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Reverting to traditional views of gender during times of relative deprivation: An experimental study in Nepal
Do individuals’ perceptions of their relative economic status affect their attitudes regarding gender roles in patriarchal societies? What role does hearing messages designed to increase support for women’s empowerment play in moderating these effects? Leveraging an original survey experiment in Nepal, we find that a prime conferring feelings of relative deprivation causes women to revert to traditional views of gender in economic decision-making; they become less supportive of women having equal control over household income, sharing house hold chores with men, and working outside the home. Women’s empowerment messaging does not attenuate these effects. Priming men to feel relatively deprived causes declines in gender equitable economic and political views, but women’s empowerment messaging nullifies these effects. The results suggest that among populations feeling relatively deprived, regressive gender norms may take hold. However, light-touch efforts to spur support for women’s empowerment may counter some reversion to traditional views of gender.
Causal Inference in Python
How many buyers will an additional dollar of online marketing bring in? Which customers will only buy when given a discount coupon? How do you establish an optimal pricing strategy? The best way to determine how the levers at our disposal affect the business metrics we want to drive is through causal inference. In this book, author Matheus Facure, senior data scientist at Nubank, explains the largely untapped potential of causal inference for estimating impacts and effects. Managers, data scientists, and business analysts will learn classical causal inference methods like randomized control trials (A/B tests), linear regression, propensity score, synthetic controls, and difference-in-differe...
Protectionism and Economic Growth
We investigate how protectionist policies influence short-run economic growth. Our empirical strategy exploits an extraordinary tax scandal that gave rise to an unexpected change of government in Sweden. A free-trade majority in parliament was overturned by a protectionist majority in 1887. We employ the synthetic control method to select control countries against which economic growth in Sweden can be compared. We do not find evidence suggesting that protectionist policies influenced economic growth and examine channels why. Tariffs increased government revenue. However, the results do not suggest that the protectionist government stimulated the economy by increasing government expenditure.
Ethics in Econometrics
Econometricians make choices on data, models, and estimation routines. Using various examples, this book shows the consequences of choices.
Handbook of Quantile Regression
Quantile regression constitutes an ensemble of statistical techniques intended to estimate and draw inferences about conditional quantile functions. Median regression, as introduced in the 18th century by Boscovich and Laplace, is a special case. In contrast to conventional mean regression that minimizes sums of squared residuals, median regression minimizes sums of absolute residuals; quantile regression simply replaces symmetric absolute loss by asymmetric linear loss. Since its introduction in the 1970's by Koenker and Bassett, quantile regression has been gradually extended to a wide variety of data analytic settings including time series, survival analysis, and longitudinal data. By foc...
Can a light-touch graduation model enhance livelihood outcomes? Evidence from Ethiopia
In recent years, a growing literature has examined the potential of multifaceted, intensive “graduation model” interventions that simultaneously address multiple barriers constraining households’ exit from poverty. In this paper, we present new evidence from a randomized trial of a lighter-touch graduation model implemented in rural Ethiopia. The primary experimental arms are a bundled intervention including a productive transfer valued at $374 (randomly assigned to be cash or an equivalent value in poultry), training, and savings groups; a simpler intervention including training and savings groups only; and a control arm. We find that three years post-baseline, the intervention inclusive of the transfer leads to some increases in assets, savings, and cash income from livestock, though there is no shift in consumption or household food security; these effects are consistent regardless of the modality of the transfer (cash versus poultry). The effects of training and savings groups alone are minimal.
Selection and Parallel Trends
One of the perceived advantages of difference-in-differences (DiD) methods is that they do not explicitly restrict how units select into treatment. However, when justifying DiD, researchers often argue that the treatment is "quasi-randomly" assigned. We investigate what selection mechanisms are compatible with the parallel trends assumptions underlying DiD. We derive necessary and sufficient conditions for parallel trends that clarify whether and how selection can depend on time-invariant and time-varying unobservables. We also suggest a menu of interpretable primitive sufficient conditions for parallel trends, thereby providing the formal underpinnings for justifying DiD based on contextual information about selection into treatment. We provide results for both separable and nonseparable outcome models and show that this distinction has implications for the use of covariates in DiD analyses. Building on our analysis of nonseparable models, we connect DiD to the literature on nonparametric identification in panel models.
Decentralization Estimators for Instrumental Variable Quantile Regression Models
The instrumental variable quantile regression (IVQR) model (Chernozhukov and Hansen, 2005) is a popular tool for estimating causal quantile effects with endogenous covariates. However, estimation is complicated by the non-smoothness and non-convexity of the IVQR GMM objective function. This paper shows that the IVQR estimation problem can be decomposed into a set of conventional quantile regression sub-problems which are convex and can be solved efficiently. This reformulation leads to new identification results and to fast, easy to implement, and tuning-free estimators that do not require the availability of high-level "black box" optimization routines.
