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

The costs of traffic noise

Traffic noise is an example of a negative externality - the impact of an action on a third party (a 'bystander') who has not consented to or played any role in the carrying out of that action. In this case, traffic noise caused by drivers on the road negatively affects people who live nearby, and those people haven't played any role in creating the noise.

To the extent that traffic noise negatively affects people living nearby, it should be reflected in property prices. Under hedonic demand theory, when someone buys a property they are really buying a bundle of characteristics of the property, one of which is the presence of traffic noise. Since traffic noise is a negative characteristic, we would expect properties that are exposed to more traffic noise to have lower prices, ceteris paribus (holding all else constant).

That means that the cost of traffic noise can be evaluated by carefully looking at the relationship between property values and traffic noise. That is what this 2025 NBER Working Paper (ungated here) by Enrico Moretti (University of California, Berkeley) and Harrison Wheeler (University of Toronto), sets out to do. They start by looking at the effect of roadside noise barriers on property prices, noting that these barriers provide an exogenous source of variation in traffic noise. They compare houses that are close to the barrier (within 500 metres directly away from the barrier) with those that are further away (between 500 and 1500 metres), using a difference-in-differences (DiD) strategy. That involves comparing the change in property prices between before and after the barrier was erected, between properties close to and those further away.

Moretti and Wheeler primarily use data from Florida, which provides details about the noise barriers that were completed, but also about barriers that were proposed but not completed. That also allows them to use a 'triple-differences' strategy, by matching areas that had a barrier erected, with those that didn't (but where one was proposed). Now, it turns out that the results from the triple-differences model are similar to the more standard DiD, but that should provide some further comfort with the robustness of the DiD results. The data on house prices and other characteristics comes from CoreLogic and covers the period from 1990 to 2022 (house prices) or 2006 to 2022 (property characteristics). Their final dataset includes nearly 600,000 home sales within 1500m of a noise barrier (and on the same side of the road as the barrier).

Focusing on their results that include parcel fixed-effects (so that time-invariant property characteristics are controlled for), Moretti and Wheeler find that:

For houses within 100 m of the barrier, the estimated effect increases to 8.59%. For houses 100–200 and 200–300 m from the barrier, the estimated effects increase to 5.79% and 4.41%, respectively. The effect on properties 300–400 m from the barrier is marginally statistically significant.

So, reducing traffic noise increases property values, and the effect is largest for properties closest to the road generating the noise. Beyond about 300 metres, the effect is statistically indistinguishable from zero, but within 300 metres, the construction of a noise barrier increases property values by between 4.41 and 8.59 percent. Moretti and Wheeler then note that:

Since our data report the construction cost of each barrier, we can compute the marginal value of public funds (MVPF), defined as the property value appreciation over costs... The average MVPF for barriers that were built amounts to 1.7, while the MVPF for barriers proposed but not built is 1.4. This is to be considered as a back-of-the-envelope calculation that ignores property taxes. Property taxes would reduce both the social benefits (since some of the home value increase gets taxed), and the social costs (since property taxes end up in local government coffers).

So, on this measure at least, noise barriers appear to be a good use of public funds, since the increases in property values exceed the costs of erecting the barriers (although noting that there may be other uses of public funds with even higher MVPF values).

Next, Moretti and Wheeler change their model in order to allow the change in price to vary based on the expected decibel reduction. They use a model where the effect is non-linear in noise reduction, and find that:

The effect plateaus at 10 dB of reduction, which represents the 96th percentile in our sample. The effect is estimated to be zero when noise reductions are 4.9 dB...

The average barrier in our sample reduces noise by 7.15 decibels. At this level of noise reduction, our estimates imply that the average price of a decibel is 0.94%...

That is the result that Moretti and Wheeler use later in their paper to estimate the economic cost of the externality. However, first they need to rule out some competing explanations for their effects. They show that air quality is somewhat better near to the barriers, but the effect is small and not statistically significant. They show that the results do not change when accounting for tree canopy or the presence of other buildings, meaning that noise barriers blocking views of the road is unlikely to explain the results. And, they show that the construction of new homes (with higher unobserved quality) does not explain the results.

Moretti and Wheeler next use their results to estimate the economic cost of the externality for each census tract in the US. From that, they find that there is:

...a negative correlation between the cost of the externality and median family incomes. The slope is -0.10 (0.01), indicating that a 10% lower income is associated with a 1% higher per capita cost. The correlations with the share of residents who are Black and the poverty rate are positive. The slopes are 0.08 (0.01) and 0.63 (0.05), respectively, indicating that a 10 percentage point higher share of Blacks or a 1 percentage point higher poverty rate are associated with 0.8% and 0.6% higher per-capita costs...

That means that the externality cost is regressive. That is, the cost of the externality is a larger proportion of income for low-income families than for higher-income families (taking into account the location of low-income and higher-income families and the property values where they live and the traffic noise they face).

In total, Moretti and Wheeler estimate the cost of traffic noise to be US$7.0 billion in Florida, and $109.75 billion for the US as a whole. Looking across cities, they find that per-capita traffic noise costs increase with urban share of the population and population density, which they suggest is because cities with greater urban share or those that are denser have both higher noise exposure, and higher property values.

Finally, Moretti and Wheeler estimate that a one-off Pigovian tax equivalent to US$974 per car would be equal to the marginal external cost of the traffic noise externality (noting that the optimal Pigovian tax is one that is equal to the marginal external cost). They also estimate the potential benefit of a move to 100 percent electric vehicles (which have lower engine noise) at US$5.39 billion for Florida, and $77.28 billion for the US as a whole.

This research tells us that traffic noise is a costly negative externality. Those costs are capitalised into property values and are borne disproportionately by lower-income households. Fortunately, the research also suggests that there are worthwhile ways of reducing those costs, including erecting noise barriers and rolling out more electric vehicles, and the benefits of those solutions may be substantial.

[HT: Marginal Revolution, last year]

Climate change and the tragedy of the commons

My ECONS102 class covered externalities and common resources this past week. In the final slide of content in my lectures, I talked about the challenges of getting global agreement on climate change, because the atmosphere's limited capacity to absorb emissions without causing harmful climate change provides a special case of the tragedy of the commons.

Why is climate change a common resource problem? Common resources are rival and non-excludable. The atmosphere's capacity to absorb emissions is both rival (one country's emissions reduce the capacity available for other countries) and non-excludable (if the capacity is reduced for one country, it is reduced for all countries). The social incentive is for all countries to reduce emissions to the point where the marginal social cost of emissions is equal to the marginal social benefit. The private incentive for each country is to reduce emissions only to where marginal private cost of emissions is equal to marginal private benefit for that country. Each country’s emissions impose a cost on other countries, meaning that each country doesn’t face the full cost of their emissions (so the marginal social cost of emissions is greater than the marginal private cost for each country), so they will emit too much. And since all countries have the same incentive, there are too many emissions relative to the socially optimal quantity.

Within a country, we might be able to solve a common resource problem like this by relying on the government to assign some form of property rights. However, there is no supra-national government to perform this role, so that means we need to arrive at a 'private solution' (albeit one where the private actors in the negotiation are countries).

The 2009 Nobel Prize winner Elinor Ostrom noted that users of a common resource may be able to solve the problem by working together (a common governance approach). A number of things would likely be necessary for such common governance to work. Ostrom noted a number of principles for common governance, one of which was that the boundary of the common resource and the group of users must be well-defined. In the case of climate change, the boundary is the environment, and the group of users is all countries. So, that principle would be no problem, provided all countries agreed to be involved (and that may be a challenge).

For common governance to be successful, the user community must also be relatively homogeneous, so that they can trust each other and develop common goals (and norms or customs) for protecting and allocating the resource. Here is where the challenge lies. The user community (countries) are not homogeneous at all. Countries at different levels of development have different goals and aspirations, and see the role of emissions in contributing to those goals and aspirations differently. And it seems unlikely that countries will really trust each other to do the right thing in relation to any climate agreement.

Ostrom also noted that protecting the resource would be best achieved through persuasion rather than coercion, since this would maintain trust within the user community. Persuading other sovereign countries to do something that makes them individually worse off is obviously a challenge. And so climate change remains one of humanity's greatest challenges. This isn't to say that it isn't an important challenge to solve, only that there are good reasons why, nearly 50 years on from the First World Climate Conference in Geneva in 1979, we are still looking for an effective agreement to protect the climate.

Don't just take my word for it though. This 2012 article by Niggol Seo (University of Sydney), published in the journal Economic Affairs (sorry, I don't see an ungated version online), outlines the case, supported by some estimates of the globally optimal policy (as it would have been at the time). Seo uses the model results to outline the incentives that each of thirteen world regions face in global negotiations over climate change. Seo compares a business-as-usual (BAU) scenario with a scenario based on the globally optimal policy (GOP). Focusing on the GOP scenario, the net costs of addressing climate change in that scenario for seven of the largest world regions are shown by Figure 2 from the paper:

Notice the large costs that China (green) and the US (blue) would face over the entire period up to the end of the century. It should be little wonder, then, that those two countries in particular would have less incentive to agree to emissions restrictions to address climate change. In contrast, the EU, India, Africa, Latin America, and Russia face more modest costs initially, and by 2075 (or 2065 in the case of Africa), the GOP scenario actually shows net benefits for those regions. Again, it should be little wonder that they have greater incentive to support of climate change agreements. Seo concludes that:

...some countries have a strong incentive to push for global regulation due to the expected reduction in climate related damages. The optimal regulation saves these countries hundreds of billions dollars annually by the century’s end. However, it would cause additional costs to China, Russia, Canada and the USA.

To that, I would observe that it particularly impacts China and the US. Climate change is a challenging problem. For an efficient global agreement that would adequately address this challenge, we need all countries to participate. However, the incentives do not necessarily help us to achieve cooperation.  Countries that face relatively low net costs from addressing climate change may need to offer transfers, concessions, technology, or some other form of compensation to countries that face high net costs, in order to change their incentives and get them on board. We may not think that outcome is fair. However, achieving an efficient and effective agreement may require some compromise on fairness, if that is what is needed to ensure that the incentives encourage all countries to participate.

Read more:

• Reason to be surprised, or not, about the Paris Agreement on climate change coming into force

This week in research #145

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

• Guzman, McGuinness, and Turner investigate 16 'mega-universities' in the US, and report that students at these mega-universities have lower average completion rates and leave with higher average student loan debt compared to students at other schools
• Melo et al. (open access) find little evidence that higher minimum wages increase job search for low-skilled jobs, and more evidence that higher minimum wages decrease the number of workers seeking employment in these jobs (file this under surprising results on the supply side of the labour market)
• Malesky et al. (with ungated earlier version here) find that cohorts exposed to the university expansion in Vietnam are 87% more likely to have a university education, but that education increases the incidence of bribe requests and perceptions of corruption

Minimum wages and the adoption of robots

Manufacturing firms typically have a choice of various production technologies. Some production technologies involve more labour. Others involve more automation (robots, as in this post). If labour becomes relatively more expensive compared with robots, firms have a greater incentive to adopt robots. That suggests that higher minimum wages, which make labour relatively more expensive for firms, may not only decrease employment (see the links at the end of this post for more on that point), but may increase the adoption of robots.

The extent to which firms adopt robots in the face of increasing minimum wages is the subject of this recent NBER Working Paper by Erik Brynjolfsson (Stanford University) and co-authors (ungated version here). They look at this question in two ways. First, Brynjolfsson et al. create a state-level measure of exposure to robots, which captures the extent to which robots are over- or under-adopted in each state, given the state's mix of industries and employment. They then correlate changes in that measure with changes in the state-level minimum wage over the period from 2003 to 2015. That correlation is illustrated in Figure 1(c) from the paper:

The regression line in the figure implies that a 10 percent increase in the minimum wage is associated with an increase in robot exposure equivalent to about 8 percent of the sample mean level of robot exposure.

Second, Brynjolfsson et al. use microdata from the US Census Bureau, including the Longitudinal Business Database and Longitudinal Firm Trade Transactions Database (LFTTD), to construct a panel dataset of robot adoption among US manufacturing firms from 1992 to 2021. They use the LFTTD data to identify which firms imported industrial robots. They then compare robot adoption between firms in adjacent counties on opposite sides of state borders, which face different state-level minimum wages but are likely to share many local economic conditions. Their measure of robot adoption in this analysis is simply whether a given firm adopted a robot in a given year, or not. In this second analysis, they find that:

...a 10% increase in minimum wage leads to an 8.4% rise in robot adoption relative to the sample average...

Notice how similar in magnitude the effects from their two analyses are, despite being quite different in nature, as well as covering different time periods. Both analyses suggest that higher minimum wages are associated with greater levels of robot adoption. The state-level relationship in the first analysis is clearly correlational, rather than causal. However, the comparison between firms in adjacent counties provides some plausibly causal effects (at least, there are plenty of other research papers that use a similar approach to estimate the causal effects of minimum wages). More generally, this research provides another example of how firms may respond to minimum wages on margins other than employment (see the links at the end of this post for more). When the relative price of labour increases, firms may change not only how many workers they employ, but also the production technology they use.

[HT: Marginal Revolution, back in February] 

Read more:

• A bunch of research findings on minimum wages in New Zealand
• The disemployment effects of Canadian minimum wages
• Jeffrey Clemens on the disemployment effects of the minimum wage
• The impact of an online minimum wage
• Seattle's minimum wage, revisited
• The latest evidence supports negative employment effects of the minimum wage
• Latest research suggests the minimum wage DOES reduce employment
• More empirical support for the disemployment effects of the minimum wage
• The minimum wage and job vacancies
• The minimum wage and hiring standards
• The employment effects of the minimum wage, from American Community Survey data
• The minimum wage and homelessness
• New York restaurants find a new way to respond to the minimum wage
• The minimum wage and workplace safety
• How employers respond to minimum wage increases
• Minimum wage increases and employer noncompliance
• Higher minimum wages and poverty revisited
• Minimum wages and health
• Effects of the minimum wage on the nonprofit sector
• 15 years of US research on the minimum wage elasticity of employment
• Local minimum wages and low-quality housing rents in Japan
• Minimum wages and student summer employment

Evidence that tradeable pollution permits can work

This week, my ECONS102 class covered externalities. As part of the topic, we spent a bit of time considering the economics of pollution control, where the government has three main options: (1) regulation (command-and-control); (2) Pigovian taxes; or (3) tradeable pollution permits. I've posted before about the latter two options (see here and here).

Tradeable pollution permits often attract attention from my students, as it seems surprising that granting polluters 'the right to pollute' might be an effective way of reducing pollution. However, economic theory suggests that this can be both an effective and a cost-effective way of reducing pollution. And the research reported in this 2025 article by Michael Greenstone (University of Chicago) and co-authors, published in the Quarterly Journal of Economics (ungated earlier version here), provides some compelling evidence that tradeable pollution permits are effective.

Greenstone et al. collaborated with the Gujarat Pollution Control Board (GPCB) in India to design and experimentally evaluate a particulate-matter emissions market, the first market of its type anywhere. They describe the experiment as follows:

GPCB launched the market for industrial plants in and around Surat, Gujarat, a rapidly growing city of 7 million people, in 2019. Under the command-and-control status quo, plants are mandated to install abatement equipment and are sporadically inspected in person by government regulators and auditors to check that they meet limits on the concentration of pollution emissions... For the present experiment, GPCB mandated a sample of 318 large, coal-burning plants to install continuous emissions monitoring systems (CEMS) to measure the total mass of particulate matter (PM) emitted, compared with the measurement under the status quo of pollution concentrations during spot visits... The emissions market experiment then randomly assigned 162 out of 318 plants to the market and 156 control plants stayed under the command- and-control regime.

The tradeable pollution permits market worked much as we describe in class:

GPCB set a cap on the total mass of particulates that could be collectively emitted by all treatment plants over a compliance period. They allocated permits to treatment plants, with permits summing to 80% of the cap distributed for free, in proportion to plant emissions potential, and 20% sold off in weekly auctions. Thereafter, treatment plants could trade permits with each other. At the conclusion of each compliance period, any treatment plant that did not hold enough permits to cover their emissions was subject to fines based on the size of the shortfall.

As I note in my ECONS102 class, the advantage of allowing pollution permits to be traded is that plants with relatively low abatement costs (low costs of reducing pollution) have an incentive to sell their surplus permits and abate pollution instead, while plants with relatively high abatement costs have an incentive to buy permits rather than undertake costly abatement. Transferability is one of the important features of efficient property rights, and having permits that are tradeable helps to ensure that.

Greenstone et al. look at compliance (did they have enough permits to cover their emissions), and then compare treatment and control plants in order to estimate the effect of the permit scheme on particulate emissions and variable abatement costs. They find that:

Treatment plants complied—held enough permits to cover their emissions—in 99% of plant-periods. By contrast, the compliance rate with concentration standards at baseline was 66%...

Second, the treatment reduced particulate emissions by 20%–30%, relative to control-plant emissions in the command- and-control regime...

Our third main finding is that the market reduced variable abatement costs by 11% at a constant level of emissions.

Taken altogether, those results provide strong field experimental evidence that the permits scheme worked as intended, that treatment plants were compliant, and that emissions decreased while also decreasing abatement costs. Greenstone et al. then conduct a cost-benefit analysis of an expansion of the market. Based on a range of assumptions on the mortality effects of particulate pollution, they estimate that the benefits of the market are at least 25 times larger than the costs. Needless to say, that suggests that tradeable pollution permits have been strongly worthwhile in this setting, and that they would be worth exploring in other settings as well. And in a postscript in the conclusion to the paper, Greenstone et al. note that the GPCB has launched another particulate market in the largest city in the state, Ahmedabad. So clearly, the GPCB was sufficiently convinced that they decided to extend the approach elsewhere.

[HT: Marginal Revolution, back in 2024]

Read more:

• The inefficiency of New Zealand's emissions trading scheme
• Pigovian taxes vs. tradeable pollution permits when clean technology becomes available
• The Greens vs. Labour on carbon emissions, taxes and permits

Taking window shopping to the next level online

When a consumer consumes a good or service, they receive utility (satisfaction, or happiness) from that consumption. That is part of the standard theory underlying consumer behaviour in neoclassical economics. Behavioural economists like the Nobel Prize winner Richard Thaler, in contrast, may distinguish between two types of utility that a consumer receives when they buy a good or service. First, there is acquisition utility, which is the net benefit from acquiring the good or service relative to what the consumer gives up to get it. This is essentially the neoclassical utility from the good or service, after allowing for the cost of purchasing it. Second, there is transaction utility, which is the utility received from how good or bad the deal itself feels to the consumer, compared to what they expected to pay for the good or service.

Transaction utility arises when consumers feel like they are 'getting a good deal'. The better the deal, the greater the transaction utility. One problem is that transaction utility is largely temporary. While acquisition utility essentially lasts as long as the good or service that is purchased, transaction utility may last only as long as the transaction. For that reason, transaction utility may explain the phenomenon of 'buyer's remorse'. The purchase seems like a great idea at the time of purchase, but later, once the transaction utility has dissipated, the purchase doesn't seem like such a great idea at all.

Ordinarily, acquisition utility and transaction utility come as a bundle. When you buy a good or service, you get both. That is, until now. An article in Rest of World reported last month:

This week, I placed orders for a $44,860 Patek Philippe hand-engraved watch, a $12,500 Hermès handbag, a $9,800 Tiffany diamond ring, and a $7,350 Cartier Love bracelet in yellow gold.

I don’t need any of them. I certainly can’t afford them. Thankfully, they’ll never arrive.

Instead of Amazon, I spent the past week browsing a new breed of websites known as “dopamine sites,” a trend that emerged in South Korea. These websites — like Dopamine Shop and FoodNeverComes — recreate the entire ritual of online shopping: You search for products, compare reviews, add items to your cart, enter a shipping address, place an order, and even track your delivery. 

Then … nothing happens. No money changes hands. No package arrives...

Purchasing and shopping are not necessarily the same act. The former is about acquiring and the latter is more of a ritual. The pleasure from dopamine websites comes from the ritual.

In some ways, this is just a high-tech version of window shopping. People have always been able to get some enjoyment from browsing goods that they have no intention of buying. But these websites go a step further by recreating the transaction itself, right through to placing the order and tracking its delivery, while removing the actual receipt of the good or service that was 'purchased'.

The article argues that the pleasure comes from ritual. However, at least some of the pleasure may be transaction utility. There is no acquisition utility here. The consumer knows from the outset that they will never receive the watch or handbag. But the 'consumer' still receives some value from the 'transaction'. These websites may come about as close as possible to offering transaction utility on its own. And since consumers are never charged for their purchase, this is a low-cost way for consumers to receive that transaction utility. All it costs is the opportunity cost of their time spent browsing the website.

It is interesting to consider what the business model of Dopamine Shop or FoodNeverComes might be. The Rest of World article is silent on this point. One possibility is advertising or affiliate links to real products and retailers. After all, retailers might value the attention of people who are shopping without buying, in the hope that some of that activity eventually spills over into 'real' purchasing behaviour.

Whatever the business model, these websites provide an interesting example that demonstrates just how much utility consumers may get from the process of buying, even if they don't actually buy anything.

[HT: Marginal Revolution]

Opportunity cost makes the news

Opportunity cost is one of the most underappreciated concepts in economics, and yet it is fundamental to good decision-making. Whenever we choose to use our resources for one thing, we give up what we could have done with them instead. The opportunity cost of something is the cost of foregoing the opportunity of using the resources for something else. More specifically, the opportunity cost is measured as the value of the next best alternative that is foregone.

Despite its importance, it is surprisingly rare to see opportunity cost mentioned in the media, even in business or economics stories. So, it was a delight to see this recent article in the New Zealand Herald:

Financial adviser Niran Iswar says almost every rental property he’s ever owned has lost money – and he thinks more people are coming around to the idea that it’s not always a surefire way to make money.

Iswar, who is head of accounting, wealth and advisory at Float, said once he counted the rates, insurance, maintenance and the opportunity cost of money tied up in rental properties, every rental he had held had gone backwards, except one that worked because it was bought at the right time “which is luck dressed up as skill”.

When making a decision about how to invest their savings, an investor has many alternatives to choose from. Each alternative comes with an opportunity cost - the return they could have earned from the best of the other alternative investments. The economic cost of an alternative includes all of the explicit costs, as well as the opportunity cost. In the case of rental properties, Iswar notes the rates, insurance, and maintenance, as well as the opportunity cost of the savings tied up in the investment.

Iswar is essentially saying that, once you take the opportunity cost into account, the cost of investing in rental properties (including the opportunity cost) exceeds the benefits. That is what he means when he says that the rental properties "have gone backwards". The savings would have been better off invested in some other alternative. As an example, an investor might be attracted to a rental property investment that offers an annual net return of six percent, but fail to consider that an alternative investment of comparable risk offers eight percent. The opportunity cost of the rental property investment is eight percent return foregone from the other investment. So although the rental property earns a positive net return of six percent, relative to the next-best alternative it actually generates an economic loss of two percent. By investing in the rental property, the investor would give up an eight-percent return in order to earn six percent.

Now, there is one important caution to note. In the context of financial investments, a straight comparison of returns ignores the role of risk. Different investments come with different risks, and different investors will have different appetites for risk. So, where investments differ substantially in risk, it is their expected returns adjusted for risk that should be compared. Only where the alternatives have broadly similar risks is a more straightforward comparison of returns appropriate.

Finally, while opportunity cost is not often mentioned explicitly, I imagine that many investors are implicitly taking it into account. Anyone who weighs up alternative uses of their savings and chooses the alternative that offers the best risk-adjusted return is already thinking in terms of opportunity cost. But making the opportunity cost explicit is useful, because it reminds us that simply earning a positive net return doesn't necessarily mean that an investment is a good one. We need to consider what else could have been done with the savings instead. That is why it was refreshing to see opportunity cost brought to the fore in the New Zealand Herald story.

Supermarket structural separation stupidity

This week, the National Party proposed that, if re-elected, it would pursue structural separation of the supermarkets (as reported in the NBR, One News, and the New Zealand Herald). This was interesting timing. My ECONS102 class has just covered firms with market power and monopolies, and one of the regulatory options available to government is structural separation. If this news had broken a week earlier, this would have made a great example for an assessment question. Instead, I'll have to console myself with a blog post.

In short, the proposed structural separation is nonsensical. Foodstuffs is currently comprised of two cooperatives - Foodstuffs North Island, and Foodstuffs South Island. The two cooperatives run three brands of supermarkets: Pak'n'Save, New World, and Four Square. The National Party is proposing to separate the business into two, with Pak'n'Save as one business and New World and Four Square as another.

There are several elements of the proposal that are plain dumb. The first problem with the proposal is that it would require a substantial reorganisation of the two existing geographically-based cooperatives into two nationwide businesses. This proposed reorganisation is somewhat ironic, given that the Commerce Commission declined a Foodstuffs merger into a nationwide business in 2024. Both of the two new supermarket chains would then have to create their own warehousing, logistics, and other backend systems, separate from the other new chain. And the two pre-existing warehousing and logistics (and other systems), which are geographically separated at present, would need to be split up in order to do this. In its own house, the government is busy trying to reduce back-office functions to increase efficiency. And yet, it seems quite happy to generate inefficiency in a private sector business?

Moreover, the cost-benefit analysis that Sense Partners completed for the Ministry of Business, Innovation and Employment demonstrates how sensitive the case is to assumptions about supply-chain costs. The preferred analysis assumes supply-chain costs rise by one percent, generating an estimated net benefit of $2.9 billion over 20 years. However, if supply-chain costs rise by two percent, the estimated benefit falls to around $920 million, and if supply-chain costs rise by more than two percent, the costs outweigh the benefits. That doesn't leave much margin for the proposal to go wrong before society is worse off overall.

The second problem is that this proposal leaves the other major player in the supermarket sector, Woolworths, structurally unchanged. While the proposal would reorganise Foodstuffs into two separate nationwide supermarket groups, Woolworths would be free to continue its current operations, which also includes three brands: Woolworths, FreshChoice, and SuperValue. Admittedly, the two businesses are not structured in exactly the same way. Woolworths operates its Woolworths supermarkets directly, while FreshChoice and SuperValue are locally owned stores franchised through a Woolworths subsidiary. So, there may be good reasons why the same form of structural separation would not be appropriate for Woolworths. But the obvious question then is, if structural separation is the answer to weak competition in the supermarket sector, why is restructuring only one of the two big market players the right approach?

The third problem is that this proposal ignores another, arguably more obvious, form of structural separation that could be enacted, which could be applied symmetrically to both of the large supermarket players. This is to structurally separate wholesale and distribution from supermarket retail. Currently, both Foodstuffs cooperatives and Woolworths benefit from substantial economies of scale in purchasing, wholesale, and distribution. Those economies of scale may themselves create a barrier to entry, because a new supermarket chain that cannot obtain groceries on similarly competitive terms faces higher costs and will struggle to compete effectively.

Structurally separating wholesale and distribution from retail could potentially address that problem by allowing existing and new retailers to access the scale economies of the established distribution networks on fair and non-discriminatory terms, without having to build their own nationwide wholesale and distribution systems. This isn't a new idea (for example, see here and here). Wholesale ownership separation was explicitly considered in a government-commissioned study in 2022, although that analysis raised significant concerns about whether an independent wholesaler would remain viable after structural separation. Despite those concerns, this remains an alternative worth considering, and the Labour Party announced a policy along these lines earlier today. Its proposal would require Foodstuffs and Woolworths to run their wholesale businesses independently from their retail businesses, although it would not require the wholesale businesses to be separately owned.

As I note in my ECONS102 class, structural separation can be an effective way of regulating a monopoly. Typically, it is used with natural monopolies, where increasing competition across the entirety of a sector is not straightforward. For example, in New Zealand we structurally separated the telecommunications backbone (now run by Chorus) from the retail. We also separated electricity lines businesses from generation and retail, although generation and retail were allowed to remain integrated in the so-called 'gentailers'.

Now, supermarket retail itself isn't obviously a natural monopoly, but wholesale and distribution may contain some of the characteristics that we associate with natural monopolies, including high fixed costs and large economies of scale. Regardless, it is clear that to date government's various measures have been largely ineffective in increasing competition in the supermarket sector. So, structural separation may be more effective than what has been tried so far. However, what is being proposed by the National Party is only one possible form of structural separation. The more important question is where in the supply chain separation would do the most to reduce barriers to entry while preserving the economies of scale that keep costs down.

This week in research #144

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

• Tompsett (open access) finds that the opening of new bridges over major rivers increase per capita economic activity

Also new from the Waikato working papers series:

• Luengo et al. investigate the causal impact of AI advice on price discovery in a controlled asset-market experiment, and find that access to AI advice significantly reduces mispricing relative to the baseline, regardless of whether the AI advice was aligned with long-term fundamentals, or myopic and based on current prices

Try this! Pull incentive sizing tool

My ECONS102 class covered intellectual property rights this week. As part of that topic, we discuss how governments might incentivise the development of new intellectual property without necessarily relying on strong intellectual property rights. One of the potential alternatives is pull funding - where the government funds successful research and development efforts once they have been developed.

In class, I focus on particular examples of advance market commitments and rewards or prizes. Advance market commitments involve governments making a commitment to buying the goods and services from intellectual property, after the IP is developed. Rewards or prizes involve the government offering to pay the reward or prize to whoever successfully develops some particular intellectual property.

That discussion leaves an important question unanswered. Pull funding creates an incentive for firms (or individuals) to develop some particular intellectual property. But how effective is that incentive? Or, turning that question around, how big does pull funding have to be to incentivise firms (or individual) to work on solving a particular problem?

The Market Shaping Accelerator team (a collaboration between researchers at Dartmouth College, the University of Chicago, and the Center for Global Development has provided a tool that gives us some idea of the answer to that question. You can find the tool here (and associated blog post here). Choose whether you want to use a prize, an AMC, or an AMC with milestones. Then decide on the number of stages in the development process (as well as how long each stage is, the cost, the probability of success of each stage, and how much cost can be covered from other sources). Then provide a discount rate for firms, and the firms' target probability of success. And then hit calculate!

The tool reports the number of firms that would be incentivised by your pull funding, the overall chance that at least one firm succeeds, and the size of the prize required. The tool also provides a comparison with push funding - where the government pays for research and development up-front, regardless of whether it will be successful or not. And the tool also allows you to compare different scenarios.

There is far more to this tool than I can do justice to with a short blog post. There are a lot of models running behind the scenes in this tool, and a lot of assumptions. Fortunately, the documentation gives you all the detail.

If you are interested in push funding, and how it can provide incentives for firms to develop new ideas, I recommend that you try the tool out for yourself. Enjoy!

The Commerce Commission goes rafting

Back in June, the Commerce Commission ruled on the case of a proposed merger between three commercial rafting operators in Rotorua. As the New Zealand Herald reported (in August):

The Commerce Commission has approved the merger of Rotorua’s three commercial rafting operators, much to the pleasant surprise of those behind the bid.

The ruling, finalised in June, came after months of investigation, during which Rotorua Rafting director Sam Sutton became so resigned to defeat that he and his fellow applicants came close to ditching the proposal.

After two decision extensions amid the commission’s concerns that the merger might lessen competition, Sutton was prepared for bad news.

I briefly covered the economics of antitrust law in my ECONS102 class today. My view still remains that the Commerce Commission's criterion for determining merger applications is deeply flawed. As I noted in this 2021 post, the Commerce Commission exists to enforce the Commerce Act, and section 47 of the Act says prohibits acquiring "assets of a business or shares" if that acquisition would have the effect of "substantially lessening competition".

Now, any merger between competing firms by its very nature removes some direct competition between them. So, the Commerce Commission's decision always hinges on whether a proposed merger meets the threshold of 'substantially' lessening competition. The Commerce Act defines 'substantial' only as "real or of substance", and the case law on this makes it clear that whether competition is substantially lessened is ultimately a matter of degree. So the exercise is inherently imprecise and subjective.

The US Federal Trade Commission (FTC) has a more objective approach, which is to compute the Herfindahl-Hirschman Index (HHI) before and after the proposed merger, and to presume that any merger that increases the HHI by 100 points in an industry that is already classed as 'concentrated' or 'highly concentrated' is substantially lessening competition.[*] The FTC may then choose to contest the proposed merger (in court). That at least provides a much transparent threshold against which mergers can initially be assessed. In my view, a more economically coherent approach would be for antitrust authorities to decide on the basis of economic welfare, as I discussed in this 2021 post.

Interestingly, the Commerce Commission's decision itself illustrates the difficulty of applying the test. Only two of the three Commissioners were satisfied that the merger was unlikely to substantially lessen competition, and all three agreed that it was a "finely balanced decision". Seven months after lodging their application, though, the rafting operators finally have their answer. And now they can get on with their business, safe in the knowledge that they are not 'substantially lessening competition'.

Read more:

• The Commerce Commission, competition and total welfare

*****

[*] The Herfindahl-Hirschman Index can be calculated by squaring the market share of each firm in the market, and adding up the resulting numbers. An industry with an HHI of under 1000 is classed as 'competitive', an industry with an HHI of between 1000 and 1800 is classed as 'concentrated', and an industry with an HHI of over 1800 is classed as 'highly concentrated'. For example, an industry with just four firms, with market shares of 40 percent, 27 percent, 19 percent, and 14 percent, would have an HHI of 40^2+27^2+19^2+14^2 = 2886, and would be classed as 'highly concentrated'.

Manufacturers' response to 'supercycles' in the market for computer memory

Back in May, David Oks wrote a fascinating Substack article about the economics of dynamic random access memory (DRAM) - the memory that is used in computers, smartphones, and gaming consoles, but also importantly in servers and data centres, such as those used to train generative AI models. I'm not going to focus on the effect of generative AI in this post, but instead on this particular part of Oks's article, which caught my attention:

And that combination—capital-intensive manufacturing plus fungibility—is a punishing combination. Because memory is fungible, the industry is intensely cyclical: the entire history of the DRAM industry is a history of boom-and-bust supercycles. First, strong demand from one sector or another—like Windows PC adoption in the 1990s—drives surging prices and a wave of investment from every player; cumulative overinvestment in an undifferentiated good produces oversupply; and then oversupply leads to collapsing prices.

And because production is so expensive, those down-cycles turn out to be existential: the memory industry is marked by constant wreckage. Intel dominated the memory game in the early 1970s but left in the 1980s, opting to focus on processors. Texas Instruments and IBM, also once major players, left in the 1990s. Germany’s Qimonda collapsed in 2009; Japan’s Elpida, once the world’s third-largest DRAM manufacturer, declared bankruptcy in 2012.

In my ECONS101 class, we teach a model of dynamic supply and demand that can be used to explain the boom-and-bust 'supercycles' that Oks describes. Consider the market for memory, and assume that it is perfectly competitive - most importantly, there are no barriers to entry into the market or barriers to exit from the market.[*] The market for memory is shown in the diagram on the left below. The diagram on the right will track changes in memory manufacturers' profits over time. Initially (at Time 0) the market is at equilibrium (where demand D0 meets supply S0) with price P0, and memory manufacturers are making profits π0. Now say there is a permanent increase in demand at Time 1, to D1. This increase in demand may be because of Windows PC adoption, or some other positive demand shock. Prices increase to P1, and memory manufacturers' profits also increase (to π1). There are no barriers to entry (this is a perfectly competitive market), so the higher profits encourage new manufacturers to enter this market (or more realistically, they encourage the existing manufacturers to increase capacity). However, new manufacturing capacity takes time to bring online, and firms make their investment decisions independently. By the time all of that new capacity becomes available, supply may have overshot the level required to simply meet the higher demand. Supply increases to S2 (more producers) at Time 2. Price falls to P2, and memory manufacturers' profits also fall (to π2).

Next, at Time 2 profits are low and some memory manufacturers will choose to exit the market (no barriers to exit because this is a perfectly competitive market), or more realistically it encourages the manufacturers to reduce their capacity. This helps explain the pattern that Oks describes, with Intel, Texas Instruments, IBM, and others exiting the market. Supply will decrease to S3 (fewer producers) at Time 3. Price will increase to P3, and memory manufacturers' profits will increase to π3. So, these 'supercycles' arise as memory manufacturers enter and exit the market in response to an initial increase in demand.

Now, memory manufacturers are not stupid. The manufacturers that remained after previous busts realised that they needed to change their approach in order to avoid these problems. Oks notes that:

And decades of collapse and consolidation left only a few players standing. In the 1990s, there were perhaps 20 meaningful producers of DRAM around the world; today there are three that account for more than 90 percent of global production. South Korea has two, SK Hynix and Samsung; and the United States has one, Micron.

And these memory makers have learned a very particular lesson from the unforgiving history of their industry: always leave demand unmet.

So, rather than rapidly increasing production in response to higher demand, the memory manufacturers have instead become much more cautious about adding capacity, deliberately allowing some demand to remain unmet. That reduces the risk that increasing supply causes a decline in prices and profits. However, the downside is, as Oks notes, that we now have a global shortage of memory as current production levels (and manufacturing capacity) are unable to keep up. That may open opportunities for new manufacturers to enter the market, even if they are less efficient (higher cost) than the incumbents. And therein lies the problem - by deliberately restricting supply tight, the incumbent manufacturers keep prices and profits high and that may eventually set off the next supercycle.

*****

[*] The market for computer memory isn't actually perfectly competitive, as there are likely to be large economies of scale in memory manufacturing. That means that a small firm entering the market would be at a large cost disadvantage to the large incumbent firms, and so small firms would be deterred from entering the market. However, there is another way of looking at this situation. Instead of considering new firms entering and exiting this market, think about the incumbent firms adding and subtracting additional manufacturing capacity. This has the same effect of increasing and decreasing supply, and leads to the same dynamic pattern in prices and profits.

Europe faces a flood of cocaine

The Financial Times reported back in July:

Cocaine production in Latin America has quadrupled in the past decade, with criminal networks in Colombia, Peru and Bolivia exploiting global trade routes to shift vast quantities of the drug. Cocaine is also increasingly shipped from Brazil into Europe via west Africa.

The UN warns that supply could soon exceed demand, increasing traffickers’ incentives to dump even more product on to Europe’s streets...

The result is that more cocaine is available in Europe today than in the 1980s — often seen as the drug’s heyday — according to the UN. Last year alone, its residue in city wastewater rose by more than a fifth, according to the EU’s drugs agency...

Lower prices and frictionless dealing on popular encrypted messaging apps have made cocaine more attainable.

My ECONS101 class covered the model of supply and demand last week, and this seems like a good example. The European market for cocaine is shown in the diagram below. The market was initially in equilibrium, where supply S0 meets demand D0. The equilibrium price of cocaine was P0, and the equilibrium quantity traded was Q0. The supply of cocaine has increased (a shift to the right, or down, of the supply curve for cocaine), to S1, due to increased production in Latin America being shipped to Europe. This lowers the equilibrium price of cocaine to P1, and increases the quantity traded to Q1.

However, the increase in cocaine supply hasn't had the same effect everywhere. The FT article also reports that:

One country where the retail price has risen rather than fallen, according to UN data, is the UK, which one former senior European police officer says may be linked either to stronger demand or to traffickers’ perception that it is riskier to smuggle cocaine into the country than elsewhere.

If cocaine traffickers believe that it is riskier to smuggle cocaine into the UK, that would decrease the supply of cocaine, and increase the price (see the diagram above, but with the change in supply reversed). That might also contribute to the increase in supply to Europe, if shipments that previously would have gone to the UK go to continental Europe instead. As for 'stronger demand', that effect is shown in the diagram below. The market was initially in equilibrium, where supply S0 meets demand D0. The equilibrium price of cocaine was P0, and the equilibrium quantity traded was Q0. If supply remained constant, and demand increased (as noted in the FT article), the demand curve shifts to the right (to D1). This increases the equilibrium price of cocaine to P1, and the quantity of cocaine traded to Q1.

The increase in demand could even lead to a price increase even with supply increasing as well. If the supply increased to S1, then the equilibrium price goes back to P0, with the quantity of cocaine traded increasing to Q1. 

Combining an increase in demand with an increase in supply is certain to lead to an increase in the equilibrium quantity. However, the change in the equilibrium price is ambiguous. Any smaller increase in supply than shown in the diagram would lead to a net increase in the price of cocaine in the UK, as would any decrease in supply. Any larger increase in supply would lead the equilibrium price to decrease.

Europe is, of course, not the only country dealing with an influx of cocaine. It has also been in the news in New Zealand recently as well. The underlying drivers are very similar, and are a recurring issue (see this post from 2017, related to the US cocaine market).

Read more:

• Cocaine makes a comeback in the U.S.

This week in research #143

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

• Magnuson and Ruggles (open access) explore the creation of the first census microdata sample at the US Census Bureau in the early 1960s (I had no idea it was so long ago!)
• Coelli and Borland (open access) find that the decline in the estimated return to a bachelor's degree in Australia over the period from 2001 to 2016 is potentially explained by strong earnings growth in low‐skill occupations, due to increases in real minimum and award wages, as well as possibly the mining boom
• Bonilla-Mejía et al. (with ungated earlier version here) find that rising violence increases intentions, plans, and preparations to emigrate from Central America, particularly to the United States
• Delavande et al. (with ungated version here) find that student attendance at a public UK university is highly dependent on timetable structure, that students compensate for marginal non-attendance at some events with increased attendance at others within the same module, and that timetable features and attendance choices do not have systematic impacts on academic achievement
• Dang (with ungated earlier version here) finds that one additional international student per thousand working-age residents of a US local labour market increases the employment-to-population ratio by 0.19 percentage points and average hourly wages by 0.48 percent
• Frank and Vickery (open access) look at how LGB students differ from their heterosexual counterparts using data from the UK from 2012 to 2020, and find that LGB men and women both have large shifts towards Humanities subjects, where they perform better, and away from law, economics, management, and STEM, and that they have lower early-career salaries than heterosexual students

The political language of economists, and the associated principal-agent problem for government

Governments often employ economists as policy analysts or consultants, to provide advice on policy. That relationship between the government and the economist is a principal-agent relationship (regardless of whether the economist is a government employee or a consultant), as my ECONS102 class covered this week. 

In a principal-agent interaction, one person or group (the agent) is given the power to decide how to use resources that ‘belong’ to someone else (the principal). In the case of economists working for government, the government is the principal and the economist is the agent. The 'resource' that the agent is using is their work time, which has been paid for by the government.

Now, the government wants the economist to use that work time to provide unbiased economic or policy advice. [*] However, the economist has their own goals and motivations. They might use that work time to scroll TikTok, and then use ChatGPT to write the advice. Or, they might use the time to provide biased advice, based on their own political preferences. Either way, this is detrimental to the interests of the government. This is the essence of the principal-agent problem (or the agency problem).

Do economists act that way? Many economists (including, at times, myself) argue that we are non-partisan, and unbiased in our policy advice, even if we hold particular political views. But is that actually the case?

That is the question addressed in this 2024 article by Zubin Jelveh (University of Maryland), Bruce Kogut, and Suresh Naidu (both Columbia University), published in The Economic Journal (ungated earlier version here). They first link over 53,000 economists from the American Economic Association member registry in 1993, 1997, 2002, and 2009 to two measures of political activity: (1) their campaign contributions (between 1979 and 2012) drawn from the Federal Election Commission's website; and (2) their signing of one or more of 35 petitions aligned with the political left or right.

Next, Jelveh et al. obtain the full text of over 62,000 academic articles and over 17,000 NBER Working Papers published by a subset of 2471 of the economists from the larger sample, between 1973 and 2011. They then rank each of the articles and papers in terms of 'ideological valence', based on the phrases that appear in it, and use that to derive several different 'ideology scores' for the writing of each economist.

Jelveh et al. then start to do some analysis of the ideology scores, finding first that:

...the fields of finance, macroeconomics and industrial organisation are more conservative, while labour is considerably more liberal than the average. Other fields, such as history and international trade, show less political valence. We further see that faculty at business schools are more conservative, as are professors affiliated with ‘freshwater’ schools, while ‘saltwater’ schools have a left-wing bent. Professors of European origin also seem to be somewhat more conservative, and there seems to be no association with Latin American origin, full professor rank or top five department ranking.

The 'saltwater schools' are predominantly those on the east coast of the US, like Harvard or MIT, while the 'freshwater schools' are predominantly those in the Midwest, such as Chicago or Minnesota. Those results, and the results by field of economics, will not surprise many people who know those schools or those fields.

The more interesting results involve the next stage of the paper, where Jelveh et al. look at several economics debates, where there is a clear left-right divide in terms of expected effects. For example, conservative economists may be more likely to believe that the minimum wage reduces employment, while liberal economists may be more likely to believe that it doesn't. Jelveh et al. take several meta-analyses on the minimum wage, and similarly political topics, and look at the relationship between the estimated elasticity in each paper in the meta-analysis, and the estimated political ideology of the authors. They find a statistically significant relationship - elasticities reported by more conservative economists tend to be consistent with more conservative policy prescriptions, while elasticities reported by more liberal economists tend to be consistent with more liberal policy prescriptions.

Economists have political leanings (as does everyone else), Jelveh et al. show that those political leanings are correlated with the results that economists report in their research. This isn't to say that economists are engaged in falsifying data to support their political beliefs. Jelveh et al. have no evidence of that. However, their results could arise if economists choose to apply methods or investigate datasets that are more likely to lead to results that are consistent with their beliefs. Or, economists may simply choose not to publish results that are inconsistent with their beliefs. Either way, this research provides some evidence that knowing the political preferences of economists may be important in interpreting the results from their research.

In my ECONS102 class, we discuss various ways that the principal can act to reduce the principal-agent problem. Those options include stricter monitoring of the agent, paying efficiency wages, or performance-based pay (or delayed payment). In this case, the government might ask the economist agent to fully document their research, or subject it to careful peer review. This would constitute stricter monitoring. However, the relationship between the government and the peer reviewer opens up an additional layer of potential principal-agent problems (what are the political preferences of the peer reviewer?). The government might pay a wage to the economist that is much higher than the equilibrium wage, hoping that would motivate them to produce higher-quality and less biased work (because if they didn't, they would lose their job and have to work elsewhere for less). Government might also use performance-based pay, or may hold back payment until after a peer review, or a replication of any analyses. However, for all of those solutions, some form of monitoring is still required in order to determine the quality of the work. There is an additional problem, though. Efficiency wages and performance-based pay work best when the agency problem involves the effort the agent puts into their work. Those solutions may be less effective when the problem arises from sincerely held beliefs about which models, methods, or evidence are most appropriate.

It seems that the principal-agent problem for governments employing economists (and, possibly, other consultants) is challenging to solve. Perhaps the best that governments can do is insist on transparency around economists' assumptions, methods, and evidence, and subject their analyses to replication and peer review. Political preferences may still matter, but good institutions can make it harder for those preferences to determine the advice that governments receive.

[HT: Marginal Revolution, back in 2024]

*****

[*] Or, maybe the government wants the economist to provide economic or policy advice that accords with the preferred policy platform of the government, rather than independent or unbiased advice. In that case, the nature of the principal-agent problem changes. Nevertheless, in both cases, what really matters is whether the economist's goals and motivations are aligned with those of the government.

Will AI make personalised pricing a reality, and is that really a bad thing?

Personalised pricing (or first-degree price discrimination) occurs when the seller sells their good or service to every consumer at a different price, as I described in this 2023 post. If executed perfectly, the seller could extract all of the consumer surplus as profits, by charging a price to every consumer that is exactly equal to the maximum the consumer is willing to pay. Fortunately for consumers, such perfect personalised pricing has remained a theoretical possibility.

But technological tools are increasingly helping firms to learn more detailed information about consumer preferences, and that allows firms to home in on consumers' maximum willingness-to-pay. The latest worry for consumers is AI, as this article in The Conversation by Patrick Dodd and Hanoku Bathula (both University of Auckland) notes:

Digital platforms can observe thousands of individual decisions. A ride-hailing platform can see which jobs a driver accepts, when they work and which incentives bring them online. A retailer can see purchases, abandoned carts and responses to discounts.

There is no strong evidence major companies already know everyone’s precise financial breaking point. But algorithmically mediated pay, personalised worker incentives, discounts and consumer offers are already real.

Notice that Dodd and Bathula also take the logic of personalised pricing for consumers, and apply it to gig-economy workers as well. Platforms such as Uber or Lyft or Doordash can increasingly use what they know about their delivery workers' preferences to determine their minimum willingness-to-accept for each delivery. The target is different (minimising how much they pay to the delivery worker, rather than maximising the price they charge the consumer), but the underlying premise of personalised pricing is the same.

Algorithms have been around for a while, though. Dodd and Bathula do not clearly lay out why they think that recent developments in AI make personalised pricing more of a reality than before. Most of what they say about 'algorithms' applies equally to statistical algorithms that have been around for years (decades, even) as to more recent developments in AI and machine learning (AI/ML). So, let me extend their argument more explicitly.

AI/ML dramatically lowers the cost of estimating individual willingness-to-pay. It can combine huge numbers of relatively weak signals about a particular consumer, learn complex patterns from the behaviour of millions of other similar consumers, experiment continually with prices and discounts, and update its estimate each time that circumstances change. That gives firms far richer models from which to estimate each particular customer's maximum willingness-to-pay (or, for their workers, to estimate their minimum willingness-to-accept). Moreover, while older statistical algorithms allowed firms to segment customers into fairly coarse categories, AI/ML allows firms to make predictions for each individual, and in real time. The better estimates from these newer models therefore allow firms to price much closer to the perfectly price-discriminating ideal. It's still not completely perfect, but it is a further improvement on what they were previously able to achieve.

Dodd and Bathula finish their article by noting the unfairness of personalised pricing. Their argument is essentially that there is asymmetry in the relationship between consumers and firms. Firms using algorithms (and now AI/ML) know increasingly more about what consumers are willing to pay, but consumers know very little about what firms are willing to accept.

However, it is worth unpacking that a bit more. Firms that don't know consumer willingness-to-pay can't raise their prices without limit, as consumers with low willingness-to-pay would stop buying from them. In practice though, personalised pricing will never be perfect. Firms may charge higher prices to consumers that they estimate have high willingness-to-pay, while offering lower prices or discounts to consumers with lower willingness-to-pay. So, relative to offering the same price to everyone, personalised pricing need not make every consumer worse off. The high-willingness-to-pay consumers are likely to be worse off, but some low-willingness-to-pay consumers may actually be better off.

Now consider which types of consumers tend to have high willingness-to-pay, and which types tend to have low willingness-to-pay. For many goods, lower-income consumers are likely, on average, to have lower willingness-to-pay, so personalised pricing could result in some of them being offered lower prices. That won't always be true though. Some lower-income consumers with few alternatives or an urgent need may have high willingness-to-pay despite having a low income. Taken together, this means that the distributional effects of personalised pricing are not necessarily straightforward. However, in some instances preventing firms from price discriminating could be making low-income consumers worse off. With that in mind, is it really fairer that firms are not allowed to offer lower prices to consumers with low willingness-to-pay?

I'm not really trying to defend price discrimination here. I'm not keen on personalised pricing for very selfish reasons - I don't want to pay more, even if I am willing to pay more! And like me, most consumers should probably not be keen on personalised pricing. But before we rail against the evils of firms price discriminating, we need to properly consider its distributional consequences. And that means thinking about which groups may be made better off by price discrimination, not just which groups are made worse off.

Read more:

• Surge pricing is coming to a supermarket near you
• What would happen if perfect personalised pricing was possible?
• Personalised pricing is now a step closer with electronic shelf labels