Innovations for Peace and Development (IPD) is an institution of the University of Texas at Austin that supplies, analyzes, and distributes information and data for the professional world of international development. As part of our vision to make complex global phenomena readily understandable to those whom implement change, IPD is launching a new project that tracks the newly formed Asian Infrastructure Investment Bank (AIIB). Our job is to analyze and summarize the global news and opinions as the story of the AIIB unfolds.
My Name is Casey McMahan and I am one of many passionate students (some undergrad, others Masters and PhDs) who is working on this project as part of IPD’s summer intern program. The project is directed by one of IPD’s founders, Dr. Kate Weaver and IPD’s Program Manager, Peter Morrison.
We aim to arm you with ample information to form opinions and challenge claims about all happenings related to the AIIB. While maintaining an extensive database of relevant news, Op-Eds, and articles, we will regularly release summaries and issue-specific blogs and policy briefs. Allow me to introduce you to our “news-mining” methodology.
Every day, at least one intern is combing through news feeds (in either English or Mandarin) and extracting summaries and important themes. We have designed a general categorization system by which we can capture certain ideas and events. Needless to say, much thought went into its craft, but I have every reason to believe the system that stands is comprehensive yet simple, and its flexibility allows for the inevitable curveballs that the course of global news will throw our way.
The first category has the very broad title of geopolitics. Its inclusion in our project originates from the mountains of speculation erected by the world’s economists, academics, and world-watchers. The ideas of AIIB’s creation range from narratives along the lines of “A Bank by China, For China” to “a compliment to U.S. interests”. In this category the base body of tags includes cooperation with other multilateral development banks, U.S. responses and reactions, and Asian economics.
The second category captures news related to AIIB’s structure and policies. Much concern has been expressed about which nations will get which percentages of voting power within the bank. This arena covers potential member states and their motivations for joining/abstaining, AIIB leadership profiles, and perhaps most interestingly, the development of lending policies, loan structures and target areas.
The final category is what we expect to later become the core of this project (as the bank becomes operational). This is aptly titled Program Cycle, and includes everything related to AIIBs actual implementation and effect on the region. Here we will track where the money goes and what the money does. Are the projects effective? Is the AIIB actually closing the Asian Infrastructure Gap, which is supposedly the justification for the bank’s existence?
With the aid of this site, you are able to search for news and events through the lens of the topics mentioned above. The website will provide readers with easy-to-navigate data to answer a myriad of questions such as:
What will be the AIIB’s geographic reach? Should the AIIB be seen as a threat to other lending banks or will it present new opportunities, or perhaps a little bit of both? What will the AIIB’s impact on the region look like? How will AIIB address issues of human rights and environmental degradation that may be associated with Bank projects?
What questions to you have? What else should we be investigating? Want to give us your opinion? Please reach us at firstname.lastname@example.org
Casey McMahan is an undergraduate studying Spanish and Geography at the University of Texas at Austin.
Originally posted on AidData's First Tranche here.
Thursday, May 9, 2013
The International Rescue Committee and researchers from Columbia University conducted an intensive assessment of Tuungane, a community driven reconstruction (CDR) program in the Democratic Republic of Congo (DRC). Tuungane organizes elections of village committees, as well as provides training in leadership, good governance, and social inclusion with the goal that local governments will be more accountable, efficient, transparent, and participatory. By nearly all measures, the program is massive:
- Targeted beneficiary population: 1,780,000 people.
- Budget for phase one: USD $46,309,000.
- Geographic Distribution: 1000s of Kilometers.
Evaluators used an impressively designed, rigorous and robust randomized intervention to assess the impacts of the program. Of the 34 outcome measures evaluated, only two were found to be statistically significant in the expected direction (willingness of the population to complain and to trust in others). Neither of the outcomes are significant at the 99% confidence level. And wonderfully, the evaluators pre-committed to an analysis plan and have stuck to it in their reporting.
By most standards, these results would be pretty damaging to the community driven development (CDD) agenda. Unsurprisingly, and correctly so, it has led to calls for more randomized evaluations on the topic. This can be a good thing as replication of RCTs is crucial.
Currently, the World Bank still supports 400 community driven development (a sister to CDRs) projects in 94 countries, valued at almost $30 billion. Thus more evidence should arrive soon. But how do we separate the push for more replication to identify the actual impact of CDD from efforts to continue to confirm previous biases?
Originally posted on AidData's First Tranche here.
Thursday, July 11, 2013
In a recent Brookings article, entitled “How Effective is the World Bank at Targeting Sub-National Poverty in Africa? A Foray into the Murky World of Geocoded Data,” Laurence Chandy, Natasha Ledlie and Veronika Penciakova, discuss the use of geocoded data to target aid at the sub-national level. Highlighting the World Bank’s Mapping for Results and IFPRI's (International Food Policy Institute) Harvest Choice data collection initiatives, the article explores the allocative efficiency of aid with respect to poverty at the first order administrative (i.e., province, state or governorate) level.
AidData staff, students, and faculty also spend a lot of time collecting high-resolution subnational aid information to assess the targeting efficiency of aid, and along the way we have learned that is critical to map aid from a variety of sources, rather than a single donor, to fully understand aid distribution in any given country.
Using a wealth of aid data from multiple donors in Malawi, we can build upon the analysis in the Brookings piece by examining how donors jointly distribute aid within a single country. The following map shows the locations of aid activities for all bilateral and multilateral donors in Malawi down to the second administrative level (i.e., district).
In order to preliminarily assess the efficiency of aid targeting, we assume an ideal scenario where total aid is distributed sub-nationally according to the proportion of poor people residing in each district. For example, a district with 5% of Malawi’s poor should receive 5% of Malawi’s aid receipts. We use poverty headcounts and population data from Malawi’s Third Integrated Household Survey (IHS3) 2010-2011, to calculate these proportions and compare them to geocoded aid disbursements collected by CCAPS and AidData.
Using this benchmark, we can identify how far the actual allocation of aid deviates from this “ideal”. If the difference for a given district is zero, then one might argue it is receiving resources appropriate to their portion of Malawi’s overall poverty burden. A significant positive or negative difference might reflect that a district is getting more or less than their fair share. The actual results show a relatively well-targeted, yet imperfect distribution of national aid resources.
Poverty rates provide a poor proxy for targeting in Malawi because a large portion of the poor live in populated areas with lower poverty rates. This touches on Chandy’s dilemma – should we target aid to areas with higher numbers of poor people, higher proportions of poor people, or both? Are we more concerned with impacting as many lives as possible or reducing pockets of highly concentrated poverty?
While these geocoded Malawi data represent a tremendous boon for aid transparency and aid effectiveness research, there are limitations to what we can learn from it. Donor project documents do not disaggregate total project funding amounts among project activity locations. Absent better reporting from donors, when project activities occurred in multiple districts, we assumed an equal distribution of resources across all locations. Yet, this is an imperfect description of where resources actually hit the ground and doesn’t convey differential aid impact per dollar spent across sectors and environmental contexts.
We agree with Chandy that targeting should not be equated with proximity to the poor. Such a metric does not take into account other relevant contextual data -- on disease rates, past performance of development projects, the quality of local governance, climate change vulnerability, etc. -- that should presumably also inform aid allocation decisions. Geocoded project information supports more nuanced analysis, but merely generating more data is insufficient. In order for donors to be held accountable, the development community must respond to the increasingly availability of geocoded data by coalescing around a set of robust methodologies for assessing the quality of subnational aid targeting efforts.
This post was written by Michael G. Findley, Josiah Marineau, Reid Porter, Jeanette Cunningham Rottas, and Kelly Steffen. Michael G. Findley is an Assistant Professor of Government at the University of Texas-Austin Josiah Marineau, Reid Porter, Jeanette Cunningham Rottas, and Kelly Steffen are Research Fellows at UT-Austin's Innovations for Peace and Development (IPD) research team.