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Odds are you’re measuring son preference incorrectly

Seema Jayachandran's picture
When investigating son-biased fertility preferences, the Demographic and Health Surveys (DHS) offer the go-to survey questions:
  • If you could go back to the time you did not have any children and could choose exactly the number of children to have in your whole life, how many would that be?
  • How many of these children would you like to be boys, how many would you like to be girls, and for how many would it not matter if it’s a boy or a girl?

Building Grit in the Classroom and Measuring Changes in it

David McKenzie's picture

About a year ago I reviewed Angela Duckworth’s book on grit. At the time I noted that there were compelling ideas, but that two big issues were that her self-assessed 10-item Grit scale could be very gameable, and that there was really limited rigorous evidence as to whether efforts to improve grit have lasting impacts.

A cool new paper by Sule Alan, Teodora Boneva, and Seda Ertac makes excellent progress on both fronts. They conduct a large-scale experiment in Turkey with almost 3000 fourth-graders (8-10 year olds) in over 100 classrooms in 52 schools (randomization was at the school level, with 23 schools assigned to treatment).

List Experiments for Sensitive Questions – a Methods Bleg

Berk Ozler's picture

About a year ago, I wrote a blog post on issues surrounding data collection and measurement. In it, I talked about “list experiments” for sensitive questions, about which I was not sold at the time. However, now that I have a bunch of studies going to the field at different stages of data collection, many of which are about sensitive topics in adolescent female target populations, I am paying closer attention to them. In my reading and thinking about the topic and how to implement it in our surveys, I came up with a bunch of questions surrounding the optimal implementation of these methods. In addition, there is probably more to be learned on these methods to improve them further, opening up the possibility of experimenting with them when we can. Below are a bunch of things that I am thinking about and, as we still have some time before our data collection tools are finalized, you, our readers, have a chance to help shape them with your comments and feedback.

Skills and agricultural productivity

Markus Goldstein's picture
Do skills matter for agricultural productivity?   Rachid Laajaj and Karen Macours have a fascinating new paper out which looks at this question.   The paper is fundamentally about how to measure skills better, and they put a serious amount of work into that.    But for those of you dying to know the answer – skills do matter, with cognitive, noncognitive, and technical skills explaining about 12.1 to 16.6 of the variation in yields.   Before we delve into that

Tony Atkinson (1944 – 2017) and the measurement of global poverty

Francisco Ferreira's picture

Sir Anthony Atkinson, who was Centennial Professor at the London School of Economics and Fellow of Nuffield College at Oxford, passed away on New Year’s Day, at the age of 72. Tony was a highly distinguished economist: He was a Fellow of the British Academy and a past president of the Econometric Society, the European Economic Association, the International Economic Association and the Royal Economic Society.  He was also an exceedingly decent, kind and generous man.

Although his contributions to economics are wide-ranging, his main field was Public Economics. He was an editor of the Journal of Public Economics for 25 years, and his textbook “Lectures on Public Economics”, co-authored with Joe Stiglitz in 1980, remains a key reference for graduate students to this day. Within the broad field of public economics, Tony published path-breaking work on the measurement, causes and consequences of poverty and inequality – from his early work on Lorenz dominance in 1970, all the way to his more recent joint work with Piketty, Saez and others on the study of top incomes. Over his 50-year academic career, he taught, supervised and examined a large number of PhD students, some of whom came to work at the World Bank at some point in their careers.

Measuring inequality isn’t easy or straightforward - Here’s why

Christoph Lakner's picture

This is the third of three blog posts on recent trends in national inequality.

In earlier blogposts on recent trends in inequality, we had referred to measurement issues that make this exercise challenging. In this blogpost we discuss two such issues: the underlying welfare measure (income or consumption) used to quantify the extent of inequality within a country, and the fact that estimates of inequality based on data from household surveys are likely to underreport incomes of the richest households. There are a number of other measurement challenges, such as those related to survey comparability, which are discussed in Poverty and Shared Prosperity 2016 – for a focus on Africa, also see Poverty in a Rising Africa, published earlier in 2016.

Biting back at malaria: On treatment guidelines and measurement of health service quality

Arndt Reichert's picture

Growing up in a tropical country, one of us (Alfredo) was acutely aware of mosquito-borne diseases such as dengue and malaria. For many years now, vector-control strategies were—and still are—promoted by government- and school-led campaigns to limit the spread of these diseases. Consequently, it is somewhat alarming to know that diseases spread by mosquitoes remain an enormous challenge facing large parts of the developing and even developed world, particularly sub-Saharan Africa. It is perhaps less surprising that our shared interest in the health sector has resulted in a joint paper on assessing the overall quality of the health care system via compliance with established treatment guidelines.

Towards a survey methodology methodology: Guest post by Andrew Dillon

When I was a graduate student and setting off on my first data collection project, my advisors pointed me to the ‘Blue Books’ to provide advice on how to make survey design choices.  The Glewwe and Grosh volumes are still an incredibly useful resource on multi-topic household survey design.  Since the publication of this volume, the rise of panel data collection, increasingly in the form of randomized control trials, has prompted a discussion abo