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‘Orderly traffic’ as a governance measure: a suggestion

Suvojit Chattopadhyay's picture

Traffic in IndiaMeasuring good governance can be tricky, but 'orderly traffic' can be used as an indicator since it offers insights beyond its limited definition.

As hard as it is to define ‘governance’, we have plenty of indicators to measure its quality: quality of key public services, extent of corruption, ease of doing business, etc. One of the challenges with these indicators is the distance between the process and outcomes, in particular, the assumptions involved in the translation of certain process into tangible outcomes. It follows that by mixing up indicators for processes and outcomes, we risk, well, measuring what doesn’t matter, and not measuring what does matter.

So as the title of this post suggests, could ‘orderly traffic’ be a good measure?

A familiar context: I live in Nairobi (and prior to that, in Delhi) and spend a considerable time waiting in traffic. What often makes traffic a problem is a complete lack of coordination amongst motorists on the road. However, I don’t think the onus of coordination at an intersection should rest on motorists – there are traffic lights or traffic police whose job it is to enforce discipline to ensure orderliness on the road. In many cities though, this is plain theory. In reality, traffic lights may not exist, or be broken; the traffic police may be absent, or just be incompetent. Motorists joust with each other every day and often end up creating gird-locks that hold everyone up. Please note that I am not talking about slow traffic caused purely due to long stops at intersections waiting for the lights to change. I am specifically concerned with the ‘orderliness’ of the flow. People waste time, fuel and a lot of their good humour (unless you are in a zen state) when they are in these gird-locks. It is usually more than evident to everyone whose fault it is and what the solution should be – and that usually only serves to raise tempers on the road. On days when the traffic flows smoothly, everyone seems happier. Zipping home after work is often the high point of the day.

Another reason to prefer Ancova: dealing with changes in measurement between baseline and follow-up

David McKenzie's picture
A few months ago, Berk blogged about my paper on the case for more T, and in particular, on the point that Ancova estimation can deliver a lot more power than difference-in-differences when outcomes are not strongly autocorrelated.

Step-by-step: How to construct an emissions reporting system

Pauline Kennedy's picture
Oil and gas field. Asian Development Bank/Creative Commons


In preparing for a climate agreement in Paris, countries all over the world are planning their domestic strategies for cutting emissions. This often requires new policies to create incentives for low-carbon development, and for that, governments need accurate and comprehensive emissions data.

One important building block is a greenhouse gas reporting program, which a growing number of countries are working on. Mexico, for example, is gathering information from its newly established emissions reporting program to support its mitigation policies. The European Union’s and California’s reporting programs are essential to their emissions trading systems, and China’s reporting program will underpin its national trading system, planned for launch in late 2016.

At Carbon Expo today in Barcelona, the World Bank Group’s Partnership for Market Readiness with the World Resources Institute released the Guide for Designing Mandatory GHG Reporting Programs. Drawing on 13 existing and proposed greenhouse gas emissions reporting programs, the report looks at successful ways to build a strong data collection system and showcases best practices. It provides step-by-step guidance on developing and implementing these reporting programs.

Improving the Granularity of Nighttime Lights Satellite Imagery: Guest Post by Alexei Abrahams

Popular data
Nighttime lights satellite imagery (DMSP-NTL) are now a popular data source among economists. In a sentence, these imagery encompass almost all inhabited areas of the globe, and record the average quantity of light observed at each pixel (nominal size ~1km2) across cloud-free nights for every year, 1992-2012. In under-developed or conflicted regions, where survey or census data at a fine level of spatial and temporal disaggregation are seldom available or reliable or comparable over space or time, NTL and other satellite imagery can be an excellent resource. Recent economics papers have used NTL to study growth of cities in sub-Saharan Africa (Storeygard (2015)), production activity in blockaded Palestinian towns of the West Bank (Abrahams (2015), van der Weide et al (2015)), and urban form in China (Baum-Snow & Turner (2015)) and India (Harari (2015)).

Can you measure flows over short periods? Aka why Justin Wolfers might (NOT) want to reconsider that parenting study

David McKenzie's picture
[EDIT: I POSTED TOO HASTILY HERE, SEE AN ADDENDUM BELOW WHERE I AGREE WITH JUSTIN AFTER ALL]
Today in the Upshot, Justin Wolfers heavily criticizes a recent study that has received lots of media attention claiming that child outcomes are barely correlated with the time that parents spend with their children. He writes:

9 pages or 66 pages? Questionnaire design’s impact on proxy-based poverty measurement

Talip Kilic's picture

This post is co-authored with Thomas Pave Sohnesen

Since 2011, we have struggled to reconcile the poverty trends from two complementary poverty monitoring sources in Malawi. From 2005 to 2009, the Welfare Monitoring Survey (WMS) was used to predict consumption and showed a solid decline in poverty. In contrast, the 2004/05 and 2010/11 rounds of the Integrated Household Survey (IHS) that measured consumption through recall-based modules showed no decline.
 
Today’s blog post is about a household survey experiment and our working paper, which can, at least partially, explain why complementary monitoring tools could provide different results. The results are also relevant for other tools that rely on vastly different instruments to measure the same outcomes.

Measuring Yields from Space

Florence Kondylis's picture

This post is co-authored with Marshall Burke.
One morning last August a number of economists, engineers, Silicon Valley players, donors, and policymakers met on the UC-Berkeley campus to discuss frontier topics in measuring development outcomes. The idea behind the event was not that economists could ask experts to create measurement tools they need, but instead that measurement scientists could tell economists about what was going on at the frontier of measuring development-related outcomes. Instead of waiting for pilot results, we decided to blog about some of these ideas and get inputs from Development Impact readers. In this series, we start with recent progress on measuring (“remote-sensing”) agricultural crop yields from space.

Dialing for Data: The Story of a High Frequency Phone Survey in Liberia

Kristen Himelein's picture

Yesterday the World Bank released their first report on the socioeconomic impacts of Ebola that was based on household data.  The report provides a number of new insights into the crisis in Liberia, showing, for example, an unexpected resiliency in agriculture, and broader economic impacts than previously believed in areas outside the main zones of infection.  As widely reported, prices for staple crops (such as rice) have jumped well above seasonal increases, but additionally we find an important income effect.  We also find the highest prices in the remote southeast of the country, an area that has been relatively unaffected by the disease. The link to the full report can be found here.

Are We Measuring the Right Things? The Latest Multidimensional Poverty Index is Launched Today – What do You Think?

Duncan Green's picture

I’m definitely not a stats geek, but every now and then, I get caught up in some of the nerdy excitement generated by measuring the state of the world. Take today’s launch (in London, but webstreamed) of a new ‘Global Multidimensional Poverty Index 2014’ for example – it’s fascinating.

This is the fourth MPI (the first came out in 2010), and is again produced by the Oxford Poverty and Human Development Initiative (OPHI), led by Sabina Alkire, a definite uber-geek on all things poverty related. The MPI brings together 10 indicators, with equal weighting for education, health and living standards (see table). If you tick a third or more of the boxes, you are counted as poor.

Almost 80 percent of the growth in remittances to developing countries over the past 20 years is an illusion

David McKenzie's picture
Remittances sent by migrant workers to developing countries have soared in the past two decades. According to the World Development Indicators, workers’ remittances to developing countries were just US$47 billion in 1980 (in constant 2011 dollars). After barely rising by 1990 ($49 billion), they doubled by 2000 ($102 billion), and from there, tripled by 2010 ($321 billion).

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