As many middle-income countries are moving towards embracing cash transfers with or without co-responsibilities attached (and the recent hype of handing cash directly to the poor), there is an important wave of programs that provide “cash plus” intervention.
Location: Sarfuddinpur, Bihar
In June this year, I was in Sarfuddinpur, a village in Muzaffarpur district in north-central Bihar. This was my tenth round of qualitative data collection in this village and I wanted to document the stories of a few Self-Help Group, or SHG, leaders; Shakuntala Devi was one of them. I first observed her presiding over an SHG meeting under the village peepal tree in July 2013. She was expertly facilitating a discussion with other SHG members around loans, but also around child health issues and the challenges faced by women in the marketplace. She disciplined free riders and rewarded contributors with a respected leader’s ease. Since then, I have seen her conduct many other meetings.
The limited availability of data on poverty and inequality poses major challenges to the monitoring of the World Bank Group’s twin goals – ending extreme poverty and boosting shared prosperity. According to a recently completed study, for nearly one hundred countries at most two poverty estimates are available over the past decade.Worse still, for around half of them there was either one or no poverty estimate available.* Increasing the frequency of data on poverty is critical to effectively monitoring the Bank’s twin goals.
Against this background, the science of “Big Data” is often looked to as providing a potential solution. A famous example of this science is “Google Flu Trends (GFT)”, which uses search outcomes of Google to predict flu outbreaks. This technology has proven extremely quick to produce predictions and is also very cost-effective. The rapidly increasing volumes of raw data and the accompanying improvement of computer science have enabled us to fill other kinds of data gaps in ways that we could not even have dreamt of in the past.
For a variety of reasons, economists have avoided getting too closely involved with the concept of culture and its relationship to economic development. There is a general acceptance that culture must have a role in guiding a population along a particular path, but, as Landes (1998) points out, a discomfort with what can be construed as implied criticism of a particular culture has discouraged broader public discourse.
As we discuss in a recent paper, the role of culture in economic development is not an easy subject to get a handle on. To start with, one faces issues of definition. The more all-encompassing the definition, the less helpful it tends to be in explaining patterns of development. Economists tend to narrowly define culture as “customary beliefs and values that ethnic, religious, and social groups transmit fairly unchanged from generation to generation” (Guiso, Sapienza and Zingales, 2006). This approach is largely dictated by the aim to identify causal relationships, by focusing on aspects of culture that are constant over time. Not surprisingly, some of the most insightful writing on the subject has been done by anthropologists. Murdock (1965) argues that a culture consists of habits that are shared by members of a society. It is the product of learning, not of heredity. Woolcock (2014) highlights how the sociologic scholarship has evolved to consider culture as “shaping a repertoire or ‘tool kit’ of habits, skills, and styles from which people construct ‘strategies of action” (Swidler, 1986, p.273).
English settlers to the New World believed that the climate of Newfoundland would be moderate, New England would be warm, and Virginia would be like southern Spain. These beliefs were based on the seemingly common sense view that climate is much the same at any given latitude around the globe.
What is striking is that these views persisted despite mounting evidence to the contrary. As late as 1620, after 13 years in the settlement, residents in Jamestown, Virginia, were still trying to import olive trees and other tropical plants, perhaps inspired by Father Andrew White, who had assured them that it was “probable that the soil will prove to be adapted to all the fruits of Italy, figs, pomegranates, oranges, olives, etc.” Eventually, the English settlers did adjust their mental models about North American climate. The accumulation of scientific data, combined with personal experience, was undeniable. But the adjustment was slow and costly, in terms of both money and lives lost.
At 23, starting graduate school for international relations, the prospect of taking economics frightened me. Having just spent my college career as a history major that marched for peace probably had something to do with it. There was also that time in 4th grade when I got a D in math, but we won’t go there.
Anyway, it was a very nice surprise when I found that the math and logic of economics made sense to me. I was proud of myself for “getting it.” And of course, for starting my own subscription to the Financial Times. Ah, the conspicuous consumption patterns of a newly-minted student of economics.
Roger Myerson, eminent theorist and winner of the Nobel in economics, brought his abiding interest in democratic decentralization and development to the World Bank recently. He was hosted by the Development Economics Vice Presidency as a visiting fellow and spent three weeks here writing, thinking and meeting with staff from the Global Practice groups, from the Research Group, and from the East Asia region.
As his main output, Professor Myerson wrote a paper titled ‘Local Foundations for Better Governance: A review of Ghazala Mansuri and Vijayendra Rao’s Localizing Development’. He presented highlights from the paper to a diverse group of Bank staff on November 13. The paper reflects on the theory and evidence for development strategies that are based on local community empowerment; it stresses that a key to viable democratic development in a nation is to increase the supply of leaders with good reputations for using public resources responsibly.
A common approach used to show high mobility is a low correlation of present and past incomes is captured, for instance, by the Hart index (cov lnyt, lnyt-1). If we assume, as is often done, that an individual’s income is comprised of a transitory component (short-term blips up or down in a self-employed person’s income that we can smooth, or even measurement error), and a permanent component where each income shock is persistent (say, an income loss after an involuntary job change (an AR (1) process with autoregressive coefficient, ρ), then the Hart index can be broken into three parts.