Women’s Education May Be Even Better Than We Thought: Estimating the Gains from Education When Schooling Ain’t Learning

Women’s schooling has long been regarded as one of the best investments in development. Using two different cross-nationally comparable data sets which both contain measures of schooling, assessments of literacy, and life outcomes for more than 50 countries, we show the association of women’s education (defined as schooling and the acquisition of literacy) with four life outcomes (fertility, child mortality, empowerment, and financial practices) is much larger than the standard estimates of the gains from schooling alone. First, estimates of the association of outcomes with schooling alone cannot distinguish between the association of outcomes with schooling that actually produces increased learning and schooling that does not. Second, typical estimates do not address attenuation bias from measurement error. Using the new data on literacy to partially address these deficiencies, we find that the associations of women’s basic education (completing primary schooling and attaining literacy) with child mortality, fertility, women’s empowerment and the associations of men’s and women’s basic education with positive financial practices are three to five times larger than standard estimates. For instance, our country aggregated OLS estimate of the association of women’s empowerment with primary schooling versus no schooling is 0.15 of a standard deviation of the index, but the estimated association for women with primary schooling and literacy, using IV to correct for attenuation bias, is 0.68, 4.6 times bigger. Our findings raise two conceptual points. First, if the causal pathway through which schooling affects life outcomes is, even partially, through learning then estimates of the impact of schooling will underestimate the impact of education. Second, decisions about how to invest to improve life outcomes necessarily depend on estimates of the relative impacts and relative costs of schooling (e.g., grade completion) versus learning (e.g., literacy) on life outcomes. Our results do share the limitation of all previous observational results that the associations cannot be given causal interpretation and much more work will be needed to be able to make reliable claims about causal pathways.

Multi-Objective Support Vector Regression Reduces Systematic Error in Moderate Resolution Maps of Tree Species Abundance

When forest conditions are mapped from empirical models, uncertainty in remotely sensed predictor variables can cause the systematic overestimation of low values, underestimation of high values, and suppression of variability. This regression dilution or attenuation bias is a well-recognized problem in remote sensing applications, with few practical solutions. Attenuation is of particular concern for applications that are responsive to prediction patterns at the high end of observed data ranges, where systematic error is typically greatest. We addressed attenuation bias in models of tree species relative abundance (percent of total aboveground live biomass) based on multitemporal Landsat and topoclimatic predictor data. We developed a multi-objective support vector regression (MOSVR) algorithm that simultaneously minimizes total prediction error and systematic error caused by attenuation bias. Applied to 13 tree species in the Acadian Forest Region of the northeastern U.S., MOSVR performed well compared to other prediction methods including single-objective SVR (SOSVR) minimizing total error, Random Forest (RF), gradient nearest neighbor (GNN), and Random Forest nearest neighbor (RFNN) algorithms. SOSVR and RF yielded the lowest total prediction error but produced the greatest systematic error, consistent with strong attenuation bias. Underestimation at high relative abundance caused strong deviations between predicted patterns of species dominance/codominance and those observed at field plots. In contrast, GNN and RFNN produced dominance/codominance patterns that deviated little from observed patterns, but predicted species relative abundance with lower accuracy and substantial systematic error. MOSVR produced the least systematic error for all species with total error often comparable to SOSVR or RF. Predicted patterns of dominance/codominance matched observations well, though not quite as well as GNN or RFNN. Overall, MOSVR provides an effective machine learning approach to the reduction of systematic prediction error and should be fully generalizable to other remote sensing applications and prediction problems.