This paper studies the effect of corporate and personal taxes on innovation in the United States over the twentieth century. We use three new datasets: a panel of the universe of inventors who patent since 1920; a dataset of the employment, location and patents of firms active in R&D since 1921; and a historical state-level corporate tax database since 1900, which we link to an existing database on state-level personal income taxes. Our analysis focuses on the impact of taxes on individual inventors and firms (the micro level) and on states over time (the macro level). We propose several identification strategies, all of which yield consistent results: i) OLS with fixed effects, including inventor and state-times-year fixed effects, which make use of differences between tax brackets within a state-year cell and which absorb heterogeneity and contemporaneous changes in economic conditions; ii) an instrumental variable approach, which predicts changes in an individual or firm's total tax rate with changes in the federal tax rate only; iii) event studies, synthetic cohort case studies, and a border county strategy, which exploits tax variation across neighboring counties in different states. We find that taxes matter for innovation: higher personal and corporate income taxes negatively affect the quantity and quality of inventive activity and shift its location at the macro and micro levels. At the macro level, cross-state spillovers or business-stealing from one state to another are important, but do not account for all of the effect. Agglomeration effects from local innovation clusters tend to weaken responsiveness to taxation. Corporate inventors respond more strongly to taxes than their non-corporate counterparts.
We design and conduct large-scale surveys and experiments in six countries to investigate how natives perceive immigrants and how these perceptions influence their preferences for redistribution. We find strikingly large misperceptions about the number and characteristics of immigrants: in all countries, respondents greatly overestimate the total number of immigrants, think immigrants are culturally and religiously more distant from them, and are economically weaker -- less educated, more unemployed, and more reliant on and favored by government transfers -- than is the case. Given the very negative baseline views that respondents have of immigrants, simply making them think about immigration before asking questions about redistribution, in a randomized manner, makes them support less redistribution, including actual donations to charities. Information about the true shares and origins of immigrants is ineffective, and mainly acts as a prime that makes people think about immigrants and reduces their support for redistribution. An anecdote about a ``hard working'' immigrant is somewhat more effective, suggesting that when it comes to immigration, salience and narratives shape people's views more deeply than hard facts.
An inventor's own knowledge is a key input in the innovation process. This knowledge can be built by interacting with and learning from others. This paper uses a new large-scale panel dataset on European inventors matched to their employers and patents. We document key empirical facts on inventors' productivity over the life cycle, inventors' research teams, and interactions with other inventors. Among others, most patents are the result of collaborative work. Interactions with better inventors are very strongly correlated with higher subsequent productivity. These facts motivate the main ingredients of our new innovation-led endogenous growth model, in which innovations are produced by heterogeneous research teams of inventors using inventor knowledge. The evolution of an inventor's knowledge is explained through the lens of a diffusion model in which inventors can learn in two ways: By interacting with others at an endogenously chosen rate; and from an external, age-dependent source that captures alternative learning channels, such as learning-by-doing. Thus, our knowledge diffusion model nests inside the innovation-based endogenous growth model. We estimate the model, which fits the data very closely, and use it to perform several policy exercises, such as quantifying the large importance of interactions for growth, studying the effects of reducing interaction costs (e.g., through IT or infrastructure), and comparing the learning and innovation processes of different countries.
We study how strongly individuals respond to tax simplicity and how they learn about the complexities of the tax system. We focus on the self-employed, who can more easily adjust to tax incentives and whose responses directly stem from their own understanding of the tax system. We use new French tax returns data from 1994 to 2012. France serves as a good quasi-laboratory: it has three fiscal regimes -- or modes of taxation-- for the self-employed, which differ in their monetary tax incentives and in their tax simplicity. Two key features are that, first, these regimes are subject to eligibility thresholds; we find large excess masses (bunching) right below the latter. Second, the regimes impact different agents heterogeneously and have changed extensively over time. Taken together, these two key elements give us measures of tax responses (the bunching) as well as the variation needed to jointly estimate a value of tax simplicity and taxable income elasticities. They also give us an opportunity to study how individuals learn about and respond over time to changing policy parameters. We estimate a large value for tax simplicity of up to 650 euros per year per individual depending on the regime and activity. We also find sizable costs of tax complexity; agents are not immediately able to understand what the right regime choice is, leave significant money on the table, and learn over time. The cost of complexity is ``regressive'' in that it affects mostly the uneducated, low income, and low skill agents. Agents who can be viewed as more informed and knowledgable (e.g., the more educated or high-skilled) are more likely to make the correct regime choice and to learn faster.
We study the optimal design of corporate taxation and R&D policies as a dynamic mechanism design problem with spillovers. Firms are heterogeneous in their research productivity, i.e., in the efficiency with which they convert a given set of R&D inputs into successful innovations and that research productivity is private information. There are non-internalized technological spillovers across firms, but the asymmetric information prevents correcting them in the first best way. We highlight that key parameters for the optimal policies are i) the relative complementarities between observable R&D investments, unobservable R&D inputs, and firm research productivity, ii) the dispersion and persistence of firms' research productivities, and iii) the magnitude of technological spillovers across firms.
We estimate our model using firm-level data matched to patent data and quantify the optimal policies. In the data, high research productivity firms get disproportionately higher returns to R&D investments than lower productivity firms. Very simple innovation policies, such as linear corporate taxes combined with a nonlinear R&D subsidy -- that provides lower marginal subsidies at higher R&D levels -- can do almost as well as the full unrestricted optimal policies. Our formulas and theoretical and numerical methods are more broadly applicable to the provision of firm incentives in dynamic settings with asymmetric information and spillovers and to firm taxation more generally.
This paper considers a dynamic taxation problem when agents can allocate their time between working and investing in their human capital. Time investment in human capital, or ``training,'' increases the wage and can interact with an agent's intrinsic, exogenous, and stochastic earnings ability. It also interacts with both current and future labor supply and there can be either ``learning-and-doing'' (when labor and training are complements) or ``learning-or-doing'' (when labor and training are substitutes). Agents' abilities and labor supply are private information to them, which leads to a dynamic mechanism design problem with incentive compatibility constraints. At the optimum, the subsidy on training time is set so as to balance the total labor supply effect of the subsidy and its distributional consequences. In a one-period version of the model, particularly simple relations arise at the optimum between the labor wedge and the training wedge that can also be used to test for the Pareto efficiency of existing tax and subsidy systems. In the limit case of learning-by-doing (when training is a direct by-product of labor) or in the case in which agents who are more able at work are also more able at training, there are important modifications to the labor wedge.
This paper considers dynamic optimal income, education, and bequest taxes in a Barro-Becker dynastic setup. Parents can transfer resources to their children in two ways: First, through education investments, which have heterogeneous and stochastic returns for children, and, second, through financial bequests, which yield a safe, uniform return. Each generation's productivity and preferences are subject to idiosyncratic shocks. I derive optimal linear formulas for each tax, as functions of estimable sufficient statistics, robust to underlying heterogeneities in preferences, and at any given level of all other taxes. It is in general not optimal to make education expenses fully tax deductible and the optimal education subsidy, income tax and bequest tax can, but need not, move together at the optimum. I also show how to derive optimal formulas using ``reform-specific elasticities'' that can be targeted to empirical estimates from existing reforms. I extend the model to an OLG model with altruism to study the effects of credit constraints on optimal policies. Finally, I solve for the fully unrestricted policies and show that, if education is highly complementary to children's ability, it is optimal to distort parents' trade-off between education and bequests and to tax education investments relative to bequests.
This paper reviews recent advances in the study of dynamic taxation, considering three main approaches: the dynamic Mirrlees, the parametric Ramsey, and the sufficient statistics approaches. In the first approach, agents' heterogeneous abilities to earn income are private information and evolve stochastically over time. Dynamic taxes are not ex ante restricted and are set for redistribution and insurance considerations. Capital is taxed only in order to improve incentives to work. Human capital is optimally subsidized if it reduces post-tax inequality and risk on balance. The Ramsey approach specifies ex ante restricted tax instruments and adopts quantitative methods, which allows it to consider more complex and realistic economies. Capital taxes are optimal when age-dependent labor income taxes are not possible. The newer and tractable sufficient statistics approach derives robust tax formulas that depend on estimable elasticities and features of the income distributions. It simplifies the transitional dynamics thanks to a newly defined criterion, the ``utility-based steady state approach" that prevents the government from exploiting sluggish responses in the short-run. Capital taxes are here based on the standard equity-efficiency trade-off.
Americans are polarized not only in their views on policy issues and attitudes towards government and society, but also in their perceptions of the same, factual reality.
In this paper we conceptualize how to think about the ``polarization of reality’’ and review recent papers that show that Republicans and Democrats (as well as Trump and non-Trump voters since 2016) view the same reality through a very different lens. Perhaps as a result, they hold different views about policies and what should be done to address different economic and social issues. We also show that providing information leads to different reassessments of reality and different responses along the policy support margin, depending on one's political leanings.This is not about having different attitudes about economic or social phenomena or policies that could justifiably be viewed differently from different angles. What is striking is to have different perceptions of realities that can be factually checked.
This paper provides a simple conceptual framework that captures how different perceptions, attitudes, and biases about immigrants or minorities can shape preferences for redistribution and reviews the empirical evidence on the effects of increasing racial diversity and immigration on support for redistribution.
In this article, we review a growing empirical literature on the effects of personal taxation on the geographic mobility of people and discuss its policy implications. We start by laying out the empirical challenges that prevented progress in this area until recently, and then discuss how recent work have made use of new data sources and quasi-experimental approaches to credibly estimate migration responses. This body of work has shown that certain segments of the labor market, especially high-income workers and professions with little location-specific human capital, may be quite responsive to taxes in their location decisions. When considering the implications for tax policy design, we distinguish between uncoordinated and coordinated tax policy. We highlight the importance of recognizing that mobility elasticities are not exogenous, structural parameters. They can vary greatly depending on the population being analyzed, the size of the tax jurisdiction, the extent of tax policy coordination, and a range of non-tax policies. While migration responses add to the efficiency costs of redistributing income, we caution against over-using the recent evidence of (sizeable) mobility responses to taxes as an argument for less redistribution in a globalized world.
This paper develops a theory of optimal capital taxation that expresses optimal tax formulas in sufficient statistics. We first consider a simple model with utility functions linear in consumption and featuring heterogeneous utility for wealth. In this case, there are no transitional dynamics, the steady-state is reached immediately and has finite elasticities of capital with respect to the net-of-tax rate. This allows for a tractable optimal tax analysis with formulas expressed in terms of empirical elasticities and social preferences that can address many important policy questions. These formulas can easily be taken to the data to simulate optimal taxes, which we do using U.S. tax return data on labor and capital incomes. Second, we show how these results can be extended to the case with concave utility for consumption. The same types of formulas carry over by appropriately defining elasticities. We show that one can recover all the results from the simpler model using a new and non standard steady state approach that respects individual preferences even with a fully general utility function or uncertainty.
Using new cross-country survey and experimental data, we investigate how beliefs about intergenerational mobility affect preferences for redistribution in France, Italy, Sweden, the U.K., and the U.S.. Americans are more optimistic than Europeans about social mobility. Our randomized treatment shows pessimistic information about mobility and increases support for redistribution, mostly for ``equality of opportunity'' policies. We find a strong political polarization. Left-wing respondents are more pessimistic about mobility, their preferences for redistribution are correlated with their mobility perceptions, and they support more redistribution after seeing pessimistic information. None of these apply to right-wing respondents, possibly because they see the government as a ``problem'' and not as the ``solution.''
This paper derives optimal income tax and human capital policies in a life cycle model with risky human capital. The government faces asymmetric information regarding agents’ ability, its evolution, and labor supply. When the wage elasticity with respect to ability is increasing in human capital, the optimal subsidy involves less than full deductibility of human capital expenses on the tax base, and falls with age. Income contingent loans or a deferred deductibility scheme can implement the optimum. Numerical results suggest that full deductibility of expenses is close to optimal and that simple linear age-dependent policies perform very well.
This paper studies the effect of top tax rates on inventors' international mobility since 1977. We put special emphasis on ``superstar'' inventors, those with the most abundant and most valuable patents. We use panel data on inventors from the United States and European Patent Offices to track inventors' locations over time and combine it with international effective top tax rate data. We construct a detailed set of proxies for inventors' counterfactual incomes in each possible destination country including, among others, measures of patent quality and technological fit with each potential destination. Exploiting the differential impact of changes in the top tax rate on inventors of different qualities, we find that superstar top 1% inventors are significantly affected by top tax rates when deciding where to locate. The elasticity to the net-of-tax rate of the number of domestic superstar inventors is relatively small, around 0.03, while the elasticity of the number of foreign superstar inventors is around 1. Inventors who work in multinational companies are more likely to take advantage of tax differentials. On the other hand, if the company of an inventor has a higher share of its research activity in a given country, the inventor is less sensitive to the tax rate in that country.
This paper proposes a new way to evaluate tax reforms, by aggregating losses and gains of different individuals using ``generalized social marginal welfare weights." A tax system is optimal if no budget neutral small reform can increase the weighted sum of (money metric) gains and losses across individuals. Optimum tax formulas take the same form as standard welfarist tax formulas by simply substituting standard marginal social welfare weights with those generalized marginal social welfare weights. Weights directly capture society's concerns for fairness allowing us to cleanly separate individual utilities from social weights. Suitable weights can help reconcile discrepancies between the welfarist approach and actual tax practice, as well as unify in an operational way the most prominent alternatives to utilitarianism such as Libertarianism, Equality of Opportunity, or Poverty alleviation.
We develop online survey experiments to analyze how information about inequality and taxes affects preferences for redistribution. Approximately 4,000 respondents were randomized into treatments providing interactive, customized information on U.S. income inequality, the link between top income tax rates and economic growth, and the estate tax. An additional 6,000 respondents were randomized into follow-up treatments to explore mechanisms underlying the initial results. The treatment has very large effects on whether respondents view inequality as a problem. By contrast, it only slightly moves policy preferences (e.g., top income tax rates and transfer programs). An exception is the estate tax---informing respondents of the small share of decedents who pay it more than doubles support for it and this effect persists in a one-month follow-up. We explore several explanations for our results. Extreme ex-ante misinformation appears to drive the large estate tax results. The small effects for all other policies can be at least partially explained by respondents' low trust in government---indeed, we show that priming people to think negatively about the government substantially reduces support for transfer programs---as well as a disconnect between concerns about social issues and the public policies that aim to address them.