Gender differences in formal wage employment in urban Tanzania

ABSTRACT

We use the Tanzania Integrated Labour Force Survey data and a censored bivariate probit model to analyse gender differences in labour force participation and gender bias in formal wage employment in urban Tanzania. We find that, compared to men, women are less likely to participate in the labour market and less likely to obtain formal wage employment, suggesting the existence of gender bias in the labour market in urban areas of Tanzania. However, after accounting for selection into labour force participation, the existing gender bias is narrowed for women with high school or university education. The regression results suggest that the existing gender differences in formal wage employment probabilities cannot entirely be explained by observable characteristics. The finding of a positive unexplained formal wage employment probability differential suggests that the possibility of gender discrimination against women in urban Tanzania cannot be completely ruled out.

KEYWORDS:

• Labour force participation
• formal wage employment
• censored bivariate probit model
• gender discrimination
• Tanzania

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 Informal economy’ is sometimes used to connote the informal sector. However, ‘informal sector’ is more commonly used in Tanzania and the National Bureau of Statistics (NBS).

2 ‘Formal sector’ is defined as including both the entire public sector and private sector enterprises and institutions that are formal in terms of registration, taxation, and official recording.

3 Some of the recent policies and regulations to improve working conditions and address gender imbalances include the Employment and Labour Relations Act of 2004 (United Republic of Tanzania [URT], 2004a), the Labour Institutions Act of 2004 (URT, 2004b), and the National Employment Policy of 2008 (URT, 2008).

4 Gender discrimination occurs when differences in job hiring decisions and wage payments between men and women are largely based on differences in the sex of an individual rather than the productivity differentials of job applicants and employees, respectively (Baah-Boateng, 2012; Boahen & Opoku, 2023).

5 Wamuthenya (2010), for example, uses a multinomial logit model (without considering labour force participation) to examine the determinants of formal and informal sector employment in the urban areas of Kenya. R. Agesa et al. (2013) also use a probit model in the first stage to correct for potential endogeneity in estimating the gender pay gap along the entire unconditional wage distribution in Kenya.

6 Studies have urged governments to incentive women to pursue training programs in STEM (Ruiters & Charteris, 2020; Schwidrowski et al., 2021).

7 These results are unweighted.

8 Although religion and ethnicity could be important in explaining labour force participation, especially for women, the information on those variables is not available in the ILFS 2014 data.

9 Spouse’s income and spouse’s labour force status may affect a woman’s decision to participate in the labour market, especially when participation is a household optimization decision. This information is only available for married individuals or those in a consensual relationship living in the same household. We present results for the case when spouse’s working status is included as an explanatory variable in Appendix Tables A1 to A3. The findings are similar to the results presented in Table 5.

10 Unexplained = $\mathrm{Avg}\{F(X_{1i}^F{\beta }_1^F,X_{2i}^F{\beta }_2^M{\rho }^F)/\mathrm{\Phi }(X_{1i}^F{\beta }_1^F)\}$-$\mathrm{Avg}\{F(X_{1i}^F{\beta }_1^F,X_{2i}^F{\beta }_2^F{\rho }^F)/\mathrm{\Phi }(X_{1i}^F{\beta }_1^F)\}$; ${\beta }_2^F$and ${\beta }_2^M$ are female and male coefficients of the formal wage employment selection equation. The male coefficients, ${\beta }_2^M,s$ are used as no-discrimination coefficients.

Additional information

Funding

This work was supported by United Nations University World Institute for Development Economics Research [grant number: ].