Population diversity and economic growth

Population diversity has a theoretically ambiguous effect on economic growth. On the one hand, having a more diverse population makes it more difficult for people to agree on things like spending on public goods (e.g. see this post), it can open the door to policies that favour certain ethnic groups, and lead to conflict over resources and the management of public services. On the other hand, having a more diverse population brings people together with different (and complementary) skills, experiences, and ways of thinking, which can boost innovation and productivity, as well as fostering connections with different communities (and other countries), which may increase international trade and investment.

Many studies have tested the relationship between population diversity and economic growth, with little consensus. That makes the literature ripe for meta-analysis, where the results of many studies are combined in order to estimate an overall relationship. That is the approach in this new article by Andreas Sintos (University of Luxembourg), published in the Journal of Economic Surveys (open access). Sintos collates the results from 83 studies, with 1537 estimates of the relationship between some measure of population diversity and some measure of economic growth.

First, Sintos establishes that there is a small publication bias overall, with studies that find a negative relationship between diversity and growth being more likely to be published than would be expected given the overall distribution of results. Then, after adjusting for publication bias and methodological quality of the studies, he finds that:

...while ethnic and linguistic diversity demonstrates a small and statistically insignificant positive effect on economic growth, the remaining dimensions of diversity—religious, genetic, birthplace, and the residual category—demonstrate a significant positive impact on economic growth, with effect sizes spanning from moderate to large.

So, population diversity (specifically religious, genetic, and birthplace diversity, as well as a residual category that captures other forms of diversity) is positively associated with economic growth. Places that have more diversity of those types (but not places that have more ethnic or linguistic diversity) grow faster. A 'moderate to large' effect here means that each standard deviation higher diversity is associated with 0.1 to 0.4 standard deviations higher economic growth. That is not to be sneezed at.

What Sintos isn't able to do, though, is explore the mechanisms that underlie that positive relationship. So, while meta-analysis can give us an overall estimate of the relationship, it can't tell us why that relationship exists. To do that, we would need to go and look at the individual studies, particularly those that found a positive relationship between diversity and growth, and see if they explored the mechanisms.

Finally, this article made me chuckle, as it is clear that substantial portions of it were written by generative AI. No human uses the word "elucidate" 14 times in a research paper, and quantitative papers rarely refer to the "scholarly discourse". I should really have been alerted to this when the first paragraph included the LLM-ese sentence: "The significance of population diversity within the economic sphere is multifaceted". Perhaps diversity's significance is multifaceted. This article doesn't tell us that though. All it tells us is that the relationship between diversity (by some measures) and economic growth is positive. More diverse places tend to grow faster.

How not to demonstrate that income inequality impacts economic growth

Many studies have estimated the relationship between income inequality and economic growth (see here and here, for example). Fewer studies have attempted to estimate a causal relationship between the two variables. Unfortunately though, that's what most of us are really interested in. Does higher inequality experience inhibit economic growth?

Theoretically, the causal relationship is not straightforward. Rising shares of income among the wealthy may hold back consumer demand, because the rich save a higher proportion of their income. That would mean that more unequal countries have lower GDP. Alternatively, governments may respond to inequality by redistribution such as progressive taxation, which reduces work and profit incentives and reduces growth. Or, high or rising inequality may reduce trust in government and undermine institutions that are critical for economic growth. On the other hand, the rich save a higher proportion of their income, and those savings are then used for investment spending, which increases economic growth. And more inequality means that those who succeed will receive very high incomes, creating incentives for entrepreneurship and innovation. So, even if inequality causes economic growth, it is unclear if inequality should cause economic growth to be higher, or lower.

This recent article by Zixiang Qi, Bicong Wang (both Beijing Wuzi University), and Yaxin Wang (Chinese Academy of Social Sciences), published in the American Journal of Economics and Sociology (sorry, I don't see an ungated version online) attempts to establish the causal relationship between inequality and economic growth in the long run, using data from 99 countries over the period from 1980 to 2018. Qi et al. find that there is an inverted-U shaped relationship between inequality and economic growth. That is, economic growth is lowest when inequality is low, and when inequality is high, but higher when inequality is middling). However, there is a major problem with the analysis.

In order to establish a causal relationship, Qi et al. rely on an instrumental variables analysis. Instrumental variables analysis involves finding some variable that is correlated with the endogenous variable (income inequality), but uncorrelated with the dependent variable (economic growth, measured as the growth rate of per capita gross national income). That means that the only effect of the instrument on the dependent variable must be through its effect on the endogenous variable.

Qi et al.'s proposed instrument is the age-dependency ratio. Here's where the problem lies. Population ageing has a direct effect on economic growth. The reason why is explained in this post. In fact, Qi et al. even acknowledge this themselves, when they write that:

...Japan's economy has been in a period of secular stagnation for several decades because of ageing population.

So, Qi et al. should know that their instrument is not a valid instrument, and yet they press ahead and use it. At that point, I think we can safely disregard the rest of their results. It is interesting that they found an inverted-U shaped correlation between income inequality and economic growth. But that is all it is - a correlation. We need better methods to establish a causal relationship, and this paper simply doesn't live up to its promise.

Read more:

• Latest research seems to support the Kuznets hypothesis
• Income inequality and economic growth in Australia
• Is there an S-shaped relationship between inequality and per capita GDP?

Is there an S-shaped relationship between inequality and per capita GDP?

This week, my ECONS102 class has been covering poverty and inequality (along with social security and predistribution/redistribution). Today in class we covered some of the negative things about inequality - mostly a laundry list of the ways in which higher inequality might create negative externalities.

One of the negative externalities is that higher inequality might inhibit economic growth. This one is contentious, and certainly not settled in the empirical literature, although there are some good theoretical reasons to expect the relationship to exist (for example, see the mechanisms discussed in this post). You can also illustrate the expected relationship narratively, as I did in class. Something like this:

Imagine you are about to start a race, and it's a race that you really want to win. How hard would you try if you found out just before the race started than some other runners were being given a two-lap head start? Now, haw hard would you try if you found out that you were being given a two-lap head start over everyone else?

In both cases, the incentives to work hard (and run fast) are reduced for most people (although one student today did perceptively point out that in the first case, you either try much harder, or not at all). Now, as I said, despite the attractiveness of this narrative, the empirical evidence is inconsistent in its support. So, I was interested to read this 2018 article by Mauro Costantini (Brunel University London) and Antonio Paradiso (Ca' Foscari University), published in the journal Economics Letters (sorry, I don't see an ungated version online). They use US annual state-level data covering the period from 1960 to 2015, and plot the relationship between GDP per capita and income inequality (measured by the Gini coefficient). The relationship is clearly shown in their Figure 1, Panel A:

The results are interesting, implying that increasing GDP per capita was associated with lower income inequality at low levels of GDP per capita, then the relationship reversed, and finally reversed again at the highest levels of GDP per capita. They refer to this relationship as 'S-shaped', and also find a similar looking relationship when controlling for expenditure on health care per capita, or expenditure on welfare per capita.

This research is far from the last word on this topic, but perhaps it might go some way towards explaining the inconsistent relationships shown in the rest of the literature so far?

Read more:

• Income inequality and economic growth in Australia

Income inequality and economic growth in Australia

The relationship between income inequality and economic growth is theoretically ambiguous. You could argue that income inequality should increase economic growth, because: (1) inequality means that there are more people with high incomes, higher income people save more, and more savings increases funds available for investment spending, which increases productivity and economic growth; or (2) inequality increases the incentives for people to work harder and get ahead, increasing productivity and economic growth. On the other hand, income inequality could decrease economic growth, because: (1) inequality creates incentives for people to engage in rent-seeking behaviour, such as buying political favours, which is wasteful of resources; (2) people dislike inequality, so when inequality is high, they pressure the government to engage in redistribution, which reduces work incentives, productivity, and economic growth. So, given the theoretical ambiguity, the only way to establish this relationship is empirically, using data.

That is what this 2017 article by Tom Kennedy (University of New England), Russell Smyth (Monash University), Abbas Valadkhani (Swinburne University of Technology), and George Chen (University of New England), published in the journal Economic Modelling (ungated earlier version here), attempts to do, using Australian data. Specifically, Kennedy et al. construct time series of inequality and economic growth at the state (and territory) level for Australia over the period from 1986 to 2013. They then apply a fairly straightforward panel regression analysis, controlling for state-level investments in physical and human capital, and find that (in their Model 1):

...the effect of inequality on growth is negative and highly significant at the 1% level, suggesting that falling income inequality can substantially boost economic growth. On average, an additional 10% rise in the growth of inequality can bring about a 2.55% fall in real output growth...

A second model, employing a somewhat different specification, results in substantially similar results. However, they look at both contemporaneous and lagged effects of each variable on economic growth. On the lagged effects, Kennedy et al. conclude that:

While policies aimed at increasing physical capital can immediately boost economic growth, the impact of a rise in human capital, or a fall in inequality, on the Australian economy appear to be statistically significant, but with one and two years delay, respectively...

These results should suggest to us that, of the competing theoretical mechanisms linking inequality and economic growth, the negative effects on balance appear to outweigh the positive effects. However, Kennedy et al. don't provide any results that might tell us which mechanisms are at play (or indeed whether there are other mechanisms we haven't thought of that drive this observed correlation). Their analysis also falls short of demonstrating a causal relationship from inequality to lower economic growth. For a better understanding of the causal relationship and underlying mechanisms, we are going to need more research in the future.

The persistent effects of the Spanish Inquisition

Continuing the economic history theme from the yesterday's post, this new article by Mauricio Drelichman (University of British Columbia), Jordi Vidal-Robert (University of Sydney), and Hans-Joachim Voth (University of Zurich), published in the Proceedings of the National Academy of Sciences (ungated earlier version here) looks at the long-run consequences of the Spanish Inquisition. As they explain:

...we investigate the long-run impact of religious persecution on economic performance, education, and trust. The Spanish Inquisition is among the most iconic examples of a state-sponsored apparatus enforcing religious homogeneity... Histories of Spain’s decline and fall as an economic power frequently emphasize the role of the Inquisition... and sociological studies have argued for a “persistence of the inquisitorial mind” in modern-day Spanish thought...

Obviously, given the time period involved (the Spanish Inquisition ran from 1478 to 1834), this relates to the Little Divergence, as discussed in yesterday's post (and this earlier post). Drelichman et al. have data on the number of people persecuted in the Inquisition for many Spanish municipalities (based on around 67,000 records of Inquisition trials), and relate that to modern-day differences in incomes (measured using nightlight intensity), and survey-based measured of religiosity (measured by frequency of church attendance), education (proportion of the population with a high school diploma) and generalised trust. In terms of income, they find that:

Municipalities with no recorded inquisitorial activity as well those in the lowest tercile of persecution have the highest GDP per capita today. Those affected but in the middle tercile already have markedly lower income. Where the Inquisition struck with highest intensity (top tercile), the level of economic activity in Spain today is sharply lower. Magnitudes are large: In places with no persecution, median GDP per capita was 19,450€; where the Inquisition was active in more than 3 y out of 4, it is below 18,000€... Our estimates imply that had Spain not suffered from the Inquisition, its annual national production today would be 4.1% higher...

In terms of their other outcome measures, they find that:

A one-SD increase in inquisition intensity is associated with an increase of between 1.3% and 3.7% in religious service attendance...

We find a consistently negative relationship between persecution and educational attainment... going from no exposure to the Inquisition to half of all years being affected by persecutions would reduce the share of the population receiving higher education today by 2.7 percentage points, relative to a mean of 47.5 percentage points—a 5.6% relative reduction...

A one-SD increase in inquisitorial intensity reduces average municipality-level trust by 0.03 to 0.05 SDs.

All of the effects are statistically significant. Of course, it would be reasonable to worry that the Inquisition targeted its activities on areas that differed in relevant ways, such as areas that were more religious, or areas that were poorer. However, Drelichman et al. find the opposite:

To address the possibility that the Inquisition might have favored locales with high levels of pre-existing religiosity, we collect data on pre-Inquisition religious figures from the Spanish Biographical Dictionary... We then estimate the local density of famous people with strong links to the church and use it to examine whether inquisitorial intensity was systematically higher in places that were more religious pre- 1480. Our data suggests the opposite—places with greater inquisitorial intensity had a lower density of famous religious individuals...

A second concern is that the Inquisition could have been attracted to poorer areas. Standard histories of the Inquisition suggest this is unlikely. The Inquisition was self-financing. It had to confiscate property and impose fines to pay for its expenses and the salaries of inquisitors. While its mission was to persecute heresy, it had strong incentives to look for it in richer places...

In relation to the question of whether targeted areas were poorer, they use the location of hospitals, which were predominantly located in rural areas and:

Largely geared to care for the poor and for out-of-towners who fell ill and had no other place to stay, hospitals were credited with reducing the number of destitute people living and dying on the streets...

So, hospitals are indicative of areas that were, in general, wealthier and with higher social capital. They find that:

Inquisitorial intensity was five times higher in places with hospitals, and the difference is highly significant...

They move on to repeat many of their analyses using coarsened exact matching (which I discussed in this recent post), and find very similar results. Again, as with much of the literature in economic history, the results do not demonstrate causality, but we are getting closer to understanding the Little Divergence. And the results of this paper might not be limited to Spain. As Drelichman et al. conclude:

...the Inquisition also operated in Southern Italy and throughout Spanish America. Inquisitorial practices were also instituted throughout the Portuguese Empire and the Papal States. All of these areas today show relatively low education, lower incomes, and less generalized trust.

[HT: Marginal Revolution]

Read more:

• Economic institutions and the 'Little Divergence'
• Portugal, gold, and the resource curse in the Little Divergence

Economic institutions and the 'Little Divergence'

In economic history, the 'Little Divergence' refers to the divergence in income per capita between countries in Europe in the early modern period (that is, after the Middle Ages). [*] Over the period from 1300 C.E. to 1800 C.E., Holland and England overtook Spain, Italy, and Portugal in terms of income per capita. This is somewhat of a surprise, given that Spain and Portugal in particular had early access to wealth from the New World.

Past research such as this article by Daron Acemoglu, Simon Johnson (both MIT), and James Robinson (Harvard), published in the journal American Economic Review (ungated earlier version here) argues that by 1500 C.E., the political institutions differed between northern and southern Europe in such a way to drive this divergence. Specifically, they argue that Spain and Portugal had absolutist monarchies, while in England and Holland the monarchies were much more constrained. Absolutist monarchies are an example of an 'extractive institution' - an institution that excludes the majority of society from political and economic power. This contrasts with 'inclusive institutions', which incentivise the participation of all people in society. You can think of societies' institutions as existing along a continuum from extractive to inclusive, with England and Holland closer to inclusive, and Spain and Portugal closer to extractive.

However, a recent paper by Antonio Henriques (Universidade de Porto) and Nuno Palma (University of Manchester) challenges this simple narrative (see also the summary of the article here). Using a variety of datasets, Henriques and Palma show that institutions were no more extractive in Spain and Portugal than in England until well after the Little Divergence had already begun.

First, looking at the number of times that parliaments met in each of the countries, they find that:

...Portugal and Spain did not perform worse than England until the mid-seventeenth century.

Second, looking at depreciation of coinage over time (which would be an indicator of an extractive ruler), they find that:

During the sixteenth century, both England and the Dutch Republic perform significantly worse than Spain and Portugal according to this measure, and it is not until the seventeenth century that the Spainish [sic] monarchy started to perform badly.

Third, they look at real interest rates in the three countries (where higher real interest rates would be an indicator that lending to the government was seen as more risky, as would be expected in a society with extractive institutions), and find that:

England and the Netherlands were not paying lower rates than their Southern rivals until late in the early modern period.

None of this is consistent with the idea that differences in institutions by 1500 C.E. were drivers of the Little Divergence. Henriques and Palma conclude that:

Iberia's economic divergence was not a consequence of inferior initial institutions. At least prior to the civil wars of the mid seventeenth century, England did not have more constraints on executive power or an environment more protective of property rights than Spain or Portugal... At some point England did have better institutions, but that point occured [sic] considerably later than 1500. Accordingly, explanations for the little divergence among Atlantic traders which rely on variation in the quality of \initial institutions" (in particular, constraints on executive power by 1500 or earlier), such as that by Acemoglu et al. (2005), cannot be correct...

Modern economic and political institutions trace their origins back to the early modern period. A lot of economic research takes advantage of this 'path dependence'. However, this paper by Henriques and Palma demonstrates the limits of path dependence as an explanation. At some points, there are going to be contemporaneous factors that influence economic and political institutions as well, and if we are not careful, we can miss those important determinants of change.

****

[*] In contrast, the 'Great Divergence' refers to the great growth in European income per capita, relative to income per capita in China and other Asian countries.

[HT: Marginal Revolution, last year]

The economic impact of universities

I've been quite critical of 'economic impact studies' in the past (for example, see here). These studies try to estimate the economic impact by aggregating up all of the spending associated with an event (for example). The key problem is that they don't usually properly take account of the counterfactual - what would have happened if the event hadn't taken place. In the case of the economic impact of a particular industry, it can be difficult to establish what the counterfactual actually is - what would have happened if that industry didn't exist? That is the case when you try to estimate the economic impact of a university, for example.

An alternative approach is to avoid trying to estimate the economic impact of a particular university, but instead to compare regions that have universities from those that don't. That can include comparing regions before and after a university is established there, as well as comparing regions with different numbers of universities.

Essentially, that is the approach taken in this 2019 article by Anna Valero (London School of Economics) and John Van Reenen (MIT), published in the journal Economics of Education Review (open access, but just in case there is an earlier ungated version here). Valero and Van Reenen use data on economic growth rates and the number of universities in 1498 regions across the world, covering the period from 1960 to 2010 in five-year time steps. They find that:

...on average, a 10% increase in the number of universities in a region is associated with around 0.4% higher GDP per person.

The results are robust to a variety of different specifications. However, that isn't all. Not only are universities associated with higher growth within a region, the higher growth also spills over into surrounding regions, such that:

...a 10% increase in universities in the rest of the country (which in most cases will represent a greater absolute increase than a 10% increase in home region universities) is associated with an increase in home region's GDP per capita of around 0.6 per cent.

They also find that the effect is greater for poorer regions, suggesting that:

...new universities have a stronger impact on laggard regions within a country.

Valero and Van Reenen then go on to investigate the important question of how universities might contribute to higher GDP per capita. They suggest four possible mechanisms:

(i) a greater supply of human capital; (ii) more innovation; (iii) support for democratic values; and (iv) demand effects.

The first two are obvious, but it turns out that changes in human capital (as measured by the share of university graduates) and changes in innovation (as measured by the cumulative number of patents) only explain a small proportion of the effect of universities on GDP per capita. In terms of the third mechanism, Valero and Van Reenen first note that:

Universities could promote strong institutions directly by providing a platform for democratic dialogue and sharing of ideas, through events, publications, or reports to policy makers.

This is over and above any effect that universities might have on human capital. Looking at the impact of universities on support for democratic institutions (using data from the World Values Survey), they find that:

...there is a highly significant association between university presence in a region and approval of a democratic system.

Interestingly, this result is not driven by university graduates, because they get the same result if they drop graduates from the World Values Survey sample.

Finally, Valero and Van Reenen look at demand effects, which is the only channel that a traditional economic impact study would consider - how much of an effect does the spending of students and staff and the university itself have on the economy? They find that:

...we can explain around 15% of the regression coefficient on universities.

In other words, the vast bulk of the economic impact of universities is not even going to be picked up in a traditional economic impact study.

Of course, this research isn't going to be the last word on this topic. Even though Valero and Van Reenen run a battery of robustness and other checks, their results are still based on correlations and so they cannot be definitive about universities causing higher GDP per capita. However, in their robustness checks they do seem to have eliminated many of the most plausible alternative explanations. They are also unable to definitively address questions about the extent to which the size or quality of universities matters. Universities do have a positive economic impact on a region (and neighbouring regions), and we are getting closer to understanding how large that impact is, and how it arises.

The geography of development and the gains from relaxing migration restrictions

I just finished reading this 2018 article by Klaus Desmet (Southern Methodist University), David Nagy (Centre de Recerca en Economia Internacional), and Esteban Rossi-Hansberg (Princeton), published in the Journal of Political Economy (it appears to be open access, but just in case there is an ungated version here). The article is quite daunting because it sets up and calibrates a complex spatial model of development, which is disaggregated down to a 1° x 1° grid across the whole world. The simulation model itself is incredibly mathematical. However, if you can put the maths aside and focus on the results, what you find is interesting and insightful.

Essentially, having calibrated their simulation model, Desmet et al. look at the 'balanced growth path' of the world economy. This is the growth path "in which the geographic distribution of economic activity is constant" - in other words, where every grid cell grows at the same rate. It can take a long time for the world economy to achieve this balanced growth path, so their model runs for 600 years into the future (starting from 2000). They also run the model backwards, and can show that it does a reasonable job of replicating the pattern of population and economic development back to 1870.

Looking forward though, they base their analysis on two main scenarios: (1) holding the current pattern of migration restrictions constant; and (2) an immediate change to the free movement of people between countries and places. Both scenarios have interesting results.

In the status quo scenario, they find that:

...over time the correlation between population and productivity across countries becomes much stronger. As predicted by the theory, in the long run, high-density locations correspond to high-productivity locations...

...the high-productivity, high-density locations 600 years from now correspond to today’s low-productivity, high-density locations, mostly countries located in sub-Saharan Africa, South Asia, and East Asia. In comparison, most of today’s high-productivity, high-density locations in North America, Europe, Japan, and Australia fall behind in terms of both productivity and population.

In case you find those results surprising, Desmet et al. explain:

This productivity reversal can be understood in the following way. The high population density in some of today’s poor countries implies high future rates of innovation in those countries. Low inward migration costs and high outward ones imply that population in those countries increases, leading to greater congestion costs and worse amenities. As a result, today’s high-density, low-productivity countries end up becoming high-density, high-productivity, high-congestion, and low-amenity countries, whereas today’s high-density, high-productivity countries end up becoming medium-density, medium-productivity, low-congestion, and high-amenity countries; the United States is among them. Australia’s case is somewhat different since its low density and high inflow barriers imply that it becomes a low-productivity, high-amenity country. 

You can group New Zealand along with Australia in that paragraph. However, the takeaway message from this scenario is that the current pattern of migration restrictions, that keeps people out of high-income countries, serves to drive population density, innovation, and economic growth in the current lower-income countries to such an extent that they overtake the current high-income countries in income per capita by the end of the simulation period. That can be clearly seen from this picture (part of Figure 3 in the paper, and there are videos of the simulation model here - this is the end of Video 1B), where warmer colours represent higher levels of income per capita:

In contrast, if migration restrictions are ended, the areas that currently have high productivity attract migrants, which boosts their population, innovation, and future economic growth. So, in the free migration scenario, Desmet et al. find that:

Because today’s poor countries lose population through migration, they innovate less. As a result, and in contrast to the previous exercise, no productivity reversal occurs between the United States, India, China, and sub-Saharan Africa... Some countries, such as Venezuela, Brazil, and Mexico, start off with relatively high utility levels but relatively low productivity levels. This means they must have high amenities. Because of migration, they end up becoming some of the world’s densest and most productive countries, together with parts of Australia, Europe, and the United States.

Coastal areas also benefit greatly when migration is free. Here's the corresponding map for the free migration scenario (part of Figure 7 in the paper, and again in the videos online you can see its evolution - this is the end of Video 3B):

You can see the difference in income per capita between the two scenarios by comparing those two maps. Another interesting point is that the effect of lifting migration restrictions is immediate, with 70.3 percent of people moving immediately once the restrictions are lifted. That demonstrates how restrictive current regulations are. There are also substantial welfare gains from free migration:

In present discounted value terms, complete liberalization yields output gains of 126 percent and welfare gains of 306 percent.

While the exact numbers depend on the particular calibration of the model, I think we can safely conclude that there are huge gains in human welfare to be had from lifting migration restrictions. As Michael Clemens noted in this article in the Journal of Economic Perspectives (open access), maintaining migration restrictions leaves trillion dollar bills on the sidewalk.

The results reported in this paper should make high-income country governments seriously reconsider their immigration policies. High immigration restrictions seriously benefit China, India and maritime Asia in the long run, and are detrimental to the future economic growth of the current high-income countries. If high-income countries want to maintain their high-income status, these restrictions need to be reconsidered. However, it would be interesting to see what happens in the simulation model when one country (or a small number of countries; or rather grid cells) lifts restrictions but others do not. Perhaps that is a future exercise, but in terms of providing input to policy decisions, it seems critical.

[HT: Marginal Revolution, last year]

The economic benefit of becoming a US state

After the Mexican-American War (1846-1848), a large chunk of northern Mexico was ceded to the United States in the so-called Mexican Cession (as a result of the Treaty of Guadalupe Hidalgo). This included the modern-day states of California, Nevada, and Utah, most of Arizona, and parts of New Mexico, Colorado, and Wyoming. We could also include the state of Texas, the annexation of which laid the seeds for the war.

This raises some obvious questions. Texas obviously thought they were better off as a state of the US. Is that actually what happened? What about the other states that joined the US as part of the Mexican Cession? And what about other territories that could easily have become states, such as Cuba or Puerto Rico?

Those are the questions that this recent working paper by Robbert Maseland (University of Groningen) and Rok Spruk (University of Ljubljana) sought to address. They use state-level and country-level data on GDP per capita, and attempt to estimate the economic growth impact of a state joining the US. This paper is interesting for several reasons, which is why we discussed it in the Economics Discussion Group at Waikato this week.

First, the paper illustrates the importance of the counterfactual - what would have happened if history had been different. In this particular case, what would economic growth have been in the Mexican Cession states, if they had remained part of Mexico? We don't know for sure of course, because we are only able to observe what happened after they became states, and we don't observe what would have happened if they didn't. Similarly, for the territories that didn't become US states that Maseland and Spruk look at, we don't observe what would have happened if they did become states, only what happened when they didn't.

Maseland and Spruk address the counterfactual problem by using the synthetic control method. Essentially, they create a model of each Mexican Cession state's observed variables (including GDP per capita) up to the time before the Mexican Cession, based on the observed variables of other countries. This creates, for each state, a synthetic version of the state that is made up of proportions of the data from other countries. For example, they find that:

...the growth pattern of prestatehood California is best reproduced by growth and development covariate values of Canada (49%), South Africa (19%), New Zealand (15%), Egypt (10%), Norway (5%), and United Kingdom (3%), respectively. On the other hand, the synthetic control group for Arizona consists of Egypt (70%), Jamaica (18%), United Kingdom (11%), and Greece (1%), respectively.

As an interesting aside, New Zealand was part of the synthetic versions of California (15%), Colorado (11%), Nevada (58%!), and New Mexico (1%).

The second interesting aspect of the paper is the results themselves. Maseland and Spruk compare the actual economic growth performance of each state with the growth performance of the synthetic version of itself. They find that there are:

...strong and pervasive effects of the admission on long-run growth. The underlying post-admission gap coefficients are both large, positive and statistically significant at 1%, respectively, and readily suggest that the effect of joining the US appears to be specific to the treated states.

For example, here's a graph of the economic growth path for actual and synthetic California:

The solid line is the actual trajectory for California, while the dashed line is the synthetic control. It is clear that they deviate from each other around 1850, and the synthetic control does much worse. This demonstrates how much better off California was after statehood.

Maseland and Spruk then go on to show that the opposite is true of Mexican states that did not join the US, finding that there are:

...large gains from the hypothetical admission of Mexican states to the United States although the variation in the long-run growth effect is notable... the states next to the US border appear to be most adversely affected by not joining the United States.

Maseland and Spruk then look at the hypothetical case of several territories joining the US, including Cuba, Puerto Rico, and the Philippines, (all of which were occupied by the US after the Spanish-American War in 1898), and Greenland (which was granted home rule by Denmark in 1979, and could hypothetically have joined the US as a state then). They find that:

By 2015, the synthetic Cuba as a hypothetical US states would be seven times richer than its real counterpart... By 2015, the difference between the synthetic Philippines as a US state, and the real Philippines is about a factor [of] 10.

...synthetic Puerto Rico as a US state would be 51 percent richer than the real non-state Puerto Rico... In terms of magnitude, the gap between synthetic Greenland as a US state and its real version as a Danish territory is 32 percent, respectively.

Finally, they look into the reasons for these substantial differences. Does statehood allow easy access to a larger domestic market and therefore drive economic growth, or does statehood lead to the adoption of better institutions? Maseland and Spruk find evidence in favour of the role of institutions:

On balance, our estimates indicate that the joint temporal and spatial variation in the level of electoral democracy can explain up to 41 percent of the statehood-induced growth premium. The corresponding variation in the level of liberal democracy accounts for up to 56 percent of the long-run growth benefits stemming from improved institutional quality upon the admission to the United States. The institutional quality bonus received by joining the US apparently drives a large part of the performance boost. This lends support to the second leg of the American Exceptionalism thesis, which is that it is America's political institutions that gave the US its unique advantage.

So, institutional quality was key to the improved economic growth performance of the Mexican Cession states after they joined the US.

There is a third reason why this paper is interesting. The original Constitution of Australia provided an opportunity for New Zealand to become a state of Australia. We chose not to. On the other hand, Western Australia became a state at that time following a referendum, when instead they could have become independent. An interesting exercise for an enterprising honours student might be to look at these two cases (data permitting) and follow the process outlined by Maseland and Spruk to construct the relevant counterfactuals for New Zealand and Western Australia. Was New Zealand better off going it alone?

[HT: Marginal Revolution]