Brandon WalcuttHankuk University of Foreign Studies

Seoul, KoreaALLIED ACADEMIES CONFERENCE

April 2010

WAGE DISCRIMINATION IN KOREA’S ESL INDUSTRY

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Contents

Background and Problem StatementThe StudyESL Industry of KoreaLiterature ReviewMethodologyDataResults/ImplicationsFuture Research and Limitations

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Background

Anecdotally, native speaking foreign teachers in Korea’s English as a Second Language (ESL) industry are subject to a substantial amount of racial, sexual and age-based discrimination.

Neoclassical economic theory suggests that “discrimination in economic life usually consists of sorting people according to traits rather than productivity” (Cooter, 1994).

As this definition applies to ESL instructors with unexplained differential earnings, wage discrimination occurs when members of different groups possess equal net marginal productivity, but are paid unequal wages.

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The Study

This study was conducted with the specific goal of identifying unexplained wage gaps which could indicate wage discrimination against certain groups within Korea’s ESL instructor population. .

Economic characteristics, such as education, certificates and years of experience should contribute to productivity and therefore should be associated with an instructor’s wages.

Noneconomic characteristics include such data as race, age, nationality and other indicators that should not be associated with the quality or productivity of an instructor.

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The Industry

English as a 2nd Language is a Huge industry in Korea, accounting for 1.9% or roughly $15 billion of Korea’s $797.8 billion 2005 nominal GDP. 

Instructors can be found teaching at public schools, universities or at private institutes.

In 2008, there were at least 18,000 English language instructors employed in Korea. Potentially many more exist within other visa categories.

The ESL system is changing as it attempts to improve the quality and depth of education provided.

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Literature Review

Wage discrimination exists if individuals with the same economic characteristics receive different wages and the differences are systematically correlated with certain noneconomic (i.e. racial, religious, etc.) characteristics of the individual.

The study of work related discrimination takes 2 primary approaches:

“Discrimination-Preference Trade” model G.S. Becker / K. Arrow - encompasses a competitive equilibrium

model in which some individuals from one group have a taste or preference for working with individuals of a particular group and are willing to sacrifice some income to satisfy their preference

“Imperfect Employer Information” model. Arrow / Phelps - based on employer perception of differences in the

predictors of a worker’s job performance or their expected net marginal value of productivity.

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Methodology and Data

Questionnaires were circulated to ESL instructors in Korea.

Primary data of interest were: Personal Demographics: Nationality, race, sex and age Educational Background: Degree, major, teaching certs,

teaching experience Primary/PT job info: Type, contract hours, average

monthly/hourly salary

Final Sample Size: ESL Instructors: 193

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Methodology and Data

GLM and linear regression analysis was used on the questionnaire data to create a controlled productivity baseline for the instructors based on common economic and noneconomic characteristics.

Tukey HSD was also used for post-hoc testing to address any Type 1 errors resulting from the multiple variance analyses in the GLM model.

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Results

An additive model was used to estimate and test the effects of various demographic, education, and experience factors on composite annual earnings. The results are below.

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Table 7. GLM Univariate ANOVA Model

Source df F η2 PNationality 10 2.82 0.18 0.00Ethnicity 7 0.29 0.02 0.96Sex 1 2.71 0.02 0.10Degree discipline 9 0.89 0.06 0.54Age 1 2.72 0.02 0.10Education level 1 6.81 0.05 0.01Years teaching in Korea 1 6.47 0.05 0.01Error 126R Squared = .941 (Adjusted R Squared = .926)

Results

Tukey HSD post-hoc tests demonstrated only Filipino and European-other earnings were significantly different from other nationalities (see table 8).

Where other European mean composite earnings were significantly higher in four comparisons, Filipino earnings were lower in all other nationality comparisons.

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Table 8. Composite Earnings Differences of Filipino and Other-European Nationalities Nationality Comparison M diff. SE PPhilippines United States -11,698,886 5,284,168 n/s

Canada -14,112,143 5,336,208 n/sUnited Kingdom -10,323,556 6,195,860 n/sAustralia -9,316,000 6,099,794 n/sNew Zealand -15,145,091 6,020,055 n/sIreland -10,652,000 8,929,184 n/sSouth Africa -4,748,000 6,655,421 n/sOther Asia -26,749,333 7,874,800 0.03Other Africa -26,672,000 8,929,184 n/sOther Europe -35,988,000 8,929,184 0.00

Other-European United States 24,289,114 7,382,358 0.05CanadaUnited Kingdom

New ZealandIrelandSouth AfricaPhilippinesOther AsiaOther Africa

Results

Linear Regression model:• Filipino, Other-European, and all other composite

nationalities were binary coded into 3 new variables and loaded with Education level and years of teaching experience were in a linear regression model to ascertain the predictive adjustment to composite annual earnings.

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Table 9. Regression Model of Education, Work and Nationality Factor B SE B β

Constant 21,762,940 4,656,351Education level 1,981,600 1,464,970 0.10Years teaching in Korea 2,103,044 637,505 0.24Filipino -11,639,112 5,158,893 -0.16Other European 21,780,805 7,236,148 0.21

Results

Wage Gaps:The previous regression model shows 4 factors

determining wages.Education level and years teaching add to productivity

and hence to wages so cannot be considered wage discrimination.

Those gaps found in relation to Filipinos and Other Europeans, however, can certainly be considered as possible areas of wage discrimination as their nationality cannot be considered an economic characteristic.

No unexplained wage gaps were found in the expected areas of sex, age and race.

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Limitations / Future Research

Potential Limitations :General sample size should be increasedSample sizes of Filipinos and Other Europeans, in

particular, are not as large as they could be. Future Studies:Expand the Filipino sample size considerably and retest

for further validation of depth/scope of wage discrimination

Assess the employer activities and perceptions that contribute to nationality-based wage differences as well as the effects of work visa status on wage discrimination

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Thank You14

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