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Determinants of Women's Job Mobility

Tiffany Li

MMSS Thesis

June 2015

Advisor: Professor Hilarie Lieb

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Table of Content

I. ACKNOWLEDGEMENTS ...................................................................................................... 3

II. ABSTRACT...................................................................................................................... 4

III. INTRODUCTION .............................................................................................................. 5

IV. LITERATURE Review ...................................................................................................... 7

V. MODEL ........................................................................................................................ 11

Logistic Regression ........................................................................................................ 11

Data .............................................................................................................................. 11

Survey and Income Program Participation ......................................................................... 11

Sampling ....................................................................................................................... 11

Assumption ................................................................................................................... 13

Dependent Variables ....................................................................................................... 13

Independent Variables..................................................................................................... 13

VI. RESULT ........................................................................................................................ 16

Summary Statistics ......................................................................................................... 16

Regression Results – Change Employer ............................................................................ 18

Regression Results – Change Occupation .......................................................................... 22

VII. CONCLUSION................................................................................................................ 26

VIII. BIBLIOGRAPHY ......................................................................................................... 28

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I. ACKNOWLEDGEMENTS

I want to express my sincere gratitude to my advisor, Professor Hilarie Lieb, for her

invaluable insights and inspirations over the past year. This project would not be possible

without her wholehearted support. I would also like to thank the TA German Bat for his

tremendous help in the data analysis process.

To my family, thank you so much for your unconditional love and shaping me to who I am

today. I would also like to thank Dean Miao for his consistent support in my life for the past

three years.

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II. ABSTRACT

Women comprise nearly half of the labor force today. Yet, women undeniably experience

different career trajectories than men. In particular, women face certain restrictions such as

fertility, childcare, gender discrimination that may not apply to men when it comes to making

voluntary decisions to switch jobs. This paper uses the data from the Survey of Income and

Program Participation of 2008 panel to empirically test the impact of several factors on the

probability for women to change employer and/or occupation without being unemployed. In

the first analysis of employer change, the results suggest that absence of spouse, the number of

children, other income, living in the West and occupation change are positively correlated to

the likelihood for women to change employers while wage, the number of the person in the

household, education level, establishment size, and general experience are negatively

correlated. In the second analysis of occupation change, absence of spouse, the number of

children, other income, and occupation change are positively correlated to the likelihood of

changing occupation, while wage, the number of the person in the household, education level,

establishment size and general experience are negatively correlated. Overall, the study reveals

that women’s job mobility decisions are complex and involve factors from multiple aspects. In

the future, a comparison with men’s results from the same population can advance our

understanding of the challenges that women face in the labor market.

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III. INTRODUCTION

Throughout the history of United States, gender equality has been widely discussed.

Despite the many efforts to fight inequality and the significant improvement of women’s status

from the past, it is still conspicuous that women face more challenges in the labor market than

men. For example, the wage gap between genders is still significant. According to the latest

data from the Bureau of Labor Statistics, women earn about 77 cents for a man's dollar.

Women also experience “the glass ceiling”, which refers to the unseen yet unbreakable barrier

that keeps women from rising to the upper level of the corporate world, regardless of their

qualification and achievement. For instance, women only account for 17% of board members

and 5% of CEOs at Fortune 500 companies1.

I was inspired to focus on women in my study for two reasons. First, women demonstrate

different career patterns than men for various reasons, including the ones we discussed above.

Second, compared to men, women’s career decisions are often influenced by factors outside of

the labor market, such as children, family, and spouse’s income, which adds complexity to the

decision process.

The other keyword in my research, job mobility, is an important aspect of labor force

participation. According to a study at Yale, job-to-job flows are large and are estimated to be

3.2% of employment per month (Moscarini and Thomsson, 2007)2. Job mobility is also closely

1 "Women and Leadership." Pew Research Centers Social Demographic Trends Project RSS. N.p., 14 Jan. 2015.

Web. 03 June 2015. 2 Moscarini, Giuseppe, and Kaj Thomsson. "Occupational and Job Mobility in the US." SSRN Journal SSRN

Electronic Journal (n.d.): n. pag. Web.

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related to an individual’s wage and the aggregate productivity of labor market, which is

discussed in further details in the literature review section.

There are ample existing studies on job mobility. However, not many studies have looked

at the intersection of women and job mobility. Diane H. Felmlee conducted a study on

determinants of internal and external job mobility for women, using data from the National

Longitudinal Survey of Labor Market Experience of Young Women. Nevertheless, her data

was from 1968 to 1973. With numerous changes happened in the labor market and our society

since 1970s, I believe a new and updated study is necessary to help us fully understand how

women make job mobility decisions today in the United States.

Given the current limited studies on women’s job mobility, this paper analyzes what factors

determine women’s probability of switching employer and switching occupation. In particular,

this paper uses logistic regression model and data from Survey of Income and Program

Participation of 2008-2012.

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IV. LITERATURE Review

In the past, there existed some empirical evidence about job mobility but relatively little

theoretical works. Economists are interested in the topic of job mobility for many reasons.

First, job mobility is associated with wage changes, which reflects individual's wage pattern

and career advancement. On a macroeconomics scale, job mobility is also related to aggregate

productivity in the labor market because it affects the allocation of resources. For example,

Lenz and Mortensen (Lenz and Mortensen, 2008) conducted a cross-country study in 2009.

They found that resources allocation from less to more productive firms accounted for more

than half of the growth in the model3.

In a recent study published in February 2015, Damir Stijepic analyzed the effects of some

determinants on internal and external job mobility, using data from the Survey of Income and

Program Participation of 19964. Internal job mobility refers to switching occupations within the

firm, and external job mobility refers to moving to another employer. For the sake of focusing

on the variables presented, Stjiepic’s subsample only encompassed white male age 25 to 55

who work at private for-profit organizations. Based on the result, Stjiepic found that education

has a significant positive effect on internal mobility, meaning that a high education degree

helps someone moving along the career trajectory within one employer. For instance,

participants with advanced education's likelihoods to switch occupations within the firm are

higher compared to others. However, such effect of education is less pronounced on external

mobility. Stjiepic also introduced a variable called versatility, which is used to estimate one’s

3 Lentz, R. and Mortensen, D. T. 2008. “An Empirical Model of Growth Through Product Innovation”.

Econometrica, 76: 1317–1373. doi: 10.3982/ECTA5997, 4 Stijepic, Damir. 2015. “Job Mobility and Sorting: Theory and Evidence”, Web.

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ability to perform various tasks and measured by the number of different courses attended in

high school. The results show a positive effect of versatility on external mobility, which

suggests the importance of the ability to perform different tasks when one tries to find a new

job at a different employer and potentially gain wage raise.

Diane H. Felmlee compared the determinants of internal and external job mobility for

women, using data from the National Longitudinal Survey of Labor Market Experience of

Young Women from 1968 to 19735. Different from Stijepic, Felmlee focused on women and

included variables with stronger implications for women such as child age, marital status and

spouse’s income in her analysis. The result shows that marriage limits the rates of job changes

and having husband decreases flexibility. In particular, geographical moves become especially

difficult, as women have to accommodate husband’s job. From the employers’ sides, they are

also hesitant to hire or promote a married women, perhaps because they expect a lower

commitment to the labor force from them. Husband income has a negative effect on job

transitions, as high income from a spouse makes it less crucial for a woman to change position

as her family is financially secure. The number of children has a small and inconsistent effect

on women's tendency to seek job transitions. Children reduce flexibility but also increase

pressure to change job frequently to meet child care demand. Overall, evidence shows that

women tend to benefit less from internal job transition (promotion) due to the constraint as we

discuss, as well as the perception and stereotype employers hold.

5 Diane H. Felmlee. 1982. “Women's Job Mobility Processes Within and Between Employers”, American

Sociological Review, Vol. 47, No. 1, pp. 142-151

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Sylvia Fuller used data from the National Longitudinal Survey of Youth (1979-2002) to

analyze the impact of job mobility on employee’s wage over their 12 years in the labor

market6. In general, wage deteriorates as external mobility rises, which means that employees

who stay with the same firm earn more than the ones who switch employers. However, Fuller

discussed reasons for why external mobility may be a path to wage growth for some workers.

On average, mobile workers, which include people with voluntary job change, layoffs,

discharges, and family related job separations, earn lower wages, because they tend to benefit

less from tenure, spend more time unemployed, or change jobs due to layoffs. In fact, statistics

shows that each career change is associated with one percent wage decrease, which tells us that

losing tenure is costly. In the first five years of a job, each year of tenure leads to 2.4 percent

higher wage for men and 2.9 percent higher wage for women. However, such effects begin to

erode after five years. Leaving a job to take advantage of a more favorable employment

opportunity early in the career can be beneficial, as they are associated with approximately 3

percent higher wages. This finding is consistent with job-search models, which presume

workers evaluate current positions against alternative possibilities and search more favorable

jobs or with higher wage (Burdett 19787; Devine and Kiefer 19918; Jovanovic 19799). On

another side, the gender differences in mobility patterns are small in this sample, which reflects

increasing convergence of career pattern between men and women. For example, results show

that men averaged 6.4 employer changes while women average 5.7 in 12 years.

6 Sylvia Fuller. 2008. “Job Mobility and Wage Trajectories for Men and Women in the United States”, American

Sociological Review February 2008 vol. 73 no. 1 158-183 7 Burdett, Kenneth. 1978. “A Theory of Employee Job Search and Quit Rates”, American Economic Review

68(1), 212–220 8 Devine, T.J., Kiefer, N.M.. 1991. “Empirical Labor Economics: the Search Approach:

New York, Oxford University Press 9 Jovanovic, Boyan. 1979. “Job Matching and the Theory of Turnover”, Journal of Political Economy 87(5), 972–

980

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Lastly, Deborah A. Cobb-Clark examined the promotion process, an important type of

internal job mobility, for young men and women in the U.S. in 200110. Cobb-Clark used data

from the National Longitudinal Survey of Youth. In particular, Cobb-Clark discussed the

importance of promotion in the labor market outcome, differences in promotion factors for

men and women, and the gender gap in subsequent wage growth. The result shows that women

are less likely to be promoted with a 5.8 percent point lower probability than similar men. If

men and women in the sample faced the same promotion standard, women would have higher

promotion rate, rising their promotion rate from 24.9 to 29.4 percent. Such discrepancy shows

that there is indeed gender discrimination when the employer considers promotion

opportunities, which is consistent with Felmlee’s findings. Gender promotion discrimination

can be viewed as a Becker-type taste that adds costs to promoting women and reduces their

marginal productivities (Olsen and Becker, 1983)11. Marriage and children both reduce the

probability of promotion though such effect is not significant. Education is not significantly

related to promotion for women, yet it has a significant positive effect for men. Other factors

also influence promotion rates such as firm structure and occupation. However, these factors

have a similar impact on both men and women. Getting ahead matters, especially for women,

associated wage gap is much larger between promoted and non-promoted women than for men.

10 Deborah A. Cobb-Clark. 2001. “Getting ahead: The determinants of and payoffs to internal promotion for

young U.S. men and women”, in Solomon Polachek (ed.) Worker Wellbeing in a Changing Labor Market

(Research in Labor Economics, Volume 20) Emerald Group Publishing Limited, pp.339 - 372 11 Olson, Craig A., and Brian E. Becker.1983. “Sex Discrimination in the Promotion Process.” Industrial and

Labor Relations Review 36 (July): 624-41

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V. MODEL

Logistic Regression

A logistic regression (logit model) is used because the dependent variables are binary

variables. A detailed description of the dependent variable is presented later in the “Dependent

Variable” section.

Data

The subsequent analysis is based on a subsample of the Panel of the Survey and Income

Program Participation of 2008-2012. I present a description of the data set and the sampling

process below.

Survey and Income Program Participation

The study relies on the Survey and Income Program Participation (SIPP), specifically, the

2008 panel. SIPP is a household-based longitudinal survey conducted by the United States

Census Bureau. SIPP is administered in panels and conducted in waves and rotation groups.

Each panel features a nationally representative sample interviewed through the phone over a

multi-year period lasting approximately four years. These groups of interviews are called

waves, and each wave of interviews lasts four months. In this particular study, four years of

panel data is obtained.

Sampling

To focus on the core topic of this study, I rely on a subsample of the Survey of Income and

Program Participation. First, I exclude all males in the data, as we are only interested in

women’s job mobility. Second, I only select white females because non-white females' job

mobility pattern might be influenced by other factors that are out of the scope of the study.

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Third, I limit my analysis to private for-profit employees because the wage is an important

independent variable. Additionally, I exclude individuals with above 99th percentile wage and

below 1st percentile wage to avoid outliers. Fourth, I restrict my subsample to individuals aged

25 to 55, as this is the prime age range for participating in the labor force. Fifth, I only look at

full-time employees, for that others' job mobility might demonstrate a different pattern. Lastly,

I exclude individuals who live in Hawaii and Alaska.

The final sample encompasses 13,701 individuals and 336,396 observations.

Figure 1 represents a visual demonstration of the sample selection process.

Figure 1: Sample Selection Process

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Assumption

An underlying assumption in the analysis is that people make job transition to maximize

utilities. In this case, utility refers to wage.

Dependent Variables

To run the analysis, I first quantify the job mobility process. Two dependent variables,

Change Employer and Change Occupation are created separately to achieve this goal.

Change Employer is a binary variable that indicates whether this individual is still working

for the same employer. It would be one if the individual changed employer and zero otherwise.

In the SIPP, the interviewer specifically ask the interviewee a question of “are you still

working for the same employer?”12

Change Occupation is a binary variable to indicate whether this individual is still working

at the same occupation. It would be one if the individual changed occupation and zero

otherwise. Since there is not a question about whether the interviewee is still working in the

same occupation in the questionnaire, I created this binary variable by comparing the

interviewee’s current occupation code to their last occupation code.

Independent Variables

Log Wage is calculated by taking the log of the hourly wage reported. Hourly wage is

converted from monthly wage using [monthly wage / (hours worked per week*weeks worked

in that month)].

12 The full questionnaire can be found at: http://www.census.gov/programs-surveys/sipp/tech-

documentation/questionnaires/2008-questionnaires.html

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Marital Status is divided into six categories: married with spouse present, married with

spouse absent, widowed, divorced, separated and never married. Individuals are assigned to

each category using a binary variable. In the logit model, married with spouse present is

excluded due to colinearity. Consequently, the result shows how having a certain marital status

affects individual’s job mobility compared to being married with spouse present.

The Number of Children denotes the number of children living in the household under 18-

year old.

The Number of Person in the Household represents the number of person living in the

household.

Other Income represents individual's household income minus her own monthly income

from the current job. This variable serves as a proxy of spouse’s income. Having this variable

allows me to analyze whether other financial source has an impact on women’s job mobility

decision process.

Education Level is divided into five categories: individuals with no high school diploma,

high school diploma, some college, bachelor's degree and advanced degree (master or doctoral

degree). Individuals are assigned to one category according to their reported education level

using a binary variable. In the logit model, no high school degree is excluded due to

colinearity. Consequently, the result shows how having a certain type of education level affects

individual’s job mobility compared having no high school diploma.

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Establishment Size is a binary variable used to represents the size of the company where

individuals work. One denotes the size of more than 100 people, and zero denote the size of

fewer than 100 people.

General Experience is a proxy that represents years of participation in the labor force. The

proxy is calculated by (age – 6 – year of education). Six plus years of education is an estimate

of years that individuals cannot participate in the labor force.

Region represents the regions where individuals live. I separate the States into five regions:

the Northeast, the Midwest, the South, and the West13. Each is assigned to one of the five

regions using a binary variable. In the logit model, the West is excluded due to colinearity.

Consequently, the result shows how living in a certain region affects individual’s job mobility

compared to living in the West.

Metro Status is a binary variable that denotes whether individual lives in the metro or the

rural area. One denotes living in the metro area and zero denotes living in rural area.

Homeownership Status represents whether individual owns the home she lives in, rent the

home she lives in or occupy the home with no payment. Three binary variables are created for

owning home, renting home and occupying home with no payment.

13 United States Census Bureau, Geography Division. "Census Regions and Divisions of the United States" (PDF).

Retrieved 2013-01-10.

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VI. RESULT

Summary Statistics

Included below are tables showing summary statistics for various relevant variables.

Table 1: Percentage of Individuals who

Change Employer

Change Employer Percentage

Yes 3.75%

No 96.25%

Table 3: Percentage of Individuals who

Change Employer & Occupation

Change Employer

& Occupation

Percentage

Yes 1.26%

No 98.74%

Table 3: Distribution of Some Variables

Variable Mean Standard Deviation Min Max

Hour Wage 16.26449 9.321706 1.605714 45.662

Number of Children .929149 1.122626 0 9

Number of person in

household

3.253939 1.514418 1 14

Other Income 3423.262 4209.14 0 99928

General Experience 20.58817 9.414623 1 40

Table 4: Breakdown by Marital Status

Marital Status Percentage

Married, spouse present 58.48%

Married, spouse absent 1.27%

Widowed 1.66%

Divorced 15.26%

Separated 2.84%

Never Married 20.49%

Table 2: Percentage of Individuals who

Change Occupation

Change Occupation Percentage

Yes 2.18%

No 97.92

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Table 6: Breakdown by Education

Highest Degree received or grade completed Percentage

Less Than 1st Grade 0.15%

1st, 2nd, 3rd or 4th grade 0.58%

5th Or 6th Grade 1.36%

7th Or 8th Grade 1.26%

9th Grade 1.28%

10th Grade 1.02%

11th Grade 1.17%

12th grade, no diploma 0.92%

High School Graduate - (diploma 25.15%

Some college, but no degree 15.57%

Diploma or certificate from a 11.96%

Associate (2-yr) college degree 12.83%

Bachelor's degree 20.30%

Master's degree 4.91%

Professional School degree 1.04%

Doctorate degree 0.50%

Table 7: Breakdown by Company Establishment Size

Establishment Size Percentage

Fewer than 100 employees 70.06%

More than 100 employees 29.94%

Table 9: Breakdown by Regions

Regions Percentage

Northeast 18.78%

South 32.73%

West 19.68%

Midwest 28.88%

Table 11: Breakdown by Home Ownership

Home ownership Percentage

Rent Home 28.07%

Own Home 69.84%

Occupy with no payment 2.09%

Table 10: Breakdown by Metro/Rural

Living in Metro/Rural Percentage

Metro 18.68%

Rural 81.32%

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Regression Results – Change Employer

Logistic regression Number of obs = 336396

LR chi2(23) = 2951.37

Prob > chi2 = 0.0000

Log likelihood = -52326.38 Pseudo R2 = 0.0274

Change Employer Coefficient Std. Err. z P>|z| [95% Conf. Interval]

Log Hour Wage -.388*** .015 -24.48 0.000 -.418 -.356

Married Spouse Absent| .233*** .073 3.21 0.001 .091 .375

Widowed .063 .075 0.84 0.398 -.083 .210

Divorced .261*** .027 9.69 0.000 .208 .313

Separated .380*** .046 8.26 0.000 .290 .470

Never married .037 .026 1.45 0.148 -.013 .088

Number of Children .047*** .013 3.66 0.000 .022 .072

Number of Person in Household -.035*** .010 -3.70 0.000 -.054 -.017

Other Income .000 .000 5.46 0.000 0.000 .000

High School -.230*** .034 -6.72 0.000 -.297 -.163

Some College -.232*** .034 -6.86 0.000 -.299 -.166

College -.411*** .041 -9.99 0.000 -.492 -.330

Advanced Degree -.345*** .054 -6.43 0.000 -.451 -.240

Establishment Size -.137*** .021 -6.47 0.000 -.179 -.096

General Experience -.049*** .004 -10.64 0.000 -.058 -.040

General Experience^2 .000 .000 6.12 0.000 .000 .000

Northeast -.339*** .031 -10.99 0.000 -.399 -.278

Midwest -.152*** .026 -5.75 0.000 -.204 -.100

South -.102*** .025 -4.11 0.000 -.151 -.053

Metro Status -.045* .024 -1.89 0.058 -.091 .002

Rent Nome .340*** .021 16.19 0.000 .299 .381

Occupy Home No Payment .554*** .050 11.01 0.000 .455 .6529

Occupation Change .623*** .047 13.23 0.000 .531 .716

Constant| -1.36*** .076 -17.96 0.000 -1.512 1.214

Figure 1: Logic Regression Result for Changing Employer. Statistical significance at the 10, 5, and 1 percent level

denoted by ∗, ∗∗, and ∗∗∗, respectively. For marital status, married spouse present is excluded. For education

level, no high school is excluded. For the region, West is excluded. For home ownership, owning home is

excluded.

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Figure 1 presents the logit regression results for employer change. Some key findings

are discussed below:

The wage is significantly negatively related to the probability of external mobility.

Increasing log hourly wage by 1 dollar leads to 38% decrease in the likelihood of switching to

another employer. This may be explained by that women with higher wage have a higher

attachment to their job and are less incentivized to change employer.

Marital status also has a significant effect on job mobility. The result shows that

compared to married women with spouse present, women within some other categories such as

married with spouse absent, divorced and separated show a higher probability of switching to

another employer. Women who do not have a partner present may have more incentives to

improve their financial condition and attempt to achieve this goal through seeking other

employment opportunities. Nevertheless, women who are widowed and never married are not

influenced by their marital status, as the coefficients for them are not statistically different from

married women with a spouse present. It is possible that greater asset enables widowed women

to change employer less often. And it is still unclear why women who are never married are

not affected. The result is consistent with Felmlee’s previous finding that marriage limits the

rate of job change for women.

Surprisingly, the number of children has a small positive effect on external mobility.

Women who have one child have 5% higher probability of changing employer than women

with no child. A previous study by Diane H. Felmlee suggests that children have opposite

effects on job mobility. In this case, the impact of financial pressure outweighs the decrease in

flexibility for women with children and motivate them to change employer.

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The increase in number of the person in the household decreases the likelihood to

change employer. Women who live in a big household face more challenges when they try to

change employer, especially when the new job may involve geographic relocation.

Other income in the household has a positive effect on women’s external mobility.

Nevertheless the coefficient is small because income is measured in large magnitude.

As education level increase, the probability of changing employer decrease. This is

consistent with previous finding by Damir Stijepic. High school, some college, college and

advanced degree graduates are less likely to switch employer with 23%, 23%, 41%, and 35%

less likelihood compared to women who did not graduate from high school. People with less

education tend to work in positions that require lower level skills. Employers have greater

incentives to keep the high-skilled workers because of the investments they made in training

these workers. As a consequence, there is less attachment between the employer and the

employees with less education and they are more likely to change employer.

Firm size has a negative effect on external mobility, suggesting that big firms have less

turnover. Compared to firms with less than 100 employees, women who work at firms with

more than 100 employees have 14% lower probability of changing employer. One possible

explanation is that bigger firms offer better benefits, and their employees are more willing to

stay.

General experience has a negative effect at external mobility. In this study, general

experience acts as a proxy for age. This result is not surprising because women who spend

more time in the labor market tend to prefer their employers because the higher level of

matching between them and their current employers after years of working in different jobs.

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The variable of the region exhibits some interesting results. It suggests that compared to

women who live in the West, women who live in Northeast, Midwest and South show less

external mobility. It is still unclear why women who live in the West change employer more

often. However, the study does not control for industries. One possible reason is that people in

the West participate more in technology jobs where employer changes tend to happen more

frequent.

Home ownership substantially decreases external mobility for women. Women who

rent home exhibit a 34% higher probability of switching employer than women who own

home. Owning home greatly decreases individual’s flexibility, since changing employer would

incur a higher cost for people who own home, such as a mortgage.

Lastly, the variable Change Occupation is statistically significant, as women who

change occupation are 62.3% more likely to change employer.

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Regression Results – Change Occupation

Logistic regression Number of observations = 336396

LR chi2(23) = 747.69

Prob > chi2 = 0.0000

Log likelihood = -33632.502 Pseudo R2 = 0.0110

Change Occupation Coefficient Standard Deviation Z P>|z| [95% Conf. Interval]

Log Hour Wage -.252*** .021 -11.91 0.000 -.293 -.210

Married Spouse Absent .430*** .090 4.75 0.000 .252 .606

Widowed -.043 .105 -0.42 0.678 -.249 .162

Divorced .159*** .036 4.36 0.000 .0874 .230

Separated .220*** .067 3.27 0.001 .088 .351

Never Married .130*** .034 3.80 0.000 .063 .197

Number of Children -.046*** .017 -2.74 0.006 -.078 -.013

Number of Person in Household .046*** .012 3.84 0.000 .023 .070

Other Income .000 3.12e-06 2.66 0.008 .000 .000

High School .208*** .052 4.04 0.000 .107 .309

Some College .201*** .051 3.91 0.000 .100 .301

College .171*** .059 2.89 0.004 .055 .286

Advanced Degree .230*** .073 3.14 0.002 .086 .372

Establishment Size -.110 *** .028 -3.97 0.000 -.164 -.056

General Experience .005 .006 0.86 0.392 -.007 .018

General Experience^2 -.000 .000 -2.15 0.031 -.000 -.000

Northeast -.0142 .038 -0.37 0.712 -.090 .061

Midwest -.117*** .036 -3.24 0.001 -.188 -.046

South -.063* .034 -1.85 0.064 -.130 .004

Metro Status -.003 .032 -0.11 0.916 -.067 .060

Rent Home .252*** .028 8.92 0.000 .197 .308

Occupy Home With No Cash .292*** .075 3.91 0.000 .146 .439

Change Employer .625*** .047 13.28 0.000 .533 .717

Constant -3.568*** .106 -33.52 0.000 -3.77 -3.36

Figure 2: Logic Regression Result for Changing Occupation. Statistical significance at the 10, 5, and 1

percent level denoted by ∗, ∗∗, and ∗∗∗, respectively. For marital status, married spouse present is excluded. For education level, no high school is excluded. For region, West is excluded. For home ownership, own

home is excluded.

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Figure 2 presents the logit regression results for occupation change. Some key findings

are discussed below.

It is important to note that since Change Employer is included as an independent

variable, individuals are making by choice occupation change without changing employer in

this model. Most of the occupation changes can be attributed to internal promotions.

A number of variables demonstrate similar effects on the probability to change

occupation relative to change employer. However, there are a few variables that show different

impacts.

Individuals with lower hour wage tend to change occupation more often. There are two

possible explanations. First, women with lower wage tend to work in positions that require

lower level skills, therefore they have invested less human capital into their occupations and

are less attached to their occupations. Second, women with lower wage try to increase their

wage by improvising their positions and pursuing different career paths.

Marital status shows significant impact on the probability of changing occupation.

Women who are married with spouse absent, divorced, separated and never married all have

higher tendency to change occupation with 43%, 16%, 22%, 13% increase in probability

compared to women who are married with spouse present. Greater flexibility and incentives to

improve financial conditions are two possible motivations for these women to change

occupations. Earlier studies done by Felmlee also suggests that married women face

discrimination in the work force as employers might expect lower job commitment from

married women regardless of their performances.

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Number of children has a small negative effect on occupation change, which is opposite

from employer change. Women who have one child have 5% lower probability of changing

occupation than women with no child. In this case, the impact of the decrease in flexibility

outweighs the financial pressure for women with children and motivate them to stay in the

same occupation. From the employer’s perspective, they might be also hesitate to promote

women with children because they expect lower job commitment from them.

Another interesting contrast to the previous model is that increasing number of the

person in the household by one slightly increase the probability to change occupation by 5%.

Since the study controls for number of children, increase in the number of person in the

household indicate adults’ presences such as parents. One possible explanation is that these

adults can help with household work and child care which allows the women achieve upward

mobility.

Education has a positive effect on the probability to change occupation. Compared to

women with no high school education, women with high school, some college, college and

advanced degree are more likely to change occupation with 21%, 20, 17% and 23% higher

probability. Women with higher education invested more in their human capital and have

greater chance to move up the hierarchy. This finding is consistent with what Stijepic found in

men’s data: a high education degree helps someone moving along the career trajectory within

one employer.

Women who work in firms with more than 100 employees are 11% less likely to

change occupation. This indicates a possible trade-off between changing occupation and

having the better benefits in bigger firms.

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General experience does not demonstrate a significant impact on occupation change.

However, general experience square have a small negative effect on occupation change. One

possible explanation is that women who spend more time in the job market tend to work in jobs

that match better with what they desire in a job and are less motivated to change occupation.

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VII. CONCLUSION

In this paper I analyze the determinants of women’s job mobility. Specifically, I focus on

external mobility as indicated by the probability of changing employer and internal mobility as

indicated by the probability of changing occupation. External and internal mobility are affected

similarly by the certain discussed factors in the results. Nevertheless, important differences

exist. For example, education has a negative impact on changing employer and positive impact

on changing occupation, suggesting people with higher education experience fewer employer

changes and are more likely to be promoted.

The study has several limitations. First, fixed effect could not be applied to the logit

regression. This may affect the accuracy of the model if other factors are not included as

independent variables. Second, the regressions do not control for industries, which can also

impact women’s job mobility. Despite these limitations, the models still reflect most of the

dynamic in women’s job mobility process due to the large sample size and many controlled

variables.

The results imply that women’s job mobility decisions are multifaceted. Family,

children, marriage and other factors outside of work place can play essential roles in the

decision process. Additionally, married women and women with children face discriminations

from the employers which can obstruct their career advancements. With the rising awareness

of gender equality and new technologies to help women achieve career goal such as egg freeze,

the career patterns of women and men are converging. However, this study unveils that women

still face many challenges in terms of job mobility. To improve our understanding of the

difference between women’s and men’s job mobility process, a comparison analysis of the

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determinants on men’s external and internal job mobility using the same dataset can be

conducted in a future study.

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