Post-2015 Consensus: Gender Equality Perspective, Braunstein
In this paper I identify and discuss the assessment paper’s methodological problems and evidentiary gaps with the intent of improving its evaluative power. Methodologically, I use a gender-aware analysis to identify the challenges of conducting benefit cost analysis based on micro experimental evidence. I then argue the case for including macroeconomic perspetives and evidence, drawing from the research on gender equality and growth.
Beginning with methodological issues, as the author points out, the key strength of randomized controlled trials is their ability to test policy changes very specifically and directly. But the reliability of BCRs from such trials must be evaluated with an awareness of their weaknesses as well as strengths.
Many assumptions have to be made about prices. From a gender equality perspective, the question of price is particularly confounding because using market prices and incomes to estimate benefits and costs incorporates value into project evaluation in ways that can disadvantage women. Using just market prices to estimate the cost of gender discrimination biases BCRs downwards.
A related issue is the invisibility of nonmarket work, much of it unpaid care work performed by women and girls. However, this is also a productive activity, and has been valued as a substantial contributor to overall GDP. Also, attributing women’s lack of lack of economic participation to insufficient training or information ignores socially-determined constraints, especially the traditional sexual division of labor. Other policy routes may therefore be needed, for example rural electrification, which can allow women to reallocate their household work in transformative ways.
A weakness of relying on controlled trials is that public externalities are missed because of the small scale. Ultimately, if we want to grapple with development’s big questions, we have to venture into the seemingly less well-defined world of macroeconomics. Macroeconomic approaches afford insights into lots of important dynamics that are simply inaccessible using randomized trials. There is also the practical argument about engaging with the widely cited instrumental case for gender equality: that gender equality is good for economic growth.
That gender inequality is bad for economic growth is one of the more compelling policy arguments proffered by development professionals these days, and a number of empirical studies have tried to estimate just how much gender discrimination costs in terms of sacrificed growth. It has been estimated that up to 20% of the difference in growth rates between East Asia and sub-Saharan Africa between 1960 and 1992 can be attributed to inequality in education and employment.
A paper by Joyce Jacobson for an earlier Copenhagen Consensus exercise estimated global GDP loss due to gender inequality in employment to be between 7 and 16%. Approximating costs of interventions can be more difficult. However, for MDG3 (promote gender equality and women’s empowerment), the UN estimated a cost of 1.1% of GDP in low income countries, although this rose to 3.2% for mainstreaming gender equality activities under other goals.
There are two main points to conclude with. First, benefit cost evaluations of policies for gender equality must be gender-aware in the sense of incorporating how gender structures, many of which exist outside the market sphere, shape choice and opportunity in economically significant ways. Second, restricting evidence to a limited sampling of micro experimental studies misses a large macro literature on gender, one whose incorporation would greatly improve the scale and scope aspects of the BCRs.