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Economic development, structural change, and women’s labor force participation:

A reexamination of the feminization U hypothesis

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Abstract

A sizable literature claims that female labor force participation (FLFP) follows a U-shaped trend as countries develop due to structural change, education, and fertility dynamics. We show that empirical support for this secular trend is feeble and depends on the data sources used, especially GDP estimates. The U also vanishes under dynamic panel estimations. Moreover, cross-country differences in levels of FLFP related to historical contingencies are more important than the muted U patterns found in some specifications. Given the large error margins in international GDP estimates and the sensitivity of the U relationship, we propose a more direct approach to explore the effect of structural change on FLFP using sector-specific growth rates. The results suggest that structural change affects FLFP consistent with a U pattern, but the effects are small. We conclude that the feminization U hypothesis as an overarching secular trend driving FLFP in the development process has little empirical support.

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Notes

  1. See Klasen and Lamanna (2009) for a more detailed discussion of the unemployment issues (and its empirical relevance for cross-country differences in labor force participation).

  2. The literature on household production also considers availability and prices of household technologies as a potential driver of female labor force participation rates. As these technologies improve over the development process, one would presume that they increase the ability of women to participate in market work. See Ramey (2009) for a discussion in the context of the USA.

  3. At the very early stages of industrialization, young unmarried women (and children) may play a significant role in the nascent industrial sectors, as they did in Britain in the late eighteenth century. But as industrialization proceeded, women’s employment in these sectors became increasingly rare, replaced by male workers who often were able to get better employment conditions and wages. For a discussion, see Marglin (1974) and Humphries (1991).

  4. Of course, agriculture also includes heavy manual labor. But if men and women share agricultural tasks, this may be no barrier to female participation if men then do the heavy manual labor (e.g., land clearing, plowing with heavy implements, etc.). Outside of the home, such sharing of tasks is generally not feasible.

  5. These results point to an inverted U, rather than a U-shaped relationship. Since both parameters are significant, the feminization U hypothesis could be rejected at a conventional significance level.

  6. Both EAPEP datasets also contain labor force projections. For the fourth revision, these extend from 1995 to 2010, and for the fifth revision, from 2009 to 2020. However, the analyses in this paper are based on the labor force estimates only, disregarding the projections.

  7. In the case of Nepal, the fourth revision reports a minor decline in female labor force participation between 1980 and 1990 (from 59 to 58 %), while the fifth revision shows an increase by around ten percentage points, albeit from a much lower level (from 45 to 55 %).

  8. That recovery is more pronounced under the fifth than under the sixth revision of the EAPEP. It seems likely that labor force estimates for the 2000s under the sixth revision are influenced by the financial crisis (through interpolations by the ILO, the 2008 recession could be reflected in earlier participation rates).

  9. As noted by Deaton (2010), there is an inherent tension in international price comparisons between surveying goods that are representative for consumption patterns in each country and specifying goods that are strictly comparable between countries. In contrast to previous ICP rounds, the 2005 round erred on the side of inter-country comparability by surveying precisely specified goods, at the expense of a potential lack of intra-country representativity.

  10. The alternative procedure, used by the World Bank, to base the entire assessment of economic performance on the latest PPP round, is also problematic as PPPs that are valid in 2005 are unlikely to have been valid 20 years earlier when products, demands, and prices differed considerably. This can also lead to substantial uncertainty about GDP trends.

  11. Interestingly, the ILO also notes that there is no significant U-shape relationship between GDP and labor force participation for men and women aged between 20 and 55 years (ILO 2011b). This is motivated by a series of graphs, which, however, only show cross-sectional patterns (despite the fact that the estimated regressions seem to be based on over-time variation only).

  12. We do not include further control variables as we are, in the spirit of this literature, interested in the reduced form of relationship between development and female participation and because some of the most likely candidates for control variables (education, fertility) are also potential transmission channels of the U.

  13. Standard errors for all turning points based on the approach outlined in Kuha and Temple (2003) are shown in the ESM (Table A.1). They are quite narrow and allow tests of significant differences in those turning points.

  14. The fourth revision data include years that are not included in the fifth revision (1950–1970). The ILO cut them out as they were deemed unreliable, but one might argue that they come from a time where the patterns of the U were more visible. We test using the overall sample whether these earlier years drive the results (by progressively cutting out the earlier observations) and find that this does not qualitatively change the results. Moreover, we think we have enough variation in economic conditions and stages of development when using the fifth revision and beyond so that a U-shaped process should be identifiable in the data. We also experimented on using IV regressions to purge our regression of possible (classical) measurement error of the GDP variables which could bias our coefficients towards zero. Specifically, we used PWT 6.3 to predict PWT 7.1 GDP. This did not change the results materially.

  15. The fixed effects regressions were estimated using Stata’s xtreg, fe command, which constrains the system so that the reported intercept is the average value of fixed effects. The full list of estimated fixed effects based on the fifth revision of the EAPEP and PWT 7.1 is included in the ESM (Table A.3). The fixed effects using other combinations of data sources are available on request.

  16. When estimating the dynamic model with data from the fourth revision, we always encountered second-order autocorrelation, which renders the moment conditions of the GMM estimator invalid. This is why this section presents the dynamic estimates only for the fifth and sixth revision data.

  17. The sample is split in such a way that one quarter of countries are expected to transition through the declining portion of the U and thus experience a fall in female labor force participation with rising per capita income, while the remaining three quarters are assumed to experience an increase in female labor force participation with rising per capita income. This corresponds approximately to the J-shaped patterns found in Section 4, with fewer observations to the left of the turning point.

  18. Of course there are also indirect effects, such as growth in overall family incomes due to structural transformation and associated income effects. Those are not directly captured by the above framework.

  19. This is because we look at relative (in percent) changes, rather than absolute (percentage point) changes. Please note that the intial female employment intensity of the growing sector does matter for the percentage change in the female labor force participation rate. Growth of sectors where the female employment intensity is high will have a larger impact on the growth of aggregate female employment rate. However, we do not have data on sector-specific female employment intensities and cannot isolate this element empirically.

  20. If there were cross-country data on male and female employment by disaggregated sector, we could also directly decompose the change in female employment into various sectoral contributions. However, here we use a regression approach to relate data on the sectoral allocation of total employment, which are not disaggregated by sex, to female labor force participation estimates from the EAPEP database.

  21. The classification is based on the ISIC 3.1 industry classification, but some of the one-digit sectors are combined in the dataset. Of course, one may argue that seven sectors are still too broad to uncover specific subsector dynamics (e.g., differential trends in female labor force participation in different types of agriculture, or manufacturing subsectors). While we are mindful of these limitations, the data do not allow estimating separate effects for different subsectors in agriculture or manufacturing.

  22. However, we have to drop West Germany and Taiwan during the analysis stage because these two countries do not have a corresponding entry in the ILO database.

  23. It is somewhat surprising that all coefficients in Table 8 are below unity. This would suggest that female labor force participation increases less than proportionately with employment growth in any sector (an across-the-board defeminization). We suspect that this weak correlation is driven by the fact that we use employment data from two different sources, which both suffer from measurement error. Another reason might be changes in female unemployment (which is included in the labor force participation rate).

  24. It should be noted that the model in Eq. 9 includes an intercept and time dummies, which capture much of the country-invariant increase in female labor force participation between 1980 and 2005. When we simulate the effect of structural change on female economic activity, we disregard those effects by basing the simulations only on sectoral growth rates.

  25. We perform the following robustness checks. First, instead of 5-year intervals, we use 4- and 3-year periods, but then most of the estimated coefficients lose significance. We also reestimate the models in Eq. 9 to 11 on data from the sixth revision of the EAPEP but again obtain much weaker correlations. Our key explanation for this finding is that the sixth revision cover a shorter time span (mostly 1990 to 2010) and that the changes in value added and employment observed during the 2008 financial crisis (and which, due to interpolations, even affect labor force participation estimates before the onset of the crisis) are different from the long-run process of structural change. Yet another potential explanation is that the effect of structural change on female labor force participation is getting even weaker over time.

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Acknowledgments

We thank R. Emre Aytimur, Friederike Greb, Tim Krieger, Inmaculada Martínez-Zarzoso, Chris Muris, Janneke Pieters, Sebastian Vollmer, two anonymous referees, the editors of this journal, and seminar participants in Göttingen and Cologne for valuable comments and advice. We are grateful to Dani Rodrik and Margaret McMillan for sharing with us the extension of the GGDC 10-Sector database. We also thank Valentina Stoevska of the ILO for sending us an earlier edition of the EAPEP data. Of course, all errors are our own.

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  1. The World Bank, Dar-es-Salaam, Tanzania

    Isis Gaddis

  2. Institute for the Study of Labor (IZA), Bonn, Germany

    Stephan Klasen

  3. Ifo Institute for Economic Research, Munich, Germany

    Stephan Klasen

  4. Department of Economics and Courant Research Centre, Poverty, Equity, and Growth in Developing and Transition Countries, University of Göttingen, Platz der Göttinger Sieben 3, 37073, Göttingen, Germany

    Isis Gaddis & Stephan Klasen

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Gaddis, I., Klasen, S. Economic development, structural change, and women’s labor force participation:. J Popul Econ 27, 639–681 (2014). https://doi.org/10.1007/s00148-013-0488-2

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  • DOI: https://doi.org/10.1007/s00148-013-0488-2

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