International mobility of people is measured much less accurately than that of goods or finances. The most common sources of global data are from national censuses, which occur only every 10 years (and take years more to come out). Specialized surveys in some countries allow more frequent measurement of some flows, but such data are still relatively rare, and poorly suited to studying short-term migration movements.
“Everybody lies.” This is the famous refrain of Dr. Gregory House that is repeated in almost every episode of the TV show House. But, we need not need to take our guidance from an eccentric TV character: academics have been heard stating similar sentiments.
This post is coauthored with Francisco Campos
When we talk about growth, we typically focus on growth rates, and so if we were to look at which countries had the greatest percentage increase in GDP per capita over the last decade (at constant international prices according to the World Development Indicators), we would get a table like this:
This post is coauthored with Francisco Campos
A bat and a ball cost Rs. 1100 in total. The bat costs Rs. 1000 more than the ball. How much does the ball cost? A culturally appropriate GRE? No, this question comes from the cognitive portion of a test designed to measure entrepreneurship in Sri Lanka.
One of my favorite papers to present is my paper on improving management in India, in part because we have wonderful photos to illustrate what bad management looks like and what improved practices look like (see the appendix to the paper for some of these). Photographing impact isn’t only useful for presentations and glossy summaries, but may potentially offer a new form of data. However, this is easier said than done, and today I thought I’d share some misadventures in trying to photograph impacts on small firms.
Mark Rosenzweig and I have just written the preface for a special issue of the Journal of Development Economics focused on measurement and survey design. Rather than just summarize the papers, we tried to draw some lessons/themes of what the 13 papers in the special issue suggest. You can find the preface here.
Here are a couple of the points – read the preface for the full list of lessons:
Worker job satisfaction has been linked to salient measures of performance such as productivity, absenteeism, and workforce turnover. As such it is a construct that economists care about. I’ve recently reviewed research on the determinants of job satisfaction in order to prepare for a study on pay-for-performance reforms in the health sector. And I’ve found a few surprises…
It’s well-worn development wisdom that transfer programs specifically targeting women result in better child outcomes. Presumably this effect works through the empowerment of women in the household, where the shift in relative earnings gives greater weight to the preferences of the woman and less to those of her husband.
I’m currently attending this large conference in lovely Toronto and trying to pack-in as many sessions as possible. A handful of papers have stood out to me – two evaluations of on-going pay-for-performance schemes in health and two methodological papers related to the economics of obesity.
Markus’s previous post on the measurement of sensitive information has started the ball rolling on a major topic that we all confront in field work – accurate measurement. This is an especially acute issue for studies that investigate socially undesirable or stigmatized behaviors such as risky sexual practices or illegal activities.
Regardless of whether we do empirical or theoretical work, we all have to utilize information given to us by others. In the field of development economics, we rely heavily on surveys of individuals, households, facilities, or firms to find out about all sorts of things. However, this reliance has been diminishing over time: we now also collect biological data, try to incorporate more direct observation of human behavior, or conduct audits of firms.
Markus’ s post yesterday is the first on what will be one recurring blog theme here- measurement. I’ll continue the trend today with a focus on one of the most fundamental welfare constructs in economics: consumption. Specifically, how might the development researcher accurately measure household consumption through survey?
Over and over again, and then again, and then some more, we get asked about evidence for the role of public opinion for development. Where's the impact? How do we know that the public really plays a role? What's the evidence, and is the effect size significant? Go turn on the television. Go open your newspaper. Go to any news website. Do tell me how we're supposed to put that in numbers.
Here's a thought: maybe the role of public opinion in development is just too big to be measured in those economic units that we mostly use in development? How do you squeeze history into a regression model? Let's have a little fun with this question. Let's assume that
y = b0 + b1x1 + b2x2 + b3x3 + b4x4 + b5x5 + b6x6 + b7x7 + b8(x1x4) + b9(x3x4) + e