Watching baseball’s Opening Day this week reminded me of how the sport sparked my passion for numbers and statistics at an early age. One of my science fair projects way back when was on a (very limited) statistical analysis of whether or not expansion teams to Major League Baseball benefited pitchers more than batters.
While the use of sabermetrics is relatively new to the game (I’d highly recommend reading Moneyball if you haven’t already), statistics have been a part of baseball since the 19th century and are as much a part of the game as hot dogs and athletic cups.
The greats of baseball were defined by their stats, from their tally of home runs or stolen bases to the number of World Series they led their teams to. Listening to or watching baseball games, you’ll hear the commentator pull some of the most ridiculous statistics, which makes you think about how each part of the game is tracked in incredible detail — down to which team’s players sport the most facial hair.
With all the data that’s collected and pored over for baseball and other sports in the United States, you would think data would be as easily accessible across all areas. Unfortunately, that’s not the case, especially for our work in developing countries. When working with statistics about the developing world, you find a lot of holes due to a whole host of problems. And for the data we do have access to, much of it is outdated or unreliable, as mentioned in a report released by the Center for Global Development last summer:
“But nowhere in the world is the need for better data more urgent than in sub-Saharan Africa — the region with perhaps the most potential for progress under a new development agenda. Despite a decade of rapid economic growth in most countries, the accuracy of the most basic data indicators such as GDP, number of kids attending school, and vaccination rates remains low, and improvements have been sluggish.
This is a problem especially apparent as the United Nations cultivates the Sustainable Development Goals, the successor to the Millennium Development Goals. In order to determine progress toward the 17 goals, the United Nations needs to collect good data to track a wide range of indicators. In the search for good data, it must accept imperfection as is done with plenty of statistics and data in the developed world.
As USAID, other agencies and donors conduct evaluations and analyses to identify critical areas or assess projects effectiveness, the work can often be hindered by the lack of (usable) data.
To address the data void, USAID is finding new and innovative ways for collection and measurement. For instance, the USAID GeoCenter has been engaging with university students in the United States and host countries through mapathons to chart unmapped areas of the world, such as Nepal, Bangladesh and the Philippines. The mapping data, openly shared, provide USAID and partners with better baseline information for monitoring projects. When combined with household surveys, the data can improve analyses and understanding of specific areas of vulnerability within a country.
The development community is far from reaching the level and reliability of statistics collected on Major League Baseball, which has been allowing general managers, coaches and fantasy baseball fanatics to make more informed decisions to improve their teams for decades.
By concentrating efforts to alleviate some of the systematic problems that lead to a lack of data in the first place, the development community would not just be improving access to reliable data, but would be solving some of the underlying problems of developing countries in the first place.
With more abundant, reliable and geocoded data about the developing world, USAID and other organizations can make more informed decisions about how to better target poverty, helping us reach the goal of eradicating extreme poverty by 2030.