World Economic Forum: “Gender equality isn’t just moral – it’s also good economics”
On June 14, 2019, Jörg Baten, Professor of Economic History at the University of Tuebingen and Alexandra de Pleijt, Postdoctoral Research Fellow in Economic History at the University of Oxford published an article on the World Economic Forum: “Gender equality isn’t just moral – it’s also good economics.”
They explain: “The empirical results suggest that economies with more female autonomy became (or remained) superstars in economic development. Institutions that excluded women from developing human capital, such as early marriage, prevented many economies from being successful in human history.”
In other words: Gender equity is often linked to rapid economic growth. For example, Botswana had the highest GDP growth rates in recent decades after reaching its highest rates of women’s equity.
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World Economic Forum: “Gender equality isn’t just moral – it’s also good economics,” by Jörg Baten and Alexandra de Pleijt
Looking at recent growth successes and failures, some of the positive stories of economies were characterised by relatively high female autonomy: Botswana had the highest GDP growth rates of the last decades and had not only favourable institutions, but also high gender equality (Robinson 2009; data on gender equality: Carmichael, Dilli and Rijpma, 2014). Similarly, China and South Korea moved from extreme gender inequality in the early 20th century to relatively high gender equality during the 1960s and 1970s, before the most dramatic economic growth process in the world economy could set in (ibid.).
A number of development economists have found that gender inequality was associated with slower development. Stephan Klasen, with co-authors, used macroeconomic regressions to show that gender inequality has usually been associated with lower GDP growth in developing countries during the last few decades (Klasen and Lamanna 2009; Gruen and Klasen 2008).
In a new study, we directly assess the growth effects of female autonomy in a dynamic historical context. Given the obviously crucial role of direction-of-causality issues in this debate, we carefully consider the causal nature of the relationship. We use the fact that lactose tolerance was a relatively exogenous genetic factor in the centuries studied, and it increased the demand for dairy farming. In dairy farming, women traditionally had a strong role in participating in income generation (Voigtländer and Voth 2013). In contrast, female participation was limited in grain farming, as it requires substantial upper-body strength (Alesina et al. 2013). Hence, the genetic factor of lactose tolerance influences long-term differences in gender-specific agricultural specialisation and can help to solve direction-of-causality issues in the statistical analysis.
We employ the new method of age-heaping-based numeracy estimates. These estimates reflect a crucial component of human capital formation namely, numerical skills that matter most for economic growth. Hanushek and Woessmann (2012) observed that math-related skills outperform simple measures of school enrolment in explaining economic development. Hence, we focus on math-related indicators. We use two different datasets: a panel dataset of European countries from 1500 to 1850, and study 268 regions in Europe, stretching from the Ural Mountains in the East to Spain in the Southwest and the UK in the Northwest.
Age at marriage is highly correlated with other indicators of female autonomy, such as the share of female household heads or the share of couples in which the wife was older than the husband. Age at marriage is particularly interesting due to the microeconomic channel that runs from labour experience to an increase in women’s human capital: after early marriage, when they drop out of labour market and switch to work in the household economy, women provided less teaching to their children, including numeracy skills. Early-married women sometimes also valued these skills less because they did not “belong to their sphere”, i.e., these skills did not allow identification (Baten et al. 2017).