A model of cocoa replanting and new planting in Bahia, Brazil, 1966-85

In 1966, 90 percent of the cocoa growing areas in Bahia, Brazil had trees more than 30 years old. By 1985 most of the area had been replanted or supplied with new trees. Throughout most of this period there were high or rising cocoa prices, and zero or negative interest rates. High prices and low interest rates directly encouraged new planting, but their relationship to replanting is more complex. In the short term, higher prices discourage replanting, which involves uprooting and a temporary loss of revenue. But over the long run, higher prices increase expectations of future profits and encourage replanting. Lowering the interest rate beloow its real level provided cocoa growers with a subsidy that encouraged both replanting and new planting.Economic Growth,Economic Theory&Research,Environmental Economics&Policies,Crops&Crop Management Systems,Banks&Banking Reform

Patterns of health care utilization in Vietnam : analysis of 1997-98 Vietnam Living Standards Survey Data

The author provides an econometric analysis of health care utilization in Vietnam based on individual and household level data from the 1997-98 Vietnam Living Standards Survey. The author focuses on the major features of health care utilization patterns, including the determinants of largely self-prescribed use of pharmaceutical drugs, and the use of government hospitals, commune health centers, and private health facilities. The role of income and health insurance is emphasized. Econometric models are estimated for use probability and frequency of contact for all major categories of care, and for individual and household medical expenditure. Econometric results reveal differential responses to income changes at different levels of income. Commune health centers and self-medication are normal goods at lower income levels but inferior goods at higher income levels. The author discusses the policy implications of these results.Health Economics&Finance,Health Systems Development&Reform,Health Monitoring&Evaluation,Public Health Promotion,Environmental Economics&Policies,Health Systems Development&Reform,Environmental Economics&Policies,Insurance&Risk Mitigation,Health Economics&Finance,Health Monitoring&Evaluation

Keynote lecture: Estimation of count-data panel models

In this talk, I will cover a number of topics related to the estimation of panel models for count data, with empirical illustrations estimated using Stata. For the theoretical background, I will rely on my book with Colin Cameron, Microeconometrics: Methods and Applications (2005, Cambridge University Press). Some of my illustrations will be based on material in my recent book with Colin Cameron, Microeconometrics Using Stata (2009, Stata Press), but several others will be based on as yet unpublished material. This talk will be operational in orientation and, for specificity, I will rely on examples estimated in Stata. I plan to cover the following topics: nonlinear panel-data modeling for exponential mean models, fixed- and random-effects panel models for the Poisson and negative binomial regression, nonlinear GMM estimation of Poisson panel regression with sample selection or endogenous regressors, dynamic panel Poisson regression with correlated random effects, dynamic panel Poisson regression with linear feedback, finite mixture models for panel Poisson regression

Modeling the Differences in Counted Outcomes using Bivariate Copula Models: with Application to Mismeasured Counts

This paper makes three contributions. First, it uses copula functions to obtain a flexible bivariate parametric model for nonnegative integer-valued data (counts). Second, it recovers the distribution of the difference in the two counts from a specifed bivariate count distribution. Third, the methods are applied to counts that are measured with error. Specifically we model the determinants of the difference between the self-reported number of doctor visits (measured with error) and true number of doctor visits (also available in the data used).

Dynamic Cost-offsets of Prescription Drug Expenditures: Panel Data Analysis Using a Copula-based Hurdle Model

This paper presents a new multivariate copula-based modeling approach for analyzing cost-offsets between drug and nondrug expenditures. Estimates are based on panel data from the Medical Expenditure Panel Survey (MEPS) with quarterly measures of medical expenditures. The approach allows for nonlinear dynamic dependence between drug and nondrug expenditures as well as asymmetric contemporaneous dependence. The specification uses the standard hurdle model with two significant extensions. First, it is adapted to the bivariate case. Second, because the cost-offset hypothesis is inherently dynamic, the bivariate hurdle framework is extended to accommodate dynamic relationships between drug and nondrug spending. The econometric analysis is implemented for six different groups defined by specific health conditions. There is evidence of modest cost-offsets of expenditures on prescribed drugs.

Post-intervention Status in Patients With Refractory Myasthenia Gravis Treated With Eculizumab During REGAIN and Its Open-Label Extension

OBJECTIVE: To evaluate whether eculizumab helps patients with anti-acetylcholine receptor-positive (AChR+) refractory generalized myasthenia gravis (gMG) achieve the Myasthenia Gravis Foundation of America (MGFA) post-intervention status of minimal manifestations (MM), we assessed patients' status throughout REGAIN (Safety and Efficacy of Eculizumab in AChR+ Refractory Generalized Myasthenia Gravis) and its open-label extension. METHODS: Patients who completed the REGAIN randomized controlled trial and continued into the open-label extension were included in this tertiary endpoint analysis. Patients were assessed for the MGFA post-intervention status of improved, unchanged, worse, MM, and pharmacologic remission at defined time points during REGAIN and through week 130 of the open-label study. RESULTS: A total of 117 patients completed REGAIN and continued into the open-label study (eculizumab/eculizumab: 56; placebo/eculizumab: 61). At week 26 of REGAIN, more eculizumab-treated patients than placebo-treated patients achieved a status of improved (60.7% vs 41.7%) or MM (25.0% vs 13.3%; common OR: 2.3; 95% CI: 1.1-4.5). After 130 weeks of eculizumab treatment, 88.0% of patients achieved improved status and 57.3% of patients achieved MM status. The safety profile of eculizumab was consistent with its known profile and no new safety signals were detected. CONCLUSION: Eculizumab led to rapid and sustained achievement of MM in patients with AChR+ refractory gMG. These findings support the use of eculizumab in this previously difficult-to-treat patient population. CLINICALTRIALSGOV IDENTIFIER: REGAIN, NCT01997229; REGAIN open-label extension, NCT02301624. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that, after 26 weeks of eculizumab treatment, 25.0% of adults with AChR+ refractory gMG achieved MM, compared with 13.3% who received placebo

Joint and separate score tests for state dependence and unobserved heterogeneity

The paper compares separate, conditional, and joint score tests of duration dependence and unobserved heterogeneity when the null is the exponential model and the alternative is the heterogeneous Weibull model. The score tests based on the conditional score function include the Neyman C(x) test as a special case. An examination of the non-null distribution of the joint test explains when all score tests have low power in the presence of multiple misspecifications. Monte Carlo experiments show that the conditional score tests are superior to the standard separate tests which confound unobserved heterogeneity and duration dependence

Models for Count Data

Many measures of health-care use that are analyzed and modeled in econometrics are event counts, for example, number of hospital admissions, doctor visits, emergency room visits. Event count models such as the Poisson regression is a common but restrictive starting point in many investigations. To overcome several key limitations of the Poisson regression model, a number of alternatives have been developed that are widely used. These include the negative binomial regression, the two-part model, the quantile count regression, and the latent class model. This article surveys popular modeling frameworks, associated issues of statistical inference, and their key features. Models for both cross-section and panel data are covered