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on Business Economics |
| By: | Agostina Brinatti; Mingyu Chen; Parag Mahajan; Nicolas Morales; Kevin Shih |
| Abstract: | We study how random variation in the availability of highly educated, foreign-born workers impacts firm performance and recruitment behavior. We combine two rich data sources: 1) administrative employer-employee matched data from the US Census Bureau; and 2) firm-level information on the first large-scale H-1B visa lottery in 2007. Using an event-study approach, we find that lottery wins lead to increases in firm hiring of college-educated, immigrant labor along with increases in scale and survival. These effects are stronger for small, skill-intensive, and high-productivity firms that participate in the lottery. We do not find evidence for displacement of native-born, college-educated workers at the firm level, on net. However, this result masks dynamics among more specific subgroups of incumbents that we further elucidate. |
| Keywords: | Immigration; Firm Dynamics; productivity; H-1B visas; High-skill immigration |
| JEL: | F22 J61 |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedrwp:98171&r=bec |
| By: | Kathryn Bonney; Cory Breaux; Cathy Buffington; Emin Dinlersoz; Lucia S. Foster; Nathan Goldschlag; John C. Haltiwanger; Zachary Kroff; Keith Savage |
| Abstract: | Timely and accurate measurement of AI use by firms is both challenging and crucial for understanding the impacts of AI on the U.S. economy. We provide new, real-time estimates of current and expected future use of AI for business purposes based on the Business Trends and Outlook Survey for September 2023 to February 2024. During this period, bi-weekly estimates of AI use rate rose from 3.7% to 5.4%, with an expected rate of about 6.6% by early Fall 2024. The fraction of workers at businesses that use AI is higher, especially for large businesses and in the Information sector. AI use is higher in large firms but the relationship between AI use and firm size is non-monotonic. In contrast, AI use is higher in young firms although, on an employment-weighted basis, is U-shaped in firm age. Common uses of AI include marketing automation, virtual agents, and data/text analytics. AI users often utilize AI to substitute for worker tasks and equipment/software, but few report reductions in employment due to AI use. Many firms undergo organizational changes to accommodate AI, particularly by training staff, developing new workflows, and purchasing cloud services/storage. AI users also exhibit better overall performance and higher incidence of employment expansion compared to other businesses. The most common reason for non-adoption is the inapplicability of AI to the business. |
| JEL: | L23 O31 O33 |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:32319&r=bec |
| By: | ADACHI Daisuke; KAWAGUCHI Daiji; SAITO Yukiko |
| Abstract: | We study the effect of a tax policy on adopting industrial robots and firm performance, notably in terms of employment. Combining the policy variation in the Tax Credit for Promoting Productivity-Enhancing Equipment Investment (TC-PPEI) in Japan and newly collected Japanese firm-level longitudinal data on robot adoption, we find that the firms eligible for the TC-PPEI increased the adoption of robots. Our event-study analysis reveals that when firms adopt robots, they do not decrease the total number of workers but significantly increase employment after 1-3 years of adoption events and sales. Our results suggest that adopting robots can create employment instead of destroying it at the firm level. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:eti:dpaper:24047&r=bec |
| By: | Kei Kawai; Jun Nakabayashi |
| Abstract: | We study the effectiveness of firms' compliance programs by conducting a field experiment in which we disclose to a subset of Japanese firms that the firm is potentially engaging in illegal bid-rigging. We find that the information that we disclose affects the bidding behavior of the treated firms: our test of bid-rigging is less able to reject the null of competition when applied to the bidding data of the treated firms after the intervention. We find evidence that this change is not the result of firms ceasing to collude, however. We find evidence suggesting that firms continue to collude even after our intervention and that the change in the bidding behavior we document is the result of active concealment of evidence by cartelizing firms. |
| JEL: | K21 L41 |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:32347&r=bec |
| By: | Ryan A. Decker; John Haltiwanger |
| Abstract: | The COVID-19 pandemic and its aftermath have featured a surge in business entry (Decker and Haltiwanger 2024). A natural question is whether the elevated entry seen in recent years will have positive implications for aggregate productivity growth given the historically important role of business entry for productivity dynamics (Decker et al. 2014, Alon et al. 2018). |
| Date: | 2024–04–19 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgfn:2024-04-19-1&r=bec |