It's not time to give up artificial sweeteners just yet

Yesterday in my first ECONS101 class of the new semester, we talked about the difference between causation and correlation. So, it was interesting to see a story in the New Zealand Herald illustrating exactly that distinction this morning:

A New Zealand health expert says an American study linking diet drinks with stroke is a warning that artificial sweeteners are not risk-free.

Published in the journal Stroke, the longterm study of post-menopausal women found that those who consumed higher amounts of diet drink had a 23 per cent greater risk of having a stroke than those who drank small amounts or none.

The higher users also had a 29 per cent higher risk of coronary heart disease and a 16 per cent higher risk of death.

Higher use was defined as consuming the equivalent of two or more cans of "diet drinks such as Diet Coke or diet fruit drinks" a week. Low use was less than one a month.

The study involved more than 81,000 women in the Women's Health Initiative Observational Study.

The original study is here (ungated), by Yasmin Mossavar-Rahmani (Albert Einstein College of Medicine) and eight other co-authors. There is also an interesting related editorial by the editors of the journal Stroke here (also ungated). The authors of the study used data from over 81,000 post-menopausal women in the U.S. Three years into the study, the women were asked how often they drank artificially-sweetened beverages (ASBs), like Diet Coke or diet juices. The study then followed those women over time to see what happened, and found that the women who said they drank more ASBs were more likely to suffer coronary heart disease, or to die.

The problem here is that this study doesn't show a causal impact of ASBs on heart disease, stroke or mortality. Although it was carefully designed, and the authors controlled for a bunch of factors also known to affect heart disease or mortality risk, like quality of diet, body mass index, etc., the study still only shows a correlation. As the editorial and the New Zealand Herald article note, this correlation might be causal (and is consistent with a causal story), but it might not. It might be a result of reverse causation. Perhaps instead of ASBs causing heart disease or stroke, being at higher risk of heart disease or stroke causes women to be more likely to drink ASBs; maybe in order to lose weight or to reduce their risk of poor health (maybe under doctor's orders). This is supported by the fact that when they look at sub-groups by BMI, the correlation is only statistically significant for obese women, but not for overweight or normal-weighted women.

Also, the sample was limited to post-menopausal women. I'm unsure how representative of the general population that sample is, even if it is large. Besides which, people's dietary behaviours change over time. This study followed women for an average of 11.9 years, and then based the risk models on one self-reported question about ASB use over the three months at the start of that period. Perhaps women who were more likely to drink ASBs were also more likely to drink sugar-sweetened beverages as they got older? Perhaps they were more likely to be yo-yo dieters, which is bad for you.

So, it is far too soon to conclude that ASBs pose an unnecessary risk to health. At the very least, we would need more careful studies that follow a more widely representative sample of people over time. And even better, if they were randomised so that some drank ASBs and others didn't (although it would be pretty difficult to get ethical clearance for that!).

Could Twitter increase student engagement?

Engaging students is probably the most important aspect of teaching. Engaged students are more likely to learn, and retain their learning for longer. I expend quite a lot of effort trying to engage with students, including through social media. My classes have had their own Facebook groups for the last several years. I know of other lecturers who have used Twitter, Instagram, WhatsApp, or WeChat, but I haven't engaged with those (either because I'm not convinced that enough students are using them to make it worthwhile or, in the case of Instagram which many students use, it isn't clear how it would be an improvement over Facebook). Also, while I know that students find the Facebook groups helpful for their learning, I haven't rigorously evaluated the effect.

So, I was interested to read this 2017 article (which appears to be open access) by Abdullah Al-Bahrani (Northern Kentucky University), Darshak Patel (University of Kentucky), and Brandon Sheridan (Elon University), published in the Journal of Economic Education. In the article, Al-Bahrani et al. evaluate the impact of Twitter on student learning in economics principles courses in three U.S. universities. All students were provided, roughly three times per week, with "short articles relating concepts from class to the real world" (probably not too dissimilar to what I do on this blog). At each institution, one section of the class received this information via the learning management system (like Moodle), while one section received this information via Twitter. Al-Bahrani et al. then looked at whether there was any difference in learning between the two groups, and found that:

In all specifications, the treatment coefficient is statistically insignificant, therefore we find no evidence that communicating through Twitter impacts students’ learning differently than a traditional LMS.

So, Twitter is no better for providing students with links to additional material than a learning management system like Moodle. Admittedly, there was a lack of statistical power in their analysis as they only had a sample of 163 students across the three institutions. So, maybe there was an effect, but their sample size was too small to detect it. However, either way I do agree with their conclusion that:

...the impact of Twitter on the educational experience may not necessarily be in the form of grades or learning, but may rather be in the form of engagement, teacher evaluations, and fostering interest in the topic.

Student engagement is important, and finding ways of engaging students on 'their turf' is even more important. More on that in a future post.

Why summer ice cream prices don't respond to changes in demand

New Zealand has been suffering through a heatwave. One of the effects was a shortage of ice cream, as the New Zealand Herald reported last month:

Hot temperatures have led to such a demand in ice cream and cold drinks that some businesses have had to turn customers away.

Havelock North McDonald's ran out of soft serve ice cream and milkshakes on Wednesday evening, forcing customers to look elsewhere.

A shortage occurs when the quantity of a good demanded exceeds the quantity of the good supplied. Some customers will miss out on the good. We might expect the price to increase to eliminate the shortage. Consider the perfectly competitive market in the diagram below:

Before the heatwave, the demand for ice creams is D0 and the supply is S. The market is in equilibrium with the price P0 and the quantity of ice creams traded is Q0. When demand increases from D0 to D1, the equilibrium should move to the intersection of D1 and S, where the price has increased to P1 and the quantity of ice creams traded has increased to Q1. However, if the price stayed at P0, the quantity supplied remains Q0, but the quantity demanded is QD - there is a shortage (or excess demand).

However, the market diagram above assumes a perfectly competitive market. In a perfectly competitive market, buyers and sellers are price takers - they have no control over the price, which is set by the market (at the intersection of supply and demand). The perfectly competitive market assumes there are many buyers and many sellers, and the sellers are all selling an identical (homogeneous) product. This is not a reasonable assumption for most markets, including the market for ice creams. In most markets, there are a many buyers, but few sellers, or the sellers are selling a differentiated product. That gives the sellers some market power - the power to choose their own price.

The diagram below shows what happens when a firm with market power faces an increase in demand. The firm is profit maximising, so it operates at the profit-maximising price and quantity where marginal revenue intersects with marginal cost - with the original (red) demand curve D0 and (red) marginal revenue curve MR0, this leads to the price P0 and the quantity of ice creams traded is Q0. When demand increases to D1 (and marginal revenue increases to MR1), the profit-maximising price increases to P1, and the quantity increases to Q1. However, if the firm kept the price at the original price P0, then the quantity demanded is QD. There is no excess demand in this case, unless the firm hadn't planned for the possibility of extra sales.

So, regardless of whether we are considering a firm with market power or a firm in a perfectly competitive market, when the demand for ice creams increases, we should expect the price to increase. So, it might be surprising that the price doesn't adjust. Why wouldn't the price adjust?

There are a few reasons that sellers don't automatically adjust prices in response to changes in demand. The first reason is menu costs - it might be costly to change prices (they're called menu costs because if a restaurant wants to change its prices, it needs to print all new menus, and that is costly). The second reason is that changing prices creates uncertainty for consumers, and if they are uncertain what the price will be on a given day, perhaps they choose not to purchase (in other words, the cost of price discovery for consumers makes it not worth their while to find out the price). The third reason is fairness. Research by Nobel Prize winner Daniel Kahneman (and described in his book Thinking, Fast and Slow) shows that consumers are willing to pay higher prices when sellers face higher costs (consumers are willing to share the burden), but consumers are unwilling to pay higher prices when they result from higher demand - they see those price increases as unfair.

Finally, in this particular case, the price of McDonald's ice creams are set at the national level. So, the seller doesn't have control over the price and can't adjust it in response to changes in demand. So, even though the simple economic models might suggest a particular outcome (an increase in price), it is easy to explain why the real world outcome differs from the model.

The economics of clearing landmines

Some years ago, I was involved in a project measuring the value of a statistical life (VSL) in Thailand (ungated earlier version here) and Cambodia (ungated version here). Part of the point of that work was to overcome earlier cost-benefit analyses of landmine clearing activities, which had wildly underestimated the benefits of clearing landmines, and often suggested that the costs outweighed the benefits.

The problem with the earlier studies is that they used the human capital approach to valuing the benefits of lives saved from clearing landmines. The human capital approach estimates VSL based on the total value of output that an average person would produce over their lifetime - essentially, it is estimated based on the total wages they would earn. However, time in work is only part of what we contribute to society, and the human capital approach therefore must underestimate the real VSL. An alternative is to use a non-market valuation approach like contingent valuation. Essentially, this involves asking people what they would be willing to pay for a small reduction in the risk of dying. Then, the average that people are willing to pay can be scaled up to work out what they would be willing to pay (on average) for a 100 percent reduction in the risk of death, which is the estimated VSL.

Our work in Thailand and Cambodia showed that the estimated benefits of landmine clearance were much larger than previously estimated. However, in spite of the higher benefits, the cost-benefit calculus only favoured landmine clearance in some areas. There were many places (typically remote, far from roads, where few people lived) where the costs of clearing landmines still outweighed the benefits.

However, benefits from lives saved (and injuries averted) are not the only benefits from clearing landmines. In a recent NBER working paper, Giorgio Chiovelli (London Business School), Stelios Michalopolous (Brown University), and Elias Papaioannou (London Business School) look at the effects of clearing landmines in Mozambique. Specifically, they estimate the impact on economic activity. Mozambique is interesting to investigate, because it is the first country ever to move from being classified as "heavily contaminated by landmines" (in 1992) to "landmine free" (which it was certified as in September 2015).

However, good data on economic activity are scarce in Mozambique due to the years of conflict. So, Chiovelli et al. make use of night-time lights data from satellite images. This is a fairly new and exciting data source, which relies on the observation that areas that are more illuminated have higher economic activity (this has been shown in many studies, but for a graphic example, look at photos that compare neighbouring North Korea and South Korea, such as this one).

Chiovelli et al. exhaustively compiled data on the landmine clearance activities in Mozambique over the period from 1992 to 2015, so that they could evaluate the impacts of clearances in different parts of the country occurring at different times. They then examined the economy-wide impacts, recognising that the main impact of landmines was on reducing market access through making roads and rail impassable. They found that:

...a one-standard deviation increase in the number of cleared CHA [Confirmed Hazardous Areas] increases log luminosity by 0.072 standard deviations... Clearing a locality from all contaminated hazardous areas increases the likelihood of the locality being lit by roughly 4%; this estimate should be compared with an average value of the locality being lit of 9.7% in 1992.

The effect is reasonably large, as:

...cleared localities (as opposed to not-contaminated ones) enjoy a boost in economic activity comparable to that of being one of the few localities endowed with a colonial railroad...

They then test for heterogeneous impacts of landmine clearance, and find that:

...reducing the number of contaminated areas along roads-railroads and clearing areas around villages and towns, especially the ones with cantinas is associated with significant increases in luminosity. On the other hand, removal of landmines in remote, rural areas (the residual category) does not seem to lead to increases in luminosity.

This is interesting, because it complements the findings from my earlier studies. The areas that are remote, with few people, not only would have lower benefits of landmine clearance due to fewer lives saved (and injuries averted), but also have lower benefits in terms of increases in economic activity.

Chiovelli et al. then move on to look at spill-over effects, and find that there are increases in economic activity even for areas with no landmines. This arises because those areas also benefit from landmine clearance, when roads and rail are cleared and areas become more accessible.

Overall, the takeaway message is that there are significant economic benefits from clearing landmines. However, that still doesn't necessarily mean that the benefits outweigh the costs in all areas.

[HT: Marginal Revolution, last June]

Taxes are not part of GDP

Our Prime Minister doesn't know the difference between GDP and the government accounts. That much is clear from her slip-up last September. However, she can almost be forgiven for that. After all, she did a communication studies degree, which almost certainly didn't include any economics. And she isn't finance minister. However, I would expect business reporters to know better. From Aimee Shaw in yesterday's New Zealand Herald:

The beer industry contributed $646 million to GDP in the year to March 2018, made up of $331m in GST and $315m in excise tax.

That sentence is exactly wrong. GST and excise tax do not contribute to GDP. To see why, let's start with the definition of GDP: The market value of all final goods and services produced within a country in a given period of time.

There are three approaches we can use to measure GDP: (1) the expenditure approach, which adds up all of the spending in the economy; (2) the income approach, which adds up all of the income in the economy; and (3) the production approach, which adds up the value of everything produced in the country. All three of these should (in theory) add up to the same value. This is easiest to see for the expenditure and income approaches, since every dollar of spending by Person A must become income to Person B.

To see why GST and excise tax are not part of GDP, let's start with the production approach. The idea is to add up the value of everything produced in the country in the last year (or quarter). So, you add up every good and every service produced in that time, and multiply each of them by their market price. The market price is the price excluding GST or excise taxes. The reason they are excluded is simple - they don't affect the underlying value of the good or service. If you included GST or excise taxes in the measure of the value of goods and services, then the government could artificially inflate GDP every year by increasing GST. So, GST and excise taxes are not included in GDP.

The expenditure approach adds up all of the spending in the economy, including spending by households (consumption), spending by businesses (investment), and spending by government, with an adjustment for net exports (the difference between the amount that overseas countries spend on goods produced in New Zealand (exports), and the amount that New Zealand spends on goods produced overseas (imports)). Again, there is no room for GST or excise tax in there.

The income approach adds up all of the income in the economy, including income from labour (wages), income from capital (rents), income from savings (interest), and income from entrepreneurship (profits). Notice there is no role for GST or excise tax in there either.

Taxes are part of the government accounts, and the difference between taxes and government spending is the budget surplus (if taxes are larger than spending) or deficit (if taxes are less than spending). Our Prime Minister might not know the difference, but we should expect better of the business media.

Identity economics and the gender gap

This post follows my review of Akerlof and Kranton's book Identity Economics yesterday. As I suggested at the end of that post, it would be interesting to look at the gender gap in economics through an identity economics lens (see my post last week for the latest on the gender gap in economics. So, that's what I'm going to attempt to do here, borrowing where I can from the 'Gender and Work' chapter of the Akerlof and Kranton book, starting with the basic (boilerplate) model:

We start with a boilerplate model of a labor market. There are firms that desire to hire workers to do tasks - yielding the demand for labor. There are men and women who desire to work - yielding the supply of labor. Some men and women are better at a task than others, with no overall difference between men and women... Solving for the wage where supply equals demand yields the number of men and women employed.

Now introduce the identity elements of the model. The 'social categories' are men and women. Some tasks are labeled appropriate for men (including economist), while other tasks are labeled appropriate for women. Then Akerlof and Kranton note:

Women lose utility from working in a man's job. And men lose utility from working in a woman's job. Men also lose utility when a women works in a man's job.

Solving this model for equilibrium, we would find that men would be more likely to be hired as economists. This is because, in the model, men would have to be paid more in order to compensate for working with female economists. To maximise profits, the employers (e.g. universities or research centres) would want to hire more men than women, because the premium they would need to pay would be lower. At equilibrium, men would resist the addition of more female economists to their workplace since that would lower their utility. Alternatively, rather than necessarily eliminating the hiring of women, the employer could segregate men and women in different parts of the firm - say, in different fields of economics.

Now, thinking about the model outlined above, this simple model seems to explain a surprising amount of the gender gap in economics. First, there are more men than women employed at each level of academia, from professors to assistant professor. This is consistent with employers having a preference for male economists. Second, there are more male than female PhD graduates. This reflects that men are preferred in hiring decisions, and students respond to those incentives. Third, the academic climate for female economists is undeniably hostile. This is consistent with men's identity utility being reduced by working with female economists. This also reinforces the second point, that there are fewer female economics PhDs, because the climate affects students as well as career academics. Fourth, male and female economists do work in different fields of economics, with men more likely to work in macroeconomics or finance, and women more likely to work in health or labour economics. This is consistent with some segregation by employers, but also with differences in the climate between the different fields.

Of course, this model is necessarily simplified, and I'm sure some will take issue with how it is presented. The implications are clear though - if the model is to be believed, then the gender gap is entrenched within economists' identities. The norms and ideals of economics need to change, so that economics is no longer considered a 'man's job'. Unfortunately, Akerlof and Kranton's book is relatively silent on how norms and ideals can be changed. Better mentoring of female students and economists has been suggested. However, if it is the norms and ideals that need to adjust, then it is the actions (and inactions) of male economists that need to change.

Book review: Identity Economics

There is a very short list of books that I would recommend that every serious undergraduate economics student (or graduate economics student) should read. These books are not central to understanding economic theory or analysis, but they are important complements to a thorough grounding in economics. The very short list includes Robert Frank's Falling Behind: How Rising Inequality Harms the Middle Class (which I discussed here), and Ziliak and McCloskey's The Cult of Statistical Significance: How the Standard Error Costs Us Jobs, Justice, and Lives (which I reviewed here). To that very short list, I would now add Identity Economics, by Nobel Prize winner George Akerlof, and Rachel Kranton. That isn't to say that the book is without faults; only that it has something important to say that I think all economics students should understand.

Akerlof and Kranton have identified an important gap in economic models, which is the omission of identity - the subtitle of the book is "How our identities shape our work, wages, and well-being". As they note in the introduction:

The discipline of economics no longer confines itself to questions about consumption and income: economists today also consider a wide variety of noneconomic motives. But identity economics brings in something new. In every social context, people have a notion of who they are, which is associated with beliefs about how they and others are supposed to behave. These notions... play important roles in how economies work.

The inclusion of identity (Akerlof and Kranton define this as people's 'social category', which differs by context) into economics allows for a much richer understanding of people's decision-making. What would previously have been considered "idiosyncratic personal differences" between decision-makers can now be properly recognised as systematic differences between groups that have different identities.

The first part of the book outlines how identity can be incorporated into a narrative economic model (that is, not a formal mathematical model). They then move onto applying the model to several real-world situations, including the economics of organisations, education, gender and work, and race and poverty. The basic outline of the model works the same in each case: there is an insider-group that has certain norms and ideals, and an outsider-group that has different norms and ideals. If a member of the outsider-group tries to act like an insider, then they face a loss of identity utility. It may therefore be better for outsiders to act like outsiders.

The basic model seems a bit circular to me when identity is itself a choice, and is not associated with an easily delineated social category. Men and women are easy social categories to define, but 'jocks' and 'burnouts', as used in their model of education, are not so simple. However, where the categories are based on defined characteristics (e.g. gender, or race), then the model strays uncomfortably close to essentialism. So, when Akerlof and Kranton note early in the book that "Social scientists in other disciplines should find identity economics useful because it connects economic models with their own work", I rather think that a critical social scientist reading this book would get rather angry at the examples and how they are described. However, they would be missing the point - I don't think this is a book for other social scientists to better link their work to economics, but rather a book for economists to better link their work to other social sciences. This is why I think it is an important read for economics students.

I do have another concern, although maybe concern is too strong a word. Akerlof and Kranton treat 'standard utility' (the utility from consumption, including consumption of leisure) and 'identity utility' as separable. I don't think that is necessarily the case. A more thorough model would recognise that standard utility may well be a function of a person's identity. In other words, identity utility might not exist in itself, but might be expressed through systematic differences in the utility function between people with different identities. However, I guess if they adopted that form in their model, it would look much more similar to earlier models adopted by Gary Becker and others.

Despite that concern, this is an important book. I'm surprised it hasn't made a bigger splash in the discipline, but at the time it was released (2010), it would have been competing with other big new ideas like behavioural economics and experimental economics. In particular, I wonder how the current debate over the gender gap in economics (which I most recently blogged about last week) would look through a lens of identity economics? That will be a topic for another post.

How the internet affects international migration decisions

In the 1960s, Everett Lee came up with a model of migration decisions (ungated version here) that remains the most widely used theory of what determines peoples' migration decisions. Lee emphasised that there are: (1) push factors - things in the origin that cause people to want to move away, like low wages or high unemployment; and (2) pull factors - things in the destination that cause people to want to move there, like high wages or low unemployment. Push factors can be positive (they make you want to move away), or negative (they make you want to stay), and likewise pull factors can be positive (they make you want to move there) or negative (they make you not want to move there). A third factor has been added to these push and pull factors - facilitating factors, or things that make migration easier, like a simplified visa process.

With the theory out of the way, we can now consider how the internet might affect international migration decisions. The internet facilitates improved communication, including between diasporas and those living in their home country. So, perhaps greater internet access might make migration easier, by increasing the flow of information about how or where to migrate to.

Alternatively, maybe the internet facilitates outsourcing of jobs to previously lower-income countries, increasing job prospects and incomes in the origin, and reducing migration (a negative push factor), or allowing people to work for a firm in the destination country without having to leave the origin country (a negative pull factor). Or maybe the internet facilitates access to goods or services (e.g. Netflix) that previously weren't available in the origin country, improving the quality of life there (again, a negative push factor).

So, it isn't clear from the theory whether the internet would increase, or decrease, international migration. What do the data say? A 2017 article by Hernan Winkler (World Bank), published in the journal Applied Economics Letters (I don't see an ungated version, but it appears to be open access), provides some answer. Winkler estimated a gravity model (which I have previously discussed here) using data from 6072 origin-destination pairs of countries over the period 1990-2010. He found that:

...a 10% increase in internet penetration in the source country is accompanied by a 1% decrease in the stock of migrants born there.

That was based on the simplest model he reported, but other models supported that the internet reduced migration. He also found similar results using an instrumental variables approach, which implies that the results are causal - that is, the internet caused a reduction in international migration. Winkler concluded that:

...the internet may weaken the importance of push factors in the decision to migrate, and that these effects dominate any declines in mobility costs associated with this new technology.

This isn't the last word on this, but it is consistent with a story that the internet is associated with job outsourcing from high-income countries to low-income countries, and weakens the incentives for workers from low-income countries to migrate. Maybe, rather than building a wall, the U.S. should be building out internet infrastructure in low-income countries?

What happens when you disconnect from Facebook?

I've written a few posts on whether Facebook or internet use makes you unhappy (see here and here and here). The problem with most (if not all) earlier studies is that they show a negative correlation between Facebook use and happiness (or life satisfaction), but fail to show a causal relationship. It might be that unhappier people are more likely to use Facebook, or to more intensively use Facebook, than happier people. Or maybe there is some third factor (e.g. work satisfaction) that affects both Facebook use (more satisfied workers use Facebook less) and happiness (more satisfied workers are happier).

A new working paper by Hunt Allcott (New York University) and co-authors (recently covered by the New York Times) addresses this by using a randomised controlled trial - they randomly selected some of their 2844 research participants to switch off Facebook for four weeks, while others only switched off Facebook for one day. They then looked at the effects of that period on a battery of different measures of online and offline activity, news knowledge, political knowledge and views, and life satisfaction, based on a comparison of the treatment group (those that switched off Facebook for four weeks) and the control group (those that switched off Facebook for a single day). As is increasingly common, they had a pre-registered analysis plan, which limits the degrees of freedom to manipulate the analysis to achieve a preferred statistical result. So the results are fairly believable.

Allcott et al. found that:

Deactivating Facebook freed up 60 minutes per day for the average person in our Treatment group. The Treatment group actually spent less time on both non-Facebook social media and other online activities, while devoting more time to a range of offline activities such as watching television alone and spending time with friends and family. The Treatment group did not change its consumption of any other online or offline news sources and reported spending 15 percent less time consuming news...

The fact that Facebook use declined is not surprising, but other online activities also declined, showing that Facebook and other online activities (including online news consumption) are complements, rather than substitutes. Moving on, they also found that:

Consistent with the reported reduction in news consumption, we find that Facebook deactivation significantly reduced news knowledge and attention to politics. The Treatment group was less likely to say they follow news about politics or the President, and less able to correctly answer factual questions about recent news events. Our overall index of news knowledge fell by 0.19 standard deviations. There is no detectable effect on political engagement, as measured by voter turnout in the midterm election and the likelihood of clicking on email links to support political causes. Deactivation significantly reduced polarization of views on policy issues and a measure of exposure to polarizing news. Deactivation did not statistically significantly reduce affective polarization (i.e. negative feelings about the other political party) or polarization in factual beliefs about current events, although the coefficient estimates also point in that direction. Our overall index of political polarization fell by 0.16 standard deviations...

We might decry Facebook as a source of fake news, but it appears to also be a significant source of real news knowledge as well, as shown by the decrease in political knowledge from deactivating Facebook. To be clear, this result arises mainly because it makes people less sure about the news statements they were presented with in the survey (and asked if the statements were true, or false, or if they were unsure). As many would expect though, it appears that Facebook contributes to political polarization. Finally, in terms of happiness or life satisfaction:

Deactivation caused small but significant improvements in well-being, and in particular on self-reported happiness, life satisfaction, depression, and anxiety. Effects on subjective well-being as measured by responses to brief daily text messages are positive but not significant. Our overall index of subjective well-being improved by 0.09 standard deviations... These results are consistent with prior studies suggesting that Facebook may have adverse effects on mental health.

Interestingly, these outcomes were about two-thirds smaller than the effects measured in past correlational studies (which they demonstrate in the paper). So perhaps Facebook isn't as negative for our overall wellbeing as it has been portrayed. However, it is worth noting that the participants that deactivated Facebook were also more likely to reduce their Facebook use after the experiment concluded. Allcott et al.'s results are also:

...consistent with reverse causality, for example if people who are lonely or depressed spending more time on Facebook, or with omitted variables, for example if lower socio-economic status is associated with both heavy use and lower well-being.

Finally, their data allows them to estimate the consumer surplus of Facebook, which is essentially a measure of the total benefits generated by Facebook for consumers. [*] This is because they asked people how much they were willing to accept to deactivate Facebook for a month - a form of non-market valuation (they are not the first to do this, as I noted in this post last year). They estimate this consumer surplus for US consumers at US$230 billion to $365 billion per year. So despite the impacts on wellbeing, Facebook does generate a lot of value.

[HT: Marginal Revolution]

Read more:

• How much money would you accept to give up Facebook for a month?
• Does the Internet make people happier?
• The more you use Facebook, the worse you feel
• Quitting Facebook might make you happier

*****

[*] Strictly speaking, the consumer surplus is the amount that consumers would be willing to pay for the service, minus the amount that they actually pay. In this case, consumers don't pay anything for Facebook use (at least monetarily - we voluntarily give Facebook lots of our data, which may or may not be valuable to us!).

The deceleration of improvements in national life expectancy

Life expectancy has been increasing over time. A broad rule of thumb is that, on average across the world, every four years life expectancy has increased by about one year. That would seem to suggest that we are about to enter an age where young people (especially young women) can expect to live to 100 (although Riccardo Trezzi remains the only living immortal). However, as I noted in this 2017 post, it pays to take care with claims about future life expectancy.

Often, those making the claims are looking at some form of projection based on past changes in life expectancy. If we take a closer look at past improvements in life expectancy, what do they tell us? A 2018 article (ungated) by Carolina Cardona and David Bishai (both Johns Hopkins Bloomberg School of Public Health), published in the journal BMS Public Health, provides an answer. Cardona and Bishai used national-level data on 173 countries over the period from 1950-2009, and looked at decadal changes in national life expectancy. They found that:

When we compare countries at a similar starting point of LEB [Life Expectancy at Birth], we find that the pace of growth of LEB has slowed over the last 60 years for countries at all levels of starting LEB. Countries with the lowest LEB (LEB < 51) had the greatest slowdown...

In the lowest group, LEB decadal improvements in the 1960–69 decade were 4.171 (SE 0.473) years lower than in the 1950–59 decade (P < 0.001), and in the 2000–09 decade were 8.491 (SE 0.982) years lower than in the 1950–59 decade (P < 0.001). The deceleration of LEB growth among the group with the highest starting LEB was smaller, at 2.961 (SE 0.181) years gained per decade slower for the 1960s (P < 0.001) and 1.893 (SE 0.264) years gained per decade slower for the 2000s (P < 0.001), both compared to 1950s.

In other words, gains in life expectancy over time have been decelerating, and have been decelerating in all countries (on average), regardless of whether they have high, or low, life expectancy. These results control for a bunch of factors known to be associated with health and life expectancy. The growth of HIV/AIDS doesn't explain it, because the slowdown has happened in countries with and without generalised HIV epidemics. The slowdown has happened in every region of the world.

To illustrate further, here's Figure 1 from the article, which plots the change in life expectancy gains over time for countries with life expectancy below 51 years (on the left), and for those with life expectancy above 70 years (on the right). This slowdown is most apparent for countries with low life expectancy.

To me, the most disappointing thing about Cardona and Bishai's article is that they didn't extrapolate to see what their results imply about future life expectancy and where the limit may lie. That would be an interesting exercise for a future honours or Masters student, since the underlying datasets are all available online.

[HT: Marginal Revolution, last February]

Read more:

• Future life expectancy is good for NZ men, but be cautious
• The limit to human lifespan, or not?

The latest on the gender gap in economics

The latest issue of the Journal of Economic Perspectives has a three-paper symposium on "Women in Economics". In the first paper (ungated), Shelly Lundberg (UC Santa Barbara) and Jenna Stearns (UC Davis) provide the latest data on trends in the gender composition of academic economists. The trends are not flash - if you're looking for a narrowing of the gender gap here, you'd have to be pretty selective about what you were focusing on. Specifically, Lundberg and Stearns find that:

In top 20 programs, the representation of women among full professors was only 3 percent in 1993, grew slowly to 10 percent in recent years, and then rose to nearly 14 percent in 2017. The female fraction of associate professors (which grew steadily throughout this period in the Chairman’s Group), increased from 10 percent to as high as 26 percent in 2011, but has declined in recent years to about 20 percent. Female representation among assistant professors stood at about 21 percent in 1993, reached a peak of 27.6 percent in 2008, and has since fallen back to 20 percent, meaning that no net progress has been made at the junior faculty level in top 20 departments over the past 24 years.

They also note that female economists are:

...more likely than men to study topics in labor and public economics and less likely to do dissertation research in macro and finance across the entire time period, there is virtually no evidence of differential trends.

Perhaps you could put that result down to differences in preferences, but given the perceptions that macro and finance appear to be rewarded better, the persistence of this particular difference contributes to the persistence of the gender gap.

In the second paper (ungated) in the symposium, Leah Boustan and Andrew Langan (both Princeton) look at the variation in women's success across different PhD programmes in the U.S. They show that the proportion of graduating economics PhDs going to women varies between 10 percent and 50 percent at different institutions. They also find that:

...departments with a greater share of women on their faculty also have more women in their student body: a 10 percentage point increase in faculty share is associated with a 2.5 percentage point increase in student share.

That points to a key role of mentoring and role modelling by female academic economists for students. This point was also confirmed through 31 interviews they conducted with faculty members and former students from five different PhD programmes. Finally, they found that:

...on average, men and women who graduated from the same program between 1994 and 2017 are no different in their propensity to be offered and accept a faculty position at a US PhD-granting department or to be promoted to associate professor within ten years of graduation. But conditional on taking a job in a US PhD-granting economics department, men land placements at higher-ranked departments and publish more in the top journals in the first seven years after obtaining their degree.

Those results are complemented by this 2017 working paper by Jihui Chen (Illinois State), Myongjin Kim and Qihong Liu (both University of Oklahoma). Chen et al. followed the outcomes of 578 economics PhD students who graduated in 2008, and found that:

...females in the Class of 2008 are less likely to receive tenure, relative to their male peers, by 14.1%.

Moreover, Chen et al.'s results show that the gender gap in tenure and promotion is greater for foreign-born academics working in the U.S.

Coming back to Boustan and Langan's paper, one of their conclusions was that:

Hiring women on the faculty strikes us as a concrete and low-cost approach to creating a productive learning environment that might encourage women to enroll in the graduate program.

That doesn't seem unreasonable, and is one of the strategies advocated in the third paper (ungated) of the symposium, where Kasey Buckles (Notre Dame) reviews strategies for closing the gender gap at all levels: undergraduate students, graduate students, assistant professors, associate professors, and in K-12 education. Mentoring of female economics students or junior academics seems to be a consistent theme, but it appears that there are no silver bullets here.

In fact, the most interesting parts of Buckles' review may be noting the interventions that don't work. For instance, she notes that double-blind peer review of papers (where the author/s' names are not known to the referee) does not reduce gender bias. To some extent, this is likely because double-blind is a convenient fiction, since reviewers may know (or can easily find out) who the authors of papers they review are. She also notes that:

...recent research has highlighted a policy that actually works against women on the tenure track—“gender-neutral” clock stopping policies that allow both men and women to add time to the tenure clock with the birth of a child. While the policies are often adopted in the interest of fairness, they can disadvantage women if men are able to be more productive during their extended time due to differences in child-care responsibilities or the impact of the birth itself.

One recommendation for K-12 students caught my eye though:

...the current AP Microeconomics course focuses on product markets, factor markets, and market failures, and the role of government... While these topics should remain central, the course could shift its content to include discussions of how economists apply these concepts to topics like health, education, family, crime, or development. These fields are relatively popular among women academics... it would not be surprising if they were also more appealing to women in high school.

That recommendation probably applies to introductory economics at university as well, and is something I already implement in ECONS102, which includes topics on health economics, the economics of education, poverty and inequality, and the economics of social security. I should start looking for more opportunities like this for my ECONS101 (business economics) paper as well.

Read more:

• Could closing the gender gap in economics be as simple as providing students with information?
• Why we should care about the gender gap in economics
• Justin Wolfers on why we should care about the gender gap in economics
• Female representation in economics
• CORE on 'missing women in economics'
• More on the gender gap in economics
• The gender gap in economics

Is language a source of gender bias?

Unconscious bias has been implicated as one of the drivers of gender inequality. Where does this unconscious bias arise from? Given that it is unconscious, it must be part of our identity or our culture - things which are difficult to change (at least in the short term). That might seem like a cop-out, but it does explain why, despite the hype, most interventions to prevent unconscious bias don't work (although, if you follow the link, you'll see that some may do).

One important aspect of culture is language, and languages differ in their treatment of gender. So-called 'gendered languages' attach genders to objects (even inanimate objects). In Spanish, think of el toro (the bull, a masculine noun), or la casa (the home, a feminine noun). English doesn't attach genders to objects like that (although we do sometimes refer to objects, like ships, as a particular gender). So if unconscious bias arises (in part) from culture, and a key aspect of culture is language, then this raises the question: do the words we use, or how we use them, affect gender bias?

This interesting question was addressed in a recent working paper by Pamela Jakiela (University of Maryland) and Owen Ozier (World Bank). They pulled together a dataset on over 4300 languages, spoken by over 99 percent of the world's population. [*] They then used their dataset to explore whether gendered language was associated with women's educational attainment, women's labour force participation, and gender attitudes among men and women. They found:

...a robust negative relationship between grammatical gender and female labor force participation. Our preferred specification suggests that grammatical gender is associated with a 12 percentage point reduction in women's labor force participation and an almost 15 percentage point increase in the gender gap in labor force participation... Taken at face value, our coefficient estimates suggest that gender languages keep approximately 125 million women around the world out of the labor force...

We find a far more muted cross-country relationship between grammatical gender and women's educational attainment. This may be due to the fact that the average within-country gender gap in educational attainment is much smaller than the gender gap in labor force participation | since many wealthy countries have no gender gap in educational attainment, particularly at the primary school level. The prevalence of gender languages is negatively associated with the gender gap in primary school completion after controlling for continent fixed effects, but the estimated relationship is only marginally statistically significant.

Using data from the World Values Survey (WVS), we show that grammatical gender predicts support for traditional gender roles. The coefficient estimate is large in magnitude, suggesting that differences in language could explain the entire gap in gender attitudes between Ukraine (at the 55th percentile of WVS countries in terms of support for gender equality) and Trinidad and Tobago (at the 80th percentile).

So, not only did gendered language explain some of the differences in gender attitudes and female labour force participation between countries, the size of the effects is meaningful. Jakiela and Ozier also showed that the results were similar when looking within countries with language heterogeneity (where some local languages are gendered and others are not), including Kenya, Nigeria, Niger, Uganda, and India. The results are correlations so fall a little short of demonstrating causality, although the paper does include a section that shows that a causal explanation is likely. Jakiela and Ozier conclude that:

Our results are consistent with research in psychology, linguistics, and anthropology suggesting that languages shape patterns of thought in subtle and subconscious ways.

The obvious policy implication to draw from their results, if they are indeed causal, is that gendered languages (e.g. Spanish, German) should immediately drop the gendered treatment of nouns. It couldn't be that simple though, could it?

[HT: Development Impact last June, although they referred to an earlier version of the same working paper]

*****

[*] As an aside, this data seems like it would be a fantastic resource for all sorts of other research, particularly where an instrumental variable for gender bias is required.

Roads go in both directions

Yesterday I wrote about declining regions, and noted that Japan could teach policy makers a lot about what to do, or what not to do, if they want to mitigate population decline in rural and peripheral regions. When it comes to public policy and affecting population change, a common refrain is that infrastructure (such as roads, rail, etc.) will increase population growth. The idea is that road facilitate movement of goods and people, increasing inter-regional trade and economic growth, creating more jobs and attracting more people. For instance, see this recent New Zealand Herald article about people commuting to Auckland from up to two hours (or more) away. However, those arguments miss a key point: roads go in both directions. So, while some people might live outside Auckland and commute in, others may well be going in the other direction. In fact, commuting against the flow of traffic isn't that uncommon.

So, is there evidence about the impact of roads on population and economic growth? Unsurprisingly, there is lots, including this recent article by Bishal Bakhta Kasu (South Dakota State) and Guangqing Chi (Penn State), published in the journal Population Research and Policy Review (it looks like it's ungated, but in case it isn't, the authors provide a good summary here). Kasu and Chi looked at the relationship between population growth and economic growth, and each of road density, railroad terminal density and the number of airports. Their data covered the county level in the US, over the period from 1970 to 2010. They found that, after controlling for various demographic, socioeconomic, and geographical differences between counties:

[t]he impact of railroads on population change is negative across the models, except for the 2000s. The impact of highways is not significant, but airports are significant in the 1980s and 2000s and from 1970 to 2010. The impact is positive, indicating that the number of airports contributes to population growth...

The impact of railroads on employment growth is negative across all periods, although the impact is not statistically significant in the 2000s. Highways have a statistically significant positive impact on employment change in the 1980s but a negative impact in the 1990s. Similarly, airports have a statistically significant [and positive] impact on employment change in the periods of the 1970s, the 2000s, and 1970–2010.

In other words, counties with more road density had no greater population or employment growth than counties with less road density. Railroads were generally negative for growth, and airports positive for growth. Of course, one of the problems with this study is that it shows correlation, not causation. That means that there could be a bunch of other variables (which weren't controlled for in the models) that affect both infrastructure and population or employment. However, this study adds to the existing evidence (reviewed in the Kasu and Chi paper) that shows inconsistent effects of roads on population change. As Kasu and Chi note:

...highway infrastructure does not produce any significant population or employment change; rather, it plays a facilitator role for the flow of people, materials, and raw and finished products.

Roads go in both directions.

Japan will be able to teach New Zealand about dealing with rural population decline

I've written a number of times about population decline in New Zealand's rural and peripheral areas (see here and here and here and here). New Zealand has seen a period of significant population growth driven by historically high net international migration. That might have been enough to turn around the fortunes of some previously-declining areas (although we won't know for sure until the delayed Census results are released), mainly through internal migration out of the larger cities (or, at least, that has been the main source of positive population change when it has occurred for those areas). However, the reprieve is likely to be short-lived as we return to more 'normal' net international migration. So, what is a declining region to do?

The Maxim Institute released a report in 2017 that made some suggestions (which I blogged about here). However, New Zealand is not the first country to face population decline in rural and peripheral areas. Japan is the poster child for population decline, and as one indicator of its ageing and declining population, consider this: Japan has been closing hundreds of schools each year over the past decade or more. The scary thing is that New Zealand's outlying regions are ageing rapidly, as Natalie Jackson and I pointed out in a 2017 article in the Journal of Population Ageing (ungated earlier version here).

What can Japan teach us about how to deal with declining regional populations? Last year, Brendan Barrett (RMIT University) wrote an interesting article in The Conversation on Japan's decline:

Everyone in Japan is aware of the challenges posed by a rapidly ageing, declining population with low birth rates. The media cover these concerns extensively.

Local governments have been trying to encourage people to move back to rural areas by providing work opportunities and sharing details of vacant houses...

There are no simple answers to these challenges. The Japanese government has been very active but past policies have tended to focus on infrastructure development and construction of public facilities (roads, dams, town halls, libraries, museums, sport facilities), rather than on the economic needs and welfare of local people...

While lots of ongoing initiatives aim to attract young people back to rural areas, the biggest concern is one of livelihoods as long-term job prospects are limited. Yuusuke Kakei covers this topic in his 2015 book Population Decline x Design, presenting proposals for new local economic activity that puts women, creativity and community at the centre. To this we should add what Joseph Coughlin describes as “The Longevity Economy” to respond to the economic and technology needs of an ageing population.

Interest in the notion of the universal basic income has also surged recently in Japan. Some commentators argue that it could play a significant role in revitalising Japan and in making rural life more attractive to young Japanese by providing them with long-term financial security.

One major challenge for local economies is access to finance, especially to support new businesses. While there are several innovative crowdfunding initiatives, Japanese municipalities should also look at the Transition Town movement for inspiration with its focus on “reclaiming the economy, sparking entrepreneurship, reimagining work”.

Specifically, it is worth exploring the potential of local entrepreneur forums. These bring together local investors from within the towns or villages with local entrepreneurs to support new, small business ventures.

The result is that communities pool their resources to support young people who have business ideas but lack financial resources. This is in line with both Masuda’s and Kakei’s recommendations to focus on local needs, rather than physical buildings and infrastructure.

Most of the initiatives highlighted by Barrett seem a little too promissory and results are lacking. A universal basic income might even make the problem of rural population decline even worse, as it allows those who lack the necessary skills for an urban job the opportunity to afford to live in an urban area (taking a broad definition of urban). Anyway, if policymakers are concerned about population decline and want to develop policy to mitigate it, they we should be keeping a close eye on initiatives in Japan, noting what works and what doesn't work. Although there will be important cultural differences to take note of, Japan is leading the way here and we will not want to make mistakes that they have already uncovered.

Read more:

• The relative (un)certainty of subnational population decline
• The Maxim Institute on dealing with population decline
• Newsflash! Population growth will be highest on the fringes of fast-growing urban areas
• Winners and losers in population growth
• Forget 'zombie towns', there's entire 'zombie districts' coming to a rural area near you

Money can't buy happiness, but it can buy life satisfaction

A vast amount of research has attempted to address the question of whether wealth (or income) and happiness (or life satisfaction) are related. Essentially, this research is tested the proposition that money can buy happiness. Some of this research has identified short-term positive effects of an unexpected increase in wealth on life satisfaction, but no such effects in the long-term. This has been referred to as hedonic adaptation - people's expectations adjust to their new circumstances. So, when your income (or wealth) increases suddenly, initially you feel a lot better off, but then you adapt to your new higher income (or wealth) and your life satisfaction returns to a more 'normal' state for you.

However, a recent NBER Working Paper by Erik Lindqvist (Stockholm School of Economics), Robert Östling (Stockholm University), and David Cesarini (New York University) calls this hedonic adaptation theory into question. They used data on over 3300 lottery winners in Sweden to investigate the effects of their lottery win (and the size of their win) on various measures of happiness and life satisfaction over a period of up to 22 years. This study is of special interest because they used a pre-registered analysis plan. This effectively neutralises any accusations that the results arise from p-hacking (for more on p-hacking and related statistical cheating, read this excellent article by Simmons, Nelson, and Simonsohn).

Lindqvist et al. found that:

...lottery wealth causes sustained increases in Overall LS [life satisfaction]. Since we did not survey any players within five years of the lottery, our research design is not suitable for studying short-run adaptation, but our results do reject the strong hypothesis of complete adaptation... Our follow-up analyses suggest that the most important mechanism explaining the increase in Overall LS is increased satisfaction with personal finances. A sustained increase in Financial LS is not easy to reconcile with a common folk wisdom that lottery winners squander their wealth through wreckless [sic] spending.

So, the increase in wealth as a result of an unexpected lottery win was associated with a persistent increase in life satisfaction. Hedonic adaptation didn't occur. However, the lottery win didn't increase 'happiness' or an index of mental health. This is likely because happiness and mental health respond to more immediate concerns and mood (they are 'affective' measures, compared with life satisfaction, which is an 'evaluative' measure).

So, it appears that money can't buy happiness, but it can buy life satisfaction.

[HT: Marginal Revolution, in June last year]