binding to growth (have smaller multipliers λG) but if left in place are likely to interact negatively with the interventions targeting the most binding constraints to growth, i.e. they create large negative secondary interaction terms (i.e. λGd[μGs (τ) −μGp(τ)]/dτY < 0 ) in expression (2). Using this approach, we can design a win-win scenario – a set of welfare enhancing interventions that close a gender gap.

# **4. Operationalizing the Approach**

In section 3 we show that our approach identifies an efficient win-win solution from an analytical perspective. In this section we show that our approach is relatively easy to operationalize because it builds on existing country growth diagnostics, which offer a list of prioritized binding constraints to growth. We also use country-level WEF data on gender gaps in labor force participation, labor productivity, wages, education, skills, access to markets, finance, land, information, discriminatory policies, laws, perceptions, customs, family norms and responsibilities, among others (WEF, 2018). The additional analysis involves figuring out which interventions addressing the binding constraints to growth in a country (i.e. those associated with large λG) will also have the effect of reducing gender-related distortions and which interventions are needed to remove gender gaps that are likely to create large negative indirect interaction effects if left in place.

What does that mean as a practical matter? First, it means that we do not focus on closing the largest gender gap. For example, the gender gap in labor force participation for highly educated workers may not be the largest, yet closing this gap in economies where returns to capital are limited by the availability of skilled labor will lift potential growth and foster the kind of productivity growth and structural transformation many policy makers hope for. Second, this approach ensures that the focus is on the constraints to national growth that align with women’s empowerment, in effect reprioritizing national objectives through a gender lens. It means that if two of the top five constraints to growth overlap with two major impediments blocking women’s economic opportunity, in a gender-enhanced growth diagnostics we would put as priority the two that overlap, even if they are ranked lower among the top five constraints to growth in the national growth diagnostics. Our purpose is twofold: (1) to demonstrate that the interventions for empowerment are important for and align or support national economic goals, and (2) to generate the necessary political will among policy officials to undertake the reforms that matter for both sets of economic objectives. We call this the “sweet spot for intervention” because it serves to alleviate bottlenecks to women’s economic activity, while at the same time contributing to national goals of lifting income and economic growth.

In practice we can tease apart the HRV framework in two ways. One way is to simply see how the binding constraints to national development affect men and women differently. Work has previously been done at the World Bank and elsewhere in examining project design from a gender perspective (Roncolato, 11