Rent control and the redistribution of wealth

Like removing GST from food, rent control is an idea that has popular appeal, but is almost universally hated by economists. In an extreme example of this dislike for rent control, the Swedish economist Assar Lindbeck wrote, in his 1972 book The Political Economy of the New Left, that "In many cases rent control appears to be the most efficient technique presently known to destroy a city - except for bombing". He may not have been wrong.

The textbook example of rent control does acknowledge that there is a redistribution from landlords to tenants (see my post on that point here). However, aside from the broad category of tenants gaining, and landlords losing, from rent control, the model is not specific about who within each group gains or loses the most. The textbook model is clear that, although tenants as a whole gain, many tenants miss out on those gains because of the excess demand for rental housing. We know from empirical experience that it tends to be low-income and minority tenants who miss out.

I recently read this interesting new working paper on the wealth redistribution of rent control, by Kenneth Ahern and Marco Giacoletti (both University of Southern California). They look changes in property values and the redistribution of wealth caused by the imposition of rent control in St. Paul, Minnesota, in November 2021. Interestingly, they note that:

St. Paul’s rent control law is particularly strict, covering all properties in the city and with no inflation-adjustment for yearly rental increases and no provision to allow rental prices to be reset to market prices upon vacancy. Annual rental growth, for all properties, is capped at 3% year-over-year.

That makes St. Paul's rent control one of the strictest around, far stricter than anything suggested here in New Zealand. Using data on nearly 150,000 property sales in St. Paul and five surrounding counties (excluding Minneapolis), Ahern and Giacoletti find that:

...the introduction of rent control caused an economically and statistically significant decline of 6–7% in the value of real estate in St. Paul.

What caused the decrease in house prices? Thinking about the standard supply and demand model, Ahern and Giacoletti find:

...a statistically significant and large increase in transaction volume in St. Paul following rent control, compared to the adjacent cities. This indicates that the decline in value was caused by a net increase of supply over demand.

In other words, property owners were selling properties at greater rates than before rent control was introduced - presumably because the returns on rental property ownership were now lower. As further evidence of this:

...we find that rental properties experienced an additional 6% decline in value compared to owner-occupied properties, for a total loss of about 12%.

Overall, Ahern and Giacoletti estimate an overall loss of over US$1.5 billion in property value in St. Paul as a result of rent control. So, clearly landlords are worse off. But so are owner-occupiers, because their houses have fallen in value as well.

Ahern and Giacoletti then turn to looking more specifically at the redistribution of wealth. They proxy the characteristics of tenants by the average characteristics of all people in the Census block group they live in (the average Census block group in St. Paul has about 400 households, and about 1100 people living in it). They then use some interesting forensic methods to identify the property owners' addresses, and if the address is residential, they take the characteristics of the property owner as the average characteristics of the Census block group of the address. Of course, this only tends to work for small-scale landlords, since large commercial landlords will have an address for service in a commercial building. They then split each sample (landlords and tenants) into high-income and low-income groups, and compare the change in property values for each combination of tenant and landlord income (high-high, high-low, low-high, and low-low). They focus most attention on what they term the 'high disparity' pairing of high-income landlords and low-income tenants, and the 'low disparity' pairing of low-income landlords and high-income tenants. They find that:

In contrast to the intended transfer from higher-income owners to lower-income renters... the value loss for the high disparity subsample is 0.89%, below the average value loss of 4%. This effect is statistically smaller than the effect for the other three subsamples. In contrast... the statistically largest effect of rent control, at 8.52%, occurs in the low disparity parts of the city where renters have higher incomes and owners have lower incomes. This implies that the impact of rent control is poorly targeted: the largest transfer of wealth is from relatively low income owners to relatively high income renters.

Ouch. However, it is fair to say that this redistribution analysis is based on some fairly heroic assumptions, such as that tenants and landlords have the average income of the area they live in, and that the landlords are correctly identified (as well as bearing in mind that the most affluent corporate landlords are excluded from the sample entirely). 

Rent controls are generally favoured because people believe that it results in a positive redistribution of wealth from landlords to tenants. However, to the extent that this paper provides us with some evidence of redistribution, it doesn't suggest that low-income tenants are strongly benefiting at the expense of high-income landlords.

[HT: Marginal Revolution]

Read more:

• Why rent controls have worse effects over longer time horizons
• The deadweight loss of rent control
• The deadly consequences of rent control in Mumbai
• Rent control and inequality in San Francisco

Working while studying may not always be bad for students

I've written before about the negative academic consequences for students who work while studying (see here or here). However, it's not certain theoretically whether working is always bad. In fact, there are a number of reasons to believe that working might be good for students in the long term. First, working might allow students to develop skills and knowledge that are valuable in the labour market later. Those skills may or may not be complementary to what they are learning in their studies (and any positive labour market effects are likely to be higher for complementary skills). Second, working might develop social skills, networks, and contacts that make it easier for students to find work after they graduate. Third, working might act as a positive signal of ability, conscientiousness, or effort, for future employers. On the other hand, working while studying comes with an opportunity cost of time spent studying, which may have negative impacts on grades, persistence, and learning (as shown in the study I discuss in this post).

With both positive and negative impacts of working while studying, what is the overall effect? I was hoping that this 2012 article by Regula Geel and Uschi Backes-Gellner (both University of Zurich), published in the journal Labour (ungated version here), would provide some answer to that question. They use longitudinal data on 1930 Swiss graduates from the year 2000, followed up one and five years after graduation. Importantly, their dataset distinguished between students who did or did not work while studying and for those who did work, it distinguishes between those who did or did not work in jobs that were related to their field of study. Now, the problem with this sample is that it is a sample of graduates, so naturally it excludes those who dropped out of university. So, it doesn't answer the overall question of the impact of working while studying on subsequent labour market outcomes, although it does provide some answer for those students who do graduate.

Geel and Backes-Gellner control for ability (using secondary school grades), motivation (based on a question that asked students how important a new challenge is), and 'liquidity' (using parental education, on the basis that students with more educated parents have more financial resources available to them and are less likely to need to work). Looking at a range of labour market outcomes one year after graduation, they find that:

...student employment per se reduces the probability of being unemployed 1 year after graduation, compared with having been a non-working student... field-related student employment reduces unemployment risk compared with having been a non-working student. Furthermore, field-unrelated student employment also reduces the unemployment risk. Consequently, students working part time in jobs related to their studies have a significantly lower short-term risk of being unemployed than both non-working students and students working part time in jobs unrelated to their studies.

...student employment significantly reduces job-search duration compared with full-time studies. Moreover, after including information about the type of student employment, we still find that field-related student employment significantly reduces job-search duration but we do not find a significantly different effect for field-unrelated student employment compared with full-time studies.

...students working part time can expect higher wages than non-working students. Again, when we differentiate the type of student employment we find that only field-related student employment, compared with full-time studies, generates such positive effects, but not field-unrelated student employment.

That all seems positive. They don't discuss the size of the effects in the text, but part-time employment while studying appears to be associated with by 1.4 percentage points lower probability of being unemployed (2.4 percentage points for those employed in work related to their study field, and 0.9 percentage points for others). It is also associated with 0.13 months shorter job search duration (and 0.29 months for those employed in work related to their study field), and wages that are 1.5 percent higher (and 2.5 percent higher for those employed in work related to their study field).

Moving on to outcomes five years after graduation, Geel and Backes-Gellner find similar effects. At that point, part-time employment while studying is associated with by 1.3 percentage points lower probability of being unemployed (1.8 percentage points for those employed in work related to their study field, and 1.0 percentage points for others). It is also associated with 0.7 percent higher wages (and 1.2 percent higher for those employed in work related to their study field). Interestingly, there is no overall impact on self-reported job responsibility (being 'great' or 'very great'), but there are statistically significant effects in opposite directions for those who were employed in work related to their study field and those who were not. Those who were employed in work related to their study field were 1.6 percentage points more likely to report great job responsibility, while those who were not employed in work related to their study field were 1.3 percentage points less likely to report great job responsibility.

Now, despite Geel and Backes-Gellner taking great care to control for a range of other variables, these are not causal estimates, they are correlations. There is likely to be selection bias in which students choose to undertake work while studying. Geel and Backes-Gellner point out that only 4 percent of working students are receiving a scholarship, but they don't tell us how many of the non-working students receive scholarships. However, since better students receive scholarships, and scholarships make work less necessary, any selection bias from scholarships would actually tend to decrease the observed positive labour market effects of working. On the other hand, if better students are more likely to work because they feel like they can better cope with the competing demands on their time, then the observed positive labour market effects of working would be overstated. For me though, the bigger issue is that these results are conditional on graduating. We don't know to what extent working while studying was associated with dropping out, rather than graduating.

Overall, this paper provides some food for thought. Working while studying might provide some benefits for some students.

Stephen Hickson on why we shouldn't remove GST from food

Stephen Hickson (University of Canterbury) wrote an excellent article in The Conversation earlier this week (and I heard him talk about it on The Panel on RNZ yesterday afternoon (at 10:15 in the recorded audio)):

Removing the goods and services tax (GST) from food is not a new idea. Te Pāti Māori are currently pushing for its removal from all foods. In 2011 Labour campaigned on removing GST from fruit and vegetables. In 2017 NZ First wanted GST removed from “basic food items”...

But the beauty of New Zealand’s tax system is its simplicity. Removing GST on food, or some types of food – for example, “healthy food” – makes that system more complex and costly.

There are a number of potential complications.

Let’s start with the obvious – what would count as “food”? Is milk powder food? Probably yes, so what about milk? Or flavoured milk? Oranges are food, so what about 100% natural orange juice? A broad definition of “food” would include lollies, potato chips, McDonalds and KFC, but many would object to removing GST from these on health grounds.

We would then need to decide what is acceptable to exempt and what is not. The arguments would go on and on.

In Australia, the question of whether an “oven baked Italian flat bread” is a bread (so not subject to GST) or a cracker (subject to GST) went to court, and involved flying a bread certification expert from Italy to testify. The only reason why that job exists is due to complexity in tax systems around the world.

In Ireland, the court was required to rule on whether Subway was serving “bread” or “confectionery or fancy baked goods” due to the difference in GST treatment.

I've written on this topic before, and the exemplar is the great Jaffa Cake controversy in the UK. We don't want to be in the position of having to have court cases to determine what is a food and what isn't, or which goods attract GST and which ones don't. The argument about reducing GST in order to help alleviate problems of rising living costs is attractive, but reducing GST is not the only, or even the most efficient, way to address living costs. As Hickson writes:

The 2018 Tax Working Group (TWG) didn’t support removing GST on food. It emphasised how such exemptions lead to “complex and often arbitrary boundaries”, particularly when trying to target specific types of food such as “healthy food”.

They also stated that such exemptions are a “poorly targeted instrument for achieving distributional aims”...

The working group explained that if the goal was to support those on low incomes, and the government was willing to give up the GST revenue from food, then it would be better to continue to collect the GST and simply refund it via an equal lump sum payment to every New Zealand household or taxpayer.

Higher income households pay more GST on food because they spend more on food than lower income households. Hence lower income households would get more back via a refund than what they pay in GST on food.

This would be simpler and a more effective way to address an issue faced by low income households.

Let's keep the tax system simple, and easy to administer. We don't need to set up a series of court cases to determine what is a cake or a biscuit, what is a bread or a cracker, or what constitutes healthy food. We shouldn't remove GST from food.

Reconciling the human capital and willingness-to-pay approaches to the value of a statistical life

There are various different approaches to measuring the value of a statistical life (VSL). As I discussed briefly in this 2019 post on the economics of landmine clearance, there are shortcomings associated with the human capital approach, which relies on estimating VSL based on the total value of output that an average person would produce over their lifetime. In my ECONS102 class, I teach that the willingness-to-pay approach, is better because it accounts for the life's worth beyond its value in labour or production. The willingness-to-pay approach essentially works out what people are willing to give up to avoid a small difference in the probability of death, and scales that up to work out what people would be willing to give up to avoid a 100 percent reduction in the probability of death.

Now, it turns out that my characterisation of these two approaches as different ways of measuring the same underlying concept (the VSL) needs some reconsideration. This new article by Julien Hugonnier (École Polytechnique Fédérale de Lausanne), Florian Pelgrin (EDHEC Business School), and Pascal St-Amour (University of Lausanne), published in The Economic Journal (ungated earlier version here) explains why. Much of the article is quite theoretical, so not for the faint of heart. However, Hugonnier et al. provide a great summary in the introduction (as well as thoroughly explaining throughout the article). Essentially, they look at three different valuations of life: the human capital value (HK), the VSL estimated using the willingness-to-pay approach, and the gunpoint value (GPV). They explain how these are related as:

An agent’s willingness to pay (WTP) or to accept (WTA) compensation for changes in death risk exposure is a key ingredient for life valuation. Indeed, a shadow price of a life can be deduced through the individual marginal rate of substitution (MRS) between mortality and wealth. In the same vein, a collective MRS between life and wealth relies on the value of a statistical life (VSL) literature to calculate the societal WTP to save an unidentified (i.e., statistical) life. The VSL’s domain of application relates to public health and safety decisions benefiting unidentified persons. In contrast, the human capital (HK) life value relies on asset pricing theory to compute the present value of an identified person’s cash flows corresponding to his... labour income, net of the measurable investment expenses. HK values are used for valuing a given life, such as in wrongful death litigation... or in measuring the economic costs of armed conflict... Finally, a gunpoint value of life (GPV) measures the maximal amount a person is willing to pay to avoid certain, instantaneous death. The GPV is theoretically relevant for end-of-life (e.g., terminal care) settings, yet, to the best of our knowledge, no empirical evaluation of the gunpoint life value exists...

So, it turns out that, while I have previously treated the VSL and HK measures as substitutes (and taught them as such):

...different life valuation methods are not substitutes, but rather complements to one another. Which of these four instruments should be relied on depends on the questions to be addressed.

It's clearly time for a bit of a re-think of how I approach the teaching of those concepts. Hugonnier et al. develop their theoretical model (which, I'm not going to lie, is heavy going), and then apply it to data on nearly 8000 people from the 2017 wave of the Panel Study of Income Dynamics (PSID). They develop a structural model (partially estimated econometrically, and partially calibrated) for people at different levels of health. They find that:

The HK value of life... [ranges] from $206,000 (poor health) to $358,000 (excellent health), with a mean value of $300,000...

The VSL mean value is $4.98 million, with valuations ranging between $1.13 million and $12.92 million...

The mean GPV is $251,000 and the estimates are increasing in both health and wealth and range between $57,000 and $651,000. The gunpoint is thus of similar magnitude to the HK value of life and both are much lower than the VSL.

Nothing unsurprising there. We know from past research (including my own, see here or here, or ungated here or here) that the HK value is much lower than the VSL value (to use Hugonnier et al.'s terms). The VSL measure is in line with the literature (as outlined in yesterday's post). The GPV is new, but people will be seriously constrained in what they can actually pay when facing certain, instantaneous death, which helps explain why it is so much lower than VSL. Now, as Hugonnier et al. noted, these different values are complementary and have different uses. They illustrate using the case of the coronavirus pandemic:

The first policy question is whether the substantial public resources allocated to vaccine development and distribution as well as in compensation for financial losses linked to shutdowns are economically justifiable on the basis of lives saved by the intervention. Our VSL estimate computes the societal willingness to pay for a mortality reduction of an unidentified person and is therefore appropriate for the relevance of public spending...

Consider next the case where an infected person j’s health deteriorates and is admitted to the intensive care unit (ICU). If access to life support in the ICU is constrained, our GPV measure calculates person j’s valuation of their own life and can be used to decide whether or not terminal care should be maintained or reallocated. If instead j dies as a result of COVID, both our HK and GPV values can be used by courts in litigation against the state, care provider, employer or other agents for insufficient intervention, malpractice or negligence.

So, in a policy context the VSL is the appropriate measure, but in the case of compensation for an identified life, the HK or GPV is more appropriate. This is where things get a little interesting though, because that implies that the value of statistical life is much higher than the value of an identified life. We know this to be untrue. The 2005 Nobel Prize winner Thomas Schelling observed a paradox wherein communities were willing to spend millions of dollars to save the life of a known victim (e.g. someone trapped in a mine), while at the same time being unwilling to spend a few hundred thousand dollars on highway improvements that would save on average one life each year. The value of an identified life is actually greater than the value of a statistical life. Hugonnier et al. don't engage with that paradox at all, so leave a serious policy problem unanswered. Nevertheless, this is an important article, and I will be changing my future teaching of the related concepts to highlight the different use cases of these measures better.