An extensive effort by Devine 1994a provides greater detail on gender differences in self-employment. She measured gender differences in self-employment rates by race, age,
marital status, years of schooling, and fullpart-time and fullpart-year status. She recorded gender differences in occupation and industry distributions and earnings levels, for
self-employed workers as well as wage-and-salary workers. In particular, she reported that among full-time, full-year workers in 1990, self-employed women earned 73 of the
annual income of female wage-and-salary workers, whereas self-employed men earned 107 of the annual income of male wage-and-salary workers.
The results of Devine’s descriptive statistics and the writings of previous researchers are suggestive of the causes for these gender differences. According to Devine, there are
noticeable differences in the personal characteristics of self-employed men and women. For example, self-employed men are more likely to be in high-paying occupations e.g.,
executive, administrative, and managerial; precision production, craft, and repair and industries e.g., construction than self-employed women. Self-employed women are more
likely to be in service occupations and industries. Self-employed men are, on average, more educated than self-employed women. Moreover, self-employed men are more likely
than self-employed women to have incorporated their self-employment business.
After controlling for some differences in personal characteristics, Moore 1983a nevertheless found that femalemale earnings ratios in self-employment were much lower
than femalemale earnings ratios in wage-and-salary employment. Moore suggested that the finding could be evidence of substantial consumer discrimination against women.
The idea that discrimination is a source of differences between the wage-and-salary and self-employment sectors has also received the attention of other researchers in other
applications. Borjas and Bronars 1989 developed the theory of consumer discrimination in the context of self-employment more fully. They proceeded to test it on data consisting
of White, Black, Asian, and Hispanic men. The results of estimations of earnings functions showed an income gap between self-employed Blacks Hispanics and Whites
that remained, even after controlling for differences in demographic characteristics. These results were consistent with the implications of their theoretical model.
4,5
In this paper, the econometric techniques used in these other settings are applied to microdata, in an effort to contribute to the understanding of gender differences in
self-employment. The methodology of the analysis is described in greater detail in the following section.
III. Methodology
This paper adopts the general approach found in many of the studies of self-employment Blau, 1985; Rees and Shah, 1986; Borjas and Bronars, 1989; Yuengert, 1994; Fairlie and
Meyer, 1996. It is assumed that workers choose between wage-and-salary employment and self-employment so as to maximize expected utility. The difference in the expected
4
In a theoretical paper by Coate and Tennyson 1992, it was argued that low self-employment rates and reduced returns to self-employment could result from discrimination, when individuals’ entrepreneurial abilities
are imperfectly observable and labor market discrimination “spills over” into markets relevant to self- employment e.g., the credit market.
5
Several studies of Black and Asian self-employment cite a sociological theory of self-employment, that suggests that low-wage workers are pushed into entrepreneurship by barriers blocking their access to good jobs
in the wage-and-salary sector Bates, 1997; Min, 1984.
Gender Differences in Self-Employment 501
utility from self-employment and the expected utility from wage-and-salary employment, I
, is a stochastic function of observable personal characteristics X. That is, I 5 Xb 1 e
1 where e is normally distributed with a mean of 0. Relevant X variables are those that may
affect the individual’s taste for self-employment versus wage-and-salary employment, indicating greater less happiness in self-employment, as X
j
increases, when b
j
is positive negative. Relevant X variables may also include those that may affect the individual’s
likelihood of a satisfying work situation in self-employment vs. wage-and-salary employ- ment, indicating greater less likelihood in self-employment, as X
k
increases, when b
k
is positive negative. An individual chooses self-employment if I
.0, but chooses wage- and-salary employment if I
,0. Probit estimation of this relationship is important in its own right. Estimation of this
relationship for men and women separately can shed light on the sources of the difference in the self-employment rates of men and women.
An understanding of the employment choice through probit estimation is also vital for an accurate understanding of the determinants of earnings in the two sectors. It is assumed
that the natural logarithm of earnings in each type of employment is a stochastic function of observable personal characteristics Z. That is,
ln Y
se
5 Z
se
g
se
1 n
se
2 and
ln Y
ws
5 Z
ws
g
ws
1 n
ws
3 where n
se
and n
ws
are normally distributed with means of 0 in the population. However, Equations 2 and 3 are estimated on subsamples of the population, consisting of workers
within each sector i.e., self-employed and employees, respectively. Steps to avoid sample-selection bias in the estimation of g require as input the results of probit estimation
of the employment choice.
6
Estimation of 2 and 3 for men and women separately, corrected for self-selection, provides a basis for studying the sources of gender differences in the determination of
earnings in each type of employment.
7
It also allows one to address gender differences in the earnings gap between self-employment and wage-and-salary employment.
Details concerning the data set used in the exploration of these issues are given in the following section.
IV. Data
