More than seven months have passed since the first case of COVID-19 was identified in the United States, and President Donald Trump has yet to put forth a national strategy or issue guidelines for states on how to get the pandemic under control. Instead, the Trump administration has actively sowed confusion, initially by transferring COVID-19 data collection responsibilities from the Centers for Disease Control and Prevention (CDC) to the U.S. Department of Health and Human Services (HHS) and more recently by pressuring the CDC to relax its testing guidelines. This abdication of responsibility has led to widespread uncertainty among state and local government officials on how best to balance public safety and economic activity, which has led to a patchwork of lockdown restrictions across states. President Trump and his administration have repeatedly chided governors who imposed lockdowns, wrongly implying that lockdowns have caused the recession. Stephen Moore, the economic adviser to the president, went so far as to opine that “lockdowns didn’t work the first time [and] won’t work the second time.” What these officials fail to realize is that the choice is not between public safety and economic activity—the latter cannot happen until the former is wholly achieved.
As outlined in a Center for American Progress piece released earlier this summer, the evidence suggests that limited interventions, including short lockdowns and no stay-at-home orders, resulted in resurgent viral outbreaks. Using both concurrent indicators (employment status, ability to meet rent or mortgage obligations, and ability to purchase normal levels of food) and future indicators (expected job loss and future rent or mortgage troubles) from the Census Bureau’s Household Pulse Survey, this analysis compared the economic impacts of lockdowns in states with short stay-at-home orders and states with long stay-at-home orders. Short stay-at-home orders were defined as orders lasting less than the median lockdown length of 53 days, while long stay-at-home orders were defined as those lasting beyond the median length. The analysis found that concurrent indicators worsened in states with short stay-at-home orders after the lockdowns ended, while these indicators slightly improved in states with long stay-at-home orders. Likewise, future indicators either worsened to a lesser degree or improved in states with long stay-at-home orders compared to states with short stay-at-home orders.
Overall, states with short lockdown periods experienced similar changes in economic outcomes as states with long lockdown periods. In some cases, the states with short lockdown periods experienced worse changes. Had these lockdown decisions only affected economic activity, this difference would not be noteworthy. Unfortunately, states with short stay-at-home periods experienced increases in COVID-19-related deaths following their decisions to reopen. And because states are so heavily economically interdependent on one another, an aggressive reopening strategy in one state may have seen only small economic gains because much of its economic activity depends on business interactions with more cautious states. In the same vein, the economic fallout resulting from a virus outbreak and subsequent reimposition of lockdowns in one state may be limited by positive economic spillovers from a more stable economic recovery occurring in a more cautious state. It is precisely the lack of a national strategy to combat the pandemic that is holding back the economic recovery as a whole.
Analyzing how different U.S. regions have fared after state lockdowns
However, these results from CAP’s analysis of the Household Pulse Survey only tell part of the story. Additional weeks’ worth of Pulse Survey data have been released since the analysis’s initial publication, painting a clearer picture of how different areas of the country fared after each state’s reopening—or lack of a lockdown period. Analyzing these data through a different lens shows stark interregional contrasts in the United States. Dividing the country into the four regions used by the Census Bureau—the Northeast, South, Midwest, and West—provides a new perspective on how state governments’ actions affected respondents’ current economic security and sentiments about their future security in each region. Overall, the analysis finds that when comparing the periods before and after lockdowns ended across regions, the South’s economy fared the worst, followed by the West, the Northeast, and the Midwest. (see Figure 1)
Part of this conclusion can be explained by how lax many state governments in the South were in imposing lockdowns and how early they lifted them compared with state governments in other regions. Consistent with the results from the earlier CAP analysis and weighting data by state population, the South, which is composed of many states with short stay-at-home orders, saw relatively bad outcomes on each of the four concurrent indicators. For example, it saw a 1.3 percentage point increase in the number of people who reported experiencing job loss after lockdowns ended compared to before. Contrast this with a 0.4 percentage point increase in the same metric in the Northeast and declines of 0.3 and 0.4 percentage points in the Midwest and West, respectively. The South also performed the worst in each of the future indicators. The share of Southern respondents who expected future rent or mortgage troubles either increased the most or decreased the least of the four regions, while the share of respondents who expected future job loss decreased the least. (see Figure 2)
The outcomes change only slightly when weighted by the number of states in each region instead of by population, which tended to skew outcomes toward very populous states. (see Appendix tables) When the population weights are removed and each state is given equal importance within a census region, the South performs the worst in every concurrent and future indicator, falling behind the West in current mortgage troubles—the only category in the population-weighted analysis in which it did not perform the worst. Moreover, the West tends to perform much better than the Northeast, likely due to California’s outsized role when using the population weights in evaluating the West overall and the state’s more recent surge in cases and deaths. The large populations of New York and New Jersey, on the other hand, played an outsized role in the Northeast’s relatively positive outcomes in the population-weighted analysis. The Midwest performs the best overall regardless of weighting.
Yet, while a regional analysis can be useful in demonstrating how different government responses to the coronavirus have affected economic outcomes, it should be noted that these groupings are imperfect. The South, for instance, includes the District of Columbia, Delaware, and Maryland—places that are less often associated with the South. Likewise, the West, which includes both California and Wyoming, is not politically or culturally homogenous, and residents across states likely view lockdowns, stay-at-home orders, and compulsory mask wearing in starkly different lights. Overall, however, this illustrative analysis is a useful tool in understanding how state governments’ decisions, or lack thereof, have affected both the realized and anticipated trajectory of the economic recovery. This is especially true for the South, which includes many—and, often, large—states, such as Florida and Texas, that forged ahead with aggressive reopenings, even as the virus was not under control.
The conclusions of these analyses reinforce the findings of CAP’s original piece: States with strict stay-at-home orders do not experience worse economic outcomes than those with long stay-at-home orders, and in some cases, they perform better economically. It is not lockdowns that are constraining the recovery—it is the collapsed consumer demand that has resulted from both the uncontrolled pandemic and the confusion over policies to address it. Americans, it would seem, are not willing to risk their health or the health of their loved ones for nonessential activities. Until the entire country knows that there is a nationally coordinated strategy to ensure the virus is under control in every community across the country, the economy will not experience a full recovery. Rather than requiring an artificial tradeoff between controlling the pandemic and reviving the economy, the data, yet again, show that protecting public health and jumpstarting the economic recovery go hand in hand.
Ryan Zamarripa is the associate director of Economic Policy at the Center for American Progress. Christian E. Weller is a senior fellow at the Center.
Tables 1 and 2 below show the population-weighted and unweighted percentage-point changes in the likelihood that people experienced certain economic conditions before and after lockdowns ended in each of the four census regions. Table 3 shows weekly percentage-point changes, relative to the week lockdowns ended, in residents’ likelihoods of experiencing certain economic conditions in each of the four census regions.