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' perceptions of immigrants influence their preferences for redistribution. We find strikingly large biases in natives' perceptions of 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, poorer, and more reliant on government transfers -- than is the case. While all respondents have misperceptions, those with the largest ones are systematically the right-wing, the non-college educated, and the low-skilled working in immigration-intensive sectors. Support for redistribution is strongly correlated with the perceived composition of immigrants -- their origin and economic contribution -- rather than with the perceived share of immigrants per se. Given the very negative baseline views that respondents have of immigrants, simply making them think about immigration in a randomized manner makes them support less redistribution, including actual donations to charities. We also experimentally show respondents information about the true i) number, ii) origin, and iii) ``hard work'' of immigrants in their country. On its own, information on the ``hard work'' of immigrants generates more support for redistribution. However, if people are also prompted to think in detail about immigrants' characteristics, then none of these favorable information treatments manages to counteract their negative priors that generate lower support for redistribution.
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 R&D policies and corporate taxation as a dynamic mechanism design with externalities using the tools of public economics. 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. There are non-internalized technology spillovers across firms, but the asymmetric information about firms' research productivity prevents the first best solution. We characterize the optimal policies for firms of different sizes and ages. We highlight that key parameters for these policies are i) the relative complementarities between observable R&D investments, unobservable R&D inputs, and firm productivity, and ii) the dispersion and persistence of firm productivity. We estimate our model using firm-level data matched to patent data and quantify the optimal policies. Simpler innovation policies, such as linear R&D subsidies and linear profit taxes, lead to large revenue losses relative to the optimal mechanism. 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.
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 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.
This paper studies optimal linear and nonlinear redistributive income taxation when there is adverse selection in the labor market. Unlike in standard taxation models, firms do not know workers' abilities and competitively screen them through nonlinear compensation contracts, unobservable to the government, in a Miyazaki-Wilson-Spence equilibrium. Adverse selection leads to different optimal tax formulas than in the standard Mirrlees (1971) model, because of the use of work hours as a screening tool by firms, which for higher talent workers results in a "rat race" and for lower talent workers in informational rents and cross-subsidies. The most surprising result is that, if the government has sufficiently strong redistributive goals, welfare is higher when there is adverse selection than when there is not. Policies that endogenously affect adverse selection are discussed. The model has practical implications for the interpretation, estimation, and use of taxable income elasticities, which are central to optimal tax design.
This paper presents a model of optimal labor income taxation in which top incomes respond to marginal tax rates through three channels: (1) standard labor supply, (2) tax avoidance, (3) compensation bargaining. We derive the optimal top tax rate formula as a function of the three corresponding behavioral elasticities. The first elasticity (labor supply) is the sole real factor limiting optimal top tax rates. The optimal tax system should be designed to minimize the second elasticity (avoidance) through tax enforcement and tax neutrality across income forms. The optimal top tax rate increases with the third elasticity (bargaining) as bargaining efforts are zero-sum in aggregate. We provide evidence on this elasticity using cross-country times series macro-evidence and CEO pay micro-evidence. The macro-evidence from 18 OECD countries shows that there is a strong negative correlation between top tax rates and top 1% income shares since 1960, implying that the overall elasticity is large. However, top income share increases have not translated into higher economic growth. US CEO pay evidence shows that pay for luck is quantitatively more important when top tax rates are low. International CEO pay evidence shows that CEO pay is strongly negatively correlated with top tax rates even controlling for firm characteristics and performance, and this correlation is stronger in firms with poor governance. These results suggest that bargaining effects play a role in the link between top incomes and top tax rates, implying that optimal top tax rates could be higher than commonly assumed.
Note: This is a long and comprehensive job market paper version and has now been replaced by two papers: ''Learning and (or) doing: Human Capital Investments and Optimal Taxation" and "Optimal Taxation and Human Capital Policies over the Life Cycle."