VoxEU Column COVID-19 Labour Markets

Unemployment effects of stay-at-home orders: Evidence from high-frequency claims data

Stay-at-home orders have been imposed in many countries to flatten the COVID-19 pandemic curve, but it’s not clear how much economic disruption is caused directly by the orders and how much by the coronavirus. This column disentangles the two by comparing the implementation of stay-at-home policies across the US and high-frequency unemployment insurance claims. The direct effect of stay-at-home orders accounted for a significant but minority share of the overall rise in unemployment claims; unemployment would have risen even without such orders. So long as the underlying public health crisis persists, undoing stay-at-home orders will only bring limited economic relief.

Closing schools, restricting economic activity, and promoting social distancing are necessary to flatten the pandemic curve (Ferguson et al. 2020), but the economic costs of implementing such lockdown policies are difficult to pin down. In the US, the most prominent of these policies is the stay-at-home (SAH) order, which typically requires people to remain at home except for tasks and work deemed essential.

Researchers have already begun quantifying the general economic disruption in the US associated with the coronavirus pandemic in terms of economic uncertainty, reduced economic activity, increased firm risk, job losses, and reduced labour-force participation (see, respectively, Baker et al. 2020, Lewis et al. 2020, Hassan et al. 2020, and Coibion et al. 2020). However, little work exists quantifying the economic effects of stay-at-home policies themselves.

Starting in mid-March, state and local officials in the US implemented stay-at-home policies in order to limit the spread and severity of the coronavirus disease. Between 16 March 2020 (when the first was issued in the Bay Area, California) and 4 April, the share of the US population under stay-at-home orders rose from 0% to 95% (see Figure 1). In the same period, 16 million US workers filed for unemployment-insurance benefits.

Figure 1 Cumulative share of population under stay-at-home order in the US

Sources: Census Bureau, The New York Times; author’s calculations.

These orders have recently come under public criticism for exacerbating the economic disruption. However, it is unclear how much of the disruption was due to the stay-at-home orders themselves and how much is due to factors that would have occurred in their absence.

In Baek et al. (2020), we take a first step in disentangling the direct effect of stay-at-home orders from the general economic disruption brought on by the coronavirus pandemic. We do so by studying the impact of such policies on initial claims for unemployment insurance, a high-frequency, regionally disaggregated indicator of real economic activity in the US.

Implementation of stay-at-home orders in the US

We use data from the New York Times to track implementation of stay-at-home orders in cities, counties, and states, measuring a state’s exposure to stay-at-home orders as the average number of weeks a state’s workers were subject to the orders. By 4 April 2020, California had the highest exposure to stay-at-home orders, with workers exposed on average to two-and-a-half weeks of such orders. Conversely, five states (Arkansas, Iowa, Nebraska, North Dakota, and South Dakota) had no counties under stay-at-home policies by 4 April. Figure 2 reports exposure to stay-at-home orders for workers in each state in the US, showing that there was considerable variation between states.

Figure 2 Employment-weighted state exposure to stay-at-home policies through week ending 4 April 2020

Sources: Census Bureau, The New York Times; author’s calculations.

The effect of stay-at-home orders on unemployment insurance claims

To provide evidence of a causal link between the implementation of stay-at-home policies and the observed increase in unemployment insurance claims, we couple the spatial and regional variation in stay-at-home implementation with high-frequency unemployment claims data by state. This allows us to isolate the economic disruption resulting from the stay-at-home policies. To make states comparable, we scale initial claims by state employment as reported in the 2018 Quarterly Census of Employment and Wages.

We compare the evolution of scaled unemployment insurance claims of ‘early adopters’, defined as those states being in the top quartile of exposure to stay-at-home orders through 4 April, to those of ‘late adopters’, defined as those states being in the bottom quartile. As shown in Figure 3, in the first few weeks, early adopters initially had a higher rise in unemployment claims relative to late adopters. By the week ending 4 April, the relative effect of adopting stay-at-home orders early largely disappears, reflecting the fact that by this point approximately 95% of the US population was under a stay-at-home order.

Figure 3 Box plots by week of initial unemployment-insurance claims relative to employment for early and late adopters of stay-at-home policies

We find a positive correlation in cumulative unemployment insurance claims and our measure of stay-at-home exposure, both measured through 4 April (Figure 4). Each bubble in the figure represents a state, with the size of the bubble indicating population and the colour indicating the severity of the local, reported coronavirus outbreak.

Figure 4 Scatterplot of stay-at-home exposure to cumulative initial weekly claims for weeks ending 21 March thru 4 April

We formalise this methodology and find that an additional week of exposure to stay-at-home policies increases unemployment insurance claims by approximately 1.9% of a state’s employment level (using 2018 employment levels by state, as reported in the Quarterly Census of Employment and Wages). This result is robust controlling for factors potentially related to both stay-at-home implementation and the magnitude of new unemployment insurance claims. For example, we control for sectoral composition related to job losses occurring in the week before the first stay-at-home policy went into effect, the share of jobs likely able to be done at home, and the severity of the COVID-19 outbreak in the state.

We calculate that the direct effect of stay-at-home orders is accountable for 4 million unemployment insurance claims between 14 March and 4 April, which accounts for approximately a quarter of the overall rise in unemployment claims in that period. The direct effect of stay-at-home orders on unemployment is therefore small relative to the aggregate increase in unemployment insurance claims, suggesting that a large majority of the increase in unemployment may have occurred in the absence of such orders.

Corroborating evidence from the Google Community Mobility Index

Along with unemployment claims, we also investigate the effect stay-at-home orders have on mobility using the Google Community Mobility Index, which tracks visits to different categories of locations (e.g. grocery stores, workplaces, or parks). We focus on mobility to retail establishments, as the main goal of stay-at-home orders was to limit non-essential business activity. The Google mobility data provide additional evidence of the effect of stay-at-home orders as it relates to a ‘demand channel’.

Figure 5 shows how a county’s retail mobility changes when stay-at-home orders are implemented (x-axis equal to 0). The extremely high-frequency nature of this data allows us to address the concern that our results are driven by anticipation effects, whereby people expect policymakers to implement stay-at-home policies imminently and reduce retail activity because the public health threat is much more salient. We find no evidence of this type of anticipation effect.

Before stay-at-home orders were implemented, retail mobility evolved similarly across counties, as evidenced by the flat line. The day they were announced, the orders reduced retail mobility by 5%. For the following two weeks, retail mobility stayed close to 10% lower relative to other counties as a result of the stay-at-home orders. Considering that during this period mobility fell by 40%, this data suggests a similar share of economic decline attributable to stay-at-home orders as the unemployment insurance data.

Figure 5 County retail mobility index

Sources: Google, The New York Times; authors’ calculations.

Policy implications

Our results support the idea that flattening the pandemic curve implies a steepening of the recession curve in the absence of any government support (Gourinchas 2020). Stay-at-home orders account for a significant but minority share of the overall rise in unemployment claims during the pandemic, implying that much of the rise in unemployment during this period would have occurred in the absence of these orders.

This suggests that any economic recovery that arises from undoing stay-at-home orders will be limited if the underlying pandemic is not resolved. Weak consumer confidence, supply-chain disruptions, and self-imposed social distancing are just a few examples of economic headwinds that could persist even in the absence of stay-at-home orders. At the same time, the orders are likely to have public-health benefits from slowing the spread of the coronavirus.1 Taken together, our results caution policymakers not to expect the reopening of the economy to be an economic panacea.


Baek, ChaeWon, Peter B. McCrory, Todd Messer, and Preston Mui (2020), “Unemployment effects of stay-at-home orders: Evidence from high frequency claims data”, IRLE Working Paper No. 101-20.

Baker, Scott R, Nicholas Bloom, Steven J Davis and Stephen J Terry (2020), “COVID-induced economic uncertainty”, NBER w26983.

Coibion, Olivier, Yuriy Gorodnichenko and Michael Weber (2020), “Labor markets during the COVID-19 crisis: A preliminary view”, NBER w227017.

Correia, Sergio, Stephan Luck, Emil Verner et al. (2020), “Fight the pandemic, save the economy: Lessons from the 1918 Flu”, No. 20200327, Federal Reserve Bank of New York.

Ferguson, Neil M, Daniel Laydon, Gemma Nedjati-Gilani, Natsuko Imai, Kylie Ainslie, Marc Baguelin, Sangeeta Bhatia, Adhiratha Boonyasiri, Zulma Cucunubá, Gina CuomoDannenburg, Amy Dighe, Ilaria Dorigatti, Han Fu, Katy Gaythorpe, Will Green, Arran Hamlet, Wes Hinsley, Lucy C Okell, Sabine van Elsland, Hayley Thompson, Robert Verity, Erik Volz, Haowei Wang, Yuanrong Wang, Patrick G T Walker, Caroline Walters, Peter Winskill, Charles Whittaker, Christl A Donnelly, Steven Riley and Azra C Ghani (2020), “Impact of non-pharmaceutical interventions (NPIs) to reduce COVID19 mortality and healthcare demand”, manuscript, Imperial College.

Gourinchas, Pierre-Olivier (2020), “Flattening the pandemic and recession curves”, working paper.

Hartl, Tobias, Klaus Wälde and Enzo Weber (2020), “Measuring the impact of the German public shutdown on the spread of COVID19”, Covid Economics 1: 25–32.

Hassan, Tarek Alexander, Stephan Hollander, Laurence van Lent and Ahmed Tahoun (2020), “Firm-level exposure to epidemic diseases: Covid-19, SARS, and H1N1”, NBER w26971.

Lewis, Daniel, Karel Mertens, and James H Stock (2020), “US economic activity during the early weeks of the SARS-Cov-2 outbreak”, NBER w26954.


1 Correia et al. (2020) present evidence from the 1918 Influenza pandemic showing that places that implemented policies akin to stay-at-home orders experienced faster economic growth once the crisis subsided. This would be another force offsetting the limited costs we identify in this paper.

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