Knowledge that Transforms

Today's weakness in the US economy results from lack of aggregate demand, due to high and growing inequality, underinvestment in public infrastructure and technology that is complementary to private capital, continuing mild austerity, difficulties encountered in making the structural transformation from manufacturing to a service-based economy, and a financial sector failing to provide adequate funds to SMEs. An agenda to restore growth includes a carbon price, inducing climate investments; increased public investments in infrastructure and technology; fighting inequality through redistribution and rewriting the rules structuring the economy; and reforming the financial sector and the global reserve system.

We develop over-identification tests that use admissions lotteries to assess the predictive value of regression-based value-added models (VAMs). These tests have degrees of freedom equal to the number of quasi-experiments available to estimate school effects. By contrast, previously implemented VAM validation strategies look at a single restriction only, sometimes said to measure forecast bias. Tests of forecast bias may be misleading when the test statistic is constructed from many lotteries or quasi-experiments, some of which have weak first stage effects on school attendance. The theory developed here is applied to data from the Charlotte-Mecklenberg School district analyzed by Deming (2014).

In this paper we analyze the relationship between turnover-driven growth and subjective well-being. Our model of innovation-led growth and unemployment predicts that: (i) the effect of creative destruction on expected individual welfare should be unambiguously positive if we control for unemployment, less so if we do not; (ii) job creation has a positive and job destruction has a negative impact on well-being; (iii) job destruction has a less negative impact in areas with more generous unemployment insurance policies; and (iv) job creation has a more positive effect on individuals that are more forward-looking. The empirical analysis using cross-sectional MSA (metropolitan statistical area)-level and individual-level data provide empirical support to these predictions.

Marijuana is the most common illicit drug with vocal advocates for legalization. Among other things, legalization would increase access and remove the stigma of illegality. Our model disentangles the role of access from preferences and shows that selection into access is not random. We find that traditional demand estimates are biased resulting in incorrect policy conclusions. If marijuana were legalized, those under 30 would see modest increases in use of 28 percent, while on average use would increase by 48 percent (to 19.4 percent). Tax policies are effective at curbing use, where Australia could raise AU$1 billion (and the United States US$12 billion).

The conventional wisdom for the healthcare sector is that idiosyncratic features leave little scope for market forces to allocate consumers to higher performance producers. However, we find robust evidence - across several different conditions and performance measures - that higher quality hospitals have higher market shares and grow more over time. The relationship between performance and allocation is stronger among patients who have greater scope for hospital choice, suggesting that patient demand plays an important role in allocation. Our findings suggest that healthcare may have more in common with "traditional" sectors subject to market forces than often assumed.

The combinatorial clock auction has become popular for large-scale spectrum awards and other uses, replacing more traditional ascending or clock auctions. We describe some surprising properties of the auction, including a wide range of ex post equilibria with demand expansion, demand reduction, and predation. Our results obtain in a standard homogeneous good setting where bidders have well-behaved linear demand curves, and suggest some practical difficulties with dynamic implementations of the Vickrey auction.

This paper studies the long run effects of financial crises using new bank and town level data from around the Great Depression. We find evidence that banking markets became much more concentrated in areas that experienced a greater initial collapse in the local banking system. There is also evidence that financial regulation after the Great Depression, and in particular limits on bank branching, may have helped to render the effects of the initial collapse persistent. All of this suggests a reason why post-crisis financial regulation, while potentially reducing financial instability, might also have longer run real consequences.

This paper estimates the incidence of state corporate taxes on the welfare of workers, landowners, and firm owners using variation in state corporate tax rates and apportionment rules. We develop a spatial equilibrium model with imperfectly mobile firms and workers. Firm owners may earn profits and be inframarginal in their location choices due to differences in location-specific productivities. We use the reduced-form effects of tax changes to identify and estimate incidence as well as the structural parameters governing these impacts. In contrast to standard open economy models, firm owners bear roughly 40 percent of the incidence, while workers and landowners bear 30–35 percent and 25–30 percent, respectively.

We explore the consequences of ethnic partitioning, a neglected aspect of the Scramble for Africa, and uncover the following. First, apart from the land mass and water bodies, split and non-split groups are similar across several dimensions. Second, the incidence, severity, and duration of political violence are all higher for partitioned homelands which also experience frequent military interventions from neighboring countries. Third, split groups are often entangled in a vicious circle of government-led discrimination and ethnic wars. Fourth, respondents from survey data identifying with split ethnicities are economically disadvantaged. The evidence highlights the detrimental repercussions of the colonial border design.

Economists have become increasingly interested in studying the nature of production functions in social policy applications, with the goal of improving productivity. Traditionally models have assumed workers are homogenous inputs. However, in practice, substantial variability in productivity means the marginal productivity of labor depends substantially on which new workers are hired--which requires not an estimate of a causal effect, but rather a prediction. We demonstrate that there can be large social welfare gains from using machine learning tools to predict worker productivity, using data from two important applications - police hiring and teacher tenure decisions.