This week in research #55

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

• Tanaka and Matsubayashi (with ungated earlier version here) use county level data from the US from 1979 to 2004, and find that suicide rates increase by 6.76% as sunlight in the current and previous months decreases by one standard deviation
• Danagoulian and Deza (with ungated earlier version here) find that traffic fatalities increase on days in which the local pollen count is particularly high, using US data from 2006 to 2016
• Galiani, Gálvez, and Nachman (with ungated earlier version here) investigate specialisation trends in economics papers from 1970 to 2016, and find that theory and econometric methods papers are becoming increasingly specialised, with a narrowing scope and steady or declining citations from outside economics and from other fields of economics research, while applied papers are covering a broader range of topics, and receiving more citations from other fields like medicine and psychology

This week in research #54

Here's what caught my eye in research over the past week (which, it appears, was a very quiet week in the lead-up to Christmas):

• Bietenbeck (open access) finds that exposure to academically motivated classmates causes an increase in student achievement among elementary school students, using data from the Project STAR randomised controlled trial in Tennessee

Onshore windfarms vs. birds

Several times recently, I've had conversations with others about environmental objections to windfarms, and specifically about their impacts on birds. I've expressed surprise that anyone could believe that large, slow-moving wind turbines could be a threat to birds. It turns out, there is research that supports the negative impacts of wind turbines on birds (see here or here), but that research doesn't actually demonstrate that wind turbines cause a decrease in bird populations. The problem, of course, is that it isn't feasible to run a randomised controlled trial with wind turbines due to cost - placing wind turbines at random across some areas and not others, and comparing the effect on bird populations in both areas. The cost of such an experiment would be enormous.

Fortunately, there are statistical methods that we can use to try and estimate the causal effects. And that is what this new article by Meng et al., published in the Journal of Development Economics (ungated earlier version here) attempts to do. Specifically, they look at the effect of onshore windfarms on bird biodiversity at the county level in China. They have two measures of biodiversity:

Bird abundance is the average number of birds of a given species per checklist observed at a county-month-year-species level. Species richness is the total number of unique species observed in a given county at the month-year level, which better reflects the diversity of the bird populations.

To establish causality, they use a difference-in-differences (two-way fixed effects) model, which essentially compares the difference in bird biodiversity before and after a windfarm is installed, between counties with and without windfarms. However, Meng et al. go a step further, using an instrumental variables approach, instrumenting for the location of windfarms by the interaction between national-level growth in windfarms interacted with county-level average windspeed at 100 metres. That instrumental variables approach should mitigate issues arising from the correlation of wind turbine location and bird biodiversity.

The novelty of this paper is not just in the methods, but in the data that Meng et al. employ. To measure bird biodiversity, they make use of data:

...from the China Birdwatching Report (CBR, similar to the eBird Reference Dataset), a citizen science dataset consisting of reports from users, including information on individual bird trips and associated characteristics, such as the specific date and time, location of a specific trip, as well as species and quality of birds encountered...

Their dataset covers the period from 2015 to 2022, and includes data collated from over 33,000 checklists. They also control for a variety of other variables:

...including average bird observed duration, average temperature, average visibility, average wind speed, total precipitation, average ozone, percentage of natural park areas in the county, average population density, and average night light value.

Using this data and the two-way fixed effects approach, Meng et al. find that:

A one standard-deviation increases in wind turbines (approximately 84 turbines)... in a given county leads to a 9.75% decrease in bird abundance per checklist from the mean value of 5.38...

...while a one standard-deviation increases in wind turbines (approximately 84 turbines) in a given county decreases the number of unique bird species by 17.67% from the mean value of 66...

Meng et al. also find evidence that the impacts are greater on migratory birds than on resident birds (important given that China is a major migration pathway for migratory birds), and that the effects are larger in forested and urban/farmland than for grassland. There is also evidence that the impact is greatest for the largest bird species.

Finally, Meng et al. show that there are effects of windfarms on neighbouring counties (as well as the counties in which the windfarms are located), and that those effects are somewhat smaller in size. That made me wonder why those analyses were not the primary results in the paper, since it seems obvious that birds may move across county borders.

So, it does appear that windfarms might cause a decrease in bird biodiversity. Meng et al. even address a bunch of concerns that jumped out at me as I was reading the paper, especially that birdwatchers, anticipating that there would be fewer birds near windfarms, do less birdwatching in those locations. On that point, Meng et al. note that:

We do not find a significant impact of wind turbine installations on birdwatcher behaviors regarding the submitted number of checklists...

And they further support that with detailed mobile phone GPS data, showing that there were not fewer trips made to the areas of windfarms, relative to areas further away. However, a couple of concerns do remain, but they are rather technical. First, I wondered why Meng et al. used the interacted variable (national growth in windfarms interacted with windspeed), rather than just windspeed alone. They describe this as a "Bartik-like variable", but we should be cautious about whether Bartik instruments are appropriate (see here). Also, two-way fixed effects models have also come in for criticism recently (see here and here). I'm not going to drag you into the technical details (read the links if you're interested). But suffice to say, this will not be the last word on whether windfarms negatively impact bird biodiversity. However, the best quality study we have so far seems to suggest they do.

Licensing of economists, and other fortune tellers

There are certain examples I use in my classes where the origins are shrouded in mystery. They likely come from some obscure note I wrote to myself after reading something online. One of those examples is that there are some states in the US that require fortune tellers to be licensed. [*] I use this as an example of the ridiculousness of occupational licensing, which in many circumstances serves no real purpose other than creating a barrier to entry into the market. After all, what harm could befall consumers from receiving the services of an unlicensed fortune teller, that licensing would help to prevent?

It turns out that the fortune teller example is true. Here's the relevant website with links to the law, as well as this hilarious article which asks the most relevant questions:

How cool would that be to have a fortune tellers license? But then I started to wonder how the licensing process would work. Is there a written examination? Do they hand you a blank piece of paper and expect you to divine the questions and then answer them? Is the test multiple choice or essay? Who grades the essays? Other fortune tellers – kind of like bar exam? Is there a road test? Is reading tea leaves or your palm akin to parallel parking?

I was in New Orleans last month. Walking along Bourbon Street, you see a lot of fortune tellers. I could tell the phony ones. They were the ones that beckoned me over. If they could tell the future, then surely they would have known that I wasn't going to walk over to them, no matter how enthusiastic they waved at me?

Anyway, if fortune tellers are licensed in Massachusetts, does that mean economists should need a licence? After all, economists are regularly asked to tell the future - what is going to happen to GDP, unemployment, interest rates, exchange rates, etc.? Whether economists should be licensed or not isn't a crazy question - there have been calls for that in the past (see here and here). And the consequences of bad fortune telling are likely to be as bad, or worse, when an economist gets it wrong as when a palm reader does. Real risk of harm is the reason that governments license doctors, dentists, and nurses. If there is a real risk of harm from people making poor financial decisions on the advice of economists (or other fortune tellers), maybe they do have to be licensed after all?

[HT: Marginal Revolution]

*****

[*] Although, as it turns out, I have referred to licensing of fortune tellers before, with a relevant link (see here).

Read more:

• Should economists be licensed?
• Should economists have licensing, or a Code of Ethics?

How university students and staff used and thought about generative AI in early 2023

Somehow, this report languished in my to-be-read pile for over a year. By Natasha Ziebell and Jemma Skeat (both University of Melbourne), it explores the relatively early use of generative AI by university students and staff, based on a small (110 research participants - 78 students and 32 academic staff) survey conducted in April-May 2023.

While the results are somewhat dated now, given the pace of change in generative AI and the ways that university students and staff are engaging with it, some aspects are still of interest. For example, Ziebell and Skeat found that while over 78 percent of academic staff had used generative AI to that point, only 52 percent of students had done so. I think many of us would be surprised that students were not more experimental in their early use of generative AI. On the other hand, perhaps they were simply reluctant to admit to having used it, given that this was a study undertaken by a university that may sanction students for the use of generative AI in assessment?

The other aspect of the paper that still warrants attention are the opportunities and challenges identified by the research participants, which still seem to be very current. In terms of opportunities:

There were a range of opportunities identified for using generative AI as a tool for teaching and learning:

• to generate study materials (e.g. revision materials, quiz/practice questions)

• to generate resources (e.g. as a teacher)

• to summarise material (e.g. coursework material, research papers)

• to generate information (e.g. similar to Wikipedia)

• to provide writing assistance (e.g. develop plans and outlines, rewording and refining text, editing)

• for learning support (e.g. explaining questions and difficult content, as an additional resource, ‘using it like a tutor’)

• as a research tool (e.g. potential for integrating generative AI with library search tools)

• as a high efficiency time-saving tool (e.g. to sort data, gather information, create materials)

• to encourage creative thinking, critical thinking and critical analysis (e.g. students generate work in an AI program and critique it)

I don't think we've moved on substantially from that list of opportunities, and if a similar survey was conducted now, we would see many of the same opportunities are still apparent. In terms of challenges:

The key challenges identified by respondents can be summarised according to the following categories:

- Reliability of generative AI (inaccurate information and references, difficulty fact checking, misinformation)

- Impact on learning (e.g. misusing generative AI, not understanding limitations of the technology)

- Impact on assessment (e.g. cheating, difficulty detecting plagiarism, assessment design)

- Academic integrity and authenticity (e.g. risk of plagiarism, collusion, academic misconduct)

- Trust and control (reliance on technology rather than human thinking, concerns about future advancements)

- Ethical concerns (e.g. copyright breaches, equitable access, impact on humanity)

Unfortunately, just as the opportunities remain very similar, we are still faced with many of the same challenges. In particular, universities have been fairly poor at addressing the impact on learning and assessment, and in my view there is a distinct 'head-in-the-sand' approach to issues of academic integrity and authenticity. Many universities seem unwilling to step back and reconsider whether the wholesale move to online and hybrid learning and assessment remains appropriate in an age of generative AI. The support available to academic staff who are on the frontline dealing with these issues is superficial.

However, academic integrity and authenticity of assessment are only an issue if students are using generative AI tools in assessment. This report suggests that, in early 2023, only a minority of students were doing so. I don't think we can rely on that being the case anymore. One example from my ECONS101 class in B Trimester serves as an illustrative case.

This year (and for many prior years going back to at least 2005), we've had weekly quizzes in ECONS101 (and before that, ECON100). These quizzes this year had 12 questions, generally consisting of ten multiple choice questions and two (often challenging) calculation questions. These quizzes are each worth about one percent of students' grades in the paper, so they are fairly low stakes. Students have generally taken them seriously, and the median time to complete a quiz has been fairly stable at 15-20 minutes over the last few years. Until B Trimester this year, when the median time to complete started the trimester at over 17 minutes, but by the end of the trimester was down to 7 minutes. It isn't clear to me that it is possible to genuinely complete the 12 questions in 7 minutes. Around 16 percent of students completed the last Moodle quiz in four minutes or less. And it wasn't that those students were rushing the test and performing badly. The average score in the quiz for students completing it in four minutes or less was 86 percent (only slightly below the 92 percent average for students who took longer than four minutes). I'm almost certain that the culprit was one of the (now several) browser extensions that will automatically answer Moodle quizzes using generative AI. Needless to say, this year sees the end of Moodle quizzes that contribute to grades in ECONS101.

Anyway, I digress. It would be really interesting to see this sort of study replicated in 2025, and with a decent sample size - it is hard to say much with a sample of 110, split across students and staff. I imagine that we would see many of the same opportunities and challenges would be salient, but that the uses of generative AI have changed in the meantime, and students would now be at least as prolific as users of generative AI as are staff.

[HT: The Conversation, last year]

The South Waikato compensating differential strikes again

The New Zealand Herald reported last month:

Robbie Winterson says he faces a battle every time he tries to hire new plumbers in his business.

He runs Go Fox Plumbing and Electrical in Putāruru in the South Waikato. When he puts a job ad on Seek or Trade Me, he says, he either gets no New Zealand applicants at all, or only people completely unsuitable for the job.

“We don’t get good-quality applicants at all. You just don’t get people applying.”

Winterson said plumbers would be paid more than electricians and his firm paid the same as they would get in Auckland, but it was still hard to hire.

The problem here is that Go Fox Plumbing is only offering to pay the same as plumbers would receive working in Auckland. To get quality applicants, they might have to offer more. They face essentially the same problem as South Waikato District Council looking for a health and safety manager in 2018, or a Tokoroa GP looking for a doctor in 2016. In all cases, despite offering what they thought was a competitive wage (compared with elsewhere), the employer was unable to attract good people for the role. 

Aside from difficulty in recruiting, there is one common thread there, and that's the location: South Waikato. Before we talk about why that's relevant (and potentially throw some shade on South Waikato), we need to review a bit of theory on compensating differentials.

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 area has negative non-monetary characteristics (e.g. it is in an area with lower amenity value, where fewer people like to live), 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. 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.

Now, if South Waikato employers (the council, or a GP, or a plumber) are all offering 'competitive' wages and unable to find quality workers, then that suggests that they are not offering wages that are high enough to compensate for the non-monetary characteristics of the job. If they have to offer a wage that is even higher than workers would receive in other locations, that suggests that there is a compensating differential at play. For some reason, it appears that workers perceive that working in South Waikato comes with negative non-monetary characteristics relative to other areas, and are unwilling to move and work there at the wages that are being offered.

Workers' perceptions have an important implication for South Waikato employers. If they want to attract quality applicants, then South Waikato employers either need to find some way to convince potential workers that there are positive reasons for living in South Waikato (South Waikato District Council is clearly trying their best), or they need to pay more to offset the perceived negative characteristics of living in South Waikato.

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

This week in research #53

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

• Dentler and Rossi (open access) use data from Rochester, NY, and find that residents are willing to pay between 7% and 18% of house valuations to avoid crime
• Kühn and Wolbring (open access) use deepfaked job application videos and find that more attractive applicants score higher in competence ratings and are more likely to be invited for a job interview than less attractive candidates, but that only men consistently profit from their looks, while women benefit from a beauty premium in female-typed, but not in male-typed jobs
• Akyildirim et al. (including my colleagues Shaen Corbet and Greg Hou) find that a cyberattack leads stock returns to decrease by -0.24%, but the effect reverses in about two weeks
• Aridor et al. (with ungated earlier version here) summarise the literature on the economics of social media
• Courtemanche et al. find no evidence that e-cigarette licensure laws affect youth use of 'electronic nicotine delivery systems', using data from the US State Youth Risk Behavior Survey (useful evidence for the New Zealand context as well, I expect)
• Cannistrà et al. (open access) evaluate the impact of an online game-based financial education tool, and find that it increases financial literacy levels by 0.313 standard deviations, in a sample across four countries
• Karadas and Schlosky look at the stock trades of US Members of Congress from 2004 to 2022, and find that politicians trade more when Congress is in session and when geopolitical risk is high, as well as making more buy trades when economic policy uncertainty and equity market volatility are high
• Ash et al. (open access) find that an increase of 0.05 rating points in Fox News viewership, induced by exogenous changes in channel placement, has increased Republican vote shares by at least 0.5 percentage points in recent presidential, Senate, House, and gubernatorial elections

And the latest paper from my own research (or, more accurately, this is the first paper from the thesis research of my PhD student Jayani Wijesinghe, on which I am a co-author along with Susan Olivia and Les Oxley):

• Our new working paper investigates the trends and factors associated with state-level life expectancy and lifespan inequality (inequality in the length of life) in the US over the period from 1968 to 2020, finding a strong and statistically significant negative correlation between life expectancy and lifespan inequality, and that greater lifespan inequality is associated with state-level education, demographics, various health variables, and income inequality

Also new from the Waikato working papers series:

• Gibson, Kim and Li examine relationships between nighttime lights (luminosity) and local economic growth for counties in China and the US and districts in Indonesia, and find that GDP-luminosity elasticities vary especially by spatial scale and metro status, and also by period and remote sensing source, meaning that claimed growth effects in previous studies based on nighttime lights may be quite inaccurate

A qualitative assessment of the implications of AI for New Zealand

Back in July, Treasury released an analytical note written by Harry Nicholls and Udayan Mukherjee on the implications of artificial intelligence for New Zealand. I read it last month, but didn't have time to write up my comments until now due to travelling. The analytical note is not a quantitative assessment on the economic or labour market or other impacts, but really just testing the waters and providing some broad overview of some of the issues. Fortunately (or unfortunately, depending on your perspective), this hasn't been superseded by subsequent quantitative analysis of the impacts, so it still provides a starting point for some discussion.

Nicholls and Mukherjee see three main issues: (1) the impacts of AI on productivity and investment; (2) the impacts on employment and the labour market; and (3) the development of regulatory approaches for AI. It may be my bias showing, but I think they miss the impact of AI on education and human capital development as a fourth main issue, but perhaps they see it as being subsumed under their first main issue (although they don't mention education at all, and mention human capital only once in the note).

The note discusses each of the three main issues in turn, but first it lays out frameworks for thinking through each issue. There are two (competing) approaches. The first is based on macroeconomic growth accounting framework, where:

AI can be thought of as a form of capital deepening, improving the quantity and quality of capital inputs. It can also be thought of as labour-augmenting technology that improves the quality of labour inputs. And it might be a form of technology that more effectively combines capital and labour, improving multi-factor productivity.

The second is a microeconomic approach, which sees:

generative AI as a form of workplace automation...

There are three types of effects [Acemoglu and Restrepo] identify in this framework:

• Displacement Effect: Automation makes it more efficient to produce some tasks by capital that were previously done by labour, so it reduces the share of labour in production.

• Reinstatement Effect: Automation can allow a more flexible allocation of tasks in production, and so can create a range of new tasks in which labour has a comparative advantage.

• Productivity Effect: Automation allows some of the tasks previously performed by labour to now be performed more cheaply by capital, and so increases the value-added in production and so potentially the overall demand for labour.

The benefit of this framework is that it helps to think through how the net impact of new automation technologies can be thought about as the result of the combination of the strength of these effects.

For example, the total effect of AI on labour demand is a combination of the Productivity Effect increasing the demand for labour and the Displacement Effect replacing labour from tasks it previously performed. Similarly, the impact of AI on wages could be thought of as the net change on marginal productivity from the Reinstatement Effect creating new tasks for labour demand and the Displacement Effect create new labour supply from replaced tasks.

I found the frameworks quite useful, but it is difficult to see how they can be reconciled (which, to be fair, is a common problem comparing macroeconomic and microeconomic frameworks). Nicholls and Mukherjee then turn to thinking through the three issues, but the frameworks seem to be a bit lost in those sections. However, they conclude by offering some potential areas for future research, which are:

• A deeper exploration of the policy levers that might accelerate the diffusion of AI, reducing the lag that characterises New Zealand’s diffusion of new technology.

• Investigating the likely impacts of AI on the productivity and competitiveness of particular sectors, or small-to-medium sized enterprises (SMEs), given their significance to the New Zealand economy.

• Taking a closer look at the implications of AI for employment and labour markets, with a particular focus on how these impacts should inform our policy settings around skills, immigration, and labour markets.

• Examining how AI intersects with our economic security, particularly if AI development is concentrated in a small number of large multinational overseas-based technology companies.

• Considering how AI could lift the productivity of New Zealand’s public sector, in order to maintain or enhance service levels in the face of pressures like an aging population.

Again, I would add the impacts on education and human capital development here, as well as research on the distributional consequences of AI. Those questions are important to understanding the future labour market impacts, as well as the need for changes to policy settings on taxes and transfers. Anyway, this is a good starting point as a think piece, and it is good to see that Treasury are actively thinking in this space, and hopefully there is more careful analytical work to come from them.

[HT: Inside Government, back in July]

Enforced class attendance might make better students worse off

Each trimester, I make it very clear to students that regular attendance at classes offers greater chances at success for them. Those statements are based on years of observational data on student performance in my papers - students who attend more regularly have higher pass rates, and better grades. Part of that is clearly selection bias - students who attend less often may have lives that are more complicated, making it difficult for them to keep up with studying. Alternatively, students who attend less often may be less motivated, and that affects both attendance and willingness to study. However, just before the pandemic, we ran an experiment in the ECONS101 class that provided some evidence that incentivised non-attendees to attend class, and that did appear to improve performance, suggesting that at least some of the effect of attendance on performance is causal (that study has still not been written up for publication, sadly).

On the other hand, I am very aware of the large (and still growing) literature comparing online, blended, and in-person teaching and learning modes (see this post, and the long list of links at the end of it). My takeaway from that literature is that teaching mode has no effect on student performance (or student learning) on average, but has important heterogeneous effects. More able (or more self-directed) students tend to do better in online and blended learning environments, whereas less able (or less self-directed) students tend to do worse in those environments. Based on that evidence, I encourage in-person attendance as I am somewhat more focused on ensuring that the less able students have the best opportunity to succeed. However, in the back of my mind, I do harbour some reservations that the top students are not necessarily getting the best learning experience as a result.

Those reservations seem to now have some support, from this 2023 article by Sofoklis Goulas (Brookings Institution), Silvia Griselda (Bocconi University), and Rigissa Megalokonomou (Monash University), published in the Journal of Economic Behavior and Organization (ungated earlier version here). They look at a relaxation of attendance rules at Greek high schools, and the effects on attendance and student performance. Specifically:

The education system in Greece has a very strict attendance policy. All high- school students attend classes back to back with short recesses in between from 8 am to 2 pm, Monday through Friday...

Until the end of the 2005-06 school year, every student was allowed a maximum of 50 unexcused and 64 hours (about 10 days) of excused class absences in a year. One absence is equal to one missed school period...

Before the beginning of the 2006-07 school year, the Ministry of Education announced a nationwide policy to encourage students’ autonomy (Gov. Gazette 65/A/30-3-2006). The policy provided eligible students with 50 additional excused class absences. Every student who had received a raw GPA higher than 75% the year before was eligible to take up more parent- approved absences in the current year.

Goulas et al. then use a difference-in-differences-in-differences approach, which involves comparing the difference between students who were eligible (greater than 75% GPA the previous year) and those that were not eligible, between Grade 11 (no relaxed policy) and Grade 12 (relaxed policy), between the 2006 and 2007 years (when the policy was implemented). That allows them to estimate the causal impact of the change in attendance policy on student outcomes, including high-stakes exam performance (in subjects that count towards university entrance) and low-stakes exam performance (in subjects that do not count towards university entrance). Goulas et al. find that:

Targeted students in the treated cohort took more absences and improved their high-stakes exam performance as a result of the increased autonomy policy. In particular, targeted students increased their total (excused) absences by four (three) additional class hours—roughly 0.09 (0.13) standard deviations—relative to non-targeted students during the year the increased autonomy policy was in effect. Targeted students' high-stakes exam performance increased by 0.07 standard deviations—or 0.019 standard deviations per missed period—due to the increased autonomy policy. Targeted students' low- stakes exam performance remained unaffected by the increased autonomy policy.

So, the policy, which was targeted at top students, appeared to increase their class absences (which was what it was designed to do), and improved those students' performance. Goulas et al. also show that: 

...the increased autonomy policy is found to be associated with an increase in university admission score of 0.13 standard deviations... also... increased autonomy is associated with being admitted to university degree programs of higher quality/selectiveness—an improvement roughly equivalent to two percentiles in the distribution of degree quality.

So, the relaxation of attendance for top students also affects their long-term prospects in a positive way. However:

The policy is also associated with an increase of roughly 0.12 standard deviations in non-targeted students’ excused absences (roughly three additional excused class absences) in the year the increased autonomy policy was introduced. The effect of the policy on non-targeted students’ total absences or on school performance is not statistically significant. The unexcused absences of targeted and non-targeted students are unaffected by the increased autonomy policy.

So, other students also had more absences, but their performance was not affected. At least they weren't made worse off. Goulas et al. then dig a little deeper, showing that the change in absences was greatest among the top quartile of eligible students (in terms of GPA). However, that analysis doesn't show that those top-quartile students perform any better than other eligible students (in fact, the effects seem to be largest for the lowest quartile of eligible students).

What might be causing these results? Goulas et al. identify five potential mechanisms:

A first mechanism through which increased autonomy could influence performance is because of the attendance-performance association, which may also differ across students... A second mechanism through which increased autonomy could affect performance is related to the effective class size. As students take more class absences due to the increased autonomy policy, the class size decreases and student performance increases. A third mechanism may be related to changes in peer characteristics due to the increased autonomy policy. As targeted students skip class more often, the effective peer characteristics may change, impacting student performance... A fourth channel of influence of increased autonomy on performance is through teachers. As targeted students skip class more often because of the increased autonomy policy, instruction might become more effective... A fifth channel of influence of the increased autonomy policy on performance is related to student motivation from agency as suggested by the Self Determination Theory... Students targeted by the increased autonomy policy might feel they are being treated as responsible individuals, capable of making optimal decisions on their own, potentially feeling motivated to perform higher...

Goulas et al. then show that there is little evidence to support the effective class size, effective peer characteristics, and the instruction quality mechanisms, because there are similar impacts on eligible students in classes with higher or lower proportions of eligible students. That leaves the attendance-performance mechanism and greater agency. Goulas et al. show that controlling for decreased attendance explains most of the effect of the policy change on student outcomes, which suggests that it is the change in attendance that most explains the change in student performance.

What can we take away from this study? Class attendance may well make the top students worse off in terms of their academic performance. However, it is likely that what students do with the spare time does need to also be considered. This policy change was directed at the top 25 percent of Greek high school students, and they were well aware that they had a high-stakes university entrance examination at the end of the year. And, the additional absences needed to be approved by parents, who are likely to maintain at least some control over their children's activities during their absences. In other contexts, and with less motivated (or less closely supervised) students, the impacts are unlikely to be the same.

Also, I'd be cautious before extending these results to the university context. In my experience, students who are not attending are likely to be working (or playing video games) rather than studying during that freed up time. And past studies have shown that working impacts negatively on student performance (see this post, and the links at the bottom of it). However, the study does give some food for thought - maybe strict attendance norms can be relaxed for top students who are clearly engaged in other outside learning activities?

This week in research #52

I started this week at the Australia New Zealand Regional Science International (ANZRSAI) Conference in Canberra. Unfortunately, I left my notebook in one of the conference rooms at the end of the second day, so most of my notes were lost. However, from memory (and the camera roll on my phone) here are some of the highlights I found from the conference:

• Andrew Leigh (economist and Australian MP) gave a great keynote on inequality in Australia based on his revised book Battlers and Billionaires, although the most interesting bit was a graph on mentions of 'inequality' in Australian parliamentary debates, which is fairly low until a massive spike from 2011 onwards (I guess politicians didn't really care about inequality before then?)
• Putu Widyastaman presented on the relationship between gentrification and prostitution in Jakarta, showing that gentrification reduces prostitution in an urban village (roughly equivalent to a neighbourhood), but increases prostitution in surrounding urban villages
• John Madden presented on the economic impact of Victoria University's west Melbourne campus (although the counterfactual of 'what would have happened if Victoria University didn't have a campus in west Melbourne' is always going to be a challenge)
• Robert Tanton presented on a geocoded synthetic Census that is being developed for Australia, allowing researchers to conduct research on Census data without access to the underlying Census data (which is not a problem in New Zealand, where access to the Census data is available through the secure Integrated Data Infrastructure)

Aside from the conference, here's what caught my eye in research over the past week:

• Duan, Yuan, and Snyder find that a one standard deviation increase in the local sex ratio raises rural unmarried males’ likelihood of temporary migration in China by 3.6 percentage points
• Vatsa and Pino (open access) find that petrol price shocks had a slightly delayed but persistent effect on one-year inflation expectations in New Zealand, whereas five-year inflation expectations were largely insensitive to these shocks
• Meng et al. (with ungated earlier version here) find that a one-standard-deviation increase in wind turbines reduces bird abundance by 9.75% and leads to a 12.2% reduction in bird species richness at the county level in China
• Arora and Roy (with ungated earlier version here) do not find any significant differences in student evaluations of teaching received by female and male professors, using an experimental approach in India
• Abel et al. find that, in an experiment, investors are more likely to continue to follow financial advice from male professionals following advice that proves incorrect, but 47 percent less likely to follow financial advice from female professionals in the same circumstances
• Cordes, Dertwinkel-Kalt, and Werner (open access) investigate the economics of 'loot boxes' in online and mobile games using an experimental study design, and find that common design features of loot boxes (such as opaque odds and positively selected feedback) double the average willingness-to-pay for lotteries
• Klöcker and Daumann (open access) develop a theoretical model to explain success and dominance in international sports
• De Acutis, Weber, and Wurm (open access) conduct a meta-analysis of 51 studies on the effect of gender quotas on company boards, and find that (among other results) stock market returns are negatively affected by quota policies (I feel like there is more to this than meets the eye on a quick skim-read though)
• König et al. (open access) find that there is a statistically significant, but very small, positive association between short-term ambient temperature changes and individuals’ general willingness to take risks, using a large survey in Augsburg, Germany
• Lin, Churchill, and Ackermann (open access) find using 14 waves of HILDA data in Australia that a 1 percent increase in the proportion of a postcode that has access to the national broadband network (NBN) is associated with a 1.573 increase in Body Mass Index and a 6.6 percentage point increase in the probability of being obese
• Oprea challenges the existence of loss aversion by showing using experimental data that it may simply arise because of complexity of experimental tasks that are used to demonstrate loss aversion (which may also explain these results, I guess?)

This week in research #51

It's been a slow week again this week, as I've been travelling for the Australia New Zealand Regional Science International (ANZRSAI) Conference in Canberra (more on that next week). Nevertheless, here's what caught my eye in research over the past week:

• Borbely et al. (open access) find that a universal free school meal policy covering the first three grades of primary schools in Scotland had a small positive effect on attendance and a small negative effect on health-related absences (potentially important results given New Zealand's free school lunch policy and mooted changes)
• Asanov, Schirmacher, and Bühren (open access) conduct a meta-analysis of 'lost letter' experiments (78 studies with an overall sample size of 53,504 letters from 18 countries), and find that the return rate is lower for political or deviant issues, stamped letters are more likely to be returned, but letters with money are not more likely to be returned, and a high socio-economic environment increases the chances of the return