Rent control and inequality in San Francisco

Rent control is a staple in introductory economics courses. The idea that a policy that has popular support from the public nevertheless has negative impacts on the very tenants that it aims to help, is an important story to tell (see here and here and here for previous posts on rent controls). The negative impacts of rent controls are supported by a simple supply and demand model of the market for rental housing. However, it is also supported by empirical data.

A new article by Rebecca Diamond, Tim McQuade, and Franklin Qian (all Stanford), published in the journal American Economic Review (ungated earlier version here), provides support in the case of San Francisco. The authors looked at what happened to tenants and properties affected by a 1994 change in rent control laws:

Rent control in San Francisco began in 1979, when acting Mayor Dianne Feinstein signed San Francisco’s first rent control law... This law capped annual nominal rent increases to 7 percent and covered all rental units built before June 13, 1979 with one key exemption: owner-occupied buildings containing 4 units or less... These “mom and pop” landlords were cast as being less profit-driven than large-scale, corporate landlords, and more similar to the tenants being protected. These small multi-family structures made up about 44 percent of the rental housing stock in 1990, making this a large exemption to the rent control law.

While this exemption was intended to target “mom and pop” landlords, in practice small multi-families were increasingly purchased by larger businesses who would then sell a small share of the building to a live-in owner so as to satisfy the rent control law exemption. This became fuel for a new ballot initiative in 1994 to remove the small multi-family rent control exemption. This ballot initiative barely passed in November 1994. Suddenly, all multi-family structures with four units or less built in 1979 or earlier were now subject to rent control. These small multi-family structures built prior to 1980 remain rent-controlled today, while all of those built from 1980 or later are still not subject to rent control.

Diamond et al. essentially compare properties (and tenants living in properties) before and after the law change, comparing those that were (the treatment group) and were not (the control group) newly subjected to rent control. This 'difference-in-differences' analysis allows them to extract the impact of rent control. They find a number of interesting things, including that:

...on average, in the medium to long term the beneficiaries of rent control are between 10 and 20 percent more likely to remain at their 1994 address relative to the control group and, moreover, are more likely to remain in San Francisco. Further, we find the effects of rent control on tenants are stronger for racial minorities, suggesting rent control helped prevent minority displacement from San Francisco... On the other hand, individuals in areas with quickly rising house prices and with few years at their 1994 address are less likely to remain at their current address, consistent with the idea that landlords try to remove tenants when the reward is high, through either eviction or negotiated payments.

On the latter point, they note that there are a number of ways that landlords can subvert rent control, such as:

First, landlords could try to legally evict their tenants by, for example, moving into the properties themselves, known as owner move-in eviction. Alternatively, landlords could evict tenants according to the provisions of the Ellis Act, which allows evictions when an owner wants to remove units from the rental market: for instance, in order to convert the units into condos or a tenancy in common.18 Finally, landlords are legally allowed to negotiate with tenants over a monetary transfer convincing them to leave. In this way, tenants may “bring their rent control with them” in the form of a lump sum tenant buyout.

On top of all that, they also found that:

...landlords actively respond to the imposition of rent control by converting their properties to condos and TICs or by redeveloping the building in such as a way as to exempt it from the regulations. In sum, we find that impacted landlords reduced the supply of available rental housing by 15 percent. Further, we find that there was a 25 percent decline in the number of renters living in units protected by rent control, as many buildings were converted to new construction or condos that are exempt from rent control.

This is a point that I made in this earlier post. Diamond et al. also note that their results imply interesting effects of rent control on inequality:

In the short run, rent control prevents displacement of the initial 1994 tenants from San Francisco, especially among racial minorities. To the extent that these 1994 tenants are of lower income than those moving into San Francisco over the following years, rent control increases income inequality. However, this short-term effect decays over time. Eight years after the law change, 4.5 percent of the tenants treated by rent control were able to remain in San Francisco because of rent control. However, five years later, this effect had decayed to 3.7 percent, and will likely continue to decline in the future.

In the long run, on the other hand, landlords are able to respond to the rent control policy change by substituting toward types of housing exempt from rent control price caps, upgrading the housing stock, and lowering the supply of rent-controlled housing. Indeed, the prior section showed that as of 2015, the average property treated by rent control has higher income residents than similar market rate properties. The long-term landlord response thus offsets rent control’s initial effect of keeping lower income tenants in the city by replacing them with residents of above-average income. In this way, rent control works to increase income inequality in both the short run and in the long run, but through different means. Rent control’s short-term effects increases the left tail of the income distribution, while the long-term effects increase the right tail.

I'm not sure that this is what advocates of rent controls would be expecting. However, it serves as another cautionary point on the effects of rent controls.

[HT: Marginal Revolution]

Making individual actions to reduce climate change

My Waikato colleague Zack Dorner had an article in The Spinoff back in September:

Regardless of how doomed you think we are, you may still think individual actions are pointless. You’re one of seven billion people in the world; your decisions are a drop in the ocean that won’t make a difference. I agree that policy change is the most important tool when it comes to climate action. But where does that leave individual actions? Do they also make a difference?...

The bottom line: when you take individual actions on climate change you are contributing to a global public good, which benefits 7 billion people now and many more in the future. And done right, you are encouraging others to change too, by helping to shift social norms. So don’t let anyone tell you your individual actions on climate change are not making a difference.

Zack's argument rests on three points: (1) global public goods; (2) the social cost of carbon; and (3) establishing new social norms. However, I think there is a stronger case to be made for individual climate action, based on social preferences (such as altruism).

I wrote a related post back in 2016, about the Paris agreement on climate change. Traditional game theoretical approaches would suggest that action on climate change is an example of the prisoners' dilemma - while every decision-maker would be better off if everyone works together, each decision-maker individually is better off if they act in their own best interests (and not with everyone else). So, in the case of individual actions to reduce climate change, we would all be better off if we drove our cars a bit less, none of us individually has a strong enough incentive to do so. Unless, as I pointed out in relation to the Paris agreement:

...the prisoners' dilemma looks quite different if the players have social preferences. For example, if players care not only about their own payoff, but also about the payoff of the other player...

The game now changes substantially, and reducing emissions becomes a dominant strategy for both players!

These points don't just apply to countries deciding whether to reduce carbon emissions. They also apply to individuals deciding whether to take individual action on climate change. If we care about other people, whether that be people living right now or people living in the future, then it starts to make sense to take individual action on climate change right now. As I discussed in the earlier post, it doesn't take much in the way of altruistic preferences for taking climate action to become the dominant strategy (this is a point I used to make in my old ECON100 class, which was unfortunately cut out when we made the transition to ECONS101 and needed to include more macroeconomics content instead).

Read more:

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

Are people willing to pay to avoid harm from international honey laundering?

You probably had to read the title of this post a couple of times. Yes, it does say honey laundering, with an "h". It's taken from the title of this paper, by Chian Jones Ritten (University of Wyoming) and co-authors, published in the Australian Journal of Agricultural and Resource Economics earlier this year (sorry, I don't see an ungated version). The paper focuses on food fraud, which refers to "the intentional substitution, addition, tampering or misrepresentation of food, food ingredients, or food packaging". They note that:

Evidence suggests that Chinese honey is being transshipped and relabelled to mask the true origin of the honey to avoid large tariffs and potential bans, also known as honey laundering... The practice of honey laundering is so prolific that an estimated one-third of honey available for sale in the United States is illegally imported from China and may contain illegal antibiotics and heavy metals...

The focus on Chinese honey is important because:

Chinese honey has the potential to contain illegal and unsafe antibiotics (specifically, Chloramphenicol, enrofloxacin, and ciprofloxacin) and high levels of herbicides and pesticides...

But do honey consumers care? Essentially, Jones Ritten et al. ran an experiment to test whether consumers were willing to pay a US$2.48 premium for an eight-ounce jar of locally produced honey, and tested whether consumers who were first given information about "the negative health implications of honey laundering" were more willing to pay the premium. They found that:

In total, 53.38% of participants across the treatments chose local honey at a $2.48 premium over honey of unknown origin...

Once we control for only honey preferences and use, access to honey laundering information significantly increases the probability (P < 0.10) of participants being willing to pay a $2.48 premium for an 8-ounce jar of local honey... When also including the influence of demographic variables on the probability of paying the premium, honey laundering information still significantly increases the probability (P < 0.05) of participants choosing local honey...

The results suggest that providing honey laundering information increases the probability of participants being willing to pay the premium by as much as 27 percentage points...

So, consumer information does affect consumers' stated preferences for honey, but not for everyone. However, my last sentence also highlights the problem with this study. They only asked what the consumers would do (their stated preference), and didn't actually require a honey purchase (which would be a revealed preference). So, we don't know whether the consumers would actually follow through on their stated preference. Perhaps they could have required a honey purchase?

It also turns out that older consumers were more willing to pay the premium, which led the authors to conclude that:

Targeting older consumers will most likely be successful at garnering more local consumers that are willing to pay for local honey than targeting younger consumers.

However, if older consumers are already more willing to buy locally produced honey, that doesn't mean that the information had a bigger effect on them. The authors could have tested that directly with their data, by running separate analyses for different age groups, or interacting the experimental treatment variable with age, but for some reason they chose not to.

Aside from those issues, it is a nice study. If you want to avoid international honey laundering, buy local.

The future impact of climate change on inequality in the U.S.

I just finished reading this 2017 article published in the journal Science, by Solomon Hsiang (UC Berkeley) and co-authors, which investigates the economic impact of climate change in the U.S. The headline results are unsurprising:

The combined value of market and nonmarket damage across analyzed sectors—agriculture, crime, coastal storms, energy, human mortality, and labor—increases quadratically in global mean temperature, costing roughly 1.2% of gross domestic product per +1°C on average.

Interestingly, the biggest contributor to economic impact is mortality:

The greatest direct cost for GMST [Global Mean Surface Temperature] changes larger than 2.5°C is the burden of excess mortality, with sizable but smaller contributions from changes in labor supply, energy demand, and agricultural production...

However, what was more interesting was the spatial impacts and their distribution, summarised in the following map:

The counties that suffer the greatest impacts of climate change are those in the South and Midwest, where the mortality impacts are likely to be the greatest due to higher summer temperatures. In contrast, in the North and Northwest, this is offset by lower winter mortality due to milder winters. However, the areas projected to suffer the greatest impacts are also the areas that include most of the poorest counties in the U.S. This is likely to increase inequality over time. As Hsiang et al. explain:

In general (except for crime and some coastal damages), Southern and Midwestern populations suffer the largest losses, while Northern and Western populations have smaller or even negative damages, the latter amounting to net gains from projected climate changes. Combining impacts across sectors reveals that warming causes a net transfer of value from Southern, Central, and Mid-Atlantic regions toward the Pacific Northwest, the Great Lakes region, and New England... Because losses are largest in regions that are already poorer on average, climate change tends to increase preexisting inequality in the United States.

The last thing the U.S. needs is another contributor to income inequality, but it seems like climate change is set to make a bad situation worse.

It would be interesting to do a similar analysis for New Zealand, not necessarily in terms of inequality, but simply looking at the impacts of climate change on mortality. On that research question, we currently know very little. To what extent will increased summer mortality, predominantly in the north of the country, offset lower winter mortality in the south? Does a wetter west and a drier east of the country matter? These and related questions might make a good project for a motivated Masters or Honours student.