Inter-Industry Gender Wage Gaps by Knowledge Intensity: Discrimination and Technology in Korea

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1 Inter-Industry Gender Wage Gaps by Knowledge Intensity: Discriination and Technology in Korea Beyza P. Ural Departent o Econoics, Syracuse University, Syracuse NY, 13244, USA. Willia C. Horrace * Center o Policy Research, 426 Eggers Hall, Syracuse University, Syracuse NY, 13244, USA. Jin Hwa Jung Departent o Agricultural Econoics and Rural Developent, Seoul National University, San 56-1 Sillidong Kwanakgu, Seoul , Korea. ABSTRACT A new gender wage gap decoposition ethodology is introduced that does not suer ro identiication probles caused by unobserved non-discriinatory wage structure. The ethodology is used to easure the relative size o Korean gender wage gaps ro 1994 to 2000 across industries, dierentiated by industrial knowledge intensity, where knowledge intensity is the extent to which industries produce or eploy high-technology products. Korea represents an iportant case study, since it possesses one o the astest growing knowledgeintensive econoies, aong industrialized countries. Epirical results indicate that over this period, discriination (the unexplained portion o the gender wage gaps) in Korea was statistically saller in knowledge-intensive industries than in industries with low knowledge intensity. Also, discriination was declining on average over the period. This suggests that continued growth in knowledge-intensive industries in Korea ay lead to urther declines in the overall gender gap. JEL Codes: C12, J31, J71 Keywords: discriination, labor arkets, wage dierential, copensation. Running Title: Inter-Industry Gender Wage Gaps by Knowledge Intensity * Corresponding Author

2 I. INTRODUCTION Although there is an extensive body o literature on the decoposition o gender wage dierentials, based on a single cross-section o data, there have been relatively ew studies that analyze how these coponents evolve over tie. For exaple, see Blau and Kahn (1999), Kidd and Shannon (2001), or Finnie and Wannell (2004). In the last ew years, any developing countries have undergone substantial changes in their industrial copositions and arket structures, due to developent strategies, shiting trade policies, and sectoral shits in the global econoy (Freean, 2004). Thereore, a dynaic analysis o the evolution o the gender wage gap in a developing country sees particularly relevant. In particular, developing countries in Asia experienced a substantial shit towards knowledge-intensive industries at the end o the last decade (OECD, 2000), where knowledge intensity is easured as the extent to which industries utilize a skilled or educated workorce or the extent to which technologically advanced processes are used in the production o output. An interesting question is then, "are these 'knowledge-based' industries ore or less prone to gender discriination than "non-knowledge-based" industries?" A second interesting question is, "to what extent has this shit towards knowledge-based industries been accopanied with a change in the prevalence o gender discriination in Asian econoies?" In this study, we analyze inter-industry gender wage gaps by knowledge intensity in Korea, using a cross sectional occupational wage survey between the years 1994 and The Korean econoy provides a good test case, as the transition towards knowledge-based industries in this country was substantial (OECD, 2000). Also, Korea experienced a steady decrease in the overall eale-ale wage dierentials. 1 We ind that in each year considered, Korean knowledge-based industries were less discriinatory than non-knowledge-based 1 For non-agricultural industries, the average eale-to-ale earnings ratio was 44.2% in 1980 but was 63.2% in 2000 (Korean Labor Institute, Labor Statistics, 2004). 2

3 industries in ters o pay dierentials. We also ind that the decrease in the overall wage gap was accopanied by a decrease in the discriinatory (unexplained) portion o the gap. To analyze inter-industry wage dierentials by knowledge intensity, we identiy a new decoposition ethodology that allows us to ake relative coparisons across industries and across tie. Conventional decoposition techniques (Oaxaca 1972, and Blinder, 1973) are not identiied in the sense that the investigator ust decide a priori on an appropriate easure o the unobserved non-discriinatory wage structure (Neuark, 1988). Our ethod identiies relative inter-industry gender wage gaps and does not require an ad hoc proxy or the nondiscriinatory wage structure, because the estiation is designed to eliinate this structure, when it can be assued to be ixed across industries and across tie. Our estiates reveal that gender discriination in knowledge-based industries was signiicantly lower than in nonknowledge-based industries in Korea in all years considered. The results hold or knowledgebased industries within both the anuacturing and service sectors o the Korean econoy, as well as or the econoy as a whole. Our analysis reveals that dynaic luctuations o discriination in the anuacturing sector at the end o the illenniu were consistent with the tiing o the Asian inancial crisis, and it ay be possible that gender discriination iproved during a period o intense industrial copetition. While this is not orally investigated or tested, it is consistent with the arguents o Becker (1971). The paper is organized as ollows. The next section suarizes theories that link industrial coposition and knowledge intensity to the agnitude o the unexplained gender wage gap. In Section III, we develop a relative estiation strategy to estiate inter-industry "non-discriinatory percentages", our noralized easure o gender discriination. Our strategy does not suer ro the lack o identiication described by Neuark (1988). Section IV describes the survey data used in this study, as well as the classiication o industries into "knowledge-based" and "non-knowledge-based" categories. Section V presents the epirical 3

4 results and copares the coponents o inter-industry wage dierentials in knowledge-based industries with non-knowledge-based industries. We repeat the analysis at a ore disaggregated level and copare the anuacturing sector and service sector by their knowledge intensity. The inal section suarizes and concludes. II. INDUSTRIAL COMPOSITION AND GENDER WAGE GAPS Krueger and Suers (1988) reueled an epirical and theoretical debate about the causes o gender wage dierentials. They ound that the structure o the wage in the United States was not copatible with a neoclassical odel (Edin and Zetteberg, 1992), showing that interindustry gender wage disparities persisted between workers with identical individual characteristics and working conditions. Several other studies, using standard wage regressions, also support the existence o inter-industry gender wage dierentials or apparently equally skilled workers; any o these studies conclude that gender discriination cannot be reuted. See Gibbons and Katz (1992), Helwege (1992), Fields and Wol (1995), and Abowd, Karaarz, Margolis (1999). 2 In the last decade, any developing countries experienced changes in industrial coposition due to developent strategies, trade liberalization, and global econoic shits (OECD, 2000). I the level o gender discriination is dierent (lower) in industries that are experiencing higher growth rates relative to other industries, then a change (decrease) in the econoies overall gender gap ay accopany these changes in the industrial coposition (ceteris paribus). There are several reasons why we ight expect dierent levels o gender discriination in dierent industries. First, productivity o labor in soe industries is an increasing unction o physical power or which the eale labor orce has coparative and 2 It is not our intent to argue the validity o wage regression or decoposing wage dierentials, because they clearly have their drawbacks. However, they have been and continue to be a airly standard tool in the literature. 4

5 absolute disadvantage. Other things being equal, it is natural to expect higher gender wage disparities in these industries relative to the industries that do not require physical strength. Clearly, this is an arguent or a arginal product dierential, but these dierences ay push eployers in these industries towards discriinatory tastes. Second, there are substantial dierences in the degree o copetition in dierent industries due to dierences in product and labor arkets, governent regulations, and trade policies. Becker (1971) clais that increasing copetition results in lower levels o discriination, which would cause interindustry dierences in the wage gaps. Finally, given today's globalization o arkets, industries that are export-oriented (and not global onopolies) ay be less likely to discriinate in their long-run labor practices, as copetition in international arket precludes survival o irs with ineicient (discriinatory) labor arket practices. Melitz (2003) develops a dynaic odel to analyze the intra-industry eects o international trade. In this odel, exposure to trade causes only the ost productive irs to survive within an industry. There is also a large epirical literature showing that exposure to trade increases the overall level o productivity in an industry through the echanis described above. In the classic Becker (1971) odel, a ir (eployer) that has tastes or discriination will eploy ewer than the proit axiizing nuber o eale eployees, and consequently will achieve suboptial proits. We expect that arket echanis would orce irs with tastes or discriination to exit the arket, causing the overall level o discriination to be lower in export-oriented industries relative to industries that trade doestically. Hellerstein, Neuark, and Troske (2002) test whether copetitive arket orces reduce or eliinate discriination using plant level longitudinal data. They ind a positive relationship between ir-level proitability and the proportion o eale labor orce. They also ind evidence that aong plants with high arket power, those that eploy a relatively large eale labor orce are ore proitable, whereas no such relationship exist or plants with 5

6 low arket power. The results are consistent with the short-run iplications o Becker s odel o eployer discriination. There is also a class o odels that posit dierential eployent search costs as support or the existence o eployer discriination. Black (1995) constructs a odel that supports eployer discriination when sequential search costs are considered. In this odel, prejudiced eployers only hire ajority workers, whereas unprejudiced eployers hire both ajority and inority workers. Since job search costs are higher or inority workers, they lower their reservation wage, creating a wage dierential between ajority and inority workers. Black's odel also predicts that as the raction o unprejudiced irs increases, the wage dierential vanishes, because search cost are eectively reduced or inority workers. Thereore, i discriination is dierent (lower) on average in developed counties than in developing countries (or a variety o reasons that will not be discussed here), then a shit towards trade liberalization and a global econoy would (change) decrease wage dierentials in developing econoies on average (ceteris paribus). I there are dierences between industries in ters o discriinatory practices, then the evolution o gender wage gaps ay be partially correlated with the changes in the industrial structure o the countries, holding worker characteristics constant. Insoar as knowledge-based industries are less capital intensive and ore huan capital intensive, they ay exhibit saller (physical) capital barriers to entry and higher potential long-run copetition than nonknowledge-based industries. Assuing the existence o discriination, as an econoy shits towards ore knowledge-based and (potentially) ore copetitive industries, the overall level o gender wage gaps should decrease even when ale-eale characteristics are unchanged. In this study, we identiy and estiate a decoposition o inter-industry gender wage gaps by knowledge intensity in Korea, a country that experienced a large transition to knowledge-based 6

7 industries in the last three decades. 3 Korea is also an extree exaple o rapid iproveent in the overall gender wage gaps, although gender wage gaps in Korea are still larger than ost OECD countries. Figure 1 shows the evolution o eale-to-ale ratio o earnings between 1980 and For non-agricultural industries, the eale-to-ale earnings ratio in Korea had increased onotonically and quickly, ro 44.2% in 1980 and 63.7% in 1998, and leveled o between 1998 and This continuous iproveent in the earnings ratio is associated with an iproveent in discriination (or the unexplained portion o the usual average wage gap). Our results indicate that there appears to be a strong correlation between a transition towards knowledge-based industries and a decrease in gender discriination in Korea at the end o the illenniu. III. DECOMPOSITION FRAMEWORK The classic Oaxaca-Blinder wage decoposition attepts to quantiy gender discriination in a highly stylized Becker (1971) odel (See Oaxaca, 1973 or Blinder, 1973). This decoposition hinges on perectly copetitive labor arkets where workers with the sae skills earn the sae wage everywhere. That is, there exists soe non-discriinatory wage structure vector, θ, that aps the deographic attributes (including education, age, and experience) o a worker into a wage, regardless o industry, occupation, or huan capital investent. 4 Epirical ipleentations posit that i worker i possess deographic characteristic vector, x i, then the worker should be paid a non-discriinatory wage, y i = x i θ, where the wage is typically in logarithic or. Then, gender discriination can be quantiied, in part, as deviations o observed ale and eale wage structures ro the unobserved or hypothetical standard, θ. 3 For years ro 1990 to 2000, or instance, the growth o real value-added o the Korean econoy was ainly led by knowledge-based anuacturing, while eployent growth ainly led by knowledge-based services. For details, see Jung and Choi (2006). 4 In what ollows, we eectively assue that this non-discriinatory structure is constant over tie, as well. 7

8 While uch has been written on the estiation o the ale and eale wage structures using regression, little has been written on the estiation o θ to which the estiated structures are to be copared, in order to quantiy discriination. The Oaxaca-Blinder procedure proceeds by substituting either the estiated ale wage structure or the estiated eale wage structure or θ to calculate discriination. According to Neuark (1988), substituting the estiated ale wage structure iplies the additional assuption that ales are paid their arginal product, while substituting the estiated eale wage structure iplies that eales are paid their arginal product. The choice o which estiate to use or the unobserved non-discriinatory structure, θ, has iplications or the easureent o discriination. An extree exaple o this range is Ferber and Greene (1982), where wage discriination or a saple o university proessors was 2 percent, based on the ale nondiscriinatory wage structure, and was 70 percent, based on the eale non-discriinatory wage structure. It is in this sense that these estiates are "not identiied." Neuark suggests an alternative estiator or θ based on a regression that pools ale and eale observations in the saple. The technique presented here use dierences in counteractual wage estiates to produce easures o relative discriination that are no longer a unction o θ, so the arbitrary decision on which structure to choose is eliinated. It is in this sense that our estiates are "identiied." 5 The goals here are: a) to partition a Korean labor arket data set by year and industry, where industries can be categorized as either "knowledge-based" or "non-knowledge-based" and b) to estiate gender wage gaps over tie and industry type to deterine i gender wage gaps have been statistically declining over tie, and i their decline is in anyway related to 5 In the context o a 'data descriptive' wage odel, 'identiication' is not identiication is the strictest sense o the word. However, the arbitrary selection o non-discriinator wage structure suggests a lack o identiication, albeit an unconventional one. It is also aditted that a single non-discriinatory wage structure across all varieties o industries ay be soewhat aretched, even in perectly copetitive labor arkets. However, this assuption is iplicit in the wage decoposition literature, when decopositions are based on wage regressions that pool workers across industries. 8

9 knowledge intensity. These estiates are calculated at various levels o aggregation in the data. The next subsection details estiation strategies at each level o aggregation considered. A. Estiation o wage gaps Let k = 1,..., K index industries at dierent levels o aggregation (e.g., knowledge-based and non-knowledge-based, or hi-tech, ediu hi-tech, ediu lo-tech, and lo-tech anuacturing). Let t = 1,..., T index tie in years. Consider the 2 T log-wage regressions. y t K = x t t + k =2 θ β d + ε t (1) y t K = xt t + k =2 θ β d + ε t (2) where y t and y t are Ft - and M t -diensional colun vectors, respectively, representing the log wage or eale and ales, respectively; x t and x t are ( F t g) and ( M t g) diensional atrices, respectively, o observable explanatory variables; θ t and θ t are g-diensional paraeter vectors; β and β are scalar paraeters; d and d are Ft - and M t - diensional vectors (respectively) o observable duy variables or industry; and ε t and ε t are Ft - and M t -diensional error vectors, respectively, satisying the usual set o regression assuptions. Deine the ollowing averages: x t = 1 ι F x t t ( 1 g) and xt = 1 ι M x t t ( 1 g) F M t t where ι F and ι t M are F t t - and M t -diensional colun vectors o ones, respectively. These are average deographic characteristics in each year or eales and ales, respectively. Ordinary least squares yields x t t 2 T K predicted counteractuals in each industry: yˆ = ˆ θ + ˆ β, (3) 9

10 yˆ = ˆ θ + ˆ β, (4) x t where ˆ β ˆ β 0. t 1 = t 1 = t These are counteractuals in the sense that we use x t average eale characteristics in year t or all industries (instead o average eale characteristics in year t in industry k) to calculate ŷ. This produces the predicted wage that an average eale in year t would ake i they were randoly placed in industry k. The procedure is siilar or calculating ŷ. This dierence is essentially how identiication is achieved. Then, counteractual ale-eale wage dierences are: T K decopositions o yˆ yˆ = {( ˆ β ˆ β ) + x ( ˆ θ θ ) x ( ˆ θ θ )} + ( x x ) θ (5) t t t t t t where θ is soe unobserved, non-discriinatory wage structure; it is the arginal product o labor o a labor arket that does not have tastes or discriination. This is siilar to the Oaxaca-Blinder decoposition with one inor dierence: the counteractual ale-eale dierential is decoposed and not the average ale-eale dierential (say, y y ). t t Using the counteractual sees reasonable, because the Oaxaca-Blinder decoposition iplicitly assues that labor arkets are copetitive, and in this highly stylized world, labor should be readily substitutable across industries, particularly when it is the average laborer being substituted. In what ollows, we cannot solve or account or this particular shortcoing o the odel. 6 A particularly appealing eature o this orulation o the decoposition is that equation (5) highlights the act that the explained portion o the gap, x x ) θ, is not ( t t identiied. Thereore, the extent to which changes in the overall gap over t due to changes in average ale-eale characteristic dierential, x x ), is not estiable without knowing ( t t 6 That is, we still ust assue that θ is constant over industries, and also over tie. 10

11 θ. Thereore, even gender wage dierences based on worker productivity dierences are not easurable in the context o the Oaxaca-Blinder decoposition. This is iportant, because the decoposition is usually dichotoized into an "explained" and an "unexplained" portion, but without knowledge o θ, nothing is truly "explained." This ay see like a soewhat gri view o the Oaxaca-Blinder decoposition, but the decoposition is salvageable (in soe sense). The proble is that the decoposition seeks to identiy a dichotoy based on soe "gold standard", θ, which assigns weights (or iportance) to average worker characteristics. However, i the gold standard is the sae across industries (as these odels typically assue), then we can use the variability over k to identiy a relative easure o discriination that is based on soe ale and eale worker o average characteristics working in any industry k and being paid soe econoy-wide gold standard, θ. (This is the essence o the identiying assuption.) Based on equation (5), discriination (the bracketed potion o the counteractual wage decoposition in the equation) is: ˆ δ ( θ, x, x ) {( ˆ β ˆ β ) + x ( ˆ θ θ ) x ( ˆ θ θ )} (6) t t = t t t t which is also not identiied, since θ is not identiied. Let ˆ δ ( θ, x, x ) = ax ˆ δ ( θ, x, x ). t[ k ] t t t t k Notice that because o the linearity (onotonicity) o the decoposition, it doesn't atter what we use or θ to ind the index o the axiu, [k]. The agnitude o ˆ δ ( θ, x, x ) is a t[ k ] t t unction i θ, but the index o the axiu, [k], is the sae regardless o what is selected orθ, because it is apping into a set o characteristics, x t or x that doesn't vary over k. Thereore, selecting θ = 0 is ine or inding [k], but θ = ˆ θ t is what is norally used (en are paid the non-discriinatory standard, and woen are paid below it). Then dierencing across k: ˆ γ ˆ ˆ ( x t, xt ) = δt[ k ]( θ, x t, xt ) δ ( θ, x t, xt ) (7) 11

12 ˆ γ (, ) ( ˆ ˆ ) ( ˆ ˆ x t xt = β t[ k ] βt[ k ] β β ) (8) These are coparisons within years but between industries, and they sweep out the nondiscriinatory wage structure, θ, and are thereore identiied. Relative estiators o this type were irst considered by Horrace and Oaxaca (2001). Horrace (2005) explains that these easures are "relative to a within saple standard," and argues that the dierencing ay reduce estiation biases associated with non-zero eans or ε t and ε t. The easure in (8) identiies relative coparisons between industries while sweeping out θ but (unortunately) not between years, because the averages x t and x t are a unction o t. To ake coparisons between years and occupations, deine the averages ale and eale characteristics across industries and years as x and x. Let F = T F t t= 1 and M = T M t t= 1 then grand eans over all years and industries are x = 1 F T t= 1 F x t t T 1 ( 1 g) and x = M t x M t= 1 t ( 1 g) Plugging these values in or x t and x t in the previous analysis, we can dierence across k and t. Let ˆ δ ( θ, x, x ) {( ˆ β ˆ β ) + x ( ˆ θ θ ) x ( ˆ θ θ )} (9) Let ˆ δ ( θ, x, x ) = ax ˆ δ ( θ, x, x ), so that and, [ ] t, k = t t ˆ γ ˆ ˆ ( x, x) = δ[ ]( θ, x, x) δ ( θ, x, x) (10) ˆ γ (, ) ( ˆ ˆ ) ( ˆ ˆ ) ( ˆ ˆ ) ( ˆ x x = β ] β [ ] β β + x θ [ t] θ t x θ [ t] ˆ θ [ t ), (11) 12

13 where [] corresponds to the index o the axial δˆ or θ = 0 over both k and t, and where [t] corresponds to index o the sae year associated with []. These are coparisons between years and industries, that sweep out θ, because the averages a unction o t. x and x are no longer There is soe industry in soe year, [], that possesses that axial value o the unexplained counteractual wage gap (discriination): δ [ ]. Then the dierence, ˆ γ 0, captures the (relative) extent to which industry k in year t is discriinatory. A convenient noralization is the "non-discriinatory percentage" exp{ ~ γ } (0,1], k = 1,..., K, t = 1,..., T. The noralization can be interpreted as ollows: "in a labor arket where skill and the nondiscriinatory wage structure are constant over industry and tie (save or the dierentials ˆ β and β ), industry-year [] is 100 percent non-discriinatory relative to all industryyears in the saple, and all other industry-years are soe raction (o 100 percent) nondiscriinatory." Clearly, certain probles inherent in the classical Oaxaca-Blinder decoposition reain here. For exaple, actual labor arkets are arked with soe level o heterogeneity across industries in ters o worker characteristics and (presuably) in ters o their non-discriinatory wage structures. However, our easure does not suer ro the lack o identiication ebodied in an arbitrary selection o, say θ = ˆ θ. Also, there is truly no t sense in which we have identiied the "unexplained portion" o soe observed wage gap, y t y t. In act we are decoposing the estiate y y ˆ ˆ, which is technically "not observed," so there is no way to relate our easure back to the overall gap, y y. t t However, this is the cost o the identiication: everything is relative to the unidentiied dierence ˆ δ [ ] ( θ, x, x ), not the "identiied" dierence y t yt. 13

14 The estiates in equation (11) are used in the epirical analyses that ollow. First, we partition the data into "knowledge-based industries" and "other industries", so that K = 2 (one duy variable in each regression). This produces two estiate o ˆ γ ( x, x ) in each o seven years, t = 1994,..., Then, we partition the data into our industries: knowledgebased anuacturing, knowledge-based services, other anuacturing, and other services, so that K = 4 (three duy variables in each regression). This produces our estiates o ˆ γ ( x, x ) in each o seven years, t = 1994,..., We now discuss variance estiation or kt the estiates in equation (11). kt B. Variance-covariance estiation Since θ will ultiately be eliinated by dierencing, we set θ = 0 in what ollows. Hence, the estiator o interest is: ˆ δ (0, x, x ) = ( x ˆ θ + ˆ β ) ( x ˆ θ + ˆ β ) (12) t Let ˆ* θ [ ˆ θ, ˆ β,..., ˆ β ] and ˆ* θ [ ˆ θ, ˆ β,..., ˆ β ] be ( g + K 1) colun vectors, so t = t t 2 tk t = t t 2 tk that ˆ * ˆ* [,..., ˆ* θ θ θ ] and ˆ * ˆ* [,..., ˆ* θ θ θ ] are T ( g + K 1) colun vectors. Let Q be a = 1 T = 1 T K 1 identity atrix bordered above by a K 1 row vector o zeros. Thereore Q is a K ( K 1) atrix. Thereore, C = I [ ι x, Q] and C = I [ ι x, Q] are TK T ( g + K 1) atrices. Then, T K t T K Δˆ (0, x, x ( TK 1) ) = C ˆ θ C ˆ θ * * (13) is a TK colun vector, and is the vector representation o the estiates in equation (12), with typical eleent δˆ. Let D be constructed ro a ( TK 1) negative identity atrix with a colun vector o ones inserted in [] colun position ro the let and then a colun o 14

15 zeroes inserted in the [] row position ro the top. For exaple i [] is the second eleent o Δˆ, then D ( TK TX ) = 0 M M M M 0 L L L L O L Then, Γˆ ( x, x TK 1 ) = [ ˆ γ ( x, x )... ˆ γ ( x, x )]' = DΔˆ (0, x, x ) (14) 11 TK is the vector representation o the ˆ γ ( x, x ) in equation (10). Since the ale and eale saples are independent, Var Γ x x = D C Var ˆ* C + CVar ˆ* {ˆ (, )} [ ( θ ) ( θ ) C ] D TK TK (15) Treating the saples in each o the T regressions as independent, ˆ* Var( θ 1) ˆ* ( ) = 0 Var θ M 0 0 ( ˆ* Var θ M 0 2 ) K K O K 0 0 M ( ˆ* Var θ T, ) a T ( g + K 1) square atrix, where Var ˆ θ ) is a ( g + K 1) square atrix returned by any ( * t regression sotware package. It ollows siilarly or Var ˆ θ ). Also notice that θ is a constant, so it is true that Thereore, Var{ˆ Δ( θ, x, x )} = Var{ˆ Δ(0, x, x )} (16) ( TK TK ) ( * t 15

16 Var {ˆ Δ ( θ, x, x )} = C Var( ˆ θ ) C + C Var( ˆ θ ) C (17) * * IV. DATA The data used or the epirical analysis are ro the 1994 to 2000 Wage Structure Survey o the Ministry o Labor o Korea, and were previously analyzed by Jung and Choi (2004, 2006). The survey provides inoration on personal characteristics and earnings data or workers eployed in irs with 10 or ore eployees in all industries, except the public adinistration sector. 7 For the epirical analysis, the agricultural and ining industries, as well as agricultural occupations, were excluded. The inal data set includes about 0.4 illion workers or years , and about 0.5 illion workers or 1999 and The industrial classiication or the epirical analysis is presented in Table 1. Knowledge-based anuacturing sectors are classiied based on their R&D intensity and knowledge-based service sectors are classiied based on the ratio o college graduates. Knowledge-based anuacturing reers to high-technology anuacturing in areas such as electronics and counication equipent, and also to ediu-high-technology anuacturing in areas such as coputers and otor vehicles. Other anuacturing includes ediu-low-technology anuacturing covering cheicals, rubber and plastic products, etals, and also low-technology anuacturing ranging ro ood and textiles to paper products. Knowledge-based services include counications, inance, business services, health, education, and cultural services. Non-knowledge-based services or "other services" include the industries like utilities, construction, wholesale and retail trade, hotels and restaurants, and transport and storage. These industrial groupings were ade based upon the two-digit Korean Standard Industrial Classiication (KSIC). 7 The Survey was extended to include sall irs with 5-9 eployees starting in 1999, but workers eployed in irs with 5-9 eployees were excluded ro the inal data set or consistency. 16

17 V. RESULTS In order to estiate non-discriinatory percentages or industry groups, wage regressions or ale and eale groups were estiated or years 1994 to The irst set o regressions included duy variables or whether an individual was eployed in a knowledge-based industry or not. Estiation results o these regressions are presented in Table 3. As an estiator o equation (11), we report two values ( k = 1, 2 ; knowledge- and non-knowledgebased) o the "non-discriinatory percentages," exp{ ˆ γ }, or seven years t = 1,..., 7. The top section o Table 5 contains these values and the standard errors o the estiators. 8 Here, the ˆ [ ] largest value o δ = corresponds to the knowledge-based industries in Thus, or knowledge-based industries in 1999, ˆ γ = 0 and exp{ ˆ γ } = 1, so in 1999 knowledge-based industries were "100 percent non-discriinatory," eaning that, relatively speaking, knowledge-based industries in 1999 were the least discriinatory industry-year in the saple. All other industry-years are evaluated relative to this standard. Table 5 shows that, in 1994 knowledge-based industries were 93.8 percent nondiscriinatory and non-knowledge-based industries were 90.7 percent non-discriinatory. In 2000, knowledge-based industries were 98.4 percent nondiscriinatory and non-knowledgebased industries were 96.2 percent non-discriinatory. Figure 2 shows the values o nondiscriinatory percentages as well as 95 percent conidence intervals based on the standard errors in equation (17). According to our estiation, knowledge-based industries had signiicantly higher non-discriinatory percentages in all years considered. Although in Note that the standard errors are or γˆ not the noralization exp{ ˆ γ }, but they can be used to calculate conidence intervals on the exp{ ˆ γ }, since it is a onotonic transoration o γˆ. We do this in the sequel. 9 Note that ˆ δ = is not readily interpretable as a easure o discriination, because it is evaluated at θ = 0. [ ] 17

18 the two estiates were relatively close, the dierence was still signiicant based on the 95 percent conidence intervals. The non-discriinatory percentages in the two groups o industries ollow a dierent trend between 1994 and Non-knowledge-based industries or "other industries" (the solid line) show a relatively ast iproveent between 1994 and 1996, ollowed by a relatively steady three years and a signiicant iproveent in Knowledgebased industries (the dashed line), however, ollow a dierent pattern. Between 1994 and 1996 non-discriination iproved very slightly, ollowed by a large iproveent in 1997 and an insigniicant decline in In 1999, knowledge-based industries have the largest nondiscriinatory percentage (100 percent), but it decreased to 98.4 percent in There is a strong possibility that we are picking up the eects o the Asian Financial Crisis that occurred between 1997 and This ay be particularly true or knowledgebased anuacturing industries in Korea, which experienced very strong growth iediately beore the inancial crisis. The OECD (2000) report characterizes Asian econoies beore the inancial crisis as industrial over-capacity due to excessive investent in anuacturing. The rapid increase in the non-discriinatory percentages or knowledge-based industries between 1996 and 1997 ay be correlated with this over-capitalization in Asian anuacturing and the subsequent steep decline in Asian currencies ater the crisis. It is not clear what echanis produced this apparent correlation, nor are we willing to speculate on it, since it is beyond the scope o this research. However, the coincidence o the decrease in the unexplained portion in the gender wage in Korean knowledge-based industries and the Asian inancial crisis is too pronounced in Figure 2 to be ignored. In the next analysis, we will show that the changes in discriination or knowledge-based industries were, in act, substantial in the anuacturing sector, but weak or non-existent in the service sector. To disaggregate the industry eects, we re-estiated the regressions with three industry duies representing the knowledge-based anuacturing, knowledge-based services, non- 18

19 knowledge-based anuacturing (other anuacturing), and the non-knowledge-based services (other services) as the oitted category. The regression results are tabulated in Table 4. Noralized estiates o equation (11) can be ound in Table 5. Figures 3 and 4 show the evolution o non-discriinatory percentages or these our industry classiications. Lower and upper liits o conidence intervals (based on standard errors reported in Table 5) show that the non-discriinatory percentages were signiicantly higher in knowledge-based industries or both anuacturing and services. The epirical inding that non-discriinatory percentages are signiicantly higher in knowledge-based industries is consistent with the hypothesis that non-productivity related discriination in knowledge-based industries is ore costly than in non-knowledge-based industries. That is, non-productivity related discriination is perhaps ore detriental to the copetitiveness o knowledge-based industries than non-knowledge-based industries, where the orer is heavily dependent upon knowledge inputs, and perhaps subject to a higher degree o copetition. Notice, also, that non-discriinatory percentages are higher in services than in anuacturing. Non-discriinatory percentages in non-knowledge-based services are lower than those in knowledge-based services and are higher than those in both knowledge-based and non-knowledge-based anuacturing. Also, the dierence in non-discriinatory percentages between knowledge-based sectors and non-knowledge-based sectors is saller or anuacturing than or services. Again, there sees to be soe (unexplained) correlation between the steep increases in the non-discriinatory percentages in the anuacturing sector (Figure 4) and the Asian Financial Crisis that occurred between 1997 and In Figure 4, we see a signiicant drop in 1999 knowledge-based anuacturing industries, as well as in non-knowledge-based industries. Although it is beyond the scope o this paper to analyze eects o the Asian crisis on Korean labor arkets, it is interesting to see that the iproveents in non-discriinatory 19

20 percentages between 1994 and 1998 were partially reversed in knowledge-based anuacturing industries and copletely reversed in non-knowledge-based anuacturing industries as the inancial crisis was itigated. In 2000, there was a signiicant iproveent in non-knowledge-based industries and a slight decrease in knowledge-based industries. Figure 3 shows that, in the service sector, the correlation between the inancial crisis and gender discriination was not as clear as in the anuacturing sector. In 1998, there was a slight decrease in non-discriinatory percentages in non-knowledge-based services, ollowed by a signiicant iproveent. In knowledge-based service industries, however, there was a slight decrease in the trend, ollowed by a signiicant iproveent. In 2000, both knowledge- and non-knowledge-based service industries experienced a decline in non-discriinatory percentages. VI. CONCLUSIONS I one accepts the validity o Oaxaca-Blinder decopositions ro linear wage regressions then the counteractual decoposition presented herein is identiied, while the usual decoposition is not. Our technique also readily lends itsel to coparisons across separate regression periods and to a convenient noralization o discriination to percentages on the unit interval. We have also provided an explanation o how to calculate standard errors or our estiates. Our technique could be applied to any partition o the data (not just a partition based on knowledge intensity) and to other ors o discriination (e.g., discriination by race), as well. Our application suggests that discriination was saller in knowledge-intensive industries in Korea than in non-knowledge-intensive industries at the end o the last decade, and this dierence sees to have been ost pronounced in the anuacturing sector. In absolute ters we do not know the dierence and by how uch it changed over tie; this is 20

21 the cost o the relative estiation procedure. There was soe volatility in the level o discriination around the tie o the Asian Financial Crisis, particularly in the anuacturing sector. Despite this volatility, discriination declined on average over the seven-year period. It would be interesting to explore the nature o the causality (i any) between the overall decline in discriination and the events surrounding the Asian Financial crisis, but this is let or uture research. ACKNOWLEDGEMENTS We would like to thank to Dan Black, To Kniesner, Devashish Mitra, Joseph Marchand, and participants in Aerican Econoic Association 2005 Meetings in Philadelphia or their helpul coents. The authors are responsible or all reaining errors. 21

22 REFERENCES Abowd, J.M., Karaarz F., and Margolis D.N. (1999) High wage workers and high wage irs. Econoetrica, 67, Altonji J.J. and Blank, R.M. (1999) Race and gender in the labor arket, in: O. Ashenelter & D. Card (ed.), Handbook o Labor Econoics, edition 1, volue 3, chapter 48, pages , Netherlands: Elsevier. Becker, G.S. (1971) The Econoics o Discriination, Chicago: University o Chicago Press. Black, D. (1995). Discriination in an equilibriu search odel. Journal o Labor Econoics, 13, Blau, F.D. and Kahn, L.M. (1999). Analyzing the gender pay gap. Quarterly Review o Econoics and Finance, 39, Blinder, A.S. (1973). Wage discriination: reduced or and structural estiates. The Journal o Huan Resources, 8, Edin, P and Zetteberg, J. (1992) Interindustry wage dierentials: evidence ro Sweden and a coparison with United States. The Aerican Econoic Review, 82, Ferber, M.A., and Green, C.A. (1982) Traditional or reverse sex discriination? A case study o a large public university. Industrial and Labor Relations Review, 35, Fields, J. and Wol E. (1995) Interindustry wage dierentials and gender wage gap. Industrial and Labor Relations Review, 49, Finnie, R and Wannell T. (2004) Evolution o the gender earnings gap aong canadian university graduates, Applied Econoics 36, Freean, R. (2004). The great doubling: the real eect o globalization on labor. Unpublished Manuscript, Departent o Econoics, Harvard University. Gibbons F. and Katz L. (1992) Does uneasured explain inter-industry wage dierentials? Review o Econoic Studies, 59, Hellerstein J.., Neuark, D. and Troske, K.R. (2002) Market orces and sex discriination. Journal o Huan Resources, 37, Helwege, J. (1992) Sectoral shits and inter-industry wage dierentials, Journal o Labor Econoics, 10, Horrace, W.C. (2005) On the ranking uncertainty o labor arket wage gaps. Journal o Population Econoics, 18, Horrace, W.C. and Oaxaca, R.L. (2001) Inter-Industry Wage dierentials and the gender wage gap: an identiication proble Industrial and Labor Relations Review, 54,

23 Jung, J.H. and Choi K. (2004) Gender wage dierentials and discriination in Korea: coparison by knowledge intensity o industries, International Econoic Journal, 18, Jung, J. H. and Choi K. (2006) The labor arket structure o knowledge-based industries: a Korean case. Journal o Asia Paciic Econoy, 11, Kidd, M.P. and Shannon M. (2001) Convergence in the gender wage gap in Australia over the 1980s: identiying the role o counteracting orces via the Juhn, Murphy and Pierce decoposition, Applied Econoics 33, Krueger B.A. and Suers L.H. (1988) Eiciency wages and inter-industry wage structure, Econoetrica, 56, Melitz, M.J. (2003) The ipact o trade on intra-industry reallocations and aggregate industry productivity, Econoetrica, 71, Neuark, D. (1988) Eployers discriinatory behavior and the estiation o wage discriination. The Journal o Huan Resources, 23, Oaxaca, R., (1973) Male-eale wage dierentials in urban labor arkets. International Econoic Review, 14, Oaxaca, R.L. and Ranso M. (1999) Identiication in detailed wage decopositions., Review o Econoics and Statistics, 81, OECD (2000) Knowledge-based Industries in Asia. OECD. Paris. 23

24 FIGURES Figure 1: Feale-to-Male Ratio o Average Earnings Source: Korean Labor Institute, Labor Statistics (2004). Figure 2: Non-Discriinatory Percentages, Knowledge-based Industries and Other Industries Knowledge Based Industries Figure plots exp{ ˆ γ } where γˆ is based on equation (11). [] = [Knowledge-based Industries, 1999]. 95% conidence bounds. Other Industries = Non-Knowledge-based Industries. Other Industries 24

25 Figure 3: Non-Discriinatory Percentages, Knowledge-based Services and Other Service Know ledge Based Services Other Services Figure plots exp{ ˆ γ } where γˆ is based on equation (11). [] = [Knowledge-based Services, 1999]. 95% conidence bounds. Other Services = Non-Knowledge-based Services. Figure 4: Non-Discriinatory Percentages, Knowledge-based Manuacturing and Other Manuacturing Know ledge Based Manuacturing Other Manuacturing Figure plots exp{ ˆ γ } where γˆ is based on equation (11). [] = [Knowledge-based Services, 1999]. 95% conidence bounds. Other Manuacturing = Non-Knowledge-based Manuacturing. 25

26 Table 1: Classiication o Knowledge-based Industries R&D Intensity 1 (1999) College Graduates 2 (2001) Knowledge- High-tech Electronical achinery based Counication equipent Manuacturing (KBM) Mediu-high-tech Oice/accounting/coputing achinery Motor vehicles Other Mediu-low- Cheicals Manuacturing tech Rubber/plastic products (OM) Non-etallic ineral products Metals Fabricated etal products Non-electrical achinery Precision instruents Other transport equipent Furniture, and Manuacturing n.e.c Low-tech Food, beverages, tobacco Textiles, apparel, leather Wood and paper products Printing Petroleu reineries/products Recycling Knowledge- Counications based Financial services Services Business services (KBS) Education services Health services/social work Culture/recreation Other Electricity, gas, water supply Services Construction (OS) Wholesale/retail trade Hotels and restaurants Transport and storage Real estate activities Other services All Industries Notes: 1) R&D expenditures as a percentage o value added in each industry. 2) The ratio o 4-year college graduates to the total eployed(%). 3) Includes printing industry. Sources: OECD(2002), Science, Technology and Industry Outlook, NSO, Korea(2002), The Econoically Active Population Survey. 26

27 Table 2: Descriptive Statistics* Feale Male Feale Male Feale Male Feale Male Feale Male Feale Male Feale Male Logarith o Hourly Wage (Won) * (0.44) (0.49) (0.45) (0.49) (0.46) (0.52) (0.47) (0.51) (0.48) (0.52) (0.52) (0.54) (0.52) (0.55) Age (11.57) (9.95) (11.67) (10.06) (11.69) (10.27) (11.70) (10.33) (11.46) (10.03) (11.09) (9.87) (11.06) (10.00) Married (0.49) (0.43) (0.50) (0.43) (0.50) (0.44) (0.50) (0.43) (0.50) (0.42) (0.50) (0.43) (0.50) (0.43) Tenure (3.67) (5.73) (3.89) (6.02) (4.03) (6.06) (4.21) (6.13) (4.35) (6.29) (4.34) (6.24) (4.47) (6.45) Education: Less Than High School (0.47) (0.41) (0.46) (0.39) (0.45) (0.37) (0.44) (0.37) (0.42) (0.35) (0.41) (0.35) (0.40) (0.35) High School (0.50) (0.50) (0.50) (0.50) (0.50) (0.50) (0.50) (0.50) (0.50) (0.50) (0.50) (0.50) (0.50) (0.50) Two-Year College (0.26) (0.28) (0.28) (0.28) (0.30) (0.29) (0.32) (0.30) (0.35) (0.31) (0.35) (0.32) (0.37) (0.32) Four-Year College or above (0.24) (0.42) (0.26) (0.43) (0.29) (0.44) (0.30) (0.44) (0.32) (0.45) (0.35) (0.46) (0.36) (0.45) * Hourly wage = [regular onthly pay + yearly bonus pay/12] /regular onthly work hours

28 Table 2: Descriptive Statistics, con t * Feale Male Feale Male Feale Male Feale Male Feale Male Feale Male Feale Male Establishent Size: Eployees (0.42) (0.41) (0.42) (0.41) (0.43) (0.42) (0.44) (0.42) (0.44) (0.42) (0.46) (0.43) (0.46) (0.45) Eployees (0.46) (0.45) (0.45) (0.44) (0.45) (0.44) (0.45) (0.44) (0.45) (0.44) (0.45) (0.43) (0.46) (0.44) Eployees (0.40) (0.41) (0.39) (0.40) (0.40) (0.40) (0.40) (0.41) (0.40) (0.41) (0.39) (0.41) (0.38) (0.40) Eployees (0.25) (0.26) (0.25) (0.26) (0.25) (0.26) (0.26) (0.25) (0.25) (0.25) (0.22) (0.23) (0.23) (0.24) 500+ Eployees (0.41) (0.42) (0.42) (0.43) (0.41) (0.43) (0.40) (0.42) (0.40) (0.42) (0.38) (0.42) (0.36) (0.39) Industries**: Knowledge-based Industries (0.48) (0.45) (0.49) (0.46) (0.49) (0.47) (0.50) (0.47) (0.50) (0.48) (0.50) (0.47) (0.50) (0.47) Knowledge-based Manuacturing (0.33) (0.30) (0.35) (0.31) (0.34) (0.32) (0.34) (0.32) (0.33) (0.32) (0.32) (0.32) (0.33) (0.32) Knowledge-based Services (0.43) (0.39) (0.44) (0.39) (0.46) (0.41) (0.46) (0.41) (0.48) (0.43) (0.48) (0.42) (0.48) (0.41) * The values in parenthesis correspond to standard deviations o the variables. ** Reer to Table 1 or classiication o the industries. 28

29 Table 3: Regression Results Two Industry Duies, Dependent Variable: Logarith o hourly wage in Korean Won. * Feale Male Feale Male Feale Male Feale Male Feale Male Feale Male Feale Male (Constant) [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] Age [121.12] [347.97] [99.56] [367.00] [132.90] [353.28] [116.15] [348.16] [103.75] [336.23] [69.86] [434.96] [75.26] [432.26] Age-squared [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [-83.03] [ ] [-94.49] [ ] High School [320.95] [334.23] [342.83] [378.20] [322.84] [379.19] [324.32] [367.50] [249.69] [320.51] [303.29] [280.44] [280.49] [308.73] 2-year college [339.77] [374.53] [384.21] [437.54] [367.68] [450.72] [380.86] [468.73] [318.14] [428.95] [340.62] [363.31] [345.92] [404.66] 4-year college [554.29] [908.09] [604.69] [992.26] [633.73] [963.95] [632.70] [960.08] [553.95] [865.10] [567.89] [797.49] [583.15] [846.72] eployees [-26.63] [-86.35] [-54.55] [-92.55] [-5.97] [-82.03] [-3.27] [ ] [-38.33] [-96.54] [14.02] [-5.71] [-6.38] [-16.12] eployees [-6.65] [ ] [3.06] [-99.46] [5.18] [ ] [27.98] [ ] [12.08] [-85.59] [30.09] [62.81] [39.15] [58.90] eployees [28.49] [-46.74] [53.67] [11.02] [46.73] [21.18] [53.95] [-7.37] [49.82] [34.29] [31.73] [83.25] [70.45] [146.73] 500+ eployees [100.26] [78.66] [100.51] [106.18] [161.89] [147.85] [180.53] [179.74] [154.85] [226.08] [135.32] [294.45] [122.82] [200.32] 29

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