G²LM|LIC

Globally, hundreds of millions of people suffer not just from chronic poverty, but also acute hardships during particular times of the year. These “lean seasons” commonly occur in rural communities in between the planting and harvest seasons, when stores from the last harvest run out and there is little agricultural work locally. Lean-season migration can help households cope with seasonal poverty by providing an additional income source when labor demand in agriculture is low. All-else equal, migration will be most effective at reducing seasonal poverty when migrants can easily remit their earnings during the lean season, either digitally or by hand.
This policy brief highlights the effect of a seasonal loan program that offered small loans timed to the lean season to households engaged in agriculture and circular labor migration in Nepal. In the sample, the researchers find evidence to remittance constraints: roughly two thirds of households engage in circular migration, over half of remittances are brought back by hand, and remittances rise sharply after the lean season when most migrants return. They designed an intervention to let households access future harvest and remittance income during the lean season when it is more valuable. They find that seasonal loans improve measures of lean season welfare and increase agricultural investment. The agricultural investments result in increased harvest revenue, and households offered the loan receive more remittances throughout the study period.

In the past decade, cash transfer programs have become a popular tool to combat poverty in low- and middle-income countries among governments and donors alike. Since March 2020 alone, over 300 new cash transfer programs have been implemented across 156 countries in response to the COVID-19 pandemic. The vast evidence on cash transfers (CT) suggests substantial positive benefits for beneficiaries after one to four years. Yet, to date, the long-term effects of such programs have rarely been studied and remain unclear. One of the few exceptions is Blattman, Fiala, and Martinez (2020), henceforward BFM (2020), who conducted a 9-year follow-up of a cash transfer program in Uganda and, unlike the short-term evaluation (BFM 2014), found only minor sustained effects. The present study pushes even further by adding a 12 year follow-up to the CT intervention examined in BFM (2020), now to look at how CT recipients handle the COVID-19 shock.

The project aims to assess the effects of the COVID-19 pandemic on Sudanese households by means of a rapid phone survey administered to 2,400 randomly selected mobile phone users. This first wave of the survey was carried out from March 4 to May 1, 2021 and inquired about changes in the ability of households to pay for goods and services, in their employment situation since February 2020, and in income and operations for household enterprises and farms. Prior to the pandemic Sudan had already been going through a great deal of economic and political turmoil associated with the overthrow of the Bashir regime in 2019, after months of protests, and the institution of a fragile transition government. Sudan has been experiencing very high inflation rates throughout this turmoil and recently introduced a major devaluation of its currency. While the project is not able to separate the effects of the pandemic from that of the ongoing political and economic turmoil, it does constitute one of the few sources on information on how households are coping through these challenging times.

Researchers in development economics have made great progress towards understanding the link between gender, jobs and poverty by rigorous evaluation of policies at a very micro level. However, the main challenge facing researchers today is to aggregate this knowledge and to gauge the external validity of each specific evaluation. In its sphere, the Jobs of the World Database tries to account for this lack of resources. The dataset harmonises available micro datasets that contain information on labour market activities and poverty to create a multi-country macro data-set that contains information about labour force participation, occupational choice (sector, casual vs regular, wage vs self-employment) split by gender, age, geographic region (urban vs rural), education levels, and wealth quintiles. This dataset covers the time period 1990-2019 and serves two purposes: (1) It would allow researchers to compare their particular setting to averages in other contexts to get a sense of how widespread patterns are and therefore the extent to which their research can be informative for other settings, and (2) It would help to identify unexplained patterns and gaps in research.