Measuring human capital using global learning data.

Human capital-that is, resources associated with the knowledge and skills of individuals-is a critical component of economic development1,2. Learning metrics that are comparable for countries globally are necessary to understand and track the formation of human capital. The increasing use of international achievement tests is an important step in this direction3. However, such tests are administered primarily in developed countries4, limiting our ability to analyse learning patterns in developing countries that may have the most to gain from the formation of human capital. Here we bridge this gap by constructing a globally comparable database of 164 countries from 2000 to 2017. The data represent 98% of the global population and developing economies comprise two-thirds of the included countries. Using this dataset, we show that global progress in learning-a priority Sustainable Development Goal-has been limited, despite increasing enrolment in primary and secondary education. Using an accounting exercise that includes a direct measure of schooling quality, we estimate that the role of human capital in explaining income differences across countries ranges from a fifth to half; this result has an intermediate position in the wide range of estimates provided in earlier papers in the literature5-13. Moreover, we show that average estimates mask considerable heterogeneity associated with income grouping across countries and regions. This heterogeneity highlights the importance of including countries at various stages of economic development when analysing the role of human capital in economic development. Finally, we show that our database provides a measure of human capital that is more closely associated with economic growth than current measures that are included in the Penn world tables version 9.014 and the human development index of the United Nations15.

Revealing a safer sex option to reduce HIV risk: a cluster-randomized trial in Botswana.

BACKGROUND: 1.8 million new HIV infections occur every year, disproportionately affecting adolescent girls and young women. Abstinence-only risk avoidance approaches have had limited impact on reducing new infections. This cluster-randomized trial examines a risk reduction approach to curbing risky sex for school-going girls in Botswana. METHODS: The unit of randomization was the school (n = 229). Intervention participants received a 1-h intervention revealing a safer sex option: dating same-age partners which have 5-9x lower HIV prevalence than older partners. Primary outcomes were pregnancy as a proxy for unprotected sex and HIV. Secondary outcomes included self-reported sexual behavior. Generalized linear multilevel models with school-level robust variance for adjusted relative risk ratios were used in an intention-to-treat analysis. RESULTS: At a 12-month follow up, the intervention reduced pregnancy with an adjusted Relative Risk Ratio (aRRR) of .657 [95% CI .433-.997] significant at the 5% level. Effects were largest at junior school (aRRR = .575 [95% CI .394-.841]) and in rural areas (aRRR = .518 [95% CI .323-.831]), significant at the 1% level. There were no significant effects for primary school students, suggesting age of sexual debut and related mechanisms are critical factors in the intervention's effectiveness. Moreover, baseline beliefs of which partner is riskiest mediate the magnitude of effects. CONCLUSIONS: Information on safe sex options can change sexual behavior. The success of the intervention working across contexts will depend on various factors, such as age of sexual debut and baseline beliefs. TRIAL REGISTRATION: Pan African Clinical Trials Registry PACTR201901837047199 . Registered 31 December 2018. Retrospectively registered. This study adheres to CONSORT guidelines.

Investing in school systems: conceptualising returns on investment across the health, education and social protection sectors.

Public policies often aim to improve welfare, economic injustice and reduce inequality, particularly in the social protection, labour, health and education sectors. While these policies frequently operate in silos, the education sphere can operate as a cross-sectoral link. Schools represent a unique locus, with globally hundreds of millions of children attending class every day. A high-profile policy example is school feeding, with over 400 million students worldwide receiving meals in schools. The benefits of harmonising interventions across sectors with a common delivery platform include economies of scale. Moreover, economic evaluation frameworks commonly used to assess policies rarely account for impact across sectors besides their primary intent. For example, school meals are often evaluated for their impact on nutrition, but they also have educational benefits, including increasing attendance and learning and incorporating smallholder farmers into corporate value chains. To address these gaps, we propose the introduction of a comprehensive value-for-money framework for investments toward school systems that acknowledges the return to a common delivery platform-schools-and the multisectoral returns (eg, education, health and nutrition, labour, social protection) emerging from the rollout of school-based programmes. Directly building on benefit-cost analysis methods, this framework could help identify interventions that yield the highest gains in human capital per budget expenditure, with direct implications for finance ministries. Given the detrimental impact of COVID-19 on schoolchildren and human capital, it is urgent to build back stronger and more sustainable welfare systems.

Experimental evidence on learning using low-tech when school is out.

School closures occurred extensively during the COVID-19 pandemic, and occur in other settings, such as teacher strikes and natural disasters. The cost of school closures has proven to be substantial, particularly for households of lower socioeconomic status, but little evidence exists on how to mitigate these learning losses. This paper provides experimental evidence on strategies to support learning when schools close. We conduct a large-scale randomized trial testing two low-technology interventions-SMS messages and phone calls-with parents to support their child in Botswana. The combined treatment improves learning by 0.12 standard deviations, which translates to 0.89 standard deviations of learning per US$100, ranking among the most cost-effective interventions to improve learning. We develop remote assessment innovations, which show robust learning outcomes. Our findings have immediate policy relevance and long-run implications for the role of technology and parents to support education provision during school disruptions.

A global panel database of pandemic policies (Oxford COVID-19 Government Response Tracker).

COVID-19 has prompted unprecedented government action around the world. We introduce the Oxford COVID-19 Government Response Tracker (OxCGRT), a dataset that addresses the need for continuously updated, readily usable and comparable information on policy measures. From 1 January 2020, the data capture government policies related to closure and containment, health and economic policy for more than 180 countries, plus several countries' subnational jurisdictions. Policy responses are recorded on ordinal or continuous scales for 19 policy areas, capturing variation in degree of response. We present two motivating applications of the data, highlighting patterns in the timing of policy adoption and subsequent policy easing and reimposition, and illustrating how the data can be combined with behavioural and epidemiological indicators. This database enables researchers and policymakers to explore the empirical effects of policy responses on the spread of COVID-19 cases and deaths, as well as on economic and social welfare.

Government responses and COVID-19 deaths: Global evidence across multiple pandemic waves.

We provide an assessment of the impact of government closure and containment measures on deaths from COVID-19 across sequential waves of the COVID-19 pandemic globally. Daily data was collected on a range of containment and closure policies for 186 countries from January 1, 2020 until March 11th, 2021. These data were combined into an aggregate stringency index (SI) score for each country on each day (range: 0-100). Countries were divided into successive waves via a mathematical algorithm to identify peaks and troughs of disease. Within our period of analysis, 63 countries experienced at least one wave, 40 countries experienced two waves, and 10 countries saw three waves, as defined by our approach. Within each wave, regression was used to assess the relationship between the strength of government stringency and subsequent deaths related to COVID-19 with a number of controls for time and country-specific demographic, health system, and economic characteristics. Across the full period of our analysis and 113 countries, an increase of 10 points on the SI was linked to 6 percentage points (P < 0.001, 95% CI = [5%, 7%]) lower average daily deaths. In the first wave, in countries that ultimately experiences 3 waves of the pandemic to date, ten additional points on the SI resulted in lower average daily deaths by 21 percentage points (P < .001, 95% CI = [8%, 16%]). This effect was sustained in the third wave with reductions in deaths of 28 percentage points (P < .001, 95% CI = [13%, 21%]). Moreover, interaction effects show that government policies were effective in reducing deaths in all waves in all groups of countries. These findings highlight the enduring importance of non-pharmaceutical responses to COVID-19 over time.

Practical lessons for phone-based assessments of learning.

School closures affecting more than 1.5 billion children are designed to prevent the spread of current public health risks from the COVID-19 pandemic, but they simultaneously introduce new short-term and long-term health risks through lost education. Measuring these effects in real time is critical to inform effective public health responses, and remote phone-based approaches are one of the only viable options with extreme social distancing in place. However, both the health and education literature are sparse on guidance for phone-based assessments. In this article, we draw on our pilot testing of phone-based assessments in Botswana, along with the existing literature on oral testing of reading and mathematics, to propose a series of preliminary practical lessons to guide researchers and service providers as they try phone-based learning assessments. We provide preliminary evidence that phone-based assessments can accurately capture basic numeracy skills. We provide guidance to help teams (1) ensure that children are not put at risk, (2) test the reliability and validity of phone-based measures, (3) use simple instructions and practice items to ensure the assessment is focused on the target skill, not general language and test-taking skills, (4) adapt the items from oral assessments that will be most effective in phone-based assessments, (5) keep assessments brief while still gathering meaningful learning data, (6) use effective strategies to encourage respondents to pick up the phone, (7) build rapport with adult caregivers and youth respondents, (8) choose the most cost-effective medium and (9) account for potential bias in samples.