The more you know about your enemy, the easier it is to beat it. This is true for wars against armies, diseases or corruption -- good information will tell you where to best deploy your soldiers, doctors and auditors. The same happens in the fight against poverty. You want your resources to go where they help the poor the most. For that you need accurate, frequent, timely, comprehensive, comparable, consistent, and accessible data. And that is exactly what we are beginning to get. In fact, data is transforming the development profession -- you can call it the revenge of the statisticians.
Here is how that transformation is happening. First, the funding of multilateral institutions -- like the World Bank -- is now more closely linked to the results they promise to achieve. To get money from taxpayers, they have to commit to specific "goals." How much will infant mortality fall? How many children will be vaccinated? How many girls will learn to read and write? What proportion of women will have access to contraceptives? By when? All this is creating a culture of monitoring and evaluation -- "M & E," in development parlance -- that is bringing light to what works and what does not work. For that, you need data.
Second, data is doing to public subsidies today what privatization did to public enterprises two decades ago: it is lifting the veil of inefficiency. With better household surveys, we can identify who exactly benefits from every dollar the government spends -- not surprisingly, this is called "benefit incidence analysis." Take education. Most developing countries spend more bankrolling free public universities than building primary schools. But the main beneficiaries of that subsidized college education are the rich (who could pay tuition) not the poor (who could not). You see the inefficiency? The same applies for subsidies to gasoline (who owns cars?), electricity (who has larger houses?), or pensions (who has formal jobs?). Statistics lets you quantify these aberrations -- and argue that the money should be redirected towards those that really need it.
Third, better data is allowing us to focus on poor people's non-cognitive skills. You see, whether you get a good job -- or any job -- does not only depend on how many exams you pass, how much you know, or what your IQ is. It also depends on things like how conscientious you are, how you react to new experiences, or how well you interact with others -- think of it as the "non-cognitive" side of your resume. Is it better to be smarter or to be on time? To know more or to listen more? To be trained or to be trainable? Household surveys are beginning to gather information that will, one day, allow us to answer those questions -- Peru is a leader in this among emerging economies. And when we get the answers, we will be able to design educational curricula to teach not just the concepts, but also the behaviors that make people more productive.
Fourth, it is possible to determine how personal circumstances affect human opportunity. We all know that children have no control or responsibility over their gender, skin color, birthplace, or parents' income. And yet, those kind of circumstances are sure-shot predictors of a child's access to vaccination, potable water, kindergarten, the internet and many other platforms without which her probability of success is close to nil -- well before she can make any choice by herself. This can now be measured, something that was impossible only a few years ago. The measure is called "Human Opportunity Index," and is beginning to change how social policy is designed.
Finally, we have broken the taboo of experimenting with people. It is no longer unusual for researchers to walk into a slum, offer child care to a sample of mothers, and then monitor whether they work outside the home more hours than those with no child care at all. (FYI: they don't always do). These type of "randomized trials" are proving really useful to assess what policies and what projects work best -- and which are a waste of time and money. From giving cheap fertilizer to farmers to making cheap loans to female micro-entrepreneurs, you can evaluate anything, as long as you have -- or create -- the data.
By now, you are probably wondering where all this data is going to come from. Isn't it true that most national statistical offices in the developing world are somewhere between weak and very weak? After all, those all-important household surveys, when they exist, get published years after they are collected. Millions are rightly being spent to upgrade statistical capacity. But it takes a long, long while before you see results -- which explains why politicians rarely care about it.
Is there a short-cut? Is there a fast way to get the data we need to help the poor? Yes, and it is probably sitting in your pocket, in your purse or on your belt. It is your cell-phone. It turns out that people will happily sign up to answer a couple of short phone surveys a month in exchange for "free minutes" of phone use. How many minutes? On average, less than five dollars worth of minutes per month. (Yes, that's how cheap we all are.) This is a bargain because you don't have to call more than a tenth of 1 percent of your population to get a good reading of how your country is doing.
As the use of cellular telephony expands among the poor -- at flash speed in places like Kenya --the possibility of turning them into data sources becomes real. In fact, some of this is already happening in Latin-America, and may soon catch on in Africa. Others will surely follow. How ironic that, in the end, the war against poverty may be won when those who try to help the poor get to literally listen to them.
Follow Marcelo Giugale on Twitter: www.twitter.com/@Marcelo_WB
Melinda Gates: Making a Difference One Life at a Time
Ian Fletcher: Free Trade Isn't Helping World Poverty
Marcelo Giugale: Memos From the Developing World: The End of Poverty
ERS/USDA Data - County-Level Poverty Rates
"Yes, let’s pursue and obtain useful data from the ground, but at a scale at which information can be easily generated, utilized, and acted upon by those we are trying to serve."
Let's not forget that the local complexities can't be always measured in numbers. Qualitative research is as, if not more, important than statistics. Participatory M
2/2 Qualitative research is as, if not more, important than statistics. Participatory M
2/2 Qualitative research is as, if not more, important than statistics. Participatory Monitoring and Evaluation, media and ICTs for development are advancing on this quest to "literally listen" to the real development experts: the beneficiaries.
As someone who has worked extensively to build the monitoring & evaluation capacity of over 300 grassroots organizations in southern and east Africa, this latest trend towards results-results-results is especially troubling when it comes to community-led initiatives. The burden of data gathering for donors on local leaders who are in the process of organizing at the grassroots level can be a tremendous drain on their time and scarce resources. Moreover, abstract metrics don’t help local leaders understand their relationship to improving the well-being of the people they serve.
Yes, let’s pursue and obtain useful data from the ground, but at a scale at which information can be easily generated, utilized, and acted upon by those we are trying to serve. Data gathering can easily become data “extraction” implemented solely for the purpose of accountability can undermine the effectiveness of the very programs it is trying to measure. Let’s always consider what is the appropriate cost and complexity needed for measurement (especially given the size and scope of programs and organizations) aiming for proportional expectations so we ensure data is a tool for learning, not policing.
Actually, econometric data from countries forced to suffer neoliberalization in exchange for World Bank loans demonstrate the catastrophic social and economic consequences of privatization.
Please stop pretending that the World Bank is interested in "the fight against poverty." Be honest about what your institution has been since the purge of Keynesian influences in the early 80s, an instrument for the brutal extraction of wealth from the masses in "developing" countries - an ideological term that mistakenly implies that capitalism is the final stage of history.
Regardless of the quality or amount of data, it will end up in the hands of the few to hold over over the many. As you have already admitted to in your article:
"Second, data is doing to public subsidies today what privatization did to public enterprises two decades ago ..."
Nothing could be worse Public policy than what the IMF and WB have done in the past with respect to Water, perhaps the most basic of human needs.
Read Paul Hawken's "Blessed Unrest" for more on the WB:
http://www.amazon.com/Blessed-Unrest-Largest-Movement-Restoring/dp/0143113658/ref=sr_1_1?s=books&ie=UTF8&qid=1305255492&sr=1-1
In particular, the chapter, "We Interrupt This Empire."
Why? Because data is about facts, but politics is about values.
Data doesn't change minds in politics. It hardens them. It's used as a weapon. It's also used to distort the truth as much as it's used to show the truth.
Which is why the most important questions will never be "how do we measure this?" or "how do we make the best use of our budget allowance?" They will be "what do we stand for" and "what are our priorities?" Data doesn't help answer those questions. It can be (and often is) used to undermine them.
It is extremely difficult for dogma to prevail in the face of incontrovertable fact.
'There are three kinds of lies: lies, damned lies, and statistics
Mark Twain
Living in a rural area, most data tends to show that it's not cost effective to do the things that data collection might say needs to be done. Because of our small numbers, we're rarely the recipients of action that "might help most."
It is the inequality that is inherent in theses two words.. subject or developer that's the problem.