But how do we know where to build these roads and schools? How do we find out who needs health facilities, and what kinds of skills exist in a particular country in order to design better employment programs? How do we know where and which kinds of deprivation exist, in order to design safety net programs that actually work?
The answer? Data. Good data. And lots of it. The World Bank Group’s 2014 Policy Research Report: A Measured Approach to Ending Poverty and Boosting Shared Prosperity, takes a carefully considered view of the progress and challenges of measuring and monitoring the twin goals, and pays special attention to data.
Good data has three essential features. It must help us learn—which usually means that over time it must collect similar information in a similar fashion that is comparable from year to year. It must be timely, to aid fast moving decision making. And the quality of the information collected must not be doubted.
It may come as a surprise to many that for many countries in the world we still do not have good data to enable us to build a solid knowledge of how many people are poor, why they are poor, and what interventions could work for them. I recently traveled to Indonesia and the Philippines, and saw firsthand how good data can help us understand not only the number of people living below the poverty line at a given time, but also to identify those who have just moved out of poverty but are still quite vulnerable to falling back down, and the sources of their vulnerabilities.
In both countries, a mini-census of the bottom 40 percent of the population was used to build up a household registry of poor and near-poor families. These “universal databases” are now being used to target a variety of social assistance and human development programs to reach both poor and vulnerable households. We can and should focus on both groups if we want to succeed in improving the lives of the least well-off in every context—not just in the short-term, but with a view toward sustained upward progress and multigenerational improvements.
We across the development community talk a lot about data gaps, and how important it is to fill them. Consider this statistic: shared prosperity (income growth of the bottom 40 percent) during the last decade can only be measured properly for less than half of the World Bank Group’s client countries.
The Policy Research Report gives us solid arguments as to why now is the time to move past discussion and take real, concrete action to improve the quality and frequency of data collection to measure progress toward the twin goals and to identify the drivers of progress in every country. More and better data are critical to build on our Systematic Country Diagnostics that are creating a solid evidence base to enhance the impact of our support to countries, while identifying data gaps that affect the quality of that evidence.
Improving the quality and frequency of data requires financing and innovation, and the collective will of the development community. It means working differently to make sure that we don’t just know how many people live in extreme poverty, or what the growth rate is among the bottom 40% in every country, but that we use these data to find what needs to be done to make markets, institutions, governments and development agencies work better for poor people. Making data an integral part of the development agenda also requires building a diverse community of stakeholders who “champion” and demand data. This is possible if the development community and governments step up their efforts in making data openly available, inviting scrutiny and free flow of ideas on the use and interpretations of data.
As the Policy Research Report points out, making data work for everyone will take efforts to improve countries’ capacity to collect and assess data, and innovations in statistical methods and data collection technologies. The combination of statistical methods and information technology offers opportunities that would have seemed implausible even a few years back. For example, World Bank teams are fielding surveys through mobile phones to collect real-time data on well-being in remote or fragile areas, and experimenting with systems to collect data, upload them to the “cloud” for validation and use in statistical models to obtain welfare estimates almost instantaneously.
That said, we must recognize that there is no short-cut – technology and statistical methods do not offer a silver bullet where capacity and institutions are lacking, which is why investments in country capacity and statistical systems must go together with expanding the frontiers of innovation in surveys. The costs of such investments are modest relative to total aid. A recent estimate places resources needed to produce such good quality data for a large number of low-income countries to be around 300 million per year.
Now is the time to mobilize real and sustained resources to fill data gaps, improve data quality, and boost country capacity to keep those gaps filled moving forward. This will provide the evidence base to ensure that policies are timely, well-targeted, and effective for the poor and most vulnerable. What are we waiting for?
I strongly agree with your ideas. The challenge that has been affecting most institutions on filling the data gaps has also been due to lack of knowledge of the need for timely and reliable data. I believe investment in statistical capacity building seminars and training sessions for the key players can foster efforts towards generating reliable data and finding solutions to different crisis presented by data.
Joel Wenyira, founder and president Inter-University statistics and capacity building conference(ISCC)-Uganda