Session 12: Frontiers of Macroeconomic Research

This paper provides new evidence on the macroeconomic impact of cash transfers in developing countries. Using a Bartik-style identification strategy, the paper documents that Brazil’s Bolsa Familia transfer program leads to a large and persistent increase in relative state-level GDP, formal employment, and informal employment. A state receiving 1% of GDP in extra transfers grows 2.2ppts faster in the first year, with R$100,000 of extra transfers generating five formal-equivalent jobs, half of which are informal. Consistent with a demand-side mechanism, the effects are concentrated in non-tradable sectors. However, an open-economy New Keynesian model only partially captures the high multipliers estimated.

We show that time variation in risk premia leads to time-varying idiosyncratic income risk for workers. Using US administrative data on worker earnings, we show that increases in risk premia lead to lower earnings for low-wage workers; these declines are primarily driven by job separations. By contrast, productivity shocks affect the earnings mainly of highly paid workers. We build an equilibrium model of labor market search that quantitatively replicates these facts. The model generates endogenous time-varying income risk in response to changes in risk premia and matches several stylized features of the data regarding unemployment and income risk over the business cycle.

We leverage recent advancements in machine learning to develop an integrated method to solve globally and estimate models featuring agent heterogeneity, nonlinear constraints, and aggregate uncertainty. Using simulated data, we show that the proposed method accurately estimates the parameters of a nonlinear Heterogeneous Agent New Keynesian (HANK) model with a zero lower bound (ZLB) constraint. We further apply our method to estimate this HANK model using U.S. data. In the estimated model, the interaction between the ZLB constraint and idiosyncratic income risks emerges as a key source of aggregate output volatility.

We leverage the rebalancings of a local-currency government bond index as an exogenous currency demand shock that moves exchange rates. Our results provide empirical support for models of inelastic financial markets where foreign exchange intervention serves as an additional policy tool to effectively stabilize exchange rates. Under inelastic financial markets, a managed exchange rate does not have to compromise monetary policy independence even in the presence of free capital mobility, relaxing the classical trilemma constraint. Countries with a free-floating exchange rate regime are most effective at stabilizing exchange rates as their volatile exchange rates generate further departure from the trilemma.

This paper investigates how a government should manage international reserves when it faces the risk of a rollover crisis. We ask, should the government accumulate reserves or reduce debt to make itself less vulnerable? We show that the optimal policy entails initially reducing debt, followed by a subsequent increase in both debt and reserves as the government approaches a safe zone. Furthermore, we find that issuing additional debt to accumulate reserves can lead to a reduction in sovereign spreads.

Constrained, “hand-to-mouth,” households with zero liquid wealth are a central build-ing block of modern heterogeneous-agent consumption models. We document empirically that many of these seemingly borrowing-constrained households actually revolve intermediate levels of high-interest credit card debt, meaning that they are not constrained at either the zero-liquid-wealth kink nor at their credit card borrowing limit. This finding presents a challenge: how can heterogeneous-agent models generate empirically realistic marginal propensities to consume without relying on borrowing- constrained households? We show that present bias induces households to revolve modest levels of credit card debt, but their indebted saving behavior still generates elevated MPCs. We then apply this insight to highlight key channels through which credit card borrowing reshapes households’ responses to fiscal and monetary policy.

Administrative data are used to establish patterns in contract terms, usage, and performance of anonymized individual credit card accounts. Non-promotional contract interest rates decline with credit score and income at the time of origination, while credit-limit-to-income ratios rise with credit score but decline with income. Utilization and default rates decline sharply with credit score and income. The workhorse incomplete markets macro model is augmented with a credit card industry to address these patterns. If individuals differ by discount factors and default costs, the model can account for almost all of these patterns. The model predicts large spreads between interest rates and default frequencies despite competitive provision of credit cards. The credit card industry lowers the average marginal propensity to consume (MPC) of lower-income individuals. Nevertheless, the average MPC in the economy is 0.32 because people are estimated to be impatient.

This paper studies how labor market power affects the efficacy of monetary policy. First, we use administrative U.S. Census data to document that firms with high monopsony power—firms who account for more than 10% of the wage bill in their local labor market—are less responsive to monetary policy in terms of their overall wage bill. Second, we construct a heterogeneous oligopsonistic New-Keynesian model to study how the decline in labor market power over the past four decades affected the transmission of monetary policy. We show that wage stickiness is key to obtain the heterogeneous response across firms. One contribution of our paper is to develop a numerical approach to solve the model. Such task is non-trivial as each local labor market consists of a finite number of firms, and a law-of-large numbers cannot be invoked to eliminate the local uncertainty resulting from wage stickiness. We calibrate the model to match key features of the U.S. labor market. We find that the decline in labor market power since the 1980s has amplified the aggregate effect of monetary policy on output by about 18%.

We document a widespread misconception that expected future movements in short-term interest rates predict corresponding future movements in long-term interest rates. In particular, people forecast similar shapes for the paths of long and short rates over the next four quarters. However, long rates should already incorporate public information about future short rates and do not positively comove with expected changes in short rates. We hypothesize that people group short- and long-term interest rates into the coarse category of ``interest rates,'' leading to overestimation of their comovement. We show that this categorical thinking persists even among professional forecasters and distorts the real behavior of borrowers and investors. Expectations of rising short rates drive households and firms to rush to lock in long-term debt before further increases in long rates, reducing the effectiveness of monetary policy. Investors sell long-term bonds because they anticipate future increases in long rates. The resulting increase in supply and decrease in demand for long-term debt cause long rates to overreact to changes in short rates, and can help explain the excess volatility puzzle in long rates.

This paper studies how measured beliefs can be used to identify monetary non-neutrality. In a general equilibrium model with both nominal rigidities and endogenous information acquisition, we analytically characterize firms’ optimal dynamic information policies and how their beliefs affect monetary non-neutrality. We then show that data on the cross-sectional distributions of uncertainty and pricing durations are both necessary and sufficient to identify monetary non-neutrality. Finally, implementing our approach in New Zealand survey data, we find that informational frictions approximately double monetary non-neutrality and endogeneity of information is important: models with exogenous information would overstate monetary non-neutrality by approximately 50%.

This paper seeks to understand the forces that maintain racial segregation and the Black-White gap in college attainment, as well as their interactions with placebased policy interventions. We incorporate race into an overlapping-generations spatial-equilibrium model with neighborhood spillovers. Race matters due to: (i) a Black-White wage gap, (ii) amenity externalities—households care about their neighborhood’s racial composition—and (iii) additional barriers to moving for Black households. We find that these forces account for 71% of the racial segregation and 64% of the Black-White gap in college attainment for the St. Louis metro area. The presence of spillovers and externalities generates multiple equilibria. Although St. Louis is in a segregated equilibrium, there also exists an integrated equilibrium with a lower college gap. We show that place-based policy interventions can reduce segregation and destabilize the segregated equilibrium.

We study the effects of diversification and competition in banking, using a rich yet tractable spatial model of deposit-taking. Market-specific risk in deposit demand increases the effective cost for banks, making geographical diversification valuable. Despite its complexity, the model lends itself to a transparent calibration strategy using micro data on deposits flows and rates. The calibrated model points to significant benefits – through reduced risk premia in deposit spreads – from the geographical expansion and consolidation in the banking industry over the last three decades. This is especially true for the smallest/poorest markets. Markups, on the other hand, have changed only modestly. The model also implies that these changes have made the banking system more exposed to aggregate shocks (e.g. to loan returns). Finally, we evaluate the equilibrium effects of replacing all ‘local’ banks with larger ones. The model predicts that this will significantly lower spreads in some markets, but leave markups more or less unchanged.

We develop a model of the production network in which the elasticity of substitution across inputs is low in the short run but grows over time due to putty-clay capital. Our model generates dynamic input-output propagation as firms slowly adjust their input mixes in response to sectoral shocks, leading to delayed aggregate responses relative to the standard model.  Quantitatively, these dynamic spillovers allow the model to match a new business cycle fact: GDP growth is strongly autocorrelated in the aggregate even though it is uncorrelated across time within individual sectors.  These dynamics also shape the propagation of sectoral demand stimulus throughout the network: in the short run, stimulus primarily increases prices, but over time slowly increases quantities as well.