Research Database

We provide sufficient conditions for the feasibility of robust Pareto-improving (RPI) fiscal policies in the class of incomplete markets models of Bewley-Huggett-Aiyagari and when the interest rate on government debt is below the growth rate (r < g). We allow for arbitrary heterogeneity in preferences and income risk and a potential wedge between the return to capital and to government bonds. An RPI improves risk sharing and can induce a more efficient level of capital. We show that the elasticities of aggregate savings to changes in interest rates are the crucial ingredients that determine the feasibility of RPIs. We establish that government debt and capital investment associated with an RPI may be complements along the transition, rather than the traditional substitutes. Our analysis shifts the focus of fiscal policy in incomplete markets from explicitly redistributive policies to using government bonds and simple subsidies to robustly improve welfare of all agents at all points in time.

A persistent rise in rents has kept inflation above target in many advanced economies. Optimal policy in the standard New Keynesian (NK) model requires policy to stabilize housing inflation. We argue that the basic architecture of the NK model—that excess demand is always satisfied by producers—is inappropriate for the housing market, and we develop a matching framework that allows for demand rationing. Our findings indicate that the optimal response to a housing demand shock is to stabilize inflation in the non-housing sector while disregarding housing inflation. Our results hold exactly in a version of the model with costless search and quantitatively in a version with housing search costs calibrated to match US data on housing tenure, vacancy rates, and the size of the real estate sector.

We present a dynamic general equilibrium model of homelessness calibrated to the U.S. data and evaluate the effectiveness of several policies in the fight against homelessness. The model is designed to capture the most at-risk groups, producing a diverse homeless population that includes a significant portion experiencing short-term homelessness due to labor market shocks and a smaller portion facing chronic homelessness mostly due to health shocks. Our policy experiments reveal that the existing housing voucher program effectively reduces homelessness especially when the general equilibrium effects are accounted for. We show that increasing the reach of the program for eligible individuals can lead to further declines in the aggregate homeless rate relative to alternative forms of subsidies. However, policies targeted to help decrease homelessness are not as popular as general poverty reduction tools such as cash subsidies, which generate a larger welfare gain in our experiments.

We develop a time series of quits and layoffs using the Current Population Survey, and analyze their relationship with labor supply decisions over the business cycle. Our findings challenge the assumption that most labor force exits from employment (EN) are voluntary quits. Instead, we show that 40% of these exits are precipitated by layoffs. With this distinction, both quits to non-participation and the share of workers exiting after a layoff falls during recessions. A workhorse search model is used to frame how these facts add nuance to our understanding of business cycles. Additional results explore regularities of these patterns in the cross section of workers, in the COVID-19 recovery, and in comparison to the JOLTS series on quits and layoffs.

In an economy with incomplete markets and consumers who are sufficiently risk averse, we show that the government can uniquely implement a permanent primary deficit using nominal debt and continuous Markov strategies for primary deficits and payments to debtholders. But this result fails if there are also useless pieces of paper (bitcoin for short) that can be traded. If there is trade in bitcoin, then there is no continuous Markov strategy for the government that leads to unique implementation. Instead, there is a continuum of equilibria with distinct real allocations in which the price of bitcoin converges to zero. And there is a balanced budget trap: continuous government policies designed for a permanent primary deficit cannot eliminate an alternative steady state in which r - g = 0 and the government is forced to balance its budget. A legal prohibition against bitcoin can restore unique implementation of permanent primary deficits, and so can a tax on bitcoin at the rate -(r - g) > 0.

This online appendix accompanies Institute Working Paper 101: Dynamics of Deterrence: A Macroeconomic Perspective on Punitive Justice Policy.

We argue that transitional dynamics play a critical role in evaluating the effects of punitive incarceration reform on crime, inequality, and labor markets. Individuals’ past choices regarding crime and employment under previous policies have persistent consequences that limit their responsiveness to policy changes. We provide novel cohort evidence supporting this mechanism. A quantitative model of this theory, calibrated using restricted administrative data, predicts nuanced dynamics of crime and incarceration that are distinct across property and violent crime and similar to the U.S. experience after 1980. Increased inequality and declining employment accompany these changes, with unequal impacts across generations.

While rising house prices are known to benefit existing homeowners, we document a new channel through which house price shocks have intergenerational wealth effects. Using panel data from school zones within a large U.S. school district, we find that higher local house prices lead to improvements in local school quality, thereby increasing children's human capital and future incomes. We quantify this housing wealth channel using an overlapping generations model with neighborhood choice, spatial equilibrium, and endogenous school quality. We find that housing market shocks generate large intergenerational wealth effects that account for around one-third of total housing wealth effects.

We measure health inequality during middle and old age by race, ethnicity, and gender and evaluate the extent to which it can explain inequalities in other key economic outcomes using the Health and Retirement Study data set. Our main measure of health is frailty, which is the fraction of one’s possible health deficits and is related to biological age. We find staggering health inequality: At age 55, Black men and women have the frailty, or biological age, of White men and women 13 and 20 years older, respectively, while Hispanic men and women exhibit frailty akin to White men and women 5 and 6 years older. The health deficits composing frailty reveal that most health deficits are more likely for Black and Hispanic people than for White people, with the notable exception of those requiring a diagnosis. Imputing medical diagnoses to Black and Hispanic people uncovers even larger health gaps, especially for Black men. Health inequality also emerges as a powerful determinant of economic inequality. If Black individuals at age 55 had the health of their White peers, the life expectancy gap between these two groups would halve, and the gap in disability duration would decrease by 40-70%. Other outcomes are similarly affected by health at age 55, indicating that targeted health interventions for minority groups before middle age could substantially reduce economic disparities in the quantity and quality of life.

We show that the first and only experiment of U.S. mass production of houses, in a factory-built home industry that became known as the Mobile Home industry (and today, as the Manufactured Home industry), was a tremendous success. Mobile Home prices-psf fell by two-thirds from 1955 to 1973, as productivity soared; home quality rose significantly, with Mobile Home building codes receiving ANSI certification in 1963 and National Fire Protection Association co-sponsorship in 1965; as production soared, Mobile Homes accounted for one-third of single-family homes produced in the early 1970s. These feats were achieved as industry leaders developed state-wide building codes for Mobile Homes. This dramatically increased the size of the market for them. Factories invested in specialized machinery to produce simple and standardized products, substituting machinery for labor. Given each factory produced under the same code, industry-induced productivity gains followed, including external effects and directed technical change. Lessons from this industry give insights into critical issues in today's residential construction industry. The poor productivity performance of today's residential construction industry is considered a puzzle. But this poor performance is not new. Our forebears before 1950 wrote extensively about the sector's poor performance, attributing it to the failure to adopt factory-built housing. Our analysis strongly supports this view - for their time and ours. It also supports their view, like that of Levitt & Sons, that factory production is the only way "to produce the homes and apartments needed to house our expanding population and our underprivileged citizens in a comfortable, dignified, decent way," (U.S. Senate 1969).

Differences in household saving rates are a key driver of wealth inequality. But what determines these differences in saving rates and wealth accumulation? We provide a new answer to this longstanding question based on new empirical evidence and a new modeling framework. In the data, we decompose U.S. household wealth into its main portfolio components to document two new empirical facts. First, the variation in wealth by income is mainly driven by differences in participation in asset markets rather than by the amounts invested. Wealth differences are a matter of to have or not to have. Second, the large heterogeneity in asset market participation closely follows observed differences in access to asset markets. Combining these two facts, we develop a new model of life-cycle wealth accumulation in which income-dependent market access is the key driver of differences in asset market participation and saving rates by income. The calibrated model accurately captures the joint distribution of income and wealth. Eliminating heterogeneity in access to asset markets increases wealth accumulation in the bottom half of the income distribution by 32%. Facilitating access to employer-sponsored retirement accounts improves broad-based wealth accumulation in the U.S. economy. Historical data support the model’s prediction.

Like most countries, the Chilean government closed schools as part of its pandemic public health mandates in order to restrict social-contact activities and reduce disease spread. We study the impact of central planner variation in school re-openings on parental labor supply, focusing on the initial three months after schools partially re-opened. We find that mothers’ labor force participation (LFP) decreased by 5.1 percentage points (ppts) one month after re-opening relative to mothers near closed schools. It decreased 9.5 ppts among householder mothers. These magnitudes weakened over time. Two or three months out, mothers who stayed in the labor force saw minimal increase in their ability to actively work and, more specifically, to work in informal jobs. In contrast, fathers’ LFP immediately increased anywhere from 2.0 to 2.9 ppts. Unplanned care disruptions during the re-opening of schools, an artifact of quarantine policies related to sickness and exposure, had differential effects on parental labor supply. Our findings support a theory that parental labor supply is uniquely sensitivity to the care transitions of children both in terms of gender and the householder status of the parent. Policies that encourage consistency in care transitions would largely benefit mothers’ ability to stay engaged in the labor force and advance in paid jobs and careers, especially when they are the householder.

Neighborhoods have a profound and lasting impact on children’s economic outcomes later in life, challenging the equality of opportunity promised by the American Dream. We develop a dynamic spatial equilibrium model in which children’s education choices are shaped by the costs and returns to education in their childhood location. Local returns depend on the moving-cost-adjusted education wage premia in all locations and local costs on the per-student school funding raised from local taxes. In the calibrated model, equalizing school funding across all students decreases differences in education outcomes across US counties and increases intergenerational mobility. However, the reform reduces the supply of educated workers in locations where the demand for them is highest, lowering aggregate output. Policies that instead broaden access to counties with good education outcomes increase intergenerational mobility without reducing output.

We present two valuation models which we use to account for the annual data on price per share and dividends per share for the CRSP Value-Weighted Index from 1929 to 2023. We show that it is a simple matter to account for these data based purely on a model of variation over time in the expected ratio of dividends per share to aggregate consumption under two conditions. First, investors must receive news shocks regarding the expected ratio of dividends per share to aggregate consumption in the long run. Second, the discount rate used to evaluate the impact of this news on the current price per share must be low. We use the approach of Campbell and Shiller (1987) and Campbell and Shiller (1988) to argue that the cash flow news in our model is not a stand-in for changes in expected returns: with our model parameters, returns are not predictable and price dividend spreads and ratios predict dividend growth at model-implied magnitudes. We illustrate which parameter choices account for differences between our results and prior findings in the literature. We conclude that the answer to Shiller’s (1981) question “Do stock prices move too much to be justified by subsequent movements in dividends?” is “not necessarily.”

While health affects many economic outcomes, its dynamics are still poorly understood. We use k-means clustering, a machine learning technique, and data from the Health and Retirement Study to identify health types during middle and old age. We identify five health types: the vigorous resilient, the fair-health resilient, the fair-health vulnerable, the frail resilient, and the frail vulnerable. They are characterized by different starting health and health and mortality trajectories. Our five health types account for 84% of the variation in health trajectories and are not explained by observable characteristics, such as age, marital status, education, gender, race, health-related behaviors, and health insurance status, but rather, by one’s past health dynamics. We also show that health types are important drivers of health and mortality heterogeneity and dynamics. Our results underscore the importance of better understanding health type formation and of modeling it appropriately to properly evaluate the effects of health on people’s decisions and the implications of policy reforms.

We study how negative sentiment around an industry impacts beliefs and behaviors, focusing on demands for racial justice after the murder of George Floyd and the salience of the “defund the police” movement. We assess stakeholder beliefs on the impact of protests on the stock prices of police-affiliated firms. In our survey experiment, laypeople and finance professionals predicted more negative stock price outcomes when they lacked details on the products supplied by such firms. Exposure to narratives about the context of the protests further reduced the prediction accuracy of these groups. In contrast, product information improved the prediction accuracy of respondents. Turning to real-life behavior, we find that mutual funds exposed to protests were 20% less likely to hold police stocks, after the protests, than funds in areas without protests. Political support for maintaining police funding, though in the majority, declined by 4.3 percentage points in protest areas. The salience of the “defund the police” narrative led to significant overreactions in both financial predictions and real-life behavior.

Using rural household survey data from West Bengal, we find that voters respond positively to excludable government welfare benefits but not to local public good programs, while reporting having benefited from both. Consistent with these voting patterns, shocks to electoral competition induced by exogenous redistricting of villages resulted in upper-tier governments manipulating allocations across local governments only for excludable benefit programs. Using a hierarchical budgeting model, we argue these results provide credible evidence of the presence of clientelism rather than programmatic politics.

After 1980, larger US cities experienced substantially faster wage growth than smaller ones. We show that this urban bias mainly reflected wage growth at large Business Services firms. These firms stand out through their high per-worker expenditure on information technology and disproportionate presence in big cities. We introduce a spatial model of investment-specific technical change that can rationalize these patterns. Using the model as an accounting framework, we find that the observed decline in the investment price of information technology capital explains most urban-biased growth by raising the profits of large Business Services firms in big cities.

We explore the evolution of income inequality and mobility in the U.S. for a large number of subnational groups defined by race and ethnicity, using granular statistics describing income distributions, income mobility, and conditional income growth derived from the universe of tax filers and W-2 recipients that we observe over a two-decade period (1998–2019). We find that income inequality and income growth patterns identified from administrative tax records differ in important ways from those that one might identify in public survey sources. The full set of statistics that we construct is available publicly alongside this paper as the Income Distributions and Dynamics in America, or IDDA, dataset. Using two applications, we illustrate IDDA’s relevance for understanding income inequality trends. First, we extend Bayer and Charles (2018) beyond earnings gaps between Black and White men and document that, unlike those for other groups, earnings for both Black men and Black women fell behind earnings for White men following the Great Recession. This trend lasted through 2019, the end of the data period. Second, we document a significant reversal in the convergence of earnings for Native earners in Native areas.

Property taxes impact the housing distribution across generations. Low property taxes lead to concentrated ownership among elderly empty-nesters, limiting housing for financially constrained young families. Conversely, high property taxes act as a “forced mortgage,” reducing upfront downpayments and enabling greater homeownership among younger households. We show in an overlapping generations model that raising property taxes in low-tax California to match those in higher-tax Texas increases homeownership in California by 4.6% and among younger households by 7.4% in steady state. Asset taxes can reallocate housing to higher-valuation households in the presence of financial constraints, providing an independent rationale for property taxes.