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

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

Study protocol: combining experimental methods, econometrics and simulation modelling to determine price elasticities for studying food taxes and subsidies (The Price ExaM Study)

Abstract Background There is a need for accurate and precise food price elasticities (PE, change in consumer demand in response to change in price) to better inform policy on health-related food taxes and subsidies. Methods/Design The Price Experiment and Modelling (Price ExaM) study aims to: I) derive accurate and precise food PE values; II) quantify the impact of price changes on quantity and quality of discrete food group purchases and; III) model the potential health and disease impacts of a range of food taxes and subsidies. To achieve this, we will use a novel method that includes a randomised Virtual Supermarket experiment and econometric methods. Findings will be applied in simulation models to estimate population health impact (quality-adjusted life-years [QALYs]) using a multi-state life-table model. The study will consist of four sequential steps: 1. We generate 5000 price sets with random price variation for all 1412 Virtual Supermarket food and beverage products. Then we add systematic price variation for foods to simulate five taxes and subsidies: a fruit and vegetable subsidy and taxes on sugar, saturated fat, salt, and sugar-sweetened beverages. 2. Using an experimental design, 1000 adult New Zealand shoppers complete five household grocery shops in the Virtual Supermarket where they are randomly assigned to one of the 5000 price sets each time. 3. Output data (i.e., multiple observations of price configurations and purchased amounts) are used as inputs to econometric models (using Bayesian methods) to estimate accurate PE values. 4. A disease simulation model will be run with the new PE values as inputs to estimate QALYs gained and health costs saved for the five policy interventions. Discussion The Price ExaM study has the potential to enhance public health and economic disciplines by introducing internationally novel scientific methods to estimate accurate and precise food PE values. These values will be used to model the potential health and disease impacts of various food pricing policy options. Findings will inform policy on health-related food taxes and subsidies. Trial registration Australian New Zealand Clinical Trials Registry ACTRN12616000122459 (registered 3 February 2016)