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Gender Wage Gap when Women are Highly Inactive: Evidence from Repeated Imputations with Macedonian Data

Abstract

The objective of this research is to understand if large gender employment and participation gaps in Macedonia can shed some light on the gender wage gap. A large contingent of inactive women in Macedonia including long-term unemployed due to the transition process, female remittance receivers from the male migrant, unpaid family workers in agriculture and so on, is outside employment, but is not necessarily having the worst labour-market characteristics. In addition, both gender wage gap and participation gap enlarge as education decreases, revealing the importance of non-random selection of women into employment. Though, the standard Heckman-type correction of the selectivity bias suggests that non-random selection exists, but the resulting wage gap remains at the same level even when selection has been considered. Instead, we perform repeated wage imputations for those not in work, by simply making assumptions on the position of the imputed wage observation with respect to the median. Then, we assess the impact of selection into employment by comparing estimated wage gaps on the base sample versus on an imputed sample. The main result is that selection explains most of the gender wage gap in the primary-education group (75 %), followed by the secondary-education group (55 %). In the tertiary group, the small initial gap vanishes once selection considered. This suggests that indeed non-working women are not those with the worst labour-market characteristics. Results suggest that gender wage discrimination in Macedonia is actually between 5.4 and 9.8 % and does not exist for the highly-educated women. The inability of the Heckman-type correction to document a role for selection in explaining the gender wage gap may be due to the criticisms to the exclusion restrictions and the large amount of missing wages.

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Fig. 1

Notes

  1. 1.

    Frequently, studies include either age or work experience and their quadratic term. Here, we use only the quadratic term of age as it is likely to be sufficient in capturing the turning point of the wage over the life cycle.

  2. 2.

    For example, in our sample, the average education of a non-working woman is 2.3, while that of working low-skilled woman is 1.8, and former are younger by about 5 years than the latter.

  3. 3.

    Should have this been the case, we will have observed that the selectivity-corrected gender wage gap increases rather than decreases.

  4. 4.

    As an additional, yet indirect, robustness check, we performed the repeated imputation with both the probit regression predicting missing wage position with respect to the median and the Mincer’s function having only gender as explanatory variable. If only gender determines the missing wage and the way of imputing wages depends only on gender, then the unadjusted gender wage gap should be reproduced. Indeed, we obtain an estimate of 12.35 %, which is very close to our estimate of 12.47 % in Table 4 (the difference is only due to randomness inflicted by the imputations).

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Acknowledgments

This research has been generously supported by the Global Development Network and the Government of Japan within the Japanese Award for Outstanding Research on Development 2013. The authors thank for the guidance and useful comments of Vladimir Gligorov and the hosts of the Institute for East and Southeast European Studies, Regensburg, Germany, during Marjan Petreski’s stay, 15.1-1.2.2014. All remaining errors are solely the authors’.

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Correspondence to Marjan Petreski.

Appendix 1 Mincer earnings functions

Appendix 1 Mincer earnings functions

Table 10 OLS estimates (Table 3 with details)

Table 10 presents the results of Table 3 in detail. Aside gender, results suggest that return to education increases with the level of education. Additional year of age brings, on average, higher salary by about 1 % with a turning point which is practically insignificant (turning point at about 350 years of age). Similarly, additional year of experience is associated with about 1 % higher wage. Full-time contract brings a wage premium of about 20 % against a part-time contract.

Only wages in three groups of occupations: armed forces, managers and professionals differ than compared to the baseline group. These three have, on average, higher wages by 10, 14 and 19 %, respectively, than compared to the elementary occupations. On the other hand, all sectors except agriculture pay different wages: financial sector and mining pay about 42 % higher wage each than industry, on average. Public sector pays about 28 % higher wage than industry and so on.

Table 11 The adjusted for characteristics and for selectivity gender wage gap, under different explanatory sets of the missing wage

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Petreski, M., Blazevski, N.M. & Petreski, B. Gender Wage Gap when Women are Highly Inactive: Evidence from Repeated Imputations with Macedonian Data. J Labor Res 35, 393–411 (2014). https://doi.org/10.1007/s12122-014-9189-1

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Keywords

  • Gender wage gap
  • Gender participation gap
  • Selection bias
  • repeated imputations

JEL classification

  • J16
  • J31
  • E24