January 17, 2020

Analysis

# UBI & the City

#### The final version of ”Universal Basic Income & The City,” supported by JFI, was released on March 25, 2021. Read the full paper here.

The final version of UBI and the City builds upon the work I describe below by exploring alternate financing mechanisms for a New York City guaranteed income policy. The authors analyze the wellbeing and economic effects of policies paid for through a wealth tax and a combination of wealth and corporate taxes, in addition to studying policies funded by a progressive income tax. Though the particulars vary, the models consistently show that the policy increases wellbeing at the cost of some loss in aggregate income and wealth. The authors also include a scenario where the guaranteed income is paid for “exogenously,” with resources coming from outside the city. This might include instances where private funders supply aid (e.g. the Give Directly Kenya experiments) or where the program is funded with state natural resources (Alaska Permanent Fund Dividend) or casino revenue (Macau’s Wealth Partaking Scheme). While not realistic for New York City, it presents a kind of worst case scenario for potential inflationary effects: what if money was figuratively dropped into the city via helicopter with no need to raise revenue through taxes? Among all the experiments, the exogenously funded guaranteed income policy is the only one that generates housing inflation, but the effect is quite modest at one percent. While no single model can offer a definitive answer to the question of the relationship between widespread unconditional cash and housing inflation, the results here suggest that concerns are overblown; intuitions based on partial equilibrium analyses (i.e. “more disposable income means more housing demand”) may be shown to be incorrect once spillover effects and feedback loops are also considered.

While this is the final version of UBI and the City, the authors plan to continue refining the model for use in peer-reviewed research. Future iterations may include population fluctuations to model the potential for inflows (households moving into the city to take advantage of the UBI) or outflows (wealthier households leaving to avoid taxes), further refinement of how “corporations” are modeled, and other tweaks. The first is potentially an important consideration for cities like Maricá, Brazil that wish to stabilize or even increase their populations.

Exploring the macroeconomic impacts of UBI through this type of modeling is both a fruitful and necessary endeavor. When the first iteration of this work came out, there was not much of a literature on this topic to speak of, as the post discusses below. In the time since, however, there has been a burst of innovative modeling work from scholars like Andre Ludivice, Nana Mukbaniani, and Diego Daruich and Raquel Fernández, with each adding features to the standard DSGE framework that offer insight into the implications of cash policy for different corners of the macroeconomy. This is fortunate because, of course, the world has changed considerably in the last fifteen months. The pandemic has made the drive for unconditional cash all the more urgent and has perhaps shifted both public and lawmaker opinion about what is possible and desirable. A year of public and private aid campaigns, proliferating pilots, and relentless advocacy has culminated in the adoption of a fully refundable child tax credit, a form of unconditional cash assistance that, while common in other countries, represents a huge expansion of the U.S. safety net.

Research on the macroeconomic effects UBI and other guaranteed income programs is, in other words, more relevant and more necessary than ever. It is for this reason that this research will be the focus of the next entry in the Jain Family Institute’s white paper series, “From Idea to Reality: Getting to Guaranteed Income.” Senior Research Fellow Claudia Sahm, Director of Research Sidhya Balakrishnan, and I will provide a review of the literature to date (including UBI and the City), discuss common findings across models and experiments and consider how existing models can be improved to address questions unique to cash welfare policy.

Skeptics of guaranteed income tend to worry about the policy’s inflationary effects; absent rent regulation, for instance, one might expect housing costs to rise in proportion to the increase in disposable income generated by the policy. In a new working paper supported by JFI, “Universal Basic Income and the City (2019),” Khalil Esmkhani, Jack Favilukis and Stijn Van Nieuwerburgh explore the effects of a UBI implemented at the city-level in New York City (the updated version of the paper, released in 2021, can be found here). The paper finds that, when financed through a progressive income tax, a UBI increases general welfare and—perhaps most notably—does not lead to housing market inflation. Their research sheds new light on the possible inflationary effects of basic income policies, and also suggests that the financing of a guaranteed income has significant implications for the policy’s outcomes. While the results are tentative, the paper already represents a major advance in the study cash transfer policy. In this post, I present an overview of the macroeconomic literature on basic income before turning back to the authors’ model, its findings, and a path for future work.

### Research gaps on the macroeconomic effects of guaranteed income

Renewed interest in cash transfer policy has been accompanied by a push for pilot research to investigate its effects on recipients. In the United States, the Stockton California SEED demonstration and the Y-Combinator experiment both launched in 2019. Chicago, IL and Newark, NJ have both held task force meetings to explore the feasibility of basic income pilots. At the state level, policymakers in Maine, Massachusetts, and New York have introduced legislation to assemble task forces and launch pilots; the legislation in Maine was signed into law and task force activities began in Fall 2019. Outside the US, the Canadian province of Ontario and the government of Finland both ran limited pilot programs, which have since concluded.

While pilots can provide important insights into the short-run impacts of unconditional cash transfers, many researchers have noted that they typically cannot speak to the effects of a large-scale program. Most pilots are small, typically limited to several hundred or several thousand participants. A universal or near-universal benefit would, however, involve an extremely large transer—labor supply effects could lead to wage fluctuations, purchasing decisions could shift prices, and so on. Such market shifts could in turn generate behavioral responses with significant implications for net transfer amounts, interest rates, and GDP. A small pilot cannot speak to these “macro” effects because the actions of a few hundred or thousand pilot participants won’t cause a shift in wages or prices. Understanding what may happen with population-wide transfers is a pressing task, but pilots are thus far ill-equipped to address them.

One remedy is to design studies that take advantage of large-scale natural experiments. For example, a 2018 paper by Damon Jones and Ioana Marinescu explores the macroeconomic effects of the Alaska Permanent Fund Dividend, which goes to all permanent residents of Alaska, using a synthetic control Difference in Difference analysis. Similarly, JFI is currently developing a study to track the possible inflationary effects of a partial basic income (paid to 52,000 recipients, a third of the city’s population) implemented in Maricá, Brazil. Another option is to carefully develop large-scale studies for this purpose. A recent paper co-authored by Johannes Haushofer makes use of a saturated site design to provide the first experimental evidence on the general equilibrium effects of cash transfers.

But natural experiments are rare, and saturated site studies are expensive and difficult to coordinate. (To our knowledge, this is the first empirical study on general equilibrium effects of cash transfers in a research area that has seen more than two decades of work.) And, perhaps most obviously and most crucially, it is difficult to guess how generalizable findings from Maricá, Brazil or rural Kenya are to a major metropolitan area in the US—let alone an entire national economy.

Rigorous study of the macroeconomic effects of cash transfer policy must include modeling work. Macroeconomic models can be calibrated to explore the effects of a policy in a variety of contexts and the sensitivity of those effects to different assumptions about, for example, household labor market response to new taxes and benefits. Perhaps most importantly, these models allow us to explore the impact of alternate financing mechanisms on policy effects. A cash transfer policy financed with a Value Added Tax (VAT) may have very different effects on GDP, prices, and wages than one paid for through a progressive income tax; paying for a policy in part by eliminating other components of the social safety net may have important implications for net effects on income distribution and poverty.

Such models have, so far, seen little use in the research literature on cash transfer policy. A 2017 paper by Michalis Nikiforos, Marshall Steinbaum and Gennaro Zezza uses the Levy Institute macroeconometric model to simulate the effects of cash transfers. The paper’s results rested on three relatively strong assumptions: that households do not reduce labor supply in response to the cash transfer (there is no “income effect”); that households do not reduce labor supply in response to new income taxes (there is no “substitution effect”); that the economy is operating below capacity such that cash transfers can be both stimulative and, if financed in part by new government debt, will not lead to an increase in interest rates. In 2018, researchers at the University of Pennsylvania used the Penn Wharton Budget Model to examine and critique these findings.

This exchange represents the extent of macroeconomic modeling work on cash transfer policy at the time of publication.

### What is macroeconomic modeling?

Macroeconomists often investigate the potential impacts of policy and economic shocks (e.g. a sudden increase in the price of oil) through the use of models. Models are abstract representations of the economy, and typically include market exchange, households, and/or businesses. The “agents” in the model produce and consume goods, sell their labor, and save. While the particular features of a given model depend on its complexity and the questions it is designed to answer, all macroeconomic models involve agents that interact in markets—buying and selling to maximize their wellbeing as represented by a “utility function,” which may also vary by agent. These interactions drive changes in prices, wages, production, consumption and savings, subject to the constraint that all markets in the model must “clear,” meaning that, at the prevailing wages and prices, supply must equal demand. This state is called “equilibrium,” and while the particular values of wages/prices/consumption/saving may fluctuate within the model over time, when the model is in equilibrium, all markets clear. Much work in macroeconomic modeling centers on perturbing a simulated economy with a stable equilibrium with a new policy or exogenous shock, to determine what the new equilibrium will look like when the proverbial dust has settled.

Within the field of macroeconomic models, one of the most dominant methods is Dynamic Stochastic General Equilibrium Modeling. These are models which allow market conditions to vary over time, account for “random” variations in household and economic characteristics, and require all included markets to clear simultaneously. This last feature is particularly important. The analysis of a change in the price of a good or the wage offered for a job might initially focus on changes to a specific market. For example, if the price of a good goes up, people will buy and consume less of it. This is sometimes termed a “partial equilibrium analysis.” One might expect, however, that there will also be a variety of spillover effects on other markets: people may use the money they used to spend on that good on other things, or they may work more to be able to afford it, causing changes in the labor supply which could affect wages. In thinking about something like a guaranteed income policy we want to be able to account for such effects. General equilibrium analysis allows researchers to account for the strength of these spillover and feedback effects—beyond the immediate effects on a given market. Importantly, the ultimate effect of a change may be quite different than a partial equilibrium analysis might suggest. (For an overview of recent advancements in macroeconomic modeling as they relate to the study of inequality, see Kathryn Holston’s related post here).

### The model and its scope

The model Esmkhani, Favilukis and Van Nieuwerburgh employ in their paper to study UBI in New York City is a Dynamic Stochastic Spatial Equilibrium model, with both overlapping generations and heterogeneous agents. It incorporates spatial components: households live in different “zones,” can build/buy/sell real estate and move, and may commute (at a cost) to work in other zones. This spatial dimension makes the model particularly useful for exploring the effects of policy on housing markets and housing affordability. Indeed, the model was initially developed to do just that: Favilukis and Van Nieuwerburgh made waves earlier this year when they used the model to understand the effects of rent control in New York City and found—contrary to prevailing wisdom—that its redistributional effects enhanced social welfare despite reductions in housing supply. Because the authors were particularly interested in the NYC housing market, the model is calibrated to reflect the particular characteristics of New York on a variety of dimensions including household characteristics, variations in housing stock, and population density in the core and periphery. Using an overlapping generations model is useful for considering how households’ decisions might change over their life-cycle, and including different types of agents (rather than one representative household) allows the researchers to include variation in household characteristics like wealth and earnings that are undoubtedly relevant for the study of UBI.