The secular rise in female labor force participation, highlighted in the recent macroe-conomics literature on growth and structural change, has been associated with the de-clining price and wider availability of home appliances. This paper uses a new and unique country dataset on the price of home appliances to test its impact on female labor supply. We assess the role of the price of appliances in raising participation by comparing it to the impact of fertility and other macroeconomic factors. A decrease in the relative price of appliances- the ratio of the price of appliances to the consumer price index- leads to a substantial and statistically significant increase in female labor force participation. The impact of the price of appliances is quantitatively of the same order of magnitude as that of fertility. This result is robust to the inclusion of additional controls, such as income per capita, government spending, and male and female unemployment rates. To assess causality, we test for exogeneity and use the lagged relative price of appliances and the food price index as instrumental variables, confirming that lower appliance prices lead to increased female participation.

Empirical researchers sometimes misinterpret how additional regressors, heterogeneity corrections, and multilevel factors impact the interpretation of the estimated parameters in binary outcome models such as logit and probit. This can result in incorrect inferences about the importance of incorporating such features in these nonlinear statistical models. Some reports of biases in binary outcome models appear related to the arbitrary variance normalization required in binary outcome models. A focus on readily interpretable numerical quantities, rather than conveniently chosen “effects” as measured by arbitrarily scaled coefficients, would eliminate nearly all of the interpretation problems we highlight in this paper.

In this paper we develop two nonparametric tests of treatment effect heterogeneity. The first test is for the null hypothesis that the treatment has a zero average effect for all subpopulations defined by covariates. The second test is for the null hypothesis that the average effect conditional on the covariates is identical for all subpopulations, that is, that there is no heterogeneity in average treatment effects by covariates. We derive tests that are straightforward to implement and illustrate the use of these tests on data from two sets of experimental evaluations of the effects of welfare-to-work programs.