We develop a method that identifies the effects of nationwide policy, i.e., policy implemented across all regions at the same time. The core idea is to track outcome paths in terms of stages rather than time, where a stage of a regional outcome at time t is its location on the support of a reference path. The method proceeds in two steps. First, a normalization maps the time paths of regional outcomes onto the reference path—using only pre-policy data. This uncovers cross-regional heterogeneity of the stage at which policy is implemented. Second, this stage variation identifies policy effects inside a window of stages where a stage-leading region provides the no-policy counterfactual path for non-leading regions that are subject to policy inside that window. We assess our method’s performance with Monte-Carlo experiments, illustrate it with empirical applications, and show that it captures heterogeneous policy effects across stages.
Summersemester 2024
The distribution of marginal propensities to consume (MPCs) is central to the transmission of shocks and policies to the economy. Recent empirical findings challenge the standard view that this distribution is mostly explained by liquidity constraints: (i) some people with substantial liquid wealth have a high MPC; (ii) current earnings, which should relax the constraint, do not reduce the MPC. I show that, in the standard consumption model, the permanent component of earnings is another important determinant of the MPC and its impact can explain (i)-(ii). An increase in the permanent component mechanically raises the magnitude of future earnings shocks, thus the risk that people face, forcing them to save more and making them more responsive to a windfall. Survey data support a large and positive effect of permanent earnings on the MPC. Numerical simulations can replicate quantitatively those survey findings in a model with rich earnings risk.
This paper combines new data and a narrative approach to identify shocks to political pressure on the Federal Reserve. From archival records, I build a data set of personal interactions between U.S. Presidents and Fed officials between 1933 and 2016. Since personal interactions do not necessarily reflect political pressure, I develop a narrative identification strategy based on President Nixon’s pressure on Fed Chair Burns. I exploit this narrative through restrictions on a structural vector autoregression that includes the personal interaction data. I find that political pressure shocks (i) increase inflation strongly and persistently, (ii) lead to statistically weak negative effects on activity, (iii) contributed to inflationary episodes outside of the Nixon era, and (iv) transmit differently from standard expansionary monetary policy shocks, by having a stronger effect on inflation expectations.
What is the relationship between international differences in income risk and countries' external asset positions? We study income risk and its higher-order moments in twelve countries. We then calibrate and discretize an income process that can account for countries' heterogeneity in income risk and embed this process in a standard heterogeneous-agents macro model. The model shows that international differences in higher-order moments of income risk give rise to sizable differences in international asset positions. We study global shocks through the lens of the model, such as the rise in automation or geopolitical fragmentation. The model predicts that higher and more unequal growth in the United States leads to important changes in other countries' saving and consumption inequality. A fall in foreign lending by countries with large international asset positions also affects substantially consumption risk in debtor economies.
This paper presents a comprehensive method for efficiently solving stochastic Integrated Assessment Models (IAMs) and performing parametric uncertainty quantification. Our approach consists of two main components: a deep learning-based algorithm designed to globally solve IAMs as a function of endogenous and exogenous state variables as well as uncertain parameters within a single model evaluation. Additionally, we develop a Gaussian process-based surrogate model to facilitate the efficient analysis of key metrics, such as the social cost of carbon, with respect to uncertain model parameters. To demonstrate the effectiveness of our method, we posit a high-dimensional stochastic IAM that aligns with cutting-edge climate science. Our computations reveal that most of the variability in the social cost of carbon stems from the parametric uncertainty in the equilibrium climate sensitivity and in the damage function.
Do larger firms have more productive or more scalable technologies? Are wealthy households more likely to invest in one over the other? We estimate nonparametric production functions using balance sheet data on the universe of Canadian firms, resulting in a joint distribution of input elasticities—therefore, returns to scale (RTS)—along with total factor productivity (TFP). We show that larger firms are characterized by significantly higher RTS, even within narrow industries. We also find that wealthier households are more likely to own firms with higher RTS. However, the relationship between TFP and firm size is more nuanced. As an application, we show that misallocation from financial frictions is more severe when the observed firm heterogeneity is driven by RTS differences, compared to the conventional view that attributes firm heterogeneity entirely to TFP differences. We develop a quantitative equilibrium model where entrepreneurs operate technologies differing in RTS and TFP.
We study the macroeconomic consequences of tax policies designed to reduce international profit shifting by multinational enterprises (MNEs) using a model that emphasizes transfer pricing of intangible capital. We prove analytically that such policies would reduce MNEs’ intangible investment, reducing output both at home and abroad. We then quantify the effects of the OECD’s proposed reforms: reallocating the rights to tax MNEs’ profits to the countries where they sell their products; and a minimum global corporate income tax. These policies would reduce profit shifting by more than two-thirds, but would also reduce output in all regions of the global economy.
We study optimal unemployment insurance in a dynamic environment with heterogeneous agents, who face potentially binding liquidity constraints, and where neither search effort nor wealth is observable. In the constrained efficient allocation, the poorest agents always search, while richer agents start searching only after a spell of inactivity or not at all. Consumption levels of committed searchers are optimally declining in duration. We characterize various (Ricardian equivalent) implementations of the constrained optimum, including one that features a lump-sum layoff payment upon separation, zero benefits during unemployment, and duration-dependent re-employment taxes. By contrast, a policy with constant benefits during unemployment, which has been shown to be optimal in settings without liquidity constraints, is severely suboptimal in our framework.
We evaluate the aggregate, distributional and welfare consequences of alternative government education policies to encourage college completion, such as making college free and improving funding for public schooling. To do so, we construct a general equilibrium overlapping generations model with intergenerational linkages, a higher education choice as well as a multi-stage human capital production process during childhood and adolescence with parental and government schooling investments. Studying the transitions induced by unexpected policy reforms we show that the “free college” and the “better schools” reform generate significant welfare gains, which take time to materialize and are lower in general than in partial equilibrium. It is optimal to combine both reforms: tuition subsidies make college affordable even for children from poorer parental backgrounds and better schools increase human capital thereby reducing dropout risk.
This paper investigates how to recover households' expectations from consumption data when the consumption basket undergoes dramatic changes. I study consumption behavior during the Covid recession and subsequent recovery. I propose a methodology for computing a measure of robust consumption that identifies a subset of goods and services that is informative about prevailing economic circumstances. Constructing this measure in micro data, reveals new facts about the Covid recession, such as differences in the response of robust consumption by education. I combine this measure of consumption, and use it to estimate households expectations during the recession and recovery when subject to a range of shocks. This delivers estimates of changing household perceptions of the persistence of the economic shocks during the course of the Covid recession. Household beliefs are shown to have an important effect on the strength of the consumption response to government stimulus policies.