Engendering Growth Diagnostics: Examining Constraints to Private Investment and Entrepreneurship

Abstract

The growth constraints diagnostic is a framework that seeks to help countries identify ‘binding’ constraints to private investment and entrepreneurship. Curiously absent from the diagnoses of the 31 countries to which this framework has been applied is any mention of gender gaps. This is surprising given the substantial literature providing evidence that gender gaps in education, income, employment, resource control and access affect economic growth and well‐being. This article ‘engenders’ the standard growth diagnostic process through disaggregating key variables by sex, reinterpreting nodes in the decision tree to reflect how they are intrinsically gendered, and adding new branches and nodes. It provides a theoretical framework for applying the gender growth diagnostic to help practitioners adopt it in country studies.

Helping when it matters: Optimal time for supporting women’s self‐employment in India

“…Despite India's economic growth over the past few decades, the country continues to lag in achieving gender equality. Scholars and policy-makers see entrepreneurialism as a route to raising women's economic participation and socioeconomic status (Ghosh & Cheruvalath, 2007;Roncolato et al, 2017;World Bank, 2012). The trend in entrepreneurial initiatives since 2000 has focused on microcredit and financial support programmes.…”

Are growth diagnostics biased against the poor? Evidence from the Millennium Challenge Corporation

“…This article has shown that criticisms of the classic HRV growth diagnostic, mostly raised by practising economists, have merit. Critics argue that the HRV diagnostic focuses too narrowly on capital accumulation as its objective, stresses the quantity rather than the quality of growth, and fails to disaggregate constraints across economic agents or space (Felipe et al, 2011; Ianchovichina & Lundstrom Gable, 2009; Martins, 2013; Roncolato et al, 2017; Sabates‐Wheeler, 2009; USAID, 2015).…”

“…Data limitations also restrict diagnostics. Roncolato et al (2017) have pointed out that there is a lack of data on constraints facing women, marginalized spheres of economic activity and issues of distribution. Hausmann et al (2008) concede that their diagnostics suffer from a data “availability bias” that leads to some constraints being more easily rejected or accepted.…”

“…These adjustments include changing the objective, interpreting existing nodes (i.e. constraints) in the decision tree differently, substituting or adding nodes and examining different data (Roncolato et al, 2017; Sabates‐Wheeler, 2009). The proponents of HRV diagnostics would not be entirely opposed to the original diagnostic being modified (Hausmann et al, 2008; Pritchett & Sharma, 2008) while several applied economists have called for doing “inclusive growth diagnostics” (inclusive diagnostic) (Ianchovichina & Lundstrom Gable, 2009; Martins, 2013; Sabates‐Wheeler, 2009; USAID, 2015).…”

“…Conducting an inclusive diagnostic also involves looking at data that help examine constraints affecting specific populations or areas whose removal could generate the greatest multiplier effect and improvements in welfare (Ianchovichina & Lundstrom Gable, 2009). One review of Harvard University’s growth diagnostics suggested that to properly “engender” the analysis and remove structural biases against women, it is necessary to employ sex‐disaggregated data (Roncolato et al, 2017). Disaggregated data, by sex or otherwise, is helpful because it is more important to ask, for example, “bad infrastructure for whom and what activities?” rather than simply whether inadequate infrastructure is a constraint to national growth (Roncolato et al, 2017).…”