive resulted rom this debate. Traditionally, the human capital theory (HC) has been embraced to link gender wage dierences to dierences in human capital characteristics. HC theory suggests that the wage gap is due to the act that women, on average, have lower education, experience, and training than men. These dierences result in declined productivity and ultimately, lower wages. Discrimination theorists presume that mechanisms in the labor market are the main source o earnings dierentials. They argue that gender wage dierences are a direct result o unequal treatment o equal workers o the opposite sex. Advocates o segregation theory propose that gender wage dierentials are an outcome o occupational gender segregation. It is the eect o men and women disproportionately employed in dierent occupations/sectors, with women dominated occupations/sectors typically earning less than men dominated occupations/sectors. According to Becker, it is expected to see industries or sectors which experienced larger reductions in trade barriers observe a greater decline in the wage dierentials between men and women, and less discrimination. In this study, I use recent developments in trade policies in Egypt since mid 1990s to test whether greater competition and participation in the global market has aected wages o men and women dierently; the degree to which women have entered traditionally "male" dominated activities and vice versa; the role o sectoral segregation in explaining the overall gender pay gaps (in particular how much wage dierences is attributed to sectoral crowding o one sex, and how much is due to productivity related actors within sectors). Some o the key questions to be answered in this study are: 2

5 1- Does trade liberalization aect sectors and industries that intensively employ one gender or the other dierently than it aects other sectors? 2- To what extent have tradable sectors, which are directly aected by outside competition, contributed to a decline (or rise) in wage inequality or discrimination in general? The rest o this paper is organized as ollows. Section II introduces trade liberalization-wage interconnection with an overview o trade reorms in Egypt, and a review o the literature on trade liberalization wages and gender inequality. Section III overviews the Egyptian economy. Section IV outlines the data sources, the estimation methodology and wage decomposition model. The main indings o the analysis are reported in Section V and section VI concludes by summarizing the results and outlining some policy implications. II. Trade Reorms in Egypt, and Review o the Literature According to Dutta (2007), there are at least three outlets through which trade liberalization or protection policies can aect the industrial/sectoral wage structure: 1) A shock to demand on labor, by which the eect o a reduction in industry protection results in a all in relative wage level in that industry (assuming imperect labor mobility or rigidity o labor movement between industries). 2) A change in the product market structure by which a more competitive market reduces distortion and anomaly o the imperect competition by reducing rents and ultimately relative wages. As Rodrik (1997) argued trade increases the own price elasticity o labor demand in absolute terms and thus erodes the bargaining power o labor vis-à-vis capital in the sharing o industry rents. 3) And a change in industry productivity, as recent evidence suggested a positive link between ree trade and productivity (Das, 2002). Egypt became a member o the World Trade Organization (WTO) in 1995 and has pledged to be in ull compliance with its trade commitments to the WTO by Egypt has made progress in liberalizing its tari structure. In 1998 Egypt reduced the maximum tari rate or most imports rom a 50 to 40 percent. To urther open the Egyptian economy, the new 2004 cabinet urther reduced the average unweighted tari rate rom 27 to 20 percent. Other measures taken by the new cabinet were to: urther reduce the number o tari rate categories rom 27 to 12 and eliminate all tari surcharges (Table A-1, in the Appendix). However, Egypt's taris remain relatively high, especially when compared with those o other developing countries with large internal markets and diversiied industrial economies. According to the World Bank, these adustments have improved Egypt s ranking rom the third on the list o countries with the highest weighted average tari rates (similar to Zambia, Romania, and Venezuela) to the middle (with countries such as Argentina, Bolivia, and Senegal). Since the mid 1990s, Egypt has signed several multi and unilateral trade agreements. Egypt belongs to the Common Market or Eastern and Southern Arica (COMESA) and the Greater Arab Free Trade Area (GAFTA). It has also signed the Agadir Accord with Tunisia, Morocco and Jordan which liberalized trade among the our countries on January An association agreement with the EU came into orce on January Egypt also has special relations with the United States under the Trade and Investment Framework Agreement (TIFA) and recently began negotiating with other bilateral partners such as Turkey and Russia. Fearing a crisis in the textile industry ater the Multiibre Arrangement quota system ended in January 2005, the government signed other agreements, involving the country s recently-established qualiied industrial zones (QIZs). Under the QIZ agreement, products produced in ree trade industrial zones could be exported duty- and quota-ree to the United States as long as they contain at least 11.7 percent o raw materials rom Israel. At the end o 2005, nearly 400 irms were licensed or the QIZs and 250 were already in operation, especially in the textile sector. (Arican Development Bank, Egypt: Country Strategy Report, multiple years). 3

6 While the literature documents a positive impact or trade liberalization on wages o the disadvantaged in developed countries, it does provide mixed evidence in developing countries. Gaston and Treler (1994) were one o the pioneers to link trade and wage discrimination in the U.S. manuacturing sector. Their indings proved a signiicant negative link between taris and wage premia. Recent studies on the eect o trade reorms on gender wage gaps suggest no generalization, and evaluate each country according to its state o development and characteristics. Jean and Nicoletti s (2002) reported positive correlation between tari protection and industry wage premia across 12 OECD countries. These results were not robust in developing countries. Artecona and Cunningham (2002), and Reilly and Dutta (2005) ound no signiicant eect o trade liberalization on gender pay gaps in Mexico and India respectively, whereas Kumar and Mishra (2006) ound that increased gender based wage premiums in the Indian manuacturing sector were attributed to trade reorms. Studies on Mexico (Feliciano, 2001), the Philippines (Hasan and Chen, 2003) and Brazil (Pavcnik et al, 2004), ound no signiicant eect. Berik et al (2003) established negative impact in Taiwan and Korea, in addition to a loss o emale employment and increased wage discrimination. A cross country study by Oostendorp (2004) suggested a narrowing gender wage gap within occupations in most cases, with the exception o high skill occupations in poorer countries. A result that was conirmed by Jacob (2007) who conointly ound reduced discrimination in industries that experienced liberalization as well. Hoekman and Winters (2005) surveyed the literature on trade, employment and wages and came out with some stylized acts: trade and trade reorms can only explain a small raction o the general increase in wage inequality observed in both developed and developing countries; the magnitude o the eects o greater liberalization on wages and inequality are small in both developed and developing countries. The literature on women s employment recognized the increased share o women in export oriented, labor intensive industries, such as textile and clothing (Joekes, 1987, 1995 and Elson, 1995). Labor-intensive irms are also more likely to hire women because they can handle women s absences related to maternity and child care better than capital-intensive ones, in addition to being more lexible, cheaper and compliant (Standing, 1989). Recent work, (El-Hamidi and Said, 2007), using household data or 2000 to 2004 (CAPMAS-LFSS) ocused on gender pay dierences and occupational segregation in the private sector. The study ound evident wage discrimination in proessionals as well as white and blue collar obs, and that the crowding o women in certain occupations is responsible or depressed women wages. III. Overview o the Egyptian Economy The Egyptian economy embarked on structural adustment policies in the early 1990s and hence transitioned towards a private sector-led market economy and largely enoyed macroeconomic stability. Between 1998 and 2006, the industrial sector has on average contributed between 25 to 30 percent o real GDP. The industrial sector grew on average by 10 percent annually during the period 2001/ /06, whereas services accounted or an average o 50 to 60 percent o real GDP. Because o higher world oil prices and increased natural gas production, oil and gas accounted or 14.6 percent o GDP in 2005/06 and earned $5.3 billion in export revenue. Industry accounted or 18.2 percent o GDP in 2005/06, led by agro-ood (sugar and beverages), textiles/clothing (cotton and wool yarn) and construction materials. Nonpetroleum exports have increased rom $2.3 billion in 2001 to $4.2 billion in In recent years, industrial production in the once-dominant public sector has declined and private- 4

7 sector production has increased sharply, in response to privatization and liberalization initiatives. According to the Ministry o Foreign Trade and Industry, by 2004/05 the private sector accounted or some 86 percent o manuacturing output. Construction was also booming, thanks to domestic demand or inrastructure and housing to accommodate the increased numbers o Iraqi reugees. The services sector contributed by 47.4 percent to the GDP in 2005/06. Income rom the Suez Canal increased 16 percent in 2005/06 due to increased world trade, especially with India and China (40 percent o the canal s revenue comes rom ships trading with Southeast Asia), and a 35 percent rise in Panama Canal charges, which caused commercial traic to reroute through Suez. Charges or use o the canal increased by 3 percent in July Agriculture s share o GDP was around 15 percnet in 2005/06 and this share has been airly constant since 1999/00. (Arican Development Bank, Egypt: Country Strategy Report, multiple years). Although the population growth rate modestly dropped rom 2.1 percent in 2000 to 1.9 percent in , and ater years o positive GDP growth rom 1993 to 2000, Egypt experienced a repeat drop in its GDP growth. According to the IMF 2, the growth o real GDP dropped by 3.8 percent in 2001, by 8.3 percent in 2002, by 7 percent in 2003, and declined by another o 5.3 percent in By 2005, there was a turning point to a positive growth o 19 percent and by another 12 percent in Among actors contributing to the change o direction in GDP growth is the exchange rate policy. In 2004, the Egyptian economy was driven by export revenues. The 25 percent depreciation on the Egyptian pound against the US dollar ollowing the introduction o partial loating exchange rate beneited exports, and resulted in a current account surplus o nearly $3.7 billion at the end o 2003/2004 (IMF, 2007). The depreciation o the Egyptian pound against the dollar, though aided exports levels and the value o the GDP at large, inlicted a huge burden on the average worker. The depreciation contributed to augmented inlation. Between 2002 and 2003, the CPI grew by 22 percent and by 197 percent between 2003 and Whereas some economists believe that the Egyptian CPI is heavily weighted by subsidized commodities and price controls and as such, is not a good measure o inlation, the WPI (wholesale price index) a less distorted measure showed an alarming inlation rate o 10 percent in 2003 and 22 percent in 2004 (IMF, 2007). The private sector has again been the economy s new engine o growth. The private sector accounted or 70 percent o the increased investment rom E 12.7 billion in 2004/2005 to E 16.4 billion in 2005/2006 (Euromonitor International, 2007). By 2006, the labor orce was estimated at million. It is dominated by men who represent 78 percent while women make up the remaining 22 percent. The labor market is highly segmented, with the government remaining a maor source o non-agricultural employment and the inormal sector continues to harbor low-productivity and low-skilled labor, especially women. A distinctive eature o the Egyptian labor market has been the mismatch between the skills needed by the labor market and what the educational system is producing. The growth o the economy though substantial has ailed to create suicient obs to match the rapid increase in the labor orce o 2.7 percent per year. With about 11 percent o the labor orce unemployed in 2006 and ob creation not keeping pace with population and labor orce expansion, the problem is intensiied. Women were indeed the most aected by downsizing and privatization, where their unemployment rate was estimated at 25 percent compared to 6.9 percent or men. IV. Data Sources and Methodology: 1 (Arican Development Bank, 2005). 2 IMF, World Economic Outlook,

8 This study addresses the question o whether increasing competitive orces rom Egypt s trade liberalization policies has aected the wages o male and emale workers dierently, by looking at two points o time: the period when the trade protection policies started to relax (1998) and the period ater signiicant strides towards global integration (2006). As the economy gains momentum in privatization eorts, this paper ocuses on the eects in private sector workers. The empirical analysis is based on the recent Egypt Labor Market Panel Survey (ELMPS 06), a ollow-up survey to the Egypt Labor Market Survey o 1998 (ELMS 98) that was carried out by the Economic Research Forum (ERF) in cooperation with CAPMAS. ELMS 1998 was carried out on a nationally-representative sample o 4,816 households. The ELMPS 2006 sample consists o a total o 8,349 households 3. The data provide inormation on monthly earnings, worker characteristics, such as age, education, gender, marital status, occupation, industry and sector o employment as well as region o residence. The working sample is restricted to private sector workers between the ages o 15 and 65, who report positive monthly earnings. Hourly real wages are calculated as the sum o wages earned in the reerence month rom primary obs, adusted or average number o work days per month and average hours per day. For comparability purposes, wages o 1998 are inlated to 2006 Egyptian pounds using the consumer price index (inlation actor is 1.43 rom 1998 to 2006). In the absence o hard data on which goods/services are internationally traded, and which goods/services are consumed locally, identiying the basis by which sectors are regarded as tradable or non-tradable is airly subective. For example, although tourism is consumed domestically, it is highly correlated with trade liberalization; thereore I opted to classiy the tourism sector as a tradable sector. On the other hand service is recognized as a non-tradable sector and thereore unlikely to be traded. Construction as well is attached physically to some part o the local economy, so it would not be traded. (see Heston et al (1995) or a complete description). Based on the economic sector o employment, workers are divided into two categories: (1) workers in tradable sectors, that are in direct competition with oreign goods, such as manuacturing and tourism and (2) workers in non-tradable sectors, which are not in direct competition with oreign goods, such as construction, electricity, water, inance, transportation, wholesale and trade 4, inance, real estate, education, health and social work. Econometric analysis is limited to both categories, but workers in agriculture, ishing and mining are included in general descriptive statistics. The model underlying employment segregation and gender pay dierences is based on the human capital theory which suggests that pay dierences can be explained by dierences in workers endowments o human capital : investments in education, training and work experience which tend to increase pay because o their positive impact on productivity. Using the model o human capital earnings unction introduced by Mincer (1974), the wage determination equation is identiied as ollows: LnW = β 0 + β k Educ ik + β 2 EXP + β 3 EXP 2 + u (1) where Educ represents dummies or dierent levels o education, EXP signiies the experience in years, EXP 2 is experience squared, and u is a random disturbance term. The speciication is shown logarithmically in order to interpret β as the rate o returns to schooling. 3 For more details, see Barsoum, G Egypt Labor Market Panel Survey 2006, Final Report, the Population Council, Cairo, Egypt. 4 I realize wholesale and trade may present an argument as de acto tradable sectors. Another test is needed, along with suicient data, to determine i the sector counts as tradable or non-tradable. 6

9 To adust the coeicients and make them nationally representative, the regressions use sample weights or the relevant years to correct or the act that the proportion o individuals and households in each sample diers rom the proportion in the true population. Following Mincer (1974), I use levels o education and years o experience (EXP) as the main explanatory variables. Levels o education are captured by six dummy variables: illiterate (base), read and write, primary, middle, secondary and university. Regional dierences are captured by ive dummies as ollows: metropolitan, lower urban, upper urban, lower rural, upper rural. Experience variables are included in the model since workers with more years o ob experience are likely to earn more (higher experience is oten associated with higher skills and higher productivity). A irm is likely to use higher wages to induce experienced workers to stay on in their obs, as the cost o training new workers could be very expensive. The experience squared variable is included to capture the possibility o a non-linear relationship between experience and earnings. A positive sign or the experience variable is expected or the reason that working experience is likely to contribute to enhancement o an individual s human capital, and negative coeicient o experience square as marginal returns rom experience tend to decline over the lietime. In this regard, it is important to keep in mind that the wage equation is built on a number o limiting assumptions. For instance, it assumes that workers have equal abilities and conront equal opportunities. Second, there is the problem o ability and the associated diiculty o measuring the quality o education. Human capital theory suggests that ability is likely to be positively correlated with schooling. Thereore, neglecting the ability actor rom the regression equation may very well result in an upward bias in the estimated returns to schooling. As a result, and because the survey data does not include variables that could be used as a proxy o ability, this problem is ignored in the estimations. A large portion o wage dierences that cannot be explained by dierences in human capital measured by educational attainment and experience highlights the importance o other unobservables such as irm size or irm proitability. A high paying industry (such as inance) is high paying because it attracts the most skilled and simply because it pays a premium to its employees. Other high paying industries (like oil and gas) oer high wages merely because the entire industry pays above average wages. Wage equations also disregard direct costs o schooling and overlooks earnings while at school. Besides, it assumes a ixed yearly return o schooling. A urther complexity o the human capital model is that wage equation assumes that education is assigned randomly across the population. As a matter o act, education is endogenous and estimating the wage-education relationship may result in an upward or downward bias, depending on how workers orm their education preerences. This is signiicant particularly in rural regions where educating women is considered secondary to educating men. Since individual and household backgrounds may inluence selection into wage employment versus non-wage employment, the Mincerian earnings equations may produce biased estimates because o selectivity bias. Following the literature in labor economics, and to account or bias resulting rom observed wages, the selection bias issue is addressed by using the popular ramework o Heckman s (1979) as the two equation selection model is estimated a priori. First, the participation equation is estimated through a probit model where individual and household characteristics (such as age, region o residence, levels o education, marital status, the presence o children below the age o six and the size o the household) aect the selection into wage employment (regular or casual) versus non-wage employment (selemployed, unemployed, or out o the labor orce). From the participation equation, a selection variable (the inverse Mills ratio term) is created, which is used in the second step, as an additional regressor in the wage equation, yielding consistent estimates o the coeicients 7

10 ree o censoring bias 5. In addition, this step allows or predicting the distribution o women across sectors i they were treated in the same manner as men. In turn, it acilitates decomposing the gender gap into ustiiable (in terms o productivity related dierences) and unustiiable components. And to urther decompose these gaps into intra-sectoral and intersectoral components. There are two concerns to note when it comes to examining pay gaps as a result o discrimination: irst is the pay gap, where women o equal characteristics to men in the same ob are paid less; second is the ob discrimination itsel, where women are kept out o higher paying obs. Empirical analysis is appropriate or measurable dierences in characteristics, such as experience and levels o education, which are commonly reerred to as explained components o the wage gap. The remainder o the gap is reerred to as unexplained, which suggests an estimate o gender based discrimination. One last important issue to consider is the unexplained component is providing an upper limit or discrimination, since this measure does not take into account the pre-market or post market actors (attachment to the labor market, tastes or personality) that may result in higher (lower) payment or males (emales). Two methods o the wage gap decomposition are then utilized. First, the standard Oaxaca- Blinder Oaxaca (1973) and Blinder (1973) decompositions. This methodology depends on identiying characteristics o individuals that aect their productivity and thereore their wages, such that the share o the wage gap not accounted or by such productive actors is the unexplained wage gap, all or some o which may be due to discrimination. Adopting Oaxaca (1973) and Blinder (1973) methodology, the dierences in the logarithmic wages between males and emales, is written as: W W m W ln = ln ln (2) where m reers to male workers and to emale workers. The operator represents the mean dierence between males and emales wages. First, separate selectivity corrected wage equations are estimated or males and emales workers. The estimated wage equations are then used to decompose the observed wage dierential between male and emale workers into components due to personal characteristics, to parameters and to sample selectivity bias. I the average observed log wage or type worker is observed characteristics, X = X / i i n lnw i = i lnwi/n. The average and the average sample selectivity bias term, λ = i X i / n where n is the number o individuals in a group. In this case, =male (m), emale (). Suppose that βˆ is the competitive wage and that emale workers are compensated at the m same wage as male workers. Then, the predicted mean wage or emale workers using competitive wages is given byβˆ m X. In other terms, the previous equation can be written, including the selection term, as: ln W m ln W = ˆ β X ˆ β X + ˆ β X m m m m ( ) + β β + m X m X ˆ ˆ m X + ˆ β X + ˆ δ m λm ˆ δ λ = ˆ β ˆ δ λ ˆ δ λ (3) m m 5 For brevity results are not reported but available in the Appendix. 8

11 The irst term on the right-hand side o the equation is the dierences in the endowments o wage-determining characteristics (X s) between males and emales wages, evaluated according to the male worker pay structure (βˆ ). This portion can also be interpreted as the m wage gain emale workers would experience i they had the same characteristics on average as male workers. The second ) term on the right-hand side is the portion due to dierences in pay structure (coeicients, β ' s ) between males and emales workers. It is the wage gain emale workers would experience, given their mean characteristics, i they were compensated as male workers. The last term represents the wage dierential attributed to sample selection bias. Accordingly, we run into an index number problem (Oaxaca, 1973; Jones, 1983). The problem arises when heterogeneous group o characteristics (X variables) are summed with two sets o wages (males and emale). Following the approach employed by Reimers (1983), which uses an unweighted average o each type o worker s coeicients, the wage dierential can be decomposed as: ( ) ( ) ˆ + ˆ ˆ ˆ + ˆ ˆ ln W ln = m W 0.5 β β β β (4) Xm X m X Xm X m δ mλm δλ Brown et al. (1980) expanded the model and incorporated the distinction between acrossoccupation and within-occupation wage dierences into the analysis o wage dierentials. According to economic theory, an individual s occupational attainment is a unction o the employers willingness to hire that person (labor demand) and the individual s desire to work in a particular occupation or sector (labor supply). Labor demand is determined by the individuals marginal productivity o labor (MPL), which, in turn, is a unction o human capital. Labor supply is derived rom an individual s utility unction, which includes at least the wage o the occupation, a taste or the work involved, and amily size (Brown et al., 1980). In this context, wage discrimination may result in one group being paid a wage higher than its MPL, or the other group being paid a wage lower than its MPL. Likewise, discrimination may occur in occupational attainment when either one group is allocated to occupations that require better skills than they possess or another group is allocated to occupations requiring less skills than what they have. Their model can be written as ollows (Brown et al., 1980; Kidd and Shannon, 1996; Meng, 1998): lnw M lnw F = n = 1 P ( X m B m X B ) + n = 1 X m B m ( P m P n n m m m m m = X B ( P P ) + X B ( P P ) = 1 n = 1 m m m + P ( X X ) B + P X ( B B ) = 1 n = 1 Where Ps are the proportions o men and women with occupation ; P"s are the proportion o women with occupation predicted using estimates o the occupational outcomes model or m men; B andb are vectors o OLS wage equation coeicient estimates or men/ women with the th occupation; Xs are mean values o the wage equation variables or men/women with the th occupation. Overall, the mean log wage dierence shown in the previous equation consists o our distinct components. The irst two represent across-occupation wage dierences, where the ) 9

12 irst part represents the explained portion and the second represents the unexplained portion. The third and ourth parts represent within-occupation wage dierences where the third part is the explained portion and the ourth is the unexplained portion. The explained term reers to wage dierentials resulting rom gender dierences in productivity-related characteristics. The unexplained term reers to wage dierentials that cannot be accounted or on the basis o productivity endowments and is commonly interpreted as a measure o labor market discrimination. I use this methodology to test the extent o discrimination in terms o access to certain sectors o the economy, in particular their allocation between tradable versus non-tradable sectors. V. FINDINGS: 1. Sample Characteristics: As acknowledged by Assaad and El Hamidi (2007), education is considered a determining actor o emale paid employment, especially employment in the government sector. This act is o great signiicance in light o the downsizing o the employer o the last resort or educated women. As the government has dropped its employment levels o educated women, the private sector took over and by 2006 had over 50 percent o all educated women in the market. This trend o hiring in the private sector appeared earlier or men. According to Table 1-Panel A, women were hard hit by the decline in public employment by 6 percent during period, an annual average drop o 0.9 percent or this period. The private sector (includes private, oint venture, oreign and others) increased its employment o women by an annual average o 3 percent between 1998 and That is approximately 2 percentage points less than the increase ensued to men during the same period. Yet, i the private sector is decomposed, and separating the agriculture work rom the non agriculture work, an exciting trend emerges. Aside rom non wage agricultural workers and its puzzling measurement inaccuracies, according to Table 1-Panel B), women appear to have gained employment in the private non agriculture wage sector, at the expense o losing employment in private agriculture wage sector. Private non agriculture obs rise by over 10% or emales in rural regions and by 8% or emales in urban regions. Further breakdown reveals that the annual growth o the ormal private non agriculture sector reached an average o 32 percent or urban women, 12 percent or rural women, 7and 11 percent or urban and rural males respectively. Women working in the urban inormal private sector surprisingly experienced an annual drop o 19 percent, whereas their counterparts in rural regions gained an average o 3 percent annually. Evidently there is a shit in the structure o economic activity by gender in terms o sector o employment. The private sector had shouldered some o the surplus labor in the last seven years. Besides, despite an increase in inormal employment between 1998 and 2006, the growth o ormal employment has accelerated considerably, especially or women in urban regions. The private sector is then considered the next engine o growth in employment opportunities, assuming avorable conditions and stable economy. Figure (1) displays employment distribution by economic sector and gender. Up until the late 1990s, ERSAP policies resulted in increased employment o men in non-tradable sectors such as transportation, utilities, and education and health (an annual growth rate o 5, 3 and 4 percent respectively). These same sectors observed an even greater increase in the new millennium (10, 5 and 12 percent in that order). The igure, as well, portrays women s share in the textile and garments industry increased by an annual growth o 6 percent between 1998 and 2006, in the same time men lost their employment at almost the same rate in that sector. Furthermore, retail and trade, as well as 10

13 transportation, education and health experienced dramatic increases. The last three sectors represented the highest growth in male employment between 1998 and A urther interesting point is the replacement o women in education and health sectors with men. In 1998, although women accounted or approximately three times the number o men in both sectors, the annual growth rate in male employment in both sectors is almost our times that o women (7 percent and 2 percent respectively). By and large, services are absorbing the bulk o private employment as opposed to goods production sectors. Table (2) asserts the increased participation o women in the private sector, rom 10 percent o total work orce in 1998 to 11.1 percent in More importantly, the table shows how emales are highly represented in the health and education sectors with an average o 68 percent o total workers being women. Ironically, this sector represents only 19 percent o all emale employment (Table 2-Column 5). Other sectors with lower barriers to women s entrance are retail and trade (22 percent o total employment) and textile and garment manuacturing 19 percent). Regrouping private sector workers into 2 categories: tradable sector, and non-tradable sector, Table 3 presents selective characteristics o workers. The table maintains that while males on average have more years o education than emales, women on the other hand enoy more years o experience. Furthermore, while women represented a raction o total employment in both sectors, their annual rate o growth in tradables was almost nine times that o men, between 1998 and 2006 (4.7 vs. 0.5). Meantime, while non-tradables recorded a higher concentration or women in 1998 (at 53 percent ) tradable sectors absorbed 59 percent o total emale employment in 2006 (Table 4) a trend that is not repeated or males. A closer look at Tables 3 and 5 shows women s earnings are lower than their men counterparts regardless o their sector o employment. Women also endured greater deterioration in their real wages than men. Table 5 reveals that between 1998 and 2006, the wage gap (women wages/men wages), widened only or tradable sectors workers remember that it has been established that tradable sectors have gained higher concentration o women by 2006, relative to non-tradable sectors. 2. Empirical Findings: Gender Wage Gap Decompositions The previous part o this section oered conventional and unreined type o analysis which turned out several conclusions. These key results however, are not inormative o the actual sectoral and gender dierentials as they do not take account o the dierences in individual and ob characteristics. In order to obtain such dierentials I begin the empirical analysis by applying the sample selection procedure, estimating the wage equation and carrying out wage decomposition detailed in the methodology in section IV. The empirical analysis proceeds at two levels. First, determinants o employment sector are estimated separately or males and emales o private sector workers or years 1998 and This step allows or predicting the distribution o women across sectors i they were treated in the same way as men. Second, wage equations are estimated separately or males and emales including selection terms obtained rom the irst step. This methodology urther acilitates decomposing the gender pay gap in the second step into ustiiable (in terms o productivity related dierences) and unustiiable components (Oaxaca, 1973), and urther decomposing gender pay dierences into intra and inter- sectoral components (Brown, 1980). The analysis is done separately or males and emales across the two sectors: tradable sectors and non-tradable sectors. This allows or dierences in wage setting in the two aggregate sectors and or dierences in parameter estimates by gender. For brevity, results o selection and regression equations are provided in the appendix. 11

14 In what ollows I look at the eect o a period o structural adustment on wage inequality in tradable sectors and non-tradable sectors. I ollow the literature and use the methodology detailed earlier to sort out the dierences in wages between male and emale workers that are due to endowments and those that are due to discrimination, more speciically the explained rom the unexplained. I group dierences due to discrimination and dierences due to selection bias in one unexplained actor. The unexplained term may include a problem o omitted variables, including attachment to the labor orce, lack o speciic training, tastes, personality and/or interrupted careers. Table 6 presents wage decompositions or males and emales by sector o employment, which separate the ustiiable (explained) and unustiiable (unexplained or pure premium) components. The table also provides the predicted wage women would receive i they were treated airly (same as men). A positive sign in the explained column indicates that men enoy a productivity wage advantage over women by the amount indicated. In other words, men have higher levels o education and/or experience, in addition to residing in regions o high demand on their labor than do women, thereore the dierence in the wage gap is ustiied in accordance to human capital dierences. Conversely, a negative sign in the explained portion indicates that the labor market exhibits some avoritism towards men vis-à-vis women. In other words, women on average have higher endowments in terms o levels o education and/or experience, and should have earned more than what they are currently being paid 6. Large components o decomposition results are not unique to this study. Considerable igures have been reported or cases in developing countries which results rom omitted variables problems and distortions in the labor markets. Moreover, the unexplained components include the selection errors (resulting rom sectoral selection module). Lower probability o employment in a typical sector contributes to the overall discrimination. Thereore, a large contribution o sample selection to wage dierentials may oset other actors that work to narrow the wage gap. The table conirms that schooling and years o experience are not treated similarly. It seems that years o schooling are more valued in the labor market than years o experience. Table 3 previously pointed to the act that women, on average, have more years o experience but lower years o schooling. Using the standard decomposition (Method A), it is evident rom the table that higher male wages are not ustiied according to human capital theory predictions or both sectors o employment and especially in tradable sectors. Recall workers characteristics in Table 3, male workers in general are more educated, but have less years o experience than their women counterparts. Decomposition igures in Table 6 support the argument that years o schooling are valued in labor market, especially in tradable sectors, than years o experience. It is also clear rom Table 6 that the part o the wage gap that is due to pure premium, or discrimination, has increased considerably between 1998 and 2006 in tradable sectors. Inequality in wages in non-tradable sectors, on the other hand, has declined considerably between 1998 and Finally, the last column o Table 6 conveys the ollowing message: by 2006, i the two groups had been treated equally according to their personal endowments, women in tradable sectors would have received double the remuneration that they currently do. Previous outcomes are indicative o the presence o a relatively high unexplained wage dierential when the dierences in endowments are taken into account in both sectors but is βˆ X 6 To clariy this point, recall the irst term on the right hand side o equation (3), ( ) sign results rom the term ( ) X m X. Given is a positive term, the negative m m βˆ m, which points to advanced levels o education or women over men. When the negative dierence is multiplied by higher male returns it results in lower values o the wage gap, due to endowments. X 12

15 more pronounced and wider in tradable than in non tradable sectors. In terms o type o endowments that is most valued, the Egyptian labor market credits years o schooling at the expense o years o experience. Advancing the previous estimation, by adding sectoral distribution into the decomposition assessment (Method B), urther breaks down the wage gap into a portion that is due to dierences between sectors and a portion due to dierences within sectors. Table 7 shows wage decomposition taking into account within and between sectoral allocations. Some dierences may appear between results o this methodology and the standard methodology o decomposition above (Table 6). The reason or the dierent indings is that, using Method A, average male workers are compared with average emale workers regardless o their sector o employment. Carrying Method B, average male workers are compared with average emale workers within and across economic sectors. Once more, the negative sign in the endowment (explained) column indicates that, i women had been treated the same as men within each occupation, their earnings would have been higher than normal, possibly due to unobserved attributes. Conversely, the positive sign in the explained column indicates that men have higher levels o education and/or experience than women, and thereore the dierence in the wage gap is ustiied in accordance to human capital theory predictions. The table in general reveals the act that the observed gap between men and women s wages is almost exclusively due to intra-sectoral dierences in wages ( pure discrimination). Across sectors, dierences between genders in their sectoral allocation are ustiied. The additional years o education or men in both sectors are well rewarded in the labor market, persistent, and well observed in non-tradable when compared with tradable sectors. Once women are employed in a tradable sector, they are exposed to greater discrimination as indicated by the positive ollowed by negative sign in explained terms. Women in non-tradable sectors experienced greater discrimination in 2006 as well. The large contribution o the intrasectoral dierences in wages is not unique to this study. Studying occupational wage dierences, Kidd and Shannon (1994) reported comparable results or Canadian labor orce, with 102 percent intra occupational wage dierentials. Taking into account the sectoral distributional eect (using Method B) leads to a signiicant drop in the explained portion o the earnings gap ound in Method A. The contribution o the explained component o the wage dierence or workers in tradable sectors dropped rom -62 percent (using standard decomposition) to -8 percent (using Method B), and increased discrimination or workers in non-tradable sectors rom -42 percent (Method A) to -66 percent (Method B). Still, the table points to a drop in sectoral segregation eect and a decline in pure wage discrimination resulting rom between sector eect (this is indicated by the negative sign o the unexplained portion between sectors which acted in the interest o women, narrowing the wage gap by 6 percent and 18 percent in both sectors respectively). Comparing the two decomposition results indicates that, the rise in the unexplained portion is due to intra occupational earnings dierences and discrimination in avor o men and against women, and to the unequal treatment o males and emales productivity related characteristics. Simply put, much o the dierences in the overall gender wage gap in Egypt cannot be explained by the dierences in workers productivity-related characteristics. The dierences are due to labor market discrimination resulting rom women being crowded in ew tradable sectors. This is contrary to what was ound in Mexico, where trade liberalization had led to a decrease in wage discrimination in industries that were orced to become competitive due to trade liberalization (Artecona and Cunningham, 2002). The increased 13

16 competition resulting rom trade liberalization in Egypt was accompanied by increased wage discrimination in both sectors, but it is more pronounced in non-tradable versus tradable sectors. Finally, it is worth taking into consideration the act that although we may arrive at a better measure o wage dierence decomposition by incorporating sectoral distribution, we are still unable to account or pre-labor market and extra-labor market actors (such as delayed or interrupted participation and women s tastes or certain or obs). VI. Conclusion and Policy Implications Using the standard decomposition (Method A), higher male wages were not ustiied according to human capital theory predictions or both sectors o employment and especially so in tradable sectors. The part o the wage gap that is due to pure discrimination, has increased considerably between 1998 and 2006 in tradable sectors and declined in nontradable sectors. Taking into account the sectoral distributional eect (Method B) led to a signiicant drop in the explained portion o the earnings gap ound in the standard decomposition (Method A). The observed gender wage gap is almost exclusively due to sectoral distribution (within sectors). Wage dierences across sectors o employment are ustiied (or example, the added years o education or men are well rewarded in the labor market), especially so in non-tradable sectors. The contribution o the explained component o the wage dierence or workers in tradable sectors dropped rom -62 percent (using Method A) to -8 percent (using Method B), and increased discrimination or workers in nontradable sectors rom -42 percent (using Method A) to -66 percent (using Method B). Furthermore, there has been a drop in sectoral segregation eect and a decline in pure wage discrimination resulting rom between sector eect, which acted in the interest o women, narrowing the wage gap in both sectors. By and large, the increased competition resulting rom trade liberalization in Egypt was accompanied by increased wage discrimination in both sectors, but it is more pronounced in non-tradable than tradable sectors. Identiying the genesis o gender remuneration dierentials inluenced by recent ree trade practices and regulations in Egypt improves our understanding o the dynamics o labor market and its interconnection with economic development. Policy analysts may ind the results o this study useul. On the one side, results may determine whether the ocus should be directed towards enorcements o pay equality within export oriented sectors or to redistributing women between sectors. On the other hand, skill acquisition and providing social protection or the poor and low wage workers may be deemed necessary in certain cases. Furthermore, as trade policies normally center on the macroeconomic level and on particular products that are traded on national or international markets, they tend to overlook micro economic actors and social changes that directly aect the underprivileged women and men. The inclusion o gender as an analytical tool in the study o economics expands its boundaries and perspectives Besides, downturns aecting one sector o the economy would be less problematic i ob seekers can move unhindered between sectors. In response to declining wages resulting rom trade reorms, restraint movement, lack o transerable skills or other barriers prevent labor rom ree movements between industries and sectors o the economy. This requires increased lexibility in the labor market through institutional reorms or providing adequate social protection in order to minimize the short term adustment costs. These adustments would be more diicult when sectors are sex segregated. 14

17 Policy recommendations centering on education and training as keys to a more equitable access to the ob market may be inadequate. Policy instruments need to deal with dynamics o the labor market (such as the within and between sectors and obs). Proposals or equal access to schooling, on the ob training, as well as inormational network monitoring the changes in wage rates and gender pay dierentials are ust a ew policies that have proved successul in countries such as Japan and Scandinavia. In short, like other structural adustment policies, trade liberalization can have the potential o enhancing the eiciency and competitiveness o host economies, but at the risk o creating or worsening inequalities or the poor and or women. Women s education and skill accumulation are central actors determining the impact o trade on women s employment and the gender wage gap. As long as women remain less qualiied than men, they are likely to remain in lower paid, less secure obs, even i better-paid obs become available through trade liberalization. Education and skills would provide greater lexibility and empower the disadvantaged to negotiate wages and work conditions. 15

18 Reerences: Arican Development Bank (Multiple Years). Arab Republic o Egypt Country Strategy Paper. Artecona, R. and W. Cunningham (2002). Eects o Trade Liberalization on the Gender Gap in Mexico, Policy Research Report on Gender and Development, Working Paper 21, World Bank, Washington D.C. Barsoum, G. (2006). Egypt Labor Market Panel Survey 2006, Final Report, the Population Council, Cairo, Egypt. Becker, Gary (1957). The Economics o Discrimination, University o Chicago Press, Chicago & London. Berik, Guinseli, Yana van der Meulen Rodgers, and Joseph E. Zveglich, Jr., (2003). International Trade and Wage Discrimination: Evidence rom East Asia, World Bank, Poverty Reduction and Economic Management Network, Policy Research Working Paper 3111, August. Blinder, A.S. (1973). Wage Discrimination: Reduced Form and Structural Variables, Journal o Human Resources, 8: El- Megharbel (2007). The Impact o Recent Macro and Labor Market Policies on Job Creation in Egypt, ECES, Working Paper, No El-Hamidi, F and Said, M (2007). Have Economic Reorms Paid O? Gender Occupational Inequality in the New Millennium in Egypt, A research study presented at the annual ECES conerence in June 2007, Cairo-Egypt. Elson, Diane and Ruth Pearson (1981). Nimble Fingers Make Cheap Workers: An Analysis o Women s Employment in Third World Export Manuacturing, Feminist Review 7: Feliciano, Zadia M. (2001). Workers and Trade Liberalization: The Impact o Trade Reorms in Mexico on Wages and Employment, Industrial and Labor Relations Review, Vol.55, No.1, pp Gaston, Noel and Daniel Treler (1994). Protection, Trade, and Wages: Evidence rom U.S. Manuacturing, Industrial and Labor Relations Review, Vol. 47, No. 4 (July), pp Hasan, Rana, and Lan Chen. (2003). Trade and Workers: Evidence rom the Philippines. East-West Center Working Paper (Economic Series) No. 61. Heston A, et al (1995). The Dierential Productivity Hypothesis and Purchasing Power Parities: Some New Evidence, Review o International Economics, Vol. 2, Issue 3. IMF, World Economic Outlook (2007). Jacob, Marilyn (2007). The Impact o Trade Liberalization on Gender and Caste Groups in India, Paper presented at the Annual Meeting o Population Association o America 16

19 Joekes, Susan P. (1987). Women in the World Economy: An INSTRAW Study. New York: Oxord University Press. Joekes, Susan P (1995). Trade-Related Employment or Women in Industry and Services in Developing Countries, Occasional Paper 5. Geneva: United Nations Research Institute or Social Development. Kumar, Utsav and Prachi Mishra (2005). Trade Liberalization and Wage Inequality: Evidence rom India, IMF Working Paper No. 05/20. Washington D.C.: International Monetary Fund. Mincer, Jacob, (1974). Schooling, Experience and Earnings, New York, Columbia University Press. Oaxaca, R. (1973). "Male-Female Wage Dierentials in Urban Labor Markets," International Economic Review, 14 (October): Oostendorp, R. H. (2004). Globalization and the Gender Wage Gap, Policy Research Working Paper 3256, World Bank, Washington, D.C. Reilly, Barry and Pua Vasudeva Dutta (2005). The Gender Pay Gap and Trade Liberalization: Evidence or India, PRUS Working Paper No. 32 (July). Revenga, Ana (1997). Employment and Wage Eects o Trade Liberalization: The Case o Mexican Manuacturing, Journal o Labor Economics, Vol.15, No.3, Part 2: Labor Market Flexibility in Developing Countries, S20-S43. Rodrik, Dani (1997). Has Globalization Gone Too Far? Washington, D.C.: Institute or International Economics. 17

20 Figure (1): Distribution o Sector o Employment o Non Government Wage Workers 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Male Female Male Female other Finance/RealState Transportation electricity/water/con Educ/Health Hot/Res, comm, person Retail&Trade other Manu Text/garm Manu. Food Manu. Source: Author s own calculation Table (1): Annual Rate o Growth by Sector o Employment and Gender, Panel A Sector Males Female Total Government Public Private Total Panel B Sector Males Females Agriculture Agriculture Non Wage Agriculture_ Wage Private Agric Wage Gov work Pub Entr Prv Non Agr Non Wage Work Source: Author s own calculation 18

21 Table (4): Employment Distribution o Private Sector Workers by Sectors and Gender Source: Author s own calculation Table (5): Wage Gap (Female Wages/ Male Wages) Source: Author s own calculation Table (6): Decomposition o Wage Dierentials by Gender and Sector o Employment Standard Decomposition Method A Source: Author s own calculation 19

22 Table (7): Decomposition o Wage Dierentials by Gender and Sector Between and Within Sectors Method B Source: Author s own calculation 20

23 Appendix Table (A-1) Source: USAID, Egyptian Trade Reorms, September

24 Table (A-2): Heckman Selection Model; Wage and Participation Equations 22

25 Table (A-3): Selectivity Corrected Regression Equations 23

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