One of the many baffling aspects of the post-2015/Sustainable Development Goal process is how little research there has been on the impact of their predecessor, the Millennium Development Goals. That may sound odd, given how often we hear ‘the MDGs are on/off track’ on poverty, health, education etc, but saying ‘the MDG for poverty reduction has been achieved five years ahead of schedule’ is not at all the same as saying ‘the MDGs caused that poverty reduction’ – a classic case of confusing correlation with causation.
So I gave heartfelt thanks when Columbia University’s Elham Seyedsayamdost got in touch after a previous whinge on this topic, and sent me her draft paper for UNDP which, as far as I know, is the first systematic attempt to look at the impact of the MDGs on national government policy. Here’s the abstract, with my commentary in brackets/italics. The full paper is here: MDG Assessment_ES, and Elham would welcome any feedback (es548[at]columbia[dot]edu):
"This study reviews post‐2005 national development strategies of fifty countries from diverse income groups, geographical locations, human development tiers, and ODA (official aid) levels to assess the extent to which national plans have tailored the Millennium Development Goals to their local contexts. Reviewing PRSPs and non‐PRSP national strategies, it presents a mixed picture." [so it’s about plans and policies, rather than what actually happened in terms of implementation, but it’s still way ahead of anything else I’ve seen]
Measuring 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.
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.
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)).
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:
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.
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.