Working Papers

The synthetic control method (SCM) is a popular approach for estimating the impact of a treatment on a single unit in panel data settings. The “synthetic control” is a weighted average of control units that balances the treated unit’s pre-treatment outcomes as closely as possible. The curse of dimensionality, however, means that SCM does not generally achieve exact balance, which can bias the SCM estimate. We propose an extension, Augmented SCM, which uses an outcome model to estimate the bias due to covariate imbalance and then de-biases the original SCM estimate, analogous to bias correction for inexact matching. We motivate this approach by showing that SCM is a (regularized) inverse propensity score weighting estimator, with pre-treatment outcomes as covariates and a ridge penalty on the propensity score coefficients. We give theoretical guarantees for specific cases and propose a new inference procedure. We demonstrate gains from Augmented SCM with extensive simulation studies and apply this framework to canonical SCM examples. We implement the proposed method in the new augsynth R package.

Contaminated sediments pose significant ecological and health threats in ports and harbors around the world. Yet there is surprisingly little progress towards cleanup in most countries. The U.S. Comprehensive Environmental Response, Compensation, and Liability Act of 1980 (CERCLA or Superfund law) facilitates cleanup projects but they often suffer lengthy delays nonetheless. Participants debate relative responsibility over cost shares. Project finance is often insufficient, in part because resources are diverted by adversarial legalism (Spadaro and Rosenthal 2003, Kagan 2001).

Technologies of sediment remediation (e.g., precision dredging, engineered cap placement, natural attenuation) can now address even the most significant contamination. These innovations allow increasing economies of operation and simplification of remediation design. Applying these technologies in risk-based proportions has produced significant successes, but far too few given the enormity of the challenge worldwide. Without innovations in funding, improved project coordination, and coherent waterfront planning, greater progress will remain difficult to achieve.

We analyze relationships between waterway cleanup of contaminated sediments and waterfront redevelopment. Using examples from North America, we evaluate possible changes in funding paradigms that, if implemented, could accelerate reclamation and remediation. We question the efficacy of the 100-percent-polluter-pays model currently employed in cleanups under the Superfund program and state-equivalent models. Despite its compensatory logic, polluter-pays has difficulty securing polluters’ participation and attributing proportions of contamination to original sources.

Further, historic polluters often lack roots in the present-day community, aiming primarily to minimize their financial exposure. By contrast, longer-term interests in waterways and on the waterfront—e.g. municipal governments, port authorities, and community organizations—can play leadership roles via planning, development regulation, and uplands remediation. Though these entities ought bear no more than their fair share of the costs, they should help coordinate the process whenever possible, given their naturally occurring stakes in cleanup and revitalization.

We propose new roles for cleanup authorities and new structures in project-finance. Tax-increment investment and other approaches can salvage public value and limit windfalls for speculators. Better funding and planning methods enhance local control over these projects and their outcomes. Importantly, new approaches can reduce levels of community displacement, as property values rise when cleanup succeeds.

Without such coordination of waterway-waterfront cleanup and redevelopment in the public interest, significant opportunities are lost. Through enlightened practices, perhaps spurred by positive regulation and negotiated solutions emphasizing local constituencies, greater numbers of effective cleanup projects providing meaningful community benefits become possible.

Keywords: Redevelopment, Municipality, Port Authority, Superfund, Funding

This paper will be presented at the WODCON XXII meetings in Shanghai, China in April of 2019. 

In this paper, we examine what groups of children are served by core childhood social safety net programs -- including Medicaid, EITC, CTC, SNAP, and AFDC/TANF -- and how they have changed over time. We find that virtually all gains in spending on the social safety net for children since 1990 have gone to families with earnings, and to families with income above the poverty line. These trends are the result of welfare reform and the expansion of in-work tax credits. We review the available research and find that access to safety net programs during childhood improves outcomes for children and society over the long run. This evidence suggests that the recent changes to the social safety net may have lasting negative impacts on the poorest children.

Scientists have long recognized two distinct forms of human thought. “Type 1” reasoning is unconscious, intuitive, and specializes in finding complex patterns. It is typically associated with the aesthetic emotion that John Keats called “beauty.” “Type 2” reasoning is conscious, articulable, and deductive. Scholars usually assume that legal reasoning is entirely Type 2. However, critics from Holmes to Posner have protested that unconscious and intuitive judgments are at least comparably important. This article takes the conjecture seriously by asking what science can add to our understanding of how lawyers and judges interpret legal texts.

This is a good time to take stock. Recent advances in cognitive psychology, brain imaging, and neural network theory have already pushed many humanities scholars to rethink postmodern interpretations that privilege politics and culture over texts. This article argues that a parallel shift is overdue in law and that Type 1 reasoning, which specializes in pattern recognition, provides a natural explanation for how judges choose among competing legal theories. Finally, and most surprisingly, the article documents cognitive psychology evidence showing that Type 1 judgments show significant universality, i.e. that humans who study subjects for long periods often make similar choices without regard to the societies they were born into. This solves a long-standing difficulty in jurisprudence, which often struggles to explain why one legal interpretation should be more convincing than another. 

The rest of the article analyzes how Type 1 thinking enters into legal reasoning and outcomes. It begins by reviewing 19th Century theories that claimed a leading role for intuitive reasoning in public policy. It then updates these theories to accommodate the relatively weak statistical correlations that psychologists have documented, arguing that modern court systems amplify these signals in approximately determinate ways. It also explains why advocates should rationally prefer formalist judges to pragmatic ones. Crucially, the existence of universality implies a measure of agreement across all lawyers regardless of personal bias or politics. This common ground gives judges a reliably neutral basis for deciding cases.
 

We develop an empirical approach to value changes to a climate in terms of total market output given optimal factor allocations in general equilibrium. Our approach accounts for unobservable heterogeneity across locations as well as the costs and benefits of adaptation in climates of arbitrary dimension. Importantly, we demonstrate that the Envelope Theorem implies the marginal product of a long-run climate can be exactly identified using only idiosyncratic weather variation. We apply this method to the temperature climate of the modern United States and find that, despite evidence that populations adapt, the marginal product of temperature has remained unchanged during 1970-2010, with high temperatures having low net value. Integrating marginal products recovers a value function for temperature, describing the causal effect of non-marginal climate changes net of adaptive re-optimization. We use this value function to consider the influence of temperature in the current cross-section and a future climate change scenario.