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Does female empowerment promote economic development?

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Abstract

Empirical evidence suggests that money in the hands of mothers (as opposed to fathers) increases expenditures on children. Does this imply that targeting transfers to women promotes economic development? Not necessarily. We consider a noncooperative model of the household where a gender wage gap leads to endogenous household specialization. As a result, women indeed spend more on children and invest more in human capital. Yet, depending on the nature of the production function, targeting transfers to women may be beneficial or harmful to growth. Transfers to women are more likely to be beneficial when human capital, rather than physical capital or land, is the most important factor of production. We provide empirical evidence supportive of our mechanism: In Mexican PROGRESA data, transfers to women lead to an increase in spending on children, but a decline in the savings rate.

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Notes

  1. At the launch of the Gender Action Plan, World Bank president Paul Wolfowitz said that “women’s economic empowerment is smart economics [...] and a sure path to development” (quoted on World Bank web page, accessed on January 17, 2014). Similarly, in 2008 then-president Robert Zoellick claimed that “studies show that the investment in women yields large social and economic returns” (speech on April 11, 2008, quoted on the World Bank web page, accessed on January 17, 2014).

  2. See http://www.worldbank.org/mdgs/gender.html, accessed on January 17, 2014.

  3. A positive relationship between the female income share and child expenditures has been documented by Thomas (1993), Lundberg et al. (1997), Haddad et al. (1997), Attanasio and Lechene (2002), Duflo (2003), Qian (2008), and Bobonis (2009).

  4. Studies that feature a preference gap between husband and wife include Lundberg and Pollak (1993), Anderson and Baland (2002), Basu (2006), Atkin (2009), Bobonis (2009), Browning et al. (2010), and Attanasio and Lechene (2014), although none of these papers explicitly considers the growth effects of transfers to women. In Browning (2000) a preference gap (women discount the future less) arises endogenously from higher female longevity. We examine the effects of transfers in a preference-based model in an earlier version of this paper (Doepke and Tertilt 2011).

  5. Specialization within the household was first discussed in the literature on the sexual division of labor (Becker 1981). However, most of this literature employs unitary or collective models of the household, whereas we embed household production in a noncooperative model. A few authors have also explored a semi-cooperative approach, e.g., Gobbi (2018) and d’Aspremont and Ferreira (2014).

  6. Assuming, of course, that men are more likely to dress in male versus female clothing, and that they spend a greater share of their private consumption on alcohol and tobacco.

  7. Throughout this paper, we use the terms “preference hypothesis” and “preference-based mechanism” for the assumption that women intrinsically care more about children than men do.

  8. In fact, we allow for a preference gap in our own previous work (Doepke and Tertilt 2009) and provide evolutionary justifications for why such a gap may exist.

  9. There is also evidence that women and men focus on different local public goods as policymakers (Chattopadhyay and Duflo 2004; Beath et al. 2017; Miller 2008), but again such differences could be equally explained by preferences or by endogenous specialization.

  10. See Lundberg and Pollak (1994) and Konrad and Lommerud (1995) for the noncooperative model of the household, and Bergstrom et al. (1986) for a discussion of the voluntary provision of public goods. Some of our theoretical results on the noncooperative model build on Browning et al. (2010) and Lechene and Preston (2011), who work out the properties of noncooperative household demand under general conditions, but do not focus specifically on the issue of differences in the opportunity cost of time within the household. Heath and Tan (2019) provide another recent application of the noncooperative model in a developing-country setting.

  11. See, e.g., Manser and Brown (1980), McElroy and Horney (1981), and Chiappori (1988, 1992).

  12. See Blundell et al. (2005) and Cherchye et al. (2012).

  13. Empirically, dynamic efficiency can be assessed by measuring couples’ bargaining power over time. Lise and Yamada (2018) show that in Japanese data, bargaining power responds to relative wages, suggesting dynamic inefficiency.

  14. They estimate that about one-fourth of American couples behave in a noncooperative way. See Mazzocco (2007) for a related test of ex-ante efficiency, Ashraf (2009) for evidence on inefficiency concerning financial choices, and Iyigun and Walsh (2007) on inefficiencies arising from pre-marital investments.

  15. Caldwell (1976) writes about families in tropical Africa: “Husbands were not expected to provide for their wives. In recent years males [...] have continued to resist assuming economic responsibility for their wives”.

  16. The role of gender equality for economic growth is also analyzed in Galor and Weil (1996), Lagerlöf (2003), and de la Croix and Vander Donckt (2010), but these papers do not analyze the effects of transfers, and instead focus on the link between gender equality and demographic change.

  17. Generalizations in terms of preferences and technologies are discussed in Appendix F (online appendix).

  18. The requirement for provision of goods and time by the same spouse can also be microfounded through a monitoring friction, i.e., spouses can provide cash to each other, but they cannot monitor how the cash is being spent. For evidence on asymmetric information and monitoring frictions in families see Chen (2006), Castilla and Walker (2013), de Laat (2014), and Hoel (2015). This still leaves open the possibility of general transfers between spouses that are not targeted towards specific public goods. Such general transfers are considered in Sect. 2.3 below.

  19. For simplicity, throughout the paper we do not impose a constraint requiring that time spent on market work has to be non-negative. This constraint is never binding if there is only wage income, and imposing the constraint leaves all results intact, while complicating the notation.

  20. It would be straightforward to derive an endogenous wage gap due to education decisions if one started with a small gender asymmetry, e.g. in time endowments due to pregnancy; see Echevarria and Merlo (1999) for such a model.

  21. Citing from Boserup (1985, p. 388) “Even though African women often provide the primary or sole economic support of children, their husbands [...] have the right to decide on the living arrangements, education, future occupation, and marriage partners of their children”.

  22. There is also evidence that the public goods provided by women are indeed more time intensive than those that husbands are in charge of. Aguiar and Hurst (2007) show that in the United States, the most time intensive home production categories are cooking and cleaning, shopping, and child care, all of which are tasks that are predominantly done by women. The only category where men provide more time than women comprises home maintenance, outdoor cleaning, vehicle repair, gardening, and pet care. This is also the smallest category overall in terms of time use.

  23. For example, Wolgast (1958) finds that women are more likely to be in charge of general household goods, while husbands are often in charge of car purchase decisions. Green and Cunningham (1975) finds that groceries fall in the female sphere, whereas life insurance and car purchase decisions are typically in the male sphere. See also Davis (1976) for a survey.

  24. Browning et al. (2010) derive a similar seperate-spheres result in a model with a finite number of public goods, but in their model specialization is driven by different preferences rather than different wages and home production.

  25. See Warr (1983) and Bergstrom et al. (1986).

  26. Browning et al. (2010) make this point in the context of a household bargaining model with a finite number of public goods.

  27. One may also wonder how the time allocation shifts in response to a transfer. However, we are not aware of empirical studies that study shifts in time use in response to transfers. In the model, total female home production increases, whereas male home production time goes down. The effect on total home time is ambiguous and depends on parameters.

  28. Our model only allows for a single homogeneous private consumption good, but it is straightforward to reinterpret the findings in a setting where male and female private consumption correspond to different bundles of goods.

  29. That is, in the benchmark we either have \(w_m=w_f\) or \(\alpha (i)\) constant, in which case the classic neutrality result in public good provision of Warr (1983) and Bergstrom et al. (1986) obtains.

  30. Some evidence on this is provided by Zelizer (1989), who shows that it was common for American women around the turn of the twentieth century to gain private resources from husbands by padding bills. An example is given from a 1890 newspaper, where it was described that women routinely engaged in domestic fraud, e.g. by getting the hatmaker to send a bill for 40 dollars when the hat had cost only 30. A second example is given where a woman would regularly tell her husband that flour is out or sugar low (when it was not) to get cash to spend according to her own desires (pp. 357–358). Zelizer also reports a similar type of deception by husbands who would misreport their paychecks. A study in Chicago from 1924 found that when asked about their husband’s paycheck, over two-thirds of women reported an amount lower than the actual earnings. This was sometimes achieved by taking money out of the paycheck envelope before bringing it home (Zelizer 1989, p. 364).

  31. The expenditure share channel also dominates in the case of a corner solution where only one spouse is contributing to public goods, i.e., a transfer to the spouse providing the public goods increases economic growth.

  32. In the empirical literature, there is no clear consensus on whether scarcity of human or physical capital is a more important reason for underdevelopment. In their summary of the “development accounting” literature, Hsieh and Klenow (2010) argue that physical capital differences explain about 20% of cross country-income differences, with human capital differences accounting for 10–30%.

  33. Note that this is the raw gap that combines all sources of wage disparities, including the education gap. Source: ILO, Annual Indicators, downloaded from www.ilo.org/ilostat, on November 18, 2015.

  34. This can be calculated using data from Tables 1 and 2: The derivative of children’s clothing expenditure share with respect to female income share is the regression coefficient 0.058. The ratio of the female income share to the expenditure share of children’s clothing is 2.55. Multiplying these two numbers gives the elasticity 0.148.

  35. We do not find significant effects on male and female clothing, although here it should be kept in mind that the spending shares of these categories are low to begin with (a little over 1% of total spending in both cases) with many households spending nothing on these items in a given period, which may make it difficult to pick up significant effects.

  36. The paper also analyzes the effect on other asset categories and finds smaller treatment effects for females for essentially all asset classes. While the gender differences for these other assets are not significant, the authors speculate that this is likely due to small sample sizes.

  37. In Doepke and Tertilt (2011) we analyze simple examples of this kind, but do not pursue them in the context of a growth model.

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Authors and Affiliations

  1. Department of Economics, Northwestern University, 2211 Campus Drive, Evanston, IL, 60208, USA

    Matthias Doepke

  2. Department of Economics, University of Mannheim, L7, 3-5, 68131, Mannheim, Germany

    Michèle Tertilt

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We thank the editor Oded Galor, two referees, Nava Ashraf, Abhijit Banerjee, Lori Beaman, Chris Blattman, Antoine Bommier, Areendam Chanda, Stefan Dercon, Garance Genicot, Doug Gollin, Andreas Irmen, Dean Karlan, Martina Kirchberger, John Knowles, Per Krusell, Ghazala Mansuri, Sonia Oreffice, Josefina Posadas, Mark Rosenzweig, Nancy Stokey, Silvana Tenreyro, Duncan Thomas, Hitoshi Tsujiyama, Dominique van de Walle, Martin Zelder, and participants at many seminar and conference presentations for helpful comments that greatly improved the paper. Financial support from the World Bank’s Gender Action Plan, the National Science Foundation (Grants SES-0820409 and SES-0748889), the European Research Council (Grant SH1-313719), and the German Science Foundation through CRC TR 224 (Project A03) is gratefully acknowledged. Titan Alon, Marit Hinnosaar, Vuong Nguyen, Xiaodi Wang, and Veronika Selezneva provided excellent research assistance.

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Doepke, M., Tertilt, M. Does female empowerment promote economic development?. J Econ Growth 24, 309–343 (2019). https://doi.org/10.1007/s10887-019-09172-4

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