Knowledge that Transforms

We study the properties of heteroskedasticity-robust confidence intervals for regression parameters. We show that confidence intervals based on a degrees-of-freedom correction suggested by Bell and McCaffrey (2002) are a natural extension of a principled approach to the Behrens-Fisher problem. We suggest a further improvement for the case with clustering. We show that these standard errors can lead to substantial improvements in coverage rates even for samples with fifty or more clusters.We recommend that researchers routinely calculate the Bell-McCaffrey degrees-of-freedom adjustment to assess potential problems with conventional robust standard errors.

We exploit the construction and eventual demise of the colonial railroads in Ghana, and most of the rest of Africa, to study the impact of transportation investments in poor countries. Using new data on railroads and cities spanning over one century, we find that railroads had large effects on the distribution of economic activity during the colonial period and these effects have persisted to date, although railroads collapsed and road networks expanded considerably after independence. Initial transportation investments may thus have large effects in poor countries. As countries develop, increasing returns solidify their spatial distribution, and subsequent investments may have smaller effects.

We assess the case for generous government-funded maternity leave, focusing on a series of policy reforms in Norway that expanded paid leave from 18 to 35 weeks. We find the reforms do not crowd out unpaid leave and that mothers spend more time at home without a reduction in family income. The increased maternity leave has little effect on children's schooling, parental earnings and labor force participation, completed fertility, marriage, or divorce. The expansions, whose net costs amounted to 0.25% of GDP, have negative redistribution properties and imply a considerable increases in taxes at a cost to economic efficiency.

We investigate the effects of automobile congestion on ambient air pollution and local infant mortality rates using data from California spanning 2002 to 2007. Constructing instrumental variables (IV) using the relationship of traffic, weather conditions, and pollutants, we show that particulate matter, even at modern levels, has large marginal effects on weekly infant mortality rates, especially for premature or low birthweight infants. We also find suggestive evidence of large effects for carbon monoxide, though results are imprecise. Finally, we check estimate sensitivity to nonclassical measurement error in local pollution and show that our IV results are robust to such concerns.

In this paper, we investigate the use of interactive effect or linear factor models in regional policy evaluation. We contrast treatment effect estimates obtained using Bai (2009) with those obtained using difference in differences and synthetic controls (Abadie and coauthors). We show that difference in differences are generically biased, and we derive support conditions for synthetic controls. We construct Monte Carlo experiments to compare these estimation methods in small samples. As an empirical illustration, we provide an evaluation of the impact on local unemployment of an enterprise zone policy implemented in France in the 1990s.

This paper investigates the impact of the European Union Emissions Trading System (EU ETS) on technological change, exploiting installations level inclusion criteria to estimate the System’s causal impact on firms’ patenting. We find that the EU ETS has increased low-carbon innovation among regulated firms by as much as 10%, while not crowding out patenting for other technologies. We also find evidence that the EU ETS has not affected patenting beyond the set of regulated companies. These results imply that the EU ETS accounts for nearly a 1% increase in European low-carbon patenting compared to a counterfactual scenario.