metadata: authors: 0: author: Savchenko,Yevgeniya
1: author: Lopez-Acevedo,Gladys C.
2: author: Robertson,Raymond
count: Sri Lanka,Cambodia
docna: 0: docna: Globalization and the gender earnings gap :
            evidence from Sri Lanka and Cambodia
docty: Policy Research Working Paper
subtopic: Labor Markets,Educational Sciences,Gender and Development,International Trade and Trade Rules
teratopic: Poverty Reduction,International Economics and Trade
abstracts: cdata!: Disasters in Bangladesh and protests
            elsewhere have created an intense debate about the value,
            particularly to women, of apparel employment in developing
            countries. This paper focuses on how the forces of
            globalization, specifically the Multi-Fibre Arrangement
            (MFA), have affected women's wages in the apparel
            sector in developing countries. The paper uses household and
            labor force surveys from Cambodia and Sri Lanka to estimate
            both apparel wage premiums relative to other industries and
            the male-female wage gap before and after the end of the
            MFA. The approach builds on new models that apply
            traditional trade theory (e.g., the Heckscher-Ohlin and
            Stolper-Samuelson theorems) to analyze the effect of
            globalization on gender-based earnings. The authors find
            large positive wage premiums and a closing of the
            male-female wage gap during the MFA period, but smaller
            premiums and a widening wage gap after the end of the MFA.
            The results suggest that the benefits of apparel exports for
            women in developing countries remain significant post-MFA.
            They also model an approach for studying the effects of
            globalization that differentiates males and females as
            separate factors. This may be a fruitful alternative to
            discrimination models or those that analyze the effects of
            globalization on women in terms of skill. Further research
            is necessary to identify the potential development effects
            of post-MFA apparel employment and to thoroughly compare the
            benefits documented in this paper with the costs that may
            come with apparel jobs.
geo_reg_key: 517198,517191,517212
repnme: repnme: Globalization and the gender earnings gap :
            evidence from Sri Lanka and Cambodia
display_title: Globalization and the gender
            earnings gap : evidence from Sri Lanka and Cambodia
listing_relative_url: /research/2016/12/27056496/globalization-gender-earnings-gap-evidence-sri-lanka-cambodia
topicv3: Trade m1326924 698,Education m1327048 470,Gender m1327621 615,Jobs m1503931 2670,Wage Gap m1582439 4439
publishtoextweb_dt: 2016-12-27T00:00:00Z
historic_topic: Poverty Reduction,International Economics and Trade
no_of_pages: 56
author_keys: 0: author_key: 675741
1: author_key: 428192
2: author_key: 636947
model_extractions: WPS7930 Policy Research Working Paper 7930 Globalization and the Gender Earnings Gap Evidence from Sri Lanka and Cambodia _Yevgeniya Savchenko Gladys Lopez Acevedo Raymond Robertson_ South Asia Region Office of the Chief Economist December 2016
Policy Research Working Paper 7930

# **Abstract**

Disasters in Bangladesh and protests elsewhere have created an intense debate about the value, particularly to women, of apparel employment in developing countries. This paper focuses on how the forces of globalization, specifically the Multi-Fibre Arrangement (MFA), have affected women’s wages in the apparel sector in developing countries. The paper uses household and labor force surveys from Cambodia and Sri Lanka to estimate both apparel wage premiums relative to other industries and the male-female wage gap before and after the end of the MFA. The approach builds on new models that apply traditional trade theory (e.g., the Heckscher-Ohlin and Stolper-Samuelson theorems) to analyze the effect of globalization on gender-based earnings. The authors find large positive wage premiums and a closing of the male-female wage gap during the MFA period, but smaller premiums and a widening wage gap after the end of the MFA. The results suggest that the benefits of apparel exports for women in developing countries remain significant post-MFA. They also model an approach for studying the effects of globalization that differentiates males and females as separate factors. This may be a fruitful alternative to discrimination models or those that analyze the effects of globalization on women in terms of skill. Further research is necessary to identify the potential development effects of post-MFA apparel employment and to thoroughly compare the benefits documented in this paper with the costs that may come with apparel jobs.

This paper is a product of the Office of the Chief Economist, South Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at gacevedo@worldbank.org.

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

# **Evidence from Sri Lanka and Cambodia**

Yevgeniya Savchenko Economist, World Bank **ysavchenko@worldbank.org** Gladys Lopez Acevedo Lead Economist, World Bank **gacevedo@worldbank.org** Raymond Robertson Professor, Texas A&M University **robertson@tamu.edu** _JEL classification:_ F16, F63, F66, J21, J24, J30, J31, J81, L67 _Keywords:_ apparel, Multi‐Fibre Arrangement, textile, wages, women, working conditions
2

# **Acknowledgements**

We are grateful to Martin Rama, Elisa Gamberoni, Ana Luisa Gouvea Abras, and Cornelia Staritz and to participants of the Second International Conference on Global Value Chains in Geneva (2016) for useful discussions and comments. This paper is a byproduct of the South Asia Report “Stitches to Riches?: Apparel Employment, Trade, and Economic Development,” https://openknowledge.worldbank.org/handle/10986/23961. These are views of the authors, and do not necessarily reflect those of the World Bank, its Executive Directors, or countries they represent.
3

# **1. Introduction**

The idea that incorporating women into the formal workforce contributes to economic development is now well established.<sup>1</sup> The effects of globalization on women’s employment and earnings, however, remain debated. The male-female wage gap changes over time but the forces that drive the changes in the wage gap are not well understood. Globalization is considered to be one of the important drivers of employment and wages in developing countries. Papers that focus on tariff reductions, such as Juhn et al. (2013, 2014) and Aguayo-Tellez (2010), find that tariff reductions favor women in both employment and consumption. Klein et al. (2010) find a similar result in Germany: increasing exports reduce gender wage inequality. Saurè and Zoabi (2014), however, find that an increase in the output price of the female-intensive sector raises both male and female wages (so that the relative wage does not change in the short run), but, as males reallocate towards the female-intensive sector, the gender gap widens.

These papers build on previous work that was equally contentious<sup>2</sup> and reflect the beginning of a shift in the way the globalization-gender earnings relationships are analyzed. Early papers were based on discrimination models that predicted that globalization would increase competition, which would make discrimination less feasible. A new wave of models, however, has begun to apply traditional trade theory (e.g., the Heckscher-Ohlin and StolperSamuelson theorems) to analyze the effect of globalization on gender-based earnings. This new wave includes several common characteristics. First, males and females are treated as separate factors that are not necessarily differentiated by skill (e.g., Galor and Wiel 1996).<sup>3</sup> Second, studies in this new wave include output price changes, either directly or indirectly (through tariffs). Third, they differentiate industries by female intensity.
4 The goal of this paper is to describe how global price changes may affect women’s wages in developing countries. Specifically, this descriptive paper contributes to this new wave of literature in three ways. First, it focuses on the apparel industry. In developing countries women often enter the formal sector though the apparel industry. Apparel is perhaps the most prominent labor-intensive and female-intensive globalized manufacturing sector. The disasters in Bangladesh apparel factories in 2012 and 2013<sup>4</sup> stimulated an intense debate around the value of apparel exports and employment in developing countries. In this debate, the costs of disasters, low wages, and poor working conditions are contrasted with the benefits of incorporating women into the formal labor force. Understanding how both the growth of apparel exports and major policy changes affect women is a critical element of the debate surrounding the apparel export model. Tremendous changes in the apparel sector over the decade from 2001 to 2010 have had significant implications for women in developing countries and, therefore, for development.

Second, we apply a simple model in which men and women are treated as different factors whose absolute and relative wages are driven by output price changes. Our simple model illustrates how the effects of changes in output prices are different in the short run and the long run, following the spirit of recent work (including Do et al. 2011). We extend these papers by differentiating the short run and long run. Adjustment costs are central in a new wave of trade models,5 and our simple theoretical model illustrates how the presence of short-run adjustment costs generates the prediction that a negative price shock to a specific sector would lead to a decrease in wages in that sector relative to other sectors in the short run. In the medium run, when factors are free to adjust, the model predicts a relative decrease of wages to a sectorspecific factor (in our case, female workers) in the overall economy.
5 Third, we apply the model to two leading apparel-exporting countries: Sri Lanka and Cambodia. They provide excellent opportunities to compare the effects of changes in the apparel exporting sector on women. Both countries are likely to be considered “small” in the theoretic sense of having negligible effects on world prices. Thus, Cambodia and Sri Lanka are excellent cases that may illustrate the potential effects of the end of the MFA on female employment and the gender wage gap. As in many low-wage developing countries, apparel is the key manufacturing industry in Sri Lanka and Cambodia. In Sri Lanka the apparel sector employed 270,000 workers, or 13 percent of its industrial workforce in 2008.<sup>6</sup> In Cambodia, apparel employed around 325,000 workers or 30 percent of the country’s total industrial workforce in 2008. Female workers dominate apparel sector employment in both countries, making up 70 percent and 83 percent of the apparel workforce in Sri Lanka and Cambodia respectively.<sup>7</sup> Both Sri Lanka and Cambodia were significantly affected by the Multi-Fibre Arrangement (MFA), which from 1974 on was the major policy governing textile and apparel trade between industrialized and developing countries.<sup>8</sup> Cambodian and Sri Lankan exports grew remarkably during the MFA era. The end of the Multi-Fibre Arrangement on January 1, 2005 induced a dramatic redistribution of the allocation of global apparel production (Staritz 2011; Lopez Acevedo and Robertson 2012)<sup>9</sup> and a dramatic drop in global apparel prices (Harrigan and Barrows 2009).

Our approach, which is similar to the approach used by Acosta and Gasparini (2007), illustrates how these short-run and medium-run responses to price changes can be estimated with conventional empirical techniques from labor economics. We find that the apparel wage premium<sup>10</sup> rose when exports began to grow under the MFA but dropped (less than the initial increase) following the MFA phase-out in both Sri Lanka and Cambodia. Even though the
6 apparel wage premium improved in the following years, it never regained its pre-MFA phase-out levels. Furthermore, the male-female wage gap widened in the post-MFA period in both countries, as predicted.

The rest of the paper is structured as follows. Section two presents the simple theoretical model. Section three provides relevant context by describing export, price, and employment trends in Cambodia and Sri Lanka. Section four contains the empirical results. We conclude in section five.

# **2. Theoretic Foundation**

Following Bernard et al. (2007) we present a simple Melitz-like model (Melitz 2003) in which firms differ by idiosyncratic technology and have two factors. Bernard et al. (2007) show that the introduction of firm-level heterogeneity may not affect the traditional Heckscher-Ohlin prediction of endowment-based trade determination and its effects (e.g., the Stolper-Samuelson relationship between output price changes and relative factor earnings). Juhn et al. (2014) apply the same logic with the two factors being defined by gender and use Mexican data to test some of the model’s implications. Our model is more modest in presentation but is meant to capture the essential features of these models, with the goal of simply illustrating the different effects and empirical implications of short- and long-run price changes.

To begin, consider a model with two goods, _(a)_ and _(b)_ . At the firm level, goods are produced with three factors: males ( _m)_ , females ( _f)_ , and a single unit of capital ( _k_ ). The fixed unit of capital that is necessary for production leads to diminishing returns to scale. The assumption of fixed capital reflects our focus on the short and medium run. We will define the medium run as the period in which labor is mobile between industries, and the short run as the period in which labor is not mobile. The capital represents an idiosyncratic amount of technology
7 represented by _φi_ for firm _i._ The ex ante productivity parameter distribution is described by 2 _g_ ( ~~~~ ,  _k_ ) and follows an exponential distribution.<sup>11</sup> As in many trade models, we take the price of good _b_ to be the numeraire. Production in industry _j_  ( _a_ , _b_ ) and firm _i_ is the result of combining inputs according to a sector-specific production function, such as Without loss of generality, we assume that industry _a_ is female intensive and, for simplicity, that the male-female employment ratio is not affected by the firm-specific technology parameter.<sup>12</sup> The variables _P, φ_ , _w_<sup>_f_</sup> _,_ and _w_<sup>_m_</sup> uniquely define the profit-maximizing level of output and the combination of male and female employment. An increase in the output price, TFP, and the individual-specific productivity parameter increase factor demand at the firm level.

The firms incur a fixed cost to set up production, which we denote _Sd._ The difference between revenue and cost must be at least as large as _Sd_ for the firm to stay in the market and produce. The result is a cutoff value for _φ_ that is required for the firm to remain in the market. Firms opting to leave the market truncate the ex post _φ_ distribution, which results in higher average productivity levels than in the ex ante distribution of firms.
8

# **A. Open Economy**

Firms incur a fixed cost to export, which we denote _Sx._ To export, the firm must be a viable domestic producer. We make the usual assumption that international transportation costs follow an “iceberg” model, which suggests that _Q_ τ goods must be exported for the quantity _Q_ to arrive (τ > 1). Firms in sector _a_ that export, therefore, earn more than firms that produce only for the domestic market. The model can be simplified by assuming that the export price ( _Px_ ) is a fixed markup over the domestic sales price ( _Px_ = τ _P_ ). The model easily accommodates foreign tariffs in that τ = premium divided by tariff. Higher foreign tariffs reduce the premium and, eventually, reduce exports to zero. Assuming away corner solutions, firms will choose to export if The result of the additional fixed cost is that firms that export are more productive and larger than firms that do not export.

# **B. General Equilibrium**

Sector _b_ produces exclusively for the domestic market with homogeneous technology.

Assume that the second sector is characterized by decreasing returns to scale. Factor demand in sector _b_ can be characterized by in which the parameters κ and λ characterize the implied factor demand functions. Under the assumption of full employment, employment of males and females in the second sector is equal
9 to the total population of each worker type minus employment of each type in the first sector. Homogenous firms implies that we can represent aggregate relative factor demand with a single demand function, and that all workers of a given type receive the same labor income. The result is that wages of each factor are determined in the aggregate labor market and equalize across sectors.

It is important at this point to distinguish wages, which we have used so far, from income. In sector _b_ , decreasing returns to scale implies that the total factor payments are less than the value of production. The remainder of the value of production is shared equally among the workers. In other words, income is the integral of the marginal revenue product from zero to total employment in sector _b_ divided by total employment in sector _b._ Income is equal to the wage, which is determined competitively in the factor demand market, plus the “profits” that accrue to the firms from having the marginal revenue product being less than the total value generated by the workers.

The income in sector _b_ effectively defines the reservation wage because sector _b_ is the residual sector. If workers are perfectly mobile between sectors, income in sector _b_ (rather than wages) becomes the minimum income in the heterogeneous sector. Workers have the chance to earn more in sector _a_ because profits are (potentially) higher in heterogeneous firms. Thus workers either work in the sector _b_ for income _Ib_ or in the heterogeneous sector for at least _Ib._ Workers in the heterogeneous sector queue for jobs in the sense that sector _a_ can hire as many workers as they want as long as they offer at least the reservation wage. Employment decisions at the firm level in the heterogeneous sector are based on _yb_ because profits are realized ex post. In the heterogeneous sector, firms sort into three categories based on the productivity draw: those that close, those that produce for the domestic market, and those that export. In this way, the
10 model generates both inter-industry and intra-industry trade, which is qualitatively similar to Bernard et al. (2007).

Income for workers in the heterogeneous sector is then which is the income they could earn in the homogenous sector plus an equal share of the firmspecific profits (note that _E_ is equal to the sum of _m_ plus _f_ ). Note that equation (5) has important implications for empirical work. A positive price shock will affect income by increasing the second component directly. The positive price shock will only affect the first component to the extent that factor markets clear. The first component is the economy-wide return to being a given factor. The second component is effectively industry specific. Our estimation approach decomposes these two terms and compares how these individual components are related to product-level prices in Cambodia and Sri Lanka.

Changes in prices generate general-equilibrium dynamics that are consistent with the predictions of the Heckscher-Ohlin model in the following sense: when workers are perfectly mobile, an increase in the relative price of the female-intensive good will increase the demand for females throughout the economy and thus will close the male-female wage gap. In the short run, however, increases in prices will only increase wages in the sector experiencing a price increase. In other words, in the short-run, a sector-specific shock will increase the sector-specific wage and will not affect the equilibrium wage differential because the wages of both males and females would increase. Once factors are able to move, the sector-specific price shock will result in a differential increase in the demand for the factor intensively used in the sector that experienced the price increase. Our empirical strategy, therefore, is to decompose wages into the
11 sector-specific and factor-specific wages and compare those with changes in industry output prices.

# **3. The Apparel Sector in Cambodia and Sri Lanka**

Sri Lanka has a long tradition in apparel. Before 1977 the Sri Lankan apparel sector was very small with a few locally owned firms working for the domestic market. Trade liberalization in 1977, however, immediately attracted foreign investors from East Asia to Sri Lanka. Over time, Sri Lankan owners started dominating the apparel sector; in 1999 approximately 80 to 85 percent of factories were locally owned (Kelegama and Wijayasiri 2004).

Due to its decades of political and civil unrest, Cambodia was a latecomer to the apparel industry. The modern Cambodian industry was established only in the mid-1990s by investors from Hong Kong SAR, China; Malaysia; Singapore; and Taiwan, China. In Cambodia, approximately 93 percent of apparel factories were foreign owned in 2009 (Natsuda, Goto, and Thoburn 2009). In this section, we provide context for our empirical results by examining the trends in exports, unit values, and the labor market.

In Cambodia the number of workers in the industry continued to grow because of industry expansion. In Sri Lanka, apparel employment declined because of industry consolidation. Given that women represent the major share of employment in apparel manufacturing, the general employment trends are directly applied to women. Employment in Cambodia’s apparel sector mushroomed from fewer than 19,000 workers in 1995 to approximately 270,000 in 2004 (Staritz and Frederick 2011a). Growth continued after the MFA phase-out, with operating employment reaching 353,017 workers in 2007 (see figure 1). After 2007, employment dipped to 281,855 in 2009, but recovered to 335,342 in 2011. Employment levels in Sri Lanka’s apparel sector increased from 102,000 in 1990 to 340,367 in 2004,
12 accounting for more than a third of manufacturing employment in 2004 (Staritz and Frederick 2011b). As the industry started consolidating<sup>13</sup> firms after the end of the MFA, employment declined by approximately 18 percent to 280,000 in 2009 (see figure 1).

Figure 1 Total Employment in the Apparel Sector in Cambodia and Sri Lanka <!-- Start of picture text --> 1995 2001 2002 2004 2005 2007 2008 2009<br>Year<br>Cambodia Sri Lanka<br>400000<br>300000<br>200000<br>Number of workers<br>100000<br>0<br><!-- End of picture text --> _Source:_ Kelegama 2005a, 2005b, 2006, 2009; Tait 2008; Barrie 2009; Saheed 2010; Garment Manufacturers’ Association in Cambodia (GMAC). _Note:_ Operating employment numbers are presented for Cambodia.

In addition to employment, working conditions directly impact a worker’s well-being through the number of hours that are worked overtime, a hazardous work environment, social benefits, or workplace discrimination. But working conditions also have an indirect impact on employment and wage opportunities through the demand side: buyers might pay lower prices or refuse to buy at all if they know that producers exploit child labor or mistreat employees.
13 The Cambodian and Sri Lankan governments had different strategies to improve working conditions in the apparel sector. Sri Lanka, as a part of its Five-Year Strategy, designed international and local campaigns to improve the image of the apparel sector and working conditions. The 2006 international image-building campaign was called _Garments without Guilt_ , and the 2008 local campaign was called _Abhimani_ (“pride”). Despite these efforts, working conditions are still far from ideal. As mentioned earlier, labor costs in Sri Lanka are lower than in China and India. Besides low wages, other issues have been problematic in parts of the apparel sector, particularly in smaller firms. These issues include the lack of appointment letters, long working hours, high work intensity, and, in particular, denial of the right of association and collective bargaining (as many firms are reluctant to recognize trade unions) (Staritz and Frederick 2011b).

Cambodia has a good record of labor compliance because of the _Better Factories Cambodia_ program that began in 1999. Through this program, compliance with international labor standards was directly linked to the apparel export quotas that Cambodia received from the United States. In the 2004 Foreign Investment Advisory Service survey of the 15 largest U.S. and European Union (EU) buyers of Cambodian apparel, Cambodia was rated the highest on “level of labor standards” and “protecting the rights of workers to organize unions” among Asian apparel-exporting countries, including Bangladesh, China, Thailand, and Vietnam (Staritz and Frederick 2011a). The MFA phase-out coincided with the expiration of the U.S. quotas in 2004, which eliminated the incentive motive of _Better Factories Cambodia_ (Staritz and Frederick 2011a). Nevertheless, the Cambodian government encouraged the apparel sector to continue with the program to maintain its reputation for compliance with good labor standards in order to remain attractive to the foreign investors.
14

# **3.1. Exports**

After the Cambodian apparel industry took off in the late 1990s, exports quadrupled within a decade, growing from US$578 million in 1998 to US$2.4 billion in 2004 (see figure 2). According to the Garment Manufacturers’ Association in Cambodia (GMAC), apparel accounted for only 3 percent of Cambodia’s total exports in the early 1990s, but by 2003 it constituted 76.4 percent. Sri Lanka, already a seasoned apparel exporter in the mid-1990s, experienced a healthy but more moderate growth than Cambodia, with exports rising from US$1.7 billion in 1995 to US$3 billion in 2004 (figure 2). In the late 1990s, however, export growth slowed, and exports even declined in 2001, 2002, and 2008 (figure 2).<sup>14</sup> <!-- Start of picture text --> Figure 2: Total Apparel Exports (Millions US$)<br>(HS61 and HS62)<br>1995 2000 2005 2010<br>Period<br>Cambodia Sri Lanka<br>6000<br>4000<br>2000<br>0<br><!-- End of picture text --> _Source:_ United Nations Commodity Trade Statistics Database (UN Comtrade). _Note:_ Exports are represented by imports reported by partner countries. Apparel Classification: HS 1992: Woven: HS62; Knit HS61.
15 Despite pessimistic expectations for the apparel sectors post-MFA, both countries continued increasing apparel exports, though growth slowed somewhat. Sri Lanka expected that exports would decrease by half and that 40 percent of firms would close in 2005 (Kelegama and Epaarachchi 2002). Sri Lankan apparel exports, however, grew 6 percent annually on average, and their value increased by approximately US$1 billion over 2005–08. In Cambodia immediately after the MFA removal, total apparel exports increased to US$2.7 billion in 2005 and to US$5.6 billion in 2011, a rise of almost 14 percent annually. The fact that these two countries continued to be important apparel producers makes them interesting cases for our study.

# **3.2. Unit Values**

In the neoclassical model presented in section 2, output prices drive wages. We use unit values as a proxy for output prices,<sup>15</sup> following Harrigan and Barrows (2009). Our unit value data come from both OTEXA and Comtrade, two well-known official sources of apparel trade data. We compute weighted averages of unit values by dividing total value by a common quantity measure (square meter equivalent in the United States data and kilograms in the European data). We use data from both the United States and the EU because the end markets of both areas are highly concentrated, with 87 to 90 percent of total Cambodian and Sri Lankan apparel exports going to those two destinations. Export products in both Cambodia and Sri Lanka are highly concentrated in a few items. The Sri Lankan apparel industry focuses on higher value-added products, such as lingerie. Unit values of apparel exports from India and Sri Lanka to the EU are higher than those of Asian competitor countries, including Bangladesh, Cambodia, China, Pakistan, and Vietnam (Tewari 2008).
16 On the other hand, the unit prices of Cambodia’s main export products are lower than the world average. For EU-15 exports, unit prices in 2005 were lower than in India, Sri Lanka, and Vietnam, but higher than in Bangladesh, China, and Pakistan (Tewari 2008). One of the reasons for the low prices of Cambodian apparel exports is that Cambodian apparel manufacturers concentrate on the production of basic products, while other countries export higher-value products.

In both Cambodia and Sri Lanka, unit values show declining trends following the end of the MFA. Figure 3 contains the weighted averages of the dollar-value square-meter equivalent apparel imports into the United States from Sri Lanka and Cambodia. The unit values for both countries fall after the end of the MFA, but recover after the great trade collapse of 2008. As Cambodia entered the global market (1998–2001), unit values rose sharply. They fell in Sri Lanka over the same time period. China’s entrance into the WTO corresponds with a drop in unit values in Cambodia, while unit values rose in Sri Lanka. U.S. import prices experienced sharper declines than those of the EU. Between 2004 and 2008 the average price of apparel exports to the United States fell by 25 percent, from US$52 to US$39 per dozen. Sri Lankan apparel export unit prices to the United States declined from US$59 to US$42 per dozen items over this period. The average price of apparel exports to the EU-15 declined by 7 percent, from €13.4 to €12.5 per kilogram between 2004 and 2008 as shown in figure 4 (U.S. International Trade Commission (USITC) and Eurostat data; see Staritz and Frederick 2011a).
17 <!-- Start of picture text --> Figure 3: Weighted Average Unit Values<br>U.S. Imports of HS 61 and 62<br>98 00 02 04 06 08 10 12 14<br>time<br>Sri Lanka Cambodia<br>60<br>50<br>40<br>30<br><!-- End of picture text --> _Source:_ Data from Office of Textiles and Apparel (OTEXA), U.S. Department of Commerce. _Note:_ Data represent the weighted U.S. dollar values per square meter equivalent of U.S. imports of HS 61 and HS 62 of each country. Data are smoothed using a six-month moving average.
18 <!-- Start of picture text --> Figure 4: Weighted Average Unit Values, Euro/KG<br>EU Imports of HS 61 and 62<br>1998m1 2000m1 2002m1 2004m1 2006m1 2008m1 2010m1 2012m1<br>time<br>Cambodia Sri Lanka<br>4<br>2<br>0<br>-2<br>-4<br><!-- End of picture text --> _Source:_ Eurostat data retrieved on 12/20/2012.

_Note:_ Data represent the weighted unit values (euro per 100 kg) of EU-27 imports of HS 61 and HS 62 of each country. Data have means removed, are deseasonalized using monthly dummy variables, and are then smoothed using a six-month moving average.

The declining trends in unit values were expected. After the MFA phase-out, the competitive pressure in apparel exports increased, which inevitably led to a decrease in apparel prices. According to the simple model described in section 2, the decline in apparel prices should have resulted in apparel manufacturing wages decreasing in the short run and in female wages in all industries declining in the long run (in approximately three to five years). The next subsection provides empirical evidence for these theoretical results.
19

# **4. Data and Empirical Results**

## **4.1 Data Description**

We combine household and labor force surveys from each country. The Cambodian data come from the cross-sectional Cambodia Socio-Economic Survey (CSES), which is collected by the National Institute of Statistics. We use data from the 1996, 1999, 2004, 2007, 2008, 2009, 2010, and 2011 surveys, which contain roughly 12,000 households each. These are crosssectional household surveys that contain detailed household and individual information, including wage, education, age, marital status, gender, location, industry, occupation, some working conditions, and the hours worked.

For Sri Lankan data, we use the 1992–2002, 2008, 2011, and 2012 waves of the crosssectional Sri Lanka Labor Force Surveys (LFS) and 2006 wave of the Sri Lanka Household Income and Expenditure Survey (HIES), which are carried out by the Sri Lankan Department of Census and Statistics. These surveys cover approximately 30,000–60,000 individuals each. They contain information about work-related activities (for example, employment status, occupation, industry, and wages); household characteristics (for example, size and location); and individual demographic characteristics such as age, gender, and education, among others.

Apparel workers in Cambodia and Sri Lanka share a number of characteristics. First, the share of the total labor force employed in the textiles and apparel industry was relatively small and remained stable after the end of the MFA: 5 percent in Cambodia and 6 percent in Sri Lanka (see table 1).
20

# **Table 1. Labor Force Characteristics for Cambodia and Sri Lanka**

||Panel A:<br>|Cambodia<br>|||||||
|---|---|---|---|---|---|---|---|---|
||1996|1999|2004|2007|2008|2009|2010|2011|
|_Employment and Education_|||||||||
|Labor force participation rate, all|33.3|56.6|17.0|27.0|21.2|20.8|25.5|27.6|
|Labor force participation rate, male|43.5|73.1|20.6|31.6|27.5|25.2|31.1|32.5|
|Labor force participation rate, female|24.1|41.0|13.9|22.3|15.5|16.8|20.4|23.3|
|Females in employment (percent)|52.3|51.5|50.1|49.2|48.6|49.7|50.8|51.2|
|Hours worked, all|39.3|45.7|37.1|39.7|39.2|36.0|39.5|39.4|
|Hours worked, male|41.3|46.1|38.0|41.0|40.6|37.3|40.8|40.9|
|Hours worked, female|37.5|45.4|36.1|38.3|37.7|34.7|38.3|38.0|
|Years of education|3.4|4.5|5.2|5.5|5.7|4.9|5.9|6.3|
|Years of education for females in T&G|4.5|5.5|6.1|6.1|5.7|5.9|6.3|6.9|
|Years of education for men in T&G|5|7.5|7.3|8.3|7.3|6.9|7.8|8.2|
|Average years of education in T&G|4.6|5.8|6.3|6.5|6.1|6.1|6.5|7.1|
|_Employment Share of the Industry_|||||||||
|Agriculture (percent)|74.3|63.5|60.7|51|45.2|57.4|46.8|48.4|
|T&G (percent)|1.6|4.2|4.3|4.7|5.5|5.3|6.5|7.8|
|Share of female in T&G (percent)|81.7|82.8|81.1|82.3|78.7|82.6|85.2|84.9|

_Source:_ Calculations based on the Cambodia Socio-Economic Survey (CSES). _Note:_ T&G = textiles and garments.
21

**Table 1 (cont). Labor Force Characteristics for Cambodia and Sri Lanka**

||||Panel|B: Sri L|anka|||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||1992|1993|1994|1995|1996|1997|1998|1999|2000|2001|2002|2006|2008|2011|2012|
|_Employment and Education_||||||||||||||||
|Labor force participation rate, all|46.1|47.1|47.2|47.1|44.2|48.0|51.8|50.4|50.5|49.4|50.2|52.1|48.7|45.0|44.8|
|Labor force participation rate, male|63.1|64.6|64.9|65.1|61.2|64.3|67.2|66.7|66.6|66.7|67.8|72.1|67.4|63.9|64.5|
|Labor force participation rate, female|29.4|30.1|29.9|29.7|28.6|32.0|36.8|34.4|34.6|32.6|33.4|33.7|31.7|27.8|27.0|
|Females in employment (percent)|28.4|29|28.9|29.3|32.4|31.3|34.1|32.9|33.2|32.1|32.4|30.7|34.1|32.8|32.1|
|Hours worked, all|40.9|41.2|42.2|42.1|39.7|38.5|39.2|39.1|38.4|41.8|40.2|n/a|40.0|46.1|46.2|
|Hours worked, male|42.4|42.5|43.6|43.5|40.5|39.9|41.3|41.0|40.1|43.6|42.1|n/a|42.4|48.7|48.7|
|Hours worked, female|37.2|38.1|38.6|38.8|37.7|35.3|35.1|35.1|35.0|37.8|36.3|n/a|35.2|40.7|40.8|
|Years of education|7.7|7.8|7.9|8|7.5|7.7|7.9|8|8|8.3|8.3|8.4|8.5|8.7|8.8|
|Years of education for females in T&G|9|9.2|9.3|9.4|9.5|9.2|9.5|9.5|9.6|9.8|9.6|10|10.2|10.3|10.2|
|Years of education for men in T&G|9.3|9.1|9.2|9.4|9.7|9.4|9.3|9.6|9.7|10.1|9.7|10.3|10.4|10.4|10.3|
|Average years of education in T&G|9.1|9.1|9.3|9.4|9.6|9.2|9.5|9.6|9.6|9.9|9.6|10.1|10.2|10.3|10.2|
|_Employment Share of the Industry_||||||||||||||||
|Agriculture (percent)|29.61|29.7|27.11|25.66|39.54|39.43|40.76|39.23|38.45|33.34|33|24.89|32.27|32.62|29.95|
|T&G (percent)|7.1|7.2|8.12|8.31|8.72|8.37|8.17|8.23|9.04|8.99|5.82|5.8|6.03|4.93|5.46|
|Share of female in T&G (percent)|53.4|59.7|54.8|56.1|57.5|61.0|58.3|56.9|59.5|55.4|72.3|69.3|73.4|72.3|71.1|

_Source:_ Calculations based on the Sri Lankan 2006 Household Income and Expenditure Survey (HIES) and the 1992–2002, 2008, 2011, and 2012 Labor Force Surveys. Hours worked were not reported in the Sri Lanka HIES 2006. _Note:_ T&G = textiles and garments.
22 Second, the apparel sector is female dominated. The female share of employment in apparel was 71 percent in Sri Lanka (2012) and 85 percent in Cambodia (2011). In both countries, this share was much higher than the proportion of females in the total employment: 51 percent in Cambodia and 32 percent in Sri Lanka. Moreover, the share of females in apparel remained relatively stable in both countries after the MFA phase-out. By nearly any definition, the apparel sector is female intensive.

Third, the labor force working in apparel was more educated than the country average. In Cambodia in 2011, an average person had 6.3 years of education, while an apparel sector employee had 7.1 years of education. In Sri Lanka in 2012, where the population is more educated on average than in Cambodia, the average person had 8.8 years of education compared to 10.2 years for an apparel sector worker.

Fourth, men working in the apparel sector are more educated than women. In Cambodia in 2011, women had on average 6.9 years of education while men had 8.2 years. This gap was smaller for Sri Lanka in 2012, with 10.2 years of schooling for female workers and 10.3 years for male workers. But these differences are representative of the gender education gap in general in these countries, rather than a gap that is specific to the apparel industry. On average, wages were higher in textiles and garments than in agriculture in both countries (see table 2) but lower than the economy-wide average.
23

**Table 2. Monthly Real Wage Levels in U.S. Dollars for Cambodia and Sri Lanka, 2005 prices**

||Panel|A: Camb|odia||||||
|---|---|---|---|---|---|---|---|---|
||1996|1999|2004|2007|2008|2009|2010|2011|
|Mean wage in T&G, all|38|51|76|64|57|54|66|64|
|Male|48|71|85|73|54|55|81|66|
|Female|34|47|73|62|58|54|63|63|
|Mean wage in agriculture, all|41|28|78|35|37|32|34|45|
|Male|44|33|85|41|42|36|39|57|
|Female|34|21|71|30|32|27|30|34|
|Mean wage, all|67|46|77|79|78|57|77|82|
|Male|76|48|79|87|87|62|87|94|
|Female|55|43|73|67|64|50|63|67|

||||||P<br>|anel B<br>|: Sri La<br>|nka<br>||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||1992|1993|1994|1995|1996|1997|1998|1999|2000|2001|2002|2006|2008|2011|2012|
|Mean wage in||||||||||||||||
|T&G, all|91|85|92|96|85|81|74|70|71|61|59|68|58|65|75|
|Male|115|99|112|118|114|108|96|87|94|77|79|94|70|91|92|
|Female|80|79|84|86|72|71|64|61|62|53|54|60|53|56|67|
|Mean wage in<br>agriculture, all|57|54|59|61|52|51|47|49|45|37|35|39|39|45|54|
|Male|63|59|66|69|58|57|53|55|50|42|39|44|46|50|60|
|Female|46|46|49|46|45|43|41|40|38|31|29|31|31|38|44|
|Mean wage, all|94|92|101|101|87|89|84|82|79|69|65|81|69|76|88|
|Male|99|96|106|105|93|96|90|87|84|73|68|85|72|80|92|
|Female|84|84|92|94|74|77|72|71|68|61|59|71|62|67|78|

_Source:_ Calculations based on Cambodia Socio-Economic Surveys, the Sri Lankan 2006 Household Income and Expenditure Survey, and the 1992–2002, 2008, 2011, and 2012 Labor Force Surveys. Note: T&G = textiles and garments. The local currency was transformed into the U.S. dollars using the Consumer Price Index (CPI) and the exchange rate from the World Development Indicators Database.

# **4.2 Decomposing the Gender Wage Differential**

The Oaxaca-Blinder decomposition is a very well-known technique that is generally applied in studies of gender wage differentials. Men and women may have different wages for
24 many reasons. Some are easily observable (such as differences in education, age, and occupation) and others, such as discrimination, are considered to be usually unobservable to the researcher. The decomposition technique separates the observed wage differences into the observable and unobservable components.

The decomposition involves estimating separate wage equations for men and women that include age, education, and other observable variables on the right-hand side. The estimated coefficients are therefore allowed to differ by gender. The approach then applies the estimated coefficients from, say, the male equation to the female data to estimate a possible counterfactual wage value for women. The difference between the two earnings can therefore be expressed as the part due to the differences in the observable and the unobservable characteristics.

The Oaxaca-Blinder decompositions shown in tables 3 and 4 include hours worked, age, age squared, and education as observable characteristics. The decompositions show that in Cambodia the gender differential falls between 11 percent and 32.5 percent, and that it grew, overall, between 1996 and 2011. About two thirds of the differential could not be explained by the observed characteristics that we included. In Sri Lanka, the wage gap also increased over time from 15.6 percent in 1993 to 23.5 percent in 2012. Similarly, unexplained characteristics accounted for a large portion of the differential. The large role of the unexplained component suggests that globalization generally, and apparel prices in particular, could be playing a significant role through the general equilibrium effects that apparel price changes have in the economy.
25

**Table 3. Oaxaca-Blinder Decomposition for Sri Lanka**

||1998|1999|2000|2001|2002|2008|2011|2012|
|---|---|---|---|---|---|---|---|---|
|VARIABLES|overall|overall|overall|overall|overall|overall|overall|overall|
|male|8.570***|8.588***|8.577***|8.575***|8.536***|8.712***|8.856***|8.933***|
||(0.011)|(0.012)|(0.013)|(0.015)|(0.013)|(0.006)|(0.007)|(0.006)|
|female|8.294***|8.367***|8.366***|8.375***|8.324***|8.462***|8.609***|8.698***|
||(0.016)|(0.017)|(0.018)|(0.020)|(0.019)|(0.011)|(0.013)|(0.011)|
|difference|0.275***|0.221***|0.210***|0.201***|0.212***|0.249***|0.247***|0.235***|
||(0.020)|(0.021)|(0.022)|(0.026)|(0.023)|(0.012)|(0.015)|(0.013)|
|explained|0.051***|0.036***|0.040***|0.036***|0.018*|-0.005|0.004|-0.014*|
||(0.008)|(0.008)|(0.008)|(0.010)|(0.009)|(0.007)|(0.008)|(0.007)|
|unexplained|0.224***|0.186***|0.171***|0.165***|0.194***|0.254***|0.243***|0.249***|
||(0.018)|(0.019)|(0.020)|(0.024)|(0.021)|(0.010)|(0.012)|(0.011)|

_Source:_ Calculations based on Sri Lankan 1998–2002, 2008, 2011, and 2012 Labor Force Surveys. _Note_ : Standard errors are in parentheses. Statistically significant coefficients at the 10, 5, and 1 percent level are indicated by one, two, and three asterisks respectively.

**<u>Table 4. Oaxaca-Blinder Decomposition for Cambodia</u>**

||1996|1999|2004|2007|2008|2009|2010|2011|
|---|---|---|---|---|---|---|---|---|
|VARIABLES|overall|overall|overall|overall|overall|overall|overall|overall|
|male|11.498***|11.703***|11.850***|12.243***|12.156***|11.940***|12.326***|12.432***|
||(0.024)|(0.020)|(0.016)|(0.024)|(0.040)|(0.019)|(0.019)|(0.017)|
|female|11.388***|11.740***|11.727***|12.072***|12.032***|11.777***|12.043***|12.107***|
||(0.026)|(0.026)|(0.020)|(0.029)|(0.043)|(0.022)|(0.022)|(0.019)|
|difference|0.110***|-0.037|0.123***|0.172***|0.124**|0.164***|0.284***|0.325***|
||(0.036)|(0.033)|(0.025)|(0.038)|(0.059)|(0.029)|(0.029)|(0.025)|
|explained|0.068***|0.014|0.054***|0.115***|0.114***|0.083***|0.135***|0.122***|
||(0.013)|(0.014)|(0.011)|(0.019)|(0.031)|(0.016)|(0.016)|(0.015)|
|unexplained|0.041|-0.052|0.068***|0.057*|0.010|0.080***|0.149***|0.203***|
||(0.033)|(0.033)|(0.025)|(0.034)|(0.057)|(0.025)|(0.025)|(0.021)|

_Source:_ Calculations based on Cambodia Socio-Economic Surveys _Note_ : Standard errors are in parentheses. Statistically significant coefficients at the 10, 5, and 1 percent level are indicated by one, two, and three asterisks respectively.
|gen,<br>age, age)<br>edu,<br>hy<br>ind,<br>OCCUP,<br>Ek<br>log(wagex,) = Bo +igen; +Bragex +Bragex + Byedu;z +Bshy +Djyjindy + BsTGxe +<br>YViAjoccup,<br>+a* year+w *year «TG, +<br>yp|
|---|
27 by equation 6. The estimated coefficients on the industry dummy variables are interpreted as “inter-industry wage differentials” following Krueger and Summers (1987).

In the long run, the price decrease would affect the “general” component of wages. In our apparel industry application, as long as industry _j_ is female-intensive (we assumed that T&G is female-intensive), any decrease in the price of industry _j_ (T&G in our case) will affect<sup></sup> 1<sup>, which</sup> represents the gender wage gap. Of course, what time frame constitutes the “long run” is not clear at the start. Estimates of the relevant time frame for “long run” effects are rare. Robertson (2004) suggests that the Stolper-Samuelson effects begin to emerge in three to five years.

# **4.3. Main Results**

Table 5 presents the results of wage regressions for Cambodia and Sri Lanka in the short run and table 6 presents the results of wage regressions for both countries in the long run. The regression analysis was carried out using the methodology described in section 2. For our analysis, we used eight rounds of the Cambodian Socio-Economic Surveys that covered the 1996–2011 period, and the 1992–2002, 2008, 2011, and 2012 Sri Lankan Labor Force Surveys.<sup>21</sup> In both countries, working in apparel pays a premium compared to the economy average.<sup>22</sup> Column 1 of table 5 shows that the MFA-era wage premium was 39.2 percent in Cambodia and column 3 of table 5 shows that this premium was 6.7 percent in Sri Lanka. This result contrasts with table 4, which might be interpreted as demonstrating that the apparel sector is a low-wage “sweatshop” sector. In comparison to international wages, wages are indeed low in the apparel sector. But table 5 shows that relative to alternatives for similar individuals (generally younger, unmarried, less-educated females), the apparel jobs (T&G dummy) pay higher than average.<sup>23</sup>
28

**Table 5. Wage Premium Regressions, Short Run**

||Cam<br>|bodia<br>|Sri L<br>|anka<br>|
|---|---|---|---|---|
||1|2|3|4|
|Female dummy|-0.205***|-0.209***|-<br>0.568***|-<br>0.570***|
||(0.016)|(0.016)|(0.023)|(0.023)|
|Hours|0.015***|0.015***|0.012***|0.012***|
||(0.000)|(0.000)|(0.000)|(0.000)|
|Age|0.042***|0.044***|0.149***|0.149***|
||(0.004)|(0.004)|(0.006)|(0.006)|
|Education|0.026***|0.026***|0.046***|0.045***|
||(0.001)|(0.001)|(0.001)|(0.001)|
|T&G dummy|0.392***|-0.136**|0.067***|0.088***|
||(0.026)|(0.063)|(0.010)|(0.013)|
|1999–2004 dummy||0.033*||0.059***|
|||(0.019)||(0.007)|
|2005 and above dummy|0.280***|0.307***|0.066***|0.092***|
||(0.013)|(0.020)|(0.007)|(0.007)|
|T&G* (1999–2004 dummy)||0.686***||-<br>0.054***|
|||(0.075)||(0.020)|
|T&G* (2005 and above<br>dummy)|-0.234***|0.383***|-0.043*|-<br>0.067***|
||(0.030)|(0.073)|(0.022)|(0.024)|
|Hazard ratio|0.002|0.010|0.681***|0.683***|
||(0.051)|(0.051)|(0.045)|(0.045)|
|Constant|10.392***|10.246***|4.806***|4.783***|
||(0.124)|(0.127)|(0.151)|(0.151)|
|Observations|31,768|31,768|153,155|153,155|

_Source:_ Calculations based on the Cambodian Socio-Economic Surveys 1996, 1999, 2004, 2007, 2008, 2009, 2010, and 2011, and the Sri Lankan 1992–2002, 2008, 2011, and 2012 Labor Force Surveys. _Note:_ Standard errors are in parentheses. Standard errors are clustered at the industry level. Statistically significant coefficients at the 10, 5, and 1 percent level are indicated by one, two. and three asterisks respectively.

The grand mean effects of the industries are calculated; post-MFA is a dummy equal to 1 for years 2005 and later; additional controls include age squared, industry, and occupation dummies. Occupation dummies are according to 1-digit ISCO-08; the omitted category is managers. Industry dummies include 15 categories: Agriculture and Forestry; Mining; Food, Beverage, and Tobacco; Textile and Apparel; Wood; Other Manufacturing; Utilities; Construction; Sales; Transport; Financial, Insurance, and Real Estate; Public Administration; Social Services; and Other Services. Log wages are measured as logarithm of real monthly individual labor income in 2005 prices. The omitted time category in cols (1) and (3) is 2004 and earlier; the omitted time category in cols (2) and (4) is 1998 and earlier.
29

**Table 6. Wage Premium Regressions, Long Run**

||Cam|bodia|Sri L|anka|
|---|---|---|---|---|
||1|2|3|4|
|Female dummy|-0.172***|-0.336***|-0.556***|-<br>0.551***|
||(0.019)|(0.030)|(0.023)|(0.024)|
|Hours|0.015***|0.015***|0.012***|0.012***|
||(0.000)|(0.000)|(0.000)|(0.000)|
|Age|0.041***|0.042***|0.148***|0.149***|
||(0.004)|(0.004)|(0.006)|(0.006)|
|Education|0.026***|0.026***|0.046***|0.045***|
||(0.001)|(0.001)|(0.001)|(0.001)|
|T&G dummy|0.281***|0.275***|0.060***|0.059***|
||(0.021)|(0.021)|(0.010)|(0.010)|
|1999–2004 dummy||-0.005||0.059***|
|||(0.023)||(0.008)|
|2005 and above dummy|0.269***|0.272***|0.076***|0.101***|
||(0.014)|(0.023)|(0.008)|(0.008)|
|Female* (1999–2004 dummy)||0.225***||-0.017|
|||(0.033)||(0.013)|
|Female* (2005 and above<br>dummy)|-0.068***|0.099***|-0.044***|-<br>0.050***|
||(0.021)|(0.032)|(0.013)|(0.014)|
|Hazard ratio|-0.002|-0.001|0.678***|0.680***|
||(0.051)|(0.051)|(0.045)|(0.045)|
|Constant|10.404***|10.389***|4.811***|4.787***|
||(0.125)|(0.128)|(0.151)|(0.151)|
|Observations|31,768|31,768|153,155|153,155|

_Source:_ Calculations based on the Cambodian Socio-Economic Surveys 1996, 1999, 2004, 2007, 2008, 2009, 2010, and 2011, and Sri Lankan 1992–2002, 2008, 2011, and 2012 Labor Force Surveys. _Note:_ Standard errors are in parentheses. Standard errors are clustered at the industry level. Statistically significant coefficients at the 10, 5, and 1 percent level are indicated by one, two, and three asterisks respectively.

The grand mean effects of the industries are calculated; post-MFA is a dummy equal to 1 for years 2005 and later; additional controls include age squared, industry, and occupation dummies. Sri Lanka Household Income and Expenditure Survey was excluded from the analysis as hours worked were not available in this survey. Occupation dummies are according to 1-digit ISCO-08; the omitted category is managers. Industry dummies include 15 categories: Agriculture and Forestry; Mining; Food, Beverage, and Tobacco; Textile and Apparel; Wood; Other Manufacturing; Utilities; Construction; Sales; Transport; Financial, Insurance, and Real Estate; Public Administration; Social Services; and Other Services. Log wages are measured as logarithm of real monthly individual labor income. Log wages are measured as logarithm of real monthly individual labor income in 2005 prices. The omitted time category in cols (1) and (3) is 2004 and earlier; the omitted time category in cols (2) and (4) is 1998 and earlier.
30 Figures 3 and 4 demonstrate that the unit values of apparel exports fell in both countries after the MFA phase-out. The theory predicts that the fall in apparel prices should translate into a drop in the apparel premium. Table 5 shows that in both countries we observe a short-run drop in the apparel premium following the end of the MFA. The interaction with post-MFA and the T&G sector (apparel) is negative and significant in both countries. In Cambodia, the apparel wage premium declined post-MFA by 23 percent compared to other industries, but still remained 16 percent higher than the economy average. In Sri Lanka the apparel premium fell by to 4.3 percent after the MFA phase-out.

An alternative specification separates the pre-MFA period using 1999 as a dividing year. This is especially important for Cambodia, which entered the international market around 1999. Figure 4, for example, shows a large increase in the unit value of apparel exports between 1998 and 2000 in Cambodia. The pattern of apparel wage premiums matches the price movements shown in figure 4. The wage premium increased 68.6 percentage points in the 1999–2004 period when the apparel price was increasing. In the MFA period, the premium fell but remained 38.3 percentage points higher than in the pre-1999 period. This pattern also matches the changes in unit values shown in figure 4.

In Sri Lanka, where the price movements were more moderate, we see smaller changes in the apparel wage premium. Figure 3 shows that the unit values in the 1999–2004 period were lower than in the 1998–1999 period, and that they continued to fall after the end of the MFA. The estimated apparel wage premiums in table 5 (column 4) follow the same pattern: they were lower in the 1999–2004 period than prior to 1999, and they continued to drop in the post-MFA period.
31 Table 5 also contains the results from the estimate of the male-female wage differential. Columns 1 and 3 shows that the male-female wage gap throughout the economy in both countries increased after the end of the MFA; this is consistent with the theoretic prediction that a drop in the price of the female-intensive good should reduce the wages of women throughout the economy. The change in the wage gap is very similar in the two countries.

When we divide the MFA period into two segments, again using 1999 as a break point, we see that the male-female wage gap closed considerably in Cambodia when the price of apparel increased between 1999 and 2004 (column 2, table 5), and widened again when the price of apparel dropped after 2004. In Sri Lanka, which did not experience the same dramatic change in unit values, the gender wage gap demonstrates no significant difference in the 1999–2004 period. The gap does increase, however, after the end of the MFA when the unit value of apparel dropped, as suggested by theory.

The results presented in table 7 combine the short-run and long-run effects in one empirical estimation. The results include interaction terms with both the T&G dummy and the female dummy. The results described above still hold: the returns to working in the apparel sector declined post-MFA in both countries and the male-female wage gap widened.
32

**Table 7. Wage Premium Regressions, Short and Long Run**

||Cam|bodia<br>|Sri L|anka<br>|
|---|---|---|---|---|
||1|2|3|4|
|Female dummy|-0.198***|-0.311***|-0.557***|-0.555***|
||(0.019)|(0.030)|(0.023)|(0.024)|
|Hours|0.015***|0.015***|0.012***|0.012***|
||(0.000)|(0.000)|(0.000)|(0.000)|
|Age|0.042***|0.043***|0.148***|0.149***|
||(0.004)|(0.004)|(0.006)|(0.006)|
|Education|0.026***|0.026***|0.046***|0.045***|
||(0.001)|(0.001)|(0.001)|(0.001)|
|T&G dummy|0.389***|-0.102|0.065***|0.084***|
||(0.026)|(0.063)|(0.010)|(0.013)|
|1999-2004 dummy||-0.024||0.062***|
|||(0.023)||(0.008)|
|2005 and above dummy|0.284***|0.267***|0.077***|0.104***|
||(0.014)|(0.023)|(0.008)|(0.008)|
|T&G* (1999–2004 dummy)||0.623***||-0.051**|
|||(0.076)||(0.020)|
|T&G* (2005 and above dummy)|-0.226***|0.350***|-0.027|-0.050**|
||(0.033)|(0.074)|(0.023)|(0.024)|
|Female* (1999–2004 dummy)||0.157***||-0.010|
|||(0.033)||(0.013)|
|Female* (2005 and above<br>dummy)|-0.015|0.099***|-0.040***|-0.043***|
||(0.022)|(0.032)|(0.013)|(0.015)|
|Hazard ratio|0.002|0.001|0.679***|0.682***|
||(0.051)|(0.051)|(0.045)|(0.045)|
|Constant|10.388***|10.316***|4.808***|4.784***|
||(0.125)|(0.128)|(0.151)|(0.151)|
|Observations|31,768|31,768|153,155|153,155|

_Source:_ Authors’ calculations based on the Cambodian Socio-Economic Surveys 1996, 1999, 2004, 2007, 2008, 2009, 2010, and 2011, and Sri Lankan 1992–2002, 2008, 2011, and 2012 Labor Force Surveys. _Note:_ Standard errors are in parentheses. Standard errors are clustered at the industry level. Statistically significant coefficients at the 10, 5, and 1 percent level are indicated by one, two, and three asterisks respectively.

The grand mean effects of the industries are calculated; post-MFA is a dummy equal to 1 for years 2005 and later; additional controls include age squared, industry, and occupation dummies. Sri Lanka Household Income and Expenditure Survey was excluded from the analysis as hours worked were not available in this survey. Occupation dummies are according to 1-digit ISCO-08; the omitted category is managers. Industry dummies include 15 categories: Agriculture and Forestry; Mining; Food, Beverage, and Tobacco; Textile and Apparel; Wood; Other Manufacturing; Utilities; Construction; Sales; Transport; Financial, Insurance, and Real Estate; Public Administration; Social Services; and Other Services. Log wages are measured as logarithm of real monthly individual labor income.
33 Since we are using cross-sectional data pooled over time, we conduct robustness exercises to check whether there were changes in returns to other factors over time (such as age, education, or hours of work) that could potentially affect our results. Tables 8 and 9 present the results of log wage regressions over time for Cambodia and Sri Lanka respectively using a simple Mincer model with Heckman correction as described above.
34

**Table 8. Wage Premiums Regressions, Cambodia**

||1996|1999|2004|2007|2008|2009|2010|2011|
|---|---|---|---|---|---|---|---|---|
||1|2|3|4|5|6|7|8|
|Female dummy|-0.314***|-0.203***|-0.183***|-0.216***|-0.183*|-0.142***|-0.303***|-0.216***|
||(0.043)|(0.043)|(0.035)|(0.041)|(0.102)|(0.034)|(0.037)|(0.027)|
|Hours|0.010***|0.011***|0.016***|0.015***|0.020***|0.018***|0.015***|0.016***|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.002)|(0.001)|(0.001)|(0.001)|
|Age|0.059***|0.049***|0.041***|0.062***|0.058***|0.052***|0.082***|0.054***|
||(0.012)|(0.008)|(0.008)|(0.009)|(0.020)|(0.009)|(0.011)|(0.007)|
|Age squared|-0.001***|-0.001***|-0.001***|-0.001***|-0.001***|-0.001***|-0.001***|-0.001***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Education|0.052***|0.043***|0.011***|0.074***|0.061***|0.047***|0.063***|0.048***|
||(0.005)|(0.011)|(0.002)|(0.010)|(0.014)|(0.004)|(0.005)|(0.004)|
|T&G dummy|-0.362***|0.543***|0.288***|0.296***|0.300**|0.102**|0.070|-0.024|
||(0.134)|(0.067)|(0.049)|(0.072)|(0.123)|(0.051)|(0.051)|(0.044)|
|Hazard ratio|0.008|0.140|-0.035|0.549***|0.136|0.054|0.594***|0.100|
||(0.131)|(0.124)|(0.102)|(0.158)|(0.297)|(0.109)|(0.131)|(0.091)|
|Constant|11.037***|10.017***|10.708***|9.540***|9.639***|10.076***|9.315***|10.335***|
||(0.514)|(0.308)|(0.248)|(0.349)|(0.717)|(0.287)|(0.337)|(0.233)|
|Observations|4,723|3,448|8,358|2,553|1,402|4,488|3,276|3,520|

_Source:_ Calculations based on the Cambodian Socio-Economic Surveys 1996, 1999, 2004, 2007, 2008, 2009, 2010, and 2011.

_Note:_ Standard errors are in parentheses. Standard errors are clustered at the industry level. Statistically significant coefficients at the 10, 5, and 1 percent level are indicated by one, two, and three asterisks respectively. The grand mean effects of the industries are calculated; additional controls include, industry, and occupation dummies. Occupation dummies are according to 1-digit ISCO-08; the omitted category is managers. Industry dummies include 15 categories: Agriculture and Forestry; Mining; Food, Beverage, and Tobacco; Textile and Apparel; Wood; Other Manufacturing; Utilities; Construction; Sales; Transport; Financial, Insurance, and Real Estate; Public Administration; Social Services; and Other Services. Log wages are measured as the logarithm of real monthly individual labor income. Log wages are measured as the logarithm of real monthly individual labor income in 2005 prices.
35

# **Table 9. Wage Premiums Regressions, Sri Lanka**

||1992|1993|1994|1995|1996|1997|1998|1999|2000|2001|2002|2008|2011|2012|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||1|2|3|4|5|6|7|8|9|10|11|12|13|14|
|Female dummy|-0.479***|-0.624***|-0.675***|-0.497***|-0.391***|-0.633***|-0.564***|-0.591***|-0.548***|-0.457***|-0.542***|-0.576***|-0.631***|-0.526***|
||(0.092)|(0.131)|(0.107)|(0.087)|(0.067)|(0.066)|(0.062)|(0.076)|(0.075)|(0.088)|(0.075)|(0.040)|(0.042)|(0.037)|
|Hours|0.012***|0.011***|0.010***|0.012***|0.012***|0.012***|0.013***|0.011***|0.014***|0.013***|0.013***|0.008***|0.010***|0.010***|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.000)|(0.000)|(0.001)|(0.001)|(0.001)|(0.001)|(0.000)|(0.000)|(0.000)|
|Age|0.134***|0.194***|0.203***|0.138***|0.110***|0.170***|0.144***|0.154***|0.150***|0.118***|0.135***|0.113***|0.124***|0.103***|
||(0.025)|(0.038)|(0.029)|(0.024)|(0.020)|(0.019)|(0.018)|(0.021)|(0.021)|(0.023)|(0.020)|(0.009)|(0.010)|(0.008)|
|Age squared|-0.002***|-0.002***|-0.003***|-0.002***|-0.001***|-0.002***|-0.002***|-0.002***|-0.002***|-0.001***|-0.002***|-0.001***|-0.001***|-0.001***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Education|0.044***|0.060***|0.054***|0.051***|0.038***|0.036***|0.036***|0.041***|0.043***|0.043***|0.037***|0.037***|0.047***|0.048***|
||(0.003)|(0.005)|(0.004)|(0.004)|(0.003)|(0.003)|(0.003)|(0.003)|(0.004)|(0.005)|(0.004)|(0.002)|(0.002)|(0.002)|
|T&G dummy|0.106***|0.047|0.086**|0.099**|-0.001|0.057**|0.067**|0.020|0.025|-0.021|0.017|0.014|0.070***|0.072***|
||(0.034)|(0.052)|(0.039)|(0.039)|(0.032)|(0.028)|(0.027)|(0.031)|(0.031)|(0.037)|(0.039)|(0.016)|(0.022)|(0.019)|
|Hazard ratio|0.459***|0.918***|0.970***|0.571***|0.433***|0.872***|0.697***|0.771***|0.742***|0.535***|0.587***|0.552***|0.639***|0.438***|
||(0.174)|(0.268)|(0.203)|(0.172)|(0.135)|(0.138)|(0.137)|(0.154)|(0.159)|(0.172)|(0.147)|(0.074)|(0.074)|(0.064)|
|Constant|5.070***|3.485***|3.786***|5.048***|6.126***|4.452***|5.101***|4.857***|4.963***|5.359***|5.055***|5.753***|5.705***|6.160***|
||(0.598)|(0.922)|(0.707)|(0.593)|(0.451)|(0.467)|(0.448)|(0.511)|(0.527)|(0.584)|(0.493)|(0.243)|(0.250)|(0.217)|
|Observations|16,849|7,029|6,748|6,826|7,200|11,472|12,177|11,991|12,358|9,215|12,378|14,820|11,428|12,664|

_Source:_ Authors’ calculations based on the Sri Lankan 1992–2002, 2008, 2011, and 2012 Labor Force Surveys.

_Note:_ Standard errors are in parentheses. Standard errors are clustered at the industry level. Statistically significant coefficients at the 10, 5, and 1 percent level are indicated by one, two, and three asterisks respectively.

The grand mean effects of the industries are calculated; additional controls include industry, and occupation dummies. Sri Lanka Household Income and Expenditure Survey was excluded from the analysis as hours worked were not available in this survey. Occupation dummies are according to 1-digit ISCO-08; the omitted category is managers. Industry dummies include 15 categories: Agriculture and Forestry; Mining; Food, Beverage, and Tobacco; Textile and Apparel; Wood; Other Manufacturing; Utilities; Construction; Sales; Transport; Financial, Insurance, and Real Estate; Public Administration; Social Services; and Other Services. Log wages are measured as the logarithm of real monthly individual labor income. Log wages are measured as the logarithm of real monthly individual labor income in 2005 prices.
36 The coefficient of the female dummy variable, which is capturing male-female wage differentials, varies over time in both countries. This reflects changes in price of a specific factor—female labor—over time. Furthermore, the results show that wages in T&G are generally higher than economy-wide average wages. The changes in wages in T&G generally trace changes in global apparel prices with a lag (figure 4). Importantly, the coefficients for age, education, and hours worked were relatively stable over time in both countries.

The point of the industrial policy employed by Cambodia and Sri Lanka (encouraging T&G in particular) supports the policy prescriptions of the "old" Krueger and Summers literature. In addition, they found that the differentials were constant, but we show that they are correlated with industry prices (and are therefore not constant).

# **4.4. Labor Force Participation**

Suarè and Zoabi (2014) find that women adjust along the extensive margin in response to labor demand shocks. To evaluate this possibility, we consider trends in women’s labor force participation. Tables 10 and 11 report marginal effects (evaluated at the means of the regressors) of the independent variables on labor force participation in Cambodia and Sri Lanka. The likelihood of labor force participation for women is lower than that of men in both countries. The female-male labor force participation differentials are higher in Sri Lanka than in Cambodia. For example, while women were 9 percent less likely than men to participate in the labor force in Cambodia in 2008, in Sri Lanka this differential was 23.2 percent in 2011. In general, in Cambodia women were 9–10 percent less likely to participate in the labor force than men over the 1996–2011 period. The gap slightly narrowed in 2007 (post-MFA) to 7.7 percent, but then picked up to 12.6 percent in 2008. In Sri Lanka, the labor force participation of women was
37 about 20 percent lower than of men over the 1992–2002 period, but the difference increased to 28 percent in 2006 and remained around 22–23 percent between 2008 and 2012. Age and education were associated with a higher probability of labor force participation in both countries. On the other hand, married individuals were less likely to be economically active compared to the never married group.
38

**Table 10. Marginal Effects for Labor Force Participation, Cambodia**

||1996|1999|2004|2007|2008|2009|2010|2011|
|---|---|---|---|---|---|---|---|---|
|Female dummy|-0.086***|-0.108***|-0.089***|-0.077***|-0.126***|-0.087***|-0.105***|-0.090***|
||(0.006)|(0.007)|(0.004)|(0.010)|(0.012)|(0.006)|(0.009)|(0.009)|
|Age (years)|0.018***|0.021***|0.027***|0.025***|0.030***|0.034***|0.041***|0.040***|
||(0.001)|(0.002)|(0.001)|(0.003)|(0.003)|(0.001)|(0.002)|(0.002)|
|Age squared|-0.000***|-0.000***|-0.000***|-0.000***|-0.000***|-0.000***|-0.001***|-0.001***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Education (years)|0.004***|0.032***|0.003***|0.026***|0.017***|0.010***|0.013***|0.016***|
||(0.001)|(0.001)|(0.000)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|Married dummy|0.091***|-0.108***|-0.101***|-0.118***|-0.097***|-0.104***|-0.099***|-0.137***|
||(0.009)|(0.012)|(0.006)|(0.016)|(0.018)|(0.009)|(0.013)|(0.014)|
|Widowed dummy|0.255***|0.002|0.044|-0.002|-0.004|0.017|0.031|0.006|
||(0.036)|(0.043)|(0.046)|(0.034)|(0.046)|(0.022)|(0.030)|(0.031)|
|Divorced dummy|0.258***|-0.002|-0.031***|-0.006|-0.027|-0.003|-0.012|-0.019|
||(0.019)|(0.017)|(0.008)|(0.025)|(0.030)|(0.017)|(0.022)|(0.023)|
|Observations|21,629|15,368|46,118|8,934|5,595|19,940|11,801|11,696|

_Source:_ Calculations based on the Cambodian Socio-Economic Surveys 1996, 1999, 2004, 2007, 2008, 2009, 2010, and 2011. _Note:_ Marginal effects of the labor force participation are evaluated at the mean of the independent variables. Standard errors are in parentheses. Never married is an omitted category for marital status. Standard errors are reported below the estimated coefficients. Statistically significant coefficients at the 10, 5, and 1 percent level are indicated by one, two, and three asterisks respectively.
39

**Table 11. Marginal Effects for Labor Force Participation, Sri Lanka**

||1992|1993|1994|1995|1996|1997|1998|1999|2000|2001|2002|2006|2008|2011|2012|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Female dummy|-0.199***|-0.190***|-0.206***|-0.194***|-0.197***|-0.187***|-0.182***|-0.199***|-0.195***|-0.205***|-0.212***|-0.280***|-0.222***|-0.232***|-0.237***|
||(0.003)|(0.005)|(0.005)|(0.005)|(0.004)|(0.004)|(0.004)|(0.004)|(0.004)|(0.005)|(0.004)|(0.004)|(0.004)|(0.004)|(0.004)|
|Age (years)|0.056***|0.057***|0.059***|0.058***|0.062***|0.059***|0.059***|0.059***|0.060***|0.058***|0.059***|0.061***|0.054***|0.055***|0.055***|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|Age squared|-0.001***|-0.001***|-0.001***|-0.001***|-0.001***|-0.001***|-0.001***|-0.001***|-0.001***|-0.001***|-0.001***|-0.001***|-0.001***|-0.001***|-0.001***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Education (years)|-0.000|0.002**|0.001*|0.001*|-0.003***|0.000|-0.000|0.002***|0.003***|0.006***|0.002***|-0.000|0.006***|0.005***|0.008***|
||(0.000)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|Married dummy|-0.038***|-0.046***|-0.045***|-0.063***|-0.067***|-0.061***|-0.065***|-0.063***|-0.065***|-0.067***|-0.064***|-0.020***|-0.055***|-0.055***|-0.063***|
||(0.004)|(0.007)|(0.007)|(0.007)|(0.006)|(0.006)|(0.006)|(0.006)|(0.006)|(0.007)|(0.006)|(0.006)|(0.006)|(0.007)|(0.006)|
|Widowed dummy|-0.030***|-0.035**|-0.020|-0.056***|-0.036***|-0.031***|-0.042***|-0.026**|-0.018|-0.013|-0.014|0.022*|0.012|0.014|0.014|
||(0.009)|(0.014)|(0.016)|(0.013)|(0.011)|(0.012)|(0.012)|(0.012)|(0.012)|(0.014)|(0.012)|(0.011)|(0.017)|(0.013)|(0.012)|
|Divorced dummy|0.043***|0.013|0.036|0.012|0.006|-0.009|-0.009|-0.006|0.005|0.043*|0.058***|0.084***|0.000|0.084***|0.051***|
||(0.016)|(0.026)|(0.032)|(0.028)|(0.021)|(0.020)|(0.019)|(0.020)|(0.021)|(0.025)|(0.021)|(0.019)|(0.011)|(0.022)|(0.018)|
|Observations|73,503|29,285|28,009|27,419|49,094|46,201|47,898|47,467|47,021|35,789|46,517|60,097|57,135|44,345|48,502|

_Source:_ Calculations based on the Sri Lankan 1992–2002, 2008, 2011, and 2012 Labor Force Surveys, and the Sri Lankan 2006 Household Income and Expenditure Survey. _Note:_ Marginal effects of the labor force participation are evaluated at the mean of the independent variables. Standard errors are in parentheses. Never married is an omitted category for marital status. Standard errors are reported below the estimated coefficients. Statistically significant coefficients at the 10, 5, and 1 percent level are indicated by one, two, and three asterisks respectively.
40 Overall, the results show that women’s labor force participation is relatively constant over time. This suggests that our results are not driven by adjustments along the extensive margin or are **not** affected by unaccounted selection bias.

# **4.5. Hours**

Table 12 shows that from 1996 to 2011 substantial numbers of employees in Cambodia worked more than 40 hours a week. A slightly higher percentage of women worked overtime than men. The percentage of women working more than 40 hours a week increased post-MFA from 68 percent in 2004 to 77 percent in 2011. The proportion fell to 69 percent in 2009. The analysis shows that women who worked overtime on average worked 54 hours a week. There was no significant difference between men and women. Furthermore, table 12 shows that 95 percent of textile and apparel workers worked more than 40 hours a day in 2011. This number was significantly higher than the economy average of 75 percent. We also find that there was a very small percentage of workers below 14 years old—on average less than one percent from 1996 to 2011. The percentage of workers in apparel younger than 14 years old was slightly higher than the economy average, but lower than the agriculture average.
41

**Table 12. Hours Worked in Cambodia**

||Percent of wo|rkers work|ing more|than 40|hours a|week||||
|---|---|---|---|---|---|---|---|---|---|
||1996|1999|<br>2004|<br>20|07|2008|2009|2010|2011|
|Male|60.55|70.82|<br>63.98|<br>72|.59|75.09|65.15|70.41|74.04|
|Female|57.78|71.57|<br>68.37|<br>73|.78|77.39|68.90|73.48|77.19|
|Agriculture|50.08|66.46|60.58|<br>67|.93|78.23|50.27|58.00|62.2|
|T&G|64.89|91.24|95.06|<br>95|.57|95.19|89.81|93.5|95.1|
|All sectors|59.5|71.1|<br>65.8|<br>73|.01|75.97|66.73|71.7|75.4|
||Percent of children l<br>|ess than 14<br>|years old<br>|in the e<br>|mploye<br>|d populatio<br> <br>|n|||
||1996|1999|2004|2007|2008|<br>2009||2010|2011|
|Male|0.27|0.29|0.89|0.51|0.11|<br>0.99||0.73|0.46|
|Female|0.99|0.28|2.07|1.4|0.37|<br>1.32||1.3|1.08|
|Agriculture|0.5|0.35|4.42|2.92|0.68|<br>2.35||2.44|2.12|
|T&G|1.91|0.18|0.34|0.55|0.48|<br>0.87||0.78|0.89|
|All sectors|0.54|0.29|1.38|0.87|0.21|<br>1.13||0.97|0.74|

_Source:_ Calculations based on the Cambodian Socio-Economic Surveys 1996, 1999, 2004, 2007, 2008, 2009, 2010, and 2011. _Note:_ T&G = textiles and garments.

Table 13 shows that on average in Sri Lanka a smaller percentage of people were working more than 40 hours a week than in Cambodia—47 and 76 percent in 2008, respectively. In Cambodia this share remained roughly the same from 2008 to 2012, while in Sri Lanka it went up to 59 percent by 2012. There was a big difference between the share of men and women working overtime in Sri Lanka—67 and 44 percent in 2012, respectively. Similar to Cambodia, people who worked overtime worked on average 55 hours a week. Also, similar to Cambodia, more people worked overtime in the apparel industry than in the economy overall—75 and 59 percent in 2012, respectively. The percentage of workers less than 14 years old in Sri Lanka is lower than in Cambodia—0.08 and 0.74 percent in 2011, respectively. There were no workers under 14 years old in the textile and apparel industry in 2012. Moreover, the percentage of girls
42 and boys under 14 years working in Sri Lanka in 2012 was approximately the same—0.05 and 0.06 percent, respectively.

**Table 13. Hours Worked in Sri Lanka**

|||Pe<br>|rcent of<br>|workers<br>|working<br>|more than<br>|40 hour<br>|s a week<br>|<br>||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||1992|1993|1994|1995|1996|1997|1998|1999|2000|2001|2002|2008|2011|2012|
|Male|52.34|52.64|54.26|54.05|49.23|46.31|49.48|49.79|48.16|55.43|51.39|52.23|65.80|66.59|
|Female|38.59|40.31|40.57|40.84|42.89|35.15|36.07|36.64|36.99|41.16|37.60|35.75|43.33|43.61|
|Agriculture|35.86|36.25|37.17|35.08|37.49|29.68|32.56|33.58|30.36|33.81|30.21|32.35|43.00|43.27|
|T&G|57.02|65.56|63.69|68.76|64.36|59.46|63.60|63.08|62.03|68.57|66.60|62.28|72.99|74.78|
|All sectors|48.60|49.06|50.24|50.13|47.24|42.82|44.98|45.46|44.44|50.86|46.91|46.61|58.51|59.30|
|||Percent o<br>|f childre<br>|n less tha<br>|n 14 yea<br>|rs old in t<br>|he emplo<br>|yed pop<br>|ulation<br>||||||
||1992|1993|1994|1995|1996|1997|1998|1999|2000|2001|2002|2008|2011|2012|
|Male|0.26|0.17|0.23|0.11|0.26|0.15|0.47|0.49|0.23|0.18|0.15|0.15|0.10|0.05|
|Female|0.50|0.47|0.31|0.21|0.24|0.10|0.63|0.61|0.38|0.33|0.29|0.10|0.05|0.06|
|Agriculture|0.21|0.15|0.2|0.11|0.38|0.22|1.02|1.07|0.55|0.49|0.5|0.43|0.22|0.05|
|T&G|0|0|0|0|0|0|1.1|0|0.75|0|0.24|0|0|0|
|All sectors|0.32|0.26|0.26|0.14|0.25|0.14|0.53|0.53|0.28|0.23|0.2|0.14|0.08|0.06|

_Source:_ Calculations based on the Sri Lanka 1992–2002 2008, 2011, and 2012 Labor Force Surveys. _Note:_ T&G = textiles and garments.

# Employment fluctuates with changes in labor demand, suggesting imperfect mobility

between industries. Working conditions fluctuate less over time, suggesting that our model which focuses on wage changes in the context of imperfect labor mobility is not at odds with the labor market characteristics of Cambodia and Sri Lanka.

Table 14 shows the regression results using the hourly wages for both Cambodia and Sri Lanka. The qualitative results are similar. The higher apparel prices in the MFA period are characterized by higher industry-specific wage premiums and smaller male-female wage differences. After the MFA, when apparel prices were lower, the apparel wage premiums are lower and the male-female wage gap is larger. These results are consistent with the hypothesis
43 that changing apparel prices have general-equilibrium effects on the male-female wage differential and on women’s wages throughout the economy (not just in apparel).

**Table 14: Short and Long Run Using Hourly Wages**

|Short and LongRun|Cambodia||Sri Lanka||
|---|---|---|---|---|
|VARIABLES|(1)<br>loghourlywage|(2)<br>loghourlywage|(1)<br>loghourlywage|(2)<br>loghourlywage|
|female|-0.193***<br>(0.020)|-0.295***<br>(0.031)|-0.546***<br>(0.022)|-0.538***<br>(0.022)|
|age|0.042***|0.043***|0.146***|0.147***|
||(0.004)|(0.004)|(0.006)|(0.006)|
|age2|-0.001***|-0.001***|-0.002***|-0.002***|
||(0.000)|(0.000)|(0.000)|(0.000)|
|education|0.026***|0.026***|0.045***|0.045***|
||(0.001)|(0.001)|(0.001)|(0.001)|
|T&G dummy|0.334***|-0.055|0.005|0.021*|
||(0.027)|(0.065)|(0.010)|(0.012)|
|1999-2004 dummy||-0.049**<br>(0.024)||0.070***<br>(0.007)|
|2005 and above dummy|0.279***|0.241***|-0.002|0.028***|
||(0.015)|(0.024)|(0.007)|(0.008)|
|T&G*(1999-2004 dummy)||0.492***||-0.042**|
|||(0.078)||(0.019)|
|T&G*(2005 and above dummy)|-0.237***|0.217***|-0.024|-0.042*|
||(0.033)|(0.076)|(0.022)|(0.023)|
|female*(1999-2004 dummy)||0.144***||-0.025**|
|||(0.034)||(0.013)|
|female*(2005 and above dummy)|-0.020|0.085**|-0.010|-0.019|
||(0.023)|(0.033)|(0.013)|(0.014)|
|Hazard ratio|0.073|0.068|0.682***|0.685***|
||(0.053)|(0.053)|(0.043)|(0.043)|
|Constant|5.823***|5.792***|0.199|0.171|
||(0.126)|(0.130)|(0.144)|(0.144)|
|Observations|31,729|31,729|146,635|146,635|
|Standard errors in parentheses<br>*** p<0.01, ** p<0.05, * p<0.1|||||

# **5. Conclusions**

Our results contribute in several ways to the debate surrounding the value of apparel exports and employment for women in developing countries. First, we demonstrate that apparel workers earn significant wage premiums relative to their other options in the economy.

Especially in Cambodia, these premiums increased significantly when Cambodia started exporting apparel. Second, we show that women’s wages relative to men (the gender wage gap)
44 follow changes in apparel prices in ways consistent with theory. Third, we show that changes in apparel prices are not followed by significant changes in our measures of hours or adjustments along the extensive margin for women.

Our results also contribute to the broader debates surrounding trade and wages and the effects of globalization on women. We demonstrate that a Heckscher-Ohlin approach which differentiates males and females as separate factors may be a fruitful alternative to discrimination models or those that analyze the effects of globalization on women in terms of skill. Under the assumption that women comprise a more apparel-specific labor input than men and that the apparel sector is a female-labor-intensive sector, the simple theoretical model predicts that a negative price shock will translate into a relative decline in apparel wages compared to other industries in the short run and a relative decrease in female wages compared to male wages across all industries in the long run.

The increased competition in apparel exports after the MFA phase-out drove down the unit values of apparel both in Cambodia and Sri Lanka. The empirical results of the post-MFA changes support the theoretical predictions. Workers, the majority of whom are female, in the apparel industry were receiving a premium comparable to that of an average worker. This premium decreased in both countries right after the MFA phase-out, but it slightly recovered in the following years. Male-female wage differentials were declining under the MFA in both countries. The gap, however, widened right after the MFA phase-out but later decreased. Finally, in terms of a very narrow measurement of working conditions, we found that substantial numbers of people were working more than 40 hours a week in apparel in both countries—95 percent in Cambodia in 2011 and 75 percent in Sri Lanka in 2012. On average, people working overtime were working 55 hours a week. On the other hand, only 0.9 percent of apparel workers
45 in Cambodia were younger than 14 years and there were no apparel workers under 14 years in Sri Lanka.

The finding of significant apparel wage premiums that are linked to apparel prices suggests economic opportunities for women in the formal labor market. Further research is necessary to identify the potential development effects of such employment and to thoroughly compare the benefits documented in this paper with the costs that may come with apparel jobs.