Returns to Compulsory Schooling in Britain: Evidence from a Bayesian Fuzzy Regression Discontinuity Analysis

In this paper we reevaluate the returns to education based on the increase in the compulsory schooling age from 14 to 15 in the UK in 1947. We provide a Bayesian fuzzy regression discontinuity approach to infer the effect on earnings for a subset of subjects who turned 14 in a narrow window around the policy change and whose schooling was affected by the policy change. Our approach and our results are quite different from previous work that has focused on large sets of cohorts and 2SLS based approaches and has reported positive earnings and wage effects of 5% and above. Our empirical analysis, using data from the UK General Household Surveys, yields considerably lower earnings and wage effects for the additional year of compulsory schooling than previous work. These findings are consistent with the implementation of the policy change that affected students at the lower end of the schooling distribution and did not lead students to acquire additional qualifications. The results add further evidence to a number of recent studies that have found no effect from this policy change on socio-economic outcomes correlated with earnings.Bayesian inference, causal effects, imperfect compliance, natural experiment, principal stratification, regression discontinuity, returns to schooling

Marijuana on Main Street: What if?

Illicit drug use is prevalent around the world. While the nature of the market makes it difficult to determine the total sales worldwide with certainty, estimates suggest sales are around 150 billion dollar a year in the United States alone. Among illicit drugs marijuana is the most commonly used, where the US government spends upwards of $7.7 billion per year in enforcement of the laws for marijuana sales (Miron, 2005). For the past 30 years there has been a debate regarding whether marijuana should be legalized. There are two important avenues through which legalization could impact use: legalization would make marijuana easier to get, and it would remove the stigma (and cost) associated with illegal behavior. Studies to date have not disentangled the impact of limited accessibility from consumption decisions based solely on preferences. However, this distinction is particularly important in the market for cannabis as legalizing the drug would impact accessibility. Hence, if most individuals do not use because they don't know where to buy it, but would otherwise use, we would see a large increase in consumption ceteris paribus, which would be important to consider for policy. On the other hand, if accessibility plays little role in consumption decisions, then making drugs more readily available would impact the supply more. In order to access the impact of legalization on use, it is necessary to explicitly consider the role played by accessibility in use, the impact of illegal actions in utility, as well as the impact on the supply side. In this paper, we develop and estimate a model of buyer behavior that explicitly considers the impact of illegal behavior on utility as well as the impact of limited accessibility (either knowing where to buy or being offered) an illicit drug on using the drug. We use the demand side estimates to conduct counterfactuals on how use would change under a policy of legalization. We conduct counterfactuals under different assumptions regarding how legalization would impact the supply as well as various tax policies on the price of cannabis

Consensus Recommendations for Clinical Outcome Assessments and Registry Development in Ataxias: Ataxia Global Initiative (AGI) Working Group Expert Guidance

To accelerate and facilitate clinical trials, the Ataxia Global Initiative (AGI) was established as a worldwide research platform for trial readiness in ataxias. One of AGI's major goals is the harmonization and standardization of outcome assessments. Clinical outcome assessments (COAs) that describe or reflect how a patient feels or functions are indispensable for clinical trials, but similarly important for observational studies and in routine patient care. The AGI working group on COAs has defined a set of data including a graded catalog of COAs that are recommended as a standard for future assessment and sharing of clinical data and joint clinical studies. Two datasets were defined: a mandatory dataset (minimal dataset) that can ideally be obtained during a routine clinical consultation and a more demanding extended dataset that is useful for research purposes. In the future, the currently most widely used clinician-reported outcome measure (ClinRO) in ataxia, the scale for the assessment and rating of ataxia (SARA), should be developed into a generally accepted instrument that can be used in upcoming clinical trials. Furthermore, there is an urgent need (i) to obtain more data on ataxia-specific, patient-reported outcome measures (PROs), (ii) to demonstrate and optimize sensitivity to change of many COAs, and (iii) to establish methods and evidence of anchoring change in COAs in patient meaningfulness, e.g., by determining patient-derived minimally meaningful thresholds of change

Marijuana on main street? Estimating demand in markets with limited access

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). (JEL D12, H25, K14, K42

Marijuana on Main Street: What if?

Abstract: Illicit drug use is prevalent. While the nature of the market makes it di ¢-cult to determine sales with certainty, estimates are around $150 billion a year in the US. Marijuana is the most common illicit drug used, where the US spends upwards of$7.7 billion per year in law enforcement (Miron, 2005). For the past 30 years there has been a debate regarding marijuana legalization. There are two important avenues through which legalization could impact use: it would make marijuana easier to get, and it would remove the stigma (and cost) associated with illegal behavior. Studies to date have not considered either of these avenues explicitly. However, both are important for policy. We develop and estimate a model of marijuana use that disentangles the impact of limited accessibility from consumption decisions based solely on preferences (and distaste for illegal behavior). We …nd that both play an important role and that individuals who have access to the illicit market are of speci…c demographics. We …nd that selection into who has access to cannabis is not random, and the results suggest estimates of the demand curve will be biased unless selection is explicitly considered. Counterfactual results indicate that making marijuana legal and removing accessibility barriers would have a smaller relative impact on younger individuals but still a large impact in magnitude. Use among teenagers would (a little less than) double and use among individuals in their thirties and forties would almost triple

Estimating Posterior Sensitivities with Application to Structural Analysis of Bayesian Vector Autoregressions

The inherent feature of Bayesian empirical analysis is the dependence of posterior inference on prior parameters, which researchers typically specify. However, quantifying the magnitude of this dependence remains difficult. This article extends Infinitesimal Perturbation Analysis, widely used in classical simulation, to compute asymptotically unbiased and consistent sensitivities of posterior statistics with respect to prior parameters from Markov chain Monte Carlo inference via Gibbs sampling. The method demonstrates the possibility of efficiently computing the complete set of prior sensitivities for a wide range of posterior statistics, alongside the estimation algorithm using Automatic Differentiation. The method’s application is exemplified in Bayesian Vector Autoregression analysis of fiscal policy in U.S. macroeconomic time series data. The analysis assesses the sensitivities of posterior estimates, including the Impulse response functions and Forecast error variance decompositions, to prior parameters under common Minnesota shrinkage priors. The findings illuminate the significant and intricate influence of prior specification on the posterior distribution. This effect is particularly notable in crucial posterior statistics, such as the substantial absolute eigenvalue of the companion matrix, ultimately shaping the structural analysis.</p