Omnibus or Ominous immigration laws? Immigration policy and mental health of the Hispanic population.

This study examines the impact of Omnibus Immigration Laws on the mental health of the Hispanic populations in the U.S. We use a Difference-in-Differences framework and data from the Behavioral Risk Factor Surveillance System Survey of Centers for Disease Control and Prevention for the 2000-2016 period that contains information on more than 400 thousand Hispanics residing in the U.S. We find that the most stringent provision, namely, "show me your papers" laws, adversely affects the mental health of Hispanics and contributes to an increase of 12%-16% in the number of unhealthy mental days and an increase of 13%-18% in the probability of having frequent mental distress in the states with "show me your papers" laws. OIL provisions that enforced the use of E-Verify or limited the use of public benefits to unauthorized immigrants did not have any effect on mental health. The study also examines (1) police stops, (2) physical health, insurance, and employment status, (3) co-ethnic density, and (4) immigration enforcement awareness as potential mechanisms that could lead to a deterioration in the mental health of Hispanics. The evidence indicates their vulnerability to strict immigration enforcement. The social and public health cost should be carefully evaluated when formulating and implementing immigration policies.

The Association of Broadband Internet Use With Drug Overdose Mortality Rates in the United States: Cross-Sectional Analysis.

BACKGROUND: The availability and use of broadband internet play an increasingly important role in health care and public health. OBJECTIVE: This study examined the associations between broadband internet availability and use with drug overdose deaths in the United States. METHODS: We linked 2019 county-level drug overdose death data in restricted-access multiple causes of death files from the National Vital Statistics System at the US Centers for Disease Control and Prevention with the 2019 county-level broadband internet rollout data from the Federal Communications Commission and the 2019 county-level broadband usage data available from Microsoft's Airband Initiative. Cross-sectional analysis was performed with the fixed-effects regression method to assess the association of broadband internet availability and usage with opioid overdose deaths. Our model also controlled for county-level socioeconomic characteristics and county-level health policy variables. RESULTS: Overall, a 1% increase in broadband internet use was linked with a 1.2% increase in overall drug overdose deaths. No significant association was observed for broadband internet availability. Although similar positive associations were found for both male and female populations, the association varied across different age subgroups. The positive association on overall drug overdose deaths was the greatest among Hispanic and Non-Hispanic White populations. CONCLUSIONS: Broadband internet use was positively associated with increased drug overdose deaths among the overall US population and some subpopulations, even after controlling for broadband availability, sociodemographic characteristics, unemployment, and median household income.

Multi-Site Bundling of Drought Tolerant Maize Varieties and Index Insurance.

Drought Tolerant Maize Varieties (DTMV) and Rainfall Index Insurance (RII) are potential complements, though with limited empirical basis. We employ a multivariate spatial framework to investigate the potential for bundling DTMV with a simulated multi-site and multi-environment RII, designed to insure against mild, moderate and severe drought risk. We use yield data from on-farm trials conducted by the International Maize and Wheat Improvement Center (CIMMYT) and partners over 49 locations in Eastern and Southern Africa spanning 8 countries and 5 mega-environments (dry lowland, dry mid altitude, wet lower mid altitude, low wetland and wet upper mid altitude) in which 19 different improved maize varieties including DTMV were tested at each location. Spatially correlated daily rainfall data are generated from a first-order two-state Markov chain process and used to calibrate the index and predict yields with a hierarchical Bayes multivariate spatial model. Results show high variation in the performance and benefits of different bundles which depend on the maize variety, the risk layer insured, and the type of environment, with high chances of selecting a sub-optimal and unattractive contract. We find that complementing RII with a specific DTMV produces contracts with lower premiums and higher guaranteed returns especially in dry lowland increasing the chances of scaling up RII within this environment.