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We theoretically illustrate how the aversion to unfairness triggers an unselfish though rational demand for redistribution. This leads the well-off to demand positive tax rates and the \"poor\" to reject extreme progressivity. We prove that the \"rich\" and the \"poor\" adjust their demand for redistribution in opposite ways when their sensitivity to fairness increases: while agents with above average expected income raise their demand for redistribution, agents with below average income lower it. We then provide empirical evidence of these behaviors using a nationally representative survey from Italy. The estimates confirm that a stronger aversion to unfair distributive outcomes is associated with a higher support for redistribution by individuals with high income and to a lower demand for redistribution by those with low income.

Health authorities using cost-effectiveness analysis (CEA) for informing reimbursement decisions on health technologies increasingly require economic evaluations encompassing both CEA and budget impact analysis (BIA). Good Research Practices advocate that the economic and clinical assumptions underlying these analyses are aligned and consistently applied. Nonetheless, CEAs and BIAs often are stand-alone analyses used in different stages of the decision-making process. This article used policy reports and Ministerial correspondence to discuss and elucidate the role of budget impact and its relationship with cost-effectiveness in reimbursement decisions in the Netherlands. The results indicate that CEAs and BIAs are both considered important for informing these decisions. While the requirements regarding CEAs—and application of the associated decision rule—are consistent across the different stages, the same does not hold for BIAs. Importantly, the definition of and evidence on budget impact differs between stages. Some important aspects (e.g. substitution and saving effects) typically are considered in the assessment and appraisal stages but are seemingly not considered in price negotiations and the final reimbursement decision. Further research is warranted to better understand why BIAs are not aligned with CEAs (e.g. in terms of underlying assumptions), vary in form and importance between stages, and do not have a clear relationship with the results of CEAs in the decision-making framework. Improving the understanding of the circumstances under which decision-makers attach a relatively larger or smaller weight to (different aspects of) budget impact may contribute to increasing the transparency, consistency, and optimality of reimbursement decisions in the Netherlands.

We present a novel experimental design to measure honesty and lying. Participants receive a die which they roll privately. Since their payoff depends on the reported roll of the die, the subjects have an incentive to be dishonest and report higher numbers to get a higher payoff. This design has three advantages. First, cheating cannot be detected on the individual level, which reduces potential demand effects. Second, the method is very easy to implement. Third, the underlying true distribution of the outcome under full honesty is known, and hence it is possible to test different theoretical predictions. We find that about 20% of inexperienced subjects lie to the fullest extent possible while 39% of subjects are fully honest. In addition, a high share of subjects consists of partial liars; these subjects lie, but do not report the payoff-maximizing draw. We discuss different motives that explain the observed behavioral pattern.

The grassroots movement that placed environmental justice issues on the national stage around 1980 was soon followed up by research documenting the correlation between pollution and race and poverty. This work has established inequitable exposure to nuisances as a stylized fact of social science. In this paper, we review the environmental justice literature, especially where it intersects with work by economists. First we consider the literature documenting evidence of disproportionate exposure. We particularly consider the implications of modeling choices about spatial relationships between polluters and residents, and about conditioning variables. Next, we evaluate the theory and evidence for four possible mechanisms that may lie behind the patterns seen: disproportionate siting on the firm side, \"coming to the nuisance\" on the household side, market-like coordination of the two, and discriminatory politics and/or enforcement. We argue that further research is needed to understand how much weight to give each mechanism. Finally, we discuss some policy options.

Regional economic divergence has become a threat to economic progress, social cohesion and political stability in Europe. Market processes and policies that are supposed to spread prosperity and opportunity are no longer sufficiently effective. The evidence points to the existence of several different modes of regional economic performance in Europe, responding to different development challenges and opportunities. Both mainstream and heterodox theories have gaps in their ability to explain the existence of these different regional trajectories and the weakness of the convergence processes among them. Therefore, a different approach is required, one that strengthens Europe’s strongest regions but develops new approaches to promote opportunity in industrial declining and less-developed regions. There is ample new theory and evidence to support such an approach, which we have labelled ‘place-sensitive distributed development policy’.

Using Danish administrative data, we study the impacts of children on gender inequality in the labor market. The arrival of children creates a long-run gender gap in earnings of around 20 percent driven by hours worked, participation, and wage rates. We identify mechanisms driving these “child penalties” in terms of occupation, sector, and firm choices. We find that the fraction of gender inequality caused by child penalties has featured a dramatic increase over the last three to four decades. Finally, we show that child penalties are transmitted through generations, from parents to daughters, suggesting an influence of childhood environment on gender identity.

In response to the coronavirus disease 2019 (COVID-19) pandemic, several national governments have applied lockdown restrictions to reduce the infection rate. Here we perform a massive analysis on near–real-time Italian mobility data provided by Facebook to investigate how lockdown strategies affect economic conditions of individuals and local governments. We model the change in mobility as an exogenous shock similar to a natural disaster. We identify two ways through which mobility restrictions affect Italian citizens. First, we find that the impact of lockdown is stronger in municipalities with higher fiscal capacity. Second, we find evidence of a segregation effect, since mobility contraction is stronger in municipalities in which inequality is higher and for those where individuals have lower income per capita. Our results highlight both the social costs of lockdown and a challenge of unprecedented intensity: On the one hand, the crisis is inducing a sharp reduction of fiscal revenues for both national and local governments; on the other hand, a significant fiscal effort is needed to sustain the most fragile individuals and to mitigate the increase in poverty and inequality induced by the lockdown.

One means by which the state reinforces inequality is by imposing administrative burdens that loom larger for citizens with lower levels of human capital Integrating insights from various disciplines, this article focuses on one aspect of human capital: cognitive resources. The authors outline a model that explains how burdens and cognitive resources, especially executive functioning, interrelate. The article then presents illustrative examples, highlighting three common life factors—scarcity, health problems, and age-related cognitive decline. These factors create a human capital catch-22, increasing people's likelihood of needing state assistance while simultaneously undermining the cognitive resources required to negotiate the burdens they encounter while seeking such assistance. The result is to reduce access to state benefits and increase inequality. The article concludes by calling for scholars of behavioral public administration and public administration more generally to incorporate more attention to human capital into their research.

In many areas, practitioners seek to use observational data to learn a treatment assignment policy that satisfies application-specific constraints, such as budget, fairness, simplicity, or other functional form constraints. For example, policies may be restricted to take the form of decision trees based on a limited set of easily observable individual characteristics. We propose a new approach to this problem motivated by the theory of semiparametrically efficient estimation. Our method can be used to optimize either binary treatments or infinitesimal nudges to continuous treatments, and can leverage observational data where causal effects are identified using a variety of strategies, including selection on observables and instrumental variables. Given a doubly robust estimator of the causal effect of assigning everyone to treatment, we develop an algorithm for choosing whom to treat, and establish strong guarantees for the asymptotic utilitarian regret of the resulting policy.

We examine the concerns that new technologies will render labor redundant in a framework in which tasks previously performed by labor can be automated and new versions of existing tasks, in which labor has a comparative advantage, can be created. In a static version where capital is fixed and technology is exogenous, automation reduces employment and the labor share, and may even reduce wages, while the creation of new tasks has the opposite effects. Our full model endogenizes capital accumulation and the direction of research toward automation and the creation of new tasks. If the long-run rental rate of capital relative to the wage is sufficiently low, the long-run equilibrium involves automation of all tasks. Otherwise, there exists a stable balanced growth path in which the two types of innovations go hand-in-hand. Stability is a consequence of the fact that automation reduces the cost of producing using labor, and thus discourages further automation and encourages the creation of new tasks. In an extension with heterogeneous skills, we show that inequality increases during transitions driven both by faster automation and the introduction of new tasks, and characterize the conditions under which inequality stabilizes in the long run.