Migration, remittances and forests : disentangling the impact of population and economic growth on forests

International migration has increased rapidly in recent decades and this has been accompanied by a remarkable increase in transfers made by migrants to their home countries. This paper investigates the effect of the rural economic growth brought about by migration and remittances on Nepal's Himalayan forests. The authors assemble a unique village-panel dataset combining remote sensing data on land use and forest cover change with data from the census and multiple rounds of living standards surveys to test various inter-relationships between population, economic growth and forests. The results suggest that rural economic growth spurred by remittances has had an overall positive impact on forests. The paper also finds that remittances caused an increase in rural wages and an increase in income, but a decrease in land prices. Considered together, however, the relationship between forests and remittances is driven largely through the income channel, indicating that the demand for amenities provided by forests in the rural Nepali setting may have been more important than factor prices in influencing land use changes for the period of the study.Population Policies,Environmental Economics&Policies,Forestry,Climate Change and Environment,Wildlife Resources

The impact of economic shocks on global undernourishment

This paper estimates the impact of the 2008 food price spike and the 2009 contraction in global growth on undernourishment rates. The analysis is based on a methodology that uses a calorie-income relationship and income distribution data. The authors find that the 2008 global food price spike may have increased global undernourishment by about 6.8 percent, or 63 million people. Moreover, they show that the sharp slowdown in global growth in 2009 could have contributed to 41 million more undernourished people compared with what would have happened if the economic crisis had not occurred.Food&Beverage Industry,Markets and Market Access,Emerging Markets,Economic Theory&Research,Regional Economic Development

Can we rely on cash transfers to protect dietary diversity during food crises ? estimates from Indonesia

The 2008"food price crisis"and more recent spikes in food prices have led to a greater focus on policies and programs to cushion their impact on poverty and malnutrition. Estimating the income elasticity of micro-nutrients and assessing how they change during such crises is an important part of the policy debate as it affects the effectiveness of cash transfer and nutritional supplementation programs. This paper assesses these issues using data from two cross-sectional household surveys in Indonesia carried out before and soon after the 1997/98 economic crisis, which led to a sharp increase in food prices. First, the authors examine how the income elasticity of the starchy staple ratio differs between the two survey rounds using non-parametric as well as regression methods. Second, they provide updated estimates of the income elasticity for important nutrients in Indonesia. The analysis finds that (i) summary measures such as the income elasticity of the starchy staple ratio may not change during crises but this masks important differences across specific nutrients; (ii) methods matter -- the ordinary least squares estimates for the income elasticity of micro-nutrients are likely to be misleading due to measurement error bias; (iii) controlling for measurement error, the income elasticity of some key micro-nutrients, such as iron, calcium, and vitamin B1, is significantly higher in the crisis year compared with a normal year; and (iv) the income elasticity for certain micro-nutrients -- vitamin C in this case -- remains close to zero. These results suggest that cash transfer programs may be even more effective during crises to protect the consumption of many essential micro-nutrients compared with non-crisis periods but in order to ensure that all micro-nutrients are consumed, specific nutritional supplementation programs are also likely to be required.Food&Beverage Industry,Nutrition,Rural Poverty Reduction,Economic Theory&Research,Inequality

The geography of human capital: insights from the subnational human capital index in Indonesia

This paper explores the spatial heterogeneity in the human capital potential of Indonesia’s next generation by constructing and analyzing sub-national human capital indices (HCI) for 34 provinces and 514 districts in Indonesia. The paper identifies data and methodological constraints in the construction of these sub-national indices and proposes and implements strategies to overcome these challenges. Several interesting findings emerge from the analysis. First, Indonesian’s young generation can only achieve 53% of their future productivity relative to the full benchmark of health and education. Second, the variation in aggregate human capital potential across space in Indonesia is staggering: some parts of country are almost at par with countries like Vietnam and China while others have human capital levels that are comparable to Chad, Niger, and Sierra Leone. Third, differences in learning outcomes as measured by harmonized test scores account for the largest share of the variation in human capital across Indonesia, suggesting that the challenge of providing quality education remains one of the most important obstacles to equalizing opportunities for the next generation of Indonesians. And fourth, the correlation between government spending and performance on HCI at the district level appears rather weak, reinforcing conclusions reached by other recent studies that have highlighted the importance of focusing on the quality of spending. Finally, this paper also shows that Indonesia’s human capital registered a modest improvement from 0.50 in 2013 to 0.53 in 2018 with stronger progress observed among the already top performing provinces

Born with a Silver Spoon : Inequality in Educational Achievement across the World

This paper assesses inequality of opportunity in educational achievement using the Human Opportunity Index methodology on data from the Programme for International Student Assessment. The findings suggest that there are large inequalities in learning outcomes as measured by demonstrated proficiency in Programme for International Student Assessment test scores in math, reading, and science. Differences in wealth, parental education, and area of residence explain a bulk of this inequality in most of the countries in the sample. Consistent with what has been documented previously in the literature, the paper also finds a strong and stable correlation between inequality of opportunity and public spending on school education. An exploration of the changes in inequality of opportunity between the 2009 and 2012 rounds of the Programme for International Student Assessment, using parametric and nonparametric techniques, suggests that there has been little progress

Monsoon Babies: Rainfall Shocks and Child Nutrition in Nepal

Do household consumption-smoothing strategies in poor countries entail significant long-run costs in terms of reduced human capital? This paper exploits the timing of monsoon rainfall shocks and the seasonal nature of agriculture to isolate income effects on early childhood anthropometric outcomes in rural Nepal and to provide evidence on the persistence of these effects into later childhood. Findings suggest that a 10 percent increase in rainfall from historic norms during the most recently completed monsoon leads to a 0.15 standard deviation increase in weight-for-age for children ages 0-36 months. This total impact consists of a negative "disease environment effect" of no more than 0.02 standard deviations and a positive "income effect" as high as 0.17 standard deviations. Consistent with this interpretation, excess monsoon rainfall also enhances child stature, but only if the monsoon rainfall shock is experienced in the second year of life. Moreover, this effect on child height is transitory, dissipating completely by age five

Applications of machine learning approaches to combat COVID-19: A survey

Machine learning (ML) and artificial intelligence (AI) approaches are prominent and well established in the field of health-care informatics. Because they have a more productive ability to predict, they are successfully applied in several health-care applications. ML approaches are needed thanks to the unsatisfactory experience of the novel virus, considerable ambiguity, complicated social circumstances, and inadequate accessible data. Several approaches have been applied as a tool to combat and protect against the new diseases. The COVID-19 outbreak has rapid growth, so it is not easy to predict the patients and resources within a specified time. ML is a strong approach in the fighting against the pandemic such as COVID-19. It is found significant to predict the susceptible, infected, recovered, or exposed persons and can assist the control strategies to block the spread of infections. This study critically examines the appropriateness and contribution of AI/ML methods on COVID-19 datasets, enhancing the understanding to apply these methods for quick analysis and verification of pandemic databases. © 2022 Elsevier Inc. All rights reserved