How Minimum Wage Hikes Affect Food Service Employment: Evidence from California and New York

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In April 2024, California implemented a $20 per hour minimum wage targeting fast food workers, while New York enacted a modest $0.50 statewide increase in January 2025. Using monthly employment data from the Federal Reserve Economic Data (FRED) and Texas as a control state, we estimate the sectoral employment response to each policy. Results show that California experienced a statistically significant decline of approximately 42,580 food service jobs relative to Texas after the policy took effect. No significant change was observed in New York, likely due to the smaller policy magnitude and limited post-treatment period. These findings suggest that the labor market impact of minimum wage increases depends critically on policy design, implementation scope, and sectoral targeting. JEL Codes: J38, J21, J88 Other Economics Macroeconomics minimum wage food service employment difference-in-differences labor policy California New York Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Minimum wage policy remains a central topic in labor economics, frequently debated for its potential to improve low-wage workers’ living standards while raising concerns about unintended employment effects. Proponents argue that wage floors can reduce inequality and boost local demand, whereas critics highlight risks such as job loss, reduced hours, or increased automation, particularly in sectors with thin profit margins. While the academic literature offers extensive analysis, empirical findings remain mixed, and outcomes appear highly dependent on policy design, sectoral exposure, and local labor market conditions. California and New York provide contrasting case studies for evaluating the labor market effects of recent minimum wage hikes. In April 2024, California implemented a $ 20 per hour minimum wage specifically targeting fast food workers, which represents one of the largest sector-specific wage increases enacted in the U.S. In contrast, New York enacted a modest statewide increase of $ 0.50 in January 2025, applied uniformly across industries. These policy differences in magnitude and scope create a natural experiment for assessing labor market responses. This study investigates the short-term effects of these policy changes on employment in the food services sector. Using monthly state-level data from the Federal Reserve Economic Data (FRED), extending through July 2025, and Texas as a control state, we apply a difference-in-differences (DiD) framework to estimate employment changes before and after each policy. Our findings indicate that California experienced a statistically significant decline in food service employment, approximately 42,580 jobs, relative to Texas following the April 2024 wage hike. In contrast, New York exhibited no significant employment change. These results suggest that the labor market consequences of minimum wage increases depend not only on timing and location but also on policy targeting and intensity. Event study analyses further support these findings by illustrating significant pre- and post-policy dynamics in California and minimal effects in New York. 2. Policy Context and Related Literature Minimum wage policies in the United States are frequently enacted at the state or local level, creating opportunities for quasi-experimental evaluation across jurisdictions. In 2024 and 2025, California and New York implemented notable minimum wage increases that differed substantially in design and scope, offering a natural contrast for empirical analysis. In April 2024, California enacted a targeted $ 20 per hour minimum wage specifically for fast food workers. The policy applies to fast food establishments with at least 60 locations nationwide and was promoted as a means of improving compensation in a sector known for high turnover, low pay, and limited benefits. This represents one of the most aggressive industry-specific wage policies in the United States and was the result of negotiations between policymakers, labor advocates, and corporate stakeholders. In contrast, New York implemented a modest statewide increase of $ 0.50 in January 2025, raising its general minimum wage to $ 16.00 per hour. This policy is part of a multi-year legislative agenda that includes planned inflation-indexed increases beginning in 2026. Unlike California’s sectoral focus, New York’s adjustment is broad-based and applies uniformly across industries and regions. These distinct policy approaches provide a useful empirical setting for testing how minimum wage changes affect labor market outcomes, particularly in the food services sector. The absence of comparable wage increases in Texas during this period strengthens the identification strategy by offering a stable control group and allows clear comparison over a multi-month post-treatment window through July 2025. This study builds on an extensive body of literature on minimum wage effects. The landmark study by Card and Krueger ( 1994 ) challenged conventional wisdom by finding no negative employment effects in fast food restaurants following a minimum wage increase in New Jersey. Subsequent work by Dube, Lester, and Reich (2010) emphasized the importance of controlling for local economic trends and found minimal disemployment effects when such controls are included. In contrast, economists such as Neumark and Wascher ( 2007 ) argue that higher minimum wages tend to reduce employment among low-skilled or young workers, particularly when policy changes are large or abrupt. By examining sector-specific labor outcomes in response to recent real-world policies, this paper contributes new evidence to this ongoing debate. More recent methodological advances in difference-in-differences and event-study designs (Callaway & Sant’Anna; Sun & Abraham) emphasize the importance of handling staggered adoption and pre-trend violations. This paper applies these insights conceptually by combining DiD with event-study checks to assess short-term policy effects in California and New York. 3. Data and Methodology 3.1 Data Sources This analysis uses monthly, seasonally adjusted state-level employment data from the Federal Reserve Economic Data (FRED) platform, focusing specifically on the food services and drinking places sector (NAICS 722). Employment series were retrieved for California, New York, and Texas using the FRED API. These data represent total non-farm payroll employment in the specified sector and are reported in thousands of workers. The series span from January 2020 through July 2025, enabling more than 12 months of pre-treatment data and at least 6–7 months of post-treatment data for both California and New York’s respective policy changes. Texas, which did not implement a minimum wage change during this period, serves as the control group. To ensure transparency and reproducibility, all data were collected and processed using Python. The data pipeline employs the fredapi, pandas, and matplotlib libraries for retrieval, transformation, and visualization. The final datasets and figures were saved locally to ensure version control and replicability throughout the analysis. Event study indicators were constructed using relative month dummies to estimate dynamic treatment effects over a ± 12 month window around each policy’s implementation date. 3.2 Policy Events as Natural Experiments Two recent minimum wage policy changes provide the empirical foundation for this study's quasi-experimental design. These events differ in both magnitude and policy scope, allowing for comparative analysis of labor market responses to wage regulation. California implemented a sector-specific minimum wage increase on April 1, 2024 , setting the wage floor at $20 per hour for fast food workers employed by chains with more than 60 locations nationwide . This policy represented a significant, targeted intervention and was among the most substantial industry-specific wage hikes enacted at the state level in the United States. New York , in contrast, enacted a broad-based minimum wage increase of $0.50 , effective January 1, 2025 , raising the statewide minimum wage to $16.00 per hour . This adjustment was part of a pre-scheduled legislative plan to incrementally raise the minimum wage across all sectors and index future increases to inflation. The staggered timing and structural differences between the California and New York policies create a natural experiment framework for evaluating short-term labor market effects. California represents a large, sector-specific wage shock beginning in April 2024, while New York’s general statewide adjustment began in January 2025. These differences informed the construction of separate difference-in-differences and event study models for each state, using Texas as a stable control group throughout. 3.3 Empirical Strategy: Difference-in-Differences (DiD) To estimate the causal effects of recent minimum wage policies on food service employment, this study employs a difference-in-differences (DiD) framework. The DiD model compares changes in employment between a treated state and a control state before and after the policy intervention. The general specification is as follows: Y_it = α + β1·Treated_i + β2·Post_t + β3·(Treated_i × Post_t) + ε_it Where: Y_it represents food service employment in state i at time t Treated_i is an indicator equal to 1 for the treated state (California or New York), and 0 for Texas Post_t is an indicator equal to 1 for months following the implementation of the minimum wage policy β3 is the DiD estimate, capturing the differential change in employment in the treated state relative to the control state after the policy Separate DiD regressions are estimated for each policy event: California vs. Texas, using April 2024 as the policy implementation date New York vs. Texas, using January 2025 as the implementation date All models are estimated using ordinary least squares (OLS) with heteroskedasticity-robust standard errors (HC1). This correction provides valid inference even when error variances are not constant across states or time, which is a realistic feature of state-level employment data. To support the parallel trends assumption, raw employment trends are visualized for each state prior to policy adoption. Annotated figures highlight the policy introduction dates and post-treatment periods. To further explore dynamic treatment effects and test for pre-trend violations, event study models are also estimated. These models introduce month-specific leads and lags relative to each policy’s implementation date, allowing for a more granular view of employment responses over time. 4. Results 4.1 Descriptive Trends This section presents descriptive evidence on food service employment trends in California, New York, and Texas before and after the recent minimum wage policy changes. Monthly employment data span from January 2020 to July 2025 and cover the food services and drinking places sector in each state. Figure 1 displays raw monthly food service employment levels over the full sample period. All three states exhibit similar patterns during the pandemic recovery, with steady growth beginning in mid-2021. However, California appears to diverge modestly from Texas following the implementation of its sector-specific $ 20 per hour minimum wage in April 2024. A noticeable flattening and slight decline in employment occurs in California during the months immediately following the policy change. Figure 2 focuses on the policy-relevant window from January 2023 through July 2025, using a three-month moving average to smooth short-term fluctuations. This visualization highlights both the April 2024 policy implementation in California and the January 2025 minimum wage adjustment in New York. While Texas continues on a relatively stable growth path, California shows signs of employment contraction following its targeted policy. New York’s trend remains largely parallel to Texas during the initial post-policy months, suggesting a weaker or delayed labor market response. These descriptive patterns provide preliminary support for a short-term employment effect in California and help validate the parallel trends assumption for the subsequent difference-in-differences analysis. However, the event study analysis later in this paper suggests some pre-treatment divergence in California, which qualifies the strength of this assumption. 4.2 California: Difference-in-Differences Analysis To estimate the short-term effect of California’s $ 20 per hour minimum wage policy on food service employment, we implement a difference-in-differences (DiD) model using Texas as the control group. The policy took effect in April 2024 and applied specifically to fast food establishments with more than 60 locations nationwide. Although the policy targets a subset of the food services industry, we use state-level employment data for the broader food services and drinking places sector (NAICS 722) to capture aggregate employment responses within this labor-intensive industry. Figure 3 displays monthly food service employment trends in California and Texas from January 2022 through July 2025. The pre-policy trajectories for California and Texas appear broadly parallel when viewed descriptively, lending initial support to the DiD strategy. However, as the event study results below illustrate, some divergence emerges in the months leading up to the policy, which suggests the parallel trends assumption may be imperfect and results should be interpreted with caution. Table 1 Difference-in-Differences Regression: California vs. Texas. The policy date is April 2024, when California implemented a $ 20 minimum wage for fast food workers. Variable Coefficient P-Value Interpretation Treated (CA) 273.86 0.000 California had ~ 273,860 more food service jobs than Texas pre-policy Post-policy (Apr 2024) 38.33 0.000 Texas employment rose ~ 38,330 jobs post-policy Treated*Post-policy -42.58 0.003 California experienced ~ 42,580 fewer jobs than Texas post-policy (significant). Note Employment is measured in thousands. Standard errors are heteroskedasticity-robust (HC1). Source Author’s calculations using FRED (NAICS 722) DiD Regression Results – California vs. Texas The DiD estimate (interaction term) indicates that food service employment in California declined by approximately 42,580 jobs relative to Texas following the April 2024 policy change. This result is statistically significant at the 1% level (p = 0.003), suggesting that California’s sector-specific minimum wage increase had a measurable and immediate impact on employment in the affected industry. The magnitude of this decline highlights the potential labor market consequences of large, targeted wage interventions, particularly in low-margin service sectors. Section 4.3: New York: Difference-in-Differences Analysis To evaluate the effect of New York’s January 2025 statewide minimum wage increase on food service employment, we conduct a difference-in-differences (DiD) analysis using Texas as a control state. Unlike California’s large, sector-specific policy, New York implemented a modest $ 0.50 increase across all industries. The policy raised the statewide minimum wage to $ 16.00 per hour as part of an ongoing series of scheduled adjustments. Figure 4 illustrates the trends in food service employment for New York and Texas between January 2022 and July 2025. The pre-policy trajectories for both states appear broadly parallel, and no visual break in trend is evident in New York following the January 2025 policy change. Figure 4. Monthly Food Service Employment in New York and Texas (2022–2025). The vertical dashed line marks New York’s $ 0.50 minimum wage increase in January 2025. No clear divergence in employment trends is observed. Source : Author’s calculations using FRED (NAICS 722). Table 2 presents the DiD regression results. The interaction term (treated × post_policy) is negative, suggesting a modest relative decline in employment, but the estimate is not statistically significant (p = 0.36). The lack of a measurable employment effect may reflect the small size of the policy change or even with seven months of post-policy data (January–July 2025), employment effects remain statistically insignificant. This strengthens the inference that the modest wage hike may not have been large enough to induce measurable short-term employment effects. Table 2 Difference-in-Differences Regression: New York vs. Texas. The policy date is January 2025, when New York increased its statewide minimum wage by $ 0.50. Variable Coefficient P-Value Interpretation Treated (NY) -529.03 0.000 NY had ~ 529,030 fewer food service jobs than TX pre-policy Post-policy (Jan 2025) 18.90 0.251 TX employment increased ~ 18,900 jobs post-policy (not significant). Treated * Post-policy -21.27 0.360 NY employment fell ~ 21,270 relative to TX (not statistically significant) Note Employment is measured in thousands. Standard errors are heteroskedasticity-robust (HC1). Source Author’s calculations using FRED (NAICS 722). DiD Regression Results – New York vs. Texas The DiD estimate for New York is negative but statistically insignificant at conventional levels, even with seven months of post-policy data now included. This suggests that New York’s broad-based $ 0.50 minimum wage increase had no detectable short-term impact on food service employment. The modest size of the policy and its uniform application across sectors likely contributed to the null result. 4.4 Event Study Analysis To complement the DiD results and test the parallel trends assumption, we conducted event studies for both California and New York. These analyses trace monthly employment effects before and after the respective minimum wage hikes. California Event Study The graph below shows the estimated monthly effects on food service employment in California relative to the policy implementation in April 2024. Key findings include: Employment levels in California were already trending downward prior to the policy, but the decline steepened significantly after implementation. The post-policy period (especially months 8 to 12) exhibits statistically significant negative effects. Pre-treatment estimates suggest some divergence from Texas, reinforcing the need for caution when interpreting the California results. Figure 5 shows the estimated monthly treatment effects of California’s minimum wage hike (April 2024) on food service employment, relative to Texas. Dots represent point estimates, and vertical bars show 95% confidence intervals. The red dashed line indicates the policy implementation date. Positive and statistically significant pre-treatment effects (months − 12 to -1) raise concerns about the parallel trends assumption, suggesting pre-existing differences in employment trajectories between California and Texas. New York Event Study New York’s policy took effect in January 2025. The event study results show: A positive trend in employment leading up to the policy, possibly due to anticipation effects or broader economic factors. A flattening or reversal of the trend immediately after the policy date. Post-policy coefficients are generally statistically insignificant, suggesting a muted or delayed response to the wage hike. Figure 6. Event study estimates for New York versus Texas around the January 2025 $ 0.50 minimum wage increase. Coefficients are plotted relative to the month of policy implementation (time 0). Pre-policy estimates remain near zero, supporting the parallel trends assumption. Post-policy estimates also cluster near zero, indicating no measurable short-term employment effect. Note : Estimates are available only through July 2025; coefficients beyond month + 5 are not estimated due to limited post-policy data. Source : Author’s calculations using FRED (NAICS 722). Figure 6 presents estimated monthly effects of New York’s January 2025 minimum wage increase on food service employment, using Texas as a control. Dots represent point estimates, and vertical bars indicate 95% confidence intervals. The red dashed line marks the policy implementation date. While pre-treatment estimates (months − 12 to -1) suggest rising employment trends in New York, post-treatment effects appear flat, indicating minimal immediate employment impact and suggesting a potential adjustment or plateau following the policy change. The event studies reinforce the main findings of the DiD analysis. California’s large, targeted wage increase appears to have had a meaningful and immediate negative effect on food service employment, although the violation of the parallel trends assumption warrants caution in interpretation. In contrast, New York’s modest, broad-based increase did not produce a statistically detectable change, consistent with its smaller policy magnitude and potentially lagged effects. Together, the results suggest that the scale and specificity of minimum wage interventions are key determinants of short-term labor market outcomes. 5. Discussion and Conclusion This paper investigates the short-term effects of recent minimum wage increases on food service employment in California and New York using a difference-in-differences framework. By comparing each state to Texas as a control, we assess whether wage policy design, specifically policy magnitude and sectoral targeting, affects employment outcomes. The results reveal a statistically significant decline in food service employment in California following the April 2024 implementation of a $ 20 per hour minimum wage for fast food workers. The estimated reduction of approximately 42,580 jobs suggests that large, sector-specific wage hikes can have measurable short-term employment effects, particularly in labor-intensive industries. Event study results support this finding by showing significant employment declines in the months following policy implementation. However, pre-policy estimates indicate early divergence between California and Texas, suggesting a potential violation of the parallel trends assumption. In contrast, New York’s broader but more modest $ 0.50 wage increase was not associated with any statistically significant change in food service employment, even with seven months of post-policy data now available. Event study analysis shows flat or muted employment responses post-implementation, reinforcing the DiD results and suggesting limited short-term labor market effects from small, broad-based wage increases. These findings contribute to the ongoing policy debate by highlighting the importance of policy design. While prior studies such as Card and Krueger ( 1994 ) and Dube, Lester, and Reich (2010) have found limited disemployment effects from moderate minimum wage increases, our results align more closely with literature that identifies employment reductions when wage hikes are large or narrowly targeted (e.g., Neumark and Wascher, 2007 ). This analysis is subject to several limitations. First, while the food service employment series provides sectoral granularity, it still aggregates across sub-industries and may not isolate fast food employment directly. Second, although the data extend through July 2025, the post-policy window for New York remains relatively short, potentially obscuring delayed effects. Finally, potential confounders such as local labor demand shocks, firm behavior, or migration trends are not explicitly modeled. While robustness checks with state-specific trends would help further address concerns about the parallel trends assumption, such models are beyond the scope of this paper but would strengthen future research. This limitation should be kept in mind when interpreting the California results. Future research should extend this analysis using longer post-policy timeframes, firm-level or establishment data, and methods that account for intra-sectoral variation. Investigating wage compression, hours worked, or worker turnover would also provide a richer understanding of how minimum wage policy interacts with labor market dynamics. Declarations Availability of data and material All data and code are publicly available at Zenodo (Version 1.0): https://doi.org/10.5281/zenodo.15843632. Funding No external funding was received for this research. Authors' contributions Peter Baker: conceptualization; methodology; data curation; formal analysis; writing—original draft; visualization. Acknowledgements The author thanks faculty and peers at George Mason University for their helpful comments and guidance during the development of this research. Conflict of interest The author declares no conflict of interest. References Baker, Peter. Minimum Wage Policy Effects on Food Service Employment: DiD and Event Study Code . Version 1.0, Zenodo, 2025. https://doi.org/10.5281/zenodo.15843632. Callaway, Brantly, and Pedro H. C. Sant’Anna. “Difference-in-Differences with Multiple Time Periods.” Journal of Econometrics , vol. 225, no. 2, 2021, pp. 200–230. Elsevier, https://doi.org/10.1016/j.jeconom.2020.12.001. Card, David, and Alan B. Krueger. “Minimum Wages and Employment: A Case Study of the Fast Food Industry in New Jersey and Pennsylvania.” American Economic Review , vol. 84, no. 4, 1994, pp. 772–793. American Economic Association. JSTOR, https://www.jstor.org/stable/2118030 Dube, Arindrajit, T. William Lester, and Michael Reich. “Minimum Wage Effects across State Borders: Estimates Using Contiguous Counties.” Review of Economics and Statistics , vol. 92, no. 4, 2010, pp. 945–964. MIT Press, https://doi.org/10.1162/REST_a_00039. Federal Reserve Bank of St. Louis. “All Employees: Food Services and Drinking Places: California [SMU06000007072200001].” FRED , 2025. https://fred.stlouisfed.org/series/SMU06000007072200001. Federal Reserve Bank of St. Louis. “All Employees: Food Services and Drinking Places: New York [SMU36000007072200001].” FRED , 2025. https://fred.stlouisfed.org/series/SMU36000007072200001. Federal Reserve Bank of St. Louis. “All Employees: Food Services and Drinking Places: Texas [SMU48000007072200001].” FRED , 2025. https://fred.stlouisfed.org/series/SMU48000007072200001. Neumark, David, and William Wascher. “Minimum Wages and Employment.” Foundations and Trends in Microeconomics , vol. 3, nos. 1–2, 2007, pp. 1–182. Now Publishers, https://doi.org/10.1561/0700000015. Sun, Liyang, and Sarah Abraham. “Estimating Dynamic Treatment Effects in Event Studies with Heterogeneous Treatment Effects.” Journal of Econometrics , vol. 225, no. 2, 2021, pp. 175–199. Elsevier, https://doi.org/10.1016/j.jeconom.2020.09.006. U.S. Bureau of Labor Statistics. “State and Metro Area Employment, Hours, and Earnings.” FRED , 2025. https://fred.stlouisfed.org. Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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08:20:44","extension":"html","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":57535,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7889672/v1/49ababcf03bcdcad148e1110.html"},{"id":94002112,"identity":"60185679-33ad-4c27-8e1f-f95fa4f5c381","added_by":"auto","created_at":"2025-10-21 08:28:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":260147,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly Food Service Employment in California, New York, and Texas (2020–July 2025). \u0026nbsp;The vertical dashed line indicates the April 2024 policy implementation date in California. \u003cem\u003eSource:\u003c/em\u003eAuthor’s calculations using FRED (NAICS 722).\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7889672/v1/c5386926bf1f08d10b00b01b.png"},{"id":94001080,"identity":"e5886544-d0b1-4be1-989f-f5860164602a","added_by":"auto","created_at":"2025-10-21 08:20:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":181763,"visible":true,"origin":"","legend":"\u003cp\u003eFood Service Employment Trends Around Minimum Wage Hikes in California and New York (2023–July 2025). Vertical dashed lines mark California’s April 2024 wage policy and New York’s January 2025 increase. Shaded regions indicate post-policy months. \u003cem\u003eSource:\u003c/em\u003eAuthor’s calculations using FRED (NAICS 722).\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7889672/v1/e854b7e03d7876caf6b245e7.png"},{"id":94003276,"identity":"57abf5e6-12fe-4ea0-8150-cd7663a353e9","added_by":"auto","created_at":"2025-10-21 08:44:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":38497,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly Food Service Employment in California and Texas (2022–2025). The vertical dashed line marks California’s $20 minimum wage implementation in April 2024. \u0026nbsp;Pre-policy trends appear broadly parallel, though event study results indicate some divergence. \u003cem\u003eSource:\u003c/em\u003eAuthor’s calculations using FRED (NAICS 722).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7889672/v1/de2d2b7a8fa86a6d4886495c.png"},{"id":94002308,"identity":"1f4c5e5b-115f-4a58-9577-63ca0b9cb4e9","added_by":"auto","created_at":"2025-10-21 08:36:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":32929,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly Food Service Employment in New York and Texas (2022–2025). The vertical dashed line marks New York’s $0.50 minimum wage increase in January 2025. No clear divergence in employment trends is observed. \u003cem\u003eSource:\u003c/em\u003eAuthor’s calculations using FRED (NAICS 722).\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7889672/v1/0af5aaf3690c9e2773326610.png"},{"id":94002117,"identity":"3bf1dd43-2841-4c12-a69a-ae47fd5774a2","added_by":"auto","created_at":"2025-10-21 08:28:43","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":22940,"visible":true,"origin":"","legend":"\u003cp\u003eCalifornia Event Study: Estimated monthly treatment effects of the April 2024 minimum wage increase on food service employment, relative to Texas. Dots represent point estimates, vertical bars show 95% confidence intervals, and the red dashed line indicates the policy date. Pre-treatment estimates suggest divergence, warranting caution. \u003cem\u003eSource:\u003c/em\u003e Author’s calculations using FRED (NAICS 722).\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7889672/v1/6b2de5c81360650a4b502e76.png"},{"id":94001082,"identity":"34d29a65-5766-4791-b517-41b6c95180b7","added_by":"auto","created_at":"2025-10-21 08:20:43","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":22030,"visible":true,"origin":"","legend":"\u003cp\u003eEvent study estimates for New York versus Texas around the January 2025 $0.50 minimum wage increase. Coefficients are plotted relative to the month of policy implementation (time 0). Pre-policy estimates remain near zero, supporting the parallel trends assumption. Post-policy estimates also cluster near zero, indicating no measurable short-term employment effect. \u003cem\u003eNote:\u003c/em\u003eEstimates are available only through July 2025; coefficients beyond month +5 are not estimated due to limited post-policy data. \u003cem\u003eSource:\u003c/em\u003e Author’s calculations using FRED (NAICS 722).\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-7889672/v1/96e5854817eefcee83df18c5.png"},{"id":94003513,"identity":"3c2b53e5-845b-4b82-b4f2-5f8c3686eb9f","added_by":"auto","created_at":"2025-10-21 08:52:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1232376,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7889672/v1/1c58c660-71b2-4f56-8ccd-1a8510c96e02.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eHow Minimum Wage Hikes Affect Food Service Employment: Evidence from California and New York\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMinimum wage policy remains a central topic in labor economics, frequently debated for its potential to improve low-wage workers\u0026rsquo; living standards while raising concerns about unintended employment effects. Proponents argue that wage floors can reduce inequality and boost local demand, whereas critics highlight risks such as job loss, reduced hours, or increased automation, particularly in sectors with thin profit margins. While the academic literature offers extensive analysis, empirical findings remain mixed, and outcomes appear highly dependent on policy design, sectoral exposure, and local labor market conditions.\u003c/p\u003e\u003cp\u003eCalifornia and New York provide contrasting case studies for evaluating the labor market effects of recent minimum wage hikes. In April 2024, California implemented a \u003cspan\u003e$\u003c/span\u003e20 per hour minimum wage specifically targeting fast food workers, which represents one of the largest sector-specific wage increases enacted in the U.S. In contrast, New York enacted a modest statewide increase of \u003cspan\u003e$\u003c/span\u003e0.50 in January 2025, applied uniformly across industries. These policy differences in magnitude and scope create a natural experiment for assessing labor market responses.\u003c/p\u003e\u003cp\u003eThis study investigates the short-term effects of these policy changes on employment in the food services sector. Using monthly state-level data from the Federal Reserve Economic Data (FRED), extending through July 2025, and Texas as a control state, we apply a difference-in-differences (DiD) framework to estimate employment changes before and after each policy. Our findings indicate that California experienced a statistically significant decline in food service employment, approximately 42,580 jobs, relative to Texas following the April 2024 wage hike. In contrast, New York exhibited no significant employment change. These results suggest that the labor market consequences of minimum wage increases depend not only on timing and location but also on policy targeting and intensity. Event study analyses further support these findings by illustrating significant pre- and post-policy dynamics in California and minimal effects in New York.\u003c/p\u003e"},{"header":"2. Policy Context and Related Literature","content":"\u003cp\u003eMinimum wage policies in the United States are frequently enacted at the state or local level, creating opportunities for quasi-experimental evaluation across jurisdictions. In 2024 and 2025, California and New York implemented notable minimum wage increases that differed substantially in design and scope, offering a natural contrast for empirical analysis.\u003c/p\u003e\u003cp\u003eIn April 2024, California enacted a targeted \u003cspan\u003e$\u003c/span\u003e20 per hour minimum wage specifically for fast food workers. The policy applies to fast food establishments with at least 60 locations nationwide and was promoted as a means of improving compensation in a sector known for high turnover, low pay, and limited benefits. This represents one of the most aggressive industry-specific wage policies in the United States and was the result of negotiations between policymakers, labor advocates, and corporate stakeholders.\u003c/p\u003e\u003cp\u003eIn contrast, New York implemented a modest statewide increase of \u003cspan\u003e$\u003c/span\u003e0.50 in January 2025, raising its general minimum wage to \u003cspan\u003e$\u003c/span\u003e16.00 per hour. This policy is part of a multi-year legislative agenda that includes planned inflation-indexed increases beginning in 2026. Unlike California\u0026rsquo;s sectoral focus, New York\u0026rsquo;s adjustment is broad-based and applies uniformly across industries and regions.\u003c/p\u003e\u003cp\u003eThese distinct policy approaches provide a useful empirical setting for testing how minimum wage changes affect labor market outcomes, particularly in the food services sector. The absence of comparable wage increases in Texas during this period strengthens the identification strategy by offering a stable control group and allows clear comparison over a multi-month post-treatment window through July 2025.\u003c/p\u003e\u003cp\u003eThis study builds on an extensive body of literature on minimum wage effects. The landmark study by Card and Krueger (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1994\u003c/span\u003e) challenged conventional wisdom by finding no negative employment effects in fast food restaurants following a minimum wage increase in New Jersey. Subsequent work by Dube, Lester, and Reich (2010) emphasized the importance of controlling for local economic trends and found minimal disemployment effects when such controls are included. In contrast, economists such as Neumark and Wascher (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) argue that higher minimum wages tend to reduce employment among low-skilled or young workers, particularly when policy changes are large or abrupt. By examining sector-specific labor outcomes in response to recent real-world policies, this paper contributes new evidence to this ongoing debate. More recent methodological advances in difference-in-differences and event-study designs (Callaway \u0026amp; Sant\u0026rsquo;Anna; Sun \u0026amp; Abraham) emphasize the importance of handling staggered adoption and pre-trend violations. This paper applies these insights conceptually by combining DiD with event-study checks to assess short-term policy effects in California and New York.\u003c/p\u003e"},{"header":"3. Data and Methodology","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Data Sources\u003c/h2\u003e\u003cp\u003eThis analysis uses monthly, seasonally adjusted state-level employment data from the Federal Reserve Economic Data (FRED) platform, focusing specifically on the food services and drinking places sector (NAICS 722). Employment series were retrieved for California, New York, and Texas using the FRED API. These data represent total non-farm payroll employment in the specified sector and are reported in thousands of workers.\u003c/p\u003e\u003cp\u003eThe series span from January 2020 through July 2025, enabling more than 12 months of pre-treatment data and at least 6\u0026ndash;7 months of post-treatment data for both California and New York\u0026rsquo;s respective policy changes. Texas, which did not implement a minimum wage change during this period, serves as the control group.\u003c/p\u003e\u003cp\u003eTo ensure transparency and reproducibility, all data were collected and processed using Python. The data pipeline employs the fredapi, pandas, and matplotlib libraries for retrieval, transformation, and visualization. The final datasets and figures were saved locally to ensure version control and replicability throughout the analysis. Event study indicators were constructed using relative month dummies to estimate dynamic treatment effects over a\u0026thinsp;\u0026plusmn;\u0026thinsp;12 month window around each policy\u0026rsquo;s implementation date.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Policy Events as Natural Experiments\u003c/h2\u003e\u003cp\u003eTwo recent minimum wage policy changes provide the empirical foundation for this study's quasi-experimental design. These events differ in both magnitude and policy scope, allowing for comparative analysis of labor market responses to wage regulation.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eCalifornia\u003c/b\u003e implemented a sector-specific minimum wage increase on \u003cb\u003eApril 1, 2024\u003c/b\u003e, setting the wage floor at \u003cb\u003e$20 per hour\u003c/b\u003e for fast food workers employed by chains with \u003cb\u003emore than 60 locations nationwide\u003c/b\u003e. This policy represented a significant, targeted intervention and was among the most substantial industry-specific wage hikes enacted at the state level in the United States.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eNew York\u003c/b\u003e, in contrast, enacted a \u003cb\u003ebroad-based minimum wage increase of $0.50\u003c/b\u003e, effective \u003cb\u003eJanuary 1, 2025\u003c/b\u003e, raising the statewide minimum wage to \u003cb\u003e$16.00 per hour\u003c/b\u003e. This adjustment was part of a pre-scheduled legislative plan to incrementally raise the minimum wage across all sectors and index future increases to inflation.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThe staggered timing and structural differences between the California and New York policies create a natural experiment framework for evaluating short-term labor market effects. California represents a large, sector-specific wage shock beginning in April 2024, while New York\u0026rsquo;s general statewide adjustment began in January 2025. These differences informed the construction of separate difference-in-differences and event study models for each state, using Texas as a stable control group throughout.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Empirical Strategy: Difference-in-Differences (DiD)\u003c/h2\u003e\u003cp\u003eTo estimate the causal effects of recent minimum wage policies on food service employment, this study employs a difference-in-differences (DiD) framework. The DiD model compares changes in employment between a treated state and a control state before and after the policy intervention. The general specification is as follows:\u003c/p\u003e\u003cp\u003eY_it\u0026thinsp;=\u0026thinsp;α\u0026thinsp;+\u0026thinsp;β1\u0026middot;Treated_i\u0026thinsp;+\u0026thinsp;β2\u0026middot;Post_t\u0026thinsp;+\u0026thinsp;β3\u0026middot;(Treated_i \u0026times; Post_t) + ε_it\u003c/p\u003e\u003cp\u003eWhere:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eY_it represents food service employment in state i at time t\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eTreated_i is an indicator equal to 1 for the treated state (California or New York), and 0 for Texas\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003ePost_t is an indicator equal to 1 for months following the implementation of the minimum wage policy\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eβ3 is the DiD estimate, capturing the differential change in employment in the treated state relative to the control state after the policy\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eSeparate DiD regressions are estimated for each policy event:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eCalifornia vs. Texas, using April 2024 as the policy implementation date\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eNew York vs. Texas, using January 2025 as the implementation date\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eAll models are estimated using ordinary least squares (OLS) with heteroskedasticity-robust standard errors (HC1). This correction provides valid inference even when error variances are not constant across states or time, which is a realistic feature of state-level employment data. To support the parallel trends assumption, raw employment trends are visualized for each state prior to policy adoption. Annotated figures highlight the policy introduction dates and post-treatment periods.\u003c/p\u003e\u003cp\u003eTo further explore dynamic treatment effects and test for pre-trend violations, event study models are also estimated. These models introduce month-specific leads and lags relative to each policy\u0026rsquo;s implementation date, allowing for a more granular view of employment responses over time.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Descriptive Trends\u003c/h2\u003e\u003cp\u003eThis section presents descriptive evidence on food service employment trends in California, New York, and Texas before and after the recent minimum wage policy changes. Monthly employment data span from January 2020 to July 2025 and cover the food services and drinking places sector in each state.\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e displays raw monthly food service employment levels over the full sample period. All three states exhibit similar patterns during the pandemic recovery, with steady growth beginning in mid-2021. However, California appears to diverge modestly from Texas following the implementation of its sector-specific \u003cspan\u003e$\u003c/span\u003e20 per hour minimum wage in April 2024. A noticeable flattening and slight decline in employment occurs in California during the months immediately following the policy change.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e focuses on the policy-relevant window from January 2023 through July 2025, using a three-month moving average to smooth short-term fluctuations. This visualization highlights both the April 2024 policy implementation in California and the January 2025 minimum wage adjustment in New York. While Texas continues on a relatively stable growth path, California shows signs of employment contraction following its targeted policy. New York\u0026rsquo;s trend remains largely parallel to Texas during the initial post-policy months, suggesting a weaker or delayed labor market response.\u003c/p\u003e\u003cp\u003eThese descriptive patterns provide preliminary support for a short-term employment effect in California and help validate the parallel trends assumption for the subsequent difference-in-differences analysis. However, the event study analysis later in this paper suggests some pre-treatment divergence in California, which qualifies the strength of this assumption.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e4.2 California: Difference-in-Differences Analysis\u003c/h2\u003e\u003cp\u003eTo estimate the short-term effect of California\u0026rsquo;s \u003cspan\u003e$\u003c/span\u003e20 per hour minimum wage policy on food service employment, we implement a difference-in-differences (DiD) model using Texas as the control group. The policy took effect in April 2024 and applied specifically to fast food establishments with more than 60 locations nationwide. Although the policy targets a subset of the food services industry, we use state-level employment data for the broader food services and drinking places sector (NAICS 722) to capture aggregate employment responses within this labor-intensive industry.\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e displays monthly food service employment trends in California and Texas from January 2022 through July 2025. The pre-policy trajectories for California and Texas appear broadly parallel when viewed descriptively, lending initial support to the DiD strategy. However, as the event study results below illustrate, some divergence emerges in the months leading up to the policy, which suggests the parallel trends assumption may be imperfect and results should be interpreted with caution.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDifference-in-Differences Regression: California vs. Texas. The policy date is April 2024, when California implemented a \u003cspan\u003e$\u003c/span\u003e20 minimum wage for fast food workers.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP-Value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eInterpretation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTreated (CA)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e273.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCalifornia had\u0026thinsp;~\u0026thinsp;273,860 more food service jobs than Texas pre-policy\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePost-policy\u003c/p\u003e\u003cp\u003e(Apr 2024)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e38.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTexas employment rose\u0026thinsp;~\u0026thinsp;38,330 jobs post-policy\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTreated*Post-policy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-42.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCalifornia experienced\u0026thinsp;~\u0026thinsp;42,580 fewer jobs than Texas post-policy (significant).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e\u003cp\u003eEmployment is measured in thousands. Standard errors are heteroskedasticity-robust (HC1).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e\u003cp\u003eAuthor\u0026rsquo;s calculations using FRED (NAICS 722)\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eDiD Regression Results \u0026ndash; California vs. Texas\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe DiD estimate (interaction term) indicates that food service employment in California declined by approximately 42,580 jobs relative to Texas following the April 2024 policy change. This result is statistically significant at the 1% level (p\u0026thinsp;=\u0026thinsp;0.003), suggesting that California\u0026rsquo;s sector-specific minimum wage increase had a measurable and immediate impact on employment in the affected industry. The magnitude of this decline highlights the potential labor market consequences of large, targeted wage interventions, particularly in low-margin service sectors.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSection 4.3: New York: Difference-in-Differences Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo evaluate the effect of New York\u0026rsquo;s January 2025 statewide minimum wage increase on food service employment, we conduct a difference-in-differences (DiD) analysis using Texas as a control state. Unlike California\u0026rsquo;s large, sector-specific policy, New York implemented a modest \u003cspan\u003e$\u003c/span\u003e0.50 increase across all industries. The policy raised the statewide minimum wage to \u003cspan\u003e$\u003c/span\u003e16.00 per hour as part of an ongoing series of scheduled adjustments.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure 4 illustrates the trends in food service employment for New York and Texas between January 2022 and July 2025. The pre-policy trajectories for both states appear broadly parallel, and no visual break in trend is evident in New York following the January 2025 policy change.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFigure 4.\u003c/b\u003e Monthly Food Service Employment in New York and Texas (2022\u0026ndash;2025). The vertical dashed line marks New York\u0026rsquo;s \u003cspan\u003e$\u003c/span\u003e0.50 minimum wage increase in January 2025. No clear divergence in employment trends is observed. \u003cem\u003eSource\u003c/em\u003e: Author\u0026rsquo;s calculations using FRED (NAICS 722).\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the DiD regression results. The interaction term (treated \u0026times; post_policy) is negative, suggesting a modest relative decline in employment, but the estimate is not statistically significant (p\u0026thinsp;=\u0026thinsp;0.36). The lack of a measurable employment effect may reflect the small size of the policy change or even with seven months of post-policy data (January\u0026ndash;July 2025), employment effects remain statistically insignificant. This strengthens the inference that the modest wage hike may not have been large enough to induce measurable short-term employment effects.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDifference-in-Differences Regression: New York vs. Texas. The policy date is January 2025, when New York increased its statewide minimum wage by \u003cspan\u003e$\u003c/span\u003e0.50.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP-Value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eInterpretation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTreated (NY)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-529.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNY had\u0026thinsp;~\u0026thinsp;529,030 fewer food service jobs than TX pre-policy\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePost-policy (Jan 2025)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e18.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.251\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTX employment increased\u0026thinsp;~\u0026thinsp;18,900 jobs post-policy (not significant).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTreated * Post-policy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-21.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.360\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNY employment fell\u0026thinsp;~\u0026thinsp;21,270 relative to TX (not statistically significant)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e\u003cp\u003eEmployment is measured in thousands. Standard errors are heteroskedasticity-robust (HC1).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e\u003cp\u003eAuthor\u0026rsquo;s calculations using FRED (NAICS 722).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eDiD Regression Results \u0026ndash; New York vs. Texas\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe DiD estimate for New York is negative but statistically insignificant at conventional levels, even with seven months of post-policy data now included. This suggests that New York\u0026rsquo;s broad-based \u003cspan\u003e$\u003c/span\u003e0.50 minimum wage increase had no detectable short-term impact on food service employment. The modest size of the policy and its uniform application across sectors likely contributed to the null result.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Event Study Analysis\u003c/h2\u003e\u003cp\u003eTo complement the DiD results and test the parallel trends assumption, we conducted event studies for both California and New York. These analyses trace monthly employment effects before and after the respective minimum wage hikes.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCalifornia Event Study\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe graph below shows the estimated monthly effects on food service employment in California relative to the policy implementation in April 2024. Key findings include:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eEmployment levels in California were already trending downward prior to the policy, but the decline steepened significantly after implementation.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eThe post-policy period (especially months 8 to 12) exhibits statistically significant negative effects.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003ePre-treatment estimates suggest some divergence from Texas, reinforcing the need for caution when interpreting the California results.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the estimated monthly treatment effects of California\u0026rsquo;s minimum wage hike (April 2024) on food service employment, relative to Texas. Dots represent point estimates, and vertical bars show 95% confidence intervals. The red dashed line indicates the policy implementation date. Positive and statistically significant pre-treatment effects (months \u0026minus;\u0026thinsp;12 to -1) raise concerns about the parallel trends assumption, suggesting pre-existing differences in employment trajectories between California and Texas.\u003c/p\u003e\u003cp\u003e\u003cb\u003eNew York Event Study\u003c/b\u003e\u003c/p\u003e\u003cp\u003eNew York\u0026rsquo;s policy took effect in January 2025. The event study results show:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eA positive trend in employment leading up to the policy, possibly due to anticipation effects or broader economic factors.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eA flattening or reversal of the trend immediately after the policy date.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003ePost-policy coefficients are generally statistically insignificant, suggesting a muted or delayed response to the wage hike.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure\u0026nbsp;6. Event study estimates for New York versus Texas around the January 2025 \u003cspan\u003e$\u003c/span\u003e0.50 minimum wage increase. Coefficients are plotted relative to the month of policy implementation (time 0). Pre-policy estimates remain near zero, supporting the parallel trends assumption. Post-policy estimates also cluster near zero, indicating no measurable short-term employment effect. \u003cem\u003eNote\u003c/em\u003e: Estimates are available only through July 2025; coefficients beyond month\u0026thinsp;+\u0026thinsp;5 are not estimated due to limited post-policy data. \u003cem\u003eSource\u003c/em\u003e: Author\u0026rsquo;s calculations using FRED (NAICS 722).\u003c/p\u003e\u003cp\u003eFigure 6 presents estimated monthly effects of New York\u0026rsquo;s January 2025 minimum wage increase on food service employment, using Texas as a control. Dots represent point estimates, and vertical bars indicate 95% confidence intervals. The red dashed line marks the policy implementation date. While pre-treatment estimates (months \u0026minus;\u0026thinsp;12 to -1) suggest rising employment trends in New York, post-treatment effects appear flat, indicating minimal immediate employment impact and suggesting a potential adjustment or plateau following the policy change.\u003c/p\u003e\u003cp\u003eThe event studies reinforce the main findings of the DiD analysis. California\u0026rsquo;s large, targeted wage increase appears to have had a meaningful and immediate negative effect on food service employment, although the violation of the parallel trends assumption warrants caution in interpretation. In contrast, New York\u0026rsquo;s modest, broad-based increase did not produce a statistically detectable change, consistent with its smaller policy magnitude and potentially lagged effects. Together, the results suggest that the scale and specificity of minimum wage interventions are key determinants of short-term labor market outcomes.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Discussion and Conclusion","content":"\u003cp\u003eThis paper investigates the short-term effects of recent minimum wage increases on food service employment in California and New York using a difference-in-differences framework. By comparing each state to Texas as a control, we assess whether wage policy design, specifically policy magnitude and sectoral targeting, affects employment outcomes.\u003c/p\u003e\u003cp\u003eThe results reveal a statistically significant decline in food service employment in California following the April 2024 implementation of a \u003cspan\u003e$\u003c/span\u003e20 per hour minimum wage for fast food workers. The estimated reduction of approximately 42,580 jobs suggests that large, sector-specific wage hikes can have measurable short-term employment effects, particularly in labor-intensive industries. Event study results support this finding by showing significant employment declines in the months following policy implementation. However, pre-policy estimates indicate early divergence between California and Texas, suggesting a potential violation of the parallel trends assumption.\u003c/p\u003e\u003cp\u003eIn contrast, New York\u0026rsquo;s broader but more modest \u003cspan\u003e$\u003c/span\u003e0.50 wage increase was not associated with any statistically significant change in food service employment, even with seven months of post-policy data now available. Event study analysis shows flat or muted employment responses post-implementation, reinforcing the DiD results and suggesting limited short-term labor market effects from small, broad-based wage increases.\u003c/p\u003e\u003cp\u003eThese findings contribute to the ongoing policy debate by highlighting the importance of policy design. While prior studies such as Card and Krueger (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1994\u003c/span\u003e) and Dube, Lester, and Reich (2010) have found limited disemployment effects from moderate minimum wage increases, our results align more closely with literature that identifies employment reductions when wage hikes are large or narrowly targeted (e.g., Neumark and Wascher, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis analysis is subject to several limitations. First, while the food service employment series provides sectoral granularity, it still aggregates across sub-industries and may not isolate fast food employment directly. Second, although the data extend through July 2025, the post-policy window for New York remains relatively short, potentially obscuring delayed effects. Finally, potential confounders such as local labor demand shocks, firm behavior, or migration trends are not explicitly modeled. While robustness checks with state-specific trends would help further address concerns about the parallel trends assumption, such models are beyond the scope of this paper but would strengthen future research. This limitation should be kept in mind when interpreting the California results.\u003c/p\u003e\u003cp\u003eFuture research should extend this analysis using longer post-policy timeframes, firm-level or establishment data, and methods that account for intra-sectoral variation. Investigating wage compression, hours worked, or worker turnover would also provide a richer understanding of how minimum wage policy interacts with labor market dynamics.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;All data and code are publicly available at Zenodo (Version 1.0): https://doi.org/10.5281/zenodo.15843632.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;No external funding was received for this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Peter Baker: conceptualization; methodology; data curation; formal analysis; writing\u0026mdash;original draft; visualization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The author thanks faculty and peers at George Mason University for their helpful comments and guidance during the development of this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The author declares no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBaker, Peter. \u003cem\u003eMinimum Wage Policy Effects on Food Service Employment: DiD and Event Study Code\u003c/em\u003e. Version 1.0, Zenodo, 2025. https://doi.org/10.5281/zenodo.15843632.\u003c/li\u003e\n\u003cli\u003eCallaway, Brantly, and Pedro H. C. Sant\u0026rsquo;Anna. \u0026ldquo;Difference-in-Differences with Multiple Time Periods.\u0026rdquo; \u003cem\u003eJournal of Econometrics\u003c/em\u003e, vol. 225, no. 2, 2021, pp. 200\u0026ndash;230. Elsevier, https://doi.org/10.1016/j.jeconom.2020.12.001.\u003c/li\u003e\n\u003cli\u003eCard, David, and Alan B. Krueger. \u0026ldquo;Minimum Wages and Employment: A Case Study of the Fast Food Industry in New Jersey and Pennsylvania.\u0026rdquo; \u003cem\u003eAmerican Economic Review\u003c/em\u003e, vol. 84, no. 4, 1994, pp. 772\u0026ndash;793. American Economic Association. JSTOR, https://www.jstor.org/stable/2118030\u003c/li\u003e\n\u003cli\u003eDube, Arindrajit, T. William Lester, and Michael Reich. \u0026ldquo;Minimum Wage Effects across State Borders: Estimates Using Contiguous Counties.\u0026rdquo; \u003cem\u003eReview of Economics and Statistics\u003c/em\u003e, vol. 92, no. 4, 2010, pp. 945\u0026ndash;964. MIT Press, https://doi.org/10.1162/REST_a_00039.\u003c/li\u003e\n\u003cli\u003eFederal Reserve Bank of St. Louis. \u0026ldquo;All Employees: Food Services and Drinking Places: California [SMU06000007072200001].\u0026rdquo; \u003cem\u003eFRED\u003c/em\u003e, 2025. https://fred.stlouisfed.org/series/SMU06000007072200001.\u003c/li\u003e\n\u003cli\u003eFederal Reserve Bank of St. Louis. \u0026ldquo;All Employees: Food Services and Drinking Places: New York [SMU36000007072200001].\u0026rdquo; \u003cem\u003eFRED\u003c/em\u003e, 2025. https://fred.stlouisfed.org/series/SMU36000007072200001.\u003c/li\u003e\n\u003cli\u003eFederal Reserve Bank of St. Louis. \u0026ldquo;All Employees: Food Services and Drinking Places: Texas [SMU48000007072200001].\u0026rdquo; \u003cem\u003eFRED\u003c/em\u003e, 2025. https://fred.stlouisfed.org/series/SMU48000007072200001.\u003c/li\u003e\n\u003cli\u003eNeumark, David, and William Wascher. \u0026ldquo;Minimum Wages and Employment.\u0026rdquo; \u003cem\u003eFoundations and Trends in Microeconomics\u003c/em\u003e, vol. 3, nos. 1\u0026ndash;2, 2007, pp. 1\u0026ndash;182. Now Publishers, https://doi.org/10.1561/0700000015.\u003c/li\u003e\n\u003cli\u003eSun, Liyang, and Sarah Abraham. \u0026ldquo;Estimating Dynamic Treatment Effects in Event Studies with Heterogeneous Treatment Effects.\u0026rdquo; \u003cem\u003eJournal of Econometrics\u003c/em\u003e, vol. 225, no. 2, 2021, pp. 175\u0026ndash;199. Elsevier, https://doi.org/10.1016/j.jeconom.2020.09.006.\u003c/li\u003e\n\u003cli\u003eU.S. Bureau of Labor Statistics. \u0026ldquo;State and Metro Area Employment, Hours, and Earnings.\u0026rdquo; \u003cem\u003eFRED\u003c/em\u003e, 2025. https://fred.stlouisfed.org.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"George Mason University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"minimum wage, food service employment, difference-in-differences, labor policy, California, New York","lastPublishedDoi":"10.21203/rs.3.rs-7889672/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7889672/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study evaluates the short-term effects of recent minimum wage increases on food service employment in California and New York using a difference-in-differences approach. In April 2024, California implemented a $20 per hour minimum wage targeting fast food workers, while New York enacted a modest $0.50 statewide increase in January 2025. Using monthly employment data from the Federal Reserve Economic Data (FRED) and Texas as a control state, we estimate the sectoral employment response to each policy. Results show that California experienced a statistically significant decline of approximately 42,580 food service jobs relative to Texas after the policy took effect. No significant change was observed in New York, likely due to the smaller policy magnitude and limited post-treatment period. These findings suggest that the labor market impact of minimum wage increases depends critically on policy design, implementation scope, and sectoral targeting.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJEL Codes:\u003c/strong\u003e J38, J21, J88\u003c/p\u003e","manuscriptTitle":"How Minimum Wage Hikes Affect Food Service Employment: Evidence from California and New York","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-21 08:20:39","doi":"10.21203/rs.3.rs-7889672/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5dc70fce-7dd6-4fbe-96ee-5724c9b41be1","owner":[],"postedDate":"October 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":56519107,"name":"Other Economics"},{"id":56519108,"name":"Macroeconomics"}],"tags":[],"updatedAt":"2025-10-21T08:20:39+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-21 08:20:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7889672","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7889672","identity":"rs-7889672","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}