JOURNAL ARTICLE
Does a One-Size-Fits-All Minimum Wage Cause Financial Stress for Small Businesses?
Published In: Management Science (INFORMS), 2023, v. 69, n. 11. P. 7095 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Chava, Sudheer; Oettl, Alexander; Singh, Manpreet 3 of 3
Abstract
The article investigates the impact of one-size-fits-all federal minimum wage increases on the financial health of small businesses in the United States, focusing on establishments in states where the effective minimum wage is equal to the federal rate ("bounded states") compared to those with higher state minimum wages ("unbounded states"). Using Dun & Bradstreet PAYDEX credit scores for approximately 15.2 million establishments from 1989 to 2013, the study finds that federal minimum wage hikes lead to delayed payments to suppliers, lower credit scores, and increased financial stress, particularly for small, young, labor-intensive, and minimum-wage-sensitive businesses in competitive and low-income areas. These financial strains are associated with higher exit rates and employment reductions among affected small businesses. The authors employ various empirical strategies—including difference-in-differences, bordering-county analyses, and fuzzy triple differences exploiting Fair Labor Standards Act exemptions—to control for confounding factors and local economic conditions, concluding that uniform federal minimum wage increases can impose unintended costs on certain small businesses.
Additional Information
- Source:Management Science (INFORMS). 2023/11, Vol. 69, Issue 11, p7095
- Document Type:Article
- Subject Area:Economics
- Publication Date:2023
- ISSN:0025-1909
- DOI:10.1287/mnsc.2022.4620
- Accession Number:173603537
- Copyright Statement:Copyright of Management Science (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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