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.
Firms are often reluctant to invest in green technology. As for the reason why – they point to high fixed costs and the resulting capital market failure. However, instruments that could possibly address this problem, such as environmental investment tax incentives, are not very popular among regulators – even though they may offer an interesting alternative to environmental taxation or even investment subsidies, since tax incentives are easier to implement at the administrative level. Could Environmental Investment (EI) tax incentives be successful at encouraging green investment? And how do firms react to the modifications in existing EI tax credits with respect to employment and innovation decisions? I try to tackle those questions using the EI tax credit reform in Spain.
Spanish Environmental Investment (EI) Tax Credit
Spain is a very interesting country in which to study a large-scale national tax incentive program because the EI tax credit went through some unusual transformations over the years of its existence. The specific EI tax incentive analyzed in this paper was first introduced in 1996 at 10% of the firm’s level of investment and survived in such form until 2006, when its slow phase-out was announced. The phase-out was implemented as the then government believed that the tax incentive was mostly financing end-of-pipe technologies (which do not affect the production process but purely reduce the pollution level at the end of the production line e.g. filters and sulphur scrubbers) rather than cleaner production technologies, very often required by law already. The phase-out was then successfully continued until tax credit’s complete elimination in January 2011. Unexpectedly, in March of 2011, this tax credit was re-introduced for 4 more years at the stable rate of 8% investment level. It was possibly done to mediate the effect of the financial crises on the industrial sectors. Figure 1. shows the chronology of events and the expected versus actual rates of the tax credit.
What makes this EI tax credit reform especially interesting is that it generated a lot of confusion until the very last moment and while introduced in March 2011 – it was done specifically with the intention to favor cleaner production over end-of-pipe technologies. In the analysis, I focus on industrial firms as the main beneficiaries of the program and consider the time period between 2008 and 2014. In the first part, I compare firms’ behavior before and after the change in this policy instrument using difference-in-difference analysis. This will show if the modification of the tax credit discouraged end-of-pipe technologies as well as how the policy reform affected green employment. In the second part of the analysis, by using instrumental variable approach with difference-in-difference, I examine the proportionate effect of an increase in the amount of the tax credit. I study its proportional effect on firms’ investment, employment and R&D outcomes. Thus, I perform the first quasi-experimental econometric analysis of the effectiveness of EI tax credit at encouraging adoption of green technologies directly, but also indirect green employment and green R&D effects.
Results
I find evidence that firms did in fact decrease their investment through the tax credit in the end-of-pipe technologies as a result of the policy change. This also includes the technologies specifically reducing air-pollution alone such as filters/sulphur scrubbers. We can, therefore, conclude that the modification was implemented quite successfully. That being said, there is no evidence to support the claim that this policy change led to an increase in the investment in cleaner production technologies. Unfortunately, the policy change also had a few unexpected indirect effects. It appears that firms reduced the number of green employees as well as the expenditure associated with the salaries of green employees, as can be seen in Figures 2a and 2b.
After performing the heterogeneous analyses, it is also clear that firms responded differently depending on their size – Figures 3a and 3b. More specifically, small firms seem to have benefited the most from the policy change, by considerably increasing their investment in cleaner production technologies. The opposite has happened to the large firms, who decreased their investment in the cleaner production technologies through the modified tax incentive.
By studying the proportional effect of the EI tax credit on investment outcomes it becomes apparent that Spanish environmental investment tax incentive was generally successful at inducing all types of green investment. This means that even during times of financial crises tax credit was drove firms’ green investment. However, they favored air-pollution-reducing over energy-efficient technologies, not necessarily end-of-pipe over cleaner production technologies, as per the concern of the government at the time. Additionally, I find further evidence that the increase in the amount of environmental investment tax credit results in a proportionate increase in the number of green employees and even private environmental R&D. Those indirect effects are quite hopeful, showing that a successful EI tax credit can also drive employment and create positive externalities through R&D.
Policy
This analysis provides a multitude of important implications for policy makers. Firstly, it encourages the usage of EI tax credits, which is also in agreement with previous literature, especially the work done by Ohrn (2019). However, this is in stark contrast to the decision of the Spanish government to eliminate this fiscal incentive from the Spanish Corporate Income Tax completely. This analysis supports its continuous use and perhaps even further modification, rather than a complete phase-out.
What we can learn from this green tax incentive is quite straightforward – adopting green depreciation incentives leads to increased business incentives and green employment outcomes, even during times of economic downturn. Additionally, the government can be successful at modifying the existing tax incentives, such that they discourage those technology choices that the central government considers undesirable. While the results clearly indicate that the tax credit should have been redefined even further so as to also encourage more investment in cleaner production technologies, this empirical work does not justify its complete phase-out. The fact that there is an increased investment in cleaner production technologies for smaller firms is also very important, as those are exactly the companies frequently faced with capital market failure – especially in the time of financial recession such as this one.
Of course, more research is needed to assess whether these types of incentives are the most efficient way to improve firms’ economic outcomes, and how the tax credit also affected their employees over the short- and long-run – especially after the complete elimination of the tax credit in 2015. Lastly, even given the financial burden that tax deductions and subsidies entail, they might still be economically justified in some cases. For instance, when positive externalities appear, such as increased green private R&D, which is the case here.
References
Ohrn, E. (2019). The effect of tax incentives on US manufacturing: Evidence from state accelerated depreciation policies. Journal of Public Economics, 180, 104084.
Steffi gave an interview to CEPR’s Tim Phillips about the team’s research:
Policies to avoid zombification of the economy
In an accompanying VoxEU column, the authors discuss the risks that government responses to COVID-19 could “zombify” the economy.
“A representative consumer survey in five EU countries indicates that many consumers do not miss certain goods and services they have cut down on since the COVID-19 outbreak,” the authors explain in their column. “Fiscal policy must recognise that some firms will become obsolete in the altered post-COVID-19 environment. To achieve a swift recovery, these obsolete firms must be allowed to fail fast so that resources can be reallocated to more efficient uses. Instead, fiscal support should be laser-like in targeting those households who are particularly hard hit by the crisis. Such support should be oriented towards helping displaced workers retrain and find new jobs.”
Prospective economic developments depend on the behavior of consumer spending. A key question is whether private expenditures recover once social distancing restrictions are lifted or whether the COVID-19 crisis has a sustained impact on consumer confidence, preferences, and, hence, spending. Changes in consumer behavior may not be temporary, as they may reflect long-term changes in attitudes arising from the COVID-19 experience. This paper uses data from a representative consumer survey in five European countries conducted in summer 2020, after the release of the first wave’s lockdown restrictions. We document the underlying reasons for households’ reduction in consumption in five key sectors: tourism, hospitality, services, retail, and public transports. We identify a large confidence shock in the Southern European countries and a permanent shift in consumer preferences in the Northern European countries. Our results suggest that horizontal fiscal support to all firms risks creating zombie firms and would hinder necessary structural changes to the economy.
Alexander Hodbod ’12 (International Trade, Finance, and Development). Counsellor to ECB Representative to the Supervisory Board, European Central Bank (DGSGO-SO), Frankfurt, Germany.
Cars Hommes. Professor of Economic Dynamics at CeNDEF, Amsterdam School of Economics, University of Amsterdam, and research fellow of the Tinbergen Institute, Amsterdam, The Netherlands, Senior Research Director (Financial Markets Department), Bank of Canada.
Stefanie J. Huber ’10 (Economics). Assistant Professor at CeNDEF, Amsterdam School of Economics, University of Amsterdam, and research candidate fellow of the Tinbergen Institute, Amsterdam, The Netherlands.
Isabelle Salle. Principal Researcher at the Bank of Canada (Financial Markets Department), research fellow at the Amsterdam School of Economics, University of Amsterdam, and research fellow of the Tinbergen Institute, Amsterdam, The Netherlands.
For patients infected by Covid-19, underlying health conditions are often cited as a source of increased vulnerability, of which exposure to high levels of air pollution has proven to be an exacerbating cause. We investigate the effect of long-term pollution exposure on Covid-19 mortality, admissions to hospitals and admissions to intensive care units in France. Using cross-sectional count data at the local level, we fit mixed effect negative binomial models with the three Covid-19 measures as dependent variables and atmospheric PM2.5 concentration (µg/m3) as an explanatory variable, while adjusting for a large set of potential confounders. We find that a one-unit increase in PM2.5 concentration raised on average the mortality rate by 22%, the admission to ICU rate by 11% and the admission to hospital rate by 14% (rates with respect to population). These results are robust to a large set of sensitivity analyses. As a novel contribution, we estimate tangible marginal costs of pollution, and suggest that a marginal increase in pollution resulted on average in 61 deaths and created a 1 million euro surcharge in intensive care treatments over the investigated period (March 19th – May 25th).
Conclusions
The study is a strong indication that air pollution is a crucial environmental factor in mortality risks and vulnerability to Covid-19. The health risks associated with air pollution are well documented, but with Covid-19 in the spotlight we hope to increase awareness of the threat caused by pollution, not only through direct increased health risks, but also through external factors, such as pandemics.
We show the aggravating effect of long-term pollution exposure to three levels of severity of Covid-19 symptoms in France: admission to hospitals for acute Covid-19 cases, admission to intensive care units for the most severe vital organ failures, and fatalities (all expressed per 100,000 inhabitants). Using cross-sectional data at the départemental (sub-regional) level, we fit mixed effect negative binomial models with the three Covid-19 measures as dependent variables and the average level of atmospheric concentration of PM2.5 (µg/m3) as an explanatory variable. We adjust for a set of 18 potential confounders to isolate the role of pollution in the spread of the Covid-19 disease across départements. We find that a one-unit increase in average PM2.5 levels increases on average the mortality rate by 22%, the admission to ICU rate by 11% and the admission to hospital rate by 14%. These results are robust to a set of 24 secondary and sensitivity analyses per dependent variable, confirming the consistency of the findings across a wide range of specifications.
We further provide numerical – and hence more tangible – estimates of the marginal costs of pollution since March 19th. Adjusting for under-reporting of Covid-19 deaths, we estimate that long-term exposure to pollution marginally resulted in an average 61 deaths across French départements. Moreover, based on average daily costs of intensive care treatments, we estimate that pollution induced an average 1 million euros in costs borne by hospitals treating severe symptoms of Covid-19. These figures strongly suggest that areas with greater air pollution faced substantially higher casualties and costs in hospital services, and raise concerns about misallocation of resources to the healthcare system in more polluted areas.
Our paper provides precise estimates and a reproducible model for future work, but is limited by the novelty of the phenomenon at the centre of the study. Our empirical investigation is restricted to the scope of France alone due to cross-border inconsistencies in Covid-19 data collection and reporting. Once Covid-19 data reporting is complete and consistent, we hope future studies will examine the effects of air pollution at a greater scale, or in greater detail. On the other hand, more disaggregated data – at the individual or hospital level – would allow more precise estimates and a better understanding of key factors of Covid-19 health risks and would also allow the use of surface-measured air pollution. Measured pollution data is available for France, but is inherently biased when aggregated at the départemental level, due to lack of territorial coverage. If precise data tracking periodic Covid-19 deaths becomes available for a wider geographic region, we specifically recommend a MENB panel regression incorporating a PCFE for spatially correlated errors. This will produce the most accurate estimates.
Going forward, more accurate and granular data should motivate future research to uncover the exact financial costs attributable to air pollution during the pandemic. Precise estimation of costs of Covid-19 treatments and equipment (e.g. basic protective equipment for personnel or resuscitation equipment), should feature in a more accurate cost analysis. Hospital responses should be thoroughly analysed to understand the true cost of treatments across all units.
It is crucial that the healthcare costs of pollution are globally recognised so that future policy decisions take them into account. Ultimately, this paper stresses that failure to manage and improve ambient air quality in the long run only magnifies future burdens on healthcare resources, and cause more damage to human life. During a global pandemic, the costs of permitting further air pollution appears ever more salient.
The relationship between institutions and development is a long-standing topic in economic research. However, economists have tended to only evaluate formal institutions (such as laws and property rights), neglecting the informal (like conventions and norms). This overspecialisation precludes the analysis of ideas and ideologies. Without considering these abstract drivers of development, the space for ethically and politically dangerous explanations of success appears (such as for genetic reasons).
Contrary to recent literature, I argue that informal constraints are actually the basis of institutions and therefore the real generators of growth and development. I show this by examining revolutions – the cauldrons where new systems, ideas, and conventions begin, and old ones end.
The illusion of separation
Scholars of comparative development have noted the increasing divergence between developed and developing countries: the gap between Northern and Southern Europe and the underdevelopment of the Middle East and sub-Saharan Africa being major examples. Several theories attempt to explain this divergence, considering possible factors such as geographical characteristics and institutional differences. Notably, comparative development has even been attributed to levels of genetic diversity (Ashraf & Galor, 2013).
In particular, the crucial historical link between institutions and development is well known. Famous examples include the advantage of limited royal power (Acemoglu, 2005); reformed constitutional arrangements and strengthened property rights (North & Weingast, 1989); and the balance of power between merchants and princes (De Long, 1993). Yet, these studies put their emphasis on formal rules, neglecting the norms, ideas and ideologies that underwrite them.
The latter are fundamental elements of institutions as they influence formal rules. In a seminal contribution to institutional economics, North (1994) distinguishes between two forms of institutions: formal rules (constitutions, laws, property rights etc.) and informal constraints (norms of behaviour, conventions, self-imposed codes of conduct etc.). In a later work, he argued that institutions evolve incrementally and successively over time (North, 1991). When those two forms are approached as two separated sources of institutions, the role of informal constraints in institutional evolution will be missed, throwing a veil over a core aspect of institutions and leading us to fallacious conclusions about the key determinants of growth and development.
Similarly, Karl Popper (1945) distinguishes an open society from a closed society based on whether a distinction exists between normative laws and natural laws. Where there is none – what Popper calls a closed “tribal society”– taboos and conventions act as if they were natural law. This gives them a powerful role in society and a fundamental role in development. By creating formal laws, societies recognise the distinction between norms and natural laws, weakening the effect of conventions (although, as we will see, they still act through both formal and informal laws).
Both North and Popper agree on this chronological development of institutions meaning a better understanding of causation is needed. Myrdal (1978) convincingly argues that the mechanisms of social systems are determined by an endogenous cycle of causation that affects the distribution of power in a society and economic, social and political stratification). This means that a change in informal constraints will alter formal rules, which will then return to affect the former. Therefore the scaffolding of institutions consists of norms of behaviour, conventions and self-imposed codes of conduct.
The revolutionary crevasse
Just as a crevasse provides a glimpse deep into the ice, revolutions open a window to the creation and destruction of social systems. Revolutions are beloved by social scientists (especially in institutional economics) as they provide natural experiments to investigate causal effects. They can shed some light on the importance of norms and convention, as well as their relationship with ideas, ideologies and leaders.
In the literature, for example, Acemoglu et al. (2008) and North & Weingast (1989) have respectively examined the impact of the French Revolution on development and the Glorious Revolution on institutional structure. However, these types of studies have focused only on the secondary changes (in laws and property rights) instead of the initial causes of change (norms and conventions). In this regard, a re-evaluation of revolutions and their characteristics is necessary to observe the initial changes.
Let us first consider which elements prepare amenable conditions for the emergence of revolutions. Gottschalk (1944) identifies three broad factors:
demand for change stemming from (a) personal discontent and (b) social dissatisfaction
hopefulness derived from (a) popular programs of reform and (b) a leader
weakness of the conservative forces – perhaps the most important.
Demand comes from widespread provocations (corruption, taxation, poor infrastructure etc.) which generate social dissatisfaction. Yet, demand by itself is not sufficient for the revolution. Some hope of success is also needed. This comes from programs of reform, as provided by the Voltaires and Rousseaus, the Lockes and Ademses, and the Marxes and Kropotkins (Gottschalk, 1994). However, tuneless emphasis on widespread provocations that are based on the formal rules underestimates the phycological mechanisms that are mainly based on informal constraints.
Personal discontent (arising for idiosyncratic reasons) only appears at the individual level yet plays an essential role in generating the leaders of revolutions. These leaders then support the new-born ideas and ideologies based on the program of reform which has a multiplier effect by coherently spreading revolutionary sentiment. This is crucial once we think of revolutions as risky events over which individuals have varying valuations of the possible outcomes. Gneezy et al. (2006) show that individuals, faced with a complex choice, may choose to stay in the old system if they value the risky benefits of revolution less than the worst outcomes of rebelling. However, once the revolutionary “lottery” is based on intellectuals’ programs of reforms and explained by leaders it becomes easier to code.
In this way, agents facing complex task (in this case revolution), might act following the leader through many of the channels identified by behavioural economics such as Tversky and Kahneman’s simple heuristics, Walker and Wooldridge’s conventions and Shiller’s narratives. These share common features which affect the majority’s decision-making processes – especially when tasks are complex.
This process is essential to notice the importance of informal constraints and how they become formal rules since leaders are the symbol of ideologies and ideas. As Axelrod indicates norms precede laws and laws strengthen norms. After the success of the revolution laws strengthen the norms through formalization. And after social conventions are entrenched, they become thoughtlessly accepted by individuals (Epstein, 2001).
As with the example of revolutions, before a change in the formal rules, an ideological revolution has occurred when intellectuals provide the programs of reforms. The ideas become conventions during the revolution, changing societal expectations. Notions of equality and liberty – in the case of the French Revolution – became the convention as the system was upended. The relationships between ideas, ideologies, norms and leaders encourage us to take them into account when evaluating growth and development.
Conclusion
I have argued that ideas, norms and ideologies are the initial drivers of development and have had an immense effect on our civilizations. However, traditional political economy’s overemphasis of formal rules fails to capture this. The insularity of this approach is highlighted by examining Revolutions, which provide evidence in favour of more inclusive definitions of institutions and the importance of ideas, ideologies and leaders in creating social systems. Therefore, I contend that a more holistic approach to analysing development is required otherwise alternative and ill-founded explanations of growth with remain.
References
Acemoglu, D., Cantoni, D., Johnson, S., & Robinson, J. A. (2008). From ancien regime to capitalism: the French Revolution as a natural experiment. Natural Experiments…, op. cit, 221-256.
Acemoglu, D., Johnson, S., & Robinson, J. A. (2005). The rise of Europe: Atlantic trade, institutional change, and economic growth. American Economic Review, 95(3), 546-579.
Ashraf, Q., & Galor, O. (2013). The ‘Out of Africa’ hypothesis, human genetic diversity, and comparative economic development. American Economic Review, 103(1), 1-46.
Axelrod, R. (1986). An evolutionary approach to norms. The American Political Science Review, 1095-1111.
De Long, J. B., & Shleifer, A. (1993). Princes and merchants: European city growth before the industrial revolution. The Journal of Law and Economics, 36(2), 671-702.
Epstein, J. M. (2001). Learning to be thoughtless: Social norms and individual computation. Computational economics, 18(1), 9-24.
North, D. C. (1991). Institutions. Journal of Economic Perspectives, 5(1), 97-112.
North, D. C. (1994). Economic performance through time. The American Economic Review, 84(3), 359-368.
North, D. C., & Weingast, B. R. (1989). Constitutions and commitment: the evolution of institutions governing public choice in seventeenth-century England. The Journal of Economic History,49(4), 803-832.
Popper, K. R. (1945). The open society and its enemies. Routledge, London.
Myrdal, G. (1978). Institutional economics. Journal of Economic Issues, 12(4), 771-783.
Gottschalk, L. (1944). Causes of revolution. American Journal of Sociology, 50(1), 1-8.
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