Gender differences in expected compensation for earnings uncertainty and skewness in business and education

The Social Science Journal 43 (2006) 343–363

Gender differences in expected compensation for earningsuncertainty and skewness in business and education

John R. Walker ∗

Department of Economics, University of Wisconsin-River Falls, 410 S. 3rd Street,River Falls, WI 54022, USA

Abstract

Using data measuring the labor market expectations of college seniors and juniors, who major inbusiness and education, this paper examines gender differences in expected compensation for earningsuncertainty and skewness. Ordinary least squares regressions indicate gender differences in uncertaintyand skewness in business. Estimates of expected beginning salaries indicate higher uncertainty andskewness coefficients for women compared to men. This could reflect a greater degree of risk aversionby women in business or their plans to engage in part-time work shortly after graduation. Later salaryestimates indicate lower uncertainty and skewness coefficients for women. Student error in estimatingthese salaries may be the cause of these results. They may also reflect the plans of women in businessto devote more time to the labor market later in their careers.© 2006 Elsevier Inc. All rights reserved.

1. Introduction

McGoldrick (1995) found women received a higher wage premium for earnings uncertaintythan men. At the same time McGoldrick’s results indicated women were less willing to give upearnings than men in selecting occupations that have a small chance of receiving a high salary(i.e., have positively skewed earnings distributions). Both results suggest women in the labormarket are less inclined to undertake earnings risk than men. The higher risk and skewnessestimates for women, however, may be overstated because McGoldrick did not control forlabor force participation or the attitudes of individuals towards job characteristics.

This study employs detailed data measuring the labor market expectations of college seniorsand juniors (majoring in business and education) to test for gender differences in expected com-pensation to earnings uncertainty and skewness. The analysis extends McGoldrick by including

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344 J.R. Walker / The Social Science Journal 43 (2006) 343–363

in the model of expected earnings controls for labor force participation plans and respondentattitudes towards job characteristics. The study also contributes to the literature on compen-sating returns by directly testing the proposition that expectations regarding future earningsuncertainty and skewness affect an individual’s current choices. Economic theory emphasizesthe role of future expectations on individual choices. The literature, however, has relied ondata indicating past outcomes to test the earnings uncertainty and skewness hypotheses.

The inclusion of business students corresponds to previous work by Chauvin and Ash (1994)who found, in a sample of business school graduates, that women were less inclined towardsearnings risk because of their selection into jobs with less contingent pay than men. Thequestion is re-examined in the present study by testing whether expected earnings uncertaintyand skewness coefficients for women in business (after controlling for labor force plans andattitudes towards jobs) are higher compared to their male counterparts.

The inclusion of education allows a test of gender differences in expected risk and skewnessin a female dominated occupation. In addition, labor unions influence wages in the educationfield. Leigh (1983) found earnings uncertainty was not significant in a test across blue-collaroccupations. He hypothesized this may be due to unions compressing wage scales while, at thesame time, increasing wages. Leigh’s analysis, however, did not control for skewness in theearnings distribution. King (1974) noted that controlling for skewness prevents understatementof the risk premium for earnings uncertainty.1

Section 2 presents a brief review of the literature as background for the study. Section 3describes the data. Section 4 presents the theoretical framework to motivate the uncertaintyand skewness hypotheses. In Section 5 the empirical model and mean values are presented.The regression results are presented in Section 6. In Section 7 the findings regarding genderdifferences in expected uncertainty and skewness are discussed. Section 8 indicates significantfindings and provides suggestions for future research.

2. Background

A small number of studies suggest a positive wage premium is paid to risk-averse workersas compensation for variation in their earnings (Feinberg, 1981; Johnson, 1977; King, 1974;Leigh, 1983; McGoldrick, 1995). In addition, King (1974) and McGoldrick (1995) found thatworkers exhibit a preference for occupations in which the earnings distribution is positivelyskewed. These workers, then, are willing to give up current earnings in exchange for a smallchance of obtaining a high salary later in that occupation.

Two of these studies examined gender differences in returns to earnings uncertainty(Feinberg, 1981; McGoldrick, 1995). Feinberg (1981) found that women were paid alower wage premium than men and speculated this reflected the concentration of womenin secondary labor markets which do not pay competitive returns to earnings uncertainty.McGoldrick (1995), however, found women received a higher risk premium than men andargued this different result stemmed from two features of her analysis (not included byFeinberg (1981)) that prevented understatement of the risk premium received by women.First, McGoldrick (1995) measured uncertainty as the standard deviation of residuals from anordinary least squares (OLS) regression that controlled for human capital characteristics. This

J.R. Walker / The Social Science Journal 43 (2006) 343–363 345

provided clearer identification of unsystematic variation in earnings for which women receivecompensating returns.2 Second, McGoldrick (1995) included in the earnings model a controlfor positive skewness.3 McGoldrick (1995) also found women gave up less earnings thanmen in the face of positive skewness. She did not explain why the uncertainty and skewnesscoefficients would be higher for women than men.

McGoldrick’s findings suggest women are less willing to undertake earnings risk than men.McGoldrick’s results, however, may be overstated because her analysis did not control forlabor force participation. De Meza (1984) indicated that individuals with a low wage elasticityof demand for leisure (i.e., are not willing to substitute work for leisure) are less likely toenter occupations with high wage uncertainty. Women, because of an emphasis on householdresponsibilities, may have a lower wage elasticity of demand for leisure than men. Thus,McGoldrick’s finding of a higher risk premium for women could reflect their lower levelof labor force participation compared to men. McGoldrick’s finding of a higher skewnesscoefficient for women may also be related to their lower level of labor force participation. Ifwomen are working less, they would have an even smaller chance of obtaining a high salarywithin a particular occupation. As a result, women would be less willing to give up earningsat the reduced chance of obtaining that high salary job.

McGoldrick’s analysis also did not control for the attitudes of women and men towardsjob characteristics. Human capital studies (Daymont & Andrisani, 1984; Filer, 1985) havesuggested the lower earnings of women stem from their preferences for jobs with fewer demandsand greater flexibility to carry out household responsibilities. Thus, including attitudes towardsjobs would also capture possible gender differences in labor market commitment and provideadditional measures to prevent overstatement of the risk and skewness estimates for women.

This paper tests for gender differences in expected risk and skewness based on a directmeasure of future earnings uncertainty obtained from respondents in this sample. In the analysisto follow, uncertainty is measured as the highest minus lowest salary estimate students expectin their job of first choice.4 Respondents also provided a point estimate of their expected salaryin this occupation. As a result, skewness is measured as (high salary − point estimate) − (pointestimate − low salary) which students expect in their job of first choice. In addition, estimatesof expected earnings include a measure of student expected labor force participation andattitudes towards 15 different job characteristics to prevent overstatement of the expected riskand skewness coefficients for women.

3. The data

The data were collected during Fall semester 1997. The target population was 753 seniorsand juniors majoring in Business Administration, Accounting, Agricultural Business, Ele-mentary and Secondary Education at the University of Wisconsin-River Falls. Surveys wereadministered to students in upper division courses and through their academic advisors. In theend, 577 surveys were returned for a response rate of 76%. Among the returned surveys, 103(18%) had incomplete information and were not fully usable in the analysis.

To measure salary expectations, students were first asked to indicate their two most pre-ferred occupations. Students then provided point estimates (in 1997 dollars) of the expected


beginning salaries and expected salaries after 10 and 20 years (for continuous employment) inthose occupations.5 To measure uncertainty regarding those future earnings, the students alsoprovided estimates of the highest possible and lowest possible value for each salary estimate(Kodde, 1986).

Labor force plans were measured by asking students to estimate the number of years overtheir working lives they planned to work full-time, part-time while raising children, part-timefor other reasons, or be a full-time homemaker. A seven-point Likert scale was used to obtainstudent attitudes towards 15 different job characteristics. The survey also provided informationabout age, gender, cumulative GPA, major, and class standing (whether a senior or junior) ofthe students.

4. Theoretical model

To formulate hypotheses regarding uncertainty and skewness this study employs a modeldeveloped by Bellante and Link (1982) and extended by McGoldrick (1995). In this framework,earnings uncertainty and skewness are entered as direct arguments in the utility function.6 It isassumed risk-averse workers view earnings uncertainty as “bad” and skewness as “good.” Thus,if earnings uncertainty increases, workers maintain the same level of total utility by receiving ahigher wage. At the same time, if positive skewness increases, workers maintain the same levelof total utility by accepting a lower wage. Bellante and Link (1982) and McGoldrick (1995)also included in the utility function a vector of nonwage job-related characteristics.

The constraint workers face in maximizing total utility is represented by a wage equation thatincludes earnings uncertainty, skewness, and the nonwage job characteristics. The coefficientsof the wage equation are a constraint because they represent an “opportunity locus” of possiblemarket rates of return for each independent variable (Bellante & Link, 1982). If there is anincrease (decrease) in the market rate of return for any independent variable, resulting in alarger (smaller) coefficient, then the individual worker will find tangency with a higher (lower)indifference curve.

The present study includes individual expectations regarding earnings, earnings uncertainty,and skewness in the utility function. Also included are the subjective preferences of individualstowards job characteristics and their expected labor force participation.

In formal terms, assume an individual chooses an occupation in order to maximize the utilityfunction:

U = U(W, R, S, P, X) (1)

where W represents the individual’s expected earnings over time, R is the measure of expectedearnings uncertainty, S is the measure of expected skewness in earnings, P is the measure ofplanned labor force participation, and X represents a vector measuring individual job prefer-ences. Following convention, it is assumed that UW > 0, UWW < 0. From the discussion above,it is assumed that UR < 0, URR < 0, and US > 0. It is also assumed UP < 0. This is consistent withthe standard model of labor supply which suggests individuals prefer leisure over work in thelabor market.


The constraint individuals expect to face in the labor market is determined by the followingearnings function:

ln Wi = β0 + β1Ri + β2Si + β3Pi + β4Xi (2)

where ln Wi is the log of real expected earnings for individual i. By maximizing Eq. (1) subjectto Eq. (2), the following first order conditions are obtained:

UW = −UR

β1= −US

β2= −UP

β3= −UX

β4= λ (3)

where λ is a Lagrangian multiplier. Focusing on the relationships of risk, skewness, and laborforce participation with expected earnings, the following tradeoffs are determined:

β1 =(−UR

UW

)> 0 (4)

β2 =(−US

UW

)< 0 (5)

β3 =(−UP

UW

)> 0 (6)

In Eq. (4), the positive β1 coefficient indicates individuals who expect greater earnings uncer-tainty will require higher earnings to enter riskier jobs. Eq. (5), where the β2 coefficient isnegative, indicates individuals who are attracted to occupations with positively skewed earn-ings distributions expect lower salaries. In Eq. (6) the positive β3 coefficient indicates thosewho plan greater labor force participation expect higher earnings.

Tradeoffs, based on individual utility, can also be derived between expected earnings andjob preferences in vector X. The signs of these coefficients are indicated in the discussion ofthe empirical model below. We now turn to an examination of the empirical model.

5. The empirical model and descriptive statistics

In the following analysis, ordinary least squares (OLS) regressions are estimated for expectedbeginning salaries and expected salaries after 10 and 20 years in the student’s job of first choice.These models are estimated in business and education on both pooled and separate samples ofwomen and men. The basic equation in these estimations is:

ln Yi = β0 + β1Ri + β2Si + β3Pi + β4Xi + β5Oi + εi (7)

where the dependent variable Y is the expected annual salary in job of first choice. Amongthe independent variables, R measures expected earnings uncertainty, S expected skewness inthe earnings distribution, P planned labor force participation, X includes measures of studentattitudes towards job characteristics, O includes controls for college major, GPA, and age whileε is the random error term. A dummy variable controlling for gender is added to the pooledestimates within the fields. Also, interaction terms are added to the pooled estimates withinbusiness and education to test for gender differences in uncertainty and skewness.


Eq. (7) is a hedonic wage function where the coefficients for uncertainty, skewness, laborforce participation, and job preferences indicate points at which individual utility is maximizedgiven the expected market rates of return to these variables. However, in Eq. (7), variablesincluded in the vector O are not directly linked to the theory of compensating returns. Inparticular, hypotheses regarding the signs of the coefficients for GPA and age rely on thestandard human capital theory of wages.

Table 1 indicates variable definitions and anticipated signs of the coefficients. As indicated,uncertainty is positive and skewness negative in each of the models. The gender-uncertaintyand gender-skewness interaction terms should not be significant if inclusion of labor force par-ticipation plans and job preferences prevents overstatement of the expected risk and skewnesscoefficients for women.

The measure of labor force participation used is the number of years students plan to workfull-time. The theory of utility preferences suggests (if the substitution exceeds the incomeeffect) a direct relationship between expected earnings and planned years worked full-time.However, human capital theory also hypothesizes that those planning more years of full-timework anticipate steeper earnings profiles (Polachek, 1976, 1981).7 As a result, Table 1 indicatesthe coefficient for planned years worked full-time is negative in estimates of expected beginningsalaries and positive after 10 and 20 years. Among other variables relating to human capital,cumulative GPA should be positive indicating higher expected earnings for those with greaterability. Age should be negative as older students would be less inclined to invest in additionalhuman capital to augment expected earnings.

In terms of job preferences, the theory of compensating returns suggests characteristicsassociated with an ambitious attitude (i.e., opportunity to advance, intellectually challenging,independent work environment) should be positive in each of the estimates. The coefficientsof characteristics associated with less commitment to the labor market (i.e., close to family,pleasant work environment, flexible hours) should be negative in the regression models.

The mean values of the dependent and independent variables (by gender) in each field arepresented in Table 2. Also indicated are t tests of the difference between means of women andmen in those fields.

The mean values for uncertainty suggest men tend to expect greater earnings risk thanwomen. Within education, average expected uncertainty is significantly higher for men ineach of the salary estimates. In business, men expect significantly greater uncertainty in salaryestimates after 20 years. The pattern for skewness is less consistent. Within education, womenexpect significantly greater skewness after 10 years. The mean values in business indicate menexpect significantly greater skewness after 20 years.

Comparing expected risk between fields, t tests (not reported to simplify Table 2) indicatedsignificantly higher average uncertainty (as 5% or less) for both women and men in businesscompared to education. This is consistent with the hypothesis that students in business expectjobs offering more variable pay while education majors expect more compressed wage scalesdue to unionization. Also consistent with this hypothesis are the higher values for skewnessamong women and men in business compared to education. For women, however, the dif-ference in average skewness is significant (at 1%) only for expected beginning salaries. Formen the mean values of expected skewness are significantly different (at 5%) after 10 and20 years.

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Table 1Variable definitions and expected signs of coefficients

Variables Definitions Expected coefficient signs

Beginning After 10years

After 20years

DependentExpected beginning salary Point estimate of expected beginning salary in job of first

choiceExpected salary 10 years Point estimate of expected salary after 10 years in job of first

choiceExpected salary 20 years Point estimate of expected salary after 20 years in job of first

choiceIndependent

UncertaintyExpected beginning salary High–low estimates for expected beginning salary +Expected salary after 10 years High–low estimates for expected salary after 10 years +Expected salary after 20 years High–low estimates for expected salary after 20 years +

SkewnessExpected beginning salary (High est. − point est.) − (point est. − low est.) for expected

beginning salary−

Expected salary after 10 years (High est. − point est.) − (point est. − low est.) for expectedsalary after 10 years

−

Expected salary after 20 years (High est. − point est.) − (point est. − low est.) for expectedsalary after 20 years

−

Labor force participation plansPlanned years worked full-time Number of years respondent plans to work full-time − + +

Job preferencesDecent salary Prefers job offering a decent salary + + +Opportunity for advancement Prefers job offering opportunity to advance + + +Intellectual challenge Prefers job that is intellectually challenging + + +Independent work environment Prefers job that offers an independent work environment + + +Close to family Prefers job that is close to family − − −Pleasant work environment Prefers job offering a pleasant work environment − − −Geographical location Prefers job in the right geographical location − − −Pleasant coworkers Prefers job with pleasant coworkers − − −Not too demanding Prefers a job that is not demanding − − −Contribution to society Prefers a job that makes a contribution to society − − −Earnings prestige Prefers a job offering earnings prestige + + +

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Table 1 (Continued )

Variables Definitions Expected coefficient signs

Beginning After 10years

After 20years

Fringe benefits Prefers a job offering fringe benefits + + +Job security Prefers a job offering job security − − −Opportunity to travel Prefers a job offering opportunities to travel − − −Flexible work hours Prefers a job with flexible work hours − − −

OtherBroad area businessa =1 broad area business, =0 otherwise ? ? ?Agricultural business =1 agricultural business, =0 otherwise ? ? ?Liberal arts business =1 liberal arts business, =0 otherwise ? ? ?Accounting =1 accounting, =0 otherwise ? ? ?Secondary education =1 secondary education, =0 otherwise + + +Elementary educationb =1 elementary education, =0 otherwise − − −GPA Cumulative grade point average + + +Age Age of the respondent − − −Class standing =1 senior, =0 junior ? ? ?

Genderc =1 male, =0 female + + +

Interaction termsd

Gender × uncertainty: beginning Gender × (high–low estimates for expected beginning salary) ?Gender × uncertainty: 10 years Gender × (high–low estimates for expected salary after 10

years)?

Gender × uncertainty: 20 years Gender × (high–low estimates for expected salary after 20years)

?

Gender × skewness: beginning Gender × [(high est. − point est.) − (point est. − low est.) forexpected beginning salary]

?

Gender × skewness: 10 years Gender × [(high est. − point est.) − (point est. − low est.) forexpected salary 10 years]

?

Gender × skewness: 20 years Gender × [(high est. − point est.) − (point est. − low est.) forexpected salary 20 years]

?

Note. Italics indicate independent variable vectors in Eq. (7). Estimate is abbreviated as est. in skewness definitions.a Excluded in estimates within business.b Excluded in estimates within education.c Included in pooled estimates within business and education.d Included in pooled estimates within business and education.

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Table 2Variable means and tests of differences by gender

Business Education

Women n Men n t statistics Women n Men n t statistics

Dependent variablesExpected beginning salary 27,846 120 29,140 107 1.46 23,583 218 24,750 120 2.83***

Expected salary 10 years 46,550 119 54,555 107 2.38** 32,842 215 34,555 119 2.47**

Expected salary 20 years 61,324 116 79,524 106 3.20*** 41,269 212 44,058 119 2.30***

Independent variablesUncertainty

High–low: expected beginning salary 15,280 118 14,619 105 −0.52 9,804 212 10,872 118 1.77*

High–low: expected salary after 10 years 31,179 117 49,646 105 1.39 12,326 204 13,978 116 2.02**

High–low: expected salary after 20 years 38,118 114 93,567 104 3.01*** 14,777 202 18,939 115 3.70***

SkewnessExpected beginning salary 4,678 118 3,548 105 −1.05 1,846 212 2,228 118 0.66Expected salary after 10 years 8,590 117 24,037 105 1.36 1,930 204 806 116 −1.72*

Expected salary after 20 years 10,266 114 52,167 104 2.62*** 1,629 202 1,661 115 0.04

Labor force participation plansPlanned years worked full-time 28.5 120 34.4 108 5.01*** 26.5 214 31.04 117 3.80***

Job preferencesDecent salary 5.62 122 5.56 107 −0.39 4.86 221 5.08 122 1.53Opportunity for advancement 6.29 121 6.17 108 −0.75 5.00 220 5.24 122 1.45Intellectual challenge 5.56 121 5.29 107 −1.85* 5.62 221 5.47 122 −1.17Independent work environment 5.35 118 5.40 107 0.06 5.32 219 5.50 122 1.31Close to family 5.47 119 5.13 107 −1.62 5.67 218 5.54 121 −0.82Pleasant work environment 6.08 118 5.80 107 −1.96** 6.35 222 6.19 122 −1.81*

Geographical location 5.39 119 5.14 106 −1.35 5.47 221 5.48 122 0.03Pleasant coworkers 5.78 120 5.58 107 −1.27 5.94 220 5.71 122 −1.96*

Not too demanding 3.47 120 3.48 104 0.07 3.13 219 3.12 122 −0.03Contribution to society 5.03 117 4.27 107 −4.16*** 6.10 219 6.17 121 0.51Earnings prestige 4.32 116 4.72 107 1.81* 3.25 221 3.56 121 1.72*

Fringe benefits 5.63 121 5.67 106 0.26 5.26 218 5.56 122 2.11**

Job security 5.94 122 5.83 105 −0.70 6.15 221 6.10 119 −0.45Opportunity to travel 4.02 118 4.08 106 0.26 3.39 218 3.60 122 1.04Flexible work hours 4.80 118 4.90 107 0.94 4.06 219 3.76 121 −1.73*

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Table 2 (Continued )

Business Education

Women n Men n t statistics Women n Men n t statistics

Other independent variablesBroad area business 0.34 122 0.30 108Agricultural business 0.12 122 0.22 108Liberal arts business 0.27 122 0.30 108Accounting 0.27 122 0.18 108Secondary education 0.53 222 0.85 122Elementary education 0.47 222 0.15 122GPA 3.04 117 2.90 107 −2.09** 3.31 221 3.11 120 −3.04***

Age 23.5 121 23.2 108 −0.61 23.70 222 24.13 122 0.71Class standing 0.69 122 0.61 108 −1.22 0.77 221 0.78 121 0.07

Note. Italics indicate independent variable vectors in Eq. (7).∗ Significant at 10%.∗∗ Significant at 5%.∗∗∗ Significant at 1%.


The data do not support the human capital hypothesis of gender differences in earnings.Consistent with the model, men, in both fields, anticipate more years of full-time work thanwomen. Expected beginning salaries, however, are not significantly lower for men. Thisis inconsistent with model’s depiction of steeper earnings profiles for those planning morework.

Job preferences suggest some differences in labor market priorities of women and men inthe sample. Within business, men place significantly greater emphasis on earnings prestigewhile women prefer having a pleasant work environment and making a contribution to society.In education, men indicate greater preference for earnings prestige while women prefer havingpleasant coworkers, a pleasant work environment, and flexible work hours.

6. Results

Regression models using the ordinary least squares method were estimated within the fieldsof business and education. Tables 3 and 4 present the coefficients from these estimates. Toreduce the length of these tables coefficients for job preferences and major are reported sepa-rately in Tables A.1 and A.2.

Results support the earnings risk and skewness hypotheses. In both business and educationuncertainty and skewness coefficients are statistically significant with the correct signs in allof the estimations.

Significant gender differences in expected earnings uncertainty and skewness are foundwithin business. The uncertainty results indicate women expect a 1.6% increase in salary (perthousand dollar increase in uncertainty) compared to 1.2% for men in estimates of beginningsalaries. After 10 years, however, men expect a higher risk premium of 1.1% compared to0.9% for women. In salary estimates after 20 years, men expect a risk premium which isslightly higher at 0.74% compared to 0.67% for women. The skewness results indicate menexpect a 1.6% reduction in salary (per thousand dollar increase in skewness) compared to1.3% for women in the estimates of beginning salaries. In salary estimates after 20 years,however, women expect a greater reduction in salary at 0.8% compared to 0.7% for men.These results are discussed in Section 7.8

The coefficients for planned years worked full-time do not support the human capital hypoth-esis in either business or education. The coefficient in business does have the hypothesizednegative sign in the estimate of expected beginning salaries for men. However, in the expectedsalary estimates after 10 and 20 years, the planned years worked full-time coefficient is negativefor both men and women. This is consistent with the suggestion by England (1982) that thoseplanning less labor market commitment, early in their careers, may not confine themselves tojobs with flat earnings profiles. Also, the results may reflect the plans of these students to retireearly suggesting expected salary increases later in their careers may have a stronger incomecompared to substitution effect. In education, planned years worked full-time is positive in eachof the total estimates. This suggests students in education, who plan more full-time work, do notexpect steeper earnings profiles. Instead, they expect higher earnings over their entire lifecycle.9

Among other results reported in Tables 3 and 4, the positive effects of age (contrary to ourhypotheses) in business and education may reflect greater skills associated with being older

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Table 3OLS estimates of expected ln salary in businessa

Dependent variable: ln expected salary

Expected beginning salary Expected salary 10 years Expected salary 20 years

Total (n = 196) Women (n = 100) Men (n = 96) Total (n = 195) Women (n = 99) Men (n = 96) Total (n = 193) Women (n = 98) Men (n = 95)

Independent variablesIntercept 9.45*** (0.201) 9.50*** (0.24) 9.63*** (0.31) 9.92*** (0.22) 9.60*** (0.29) 10.23*** (0.331) 9.94*** (0.26) 9.5*** (0.33) 10.54*** (0.388)

UncertaintyBeginning 0.018*** (0.003) 0.016*** (0.003) 0.012*** (.004)After 10 years 0.009*** (0.001) 0.009*** (0.001) 0.0109*** (0.001)After 20 years 0.007*** (0.001) 0.0067*** (0.001) 0.0074*** (0.001)

SkewnessBeginning −0.014*** (0.004) −0.013*** (0.003) −0.016*** (0.004)After 10 years −0.011*** (0.002) −0.01*** (0.001) −0.0106*** (0.001)After 20 years −0.008*** (0.001) −0.008*** (0.001) −0.007*** (0.001)

Labor force participationPlanned years work

full-time−0.001 (0.002) 0.003 (0.002) −0.006* (0.003) −0.008*** (0.002) −0.0048** (0.002) −0.015*** (0.003) −0.007*** (0.002) −0.006** (0.003) −0.015*** (0.004)

Other independent variablesGPA −0.0003 (0.032) 0.05 (0.035) −0.07 (0.056) 0.063* (0.036) 0.101** (0.043) 0.054 (0.063) 0.08* (0.041) 0.14*** (0.049) 0.015 (0.074)Age 0.013*** (0.003) 0.006 (0.004) 0.017*** (0.005) 0.006* (0.004) 0.008 (0.005) 0.001 (0.006) 0.007* (0.004) 0.015** (0.006) −0.001 (0.006)Class standing 0.015 (0.031) 0.04 (0.038) 0.02 (0.046) −0.007 (0.034) 0.005 (0.045) −0.036 (0.051) −0.006 (0.04) 0.03 (0.05) −0.045 (0.057)Gender 0.14** (0.059) 0.12*** (0.046) 0.16*** (0.05)

Interaction termsGender × uncertainty −0.003 (0.005) 0.0016 (0.001) −0.0006 (0.001)Gender × skewness −0.005 (0.005) −0.0009 (0.002) −0.002* (0.001)

Adjusted R2 .33 .541 .261 .702 .74 .69 .71 .74 .732F statistic 4.50*** 5.87*** 2.40*** 17.9*** 12.7*** 9.86*** 18.5*** 12.7*** 11.7***

Note. Coefficients for job preferences and major reported separately in Table A.1.a Standard errors in parentheses.∗ Significant at 10% level.∗∗ Significant at 5% level.∗∗∗ Significant at 1% level.

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Table 4OLS estimates of expected ln salary in education


Expected beginning salary Expected salary 10 years Expected salary 20 years


Independent variablesIntercept 9.63*** (0.131) 9.52*** (0.162) 9.49*** (0.251) 9.83*** (0.15) 9.57*** (0.18) 10.36*** (0.281) 9.97*** (0.165) 9.75*** (0.205) 10.25*** (0.35)

UncertaintyBeginning 0.0097*** (0.002) 0.0098*** (0.003) 0.014*** (0.003)After 10 years 0.0098*** (0.002) 0.0105*** (0.002) 0.0139*** (0.003)After 20 years 0.0086*** (0.002) 0.0084*** (0.002) 0.092*** (0.002)

SkewnessBeginning −0.0114*** (.003) −0.012*** (0.003) −0.0125*** (0.003)After 10 years −0.0094*** (0.002) −0.0102*** (0.002) −0.0068*** (0.004)After 20 years −0.0073*** (0.002) −0.0076*** (0.002) −0.005** (0.002)

Labor force participationPlanned years work

full-time0.0017** (0.001) 0.0018* (0.001) 0.00084 (0.002) 0.0026*** (0.001) 0.0028*** (0.001) −0.0004 (0.002) 0.0022** (0.001) 0.0019 (0.001) 0.0015 (0.002)

Other independent variablesGPA 0.006 (0.021) 0.004 (0.029) −0.008 (0.032) 0.009 (0.023) 0.02 (0.03) −0.017 (0.036) 0.026 (0.025) 0.045 (0.035) −0.008 (0.041)Age 0.0013 (0.002) 0.0008 (0.002) 0.004 (0.003) 0.005** (0.002) 0.007*** (0.002) 0.004 (0.004) 0.006*** (0.002) 0.007** (0.003) 0.074* (0.005)Class standing −0.011 (0.021) −0.017 (0.026) −0.009 (0.036) −0.016 (0.023) −0.02 (0.028) −0.032 (0.041) −0.022 (0.025) −0.026 (0.032) −0.042 (0.045)Gender −0.014 (0.039) −0.057 (0.041) −0.06 (0.042)

Interaction termsGender × uncertainty 0.0016 (0.004) 0.002 (0.003) −0.0007 (0.002)Gender × skewness −0.0005 (0.004) 0.003 (0.003) 0.003 (0.003)

Adjusted R2 .225 .221 .217 .291 .332 .289 .316 .309 .298F statistic 4.39*** 3.4*** 2.32*** 5.65*** 5.05*** 2.88*** 6.17*** 4.59*** 2.97***

Note. Coefficients for job preferences and major reported separately in Table A.2.aStandard errors in parentheses.

∗ Significant at 10% level.∗∗ Significant at 5% level.∗∗∗ Significant at 1% level.


(Lazear, 1976). Also, within business, the results regarding GPA suggest women anticipatebeing rewarded for greater ability in the later salary estimates. However, the significant gendercoefficient in the total estimates in business suggests (all else equal) men anticipate at least12% higher earnings than women over the entire lifecycle.

7. Discussion

The results suggest there are gender differences in expected earnings risk in business. Inter-pretation of these results, however, is hampered by the possibility that student error in predictingfuture wages may be influencing the uncertainty and skewness coefficients.

The literature suggests two sources of error. First, Slovic, Finucane, Mertz, Flynn, andSatterfield (2000) indicated that White males perceive less risk in the world comparedto other groups. Thus, men in business may overstate expected earnings uncertainty andskewness causing downward bias in the estimated coefficients.10 Second, Subich, Barrett,Doverspike, and Alexander (1989) and Smith and Powel (1990) suggest men overstate theirexpected salaries which would cause upward bias in the expected uncertainty and skewnesscoefficients.11 The relative size of the risk and skewness coefficients between women and menin business, then, may depend on the proportional effects of these two opposing sources ofbias.12

We may, however, be confident student error is not impacting the results in business forexpected beginning salaries. Betts (1996) found college seniors and juniors who predict wageswithin their fields (as is the case in this sample) are knowledgeable about beginning salaries.In addition, Table 2 indicates average beginning salaries, uncertainty, and skewness are similarfor women and men in business. This suggests men have not overstated earnings and risk inthese salary estimates. The remaining discussion, then, focuses on possible explanations of thelarger uncertainty and skewness coefficients for women compared to men in business in theestimates of expected beginning salaries. The discussion also cautiously offers an explanation(in addition to student error) regarding why the uncertainty and skewness coefficients are lowerfor these women in the later salary estimates.

Studies suggest women in business, as a result of gender socialization, may be morerisk averse than men (Muldrow & Bayton, 1979; Sexton & Bowman-Upton, 1991; Subichet al., 1989). A greater degree of risk aversion by women in business in this sample couldexplain why they require a higher expected return for earnings uncertainty than men in esti-mates of beginning salaries. In addition, if these women are more risk averse they wouldalso be less willing to forgo earnings in the face of positive skewness. This could explainthe larger value of the skewness coefficient for women in business in the beginning salaryestimates.

The larger uncertainty and skewness coefficients, in the estimates of expected beginningsalaries, could also reflect the plans of women in business to engage in part-time work earlyin their career. Results not reported indicated women in business planned, on average, 4.5years of part-time work to raise children compared to 1.4 years for men. If women in businessplanned to begin their families shortly after graduation they may have anticipated morepart-time work at this early career stage. This would translate into a lower wage elasticity


of demand for leisure and a higher expected return to earnings uncertainty in the beginningsalary estimates (De Meza, 1984). It would also translate into a larger skewness coefficient aswomen in business would be less willing to give up earnings when facing positive skewnessin the distribution of beginning salaries.

A problem with this explanation is that evidence suggests women, with higher levels ofeducation, are delaying marriage and children in order to pursue their careers (Hayghe andBianchi, 1994; Rindfuss, Morgan, & Offutt, 1996). However, Rindfuss et al. (1996) foundbirth rates for women with a bachelor’s degree rose steadily through the age ranges 21–23years and 24–26 years before peaking at 27–29 years. Additional information from this samplesuggests women in business could have anticipated a rise in birth rates at least as steep asRindfuss et al. (1996). Of the 122 women in business, 92 indicated they planned to marryand have (on average) 2.5 children. This exceeds the national fertility rate of 2.0 (Downs,2003) and suggests, in planning larger families, these women may have expected to beginhaving children early. Among the remaining 30 women in business, 28 were already married.Most of these women (19) had children and 5 were planning to have more. This segmentof the sample, being already involved in childcare, could have planned part-time work upongraduation contributing to the higher values of uncertainty and skewness in the beginning salaryestimates.13

The lower values of uncertainty and skewness for women in business, in the later salaryestimates, may reflect student error in predicting those salaries. These results, however, couldalso reflect plans of women in business to take on full-time work after their children are older. Ifwomen in business anticipate being part of a dual earner family the transition to full-time work,particularly after 10 years when some children may still be young, would be easier as they wouldexpect sufficient income to cover childcare costs. This is consistent with studies suggestinghusband’s income encourages the labor force participation of career-oriented women (Long &Jones, 1980; Phang, 1995). While this explanation cannot be ruled out it is offered with cautionbecause error may be impacting these estimates.

The findings in education, which indicate there are not significant gender differences inexpected uncertainty and skewness, may reflect union influence which limits variation of wages.These results, however, suggest expected uncertainty and skewness do impact the currentchoices of students in education. This is consistent with Leigh (1983) who found unions werenot the reason workers in blue-collar occupations failed to receive compensating returns forwage uncertainty.

8. Conclusion

This study has confirmed the uncertainty and skewness hypotheses based on sample datameasuring salary expectations of seniors and juniors majoring in business and education. Thesefindings complement previous studies supporting the uncertainty and skewness hypothesesbased on actual wages paid (King, 1974; McGoldrick, 1995). In addition, these hypotheses havebeen confirmed in two fields with different wage structures. Business offers greater earningsrisk through variable pay schemes based on individual incentives. Education has less risk dueto emphasis on group pay schemes negotiated through unions.


The findings in business, which indicated larger uncertainty and skewness estimates forwomen in expected beginning salaries, are consistent with Chauvin and Ash (1994). Thisprevious work found women in business preferred less earnings risk due to their selection intojobs with less variable pay than men. Neither of these studies, however, has pinpointed thereason why women in business are less willing to take on earnings risk than men. Businessis increasingly becoming the field of choice for women pursuing careers in nontraditionaloccupations. As a result, future work should continue to examine the determinants of earningsrisk preferences of women and men in this field. Labor force participation and risk aversionare two possible determinants of gender differences in earnings risk that are worthy of furtherconsideration.

Notes

1. Leigh (1983) estimated an additional earnings model that included percent of work-ers unionized. The uncertainty coefficient was still not significant and Leigh (1983)concluded unions did not cause the insignificant uncertainty coefficient. Instead, Leigh(1983) speculated compensating returns may not be paid in blue collar industries becauseearnings risk is due to accidents, layoffs, and strikes. Leigh’s results, however, are stillambiguous because he did not control for skewness.

2. McGoldrick (1995) argued a greater percentage of total variation in wages of women(compared to men) is due to systematic differences in human capital. Thus, to preventunderstatement of women’s risk premium, it is important to identify unsystematic vari-ation in earnings. Feinberg (1981) measured uncertainty as the standard deviation ofresiduals in an OLS regression that did not control for human capital characteristics.This measure, then, included systematic variation causing an understated risk premiumin women’s estimates.

3. As noted above, King (1974) indicated including skewness prevents understatement ofthe uncertainty coefficient. Results in the study by McGoldrick (1995) showed a higherpercent skewness in the earnings distribution of women suggesting a greater degreeof understatement would occur in women’s estimates. McGoldrick (1995) measuredskewness as the sum of the residuals cubed.

4. A similar measure was employed by Kodde (1986) in analyzing the impact of expectedearnings risk on the demand for higher education.

5. This follows Blau and Ferber (1991) who measured the labor market expectations ofseniors in the College of Commerce and Business Administration at the Universityof Illinois, Urbana-Champaign to test human capital model of gender differences inearnings. The emphasis on expected salaries over time (for continuous employment)allows for a test of the human capital hypothesis that women, planning less laborforce participation, select occupations with flatter earnings profiles (Polachek, 1976,1981).

6. Bellante and Link (1982) were following the mean–variance analysis developed by Tobin(1958). See Tobin (1958, pp. 74–77), for a defense of placing uncertainty regarding therate of return on bonds as a direct argument in the utility function.


7. Regressions were also run using planned years worked part-time while raising childrenas measures of labor force plans. Neither of these variables was significant in any of theestimations within business and education. Also, the expected uncertainty and skewnessresults were similar in these estimations to those reported below.

8. Chow tests indicated the separate regressions for women and men in business (in eachof the expected salary estimates) are significantly different (at the 1% level). Thus, ininterpreting these results, greater emphasis is placed on the relative size of the uncertaintyand skewness coefficients in the separate regressions and less weight on the interactionterms in the pooled estimates which are mostly insignificant. In education, Chow testsindicated the separate equations for women and men (in each of the expected salaryestimates) are not significantly different. As a result, greater weight is placed on theinteraction terms in the pooled estimates which suggest there are not significant genderdifferences in expected risk and skewness.

9. The results of Blau and Ferber (1991) also did not support the human capital model.In their analysis, however, planned years worked full-time was not significant in anyof the estimations. A replication study by Walker (1998) (which focused on seniors inbusiness and education) found planned years worked full-time was not significant inestimates within business. In education, planned years worked full-time was positiveand significant in estimates of expected beginning salaries but not after 10 and 20 years.

10. Skewness would be overstated if men in business overestimated the highest compared tolowest salary estimate.

11. The uncertainty coefficient would be biased upward because overstatement of expectedearnings would result in a higher risk premium for a given level of uncertainty. At thesame time, overstatement of expected earnings would cause understatement of skewnessresulting in a higher value for the skewness coefficient.

12. An instrumental variables estimation was attempted to test the robustness of the uncer-tainty and skewness results. Instruments in the Two-Stage Least Squares estimationincluded age, planned years worked full-time, and the job preferences of the students.Unfortunately, these estimates were unreliable because the instruments in the first stageestimations were not strongly related to the uncertainty and skewness variables. Inaddition, predicted values of uncertainty and skewness in the second stage were notsignificantly related to expected earnings.

13. Of the 19 women with children, 12 were between the ages of 21 and 33. This suggeststheir children were younger requiring more time. Also, three of the five who plannedmore children were 30 years old indicating the intention of some of the older women tostill be involved in child care.

Acknowledgements

I thank the students who participated in this study and Alexander Ansah for excellent researchassistance. Comments provided by Mary Ellen Benedict, Jim Peach, Saif Zahir, and the anony-mous reviewers were also greatly appreciated.

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Appendix A

Table A.1Coefficients for job preferences and major in businessa


Expected beginning salaries Expected salaries 10 years Expected salaries 20 years


Independent variablesJob preferences

Decent salary 0.0158 (0.014) −0.003 (0.018) 0.016 (0.022) 0.022 (0.016) 0.039* (0.022) 0.005 (0.023) 0.039** (0.018) 0.066** (0.025) 0.029 (0.026)Advancement 0.005 (0.013) −0.008 (0.021) 0.011 (0.018) 0.026* (0.015) 0.052 (0.026) 0.018 (0.020) 0.033** (0.016) 0.056* (0.03) 0.023 (0.022)Intellectual challenge 0.002 (0.015) 0.021 (0.020) 0.01 (0.023) 0.03* (0.017) 0.01 (0.024) 0.061** (0.026) 0.03 (0.019) 0.024 (0.028) 0.035 (0.029)Work autonomy 0.014 (0.013) 0.013 (0.015) 0.004 (0.021) 0.011 (0.014) 0.018 (0.018) −0.006 (0.022) 0.016 (0.016) 0.013 (0.021) 0.015 (0.025)Near family −0.009 (0.011) 0.003 (0.013) 0.007 (0.017) −0.017 (0.012) 0.005 (0.016) −0.009 (0.018) −0.013 (0.013) −0.0002 (0.018) −0.002 (0.021)Work environment −0.031 (0.018) −0.061*** (0.021) 0.019 (0.032) 0.012 (0.014) −0.006 (0.026) 0.014 (0.033) −0.006 (0.023) 0.013 (0.021) −0.013 (0.037)Geographical location −0.013 (0.012) −0.061*** (0.017) 0.023 (0.018) −0.031** (0.013) −0.072*** (0.02) 0.0006 (0.019) −0.035** (0.015) −0.073*** (0.023) −0.022 (0.021)Pleasant coworkers 0.0048 (0.017) 0.024 (0.021) −0.0021 (0.026) 0.03 (0.019) 0.024 (0.027) 0.03 (0.028) 0.018 (0.022) −0.008 (0.031) 0.046 (0.031)Not demanding −0.010 (0.011) −0.005 (0.012) −0.023 (0.018) 0.008 (0.012) 0.023 (0.015) −0.014 (0.019) 0.009 (0.013) 0.04** (0.017) −0.022 (0.022)Contribution to society 0.0018 (0.012) 0.034** (0.015) 0.0057 (0.019) −0.0024 (0.013) 0.029 (0.018) −0.02 (0.020) −0.007 (0.015) 0.018 (0.022) −0.015 (0.022)Earnings prestige 0.0014 (0.01) 0.011 (0.011) 0.008 (0.016) 0.0008 (0.011) −0.002 (0.014) 0.006 (0.017) 0.001 (0.012) −0.00004 (0.016) −0.0068 (0.019)Fringe benefits 0.03* (0.014) 0.018 (0.017) −0.009 (0.025) 0.018 (0.016) 0.007 (0.02) −0.0003 (0.027) 0.013 (0.018) −0.0002 (0.023) 0.013 (0.03)Job security 0.021 (0.015) 0.024 (0.015) −0.002 (0.025) 0.006 (0.016) −0.009 (0.02) −0.009 (0.027) 0.01 (0.018) −0.02 (0.021) 0.016 (0.031)Opportunity to travel −0.008 (0.008) −0.02 (0.010) 0.01 (0.014) 0.026* (0.009) −0.007 (0.012) 0.027* (0.015) 0.001 (0.016) −0.005 (0.03) 0.012 (0.017)Flexible hours −0.003 (0.01) 0.018 (0.013) −0.008 (0.016) −0.005 (0.012) −0.018 (0.015) 0.02 (0.017) −0.004 (0.013) −0.034* (0.018) 0.035* (0.019)

Majorb

Accounting 0.073* (0.039) 0.096** (0.043) 0.062 (0.070) 0.018 (0.044) 0.026 (0.053) −0.003 (0.075) 0.005 (0.05) −0.0009 (0.06) −0.003 (0.083)Liberal arts business 0.058 (0.042) 0.08* (0.041) 0.053 (0.057) −0.02 (0.039) 0.039 (0.050) −0.077 (0.063) −0.052 (0.045) −0.005 (0.058) −0.102 (0.07)Agricultural business −0.009 (0.042) −0.021 (0.053) 0.067 (0.064) −0.072 (0.046) −0.145** (0.065) 0.004 (0.068) −0.095* (0.053) −0.17** (0.075) −0.057 (0.077)

a Standard errors in parentheses.b Broad area business is excluded in the estimations.∗ Significant at 10% level.∗∗ Significant at 5% level.∗∗∗ Significant at 1% level.

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Table A.2Coefficients for job preferences and major in educationa

Dependent variable: ln expected salaries

Expected beginning salaries Expected salaries 10 years Expected salaries 20 years


Independent variablesJob preferences

Decent salary 0.017** (0.008) 0.02** (0.01) 0.017 (0.014) 0.021** (0.01) 0.026** (0.011) 0.02 (0.016) 0.01 (0.01) 0.014 (0.013) 0.019 (0.018)Advancement −0.0013 (0.007) −0.008 (0.008) 0.008 (0.012) −0.004 (0.007) −0.0021 (0.009) −0.003 (0.013) 0.003 (0.008) 0.0026 (0.01) 0.014 (0.015)Intellectual challenge 0.0086 (0.009) 0.0082 (0.012) −0.005 (0.016) 0.005 (0.01) 0.008 (0.012) −0.01 (0.018) 0.0056 (0.01) 0.0083 (0.014) −0.0098 (0.021)Work autonomy 0.0072 (0.012) 0.011 (0.01) −0.0055 (0.014) 0.0051 (0.009) −0.0014 (0.01) 0.022 (0.016) 0.0046 (0.01) 0.0013 (0.012) 0.014 (0.017)Near family 0.0055 (0.006) 0.005 (0.009) 0.0025 (0.01) 0.0014 (0.007) 0.007 (0.01) −0.012 (0.011) 0.01 (0.008) 0.02* (0.011) −0.008 (0.012)Work environment 0.017 (0.013) 0.0475*** (0.018)−0.012 (0.019) 0.086 (0.014) 0.051*** (0.019) −0.045** (0.021) −0.002 (0.015) 0.035 (0.022) −0.005 (0.023)Geographical location −0.007 (0.009) −0.004 (0.012) −0.013 (0.015) 0.0041 (0.009) 0.007 (0.019) −0.002 (0.016) 0.003 (0.01) −0.0005 (0.014) 0.01 (0.018)Pleasant coworkers −0.0037 (0.01) 0.0036 (0.014) −0.01 (0.016) 0.0038 (0.011) 0.0023 (0.015) −0.005 (0.018) −0.0083 (0.013) 0.014 (0.018) −0.005 (0.02)Not demanding −0.0054 (0.007) −0.011 (0.009) 0.009 (0.012) −0.0052 (0.008) −0.0085 (0.01) −0.003 (0.014) −0.003 (0.009) −0.0064 (0.012) −0.0007 (0.016)Contribution to society −0.0008 (0.009) −0.007 (0.011) 0.019 (0.016) 0.0013 (0.009) −0.007 (0.011) 0.0086 (0.018) 0.0086 (0.01) 0.0045 (0.013) 0.015 (0.02)Earnings prestige 0.012* (0.007) 0.008 (0.009) 0.019* (0.011) 0.015** (0.007) 0.0075 (0.009) 0.032** (0.012) 0.016** (0.008) 0.017 (0.011) 0.015 (0.013)Fringe benefits −0.006 (0.008) −0.005 (0.01) −0.003 (0.015) −0.003 (0.009) 0.009 (0.01) −0.037** (0.017) −0.0004 (0.01) 0.003 (0.012) −0.019 (0.018)Job security −0.0037 (0.012) −0.013 (0.015) −0.011 (0.021) −0.0157 (0.013) −0.036** (0.016) 0.022 (0.024) −0.021 (0.015) −0.05*** (0.019) 0.024 (0.026)Opportunity to travel −0.0015 (0.006) 0.0035 (0.007) −0.013 (0.01) 0.0215 (0.006) −0.002 (0.008) 0.00035 (0.011) 0.009 (0.007) 0.0026 (0.01) 0.009 (0.015)Flexible hours −0.0055 (0.007) −0.007 (0.009) −0.0065 (0.012) 0.019 (0.008) 0.0012 (0.009) 0.00032 (0.014) −0.007 (0.009) −0.0088 (0.011) −0.014 (0.015)

Majorb

Secondary education 0.075*** (0.019) 0.081*** (0.023) −0.18 (0.041) 0.056*** (0.021) 0.093*** (0.024) 0.019 (0.048) 0.12*** (0.024) 0.135*** (0.029) 0.08 (0.053)a Standard errors in parentheses.b Elementary education is excluded in the estimations.∗ Significant at 10% level.∗∗ Significant at 5% level.∗∗∗ Significant at 1% level.


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Documents

Gender differences in expected compensation for earnings uncertainty and skewness in business and education