It’s hard to argue against the idea that giving cash to someone in need is the best you can do for that person in most circumstances: money maximizes your choice set and any conditions, strings attached, etc. makes that set smaller. With the advance of mobile technologies and better, bigger data, you can now send someone anywhere in the world money and make that person’s life instantly better – at least in the short run. But, what if I told you that with every dollar you send to one poor person, you’re taking away food from a few other people? How should we evaluate the impact of your transfer then?
Along with the Center for Experimental Social Science at Nuffield College at Oxford, eMBeD co-organized a conference called “Measuring the Tricky Things.” The lineup included Susan Fiske presenting a magisterial overview of her decades-long work on the stereotype content model, Armin Falk on his groundbreaking study of time, risk, and social preferences among 80,000 individuals in 65 countries, Karla Hoff on using lab in field experiments to identify the honor ethic among higher caste villagers in North India, Ryan Enos on measuring racial attitudes, Rachel Glennerster on measuring women’s empowerment, Julian Jamison on how and why to use item count techniques to mitigate social desirability bias, Henry Travers on debiasing estimates of wildlife survival, Amandi Mani on assessing the effect of financial worry on cognitive performance with cell phones, and Sheheryar Banuri on using videos to probe the effect of pro-poor bonuses on doctor’s decisions on which patients to see. My eMBeD co-head Renos Vakis assessed the strengths and weaknesses of World Bank surveys on socio-emotional skills. I discussed the reliability and validity of measurements of social norms with respect to women’s labor force participation in Jordan.
- At VoxDev, Sarah Baird, Berk, and I explain why the Econ 101 leisure-labor trade-off model can lead us so far astray in considering the labor impacts of cash transfers of different types (UCTs, CCTs, remittances, pensions, etc.) – and our view of what research needs to measure going forward.
- On the Education for Global Development blog, Holla, Molina and Pushparatnam ask us what The Wire can teach us about psychometrics – with examples of what things to look for in “validating” a test score.
- Women tend to have preferences that are more pro-social and are less risk-taking and less patient on average (the latter I was surprised by, but the difference is not so large). In Science this week, Falk and Hermle look at how these gender differences in preferences are correlated with economic development and gender equality using survey data on 80,000 individuals in 79 countries! They find gender differences increase with GDP and with gender equality – their explanation is “As suggested by the resource hypothesis, greater availability of material resources removes the human need of subsistence, and hence provides the scope for attending to gender-specific preferences. A more egalitarian distribution of material and social resources enables women and men to independently express gender-specific preferences.”
- development impact links
Helena Costa, a smallholder from Sao Tome & Principe, has been investing in her family’s small agribusiness for a decade, wanting it to be more productive, more profitable, and produce quality fruits and vegetable products to supply local and export markets. The quality improvements she’s invested in include food safety practices, shifting to organic production, and planting biofortified crops. However, these food quality improvements are not yet recognized by the market. So, for Helena, improving the nutritional value of her food products is an extra cost that puts her at a disadvantage in relation to her competitors.
“We had lost hope,” said Muneera’s father. “As her health deteriorated and her body weakened, we worried that she could not last much longer.” Six months short of her fourth birthday, Muneera was suffering the effects of malnutrition, which had put her life in danger. Though she lived near Yemen’s capital, Sana’a, Muneera’s family did not have the resources to take her for medical care. Like thousands of other children in Yemen, the deteriorating conditions due to ongoing instability had led to malnutrition.
During the days coming up to, and after October 17, when many stories, numbers, and calls for action will mark the International Day for the Eradication of Poverty, we want to invite you to think for a second on what you imagine a poor household to be like. Is this a husband, wife, and children, or maybe an elderly couple? Are the children girls or boys? And more importantly, do all experience the same deprivations and challenges from the situation they live in? In a recent blog post and paper, we showed that looking at who lives in poor homes—from gender differences to household composition more broadly—matters to better understand and tackle poverty.
Globally, female and male poverty rates—defined as the share of women and men who live in poor households—are very similar (12.8 and 12.3 percent, respectively, based on 2013 data). Even in the two regions with the largest number of poor people (and highest poverty rates)—South Asia and Sub-Saharan Africa—gender differences in poverty rates are quite small. This is true for the regions, but also for individual countries, irrespective of their share of poor people. Why is that the case? As Chapter 5 of the 2018 Poverty and Shared Prosperity Report explains, our standard monetary poverty indicator is measured by household, not by individual. So, a person is classified as either poor or nonpoor according to the poverty status of the household in which she or he lives. This approach critically assumes everyone in the household shares equally in household consumption—be they a father, a young child, or a daughter-in-law. By design, it thus masks differences in individual poverty within a household.
Notwithstanding this shortcoming, when we look a bit deeper the information we have today still shows visible gender differences in poverty rates. Take age, for example. We know that there are more poor children than poor adults, and while we do not find that poverty rates differ much between girls and boys at the early stages of life, stark differences appear between men and women during the peak productive and reproductive years.
Against the backdrop of catastrophic natural disasters that struck in Indonesia, the World Bank Group and IMF Annual Meetings took place last week in Bali. No scene could be more illustrative of the fragility of infrastructure in the face of more extreme and frequent weather events—and the urgent need for meticulous planning, with an eye for resilience.
To mark this year’s End Poverty Day, the World Bank has released its biennial Poverty and Shared Prosperity Report “Piecing Together the Poverty Puzzle”, which documents the dramatic reduction in extreme poverty achieved from 1990 to 2015. In the span of 25 years, the share of people around the world living in extreme poverty line fell from 36% to 10% (from 1.9 billion to 736 million), despite the global population growing from 5 to 7 billion.
The Philippines’ economy has been booming since 2010, growing over 6% per year on average. The country is one of the top performers in the East Asia Pacific region, and its impressive economic performance is reflected in the towering skylines, luxurious condos, and huge shopping malls of Makati and Bonifacio Global City, the financial centers of Metro Manila. However, the country still has over 20% of the population living below national and international poverty line. Old jeepneys, the most popular means of transportation, carrying a massive number of commuters to and from expanding swathes of blighted areas portrait perfectly this contrast. My personal observation was quickly confirmed by the graph below.
The other day I asked my five-year-old daughter if she knew what being poor was. She hesitated at first but soon she was on a roll. She mentioned that being poor was not having enough to eat, not living in a “germ-free” house, and – my favorites – not having gummy bears or a blanket. All this within the first couple of minutes of possibly her first time ever thinking about what being poor meant. The idea of poverty is very intuitive – even for a five-year-old – but equally hard to put boundaries around. It is common to say that poverty doesn’t mean the same thing in different contexts or that it goes beyond monetary dimensions. But what do we mean by that?