Knowledge that Transforms

A rapidly growing body of research applies panel methods to examine how temperature, precipitation, and windstorms influence economic outcomes. These studies focus on changes in weather realizations over time within a given spatial area and demonstrate impacts on agricultural output, industrial output, labor productivity, energy demand, health, conflict, and economic growth, among other outcomes. By harnessing exogenous variation over time within a given spatial unit, these studies help credibly identify (i) the breadth of channels linking weather and the economy, (ii) heterogeneous treatment effects across different types of locations, and (iii) nonlinear effects of weather variables. This paper reviews the new literature with two purposes. First, we summarize recent work, providing a guide to its methodologies, datasets, and findings. Second, we consider applications of the new literature, including insights for the “damage function” within models that seek to assess the potential economic effects of future climate change.

This paper undertakes an assessment of a rapidly growing body of economic research on financial literacy. We start with an overview of theoretical research which casts financial knowledge as a form of investment in human capital. Endogenizing financial knowledge has important implications for welfare as well as policies intended to enhance levels of financial knowledge in the larger population. Next, we draw on recent surveys to establish how much (or how little) people know and identify the least financially savvy population subgroups. This is followed by an examination of the impact of financial literacy on economic decision-making in the United States and elsewhere. While the literature is still young, conclusions may be drawn about the effects and consequences of financial illiteracy and what works to remedy these gaps. A final section offers thoughts on what remains to be learned if researchers are to better inform theoretical and empirical models as well as public policy.

This paper uses firm-level survey data matched with official tax records to estimate the unobserved true sales of formal firms in Mongolia. Taking into account firm-level incentives to comply with taxes and a production function technology linking unobserved true sales with observable firm-level production characteristics, we derive a multiple-indicators, multiple-causes model predicting true sales. We find that firms underreport sales to the tax office by 38.6%, but firm-level survey data also suffer from significant underreporting. Finally, we compare our approach with two alternative approaches of measuring underreporting and discuss the practical implications of the findings for firm-level analyses of underreporting.

Physician-induced demand, whereby physicians alter patient treatment for personal gain, lies at the heart of concerns about publicly provided health care. However, little is known about how payment systems affect the ultimate outcome of patient health. Exploiting a unique policy induced variation in Denmark, I investigate the impact of physician payment contracts on infant health. In a difference-in-differences framework, I find that firstborn infants exposed in the womb to the care of general practitioners with capitation contracts have poorer infant health outcomes than infants exposed to fee-for-service contracts. The firstborn children of younger women primarily drive the effects.

In this paper, I construct product-level U.S.-manufacturing-imports data for new products. I show that consistent with product cycles, the North's new-products exports to the United States, relative to its old-products exports, grow faster than the South's for over a decade; then the South catches up with the North, and this pattern is reversed. This finding holds up in parametric, nonparametric, and semiparametric estimations, and only when new products are properly identified and old products within the same industries are used as controls. There is also evidence that product cycles become shorter over time and they are technology related.

We model technological progress as an external effect of organizational design, focusing on how factories, based on labor division, could spawn the Industrial Revolution. Dividing labor, as Adam Smith argued, facilitates invention by observers of production processes. However, entrepreneurs cannot internalize this benefit and choose labor division to facilitate monitoring. Equilibrium with few entrepreneurs features low wage shares, and high specialization, but a limited market for innovations. Conversely, with many entrepreneurs, there is a large market for innovation but little specialization because of high wage shares. Technological progress therefore occurs with a moderate scarcity of entrepreneurs. Institutional improvements affect growth ambiguously.

This paper presents a New Keynesian (NK) model that is extended to differentiate between straight time and overtime work. The model proposes that the New Keynesian Phillips curve (NKPC) should be estimated with marginal cost measured in terms of overtime labor; the resulting coefficient estimates are in accordance with theory and statistically significant for the hybrid NKPC (which allows for backward-looking price setters) but not for the purely forward-looking NKPC. In the hybrid model, backward-looking behavior is found to be predominant. The paper also shows that the incorporation of employment frictions (predetermined employment and convex adjustment costs) in NK models helps reconcile the frequent price changes found in the microdata with the degree of sluggishness in inflation adjustment to output changes at the macro level.

In this paper, we explore whether information from firm-level data can improve forecasts of aggregate productivity growth. We generate firm-level productivity measures and aggregate them into time-series components that capture within-firm productivity and the productivity contribution of reallocation. We show that these components improve aggregate total factor productivity forecasts in a simple univariate setting, even when firm-level data are available with a time lag. Lagged firm-level information also improves aggregate productivity forecasts when we combine results from a variety of different multivariate forecasting models using Bayesian model averaging techniques.

This paper introduces a new confidence interval (CI) for the autoregressive parameter (AR) in an AR(1) model that allows for conditional heteroskedasticity of a general form and AR parameters that are less than or equal to unity. The CI is a modification of Mikusheva's (2007a) modification of Stock's (1991) CI that employs the least squares estimator and a heteroskedasticity-robust variance estimator. The CI is shown to have correct asymptotic size and to be asymptotically similar (in a uniform sense). It does not require any tuning parameters. No existing procedures have these properties. Monte Carlo simulations show that the CI performs well in finite samples in terms of coverage probability and average length, for innovations with and without conditional heteroskedasticity.

We propose a random coefficients nested logit (RCNL) model to compare the tractable nested logit (NL) model with the more complex random coefficients logit (RC) model. After a simulation study, we use data on the European automobile market. Both the NL and RC models are rejected against the RCNL model. The RC model results in different substitution patterns and a wider market definition than the NL and RCNL models. Nevertheless, the predicted price effects from mergers are robust across models. Our findings stress the importance of accounting for discrete sources of market segmentation not captured by continuous product characteristics.