Policy Research Working Paper Series Publication roundup for the weeks of February 1 and 8

This blog is a biweekly feature highlighting recent working papers from around the World Bank Group that were published in the World Bank’s Policy Research Working Paper Series. This entry introduces seven papers published during the weeks of February 1 and 8 on various topics, including the COVID-19 pandemic crisis, the use of a new data source (such as nighttime data), and the measurement of poverty. Here are the highlights of select findings.

Satellite imagery data have become widely adopted in economic research. In this week’s entry, we introduce two papers exploring the potential of nighttime light data as alternative proxies for measuring economic activity. A paper by Mark Roberts, Tracking Economic Activity in Response to the COVID-19 Crisis Using Nighttime Lights investigates the use of high-frequency nighttime light data to facilitate the real-time tracking of the economic impacts of the COVID-19 crisis in Morocco. The next paper by Sam Asher and coauthors, Development Research at High Geographic Resolution introduces the Socioeconomic High-resolution Rural-Urban Geographic Dataset on India (SHRUG), an open-data platform that the authors developed. With a consistent set of identifiers, the SHRUG can link a broad spectrum of data quickly. In the paper, the authors present three analyses that are possible only with high-resolution data, such as the SHRUG.

• Tracking Economic Activity in Response to the COVID-19 Crisis Using Nighttime Lights: The results support the use of nighttime light data to track the economic impacts of the COVID-19 crisis at both higher temporal frequencies and the subnational level. The author finds a strong correlation between quarterly movements in Morocco’s overall nighttime light intensity and movements in its real GDP. At the subnational level, Rabat – Salé – Kénitra, Tanger – Tetouan – Al Hoceima, and Fès – Meknès suffered much larger declines than other regions did. 
• Development Research at High Geographic Resolution: The analysis confirms that night lights are highly significant proxies for population, employment, per-capita consumption, and electrification at very local levels. However, elasticities between night lights and these variables are far lower in a time series than in a cross-section, and they vary widely across context and level of aggregation. It suggests that researchers should exercise extreme caution when applying regional luminosity to GDP elasticities. 

The next two papers measure poverty, vulnerability, and a broader concept of quality of life in unique environments: a refugee camp and a disaster-prone area. Refugee camps are believed to be safe havens for forcibly displaced persons. Yet, little evidence exists on how living arrangements affect the refugees' quality of life. A paper by Chinedu Temple Obi, The Impact of Living Arrangements (In-Camp versus Out-of-Camp) on the Quality of Life investigates how Syrian refugees’ quality of life in camps in Jordan differs from that of Syrian refugees residing outside of camps. In a disaster-prone area, consumption is highly volatile due to repeated droughts or flooding. So, understanding “vulnerability to poverty” has more important policy implications than standard poverty measures do. A paper by  Emmanuel Skoufias, Katja Vinha, and Berhe Mekonnen Beyene, Quantifying Vulnerability to Poverty in the Drought-Prone Lowlands of Ethiopia estimates vulnerability to poverty in the drought-prone lowlands of Ethiopia.

• The Impact of Living Arrangements (In-Camp versus Out-of-Camp) on the Quality of Life explains that refugees who live in camps have a lower quality of life than those living outside of them do. On average, refugees in camps are 36% more likely to live below the national abject poverty line, meaning that they find it difficult to meet their daily basic needs. In addition, they are 37% more likely to live in overcrowded shelters. They own fewer household assets than refugees living outside of camps do and are less satisfied with water, electricity, and sewerage access. 
• Quantifying Vulnerability to Poverty in the Drought-Prone Lowlands of Ethiopia reveals that vulnerability is remarkably higher in the drought-prone lowlands than in the other ecological zones, although the differences in the poverty rates are modest. The vulnerability rate is more than two times larger than the poverty rate is in the lowlands. The analysis also reveals important distinctions in the sources of vulnerability.

Finally, we introduce two papers investigating gender differences in antibiotic use in Mali and a place-based policy in India. In Sub-Saharan Africa, excess female mortality has been an important development challenge, but little evidence exists that men and women are receiving systematically different levels of health care. A paper by Christine Blandhol and Anja Sautmann, Gender Differences in Children's Antibiotic Use and Adherence examines gender differences in the use of antibiotics for children under 5 years in poor households in Mali.  The next paper by David Blakeslee and coauthors, Land Rezoning and Structural Transformation in Rural India explores the effects of the Industrial Areas (IA) program in Karnataka, India. Under this program, the state government rezones agricultural land for industrial use. However, in contrast to other place-based policies, no financial incentives are offered to firms in IAs. This paper provides new evidence on whether such limited incentives succeed in drawing manufacturing firms to IAs in rural locations.  

• With unique daily in-home health records of 1,763 children in Mali, Gender Differences in Children's Antibiotic Use and Adherence shows that boys are much more likely to take the antibiotic course for longer periods (Figure1). Girls are 10 to 14 percentage points less likely to complete an antibiotics course, and they have an almost five-percentage-point-higher daily probability of ending treatment before the recommended treatment duration has been reached. By contrast, authors finds no gender differences in starting antibiotic treatment. 

Figure 1: Survival distribution of antibiotic treatment lengths (adopted by Blandhol and Sautmann (2021))