Knowledge that Transforms

Work on relational employment agreements often predicts low payments or termination for poor performance. The possibility of saving can, however, limit the effectiveness of monetary incentives in motivating an employee with diminishing marginal utility for consumption. We study the role of savings and their observability in optimal relational contracts. We focus on the case where players are not too patient, and hence the constant first‐best effort cannot be implemented. If savings are hidden, the relationship eventually deteriorates over time. In particular, both payments and effort decline. On the other hand, if savings are public, consumption is initially high, so the agent's savings fall over time, and effort and payments to the agent increase. The findings thus suggest how tacit agreements on consumption can forestall the deterioration of dynamic relationships in which the agent can save.

What drives the dynamics of top wealth inequality? To answer this question, I propose an accounting framework that decomposes the growth of the share of aggregate wealth owned by a top percentile into three terms: a within term, which is the average wealth growth of individuals initially in the top percentile relative to the economy; a between term, which accounts for individuals entering and exiting the top percentile due to changes in their relative wealth rankings; and a demography term, which accounts for individuals entering or exiting the top percentile due to death and population growth. I obtain closed‐form expressions for each term in a wide range of random growth models. Evidence from the Forbes 400 list suggests that the between term accounts for half of the recent rise in top wealth inequality.

We develop a new approach for the identification of Pareto‐improving tax reforms. This approach yields necessary and sufficient conditions for the existence of Pareto‐improving reform directions. A main insight is that “Two brackets are enough”: When the system cannot be improved by altering tax rates in one or two income brackets, then there is no continuous reform direction that is Pareto‐improving. We also show how to check whether a given tax reform is Pareto‐improving. We use these tools to study the introduction of the Earned Income Tax Credit (EITC) in the United States in 1975. A robust finding is that, prior to the EITC, the U.S. tax‐transfer system was not Pareto‐efficient. Under plausible assumptions about behavioral responses, the 1975 reform was not Pareto‐improving. Qualitatively, though, it had the right properties: A similar reform with earnings subsidies made available to a broader range of incomes would have been Pareto‐improving.

Using an endogenous growth model, Liu, Mian, and Sufi (2022) (LMS) show that a decline in the interest rate can lead to a fall in productivity growth and a rise in leader‐laggard productivity gaps and firm profits. We identify two issues in their quantitative analysis of transition dynamics: a time‐scale error and the omission of composition terms in calculating productivity growth along the transition to a new balanced growth path. Correcting the time‐scale error and including the composition terms, the decline in the interest rate that LMS study leads to a large and protracted productivity boom lasting about 20 years. In addition, the average leader‐laggard gap grows much more slowly than reported in their paper. We also point out an issue in their quantitative analysis of steady‐state profit shares. These issues are related to the quantitative exercises, and do not affect the key theoretical contributions of LMS.

Using a new survey of firms in New Zealand, we document how exogenous variation in the macroeconomic uncertainty perceived by firms affects their economic decisions. We use randomized information treatments that provide different types of information about the first and/or second moments of future economic growth to generate exogenous changes in the perceived macroeconomic uncertainty of some firms. The effects on their decisions relative to their initial plans as well as relative to an untreated control group are measured in a follow‐up survey six months later. We find that as firms become more uncertain, they reduce their prices, employment, and investment, their sales decline, and they become less likely to invest in new technologies or open new facilities. These ex post effects of uncertainty are similar to how firms say they would respond to higher uncertainty when asked hypothetical questions.

We show that, in a general family of linearized structural macroeconomic models, knowledge of the empirically estimable causal effects of contemporaneous and news shocks to the prevailing policy rule is sufficient to construct counterfactuals under alternative policy rules. If the researcher is willing to postulate a loss function, our results furthermore allow her to recover an optimal policy rule for that loss. Under our assumptions, the derived counterfactuals and optimal policies are robust to the Lucas critique. We then discuss strategies for applying these insights when only a limited amount of empirical causal evidence on policy shock transmission is available.

We use a two‐stage experiment on agricultural lending in Mali to test whether selection into lending is predictive of heterogeneous returns to capital. Understanding this heterogeneity, and the selection process which reveals it, is critical for guiding modeling of credit markets in developing countries, as well as for policy. We find such heterogeneity: returns to capital are higher for farmers who borrow than for those who do not. In our first stage, we offer loans in some villages and not others. In the second stage, we provide cash grants to a random subset of all farmers in villages where no loans were offered, and to a random subset of the farmers who do not borrow in villages where loans were offered. We estimate seasonal returns to the grant of 130% for would‐be borrowers, whereas we find returns near zero for the sample representative of non‐borrowers. We also provide evidence that there are some farmers—particularly those that are poor at baseline—that have high returns but do not receive a loan.

This paper studies the welfare effects of encouraging rural–urban migration in the developing world. To do so, we build and analyze a dynamic general‐equilibrium model of migration that features a rich set of migration motives. We estimate the model to replicate the results of a field experiment that subsidized seasonal migration in rural Bangladesh, leading to significant increases in migration and consumption. We show that the welfare gains from migration subsidies come from providing better insurance for vulnerable rural households rather than from correcting spatial misallocation by relaxing credit constraints for those with high productivity in urban areas that are stuck in rural areas.

We develop an urban growth model where human capital spillovers foster entrepreneurship and learning in heterogeneous cities. Incumbent residents limit city expansion through planning regulations so that commuting and housing costs do not outweigh productivity gains from agglomeration. The model builds on strong microfoundations, matches key regularities at the city and economy‐wide levels, and generates novel predictions for which we provide evidence. It can be quantified by relying on few parameters and gives us a basis to estimate them. We examine counterfactuals relaxing planning regulations or constraining city growth to assess the effect of cities on economic growth and aggregate output.

We propose a framework for analyzing the sensitivity of counterfactuals to parametric assumptions about the distribution of latent variables in structural models. In particular, we derive bounds on counterfactuals as the distribution of latent variables spans nonparametric neighborhoods of a given parametric specification while other “structural” features of the model are maintained. Our approach recasts the infinite‐dimensional problem of optimizing the counterfactual with respect to the distribution of latent variables (subject to model constraints) as a finite‐dimensional convex program. We also develop an MPEC version of our method to further simplify computation in models with endogenous parameters (e.g., value functions) defined by equilibrium constraints. We propose plug‐in estimators of the bounds and two methods for inference. We also show that our bounds converge to the sharp nonparametric bounds on counterfactuals as the neighborhood size becomes large. To illustrate the broad applicability of our procedure, we present empirical applications to matching models with transferable utility and dynamic discrete choice models.