How Alaska is like South Waikato

I've written a couple of times about the challenges that South Waikato faces in attracting workers (see here and here). This problem is not unique to South Waikato - a lot of rural and remote areas face the same challenges. In just the latest example I've seen, Jordan McGillis reports in City Journal on the situation in Alaska:

My analysis of Census data finds that more than 40 percent of civilian, prime-age, noncollege Alaska men employed in blue-collar occupations earn at least $75,000. More strikingly, 9.4 percent of Alaska’s civilian, prime-age, noncollege men work a blue-collar job and earn at least $100,000, compared with 3.6 percent nationally, ranking Alaska first in the country. Among those in such occupations more than one in four earns six figures...

That’s no surprise to [Ray Weber, Dean of technical and vocational education at the University of Alaska Anchorage]. “Can [wages in Alaska] be higher [than in the rest of the country]? Yes, especially Slope jobs or jobs that suck. Like, we have electrical linemen that go across the state. They’re going to Unalakleet, the only way to get there is by airplane. And there’s one pizza joint. One. No other restaurant. You generally end up sleeping either in a bunkhouse, if you’re lucky. Or you’re sleeping in the school auditorium. . . . I like Alaska, but we’re asking the wrong questions if you’re saying, ‘high-paying, lucrative jobs.’ What do the younger generation consider important? The answer is: Things that are not in Alaska.”

Economists call this a compensating wage differential: the premium required to induce workers to accept jobs with undesirable nonpecuniary characteristics. Work in Alaska is colder, darker, lonelier, and often more dangerous. The roughly $120,000 premium earned by North Slope oil-and-gas workers over their counterparts elsewhere, and the roughly $40,000 premium earned by electricians deploying to isolated communities such as Unalakleet, are partly the price employers must pay to fill jobs few want.

Workers with the same skills can often accept lower wages in Texas or Louisiana in exchange for a more attractive lifestyle.

Wages differ for the same job in different firms or locations. Consider the same job in two different locations. If the job in the first location has positive non-monetary characteristics (e.g. it is in an area that has high amenity value, where people like to live), then more people will be willing to do that job. This leads to a higher supply of labour for that job, which leads to lower equilibrium wages. In contrast, if the job in the second location (Alaska, for example) has negative non-monetary characteristics (e.g. it is in an area with lower amenity value, where fewer people like to live, where it is cold, dark, lonely, and more dangerous), then fewer people will be willing to do that job. This leads to a lower supply of labour for that job, which leads to higher equilibrium wages. As McGillis notes, the difference in wages between the attractive job that lots of people want to do and the unattractive job that fewer people want to do is called a compensating differential. The compensating differential essentially compensates workers for working in jobs with negative non-monetary characteristics compared with working in jobs with positive non-monetary characteristics.

And so, jobs that pay less in the rest of the US must pay much more in Alaska in order to attract workers.

Read more:

• Reason to be wary if a job in Taumarunui offers an Auckland salary
• The compensating differential for rural GPs must be enormous
• Compensating differentials are alive and well in Tokoroa
• Rural Australia sets a new high bar for compensating differentials
• The $300,000 job that no one wants
• The South Waikato compensating differential strikes again

This week in research #146

Here's what caught my eye in research over the past week:

• James and Kjorstad find that spelling and grammar errors significantly reduced perceived research quality within a sample of US economists, while manuscripts prepared in LaTeX were generally evaluated more favourably than those prepared in Microsoft Word within an international sample of economists
• Fornwagner (with ungated earlier version here) finds no effect of menopause on women’s risk preferences, using an online sample of over 1700 UK women
• Wang, Wang, and Zhou (with ungated earlier version here) use global air traffic data as instrumental variable to estimate the effect of in-person interactions on bilateral trade, finding that online interactions are an imperfect substitute for in-person contact
• Garthwaite et al. (with ungated earlier version here) find that the rent-sharing between college basketball and football programmes and other sports transfers spending away from students who are more likely to be Black and come from poor neighbourhoods toward students more likely to be White and come from higher-income neighbourhoods

The declining returns to higher education in Australia

In yesterday's post, I discussed a recent meta-analysis on the returns to compulsory schooling. That research was necessarily focused on secondary education. What about the returns to higher education? Two recent articles tell an interesting story about the change over time in returns to higher education in Australia.

The first article is this one by Elisa Birch and Alison Preston (both University of Western Australia), published in the journal Economic Record (open access). Using data from the Household, Income and Labour Dynamics in Australia (HILDA) survey, they document a striking decrease in the returns to higher education over the period from 2001 to 2023. They apply a fairly standard Mincer wage equation, comparing hourly earnings between workers aged 28 to 38 years with no post-secondary qualification to those with one of seven categories of education: PhD; Master’s; Graduate Diploma; Graduate Certificate; Honours; Bachelor’s; and Diploma/Certificate. Strictly speaking, these estimates are wage premiums associated with different qualifications, rather than necessarily causal estimates of the returns to obtaining those qualifications. Nevertheless, they do show some interesting trends, and their headline results are summarised in Figure 2 from the paper:

Notice that, in all cases (except, arguably, Graduate Certificates), the general trend in the returns to higher education has been downward. The wage premium between higher education and no post-secondary education is lower in 2019-2023 than it was in 2001-2005. Birch and Preston also look in more detail at the difference in returns between the early period in their data (from 2001-2011) and the later period (from 2011-2023), and the differences by gender, and find that:

...over the study period, the wage premium for a Master’s degree declined by 13.2 per cent for males. For females, the decline in the Master’s wage premium was even more pronounced, falling by 16.6 per cent... The reduction in the return to a Bachelor’s degree was similar for both males and females, with a decrease of approximately 13 per cent in both groups. In Period 2, the wage premium on a Master’s degree was equal to 35 per cent among men and 26 per cent among women (a gender gap of 9 percentage points). Among Bachelor’s degree holders the wage premium in 2012–23 was equal to 27 per cent among men and 22 per cent among women (a gender gap of 5 percentage points).

So, there isn't even anything positive to say about the changes in relation to the gender wage gap. The higher wage premium for male graduates remained the same, or increased, compared to the wage premium for female graduates. Even worse, when looking across the whole wage distribution, Birch and Preston find that:

While the male wage structure appears to have shifted downward and flattened, the female wage structure shows both a downward shift and a notable decline in returns at higher wage levels.

So, the returns to higher education have been declining in Australia (a result that other studies have also found), particularly at the upper end of the wage distribution for women. Birch and Preston largely leave open the question of why. That is where this new article by Michael Coelli and Jeff Borland (both University of Melbourne), published in the journal Australian Economic Review (open access), comes in.

Coelli and Borland first note that the decline in the returns to higher education is concentrated in the period after 2001, using five-yearly Australian Census data from 1981 to 2021, and using income as a measure (rather than hourly earnings, which isn't available in the Census). Then, they explore different explanations for why the returns to higher education changed, focusing on explanations that might explain a decrease starting around 2001. They find little support for a slowdown in skill-biased technical change relative to increases in the supply of skilled workers. Instead, they find that the decrease is associated with a relative increase in the wages of workers with no post-secondary education since 2001, and link that to two main probable causes.

First, the Federal Minimum Wage began increasing in real terms from 1996. On top of that:

The method of setting wages for employees covered by awards during 1993 to 2010 may also have raised the relative wages of low‐skill employees. During that time, the annual increases to rates of pay for employees covered by awards were made in flat dollar amounts, resulting in higher percentage growth in wages for lower wage earners. This may have underpinned stronger wage growth in low‐skill occupations... Award rate increases were above inflation at the lower end of the wage distribution (especially at the minimum wage), but below inflation further up the distribution. Since 2011, a common percentage increase has been set for employees covered by all awards.

So, the process of wages being determined by awards, alongside an increasing real minimum wage, may have compressed the wage distribution, especially before 2011. This would have the effect of raising wages for workers at the bottom of the wage distribution, including many workers without post-secondary education, relative to workers at the top of the wage distribution, including many workers with higher education.

Second, Coelli and Borland note that:

Starting around 2001, Australia experienced a large and extended boom in mining, coinciding with the rise of China in global trade after its accession to the WTO. This affected employment in mining and in construction. Its impact was felt more strongly among low‐skilled workers than among the higher‐skilled.

Since workers with no postsecondary education are more likely to be working in mining and construction than workers with higher education, the former workers likely benefited disproportionately from the mining boom.

Alongside those two explanations, Coelli and Borland also note a phenomenon that they call 'occupational downgrading':

Growth in the supply of workers with a bachelor's degree generally exceeded demand growth throughout the 1981 to 2021 period, leading to them moving down the jobs ladder.

These three explanations (minimum wages and wage awards; the mining boom; and occupational downgrading) are Coelli and Borland's explanations for the decrease in the returns to higher education. They do leave some questions from Birch and Preston unanswered though - why has the decrease in the returns to higher education been concentrated among the top of the wage distribution for female workers? The top of the wage distribution is least likely to be affected by minimum wages and wage awards. However, that leaves the other explanations for the decrease. Is it because female workers are more likely to 'occupationally downgrade'? Or, did female workers miss out on the mining boom? Clearly, there is more research to be done here.

Read more:

• Internal rates of return for college majors in the US
• The returns to each additional year of compulsory schooling

The returns to each additional year of compulsory schooling

This week, my ECONS102 class is covering the economics of education. Part of that topic considers the private education decision - the decision each individual makes about the amount of education they receive. This depends on the private costs and benefits of education. Arguably the primary benefit of education is incremental income - the additional lifetime earnings that education brings.

How big are those gains? Previous studies I have seen (such as a study reported in Paul Oyer's book Everything I Ever Needed to Know about Economics I Learned from Online Dating (which I reviewed here)) have estimated effects in the order of 10 percent per additional year of education. However, that was a single study, which could easily provide a biased view if considered in isolation.

What are the effects more generally? This recent article by Gregory Clark and Christian Nielsen (both University of Southern Denmark), published in the journal Kyklos (open access), provides an answer based on a meta-analysis of 79 causal estimates of the effect of an additional year or years of compulsory schooling on earnings, drawn from 53 other papers that apply methods consistent with causal inference. Meta-analysis provides a method of combining the estimates across many studies into one overall estimate. Importantly, Clark and Nielsen apply various methods to correct for the effects of publication bias (which is the tendency for statistically significant results to be more likely to be published, while statistically insignificant results tend to be missing from the research record).

Clark and Nielsen report baseline results that are somewhat lower than I expected:

With these procedures the average percentage gain in earnings from an additional year of schooling was 8.2% for 79 independent estimates, from 53 papers... If these estimates are weighed by their precision in a random effects estimation, the gains are reduced to 6.0%, and similarly 6.2% for a fixed effects weighting.

Given that the true effect is likely to vary across populations and study settings, I would consider the random-effects estimate of 6.0 percent for each additional year of schooling as the more plausible of the three. However, Clark and Nielsen also employ meta-regression, using PET (Precision Effect Test) and PEESE (Precision Effect Estimate with Standard Error) regression models, which are designed to address imprecision and publication bias in the overall sample of results. After removing some outliers, these approaches suggest:

...a true effect of 6.1%–6.4% to an extra year of education and an insignificant effect of publication selection...

So again, it seems like the effect may be in the order of six percent per additional year of schooling. Interestingly, Clark and Nielsen themselves note that the PET-PEESE evidence for publication bias is highly model-dependent, and that these results could instead reflect genuine heterogeneity in the true effect. However, Clark and Nielsen go a little further, looking in more detail at the distribution of effects across the 79 estimates. They note that if the estimates of the earnings return were normally distributed, then there are a lot of 'missing' studies with negative returns. They also consider whether the underlying returns might be log-normally distributed. Once you consider sampling error, the log-normal distribution can still generate negative estimates, and Clark and Nielsen argue that there are fewer negative estimates in the published literature than the model would predict. Consider Figure 10 from the paper:

In the figure, the yellow bars show a histogram of the 79 estimates in the sample. The blue line shows a log-normal distribution based on the estimates. Notice that, compared with the log-normal distribution, there are more studies than expected with estimates in the 0-4 percent range, and too few in the negative range. Clark and Nielsen argue that, if the 'missing' studies were included, a better estimate of the returns to each additional year of schooling may be in the range of 0-3 percent.

However, I’m not convinced that Clark and Nielsen’s distributional assumptions are justified here. Figure 10 (like Figure 8 in the paper) shows that the observed distribution of estimates doesn’t match their chosen distribution. But they haven’t established that their chosen distribution is what we should expect in the absence of publication bias. That distribution would depend on differences in estimation methods, the precision of the estimates, and genuine differences in returns across the populations studied. A mismatch between their chosen distribution and the observed study estimates therefore does not, by itself, demonstrate that studies with negative estimates are missing. So, while publication bias remains a possible explanation, I would treat Clark and Nielsen’s proposed 0-3 percent range for the average return very cautiously.

Nevertheless, overall it does seem clear from their other results that the returns to an additional year of schooling may be somewhat lower than the 10 percent found in other studies. What would be interesting to know next is whether the estimated returns have been changing over time (which my next blog post will look at, for Australia), and what study features lead to different estimates. Clark and Nielsen do note that instrumental variable regression leads to higher estimates than difference-in-differences or regression discontinuity estimates. However, there are other contextual variables that may matter as well. Hopefully, someone else can pick up on this work and take it to the next step. That might help to distinguish publication bias from the more mundane possibility that there simply isn't a single 'true' return to an additional year of schooling.

Read more:

• Internal rates of return for college majors in the US