NBER Summer Institute 2022 Round-up: Week 1

On July 11, the National Bureau of Economic Research kicked off its summer institute, an annual 3-week conference featuring discussions and paper presentations on various topics in economics, including monopsony power, inflation, and discrimination in housing policy. This year’s NBER event is being held virtually and is being livestreamed on YouTube.

We’re excited to see Equitable Growth’s grantee network, our Steering Committee, and our Research Advisory Board and their research well-represented throughout the program. Below are abstracts (in no particular order) of some of the papers that caught the attention of Equitable Growth staff during the first week of the conference.

Come back next week and the following for round-ups from weeks 2 and 3.

“The Effects of Federal “Redlining” Maps: a Novel Estimation Strategy”
Luca Perdoni, Yale University, Equitable Growth grantee
Disa M. Hynsjo (deceased), Yale University, Equitable Growth grantee

Abstract. “This paper proposes a new empirical strategy to estimate the causal effects of 1930s federal “redlining” – the mapping and grading of US neighborhoods by the Home Owners’ Loan Corporation (HOLC). Our analysis exploits an exogenous population cutoff: only cities above 40,000 residents were mapped. We employ a difference-in-differences design, comparing areas that received a particular grade with neighborhoods that would have received the same grade if their city had been mapped. The control neighborhoods are defined using a machine learning algorithm trained to draw HOLC-like maps using newly geocoded full-count census records. For the year 1940, we find a substantial reduction in property values and homeownership rates in areas with the lowest grade along with an increase in the share of African American residents. We also find sizable house value reductions in the second-to-lowest grade areas. Such negative effects on property values persisted until the early 1980s. Our results illustrate that institutional practices can coordinate individual discriminatory choices and amplify their consequences.’

Note: This research was funded in part by Equitable Growth.

“Measuring Common Ownership: the Role of Blockholders and Insiders”
Amir Amel-Zadeh, University of Oxford
Fiona Kasperk, University of Oxford
Martin C. Schmalz, Oxford University

Abstract. “We construct a novel data set to show that, between 2003-2020, up to one-fifth of America’s largest firms had a non-financial blockholder or insider as their largest shareholder. Blockholders and insiders tend to be less diversified than institutional investors. Measures of `”universal” and “common” ownership of firms are therefore lower than previously believed based on analyses of institutional investors’ holdings alone, and the heterogeneity in ownership structures across firms is greater. Consolidation in the asset management industry increases universal ownership and common ownership of industry rivals. Extant results claiming indexing alone explains the rise of universal ownership cannot be confirmed with the new, more comprehensive data.”

Note: This abstract is from an earlier version of theis paper.

“Wealth and Property Taxation in the United States”
Sacha Dray, London School of Economics
Camille Landais, London School of Economics
Stefanie Stantcheva, Harvard University and NBER, Equitable Growth grantee

Abstract. “We study the history and geography of wealth accumulation in the US, using newly collected historical property tax records since the early 1800s. The property tax in the US was a comprehensive tax on all kinds of properties (real estate, personal property, and financial wealth), making it one of the first “wealth taxes.” Our new data allows us to reconstruct wealth series at the city, county, and state levels over time and to study the effects of property taxes on property values, migration, and investment. We first document the long-term evolution of household wealth in the US since the early 1800s, offering new fine-grained and high-frequency estimates of household wealth over a long period of time. The US had significantly lower wealth than Europe and only caught up with Europe after WW1, despite GDP per capita having been larger than that of France or the UK since the late 1870s. Second, we study the spatial allocation of wealth in the US over the long run. The geography of wealth is highly persistent and factors related to geography and demographics correlate strongly with wealth at the city, county, and state levels. Finally, we study the role of the property tax (i.e., a “wealth tax”) on wealth accumulation, using the large variation in property tax rates across more than 300 municipalities. We find an implied elasticity of capital income with respect to the net-of-tax rate on income of about .70 after 10 years. This elasticity can be broken down into an (extensive) elasticity of migration of about .26 and an (intensive) elasticity of per capita income of about .44. The intensive margin elasticity appears to be driven in part by reporting and avoidance responses, but also by significant capitalization of property taxes in local real estate prices.”

“Learning About the Long Run”
Leland Farmer, University of Virginia
Emi Nakamura, University of California, Berkeley and NBER, Equitable Growth grantee
Jón Steinsson, University of California, Berkeley and NBER, Equitable Growth grantee

Abstract. “Forecasts of professional forecasters are anomalous: they are biased, forecast errors are autocorrelated, and forecast revisions predict forecast errors. Sticky or noisy information models seem like unlikely explanations for these anomalies: professional forecasters pay attention constantly and have precise knowledge of the data in question. We propose that these anomalies arise because professional forecasters don’t know the model that generates the data. We show that Bayesian agents learning about hard-to-learn features of the data generating process (low frequency behavior) can generate all the prominent aggregate anomalies emphasized in the literature. We show this for two applications: professional forecasts of nominal interest rates for the sample period 1980-2019 and CBO forecasts of GDP growth for the sample period 1976-2019. Our learning model for interest rates also provides an explanation for deviations from the expectations hypothesis of the term structure that does not rely on time-variation in risk premia.”

Minority Unemployment, Inflation, and Monetary Policy
Munseob Lee, University of California at San Diego
Claudia Macaluso, Federal Reserve Bank of Richmond
Felipe Schwartzman, Federal Reserve Bank of Richmond

Abstract. “Persistent income inequality between Black and white households has generated a vigorous debate on what policy instruments may be effective to reduce such disparities. One such instrument to be evaluated is monetary policy. In this paper, we study precisely how monetary policy affects the real income volatility of Black and white households, in a framework where the monetary authority faces a trade-off between unemployment and inflation. Our assessment is informed by four empirical regularities: (i) the unemployment rate for Black individuals is about twice as high than the unemployment rate for white individuals, at all times; (ii) though the levels differ, the unemployment rates for Black and white individuals move closely over the business cycle; (iii) labor income represents a larger portion of overall income for Black than for white households; (iv) Black households experience higher price volatility with respect to white households. We argue that (i) and (ii) imply that proposals postulating that monetary policy targets Black unemployment are equivalent to a policy accepting larger inflation fluctuations. At the same time, (iii) and (iv) imply that the real income volatility of Black households is more directly affected by inflation changes. In our quantitative evaluation, we find that Black households gain disproportionately more from accommodative monetary policy so long as inflation expectations remain anchored. With unanchored expectations, on the other hand, the benefit from reducing unemployment become smaller and the erosion of real income from higher inflation dominates.”

“Minimum Wages, Efficiency and Welfare”
David W. Berger, Duke University and NBER, Equitable Growth grantee
Kyle F. Herkenhoff, University of Minnesota and NBER, Equitable Growth grantee
Simon Mongey, University of Chicago and NBER, Equitable Growth grantee

Abstract. “It has long been argued that a minimum wage could alleviate efficiency losses from monopsony power. In a general equilibrium framework that quantitatively replicates results from recent empirical studies, we find higher minimum wages can improve welfare, but most welfare gains stem from redistribution rather than efficiency. Our model features oligopsonistic labor markets with heterogeneous workers and firms and yields analytical expressions that characterize the mechanisms by which minimum wages can improve efficiency, and how these deteriorate at higher minimum wages. We provide a method to separate welfare gains into two channels: efficiency and redistribution. Under both channels and Utilitarian social welfare weights the optimal minimum wage is $15, but alternative weights can rationalize anything from $0 to $31. Under only the efficiency channel, the optimal minimum wage is narrowly around $8, robust to social welfare weights, and generates small welfare gains that recover only 2 percent of the efficiency losses from monopsony power.”

Note: This research was funded in part by Equitable Growth.

“New Pricing Models, Same Old Phillips Curves?”
Adrien Auclert, Stanford University and NBER, Equitable Growth grantee
Rodolfo D. Rigato, Harvard University
Matthew Rognlie, Northwestern University and NBER
Ludwig Straub, Harvard University and NBER

Abstract. “We show that, in a broad class of menu cost models, the dynamics of aggregate inflation in response to arbitrary shocks to aggregate costs are nearly the same as in Calvo models with suitably chosen Calvo adjustment frequencies. We first prove that the canonical menu cost model is first-order equivalent to a mixture of two timedependent models, which reflect the extensive and intensive margins of price adjustment. We then show numerically that, in any plausible parameterization, this mixture is well-approximated by a single Calvo model. This close numerical fit carries over to other standard specifications of menu cost models. Thus, the Phillips curve for a menu cost model looks like the New Keynesian Phillips curve, but with a higher slope.”