cssi/SciSciGPT-SciSciNet huggingface dataset:
Lattice charge overlap and the elastic limit

Abstract The results of a lattice simulation of time-separated charge overlap for the charged pion are discussed. The expected result ∼ exp[-( E q - mπ )t] for large charge density overlap time separation, t , is clearly visible in the Fourier transform, indicating that the elastic limit can be achieved at low to medium momentum values on present-sized lattices. The implications of this result for direct lattice simulations of hardon structure functions are discussed and a brief presentation of the lattice formalism is given.
Design of Massive-MIMO-NOMA With Limited Feedback

In this letter, a low-feedback nonorthogonal multiple access (NOMA) scheme using massive multiple-input multiple-output (MIMO) transmission is proposed. In particular, the proposed scheme can decompose a massive-MIMO-NOMA system into multiple separated single-input single-output (SISO) NOMA channels, and analytical results are developed to evaluate the performance of the proposed scheme for two scenarios, with perfect user ordering and with one-bit feedback, respectively.
Strangeness in ultrarelativistic nucleus-nucleus collisions

Abstract I discuss strangeness production in nucleus-nucleus reactions at ultrarelativistic energies (up to 200 AGeV). In these reactions matter may be created with densities and temperatures in the transition region between quark-gluon plasma (QGP) and hadron gas. Strange anti-baryon enhancement at 200 AGeV and probably even more so at 10 AGeV signals importance of interactions beyond hadron gas dynamics. The systematics of strangeness production indicates that energy and baryon density are key variables while the size of the production volume plays no visible role. Analysis of strangeness appears useful to explore thermalization, flow and the post-equilibrium stage in ultrarelativistic nucleus-nucleus collisions.
Fitting the Density Substructure of the Stellar Halo with MilkyWay@home

We propose and test a method for applying statistical photometric parallax to main sequence turn off (MSTO) stars in the Sloan Digital Sky Survey (SDSS). Using simulated data, we show that if our density model is similar to the actual density distribution of our data, we can reliably determine the density model parameters of three major substructures in the Milky Way halo using the computational resources available on MilkyWay@home (a twenty parameter fit). We fit the stellar density in SDSS stripe 19 with a smooth stellar spheroid component and three major streams. One of these streams is consistent with the Sagittarius tidal stream at $21.1$ kpc away, one is consistent with the trailing tail of the Sagittarius tidal stream in the north Galactic cap at $48$ kpc away, and one is possibly part of the Virgo Overdensity at $6$ kpc away. We find the one sigma widths of these three streams to be $1.0$ kpc, $17.6$ kpc, and $6.1$ kpc, respectively. The width of the trailing tail is extremely wide ($41$ kpc full width at half maximum). This large width could have implications for the shape of the Milky Way dark matter halo. The width of the Virgo Overdensity-like structure is consistent with what we might expect for a "cloud"-like structure; analysis of additional stripes of data are needed to outline the full extent of this structure and confirm its association with the Virgo Overdensity.
Heat kernel measures on random surfaces

The heat kernel on the symmetric space of positive definite Hermitian matrices is used to endow the spaces of Bergman metrics of degree k on a Riemann surface M with a family of probability measures depending on a choice of the background metric. Under a certain matrix-metric correspondence, each positive definite Hermitian matrix corresponds to a Kahler metric on M. The one and two point functions of the random metric are calculated in a variety of limits as k and t tend to infinity. In the limit when the time t goes to infinity the fluctuations of the random metric around the background metric are the same as the fluctuations of random zeros of holomorphic sections. This is due to the fact that the random zeros form the boundary of the space of Bergman metrics.
NUMERICAL SUNSPOT MODELS: ROBUSTNESS OF PHOTOSPHERIC VELOCITY AND MAGNETIC FIELD STRUCTURE

MHD simulations of sunspots have successfully reproduced many aspects of sunspot fine structure as a consequence of magneto-convection in inclined magnetic field. We study how global sunspot properties and penumbral fine structure depend on the magnetic top boundary condition as well as on grid spacing. The overall radial extent of the penumbra is subject to the magnetic top boundary condition. All other aspects of sunspot structure and penumbral fine structure are resolved at an acceptable level starting from a grid resolution of 48 [24] km (horizontal [vertical]). We find that the amount of inverse polarity flux and the overall amount of overturning convective motions in the penumbra are robust with regard to both resolution and boundary conditions. At photospheric levels Evershed flow channels are strongly magnetized. We discuss in detail the relation between velocity and magnetic field structure in the photosphere and point out observational consequences.
Cooling a Single Atom in an Optical Tweezer to Its Quantum Ground State

Individual, neutral atoms trapped in optical tweezers have been cooled to their quantum ground state, raising hopes that they can be used to process quantum information.
Bright Lenses and Optical Depth

In gravitational lensing, the concept of optical depth assumes that the lens is dark. Several microlensing detections have recently been made in which the lens may be bright. Relations are developed between apparent and absolute optical depth in the regime of the apparent and absolute brightness of the lens. An apparent optical depth through bright lenses is always less than the true, absolute optical depth. The greater the intrinsic brightness of the lens, the more likely that it will be found nearer the source.
GRAVITATIONAL WAVES AND STALLED SATELLITES FROM MASSIVE GALAXY MERGERS AT z ⩽ 1

We present a model for merger-driven evolution of the mass function for massive galaxies and their central supermassive black holes at late times. We discuss the current observational evidence in favor of merger-driven massive galaxy evolution during this epoch, and demonstrate that the observed evolution of the mass function can be reproduced by evolving an initial mass function under the assumption of negligible star formation. We calculate the stochastic gravitational wave signal from the resulting black hole binary mergers in the low redshift universe (z ≤ 1) implied by this model, and find that this population has a signal-to-noise ratio 2 × to 5 × larger than previous estimates for pulsar timing arrays, with a {2σ, 3σ} lower limit within this model of h c(f = 1 yr–1) = {1.1 × 10–15, 6.8 × 10–16}. The strength of this signal is sufficient to make it detectable with high probability under conservative assumptions within the next several years. A principle reason that this result is larger than previous estimates is our use of a recent recalibration of the black hole-stellar mass correlation for the brightest cluster galaxies, which increases our estimate by a factor of ~2 relative to past results. For cases where a galaxy merger fails to lead to a black hole merger, we estimate the probability for a given number of satellite black holes to remain within a massive host galaxy, and interpret the result in light of ULX observations. We find that in rare cases, wandering supermassive black holes may be bright enough to appear as ULXs.
Nonsmoothable, locally indicable group actions on the interval

By the Thurston Stability Theorem, a group of C^1 orientation-preserving diffeomorphisms of the closed unit interval is locally indicable. We show that the local order structure of orbits gives a stronger criterion for nonsmoothability that can be used to produce new examples of locally indicable groups of homeomorphisms of the interval that are not conjugate to groups of C^1 diffeomorphisms.
Contract costs, stakeholder capitalism, and ESG

When do market forces push firms toward stakeholder goals. When do market forces push firms toward ESG goals? I offer thoughts, first on stakeholder capitalism (Chapter 1) and then ESG (Chapter 2).
The Real Effects of Financial Sector Interventions During Crises

We assess the importance of supply-side credit market frictions by studying the impact of bank recapitalization on firm growth in 50 countries during the recent crisis. Our identification strategy exploits the crisis as a shock to credit supply and combines an exogenous measure of firms’ dependence on external financing with policy interventions aimed at restoring bank capital. We find that the growth of financially dependent firms is disproportionately positively affected by bank recapitalization. This effect is quantitatively important and robust to controlling for other policies. These results provide new evidence of the influence of credit market frictions on economic activity.
RAFT: Recurrent All-Pairs Field Transforms for Optical Flow

We introduce Recurrent All-Pairs Field Transforms (RAFT), a new deep network architecture for optical flow. RAFT extracts per-pixel features, builds multi-scale 4D correlation volumes for all pairs of pixels, and iteratively updates a flow field through a recurrent unit that performs lookups on the correlation volumes. RAFT achieves state-of-the-art performance. On KITTI, RAFT achieves an F1-all error of 5.10%, a 16% error reduction from the best published result (6.10%). On Sintel (final pass), RAFT obtains an end-point-error of 2.855 pixels, a 30% error reduction from the best published result (4.098 pixels). In addition, RAFT has strong cross-dataset generalization as well as high efficiency in inference time, training speed, and parameter count. Code is available at https://github.com/princeton-vl/RAFT.
Modern Monte Carlo methods for efficient uncertainty quantification and propagation: A survey

Uncertainty quantification (UQ) includes the characterization, integration, and propagation of uncertainties that result from stochastic variations and a lack of knowledge or data in the natural world. Monte Carlo (MC) method is a sampling-based approach that has widely used for quantification and propagation of uncertainties. However, the standard MC method is often time-consuming if the simulation-based model is computationally intensive. This article gives an overview of modern MC methods to address the existing challenges of the standard MC in the context of UQ. Specifically, multilevel Monte Carlo (MLMC) extending the concept of control variates achieves a significant reduction of the computational cost by performing most evaluations with low accuracy and corresponding low cost, and relatively few evaluations at high accuracy and correspondingly high cost. Multifidelity Monte Carlo (MFMC) accelerates the convergence of standard Monte Carlo by generalizing the control variates with different models having varying fidelities and varying computational costs. Multimodel Monte Carlo method (MMMC), having a different setting of MLMC and MFMC, aims to address the issue of uncertainty quantification and propagation when data for characterizing probability distributions are limited. Multimodel inference combined with importance sampling is proposed for quantifying and efficiently propagating the uncertainties resulting from small datasets. All of these three modern MC methods achieve a significant improvement of computational efficiency for probabilistic UQ, particularly uncertainty propagation. An algorithm summary and the corresponding code implementation are provided for each of the modern Monte Carlo methods. The extension and application of these methods are discussed in detail.
Unsupervised Machine Learning for Clustering in Political and Social Research

In the age of data-driven problem-solving, applying sophisticated computational tools for explaining substantive phenomena is a valuable skill. Yet, application of methods assumes an understanding of the data, structure, and patterns that influence the broader research program. This Element offers researchers and teachers an introduction to clustering, which is a prominent class of unsupervised machine learning for exploring and understanding latent, non-random structure in data. A suite of widely used clustering techniques is covered in this Element, in addition to R code and real data to facilitate interaction with the concepts. Upon setting the stage for clustering, the following algorithms are detailed: agglomerative hierarchical clustering, k-means clustering, Gaussian mixture models, and at a higher-level, fuzzy C-means clustering, DBSCAN, and partitioning around medoids (k-medoids) clustering.
NNgSAT: neural network guided SAT attack on logic locked complex structures

The globalization of the IC supply chain has raised many security threats, especially when untrusted parties are involved. This has created a demand for a dependable logic obfuscation solution to combat these threats. Amongst a wide range of threats and countermeasures on logic obfuscation in the 2010s decade, the Boolean satisfiability (SAT) attack, or one of its derivatives, could break almost all state-of-the-art logic obfuscation countermeasures. However, in some cases, particularly when the logic locked circuits contain complex structures, such as big multipliers, large routing networks, or big tree structures, the logic locked circuit is hard-to-be-solved for the SAT attack. Usage of these structures for obfuscation may lead a strong defense, as many SAT solvers fail to handle such complexity. However, in this paper, we propose a neural-network-guided SAT attack (NNgSAT), in which we examine the capability and effectiveness of a message-passing neural network (MPNN) for solving these complex structures (SAT-hard instances). In NNgSAT, after being trained as a classifier to predict SAT/UNSAT on a SAT problem (NN serves as a SAT solver), the neural network is used to guide/help the actual SAT solver for finding the SAT assignment(s). By training NN on conjunctive normal forms (CNFs) corresponded to a dataset of logic locked circuits, as well as fine-tuning the confidence rate of the NN prediction, our experiments show that NNgSAT could solve 93.5% of the logic locked circuits containing complex structures within a reasonable time, while the existing SAT attack cannot proceed the attack flow in them.
The Impact of the Novel Coronavirus Disease 2019 on Therapy Service Delivery for Children with Disabilities.

Objective To assess the impact of the novel coronavirus disease 2019 (COVID-19) pandemic on the delivery of, and parent satisfaction with, therapy services for children with disabilities in early intervention, school, and outpatient settings. Study design There were 207 parents of children with disabilities who completed a web-based survey about their child[ren]'s access to, and satisfaction with, therapy services during COVID-19. Parents also completed the Family-Provider Partnership Scale and the Telehealth Satisfaction Scale. Satisfaction was compared between families receiving therapies in school, early intervention, outpatient, and multiple settings. Results Forty-four percent of parents reported low satisfaction with their child[ren]'s therapy services during the pandemic. Access to telehealth positively predicted overall satisfaction and satisfaction with the family-provider partnership, whereas receiving school-based therapies negatively predicted overall satisfaction and satisfaction with the family-provider partnership. Conclusions School-based therapies are legally mandated for eligible students, free of cost to families, integrated in the academic setting, and less burdensome on parents than other services. Thus, given the disparity in parental satisfaction regarding school-based service delivery, addressing therapy delivery in school-based settings during the duration of COVID-19 is critical for preventing increased disparities and more effectively meeting children's needs. Telehealth seems to be a promising option for continuing high-quality services during the duration of the COVID-19 pandemic and for families who face barriers in accessing services in general. Future studies are warranted with larger and more diverse samples, as well as longitudinal studies that monitor service access and parent satisfaction throughout the remainder of the pandemic.
The Invention of Fanaticism

This article examines the Western construction of “religion” as passionate and non-rational. First, it summarizes the history of the category “religion” and the creation of the myth of religious violence. It then examines the construction of one of the stock characters of modernity, the religious fanatic, and shows how fanaticism migrated from an accusation against heretics to an accusation of intolerance, and from an indictment of false prophecy and belief to an indictment of an irrational and violent passion. The article then shows how the construction of “religious fanaticism” can promote secularist rationales for violence.
Multi-Temporal Analysis and Scaling Relations of 100,000,000,000 Network Packets

Our society has never been more dependent on computer networks. Effective utilization of networks requires a detailed understanding of the normal background behaviors of network traffic. Large-scale measurements of networks are computationally challenging. Building on prior work in interactive supercomputing and GraphBLAS hypersparse hierarchical traffic matrices, we have developed an efficient method for computing a wide variety of streaming network quantities on diverse time scales. Applying these methods to 100,000,000,000 anonymized source-destination pairs collected at a network gateway reveals many previously unobserved scaling relationships. These observations provide new insights into normal network background traffic that could be used for anomaly detection, AI feature engineering, and testing theoretical models of streaming networks.
Stochastic Optimal Power Flow with Network Reconfiguration: Congestion Management and Facilitating Grid Integration of Renewables

There has been a significant growth of variable renewable generation in the power grid today. However, the industry still uses deterministic optimization to model and solve the optimal power flow (OPF) problem for real-time generation dispatch that ignores the uncertainty associated with intermittent renewable power. Thus, it is necessary to study stochastic OPF (SOPF) that can better handle uncertainty since SOPF is able to consider the probabilistic forecasting information of intermittent renewables. Transmission network congestion is one of the main reasons for renewable energy curtailment. Prior efforts in the literature show that utilizing transmission network reconfiguration can relieve congestion and resolve congestion-induced issues. This paper enhances SOPF by incorporating network reconfiguration into the dispatch model. Numerical simulations show that renewable curtailment can be avoided with the proposed network reconfiguration scheme that relieves transmission congestion in post-contingency situations. It is also shown that network reconfiguration can substantially reduce congestion cost, especially the contingency-case congestion cost.
Long regularly-spaced and convex sequences in dense sets of integers

Abstract Let A be a set of integers which is dense in a finite interval. We establish upper and lower bounds for the longest regularly-spaced and convex sequences in A and in A − A .
FOREIGN CURRENCY FOR LONG-TERM INVESTORS*

Conventional wisdom holds that conservative investors should avoid exposure to foreign currency risk. Even if they hold foreign equities, they should hedge the currency exposure of these positions and hold only domestic Treasury bills. This paper argues that the conventional wisdom may be wrong for long-term investors. Domestic bills are risky for long-term investors, because real interest rates vary over time and bills must be rolled over at uncertain future interest rates. This risk can be hedged by holding foreign currency if the domestic currency tends to depreciate when the domestic real interest rate falls. Empirically this effect is important.
The Interaction of Communicating Measurement Uncertainty and the Dark Triad on Managers' Reporting Decisions

ABSTRACT: I conduct an interactive experiment with participants in manager- and investor-like roles to examine whether and how mandating range disclosures for uncertain estimates will influence managers' reporting decisions. I find that managers report less aggressively when ranges are disclosed, such that investors have little aggressive reporting to identify using range disclosures. However, consistent with psychology theory, range disclosures have the greatest effect on managers with stronger levels of psychopathy, narcissism, or Machiavellianism (“the Dark Triad” of personality in psychology). Range disclosures discipline these managers' aggressive reporting, while managers with lower levels of all of these personalities have less aggressiveness to discipline and are insensitive to range disclosure. Consequently, mandating range disclosures should have the greatest effect on managers most in need of reining in—and is unlikely to reveal aggressive reporting to investors (as might be expected) because t...
Ability, incentives, and management feedback: organizational change to reduce pressure ulcers in a nursing home.

Objective Quality improvement (QI) processes in nursing homes are highly variable and often ineffective. This study evaluated an innovative QI process to reduce pressure ulcers (PUs) in a nursing home with a high rate of PUs. Design This was a 48-week, longitudinal study comparing the incidence of PUs during 12-week baseline and intervention and post-intervention periods. Setting Not-for-profit, 136-bed nursing home in urban Western Pennsylvania. Patients or Other Participants All residents and all staff at the nursing home participated in this study. Interventions The intervention consisted of 3 components: Ability enhancement, incentivization, and management feedback. To enhance ability, all staff members completed a computer-based interactive video education program on PU prevention and were mandated to use penlights to promote early detection. Incentivization included $75 for each staff member if the desired reduction in PU incidence was achieved. Management feedback provided real-time information of staff”s adherence to the mandated training. Main Outcome Measures Outcome measures consisted of staff’s adherence to mandated training and the incidence of new PUs during the baseline period compared to the intervention and post-intervention periods. Results Management responded to noncompliance with training with both rewards and stepped discipline. Adherence to protocol, as measured by training compliance, was 100%. There was a significant reduction ( P Conclusion An innovative QI initiative resulted in a significant decrease in PUs in 1 facility. This intervention was not sustainable when the 3 components of the QI intervention were no longer actively maintained.
Why do HMOs spend less? Patient selection, physician price sensitivity, and prices

Abstract Cost-control incentives have become an important part of the health insurance landscape in the United States. These incentives are the strongest in capitated managed care organizations, especially HMOs, because such organizations are paid a fixed amount regardless of the spending they generate. Using a sample of insurance claims from about 500 plans, I find that in HMOs, spending on anti-cholesterol drugs is 19% lower than in other insurance plans. This spending difference could reflect the strong cost-control incentives generated by capitation, but it could also reflect the selection of healthy, price-sensitive patients into HMOs, or it could simply reflect lower prices. To understand the difference, I estimate a model of physician prescribing and patient refill decisions. Patients in HMOs are nearly twice as sensitive to copays as are other patients, consistent with selection of healthy patients into HMOs. Even after adjusting for this selection, however, HMO physicians remain highly sensitive to drug procurement costs. This high sensitivity explains about a 20% of the spending difference; combined with lower prices, it explains about 25–55% of the difference. I find no evidence that these spending reductions result in fewer patient refills or the prescribing of less effective statins.
Efficiency in Employee-Owned Enterprises An Econometric Case Study of Mondragon

The authors undertake the first econometric study of efficiency for Eroski, the largest member of the Mondragon group of worker cooperatives. Three types of stores are found within Eroski: 1) coope...
Supersparse linear integer models for optimized medical scoring systems

Scoring systems are linear classification models that only require users to add, subtract and multiply a few small numbers in order to make a prediction. These models are in widespread use by the medical community, but are difficult to learn from data because they need to be accurate and sparse, have coprime integer coefficients, and satisfy multiple operational constraints. We present a new method for creating data-driven scoring systems called a Supersparse Linear Integer Model (SLIM). SLIM scoring systems are built by using an integer programming problem that directly encodes measures of accuracy (the 0---1 loss) and sparsity (the $$\ell _0$$l0-seminorm) while restricting coefficients to coprime integers. SLIM can seamlessly incorporate a wide range of operational constraints related to accuracy and sparsity, and can produce acceptable models without parameter tuning because of the direct control provided over these quantities. We provide bounds on the testing and training accuracy of SLIM scoring systems, and present a new data reduction technique that can improve scalability by eliminating a portion of the training data beforehand. Our paper includes results from a collaboration with the Massachusetts General Hospital Sleep Laboratory, where SLIM is being used to create a highly tailored scoring system for sleep apnea screening.
Inflation-Based Adjustments in Federal Civil Monetary Penalties

Civil monetary penalties play a vital role in federal law. The Federal Civil Penalties Inflation Adjustment Act of 1990, Pub. L. No. 101-410, prescribes rules for the regular adjustment of federal civil monetary penalties in response to inflation. Three statutory defects have undermined the Inflation Adjustment Act. First, the Act's 10 percent cap on initial adjustments creates an "inflation gap" relative to the level that would properly reflect inflation. Second, the Act directs federal agencies to use Consumer Price Index data that are at least 7 months and as many as 18 months out of date. This creates "CPI lag" in the adjustment of civil monetary penalties. Third, the Act's rounding rules can force some agencies to wait 15 years or more between adjustments.Originally prepared as a report for the Administrative Conference of the United States, this article examines the Inflation Adjustment Act and recommends possible legislative remedies for the Act's defects.
Working sick: lessons of chronic illness for health care reform.

Although chronic illness is generally associated with the elderly or disabled, chronic conditions are widespread among working-age adults and pose significant challenges for employer-based health care plans. Indeed, a recent study found that the number of working-age adults with a major chronic condition has grown by 25 percent over the past 10 years, to a total of nearly 58 million in 2006. Chronic illness imposes significant costs on workers, employers, and the overall economy. This population accounts for three-quarters of all personal medical spending in the United States, and a Milken Institute study recently estimated that lost workdays and lower productivity as a result of the seven most common chronic diseases results in an annual loss of over $1 trillion dollars.I am focusing on this significant and growing population as a challenge for employers and as a critical test case for current health care reform proposals. Many of the cost-control methods used by employer-based plans simply shift rather than lower health care costs. This disproportionately burdens people with chronic illnesses and creates long-term social and economic costs. The experiences and challenges of workers with chronic illness provide an opportunity to examine the larger framework of health care reform, not just the employer’s role in isolation, and they make clear that chronic illness is an issue that must be addressed by employers and policymakers.
Estimation and inference with a (nearly) singular Jacobian

This paper develops extremum estimation and inference results for nonlinear models with very general forms of potential identification failure when the source of this identification failure is known. We examine models that may have a general deficient rank Jacobian in certain parts of the parameter space. When identification fails in one of these models, it becomes underidentified and the identification status of individual parameters is not generally straightforward to characterize. We provide a systematic reparameterization procedure that leads to a reparametrized model with straightforward identification status. Using this reparameterization, we determine the asymptotic behavior of standard extremum estimators and Wald statistics under a comprehensive class of parameter sequences characterizing the strength of identification of the model parameters, ranging from nonidentification to strong identification. Using the asymptotic results, we propose hypothesis testing methods that make use of a standard Wald statistic and data‐dependent critical values, leading to tests with correct asymptotic size regardless of identification strength and good power properties. Importantly, this allows one to directly conduct uniform inference on low‐dimensional functions of the model parameters, including one‐dimensional subvectors. The paper illustrates these results in three examples: a sample selection model, a triangular threshold crossing model, and a collective model for household expenditures. Reparameterization deficient rank Jacobian asymptotic size uniform inference subvector inference extremum estimators identification nonlinear models Wald test weak identification underidentification C12 C15
Momentum Traders in the Housing Market: Survey Evidence and a Search Model

This paper studies household beliefs dur ing the recent US housing boom. The first part presents evidence from the Michigan Survey of Consumers. To characterize the heterogeneity in households’ views about housing and the econ omy, we perform a cluster analysis on survey responses at different stages of the boom. The estimation always finds a small cluster of house holds that believe it is a good time to buy a house because house prices will rise further. The size of this “momentum” cluster strongly increased toward the end of the boom. The second part of the paper provides a simple search model of the housing market to show how a small number of optimistic investors can have a large effect on prices without buying a large share of the housing stock. The raw survey data suggest that the housing boom had two distinct phases. During the early boom years 2002–2003, a large and increasing fraction of households believed that the time for buying a house was good. This fraction peaked at 85.2 percent in 2003:II. The most important reason—cited by up to 72 percent of house
How General Do Theories of Explanation Need To Be

Theories of explanation seek to tell us what distinctively explanatory information is. The most ambitious ones, such as the DN-account, seek to tell us what an explanation is, tout court. Less ambitious ones, such as causal theories, restrict themselves to a particular domain of inquiry. The least ambitious theories constitute outright skepticism, holding that there is no reasonably unified phenomenon to give an account of. On these views, it is impossible to give any theories of explanation at all. I argue that both the less ambitious and outright skeptical varieties are committed to a certain context-sensitivity of our explanatory discourse. And though this discourse is almost certainly context-sensitive in some respects, it does not exhibit the context-sensitivity less than fully ambitious theories are committed to. Therefore, all accounts that seek to restrict themselves in scope, including causal accounts of explanation, fail.
State Court Judges Are Not Bound by Nonoriginalist Supreme Court Interpretations

In this brief Essay, I provide a tentative argument for modest state court interpretative independence. I argue that state courts possess interpretative independence from nonoriginalist U.S. Supreme Court interpretations. I also argue that state courts must follow all U.S. Supreme Court judgments (within the Court's jurisdiction) and originalist Supreme Court opinions. I close by suggesting that this modest state court interpretative independence is likely to advance federalism’s three primary values.
A Theory of Just-in-Time and the Growth in Manufacturing Trade

This paper argues the widespread adoption of Just-in-Time (JIT) logistics provides a key to understanding the growth in the U.S. trade share. To do so, I develop a dynamic trade model based on the choice of the logistics technology used in a firm's supply chain. The model's predicted trade dynamics depend on how the set of firms using JIT with international suppliers changes over time. A numerical example shows the model is capable of generating growth in the trade share. I present evidence showing the theory is consistent with aggregate data as well as industry-level panel data.
Contingent Compensation of Post-Conviction Counsel: A Modest Proposal to Identify Meritorious Claims and Reduce Wasteful Government Spending

This contribution to a symposium on post-conviction litigation argues that the lack of properly-incentivized counsel is a primary problem with our failing system of habeas litigation. The lack of counsel causes a great flood of frivolous petitions by pro se prisoners, while also preventing prisoners with meritorious claims from getting relief. The lack of counsel, and more fundamentally, the lack of funding therefor, thus perpetuates the problem of incarceration waste. Government-funded contingent compensation of post-conviction counsel may be the most promising way to help courts identify the bona fide cases deserving of relief, providing more accurate justice and saving money on net.In Part I, I lay out the problem of incarceration waste, identifying the types of prisoners who should be released even under current law and foreseeable changes thereto. I also show that, without a constitutional or statutory right to counsel, even those prisoners that are being wastefully incarcerated are unable to persuasively reveal that status to their captors. In Part II, I present a proposal for rational governments to pay post-conviction counsel, but do so through a contingent fee system that would incentivize the attorneys to identify such prisoners and cogently present their cases to prosecutors and courts. Such a contingent funding system would be more politically feasible, since it does not shower money upon prisoners who deserve to be there, and it creates the proper incentives for attorneys to provide a screening function for the most meritorious cases. In Part III, I identify other structural and doctrinal impediments to governments achieving a rational policy for reducing incarceration waste, and suggest that they be reconsidered through this lens. I conclude that, although government-paid contingent compensation of post-conviction counsel may be a useful way to get representation for those prisoners that have the most meritorious claims, and to save some money for governments on the margins, it is very far from a solution to the overwhelming problem of mass incarceration.
Fertility, Child Work and Schooling Consequences of Family Planning Programs: Evidence from an Experiment in Rural Bangladesh

Abstract Despite the attractiveness of experiments from the perspective of program evaluation, there have been very few program experiments in the area of family planning. This article evaluates an ongoing family planning program experiment in rural Bangladesh. The article estimates the effect of mothers' program exposure on fertility and children's time allocation. The results show that while the program was effective in reducing fertility, it had no significant impact on children's school enrollment. However, the program appears to have significantly raised boys' participation in the labor force.
The "Big C": Identifying and Mitigating Contagion*

This paper defines "interdependence" as high correlations across markets during all states of the world and "contagion" as the spillovers from extreme negative events. Interdependence has increased dramatically over time, especially within the euro area, even after controlling for global shocks and changes in volatility. Not surprisingly, negative events in one country also quickly affect others. Regression analysis shows that a country is more vulnerable to contagion if it has a more levered banking system, greater trade exposure, weaker macroeconomic fundamentals, and larger international portfolio investment liabilities. Countries are less vulnerable, however, if they have larger international financial portfolio investment assets (which can provide a buffer against shocks) and are less reliant on debt (versus equity) for international financing. The analysis has a number of implications for evaluating policies aimed at mitigating contagion. Instead of limiting integration through trade and financial flows, countries should focus on reducing leverage in their domestic banking systems, strengthening macroeconomic fundamentals, balancing investment liabilities with international assets, and avoiding preferential treatment for debt financing. Countries that face imminent risks of contagion should evaluate additional policies based on whether they can permanently thwart a channel for contagion versus simply delay an inevitable adjustment. Any analysis of policy options should also consider the international externalities generated by contagion. Although this paper does not provide a complete analysis of current challenges in the euro area, the analysis and framework can help prioritize various proposals. Certain policies (such as well-designed deposit insurance to prevent bank runs) can be better justified to mitigate contagion than several other proposals that could instead increase contagion through other channels.
Mortgage Dollar Roll

Mortgage dollar roll, the most common financing strategy for agency MBS, differs from repo in that the returned collateral can differ from those received. Also, MBS ownership changes hands in the funding period. We show that dollar roll “specialness,” how much implied financing rates fall below MBS repo rates, (1) increases in the value of the cheapest-to-deliver option, (2) decreases in the leverage of primary dealers, (3) decreases in prepayment risk exposure during the financing period, and (4) decreases in MBS returns. The Federal Reserve’s dollar roll sales in quantitative easing operations are associated with lower specialness. Received February 3, 2016; editorial decision July 30, 2018 by Editor Itay Goldstein. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.
Customer-Driven Misconduct: How Competition Corrupts Business Practices

Competition among firms yields many benefits but can also encourage firms to engage in corrupt or unethical activities. We argue that competition can lead organizations to provide services that customers demand but that violate government regulations, especially when price competition is restricted. Using 28 million vehicle emissions tests from more than 11,000 facilities, we show that increased competition is associated with greater inspection leniency, a service quality attribute that customers value but is illegal and socially costly. Firms with more competitors pass customer vehicles at higher rates and are more likely to lose customers whom they fail, suggesting that competition intensifies pressure on facilities to provide illegal leniency. We also show that, at least in markets in which pricing is restricted, firms use corrupt and unethical practices as an entry strategy.
The optimal probability and magnitude of fines for acts that definitely are undesirable

Even when society would wish to deter all acts of some type, such as tax evasion and many common crimes, the benefits from deterrence often will be insufficient to justify the expenditures on enforcement that would be required to deter everyone. If some individuals are not deterred, however, they will bear risk when fines are employed as a sanction. As a result, it may be optimal to reduce total risk-bearing costs by reducing the number of individuals who bear any risk. This can be accomplished by increasing enforcement above the level that would be justified considering only the benefits of deterrence and the direct costs of enforcement. Another possibility is that it may be optimal reduce the risk borne by those who act, by employing fines below the maximum feasible level. This latter result constitutes an instance in which the well-known implication of Becker's analysis that it is optimal to employ extreme sanctions for all offenses is invalid.
Regulatory Competition and the Market for Corporate Law

This article develops an empirical model of firms’ choice of corporate laws under inertia. Delaware dominates the incorporation market, though recently Nevada, a state whose laws are highly protective of managers, has acquired a sizable market share. Using a novel database of incorporation decisions from 19952013, we show that most firms dislike protectionist laws, such as anti-takeover statutes and liability protections for ocers, and that Nevada’s rise is due to the preferences of small firms. Consistent with the bonding hypothesis, our estimates indicate that despite inertia, Delaware would lose significant market share and revenues if it adopted protectionist laws. Associate Professor, Duke University School of Law. Ph.D. (Financial Economics), Yale Uni
Helping Friends or Influencing Foes: Electoral and Policy Effects of Campaign Finance Contributions

Campaign finance contributions may influence policy by affecting elections or by influencing the choices of politicians once in office. We study the trade-offs between these two mechanisms using a game in which contributions may affect electoral outcomes and signal policy-relevant information to politicians. In the model, an interest group and two politicians each possess private information about a policy-relevant state of the world. The interest group may contribute to its preferred candidate or to an opposing candidate. Contributions that increase the likelihood of electing the opposing candidate may allow interest groups to signal good information about their preferred policy and influence the opponent’s policy choice. The welfare effects of contributions can be positive or negative depending on whether they generate enough policy information to offset the possible costs of interest group control over electoral outcomes. ∗Assistant Professor of Public Policy, Martin School of Public Policy and Administration, University of Kentucky. Contact: keith.schnakenberg@gmail.com. †Assistant Professor of Political Science, Texas AM Grossman and Helpman, 1994; Groseclose and Snyder, 1996; Diermeier and Myerson, 1999) or from contributions aimed at gaining access (e.g., Cotton, 2012, Forthcoming; Grimmer and Powell, 2015; Kalla and Broockman, Forthcoming).
Working conditions and factory survival: Evidence from better factories Cambodia

A large and growing literature has identified several conditions, including exporting, that contribute to plant survival. A prevailing sentiment suggests that anti‐sweatshop activity against plants in developing countries adds to the risk of closure, making survival more difficult by imposing external constraints that may interfere with optimizing behavior. Using a relatively new plant‐level panel data set from Cambodia, this paper applies survival analysis to estimate the relationship between changes in working conditions and plant closure. The results find little, if any, evidence that improving working conditions increases the probability of closure. In fact, some evidence suggests that improvements in standards relating to compensation are positively correlated with the probability of plant survival.
Should the forensic accounting profession be regulated

Abstract Adopting Huber’s (2012) argument that forensic accounting has become a profession, this paper examines whether the forensic accounting profession and the forensic accounting certification industry should be regulated. Several recent studies have uncovered significant problems within the forensic accounting profession and the forensic accounting certification industry. The failure of forensic accounting corporations to disclose either their legal status or the qualifications of their officers and directors, their failure to publish financial statements, and their failure to adopt or enforce a Code of Ethics or Standards of Practice, were among the most significant problems uncovered. The failures of the corporations were exacerbated by forensic accountants’ failure to investigate diligently the corporations that issued their certifications prior to obtaining their certifications. This resulted in a significant number of forensic accountants holding certifications from corporations that were inconsistent with their beliefs that a forensic accounting corporation should be not-for-profit, and their officers and directors should be qualified. Those studies suggested three alternatives for addressing the problems: voluntary action by the corporations, establishing an independent agency for accrediting the corporations and certifications, and regulatory intervention. However, the feasibility of the recommended alternatives was not sufficiently evaluated to be able to arrive at a conclusion for recommending which alternative should be implemented. This paper evaluates the feasibility of alternative solutions. It concludes that the most realistic alternative is for government regulation of forensic accounting in the form of legislation at the state level.