The Effects of the Louisiana Scholarship Program on Student Achievement After Two Years

The Louisiana Scholarship Program (LSP) is a statewide initiative offering publicly-funded vouchers to enroll in local private schools to students in low-performing schools with family income no greater than 250 percent of the poverty line. Initially established in 2008 as a pilot program in New Orleans, the LSP was expanded statewide in 2012. This paper examines the experimental effects of using an LSP scholarship to enroll in a private school on student achievement in the first two years following the program’s expansion. Our results indicate that the use of an LSP scholarship has negatively impacted both ELA and math achievement, although only the latter estimates are statistically significant. Moreover, we observe less negative effect estimates in the second year of the program

Impact evaluation of a conditional cash transfer program

"This paper presents the main findings of a quantitative evaluation of the Red de Protección Social (RPS), a conditional cash transfer program in Nicaragua, against its primary objectives. These included supplementing income to increase household expenditures on food, reducing primary school desertion, and improving the health care and nutritional status of children under age 5. The evaluation design is based on a randomized, community-based intervention with measurements before and after the intervention in both treatment and control communities. Where possible, we erred on the side of assessing effects in conservative manners, for example, in the calculation of standard errors and the treatment of possible control group contamination. Overall, we find that RPS had positive (or favorable) and significant double-difference estimated average effects on a broad range of indicators and outcomes. Where it did not, it was often due to similar, smaller improvements in the control group that appear to have been stimulated indirectly by the program. Most of the estimated effects were larger for the extreme poor. The findings presented here played an important role in the decision to continue this effective program." Authors' AbstractImpact evaluation ,Transfer payments ,Human capital ,Impact assessment ,

A Microbiome-Based Index for Assessing Skin Health and Treatment Effects for Atopic Dermatitis in Children.

A quantitative and objective indicator for skin health via the microbiome is of great interest for personalized skin care, but differences among skin sites and across human populations can make this goal challenging. A three-city (two Chinese and one American) comparison of skin microbiota from atopic dermatitis (AD) and healthy pediatric cohorts revealed that, although city has the greatest effect size (the skin microbiome can predict the originated city with near 100% accuracy), a microbial index of skin health (MiSH) based on 25 bacterial genera can diagnose AD with 83 to ∼95% accuracy within each city and 86.4% accuracy across cities (area under the concentration-time curve [AUC], 0.90). Moreover, nonlesional skin sites across the bodies of AD-active children (which include shank, arm, popliteal fossa, elbow, antecubital fossa, knee, neck, and axilla) harbor a distinct but lesional state-like microbiome that features relative enrichment of Staphylococcus aureus over healthy individuals, confirming the extension of microbiome dysbiosis across body surface in AD patients. Intriguingly, pretreatment MiSH classifies children with identical AD clinical symptoms into two host types with distinct microbial diversity and treatment effects of corticosteroid therapy. These findings suggest that MiSH has the potential to diagnose AD, assess risk-prone state of skin, and predict treatment response in children across human populations.IMPORTANCE MiSH, which is based on the skin microbiome, can quantitatively assess pediatric skin health across cohorts from distinct countries over large geographic distances. Moreover, the index can identify a risk-prone skin state and compare treatment effect in children, suggesting applications in diagnosis and patient stratification