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Why do women become teachers while men don’t?

  • David Carroll , Jaai Parasnis ORCID logo EMAIL logo and Massimiliano Tani


Across countries, almost all primary and pre-primary teachers are women while few men in the occupation tend to specialise in secondary schooling and administration. We investigate the decision to become a teacher versus alternative occupations for graduates in Australia over the past 15 years. We find that this gender distribution reflects relative returns in the labour market: women with bachelor qualifications receive higher returns in teaching, while similarly educated men enjoy substantially higher returns in other occupations. We also find evidence that schools which can, and do, make higher wage offers successfully attract more male teachers as well as more female teachers with a degree in science, technology, engineering, and mathematics. These results are consistent with the predictions of theoretical models of self-selection of intrinsically motivated workers.

JEL codes: I26; J16; J24; J31

Corresponding author: Jaai Parasnis, Monash University, 20 Chancellors Walk, 3800Clayton, Victoria, Australia, E-mail:

Funding source: Graduate Careers Australia

Award Identifier / Grant number: MON


We acknowledge funding support from Graduate Careers Australia under the Graduate Research Program research grants scheme. We thank the editor, Francesca Barigozzi, and two anonymous reviewers for their insightful comments and suggestions.


Appendix 1: Decomposition Methods

The Kitagawa-Blinder-Oaxaca Decomposition

For the conditional mean decomposition, the logarithms of the wage equations for males (M) and for females (F) are modelled as.


respectively, where t refers to the graduation year. These equations can be estimated separately or in a pooled regression by adding a gender indicator. Using the assumption of linearity (and separability) of wages as a function of observable and unobservable characteristics, it is possible to write the difference in the mean wages Δt=WMtWFt into the three components:



  1. (XMtXFt)βFt is the explained component due to observed group differences in the X’s (also known as endowment effect);

  2. βMtβFt)XFt is the unexplained component due to differences in the coefficients (the β’s); and

  3. the interaction term (XMtXFt)(βMtβFt) reflects that differences in endowments and coefficients between the two groups exist simultaneously.

Under the assumption that the conditional expectation of wages given a set of covariates is linear, it is possible to further subdivide composition and wage structure effects into the contribution of each covariate.

The Firpo, Fortin and Lemieux Decomposition

This consists of two steps: the first is to perform a regression of the probability of the wage observation being above a quantile of interest (or other statistic such as variance or Gini coefficient). In the second, the Kitagawa-Blinder-Oaxaca decomposition is applied.

Hence, the wage gap at quantile q(τ) can be written as the difference between female and male quantiles:


The unconditional quantile regression approach first replaces the dependent variable of models (1) and (2) with the ‘recentered influence function’ (RIF) of the wages WMt and WFt for the quantile of interest, which is defined as:


where the expression I(Witq)(1τ)fW(q(τ)) is the influence function.[6] The RIF functions for males and females are therefore:




respectively. The quantile wage gap is obtained as the difference in conditional expected value of the RIF between the two groups. This can be decomposed using the Kitagawa-Blinder-Oaxaca decomposition as:


where the term (XMtXFt)δFt,τ explains the effect of the covariates on the unconditional quantile and the term (δMt,τδFt,τ)XMt captures unexplained differences between males and females.

A RIF regression[7] is therefore analogous to performing a linear regression model on the probability of the wage observation being above the quantile of interest. The only difference with a simple linear probability model is that in the RIF case the coefficients are normalized by the density function evaluated at the quantile q(τ). As shown in Firpo, Fortin, and Lemieux (2009), instead of decomposing the quantile gap Δt(τ), this methodology uses the corresponding gap in the probability, on which it then applies the Kitagawa-Blinder-Oaxaca decomposition. The main advantage of this approach is to allow one to separate the overall components of the decomposition into the contribution of each single variable.

Appendix 2: Robustness Tests

One potential concern is whether the results are sensitive to omitted variables. If any non-random choice of occupation is captured by other control variables, such as ATAR, included in the analysis, our estimates remain valid. Otherwise, the results are biased. If the occupational choice is associated with unobserved characteristics, the extent of omitted variable bias can be investigated the methodology by developed by Oster (2019) for linear models. This effectively extends the approach developed by Altonji, Elder, and Taber (2005) to the case where the R2 value of a regression is no longer assumed to be equal to 1.[8] Oster’s test can be viewed as a critical node in the decision of whether or not the coefficient of the treatment variable is suffering from omitted variable bias. In essence, the test measures the amount of unobserved, relative to observed, selection that would result in the OLS coefficient of interest being zero. Using the Stata command psacalc, we estimate such ratio to be about two in the original regression on females and 11 in the case of males.[9] This implies that, in our original regression for females, the selection on unobservable would have to be twice as large as that on observables for the coefficient of the teacher variable to be zero. This selection of unobservables would have to be 11 times as large in the case of males. These magnitudes are highly unlikely given the control variables on demographic characteristics and academic ability, as well as institutional factors. These results strongly suggest that the findings are not subject to omitted variable bias.

Besides Oster (2019) method, we conduct several other robustness tests: we check the sensitivity of our results by changing the definition of teachers to progressively include Special Education Teachers, Vocational Education Teachers and Education Officers; we also estimate the models by varying the sample restrictions: including all individuals, excluding individuals who report wages above $200,000, excluding the top and bottom 10th percentile of the wage distribution and using derived hourly wages instead of the annual wage reported by graduates. The main results that (i) women earn more as teachers, while men earn more as non-teachers and (ii) when non-teachers earn more compared to teachers, the wage differential between non-teachers and teachers is smaller for women compared to men, are consistent across all specifications. In order to account for flexibility of occupations, we include the proportion of part-time workers within the occupation as an additional control variable. Except for the master’s degree holders, women have lower opportunity costs for becoming a teacher. The specifications and results of these robustness tests are summarised in Table A1 in Appendix 2.

Figure 1 illustrates the differences in the distribution of male and female teachers across pre-primary, primary and secondary levels. We account for these differences by estimating the differences between teachers and non-teachers separately by gender and teacher groups. The results, available from authors, confirm the main findings. Male pre-primary teachers have a wage disadvantage of 18 percent compared to 12 percent for female pre-primary teachers. Similarly, men who work as primary teachers have a 14 percent wage disadvantage compared to non-teachers while women primary teachers have wage disadvantage of only two percent. Women working as secondary school teachers earn $2948 (6.6%) more than non-teachers, while male secondary school teachers earn $2532 (4.8%) less.

Table A1:

Summary of results from alternative specifications.

SpecificationEducation levelWomenMen
1. Definition of teachers: Include special education, vocational education and education officers as teachersGraduatesDifference between teachers and non-teachers not significant
Graduate diploma
2. Definition of teachers: Include special education and vocational education as teachers, education officers as non-teachersGraduatesDifference between teachers and non-teachers not significant
Graduate diploma
3. Log hourly wages instead of annual wagesGraduatesTeachers earn more; differential in favour of teachers greater for females than males.
Graduate diploma
MastersTeachers earn more
4. Including full wage range (sample not restricted to $10,000– <$150,000)Graduates
Graduate diploma
5. Sample restricted to wage < $200,000GraduatesTeachers earn more; differential in favour of teachers greater for females than males.
Graduate diploma
6. Excluding ATAR as a control variableGraduates
Graduate diploma
7. Excluding ATAR as a control variable and restricting sample to education qualifications only.Graduates
Graduate diploma
8. Controlling for proportion of part time workers in the occupationGraduatesTeachers earn more, differential in favour of teachers greater for females than males.
Graduate diploma
MastersTeachers earn more, differential in favour of teachers greater for males than females.
  1. Notes: Symbol ✓ denotes that results are consistent with the results reported in Tables 2 and 5.


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Received: 2020-07-29
Accepted: 2021-01-07
Published Online: 2021-01-22

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