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Enrolment in higher education has risen dramatically in Latin America, especially in Chile. Yet graduation and persistence rates remain low. One way to improve graduation and persistence is to use data and analytics to identify students at risk of dropout, target interventions, and evaluate interventions' effectiveness at improving student success. We illustrate the potential of this approach using data from eight Chilean universities. Results show that data available at matriculation are only weakly predictive of persistence, while prediction improves dramatically once data on university grades become available. Some predictors of persistence are under policy control. Financial aid predicts higher persistence, and being denied a first-choice major predicts lower persistence. Student success programmes are ineffective at some universities; they are more effective at others, but when effective they often fail to target the highest risk students. Universities should use data regularly and systematically to identify high-risk students, target them with interventions, and evaluate those interventions' effectiveness.

High-stakes testing pressures schools to raise test scores, but schools respond to pressure in different ways. Some responses produce real, broad increases in learning, but other responses can raise reported test scores without increasing learning. We estimate the effect of an accountability program on reading scores and math scores in Chile. Over a 6-year period, fourth-grade reading and math scores rose by 0.2 to 0.3 standard deviations, on average, and half the rise was due to the accountability program. However, many schools, especially schools serving disadvantaged students, inflated their accountability ratings by having low-performing students miss high-stakes tests. To encourage healthier responses to accountability, we recommend setting accountability goals that are attainable for schools with disadvantaged students, and providing incentives for all students to take high-stakes tests.

Debates in education policy draw on different theories about how to raise children's achievement. The "school competition" theory holds that achievement rises when students can choose among competing schools. The "school resources" theory holds that achievement rises with schools' resources per student. The "family resources" theory holds that achievement rises as parents become more educated and earn higher incomes. We test all three theories in Chile between 2002 and 2013, when reading and math scores rose by 0.2 to 0.3 standard deviations, as school competition, school resources, and family resources all increased. We compare Chilean municipalities in a difference-in-differences analysis, asking whether test scores rose fastest in municipalities with greater increases in school competition, school resources, or family resources. We find that municipal test scores did not rise with school competition but did rise with family resources (parental education, not income) and, to a smaller extent, with school resources (as measured by class sizes). Results favor the family resource theory, and to a lesser extent the school resource theory, but not the school competition theory.

A common criticisms of school choice programs is that, instead of improving student achievement, they would increase school segregation. Parents may use different criteria to choose a school, such as proximity, school quality, or the school's ethnic/racial composition. As a result, the system would be segregated based on the parent's preferences. This research examines the school preferences of indigenous parents and whether ethnic discrimination influences their decision-making process. Longitudinal national-level data from Chile were analyzed using OLS with fixed effects. The results show that indigenous students, particularly those who have suffered ethnic discrimination in middle school, prefer high schools with a higher percentage of indigenous students. Furthermore, it was found that the level of acts of discrimination occurring in middle schools increases as the percentage of indigenous students rises. However, when the proportion of indigenous and non-indigenous students is similar, indigenous students are less likely to face discrimination. [ABSTRACT FROM AUTHOR]

Enrolment in higher education has risen dramatically in Latin America, especially in Chile. Yet graduation and persistence rates remain low. One way to improve graduation and persistence is to use data and analytics to identify students at risk of dropout, target interventions, and evaluate interventions' effectiveness at improving student success. We illustrate the potential of this approach using data from eight Chilean universities. Results show that data available at matriculation are only weakly predictive of persistence, while prediction improves dramatically once data on university grades become available. Some predictors of persistence are under policy control. Financial aid predicts higher persistence, and being denied a first-choice major predicts lower persistence. Student success programmes are ineffective at some universities; they are more effective at others, but when effective they often fail to target the highest risk students. Universities should use data regularly and systematically to identify high-risk students, target them with interventions, and evaluate those interventions' effectiveness. [ABSTRACT FROM AUTHOR]

Current methods used to estimate people's evacuation times during a natural disaster assume that human responses across different social groups are similar. However, individuals respond differently based on their socioeconomic and demographic characteristics and previous knowledge. This article develops the Response Time by Social Vulnerability Index (ReTSVI), which is a methodology to estimate how human response time to evacuation warnings during a natural hazard is affected by considering characteristics related to both physical and social vulnerability. ReTSVI is a three-step methodology: first we calculate a population's evacuation curves considering social vulnerability level, certain demographic information and a model that describes an inundation hazard. Then, we use a mobilization model to generate evacuation maps per level of vulnerability and we also estimate the social vulnerability index for the area of study. In the third step, we combine the results from the second step to generate a map that indicates the percentage of people that could evacuate a hazard zone according to their social vulnerability level. Finally, we provide an example of the application of ReTSVI in a potential case of a severe flood event in Huaraz, Peru. The results show that during the first 5 minutes of the evacuation, the population that lives in neighborhoods with high social vulnerability evacuate 15 % and 22 % fewer people than the neighborhoods with medium and low social vulnerability. These differences gradually decrease over time after the evacuation warning and social vulnerability becomes less relevant after 30 minutes. Using a methodology such as ReTSVI allows first responders to identify areas where the same level of physical vulnerability affects distinct groups differently, providing them with a tool to quantify the differences in time to evacuate and where the resources before and during an evacuation should be preferentially allocated. [ABSTRACT FROM AUTHOR]