Over the past decades, the Blinder-Oaxaca method has been used as the basis for many comparisons of male and female wages. The major types of decomposition used over the past decade have incorporated human capital variables, human capital plus firm (employer) variables, and human capital plus industry/occupation variables. The remainder of this section summarises the findings from these three types of study.
While the original Blinder and Oaxaca models included firm-based and industry/occupation variables, one study used only human capital variables. Waldfogel (1998) found that even when the gender pay gap had been closing, the pay gap between women with children and women without children has been widening in the US. This phenomenon is called the "family gap". When only women with children are considered, sole parent mothers fared worst, including women who had been previously married. The two decompositions performed by Waldfogel (on 1980 and 1991 data) indicate that the presence of children has such a negative effect on women's earnings that if there was no difference between women with children and women without children, especially for human capital, then the family gap in pay would still exist. Waldfogel suggests that this result is mediated by the provision of maternal leave, as countries with better maternal leave policies (e.g., longer leave provisions, paid leave provisions) have smaller gender pay gaps, possibly due to the positive effect of maternal leave provisions on tenure and work experience.
The addition of firm-based variables in the model, such as number of employees, provides more information on the source of differences in male and female wages.
Chauvin and Ash (1994) used a sample of American business school graduates to decompose the gender pay gap in base pay, contingent pay 8 , and total pay. (Three indicator variables relating to occupation in the professional, technical, and sales categories were included in the model.) The authors found a 9% unexplained pay gap for total pay, and no unexplained pay gap for base pay, adjusted for differences in means. Further decomposition work showed that the unexplained pay gap in total pay was due to gender differences in contingent pay; this gap disappeared when contingent pay was added to the model. This suggests that the size of the gender pay gap is strongly dependent on the type of pay data used as the dependent variable.
Swaffield (2000) found that full-time education of up to five years duration had a positive effect on women's hourly wage, but no effect on that for men. Unemployment had a negative impact on the hourly wage of both males and females, but duration of unemployment of over one year had no additional effect. While employment in a male-dominated occupation 9 increased the female hourly wage, the women employed in such occupations were penalised more heavily for exiting the paid labour force. The interaction between being employed in a male-dominated occupation and time spent outside the labour force had a significantly negative effect on the female hourly wage.
One primary advantage of using employer-employee linked data is the ability to include a measure of the proportion of women in the employer's workforce within the model. The consistent finding from these studies is that a predominantly female workforce - at the employer level - significantly decreases both male and female wages. The following two studies used linked employer-employee data.
Reilly and Wirjano (1998) used a Mincerian analysis of the 1979 Canadian data from the General Segmentation Study. Individuals were included if they worked at least 30 hours per week. The authors found that the largest single (negative) effect on wages was the proportion of women in the employer's workforce. Education and experience both increased wages, as did tenure to a lesser degree. In a later Blinder-Oaxaca decomposition, the proportion of females in the employer workforce was found to account for 26% of the mean gap in log wages. The practical significance of these results is increased by the fact that males tended to be employed in male-dominated workplaces and females in female-dominated workplaces. The implication from the model is that increased workplace segregation will increase the gender pay gap.
Reiman (2001) used data from the 1995 Australian Workplace Industrial Relations Survey (AWIRS95) initially to construct a regression of log wages against a set of 36 human capital and employer-based variables (incorporating 28 indicator variables). He found that males earned 7.56% higher than females, indicating that a gender pay gap existed in Australia in 1995. Working full-time (35 hours per week), and working for an employer with a predominantly female workforce, were associated with lower wages. Higher years of schooling and having English as the primary language were associated with higher wages. With further analysis, Reiman found six variables that produced statistically significant differences between males and females. Compared to males, females were paid more in full-time work, when they had children aged less than five years, and in metropolitan areas. Females were paid less in South Australia and when the employer was at least partially foreign-owned. Reiman's model reduced the gender wage gap from 13.4% to 8.1%, which is a reduction of 39%. In dollar terms, the adjusted gender pay gap equated to $1.23 Australian dollars per hour, before tax.
Groshen (1991) used the data from five American Industry Occupational Wage Surveys in a Blinder-Oaxaca decomposition. Because different industry-based surveys were used, the data periods range from 1974 to 1983. While the industries were decomposed separately, the five findings actually relate to three different years. Occupation was found to be highly segregated, and wages were found to be strongly related to the proportion of females in the occupation. Occupation was found to account for over half the observed gender wage gap. While males and females who worked in the same occupation for the same employer (termed a "job cell") earned roughly the same amount, most occupations were segregated and within employers most occupations were totally segregated.
Bayard, Hellerstein, Neumark, and Troske (1999) matched employee records from the 1990 Worker-Establishment Characteristics Database constructed from the American Dicennial Census to employers listed in the US Census Bureau's American Standard Statistical Establishment List. The authors could not replicate Groshen's finding of sex segregation causing the gender pay gap. In this later study, while females were found to be segregated into lower paying industries and occupations, the largest influences on the gender pay gap arose from the lower wages of females compared to males for the same job cell and in job cell segregation. The level of occupation disaggregation in the model was important; as occupation was more highly specified (from a 1-digit level, representing 13 occupations through to a mix of 3- and 4-digit occupations, representing 491 occupations) the size of the gender coefficient decreased.
Macpherson and Hirsch (1995) examined the influence of feminised occupations on the gender pay gap, primarily using 1983 to 1993 data from the American Current Population Survey Outgoing Rotation Group. Occupation was analysed at the 3-digit level, and the proportion of female workers in each occupation was calculated. Macpherson and Hirsch found significantly lower wage rates for all workers in feminised occupations (containing at least 75% women) and also in masculinised occupations (containing at least 75% men). The lowest average wage is associated with the group of feminised occupations. For all occupation groups (0-25% women, 25-50% women, 50-75% women, 75-100% women) the average female wage was lower than the average male wage. When extra variables associated with job characteristics were introduced into the model, the influence of occupation feminisation on wages was reduced. The proportion of females in a job effectively acts as a proxy variable for differences in job characteristics (e.g., physical requirements), worker-based productivity differences, and preferences for job characteristics. For example, feminised jobs typically have a lower requirement for training (and, therefore, lower levels of human capital).
Finally, the following three studies also incorporate public sector variables into their models. Naur and Smith (1996) used three 10-year cohorts in their decomposition of Danish employees. They found that the youngest cohort (aged 20 to 29 years) had the smallest gender pay gap in 1980, but this gap widened in the ten years to 1990. Only the oldest cohort (aged 40 to 49 years) experienced a decreased gender pay gap. The middle cohort consistently had the largest gender pay gap in the 1980s due to the low wages paid to women, especially in the Danish public sector that was the primary employer of the middle and youngest cohort women. In comparison, the lower pay for women in the oldest cohort was mainly due to a lack of human capital. These findings suggest that occupational and industrial factors can override increased human capital (e.g., higher educational attainment) investment by women.
Further work by Gupta, Oaxaca and Smith (1998), using data from the Danish Longitudinal Sample, found little change in the gender pay gap in either the private or public sector between 1983 and 1994, with a decrease in public sector wages compared to the private sector over the time period. There was also an increase in wage dispersion, particularly in the public sector. Given that Danish women were concentrated on lower paying jobs, this increase in wage dispersion should have increased the gender pay gap. In both the private and the public sectors there was an increased return on qualifications, higher for females compared to males, so this human capital factor reduced the gender pay gap for both sectors. Qualification was decomposed into education, experience, and occupational position factors. The biggest contributor to the qualification effect was the increased labour experience of women in the public sector, and the increased educational attainment of women also decreased the gender pay gap in this sector. Changes in occupational position should have slightly decreased the gender pay gap, although this effect was countered by relative pay changes within the occupational groups.
The finding that increased educational attainment of women decreases the gender pay gap is robust across different cultures. For example, Sung, Zhang and Chan (2000) decomposed Hong Kong census data from 1981, 1986, 1991 and 1996, incorporating seven occupational groups at the 1-digit level. The authors also used the Brown, Moon and Zoloth extension to the Blinder-Oaxaca method to separate intra- and inter-occupational wage differences. From 1981 to 1996 there was a decrease in the gender pay gap from 29% to 16.1%, partly due to increased female educational attainment and also due to the Hong Kong economy shifting from manufacturing to services-based, resulting in females shifting to the more highly paid services occupations. Regarding occupation, the gender pay gap is mainly intra-occupational rather than inter-occupational, with inter-occupational effects actually reducing the gender pay gap.
Little published research has been performed in New Zealand using the Blinder-Oaxaca decomposition to investigate the gender pay gap. Primary New Zealand research using this method is outlined below in chronological order.
Dixon (1996a) performed a number of decompositions of salaried and waged employees using the Household Economic Survey (HES). The model included human capital, industry, and occupational variables. Initial decomposition models containing an indicator variable for gender was entered into the model, although latter analyses decomposed males and females separately. Qualifications and gender had the largest effect on earnings, with university qualifications and being males producing higher wages. Part-time work status was associated with lower hourly income.
Dixon (1998) examined the changes in income inequality between 1984 and 1997 using the Household Economic Survey (HES) conducted by Statistics New Zealand. She found that the gender pay gap in average hourly earnings decreased, as did the gender pay gaps in full-time weekly earnings and median hourly earnings. The reason for this reduction was that female earnings increased more than male earnings from 1984 to 1995, with no reduction evident in 1996 or 1997.
Dixon (2000) used the Blinder-Oaxaca decomposition method to examine the gender pay gap of New Zealand salaried and waged employees aged 20 to 59 years. Due to the low hourly pay gap between part-time and full-time female earners, part-time employees were also included in the analysis. Qualification (4 indicator variables), ethnicity (3 indicator variables), country of birth (2 indicator variables), part-time status, region (based on Regional Council), industry (2-digit or 3-digit level, included as indicator variables), and occupation (2-digit or 3-digit level, included as indicator variables) were included in the models. Two models decomposed log wages using 2-digit industry and occupation classifications. The HES data showed a log wage gap of 0.136, with between 14% and 30% of the gap attributable to industry and 4% and 10% attributable to occupation 10 . The Household Labour Force Survey Income Supplement (IS) data gave a log wage gap of 0.171, with between 20% and 24% of the gap attributable to industry and -9% and 5% attributable to occupation. When the decomposition of the IS data was based on 3-digit industry and occupation classifications, the log wage gap remained at 0.171, with a lower effect of industry (between 12% and 18%) and a larger effect of occupation (between 6% and 23%).
In other modelling work on the gender pay gap, Kirkwood (1998) used a tree analysis to examine 1997 earnings data on males and females in full-time employment (defined as at least 30 hours per week, not including the self-employed). A pruned tree with 12 terminal nodes explained 29% of average earnings. The most important variable was occupation (based on the one-digit New Zealand Standard Classification of Occupation - NZSCO - group) followed by hours worked. Age, highest education qualification, and sex were also found to be important in the model, although industry was not. In a subsequent standardisation procedure using these four variables plus ethnicity, hours worked was found to have the greatest influence on average weekly earnings. The standardisation model also decreased the gender pay gap from 21% to 14% due to a better model specification resulting from the inclusion of additional variables. These findings suggest that gender is a secondary explanatory factor for earnings.
8 Pay directly contingent upon job performance, e.g., bonus, commission, and profit sharing.
9 At least 60% of the full-time employees for that occupation are male.
10 Four models were run in a 2x2 design (two experience calculations x two coefficient weighting methods). The range represents the minimum and maximum values from the four models.