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.
New paper in Journal of Empirical Legal Studies co-authored by Ria Ivandić ’13 (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.
Jakob Poerschmann ’21 explains how to teach your regression the distinction between relevant outliers and irrelevant noise
Jakob Poerschmann ’21 (Data Science) has written an article called “Stop Dropping Outliers! 3 Upgrades That Prepare Your Linear Regression For The Real World” that was recently posted on Towards Data Science.
The real world example he uses to set up the piece will resonate with every fan of FC Barcelona (and probably scare them, too):
You are working as a Data Scientist for the FC Barcelona and took on the task of building a model that predicts the value increase of young talent over the next 2, 5, and 10 years. You might want to regress the value over some meaningful metrics such as the assists or goals scored. Some might now apply this standard procedure and drop the most severe outliers from the dataset. While your model might predict decently on average, it will unfortunately never understand what makes a Messi (because you dropped Messi with all the other “outliers”).
The idea of dropping or replacing outliers in regression problems comes from the fact that simple linear regression is comparably prone to extremes in the data. However, this approach would not have helped you much in your role as Barcelona’s Data Scientist. The simple message: Outliers are not always bad!
Dig into the full article to find out how to prepare your linear regression for the real world and avoid a tragedy like this one!
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.
Patrick Altmeyer, Eduard Gimenez, Simon Neumeyer and Jakob Poerschmann ’21 competed against 57 teams from 14 countries.
The Novartis Datathon is a Data Science competition taking place annually, usually in Barcelona. In 2020, the Barcelona GSE team “Non-Juvenile Asymptotics” consisting of Eduard Gimenez, Patrick Altmeyer, Simon Neumeyer and Jakob Poerschmann won third place after a fierce competition against 57 teams from 14 countries all over the globe. While the competition is usually hosted in Barcelona, the Covid-friendly version was fully remote. Nevertheless, the increased diversity of teams clearly made up for the missed out atmosphere.
This year’s challenge: predict the impact of generic drug market entry
The challenge of interest concerned predicting the impact of generic drug market entry. The risk of losing ground against cheaper drug replicates once the patent protection runs out is evident for pharmaceutical companies. The solutions developed helped solving exactly this problem, making drug development much easier to plan and calculate.
While the problem could have been tackled in various different ways, the Barcelona GSE team focused on initially developing a solid modeling framework. This represented a risky extra effort in the beginning. In fact more than half of the competition period passed without any forecast submission by the Barcelona GSE team. However, the initial effort clearly paid off: as soon as the obstacle was overcome, the “Non-Juvenile Asymptotics” were able to benchmark multiple models at rocket speed.
Fierce competition until the very last minute
The competition was a head-to-head race until the last minute. Still in first place until minutes before the final deadline, the predictions of two teams from Hungary and Spain ended up taking the lead by razor sharp margins.
Article by Dennis Hutschenreiter, Tommaso Santini, and Eugenia Vella
In this paper, we study the sectoral reallocation of employment due to automation in Germany. Empirical evidence by Dauth et al. (2021) shows that robot adoption has induced a shift of employment from the manufacturing to the service sector, leaving total employment unaffected. We rationalize this evidence through the lens of a two-sector, general equilibrium model with matching frictions and endogenous participation.
Few papers have studied the effect of automation on employment in a multi-sectoral model. Berg et al. (2018) argue that the inclusion of a non-automatable sector amplifies the difference between the effect of automation on low- and high-skill workers. Sachs et al. (2019) also include a non-automatable sector in an overlapping generations model. They study the possibility of one generation improving their welfare at future generations’ expense through robot adoption. To the best of our knowledge, we are the first to build a two-sector general equilibrium model with search and matching frictions to analyze the long-run impact of automation on both sectoral and aggregate employment.
We consider a representative household that decides how to allocate its members between non-participants in the labor markets, job-searchers in manufacturing, and job-searchers in services. The household also accumulates capital. On the production side, there is a representative ﬁrm in each sector. The manufacturing ﬁrm decides how many vacancies to post and how much capital to borrow from the household. Automation increases the capital intensity of the technology in the manufacturing sector. This can be motivated by the idea that some work operations, formerly performed by humans, are now executed by robots (Acemoglu and Restrepo (2018)). In the service sector, we assume for simplicity that no capital is needed, and thus the representative firm decides only the number of vacancies to post.
After calibrating our model to the German economy in 1994, we perform steady-state comparative statics to study the long-run impact of automation on sectoral reallocation of employment. As the stock of robots increased by 87% in Germany between 1994 to 2014, we can qualitatively compare the model economy’s reaction to an increase in the degree of automation with the sectoral employment shares we observe in Germany over time.
The right panel of Figure 1 shows the model implied values of sectoral employment levels together with total employment. A higher degree of automation, which we take as a proxy of robot adoption, increases employment in services and decreases it in manufacturing. Despite the adjustments of sectoral employment, total employment remains constant, consistently with the empirical evidence of Dauth et al. (2021). In the left panel of Figure 1, we plot the employment shares in the German economy, which we compute using data from the German Federal Statistical Office (DESTATIS). The model qualitatively replicates the observed pattern in sectoral employment.
To assess how well our model can explain the sectoral reallocation of employment in Germany, we focus on comparing two steady states. These two steady states correspond to the start and end years in the empirical analysis of Dauth et al. (2021). The model predicts a decline of 27% in the ratio of manufacturing employment to service employment, which is reasonably close to the one found in the aggregate data for the German economy, i.e., 32%.
Having shown that the model replicates the sectoral reallocation we observe in the data, we then ask the following question. What determines the extent of sectoral reallocation?
Two main parameters govern the strength of the sectoral reallocation of employment in the model: (1) the elasticity of substitution between capital and labor in the manufacturing sector αand (2) the elasticity of substitution between the outputs of the two sectors χ. Intuitively, in the first case, as α decreases, capital and labor become stronger complements in the production of the manufacturing good. As automation raises the return of capital for a given capital stock, in the long-run this leads to a higher capital stock in the steady state. The stronger the complementarity between the two inputs in manufacturing (i.e., the lower α), the higher is the relative demand for manufacturing workers. Therefore, the sectoral reallocation of employment due to automation is mitigated for lower values of α, as Figure 2 demonstrates.
Concerning the second parameter, we need to distinguish two different effects on production and employment in the service sector. Firstly, since automation leads to a higher accumulation of capital in the long run and, thus, to higher household wealth, this will lead to a higher demand for services, which is a normal good. Secondly, the stronger is the complementarity between the two goods in the economy, the higher is the increase in the demand for services. Consequently, a higher substitutability (i.e., a higher χ) between service and manufacturing goods mitigates the increase in the demand for services and, thus, the sectoral reallocation of employment, as Figure 2 shows.
To sum up, we build a general equilibrium model with an automatable and a non-automatable sector and labor market frictions that is able to rationalize the empirical evidence presented by Dauth et al. (2021) on (i) the substantial sectoral reallocation of employment and (ii) the null-effect on total employment. We show that our calibrated model can reasonably explain the empirical strength of the sectoral reallocation of labor. Furthermore, we analyze which key parameters govern the magnitude of this effect in the model.
An interesting extension of our model would be to include heterogeneous agents and capital-skill complementarity (see e.g. Dolado et al. (2021) and Santini (2021)). With that extended framework, one could study the interplay between automation, sectoral automation, and inequality. We leave this topic for future research.
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.
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.
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.
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.
Ohrn, E. (2019). The effect of tax incentives on US manufacturing: Evidence from state accelerated depreciation policies. Journal of Public Economics, 180, 104084.
This paper explores the effects of austerity measures on labour markets and on income inequality and finds evidence of a mechanism that can mitigate the size of the economic contraction. The results indicate that: (i) Fiscal consolidation causes greater distortions for the youth, hence they deserve a special attention to avoid severe long-term economic costs. (ii) While at first glance transfers cuts seem to be ideal, a careful examination suggests that these policies can jeopardise the success of fiscal consolidation. (iii) Tax hikes, negatively affecting the productive sector, trigger frictions in the labour market that give rise to recessionary effects. (iv) Spending cuts, targeting public sector wages and employment, can endanger the capabilities of the current and future labour force. (v) Lastly, income inequality increases with tax hikes and spending cuts, whereas the muted response to transfers cuts is explained by the reaction of labour demand.
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