Occupational Skills and Gender Wage GapOccupational Skills and Gender Wage Gap Yu Zhou (ABSTRACT FOR...

Occupational Skills and Gender Wage Gap

Yu Zhou

Dissertation submitted to the Faculty of the

Virginia Polytechnic Institute and State University

in partial fulfillment of the requirements for the degree of

Doctor of Philosophy

in

Economics, Science

Suqin Ge, Chair

Richard A. Ashley

Djavad Salehi-Isfahani

Nicolaus Tideman

May 9, 2018

Blacksburg, Virginia

Keywords: Occupational Skills, Decomposition, Gender Differences, OECD Countries

Copyright 2018, Yu Zhou


Yu Zhou

(ABSTRACT)

This dissertation consists of three essays studying the occupational wages, skills, and gender

wage gap in U.S. and other OECD countries. The analysis especially focuses on how the

gender differences in skill levels and skill returns could explain the gender wage gaps and

changes. The first chapter outlines the dissertation by briefly discussing the motivations,

methods, and main findings in each of the following chapters.

Chapter 2 focuses on the well-documented wage and employment polarizations in the U.S..

The occupations moving into the lower tail (“in” occupations) have more immigrant workers,

more part-time workers, and less female workers. In addition, the wage gaps between

domestic/immigrant, full-time/part-time, and male/female workers are also larger in “in”

occupations. The opposite facts hold true in the occupations moving out of the lower tail

(“out” occupations). Utilizing the regional differences, we also find stronger spillover effect

from high-wage occupations to the “out” occupations than the effect to the “in” occupations.

Chapter 3 investigates how gender differences in skills beyond education and experience can

account for the observed gender wage gap and its changes between 1980 and 2015 by using

data from the Dictionary of Occupational Titles (DOT) and the Occupational Information

Network (O*NET). The main empirical finding is that female workers possess much higher

level of caring skills, and the returns to caring skills are significantly negative but have

increased over time, accounting for a major part of the persistent gender wage gap and the

narrowing gender wage gap from 1980 to 2015. Another significant portion of the narrowed

gender wage gap can be attributed to the faster growth in female workers’ average directness

skills and the fact that the returns to directness skills are significantly positive and stable

over time.

In the last chapter, we document significant cross-country variation in gender wage gaps

among OECD countries by using the data from Survey of Adult Skills (PIAAC). We find

significant cross-country variation in the gender differences in returns. The gender differences

in returns to basic labor and experience are the most important factors in explaining the

gender wage gap. In addition, gender differences in returns to cognitive and directness skills

are playing milder but substantial roles in explaining the wage gap. We also find the social

institutions and attitudes indicators are related to the cross-country variation in gender

differences.


Yu Zhou

(ABSTRACT FOR GENERAL AUDIENCE)

This dissertation makes effort to understand the changes in wages in the U.S. and other

OECD countries. I focus on two important features of the changes, namely, wage polarization

and change in gender wage gap.

Wage polarization describes the uneven changes in wages in different occupations; there is fast

wage growth in the high-wage occupations, mild wage growth in the low-wage occupations,

and slow even negative wage growth in middle-wage occupations. The analysis shows that

technology advancement has increased the productivity of the high-wage occupations. There-

fore, the wages in these occupations also increase. Meanwhile, there is strong spillover effect

from the high-wage occupations to the low-wage occupations because low-wage occupations

mostly provide services to the high-wage occupations. The spillover effect is the most likely

cause on the wage growth in the low-wage occupations. In contrast, jobs in the middle-wage

occupations are crowded out by the technology advancement. This harms the wage growth

in these occupations.

Gender wage gap is defined as the gender difference in the social average wages. In the

U.S., female workers only earned 55% of what male workers earned in 1980. This number

has increased to 70%. In our analysis, we argue that genders have different skills in the

daily interaction with people. Female workers have much stronger skills in caring for others.

However, this caring skills are negatively rewarded. Fortunately, the rewards to the caring

skills are increasing. The negative reward to caring skills and changes in the rewards could

account for the remaining gender wage gap and its change.

Gender wage gap also presents a significant cross-country variation. Slovenia has gender

wage gap at a level of 4% but Japan has a level of 40%. The analysis shows that potential

explanation to the variation is social institutions and social attitudes. In a society emphasizing

on competition or providing better benefits to maternity leaves, low-skill female workers are

more likely to receive lower average wages.

Acknowledgments

“But seek first His kingdom and His righteousness, and all these things will be given to you as

well.” (Matthew 6:33) Though I have many names to whom I want to give thanks, I know

that God grants me this opportunity. He calls me to Blacksburg and carries me through my

study. Obtaining the degree is golden but knowing Him is much more precious than any gold.

Praise be to God.

God have given me many blessings. My parents, Zengqi Zhou and Lijuan Liu, are the first.

Their love and support surround me since my first cry and their encouragements bring me

strength through my study. They ask for nothing from me and my thanks is the only thing

that I could offer.

My special thanks goes to my advisor, Dr. Suqin Ge. In six years, she poured her heart on

me and lead me to academic maturity. She shows me extraordinary patience and inspires me

with perspectives. Her tender soul and academic excellence are my examples.

My special thanks also extends to Dr. Richard Ashley and Dr. Martha Ann Bell. They help

me extend my research interests to interdisciplinary study. Their guidance and support are

much appreciated.

Many people have helped on my way to finish this dissertation. Dr. Nicolaus Tideman, Dr.

Kwok Ping Tsang, and Dr. Djavad Salehi-Isfahani have provided many precious insights and

suggestions on improving the dissertation. Dr. Tiefeng Qian, Dr. Xiaojin Sun, and Dr. Xi

Chen are good friends to whom I can always turn for help. Many other colleagues have also

helped me in a way or another. I apologize that I cannot list their names.

At last, I want to thank both my families in China and in Blacksburg. I want to thank

my relatives in China for always sending their best wishes to me. I also want to express

my gratitude to Bob and Sandra Jackson, Jay and Michelle Lester, Dr. Johnny Yu and Dr.

Jessie Chen-Yu. They are my family in Blacksburg.

iv

Contents

1 Introduction 1

2 Occupation Characteristics and Changes in Low-Skill Occupations 4

2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

2.3 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

2.3.1 Sample Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

2.3.2 Occupation Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

2.3.3 Plotting Wage and Employment Share Change . . . . . . . . . . . . . 16

2.4 Characteristics of Occupation Groups . . . . . . . . . . . . . . . . . . . . . . 17

2.4.1 Immigration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

2.4.2 Part-Time Jobs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

2.4.3 Gender Composition . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

2.4.4 Marriage Wage Premium . . . . . . . . . . . . . . . . . . . . . . . . . 23

2.4.5 Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

2.5 Empirical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

2.5.1 Commuting Zones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

2.5.2 Empirical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

2.6 Conclusion and Future Research Plan . . . . . . . . . . . . . . . . . . . . . . 33

2.7 Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

v

2.7.1 Occupation Groups in the First Quintile of Skill Ranking . . . . . . . 35

2.7.2 Empirical Results with Different Samples and Model Specifications . 36

2.7.3 Change of Labor Union Coverage . . . . . . . . . . . . . . . . . . . . 44

2.7.4 Regional Variation in Wage Growth and Employment Share Change . 44

3 Skill Requirements, Returns to Skills and Gender Wage Gap 47

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

3.2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

3.3 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

3.3.1 CPS Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

3.3.2 Definitions of Occupations . . . . . . . . . . . . . . . . . . . . . . . . 54

3.3.3 Choices of Skill Measurements . . . . . . . . . . . . . . . . . . . . . . 56

3.4 Empirical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

3.4.1 Stylized Fact of the Gender Skill Differences . . . . . . . . . . . . . . 58

3.4.2 Returns to Skills . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

3.4.3 Decomposition on the Gender Wage Gap . . . . . . . . . . . . . . . . 63

3.4.4 Decomposition on the Gender Wage Gap Change . . . . . . . . . . . 64

3.4.5 Decomposition within Different Education Groups . . . . . . . . . . . 66

3.4.6 Decomposition within Different Marital Status Groups . . . . . . . . 72

3.4.7 Decomposition within Different Race Groups . . . . . . . . . . . . . . 76

3.4.8 Decomposition within Different Age Groups . . . . . . . . . . . . . . 77

3.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

4 Returns to Skills and Gender Wage Gap Across OECD Countries 86

4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

4.2 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

4.3 Empirical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

vi

4.3.1 Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

4.3.2 Decomposition on Gender Wage Gap . . . . . . . . . . . . . . . . . . 95

4.4 Explaining the Gender Differences in Returns . . . . . . . . . . . . . . . . . 97

4.5 Ending Notes and Work for the Future . . . . . . . . . . . . . . . . . . . . . 103

4.6 Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

4.6.1 Definition of Directness Skills . . . . . . . . . . . . . . . . . . . . . . 103

4.6.2 Correlation between Gender Differences in Returns with Social Indicators104

References 106

vii

List of Figures

2.1 Smoothed Wage Growth in US . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.2 Smoothed Employment Share Change in US . . . . . . . . . . . . . . . . . . 6

2.3 Actual and Counter-factual Wage Growth in US . . . . . . . . . . . . . . . . 7

2.4 Actual and Counter-factual Employment Share Change in US . . . . . . . . 8

2.5 Smoothed Wage Change if Wage Ranking Allowed to Change . . . . . . . . 9

2.6 Representative Commuting Zones with Different Polarization Pattern . . . . 29

3.1 Gender Gap in Hourly Wage . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

3.2 Gender Differences in Education and Potential Experience . . . . . . . . . . 50

3.3 Gender Differences in Skills . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

3.4 Gender Gap in Hourly Wage by Education . . . . . . . . . . . . . . . . . . . 66

3.5 Gender Differences in Skills by Education . . . . . . . . . . . . . . . . . . . . 67

3.6 Gender Gap in Hourly Wage by Marital Status . . . . . . . . . . . . . . . . 72

3.7 Gender Differences in Skills by Marital Status . . . . . . . . . . . . . . . . . 73

3.8 Gender Gap in Hourly Wage by Race . . . . . . . . . . . . . . . . . . . . . . 76

3.9 Gender Differences in Skills by Race . . . . . . . . . . . . . . . . . . . . . . . 77

3.10 Gender Gap in Hourly Wage by Age Group . . . . . . . . . . . . . . . . . . 80

3.11 Gender Differences in Skills by Age Group . . . . . . . . . . . . . . . . . . . 84

4.1 Gender Waqe Gap by Country . . . . . . . . . . . . . . . . . . . . . . . . . . 88

4.2 Correlation between Gender Difference in Basic Labor with Selected Indicators 97

viii

4.3 Correlation between Gender Difference in Returns to Experience with Selected

Indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99

4.4 Correlation between Gender Difference in Returns to Education with Selected

Indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100

4.5 Correlation between Gender Difference in Returns to Cognitive Skills with

Selected Indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101

4.6 Correlation between Gender Difference in Returns to Directness Skills with

Selected Indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102

ix

List of Tables

2.1 Labor Share Change and Wage Growth in Each Occupation Group . . . . . 18

2.2 Share of Immigration Working Hour s in Each Occupation Group . . . . . . 19

2.3 Wage Differences Between Immigrants and Natives . . . . . . . . . . . . . . 20

2.4 Labor Share of Part-time Working Hours in Each Occupation Group . . . . 20

2.5 Part/Full-Time Wage in Each Occupation Group . . . . . . . . . . . . . . . 21

2.6 Labor Share of Female Labor in Each Occupation Group . . . . . . . . . . . 22

2.7 Gender Wage Gap in Each Occupation Group . . . . . . . . . . . . . . . . . 22

2.8 Gender Wage Change in Each Occupation Group . . . . . . . . . . . . . . . 22

2.9 Married Ratios by Gender in Each Occupation Group . . . . . . . . . . . . . 23

2.10 Marriage Wage Premium by Gender in Each Occupation Group . . . . . . . 24

2.11 Wage Change in Each Occupation Group by Marital Status . . . . . . . . . 24

2.12 Working Hours Share of Non-college Educated in Each Occupation Group . . 25

2.13 College/Non-college Average Wage in Each Occupation Group . . . . . . . . 26

2.14 Correlations with Changes in the Occupation Characteristics . . . . . . . . . 31

2.15 List of “Out” Occupations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

2.16 List of “In” Occupations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

2.17 List of Constant Occupations . . . . . . . . . . . . . . . . . . . . . . . . . . 37

2.18 Correlations in All Commuting Zones, Constant Occupations . . . . . . . . . 38

2.19 Correlations in Large Zones, Constant Occupations . . . . . . . . . . . . . . 39

2.20 Correlations in All Commuting Zones, “Out” Occupations . . . . . . . . . . 40

x

2.21 Correlations in Large Zones, “Out” Occupations . . . . . . . . . . . . . . . . 41

2.22 Correlations in All Commuting Zones, “In” Occupations . . . . . . . . . . . 42

2.23 Correlations in Large Zones, “In” Occupations . . . . . . . . . . . . . . . . . 43

2.24 Working Hours Ratio of Workers Covered by Labor Union in Each Occupation

Group . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

2.25 Employment Share of Each Occupation Group in Each Region . . . . . . . . 44

2.26 Each Region’s Share of the Whole Population in Each Occupation Group . . 45

2.27 Regional Average Wage of Each Occupation Group . . . . . . . . . . . . . . 46

3.1 Correlations between Skills . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

3.2 Returns to Skills . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

3.3 Return to Skills with One People Skills . . . . . . . . . . . . . . . . . . . . . 62

3.4 Wage Gap Decomposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

3.5 Gender Wage Gap Change Decomposition . . . . . . . . . . . . . . . . . . . 65

3.6 Returns to Skills by Education Level . . . . . . . . . . . . . . . . . . . . . . 68

3.7 Decomposition on the Gender Wage Gap in Education Groups . . . . . . . . 69

3.8 Decomposition on the Gender Wage Gap Change in Education Groups . . . 71

3.9 Decomposition on Gender Wage Gap Level by Marital Status . . . . . . . . 74

3.10 Decomposition on Gender Wage Gap Change by Marital Status . . . . . . . 75

3.11 Decomposition on Gender Wage Gap Level by Marital Status . . . . . . . . 78

3.12 Decomposition on Gender Wage Gap Change by Race . . . . . . . . . . . . . 79

3.13 Decomposition on Gender Wage Gap Level by Age Groups . . . . . . . . . . 81

3.14 Decomposition on Gender Wage Gap Change by Age Groups . . . . . . . . . 82

4.1 Returns to Productive Characteristics and Skills by Country . . . . . . . . . 93

4.2 Percentage of Gender Wage Differnce Explained by the GEnder Difference in

the Returns and Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

4.3 Components of Directness Skills . . . . . . . . . . . . . . . . . . . . . . . . . 104

xi

4.4 Correlations with Social Institutions . . . . . . . . . . . . . . . . . . . . . . . 104

4.5 Correlations with Social Attitudes . . . . . . . . . . . . . . . . . . . . . . . . 105

4.6 Correlations with Social Values . . . . . . . . . . . . . . . . . . . . . . . . . 105

xii

Chapter 1

Introduction

This dissertation seeks to understand the causes on gender wage gaps both in the U.S. and

in a broader scope of OECD countries. In the U.S., the gender wage gap has narrowed

significantly in recent decades. In 1980, female workers earned 55% of male average hourly

wage, but in 2015, the number increased to 70%.(Goldin, 2014) Most of the decline in the

gender wage gap took place between 1980 to the mid-1990s and the change has been small

since then. In addition, there is a significant cross-country variation in the gender wage gaps

among OECD countries. Women’s average hourly wage is almost as high as men’s hourly

wage in Slovenia. The gap is lower than 4%. However, in Japan women are earning average

hourly wage that is lower than 60% of men’s wage.

As an important indicator of gender equality, both the changes and levels of gender wage

gaps have attracted great attention in the literature. The conventional explanations for the

narrowing gender wage gap are changes in human capital variables.(Altonji and Blank, 1999;

Blau and Kahn, 2006) However, as the gender differences in education and in experiences

have been narrowed dramatically both in the U.S. and other OECD countries, new theories

are in need to explain the sizable residual gender wage gaps. A recent literature is trying

to solve the puzzle from the task/skill approach.(Bacolod and Blum, 2010; Beaudry and

Lewis, 2014; Black and Spitz-Oener, 2010; Borghans et al., 2014; Yamaguchi, 2016)

They use new micro data to measure gender differences in skills conditional on the usual

human capital variables. They find that growing returns to cognitive and people skills and

declining returns to motor skills can account for part of the narrowing gender wage gap.

In line with the literature, we further argue that there should be more than one type

of people skills. (Borghans et.al, 2008a, b) .We distinguish the directness skills that are

important in making decisions, negotiation, and persuasion from the caring skills that are

1

Yu Zhou Chapter 1. Introduction 2

important in coordination and providing services. We contribute the literature in the following

ways. First, our empirical results shows that these two skills play different roles in explaining

the gender wage gap. The increasing returns to caring skills and increasing level in directness

skills in female workers account for the major part of the decline in the gender wage gap

in the U.S.. The recent slowdown of the declining gender gap is due to the slowdown in

the growth of female directness skills and returns to caring skills. Second, we show that the

females’ higher level in caring skills actually is the main reason why their wages are lower.

Although the literature agrees on the importance of people skills and their contribution to

female wage growth, it is necessary to distinguish directness skills from caring skills. Third,

this paper documents a significant gender difference in caring skills, which can be found in

all groups regardless of education, race, marital status, and age. This leads to future work to

investigate the sources of the higher caring skills in women.

In our study on the OECD countries, we find that including the skills into the wage deter-

mining equation could better explain the gender wage gaps in all countries. Though the

gender differences in returns to skills account smaller parts of the gender wage gaps than the

gender differences in the returns to basic labor and experiences do, they still play substantial

roles in explaining the gender wage gaps. In addition, we find that the gender differences in

the returns vary significantly across countries. We make hypotheses that these variations

could be correlated with the social institutions such as employment protection and maternity

leave. In addition, social attitudes such as people’s opinions on female working could also be

correlated to the cross-country variation in gender differences in returns. In the last section

of the dissertation, we provide potential evidences on the existence of the correlations.

The dissertation consists of three major parts. Chapter 2 provides the general background of

the structure change in occupational wages in the U.S.. We focus on the lower tail of the

wage ranking and analyze the features of the winners and losers in term of ranking changes.

We find that the wage changes in the lower tail is improving the wage condition of females.

In Chapter 3 and Chapter 4, we narrow our focus on the changes in the gender wage gaps. In

Chapter 3, we investigate the gender gap and its change over time in the U.S.. We emphasize

the importance of distinguishing the directness skills from the caring skills in explaining the

level and change in the gender wage gap. In Chapter 4, we make effort to apply the skill

approach analysis to a broader group of OECD countries.

Chapter 2 focuses on the well-documented wage and employment polarizations in the U.S..

The occupations moving into the lower tail (“in” occupations) have more immigrant work-

ers, more part-time workers, and less female workers. In addition, the wage gaps between

domestic/immigrant, full-time/part-time, and male/female workers are larger in “in” occupa-

Yu Zhou Chapter 1. Introduction 3

tions. The opposite facts hold true in the occupations moving out of the lower tail (“out”

occupations). Utilizing the regional differences, we also find stronger spillover effect from the

high-wage occupations to the “out” occupations than the effect to the “in” occupations.

Chapter 3 investigates how gender differences in skills beyond education and experience can



Network (O*NET). The main empirical finding is that female workers possess much higher

level of caring skills than male workers do, and the returns to caring skills are significantly

negative but have increased over time, accounting for a major part of the persistent gender

wage gap and the narrowing gender wage gap from 1980 to 2015. Another significant portion

of the narrowed gender wage gap can be attributed to the faster growth in female workers’

average directness skills relative to those of male workers and the fact that the returns to

directness skills are significantly positive and stable over time.

In the last chapter, we document significant cross-country variation in gender wage gap

among OECD countries by using the dataset from Survey of Adult Skills (PIAAC). We find

significant cross-country variation in the gender differences in returns. The gender differences

in returns to basic labor and experience are found to be the most important factors in

explaining the gender wage gap. In addition, gender differences in returns to cognitive and

directness skills are also playing milder but substantial roles in explaining the wage gap. We

also find the social institutions and attitudes indicators are related to the cross-country vari-

ation in gender differences. These indicators provide potential direction for the future research.

Chapter 2

Occupation Characteristics and

Changes in Low-Skill Occupations

(ABSTRACT)

The well-documented wage polarization in the U.S.. is weakened if the wage rankings of

occupations are allowed to change over time. The occupations moving into the lower tail

(“in” occupations) have more immigrant workers, more part-time workers, and less female

workers. In addition, the wage gaps between domestic/immigrant, full-time/parttime, and

male/female workers are also larger in “in” occupations. The opposite holds true in the

occupations moving out of the lower tail (“out” occupations). Regional differences in wage

and employment polarizations are found across commuting zones. Utilizing the regional

difference, the higher share of part-time workers is found to be the main reason lowering the

wage of the “in” occupations relative to the “out” occupations. We also show that the wage

growths in “out” occupations are strongly correlated to the wage growths and employment

growths of the occupations in the upper tail of the wage rankings. In contrast, only the

wage growth in the upper tail correlates to the wage growth of the “in” occupations. This

suggests that the spillover effect of the wage growth in the upper tail is stronger to the “out”

occupations.

4

Yu Zhou Chapter 2. 5

2.1 Introduction

The literature shows that since 1980, both employment share change and wage growth are

higher among the occupations in both tails of wage ranking1 than the ones in the middle of

wage ranking. This is called labor market polarization. This phenomenon is well-documented

in the U.S. (Autor and Dorn, 2013; Autor et al., 2006, 0) and many European countries

(Goos and Manning, 2007; Goos et al., 2009, 1). Figure 2.1 shows the wage growth in the U.S.

from 1980 to 2010. Figure 2.2 shows the corresponding employment share change. To be con-

sistent with the existing literature, I keep the horizontal axis fixed at the wage ranking of 1980.

Figure 2.1: Smoothed Wage Growth in US

These figures show that the employment polarization enlarged over the past three decades

but wage polarization peaked at 2000 and became less pronounced from 2000 to 2010.

The literature attributes the causes of polarization to the routine-task biased technolo-

gy change (RBTC hereafter). The theory says that the ongoing technology change since

1980 is substitutive to the routine tasks that are more intensive in middle-skill occupation-

s. Meanwhile RBTC is complementary to the abstract tasks that are more intensive in

1In the literature, wage ranking is exchangeable with skill ranking.


Figure 2.2: Smoothed Employment Share Change in US

high-skill occupations. This change leads to both job dissolution and wage decrease in the

middle-skill occupations and wage increase in the high-skill occupations. In turn, higher wage

in high-skill occupations will increase the demand for the labors performing manual task2

and the wages in the corresponding occupations. In Autor and Dorn (2013), they further

connect the lower tail polarization to the rise of service occupations. As what they put in

the paper, “workers in service occupations tend to be at the bottom of the wage-ranked

occupational skill distribution (i.e., at the left-hand side of polarization graphs) while routine

occupations are toward the middle of the distribution”. If labor share of service occupations

is assumed to be unchanged since 1980 and their wage growths are kept at the same speed of

production occupations3, the counter-factual graphs are presented in Figure 2.3 and Figure 2.4.

These figures demonstrate that both wage and employment polarization are mitigated if the

wage and employment share growth of service occupations are fixed. However, the figures also

bring up one fact unfit to the RBTC theory. In the year 2000 when the highest wage polar-

ization happened, the labor share change in service occupations is not significant. In contrast,

2In RBTC model, manual labor provides service which is complementary to the good consumption by theabstract task labor.

3Production occupations are the ones shown to be most routine-task intensive in Autor and Dorn (2013)


Figure 2.3: Actual and Counter-factual Wage Growth in US

when employment polarization is substantial in 2010, the wage polarization is less significant

than in 2000. In addition, the wage polarization in Figure 2.1 is based on the assumption

that the wage ranking does not change over time. However, it is possible that the wage rank

also changes over time because the growths of wages are very different across occupations.

Figure 2.5 shows the wage change when the skill (wage) ranking is allowed to change over time.

Figure 2.5 shows that if the skill ranking of occupations is allowed to change over time, instead

of having a significant wage polarization, the occupations in the first quintile have relatively

lower wage growth than second quintile. This implies, instead of making all labors in low-skill

occupations better off, RBTC is beneficial to some occupations but not for the rest. In this

paper, I investigate the occupational ranking change from 1980 to 2010. The finding is that

some occupations stay in the first quintile with low wage growth and a large employment

increase. I call them constant occupations in the paper. In contrast, other occupations enjoy

high wage growth and move out of the first quintile meanwhile the employment shares are

stable or mildly decreasing. I call them “out” occupations. The data also shows that some

occupations that were not in the first quintile in 1980 experienced wage decrease and ended

up in the first quintile in 2010. I call them “in” occupations. The details are reported in

section 2.3.


Figure 2.4: Actual and Counter-factual Employment Share Change in US

To understand the different labor market performance in these occupations, I investigate the

characteristics that may affect the changes in wage growths and employment shares, namely

the immigration labor ratio4, the part-time job ratio5 , the marriage ratio6, the female labor

ratio7, the non-college educated labor ratio8, and the average experience weighted by working

hours9. I document that patterns of changes in immigration ratio, part-time job ratio, and

female labor ratio are very different across different occupations.

Constant occupations have fast increase in immigration ratio and the wages of immigrants

are decreasing relatively to the ones of natives. These occupations also have stable and high

ratio of part-time jobs. The wages of part-time worker grow slower relatively to full-time jobs.

4The ratio of working hours of labors who indicate themselves as immigrants to the total working hours ofwhole sample.

5The ratio of working hours of labors who work as part-time jobs (working less than 40 weeks per yearand less than 35 hours per week) to the total working hours of whole sample.

6The ratio of working hours of married labors to the total working hours of whole sample.7The ratio of female labor working hours to the total working hours of whole sample.8The ratio of working hours of labors who receive education less than 13 years to the total working hours

of whole sample.9The potential working experience is age minus education years minus 7.


Figure 2.5: Smoothed Wage Change if Wage Ranking Allowed to Change

Both female labor ratio and gender wage gap are decreasing in constant occupations. There

is also a clear decreasing trend in marriage ratio. In contrast, the marriage wage premium

increases. All these factors are negatively correlated to the wage growth.

In contrast, “out” occupations have slow increase in immigration ratio and wages of natives

and immigrants are growing almost at the same speed. The part-time job ratio is also stable

and is only as half high as in constant occupations. The wage gap between full-time jobs

and part-time jobs is enlarging over time but not as pronounced as in constant occupations.

Female working ratio is also decreasing. However, the wage growths of both genders are

not substantially different. The marriage ratio in “out” occupations are nearly constant

and marriage wage premium stays at a low level. Compared to constant occupations, “out”

occupations are less affected by the factors that hinder the wage growth.

The evidence for “in” occupations is mixed. They have the fastest growth in immigra-

tion ratio and wages of immigrants decreases faster than the one of natives. Part-time job

ratio increases slightly in these occupations and wage gap between full-time jobs and part-time

jobs enlarges over time. These facts indicate decrease in wages of these occupations. However,

the change of marriage labor ratio and female labor ratio point to the other direction. There


is a minor increase of female labor ratio and gender wage gap also shrinks over time. Marriage

labor ratio decreases significantly and marriage wage premium decreases simultaneously.

However, given the general decreasing trend of wage in any cohort, these two factors are at

best mitigating the wage decrease but not reversing the trend.

I hypothesize that the released labor from routine task occupations (as the RBTC the-

ory predicts) may flow to some occupations with certain characteristics asymmetrically and

cause the different wage and labor share change in the low-skill occupations. By utilizing

the regional variation among commuting zones, I find some inconclusive evidence that im-

migration ratio, part-time job ratio and female labor ratio are negatively correlated with

the wage growth. On the other hand, employment share change is positively correlated with

immigration ratio, non-college educated labor ratio and married labor ratio. Surprisingly,

labor share is negatively correlated with part-time job ratio.

This paper do not intend to provide a new framework to analyze wage and employment

polarization or find the causation of polarization. However, the findings indicate that occupa-

tion characteristics play an important role in the shape and magnitude of polarization. The

asymmetric growth in wage and employment may result from skill matching between labors

released from routine-task intensive occupations and the absorbing occupations. Checking

the task/skill components of each occupation is our next step in progress.

The rest of paper constructs as follow. Section 2.2 is a literature review about wage and

employment polarization in the U.S. and other economies. In section 2.3, I present the data

and statistics that provide initial evidence. Section 2.4 provides details in the occupation

groups. In section 2.5, by utilizing the regional variation, I create a panel data set to test our

hypotheses. Section 2.6 concludes and provide future research plan.

2.2 Literature Review

Labor market polarization is the start point of this paper. There is fast growing literature on

the topic that provides valuable analyzing framework and different explanations of polarization.

Though labor market polarization is not documented in Autor et al. (2003), the task-

based analysis framework launched in this paper provides the foundation of following studies.

The authors categorise tasks performed in each job into two categories, routine tasks and

non-routine tasks (problem solving, complex communications). The technology change is


routine-task biased (RBTC) which means the technology change (represented by the comput-

erization) substitutes to routine tasks but complements non-routine tasks. The decreasing

prices of computers and vast computerization lead to both employment and wage decrease in

the routine tasks and employment and wage increase of non-routine tasks. Their empirical

results confirm that computerization is a major cause in the increase demand for college

labors and decrease for routine tasks.

The first documentation of employment and wage polarization in the U.S. is in Autor

et al. (2006). In this paper, they also employ the RBTC model and show the effect of

computerization on the labor market. Instead of categorizing tasks into two groups, there

are three groups of tasks, namely, abstract tasks, routine tasks, and manual tasks. College

educated workers perform the abstract tasks and non-college educated workers can decide

to provide either routine or manual tasks. Non-college workers have a unit endowment of

manual input and a random routine endowment which is identically independently uniform

distributed. The conclusion is that as prices of computers decrease, the return to each unit

of routine input decreases so more non-college labor will sort to serve as manual input. This

model fits the employment polarization very well. However, if technology change only affects

routine tasks and is neither substitutive nor complementary to the manual tasks, The model

has very little power on explaining the wage polarization.

Autor and Dorn (2013) can be considered as an updated version of Autor et al. (2006).

One important improvement is the modification on the RBTC to be compatible with wage

polarization under the assumption that “goods and services are gross complements”.10 The

focus of this paper is checking the role of routine tasks in wage and job polarization. They

utilize the regional variation among commuting zones and shows that commuting zones with

higher routine task ratio in 1980 experienced “greater adoption of information technology

and both wage and employment in service occupations have higher growth rate.” This

paper is the most comprehensive attempt on explaining labor market polarization and the

evidence partly provide support to the adapted RBTC model. However, the explanation

power is weakened by the following flaws. First, since it does not show the complementary

relation between goods and service in the paper, the growth in wage and employment shown

in the paper is more like facts than causation of polarizations. Second, the paper takes

a bold assumption that the service occupations compose the main parts of lower-tail of

skill (wage) ranking. But as shown in section 2.3, the composition of lower-tail occupations

is more complex. In addition, even among the occupations in service sector, the wage

and employment changes are not uniform. Therefore, I hypothesis that RBTC may not be

10However, the wage polarization achieved in the model is symmetric on both tails which is not consistentwith patterns shown in the data.


the only reason causing the polarization, the characteristic of occupations may also play a part.

Though analysis based on RBTC is of many merits, the theory cannot perfectly explain the

exact shape of labor market polarization. Michaels et al. (2014) try to make a reconciliation

by avoiding to curve the exact shape of polarization. Instead, they focus on the technol-

ogy change in the demand of labors in different education levels. First, they connect the

education level with occupations’ task composition. Highly educated workers cluster in the

occupations with more requirements on non-routine cognitive tasks; middle-educated workers

are concentrated in occupations with more routine cognitive tasks; least educated workers are

mostly in the occupations with more non-routine manual tasks but less non-routine cognitive

tasks. Using data from 11 developed countries, they find that country-industry pairs with

faster technology growth demand more highly educated workers and less middle-educated

workers. The effect on least educated worker is ambiguous. Though polarization cannot

be well explained, this paper enlightens me to check the effect of education on the low-skill

(wage) occupations and education/skill match.

Similar evidence is also shown in Lindley and Machin (2014). They try to explain employment

polarization in a spatial view. Empirically, using data the U.S. they find concentration of

highly educated workers in some states and MSAs. They also find a positive correlation

between polarization and educated labor share. As they conclude, though hypothetically, the

concentration of highly educated people increase the wage inequality in some areas, the higher

wages also induce a higher demand of service (low-skill) and consequently an employment in

these areas.

Most of the literature focuses on the wage and employment share changes that happened

in the past three decades. Barany and Siegel (2018) contribute to the literature in two

ways. First, they shows that the labor market polarization can be dated back to 1950s or

1960s which is much earlier than the RBTC took place. Second, they show that in a more

aggregate level than occupations, ten occupations groups also present a pattern of wage and

employment polarization along the axis of 1980’s median wage. Their model shows that as

the productivity in manufacturing sector which is in the middle of skill-ranking increase,11

workers will sort themselves to either tail side of skill-ranking (low-skill service sector or

high-skill service sector as defined in the paper). Their conclusion is rather than the RBTC

causing the labor market polarization, industry structural change leads to the wage and

employment share changes in the past 50 years. This research suggests to check the possible

factors affecting the labor supply that may cause the different labor market performance in

11This contradicts to most of literature that assumes the technology change enhancing the productivity inhigh-skill sectors instead of in middle skill sectors.


different occupations.

Labor market polarization is not unique in the U.S., Goos and Manning (2007) docu-

ment the same employment polarization in British labor market. Yet, The evidence of the

wage polarization is missing. A merit in this paper is that they consider several shifters

on both demand and supply as competing explanations. They find insignificant influence

of international trade, outsourcing and production demand on labor demand. They try to

reconcile the observation of increasing college educated labor with the increase of low-skill job

share by the concept of “over-education” or “credentialism”. They also find the insignificant

role of immigration as labor supply. This is maybe due to the fact that Britain are not facing

as many immigrant labor supply as the U.S. does. This difference in immigrant labor market

may be the cause on the difference in wage growths.

In Goos et. al (2009, 2011), they show the pervasive employment polarizations in 16

EU countries. They listed three possible explanations. First, RBTC causes job dissolution

in middle-skill occupations and workers move to both tail ends of skill ranking. Second,

developed countries outsource middle-skill occupations to developing countries. Third, under

the framework of RBTC, growing wage of upper-tail occupations creates more demand of

services provided by the occupations in the lower tail. They find the dominant role of

routineness on the other two explanations (as how they put, “offshoring loses a ‘horse race’

against RTI (routineness)”). They also shows that both within and between industry labor

share negatively changes with the routineness of occupations. One drawback in the paper is

that it cannot explain the relative higher increase in wage and employment changes in the

upper-tail.

In sum, the employment polarization is pervasive in developed countries but the wage

polarization is not a uniform trend. This is the main reason causing difficulty on forming a

theory that could apply to all cases. However, the literature leaves many clues that could

affect the shape of wage and employment share change. No matter the technology change is

favoring middle-skill occupations (as in industry structural change model) or against these

occupations (as in RBTC model), jobs are dissolved and labors are released from these

occupations. The unanswered question is how the other occupations can absorb these labors.

In this paper, I investigate the characteristics that may affect the ability of occupations in

the lower tail of skill ranking to absorb labor supplies. A follow-up study is to check how

the skill of released labors match with the skill requirement in different low-skill occupations.

Depending on the availability of data, a study of cross-country is very helpful to answer the

question.


2.3 Data

2.3.1 Sample Description

I employ the 5% census data in 1980, 1990, 2000 and five-year ACS data in 2010 from IPUMS.

The reason to choose census data over CPS data is twofold. The sample size is the first

concern. Since I intend to check the wage and employment share in occupational level, large

sample size can help reduce the bias caused by outliers. The second reason is Census data

have richer geographic information. As shown later in this part, commuting zones are essential

to this study. In contrast, CPS data only record the information of Metropolitan Areas and

about 1/3 observations have record either ”unidentified area” or “not in metropolitan area”.

This drawback plus the small sample size of CPS almost prevents me from implementing

regional analysis. Taking these two consideration, I choose census data as working sample.

The working age is defined as from 16 to 65. Any observation has zero working hour

per week or zero working week are excluded from the sample. I also cleanse the data by

excluding the observations who are self-employed12, with missing or zero wage income and

within institutional group quarters13.

I use the variable “wage and salary income” as the measurement of income. The vari-

able used in empirical analysis is hourly wage which is wage income divided by total yearly

working hours. Total yearly working hours is the product of weeks working last year14 and

usual working hours per week. I also take care of the outliers. I set the lower bar of hourly

wage as the first percentile value over the whole economy and any observation with hourly

wage lower than the lower bar is adjusted to this value. I set the higher bar to different

state15. First, I multiply the top-code income values by 1.516. The higher bars are set to be

these values divided by 1750 (50 weeks by 35 hour s per week as the definition of full-time

full-year job). Any observation has a higher hourly wage than higher bar is adjusted to the

12In Card and DiNardo (2002), they investigated the wage change in the U.S. and “found that ... eliminatingthe observations with self-employment income do not have much impact” on the wage change pattern. Iexclude the self-employment observations to control outliers.

13In census data, this group includes correctional institutions, mental institutions and institutions for theelderly, handicapped and poor.

14ACS data stopped to report the integer numbers of working weeks from 2007. Only an interval variableof working weeks is reported. In our sample, I calculate the mean working weeks in each interval in 2000 anduse these values as a proxy for working weeks in ACS 2010 five years data. In 2007 ACS data, I find thatusing mean working weeks instead of exact working weeks has very limited impact on occupation wages.

15Topcode varies across state since 1990.16There is no consent on how to deal with topcode values. Some literature choose not to adjust it, some

choose to multiply it by a factor of 1.33, 1.4 or 1.5. Since this change will only affect the upper tail wagedistribution, the way that I choose has limited effect on our conclusion about lower tail wage distribution.


higher bar.

2.3.2 Occupation Systems

Both wage and employment polarization are based on the ranking of occupational skills

(wages). This means the shape of polarization depends on how broadly an occupation is

defined. Without limit of data size, a more detailed occupation system can reflect the wage

change pattern better.

In the literature, two major occupation systems are employed. One is detailed defined.

In Autor and Dorn (2013) and Lindley and Machin (2014), based on the U.S. census and

ACS data, they derive a compatible occupation to the 1990 census occupation system17. In

this system, there are 330 consistently defined occupations and 323 non-farm/non-military

occupations. Similar system is adopted in Goos and Manning (2007) when they study British

data. They use a 3-digit occupation system and approximately 370 occupations are defined.

The second type of occupation system are employed in Goos et al. (2009, 1). They use a

pooled data set from European countries and only 20 occupations are broadly defined. This

system is more like a sector system than an occupation one.

In this study, since I also use the U.S. census and ACS data, I adopt the same occupa-

tion system as in Autor and Dorn (2013) and Lindley and Machin (2014) at first. However,

this detailed system is not very handy when I intend to check the regional variation commuting

zones. As shown in next section, 363 out of 741 commuting zones have population less than

100,000. In a 5% sample, many occupations are missing in commuting zone level. Since I fix

each the weight of each occupation in wage percentile when I draw wage and employment

change, too many missing values harm the comparability between commuting zones. To solve

this problem, I search for an alternative occupation system. I constructed a new one based

on 2010 standard occupational classification from Bureau of Labor Statistics (BLS-SOC).

In BLS-SOC, there are 16 major groups, 96 minor groups, 449 broad occupations and 821

detailed occupations. In addition, “occupations with similar skills or work activities are

grouped at each of the four levels of hierarchy to facilitate comparisons”18. Yet, several

minor groups are too generally defined to present the skill difference in the group. I partition

these minor groups according to the mean wages of contained occupations and 98 minor

non-farm/non-military groups are defined after adjustment.

17The occupation definition changes each time in census data, and ACS data change theirs accordingly.18See http://www.bls.gov/soc/socguide.htm for details


Since BLS-SOC groups occupations according to their skills and tasks, it is suitable to

the task-based analysis. In addition, I create a crosswalk between the occupation system in

Autor and Dorn (2013) and BLS-SOC. The polarization pattern can be replicated very well

with BLS-SOC. In addition, ACS data started to report the SOC occupation since 2000, the

matching is consistent with the way how they pair census occupation and BLS-SOC.19

BLS-SOC solves the problem caused by missing occupation well and enables me to compare

the polarization pattern between commuting zones. Though in the empirical analysis, I still

use the census occupation system because I group occupation in a more broad way, I could

see BLS-SOC is useful in consequent research such as investigating labor task match between

occupations.

2.3.3 Plotting Wage and Employment Share Change

First, I calculate occupations’ log hourly mean wages and employment shares in the decennial

data from 1980 to 2010. There are 323 non-farm/non-military occupations (98 occupations

when using BLS-SOC). All variables are weighted by personal weight and labor supply weight

(working hours). Then I order occupations according to their log hourly wages in 1980.

The in-percentile weight of each occupation is calculated in the following manner: after

ordering the occupations, I calculate the accumulated employment share of each occupation.

This enables me to see how much actual employment share each occupation allocate in the

percentile. For example, if occupation A in order 3 has accumulated employment share

as 0.024 and occupation B in order 4 has accumulated employment share as 0.031. Then

occupation B has total employment share of 0.007. In percentile 2, occupation A allocates

0.004 employment share and occupation B allocates 0.006 and 0.001 in percentile 2 and

percentile 3 respectively. Occupation B’s weight in percentile 2 is 0.006/0.007= 0.857 and

in percentile 3 is 0.001/0.006=0.143. Keeping this weight fixed, the percentile wages and

percentile employment shares are calculated in year 1990, 2000 and 2010. The wage and

employment share change in each year can be easily calculated.

This is the common way in literature. However, the in-percentile method faces a prob-

lem of misrepresenting. Taking the example above, if occupation A’s employment share

is only 0.004, then its weight in percentile 2 is 0.004/0.004=1. In this case, the wage of

percentile 2 is mainly decided by occupation A’s wage though occupation B holds a larger

19In ACS data, BLS-SOC is reported in detailed occupation level.


actual share in percentile 2. I try to mitigate this problem by using the actual share in each

percentile as weight. Though I observe substantial change in percentile wages, the smoothed

graph does not show much difference, especially, the polarization pattern is preserved. This

is because smoothing can be heavily affected by outliers and trend. This is another intuition

for me to scrutinize the occupations in the lower tail because the whole wage level could be

pulled up by some fast growing occupations.

2.4 Characteristics of Occupation Groups

I scrutinize the occupations in the first quintile of skill (wage) ranking and find that the wage

growth and employment share change are not uniform among these occupations. Comparing

the wage ranking between 1980 and 2010, I find wage rank are stable in some occupations, such

as waiters, cashiers, and dressmakers. In contrast, some occupations experience significant

wage growths such as recreation facility attendants, licensed practical nurses, and typists.

In addition, wages are decreasing in some occupations such as stock and inventory clerks,

packers, and production helpers. I define constant occupations as the ones stay in the first

quintile both in 1980 and in 2010; “out” occupations are the ones that were in the first quintile

in 1980 but not in 2010; “in” occupations are the one that were not in the first quintile in

1980 but in 2010. One interesting observation is that half of the constant occupations are

categorized as service sector in Autor and Dorn (2013) but also 1/4 occupations of “in” group

are service occupations too. This implies that occupations with similar skill requirement have

different skill compensation.20 A detailed occupation ranking change is in section 2.7.1. In

Table 2.1, I try to make a simple comparison among these occupation groups.

I could see that both employment share change and wage growth present different patterns.

The constant occupations have a significant employment share expansion and the wage is

stable from 1980 to 1990 and have a jump in 2000 and decrease in 2010. The patterns of

“out” occupations are the opposite: the employment share shrinks all the time but the wage

increases all the time. In “out” occupations, wage stays almost unchanged from 1980 to

2000 and decreases clearly in 2010. This implies, at least largely if not all the employment

polarization of the lower tail can be explained by the increase of the labor share growth in

constant occupations. The data also shows that, very possibly at 1990, there is a shift in the

wage ranking. The “out” occupations moves out of the first quintile and “in” occupations

20In Autor and Dorn (2013), they take the consideration the difference between private service occupationsand service sector which also includes high income occupations such as financial and real estate service. Here,service sector only includes low-skill private service occupations.


Table 2.1: Labor Share Change and Wage Growth in Each Occupation Group

Year Constant Occupations “Out” Occupations “In” OccupationsEmployment Log hourly Employment Log hourly Employment Log hourly

Share wage Share wage Share wage1980 0.126 2.329 0.072 2.526 0.041 2.6331990 0.125 2.338 0.062 2.567 0.038 2.6222000 0.131 2.493 0.058 2.790 0.036 2.6432010 0.149 2.408 0.055 2.811 0.038 2.537Source: Census 5 percent samples for 1980, 1990, 2000; American Community Survey,5-years, 2010

drop into the first quintile. This can partially explain the lower tail of wage change becomes

flatter if I allow the wage ranking to change if Figure 2.3. The table also shows that some

important change in wage and employment share may be concealed if I take all the low-skill

occupations as a whole group. Instead, by dividing occupations into groups, I could learn the

factors that affect each group differently.

As literature indicates, the labor market polarization is possibly induced by RBTC which

increases the productivity in the upper tail and the labor supply in the lower tail. So one

possible story behind these different wage and employment change patterns is that the fast

growth of high-skill occupations in both employment share and wage induce the demand of

low-skill occupations. These occupations may perform as private services and complementary

tasks to the high-skill occupations. Yet, with different labor supply to low-skill occupations,

the wage and employment growth may have different patterns in different occupations. Hence,

I need to look at the major causes of labor supply. In the literature, many occupational char-

acteristics that may influence labor supply and wage have been examined such as education,

experience, gender, marriage, and labor union. However, another two factors, immigrant

labor supply and part-time worker are somehow overlooked. In the rest of this part, I report

the statistics of these characteristics of each occupation group.

2.4.1 Immigration

The literature about immigrants usually focuses on two questions. The first one is how

immigrants impact on the job opportunities of natives and earlier immigrants. The second

one is how the wages of natives an earlier immigrants are affected by new immigrants. The

findings in the first questions is mixed. All focusing on the low-skill industries (occupations),

Altonji and Card (1991) and Card (2001, 2005) find no evidence that immigrants substitute


out the native workers but Borjas (2003)and Cortes (2008) find the substitution in different

levels. However, there is a consensus that the wages decrease in industries (occupations)

where immigrants concentrate. So I compare the immigration ratio in different occupation

group in the follow.

Table 2.2: Share of Immigration Working Hour s in Each Occupation Group

YearWhole Constant “Out” “In”

Economy Occupations Occupations Occupations1980 0.063 0.087 0.048 0.0781990 0.100 0.141 0.074 0.1492000 0.141 0.192 0.098 0.2212010 0.170 0.224 0.115 0.265Source: Census 5 percent samples for 1980, 1990, 2000;American Community Survey, 5-years, 2010.

Table 2.2 shows that the labor share of immigrants increases rapidly in constant and “in”

occupations and whole economy . Meanwhile, “out” occupations have relatively small increase

in immigration labor share. This implies that the immigrant labor supply could be one cause

in the difference in employment share change and wage growth. Immigrants asymmetrically

cluster in the constant and “in” occupations meanwhile labor supply to the “out” occupa-

tions does not increase much Therefore, wage growths in constant and “out” occupations

are hindered by the fast growth of labor supply but “out” occupations enjoy a fast wage growth.

However, the negative impact of immigration labor ratio on wage may not work only through

over-supply. If I could find evidence that wage is substantially different between natives and

immigrants controlling on occupations, the difference of wage growth can be caused by the

different immigration ratio growth.

Table 2.3 shows the difference of wage growth between immigrants and natives in each

occupation group. Though immigrants have a higher wage than natives at 1980, wage growth

of natives is substantially faster in constant occupations so that the wage of natives surpasses

the one of immigrants in 2010. In contrast, though the wage gap between natives and

immigrants are shrinking all the time, both cohorts present very similar wage growth rate in

the “out” group. The wage difference between natives and immigrants enlarges continually

among “in” occupations.

In sum, I find a substantial immigration ratio increase and lower wage growth in both constant

and “in” occupation. On the other hand, immigration ratio only increases mildly in “out”


Table 2.3: Wage Differences Between Immigrants and Natives

Year Constant Occupations “Out” Occupations “In” Occupations

1980 Immigrants 2.377 2.578 2.603Natives 2.324 2.524 2.636

1990Immigrants 2.366 2.68 2.501

Natives 2.339 2.618 2.579

2000Immigrants 2.499 2.830 2.575

Natives 2.491 2.786 2.663

2010 Immigrants 2.396 2.839 2.458Natives 2.413 2.807 2.568

Source: Census 5 percent samples for 1980, 1990, 2000; American CommunitySurvey, 5-years, 2010.

occupations and the wage growth rate are almost the same between immigrants and natives

cohorts. Together with the fact that constant and “in” occupations have lower wage growth

rate than “out” occupations, I hypothesize that both the increase of immigrant labor supply

and the worsened immigrants wage condition hinders the wage growth of constant occupations.

2.4.2 Part-Time Jobs

Part-time jobs are also overlooked in the literature. As how RBTC predicts, labors are

substituted out by technologies from routine tasks. One question left is where these labors

can find a job. Part-time jobs may not be ideal but could be the easiest ones that they could

find. In this sense, the labor supply to part-time jobs is larger than to full-time jobs. The

literature also shows that part-time jobs have a disadvantaged wage condition comparing

to full-time jobs (Aaronson and French, 2004; Hirsch, 2005), especially the involuntary

part-time jobs (Barrett and Doiron, 2001).

Table 2.4: Labor Share of Part-time Working Hours in Each Occupation Group

Year Constant Occupations “Out” Occupations “In” Occupations1980 0.338 0.177 0.1871990 0.327 0.178 0.1892000 0.300 0.166 0.1822010 0.318 0.167 0.199Source: Census 5 percent samples for 1980, 1990, 2000; AmericanCommunity Survey, 5-years for 2010.


Table 2.4 shows that the part-time labor shares of each occupation group are very stable

over years but constant occupations present a significant higher level than the other two

occupation groups. Next I am going to check if there is a wage differential between part-time

jobs and full-time jobs.

Table 2.5: Part/Full-Time Wage in Each Occupation Group

Year Constant Occupations “Out” Occupations “In” Occupations

1980 Part-time 2.314 2.5 2.543Full-time 2.337 2.532 2.652

1990Part-time 2.283 2.565 2.425Full-time 2.364 2.634 2.597

2000Part-time 2.483 2.767 2.601Full-time 2.497 2.795 2.652

2010 Part-time 2.328 2.702 2.389Full-time 2.444 2.831 2.571

Source: Census 5 percent samples for 1980, 1990, 2000; American CommunitySurvey, 5-years for 2010

Table 2.5 shows that the pattern of wage change is not uniform. One prominent observation is

in 2000, the year in which most pronounced wage polarization happened, part-time wages have

a jump in all three occupation groups. For the other years, part-time jobs’ wages are almost

stable over time while full-time jobs has a substantial wage growth in constant occupations.

In “out” occupations, full-time jobs always enjoy a faster wage growth than part-time jobs but

the gap of wage growth is not as pronounced as in the constant occupations. For the “in” oc-

cupation group, I observe a faster wage decrease in the part-time jobs than in the full-time jobs.

Evidence from the part-time working hours ratio change is not clear cut. The ratio does

not change much over time. However, when compare the constant occupations to “out”

occupations, both higher part-time ratio and lower wage growth in constant occupations

hinders the wage growth.

I need some outside evidence from literature to analyze the employment share effect. The

literature shows that labors are released from the routine-intensive occupations and moves

to the low-skill occupations. However, most of them have to move to the occupations with

slower wage growth. This is due to the easiness to find a job which means they move to these

occupations involuntarily. Similar to the story of immigrant ratio, higher part-time ratio in

constant occupations drag down the wage growth through two channels, the first one is larger

labor supply to these occupations and the other is worsened wage conditions of part-time jobs.


2.4.3 Gender Composition

Existence of gender wage inequality or gender wage gap is another stylized fact in wage

study (Blau and Kahn, 1997; Card and DiNardo, 2002; Petersen and Morgan, 1995). In

Blau and Kahn (1997), they also shows that gender wage gap is more severe in high-skill

occupations than the middle-skill and low-skill ones. Potentially, gender composition change

could be another reason for differential wage change in different occupation groups.

Table 2.6: Labor Share of Female Labor in Each Occupation Group

Year Constant Occupations “Out” Occupations “In” Occupations1980 0.749 0.889 0.2281990 0.708 0.860 0.2822000 0.696 0.809 0.3032010 0.686 0.792 0.304Source: census 5 percent samples for 1980, 1990, 2000; American CommunitySurvey, 5-years for 2010.

Table 2.7: Gender Wage Gap in Each Occupation Group

Year Constant Occupations “Out” Occupations “In” Occupations1980 0.235 0.132 0.2891990 0.176 0.072 0.2312000 0.111 0.110 0.1652010 0.076 0.098 0.167Source: census 5 percent samples for 1980, 1990, 2000; AmericanCommunity Survey, 5-years for 2010.

Table 2.8: Gender Wage Change in Each Occupation Group

Year Constant Occupations “out” Occupations “In” OccupationsFemale Male Female Male Female Male

1980-1990 0.018 -0.041 0.101 0.173 -0.008 -0.0661990-2000 0.193 0.069 0.257 0.235 0.122 -0.0022000-2010 0.119 -0.040 0.279 0.245 0.013 -0.109Source: Census 5 percent samples for 1980, 1990, 2000; AmericanCommunity Survey, 5-years for 2010.


Tables 2.6, 2.7, 2.8 show how the gender composition affects the different wage performance

in each occupation group. In general, the gender wage gap is shrinking in all three occupation

groups. Both in constant and “out” groups, decreasing female labor share is observed. Howev-

er, in constant group, female labors enjoy wage increase over time, but male labors’ wage has

a decreasing trend. In “out” group, both genders have positive wage growth and the increase

levels are similar between genders. Therefore, partially, the inferior wage performance in

constant group to “out” group can be explained by the rising share of male labors and their

decreasing wage trend in constant group. In the case of “in” group, I observe an increase

of female labor share, however, though female labors enjoy wage growth in general, the

magnitude is not big enough to compensate the wage decrease in male labor and a decrease

in average age was induced.

2.4.4 Marriage Wage Premium

Marriage is also found to be an important factor influencing wage in the literature and usually

married men enjoy higher wages than unmarried men (Antonovics and Town, 2004; Hersch

and Stratton, 2000; Korenman and Neumark, 1991). As shown above, gender composition

has changed significantly and more male labors are working in constant and “out” occu-

pations, I want to see if marriage wage premium work interactively with gender composition.21

Table 2.9: Married Ratios by Gender in Each Occupation Group

Constant Occupations “Out” Occupations “In” OccupationsYear Male Female Male Female Male Female1980 0.455 0.545 0.639 0.577 0.636 0.5611990 0.406 0.505 0.613 0.583 0.574 0.5362000 0.412 0.483 0.609 0.553 0.527 0.5132010 0.359 0.422 0.588 0.562 0.478 0.465Source: census 5 percent samples for 1980, 1990, 2000; AmericanCommunity Survey, 5-years for 2010.

Table 2.9, 2.10, 2.11 show how marriage premium affects wage growth. Consistent with

the literature, marriage premium is more substantial in male labors than in female labors.

However, there is an increasing trend in female marriage premium in both constant and “out”

groups. One interesting observation is the marriage ratio in constant and “in” group drop

21I did not control other variate when calculating the marriage wage premium. Marriage labor is definedas married with spouse present and married with spouse absent in census data and ACS data.


Table 2.10: Marriage Wage Premium by Gender in Each Occupation Group

Constant Occupations “Out” Occupations “In” OccupationsYear Male Female Male Female Male Female1980 0.281 0.046 0.288 0.002 0.376 0.1081990 0.297 0.069 0.259 0.010 0.286 0.0422000 0.252 0.050 0.261 0.018 0.239 0.0112010 0.256 0.118 0.282 0.047 0.242 0.059Source: census 5 percent samples for 1980, 1990, 2000; AmericanCommunity Survey, 5-years for 2010.

Table 2.11: Wage Change in Each Occupation Group by Marital Status

Panel A: Wage Change in Male Workers by Marital StatusConstant Occupations “Out” Occupations “In” Occupations

Year Married Unmarried Married Unmarried Married Unmarried1980-1990 -0.019 -0.035 0.040 0.069 -0.049 0.0411990-2000 0.066 0.095 0.235 0.262 0.009 0.1462000-2010 -0.027 -0.002 0.258 0.264 -0.085 0.049

Panel B: Wage Change in Female Workers by Marital StatusConstant Occupations “Out” Occupations “In” Occupations

Year Married Unmarried Married Unmarried Married Unmarried1980-1990 0.03 0.007 0.104 0.096 -0.086 -0.021990-2000 0.197 0.193 0.264 0.248 0.030 0.1272000-2010 0.164 0.092 0.298 0.253 -0.053 -0.004Source: census 5 percent samples for 1980, 1990, 2000; American CommunitySurvey, 5-years for 2010.


sharply compared to “out” group. The studies in marriage wage premium could provide some

inspiring thoughts on the wage differential. What may cause the marriage ratio difference

may be also correlated with wage rate difference.

Comparing constant with “out” occupations, though there are more male labors in both

groups, wages of male labors decrease both in married and unmarried cohorts in constant

group but increase in “out” group. Another interesting trend is in unmarried male labor

in “in” group. It is the only cohort enjoy constant wage increase in “in” group. However,

the increasing share of unmarried man does not reverse the decreasing trend of wage in “in”

group. Therefore, though there is a substantial difference in the marriage ratio, the effect of

marriage on wage is not very likely through the channel of marriage wage premium.

2.4.5 Education

College wage premium is another well-documented fact in the literature. Very often used

as an proxy of skill, education level is highly correlated with wage rate. Here, I check the

non-college labor working hours ratio and wage condition of non-college labors.22

Table 2.12: Working Hours Share of Non-college Educated in Each Occupation Group

Constant Occupations “Out” Occupations “In” Occupations1980 0.793 0.621 0.8451990 0.657 0.422 0.7452000 0.698 0.463 0.7942010 0.630 0.412 0.757Source: census 5 percent samples for 1980, 1990, 2000; AmericanCommunity Survey, 5-years for 2010.

Table 2.13 shows that the college premium is pervasive among all the occupation groups and

there is a decreasing trend of non-college labor ratio in all three occupation groups. Starting

with the lowest level, “out” occupations have the most substantial drop in non-college labor

ratio. In contrast, “in” occupations have the most mild fall in non-college labor ratio though

the level was the highest in this group. Constant occupations are in the middle both in term

of level and change of the ratio. Though the grouping standard is simple, the pattern still

shows an possible twist in education-wage relation. With more than 20% higher ratio of

22In the sample of this paper, non-college labor is defined as people who have education levels not higherthan 12 grades, namely the high school graduates and high school drop-outs.


Table 2.13: College/Non-college Average Wage in Each Occupation Group

Constant Occupations “Out” Occupations “In” OccupationsYear Non-College College Non-College College Non-College College1980 2.304 2.422 2.507 2.557 2.624 2.6921990 2.286 2.429 2.580 2.652 2.536 2.6572000 2.447 2.592 2.731 2.839 2.615 2.7522010 2.341 2.513 2.744 2.856 2.501 2.643Source: census 5 percent samples for 1980, 1990, 2000; American CommunitySurvey, 5-years for 2010

non-college labor, “in” group had substantial higher wage than “out” group in 1980. Though

this twist is solved as time passes, there is still a similar twist if I compare constant group and

“in” group in 2010. I have two possible explanation for this phenomenon. First, occupations

alone cannot be enough to represent to skill (wage) level, rising and falling of industries also

impact the wage rate. The reverse education wage premium may be caused by a relative

higher wage rate in some industries where these occupations concentrate. The other reason

may be the change of returns to different tasks and tasks composition in the occupations

that are as what RBTC indicates. In this sense, it is worthy to investigate the change in the

task composition of each occupation.

I also check the impact of labor union and potential experience on wage. I could not

find any substantial difference in these characteristics. The status of labor union is not

reported in census data and ACS data. I switch to CPS data to check the labor union

coverage. There is a decreasing trend in all occupation groups but the coverage is small. In

addition, the data size of CPS does not allow me to implement regional analysis. I report the

statistics of these two variables in section 2.7.3

In sum, I find that many occupational characteristics present different changing trend in

different occupation groups. Immigration working hours are growing faster in constant group

than “in” or “out” groups. There is also a worsening wage condition for immigrants. These

two factors both imply a higher wage growth rate in “out” occupations. Higher part-time

job working hours ratio and enlarging wage gap between full-time jobs and part-time jobs

also hinder the wage growth in constant and “in” occupations. Gender wage differential also

plays a role. I observe a shrinking female labor shares in both constant and “out” groups.

Yet, male wage in constant group is decreasing meanwhile their counterparts in “out” group

enjoy almost the same wage growth as the one of female labors. The evidence from marriage

wage premium and education is mixing. However, I observe a sharp fall of marriage ratio in

constant and “in” groups. I speculate that the factors affecting marriage may also play a role


in the wage and employment share change in each occupation group. There is also a reverse

college wage premium which indicate suggest to seek explanations in industry and task change.

I hypothesis that immigration ratio would negatively correlated with the wage growth

and positively correlated with employment share in constant and “in” group, but the impact

on “out” group is not clear. The effect on part-time jobs ratio on wage growth is supposed to

be the same as immigration ratio. However, there is no substantial part-time job ratio change

in aggregate level, the effect on labor share change is not a clear cut. As I hypothesized,

higher part-time ratio may absorb more released labor from routine-intensive occupations,

the correlation between part-time ratio and employment share change is possibly positive.

The gender effect on wage growth should be positive, which means higher female labor ratio

should correlated with higher wage growth rate. Higher female ratio should be correlated with

lower employment share change. As discussed above, the effect of marriage and education is

not clear.

2.5 Empirical Analysis

2.5.1 Commuting Zones

I utilize the regional variation among commuting zones to test the hypotheses from last part.

The concept of commuting zones is introduced in Tolbert and Sizer (1996) and becomes

more frequently used in the research of local labor markets. Compared to other economic

statistic systems such MSAs or PUMAs, commuting zones have three advantages. First,

commuting zones are created based on the journey-to-work data from 1990 Census so this

system can better reflect the economic connection in each zone. Second, one commuting zone

is a combination of one or more counties. This allows researcher to construct a consistently

defined economic statistic area even for the years before 1990. Third, commuting zones

are either not restricted by the population as in PUMA nor by the center city status as in

MSAs. This feature allows commuting zones to cover entire U.S. including the rural areas.

This increased the sample size of this paper by 2 times comparing with using MSAs. The

commuting zones update every 10 years. In this paper, I adopt the system of 1990. 741

commuting zones are consistently defined. One drawback of commuting zone system is since

they do not control on population, there are many commuting zones with small populations.

In this sample, 363 commuting zones have a population less than 100,000. 243 commuting

zones have a population higher than 200,000. So in the empirical analysis, I only report the

result based on the commuting zones having large populations (higher than 200,000). The


results for all commuting zones are reported in the section 2.7.2.

First, I plot wage and employment share change distribution for each commuting zone

as what I did to the whole economy. I observe different polarization patterns. Following

is some examples of different wage and employment polarization. Figure 2.6 shows some

representative commuting zones that have difference polarization levels.

In commuting zone 85, neither wage nor employment presents a substantial polarization.

In commuting zone 193, there is a pronounced wage polarization and a mild employment

polarization. In commuting zone 218, the employment polarization is substantial but not the

wage polarization. However, I could find a common trend in three commuting zones, that is,

as the employment share of lower tail increases, the wage decreases simultaneously.

2.5.2 Empirical Results

Based on the hypotheses from data statistics, if I control the other variables that affect

average wage, such like marriage, education and experience, in the commuting zones where

higher lower tail wage polarization occurs, I would see lower immigration level and part-time

job ratio. In contrast, areas that have higher employment growth polarization should have

higher level of immigration growth and part-time growth. However, since I see the part-time

ratio in the whole economy is very stable over time, the effect of part-time ratio change on

employment share growth could be limited (only through the change of occupation group

composition) but the one on wage growth could work both through composition change and

deteriorating part-time wage condition. The areas with higher female labor share would

experience higher wage increase. The estimation are based on the following two regressions.

dhourlywageijt = αjt + ΓX ′ijt + µjit, (2.1)

dlaborshareijt = βjt + ΘX ′ijt + εjit, (2.2)

i is the index of commuting zones, j is the index of occupation groups, t is the index of time.

Because I take first order difference, t = 1990, 2000, 2010. Since polarization is the result of

an accumulative change from 1980, all variables take difference between the level in 1980 and


6a 6b

6c 6d

6e 6f

Figure 2.6: Representative Commuting Zones with Different Polarization Pattern


in observation year. I check change of each control variable on wage growth and employment

share change.

dhoulywage is the wage growth. dlaborshare is the employment share change. In the

control variable set X, I include variables that potentially may influence the wage growth and

employment share changes. dimmigration is the change in immigration working hours ratio;

dparttime is the change in part-time working hours ratio; dfemale is the the change in female

working hours ratio; dnoncollege is the change in non-college educated labor working hours

ratio; dexperience is change in average experience weighted by working hours; dmarriage is

the change of married labor working hours ratio. In addition, I include dlaborshare˙top which

is the employment share change in the fifth quintile and dhourlywage˙top which is the wage

growth in the fifth quintile in the control variable set in order to check the demand side effect.23

Since I am using panel data, choosing between fixed-effect model and random-effect model is

a question I could not avoid. Table 2.14 shows the results. Here, I choose fixed-effect model

for two reasons. First, I check the regional variation24 in employment share changes and wage

changes. I could not find any concentration trend of these occupation groups. One possible

reason is the occupation concentration may happen within each region but I could not verify

this hypothesis without further detailed data. I report the regional statistics in section 2.7.4.

Second, I also use tests to compare random-effect model and fixed-effect model and the

tests suggest fixed-effect model over random-effect model. The comparison is reported in

section 2.7.2. The reason to add year dummy variables is that I observe uniformly significant

wage increase after I control any characteristics. There is potentially a wage shock on that year.

In wage growth equation, the effects of occupation characteristics have a substantial difference

between each occupation group. As predicted, immigration ration has a significant negative

impact on wage growth of constant occupations both due to the growth of immigration ratio

and inferior wage rate of immigrants to natives. Immigration ratio has insignificant effect

on wages of “out” group. This is because though immigration ratio grows mildly over time,

the wage differential between natives and immigrants is almost constant and immigrants

even enjoy a higher wage rate. However, the constant group has another story. The case of

“in” group is out of expectation, the effect of immigration ratio on wage growth is positive

and insignificant, however, in other model specifications, and the result is consistent with

predication as significantly negative.

The results for part-time job ratio is more consistent with the prediction. Wage growth rates

23See Cortes and Tessada (2011) and Mazzolari and Ragusa (2013) for evidence of spillover effect24I use the regional division reported in census and ACS data where me is divided into 9 regions.


Table 2.14: Correlations with Changes in the Occupation Characteristics

dhourlywage dlaborshareConstant “Out” “In” Constant “Out” “In”

Occs Occs Occs Occs Occs Occs

dimmigration-0.136** -0.150 0.028 0.021* -0.037*** 0.022***(0.068) (0.100) (0.067) (0.012) (0.011) (0.003)

dparttime-0.552*** -0.146 -0.289*** -0.074*** -0.038*** -0.020***(0.089) (0.093) (0.108) (0.016) (0.010) (0.026)

dfemale-0.427*** -0.150** -0.143* -0.011 0.002 -0.011**(0.099) (0.066) (0.086) (0.018) (0.007) (0.005)

dmarriage0.068 -0.027 0.008 0.041*** -0.015** -0.008*

(0.082) (0.056) (0.081) (0.015) (0.006) (0.004)

dnoncollege-0.028 0.005 -0.097 0.037*** 0.007 -0.005(0.074) (0.045) (0.084) (0.013) (0.005) (0.004)

dexperience0.001 -0.002 0.002 0.001** 0.001*** 0.000

(0.002) (0.001) (0.002) (0.000) (0.000) (0.000)

dlaborshare top0.589** 0.464** 0.296 -0.245*** -0.098*** -0.105***(0.193) (0.182) (0.291) (0.034) (0.020) (0.015)

dhourlywage top0.080*** 0.137*** 0.163** -0.010 -0.019*** 0.003(0.045) (0.040) (0.066) (0.008) (0.004) (0.003)

Year20000.140*** 0.145*** 0.059*** 0.002 -0.002** -0.004***(0.010) (0.008) (0.015) (0.002) (0.001) (0.001)

Year20100.062*** 0.100*** -0.048** 0.026*** -0.002 -0.004***(0.015) (0.011) (0.022) (0.003) (0.001) (0.001)

R2 0.579 0.732 0.299 0.435 0.422 0.254The dependent variables are wage growth and employment share change in each occupation group.

Fixed-Effect Model with Time Effect is employed. 729 observations consist 243 large commuting

zones with population higher than 200,000 by three time periods, 1980-1990, 1980-2000, 1980-2010.

*** Significant at the 1 percent level.

** Significant at the 5 percent level.

* Significant at the 10 percent level.


in both constant group and “in” group are negatively correlated with the part-time job ratio

due to the worsened wage condition of part-time jobs. In contrast, since the part-time job

ratio is low and continuously decreasing, there is no significant impact of part-time job ratio

on wage growth of “out” occupations.

The results of female labor ratio impact is uniformly negative for all three occupation

groups. This result makes more sense in “out” group. Since wage rates grow at almost same

speed for both genders, higher female labor ratio will hinder the wage growth rate due to

the gender wage gap remaining over time. However, the result is not consistent with the

expectation from last section. I see wage increase of female labors and wage decrease of male

labors. Meanwhile the hypothesis is higher female labor ratio would be probably correlated

with a higher wage growth. Yet, this result does not totally reject the hypothesis. Since in

empirical analysis, I look at the variation within one commuting zones, higher female labor

share may be correlated with higher female labor supply that possibly negatively impact on

the wage growth. This also implies that checking the industry change is necessary because

this may influence on the labor supply significantly.

The wage spillover effect in the literature is also shown in the results. The wage growth

rate employment share growth in high-skill occupations are positively correlated with the

counterparts in all three occupation groups. The only exemption is effect of the high-skill

employment share growth on wage growth of “in” occupations. The sign is positive but it is

not significant. Yet, with other model specification, the sign is significantly positive.

In the employment share change equation, immigration ratio is positively correlated with

employment share growth of constant group and “in” group and is negatively correlated

with “out” group. This is consistent with the facts in section 4 where the expansion of

constant group is associated with an substantial increase in immigration ratio meanwhile

as the immigration grows mildly, the employment share of “out” decreases. The effect of

part-time job ratio is not consistent with intuition. The statistics in section 4 shows that

the higher part-time ratio is, the higher employment share change is. However, the data

also shows that part-time job ratio does not vary much over time in any of three occupation

groups. Within a commuting zone, the change of part-time job ratio may reflect the rising

or falling of an occupation. Marriage ratio is another factor that is significant over three

occupation groups and the effects are not identical over groups. Higher marriage ratio is

positively correlated with employment share change in constant occupations but is negatively

correlated with “in” and “out” occupations.

The spillover effect is not found in the employment share change equation, instead, in-


creasing employment share in high-skill occupations is correlated with decreasing employment

share.

2.6 Conclusion and Future Research Plan

In this paper, I check the lower tail of occupation skill (wage) ranking. I find, not quite

consistent with the literature, occupations in the lower tail do not change uniformly in the

wage or employment share. Some occupations are with mild wage growth but substantial em-

ployment expansion and the others have significant wage growth but a shrinking employment

share. In addition, if I allow the wage ranking to change over time, the wage polarization

documented in the literature is weakened significantly. These findings lead me to investigate

possible occupational characteristics that may be correlated with labor market performance.

Grouping the occupations by the change of their wage ranking, I find many occupational

characteristics vary substantially from group to group, namely the change of immigration ratio,

part-time job ratio, female labor ratio and ratio of married. Moreover, these characteristics

are highly correlated with labor supply. In addition, in each characteristics, I find the wage

differential between different cohorts. Therefore, these characteristics may influence both on

employment share change and wage growth.

Education level is another characteristic that is significantly vary between occupations

groups. In addition, a reverse education wage premium exists between different groups in

different time. Related to the literature, this phenomenon may correlated with the rising and

falling of industries and tasks change of each occupation.

Utilizing the regional variation among commuting zones, I create a panel data set to test the

hypotheses that these characteristics should have different effects on different occupations.

The results partially support these hypotheses. I find that increase in immigration ratio

change is correlated with lower wage growth in the constant group but not in the other two

groups. Part-time ratio increase and female labor ratio is correlated with lower wage growth

in all three occupations.

As to the employment share change, immigration ration has a positive effect on the constant

group but has a negative effect on the “out” group. The evidence for “in” group is inconclusive.

I also check how the growth of wage and employment share in the high-skill occupations


impact the wage and employment change in the low-skill occupations. I find both the wage

growth and employment share increase are positively correlated with the wage growth in

low-skill occupations. The magnitude of each variable varies from group to group. According

to the literature, this is possibly due to the level of complementarity of each group with the

high-skill occupations.

In sum, I find that the well-documented labor market polarization is weakened if I allow the

wage ranking to change over time. In addition, the labor market performance in the occupa-

tions in the lower tail of wage ranking is not uniform. Occupational characteristics correlated

with labor supply such as Immigration ratio, part-time job ratio, female labor ratio and ratio

of married labors vary significantly between groups. Evidence from commuting zones shows

that these characteristics are correlated with different occupations in different direction and

magnitude. This result suggests that studying on labor supply to the occupations in the lower

tail of skill (wage) rankings is a possible way to explain the wage and employment polarization.

The result in this paper is preliminary. However, it provides me several direction to work on

in the future.

First, add to the labor supply differential, I will investigate on the task composition of

each occupation and occupation group. As what RBTC suggests, labors are substituted

out from routine-task jobs. Afterwards, their endowed skills (tasks) highly decide where

they could find new jobs. Skill (task) matching could affect the labor supply to different

occupation groups. This is possible cause on the wage and employment polarization.

Second, I will investigate the role of industry change on labor market polarization. In-

dustry change caused by factors other than technology, such as outsourcing may affect

occupations concentrate in certain industries heavily. The data shows the concentration in

some level. I also expect the industry change can help explain the reverse education wage

premium.

Third, I will also check the demand side. The result is not consistent with the litera-

ture on high-skill spillover effect. How to reconcile this result and to what extent the spillover

effect on different occupation groups are in what I are interested. In addition, the literature

also provide some other demand side factors to look at, such as caring of aged people and

substitutive housework of working high-skill females. Though these factors all demand

low-skill occupations in general, the actual affected occupations are very different. Along

with the labor supply, these demands are also possible reasons of labor market polarization.


2.7 Appendix

2.7.1 Occupation Groups in the First Quintile of Skill Ranking

Table 2.15: List of “Out” Occupations

OCC Rank RankSector Occupation Concept

1990dd 1980 1990176 38 129 1 Clergy and religious workers177 15 164 1 Welfare service workers207 52 162 1 Licensed practical nurses313 55 114 2 Secretaries and stenographers315 29 87 2 Typists316 48 117 2 Interviewers, enumerators, and surveyors335 43 68 2 File clerks347 49 64 2 Office machine operators, n.e.c.357 42 145 2 Messengers385 53 72 2 Data entry keyers425 40 65 3 Crossing guards445 19 79 3 Dental Assistants459 50 108 3 Recreation facility attendants464 57 59 3 Baggage porters, bellhops and concierges658 51 58 4 Furniture/wood finishers, other prec. wood workers749 32 88 5 Miscellaniome textile machine operators765 35 144 5 Paper folding machine operators


Table 2.16: List of “In” Occupations


1990dd 1980 1990365 98 42 2 Stock and inventory clerks453 63 41 3 Janitors467 166 17 3 Motion picture projectionists686 155 49 4 Butchers and meat cutters687 65 25 4 Bakers727 75 43 5 Sawing machine operators and sawyers754 89 30 5 Packers, fillers, and wrappers764 119 54 5 Washing, cleaning, and pickling machine operators769 101 50 5 Slicing, cutting, crushing and grinding machine774 74 55 5 Photographic process workers809 62 44 6 Taxi cab drivers and chauffeurs865 60 37 6 Helpers, constructions866 72 31 6 Helpers, surveyors873 105 39 6 Production helpers878 80 46 6 Machine feeders and offbearers887 59 22 6 Vehicle washers and equipment cleaners

2.7.2 Empirical Results with Different Samples and Model Speci-

fications


Table 2.17: List of Constant Occupations


1990dd 1980 1990155 39 15 1 Kindergarten and earlier school teachers276 24 12 2 Cashiers317 8 10 2 Hotel clerks319 25 29 2 Receptionists and other information clerks329 21 21 2 Library assistants356 56 48 2 Mail clerks, outside of post office383 20 18 2 Bank tellers387 12 9 2 Teacher’s aides405 4 3 3 Housekeepers, maids, butlers, and cleaners408 10 11 3 Laundry and dry cleaning workers427 13 8 3 Protective service, n.e.c.433 47 47 3 Supervisors of food preparation and service434 18 16 3 Bartenders435 2 6 3 Waiters and waitresses436 5 4 3 Cooks439 7 7 3 Food preparation workers444 3 2 3 Miscellanious food preparation and service workers447 17 27 3 Health and nursing aides451 36 25 3 Gardeners and groundskeepers457 41 40 3 Barbers458 23 17 3 Hairdressers and cosmetologists461 45 45 3 Guides462 37 38 3 Ushers466 30 34 3 Recreation and fitness workers468 1 1 3 Child care workers469 27 23 3 Personal service occupations, n.e.c472 14 14 3 Animal caretakers, except farm666 28 28 4 Dressmakers, seamstresses, and tailors669 22 51 4 Shoemakers, other prec. apparel and fabric workers688 54 37 4 Batch food makers729 44 32 5 Nail, tacking, shaping and joining mach ops (wood)738 26 33 5 Winding and twisting textile and apparel operatives739 31 41 5 Knitters, loopers, and toppers textile operatives743 34 22 5 Textile cutting and dyeing machine operators744 6 5 5 Textile sewing machine operators745 9 13 5 Shoemaking machine operators747 11 19 5 Clothing pressing machine operators813 33 30 6 Parking lot attendants885 16 20 6 Garage and service station related occupations888 46 26 6 Packers and packagers by hand


Table 2.18: Correlations in All Commuting Zones, Constant Occupations

dhourlywage dlaborshare

FEFE time

RE FEFE time

REdummy dummy

dimmigration-0.312*** -0.086 -0.364*** 0.104*** 0.054*** 0.091***(0.067) (0.053) (0.048) (0.011) (0.011) (0.010)

dparttime-0.742*** -0.492*** -0.276*** -0.044*** -0.051*** -0.075***(0.067) (0.049) (0.054) (0.011) (0.010) (0.011)

dfemale-0.067 0.010 -0.050 -0.064*** -0.077*** -0.067***(0.076) (0.055) (0.063) (0.013) (0.012) (0.011)

dmarriage-0.002 0.022 0.154*** -0.038*** 0.011 -0.017**(0.049) (0.046) (0.041) (0.008) (0.010) (0.007)

dnoncollege0.718*** -0.081* 0.499*** -0.027*** 0.035*** -0.043***(0.047) (0.042) (0.039) (0.008) (0.009) (0.006)

dexperience0.004** -0.001 0.002** 0.001*** 0.001*** 0.001***(0.001) (0.001) (0.001) (0.000) (0.000) (0.000)

dlaborshare top0.619*** 0.527*** 0.119*** -0.328*** -0.304*** -0.351***(0.165) (0.121) (0.097) (0.027) (0.026) (0.022)

dhourlywage top0.503*** 0.133*** 0.594*** -0.009** -0.018** -0.006*(0.028) (0.024) (0.024) (0.004) (0.005) (0.004)

Year20000.154*** -0.002(0.006) (0.001)

Year20100.066*** 0.016***(0.006) (0.002)

R2 0.415 0.518 0.473 0.216 0.292 0.248N 2223 2223 2223 2223 2223 2223

Hausman test: chi2(8)= 319.88, Hausman test: chi2(7)= 400.23,

Prob¿chi2 = 0.000 Prob¿chi2 = 0.000

Time effect test: F(2,1472)=654.62, Time effect test: F(2,1472)=73.91,

Prob¿F=0.000 Prob¿F=0.000

BP test: chi2(1)=132.36, BP test: chi2(1)=793.40,

Prob¿chi2=0.000 Prob¿chi2=0.000

The dependent variables are wage growth and employment share change in constant occupation group.

2223 observations consist 741 commuting zones by three time periods, 1980-1990, 1980-2000, 1980-2010.





Table 2.19: Correlations in Large Zones, Constant Occupations


FEFE time

RE FEFE time

REeffect effect

dimmigration -0.580*** -0.136** -0.489*** 0.112*** 0.021* 0.083***(0.081) (0.068) (0.056) (0.013) (0.012) (0.013)

dparttime-0.734*** -0.552*** -0.414*** -0.071*** -0.074*** -0.090***(0.118) (0.089) (0.099) (0.019) (0.016) (0.020)

dfemale-0.798*** -0.427*** -0.454*** 0.032 -0.011 0.007(0.131) (0.099) (0.105) (0.021) (0.018) (0.022)

dmarriage0.175** 0.068 0.089 -0.077*** 0.041*** -0.061***(0.087) (0.082) (0.079) (0.014) (0.015) (0.016)

dnoncollege0.720*** -0.028 0.672*** -0.067*** 0.037*** -0.079***(0.083) (0.074) (0.073) (0.014) (0.013) (0.013)

dexperience0.007*** 0.001 0.006** 0.001*** 0.001** 0.001**(0.002) (0.002) (0.002) (0.000) (0.000) (0.001)

dlaborshare top0.612** 0.589** 0.469*** -0.245*** -0.245*** -0.343***(0.261) (0.193) (0.174) (0.027) (0.034) (0.037)

dhourlywage top0.499*** 0.080*** 0.503*** 0.003 -0.010 0.012(0.042) (0.045) (0.037) (0.007) (0.008) (0.008)

Year20000.140*** 0.002(0.010) (0.002)

Year20100.062*** 0.026***(0.015) (0.003)

R2 0.47 0.579 0.5 0.2 0.435 0.253N 729 729 729 729 729 729

Hausman test: chi2(8)=324.72, Hausman test: chi2(8)=26.91,



Prob ¿ F = 0.000 Prob¿F=0.000



The dependent variables are wage growth and employment share change in constant occupation group.

729 observations consist 243 Large commuting zones (with population higher than 200,000) by three

time periods, 1980-1990, 1980-2000, 1980-2010.





Table 2.20: Correlations in All Commuting Zones, “Out” Occupations


FEFE time

RE FEFE time

REeffect effect

dimmigration -0.084 -0.209** -0.064 -0.026*** -0.032*** -0.055***(0.108) (0.093) (0.070) (0.009) (0.009) (0.009)

dparttime-0.289*** -0.143** -0.354*** -0.044*** -0.041*** -0.057***(0.066) (0.056) (0.063) (0.005) (0.005) (0.005)

dfemale-0.309*** 0.117*** -0.295*** -0.005 -0.005 -0.007*(0.049) (0.043) (0.037) (0.004) (0.004) (0.004)

dmarriage-0.101*** 0.054 0.008 -0.031*** -0.028*** -0.020***(0.038) (0.034) (0.032) (0.003) (0.003) (0.003)

dnoncollege0.090*** -0.125*** 0.048** -0.003 0.005 -0.001(0.033) (0.033) (0.023) (0.003) (0.003) (0.002)

dexperience0.005*** -0.001 0.003*** 0 -0.0002** 0.0001*(0.001) (0.001) (0.001) (0.000) (0.0001) (0.0000)

dlaborshare top0.651*** 0.825*** 0.202** -0.076*** -0.072*** -0.111***(0.150) (0.128) (0.088) (0.012) (0.012) (0.012)

dhourlywage top0.689*** 0.229*** 0.667*** -0.009*** -0.015*** -0.005*(0.020) (0.026) (0.016) (0.002) (0.003) (0.002)

Year20000.141*** 0.001**(0.006) (0.001)

Year20100.167*** 0.002***(0.007) (0.001)

R2 0.627 0.689 0.638 0.189 0.194 0.258N 2223 2223 2223 2223 2223 2223


Prob¿chi2 = 0.000 Prob¿chi2 = 0.0000

Time effect test: F(2,1472)=288.40 Time effect test: F(2,1472)=6.81,

Prob¿F=0.000 Prob¿F=0.0011



The dependent variables are wage growth and employment share change in “out” occupation group.






Table 2.21: Correlations in Large Zones, “Out” Occupations


FEFE time

RE FEFE time

REeffect effect

dimmigration-0.178 -0.15 -0.11 -0.033** -0.037*** -0.047***

(0.119) (0.100) (0.079) (0.011) (0.011) (0.011)

dparttime-0.278** -0.146 -0.244** -0.037*** -0.038*** -0.041***(0.114) (0.093) (0.113) (0.010) (0.010) (0.010)

dfemale-0.458*** -0.150** -0.296*** 0.007 0.002 -0.004(0.078) (0.066) (0.061) (0.007) (0.007) (0.028)

dmarriage-0.080 -0.027 0.053 -0.016*** -0.015** -0.006

(0.066) (0.056) (0.050) (0.006) (0.006) (0.005)

dnoncollege0.193*** 0.005 0.132*** 0.001 0.007 0.001(0.046) (0.045) (0.046) (0.004) (0.005) (0.004)

dexperience0.009*** -0.002 0.008** 0.001*** 0.001*** 0.001***(0.001) (0.001) (0.001) (0.000) (0.000) (0.000)

dlaborshare top0.443** 0.464** 0.353** -0.097*** -0.098*** -0.130***(0.223) (0.182) (0.144) (0.020) (0.020) (0.016)

dhourlywage top0.550*** 0.137*** 0.595*** -0.025*** -0.019*** -0.025***(0.035) (0.040) (0.031) (0.003) (0.004) (0.003)

Year20000.145*** -0.002**(0.008) (0.001)

Year20100.100*** -0.002(0.011) (0.001)

R2 0.739 0.732 0.753 0.42 0.422 0.453N 729 729 729 729 729 729




Prob¿F=0.000 Prob¿F=0.0428

BP test: chi2[1]=147.61, BP test:chi2[1]=339.04,

Prob¿chi2 = 0.0000 Prob¿chi2=0.0000

The dependent variables are wage growth and employment share change in “out” occupation group.


time periods, 1980-1990, 1980-2000, 1980-2010.





Table 2.22: Correlations in All Commuting Zones, “In” Occupations


FEFE time

RE FEFE time

REeffect effect

dimmigration-0.498*** -0.375*** -0.476*** 0.051*** 0.054*** 0.055***(0.042) (0.041) (0.054) (0.003) (0.003) (0.005)

dparttime-0.593*** -0.276*** -0.528*** -0.039*** -0.047*** -0.036***(0.067) (0.063) (0.063) (0.004) (0.004) (0.004)

dfemale0.047 -0.063 -0.022 -0.011*** -0.006 -0.009***(0.056) (0.051) (0.050) (0.003) (0.003) (0.003)

dmarriage0.089** -0.043 0.214*** -0.014*** -0.022*** -0.012***(0.045) (0.048) (0.042) (0.003) (0.003) (0.003)

dnoncollege0.338*** -0.053 0.271*** -0.018*** -0.009*** -0.008(0.049) (0.049) (0.046) (0.003) (0.003) (0.003)

dexperience-0.005*** -0.002 -0.004*** 0.000 0.000 0.0002***(0.002) (0.001) (0.001) (0.000) (0.000) (0.0000)

dlaborshare top0.347 0.332* 0.189 -0.102*** -0.112*** -0.148***(0.214) (0.195) (0.147) (0.013) (0.013) (0.012)

dhourlywage top0.331*** 0.250*** 0.441*** -0.021*** -0.007*** -0.019***(0.031) (0.040) (0.032) (0.002) (0.002) (0.002)

Year20000.069*** -0.005***(0.010) (0.001)

Year2010-0.022* -0.005***(0.013) (0.001)

R2 0.236 0.268 0.265 0.415 0.449 0.449N 2223 2223 2223 2223 2223 2223



Time effect test: F(2,476)=161.23, Time effect test: F(2,476)=40.45 ,

Prob¿F=0.000 Prob¿F=0.0000



The dependent variables are wage growth and employment share change in “in” occupation group.






Table 2.23: Correlations in Large Zones, “In” Occupations


FEFE time

RE FEFE time

REeffect effect

dimmigration-0.295*** 0.028 -0.318*** 0.024*** 0.022*** 0.023***(0.072) (0.067) (0.057) (0.003) (0.003) (0.003)

dparttime-0.729*** -0.289*** -0.673*** -0.014** -0.020*** -0.016***(0.122) (0.108) (0.111) (0.006) (0.026) (0.006)

dfemale0.086 -0.143* 0.138* -0.015*** -0.011** -0.010**(0.100) (0.086) (0.082) (0.005) (0.005) (0.005)

dmarriage0.297*** 0.008 0.386*** -0.002 -0.008* -0.001(0.076) (0.081) (0.068) (0.003) (0.004) (0.003)

dnoncollege0.517*** -0.097 0.389*** 0.015*** -0.005 0.012***(0.085) (0.084) (0.079) (0.004) (0.004) (0.004)

dexperience-0.003 0.002 -0.002 0.000 0.000 0.000(0.002) (0.002) (0.002) (0.000) (0.000) (0.000)

dlaborshare top0.561 0.296 0.610*** -0.107*** -0.105*** -0.098***

(0.344) (0.291) (0.225) (0.016) (0.015) (0.012)

dhourlywage top0.308*** 0.163** 0.387*** -0.011*** 0.003 -0.010***(0.052) (0.066) (0.047) (0.002) (0.003) (0.003)

Year20000.059*** -0.004***(0.015) (0.001)

Year2010-0.048** -0.004***(0.022) (0.001)

R2 0.256 0.299 0.284 0.237 0.254 0.247N 729 729 729 729 729 729




Prob¿F=0.000 Prob¿F=0.0000



The dependent variables are wage growth and employment share change in “in” occupation group.


time periods, 1980-1990, 1980-2000, 1980-2010.





2.7.3 Change of Labor Union Coverage

Table 2.24: Working Hours Ratio of Workers Covered by Labor Union in Each OccupationGroup

Year Constant Occupations “Out” Occupations “In” Occupations1990 0.026 0.026 0.0522000 0.021 0.025 0.0382010 0.01 0.017 0.022Data source: IPUMS-CPS ASEC samples. 3-years pooled data for 1990(1990-1992). 5 years pooled data for 2000 and 2010 (1998-2002, 2008-2012 respectively)

2.7.4 Regional Variation in Wage Growth and Employment Share

Change

Table 2.25: Employment Share of Each Occupation Group in Each Region

New England Middle Atlantic East North CentralYear Con Out In Con Out In Con Out In1980 0.119 0.072 0.037 0.128 0.08 0.041 0.111 0.067 0.0441990 0.114 0.059 0.031 0.123 0.07 0.038 0.115 0.061 0.0412000 0.125 0.054 0.030 0.137 0.063 0.037 0.120 0.054 0.0372010 0.143 0.053 0.032 0.154 0.061 0.04 0.141 0.054 0.039

West North Central South Atlantic East South CentralYear Con Out In Con Out In Con Out In1980 0.124 0.070 0.045 0.14 0.077 0.042 0.146 0.066 0.0441990 0.124 0.065 0.042 0.132 0.064 0.036 0.141 0.059 0.0412000 0.125 0.06 0.039 0.134 0.057 0.034 0.135 0.057 0.0392010 0.138 0.058 0.040 0.149 0.055 0.036 0.147 0.058 0.042

West South Central Mountain PacificYear Con Out In Con Out In Con Out In1980 0.122 0.072 0.039 0.126 0.07 0.039 0.126 0.068 0.0391990 0.128 0.064 0.040 0.134 0.063 0.037 0.123 0.056 0.0362000 0.132 0.059 0.037 0.136 0.058 0.034 0.134 0.052 0.0352010 0.148 0.055 0.04 0.154 0.055 0.036 0.156 0.052 0.038

Data source: 5% Census data for 1980, 1990, 2000. ACS five-years data for 2010


Tab

le2.

26:

Eac

hR

egio

n’s

Shar

eof

the

Whol

eP

opula

tion

inE

ach

Occ

upat

ion

Gro

up

New

Engl

and

Mid

dle

Atl

anti

cE

ast

Nor

thC

entr

alY

ear

Tot

alC

onO

ut

InT

otal

Con

Out

InT

otal

Con

Out

In19

800.

058

0.05

50.

058

0.05

20.

161

0.16

40.

178

0.15

90.

190

0.16

70.

176

0.20

219

900.

057

0.05

20.

054

0.04

70.

151

0.14

90.

168

0.14

90.

170

0.15

70.

167

0.18

320

000.

052

0.05

00.

049

0.04

40.

138

0.14

50.

153

0.14

20.

168

0.15

40.

159

0.17

520

100.

050

0.04

80.

048

0.04

10.

136

0.14

00.

149

0.14

10.

153

0.14

60.

149

0.15

7W

est

Nor

thC

entr

alSou

thA

tlan

tic

Eas

tSou

thC

entr

alY

ear

Tot

alC

onO

ut

InT

otal

Con

Out

InT

otal

Con

Out

In19

800.

074

0.07

30.

073

0.08

10.

164

0.18

20.

175

0.16

80.

060

0.06

90.

055

0.06

319

900.

070

0.06

90.

072

0.07

70.

183

0.19

30.

186

0.17

40.

058

0.06

50.

055

0.06

220

000.

071

0.06

80.

075

0.07

80.

189

0.19

40.

189

0.17

90.

059

0.06

00.

059

0.06

420

100.

070

0.06

50.

074

0.07

40.

194

0.19

50.

192

0.18

20.

057

0.05

60.

059

0.06

2W

est

Sou

thC

entr

alM

ounta

inP

acifi

cY

ear

Tot

alC

onO

ut

InT

otal

Con

Out

InT

otal

Con

Out

In19

800.

103

0.10

00.

103

0.09

60.

050.

050

0.04

90.

047

0.14

00.

140

0.13

30.

039

1990

0.10

20.

104

0.10

50.

106

0.05

30.

057

0.05

40.

052

0.15

60.

153

0.14

00.

036

2000

0.10

90.

110

0.11

20.

111

0.06

40.

066

0.06

50.

060

0.15

10.

154

0.13

80.

035

2010

0.11

60.

116

0.11

60.

122

0.07

0.07

30.

069

0.06

60.

154

0.16

10.

144

0.03

8D

ata

sourc

e:5%

Cen

sus

dat

afo

r19

80,

1990

,20

00.

AC

Sfive

-yea

rsdat

afo

r20

10


Table 2.27: Regional Average Wage of Each Occupation Group

New England Middle Atlantic East North CentralCon Out In Con Out In Con Out In

1980 2.316 2.491 2.577 2.400 2.58 2.669 2.364 2.547 2.7421990 2.508 2.722 2.677 2.467 2.727 2.68 2.303 2.587 2.6322000 2.602 2.885 2.736 2.574 2.890 2.727 2.482 2.771 2.6812010 2.545 2.919 2.655 2.497 2.895 2.619 2.379 2.779 2.568

West North Central South Atlantic East South CentralCon Out In Con Out In Con Out In

1980 2.261 2.439 2.636 2.284 2.503 2.521 2.212 2.433 2.5261990 2.231 2.500 2.528 2.316 2.597 2.488 2.178 2.478 2.4522000 2.427 2.683 2.621 2.481 2.764 2.609 2.405 2.671 2.5812010 2.343 2.710 2.531 2.388 2.797 2.492 2.277 2.700 2.468

West South Central Mountain PacificCon Out In Con Out In Con Out In

1980 2.235 2.474 2.511 2.308 2.487 2.615 2.422 2.605 2.7151990 2.189 2.534 2.407 2.266 2.542 2.476 2.426 2.723 2.6252000 2.369 2.701 2.519 2.469 2.751 2.609 2.555 2.883 2.6752010 2.280 2.725 2.411 2.413 2.784 2.514 2.486 2.901 2.578

Data source: 5% Census data for 1980, 1990, 2000. ACS five-years data for 2010

Chapter 3

Skill Requirements, Returns to Skills

and Gender Wage Gap

(ABSTRACT)

This paper investigates how gender differences in skills beyond education and experience can



Network (O*NET). Besides math and motor skills, we emphasize the differences between two

types of inter-personal skills: directness skills and caring skills. Directness skills are related to

decision-making, persuasion and negotiation, whereas caring skills are more about cooperation

and coordination. The main empirical finding is that female workers possess much higher

level of caring skills than male workers, and the returns to caring skills are significantly

negative but have increased over time, accounting for a major part of the persistent gender

wage gap and the narrowing gender wage gap from 1980 to 2015. Another significant portion

of the narrowed gender wage gap can be attributed to the faster growth in female workers

average directness skills relative to those of male workers and the fact that the returns to

directness skills are significantly positive and stable over time.

3.1 Introduction

Gender wage gap in the U.S. has narrowed significantly in recent decades. In 1980, female

workers earned 55% of male average hourly wage, but in 2015, the number increased to

47


79%.1 Most of the decline in the gender wage gap took place between 1980 to the mid 1990s

(figure 3.1), and the change has been small since then. The conventional explanations for

the narrowing gender wage gap include changes in human capital variables (Altonji and

Blank, 1999; Blau and Kahn, 2006) and changes in selection (Mulligan and Rubinstein,

2008). However, as shown in figure 3.2, gender differences in residual wages, after controlling

for education, experience and other demographic variables, remain substantial. To account

for the residual gender wage difference, explanations such as the role of bargaining and

competition, gender segregation, and working hours have been proposed, but there is still no

consensus on the determinants of the narrowing gender wage gap.2

A recent literature uses the task/skill3 approach to explain the residual difference. (Ba-

colod and Blum, 2010; Beaudry and Lewis, 2014; Black and Spitz-Oener, 2010; Borghans

et al., 2014; Yamaguchi, 2016). They use new micro data to measure gender differences in

skills conditional on the usual human capital variables. They find that growing returns to

cognitive and people skills and declining returns to motor skills can account for part of the

narrowing gender wage gap. In this line of research, people skills are typically defined as a

one-dimensional index that is supposed to measure all kinds of skills present in interactions

between people, such as cooperation, negotiation, directing, and persuasion. However, sub-

stantial gender difference exists in personalities and in men and women’s attitudes towards

people and towards competition, according to research in the psychology literature.4 Using a

one-dimension measurement of people skills may disguise important gender differences in skills.

In addition, different occupations may require different types of people skills. For example,

in occupations such as managers, lawyers, and university instructors, people skills are highly

appreciated. People skills are also highly valued in occupations such as social workers, nurses,

and school teachers. But these two occupations require different kinds of people skills, and

wages are also substantially different, the distinction between different people skills may be

important. In this paper, we follow Borghans et al. (2008b) and distinguish people skills

into directness skills and caring skills. Directness skills represent skills of decision-making,

negotiation, and persuasion. In contrast, caring skills are related to creating cooperation and

providing assistance.

1Data from March-CPS, 1980-2015. The sample includes civilian wage earners with age 18-65.2Babcock and Laschever (2003) and Gneezy et al. (2003) checks the role of bargaining and competition in

individual wage dermination. Bayard et al. (2003) focus on gender segregation in occupations. Goldin (2014)argues that the non-linear returns to working hours bring disadvantage to female labors because they workfor fewer hours.

3In the literature, skills and tasks are interchangeable. Autor et al. (2003) uses the term tasks for they usethe direct measurement of working content in DOT. Bacolod and Blum (2010) uses the term skills becauseeach skill measurement is a combination of several tasks. Here, we use the term skills.

4For an introduction of personalities and their application in economics, see Borghans et al. (2008a).


Figure 3.1: Gender Gap in Hourly Wage

Despite the rising consensus on the importance of skills in wage determination, datasets

recording both individual characteristics and workers’ skills are still rare, especially in the

U.S..5 In this paper, we merge micro data with skill measurements at occupational level.

Our individual characteristics are from March Current Population Survey (CPS) 1980-2015.

Our occupational skill measurements are from the Dictionary of Occupational Titles (DOT,

fourth edition in 1977, revised fourth edition in 1991), and the Occupational Information

Network 1998 (O*NET98). Though O*NET is the successor of DOT, a major revision on

the skill dimensions took place when O*NET launched. This revision leads many studies to

focus on one dataset or another. To account for the skill changes, we construct a crosswalk

between DOT and O*NET through text searching. Out of 44 skill measurements in DOT, 43

are successfully matched with the new measurements in O*NET. The details of the crosswalk

are recorded in session 3.

We classify skills into four categories: math skills, directness skill, caring skills, and motor

skills. Consistent with the existing research, we find fast growth of math skills for female

workers and persistent higher motor skills for male workers. Contrasting to the common

5Survey of Adult Skills (PIAAC) is an exception but the dataset is very limited in size and direct incomemeasurement is not available in public-use data.


Figure 3.2: Gender Differences in Education and Potential Experience

conclusion that females have higher people skills, we find that female have disadvantage in

directness skills initially. The disadvantage narrows and reverses as time goes by. Female

workers always posses much higher caring skills compared to male workers.

We employ a Mincerian wage equation including human capital variables and the skill

measurements to estimate the market returns to different skills. While math, directness,

and motor skills are positively rewarded in the labor market, caring skills’ market returns

are significantly negative. The negative returns of caring skills decreased (in absolute value)

over the years. With standard Oaxaca-Blinder decomposition, we find that the increasing

returns to the caring skills can account for 20.4% of the change in the gender wage gap. In

addition, 22.5% of the total change can be explained by the increasing directness skills of

female workers. These two driving forces in reducing the gender wage gap slowed down after

mid-1990s. The higher caring skills still hold female wages in disadvantage. In 2015, 41.5%

of the gender wage gap can be explained by the gender difference in caring skills.

The gender differences in directness skills can also explain the between-group differences found

in subgroups by education, race, marital status, and age. The low education group (without

college degrees) experienced faster decline in gender wage gap than the high education group


(with college degrees or above). This pattern can be largely attributed to the faster growth in

directness skills and in returns to caring skills among the low education group. In the black

group, unmarried group, and young group (age 25-34), females either have higher directness

skills or their differences with males are much smaller than in other groups. We find smaller

gender wage gaps in these groups.

This paper contributes to the literature in the following ways. First, we emphasize the

importance to distinguish directness skills and caring skills and show how these two skills

play different roles in explaining the gender wage gap. We show that the increasing returns

to caring skills and increasing level in directness skills for female workers account for the

major part of the gender wage gap decline. The recent slowdown of the declining gender gap

is due to the slowdown in the growth of female directness skills and returns to caring skills.

Second, we show that the higher level in caring skills actually is the main reason why female

wages are lower. Although the literature agrees on the importance of people skills and their

contribution to female wage growth, it is necessary to distinguish directness skills with caring

skills. Third, this paper documents a significant gender difference in caring skills, which can

be found in all groups regardless of education, race, marital status, and age. This leads to

future work to investigate the sources of the higher caring skills among women.

The rest of the paper is organized as follows: section 3.2 is the literature review on the

gender differences in wage and skills; section 3.3 provides detailed data description; section

3.4 presents the empirical model and results; section 3.5 concludes.

3.2 Literature Review

Gender wage difference attracts great attention in the literature as it narrows dramatically in

the 1980s but merely changes in the 1990s even though many human capital variables keep

changing. In their handbook chapter, Altonji and Blank (1999) summarize the researches

focusing on human capitals such as working experience and on job characteristics such as

job training and seniority. Mulligan and Rubinstein (2008) turn the scope to labor force

composition and argue that the selection of female labors into labor force turns from negative

to positive from 1970s to 1990s. The change in the selection increases the average skill

(proxied by IQ) and female wages. In contrast, Blau and Kahn (2006) argues that human

capitals cannot explain much of the gender wage gap change. Instead, the occupational

upgrading and deunionization contribute most in the narrowing of gender wage gap. But as

they conclude, much of the slowing convergence and the gender wage gap are attributed to


“unexplained” factors.

Several explanations have been proposed as the “unexplained” factors. Babcock and Laschever

(2003) and Gneezy et al. (2003) focus on the gender difference in the attitude towards bar-

gaining and competition. Females are often found less likely to bargain for the compensation.

Females also behave less aggressively in the competition especially when they compete with

males. Both behaviors could result in wage disadvantage for females.

Gender segregation is another well-documented reason in the literature. Bayard et al.

(2003) found that females are segregated “into low-paying occupations, industries, establish-

ments and occupations in establishments.” As a particular type of occupational segregation,

it is less likely for females to get promoted in the industry (Addison et al., 2014; Blau and

DeVaro, 2007; Cobb-Clark, 2001; McCue, 1996) or in the academics (Ceci et al., 2014;

Ginther and Kahn, 2014). Blau and Kahn (2017) have a section discussing the level and

change of gender segregation at large. Goldin (2014) and Erosa et al. (2017) highlight a

particular aspect of occupations, working hours, as the factor causing gender wage difference.

They find that instead of a constant number, hourly payment is increasing with the working

hours. Meanwhile, females are more likely to work in part-time jobs or in full-time jobs

without working overtime. In this sense, gender wage difference is not only found in yearly

earnings but also in hourly payments.

With many characteristics of occupations scrutinized, scholars turn their focus on the

individual difference and a growing literature is investigating the role of skills/tasks in the

gender wage difference. Though gender differences in skills/tasks are strongly correlated

with occupation difference, skills/tasks approach could still help us understanding the wage

difference by telling the importance of each skill/task. This approach starts with the work of

Autor et al. (2003) (hereafter ALM 2003). They explore the information of occupational skills

contained in the DOT and propose a “tasks” model. In this model, “tasks” are the inputs

of the production and rewarded the market returns. They distinguish tasks into two major

categories, routine tasks and non-routine tasks based on the programmable feasibility of each

task. As the model predicts, the skill-biased technology change (SBTC, computerization)

increases the return to non-routine tasks and lowers the one to the routine tasks. The

“tasks” model becomes a powerful tool in linking the wage change with the technology change.

Following this line, Beaudry and Lewis (2014) and Yamaguchi (2016) find that much of

the narrowing in gender wage difference could be explained by the increasing return to the

cognitive skills (loosely similar to the non-routine tasks in ALM 2003) and the decreasing

return to the motor skills (the routine tasks) caused by the SBTC.


To further account for the gender difference in skills, Bacolod and Blum (2010), Black

and Spitz-Oener (2010), Borghans et al. (2014), Deming (2017), and Spitz-Oener (2006)

introduce people skills (non-routine interactive tasks) into the skills/tasks approach. Deming

(2015) provides a model in which people skills and cognitive skills are complementary. The

SBTC will improve the returns to cognitive skills and to people skills. Using CPS and DOT

data, Bacolod and Blum (2010) show that the major part of narrowing in gender wage gap

could be explained by the increasing return to the cognitive and people skills from 1970

to 1990. Black and Spitz-Oener (2010) and Spitz-Oener (2006) use survey data from West

Germany in which they could observe the task composition in an occupation. They find that

female labors experience higher task shift toward to the non-routine analytic and non-routine

interactive tasks. This shift helps narrowing the gender wage gap.

Based on the gender difference in personality documented in the psychology literature,

latest development in the skills/tasks approach is the distinguishing of different types of

people skills. Borghans et al. (2008b) argue that two interpersonal styles, directness skills

and caring skills, could have different market returns due to their supply and importance in

the jobs. Employing survey data from Germany and Great Britain, they show that directness

skills bring positive return and caring skills bring negative return. Especially the ratio of

directness to caring has positive return. In the research about U.S labor market, Fortin (2008)

shows the gender difference in the attitude toward family/people and money/success in the

high school students. Female students value family more while male students consider money

more important. As to the market outcomes, the valuation on family negatively impacts on

individual income but valuation on money has positive influence. In out study, we assume

that the gender difference in attitude could translate into the differences in the two types of

people skills presented in the workplace. We show that distinguishing the people skills could

help us better explain the change of gender difference.

As far as we know, this is the first study about U.S. labor market by looking at the

gender differences in caring skills and directness skills. Loosely related, Ngai and Petrongolo

(2017) look into how the rise of the service economy in U.S help narrows the gender gaps

because females have advantage in the service sector. Instead of focusing on individual skills

as we do, their emphasize the role of the change in sectors caused by technology. However,

caring skills which is abundant in service sector have increasing return over time in our result.


3.3 Data

There are three main sources of data in this study. The first one is the Current Population

Survey (CPS) March 1980-2015. The second one is the fourth edition DOT (1977)6 and

the revised fourth edition DOT (1991)7 and the last one is the O*NET 98 (created by U.S.

Department of Labor, Employment and Training Administration) data.8

3.3.1 CPS Data

CPS-March sample contains the Annual Social and Economic Supplement (ASES) which

reports social and economic characteristics of each person in the surveyed households. These

characteristics include age, gender, race, region, statistical metropolitan area (SMSA), edu-

cation, income, and work information such as working hours and occupations. In practice,

we use a sample of workers who are 16-65 years old, within labor force, wage earning,

non-institutionalized, in non-agricultural and non-military occupations. Many researches

constrain the sample to full-year-full-time workers (who work at 35 hours per week and 50

weeks per year). Our research keeps all the workers in the working age because working

hours are also highly correlated with the occupation skills and they could be a resource of the

negative return to the caring skills. In addition, observations with zero incomes and negative

individual weights are deleted. Though the record of the key variables such as income and

occupation could be traced back to 1962 in the public-use data, detailed working hours are

only reported from 1976. Because we focus on workers’ hourly incomes which most accurately

reflect the returns to the worker characteristics and skills, the study has to be conducted on

the sample after 1976. In addition to the data availability, gender wage gap were not present-

ing significant change in the 1970s. So in the study, we constrain our sample from 1980 to 2015.

3.3.2 Definitions of Occupations

The schemes of occupations change over time and vary among different data sources. The

Bureau of Labor revises the scheme of occupations in each Census period. The new scheme

is usually adapted into CPS data two years after the Census. Several scholars put effort on

constructing a consistent definition of occupations over time. In ALM 2003, they construct

6https://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/78457https://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/61008https://www.onetcenter.org/db releases.html


crosswalks from Census 1970 scheme to Census 2000 scheme. In Autor and Dorn (2013), they

construct a consistent scheme over the past four decades based on the scheme in 1990 Census.

The public-use CPS data also provide two consistent definitions based on the schemes in 1950

and 1990 Census respectively. In this study, we adopt the one based on the definition in 1990

Census which has 315 occupations.9 In the DOT 1977, over 13,000 detailed occupations are

defined and there are over 1,200 occupations define in O*NET 98 data. In the literature, The

bridge that connect census occupation scheme to the detailed occupation scheme in DOT

1977 is the special version of the April 1971 CPS issued by the National Academy of Sciences

(2001). This dataset contains 60,441 individual observations. In each observation, both the

detailed DOT 1977 occupation and the 1970 Census occupation are recorded. The skills are

recorded on the DOT occupation level. It takes two steps to obtain the skill measurements

on the Census occupations level.

First, because there are 44 skill measurements recorded in DOT, we need to reduce the dimen-

sions by grouping the measurements into 4 categories, namely math skills, directness skills,

caring skills, and motor skills.10 Second, we group the observations by census occupations.

Within each group (occupation), observations may have same or different skill measurements

depending on their DOT occupations. Using the individual weight, the weighted average skill

measurements could be constructed for each census occupation. The skill measurements on

census occupational level will be merged to each annual CPS data as proxied individual skills.

There is no file connecting the DOT 1991 or O*NET 98 occupation to Census occupa-

tions directly. We use the crosswalk between DOT 1977 and DOT1991/O*NET 1998 data

online11 and construct the Census occupational skill measurements by repeating the two

steps above. The crosswalk between DOT 1977 and O*NET 1991 is straightforward but

some explanations are needed for the crosswalk between DOT 1977 and DOT 1991. From

the fourth edition to the revised fourth edition, 2453 out of 12,742 occupations have been

updated. Also, some occupations were deleted and some new occupations were added to

reflect the change of the labor market. We use “tables contain occupations with a DOT code

and/or title which changed or was deleted between 1979 and 1991” to match the DOT 1977

with the DOT 1991 The matching of DOT 1991 and CPS 1971 is 98% successful.

9This is the maximum number of occupations that we can trace back from the special 1971 CPS data.10See next subsection for details.11 https://www.onetcenter.org/taxonomy.html


3.3.3 Choices of Skill Measurements

How to choose the skill measurements is another key question in this study. In both DOT

1977 and DOT 1991, 44 occupational characteristics are recorded for each detailed job. These

44 characteristics fall into seven categories: work functions; required General Educational

Development (GED); aptitudes needed; temperaments needed; interests; physical demands;

and working conditions in the environment. Though the DOT provide a rich source of

occupational characteristics, it is unfeasible to employ all 44 characteristics in the empirical

analysis. Another issue is the choice between similar characteristics. For example, the

math GED and the aptitude “numerical” can both possibly represent the labor’s cognitive

analytic skills. However, when we plot the average levels of two genders over years, two

measurements have different convergent trends. For math GED, female workers catch up

and even surpass male workers but for aptitude numerical, the narrowing slowed down in

the 1990s and a gender gap remained until 2015. This implies that some important gender

differences may be overlooked if measurement is picked up arbitrarily. Our solution to the

problems is two-fold. We first set four skill categories according to the gender difference

based on the literature; a subset of the 44 characteristics are assigned to each category. Then

following Bacolod and Blum (2010), we construct an index for each skill category by using

principle component analysis (PCA). The indices will be comprehensive representatives of all

the included characteristics in each category.12

The first category of skills is the math skills. In ALM 2003, they use math skills to represent

non-routine cognitive tasks. In contrast, in Bacolod and Blum (2010), they have a broader

definition of cognitive skills which includes the verbal and intelligence aptitudes. Though

the broader definition could reflect the comprehensive development of cognitive skills, the

inclusion of verbal and intelligence aptitudes causes a high correlation between the cognitive

skills and directness skills defined in the following. In addition, gender gap is more likely to

exist in the dimension of “hard science” than in the general cognitive development. So in

our paper, we decide to use math skills as the measurement of cognitive skills. Math skills

include three measurements in DOT: the complexity of the job in relation to data; math

GED; aptitude for numeric.

The second category of skills is the directness skill. In ALM 2003, they use single apti-

tude “adaptability to accepting responsibility for the direction control or planning of an

12Other solutions are also suggested in the literature. Autor and Dorn (2013)and Deming (2017) suggests touse the arithmetic mean of the characteristics in each category as the skill measurement. ALM 2003 suggestsa two-step procedure: first, calculate the arithmetic mean for each occupation then merge the measurementsto Census 1960, replace the measurements by the percentiles in the population distribution. We have triedall the methods, the results are qualitatively consistent.


activity (DCP)” as the measurement of the non-routine interactive skill. In contrast, Bacolod

and Blum (2010) include four aptitudes to measure people skills which cover from the skill of

directing to the skill of caring. In Borghans et al. (2008b) and Deming (2017), they present

the evidence of different market outcomes of high-end people skills and caring skills. In Fortin

(2008), she presents the gender differences in the personality traits of ambition and caring

for family. The former trait reflect person’s directness skills. The later one is correlated

with person’s caring skills. It is of interest to ask whether genders present skill differences in

different people skills in the workplace and how the differences influence the gender wage

gap. So in our paper, we decide to categorize people skills into two subsets. The directness

skills present the high-end interactive skills which include four measurements: the complexity

of the job in relation to people; DCP; adaptability to influencing people in their opinions,

attitudes or judgments about ideas or things (Influ); adaptability to dealing with people

beyond giving and receiving instructions (Depl). In general, directness skills reflect people’s

skills in decision making, persuasion, and negotiation.

The caring skills include two aptitudes: a preference for activities involving business contact

with people and a preference for working for the presumed good of people. In general, caring

skills reflect people skills in cooperation and coordination.

Similar to Bacolod and Blum (2010), we use nine DOT variables to measure the motor

skills: the complexity of the job in relation to things; aptitudes for manual dexterity; finger

dexterity; motor coordination; eye-hand-foot coordination; spatial and form perception; color

discrimination; and adaptability to situations requiring attainment of standards.

The revision from DOT to O*NET is significant. Instead of analyzing occupations from 44

characteristics, O*NET provides 483 descriptors for each occupation. The descriptors include

worker characteristics, worker requirements, experience requirements, and occupational re-

quirements. Though the revision prevent us from attaining a consistent skill measurements,

DOT and O*NET provides detailed text descriptions on what the characteristics/descriptors

are measuring. So through text search, we could match the characteristics with the descrip-

tors. Using PCA, we could produce similar skill indices with O*NET descriptors as what

we did with DOT data. In the appendix, we provide a complete crosswalk from the DOT

characteristics to the O*NET descriptors. For each worker requirements descriptor, two

variables, level and importance respectively, are reported. We choose to use the variable of

level which measures the skill level presented by the workers in the occupation.

Table 3.1 presents the correlations between skills. The results show that the correlations

between skills in O*NET98 are still comparable with the ones in DOT1991 in the terms of


the sign and magnitude.

Table 3.1: Correlations between Skills

Panel A: Correlation between Skills in DOT1991Math Directness Caring Motor

Math 1Directing 0.588 1Caring 0.251 0.63 1Motor 0.028 -0.435 -0.615 1

Panel B: Correlation between Skills in O*NET98Math Directness Caring Motor

Math 1Directing 0.774 1Caring 0.461 0.77 1Motor 0.052 -0.133 -0.213 1

Data are from March CPS 1980-2015, DOT1991and O*NET98

At last, we are aware that DOT/O*NET reflects the skill requirement on occupational level

instead of individual level. But we argue that the purpose of the DOT is to guide labors

choose occupations according to their skills. So the DOT should partially reflect the skill

levels of labors.

3.4 Empirical Results

3.4.1 Stylized Fact of the Gender Skill Differences

Figure 3.3 shows the gender differences in skills. Females started with disadvantage in math

skills. As math skills grew fast in female labors and slightly decreased in male labors, the gap

reversed in the mid-1990s. The gap maintains stable afterward. The reversed gap is partially

due to the higher education achievements of female workers as we include GED-math in our

skill measurements.

Female workers also had less directness skills in 1980 but they caught up quickly. The


gap reversed in 1987 and expanded fast until 2000. The gap grew much slower after 2000. In

contrast, caring skills are always abundant in female workers. The gap also remains stable

over time.

Motor skills present very small gender difference in 1980 with higher level in male workers.

Over the period, motor skills decrease in both female and male labors but with much sharper

decrease in female workers until 2000. The gap remained stable afterward.

Figure 3.3: Gender Differences in Skills

3.4.2 Returns to Skills

We specify the wage equation for each individual i at time t :

lnωit = Z ′itαt +X ′

itβt + εit, (3.1)

where ωit is the hourly wage, Zit are worker’s skills including math, directness, caring, and

motor skills which are proxied by the occupational skill requirements. Xit are common


worker characteristics including age, potential working experience, region, race, SMSA status,

and education. αt and βt are the corresponding market returns to the skills and worker

characteristics. Since the individual level skills are not observable in our data, we need to

merge the individual observations with the skill measurements at occupational level from

DOT and O*NET. This leaves our specification with estimating the effects of aggregate

variables on micro units which is discussed by Moulton (1990). Moulton argues that the

estimated standard errors of the aggregate variables are downward biased. In our estimation,

following Bacolod and Blum (2010), we try to correct the bias by clustering the standard

errors at the occupational level.

Table 3.2 reports the estimated returns to skills in selected years. As comparison, Ta-

ble 3 reports the skill returns when skills is categorized into 3 groups: math skills, people

skills, and motor skills with people skills is a combination of directness skills and caring skills.

Table 3.2: Returns to Skills

year math directness caring motor1980 1.157*** 2.171*** -2.189*** 0.841***

[0.248] [0.261] [0.223] [0.174]1985 1.428*** 1.679*** -1.798*** 0.608***

[0.270] [0.343] [0.267] [0.217]1990 1.362*** 1.862*** -1.686*** 0.623***

[0.248] [0.346] [0.292] [0.211]1995 1.276*** 1.768*** -1.471*** 0.394**

[0.236] [0.326] [0.302] [0.191]2000 1.284*** 1.953*** -1.628*** 0.349*

[0.264] [0.383] [0.300] [0.205]2005 1.416***1 1.866*** -1.360*** 0.446*

[0.285] [0.349] [0.321] [0.244]2010 1.484*** 1.972*** -1.285*** 0.515

[0.295] [0.352] [0.330] [0.260]2015 1.430*** 1.998*** -1.308*** 0.452

[0.304] [0.364] [0.340] [0.266]Note: Included in the regression specification areexperience, experience squared, education level d-ummies, race dummies,region dummies, SMSA st-atus dummy, and skills. Robust standard errors a-re reported in parentheses. *significant at 10%, **significant at 5%, ***significant at 1%


The return to the math skills which are the the representative cognitive skills presents two

waves over the period. The return increased from 1980 to 1985. Then it deceased until 1991.

From then on, the return grew constantly until 2014. This result is in general consistent

with the consensus that the importance of the cognitive skills is increasing as they are most

complementary with the technology advancement.

Directness skills as the “high-end” people skills are always positively rewarded with the return

presenting some fluctuations in a small range. This result is not in line with the literature

where two types of people skills are not distinguished. In the literature, people skills usually

present an increasing trend in the return. Here we argue, the increasing trend is mainly

caused by the increasing return of the caring skills. Because directness skills relate to the

decision making, persuasion, and negotiation, their importance in the wage shouldn’t change

too much in the market.

Caring skills, in contrast, have a clear increasing trend in the return. Starting with a

magnitude negating the positive return of the directness skills, the return to caring skills

increases almost 40% over the period. A close look shows that more than 50% of the increase

happened between 1980 and 1990 when the female relative wage to male’s also increased the

most. The increase in the return slowed down after 1995 which also synchronized with the

slow-down of the gender wage gap narrowing. One the one hand, as caring skills relate to the

cooperation, coordination and serving attitude, this increasing return could be considered

as the product of the rising of the service sector (Autor and Dorn, 2013). On the other

hand, the negative magnitude of the return could be explained by the skill definition. Given

everything else equal, higher level of caring skills means sacrificing own time and putting

higher effort to increase others’ productivity. One example is the real estate occupations and

secondary school teachers. The requirements on the math skills, directness skills, and motor

skills are almost identical in the two occupations. However, secondary school teachers require

higher caring skills. So the wage gap between the two occupations could be explained by the

sacrifices made by the teachers.

As a comparison, in Table 3.3 we report the returns to skills when people skills are not

distinguished into two skills. Though the returns to math skills and to motors are almost

unchanged, the return to people skills is negative at the beginning and grows to insignificantly

positive.13 This implies that mixing two types of peoples skills disguises the importance of

the role played by the people skills in the gender wage difference.

13This may seem insistent with the literature in which people skills are found to have positive returns mostof the time. However, the people skills that are used in the literature are more equivalent to the directnessskills which means caring skills are missing in most of the current researches.


Table 3.3: Return to Skills with One People Skills

year math people motor1980 1.442*** -0.585* 0.847***

[0.349] [0.329] [0.251]1985 1.442*** -0.493* 0.697***

[0.329] [0.337] [0.256]1990 1.373*** -0.229 0.704***

[0.282] [0.311] [0.244]1995 1.174*** -0.147 0.442**

[0.262] [0.286] [0.235]2000 1.277*** -0.169 0.377*

[0.272] [0.287] [0.255]2005 1.336*** 0.023 0.530*

[0.289] [0.307] [0.289]2010 1.385*** 0.204 0.587*

[0.286] [0.318] [0.301]2015 1.345*** 0.194 0.492

[0.299] [0.324] [0.313]Note: Included in the regression spe-cification are experience, experiencesquared, education level dummies,r-ace dummies, regiondummies, SMSAstatus dummy, and skills. Robust st-andard errors are in parentheses. *si-gnificant at 10%, **significant at 5%,***significant at 1%

At last, the return to the motor skills decreased from 1980 to 2000 and increased slightly after-

ward but remained insignificant. This mostly replicates the existing results in the literature.

The decreasing return is usually attributed to the substitute effect of the computerization.

As the machines have better capabilities and lower prices, many routine tasks that required

workers’ motor skills could be achieved by machines. The return to the motor skills decreases

as the prices of the machines go down.


3.4.3 Decomposition on the Gender Wage Gap

First, we record how each of the worker characteristics and skills account for the gender wage

gap in each year. Since we assume a gender-neutral return, the gender wage gap could simply

decomposed by the following equation:

lnωMt − lnωF

t =(ZM

t

′− ZF

t

′)αt +

(XM

t

′−XF

t

′)βt +

(εMt − εFt

), (3.2)

lnωMt and lnωF

t are average log wages for males and females in year t. XMt

′−XF

t

′are the

mean gender differences in worker characteristics. ZMt

′−ZF

t

′are the mean gender differences

in skills. εMt −εFt are the gender differences in the positions in the residual wage distribution.

Table 3.4: Wage Gap Decomposition

1980 1990 2000 2015

Wage Percent Wage Percent Wage Percent Wage PercentDifference Difference Difference Difference

Observed 0.447 (100.0%) 0.317 (100.0%) 0.261 (100.0%) 0.202 (100.0%)Wage Gap

Characteristics 0.089 (19.9%) 0.008 (2.5%) -0.007 (-2.8%) -0.024 (-11.8%)Skills 0.180 (40.2%) 0.113 (35.6%) 0.084 (32.2%) 0.066 (32.5%)Math 0.006 (1.3%) -0.003 (-0.9%) -0.006 (-2.1%) -0.006 (-2.8%)Directness 0.020 (4.5%) -0.007 (-2.2%) -0.025 (-9.7%) -0.032 (-16.1%)Caring 0.124 (27.8%) 0.094 (29.6%) 0.097 (37.3%) 0.083 (41.2%)Motor 0.034 (7.7%) 0.027 (8.5%) 0.014 (5.2%) 0.017 (8.3%)

Unexplained 0.236 (38.8%) 0.196 (62.8%) 0.184 (72.2%) 0.160 (81.3%)

The worker characteristics only have significant explanation power in the 1980s. In 1980, 20%

of the gender wage gap could be explained by the gender difference in the worker characteris-

tics, mainly by the differences in education and working experience. However, as more female

workers were receiving higher education, the gender gap in education diminished and even

reversed in 1990. Meanwhile, the gender difference in working experience was also decreasing

in the 1980s. As a result, in 1990, worker characteristics almost lost the explanation power.

Actually, the reserved gender gaps didn’t stop growing. If the characteristics were the only

wage determinants, we would expect that female wages are 0.7% and 2.4% higher than the

male wages in 2000 and 2015 respectively.


Differences in skills could consistently account for 30%-40% of the gender wage gap over the

period. In table 4, the math skills are almost irrelevant to the gender wage gap. This may

contrast to people’s general perception of the gender difference in math skills. Here, we argue

that importance of the math skills may be disguised by grouping the high and low education

workers. Subsection 4.4 provides a detailed discussion.

Directness skills accounts for 4.5% of the total gender wage gap in 1980 but female labors

soon caught up and presented higher directness skills in the workplace in the mid-1980s.

Again if the directness skills were the only wage determinant, we should have seen higher

wages for female labors after 1990.

The major source of the gender wage gap is the gender difference in caring skills. The

abundant caring skills in female workers cause their wages 12.4% lower than male wages in

1980. This accounts for 27.8% of the total wage gap. Though the return to the caring skills

is increasing constantly, the gender difference in the skills is persistent. In 2015, the caring

skills still cause female wages 8.3% lower than male wage. This accounts for 41.2% of the

total wage gap.

Motor skills plays a mild role in the gender wage gap. It accounts for 5%-8% of the

total wage gap over the period. Since the return decreases over time, the magnitude of the

wage gap caused by the motor skills also decreases.

3.4.4 Decomposition on the Gender Wage Gap Change

In this session, we turn our focus to the change in the gender wage gap. Both the changes

in the returns to skills and the changes in the gender differences in skills could result in

the change in the gender wage gap. So again, we need a decomposition method to isolate

out the importance of each factor. Denote that ∆ωt = lnωMt − lnωF

t , ∆X ′t = XM

t

′−XF

t

′,

∆Z ′t = ZM

t

′− ZF

t

′, and ∆εt = εMt − εFt .

The change in the gender wage gap could be decomposed by the following equation:

∆ωt+1 − ∆ωt =[(

∆X ′t+1 − ∆X ′

t

)βt+1 +

(∆Z ′

t+1 − ∆Z ′t

)αt+1

]+[∆X ′

t

(βt+1 − βt

)+ ∆Z ′

t (αt+1 − αt)]

+ (∆εt+1 − ∆εt)

(3.3)


The first bracket measures the impact of the changes in the gender differences in skills

and worker characteristics. We refer it as quantity effect. The second bracket measures

the impact of the changes in returns to the skills and worker characteristics. We refer it

as price effect in the following parts. The last term measures the changes of the positions

of female wages in the residual wage distributions. Table 3.5 reports the decomposition results.

Table 3.5: Gender Wage Gap Change Decomposition

1980-1990 1990-2000 2000-2015 1980-2015

Gap Change -0.130 (100.0%) -0.056 (100.0%) -0.059 (100.0%) -0.245 (100.0%)Total Wage

Changes inCharacteristics -0.039 (29.9%) -0.023 (40.5%) -0.031 (52.9%) -0.091 (37.0%)Skills -0.030 (27.9%) -0.014 (28.8%) 0.001 (-1.0%) -0.048 (19.7%)Math -0.008 (6.1%) -0.003 (5.5%) 0.003 (-2.7%) -0.003 (2.2%)Directness -0.028 (21.7%) -0.017 (30.5%) -0.006 (10.8%) -0.055 (22.5%)Caring -0.003 (2.0%) 0.007 (-12.2%) 0.006 (-10.3%) 0.014 (-5.9%)Motor 0.002 (-1.6%) -0.003 (5.0%) -0.001 (1.3%) -0.004 (1.4%)

Changes in returns toCharacteristics -0.001 (0.6%) -0.002 (3.4%) -0.000 (0.6%) -0.012 (5.0%)Skills -0.042 (32.4%) -0.015 (27.1%) -0.017 (27.9%) -0.066 (27.0%)Math 0.001 (-0.4%) 0.000 (-0.4%) -0.001 (-1.4%) 0.001 (-0.6%)Directness -0.002 (1.9%) -0.000 (0.6%) -0.001 (1.0%) -0.002 (0.7%)Caring -0.030 (23.0%) -0.012 (21.2%) -0.019 (32.3%) -0.050 (20.4%)Motor -0.010 (8.0%) -0.003 (5.7%) 0.004 (-6.8%) -0.016 (6.5%)

Unexplained -0.010 (9.1%) 0.001 (-3.9%) -0.013 (21.6%) -0.013 (10.2%)

The changes in the characteristics, mainly in education and working experience take the

biggest share of the change in the gender wage gap from the lowest of 30% in the 1980s to the

highest of 52.9% after 2000. However, the changes in the associated returns don’t contribute

much to the change in the gender wage gap.

As to the changes in the skills, only the one in the directness skills significantly contributes

to the change in the gender wage gap. As we have shown in Session 3, directness skills are

the only skill category in which the gender difference change significantly over the period.

Though the gender difference in caring skills doesn’t change much over time, the returns

does help explain the change in the gender wage gap. Depending on the period, 20%-30% of

the change could be explained by the change of the return to the caring skills alone. This


finding helps explain the slow-down of the gender wage gap. The gender gap favoring female

labors in the directness skills stopped growing after 2000. Only the growth in the return

to the caring skills which also slows down keeps the female wages growing slowly. A point

to mention is that gender difference in the caring skill expanded slightly after 1990. This

quantity effect offsets some growth of the female wages. Without growth in other skills, the

higher caring skills in female labors indicates lower female wages given the return to caring

skills is negative.

3.4.5 Decomposition within Different Education Groups

Prada and Urzua (2017) find that motor skill (mechanical ability) brings higher rewards to

the workers without college degrees than to the group with college degrees or above. Since

the gender wage gaps in different education groups present diverging trend (Figure 3.4), it is

of interest to investigate if the return to the same skills varies with the education level and

how much the variation could account for the diverging trends. Figure 3.5 shows the gender

differences in skills in two education groups.

Figure 3.4: Gender Gap in Hourly Wage by Education


Figure 3.5: Gender Differences in Skills by Education

Though female labors had disadvantage in the math skills as a whole group, low education

females always have higher math skills than the males in the same group. In contrast, even

the skill gap decreases over time in the high education group, female labors still have lower

math skills.

The between group difference in directness skills is also interesting. Female labors in

both groups increased their directness skills in the 1980s quickly. However, high educated

female workers just maintained the slight advantage afterward whereas low educated females

expanded their advantage until 2000.

Caring skills have a gap favoring female labors in both groups. The gap is larger in the low

education group. Both gaps are persistent over time with a vague increasing slope.

As to the motor skills, the gender difference in high education group has a flat reserved-U

shape curve but the difference remains small in magnitude. Low education group, however,

observes an sharp increase in the skill difference before 2000. Afterward, with fluctuation,

the gender gap remains stable.


Table 3.6: Returns to Skills by Education Level

High Education Low Educationyear math directness caring motor math directness caring motor1980 1.508*** 1.727*** -1.566*** 0.027 -0.005 1.980*** -1.982*** 0.747***

[0.241] [0.398] [0.350] [0.243] [0.267] [0.277] [0.188] [0.154]1985 1.707*** 1.370*** -1.303*** 0.028 0.275 1.584*** -1.629*** 0.572***

[0.213] [0.375] [0.370] [0.256] [0.315] [0.333] [0.213] [0.187]1990 1.519*** 1.828*** -1.332*** -0.002 0.328 1.615*** -1.553*** 0.634***

[0.239] [0.399] [0.383] [0.255] [0.275] [0.310] [0.243] [0.198]1995 1.685*** 1.374*** -1.022*** -0.089 0.143 1.820*** -1.438*** 0.451***

[0.227] [0.395] [0.415] [0.259] [0.256] [0.319] [0.262] [0.181]2000 1.652*** 1.736*** -1.208*** -0.188 0.305 1.833*** -1.402*** 0.412***

[0.273] [0.507] [0.446] [0.278] [0.297] [0.381] [0.260] [0.213]2005 1.717*** 1.641*** -1.001*** -0.048 0.359 1.773*** -1.300*** 0.492***

[0.266] [0.460] [0.433] [0.278] [0.320] [0.367] [0.277] [0.221]2010 1.774*** 1.834*** -0.956*** 0.067 0.545 1.722*** -1.127*** 0.613***

[0.279] [0.442] [0.418] [0.303] [0.327] [0.363] [0.282] [0.244]2015 1.649*** 1.823*** -1.006*** 0.065 — 0.402 1.896*** -1.148*** 0.490***

[0.302] [0.473] [0.466] [0.282] [0.335] [0.366] [0.283] [0.212]Note: Included in the regression specification are experience, experience squared, race dum-my, region dummies, SMSA status dummy and skills. Robust standard errors are reportedin parentheses. *significant at 10%, **significant at 5%, ***significant at 1%


Gender wage gaps in education groups diverged after 2000. Meanwhile, variations on the

gender skills differences and returns to skills are also observed on the education level. It is of

interest to ask whether the variations could explain the divergence in the gender wage gaps.

We repeat the decomposition exercises on the education level. Table 3.6 reports the returns

to skills and Table 3.7 reports level decomposition of the gender wage gap.

Table 3.7: Decomposition on the Gender Wage Gap in Education Groups

Panel A: Decomposition on Gender Wage Gap in High Education Group

1980 1990 2000 2015


Observed 0.411 (100.0%) 0.291 (100.0%) 0.268 (100.0%) 0.245 (100.0%)Wage GapCharacteristics 0.064 (15.5%) 0.043 (14.9%) 0.037 (13.9%) 0.019 (7.7%)Skills 0.121 (29.5% 0.080 (27.4% 0.065 (25.2% 0.057 (26.9%Math 0.014 (3.4%) 0.015 (5.0%) 0.016 (6.0%) 0.016 (6.5%)Directness 0.002 (0.6% -0.005 (-1.7%) -0.013 (-4.7%) -0.010 (-4.1%)Caring 0.107 (26.0%) 0.069 (23.9%) 0.063 (23.5%) 0.060 (24.5%)Motor -0.002 (-0.4%) 0.001 (0.2%) 0.001 (0.4%) 0.000 (0.0%)

Unexplained 0.226 (55.0%) 0.167 (57.6%) 0.165 (60.9%) 0.169 (65.5%)

Panel B: Decomposition on Gender Wage Gap in Low Education Group

1980 1990 2000 2015


Observed 0.435 (100.0%) 0.310 (100.0%) 0.259 (100.0%) 0.220 (100.0%)Wage GapCharacteristics -0.003 (-0.7%) -0.017 (-5.5%) -0.021 (-8.1%) -0.013 (-6.0%)Skills 0.173 (39.7%) 0.113 (36.6%) 0.086 (32.0%) 0.070 (31.7%)Math -0.001 (-0.2%) -0.005 (-1.7%) -0.003 (-1.1%) -0.003 (-1.4%)Directness -0.006 (-1.3%) -0.021 (-6.8%) -0.037 (-14.4%) -0.042 (-19.1%)Caring 0.133 (30.6%) 0.101 (32.4%) 0.092 (35.5%) 0.086 (39.2%)Motor 0.046 (10.6%) 0.039 (12.6%) 0.031 (11.9%) 0.028 (12.8%)

Unexplained 0.266 (61.0%) 0.213 (68.9%) 0.195 (76.1%) 0.163 (74.2%)

Returns to math skills in the high education group is much higher than the one to the low

education group. So math skills influence the gender wage gap in each group differently. It

could account for 3.4%-6.5% of the gender wage gap in the high education group but it’s still

irrelevant to the gender wage gap in the low education group.


Directness skills and caring skills are playing similar role in the gender wage gaps in both

groups though the gender differences are larger in both skills in the low education group. So

the directness skills narrows the wage gap more in the low education group but caring skills

also expands the gender wage gap more in the same group. Over the period, low education

group also experiences larger change in the share of the wage gap accounted by the two skills.

Motor skills also present significant difference between the education groups. Motor skills

difference is larger and persistent in the low education group and the skills are positively

rewarded. The return has a decreasing trend. 10%-13% of the wage gap could be attributed

to motor skills in the low education group. In contrast, the gender difference is small and

diminishing in the high education group. In addition, the return is insignificantly different

from zero. The share of the gender wage gap attributed to the motor skills is negligible in

the high education group.

Table 3.8 reports the decomposition on the change of the gender wage gap in skills.

First, worker characteristics only influence the gender wage gap change in the 1980s through

quantity effect. This result is similar to the one from the whole sample. However, since

education no longer enters the education specific regression as an explanatory variable, less

change could be explained by the workers characteristics. Most of the change results from

the change in working experience.

Second, we are interested in how the skills differences between two groups cause the di-

verging trends in the gender wage gaps after 1990. Comparing the results in Panel A and

Panel B, we find the quantity effect of the directness skills and both the price and the quantity

effects of the caring skills can powerfully explain the difference.

In the 1990s, the return of the caring skills in the high education group didn’t grow as

strong as the one in the low education group. Meanwhile, the difference in the directness skills

(with female labors having the advantage) also expands more in the low education group.

Though only the high education group has a quantity effect in caring skills favoring females,

the total change accounted by these three effects is still 0.006 less in the high education group.

This is 0.006/(0.051 − 0.023) = 21.4% of the divergence. In the period 2000-2015, we could

observe the same changes in these effects and 83.3% of the divergence could be explained by

the differences in these effects.

In summary, caring skills and directness skills are playing similar role in the gender wage gap

level in both groups. Higher level in caring skills results in lower wage level for female workers


Table 3.8: Decomposition on the Gender Wage Gap Change in Education Groups

Panel A: Decomposition on Gender Wage Gap in High Education Group

1980-1990 1990-2000 2000-2015 1980-2015


Changes inCharacteristics -0.034 (28.0%) -0.006 (27.7%) -0.008 (34.1%) -0.045 (27.2%)Skills -0.023 (23.5%) -0.014 (63.2%) 0.002 (-8.0%) -0.035 (23.4%)Math -0.006 (5.3%) -0.001 (2.3%) -0.001 (2.4%) -0.006 (3.7%)Directness -0.007 (6.2%) -0.009 (38.9%) 0.003 (-12.9%) -0.013 (8.1%)Caring -0.015 (12.5%) -0.005 (20.7%) 0.000 (-1.2%) -0.021 (12.6%)Motor 0.001 (-0.8%) 0.000 (-0.8%) -0.001 (3.6%) 0.002 (-1.0%)

Changes in returns toCharacteristics -0.008 (6.7%) -0.000 (0.3%) -0.002 (6.8%) -0.004 (2.5%)Skills -0.020 (16.8%) -0.000 (0.5%) -0.011 (48.4%) -0.036 (21.7%)Math 0.003 (-2.4%) 0.002 (-9.4%) 0.001 (-2.4%) 0.006 (-3.6%)Directness 0.000 (-9.4%) 0.000 (-1.8%) -0.001 (3.1%) -0.000 (0.1%)Caring -0.026 (21.6%) -0.005 (20.2%) -0.009 (42.1%) -0.043 (26.1%)Motor 0.003 (-2.4%) 0.002 (-8.6%) -0.001 (5.6%) 0.001 (-0.9%)

Unexplained -0.033 (27.5%) -0.002 (8.4%) -0.005 (21.7%) -0.078 (25.2%)

Panel B: Decomposition on Gender Wage Gap in Low Education Group

1980-1990 1990-2000 2000-2015 1980-2015


Changes inCharacteristics -0.030 (24.3%) -0.002 (4.7%) -0.007 (17.7%) -0.043 (20.0%)Skills -0.021 (16.6%) -0.008 (26.5%) -0.002 (5.9%) -0.031 (15.1%)Math -0.001 (0.9%) -0.001 (1.7%) 0.000 (-1.2%) -0.001 (0.5%)Directness -0.020 (16.1%) -0.012 (23.6%) -0.004 (10.1%) -0.039 (18.3%)Caring -0.002 (1.8%) 0.003 (-6.6%) 0.003 (-7.5%) 0.012 (-5.6%)Motor 0.003 (-2.3%) -0.004 (7.8%) -0.001 (1.6%) -0.003 (1.6%)

Changes in returns toCharacteristics -0.003 (2.3%) -0.003 (6.0%) 0.001 (-1.7%) -0.002 (1.1%)Skills -0.041 (32.3%) -0.020 (39.0%) -0.017 (43.5%) -0.073 (33.7%)Math -0.001 (-1.1%) 0.002 (-4.5%) -0.002 (4.1%) -0.001 (0.4%)Directness 0.001 (-0.9%) -0.003 (5.3%) -0.001 (2.2%) 0.000 (-0.2%)Caring -0.031 (24.6%) -0.016 (32.1%) -0.020 (50.4%) -0.053 (24.5%)Motor -0.009 (7.5%) -0.003 (5.9%) 0.006 (-13.0%) -0.019 (9.0%)

Unexplained -0.031 (24.5%) 0.017 (23.8%) -0.013 (33.5%) -0.067 (31.1%)


but higher level in directness skills mitigates the impact. But math skills bring advantage to

the male wages only in the high education group. Motor skills also benefit males but only in

the low education group. As to the gender wage gap change, most of the change comes from

the change in the the quantity of directness skills and the returns to the caring skills. A good

amount of the divergence between two education groups could be explained by these two effects.

3.4.6 Decomposition within Different Marital Status Groups

Substantial between-group difference could also be found in the gender wage gaps in different

marital status groups. (Figure 3.6). This session, we present the skill differences (Figure 3.7)

and the decomposition results within each martial group. Table 3.9 and 3.10 reports the

decomposition results on level and change respectively.

Figure 3.6: Gender Gap in Hourly Wage by Marital Status

Starting with a low level (20%), gender wage gap in unmarried group decreased from 1980

until mid-1990s to a level about 7%. Though the gap expanded a bit in the second half

of 1990s, it returned to a level 7% at the year 2015. The gender wage gap in the married

group, in contrast, started with a level over 50%. The gap decreases over time with a faster


Figure 3.7: Gender Differences in Skills by Marital Status

speed before 1990. The gap is still about 25% at the year 2015. What can account for the

between-group differences? Worker characteristics is the first cause. Female workers obtain

higher education achievement than males in the unmarried group all the time while the

opposite holds in the married group until 2000. The second reason is the differences in skills.

Female workers are rich in both directness and caring skills in the unmarried group. The

positive return to directness skills offsets the negative return to caring skills. But female

labors are only rich caring skills in the married group before 2000 and the skill difference

is even bigger than the one in unmarried group. After 2000, though female labors have

advantages in the directness skills, the difference in caring skills even expands which slows

down the growth in female relative wage. The between-group difference is even more salient

in the change of the gender wage gap. On the one hand, as the most important single factor

influencing the wage gap change in the married group, directness skills has much smaller

quantity effect in the unmarried group. On the other hand, the increasing return to caring

skills benefits female labors in both groups. However, this price effect is largely offset by the

increasing difference in skills in the married group.


Table 3.9: Decomposition on Gender Wage Gap Level by Marital Status

Panel A: Decomposition on Gender Wage Gap in Married Subgroup

1980 1990 2000 2015



Characteristics 0.036 (7.1%) 0.031 (8.0%) 0.008 (2.50%) -0.027 (-10.7%)Skills 0.175 (34.5%) 0.119 (30.3%) 0.088 (26.9%) 0.066 (26.6%)Math 0.000 (-0.1%) -0.000 (0.0%) -0.001 (-0.2%) -0.002 (-1.0%)Directness 0.043 (8.5%) 0.008 (1.9%) -0.018 (-5.7%) -0.035 (-14.0%)Caring 0.101 (19.9%) 0.084 (21.5%) 0.090 (27.5%) 0.086 (34.6%)Motor 0.031 (6.2%) 0.027 (6.9%) 0.017 (5.2%) 0.016 (6.3%)Unexplained 0.295 (58.3%) 0.242 (61.6%) 0.229 (70.6%) 0.208 (84.1%)

Panel B: Decomposition on Gender Wage Gap in Unmarried Subgroup

1980 1990 2000 2015



Characteristics -0.022 (-10.9%) -0.033 (-30.7%) -0.035 (-32.6%) -0.036 (-51.7%)Skills 0.084 (41.9%) 0.050 (47.5%) 0.039 (36.2%) -0.037 (52.4%)Math -0.002 (-1.2%) -0.005 (-4.6%) -0.007 (-6.9%) -0.006 (-7.9%)Directness -0.007 (-3.6%) -0.014 (-13.1%) -0.032 (-29.7%) -0.038 (-54.15)Caring 0.075 (37.4%) 0.055 (52.4%) 0.070 (64.9%) 0.065 (92.5%)Motor 0.019 (9.3%) 0.013 (12.9%) 0.008 (7.8%) 0.015 (22.0%)Unexplained 0.139 (69.0%) 0.088 (83.2%) 0.104 (96.4%) 0.070 (99.3%)


Table 3.10: Decomposition on Gender Wage Gap Change by Marital Status

Panel A: Decomposition in the Married Subgroup

1980-1990 1990-2000 2000-2015 1980-2015

Total Wage -0.113 (100.0%) -0.068 (100.0%) -0.078 (100.0%) -0.258 (100.0%)Gap ChangeChanges inCharacteristics -0.014 (12.3%) -0.028 (40.8%) -0.034 (43.6%) -0.081 (31.4%)Skills -0.030 (26.7%) -0.016 (23.6%) -0.007 (9.3%) -0.061 (23.6%)Math -0.002 (1.5%) -0.000 (0.7%) -0.001 (1.4%) -0.006 (2.1%)Directness -0.031 (27.8%) -0.026 (38.5%) -0.016 (20.3%) -0.076 (29.4%)Caring 0.001 (-1.3%) 0.012 (-17.3%) 0.011 (-13.9%) 0.022 (-8.4%)Motor 0.001 (-1.3%) -0.001 (1.7%) -0.001 (1.4%) -0.001 (0.5%)

Changes in returns toCharacteristics 0.009 (-8.1%) 0.004 (-6.2%) -0.001 (1.1%) 0.018 (-7.0%)Skills -0.025 (22.0%) -0.016 (23.8%) -0.015 (18.7%) -0.048 (18.50%)Math 0.002 (-1.5%) -0.000 (0.1%) -0.000 (0.6%) 0.004 (-1.4%)Directness -0.004 (3.4%) 0.000 (-0.4%) -0.000 (0.6%) -0.001 (0.5%)Caring -0.018 (15.7%) -0.007 (9.7%) -0.014 (18.4%) -0.036 (14.1%)Motor -0.005 (4.5%) -0.010 (14.4%) 0.001 (-0.9%) -0.014 -5.30%

Unexplained -0.053 (47.1%) -0.012 (18.1%) -0.021 (27.3%) -0.087 (33.5%)

Panel B: Decomposition in the Unmarried Subgroup

1980-1990 1990-2000 2000-2015 1980-2015

Total Wage -0.095 (100.0%) 0.002 (100.0%) -0.038 (100.0%) -0.131 (100.0%)Gap ChangeChanges inCharacteristics -0.008 (8.2%) -0.002 (-98.4%) -0.013 (34.8%) 0.036 (27.8%)Skills -0.012 (12.30%) 0.004 (173.6%) 0.004 (-9.4%) -0.023 (17.3%)Math -0.001 (0.5%) -0.002 (-87.4%) 0.002 (-4.6%) -0.001 (0.7%)Directness -0.007 (7.5%) -0.002 (-64.4%) -0.003 (8.7%) -0.029 (22.3%)Caring -0.004 (4.1%) 0.008 (337.8%) 0.006 (-15.7%) 0.009 (-6.8%)Motor -0.000 (0.1%) 0.000 (-12.3%) -0.001 (2.2%) -0.001 (1.1%)

Changes in returns toCharacteristics -0.008 (8.4%) -0.003 (-107.7%) 0.001 (-2.2%) 0.000 (-0.2%)Skills -0.021 (22.4%) 0.001 (60.7%) -0.008 (20.1%) -0.023 (17.4%)Math -0.002 (2.0%) 0.000 (7.6%) -0.000 (0.4%) -0.002 (1.4%)Directness 0.001 (-0.8%) -0.003 (-126.8%) -0.002 (5.5%) -0.001 (0.9%)Caring -0.016 (16.4%) 0.008 (333.7%) -0.012 (32.8%) -0.019 (14.4%)Motor -0.005 (4.8%) -0.003 (-153.8%) 0.007 (-18.7%) -0.001 (0.7%)

Unexplained -0.046 (48.8%) 0.002 (71.9%) -0.021 (56.6%) -0.049 (37.7%)


3.4.7 Decomposition within Different Race Groups

Figure 3.8 and figure 3.9 present the gender wage gaps and differences in skills of the white

and black race subgroups. Table 3.11 and Table 3.12 present the decomposition results. As

in the results above, a big portion of between-group difference could be explained by the

differences in skills.

Figure 3.8: Gender Gap in Hourly Wage by Race

Both the level and change of the gender wage gap in white subgroup are larger. The gaps in

both groups decreased before 1990. After 1990, the gender wage gap in the black subgroup

fluctuate around 10% but the gap in the white subgroup continues decreasing with a flatter

slope.

The motor and caring skills are playing almost identical roles and account for biggest

shares of the gender wage gaps in both subgroups in the level decomposition. Though the

returns to skills are group-specific, the estimates are not significantly different. What’s more,

the size of the gaps in two subgroups are also similar. Higher motor skills bring male workers

higher wages while higher caring skills hinder female workers’ wages.


Figure 3.9: Gender Differences in Skills by Race

Directness skills play more important role in explaining the change of the wage gaps. The

difference in directness skills is significantly higher in black group with female having ad-

vantage. The difference doesn’t change much except in the 1990s. The difference is much

smaller in the white subgroup with the sign reversed in 1990 favoring female labors. The

difference decreases constantly. Reflected on the change in the wage gap, directness could

explain 20%-41% of the change over time in the white subgroup.

3.4.8 Decomposition within Different Age Groups

Goldin (2014) shows that gender wage gap peaks at the age around 40 in the full-time-full-year

college graduates sample from Census data 1940-2010. With different sample, we show that

gender wage gap increases with age. (Figure 3.10). This holds in all three age subgroups,

age 25-34, age 35-44, and age 45-54. Before 1990, three gaps approximately parallel to

each other. After 1990, the gaps in the two older groups narrow more than the one in

the youngest group. The difference in directness and caring skills can partially explain the

between-group difference. (Figure 3.11). Starting with similar level, gender difference in the


Table 3.11: Decomposition on Gender Wage Gap Level by Marital Status

Panel A: Decomposition on Gender Wage Gap in Married Subgroup

1980 1990 2000 2015



Characteristics 0.036 (7.1%) 0.031 (8.0%) 0.008 (2.50%) -0.027 (-10.7%)Skills 0.175 (34.5%) 0.119 (30.3%) 0.088 (26.9%) 0.066 (26.6%)Math 0.000 (-0.1%) -0.000 (0.0%) -0.001 (-0.2%) -0.002 (-1.0%)Directness 0.043 (8.5%) 0.008 (1.9%) -0.018 (-5.7%) -0.035 (-14.0%)Caring 0.101 (19.9%) 0.084 (21.5%) 0.090 (27.5%) 0.086 (34.6%)Motor 0.031 (6.2%) 0.027 (6.9%) 0.017 (5.2%) 0.016 (6.3%)Unexplained 0.295 (58.3%) 0.242 (61.6%) 0.229 (70.6%) 0.208 (84.1%)

Panel B: Decomposition on Gender Wage Gap in Unmarried Subgroup

1980 1990 2000 2015



Characteristics -0.022 (-10.9%) -0.033 (-30.7%) -0.035 (-32.6%) -0.036 (-51.7%)Skills 0.084 (41.9%) 0.050 (47.5%) 0.039 (36.2%) -0.037 (52.4%)Math -0.002 (-1.2%) -0.005 (-4.6%) -0.007 (-6.9%) -0.006 (-7.9%)Directness -0.007 (-3.6%) -0.014 (-13.1%) -0.032 (-29.7%) -0.038 (-54.1Caring0.075 (37.4%) 0.055 (52.4%) 0.070 (64.9%) 0.065 (92.5%)Motor 0.019 (9.3%) 0.013 (12.9%) 0.008 (7.8%) 0.015 (22.0%)Unexplained 0.139 (69.0%) 0.088 (83.2%) 0.104 (96.4%) 0.070 (99.3%)


Table 3.12: Decomposition on Gender Wage Gap Change by Race

Panel A: Decomposition in the White Subgroup

1980-1990 1990-2000 2000-2015 1980-2015

Total Wage -0.128 (100.0%) -0.055 (100.0%) -0.072 (100.0%) -0.255 (100.0%)Gap ChangeChanges inCharacteristics -0.034 (26.5%) -0.028 (52.1%) -0.034 (47.5%) -0.106 (41.4%)Skills -0.026 (20.5%) -0.014 (25.0%) -0.005 (6.7%) -0.052 (20.5%)Math -0.002 (1.4%) -0.001 (1.7%) -0.001 (1.0%) -0.004 (1.6%)Directness -0.026 (20.4%) -0.022 (41.0%) -0.016 (22.4%) -0.07 (27.5%)Caring 0.001 (-0.6%) 0.01 (-18.0%) 0.013 (-17.8%) 0.022 (-8.6%)Motor 0.001 (-0.7%) -0.000 (0.2%) -0.001 (1.1%) -0.000 (0.1%)

Changes in returns toCharacteristics 0.002 (-1.9%) 0.003 (-5.4%) -0.000 (0.6%) 0.014 (-5.5%)Skills -0.028 (21.7%) -0.009 (15.7%) -0.010 (14.5%) -0.039 (15.5%)Math 0.001 (-0.9%) -0.000 (0.1%) -0.000 (0.3%) -0.001 (0.6%)Directness -0.001 (2.8%) 0.000 (-0.2%) -0.001 (1.7%) 0.001 (-0.2%)Caring -0.004 (14.5%) -0.001 (1.1%) -0.012 (17.1%) -0.030 (11.8%)Motor -0.019 (5.3%) -0.008 (14.8%) 0.003 (4.6%) -0.011 (4.5%)

Unexplained -0.043 (33.2%) -0.007 (12.5%) -0.022 (30.8%) -0.072 (28.1%)

Panel B: Decomposition in the Black Subgroup

1980-1990 1990-2000 2000-2015 1980-2015

Total Wage -0.095 (100.0%) 0.024 (100.0%) -0.032 (100.0%) -0.102 (100.0%)Gap ChangeChanges inCharacteristics 0.006 (-6.1%) 0.011 (45.1%) -0.007 (22.5%) 0.006 (-5.7%)Skills -0.025 (26.6%) -0.005 (-21.1%) 0.007 (-21.2%) -0.024 (23.5%)Math -0.006 (6.8%) -0.000 (-0.9%) 0.003 (-9.5%) -0.005 (4.6%)Directness -0.002 (2.7%) -0.019 (-78.2%) 0.005 (-16.7%) -0.015 (14.9%)Caring -0.018 (18.6%) 0.018 (69.1%) -0.001 (3.9%) -0.003 (2.6%)Motor 0.001 (-1.6%) -0.003 (-11.1%) -0.000 (1.1%) -0.001 (1.5%)

Changes in returns toCharacteristics -0.009 (9.8%) -0.003 (-13.7%) 0.003 (-9.0%) -0.006 (6.0%)Skills 0.000 (-0.3%) -0.005 (-20.3%) -0.029 (90.0%) -0.033 (32.2%)Math -0.000 (0.0%) 0.005 (19.2%) -0.006 (20.0%) -0.001 (0.7%)Directness -0.000 (0.3%) -0.007 (-27.7%) 0.004 (-11.0%) -0.004 (4.2%)Caring -0.006 (6.6%) 0.004 (14.8%) -0.022 (69.2%) -0.024 (23.8%)Motor 0.007 (-7.2%) -0.006 (-26.7%) -0.004 (11.9%) -0.004 (3.5%)

Unexplained -0.066 (69.9%) 0.027 (110.0%) -0.006 (17.7%) -0.045 (44.0%)


caring skills is more persistent in the older groups. As to directness skills, youngest group

has the smallest initial gender difference but females in older groups experience faster growth.

At 1980, females in older groups bear wage disadvantage from both higher caring skills

and lower directness skill while only caring skills hinder the female wages in the youngest

group. Though older females have faster growth in directness skills, the between-group

difference in the gender wage gap would remain as long as the negative return to caring skills

is not totally offset by the positive return to directness skills. (Table 3.13). What about

the between-group difference in change? As Table 3.14 shows, all three groups have similar

positive price effect and negative quantity effect from caring skills, older groups have much big-

ger quantity effect from directness skills which help female workers narrow the wage difference.

Figure 3.10: Gender Gap in Hourly Wage by Age Group


Table 3.13: Decomposition on Gender Wage Gap Level by Age Groups

Panel A: Decomposition on Gender Wage Gap in Subgroup Age 25-34

1980 1990 2000 2015



Characteristics 0.041 (12.4%) 0.001 (-0.5%) -0.020 (-10.9%) -0.045 (-39.1%)Skills 0.121 (36.3%) 0.056 (30.1%) 0.047 (25.8%) 0.046 (40.0%)Math 0.000 (0.0%) -0.005 (-2.9%) 0.005 (2.5%) -0.004 (-3.8%)Directness 0.005 (1.5%) -0.017 (-9.0%) -0.029 (-15.8%) -0.036 (-31.3%)Caring 0.084 (25.2%) 0.059 (31.9%) 0.060 (32.9%) 0.067 (58.8%)Motor 0.032 (9.6%) 0.018 (10.1%) 0.020 (11.2%) 0.019 (16.4%)

Unexplained 0.170 (51.2%) 0.130 (70.4%) 0.155 (85.2%) 0.114 (99.1%)

Panel B: Decomposition on Gender Wage Gap in Subgroup Age 35-44

1980 1990 2000 2015



Characteristics 0.001 (11.6%) 0.051 (14.3%) 0.008 (2.8%) -0.025 (-15.0%)Skills 0.155 (29.8%) 0.101 (28.3%) 0.072 (24.8%) 0.046 (27.3%)Math 0.001 (0.1%) 0.001 (0.2%) -0.001 (-0.2%) -0.003 (-1.8%)Directness 0.053 (10.1%) 0.012 (3.2%) -0.02 (-7.0%) -0.044 (-25.9%)Caring 0.082 (15.7%) 0.068 (19.2%) 0.084 (28.7%) 0.080 (47.2%)Motor 0.020 (3.8%) 0.020 (5.7%) 0.010 (3.3%) 0.013 (7.7%)

Unexplained 0.305 (58.5%) 0.204 (57.4%) 0.211 (72.4%) 0.149 (87.7%)

Panel C: Decomposition on Gender Wage Gap in Subgroup Age 45-54

1980 1990 2000 2015



Characteristics 0.063 (11.9%) 0.050 (11.4%) 0.035 (10.7%) -0.001 (-0.3%)Skills 0.179 (33.5%) 0.137 (31.1%) 0.090 (26.9%) 0.065 (23.0%)Math 0.005 (0.9%) 0.003 (0.6%) 0.000 (0.1%) -0.002 (-0.6%)Directness 0.062 (11.6%) 0.023 (5.2%) -0.012 (-3.5%) -0.029 (-10.3%)Caring 0.088 (16.5%) 0.088 (20.1%) 0.087 (26.0%) 0.077 (27.3%)Motor 0.024 (4.5%) 0.023 (5.3%) 0.014 (4.2%) 0.019 (6.7%)

Unexplained 0.292 (54.7%) 0.252 (57.5%) 0.208 (62.40%) 0.218 (77.3%)


Table 3.14: Decomposition on Gender Wage Gap Change by Age Groups

Panel A: Decomposition in the Subgroup Age 25-34

1980-1990 1990-2000 2000-2015 1980-2015

Total Wage -0.147 (100.0%) -0.002 (100.0%) -0.067 (100.0%) -0.218 (100.0%)Gap ChangeChanges inCharacteristics -0.041 (27.9%) 0.002 (-99.6%) -0.029 (42.5%) -0.101 (46.5%)Skills -0.029 (19.7%) -0.002 (92.2%) 0.006 (-9.9%) -0.038 (17.4%)Math -0.005 (3.7%) 0.000 (3.8%) 0.001 (-2.9%) -0.004 (2.1%)Directness -0.021 (14.4%) -0.001 (43.6%) -0.006 (8.9%) -0.041 (18.9%)Caring -0.003 (2.3%) -0.001 (81.8%) 0.016 (-24.3%) 0.011 (-5.0%)Motor 0.001 (-0.7%) -0.002 (-37.0%) -0.006 (8.3%) -0.003 (1.4%)

Changes inreturns toCharacteristics -0.001 (0.7%) -0.001 (59.0%) 0.004 (-5.8%) 0.015 (-6.9%)Skills -0.036 (24.4%) -0.001 (37.0%) -0.008 (11.6%) -0.037 (16.9%)Math 0.000 (-0.1%) 0.000 (-2.9%) -0.002 (2.5%) -0.000 (0.0%)Directness -0.000 (0.2%) -0.002 (90.4%) -0.001 (1.5%) 0.000 (-0.2%)Caring -0.022 (14.6%) 0.000 (-17.3%) -0.009 (13.5%) -0.027 (12.6%)Motor -0.014 (9.6%) 0.001 (-33.2%) 0.004 (-5.8%) -0.010 (4.5%)

Unexplained -0.040 (27.3%) -0.000 (11.5%) -0.042 (61.6%) -0.057 (26.0%)

Panel B: Decomposition in the Subgroup Age 35-44

1980-1990 1990-2000 2000-2015 1980-2015

Total Wage -0.164 (100.0%)% -0.065 (100.0%) -0.121 (100.0%) -0.351 (100.0%)Gap ChangeChanges inCharacteristics -0.010 (6.3%) -0.058 (87.9%) -0.037 (30.9%) -0.117 (33.3%)Skills -0.028 (17.2%) -0.019 (28.4%) -0.014 (20.6%) -0.072 (20.6%)Math -0.002 (1.5%) -0.002 (2.7%) -0.002 (1.7%) -0.008 (2.4%)Directness -0.033 (20.3%) -0.033 (50.0%) -0.023 (19.0%) -0.093 (26.7%)Caring 0.004 (-2.8%) 0.016 (-25.2%) 0.011 (-8.9%) 0.028 (-8.0%)Motor 0.003 (-1.8%) -0.001 (0.8%) 0.000 (-0.4%) 0.002 (-0.5%)

Changes inreturns toCharacteristics 0.001 (-0.4%) 0.015 (-22.4%) 0.004 (-3.1%) 0.030 (-8.7%)Skills -0.026 (15.9%) -0.010 (15.5%) -0.012 (9.8%) -0.037 (10.4%)Math 0.002 (-1.5%) 0.000 (-0.5%) -0.000 (0.1%) 0.005 (-1.4%)Directness -0.008 (4.7%) 0.001 (-1.3%) -0.001 (0.5%) -0.003 (0.9%)Caring -0.018 (11.1%) -0.001 (1.7%) -0.014 (11.7%) -0.030 (8.5%)Motor -0.003 (1.6%) -0.010 (15.5%) 0.003 (-2.5%) -0.009 (2.4%)

Unexplained -0.100 (61.0%) 0.006 (-9.5%) -0.062 (51.1%) -0.156 (27.1%)


Table 3.14 continue

Panel C: Decomposition in the Subgroup Age 45-54

1980-1990 1990-2000 2000-2015 1980-2015

Total Wage -0.095 (100.0%) -0.105 (100.0%) -0.052 (100.0%) -0.252 (100.0%)Gap ChangeChanges inCharacteristics -0.016 (16.5%) -0.038 (36.1%) -0.039 (76.4%) -0.094 (37.3%)Skills -0.027 (28.5%) -0.018 (17.5%) -0.016 (31.9%) -0.072 (28.7%)Math -0.002 (2.0%) -0.002 (2.5%) -0.002 (4.3%) -0.007 (2.8%)Directness -0.038 (40.2%) -0.032 (30.1%) -0.015 (30.5%) -0.090 (35.8%)Caring 0.011 (-11.8%) 0.016 (-15.2%) -0.001 (1.7%) 0.022 (-8.5%)Motor 0.002 (-1.8%) -0.000 (0.1%) 0.002 (-4.6%) 0.004 (-1.4%)

Changes inreturns toCharacteristics 0.002 (-2.5%) 0.023 (-22.4%) 0.003 (-5.5%) 0.030 (-11.7%)Skills -0.015 (15.7%) -0.028 (27.0%) -0.008 (16.0%) -0.041 (16.5%)Math -0.000 (0.0%) 0.001 (-0.5%) -0.000 (0.0%) 0.001 (-0.4%)Directness -0.001 (1.2%) -0.003 (2.6%) -0.002 (3.2%) -0.001 (0.4%)Caring -0.012 (12.1%) -0.017 (16.3%) -0.010 (17.4%) -0.033 (13.1%)Motor -0.002 (2.5%) -0.009 (8.6%) 0.002 (-4.7%) -0.008 (3.4%)

Unexplained -0.040 (41.8%) -0.044 (41.7%) 0.010 (-18.8%) -0.074 (29.3%)


Figure 3.11: Gender Differences in Skills by Age Group

3.5 Conclusion

In this paper, we spend effort on understanding the gender wage gap in the U.S by looking

at the gender differences in the skills presented in the work place. Added to the roles of

cognitive skills and motor skills, the roles of two distinguished people skills, directness skills

and caring skills, are emphasized in this paper. As gender difference in the personalities and

attitudes are well-documented in the psychology and economics literature, we find substantial

gender differences in directness skills and caring skills. Males had advantage in directness

skills which is related to decision making, negotiation, and persuasion at beginning of 1980

but the females caught up and reversed the difference in 1990. However, caring skills which

is important in creating cooperation and providing assistance always present a higher level

in the female workers. The difference is stable over time. The market outcomes are also

different: directness skills bring positive and stable return but caring skills bring negative

return with an increasing trend (decreasing in absolute values). We find that 25%-32% of the

gender wage gap could be explained by the gender differences in the caring and directness

skills. Over our sample period, 22.5% of the change in the gender wage difference could be

explained by the growing directness in female workers and the increasing return to caring

skills could account for another 20.4% of the change. Similar to the previous studies, we also


find that the growing math skills in female workers and decreasing return to motor skills also

help narrowing the gender wage difference.

We also document the fact that the gender wage gap in the low education group nar-

rows faster than the one in the high education group after 2000. A between-group comparison

shows that the return to caring skills grows faster in the low education group and also that

females in the low education group obtain more advantage in the directness skills. More than

80% of the between-group difference could be explained by these two factors. In addition,

we find that math skills only matter in the high education group and females are still in

disadvantage in math skills despite of their higher education achievements. In contrast, motor

skills only matter in the low education group and males also hold advantage in this category.

Recently studies document that the gender wage differences in the higher percentiles of the

wage distribution narrow much slower that the ones in the lower percentiles. Much of the

explanation emphasizes on the gender differences in STEM majors/occupations. Our study

supplements the explains with an angle of people skills. The difference in the directness skills

and caring skills may also account for the slower change in the high income groups.

Between-group differences can also be found in the sub-groups by race, marital status,

and age. Substantial smaller wage differences are found in the black, unmarried, and young

(age 25-34) sub-groups respectively. A common feature in these groups is the better position

of female workers in the directness skills. In 1980, female workers have either advantage or

negligible disadvantage in directness skill in these sub-groups. Since directness skill grows

faster in female workers, they obtain and enlarge their advantage over males from 1983.

In sum, we find that distinguishing the people skills into directness skills and caring skills can

better explain the gender difference both in skills and in wage. This distinguish also partially

account for the between-groups that are found in different education, race, marital status,

and age groups. An interesting question for us to answer is how could genders could have

such prominent differences in people skills? Current literature provide many clues such as

personalities (nature), education, and social norms (nurture). But the driving reason is still

waiting to be found.

Chapter 4

Returns to Skills and Gender Wage

Gap Across OECD Countries

(ABSTRACT)

Using the dataset from Survey of Adult Skills (PIAAC), we document significant cross-country

variation in gender wage gap among OECD countries. In the analysis of the returns to

productive characteristics and skills, we find significant cross-country variation in the gender

differences in returns. The gender differences in returns to basic labor and experience are

found to be the most important factors in explaining the gender wage gap. In addition, gender

differences in returns to cognitive and directness skills are playing milder but substantial

roles in explaining the wage gap. We also find the social institutions and attitudes indicators

are related to the cross-country variation in gender differences. These indicators provide

potential direction for the future research.

4.1 Introduction

Gender gap on average wage as an important indicator of gender equality shows significant

cross-country variation from less than 4% in Slovenia to more than 40% in Japan. (Figure

4.1) Many studies have made effort to understand the cross-country variation.1 Several

hypothesis have been proposed such as the childcare policy and wage setting institutions

1See Weichselbaumer and Winter-Ebmer (2005) for a worldwide study; Arulampalam et al. (2007); Blauand Kahn (1992, 1996, 2003) for studies in the industrialized countries; Brainerd (2000); Newell and Reilly(2001); Orazem and Vodopivec (2000) for the transition countries

86


(Arulampalam et al., 2007), unequal returns to skills in females (Blau and Kahn, 1996),

collective bargaining power (Blau and Kahn, 2003), and the industry structure (Olivetti

and Petrongolo, 2014). These studies have provided tremendous insights but due to the

restriction of data availability, most studies have two drawbacks: weak comparability of the

data from different countries and lack of direct measurements of workers’ skills. As skill

measurements other than proxies for skills such education achievements have been shown

as important wage determinants (Bacolod and Blum, 2010; Beaudry and Lewis, 2014;

Black and Spitz-Oener, 2010; Borghans et al., 2014; Yamaguchi, 2016), it is important to

check the cross-country variation in the returns to skills by employing coherent data from

different countries. The recently released dataset, Survey of Adult Skills (PIAAC) from

OECD countries, contain the direct measurements of workers’ skills, Using the PIAAC data,

in this study we intend to answer three questions. The first one is how the returns to observed

and unobserved skills vary across countries. The second one is how the gender differences in

the returns to skills explain the gender wage gaps. The last one is what are the resources of

the cross-country variation in gender differences in returns to skills. To our best knowledge,

the study from Hanushek et al. (2015) is the only one investigating the cross-country variation

in returns to skills using PIAAC data but they do not focus on the gender differences. We

document significant cross-country variation in the gender differences in returns. The gender

differences in returns to basic labor and experience are found to be the most important factors

in explaining the gender wage gap. In addition, gender differences in returns to cognitive and

directness skills are also playing milder but substantial roles in explaining the wage gap. We

also find that institutions are more relevant to the gender difference in basic labor and in expe-

rience, social attitudes toward female working are more relevant to the difference in education

premium, and people’s valuation in jobs are more relevant to the differences in returns to skills.

The rest of paper is organized as following. Section 4.2 introduces the PIAAC data and other

dataset employed in this study. Section 4.3 presents the empirical analysis results on gender

wage gap decompositions. Section 4.4 presents the correlations between gender differences in

returns with social institutions, attitudes, and values. Section 4.5 provides an ending note

and extensions for the future work.


Figure 4.1: Gender Waqe Gap by Country

4.2 Data

The main dataset is the public use files (PUF) of Survey of Adult Skills. This survey is

developed and conduct by the Programme for the International Assessment of Adult Compe-

tencies (PIAAC) in OECD. The purpose of this survey is to collect information and data on

how adults use their skills at home and at work. Participants of the survey are asked to take

tests that examine the adults’ proficiency in cognitive skills. The test results are processed

into scores that are presenting the individual skill level in three aspects: literacy, numeracy

and, problem solving in technology-rich environments.2 PIAAC also collects rich informa-

tion from participants such as demographics, education, occupation, income, and tasks in jobs.

The survey has been conducted in more than 40 countries. However, the PUF only contains

the data from 31 countries.3 Due to the confidentiality restriction, the income information

is missing in the countries of Austria, Canada, Germany, New Zealand, Singapore, Sweden,

Turkey, and United States. In addition, the data from Russia is still under revision. We

include the Russian data in our sample with caution.

All the participants of the survey are between the age 16 to 65. We do not add age

2For each skill, literacy, numeracy, and problem solving, 10 plausible values are reported in the PUF files.We take the first plausible values as they are commonly used in the literature.

3These countries are Austria, Belgium, Canada, Chile, Cyprus, Czech Republic, Denmark, Estonia,Finland, France, Germany, Greece, Ireland, Israel, Italy, Japan, Korea, Lithuania, Netherlands, New Zealand,Norway, Poland, Russia, Singapore, Slovak Republic, Slovenia, Spain, Sweden, Turkey, United Kingdom,United States. These countries participate in the surveys in two different waves: the first wave happenedbetween 2008 to 2012; the second wave happened between 2012 to 2016.


restriction to our sample selection. Individual hourly wage is reported in 23 countries in PUF.

All the wages has been converted to the 2012 U.S dollars through Purchasing Power Parity

(PPP). The conversion enables us to conduct between-country analysis on the returns to

productive characteristics and skills. We keep observations from all the civilian wage earners

with positive hourly wage in non-agricultural occupations. As our sample is much smaller

than Census data, the average wage is more likely to be biased by the extreme values. As

a control to the extreme values, we change all income observations below the 1% level in a

country’s wage distribution to the 1% level and we lower the observations above the 99%

level to the 99% level. In our analysis, income is natural logged. The numbers of gender

differences in wages could be interpreted as the percentage differences.

Education in PIAAC is reported with a scale between 1 to 15 under the scheme of Internation-

al Standard Classification of Education (ISCED 1997).4 Since the length of education cannot

be recovered, we could not observe the return to education directly. In order to investigate

the return to education, more specifically, the higher education premium, we convert the

education to five categories: high school drop (hsd), high school graduate (hsg), some college

(sc), college graduate (cg), and post graduate (pg).

An advantage of PIAAC is the availability of individual actual working experience which is

absent in most survey or census data. Yet, the variable only measures the experience in the

industry of current job.

Cognitive skills are reported in a scale between 0 to 400 with most observations having

values in the range 300-400. In order to interpret the returns to the skills, we re-scale the

skills measurements to be with mean at 0 and standard deviation at 1. The returns can

be interpreted as the wage growth rate as the skill level move up one standard deviation.

Since all three skills are highly correlated to each other, we choose the numeracy skills as

our measurement of cognitive skills. We also use literacy and problem solving skills as the

measurement, the results are robust to the choice of measurement.

Though cognitive skills are important measurement of workers’ skills, a growing interest in the

literature is the interactive or people skills.(Bacolod and Blum, 2010; Black and Spitz-Oener,

2010; Borghans et al., 2014; Deming, 2017; Spitz-Oener, 2006). In Borghans et al. (2008b),

they find directness skills which represent people’s ability in making decisions, persuading, and

negotiating. However, people skills are not directly measured in PIAAC. Following Borghans

et al. (2008b), we constructed the measurement of directness skills from the 8 job tasks of each

person. A detailed list of the tasks are in section 4.6.1. Using principle component analysis,

4Education is not reported in the data from Estonia


we reduce the skills measurements to one dimension. Then similar to what we do for the

cognitive skills, we re-scale the measurement to be with mean at 0 and standard deviation at 1.

In order to explain the cross-country variation in the gender differences in returns to produc-

tive characteristics and skills, we use several resources to examine the cross-country differences

in institutions, social attitudes, and social values.

OECD Indicators of Employment Protection Legislation (EPL) “measure the procedures and

costs involved in dismissing individuals or groups of workers and the procedures involved in

hiring workers on fixed-term or temporary work agency contracts.”5 The data provides four

indicators: protection of permanent workers against (individual) dismissal, specific require-

ments for collective dismissal, regulation on temporary forms of employment, and protection

of permanent workers against individual and collective dismissals that is a weighted average

of the first two indicators with weights 5/7, 2/7 respectively.6 Higher values of indicators

imply higher labor market protection. All observations are from the year 2013 except the

ones for United Kingdom and Slovenia which are from the year 2014.

Another measurement of labor market protection is the trade union density (TUD). We

extract the data from OECD website.7 The numbers are the percentages of the total wage and

salary earners that are covered by the trade unions in each country. Survey data are employed

in producing TUD when they are available otherwise administrative data are employed.8 All

observations are from the year 2012 except the one for United Kingdom which is from the

year 2013.

The market protection level could also result in wage differentials. According to OECE,

Product Market Regulation (PMR) indicators “are a comprehensive and internationally-

comparable set of indicators that measure the degree to which policies promote or inhibit

competition in areas of the product market where competition is viable”.9 Higher values

in the indicators imply higher market protection level.10 All observations are from the year

2013.

We also collect the information of total paid leave weeks available to mothers and the

5Data source: http://www.oecd.org/els/emp/oecdindicatorsofemploymentprotection.htm6See Venn (2009) for details7Data source: https://stats.oecd.org/Index.aspx?DataSetCode=TUD8See Visser (2011) for details9Data source: http://www.oecd.org/eco/growth/indicatorsofproductmarketregulationhomepage.htm.

10See Wolfl et al. (2009) for details.


public spending on early childhood education and care.11 The public spending is measured

as the percentage of GDP. All observations are from the year 2013.

As social attitudes and social values could also influence on the work choices and wage

determination, we also collect the relevant information from the World Value Survey (WVS)12

and European Value Survey (EVS)13. Though WVS has been conducted six times (1981,

1989, 1994, 1999, 2005, 2010) and EVS has been conducted 4 times (1981, 1990, 1999, 2008),

we couldn’t construct a panel for our sample countries because countries didn’t participate in

all the survey waves. To deal with the dynamics of attitudes and values, we try to restrict

our sample to the waves after 1999 unless some countries only have observations in the earlier

years. The final values are the average of all selected waves. Following Fortin (2005), we

select five social attitudes and four social values from the survey. We make the attitudes and

values indicators to be gender specific.

All social attitudes are extracted from the questions relative to the level of discrimina-

tion toward female working. The first question is “when job are scarce men should have

more right to a job than women.” People could choose agree (value 1), disagree (value 2),

and neither agree nor disagree (value 3) to the statement. We re-scale value 2 and 3 to 0 in

which higher value implies more discriminatory attitude toward female working. The second

question is “ a working mother can establish just as warm and secure a relationship with her

children as a mother who does not work.” The third question is “being a housewife is just as

fulfilling as working for pay.” The fourth one is “both the husband and wife should contribute

to household income.” For these question, people could answer strongly agree (value 1), agree

(value 2), disagree (value 3), or strongly disagree (value 4) to the statements. We re-assign

value 0 to the answers disagree and strongly disagree and value 1 to the answers agree and

strongly agree. Higher values in the second and fourth questions imply more positive attitude

toward working females and higher value in the third question implies more traditional view

toward females. The last questions is “competition is good or harmful.” The answers are in

a scale 1-10 with 1 as good. We re-scale the answers to have a range 0-1 where 1 still stands

for “competition is good”.

Four working values are extracted from people’s answers to the questions if they think

“good pay”, “good hours”, “meeting people”, and “useful to the society” would be important

factors in their job choices. If they mentioned one or more factors, value one would be

assigned to the corresponding factor(s).

11Data source: http://www.oecd.org/els/family/database.htm.12Data source: http://www.worldvaluessurvey.org/wvs.jsp.13Data source: http://www.europeanvaluesstudy.eu/.


4.3 Empirical Analysis

4.3.1 Model

In line with the literature, we employ the expanded Mincerian equation as the basic analysis

model. We include education, experience, and skills measurements as wage determinants.

In order to implement gender difference study, we obtain the returns to the productive

characteristics and skills from gender specific regressions for each country.14We specify the

wage equation for each individual i with gender g in a country j :

lnωigj = Z ′igjαgj +X ′

igjβgj + εigj, (4.1)

where ωigj is the hourly wage, Zigj are worker’s skills including cognitive and directness skills.

Xigj are common worker characteristics including working experience and education. αgj and

βgj are the corresponding market returns to the skills and worker characteristics. We report

the country specific returns in Table 4.1.

Coefficients on constant could be interpreted as the return to the basic labor, labors with

lowest education, labor market experience and skill levels. In most countries, the gender

difference in this return are not significant. However, East European countries and countries

in transition stand out. Six countries in our sample, Czech Republic, Estonia, Lithuania,

Poland, Slovak Republic, and Slovenia have the highest differences though Slovenia has the

lowest gender wage gap in our sample. The only exception is Russia in which we find most of

the returns are very different from the rest of the sample.

For most countries, males have significantly higher return to experiences. Belgium, Greece,

Ireland, and Netherlands are the only exceptions. The higher returns in males may result

from the better career continuity of males. We also hypothesis that the exceptions may step

from the labor market institutions.

Education premium are higher in females in most cases. This implies that well-educated

females are in relatively better position than their counterparts with lower education achieve-

14We also test the gender difference within a equation, 18 out of 23 countries’ results suggest significantgender difference.


Tab

le4.

1:R

eturn

sto

Pro

duct

ive

Char

acte

rist

ics

and

Skills

by

Cou

ntr

y

Cou

ntr

yB

asic

Lab

orE

xp

erie

nce

Educa

tion

Cog

nit

ive

Skills

Dir

ectn

ess

Skills

Mal

eF

emal

eM

ale

Fem

ale

Mal

eF

emal

eM

ale

Fem

ale

Mal

eF

emal

eB

elgi

um

2.46

4***

2.28

2***

0.02

5***

0.02

6***

0.16

9***

0.39

2***

0.05

2***

0.04

0***

0.03

2***

0.01

8**

(0.0

24)

(0.0

24)

(0.0

02)

(0.0

02)

(0.0

64)

(0.0

49)

(0.0

10)

(0.0

10)

(0.0

09)

(0.0

08)

Chile

1.68

6***

1.79

7***

0.00

9***

-0.0

14**

*0.

909*

**0.

765*

**0.

140*

**0.

182*

**0.

034*

0.03

5(0

.059

)(0

.068

)(0

.005

)(0

.006

)(0

.081

)(0

.083

)(0

.024

)(0

.027

)(0

.019

)(0

.021

)C

ypru

s1.

889*

**1.

665*

**0.

053*

**0.

038*

**0.

489*

**0.

703*

**0.

053*

**0.

064*

**0.

002

0.03

5**

(0.0

77)

(0.0

62)

(0.0

06)

(0.0

05)

(0.0

64)

(0.0

57)

(0.0

18)

(0.0

14)

(0.0

15)

(0.0

14)

Cze

chR

epublic

1.86

3***

1.55

4***

0.02

2***

0.01

9***

0.24

3***

0.52

5***

0.05

2***

0.02

8**

0.04

6***

0.01

5(0

.036

)(0

.033

)(0

.004

)(0

.003

)(0

.066

)(0

.073

)(0

.015

)(0

.014

)(0

.014

)(0

.012

)D

enm

ark

2.47

8***

2.53

1***

0.04

1***

0.03

0***

0.25

1***

0.17

2***

0.06

2***

0.04

2***

0.06

1***

0.03

4***

(0.0

27)

(0.0

25)

(0.0

02)

(0.0

02)

(0.0

31)

(0.0

24)

(0.0

09)

(0.0

07)

(0.0

08)

(0.0

07)

Est

onia

2.16

2***

1.83

1***

0.02

1***

0.01

3***

..

0.11

70.

152*

**0.

116*

**0.

132*

**(0

.035

)(0

.028

)(0

.004

)(0

.003

).

.(0

.014

)(0

.012

)(0

.014

)(0

.011

)F

inla

nd

2.44

7***

2.37

9***

0.03

0***

0.01

9***

0.23

6***

0.25

4***

0.04

9***

0.06

5***

0.06

8***

0.04

1***

(0.0

33)

(0.0

32)

(0.0

02)

(0.0

02)

(0.0

36)

(0.0

32)

(0.0

10)

(0.0

09)

(0.0

08)

(0.0

07)

Fra

nce

2.14

5***

2.08

8***

0.03

6***

0.02

6***

0.24

2***

0.29

7***

0.06

9***

0.05

3***

0.04

4***

0.03

3***

(0.0

33)

(0.0

27)

(0.0

03)

(0.0

03)

(0.0

33)

(0.0

30)

(0.0

10)

(0.0

10)

(0.0

09)

(0.0

08)

Gre

ece

1.58

4***

1.55

4***

0.03

4***

0.03

6***

0.51

4***

0.46

3***

-0.0

020.

009

0.02

90.

001

(0.0

57)

(0.0

49)

(0.0

06)

(0.0

06)

(0.0

65)

(0.0

52)

(0.0

19)

(0.0

19)

(0.0

19)

(0.0

17)

Irel

and

2.38

7***

2.18

6***

0.03

7***

0.04

8***

0.34

9***

0.47

8***

0.08

7***

0.07

6***

0.06

7***

0.04

5***

(0.0

54)

(0.0

43)

(0.0

04)

(0.0

04)

(0.0

52)

(0.0

42)

(0.0

17)

(0.0

15)

(0.0

13)

(0.0

11)

Isra

el1.

732*

**1.

808*

**0.

054*

**0.

032*

**0.

283*

**0.

343*

**0.

095*

**0.

105*

**0.

050*

*0.

036*

(0.0

60)

(0.0

55)

(0.0

06)

(0.0

06)

(0.0

54)

(0.0

49)

(0.0

23)

(0.0

23)

(0.0

20)

(0.0

19)

Ital

y2.

168*

**2.

187*

**0.

026*

**0.

017*

**0.

462*

**0.

400*

**0.

054*

**0.

066*

**0.

065*

**0.

055*

**(0

.045

)(0

.051

)(0

.004

)(0

.005

)(0

.043

)(0

.042

)(0

.015

)(0

.016

)(0

.014

)(0

.014

)Jap

an2.

036*

**2.

091*

**0.

049*

**0.

016*

**0.

215*

**0.

268*

**0.

099*

**0.

078*

**0.

069*

**0.

102*

**


Tab

le1

Con

tinue

Cou

ntr

yB

asic

Lab

orE

xp

erie

nce

Educa

tion

Cog

nit

ive

Skills

Dir

ectn

ess

Skills

Mal

eF

emal

eM

ale

Fem

ale

Mal

eF

emal

eM

ale

Fem

ale

Mal

eF

emal

e(0

.039

)(0

.037

)(0

.003

)(0

.003

)(0

.037

)(0

.046

)(0

.013

)(0

.013

)(0

.013

)(0

.012

)K

orea

,R

ep.

2.15

3***

2.09

9***

0.03

9***

0.01

7***

0.34

7***

0.46

3***

0.05

6***

0.03

50.

127*

**0.

082*

**(0

.047

)(0

.046

)(0

.005

)(0

.006

)(0

.044

)(0

.054

)(0

.018

)(0

.022

)(0

.017

)(0

.019

)L

ithuan

ia1.

707*

**1.

337*

**0.

013*

**0.

012*

**0.

373*

**0.

554*

**0.

089*

**0.

061*

**0.

049*

**0.

047*

**(0

.059

)(0

.037

)(0

.004

)(0

.003

)(0

.071

)(0

.043

)(0

.016

)(0

.012

)(0

.019

)(0

.012

)N

ether

lands

2.16

3***

2.02

5***

0.04

5***

0.05

6***

0.39

5***

0.42

3***

0.06

5***

0.02

5**

0.07

2***

0.03

7***

(0.0

38)

(0.0

33)

(0.0

03)

(0.0

03)

(0.0

29)

(0.0

28)

(0.0

11)

(0.0

12)

(0.0

09)

(0.0

10)

Nor

way

2.67

9***

2.59

0***

0.03

7***

0.02

9***

0.17

8***

0.21

7***

0.08

0***

0.06

2***

0.04

5***

0.00

4(0

.024

)(0

.023

)(0

.002

)(0

.002

)(0

.023

)(0

.017

)(0

.009

)(0

.009

)(0

.008

)(0

.007

)P

olan

d1.

704*

**1.

410*

**0.

029*

**0.

027*

**0.

131*

**0.

349*

**0.

068*

**0.

082*

**0.

065*

**-0

.013

(0.0

25)

(0.0

40)

(0.0

03)

(0.0

03)

(0.0

41)

(0.0

48)

(0.0

12)

(0.0

14)

(0.0

11)

(0.0

11)

Russ

ian

Fed

erat

ion

1.32

7***

1.39

1***

0.00

20.

009

0.32

00.

035

0.18

8***

0.05

5-0

.027

0.05

7(0

.182

)(0

.204

)(0

.015

)(0

.011

)(0

.236

)(0

.240

)(0

.056

)(0

.043

)(0

.057

)(0

.035

)Slo

vak

Rep

ublic

1.79

8***

1.43

7***

0.01

8***

0.00

60.

167*

*0.

460*

**0.

080*

**0.

073*

**0.

057*

**0.

009

(0.0

44)

(0.0

42)

(0.0

04)

(0.0

04)

(0.0

75)

(0.0

56)

(0.0

17)

(0.0

16)

(0.0

17)

(0.0

13)

Slo

venia

1.76

8***

1.54

5***

0.01

9***

0.01

9***

0.47

2***

0.64

5***

0.09

6***

0.05

8***

0.05

9***

0.02

6**

(0.0

36)

(0.0

43)

(0.0

03)

(0.0

03)

(0.0

39)

(0.0

37)

(0.0

10)

(0.0

12)

(0.0

11)

(0.0

11)

Spai

n2.

048*

**2.

018*

**0.

021*

**0.

016*

**0.

422*

**0.

458*

**0.

071*

**0.

085*

**0.

052*

**0.

028*

*(0

.039

)(0

.037

)(0

.003

)(0

.004

)(0

.042

)(0

.041

)(0

.016

)(0

.016

)(0

.013

)(0

.012

)U

nit

edK

ingd

om2.

179*

**2.

173*

**0.

039*

**0.

029*

**0.

384*

**0.

412*

**0.

121*

**0.

097*

**0.

092*

**0.

063*

**(0

.031

)(0

.026

)(0

.002

)(0

.002

)(0

.027

)(0

.022

)(0

.010

)(0

.009

)(0

.009

)(0

.008

)D

ata

sourc

e:P

IAA

C**

*S

ign

ifica

nt

atth

e1

perc

ent

leve

l.**

Sig

nifi

can

tat

the

5pe

rcen

tle

vel.

*S

ign

ifica

nt

atth

e10

perc

ent

leve

l.


ments.

Cognitive skills and directness skills also reward males more in most countries. We hy-

pothesis that the gender difference in returns may result from occupation choices which could

be influenced by the society’s attitudes and values.

4.3.2 Decomposition on Gender Wage Gap

We try to identity the resource of gender wage gap by constructing the counterfactual female

wages. By assuming females having either the level or the return to a single productive

characteristic or a type of skills of males, a counterfactual gender wage gap is calculated. The

difference between actual wage gap and the counterfactual wage gap would be the part than

could be explained by the level or the return to the wage determinant. For example, if we

assume females having the same average education level as males, then the counterfactual

wage gap would reflect any other difference but the education level. Meanwhile, the actual

wage gap is the sum of education difference and other differences. In this sense, the difference

between the actual difference and the counterfactual difference would be accounted by the

differences in education. Table 4.2 present the percentage of the gender wage gap account by

each wage determinant’s level and return.

Gender difference in basic labor is the most important factor affecting the gender wage gap.

For ten countries, the difference in the basic labor can fully account the wage gaps. The

next important factor is return to experience. Another five countries’ gender wage gaps are

fully accounted by the gender differences in the returns to experience. The level difference

in experience has much milder power in explaining the wage gap which implies the general

increasing trend in the female working experience. In contrast, when education level or

education return are set as the counterfactual factor, gender wage gaps get enlarged in many

countries. This reflect the general higher education levels and returns in females. Take the

factors above together, we could see females have disadvantages in the starter wage and

also in the reward to their experiences and their disadvantages have to be compensated by

achieving higher education levels.

The returns to skills play minor but still significant roles. Either the returns to cogni-

tive skills or directness skills can account more than 10% of the gender wage gaps in twelve

countries. A reason causing the smaller effects of the returns to skills is the scale of the skill

measurement. The results show that compared to the gender differences in skill levels, the


Tab

le4.

2:P

erce

nta

geof

Gen

der

Wag

eD

iffer

nce

Expla

ined

by

the

GE

nder

Diff

eren

cein

the

Ret

urn

san

dL

evel

s

Cou

ntr

yB

asic

Lab

orE

duca

tion

Exp

erie

nce

Cog

nit

ive

Skills

Dir

etnes

sSkills

Ret

urn

Lev

elR

eturn

Lev

elR

eturn

Lev

elR

eturn

Lev

elB

elgi

um

244.

2%-1

07.8

%-4

1.5%

-63.

5%30

.4%

14.3

%-0

.2%

-2.0

%2.

0%C

hile

-0.6

%76

.1%

-3.8

%10

2.3%

30.3

%49

.3%

17.7

%18

.1%

16.9

%C

ypru

s16

8.6%

-166

.3%

-56.

1%80

.7%

34.1

%-0

.9%

-6.8

%-3

.2%

-4.7

%C

zech

Rep

ublic

149.

1%-5

1.3%

-7.0

%13

.3%

4.3%

8.1%

3.3%

2.6%

4.1%

Den

mar

k-6

8.9%

0.5%

-21.

8%16

9.4%

25.8

%14

.5%

8.7%

4.6%

6.1%

Est

onia

92.4

%-0

.6%

-0.6

%5.

8%0.

2%5.

1%-1

.0%

-0.8

%-5

.5%

Fin

land

43.8

%-1

5.3%

-21.

5%75

.5%

3.1%

8.7%

-1.5

%0.

5%-5

.0%

Fra

nce

49.4

%-3

9.2%

-27.

7%76

.7%

13.6

%6.

0%-1

.6%

-2.4

%0.

2%G

reec

e24

.9%

25.4

%-3

6.0%

-19.

3%39

.3%

-4.7

%-7

.8%

-7.5

%-5

.5%

Irel

and

248.

8%-9

7.4%

-24.

1%-4

3.7%

38.3

%34

.4%

17.1

%15

.8%

16.0

%Is

rael

-85.

7%-8

0.0%

-35.

4%27

0.6%

15.1

%19

.3%

2.2%

3.4%

4.2%

Ital

y-1

4.3%

33.8

%-5

8.6%

148.

8%60

.6%

18.2

%14

.8%

12.8

%9.

0%Jap

an-1

1.3%

0.1%

10.6

%75

.1%

9.9%

7.0%

0.9%

3.6%

14.0

%K

orea

,R

ep.

19.9

%-1

2.8%

15.4

%52

.1%

13.2

%4.

5%2.

7%-1

.1%

8.8%

Lit

huan

ia20

3.2%

-34.

70%

-35.

0%-1

9.3%

1.5%

7.0%

9.2%

7.0%

1.1%

Net

her

lands

125.

8%-2

1.3%

-7.5

%-4

0.1%

19.7

%8.

8%2.

1%-0

.4%

8.3%

Nor

way

63.5

%-1

7.7%

-11.

6%48

.3%

5.1%

11.1

%0.

4%-1

.1%

1.4%

Pol

and

360.

2%-1

06.5

%-1

08.5

%-3

9.8%

24.7

%10

.8%

11.2

%19

.3%

14.7

%R

uss

ian

Fed

erat

ion

-20.

6%80

.5%

13.1

%91

.4%

22.7

%17

.6%

19.8

%20

.1%

6.6%

Slo

vak

Rep

ublic

158.

6%-6

6.2%

-36.

7%12

.1%

-6.6

%-8

.0%

-7.0

%-6

.4%

-8.5

%Slo

venia

579.

9%-2

89.2

%-2

81.1

%-4

8.4%

-11.

8%-2

1.7%

-14.

9%-2

7.4%

-30.

0%Spai

n24

.3%

47.6

%-2

8.0%

24.5

%36

.4%

18.2

%2.

2%2.

4%3.

8%U

nit

edK

ingd

om28

.9%

4.3%

16.7

%10

1.1%

29.6

%39

.9%

26.5

%25

.4%

21.3

%D

ata

Sou

rce:

PIA

AC


gender differences in returns to skills are more important in explaining the gender wage gaps.

4.4 Explaining the Gender Differences in Returns

In this section, we want to find some social institutions or social norms that are quantitively

measurable and potentially could affect the male/female returns differently. As suggested in

the literature, we explore five institution indicators, four attitude indicators toward female

working, and four value indicators in job decisions. We correlate the gender differences in

returns with each individual indicator. Here, we pick up the most relevant factors and plot

them against the return differences. The full correlation table is reported in section 4.6.2.

Figure 4.2: Correlation between Gender Difference in Basic Labor with Selected Indicators

The gender difference in returns to basic labors is weakly correlated with many institutions

and social attitudes toward female working. Among these indicators, the length of paid

maternity leave and female attitudes toward competition stands out. (Figure 4.2) In general,

the longer the paid leave is and the more comfortable females feel about competition, the


larger gap could be observed in a country. The impact of paid maternity leave on the females’

labor outcome has been studied in several countries. Lalive and Zweimuller (2009) investigate

the impact of two maternity leave reforms in Austria and they find reforms (longer maternity

leave) increase the fertility rate and reduce the female employment postbirth. They also find

female earnings also decrease in the short-run of the reforms. In their study of maternity

leave reforms in Germany, Schonberg and Ludsteck (2014) also find the decrease of mothers’

postbirth employment rates in the short-run of all five reforms in Germany. The positive

correlation shown here add another piece of potential evidence to the story. Since longer paid

maternity leave is associated with lower rate of return to work, employers have the incentive

to set lower wages for females at the first hand. The precaution should impact on the basic

labors most since they have the least bargain power.

The positive correlation of women’s positive attitudes toward competition and the gen-

der difference in returns to basic labor seems counter-intuitive at first glance because the

consensus in the literature attributes the gender wage gap to females’ inferior performances

under competition.15 A plausible explanation is that in a society emphasizing on competition

would provide less protection on the less competitive workers. Since the basic female workers

are most likely to be the least competitive workers, their wages are relatively worsened even

compared to their male counterparts.

The most relevant indicators to the gender difference in returns to experience are the em-

ployment protection legislation (EPL) level and the social positive attitude toward the value

of being a housewife. (Figure 4.3) Higher EPL level would enhance the chance, especially

for females, to have longer work experience which is expected to increase the returns to

experience. As expected, a negative correlation exists between the gender difference in returns

and the EPL levels. In contrast, if females in a society agrees more on the traditional role of

females i.e. it’s fulfilling to be a housewife, then their work experience would be shortened

and the return to experience would also be lower. A positive correlation between females’

attitudes and the gender difference is observed.

As shown in Figure 4.4, interesting correlations exist between gender differences in returns to

education and female attitudes. On the one hand, when females hold more positive attitudes

toward competition, their education premium is larger compared to the premium in males.

This is consistent with consensus in the literature. When females feel comfortable with

competition and have better competitiveness, they would outperform less competitive females

more. On the other hand, a discriminatory attitude against female working, i.e. males have

priority to work when jobs are scarce, is associated with a higher education premium in

15See Gneezy et al. (2003)


Figure 4.3: Correlation between Gender Difference in Returns to Experience with SelectedIndicators

females. A possible explanation is that when facing discrimination, females who would obtain

higher education level and work afterward would be ones with better abilities.

The only indicator seemingly correlated to the gender difference in returns to cognitive skills

is the males’ valuation in good working hours. (Figure 4.5) When males giving higher weight

to the good working hours in their job decisions, their returns to cognitive skills become lower

relative to the returns to females. This is understandable since when people with higher

valuation in good hours would intend to sacrifice some wages for good hours even though

they have the same cognitive skills as people who make the opposite job choices.

The correlations between the gender difference in returns to directness skills with indicators

are the hardest to interpret. (Figure 4.6) On the one hand, when males have higher valuation

in good hours comparing to females, i.e, the male to female gap in good hours’ valuation

getting bigger, the returns to males are also increasing. On the other hand, when males hold

higher valuation in the usefulness of a job to the society, their returns to directness decreases

relative to the returns to females. It seems that when males are choosing jobs according to

working hours, they still consider using their people skills for pecuniary returns but when


Figure 4.4: Correlation between Gender Difference in Returns to Education with SelectedIndicators

they choose their jobs according to altruistic intention, people skills no longer have impact

on the wages.


Figure 4.5: Correlation between Gender Difference in Returns to Cognitive Skills with SelectedIndicators


Figure 4.6: Correlation between Gender Difference in Returns to Directness Skills withSelected Indicators


4.5 Ending Notes and Work for the Future

In this paper, we investigate the gender wage gaps in 23 OECD countries and the gap levels

vary significantly across countries. With a simple Micerian wage equation, we also find that

the males and females are earning different returns to wage determinants and the gender

differences in returns also vary significantly across countries. By constructing counterfactual

wage gaps, we find that the gender differences in returns to basic labor and in returns to

experiences can account the biggest part of the gender wage gap. In addition, the gender dif-

ferences in returns to skills also play milder but still substantial roles in explaining the gender

wage gaps. As attempt to understand the resources of the gender differences in returns, we

resort to the across countries variation. Following the literature, we pick up twelve indicators

that are potentially correlated with the differences in returns: four institutional indicators

that reflect important aspects of labor and product markets, four attitude indicators that

reflect the attitudes toward female working, and four value indicators that reflect people’s

valuations in factors in job choices. By correlation the gender differences in returns with the

indicators, we find that institutions are more relevant to the gender difference in returns to

basic labor and in experience, social attitudes toward female working are more relevant to

the difference in education premium, and people’s valuation in factors are more relevant to

the differences in returns to skills.

One missing factor that are potentially important in determining the gender differences

is sectoral/industrial specific productivity change caused by the technology. One the one

hand, the productivity change may change or determine the industry structure which is an

important factor influences the labor demand. On the other hand, the technology change

also directly determines the returns to the skills. The gender wage gap could be mitigated or

enlarged as the returns to skills change with the technology.

4.6 Appendix

4.6.1 Definition of Directness Skills

According to the definition in Borghans et al. (2008b), 6 variables from PIAAC are included

as components of directness skills.


Table 4.3: Components of Directness Skills

Variable Name in PUF ContentF Q02b instructing, training or teaching peopole, individually or in groupsF Q02c making speeches or giving presentations in front of five or more peopleF Q02d selling a product or selling a serviceF Q02e advising peopleF Q04a persuading or influencing peopleF Q04b negotiating with people either inside or outside your firm or organisation

4.6.2 Correlation between Gender Differences in Returns with So-

cial Indicators

Table 4.4: Correlations with Social Institutions

Social Institutions Gender Differences in Returns toBasic Labor Experience Education Cognitive Directness

dismissal 0.186 -0.460 -0.166 0.239 0.034individual dismissal -0.124 -0.077 0.220 0.259 -0.145collectuve dismissal 0.360 -0.471 -0.439 0.007 0.199temprary dismissal 0.274 -0.042 -0.148 -0.365 0.168

pmr -0.212 0.262 0.166 0.284 -0.333union density -0.298 -0.092 0.225 0.052 0.102

childcare Spending -0.356 0.0301 0.275 0.263 0.190paid leave weeks 0.450 0.102 -0.188 -0.149 0.113

Data Source: PIAAC, OECD


Table 4.5: Correlations with Social Attitudes

Social Attitudes Gender Differences in Returns toBasic Labor Experience Education Cognitive Directness

scarce jobs male 0.333 0.193 -0.232 0.052 -0.296scarce jobs female 0.294 0.266 -0.358 0.050 -0.239

scarce jobs gap 0.262 -0.012 0.081 0.035 -0.273working mother male -0.442 0.263 0.384 0.327 -0.346

working mother female -0.408 0.185 0.371 0.276 -0.288working mother gap -0.144 0.273 0.072 0.194 -0.215

housewife male 0.216 0.292 -0.250 0.143 -0.255housewife female 0.076 0.389 -0.187 0.133 -0.207

housewife gap 0.327 -0.347 -0.104 -0.010 -0.063both income male 0.257 0.016 -0.135 -0.204 0.012

both income female 0.245 0.031 -0.131 -0.251 -0.035both income gap 0.120 -0.071 -0.055 0.178 0.229

Data Source: PIAAC, OECD

Table 4.6: Correlations with Social Values

Social Values Gender Differce in Returns onBasic Labor Experience Education Cognitive Directness

good pay male 0.415 -0.171 -0.071 0.009 -0.364good pay female 0.357 -0.066 -0.069 -0.027 -0.382

good pay gap -0.115 -0.206 0.045 0.108 0.316good hours male 0.220 0.218 -0.151 -0.397 -0.318

good hours female 0.156 0.250 -0.030 -0.264 -0.483good hours gap 0.164 -0.161 -0.375 -0.354 0.642useful jobs male 0.083 0.018 -0.165 -0.322 -0.096

useful jobs female 0.093 -0.033 -0.179 -0.323 0.054useful jobs gap -0.005 0.148 -0.002 -0.080 -0.443

meeting people male 0.197 -0.266 -0.132 -0.158 -0.117meeting people female 0.315 -0.405 -0.125 -0.022 -0.173

meeting people gap -0.284 0.333 -0.021 -0.337 0.134comp ok male 0.427 -0.077 -0.324 0.182 0.294

comp ok female 0.559 -0.105 -0.451 0.089 0.365comp ok gap -0.391 0.082 0.366 0.216 -0.218

Data source: PIAAC, OECD

References

Aaronson, D. and French, E. (2004). The effect of part-time work on wages: Evidence from

the social security rules. Journal of Labor Economics, 22(2):329–252.

Addison, J. T., Ozturk, O. D., and Wang, S. (2014). The role of gender in promotion and

pay over a career. Journal of Human Capital, 8(3):280–317.

Altonji, J. G. and Blank, R. M. (1999). Race and gender in the labor market. Handbook of

Labor Economics, 3:3143–3259.

Altonji, J. G. and Card, D. (1991). The effects of immigration on the labor market outcomes

of less-skilled natives. In Immigration, trade, and the labor market, pages 201–234.

University of Chicago Press.

Antonovics, K. and Town, R. (2004). Are all the good men married? uncovering the sources

of the marital wage premium. American Economic Review, 94(2):317–321.

Arulampalam, W., Booth, A. L., and Bryan, M. L. (2007). Is there a glass ceiling over Europe?

exploring the gender pay gap across the wage distribution. ILR Review, 60(2):163–186.

Autor, D. H. and Dorn, D. (2013). The growth of low-skill service jobs and the polarization

of the US labor market. American Economic Review, 103(5):1553–1597.

Autor, D. H., Katz, L. F., and Kearney, M. S. (2006). The polarization of the US labor

market. American Economic Review, 96(2):189–194.

Autor, D. H., Levy, F., and Murnane, R. J. (2003). The skill content of recent technological

change: An empirical exploration. Quarterly Journal of Economics, 118(4):1279–1333.

Averett, S. L. and Hotchkiss, J. L. (1996). Discrimination in the payment of full-time wage

premiums. ILR Review, 49(2):287–301.

Babcock, L. and Laschever, S. (2003). Women Don’t Ask: Negotiation and the Gender Divide.

Princeton University Press.

Bacolod, M. P. and Blum, B. S. (2010). Two sides of the same coin us residual inequality

and the gender gap. Journal of Human Resources, 45(1):197–242.

Barany, Z. L. and Siegel, C. (2018). Job polarization and structural change. American

Economic Journal: Macroeconomics, 10(1):57–89.

Barrett, G. F. and Doiron, D. J. (2001). Working part time: by choice or by constraint.

Canadian Journal of Economics, pages 1042–1065.

106

Yu Zhou References. 107

Bayard, K., Hellerstein, J., Neumark, D., and Troske, K. (2003). New evidence on sex

segregation and sex differences in wages from matched employee-employer data. Journal

of Labor Economics, 21(4):887–922.

Beaudry, P. and Lewis, E. (2014). Do male-female wage differentials reflect differences in

the return to skill? cross-city evidence from 1980-2000. American Economic Journal:

Applied Economics, 6(2):178–194.

Black, S. E. and Spitz-Oener, A. (2010). Explaining women’s success: Technological change

and the skill content of women’s work. Review of Economics and Statistics, 92(1):187–

194.

Blau, F. D. and DeVaro, J. (2007). New evidence on gender differences in promotion rates:

An empirical analysis of a sample of new hires. Industrial Relations: A Journal of

Economy and Society, 46(3):511–550.

Blau, F. D. and Kahn, L. M. (1992). The gender earnings gap: learning from international

comparisons. American Economic Review, 82(2):533–538.

Blau, F. D. and Kahn, L. M. (1996). International differences in male wage inequality:

institutions versus market forces. Journal of Political Economy, 104(4):791–837.

Blau, F. D. and Kahn, L. M. (1997). Swimming upstream: Trends in the gender wage

differential in the 1980s. Journal of Labor Economics, 15(1, Part 1):1–42.

Blau, F. D. and Kahn, L. M. (2003). Understanding international differences in the gender

pay gap. Journal of Labor economics, 21(1):106–144.

Blau, F. D. and Kahn, L. M. (2006). The US gender pay gap in the 1990s: Slowing convergence.

ILR Review, 60(1):45–66.

Blau, F. D. and Kahn, L. M. (2017). The gender wage gap: Extent, trends, and explanations.

Journal of Economic Literature, 55(3):789–865.

Borghans, L., Duckworth, A. L., Heckman, J. J., and Ter Weel, B. (2008a). The economics

and psychology of personality traits. Journal of Human Resources, 43(4):972–1059.

Borghans, L., Ter Weel, B., and Weinberg, B. A. (2006). People people: Social capital and

the labor-market outcomes of underrepresented groups. Technical report, National

Bureau of Economic Research.

Borghans, L., Ter Weel, B., and Weinberg, B. A. (2008b). Interpersonal styles and labor

market outcomes. Journal of Human Resources, 43(4):815–858.

Borghans, L., Ter Weel, B., and Weinberg, B. A. (2014). People skills and the labor-larket

outcomes of underrepresented groups. ILR Review, 67(2):287–334.

Borjas, G. J. (2003). The labor demand curve is downward sloping: Reexamining the impact

of immigration on the labor market. Quarterly Journal of Economics, 118(4):1335–1374.

Brainerd, E. (2000). Women in transition: Changes in gender wage differentials in Eastern

Europe and the former Soviet Union. ILR Review, 54(1):138–162.

Card, D. (2001). Immigrant inflows, native outflows, and the local labor market impacts of


higher immigration. Journal of Labor Economics, 19(1):22–64.

Card, D. (2005). Is the new immigration really so bad? The Economic Journal, 115(507).

Card, D. (2009). Immigration and inequality. American Economic Review, 99(2):1–21.

Card, D. and DiNardo, J. E. (2002). Skill-biased technological change and rising wage

inequality: Some problems and puzzles. Journal of Labor Economics, 20(4):733–783.

Ceci, S. J., Ginther, D. K., Kahn, S., and Williams, W. M. (2014). Women in academic science:

A changing landscape. Psychological Science in the Public Interest, 15(3):75–141.

Cobb-Clark, D. A. (2001). Getting ahead: the determinants of and payoffs to internal

promotion for young US men and women. In Worker Wellbeing in a Changing Labor

Market, pages 339–372. Emerald Group Publishing Limited.

Cortes, P. (2008). The effect of low-skilled immigration on US prices: evidence from CPI

data. Journal of Political Economy, 116(3):381–422.

Cortes, P. and Tessada, J. (2011). Low-skilled immigration and the labor supply of highly

skilled women. American Economic Journal: Applied Economics, 3(3):88–123.

Deming, D. J. (2017). The growing importance of social skills in the labor market. Quarterly

Journal of Economics, 132(4):1593–1640.

Erosa, A., Fuster, L., Kambourov, G., and Rogerson, R. (2017). Hours, occupations, and

gender differences in labor market outcomes. Technical report, National Bureau of

Economic Research.

Fortin, N. M. (2005). Gender role attitudes and the labour-market outcomes of women across

OECD countries. Oxford Review of Economic Policy, 21(3):416–438.

Fortin, N. M. (2008). The gender wage gap among young adults in the United States: the

importance of money versus people. Journal of Human Resources, 43(4):884–918.

Ginther, D. and Kahn, S. (2014). Academic womens careers in the social sciences. The

Economics of Economists: Institutional Setting, Individual Incentives, and Future

Prospects, page 285.

Gneezy, U., Niederle, M., and Rustichini, A. (2003). Performance in competitive environments:

Gender differences. Quarterly Journal of Economics, 118(3):1049–1074.

Goldin, C. (2014). A grand gender convergence: Its last chapter. American Economic Review,

104(4):1091–1119.

Goos, M. and Manning, A. (2007). Lousy and lovely jobs: The rising polarization of work in

Britain. Review of Economics and Statistics, 89(1):118–133.

Goos, M., Manning, A., and Salomons, A. (2009). Job polarization in Europe. American

Economic Review, 99(2):58–63.

Goos, M., Manning, A., and Salomons, A. (2011). Explaining job polarization: the roles of

technology, offshoring and institutions.

Hanushek, E. A., Schwerdt, G., Wiederhold, S., and Woessmann, L. (2015). Returns to skills

around the world: Evidence from piaac. European Economic Review, 73:103–130.


Hersch, J. and Stratton, L. S. (2000). Household specialization and the male marriage wage

premium. ILR Review, 54(1):78–94.

Hirsch, B. T. (2005). Why do part-time workers earn less? the role of worker and job skills.

ILR Review, 58(4):525–551.

Korenman, S. and Neumark, D. (1991). Does marriage really make men more productive?

Journal of Human Resources, 26(2):282–307.

Lalive, R. and Zweimuller, J. (2009). How does parental leave affect fertility and return

to work? evidence from two natural experiments. Quarterly Journal of Economics,

124(3):1363–1402.

Lindley, J. and Machin, S. (2014). Spatial changes in labour market inequality. Journal of

Urban Economics, 79:121–138.

Manacorda, M., Manning, A., and Wadsworth, J. (2012). The impact of immigration on

the structure of wages: theory and evidence from Britain. Journal of the European

Economic Association, 10(1):120–151.

Mazzolari, F. and Ragusa, G. (2013). Spillovers from high-skill consumption to low-skill labor

markets. Review of Economics and Statistics, 95(1):74–86.

McCue, K. (1996). Promotions and wage growth. Journal of Labor Economics, 14(2):175–209.

Michaels, G., Natraj, A., and Van Reenen, J. (2014). Has ICT polarized skill demand?

evidence from eleven countries over twenty-five years. Review of Economics and

Statistics, 96(1):60–77.

Moulton, B. R. (1990). An illustration of a pitfall in estimating the effects of aggregate

variables on micro units. Review of Economics and Statistics, 72(2):334–338.

Mulligan, C. B. and Rubinstein, Y. (2008). Selection, investment, and women’s relative wages

over time. Quarterly Journal of Economics, 123(3):1061–1110.

Newell, A. and Reilly, B. (2001). The gender pay gap in the transition from communism:

some empirical evidence. Economic Systems, 25(4):287–304.

Ngai, R. and Petrongolo, B. (2017). Gender gaps and the rise of the service economy.

American Economic Journal: Macroeconomics, 9(4):1–44.

Olivetti, C. and Petrongolo, B. (2008). Unequal pay or unequal employment? a cross-country

analysis of gender gaps. Journal of Labor Economics, 26(4):621–654.

Olivetti, C. and Petrongolo, B. (2014). Gender gaps across countries and skills: Demand,

supply and the industry structure. Review of Economic Dynamics, 17(4):842–859.

Orazem, P. F. and Vodopivec, M. (2000). Male-female differences in labor market outcomes

during the early transition to market: The cases of Estonia and Slovenia. Journal of

Population Economics, 13(2):283–303.

Ottaviano, G. I. and Peri, G. (2005). Rethinking the gains from immigration: Theory and

evidence from the us. Technical report, National Bureau of Economic Research.

Ottaviano, G. I. and Peri, G. (2012). Rethinking the effect of immigration on wages. Journal


of the European Economic Association, 10(1):152–197.

Petersen, T. and Morgan, L. A. (1995). Separate and unequal: Occupation-establishment sex

segregation and the gender wage gap. American Journal of Sociology, 101(2):329–365.

Prada, M. F. and Urzua, S. (2017). One size does not fit all: Multiple dimensions of ability,

college attendance, and earnings. Journal of Labor Economics, 35(4):953–991.

Schonberg, U. and Ludsteck, J. (2014). Expansions in maternity leave coverage and mothers

labor market outcomes after childbirth. Journal of Labor Economics, 32(3):469–505.

Spitz-Oener, A. (2006). Technical change, job tasks, and rising educational demands: Looking

outside the wage structure. Journal of Labor Economics, 24(2):235–270.

Tolbert, C. M. and Sizer, M. (1996). US commuting zones and labor market areas. In US

commuting zones and labor market areas. Economic Research Service, Rural Economy

Division.

Venn, D. (2009). Legislation, collective bargaining and enforcement. In Legislation, collective

bargaining and enforcement. OECD Publishing.

Visser, J. (2011). Database on institutional characteristics of trade unions, wage setting, state

intervention and social pacts, 1960-2010 (ictwss). University of Amsterdam http://www.

uva-aias. net/208.

Weichselbaumer, D. and Winter-Ebmer, R. (2005). A meta-analysis of the international

gender wage gap. Journal of Economic Surveys, 19(3):479–511.

Wolfl, A., Wanner, I., Kozluk, T. J., and Nicoletti, G. (2009). Ten years of product market

reform in OECD countries-insights from a revised PMR indicator. In Ten years of

product market reform in OECD countries-insights from a revised PMR indicator.

OECD Publishing.

Yamaguchi, S. (2016). Changes in returns to task-specific skills and gender wage gap. Journal

of Human Resources, pages doi:10.3368/jhr.53.1.1214–6813R2.

Occupational Skills and Gender Wage GapOccupational Skills and Gender Wage Gap Yu Zhou (ABSTRACT FOR...

Documents

Transcript of Occupational Skills and Gender Wage GapOccupational Skills and Gender Wage Gap Yu Zhou (ABSTRACT FOR...