Best paper award for Inês Xavier (Economics ’15, UPF PhD ’21)
Congratulations to @Ines_MXavier for winning the @ITAXjournal Best PhD student paper award for her paper "Wealth Inequality in the US: The Role of Heterogenous Returns".
— International Tax and Public Finance (@ITAXjournal) August 26, 2021
Paper abstract
Why is wealth so concentrated in the United States? In this paper, I investigate the role of return heterogeneity as a source of wealth inequality. Using household-level data from the Survey of Consumer Finances (1989-2019), I provide new empirical evidence on returns to wealth in the United States, and find that wealthier households earn, on average, higher returns: moving from the 20th to the 99th percentile of the wealth distribution raises the average yearly return from 3.6% to 8.3%. To understand how these return differences shape the distribution of wealth, I introduce realistic return heterogeneity in a partial equilibrium model of household saving behavior. This exercise suggests that considering both earnings and return heterogeneity can fully account for the top 10% wealth share observed in the data (76%), which cannot be explained by earnings differences alone.
Editor’s note: This post is part of a series showcasing BSE master projects. The project is a required component of all Master’s programs at the Barcelona School of Economics.
Abstract
We construct a theoretical Overlapping Generations (OLG) model to describe how sovereign debt crises can propagate in the economy under certain financial constraints.
In the model, households work when young and deposit their savings in exchange for a dividend, banks invest deposits in assets and government bonds. Banks, subject to legal and market requirements, invest a fixed fraction of deposits and own equity in assets. When prices of bonds fall due to perceived sovereign debt risks, banks can invest less on capital goods directly affecting the business cycle. This paper simulates the deviations from steady-state produced by a shock to government securities and provides insights into macro-prudential policy implications.
We find that a sovereign debt crisis affects young and old generations differently, with the latter facing higher fluctuations in consumption. We also find that the macro-prudential policy can be effective only at very high levels on the old, but ineffective for the younger generation.
Conclusions
This paper draws three main conclusions about the impact of a sovereign debt crisis on the business cycle within the proposed OLG theoretical framework:
A decline in government bond prices leads to lower output, wages and dividend negatively affecting present and future consumption. However, this effect is different for young and old generations. In particular, the old seem to face more sudden changes and higher deviations from steady-state values when a sovereign debt crisis takes place.
The proposed macro-prudential policy does not seem to offset the impact of a fall in government bonds prices on the business cycle. In fact, almost all the macroeconomic variables of interest in our theoretical model do not change significantly, relative to their steady-state values, when the supervising authority modifies the capital requirements for banks.
A very aggressive policy on capital requirements (i.e. x=0.9 for the whole period) can compensate for the negative shock bonds prices have on dividends and, therefore, consumption for the old.
In the Catalan elections held on 14 February 2021, just a few weeks after the peak of the third wave of COVID-19, voter turnout was significantly lower than in 2017 (51.3% compared to 79.1%) and the lowest in history. Despite the political context being different compared to 2017, in the run-up to the election predicted turnout remained at similar levels until the emergence of Covid-19 – whereupon it dropped sharply. In these extraordinary elections, the vote share of the parties changed which shifted the Catalan political spectrum.
We study this relationship between Covid-19 and electoral results in Catalonia. To estimate the effect of the pandemic we explore the differences in cumulative Covid-19 incidence at the municipal level and compare it with electoral outcomes controlling for economic and demographic variables (population density, percentage of over-65 years old, share of foreign population and the unemployment rate).
The most widely used model of electoral participation, that of Riker and Ordeshook (1968), considers that the decision to participate is based on a cost-benefit trade-off between the expressed benefit of voting (feeling fulfilled, considering that civic duty is fulfilled, etc.) and the cost that individuals associate with participation. This cost rose sharply, because voting implied a higher risk of contracting the disease and the inconvenience caused by anti-covid measures. Therefore, higher costs would imply lower turnout as shown by several empirical studies, which find that with small increases in cost turnout drops significantly (eg. Aldrich, 1993). We assume that the increase in cost is the same for all (although there could be differences by age, and so we control for the most vulnerable group, the over-65s), which would imply a fall in participation, but could also induce changes in the outcome. This change may be due to differences in the “sentimental” benefit of voting between voters of different parties.
Economic theory also indicates who voters choose, conditional on voting. According to “retrospective” voting theory, voters support or punish parties in government in response to their performance in a crisis such as Key (1966). In contrast, according to “prospective” voting theory, the individual votes for the party that he believes will do better or, as Leininger and Schaub (2020) argue, seeks to match the party in regional and national government for more optimal crisis management.
Effect on participation
As seen in the maps above, the areas with the highest cumulative Covid incidence also have a lower percentage of participation, especially the Barcelona metropolitan area. Our analysis shows that an increase of 100 points in cumulative incidence in the last 14 days is related to a drop of 2.6 percentage points in turnout. To understand the magnitude, this is equivalent to one extra Covid case in a municipality of 1,000 inhabitants, so we estimate that the effect of the pandemic is quite high. To understand the impact of the second and third waves of Covid-19, we have conducted the analysis with cumulative incidence measures in the last month and in the last 4 months prior to the elections. However, as the period lengthens, the effect on turnout decreases (1.3 and 0.4 percentage points respectively).
However, the political context also changed: while in 2017 the voting framework was centred on the independence process (which led to the historical record turnout); in 2021 it was the pandemic that defined the elections. However, the trigger for this change of context (and the neglect of the independence process) was the eruption of the virus. As can be seen in the graph below showing the predicted turnout for the elections to the Parliament of Catalonia. Since Covid-19 appears to be the only element of exogenous variation between municipalities when comparing the 2017 and 2021 elections, we can infer causality.
These results are in line with the theory of the myopic voter who takes into account episodes closer to the election when voting. In this case, the myopic voter is acting rationally, as they account for the actual risk at the time of the vote rather than the risk of the past few months.
Effect on results
Secondly, the elections brought a major shift in the political spectrum, as can be seen in the bar charts above. For this reason, we have not been able to include other elections, as they were not comparable with each other. We have grouped the political parties into the following ideological and identity groups:
Pro-independence left: ERC and CUP.
Pro-independence right-wing: JUNTSXCAT and PDECAT
Non-independence left-wing: PSC and PODEMOS
Non-independence right: PP, Cs, and VOX
First, we respectively analyse the pro-independence and non-independence groups, the victors (the pro-independence coalition), and the opposition. The results show that the pandemic has a positive effect on the percentage of the vote of the pro-independence parties and a negative effect on the non-independence group. This effect can be seen as a retrospective vote, showing a certain approval by pro-independence voters of how the regional government has handled the pandemic.
On the other hand, in the analysis of the groups divided into identity and ideological groups, we find that the group with the highest positive coefficient is the non-independence left – the coalition in charge of the central government at the time of the elections. This behaviour is associated with the prospective vote. With the arrival of Salvador Illa (The Spanish Minister of Health during the pandemic) as the PSC candidate, the party was reinforced and ends up as the winner of the elections. This positive effect can be seen as an attempt to align the post-pandemic recovery strategy in Catalonia with that of the rest of Spain.
In conclusion, our results suggest that COVID-19 had a significant outcome in the Catalan elections that translated into a negative relationship between the virus and turnout. In contrast, the relationship is positive between cumulative incidence and the vote of pro-independence parties and the non-independence left-wing group. At the same time, the retrospective theory seems to hold true as there has been strong support for the government in office based on their management of the pandemic.
Connect with the authors
Iván Auciello Estévez ’21 is a student in the Barcelona GSE Master’s in Economics. After graduating he plans to work as a Research Assistant at Banco de España.
Pau Jovell Codina ’21 is a student in the Barcelona GSE Master’s in Economics. After graduating he plans to work as a Research Assistant at Banco de España.
Since the 2007 implementation of the “Dependency Act”, people with functional limitations in Spain can request Long-Term Care (LTC) benefits. The Act’s main objective is to improve the care and quality of life of people who have lost their autonomy. Fourteen years later, evidence on the impact of the Dependency Act in Spain on its beneficiaries remains scarce. This is partly because we need data on the quality of life of this population in order to fully evaluate its impact and we still don’t gather a suitable indicator. However, we can assess the impact of benefits by utilising data on the use of healthcare services as a proxy to estimate quality of life and health, and that is the approach we have taken in our research.
The relationship between LTC benefits and healthcare use
The effect of LTC benefits on healthcare use is not trivial and may have implications not just for the quality of life of recipients, but also for the management of healthcare services.
If access to LTC improves the health status of dependent people (for example through better treatment management, better nutrition or avoiding domestic accidents), investments in the LTC system could save healthcare providers money in the future. On the other hand, LTC benefits might increase the demand for healthcare, for example through greater health-monitoring by caregivers.
Using data on the type of healthcare service, type of admission and diagnoses, we can better understand the relationship between benefits and healthcare use, and therefore increase the efficiency in the allocation of social care and healthcare resources to design a better integrated care system.
The data
To study the effects of LTC benefits on healthcare use, we needed to gain access to data from social services and healthcare providers and then link it. As others have shown, this was not easy, but fortunately, the interest of the institutions involved in this research —CatSalut, AQuAS, the Departament de Treball, Afers socials i Famílies de la Generalitat de Catalunya (DTASF) (Labour, Family and Social services department) and CRES (UPF) — helped to facilitate this process.
Even with access to the data, measuring the effect of LTC benefits on the health system is not straightforward. Those who qualify for benefits will by definition have worse health and, regardless of the new policy, will probably make greater use of the healthcare system than those who don’t qualify for benefits. Therefore, simply comparing those applicants who receive LTC benefits with those who don’t would not help us to identify the effects of the Dependency Act.
To deal with this, we use an instrumental variable technique based on the “leniency” of the evaluators. The idea is as follows. When there is an evaluation guided by objective criteria such as when grading an exam, imposing a judicial sentence or assigning the severity of a medical case, there is always a degree of subjectivity from the person performing the assessment. It is common in research literature to consider this as a source of exogenous variation, because there is no predictable basis by which assessors should differ. This allows us to use traditional statistical methods that can identify any consequences associated with new policies. In our context, despite the fact that the assessment is based on the Dependency Assessment Scale, each examiner has a small margin of subjective interpretation. Thus, there are examiners who, on average, tend to provide slightly higher scores, so that their applicants qualify for greater LTC benefits. Since the applicant cannot choose his/her examiner, being assessed by one examiner or another affects the probability of receiving a benefit which is exogenous to the assessment process.
The results
In the two graphs below, we summarize the most important results from our research.
Figure 1 shows that access to LTC benefits decreases by 7 percentage points the probability of a group of hospitalizations considered by the medical literature as avoidable with continuous care for the elderly (such as hospitalizations for injuries, ulcers and nutritional deficiencies). This represents a 60% reduction in this type of hospitalization.
Figure 1. Effect of LTC benefits on the probability of avoidable hospitalizations
Figure 2 shows that unscheduled visits to primary care decrease by almost 10 visits two years after receiving LTC benefits, a 50% reduction with respect to the mean. Our analysis by diagnosis indicates that this reduction is explained by a sharp drop in visits caused by the economic and family situation of the individual.
Figure 2. Effect of LTC benefits on visits to primary care. Scheduled vs Unscheduled
Conclusions
Our results show that LTC benefits can mitigate the use of healthcare services, in line with the conclusions of previous research. Additionally, our data allows us to go one step further, identifying in detail the types of services which are the most affected.
Particularly interesting are the results relating to primary care, where LTC benefits strongly reduce visits not strictly related to health causes. It seems that reinforcing the LTC system will not only improve the quality of life of dependents and caregivers, but may also reduce the pressure on the healthcare system.
Undoubtedly, these results are important given the chronic under-financing of the LTC system, especially in a context where COVID-19 has highlighted a need for real integration between social and health care. This is just one example of how access to, and analysis of administrative data can contribute to the evaluation of public policies, facilitating better informed decision making.
As we are approaching the COP26 meeting to be held in Glasgow later this year, a highly anticipated milestone that is to be expected is the finalization of the rulebook for Article 6 of the Paris Agreement. Article 6 calls for ‘voluntary cooperation’ between public and private actors in carbon markets and other forms of international cooperation to meet the climate goals.
Ex-ante policy modelling assessments have shown that international cooperation on carbon pricing can result in economic and environmental gains that potentially could be used to boost the ambition of the climate targets. In our OECD working paper (jointly with Sonja Peterson, Daniel Nachtigall and Jane Ellis) we present a review of the literature on ex-ante policy modelling studies that examine the economic and environmental gains that could be realised if nations cooperate on climate action. Ex-ante modelling studies usually use Computable General Equilibrium (CGE) models or Integrated Assessment Models (IAM) to understand the socio-economic and environmental impacts of climate policies. We group the research articles into the following five types of cooperative actions that could be realised between countries – carbon price harmonization, extending the coverage of carbon pricing systems, implementing a multilateral fossil fuel subsidy reform, establishing international sectoral agreements and, mitigating carbon-leakage through strategic climate coalitions and border carbon adjustment.
The literature shows that all forms of international cooperation could potentially deliver economic and environmental benefits. Extending carbon markets to include new regions would reduce the aggregate mitigation costs but would not lead to unanimous gains for each of the participating countries and thus compensation mechanisms would be needed to incentivize participation from countries that would face costs. Sectoral agreements have a limited impact but could help in the reduction of GHG emissions though not cost-effectively. All of the studies unambiguously show that removal of fossil fuel subsidies would lead to an improvement in aggregate global welfare.
Further details about the results and individual papers can be found here:
Strong Central Bank’s anti-inflationary postures are often viewed as a way to implement policies consistent with the preferences of the poor. Five examples:
Mankiw (2006): inflation “is not a tax on all assets but only on non-interest-bearing assets, such as cash. The rich are able to keep most of their wealth in forms that can avoid the inflation tax”.
FED Kansas City President, George (2017): “not as enthusiastic or encouraged as some when I see inflation moving higher,” because “inflation is a tax and those least able to afford it generally suffer the most.”
Cœuré, ECB (2012): “inflation is also particularly harmful to the poorest parts of the population”; “poorer households tend to hold a larger fraction of their financial wealth in cash, implying that both expected and unexpected increases in inflation make them even poorer.”
Central Bank of Colombia: low & stable inflation is important because “increasing inflation means a redistribution of income against the poor.”
Central Bank of Chile: inflation tends to hurt those who have a greater proportion of their wealth in money, that is, the poorest.
But do the poor prefer stronger anti-inflationary policies than the rich?
This is not obvious: anti-inflationary policies often come at the cost of less economic activity and higher unemployment rates, and these side effects of contractionary monetary policies are not necessarily evenly spread across the income distribution.
Accordingly, preferences vis-à-vis inflation versus unemployment might also not be evenly distributed across income groups. We study these relative preferences across the income distribution.
We find that:
Both the poor and the rich dislike inflation and unemployment and they both dislike extra points of unemployment more than extra points of inflation.
The aversion to unemployment relative to inflation is higher in Latin America than in Europe.
Our main point: the poor have a higher aversion to unemployment relative to inflation than the rich. This finding is at odds with the commonly held view by Central Banks that hawkish monetary policies line up with the poor’s preferences.
The idea that a compassionate Central Bank should fight inflation strongly notwithstanding the consequences on unemployment is at odds with the preferences along the income distribution estimated in our paper.
Cities compete to attract large firms. When Amazon announced in 2017 the opening of its second headquarters, 238 US cities signed up for it. Large firms bring jobs and can boost local productivity through spillovers. However, the downside is that they generate excessive local volatility.
We leverage quarterly data on size of all German establishments from 1990 to 2014 to show that a buildup of concentration of economic activity in the hands of few sizable firms is systematically associated with higher volatility in local labor markets in subsequent months.
The reason is granularity. When concentration is high, shocks to large firms do not average out with shocks to smaller ones and the evolution of local employment ends up mimicking the evolution of employment in the large firm. The economy experiences “granular” business cycles.
Our paper is the first to provide solid time-series support to granular business cycles as in Carvalho and Grassi. However, we show that large firms do not seem capable to trigger both booms and busts alike. Our evidence points in favor of granularity-driven recessions only.
Finally, we calibrate the parameters governing local firm dynamics to match the local employment law of motion, because we want to see what are the causes of the disproportionate presence of large firms in big cities. We find that it’s because of higher growth opportunities in big cities.
Bottom line: the volatility externality imposed by large firms encourages short-time work schemes as opposed to layoffs and may justify using size-dependent forms of public support for crisis management, but the benefit might have to be weighed against potential moral hazard.
Federica Daniele ’13 is an economist at the Bank of Italy. She holds a PhD from UPF and Barcelona GSE and is an alum of the Barcelona GSE Master’s in Economics.
In England, domestic violence accounts for one-third of all assaults involving injury. A crucial part of tackling this abuse is risk assessment – determining what level of danger someone may be in so that they can receive the appropriate help as quickly as possible. It also helps to set priorities for police resources in responding to domestic abuse calls in times when their resources are severely constrained. In this research, we asked how we can improve on existing risk assessment, a research question that arose from discussions with policy makers who questioned the lack of systematic evidence on this.
Currently, the risk assessment is done through a standardised list of questions – the so-called DASH form (Domestic Abuse, Stalking and Harassment and Honour- Based Violence) – which consists of 27 questions that are used to categorise a case as standard, medium or high risk. The resulting DASH risk scores have limited power in predicting which cases will result in violence in the future. Following this research, we suggest that a two-part procedure would do better both in prioritising calls for service and in providing protective resources to victims with the greatest need.
In our predictive models, we use individual-level records on domestic abuse calls, crimes, victim and perpetrator data from the Greater Manchester Police to construct the criminal and domestic abuse history variables of the victim and perpetrator. We combine this with DASH questionnaire data in order to forecast reported violent recidivism for victim-perpetrator pairs. Our predictive models are random forests, which are a machine-learning method consisting of a large number of classification trees that individually classify each observation as a predicted failure or non-failure. Importantly, we take the different costs of misclassification into account. Predicting no recidivism when it actually happens (a false negative) is far worse in terms of social costs than predicting recidivism when it does not happen (a false positive). While we set the cost of incurring a false negative versus a false positive at 10:1, this is a parameter that can be adjusted by stakeholders.
We show that machine-learning methods are far more effective at assessing which victims of domestic violence are most at risk of further abuse than conventional risk assessments. The random forest model based on the criminal history variables together with the DASH responses significantly outperforms the models based on DASH alone. The negative prediction error – that is, the share of cases that would be predicted not to have violence yet violence occurs in the future – is low at 6.3% as compared with an officer’s DASH risk score alone where the negative prediction error is 11.5%. We also examine how much each feature contributes to the model performance. There is no single feature that clearly outranks all others in importance, but it is the combination of a wide variety of predictors, each contributing their own ‘insight’, which makes the model so powerful.
Following this research, we have been in discussion with police forces across the United Kingdom and policy makers working on the Domestic Abuse Bill to think how our findings could be incorporated in the response to domestic abuse. We hope this research acts as a building block to increasing the use of administrative datasets and empirical analysis to improve domestic violence prevention.
New research venture created by Francesco Amodio (Econ ’10), Giorgio Chiovelli (Econ ’11) and Serafín Frache
A pair of Barcelona GSE Alumni and their frequent co-author and friend have recently launched a new research centre to provide a platform for their vision of ideal research collaboration and to bring those learnings to a wider audience. This venture is Mont^2, the Montréal x Montevideo Econ Lab.
This initiative was not without its challenges despite appearing to be an easy path for a group of friends and co-authors from the outside. Its genesis happened just before the COVID-19 pandemic struck both Canada and Uruguay, where the founders are based (not to mention the rest of the world) forcing them to adapt their plans for the launch of a physical working group to an online one.
Francesco Amodio ’10 and Giorgio Chiovelli ’11 are Economics Program alumni and became firm friends after meeting as TA and student in an econometrics class. They began collaborating during their respective PhD’s, Francesco at UPF and Giorgio at Bologna in Italy. After graduating and starting their careers in Montréal and Montevideo respectively, they included a third member into their collaborative efforts, Serafín Frache, and started laying the groundwork for what would ultimately become Mont^2. Serafín had local knowledge of Uruguayan administrative data and its potential to answer exciting economic questions. From these roots, the three researchers began thinking about their long-term career plans and how they can make an impact on their communities and give back to their respective local communities and the wider academic and policy-world.
It is with this foundation that Mont^2 was created. The professors applied to Social Sciences and Humanities Research Council (SSHRC) for seed funding to utilise the unique Uruguayan data and begin building the infrastructure of Mont^2.
They also aim for the lab to structure the mentorship of the professors’ current and future pre-doctoral research assistants. This would give them the tools to work with big data and be prepared for their future careers where this skill is in demand, whether in academia or the private sector. The trio also want to bring attention to the role academic research has to play with policy-making institutions regardless of where they might be located.
Mont^2 has just been launched, but already they are hard at work on a handful of projects with RAs already enlisted. It is a working environment meant to provide a formalised structure to the growing network of researchers and collaborators that began with Francesco, Giorgio and Serafín but now stretching far beyond. The hope is for Mont^2 to strengthen their ties with policy institutions and begin to promote best practices when dealing with confidential government big data.
Is ethnic diversity good or bad for economic development? When different languages, ethnicities or races coexist in the same society, there are challenges for the economy, but also opportunities. On one hand, if individuals within ethnic groups are homogeneous, and groups differ in preferences toward policies or public goods, then conflicting preferences can lead to inefficiencies in public good provision or to policy choices that may not benefit the entire society. Inter-group tensions can also result in civil conflicts or exacerbate mistrust and lack of cooperation. However, on the other hand, if ethnic groups differ in subsistence activities or skills, then complementary specializations can generate economic gains, stimulate innovation, and promote inter-group trade. Alesina and La Ferrara (2005) provide a review of this literature. While there is a general understanding that diversity brings opportunities and challenges, there is scarce evidence on which factors determine its positive or negative consequences. When is ethnic diversity good for economic development, and when is it bad?
I ask whether the effect of ethnic diversity on economic development depends on one characteristic of ethnic groups that has received little attention: the heterogeneity of individuals within ethnic groups. Underlying previous literature is the assumption that individuals within ethnic groups tend to be homogeneous. However, individuals may differ in many dimensions, including preferences, economic activities or skills, as well as cultural, genetic, and linguistic traits. I focus on having different economic specializations and skills within the same ethnic group, and I study whether ethnic groups with more heterogeneous individuals do better in multi-ethnic societies.
Consider two ethnic groups, A and B. The two groups differ in ethnicity. In turn, ethnic group A has individuals with diverse skills due to their different economic specializations, while ethnic group B is more homogeneous (individuals from ethnic group B have similar skills). The idea is that it may be easier for individuals of ethnicity A to live and to interact in a multi-ethnic society–they come from an ethnic group that is already highly heterogeneous. They will already be used to diverse environments. They will be more familiar with having to interact with heterogeneous individuals. If you come from an ethnic group that is highly heterogeneous, in terms of skills, you may be more willing to live and to interact with other ethnicities. In this case, positive interactions, mutually beneficial exchange, between ethnic groups will become more frequent.
The 16th Century resettlement of Peruvian ethnic groups
To study this, I collect new data on a natural experiment from Peru’s colonial history. I focus on highland Peru. There, Spanish colonizers resettled native populations in the 16th century. They forced together different ethnic groups in new villages, and this happened unintentionally. Importantly, in some ethnic groups, individuals had already been living in very different ecological zones of the Andes, at different altitudes, during the pre-colonial period, before the Spanish conquest. This creates within-group heterogeneity. In some cases, individuals from the same ethnic group were very different in terms of ecological specializations and skills – the types of lands and crops that they were used to cultivate. In other ethnic groups, everyone lived in the same climate zone, at the same altitude. I am asking: did the more heterogeneous ethnic groups do better once they were resettled in multi-ethnic villages?
Firstly, I use a map of the spatial distribution of ethnic groups at the time of the Spanish conquest. It allows me to compute the distance from each village to the closest ethnic frontier and use it as a source of quasi-random variation in ethnic diversity. During the pre-colonial period, individuals from the same ethnic group were distributed vertically, at different altitudes. This is the thesis of the anthropologist John Murra. He documents this vertical settlement pattern as a subsistence strategy in an environment in which differences in elevation create a variety of ecological zones and climates. At the time of the resettlement, the mountain environment of the Andes was new to Spanish colonizers – they were used to a flatter world. As a result, in villages that were created close to ethnic borders, they concentrated individuals from different ethnicities unintentionally (Pease 1978; Wachtel 1976). Secondly, I use spatial data on the distribution of ecological zones to compute a proxy for the heterogeneity of skills within each ethnic group prior to the conquest. It is important to note that ethnic groups with more heterogeneous skills may be different in other dimensions (e.g., group size, population density, etc). In the analysis, I use all the available data on the pre-colonial characteristics of ethnic groups to account for the main correlates of within-group heterogeneity.
The first result in the paper documents the direct effect of ethnic diversity, which I benchmark against previous results in the literature. I find that ethnic diversity is robustly associated with lower living standards in the long run. Specifically, I explore a variety of outcomes that capture contemporary living standards. As proxies for local economic activity, I use light intensity per capita (2000-2003) and a measure of non-subsistence agriculture from the agricultural census of 1994. For access to public infrastructure, I use data from the 1993 population census on access to public sanitation and the public network of water supply. This result is in line with the literature on the costs of ethnic diversity, though it also highlights the persistent consequences of forced diversity at the local level. When examining the effect of ethnic diversity and within-group heterogeneity, I find the following pattern:
The figure shows the average effect size of ethnic diversity as a function of within-group heterogeneity. I find a robust pattern: the more heterogeneous an ethnic group was prior to resettlement, the lower the cost of ethnic diversity. On average, where ethnic groups have more heterogeneous individuals in terms of skills, the negative effect of ethnic diversity is reduced, and ethnic diversity may even become an advantage for economic development. To understand the evolution of these long-term effects, I use data from the 1876 population census on occupations and literacy rates, showing that the documented pattern persists over time.
Why is this happening? When exploring potential channels, I find evidence consistent with cultural transmission. Individuals from more heterogeneous ethnic groups in terms of skills are more likely to interact with other ethnicities. Using data from colonial records, I find evidence suggesting cooperative behavior and more open attitudes when interacting with other ethnic groups. Overall, understanding whether individuals from more heterogeneous ethnic groups are better able to integrate in a multi-ethnic society is a relevant question, not only in an increasingly globalized world, but also in the context of forced displacements and migrations, like in the case of refugees.
References
Alesina, A., and La Ferrara, E. (2005). “Ethnic Diversity and Economic Performance.” Journal of Economic Literature, 43 (3), pp. 762-800.
Murra, J. V. (1975). Formaciones económicas y políticas del mundo andino. Instituto de Estudios Peruanos.
Pease, F. G. Y. (1978). Del Tawantinsuyu a la historia del Perú, Instituto de Estudios Peruanos.
Wachtel, N. (1976). Los vencidos: los indios del Perú frente a la conquista española (1530-1570). Alianza.
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