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We use a unique data set and propose a novel identification strategy to estimate the impact of minimum wage increases on labor demand as measured by vacancies, a labor market outcome variable that has not been studied in the large empirical minimum wage literature. Our identification strategy builds on the idea that not all occupations are similarly affected by minimum wage increases. A much larger fraction of workers earn at or near the prevailing minimum wage level in some occupations than in others. Intuitively, one would expect these occupations to be affected differently by a minimum wage increase. We formalize this idea by identifying six 2-digit occupations as potentially at-risk occupations....First, we designate workers who earn at or below 110 percent of the effective state-level minimum wage as those who earn close to the minimum wage. The 110 percent threshold is partly informed by the distribution of the minimum wage increases shown in Figure 3. Second, we consider an occupation to be in the at-risk group, if during the entire sample period, the fraction of workers earning at or below 110 percent of the effective minimum wage is at least 5 percent. In Section 6, we show that our results are robust to variations in these two threshold level...Our results show statistically significant and large negative effects of minimum wage increases on vacancies in at-risk occupations. More specifically, vacancies in at-risk occupations drop by 2.4 percent in response to a 10 percent rise in the prevailing minimum wage relative to other occupations. This baseline result is driven by a strong preemptive response by firms cutting vacancies in advance of the minimum wage change. Furthermore, the decline in vacancies is larger for occupations that generally employ workers with lower educational attainment and those in counties with higher poverty rates....Workers who are more susceptible to the adverse effects of minimum wage hikes are naturally low-wage workers. If low-wage workers are more likely to be employed in occupations that do not require high levels of educational attainment, then we might expect to see more adverse effects from minimum wage increases on these occupations. In Column (3) of table 5, we see that this is indeed the case. We find that a 10 percent minimum wage increase reduces low-education vacancies by about 3.8 percent.

At-risk occupations

"...We designate an occupation as an “at-risk occupation” if a large share of workers in the occupation earn at or close to the effective minimum wage...To identify at-risk occupations, we construct hourly wage distributions of the occupations at the two-digit level by state using micro-level data from the CPS. To construct these distributions, we use hourly wage information (from the fourth and eighth months interviews in the CPS). When hourly wage data are not available, we use weekly hours worked and weekly earnings to compute an hourly wage. We restrict our sample to employed individuals of age 16 and older, and exclude those who are self-employed or work without pay....First, we designate workers who earn at or below 110 percent of the effective state-level minimum wage as those who earn close to the minimum wage. The 110 percent threshold is partly informed by the distribution of the minimum wage increases shown in Figure 3. Second, we consider an occupation to be in the at-risk group, if during the entire sample period, the fraction of workers earning at or below 110 percent of the effective minimum wage is at least 5 percent. In Section 6, we show that our results are robust to variations in these two threshold levels..."

Vacancy data

"...The vacancy data in our analysis come from the job openings data from the Conference Board as a part of its Help Wanted OnLine (HWOL) data series..."

"...Workers who are more susceptible to the adverse effects of minimum wage hikes are naturally low-wage workers. If low-wage workers are more likely to be employed in occupations that do not require high levels of educational attainment, then we might expect to see more adverse effects from minimum wage increases on these occupations. In Column (3) of table 5, we see that this is indeed the case We find that a 10 percent minimum wage increase reduces low-education vacancies by about 3.8 percent...."

Marianna Kudlyak, Murat Tasci, and Didem Tüzemen, "Minimum Wage Increases and Vacancies,"Federal Reserve Bank Of Cleveland Working Paper Series, April 2022https://www.clevelandfed.org/en/newsroom-and-events/publications/working-papers/2022-working-papers/wp-1930r-minimum-wage-increases-and-vacancies.aspx

The Evidence

Christopher Esposito, Edward Leamer and Jerry Nickelsburg, "Who Paid Los Angeles' Minimum Wage? A Side-by-Side Minimum Wage Experiment in Los Angeles County," National Bureau Of Economic Research, June 2021, https://www.nber.org/papers/w28966

Restaurant Closures, “…The closure rate was notably higher in the southern portion of the City Wage Region. This is a part of the county with a larger proportion of lower income census tracts. The interior parts of the City Wage region and, Beverly Hills, West Hollywood, and Culver City in the State Wage region, more affluent parts of the county, had relatively low closure rates. In Table 10, we aggregate closures and compute closure rates at the strata level and, with raw closure rates reported for the reference group of restaurants (the State Non-Border) and closure rates relative to the reference group for the other three strata. Closure rates for restaurants in both high and low-income census tracts are shown to be higher in the City Border in Table 10. For robustness, we investigated the closure rate of restaurants using the alternative cross-border competitor construction described in Section 3. We report these results in Table 11. The first column of the table shows the closure rate for the reference group, State Wage restaurants not exposed to City Wage competitors. Columns 2-4 show closure rates relative to the reference group. State Wage Non-Border restaurants had a closure rate of 25.6% in low-income neighborhoods and 23.6% in high-income neighborhoods respectively. State Wage Border restaurants had lower closure rates, and City Wage Border restaurants had higher closure rates. The highest closure rates were for City Wage restaurants in low-income tracts that bordered on State Wage, low-income census tracts. In the City Wage region, the lowest closure rate was in the Non-Border high-income census tracts. is the same group of restaurants that also increased prices by the largest amount….”

Menu Changes, “…The above table provides evidence that restaurants changed their menus more frequently when subjected to the larger increase in the minimum wage. This result was particularly apparent among restaurants in high-income census tracts, where City Border and City Non-Border restaurants were respectively 4.5 and 13.3 percentage points more likely to change their menus than the reference group. If changes in menu items in response to the higher minimum wage are attempts by the restaurant to lower costs, then at least some of the incidence of the wage was borne by customers who purchased the inferior food items. Although we cannot determine if the menu changes served that purpose, the fact that they occurred more often at restaurants in low-income neighborhoods - where, as we found earlier, price increases were smaller- suggests that they may have been implemented to reduce costs…”

"...In Table 4 we display mean price changes taken between rounds 1 and 7 of our price survey broken out by these eight groups of restaurants. The first column shows mean price changes for the reference group, State wage restaurants not exposed to City wage competitors, while columns 2-4 show price increases for the other three groups of restaurants relative to the reference group. The results presented in Table 4 provide a robustness check on the results presented in Table 3. Both tables report exceptionally large price increases in the high-income State Border region which had the lower increase in minimum wages. Moreover, when price changes are computed between different rounds of the survey (Appendix A) the same result is obtained. This shifts attention to the pre-equilibrium hypothesis regarding exceptional closures of restaurants in the City Border region discussed in Section 8. The price changes reported in Table 4 for restaurants in low-income census tracts, however, are different from our Table 3 statistics. Table 3 reports no statistically significant difference in price changes in any of the three low income regions. Table 4 reports large (7.3) and statistically significant price increases in the City Border. Caution should be exercised when interpreting this result because few observations (33) were taken of items in low-income census tracts in the City Border that were exposed to State wage competitors...."

New NBER looks at how minimum wage increase are "paid for" via pass throughs, in high income neighborhoods the higher costs were passed on to customers, in low income neighborhoods they fell on landlords and restaurant owners"...We exploit a geographical discontinuity in Los Angeles County, where in 2015 the City of Los Angeles passed a minimum wage law and in 2016 the State of California passed a different minimum wage law. This created two minimum wage schedules in the county that remained unequal for over five years. Using a novel data set from a multi-year price survey, our analysis shows that the incidence of Los Angeles City’s higher minimum wage fell on customers in high-income neighborhoods, and on landlords and restaurant owners in low-income neighborhoods.We further show that the mix of responses at restaurants subject to the LA City minimum wage, including price increases, menu changes, and restaurant closures, was affected by proximity to restaurants subject to the lower California State minimum wage..."
Core Finding, "... Specifically, we found that restaurants in high-income neighborhoods and located far from lower-wage competition passed most of the City Wage differential through to their customers by raising prices. Restaurants in low-income neighborhoods including those located near lower-wage competition were found to not have larger price increases than those in the State Wage region. Instead, restaurants in low-income neighborhoods changed their menus more often and restaurants exposed to lower-wage competition were more likely to close. We infer that the spatial discontinuity in minimum wages caused demand for restaurant meals to spill across the wage border and to nearby restaurants subject to the State Wage. In support of this inference, we found that State Wage restaurants in high-income census tracts and located near City Wage competition increased prices and were less likely to close than their nearby City Wage competitors....

Evidence, "...In the summer of 2015, the City of Los Angeles enacted a schedule of minimum wage increases that would reach $15/hour in July 2020. Los Angeles County, which encompasses the City of Los Angeles, is comprised of 88 governmental jurisdictions not legally subject to the City’s minimum wage ordinance. In 2016 California enacted a statewide schedule of minimum wage increases which set a minimum for all 89 jurisdictions. The California state schedule of increases differed from the City’s in three important ways. First the California state minimum wage would not reach $15/hour until January 2022. Second, the two minimum wages are indexed to the CPI, but the indexation starts earlier for the City’s minimum wage. Third, in a recession, the Governor of California can suspend increases in the state minimum wage thereby extending the lower wage longer, while the City’s wage has no such provision. Absent the latter two differences, the minimum wage in Los Angeles City and in a handful of jurisdictions within Los Angeles County that elected to follow the City’s minimum wage schedule will be $1 to $1.50/hour higher than in the rest of the County between July 2017 and January 2022 (Figure 1)....In our study, we control for neighborhood income levels because customers prefer restaurants close to where they live, and because residents of high-income neighborhoods may have relatively inelastic demand for restaurant meals. Thus, restaurant owners in these neighborhoods should, on average, be able to pass along additional wage costs to their customers without a reduction in sales. Tellingly, in low-income neighborhoods we find that restaurants subject to the City Wage did not have statistically significant price increase for existing menu items, but they did change menu items more often. Finally, we note that small mom-and-pop restaurants are only subject to minimum wage requirements on statutory employees. Family members working at the restaurant are not considered employees. Mom and-pop restaurants are more concentrated in low-income neighborhoods, and this might also help to explain the lack of price increases in these regions...Although increases in the minimum wage occurred throughout the four years of the survey, we do not find restaurants responding to individual between-round increases in the minimum wage by raising prices in a way that is coordinated with the minimum wage increases…"
"...Table 1 has regressions that put the moving pieces in these figures together, explaining the log of restaurant employment and wages as a function of (1) a lagged dependent variable to account for the persistence of departures from normal, (2) the log of the California minimum wage, (3) the log of the overall employment or wages depending on which is the dependent variable, and (4) quarterly dummy variables that measure departures from QTR=1, which is the omitted effect absorbed by the constant C. An important problem with this regression is that much of the County is subject to the City minimum wage which generally exceeds the State minimum wage. This means there is an omitted variable equal to the City wage minus the State wage, which is pretty constant except for the fact that the City increment comes in July while the State increment comes in January. A second, and possibly more important problem, is that the minimum wage schedules are announced in advance, and the response of employment and wages need not be timed with the arrival of the pre-announced increases. The third, and most important problem, is that there is no control group without any minimum wage increases. With those caveats, the regressions in Table 1 are quite interesting. Attention in this table is focused with shading green or red on the t-statistics greater than 2 or less than minus 2. For the employment variables, the effect that is most statistically apparent is the lagged dependent variable (persistence). This persistence reinforces our view that the response of employment to a schedule of future minimum wage increases cannot be timed to the minimum wage increments but must take into account the future minimum wages. Employment in limited-service restaurants tracks overall employment in a statistically discernable way while employment in full service restaurants does not.For wages, the persistence effect is not detectable, but both the minimum wage and the overall LA County wage are statistically significant. It is worth mentioning that the minimum wage coefficients in both employment equations are negative, though not statistically significant. This could be summarized with the conclusion that effects on wages are apparent with this kind of regression model because the law requires actual wages to move with the minimum wages but the effects on employment are disguised by the fact that employment changes are not necessarily timed to the minimum increase: there can be adjustments in advance and adjustments long after. These results do conform with the minimum wage literature which finds apparent wage effects but often no apparent employment effects. This is natural segue into our study of restaurant prices: To what extent do LA county restaurants pass on the rising wages to customers via price increases, and to what extent to the City and State minimum wages produce different increments in prices?..."

More evidence that minimum wage increases reduced low skilled workers net welfare. In this case via several different channels, hour reduction, lower benefits eligibility and less consistent schedules based on evidence from a retailer in California.

Overall impact on workers, "...We further show that increasing the minimum wage can diminish worker welfare due to the changes in firms’ scheduling practices, even when it does not reduce the overall employment. For an average worker in a California store in our data,we estimate the net loss of welfare due to their reduction of hours, lower eligibility for benefits, and less consistent schedules (that resulted from a $1 increase in the minimum wage) to be at least $1,590 annually or 11.6% of the worker’s total wage compensation... loss in worker welfare due to the reduction in weekly hours, eligibility of benefits, and consistency of their work schedules is estimated to be $4,710 (= $1, 872 + $467 + $2, 371) annually, while the value that workers may derive from the reduced hours may be worth up to $3,120. As a result, the net loss in worker welfare is estimated to be at least $1,590 annually or 11.6% of the worker’s total wage compensation. We note that this net loss in worker welfare is entirely due to workers’ reduced eligibility for benefits and the consistency of theirs schedules not directly due to the reduced hours, when assuming workers can use all the reduced hours in their second jobs.

Impact on benefits, "This decrease in the average number of hours worked not only reduces total wages but also impacts workers’ eligibility for benefits. We show that the percentage of workers with weekly hours larger than 20 (who may be eligible for retirement benefits according to ERISA (1974)) and those with weekly hours larger than 30 (who may be eligible for healthcare insurance according to the Affordable Care Act (ACA)) decreases by 23.0% and 14.9%, respectively. These results suggest that, as the minimum wage increases, firms may strategically adjust their scheduling practice to reduce the number of workers who are eligible for benefits.This is consistent with the results in Clemens et al. (2018) which shows using survey data from the US that minimum wage increase reduces workers’ likelihood to receive healthcare insurance especially in the low wage sectors. To demonstrate the significant financial incentive for firms to do so, we provide a rough estimate of a store’s savings associated with reducing workers’ eligibility for benefits in Section 6.2.1."..."

Impact on change in consistency of workers schedule, "...Besides the direct reduction in total wage compensation and eligibility for benefits,we also show that increasing the minimum wage leads to less consistent worker schedules both in terms of the number of hours they work from one week to another and in terms of the timing of their shifts. In particular, for each $1 increase in the minimum wage, the absolute (relative) deviation in the number of weekly hours worked by each worker increases by up to 33.0% (6.7%). In addition, we show that the absolute (relative) deviation in the number of daily hours increases by up to 9.5% (2.0%). The absolute deviation in the starting time of the work shift increases by up to 9.8%..."
Implication for firms costs, "...In this subsection, we first provide an approximation of the savings in benefit costs for an average store in California in our data through the scheduling adjustments associated with the minimum increase of $1. This will help demonstrate the strong financial motivations for the stores to adopt the schedule adjustments. We then provide a rough quantification for the resultant total welfare loss of an average worker in a California store in our data from the scheduling adjustments. This will help demonstrate the importance of the scheduling implications when analyzing the effect of the minimum wage on worker welfare.... To this end, we consider the average annual cost of retirement benefits per worker for the retail store to be $1,228. The total annual cost of healthcare insurance and retirement benefits for the store is thus estimated to be $14,638 (= 15∗ (3, 800∗ 30.3%∗ 71.2% + 1, 228∗ 56.0%∗ 22.7%)). Based on our results in Section 5.1, when increasing the minimum wage by $1, the percentage of workers with weekly hours longer than 20 and 30 decreases by 23.0% and 14.9%, respectively. Meanwhile, the number of workers for an average store in California increases from 15 to 19. With such schedule adjustments, the total annual cost of healthcare insurance and retirement benefits for this average store is thus estimated to be $9,664 (= 19∗(3, 800∗15.4%∗71.2% + 1, 228∗33.0%∗22.7%)). Therefore, the annual reduction in healthcare and retirement benefit costs is $4,974 (= $14, 638 − $9, 664). Given that the total labor hours per week is 348 for an average store in California, the total increase in the annual labor cost as a result of $1 increase in the minimum wage is $18,096 (= 1∗52∗348), and the reduction in the benefit costs represents 27.5% of the total wage cost increase..."
Qiuping Yu, Shawn Mankad and Masha Shunko, "Evidence of The Unintended Labor Scheduling Implications of The Minimum Wage," Social Science Research Network, June 2021, https://papers.ssrn.com/sol3/papers.cfm

The sample, "...highly granular dataset of worker schedules from a medium-sized chain of fashion retail stores in the United States. Specifically, we study worker scheduling and minimum wage data from 2015 to 2018 for5,760 workers at 45 stores in California and 17 stores in Texas. All stores share the same brand. Our data include all workers employed at the stores. They are all paid by the hour, and most of them are paid with the minimum wage. The key advantage of our dataset is that it allows the precise measurements of labor hours for a store as a whole and for all individual workers within a store, as well as the timings of the shifts for each worker..."
The results, "...Our results show that the minimum wage has no statistically significant impact on the total labor hours employed at the stores, which is consistent with the literature on the employment effect of minimum wage, especially in the non-tradable sectors (e.g., retail and service)…However, we show that the way in which the stores allocate these hours among their workers does change. Specifically, when the minimum wage increases by $1, the number of workers scheduled to work each week goes up by 27.7%, while the average hours per worker per week decrease by 20.8%. For an average store in California, for example, these changes translate into four extra workers per week and five fewer hours per worker per week. This means, for an average worker in California paid the minimum wage, her total wage compensation is reduced by 13.6% when increasing the minimum wage from $11 to $12…”

Neumark summarizes his recent lit review forWSJ. "...consensus of economic research on the effects of minimum wages pointsclearly to jobloss …The results are stark. Across all studies, 79% report that minimum wages reduced employment. In 46% of studies the negative effect was statistically significant. In contrast, only 21% of studies found small positive effects of minimum wages on employment, and in only a minuscule percentage (4%) was the evidence statistically significant. A simplistic but useful calculation shows that the odds of nearly 80% of studies finding negative employment effects if the true effect is zero is less than one in a million. Across all the studies, the average employment elasticity is about minus-0.15, which means, for example, that a 10% increase in the minimum wage reduces employment of the low-skilled by 1.5%. Extrapolating this to a $15 minimum wage, this 107% increase in the states where the federal minimum wage of $7.25 now prevails would imply a 16% decline in low-skilled employment (broadly consistent with the recent CBO study). That sounds like a substantial job loss....Our survey finds other important results. First, contrary to what is sometimes claimed, there is no tendency for the most recent research to provide less evidence of job loss. Second, the sharper a study’s focus on workers directly affected by the minimum wage, the stronger the evidence of job loss. For example, the average employment elasticity for those with at most a high school education is minus-0.24, implying that a 10% increase in the minimum wage reduces their employment by 2.4%. The only studies that produce more mixed evidence are studies of low-wage industries, like retail or restaurants. Notably, in these studies the job loss among those most affected by the minimum wage may be masked by employers substituting from lower-skilled to higher-skilled workers...."
David Neumark, "Raising the Minimum Wage Will Definitely Cost Jobs," Wall Street Journal, March 18, 2021, https://www.wsj.com/articles/raising-the-minimum-wage-will-definitely-cost-jobs-11616108030