The Unassuming Economist

From a paper by by Luca Bettarelli, Davide Furceri, Michael Ganslmeier, Marc Tobias Schiffbauer:

“Beyond its environmental damage, climate change is predicted to produce significant economic costs. Combining novel high-frequency geospatial temperature data from satellites with measures of economic activity for the universe of US listed firms, this article examines a potentially important channel through which global warming can lead to economic costs: temperature uncertainty. The results show that temperature uncertainty—by increasing power outages, reducing labor productivity, and increasing the degree of exposure of firms to environmental and non-political risks, as well as economic uncertainty at the firm-level—persistently reduce firms’ investment and sales. This effect varies across firms, with those characterized by tighter financial constraints being disproportionally more affected.”

From a paper by Nestor Garza, Ivo Gasic, Clemente Larrain:

“This paper aims to build a set of long-term, geographically controlled land value indices for Santiago de Chile, with which to test land rent theory predictions regarding macroeconomic impacts. This paper uses a geographic cluster approach to the Laspeyres estimator, weighted by the stock of available land plots and their market offers per zone, to create two quarterly land value indices for Gran Santiago during the period 1983Q4–2016Q2. Subsequently, this paper implements dynamic time series methods (Vector Error Correction) as a baseline to determine the effect of economic performance and interest rate on urban land values. The two land value indices are correctly predicted by economic and interest rate shocks, as theoretically expected. In addition, this paper found that land values grew faster-than-predicted during the period of the so-called “Chilean Miracle” (1992–1998), a situation associated in the literature with worsened housing affordability and socio-spatial inequality.” 

From a paper by David Autor, David Dorn, Gordon Hanson, Maggie R. Jones, Bradley Setzler:

“This chapter analyzes the distinct adjustment paths of U.S. labor markets (places) and U.S. workers (people) to increased Chinese import competition during the 2000s. Using comprehensive register data for 2000–2019, we document that employment levels more than fully rebound in trade-exposed places after 2010, while employment-to-population ratios remain depressed and manufacturing employment further atrophies. The adjustment of places to trade shocks is generational: affected areas recover primarily by adding workers to non-manufacturing who were below working age when the shock occurred. Entrants are disproportionately native-born Hispanics, foreign-born immigrants, women, and the college-educated, who find employment in relatively low-wage service sectors such as medical services, education, retail, and hospitality. Using the panel structure of the employer-employee data, we decompose changes in the employment composition of places into trade-induced shifts in the gross flows of people across sectors, locations, and non-employment status. Contrary to standard models, trade shocks reduce geographic mobility, with both in- and out-migration remaining depressed through 2019. The employment recovery stems almost entirely from young adults and foreign-born immigrants taking their first U.S. jobs in affected areas, with minimal contributions from cross-sector transitions of former manufacturing workers. Although worker inflows into non-manufacturing more than fully offset manufacturing employment losses in trade-exposed locations after 2010, incumbent workers neither fully recover earnings losses nor predominantly exit the labor market, but rather age in place as communities undergo rapid demographic and industrial transitions.”

From a paper by Tomas Kabrt:

“This paper focuses on the demographic determinants of current account balance (CAB) across income groups, continents, and time periods between 1993 and 2021. The benchmark model employs a panel data analysis, particularly a two-way effects estimator (Baltagi, 2021), with CAB as the main dependent variable, while savings rate (SAV) is used as an alternative dependent variable. It was found that the old dependency ratio, fertility rate, life expectancy, population growth, and mortality rate have a statistically significant effect on CAB and SAV, but the effects are heterogeneous across income groups and continents. In Africa, an increase in the old dependency ratio has a negative effect on CAB in accordance with the theories of Modigliani and Sterling (1983), Graham, (1987), and Masson and Tryon (1990). Fertility rate has a negative effect on CAB in Africa while having an ambiguous effect in Asia and Europe. In line with the findings of Mason and Lee (2006), there is a positive relationship between fertility rates and CAB and SAV in lower-middle-income countries and negative relationship in high-income countries. Conversely, population growth affects negatively CAB and SAV in lower middle-income countries and positively in high-income countries.”

From a paper by Alessandra Fogli, Veronica Guerrieri, Mark Ponder, and Marta Prato:

“Since the 1980s, the US has experienced not only a steady increase in income inequality, but also a contemporaneous rise in residential segregation by income. What is the relationship between inequality and residential segregation? How does it affect intergenerational mobility? We first document a positive correlation between inequality and segregation, both over time and across metro areas. We then develop a general equilibrium model where parents choose the neighborhood where they raise their children and invest in their children’s education. In the model, segregation and inequality amplify each other because of a local spillover that affects the return to education. We calibrate the model to a representative US metro in 1980 and use the micro estimates of neighborhood exposure effects in Chetty and Hendren (2018b) to discipline the strength of the local spillover. We first use the calibrated version of the model to explore the economy’s response to an unexpected skill premium shock. We find that segregation dynamics played a significant role in amplifying the increase in inequality and in dampening intergenerational mobility. We then use the model to explore the effects of policies designed to move poor families to better neighborhoods, like the Moving To Opportunity (MTO) program. We show that scaling up MTO policies induces general equilibrium effects that limit their efficacy.”