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Have the MDGs affected developing country policies and spending? Findings of new 50 country study.

Duncan Green's picture

Portrait of childrenOne 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]

‘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.

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.

Why Performance Measurement for Development Professionals is Critical: Learning from Miller’s Pyramid

Tanya Gupta's picture

According to a training report  no less than $55.4 billion in 2013 was spent on training, including payroll and external products and services, in the US alone. The US and other countries spend a significant amount of money on employee development with the implicit assumption that training is correlated to improved on- the- job performance.   However, what exactly should we measure to ensure that this money is well spent? What is it that we need to measure to determine that employees are performing as expected and thus benefitting from these training expenditures?

Two responses that we often get to this “what should be measured” question are “performance” and “competencies”. The Government Accountability Office (GAO) of the United States defines  performance measurement as the “ongoing monitoring and reporting of program accomplishments, particularly progress toward pre-established goals.”  Performance measures, therefore, help define what success at the workplace means (“accomplishments”), and attempt to quantify performance by tracking the achievement of goals. Competencies are generally viewed as “a cluster of related knowledge, skills, and attitudes” (Parry 1996), and are thought to be measurable, correlated to performance, and can be improved through training.  While closely connected, they are not the same thing. Competencies are acquired skills, while performance is use of those competencies at work. Measurement of both is critical.

How should a Post-2015 Agreement Measure Poverty? Vote for Your Preferred Methodology

Duncan Green's picture

The blog’s been insufficiently techie of late, so step forward ODI’s Emma Samman with a piece + poll on measurement. Maybe the start of a ‘Friday geek ‘ series?

Some one in five people today still cannot provide for their most basic needs, progress on Millennium Development Goal (MDG) 1 (to halve extreme poverty and hunger) notwithstanding. The High-Level Panel report affirms that ‘eradicating extreme poverty from the face of the earth by 2030’ should be at the core of a post-2015 agreement: ‘This is something that leaders have promised time and again throughout history. Today it can actually be done.’ The World Bank has endorsed this viewpoint, as have David Cameron, Barack Obama and The Economist, alongside several NGOs.

But is the goal ambitious enough – in terms of who it targets, and how? We’re exploring these issues as part of Development Progress, a four year project that aims to explore what’s working in development and why. We asked several experts to make proposals as to how to measure poverty in a post-2015 agreement. Their contributions show some consensus, but also several areas of contention.

How Do You Measure History?

Anne-Katrin Arnold's picture

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