While the role of cultural norms in determining corruption is well‐explored in the empirical literature, the relationship between a specific aspect of culture, that is, individualism versus collectivism, and corruption is rather unexplored. This paper investigates the relationship between individualism/collectivism and corruption in a large cross‐section of countries. To establish causality, the paper uses an index of historical prevalence of infectious diseases and a measure of genetic distance between the population in a country from that in the United States to instrument the individualism/collectivism variable. We find that more individualistic countries have lower levels of corruption (perception). This relationship is robust to the inclusion of a rich set of control variables and to the use of alternative measures of corruption.

This article examines the effect of access to health care center, in terms of travel time, on childhood immunization in Nepal using the 2004 and 2011 waves of the Nepal Living Standards Measurement Surveys. We employ probit and instrumental variable probit estimation methods to estimate the causal effect of travel time on the probability of immunization. Results indicate that travel time to the nearest health center displays a significant negative association with the probability of immunization (coefficient = -0.015,P< .05). Furthermore, the effect of travel time tends to be stronger in rural and distant areas of Nepal's mountain and hill regions. The results suggest that policy interventions should increase the number of mobile clinics in rural villages and provide conditional cash transfer to incentivize immunization coverage at the household level. In addition, household income, parental education, ethnicity, and household location emerge as important determinants of immunization in Nepal.

In this study, we examine the importance of multifactor productivity (MFP) growth in goods and services for U.S. States during 1980–2007 by applying the dual growth accounting framework. We find that MFP growth was relatively high and converged in the goods sector, but was low and did not converge in services. Although low growth in MFP in services was due to declining real user cost, particularly in real estate services, the lack of convergence itself was due to variation in wage growth. We also document that while the gap between productivity and wage growth was higher in goods, the two series were more strongly correlated in services. Finally, states with higher initial human capital experienced higher growth in both sectors. (JEL O47, R11)

Objective To evaluate the effect of an employer-mandated obstructive sleep apnea (OSA) diagnosis and treatment program on non-OSA-program trucker medical insurance claim costs. Methods Retrospective cohort analysis; cohorts constructed by matching (randomly, with replacement) Screen-positive Controls (drivers with insurance screened as likely to have OSA, but not yet diagnosed) with Diagnosed drivers (n = 1,516; cases = 1,224, OSA Negatives = 292), on two factors affecting exposure to medical claims: experience level at hire and weeks of job tenure at the Diagnosed driver’s polysomnogram (PSG) date (the “matching date”). All cases received auto-adjusting positive airway pressure (APAP) treatment and were grouped by objective treatment adherence data: any “Positive Adherence” (n = 932) versus “No Adherence” (n = 292). Bootstrap resampling produced a difference-in-differences estimate of aggregate non-OSA-program medical insurance claim cost savings for 100 Diagnosed drivers as compared to 100 Screen-positive Controls before and after the PSG/matching date, over an 18-month period. A two-part multivariate statistical model was used to set exposures and demographics/anthropometrics equal across sub-groups, and to generate a difference-in-differences comparison across periods that identified the effect of OSA treatment on per-member per-month (PMPM) costs of an individual driver, separately from cost differences associated with adherence choice. Results Eighteen-month non-OSA-program medical claim costs savings from diagnosing (and treating as required) 100 Screen-positive Controls: $153,042 (95% CI: −$5,352, $330,525). Model-estimated effect of treatment on those adhering to APAP: −$441 PMPM (95% CI: −$861, −$21). Conclusions Results suggest a carrier-based mandatory OSA program generates substantial savings in non-OSA-program medical insurance claim costs.

Forecasting of rice area, production, and productivity of Odisha was made from the historical data of 1950-51 to 2008-09 by using univariate autoregressive integrated moving average (ARIMA) models and was compared with the forecasted all Indian data. The autoregressive (p) and moving average (q) parameters were identified based on the significant spikes in the plots of partial autocorrelation function (PACF) and autocorrelation function (ACF) of the different time series. ARIMA (2, 1, 0) model was found suitable for all Indian rice productivity and production, whereas ARIMA (1, 1, 1) was best fitted for forecasting of rice productivity and production in Odisha. Prediction was made for the immediate next three years, that is, 2007-08, 2008-09, and 2009-10, using the best fitted ARIMA models based on minimum value of the selection criterion, that is, Akaike information criteria (AIC) and Schwarz-Bayesian information criteria (SBC). The performances of models were validated by comparing with percentage deviation from the actual values and mean absolute percent error (MAPE), which was found to be 0.61 and 2.99% for the area under rice in Odisha and India, respectively. Similarly for prediction of rice production and productivity in Odisha and India, the MAPE was found to be less than 6%.