How much did slavery cost the American economy?

What was the economic cost of slavery in the United States? Answering that question requires first recognising that the spatial distribution of economic activity could have been very different if there was no slavery. And accounting for that difference is the approach adopted in this 2025 NBER Working Paper (ungated version here) by Treb Allen (Dartmouth College), Winston Chen (Yale University), and Suresh Naidu (Columbia University).

Using microdata from the 1860 US Census, Allen et al. first establish three stylised facts (with several elements to each):

Stylized Fact #1(a): Free workers sort into higher productivity locations, enslaved workers are sorted even more so into higher productivity locations...

Stylized Fact #1(b): Free workers sort into higher amenity locations, enslaved workers are sorted into lower amenity locations...

Stylized Fact #1(c): Free workers sort into locations with less coercion, enslaved workers are sorted into places with more coercion...

Stylized Fact #2(a): There is no correlation between cotton suitability and agricultural employment share for either enslaved or free workers...

Stylized Fact #2(b): Free workers sort out of agriculture in high malaria areas, whereas enslaved workers are sorted into agriculture in high malaria areas...

Stylized Fact #2(c): Both free and enslaved workers are more likely to be employed in agriculture the greater the distance to freedom, but this is especially true for enslaved workers...

Stylized Fact #3(a): Both free workers’ wages and the price of enslaved workers are higher in locations with higher productivities...

Stylized Fact #3(b): free workers’ wages are lower in locations with better amenities, whereas the price of enslaved workers are higher in locations with better amenities...

Stylized Fact #3(c): free workers’ wages are lower in locations with greater coercion, whereas the price of enslaved workers is (slightly) higher in locations with greater coercion.

The first stylised fact shows that enslaved and free workers are geographically sorted differently. While both enslaved and free workers locate more in high-productivity areas, this is for different reasons. Free workers can capture higher wages in those areas, while enslaved workers are more profitable for their owners in those areas. Allen et al. use malaria suitability as a negative indicator of amenity. Free workers avoid areas that are more susceptible to malaria (low amenity), whereas enslaved workers have no choice and cannot avoid those areas. Allen et al. measure the extent of coercion with the distance to the North, since slaves who were located further from the North could be more heavily coerced in work without prompting them to attempt to escape to the North. Unsurprisingly, there are more slaves further from the North, where coercion is easier for slave owners.

The second stylised fact shows that enslaved and free workers are sorted into different sectors of the economy, with enslaved workers more likely to be engaged in agriculture, especially in low amenity (high malaria) areas and areas with more coercion (further from the North). This sorting doesn't simply reflect the suitability of areas for agriculture (specifically, suitability for cotton).

The third stylised fact shows that wages are higher where productivity is higher. Lower wages for free workers in high amenity areas reflects a compensating differential. That is, wages need to be higher in low amenity areas in order to attract free workers. Where coercion is greater (further from the North), free workers earn lower wages as they need to compete with more enslaved workers.

After establishing those stylised facts, Allen et al. turn to a general equilibrium model and use their model to estimate the economic impacts of slavery. In that analysis, they find that:

In equivalent variation terms, slaveowners would have to pay an enslaved worker 1,192% more to get voluntary consent to the allocation of labor induced under slavery.

Breaking that down into three different sources of misallocation, they find that:

...allowing enslaved individuals to choose their own labor supply while remaining in slavery (i.e. removing coercion) yields a welfare gain equivalent to a 361% increase in wages; allowing enslaved workers to retain all of their earnings yields a welfare gain equivalent to a further 81% wage increase; and allowing enslaved workers to choose any occupation in any location in the U.S. (so that they act identically to free workers) leads to a welfare gain equivalent to a 55% wage increase.

Needless to say, those are massive welfare gains from removing slavery, but they likely understate the welfare costs of slavery. Importantly, their model does not capture all of the costs of slavery, including the physical and psychological harms associated with violence, abuse, and the loss of freedom. So, the welfare estimates from the model are best considered as lower-bound estimates of the full welfare costs of slavery (and the gains from removing slavery). It should also be noted that the percentage welfare gains from each source of misallocation compound rather than simply adding together, which explains why the overall welfare gain is so much greater than the sum of the three individual percentages.

Turning to the economy as a whole, Allen et al. then find that:

The movement of formerly enslaved workers out of agriculture causes services and manufacturing sectors to expand: for example, manufacturing output increases by 29% in the North, 19% in the South, and 27% overall. Agriculture expands in the U.S. North by 24%, but contracts in the U.S. South by 29%, for a modest decline of 5.4% overall. All told, aggregate GDP rises by 9.1%, with the reallocation of labor toward higher amenity locations offset by a combination of this structural transformation and the ability of the formerly enslaved to move to the (more productive) U.S. North.

A 9.1 percent increase in GDP is nothing to be sneezed at. Of course, this is based on the structure of the US economy in 1860, and can't be compared to what actually transpired later, after the Civil War.

Interestingly, but not unsurprisingly, not all workers would have been made better off by removing slavery. Free persons' welfare would decline by 0.7 percent overall, reflecting that many previously-enslaved workers would move to high-amenity and high-productivity areas, competing with free workers in those areas and lowering their welfare. However, that welfare loss pales in comparison to the welfare gains that previously-enslaved workers would receive, as well as the overall economic benefits.

US slavery was an immensely profitable institution for southern slave owners. There should be no doubt about that. The wealth accumulated through slavery remains visible today in the large plantation houses dating to the Antebellum period, some of which I may see after I cross the Mason-Dixon Line into the South for a couple of days next week. However, those plantation houses tell us little about the immense costs that slavery imposed on enslaved people, or the wider US economy. Allen et al.'s results provide some numbers to quantify those costs, even if they cannot capture the full human toll.

[HT: Marginal Revolution]

This week in research #151

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

• Dylong and Uebelmesser (open access) find that survey respondents in eastern Germany who receive information about the true size of the immigrant population in the region show more supportive attitudes toward immigration, with respondents who have less contact with immigrants prior to the intervention responding more strongly to the treatment
• Luo and Sun find that days above 27°C are significantly associated with increased traffic accident incidence in Taipei, with larger effects for accidents caused by drivers of two‐wheel vehicles than those of four‐wheel vehicles
• Duncan and Ross (open access) find, using a novel natural experiment based on the 2017 solar eclipse, that a 1 percent increase in Airbnb listings raises housing prices by 0.037-0.043 percent

Book review: An Economic Theory of Greed, Love, Groups, and Networks

I just finished reading An Economic Theory of Greed, Love, Groups, and Networks by Paul Frijters with Gigi Foster, which has been sitting on my bookshelf for over ten years (and originally published in 2013), and kept being passed over in favour of other, seemingly more exciting, books. The book is clearly a labour of love for Frijters, and aims to add some of what he sees as the key missing parts of economics - not greed, which is inherent in the neoclassical model of individual utility maximisation, but love, groups, and networks. Frijters notes that the aim of the book is to add and integrate these into the mainstream view of economics, and:

What results is an enhanced view of how society works that increases the number of phenomena covered, and deepens the existing story.

In her preface to the book, Foster warns that the book may be an extremely uncomfortable read for those who hold mainstream economic views. She is right in that. I found it uncomfortable, because I was constantly thinking "yes, and...". The book has sections devoted to greed (the mainstream economic view, according to Frijters), love, groups and power, and networks and markets. The most uncomfortable aspect, I found, was that I thought the story they tell would have been much more compelling if told in the reverse order, starting with networks, and ending with greed and love.

There is a lot to like about the book. Frijters writes well, and tells a story that is sufficiently plausible to be persuasive. The problem is that much of the book reads like a collection of just-so stories, plausible enough individually and collectively, but often difficult to distinguish empirically from alternative explanations. I don't often find myself using the word epistemology, but this time I can't really avoid it. I don't buy into Frijters's epistemological stance.

As one example, Frijters conceptualises love in the form of loyalty to others. He dismisses altruism as a more limited concept than love. However, I was surprised by how little attention he pays to the broader literature on social preferences, particularly negative social preferences. People don't just care about others in a positive way. We can be spiteful, or envious. It is therefore rather ironic that later in the book, Frijters critiques the work of Akerlof and Kranton (whose book Identity Economics I reviewed here) as being "rather limited". I would say that a theory that considers positive social preferences, but gives little attention to negative social preferences, is rather limited as well.

Where I found the book was strongest was on the treatment of networks and markets (which is why, I think, it should have come first). I especially liked the way that Frijters leverages networks into explanations of the construction of cliques, groups, and ultimately, nations. The different forms of groups were also interesting, but I found this part of the book less convincing. I expect that there are many other categorisations of groups that could be developed, and I would have liked to see deeper engagement with the extensive literature on collective action, such as the work of Mancur Olson or Nobel Prize winner Elinor Ostrom. In her preface, Foster asks readers to forgive instances where particular authors' contributions are not acknowledged. However, I think the lack of engagement with the collective action literature is a more substantial omission.

The book is mostly heavy going, but there are some lighter parts. I particularly liked this bit:

The type of economist who depicts the rest of the world as being inhabited by agents who are rational and have complete information is, by implication, redundant. Instead of working out how the economic system works, such an economist could simply walk up to the first person on the street and ask him how the economy works...

In that, Frijters is undoubtedly correct. Overall, I think the book presents some interesting points to think about, things that could expand the horizons of economic theory. However, it's unclear to me who the intended audience for this book is (or was). I don't think it will engage the general reader, and a specialist economist may well struggle, as I did, with the epistemological approach. Sadly, it's not a book that I would recommend for any but those who are looking at extended mainstream theoretical economic models, and even then, they would likely be better served by looking at Frijters's more focused academic research.

New evidence that declining fertility rates may not stabilise

It is not news that total fertility rates across most countries are declining, and have been for some time (see the links at the end of this post). However, in spite of declining fertility, many population projections assume that fertility decline will come to an end, or even reverse. For example, the UN World Population Prospects projections assume that fertility rates eventually enter a relatively stable phase, with an average long-run level of about 1.6 births per woman, although the assumed stabilisation level varies across countries (see page 29 of this methodological report). Similarly, Stats NZ assumes that the median period total fertility rate will slowly decline from 1.59 in 2025 to 1.55 in 2054 and beyond (see here).

Given that it has been relatively rare for total fertility rates to reverse trajectory, how realistic is it that they will attain some stable equilibrium in the future? This new NBER Working Paper by Diane Coffey, Michael Geruso, Dean Spears (all University of Texas at Austin), and Sangita Vyas (City University of New York), digs into the data to provide an answer. Specifically, they look at data on:

...period total fertility rates (TFRs) for all country-years since 1950 in the United Nations World Population Prospects, in completed cohort fertility for country-cohorts in the Human Fertility Database, and in fertility measures from subnational geographies in India, Japan, and the United States.

It is worth taking a moment to explain the difference between a period total fertility rate and a cohort total fertility rate. Most of the time, the statistics we see reported are period total fertility rates. Those are the number of births a woman would have across their lifetime if they experienced the age-specific fertility rate at each age in that year. Of course, each woman only experiences the age-specific fertility rate of one age in any one year, not all of them collectively. That brings us to the cohort total fertility rate, which is the average number of births each age-cohort of women (born in the same year) actually have across their lifetime. Completed cohort fertility is computed after an age cohort of women has effectively moved out of childbearing age, which is why this statistic isn't reported often - you have to wait until women are over 50 years old before you can be confident that you have an accurate measure of the cohort total fertility rate. This distinction between the two measures matters because period fertility rates can decline if women delay having children, even though the cohort fertility rate (the average number of children women have over their childbearing years) remains unchanged.

Now, coming back to the working paper by Coffey et al., they use phase diagrams to illustrate the trajectory of total fertility rates over time. A phase diagram graphs the change in a variable against the level of the variable. It essentially shows, for each level of the variable, whether the variable is increasing, or decreasing. If a total fertility rate is to stabilise at some level, then the phase diagram should show that for low levels of the rate, the change is positive, while for high levels of the rate, the change is negative. The level where the change is equal to zero is the level at which the rate would stabilise. In other words, for fertility rates to stabilise at 1.6 births per woman, we would expect them to increase if the rate is below 1.6 and decrease if the rate is above 1.6.

What do Coffey et al. find? The results for past data from the UN World Population Prospects is summarised in Figure 3 from the paper:

Notice that for most levels of the total fertility rate, the point estimate of the change (growth) in the rate is negative. The most charitable interpretation of these results is that maybe there is some weak evidence for a cross-over at a total fertility rate of between the 1.25-1.35 bin and the 1.35-1.45 bin. However, none of the estimates below the 1.45-1.55 bin are statistically significantly different from zero, and the point estimates at the lowest total fertility rates are negative.

Moving onto data from the Human Fertility Database, the results are summarised in Figure 4 from the paper:

In this case, there is no evidence to support a stabilisation of cohort total fertility rates in these data. All point estimates are negative, and all of those from the 1.45-1.55 bin and above, are statistically significantly below zero. The results for Japanese prefectures since 2010, US states since 2010, and Indian districts (based on data from the Demographic and Health Survey 2019-2021) all similarly show negative and statistically significant change in total fertility rates across almost all levels of observed total fertility rates (the one exception is the 1.05-1.15 bin for Japanese prefectures, but that result is positive but statistically insignificant).

Taken altogether, these results demonstrate that there is no evidence that declining fertility rates will reverse course, at least not on average across countries, or across subnational areas in the three countries Coffey et al. looked at. There isn't even any evidence that total fertility rates will stabilise, at least not at the levels that have been observed so far.

Low and declining total fertility rates are the norm across developed countries, and increasingly in developing countries as well. They aren't returning to replacement levels. That doesn't mean that fertility rates will decline indefinitely, but it does mean that assuming they will stabilise seems unlikely to be borne out. So, policymakers might be better off planning for continuing low fertility, rather than blaming pet ownership and waiting for a rebound.

[HT: Marginal Revolution]

Read more:

• The pandemic 'baby bust' in other countries
• Cohort effects and the downturn in US fertility since the Great Recession
• The new economics of fertility
• The economics of the falling total fertility rate in New Zealand
• The economics of fertility in high-income countries
• Can fertility return to replacement levels?