Sex, Drugs and Economics: Labour markets

Showing posts with label Labour markets. Show all posts

Wednesday, 10 June 2026

Is it working from home, and not generative AI, that is harming the prospects of young workers?

There is growing evidence that the labour market for young workers is challenging. Graduates are finding it more difficult to get jobs after graduation. Several research papers have noted that generative AI may be to blame (see this post, for example), with one research paper referring to the changes in the labour market as seniority-biased technological change (see this post).

But the challenge with trying to attribute changes in the labour market to the rise of generative AI is that there are other contemporaneous changes affecting the labour market as well. One of those changes is the rise of working from home (as I noted in yesterday's post). Working from home may reduce the prospects for junior workers in part because it costs more to supervise and monitor them when they are working from home. Junior workers also benefit from on-the-job learning when they work with other people, and that on-the-job learning is less effective when they work from home. Combining those two effects, working from home reduces the incentive for employers to hire junior workers.

This new working paper by Peter Lambert (University of Warwick) and Yannick Schindler (Ellison Institute of Technology, Oxford) tries to disentangle the effects of generative AI and working from home on employment of younger workers. They use data from Revelio Labs that is made up of monthly matched employer-employee records collected from résumés (predominantly from LinkedIn) to construct a measure of the junior share of all new hires. They also use data from Lightcast on the near-universe of online job postings across thousands of online job sites and other websites. They use the Lightcast data to construct a measure of the share of job postings that require three or fewer years of experience. Their data from both sources covers the period from 2017 to 2025, and includes four countries: the US, the UK, Canada, and Australia.

Lambert and Schindler then use that data, along with measures of 'exposure to generative AI' and 'exposure to working from home' at the occupation level, in a difference-in-differences strategy. That means that they essentially compare the change in the share of junior job hires (or job postings) between occupations that are more or less exposed to generative AI (or working from home). Their main results are neatly summarised in Figure 3 from the paper:

Panel (a) shows that the junior share of new hires decreases significantly in jobs that are more exposed to working from home, from 2023 onwards (the black line). When they also control for exposure to generative AI (the red line), the effect of working from home barely changes. In contrast, Panel (b) shows that the junior share of new hires also decreases significantly in jobs that are more exposed to generative AI, from 2023 onwards (the black line). However, when they also control for exposure to working from home (the blue line), the effect of generative AI becomes much smaller and statistically insignificant. The results are similar for the share of job postings requiring three or fewer years' experience, as shown in Panels (c) and (d) of the figure.

The size of the effects are quite large too. A one-standard-deviation increase in exposure to working from home reduces the junior share of new hires by about two percentage points, and the share of job postings requiring three or fewer years' experience by 1.5 percentage points.

Lambert and Schindler conclude that, based on their results, working from home is a better predictor of the decline in junior hiring than generative AI. Given potential benefits of working from home, they are reluctant to recommend policies against working from home, instead noting that:

...micro-level adjustments may be required to help firms adapt their organizational practices, so as to enjoy the benefits of WFH [work from home] arrangements while simultaneously managing the development of early-career talent.

Seen alongside the negative mental health impacts of working from home (as noted in yesterday's post), this should give us further pause for thought. However, it is worth noting that even if working from home is a better predictor of reductions in junior hiring than generative AI within their model, that doesn't let generative AI off the hook entirely. Since both trends are happening at the same time, reducing working from home might not eliminate the negative impacts on junior hiring, but instead make generative AI appear more important as an explanation. Lambert and Schindler note early in their paper that it is often the same occupations (white-collar occupations) that are most exposed to both working from home and generative AI. Given that, perhaps Lambert and Schindler's recommendation for micro-level changes in organisational practice may be the best mitigation strategy available to us.

[HT: Marginal Revolution]

Did employers value an AI-related qualification in 2021?

Many universities are rapidly adapting to education in the age of generative AI by trying to develop AI skills in their students. There is an assumption that employers want graduates with AI skills across all disciplines, but is there evidence to support that? This recent discussion paper by Teo Firpo (Humboldt-Universität zu Berlin), Lukas Niemann (Tanso Technologies), and Anastasia Danilov (Humboldt-Universität zu Berlin) provides an early answer. I say it's an early answer because their data come from 2021, before the wave of generative AI innovation that became ubiquitous following the release of ChatGPT at the end of 2022. The research also focuses on AI-related qualifications, rather than the more general AI skills, but it's a start.

Firpo et al. conduct a correspondence experiment, where they:

...sent 1,185 applications to open vacancies identified on major UK online job platforms... including Indeed.co.uk, Monster.co.uk, and Reed.co.uk. We restrict applications to entry-level positions requiring at most one year of professional experience, and exclude postings that demand rare or highly specialized skills...

Each identified job posting is randomly assigned to one of two experimental conditions: a "treatment group", which receives a résumé that includes additional AI-related qualifications and a "control group", which receives an otherwise identical résumé without mentioning such qualifications.

Correspondence experiments are relatively common in the labour economics literature (see here, for example), and involve the researcher making job applications with CVs (and sometimes cover letters) that differ in known characteristics. In this case, the applications differed by whether the CV included an AI-related qualification or not. Firpo et al. then focus on differences in callback rates, and they differentiate between 'strict callbacks' (invitations to interview), and 'broad callbacks' (any positive employer response, including requests for further information). Comparing callback rates between CVs with and without AI-related qualifications, they find:

...no statistically significant difference between treatment and control groups for either outcome measure...

However, when they disaggregate their results by job function, they find that:

In both Marketing and Engineering, résumés listing AI-related qualifications receive higher callback rates compared to those in the control group. In Marketing, strict callback rates are 16.00% for AI résumés compared to 7.00% for the control group (p-value = 0.075...), while broad callback rates are 24.00% versus 12.00% (p-value = 0.043...). In Engineering, strict callback rates are 10.00% for AI résumés compared to 4.00% for the control group (p-value = 0.163...), while broad callback rates are 20.00% versus 8.00% (p-value = 0.024...).

For the other job functions (Finance, HR, IT, and Logistics) there was no statistically significant effect of AI qualifications on either measure of callback rates. Firpo et al. then estimate a regression model and show that:

...including AI-related qualifications increases the probability of receiving an interview invitation for marketing roles by approximately 9 percentage points and a broader callback by 12 percentage points. Similarly, the interaction between the treatment dummy and the Engineering job function dummy in the LPM models is positive and statistically significant, but only for broad callbacks. AI-related qualifications increase the probability of a broad callback by at least 11 percentage points...

The results from the econometric model are only weakly statistically significant, but they are fairly large in size. However, I wouldn't over-interpret them because of the multiple-comparison problem (around five percent of results would show up as statistically significant just by chance). At best, the evidence that employers valued AI-related qualifications in 2021 is pretty limited, based on this research.

Firpo et al. were worried that employers might not have noticed the AI qualifications in the CVs, so they conducted an online survey of over 700 professionals with hiring experience and domain knowledge, but that survey instead shows that the AI-related qualification was salient and a signal of greater technical skills, but lower social skills. These conflicting signals are interesting, and suggestive that employers are looking for both technical skills and social skills in entry-level applicants. Does this, alongside the earlier results for different job functions, imply that technical skills are weighted more heavily than social skills for Engineering and Marketing jobs? I could believe that for Engineering, but for Marketing I have my doubts, because interpersonal skills are likely to be important in Marketing. Again though, it's probably best not to over-interpret the results.

Firpo et al. conclude that:

...our findings challenge the assumption that AI-related qualifications unambiguously enhance employability in early-career recruitment. While such skills might be valued in abstract or strategic terms, they do not automatically translate into interview opportunities, at least not in the entry-level labor market in job functions such as HR, Finance, Marketing, Engineering, IT and Logistics.

Of course, these results need to be considered in the context of their time. In 2021, AI-related skills might not have been much in demand by employers. That is unlikely to hold true now, given that generative AI use has become so widespread. It would be interesting to see what a more up-to-date correspondence experiment would find.

[HT: Marginal Revolution]

ChatGPT and the labour market

Saturday, 8 November 2025

Survey evidence on the labour market impacts of generative AI

A picture of the labour market impacts of generative AI is slowly emerging. At this stage, there is little consensus on what the impacts will be. I just stumbled across this working paper, by Jonathan Hartley (Stanford University) and co-authors, which I had put aside to read earlier this year. Unlike some of the research I have discussed in recent posts (linked at the end of this post), Hartley et al. make use of a nationally representative survey of US workers.

The survey has had three waves in the US (plus one Canadian wave), and the first US wave had over 4200 respondents (Hartley et al. don't report how many respondents there were for the other waves). The results make for interesting reading. First, in terms of who is using generative AI, they report that:

...LLM adoption at work among U.S. survey respondents above 18 has increased rapidly from 30.1% as of December 2024, to 43.2% as of March/April 2025, and to 45.9% as of June/July 2025...

Conditional on using Generative AI at work, about 33% of workers use Generative AI five days per week at work (every weekday). Roughly 12% of Generative AI users use such tools at work only 1 day at work. About 17% and 18% of Generative AI users use Generative AI tools at work two and three days per week respectively...

That is a lot of people using generative AI for work, and using it often when they do. It is interesting to sit these results alongside those of Chatterji et al. (whose paper I discussed in this post). They found growth in both work-related and non-work-related ChatGPT messages over time.

Who is using generative AI at work, though? Hartley et al. find that:

...Generative AI tools like large language models (LLMs) are most commonly used in the labor force by younger individuals, more highly educated individuals, higher income individuals, and those in particular industries such as customer service, marketing and information technology.

These results are similar to those of Chatterji et al., except that Hartley et al. also report gender differences (with greater use of generative AI by men), whereas Chatterji et al. report that the gender gap that was apparent among early adopters of ChatGPT has closed completely.

Hartley et al. then move on to estimating the productivity gains from generative AI. Given that this is survey-based, and not observational or experimental, we should take these results with a very large grain of salt. Hartley et al. ask their respondents how long it takes then to complete various tasks with and without generative AI. The results are summarised in Figure 12 in the paper:

Notice that every task is reported to take less time with generative AI (the green dots) than without (the blue dots). The productivity gains are different for different tasks. However, I find this figure and the data to be very fishy. How could generative AI create a huge decrease in time on 'Persuasion' tasks? Or 'Repairing' (which has one of the biggest productivity gains). Also, notice how almost every task takes between 25 and 39 minutes with generative AI. I strongly suspect that the research participants are anchoring their responses to this question on 30 minutes with GenAI for some reason. Without seeing the particular questions that are being asked though, it is hard to tell why. [*]

Hartley et al. then try to estimate the impact of generative AI on job postings, employment, and wages, using a difference-in-differences research design. They find no impact on job postings or employment, but significant impacts on wages. However, here things get strange. The coefficients that they report in Tables 6 and 7 of the paper are clearly negative, and yet Hartley et al. write that:

Our estimated coefficients... imply economically meaningful wage effects: a one-standard deviation increase in occupational Generative AI exposure corresponds to a significant increase in median annual wages...

Going back to their regression equations, their 'exposure to generative AI variable' is more positive when exposure is high, so a negative coefficient should imply that more exposure to generative AI is associated with lower wages. I must be missing something?

Given the deficiencies in the data and the regression modelling, I don't think that this paper really adds much to our understanding of the labour market effects of generative AI. Which is disappointing, because survey-based evidence would provide us with a complementary data source that would help us to triangulate with the results from other data sources and methods.

[HT: Marginal Revolution]

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[*] On a slightly more technical note, we might expect there to be as much variation (in relative terms) in the 'with GenAI' data as in the 'without GenAI' data. However, the coefficient of variation (the standard deviation expressed as a percentage of the mean) is 0.109 for the 'with GenAI' data, but 0.226 for the 'without GenAI' data. So, there is less than half the variation in the reported task times with GenAI than without. Again, that suggests that this data is fishy.

ChatGPT and the labour market

Monday, 3 November 2025

Generative AI and entry-level employment

Does technological change increase employment, or decrease employment? The answer depends on what you believe about the technological change. If the technological change primarily automates tasks that were previously done by human workers, then it may decrease the demand for labour and reduce employment [*]. On the other hand, if the technological change primarily makes workers more productive, so that they generate more value for their employers, then it may increase the demand for labour and increase employment [**].

Which of those two situations is AI creating? In reality, it's probably a bit of both. However, which effect is stronger? We can get a sense of where things are at from this recent working paper by Erik Brynjolfsson, Bharat Chandar, and Ruyu Chen (all Stanford University). They use data from ADP, the largest payroll processing firm in America, which contains records on between 3.5 and 5 million workers each month between January 2021 and July 2025. Using this data, Brynjolfsson et al. demonstrate six key facts about the labour market over that time:

First, we find substantial declines in employment for early-career workers in occupations most exposed to AI, such as software development and customer support.

Second, we show that economy-wide employment continues to grow, but employment growth for young workers has been stagnant.

Third, entry-level employment has declined in applications of AI that automate work, with muted effects for those that augment it.

Fourth, these employment declines remain after conditioning on firm-time effects, with a 13% relative employment decline for young workers in the most exposed occupations.

Fifth, these labor market adjustments are more visible in employment than in compensation.

Sixth, we find that these patterns hold in occupations unaffected by remote work and across various alternative sample constructions.

The first key fact is demonstrated by looking across all occupations. However, it is most clearly seen in Figure 1 from the paper, which shows how the number of workers (by age group) employed as software developers or customer service representatives (two of the occupations most cited in the media as being affected by AI) have changed compared with October 2022:

Notice how the blue line (employment of early career workers aged 22 to 25 years) trends downwards, starting from 2022, while employment of the most senior workers (especially those aged 35 years and over) continue to trend upwards.

The second key fact builds on this, to show the trend is apparent when you pool all workers together, as demonstrated in Figure 4 from the paper:

Note that the trends are not as wildly different as they are for software developers or customer service representatives, but remember that figure shows the trends across all workers, including those in jobs like nurses, welders, or baristas, whose employment is unlikely to be very impacted by AI (yet!).

Their third key fact relates most closely to the point I raised at the beginning of this post. On this, Brynjolfsson et al. find that:

...occupations with the highest estimated automation shares have experienced declining employment for the youngest workers...

...occupations with the highest estimated augmentation shares have not experienced a similar pattern...

The relevant figures are Figures 7 and 8 from the paper (which are too large for me to reproduce sensibly here). For their fourth key fact, Brynjolfsson et al. use a Poisson regression model within each age group, which allows them to control for firm-time-specific and firm-quintile-specific effects (where the quintiles are quintiles of exposure to AI, drawn from the paper that I discussed in this 2023 post). The firm-time effects will control for firm-specific shocks that affect all of the firm's workers, while the firm-quintile effects will control for different trends affecting a firm's workers that have similar exposure to AI. The analysis won't control for all of the relevant differences, and the analysis remains correlational rather than causal. Nevertheless, Brynjolfsson et al. find that:

For workers aged 22-25, estimates for higher quintiles are large and statistically significant, with a 12 log point decline in relative employment... Estimates for other age groups are generally much smaller in magnitude and not statistically significant.

A 12-log point change is about 11.3 percent, so relative to older workers working in the same firm and with the same exposure to AI, the youngest workers have suffered an 11.3 percent decrease in employment.

Brynjolfsson et al.'s fifth point is simply that the effects show up for employment, but not for wages. And their sixth point is that the effect is robust to various alternatives, including: excluding tech occupations; looking separately at jobs that are, or are not, amenable to remote work; extending the pre-period back to 2018; looking separately by gender; and using the Current Population Survey instead of the payroll data. Interestingly, looking differently at occupations depending on the education level of workers, the results show that:

Occupations with a high share of college graduates have declining employment overall, with muted differences between more-exposed and less-exposed occupations compared to our main results. In contrast, occupations with a low share of college graduates have rising overall employment, with the least AI-exposed occupations growing and the most exposed occupations declining in employment.

All of this is not great news for current university students. Think about all of the results taken together. Firms have been employing fewer entry-level workers, which are your typical graduates. Occupations that have a high share of college graduates are experiencing declining employment overall, in both occupations that are more exposed to AI and occupations that are less exposed to AI. And employment in AI-exposed occupations with a low share of college graduates has also been declining. It seems that the only groups of entry-level workers who aren't experiencing negative trends are non-college-educated workers in occupations that are not exposed to AI.

Brynjolfsson et al. try to finish on a positive note though, noting that:

The adoption of new technologies typically leads to heterogeneous effects across workers, resulting in an adjustment period as workers reallocate from displaced forms of work to new forms with growing labor demand... Past transitions such as the IT revolution ultimately led to robust growth in employment and real wages following physical and human capital adjustments, with some workers benefiting more than others...

Here's hoping that we don't have to wait too long for those adjustments.

[HT: Marginal Revolution]

*****

[*] However, decreasing employment (and wages) in the automating industry may increase the supply of workers into other industries, increasing employment (but decreasing wages) in those other industries. The general equilibrium effect is not straightforward.

[**] Arguments that increasing productivity will reduce the demand for labour, since fewer workers are needed to complete the same amount of work, run into the 'lump of labour' fallacy. They forget that firms can expand production if they have more productive workers, and will want more workers if their workers are more productive and therefore more profitable to employ.

ChatGPT and the labour market

Sunday, 28 September 2025

Minimum wages and student summer employment

The research literature is starting to coalesce around minimum wages have small disemployment effects, consistent with the standard demand-and-supply model (see the meta-analysis in this post, or the links at the end of this post, for more details). To add to that evidence, this recent article by Adam Wright, Darius Martin, and John Krieg (all Western Washington University), published in the journal Contemporary Economic Policy (), looks at the impact on student summer employment. Specifically, Wright et al. use data from Washington state, and focus on students enrolled in Western Washington University in Bellingham, which has about 15,000 undergraduate students.

Wright et al. match student data from 2013 to 2019 to employment records from the Washington State Employment Security Division (ESD) from 2009 to 2019. The ESD data includes both earnings and hours worked, but only for employers that pay into the state's unemployment insurance programme. This excludes workers for the federal government and the self-employed, who are unlikely to be students. However, it also excludes students "students working under financial assistance programs provided by the school", so Wright et al. supplement the ESD data with data on student employees at WWU. Because the employment data starts before the student data, Wright et al. can control for past work experience. And because the data are quarterly, they are able to look separately at employment effects over summer, and during term time.

There are over 31,000 students in the analysis sample, and nearly 260,000 student-by-quarter observations. Wright et al. apply a fairly straightforward panel regression model, controlling for work experience and for the unemployment rate of Whatcom County (where the WWU campus is located). They look at three outcome variables: (1) hours worked; (2) wage income; and (3) a binary variable indicating whether a student worked any hours at all. The overall effects of the minimum wage are not statistically significant. However, when looking at the results by season, Wright et al. find that:

In summer, when students are most likely to work, higher minimum wages significantly predict reduced work hours and the probability of employment, whereas the relationship with income is negative but imprecisely measured. In particular, the coefficient estimates imply that a 100% increase in the minimum wage is associated with 90.08 fewer hours worked in the summer and a reduction in the probability of work by 34.2% points. This suggests that the 16% minimum wage increase experienced in 2016–2017 was linked to a 14.4 h decrease in summer hours worked (an 8.5% decrease relative to the average) and a 5.5% point decrease in summer employment (an 8% decrease)... Minimum wage policy does not appear to predict disemployment in non-summer quarters, leading wage income to rise with increasing minimum wages in winter and spring...

So, minimum wages reduce student summer employment. It is unclear why Wright et al. decided to illustrate the size of the effect with a 100% change in the minimum wage (although that is what the coefficient in the table represents), since that size of change is never observed in the data. It is better to say that a 10% increase in the minimum wage would reduce hours worked in summer by about nine hours (compared with a mean of 170 hours of work in the summer quarter). It's a small effect, but not zero. And since student summer employment tends to be concentrated in low-wage industries like retail or hospitality, that makes sense. Turning to the effect of previous work experience, Wright et al. find that:

...higher wage income in winter and spring quarters when minimum wages increase only holds for students with prior work experience. However, there is statistically significant drop in employment in summer for both students with and without work experience. We estimate that a 100% minimum wage increase for workers with no pre‐matriculation work experience is associated with 115 fewer hours of work and a 38.8% point decrease in the probability of employment during the summer quarter. These estimates imply that the 16% minimum wage increase in 2017 corresponded with inexperienced students working 18.1 fewer hours and being 12.8% points less likely to be employed in the summer. These results are attenuated for those who entered WWU with work experience: a 100% minimum wage increase corresponds to 21.4% point reduction in summer employment for this group whereas the relationship between minimum wages and hours worked is negative but statistically insignificant.

The key finding is that students with no prior work experience and more negatively affected by the minimum wage increase than students with prior work experience. This is to be expected, since employers facing a higher minimum wage would be likely to concentrate employment in more experienced (and more productive) workers, even within summer student workers.

Next, comparing the effects between students who are local to Whatcom County and those who are not, Wright et al. find that:

...the negative relationship between minimum wages and summer employment only occurs for non‐locals, with estimates comparable to those experienced by students with no prior pre‐matriculation work history... Taken together, the full sample and quarterly results indicate that those with higher search costs may be more negatively impacted by minimum wage changes.

The mention of search costs here is important. In a search model of the labour market, workers face a search cost, made up of the time and effort spent looking for a job. Locals face lower search costs, because they likely have networks of local acquaintances and friends who can more easily help them find work, compared with non-locals. These results show that, in the context of higher minimum wages, those differences in search costs really matter.

Wright et al. are careful to point out that their results are correlations rather than causal. Specifically, their analysis lacks a control group. Nevertheless, it provides some descriptive evidence that is consistent with the emerging consensus of small but significant disemployment effects of the minimum wage. However, it would be interesting to see whether these results stood up to a more careful analysis using methods designed to elicit causal impacts. Nevertheless, Wright et al. conclude that:

Our results suggest that minimum wages particularly hurt inexperienced workers in summer, the quarter in which students tend to work most.

As someone who teaches students who rely on summer employment to build up reserves that they can draw on during term time, these results, even if they are not definitively causal, are a worry.

The Cristiano Ronaldo effect on the Saudi Pro League

This past week, my ECONS102 class covered labour markets. Part of that topic is a discussion of superstar and tournament effects, which are explanations for why, within a particular labour market, some workers get paid a lot while most workers get paid very little. As an example, in the labour market for actors, the top actors get paid a lot, while the 'average' actor barely earns enough to get by (or doesn't earn enough to get by, which is why so many aspiring actors work as waitstaff at restaurants).

Superstar effects arise when the worker (the superstar) earns a lot of value for their employer. Labour markets with superstar effects generally have two features:

Scale – the top performers can satisfy the demand of a lot of consumers (which is more likely when the output is non-rival). That generates a very high value (technically, the value of the marginal product of labour, or VMPL) for the employer, with little additional cost; and
Non-substitutability – the particular job (or skills, or ‘style’) performed by the top performer is unique, and cannot be easily replicated by the ‘average’ worker.

Because of the high value created by the superstar, and non-substitutability, employers compete fiercely over these top performers, and so they will receive a very high wage. Movie stars provide a good example of the superstar effect in action.

In some labour markets, workers are rewarded for their relative (rather than absolute) performance. In these markets, workers essentially compete for a ‘prize’ – maybe a raise or a promotion, and they only need to be a little bit better than the second-best person in order to ‘win’ the prize. These labour markets are said to exhibit tournament effects. In these markets, the extra pay for the top workers arises not because they create more value for the employer, but as a way of incentivising workers to work hard (in order to try and 'win' the tournament). Good examples of tournament effects in action are the pay for CEOs, sports stars, or hedge fund managers.

How can we distinguish between superstar and tournament effects? Some markets actually have both, like the market for sports stars. However, for the high wages to be a superstar effect, the worker must generate much more value for the employer than alternative workers do. And that brings me to this recent article by Dominik Schreyer (WHU – Otto Beisheim School of Management) and Carl Singleton (University of Stirling), published in the journal Contemporary Economic Policy (open access).

Schreyer and Singleton look at the impact of Cristiano Ronaldo on the Saudi Pro League, after he was surprisingly signed by the Al Nassr club a few days after the 2022 FIFA World Cup. They note the potential for superstar effects here, specifically:

The Ronaldo signing, and subsequent player moves, could attract international tourists and foreign investments to the country, help market the TV product abroad...

Schreyer and Singleton look at the impact of Cristiano Ronaldo on stadium attendance, as a measure of his positive impact on the league. Their data comes from 240 matches played by 16 different clubs during the 2022–2023 season, noting that Ronaldo made his debut for Al Nassr on January 22 and played in 16 matches over the course of the rest of the season. In their preferred regression specification, Schreyer and Singleton find that:

...the estimated average effect of Ronaldo playing at home is 20% points of capacity, and the average effect of him playing away is 15% points, significantly different from zero at the 10% level (two‐sided test).

Moreover, when they look at whether there was a general impact of Ronaldo joining the league on attendance, they find that:

...the post‐Ronaldo‐playing period of the season 2022–2023 was associated with generally higher attendance demand across all matches, by 3% points of stadium capacity, significantly different from zero at the 10% level (two‐sided test), with a further significant 17% point effect when he played at Al‐Nassr's home.

Schreyer and Singleton conclude that this is evidence in favour of a superstar effect. However, I am not entirely convinced. How much additional value is Ronaldo generating for Al Nassr and the Saudi Pro League? Mean stadium capacity in Schreyer and Singleton's sample is just 26,000. So, an increase of 15-20 percentage points is an increase of 4000-5000 people in attendance at the game. That isn't going to generate anywhere near enough additional revenue to cover Ronaldo's salary of €180 million per year. Even with jersey sales and an increase in advertising or sponsorship revenue, this will not break even for Al Nassr. On the other hand, for Saudi Arabia generally this might be a good deal. Schreyer and Singleton note that:

The Ronaldo signing... [could] legitimize KSA's other foreign sports investments, including the 2021 takeover of Newcastle United FC...

Perhaps those broader benefits are worth more than Ronaldo's salary? Saudi Arabia may value the legitimisation quite highly (hence all the accusations of sportswashing). Still, it seems to me that at least part of Ronaldo's salary is a tournament effect. If Al Nassr had signed Kylian Mbappe instead, I'm sure they would have paid a hefty salary for the privilege. Instead, Mbappe is being paid the comparatively pauper-like salary of €36 million at Real Madrid. That suggests that the next best player earns far less than Ronaldo [*], which is indicative of Ronaldo's salary being a tournament effect.

*****

[*] We could argue endlessly about who are the top and second-best players. However, choose any from this list of the top-paid footballers, and the argument still holds up.

Wednesday, 5 March 2025

Minimum wages and alcohol consumption

There are several reasons to believe that higher minimum wages will affect alcohol consumption. First, higher incomes for those on the minimum wage gives them greater purchasing power. If alcohol is a normal good (which it is), then as their incomes increase people will consume more alcohol. On the other hand, higher minimum wages may lead to disemployment, especially among young people and those in the food and beverage industries. In that case, those workers without jobs have lower incomes and would consume less alcohol. However, losing a job (or not having a job) can be a stressful experience, and increase the incentives to drink alcohol as a coping strategy. And having a job that pays more due to a higher minimum wage may reduce financial stress and reduce the incentives to drink. Overall, there is a lot going on, and it isn't clear at all whether, overall, a higher minimum wage should lead to more alcohol consumption, or less alcohol consumption.

That's where this new article by Yihong Bai (Western University in Ontario) and Michael Veall (McMaster University), published in the journal Economics and Human Biology (open access), comes in. Bai and Veall use longitudinal data from the Canadian National Population Health Survey (NPHS) from 1994/95 to 2010/11, and look at how alcohol consumption is related to the province-level minimum wage (adjusted for inflation). Their full sample includes over 18,000 observations for a little over 4000 individuals in Canada. They measure alcohol consumption in six different ways: (1) whether each person is a drinker or not; (2) whether they binge drink at least once per month on average; (3) whether they are a 'heavy drinker' (defined by binge drinking at least once per week on average, or having average daily alcohol consumption [ADAC] of two drinks for men, or one drink for women; (4) average number of drinks over the last month; (5) number of binge drinking events over the last month; and (6) ADAC.

Bai and Veall apply a two-way fixed effects approach to identify the effects of the minimum wage on alcohol consumption, and find that:

...almost all the estimated coefficients are very small with reasonably tight confidence intervals that cover zero.

In other words, there is very little evidence that higher minimum wages increase alcohol consumption. However, that is based on the whole population, and minimum wages are more likely to affect low-income workers. Rather than looking at low-income workers directly, Bai and Veall look at low-education workers (being those with high school education or less), who are also more likely to be affected by minimum wage changes. For that group, they also find that almost all of the coefficients are not statistically significant. Another group that tends to be more affected by minimum wage changes is young people, and Bai and Veall report that:

We also estimate using samples for a sample of ages 21–25 and ages 15–20 and find no evidence of minimum wages increasing drinking. However, our confidence intervals are wide and this finding must be treated with caution.

One of the key issues with this paper is that they perform a large number of regressions (with six dependent variables), but don't adjust for multiple comparisons. This is important because the more comparisons they make, the more likely it is that some will turn out to be statistically significant just by chance. That's why I discount their finding that the ADAC decreases when the minimum wage increases. I doubt that it would be robust to an adjustment for multiple comparisons, and the ADAC results are inconsistent with the effects in the other models.

On the other hand, the two-way fixed effects approach is problematic and has attracted a lot of criticism recently (which is nicely outlined in two posts on the Development Impact blog, here and here, as well as this post). The short version is that the two-way fixed effects approach is likely to lead to biased estimates of the treatment effect - in this case, it would lead to a biased estimate of the effect of minimum wages on alcohol consumption. It isn't clear what direction the bias would lead.

So, by itself this paper doesn't give an answer to the question of whether minimum wages affect alcohol consumption or not, and if they do affect alcohol consumption whether minimum wages lead to an increase, or a decrease, in alcohol consumption. This research question is far from settled and is an area where future research would be useful.

Tuesday, 4 March 2025

Local minimum wages and low-quality housing rents in Japan

In this 2023 post, I discussed the impact of higher minimum wages on homelessness. Part of the story related to housing rents:

There are a couple of reasons to expect that higher minimum wages might increase homelessness. If minimum wages decrease employment (a result that is contested, but I believe it is likely given the galaxy of literature we have to date; again, see the links at the end of this post), then higher minimum wages may directly increase the risk of people becoming homeless. That's because when low-income people workers lose their jobs, they may no longer be able to afford to pay rent, and may lose their homes. Second, if minimum wages increase incomes for those that are not made unemployed, they may increase the demand for housing, pushing up rents. This may indirectly increase the risk of people becoming homeless, who can no longer afford the higher market rent.

The research that I referred to in that post found that higher minimum wages increased homelessness, and that they also increased housing rents, consistent with the mechanism outlined above. However, we shouldn't believe just a single paper's research findings. This 2021 article by Atsushi Yamagishi (Princeton University), published in the journal Regional Science and Urban Economics (ungated earlier version here), provides some additional evidence, this time from Japan.

Japan provides an interesting case study for examining the effects of the minimum wage on housing, because:

Japan has forty-seven prefectures and each has a different minimum wage rate. There is no difference in the minimum wage rate within a prefecture...

And on top of that, each prefecture has little control over its local minimum wage. Yamagishi notes that:

...the minimum wage setting in Japan is highly centralized and unresponsive to trends in local housing markets due to institutional features. Japanese prefectural minimum wages are determined by the following process. First, the central government classifies prefectures into four categories, and it assigns the targeted amount of minimum wage increase to each category. The categorization is reviewed only once every five years and changes in the classification are rare.

Yamagishi uses data from 2007 to 2013, and exploits an interesting natural experiment, where:

From 2007 to 2012, a new consideration took the primary role in setting minimum wages due to the national policy change... after the revision of the Minimum Wage Law in 2007, the primary consideration in setting the minimum wage rate became closing the gap between the quality of life of minimum wage workers and people relying on Public Assistance (seikatsu-hogo, PA henceforth)...

Since the gap was generally larger in urban areas, the policy resulted in a plausibly exogenous minimum wage increase in urban prefectures...

So, not only is there variation in minimum wages across prefectures, and that variation is not related to local housing markets, there is a change in the variation driven by the policy change. Yamagishi uses data on advertised apartment [*] rents from At Home, "one of the most popular online real estate search engines in Japan". Using both an event study research design and a difference-in-differences design, Yamagishi found that:

...low-quality apartments experience around a 2.5-4.5% rent increase in response to a 10% minimum wage increase.

When looking at differences by apartment quality (proxied by the age of the apartment, with 'old' apartments being over 25 years old and 'very old' apartments being over 35 years old), Yamagishi found that:

An old apartment experiences a rent increase of around 3.3% when the minimum wage increases by 10%, which is statistically significant at the 1% level. A very old apartment experiences an increase of around 4%, which is also significant at 1% level. Overall, the result reveals the larger impact on the rents of lower-quality apartments...

Of course, the key point here is that workers on the minimum wage are more likely to live in low-quality apartments than in higher-quality apartments. So, the takeaway from this paper is that higher minimum wages may make some workers better off in terms of higher wages (while also considering the disemployment effects of the higher minimum wage), but that the gains of those workers would be offset somewhat by higher rents. Yamagishi estimates that landlords gain between 7.5-13.5 percent of the increased minimum wage, but the assumptions necessary to arrive at that estimate are a little difficult to justify.

Nevertheless, the overall point stands. The minimum wage workers lose some of their higher minimum wage to higher rents.

[HT: Marginal Revolution, back in 2023]

*****

[*] As an interesting aside, Yamagishi notes that in Japan, 'apartments' are generally low quality. A high quality apartment is referred to as a 'mansion' (see here).

15 years of US research on the minimum wage elasticity of employment

It's time to pick up my recent thread of posts on the minimum wage (most recently in this post). I want to return for a moment to more conventional research on the minimum wage, specifically looking at the effects of higher minimum wages on employment. The majority of minimum wage research has focused on estimating some variation on the minimum wage elasticity of employment - that is, the responsiveness of employment to a change in the minimum wage.

This 2019 article by Paul Wolfson (Dartmouth College) and Dale Belman (Michigan State University), published in the journal Labour (ungated version here), presents a meta-analysis of the findings of 15 years of such research in the US context. As they explain:

The beginning of the New Minimum Wage Research can be dated to a 1991 conference at Cornell University, and the exchange between Neumark and Wascher (2000) and Card and Krueger (2000) in the December 2000 issue of the American Economic Review marks the end of its first period. Our study includes analyses of US data that have appeared after December 2000. We identified 60 analyses, working papers, and published articles that both satisfied these criteria and included at least one estimate of the effect of the minimum wage on employment. In 37 of these 60, either the analysis explicitly reported one or more elasticities and their standard errors or it was possible to calculate them...

As a reminder, a meta-analysis involves combining the results from many other studies in order to estimate an overall effect. Wolfson and Belman's meta-analysis overall includes 739 estimates of the minimum wage elasticity of employment, drawn from those 37 studies. They also run analyses based on the 'best' estimate from each study, as well as the average estimate from each study. All of the analyses result in similar findings.

The starting point for Wolfson and Belman is an estimate from a survey article by Brown et al. published in 1982, which estimated that the elasticity was between -0.1 and -0.3. That would mean that a 10 percent increase in the minimum wage would result in a decrease in employment of between 1 percent and 3 percent. Brown et al.'s estimates were not based on a meta-analysis, but instead based on a narrative review of the literature up to 1982. Obviously, there literature has moved on a lot since then, and new methods and better data have been applied to the question of the employment impacts of the minimum wage.

So, what do Wolfson and Belman find? They report that:

...the range of the employment elasticity has shifted toward zero since Brown et al. (1982), from [-0.3, -0.1] to [-0.13, -0.07]... Teenagers, and eating and drinking establishments together account for more than half of the estimates in our sample. Estimating separate models for teens and for eating and drinking places has little effect on our estimated range, moving it from [-0.13, -0.10] to [-0.11, -0.07]... The minimum wage then has negative employment effects, but estimates of them have become smaller and are largely localized to teenagers, who comprise a declining share of the labor force.

The estimated elasticity range overall of -0.07 to -0.13 implies that a 10 percent increase in the minimum wage would result in a decrease in employment of between 0.7 and 1.3 percent. That effect is small, but it is not zero either. Overall, estimates from the literature do tend to show that higher minimum wages decrease employment (as also noted in some of the more recent findings in my earlier posts - see the list below).

Effects of the minimum wage on the nonprofit sector

After a few days of 'rest' (by which I really mean some intensely long work days), I'm going to pick up again on my recent series of posts about the minimum wage (see here for the most recent post), but returning to more familiar ground - the disemployment effects of the minimum wage. The story we tell using basic supply and demand is that a minimum wage that is above the equilibrium wage in a labour market will reduce the number of jobs (reduce the quantity of labour demanded by employers).

Considering this post from last week (along with others), we should now be recognising that the simple story is incomplete, because there are other margins that employers may adjust along. They might not reduce jobs, but they might change some of the non-monetary characteristics of jobs, for example. Employers may also absorb some of a higher minimum wage in the form of higher costs, and reduced profits.

Minimum wages don't only affect for-profit firms though. They also affect nonprofit firms. And non-profit firms have one margin that they cannot adjust - profits. A non-profit firm cannot sustainably absorb higher costs by accepting lower profits. So, we might expect to see larger disemployment (and other) effects on firms operating in the nonprofit sector.

That is essentially what this 2023 article by Jonathan Meer (Texas A&M University) and Hedieh Tajali (University of Edinburgh), published in the journal Oxford Economic Papers (ungated earlier version here), looks at. Specifically, Meer and Tajali use data from electronic charity filings to the US Internal Revenue Service (IRS) from 2011 to 2017, as well as data for the same years from the Quarterly Census of Employment and Wages, collected by the Bureau of Labour Statistics (BLS). The two different data sources paint a generally similar picture, but the IRS data offers somewhat more detail for the analyses.

Meer and Tajali then look at the difference in impacts of higher state-level minimum wages, differentiating (similar to what Clemens and Strain did in the paper I discussed here) between states that had large minimum wage rate changes (more than US$2), small minimum wage rate changes (less than US$2), and indexed rates (that change annually in step with inflation). Notice though that the threshold between large and small changes is $2, rather than the $1 that Clemens and Strain used. I'm unsure if that is material, but they don't present any alternative analyses based on other thresholds.

Meer and Tajali find that, using the IRS data:

Large statutory changers have a statistically significant 7.1% (s.e. = 2.3%) decrease in employment relative to nonchangers. But states with smaller minimum wage increases see little impact on employment... States with inflation-indexed minimum wages also see a negative effect despite relatively small increases.

The effect for small (less than $2) minimum wage increases is a statistically insignificant decrease in employment of 1.6 percent, while for indexers a minimum wage increase is associated with a 2.3 percent decrease in employment. Those are quite substantial effects (particularly for large minimum wage increases). The BLS data shows that:

...states with large statutory increases see a 2.7% (s.e. = 1.1%) decrease in employment relative to states that did not increase their minimum wage. Small statutory increasers see a negative but imprecise effect, while there is no meaningful impact on indexers.

So the effects are smaller using the BLS data. However, the BLS data:

...only includes organizations with an employee covered by unemployment insurance, it does not include nonprofits without paid workers...

You might think that shouldn't make much difference, but a higher minimum wage will also affect employers who don't pay any of their workers (because those workers are volunteers), because for some of those volunteers a higher minimum wage represents a better 'outside option'. So, with higher minimum wages, nonprofit firms might lose some workers to for-profit firms (or to other nonprofit firms) that are paying the now-higher minimum wage. That would mean that there would likely be larger disemployment effects showing up in the IRS data than in the BLS data. However, I doubt it explains a large proportion of the difference, and in any case, both datasets show statistically significant disemployment for nonprofit firms when minimum wage increases are large.

Moving on to other aspects of nonprofit firms, Meer and Tajali find (using the more-detailed IRS data) that large minimum wage changes are associated with lower grant receipts, less fundraising expenditures (which might partially explain the lower grant receipts), and lower total expenses. Looking at different sizes of nonprofit firms, they find that:

The smallest nonprofits, with three or fewer employees (including those that are entirely volunteer-run), are the most affected. Aggregate employment in this size bin is 25.0% (s.e. = 15.6%) lower in states with large statutory changes relative to nonchanging states. Estimates for other size categories are negative but not statistically significant.

Altogether, it is apparent that employment in nonprofit firms is negatively affected by large increases in the minimum wage, which is consistent with my impression of the overall literature on employment generally.

[HT: Marginal Revolution, back in 2023]

Minimum wages and health

Picking up again on the theme of last week's posts about recent research on the minimum wage, this 2024 article by David Neumark (University of California-Irvine), published in the journal Labour (open access), reviews the literature on the impacts of minimum wages on health and health behaviours. It's somewhat of a systematic review, although it doesn't closely follow the PRISMA reporting guidelines. Nevertheless, it is a helpful summary of the literature relating minimum wages to health, which is important in light of statements such as this one from the American Public Health Association, claiming unambiguously that higher minimum wages would improve health.

As you might expect the reality is somewhat more nuanced. Neumark starts by pointing out why the effect of higher minimum wages on health is theoretically ambiguous:

The potential for higher minimum wages to improve health is clear, as a higher minimum wage unambiguously raises incomes for some workers (and their families). On the other hand, job loss can reduce income among other workers and their families... it is entirely possible that health benefits from income gains for some workers outweigh adverse health effects for others who lose their jobs, perhaps because there are almost certainly more income gainers than job losers. This net gain might be more likely if there was clear evidence that minimum wages raise incomes in lower income families (rather than for low-wage workers). However, the evidence on family income is ambiguous, in part because many minimum wage workers are not in poor or low-income families, and many low-income families have no workers...

That latter point relates to my most recent post on the effect of minimum wages on poverty, covering research by Burkhauser et al. that demonstrated (as has been shown before) that only a minority of minimum wage workers live in poor families. Neumark's review covers 63 published and peer-reviewed articles, mostly using US data, and mostly published in the last decade. He separated his review into sections on adult and teen health, infant and child health, diet and obesity, mental health, suicide, family structure and children, risky behaviour, crime (which seems a little out of place, but many studies that consider risky behaviour also consider crime), and mechanisms that can affect health (like access to health insurance). Neumark briefly summarises each paper, notes some of the positives and negatives of the methods employed, and draws a conclusion about how convincing (or otherwise) each study is (generally on the basis of the methods employed).

There is a lot to unpack in the review, and I'm not going to try to summarise it all here. Instead, here's what Neumark says in the concluding section:

...the evidence, even focusing on the more-compelling studies (which I do), is decidedly mixed. The evidence on overall physical health points in conflicting directions, and may lean toward adverse effects—possibly a reflection, in part, of the conflicting influences of minimum wages on factors that can affect health (related to how higher income is spent). In particular, research on the effects of minimum wages on diet and obesity sometimes points to beneficial effects, whereas other evidence indicates that higher minimum wages increase smoking and drinking and reduce exercise (and possibly hygiene). In contrast, there is rather strong evidence that higher minimum wages reduce suicides, perhaps partly consistent with the evidence on effects on other measures of mental health/depression being either positive or mixed.

Going a little farther afield, research on minimum wage effects on family structure and children indicates that mothers spend more time with children, provides no clear indication of changes in treatment of children, but point to declines in children's test scores—clearly a mixed picture. There are many good studies of the effects of minimum wages on crime, but the conclusions are mixed. Turning to channels of influence on health (most notably, health insurance), the stronger evidence points to declines in employer-provided health insurance, and other adverse effects on potential influences on health, but there is no clear evidence of effects on unmet medical needs.

When Neumark narrows his focus only to those studies where the evidence is most convincing, he concludes that:

...the mixed conclusions on how minimum wages affect health and related behaviors undermine the evidence base for concluding that the minimum wage is an effective means of improving health.

However, one thing that this review highlights is the comparative lack of research on the effect of minimum wages on health, particularly in comparison to, say, studies on the effect of minimum wages on labour market outcomes (of which there are many). It also highlights that few studies, even relatively recent studies, perform even basic supplementary analysis such as placebo checks on the effects of minimum wages on groups unlikely to be affected by higher minimum wages (such as those with high education), and many studies over-control by including unemployment, income, or poverty in their analyses. Clearly, there is substantial scope for additional research in this space. Indeed, in a footnote to the paper, Neumark notes that:

Effects of minimum wages on drug use, perhaps particularly opioids, could impact health and suicides (as well as other outcomes). This would be a natural question to consider. However, I have not found any such evidence.

So, not only is there scope to improve on the extant studies, there is also scope for studies on areas of health that have not been considered to date. Clearly, there will be more research to come on this theme.

Higher minimum wages and poverty revisited

One of the key purposes of a minimum wage is to decrease poverty. However, there is no certainty that poverty would be reduced by a higher minimum wage. In the simplest sense, we can think about several effects of higher minimum wages on poverty. Income goes up for those earning the minimum wage, and therefore poverty may decrease. However, if there is any disemployment (workers losing their jobs) resulting from the minimum wage, poverty may increase. Poverty may also increase if there is significant pass-through of higher minimum wages into prices (as noted in this post), increasing the cost of living for all households. So, whether higher minimum wages increase or decrease poverty, or don't affect poverty at all, is essentially an empirical question (and one that I have written about before).

Until relatively recently though, there was some general consensus among economists that the minimum wage is ineffective at reducing poverty. Aside from the offsetting effects I noted above, the minimum wage isn't really well targeted at the poor (consider, for example, the number of teens from relatively high-income families who work in jobs paying the minimum wage).

So, the lack of an effect of minimum wages on poverty was generally agreed on by economists. Until this 2019 article by Arindrajit Dube, published in the American Economic Journal: Applied Economics (open access), which claimed to show that there was a large effect of higher minimum wages on poverty in the US between 1983 and 2012. That Dube article got a lot of press at the time, and encouraged support for a much higher federal minimum wage, as encapsulated in the US Raise the Wage Act of 2021 (which died in Committee).

Dube's article also shook the consensus among economists, and thus it also attracted attention from other researchers. And unsurprisingly, some have looked closely at the analyses. This 2023 NBER Working Paper by Richard Burkhauser (Cornell University), Drew McNichols (Amazon), and Joseph Sabia (San Diego State University) is one such effort. As they explain:

This study revisits the relationship between minimum wage increases and poverty. We highlight four key results. First, we replicate and reassess the findings of Dube (2019), based on poverty data from the March 1984 to March 2013 CPS (corresponding to calendar years 1983-2012). After precisely replicating his estimates, we show that his results are driven by two specification choices: (1) the inclusion of macroeconomic controls (the state unemployment rate and per capita state Gross Domestic Product) that may also capture a mechanism through which the minimum wage affects poverty: its employment and hours effects, and (2) restricting treatment states’ counterfactuals to states within the same census division (“close controls”), even when geographically proximate states are rejected by a data-driven synthetic control approach to generate counterfactuals. When we (1) use the state house price index and the unemployment and average wage rate among more highly educated individuals to control for state macroeconomic conditions that are less likely to capture pathways through which minimum wages affect poverty in a difference-in-differences framework, or (2) allow states outside a treatment state’s census division to serve as potential donors in a synthetic control framework, we find no evidence of poverty-reducing effects of the minimum wage over the 1983-2012 period... The 95 percent confidence intervals around our preferred estimates rule out poverty elasticities with respect to the minimum wage of less than -0.138, which include central estimates reported by Dube (2019).

In other words, Burkhauser et al. show that Dube's results are not robust to several modelling choices that Dube made. When the models are run with different control variables, or with a different selection of control states, there are no negative effects of higher minimum wages on poverty. Also:

...when we explore the most recent decade of CPS data, which captures the years following the Great Recession (2010-2019), the contemporaneous and longer-run poverty findings reported by Dube (2019) are largely absent, including in models that use Dube’s preferred macroeconomic controls or controls for spatial heterogeneity. Specifically, we find no evidence that post-Great Recession minimum wage increases had a statistically significant or economically important effect on poverty.

So, Burkhauser et al. show that Dube's results are sensitive to the choice of the time period that the dataset covers. And then:

...when we combine the two data windows discussed above and amass our “full panel” from 1983-2019, we find little support for the hypothesis that minimum wage increases reduce poverty over this 37-year period. Estimated elasticities below -0.131 for non-elderly individuals (and below -0.129 for all persons) lie outside of our 95% confidence interval, which would rule out the central long-run estimate reported by Dube (2019). Our preferred estimate shows that a 10 percent increase in the minimum wage is associated with a (statistically insignificant) 0.17 percent increase in the probability of poverty among all persons.

So, using the broader dataset from 1983 to 2019, the effect of higher minimum wages on poverty is small and statistically insignificant. Finally, Burkhauser et al. reiterate earlier findings in the literature, by showing that:

...less than 10 percent of those whose hourly wage rate would be directly impacted by a $15 minimum wage live in poor families. Approximately two-thirds live in families with incomes over two times the poverty line and nearly half live in families with incomes over three times the poverty line.

Burkhauser et al. conclude that:

In summary, our findings provide little compelling evidence that raising the minimum wage will be an effective or target efficient policy tool for alleviating poverty.

Burkhauser et al.'s paper is a comprehensive and systematic take-down of Dube's earlier work. And, it reestablishes the earlier consensus - higher minimum wages do not reduce poverty (at least, in the US - the paper I discussed in this earlier post showed some short run, but not long run, effects on poverty in Brazil). As with all research, it pays not to overcorrect greatly on the basis of a single new research paper. Policy makers would do well to remember that.

[HT: Marginal Revolution, back in 2023]

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Saturday, 8 November 2025

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Monday, 3 November 2025

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Sunday, 28 September 2025

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Sunday, 27 July 2025

The Cristiano Ronaldo effect on the Saudi Pro League

Wednesday, 5 March 2025

Minimum wages and alcohol consumption

Tuesday, 4 March 2025

Local minimum wages and low-quality housing rents in Japan

Monday, 3 March 2025

15 years of US research on the minimum wage elasticity of employment

Wednesday, 19 February 2025

Effects of the minimum wage on the nonprofit sector

Saturday, 15 February 2025

Minimum wages and health

Wednesday, 12 February 2025

Higher minimum wages and poverty revisited

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