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Home / Publications / Research / Coronavirus Economic Impact of COVID-19 November 12, 2020 The Employment Recovery of Small and Large Employers Article by: Marios Karabarbounis, Matthew Murphy and Nicholas Trachter Policymakers, national pundits, and researchers often point to the importance of small rms in job creation. Did small rms play a special role in the recovery of aggregate employment during the pandemic? In this article, we use company-level employment data from Ultimate Kronos Group (UKG), a workforce management software provider, to study this question. Firms use the UKG software to manage the time of hourly workers. These employees "punch-in" via the UKG timekeeping system when arriving at work (both in person and virtually). The system computes the hours worked by each employee each week, and this information is then used to pay the employee. As a result, the UKG data are a good indicator of the number of shifts worked by hourly employees in the United States. We thus interpret aggregate weekly punches in the UKG dataset as a measure of aggregate U.S. employment. The UKG data are available at a weekly frequency from January 2020 to September 2020 and provide weekly punches aggregated to the level of the parent rm or company. There are 12,224 rms in the UKG dataset. The dataset provides an industry classi cation for nearly three-quarters of the rms, spanning a wide range of industries: 34.1 percent of rms are in services and distribution, 22.5 percent of rms are in manufacturing, 12.6 percent are in retail, 17.2 percent are in health care, and the rest are in the public sector. In the analysis that follows, we focus on the number of weekly punches across time for rms that were active (e.g., reporting a positive number of punches) during at least one week during the months of January and February. Aggregate Statistics Figure 1 shows the time series of total weekly punches. Before the pandemic, there were around 15.4 million punches logged in the UKG system each week. By the middle of April, total weekly punches fell to 10.7 million, a 31 percent decrease. As states opened, the number of weekly punches gradually increased to 14 million. This implies that, as of the last / week of September, aggregate hourly employment had recovered to around 9 percent below the pre-pandemic level. Notice the temporary drops in employment during Memorial Day week, the week around July 4, and the week around Labor Day. Figure 2 shows the number of active rms each week, de ned as rms with at least one week of positive punches in January and February (normalized by the average number of active rms in January and February). The initial pandemic shock led to a massive drop in active rms: 10 percent of rms active in March were inactive in late April. The number of active rms increased between late April and late June but then began to decline again. Overall, 8 percent of active rms in March were inactive by late September. / Small Versus Large Firms We group rms according to their average number of weekly punches between January 2020 and February 2020. The rst group includes rms with fewer than 250 average weekly punches, while the last group includes rms with more than 5,000 average weekly punches. Table 1 presents some aggregate statistics across these groups. While smaller rms account for a large fraction of rms, they account for a small fraction of aggregate weekly punches: Nearly 60 percent of rms have 500 or fewer weekly punches, but they represent only about 8 percent of the total punches. / Table 2 shows rm shutdowns by size. There is substantial variation: While only 4 percent of the largest rms shut down operations for at least one week, 31.2 percent of the smallest rms (fewer than 250 weekly punches) experienced at least a one-week shutdown. Further, the incidence of a shutdown declines monotonically with rm size. Conditional on shutting down the rm for at least a week, the incidence of long, maybe permanent, shutdowns is similar across the rms of di erent sizes: Around half of rms that shut down did so for at least eight weeks. Despite being more likely to shut down, smaller rms experienced larger rebounds in terms of total weekly punches than larger rms during the recovery that followed the initial pandemic shock. Figure 3 plots the number of weekly punches across rms of di erent sizes, where weekly punches for each group are normalized with respect to the group's average of weekly punches during January and February. As mentioned, the gure shows that while small rms experienced more shutdowns, they grew more quickly than larger rms during the recovery. / Final Observations We used rm-level data from UKG to analyze the path of the employment recovery for small and large rms. Small rms were more likely to experience shutdowns than large rms. However, small rms grew faster as the economy recovered. A natural question is whether the exceptional employment recovery of small rms is persistent. For example, small rms might expand their employment in response to seasonal demand. If this is the case, we should expect the employment gains of small rms to vanish soon, negatively a ecting unemployment in the next months. Another aspect to consider is the role of government programs in aiding small rms during the pandemic. Does the large employment growth of small rms re ect to some extent their eligibility for loans through the Paycheck Protection Program? While with our data we cannot make conclusive statements about whether the employment growth of small rms re ects seasonal or persistent trends, or whether the success of small rms is due to the recent government programs, we view our ndings as a meaningful contribution to the discussion about the merits of size-dependent policies. Marios Karabarbounis is an economist, Matthew Murphy is a research associate, and Nicholas Trachter is a senior economist in the Research Department at the Federal Reserve Bank of Richmond. / This article may be photocopied or reprinted in its entirety. Please credit the authors, source, and the Federal Reserve Bank of Richmond and include the italicized statement below. Views expressed in this article are those of the authors and not necessarily those of the Federal Reserve Bank of Richmond or the Federal Reserve System. Contact Us Research Department (804) 697-8000 © 1997-2020 Federal Reserve Bank of Richmond /
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