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Covid-19 federal assistance to state and local governments and its consequences

California governor and presidential hopeful Gavin Newsom recently reached an agreement with state legislators on yet another round of cheques to be mailed to households around the state (Luna 2022). Households will receive up to $1,050 in cash from the state government, in a manoeuvre estimated to cost almost $10 billion. This follows two previous rounds of so-called Golden State Stimulus payments to California residents in 2021.

It all constitutes quite the change of tune from May 2020, when Newsom proposed significant spending cuts in response to the downturn triggered by the Covid-19 pandemic (Office of Governor Gavin Newsom 2020). What happened?

In the early months of the pandemic, analysts and policymakers alike expressed significant concern about the impact the initial downturn and widespread lockdowns would have on state and local budgets (Bartik 2020, McNichol et al. 2020). As state and local governments in the US are generally bound by balanced-budget requirements, reduced revenues can trigger sudden disruptions of service provision and curtail the employment of state and local government employees (Clemens and Miran 2012, Shoag et al. 2019). To avoid such turmoil, the federal government has assumed responsibility for the stabilisation of state and local budgets.

And assume responsibility it did. Across four major Covid-19 relief bills, the federal government allocated about $900 billion in funds to state and local governments. This was in line with the most pessimistic estimates of revenue from early in the pandemic (e.g. Bartik 2020). Those estimates, however, ended up dramatically overestimating the impact the pandemic would ultimately have on state and local budgets. In fact, state tax revenues since early 2020 have exceeded pre-pandemic forecasts (Dougherty and de Biase 2021, National Association of State Budget Officers 2021).

There are two main reasons why early forecasts of revenue losses missed the mark (Clemens and Veuger 2020, forthcoming). First, they did not account for the other components of the policy response to the downturn, which included unprecedented amounts of support for households and firms as well as the rapid development and deployment of vaccines. These policies indirectly supported state and local governments’ tax bases, making direct assistance to state and local government less necessary. Second, they frequently relied on historical relationships between macroeconomic indicators and revenue that were quite different during the Covid-19 crisis. 

We assess the consequences of the perhaps overly generous federal fiscal assistance to state and local governments in Clemens et al. (2022a, 2022b). In both papers, we exploit the fact that allocations of federal aid were far more generous to the residents of states that are more favourably represented in Congress than to the residents of states that are less favourably represented. Representation does not scale proportionately with population, in large part because each state elects precisely two Senators regardless of its population, and in part because each state gets at least one member of the House of Representatives. The resulting over-representation of low-population states (or ‘small’ states) strongly predicts their aid allocations. This leads us to use a measure of states’ per-resident congressional representation as an instrumental variable. As Figure 1 (from Clemens and Veuger 2021) illustrates, this ‘small-state bias’ led to significant differences in the amount of Covid-19 relief funding per resident received by the public sector in small states relative to large states. 

Figure 1 Aid per resident by level of congressional representation

The differences in federal funds flowing to different states predicted by differences in congressional representation cannot be explained by other factors, such as prognosticated revenue shortfalls, severity of the threat to public health, or other proxies for funding needs. As a result, they allow us to avoid a standard source of bias, namely that aid tends to flow most generously to states in greatest need, as we estimate the impact of fiscal assistance on macroeconomic outcomes. 

The first and main outcome of interest we analyse in Clemens et al. (2022b) is state and local government employment. Preserving state and local government employment is important both in and of itself, to ensure continued service provision, and for its role in stabilising the broader macroeconomy.  

Figure 2 shows the local-projection impulse response of state and local government employment to fiscal assistance. It indicates that each $1 million in aid preserved modestly less than 18 public sector job-months across the 18 months of our sample, or to put this in more familiar terms, fiscal assistance of $855,000 was allocated for each state or local government job-year preserved. This number is quite high relative to comparable estimates from past downturns. Research on the effects of components of the 2009 American Recovery and Reinvestment Act, for example, has estimated costs per job-year ranging from $26,000 to $202,000 (Chodorow-Reich et al. 2012, Wilson 2012, Conley and Dupor 2013). In the pandemic context, the Paycheck Protection Program has been estimated to cost between $169,000 and $258,000 per job-year (Autor et al. 2022a, 2022b). 

Figure 2 Effects of federal aid per resident on state and local government employment

Figure 3, from the same paper, shows that the impact on the broader economy was (even) more modest (see also Auerbach et al. 2021). Federal assistance to states and localities does not appear to have had a statistically significantly effect on private-sector employment, wages, income, or output. This too can be contrasted with past work on the multiplier effects of federal spending (e.g. Inoue et al. 2022). Estimates from historical downturns from the Great Depression through the Great Recession have tended to produce multiplier estimates that range between 0.5 and 2 (Ramey 2019, Chodorow-Reich 2020).

Figure 3 Effects of federal aid per resident on macro outcomes

In Clemens et al. (2022a), we and John Kearns report some more positive findings. Federal assistance appears to have helped states roll out more effective testing operations. As shown in the figure below, the testing advantage of states that received more federal funds has grown steadily since the summer of 2020.

Figure 4 Effects of federal aid per resident on total Covid-19 tests administered per 100,000

In our analysis of states’ vaccination campaigns, we find that states that received more federal funds per resident did not outperform their peers by having higher vaccination rates. It does appear that greater federal funds have led to more equitable vaccination patterns: states that received more federal money saw smaller gaps arise between the vaccination rates of residents with a college education relative to those with a high school education. 

The overall picture painted by the work summarised here suggests that federal assistance to state and local governments was only modestly effective, if that, as a macroeconomic stimulus. In addition to the overly generous support allocated to state and local governments, two factors are likely key in explaining this. First, for much of the past two years, the public health situation led governments to impose restrictions on, and households and firms to voluntarily refrain from, a wide range of economic activity. These restrictions and voluntary pullbacks on economic activity may have shut down a key mechanism through which fiscal stimulus traditionally operates. Second, well before state and local governments made use of the full amount of funds they were allocated, the economy had entered a period of significant inflationary pressure. This sets the recent macroeconomic context apart from, for example, the period after the global crisis, when aggregate demand shortfalls were rampant. 

The extent to which federal funds have advanced other goals of interest remains to be seen. Our analysis of states’ testing and vaccination campaigns suggests that federal funds have advanced at least some objectives of interest. The effects of federal funds on education, law enforcement, and other local public services will need to be assessed in future research. 

References

Auerbach, A, Y Gorodnichenko, P B McCrory and D Murphy (2021), “What Covid-19 Teaches Us About Fiscal Multipliers”, VoxEU.org, 23 December.

Autor, D, D Cho, L D Crane, M Goldar, B Lutz, J K Montes, W B Peterman, D D Ratner, D Villar Vallenas and A Yildirmaz (2022a), “An Evaluation of the Paycheck Protection Program Using Administrative Payroll Microdata”, NBER Working Paper 29972.

Autor, D, D Cho, L D Crane, M Goldar, B Lutz, J K Montes, W B Peterman, D D Ratner, D Villar Vallenas and A Yildirmaz (2022b), “The $800 Billion Paycheck Protection Program: Where Did the Money Go and Why Did it Go There?”, Journal of Economic Perspectives 36(2): 55-80.

Bartik, T J (2020), “An Updated Proposal for Timely, Responsive Federal Aid to State and Local Governments During the Pandemic Recession”, W.E. Upjohn Institute for Employment Research, 22 May.

Chodorow-Reich, G (2020), “Regional Data in Macroeconomics: Some Advice for Practitioners”, Journal of Economic Dynamics and Control 115: 103875. 

Chodorow-Reich, G, L Feiveson, Z Liscow and W G Woolston (2012), “Does State Fiscal Relief during Recessions Increase Employment? Evidence from the American Recovery and Reinvestment Act”, American Economic Journal: Economic Policy 4(3): 118-45.

Clemens, J, P Hoxie, J Kearns and S Veuger (2022a), “How Did Federal Aid to States and Localities Affect Testing and Vaccine Delivery?”, NBER Working Paper 30206.

Clemens, J, P Hoxie and S Veuger (2022b), “Was Pandemic Fiscal Relief Effective Fiscal Stimulus? Evidence from Aid to State and Local Governments”, NBER Working Paper 30168.

Clemens, J and S Miran (2012), “Fiscal Policy Multipliers on Subnational Government Spending”, American Economic Journal: Economic Policy 4(2): 46-68.

Clemens, J and S Veuger (2020), “Fiscal Federalism and the COVID-19 Shock in the US”, VoxEU.org, 28 September.

Clemens, J and S Veuger (2021), “Politics and the Distribution of Federal Funds: Evidence from Federal Legislation in Response to COVID-19”, Journal of Public Economics 204: 104554.

Clemens, J and S Veuger (Forthcoming), “Lessons from COVID-19 Aid to State and Local Governments for the Design of Federal Automatic Stabilizers”, Aspen Economic Strategy Group.

Conley, T G and B Dupor (2013), “The American Recovery and Reinvestment Act: Solely a Government Jobs Program?”, Journal of Monetary Economics 60(5): 535-549.

Dougherty, S and P de Biase (2021), “State and Local Government Finances in the Time of COVID-19”, VoxEU.org, 26 October.

Inoue, A, B Rossi and Y Wang (2022), “Why Fiscal Multipliers Estimates Change over Time and What Determines Their Magnitude”, VoxEU.org, 14 April.

Luna, T (2022), “Deal Reached on Plan for More than $9 Billion in Gas Refunds to California Drivers”, Los Angeles Times, 24 June.

McNichol, E, M Leachmen and J Marshall (2020), “States Need Significantly More Fiscal Relief to Slow the Emerging Deep Recession”, April 14, Center on Budget and Policy Priorities.

National Association of State Budget Officers (2021), “The Fiscal Survey of the States: Fall 2021. An Update of State Fiscal Conditions”.

Office of Governor Gavin Newsom (2020), “Governor Newsom Submits May Revision Budget Proposal to Legislature 5.14.20”, 14 May.

Ramey, V (2016), “Macroeconomic Shocks and Their Propagation”, in J B Taylor and H Uhlig (eds.), Handbook of Macroeconomics 2: 71-162.

Ramey, V (2019), “Ten Years After the Financial Crisis: What Have We Learned from the Renaissance in Fiscal Research?”, Journal of Economic Perspectives 33(2): 89-114. 

Shoag, D, C Tuttle and S Veuger (2019), “Rules Versus Home Rule: Local Government Responses to Negative Revenue Shocks”, National Tax Journal 72(3): 543-574.

Wilson, D J (2012), “Fiscal Spending Jobs Multipliers: Evidence from the 2009 American Recovery and Reinvestment Act”, American Economic Journal: Economic Policy 4(3): 251-282.

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