Improvements in home production play a crucial role in shaping intensive and extensive margins of labor supply and time allocation decisions, with potential differences in impact between men and women. This study leverages nationally representative data from Nepal to examine the effect of clean energy access, which enhances home production, on labor supply and time allocation decisions. A range of econometric models, including Tobit, Heckman selection, and a selection bias model with multiple choices, are employed, with the instrumental variable incorporated into these models. I found that clean stove adoption causes an increase in men's labor participation while reducing women's. Men worked 2.5 more hours and 25 more days per year with clean stoves, whereas women's work hours did not significantly change. Although fewer women participated in the labor force, those who did work 27 more days per year by adopting clean cookstoves. In the non-farm sector, the women work 20 more days, and the men work 15 more days. Self-employed women work 19 more days per year, while self-employed men work 25 more days per year.
Presentation: 2025 USAEE/IAEE North American Conference (Finalist, Best Student Paper Awards, Scheduled), IAEE session at 2026 ASSA Conference (Scheduled)
We study external validity within the context of instrumental variable estimation. The key assumption we impose for external validity is conditional external unconfoundedness among compliers, which means that the treatment effect and target selection are independent among compliers conditional on covariates. We study this assumption by using a case study about the impact of solid fuel usage on women's average cooking time. Among the six countries examined, we find no statistical evidence that the assumptions for external validity are violated for four countries (Ethiopia, Honduras, Kenya, and Zambia). Conversely, in Cambodia and Nepal, we find low external validity. These results provide suggestive evidence that the assumptions for external validity are violated for these two countries.
Presentation: 2025 ASSA Conference
Using data from the Multi-Tier Framework Survey (MTF) conducted in Nepal, we explore how geographical factors, specifically land slope and elevation, impact the adoption of LPG stoves. We employ a logit model to analyze the factors influencing households' choices regarding LPG stove adoption and generate slope and elevation data. Overall, we find that the estimates of the average slope are robust and statistically significant, but those for the average elevation are sensitive to model specifications and smaller compared to the slope. We find that the Kathmandu region is important in the analysis, and slope and elevation have nonlinear effects. Additionally, we show that geographical factors are similarly important across different household expenditure quintile groups, except for the lowest.
I examine how accessibility influences LPG stove demand and provide empirical evidence that household transportation ownership positively affects the adoption of LPG stoves. Using data from Nepal’s Multi-Tier Framework Survey (MTF), I apply Propensity Score Matching to address self-selection bias; the Average Treatment Effect on the Treated (ATET) is positive and significant across model specifications. To control community-level information, I merge MTF data with the Household Risk and Vulnerability Survey (HRVS) at the district level. Although this reduces the sample size, the results remain robust, with a larger and still significant ATET.
Presentaion: 2026 ASSA Conference (Poster)