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.
MEF-Seminar Summersemster 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.
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