In the United States, the official poverty rate for 2012 stood at 15 percent based on the national poverty line which is equivalent to around $16 per person per day. Of the 46.5 million Americans living in poverty, 20.4 million live under half the poverty line. This begs the question of just how poor America’s poorest people are.

Poverty, in one form or other, exists in every country. But the most acute, absolute manifestations of poverty are assumed to be limited to the developing world. This is reflected in the fact that rich countries tend to set higher poverty lines than poor countries, and that global poverty estimates have traditionally excluded industrialized countries and their populations altogether.

An important study on U.S. poverty by Luke Shaefer and Kathryn Edin gently challenges this assumption. Using an alternative dataset from the one employed for the official U.S. poverty measure, Shaefer and Edin show that millions of Americans live on less than $2 a day—a threshold commonly used to measure poverty in the developing world. Depending on the exact definitions used, they find that up to 5 percent of American households with children are shown to fall under this parsimonious poverty line.

Methodologies for measuring poverty differ wildly both within and across countries, so comparisons and their interpretation demand extreme care.

These numbers are intended to shock—and they succeed. The United States is known for having higher inequality and a less generous social safety net than many affluent countries in Europe, but the acute deprivations that flow from this are less understood. A crude comparison of Shaefer and Edin’s estimates with the World Bank’s official $2 a day poverty estimates for developing economies would place the United States level with or behind a large set of countries, including Russia (0.1 percent), the West Bank and Gaza (0.3 percent), Jordan (1.6 percent), Albania (1.7 percent), urban Argentina (1.9 percent), urban China (3.5 percent), and Thailand (4.1 percent). Many of these countries are recipients of American foreign aid. However, methodologies for measuring poverty differ wildly both within and across countries, so such comparisons and their interpretation demand extreme care.

This brief is organized into two parts. In the first part, we examine the welfare of America’s poorest people using a variety of different data sources and definitions. These generate estimates of the number of Americans living under $2 a day that range from 12 million all the way down to zero. This wide spectrum reflects not only a lack of agreement on how poverty can most reliably be measured, but the particular ways in which poverty is, and isn’t, manifested in the U.S.. In the second part, we reexamine America’s $2 a day poverty in the context of global poverty. We begin by identifying the source and definition of poverty that most faithfully replicates the World Bank’s official poverty measure for the developing world to allow a fairer comparison between the U.S. and developing nations. We then compare the characteristics of poverty in the U.S. and the developing world to provide a more complete picture of the nature of poverty in these different settings. Finally, we explain why comparisons of poverty in the U.S. and the developing world, despite their limitations and pitfalls, are likely to become more common.

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One of global society’s foremost structural changes underway is its rapid aggregate shift from farmbased to city-based economies. More than half of humanity now lives in urban areas, and more than two-thirds of the world’s economies have a majority of their population living in urban settings. Much of the gradual movement from rural to urban areas is driven by long-term forces of economic progress. But one corresponding downside is that city-based societies become increasingly disconnected—certainly physically, and likely psychologically—from the practicalities of rural livelihoods, especially agriculture, the crucial economic sector that provides food to fuel humanity.

The nature of agriculture is especially important when considering the tantalizingly imminent prospect of eliminating extreme poverty within a generation. The majority of the world’s extremely poor people still live in rural areas, where farming is likely to play a central role in boosting average incomes. Agriculture is similarly important when considering environmental challenges like protecting biodiversity and tackling climate change. For example, agriculture and shifts in land use are responsible for roughly a quarter of greenhouse gas emissions.

As a single word, the concept of “agriculture” encompasses a remarkably diverse set of circumstances. It can be defined very simply, as at dictionary.com, as “the science or occupation of cultivating land and rearing crops and livestock.” But underneath that definition lies a vast array of landscape ecologies and climates in which different types of plant and animal species can grow. Focusing solely on crop species, each plant grows within a particular set of respective conditions. Some plants provide food—such as grains, fruits, or vegetables—that people or livestock can consume directly for metabolic energy. Other plants provide stimulants or medication that humans consume—such as coffee or Artemisia—but have no caloric value. Still others provide physical materials—like cotton or rubber—that provide valuable inputs to physical manufacturing.

One of the primary reasons why agriculture’s diversity is so important to understand is that it defines the possibilities, and limits, for the diffusion of relevant technologies. Some crops, like wheat, grow only in temperate areas, so relevant advances in breeding or plant productivity might be relatively easy to diffuse across similar agro-ecological environments but will not naturally transfer to tropical environments, where most of the world’s poor reside. Conversely, for example, rice originates in lowland tropical areas and it has historically been relatively easy to adopt farming technologies from one rice-growing region to another. But, again, its diffusion is limited by geography and climate. Meanwhile maize can grow in both temperate and tropical areas, but its unique germinating properties render it difficult to transfer seed technologies across geographies.

Given the centrality of agriculture in many crucial global challenges, including the internationally agreed Sustainable Development Goals recently established for 2030, it is worth unpacking the topic empirically to describe what the term actually means today. This short paper does so with a focus on developing country crops, answering five basic questions: 

1. What types of crops does each country grow? 

2. Which cereals are most prominent in each country? 

3. Which non-cereal crops are most prominent in each country? 

4. How common are “cash crops” in each country? 

5. How has area harvested been changing recently? 

Readers should note that the following assessments of crop prominence are measured by area harvested, and therefore do not capture each crop’s underlying level of productivity or overarching importance within an economy. For example, a local cereal crop might be worth only $200 per ton of output in a country, but average yields might vary across a spectrum from around 1 to 6 tons per hectare (or even higher). Meanwhile, an export-oriented cash crop like coffee might be worth $2,000 per ton, with potential yields ranging from roughly half a ton to 3 or more tons per hectare. Thus the extent of area harvested forms only one of many variables required for a thorough understanding of local agricultural systems. 

The underlying analysis for this paper was originally conducted for a related book chapter on “Agriculture’s role in ending extreme poverty” (McArthur, 2015). That chapter addresses similar questions for a subset of 61 countries still estimated to be struggling with extreme poverty challenges as of 2011. Here we present data for a broader set of 140 developing countries. All tables are also available online for download.

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Executive Summary

Cities are creations of their states. Their boundaries, their powers, and their responsibilities are all substantially prescribed by state law. With the advent of the new federalism—beginning in the 1970s and resurgent today—the devolution of power from Washington to state capitals has increased the importance of state decision making for cities.

Yet, this shift occurred precisely as cities were losing political clout in state legislatures due to population decline within city limits and rampant growth in suburban jurisdictions.

This paper argues that in response to shifting population distributions within states, cities need to build new coalitions to effectively achieve their legislative goals within state legislatures. Case studies—New York City, Chicago, Detroit, and the three largest cities in Ohio (Cincinnati, Cleveland, and Columbus)—are used to more closely examine coalition-building methods.

Overall, the authors find:

• Cities' dependence on state government has increased as the federal government has ceded more power to the states. As cities' populations have declined, they have become weaker in state legislatures that have grown more powerful due to federal policy. In the peak year of 1978, about 15 percent of city revenues came from the federal government. By 1999 that had decreased to 3 percent. Concurrently, the federal government has shifted a number of programs to the states, which control the rules and revenue mechanisms cities operate under.

• Traditional political coalitions cities have used to achieve their state legislative goals are no longer as effective. Partisan (usually Democratic) coalitions are less reliable as focus has shifted to suburban swing districts. Moreover, as their power has decreased, cities' agendas have become more reactive, aiming to preserve the status quo in funding, infrastructure projects, and autonomy.

• Older, inner-ring suburbs are a logical new partner for cities in state legislatures. Increasingly, these suburbs, and some outer ones, have common interests with central cities as they address immigration, fiscal stress, and infrastructure woes. Such alliances would also better address metropolitan-wide issues on a metropolitan basis.

There remain many obstacles to forging such coalitions, however, including longtime distrust among big cities and their neighbors, racial disparities, and in some cases, growing investment in central cities while surrounding suburbs languish. Nonetheless, for cities to effectively influence their state governments more creative approaches to coalition building must be found.

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Should increasing exports be part of the solution to Greater Cleveland's -- and the nation's -- economic doldrums? Can export growth make this recovery job-filled rather than jobless?

That's a counterintuitive proposition, but one that is gaining traction in Northeast Ohio. Cleveland, Youngstown and other metros often see themselves on the losing end of globalization, as manufacturing has moved abroad and trade barriers and currency manipulations impede the entry of U.S.-made goods into foreign markets.

But exports bring tremendous benefits to workers, companies and the nation as a whole. Exporting companies tend to be more innovative. They pay higher wages across all skill levels. And they are a response to a new global reality: 95 percent of the world's customers live outside the United States.

Any successful export strategy, including the one that the Obama administration is developing, must start with where U.S. exports come from. Our major metropolitan areas are the nation's export hubs. In 2008, they produced about 64 percent of U.S. exports, including more than 62 percent of manufactured goods and 75 percent of services.

Northeast Ohio's major metros are leaders in exports, oriented toward global consumers in a way that most American regions are not. Exports contribute more than 12 percent of the gross metropolitan product in Akron, 13 percent in Cleveland, and a jaw-dropping 18 percent in Youngstown, compared to a national metro average of 10.9 percent.

Exports are also a source of much-needed jobs in these metros. As of 2008 (the most recent year for which we have data) there were 110,000 export jobs in the Cleveland metro and about 30,000 each in greater Akron and Youngstown. Every $1 billion in exports from the average metropolitan area in 2008 supported 5,800 jobs.

To leverage the powerful export activity already occurring in Cleveland and elsewhere, the Obama administration should connect its macroeconomic vision for export growth with the metro reality where the doubling will mostly occur.

For example, the president's export advisory council should include state and local leaders, and revamp export guidance and support to meet the needs of small firms, which find it hard to enter new markets.

But Northeast Ohio metros have their own work to do. The rate of export growth between 2003 and 2008 in Cleveland and Akron is lackluster when compared to the large metro average. U.S. companies dominate the global market in service exports, and the nation actually has a generous service trade surplus, but service exports' share of overall output in Northeast Ohio metros is smaller than the large metro average, and growth in service exports is slower.

Most troubling, Cleveland and its neighbors are underperforming when it comes to innovation, which is a critical ingredient for future international success. Metros that are manufacturing-oriented or export-intensive (or both) tend to create patents at a rate of just over five patents per 1,000 workers. But Cleveland, Akron and Youngstown fall short, with 2.8, 4.5, and 1 patent per 1,000 workers, respectively.

Northeast Ohio must accelerate its efforts to increase the region's innovation and export capacity, through regional organizations such as NorTech and JumpStart. Just as the president set an export goal for the nation, Northeast Ohio should embrace the opportunity to set its own aggressive export goals. Business groups, the Fund for Our Economic Future, universities and regional economic development organizations have made a start but need to devote more resources and collaborate to achieve those goals.

The region can make this happen. Organizations like the Manufacturing and Advocacy and Growth Network (MAGNET) and its partners, with support from the Fund and chambers, are working directly with companies to increase manufacturing innovation in Northeast Ohio, with increasing exports one of their major emphases.

For too long, the debate over export policy has been the exclusive domain of macro policymakers in Washington and a narrow clique of trade constituencies. It is time to include a larger portion of the business sector and, just as importantly, the places like Northeast Ohio, where exporting companies can thrive.

       

       

       

       

       

       

       

       

       

       

       

       

       

       

       

       

       

       

       

       

2015 is a pivotal year for global development; this fall is a pivotal moment. Meetings this fall will determine the global vision for sustainable development for 2030.

Three papers being released today—“Action implications focusing now on implementation of the post-2015 agenda,” “Systemic sustainability as the strategic imperative for the post-2015 agenda,” and “Political decisions and institutional innovations required for systemic transformations envisioned in the post-2015 sustainable development agenda”—set out some foundational ideas and specific proposals for political decisions and institutional innovations, which focus now on the implementation of the new global vision for 2030. This blog summarizes the key points in the three papers listed below.

Fundamentals for guiding actions, reforms and decisions

1) Managing systemic risks needs to be the foundational idea for implementing the post-2015 agenda.

The key political idea latent but not yet fully visible in the post-2015 agenda is that it is not a developing country poverty agenda for global development in the traditional North-South axis but a universal agenda based on the perception of urgent challenges that constitute systemic threats.

The term “sustainable development” by itself as the headline for the P-2015 agenda creates the danger of inheriting terminology from the past to guide the future.

2) Goal-setting and implementation must be effectively linked.

The international community learned from the previous two sets of goal-setting experiences that linking implementation to goal-setting is critical to goal achievement.  G-20 leader engagement in the post-2015 agenda and linking the success of the G-20 presidencies of Turkey (2015), China (2016), and Germany (2017) would provide global leadership for continuity of global awareness and commitment.

3) Focus on the Sustainable Development Goals must be clear.

Criticism of the 17 Sustainable Development Goals (SDGs) as being too defuse and too detailed is ill-founded and reveals a lack of political imagination. It is a simple task to group the 17 goals into a few clusters that clearly communicate their focus on poverty, access, sustainability, partnership, growth, and institutions and their linkages to the social, economic, and environmental systemic threats that are the real and present dangers.

4) There must be a single set of goals for the global system.

The Bretton Woods era is over. It was over before China initiated the creation of the Asian Infrastructure Investment Bank (AIIB) and the BRICS New Development Bank (NDB). Never has it been clearer than now that maintaining a single global system of international institutions is essential for geopolitical reasons. For the implementation of the post-2015 agenda, all the major international institutions need to commit to them.

Proposals for political action and institutional innovations

In a joint paper with Zhang Haibing from the Shanghai Institutes of International Studies (SIIS), we make five specific governance proposals for decision-makers: 

1) Integrating the SDGs into national commitments will be critical.

The implementation of the post-2015 agenda requires that nations internalize the SDGs by debating, adapting and adopting them in terms of their own domestic cultural, institutional, and political circumstances. It will be important for the U.N. declarations in September to urge all countries to undertake domestic decision-making processes toward this end.

2) Presidential coordination committees should be established.

To adequately address systemic risks and to implement the P-2015 agenda requires comprehensive, integrated, cross-sectoral, whole-of-government approaches.  South Korea’s experience with presidential committees composed of ministers with diverse portfolios, private sector and civil society leaders provides an example of how governments could break the “silos” and meet the holistic nature of systemic threats.

3) There needs to be a single global system of international institutions.

China’s Premier Li Keqiang stated at the World Economic Forum in early 2015 that “the world order established after World War II must be maintained, not overturned.” Together with a speech Li gave at the OECD on July 1st after signing an expanded work program agreement with the OECD and becoming a member of the OECD Development Center, clearly signals of China’s intention to cooperate within the current institutional system. The West needs to reciprocate with clear signals of respect for the increasing roles and influence of China and other emerging market economies in global affairs.

4) We must move toward a single global monitoring system for development targets.

The monitoring and evaluation system that accompanies the post-2015 SDGs will be crucial to guiding the implementation of them. The U.N., the OECD, the World Bank, and the IMF have all participated in joint data gathering efforts under the International Development Goals  (IDGs) in the 1990s and the Millennium Development Goals (MDGs) in the 2000s. Each of these institutions has a crucial role to play now, but they need to be brought together under one umbrella to orchestrate their contributions to a comprehensive global data system.

5) Global leadership roles must be strengthened.

By engaging in the post-2015 agenda, the G-20 leaders’ summits would be strengthened by involving G-20 leaders in the people-centered post-2015 agenda. Systemically important countries would be seen as leading on systemically important issues. The G-20 finance ministers can play an appropriate role by serving as the coordinating mechanism for the global system of international institutions for the post-2015 agenda. A G-20 Global Sustainable Development Council, composed of the heads of the presidential committees for sustainable development from G20 countries, could become an effective focal point for assessing systemic sustainability.

These governance innovations could re-energize the G-20 and provide the international community with the leadership, the coordination, and the monitoring capabilities that it needs to implement the post-2015 agenda.

       

       

       

       

     

       

Event Information

Lifting an estimated 1.2 billion people from extreme poverty over the next generation will require robust and broadly-shared economic growth throughout the developing world that is sufficient to generate decent jobs for an ever-expanding global labor force. Innovative but affordable solutions must also be found to meet people’s demand for basic needs like food, housing, a quality education and access to energy resources. And major investments will still be required to effectively address global development challenges, such as climate change and child and maternal health.  On all these fronts, the private sector, from small- and medium-sized enterprises to major global corporations, must play a significant and expanded role.

On August 4-6, 2013, Brookings Global Economy and Development is hosting the tenth annual Brookings Blum Roundtable on Global Poverty in Aspen, Colorado. This year’s roundtable theme, “The Private Sector in the New Global Development Agenda,” brings together global leaders, entrepreneurs, practitioners and public intellectuals to discuss how the contribution of the private sector be enhanced in the push to end poverty over the next generation and how government work more effectively with the private sector to leverage its investments in developing countries. 

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Roundtable Agenda

Sunday, August 4, 2013

Welcome: 8:40AM - 9:00AM MST
Brookings Welcome
• Strobe Talbott, Brookings

Opening Remarks
• Richard C. Blum, Blum Capital Partners, LP and Founder of 
the Blum Center for Developing Economies at UC Berkeley
• Julie Sunderland, Bill and Melinda Gates Foundation
• Kemal Derviş, Global Economy and Development, Brookings

Session I: 9:00AM - 10:30AM MST
Framing Session: Reimagining the Role of the Private Sector
In this opening discussion, participants will explore the overarching questions for the roundtable: How can the contribution of the private sector be enhanced in the push to end poverty over the next generation? What are the most effective mechanisms for strengthening private sector accountability? How can business practices and norms be encouraged that support sustainable development and job creation? How can business build trust in its contributions to sustainable development?

Moderator
• Nancy Birdsall, Center for Global Development

Introductory Remarks
• Homi Kharas, Brookings Institution
• Viswanathan Shankar, Standard Chartered Bank
• Shannon May, Bridge International Academies


Session II: 10:50AM - 12:20PM MST
Private Equity
Participants will explore the following questions for the roundtable: What are the constraints to higher levels of private equity in the developing world, including in non-traditional sectors? How can early-stage investments be promoted to improve deal flow? How can transaction costs and technical assistance costs be lowered?

Moderator
• Laura Tyson, University of California, Berkeley

Introductory Remarks
• Robert van Zwieten, Emerging Markets Private Equity Association
• Runa Alam, Development Partners International
• Vineet Rai, Aavishkaar

Dinner Program: 6:45PM - 9:15PM MST
Aspen Institute Madeleine K. Albright Global Development Lecture

Featuring
• Dr. Paul Farmer, Chief Strategist and Co-Founder, Partners in Health

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Monday, August 5, 2013

Session III: 9:00AM - 10:30AM MST
Goods, Services and Jobs for the Poor
Participants will explore the following questions for the roundtable: In what areas are the most promising emerging business models that serve the poor arising? What are the major obstacles in creating and selling profitable, quality, and beneficial products to the poor and how can they be overcome? What common features distinguish successful and replicable solutions?

Moderator
• Mary Robinson, Mary Robinson Foundation

Introductory Remarks
• Ashish Karamchandani, Monitor Deloitte
• Chris Locke, GSMA
• Ajaita Shah, Frontier Markets
• Hubertus van der Vaart, SEAF

Session IV: 10:50AM - 12:20PM MST
Blended Finance
Participants will explore the following questions for the roundtable: Can standard models of blended finance deliver projects at a large enough scale? How can leverage be measured and incorporated into aid effectiveness measures? Should governments have explicit leverage targets to force change more rapidly and systematically?

Moderator
• Henrietta Fore, Holsman International

Introductory Remarks
• Elizabeth Littlefield, OPIC
• Ewen McDonald, AusAID
• Laurie Spengler, ShoreBank International 

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Tuesday, August 6, 2013 

Session V: 9:00AM - 10:30AM MST
Unlocking Female Entrepreneurship
Participants will explore the following questions for the roundtable: How is the global landscape for female entrepreneurship changing? What types of interventions have the greatest ability to overturn barriers to female entrepreneurship in the developing world? Who, or what institutions, should lead efforts to advance this agenda? Can progress be made without a broader effort to end economic discrimination against women?

Moderator
• Smita Singh, Independent

Introductory Remarks
• Dina Powell, Goldman Sachs
• Carmen Niethammer, IFC
• Randall Kempner, ANDE

Session VI: 10:50AM - 12:20PM MST
U.S. Leadership and Resources to Engage The Private Sector
Participants will explore the following questions for the roundtable: How can U.S. foreign assistance be strengthened to more effectively promote the role of the private sector? How can U.S. diplomacy support private sector development in the emerging economies and multinational enterprises investing in the developing world? What can the US do to promote open innovation platforms?

Moderator
• George Ingram, Brookings

Introductory Remarks
• Sam Worthington, InterAction
• John Podesta, Center for American Progress
• Rajiv Shah, USAID

Closing Remarks
• Richard C. Blum, Blum Capital Partners, LP and Founder of the Blum Center for Developing Economies at Berkeley
• Kemal Derviş, Global Economy and Development, Brookings

Public Event: 4:30PM - 6:00PM MST
Brookings and the Aspen Institute Present: "America's Fiscal Health and its Implications for International Engagement"
Global Economy and Development at Brookings and the Aspen Institute will host the 66th U.S. Secretary of State Condoleezza Rice and Administrator of the U.S. Agency for International Development Rajiv Shah for a discussion on the current state of the U.S.'s fiscal health and its impact on American diplomatic and development priorities. Moderated by Ambassador Nicholas Burns, Director, Aspen Strategy Group.

Moderator
• Nicholas Burns, Director, Aspen Strategy Group

Panelists
• Condoleezza Rice, 66th United States Secretary of State
• Rajiv Shah, Administrator of the United States Agency for International Development

 

Event Materials

• BBR Participant List 2013_FINAL

       

       

Event Information

Register for the Event

The last time policymakers substantially reviewed federal communications policy, it was the early 1990s. At that time, the Internet was only beginning to reveal itself to be the dynamic technology seen today. Mobile devices and services, such as 100 megabit broadband, smartphones, applications, social networks, tablets, and digital streaming, were barely imagined, let alone factored into policy discussions. As the recent debate around net neutrality highlights, policymakers today can be hamstrung in efforts to fit today's communications technologies and services into last century's communications law. Given that most major communications laws are out of step with today’s advanced mobile capabilities, what shape would smart, updated legislation and regulatory changes take? What are the major changes to U.S. communications law that most need to be addressed and implemented?

On March 24, the Center for Technology Innovation at Brookings hosted a conversation with Craig Silliman, general counsel and executive vice president for public policy at Verizon, to examine what 21st century communications polices might look like.

Video

• Updating communications law and regulations for the mobile era

Audio

• Updating communications law and regulations for the mobile era

Transcript

• Uncorrected Transcript (.pdf)

Event Materials

• 20150324_communications_law_transcript

      

There is a lot of hope around the potential of Big Data—massive volumes of data (such as cell phone GPS signals, social media posts, online digital pictures and videos, and transaction records of online purchases) that are large and difficult to process with traditional database and software techniques—to help achieve the sustainable development goals. The United Nations even calls for using the ongoing Data Revolution –the explosion in quantity and diversity of Big Data—to make more and better data usable to inform development analysis, monitoring and policymaking: In fact, the United Nations believes that that “Data are the lifeblood of decision-making and the raw material for accountability. Without high-quality data providing the right information on the right things at the right time; designing, monitoring and evaluating effective policies becomes almost impossible.” The U.N. even held a “Data Innovation for Policy Makers” conference in Jakarta, Indonesia in November 2014 to promote use of Big Data in solving development challenges.

Big Data has already played a role in development: Early uses of it include the detection of influenza epidemics using search engine query data or the estimation of a country’s GDP by using satellite data on night lights. Work is also under way by the World Bank to use Big Data for transport planning in Brazil.

During the Data for Development session at the recent NetMob conference at MIT, we presented a paper in which we jump on the Big Data bandwagon. In the paper, we use mobile phone data to assess how the opening of a new toll highway in Dakar, Senegal is changing how people commute to work (human mobility) in this metropolitan area. The new toll road is one of the largest investments by the government of Senegal and expectations for its developmental impact are high. In particular, the new infrastructure is expected to increase the flow of goods and people into and out of Dakar, spur urban and rural development outside congested areas, and boost land valuation outside Dakar. Our study is a first step in helping policymakers and other stakeholders benchmark the impact of the toll road against many of these objectives.

Assessing how the impact of the new toll highway differs by area and how it changes over time can help policymakers benchmark the performance of their investment and better plan the development of urban areas.

The Dakar Diamniadio Toll Highway

The Dakar Diamniadio Toll Highway (in red in Figure 1), inaugurated on August 1, 2013 is the first section (32 km or 20 miles) of a broader project to connect the capital, Dakar, through a double three-lane highway to a new airport (Aeroport International Blaise Diagne, AIBD) and a special economic zone, the Dakar Integrated Special Economic Zone (DISEZ) and the rest of the country.

Note: The numbers indicate the incidence of increased inter cell mobility and were used to calculate the percentage increase in mobility.

The cost of this large project is estimated to be about $696 million (FCFA 380.2 billion or 22.7 percent of 2014 fiscal revenues, excluding grants) with the government of Senegal having already disbursed $353 million. The project is one of the first toll roads in sub-Saharan Africa (excluding South Africa) structured as a public-private partnership (PPP) and includes multilateral partners such as the World Bank, the French Development Agency, and the African Development Bank.

In our study, we ask whether the new toll road led to an increase in human mobility and, if so, whether particular geographical areas experienced higher or lower mobility relative to others following its opening.

Did the Highway Increase Human Mobility?

Using mobile phone usage data (Big Data), we use statistical analysis in our paper to approximate where people live and where they work. We then estimate how the reduction in travel time following the opening of the toll road changes the way they commute to work.

As illustrated in the map of Figure 1, we find some interesting trends:

• Human mobility in the metropolitan Dakar area increased on average by 1.34 percent after the opening of the Dakar Diamniadio Toll Highway. However, this increase masks important disparities across the different sub-areas of the Dakar metropolitan areas. Areas in blue in Figure 1 are those for which mobility increased after the opening of the new road toll while those in red experienced decreased mobility.
• In particular, the Parcelles Assainies suburban area benefited the most from the toll road with an increase in mobility of 26 percent. The Centre Ville (downtown) area experienced a decrease in mobility of about 20 percent.

These trends are important and would have been difficult to discover without Big Data. Now, though, researchers need to parse through the various reasons these trends might have occurred. For instance, the Parcelles Assainies area may have benefited the most because of its closer location to the toll road whereas the feeder roads in the downtown area may not have been able to absorb the increase in traffic from the toll road. Or people may have moved from the downtown area to less expensive areas in the suburbs now that the new toll road makes commuting faster.

The Success of Big Data

From these preliminary results (our study is work in progress, and we will be improving its methodology), we are encouraged by the fact that our method and use of Big Data has three areas of application for a project such as this:

Benchmarking: Our method can be used to track how the impact of the Dakar Diamniadio Toll Highway changes over time and for different areas of the Dakar metropolitan areas. This process could be used to study other highways in the future and inform highway development overall.

Zooming in: Our analysis is a first step towards a more granular study of the different geographic areas within the Dakar suburban metropolitan area, and perhaps inspire similar studies around the continent. In particular, it would be useful to study the socio-economic context within each area to better appreciate the impact of new infrastructure on people’s lives. For instance, in order to move from estimates of human mobility (traffic) to measures of “accessibility,” it will be useful to complement the current analysis with an analysis of land use, a study of job accessibility, and other labor markets information for specific areas. Regarding accessibility, questions of interest include: Who lives in the areas most/least affected? What kind of jobs do they have access to? What type of infrastructure do they have access to? What is their income level? Answers to these questions can be obtained using satellite information for land prices, survey data (including through mobile phones) and data available from the authorities. Regarding urban planning, questions include: Is the toll diverting the traffic to other areas? What happens in those areas? Do they have the appropriate infrastructure to absorb the increase in traffic?

Zooming out: So far, our analysis is focused on the Dakar metropolitan area, and it would be useful to assess the impact of new infrastructure on mobility between the rest of the country and Dakar. For instance, the analysis can help assess whether the benefits of the toll road spill over to the rest of the country and even differentiate the impact of the toll road on the different regions of the country.

This experience tells us that there are major opportunities in converting Big Data into actionable information, but the impact of Big Data still remains limited. In our case, the use of mobile phone data helped generate timely and relatively inexpensive information on the impact of a large transport infrastructure on human mobility. On the other hand, it is clear that more analysis using socioeconomic data is needed to get to concrete and impactful policy actions. Thus, we think that making such information available to all stakeholders has the potential not only to guide policy action but also to spur it. 

References

Atkin, D. and D. Donaldson (2014). Who ’ s Getting Globalized ? The Size and Implications of Intranational Trade Costs . (February).

Clark, X., D. Dollar, and A. Micco (2004). Port efficiency, maritime transport costs, and bilateral trade. Journal of Development Economics 75(2), 417–450, December.

Donaldson, D. (2013). Railroads of the Raj: Estimating the Impact of Transportation Infrastructure. forthcoming, American Economic Review.

Fetzer Thiemo (2014) “Urban Road Construction and Human Commuting: Evidence from Dakar, Senegal.” Mimeo

Ji, Y. (2011). Understanding Human Mobility Patterns Through Mobile Phone Records : A cross-cultural Study.

Simini, F., M. C. Gonzalez, A. Maritan, and A.-L. Barab´asi (2012). A universal model for mobility and migration patterns. Nature 484(7392), 96–100, April.

Tinbergen, J. (1962). Shaping the World Economy; Suggestions for an International Economic Policy.

Yuan, Y. and M. Raubal (2013). Extracting dynamic urban mobility patterns from mobile phone data.

      

It is estimated that there are 95 mobile phone subscriptions per 100 inhabitants worldwide, and this boom has not been lost on the developing world, where the number of mobile users has also grown at rocket speed. In fact, in recent years the information communication technology (ICT) revolution has provided opportunities leading to “death of distance,” allowing many obstacles to better livelihoods, especially for those in remote regions, to disappear. Remarkably, though, the huge proportion of poverty-stricken populations in so many of those same regions persists.

How might, then, we think differently on the relationship between these two ideas? Can and how might ICTs act as an engine for eradicating poverty and improving the quality of life in terms of better livelihoods, strong education outcomes, and quality health? Do today's communication technologies hold such potential?

In particular, the mobile phone’s accessibility and use creates and provides us with an unprecedented volume of data on social interactions, mobility, and more. So, we ask: Can this data help us better understand, characterize, and alleviate poverty?

Mapping call data records, mobility, and economic activity

The first step towards alleviating poverty is to generate poverty maps. Currently, poverty maps are created using nationally representative household surveys, which require manpower and time. Such maps are generated at a coarse regional resolution and continue to lag for countries in sub-Saharan Africa compared to the rest of the world.

As call data records (CDRs) allow a view of the communication and mobility patterns of people at an unprecedented scale, we show how this data can be used to create much more detailed poverty maps efficiently and at a finer spatial resolution. Such maps will facilitate improved diagnosis of poverty and will assist public policy planners in initiating appropriate interventions, specifically at the decentralized level, to eradicate human poverty and ensure a higher quality of life.

How can we get such high resolution poverty maps from CDR data?

In order to create these detailed poverty maps, we first define the virtual network of a country as a “who-calls-whom” network. This signifies the macro-level view of connections or social ties between people, dissemination of information or knowledge, or dispersal of services. As calls are placed for a variety of reasons, including request for resources, information dissemination, personal etc., CDRs provide an interesting way to construct a virtual network for Senegal.

We start by quantifying the accessibility of mobile connectivity in Senegal, both spatially and across the population, using the CDR data. This quantification measures the amount of communication across various regions in Senegal. The result is a virtual network for Senegal, which is depicted in Figure 1. The circles in the map correspond to regional capitals, and the edges correspond to volume of mobile communication between them. Thicker edges mean higher volume of communication. Bigger circles mean heavier incoming and outgoing communication for that region.

Figure 1: Virtual network for Senegal with MPI as an overlay

Source: Author’s rendering of the virtual network of Senegal based on the dataset of CDRs provided as a part of D4D Senegal Challenge 2015

Figure 1 also shows the regional poverty index[1] as an overlay. A high poverty index corresponds to very poor regions, which are shown lighter green on the map. It is evident that regions with plenty of strong edges have lower poverty, while most poor regions appear isolated. 

Now, how can we give a more detailed look at the distribution of poverty? Using the virtual network, we extract quantitative metrics indicating the centrality of each region in Senegal. We then calculate centrality measures of all the arrondissements[2] within a region. We then correlate these regional centrality measures with the poverty index to build a regression model. Using the regression model, we predict the poverty index for each arrondissement.

Figure 2 shows the poverty map generated by our model for Senegal at an arrondissement level. It is interesting to see finer disaggregation of poverty to identify pockets of arrondissement, which are most in need of sustained growth. The poorer arrondissements are shown lighter green in color with high values for the poverty index.

Figure 2: Predicted poverty map at the arrondissement level for Senegal with MPI as an overlay

Source: Author’s rendering of the virtual network of Senegal based on the dataset of CDRs provided as a part of D4D Senegal Challenge 2015.

What is next for call data records and other Big Data in relation to eradicating poverty and improving the human development?

This investigation is only the beginning. Since poverty is a complex phenomenon, poverty maps showcasing multiple perspectives, such as ours, provide policymakers with better insights for effective responses for poverty eradication. As noted above, these maps can be used for decomposing information on deprivation of health, education, and living standards—the main indicators of human development index.

Even more particularly, we believe that this Big Data and our models can generate disaggregated poverty maps for Senegal based on gender, the urban/rural gap, or ethnic/social divisions. Such poverty maps will assist in policy planning for inclusive and sustained growth of all sections of society. Our methodology is generic and can be used to study other socio-economic indicators of the society.

Like many uses of Big Data, our model is in its nascent stages. Currently, we are working towards testing our methodology at the ground level in Senegal, so that it can be further updated based on the needs of the people and developmental interventions can be planned. The pilot project will help to "replicate" our methodology in other underdeveloped countries.

In the forthcoming post-2015 development agenda intergovernmental negotiations, the United Nations would like to ensure the “measurability, achievability of the targets” along with identification of 'technically rigorous indicators' for development. It is in this context that Big Data can be extremely helpful in tackling extreme poverty.

Note: This examination was part of the "Data for Development Senegal" Challenge, which focused on how to use Big Data for grass-root development. We took part in the Data Challenge, which was held in conjunction with NetMob 2015 at MIT from April 7-10, 2015. Our team received the National Statistics prize for our project titled, "Virtual Network and Poverty Analysis in Senegal.” This blog reflects the views of the authors only and does not reflect the views of the Africa Growth Initiative.

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[1] As a measure of poverty, we have used the Multidimensional Poverty Index (MPI), which is a composite of 10 indicators across the three areas: education (years of schooling, school enrollment), health (malnutrition, child mortality), and living conditions.

[2] Senegal is divided into 14 administrative regions, which are further divided into 123 arrondissements.

      

Mobile devices are making a big difference in the lives of billions of people around the world who use them every day. Internet-enabled smartphones and tablets provide access to information and a channel of communication for users. Building wireless networks to support mobile devices requires large capital investments from wireless carriers who must purchase wireless spectrum and infrastructure. To ensure that mobile services are reliable and affordable, national governments must allocate enough wireless spectrum to commercial carriers to satisfy demand. This is the subject of a new paper from Shamika Ravi and Darrell M. West titled “Spectrum Policy in India."

A scarce resource

Mobile devices typically operate on frequencies from 30 kHz to 300 GHz on the radio spectrum. Unless spectrum is allocated efficiently, the scarcity of available frequencies leads to poor quality and high costs for mobile broadband. The growing demand for mobile service in India currently exceeds the amount of spectrum available to wireless carriers. The scarcity of wireless spectrum limits reliable Internet access for mobile subscribers who have no alternative point of access. According to the Cellular Operators Association of India, nearly 60 percent of Internet users only have access through their mobile phones.

Mobile service in India is relatively expensive for many consumers because the Indian military reserves so much spectrum for their own use. Much of this spectrum goes underutilized, even as commercial carriers plead for more spectrum to be released. When the Indian government does release spectrum, it is typically through auctions with high starting bids. Setting high starting bids for blocks of spectrum can lead to high selling prices that force wireless carriers to take out large loans. Higher prices for spectrum raise costs for consumers and reduce private sector investment in wireless infrastructure. Rather than make spectrum artificially scarce, the Indian government should work with wireless carriers to lower the prices for consumers. 

Investing in India’s future

Reliable mobile service has the potential to greatly enhance economic growth in India. Analysis from the Boston Consulting Group found that the India’s mobile sector grew at 12.4 percent annually from 2009-2014; it now accounts for 2.2 percent of India’s gross domestic product. Potential growth comes from filling gaps in educational and health care spending in rural communities. Innovative mobile applications provide a low cost method of sending education and health care resources to underserved rural communities that lack physical infrastructure. In India’s rapidly growing cities, mobile services are seen as a way to improve the quality of government services and promote entrepreneurship. Prime Minister Narendra Modi recently designated 100 “smart cities” that would use technology to overcome the challenges of India’s rapid urbanization.

India could free up spectrum by adopting the “NATO Band” of spectrum for military uses and auctioning off the remaining spectrum.  The NATO band is used by the militaries of NATO member countries and several of their allies, and it already overlaps with much of the Indian military’s spectrum.  Furthermore, the Indian government must lower the minimum bids at spectrum auctions and lower taxes so that wireless carriers have enough profits to build their networks.  Mobile technologies are rapidly evolving, and each new generation has greater demands for spectrum. Regulators in India will not only have to maintain affordable prices for the current generation of mobile technology, but also anticipate upgrades that will deliver more data at faster speeds.

      

Last month, we experienced yet another breakthrough in the epic battle of man against machine. Google’s AlphaGo won against the reigning Go champion Lee Sedol. This success, however, was different than that of IBM’s Deep Blue against Gary Kasparov in 1987. While Deep Blue still applied “brute force” to calculate all possible options ahead, AlphaGo was learning as the game progressed. And through this computing breakthrough that we can learn how to better leverage the data revolution.

In the game of Go, brute-force strategies don’t help because the total number of possible combinations exceeds the number of atoms in the universe. Some games, including some we played since childhood, were immune to computing “firepower” for a long time. For example, Connect Four wasn’t solved until 1995 with the conclusion being the first player can force a win. And checkers wasn’t until 2007, when Jonathan Schaeffer determined that in a perfect game, both sides could force a draw. For chess, a safe strategy has yet to be developed, meaning that we don’t know yet if white could force a win or, like in checkers, black could manage to hold on to a draw.

But most real-life situations are more complicated than chess, precisely because the universe of options is unlimited and solving them requires learning. If computers are to help, beyond their use as glorified calculators, they need to be able to learn. This is the starting point of the artificial intelligence movement.  In a world where perfection is impossible, you need well-informed intuition in order to advance. The first breakthrough in this space occurred when IBM’s Watson beat America’s Jeopardy! champions in 2011. These new intelligent machines operate in probabilities, not in certainty.

That being said, perfection remains important, especially when it comes to matters of life and death such as flying airplanes, constructing houses, or conducting heart surgery, as these areas require as much attention to detail as possible. At the same time, in many realms of life and policymaking we fall into a perfection trap. We often generate obsolete knowledge by attempting to explain things perfectly, when effective problem solving would have been better served by real-time estimates. We strive for exactitude when rough results, more often than not, are good enough.

By contrast, some of today’s breakthroughs are based on approximation. Think of Google Translate and Google’s search engine itself. The results are typically quite bad, but compared to the alternative of not having them at all, or spending hours leafing through an encyclopedia, they are wonderful. Moreover, once these imperfect breakthroughs are available, one can improve them iteratively. Only once the first IBM and Apple PCs were put on the market in the 1980s did the cycle of upgrading start, which still continues today.

In the realm of social and economic data, we have yet to reach this stage of “managed imperfection” and continuous upgrading. We are producing social and economic forecasts with solid 20th century methods. With extreme care we conduct poverty assessments and maps, usually taking at least a year to produce as they involve hundreds of enumerators, lengthy interviews and laborious data entry. Through these methods we are able to perfectly explain past events, but we fail to estimate current trends—even imperfectly.

The paradox of today’s big data era is that most of that data is poor and messy, even though the possibilities for improving it are unlimited. Almost every report from development institutions starts with a disclaimer highlighting “severe data limitations.” This is because only 0.5 percent of all the available data is actually being curated to be made usable. If data is the oil of the 21st century, we need data refineries to convert the raw product into something that can be consumed by the average person.

Thanks to the prevalence of mobile device and rapid advances in satellite technology, it is possible to produce more data faster, better, and cheaper. High-frequency data also makes it possible to make big data personal, which also increases the likelihood that people act on it. Ultimately, the breakthroughs in big data for development will be driven by managerial cultures, as has been the case with other successful ventures. Risk averse cultures pay great attention to perfection. They nurture the fear of mistakes and losing. Modern management accepts failure, encourages trial and error, and reaches progress through interaction and continuous upgrading.

       

La monnaie électronique a émergé dans les pays de l'Union Economique et Monétaire Ouest Africaine, à la faveur de l'adoption, en 2006, d'une Instruction de la Banque Centrale, instaurant un cadre réglementaire souple et incitatif pour l’exercice de cette activité. L'implication des opérateurs de télécommunications dans l'offre de services financiers basés sur la téléphonie mobile a donné,  dès 2009, une nouvelle dimension à cette activité par l'accroissement du nombre des utilisateurs et des volumes de transactions.

Une activité en expansion

A fin septembre 2015, 22 millions de personnes, soit près d'un quart de la population de l'Union, ont souscrit à des services financiers via la téléphonie mobile. Environ 30% de ces abonnés réalisent au moins une opération sur une période de 90 jours.

Près de 500 millions de transactions ont été aussi réalisées au cours des neuf premiers mois de l'année 2015. La valeur cumulée des transactions atteint 5000 milliards de FCFA (8,5 milliards USD) à fin septembre 2015. De septembre 2013 à septembre 2014, cette valeur est passée de 1000 milliards à 2068 milliards de F CFA, soit une hausse de 107%.

Le réseau de distribution des services financiers via la téléphonie mobile suit également cette tendance haussière, en passant de 93 621 points de services en 2014 à plus de 132 658 points de services à fin septembre 2015.

Source: BCEAO

Le contexte socioéconomique de l'Union explique pour une large part, le succès des services de paiement via la téléphonie mobile. En effet, ce mode de prestation des services de transfert ou de paiement se révèle particulièrement adapté pour les personnes n'ayant pas accès au système bancaire classique, tout en offrant l'opportunité à des institutions non bancaires, en contrepartie de dépôt d'espèces, de mettre à la disposition des usagers une monnaie autre que fiduciaire, dont l'encours leur permet d'effectuer des transactions financières diverses.

L'implication croissante des opérateurs de télécommunications

Les partenariats entre les banques et les opérateurs de télécommunications occupent une place dominante sur le marché. En fin 2015, sur les 33 émetteurs de monnaie électronique sous licence, 25 appartenaient aux dits partenariats.

Au titre du modèle non bancaire, sept acteurs non bancaires ont été agréés pour émettre la monnaie électronique en qualité d'Etablissement de Monnaie Electronique (EME).[1]

Source: BCEAO

Un cadre réglementaire rénové

A la faveur de l'expansion des services financiers via la téléphonie mobile et de l'implication croissante des opérateurs de télécommunication, la Banque Centrale a rénové son cadre réglementaire afin de renforcer la sécurité et la qualité des services de paiement adossés à la monnaie électronique. Les principaux axes d'amélioration portent sur:

• une responsabilisation accrue des émetteurs en clarifiant leurs rôles dans les partenariats avec des prestataires techniques. Ainsi, les activités de prestataire technique sont limitées, sous la responsabilité de l'émetteur, au traitement technique de la monnaie électronique ou à sa distribution. De même, les émetteurs demeurent responsables, de l’intégrité, de la fiabilité, de la sécurité, de la confidentialité et de la traçabilité des transactions réalisées par chacun de leurs distributeurs;


• une stimulation de la concurrence par la transparence de la tarification avec l'obligation faite aux émetteurs de publier leurs tarifs;


• la formulation d'exigences spécifiques en matière de gouvernance et de contrôles interne et externe pour les établissements de monnaie électronique, en exigeant l'honorabilité des dirigeants, le respect du secret professionnel et des audits réguliers des infrastructures;


• une protection accrue des détenteurs de monnaie électronique avec d'une part, le cantonnement des fonds dans  des comptes dédiés, et l'exigence d'une équivalence continue entre l'encours de monnaie électronique et les soldes des comptes de cantonnement et d'autre part, l'obligation de la mise en place d'un mécanisme de recueil et de traitement des réclamations des porteurs de monnaie électronique;


• le renforcement du dispositif de supervision, par la réduction des délais de reporting des activités des émetteurs à la Banque Centrale, et l'adoption de sanctions pour les infractions aux dispositions réglementaires.

L'offre de services financiers via la téléphonie mobile

L'offre de services financiers via la téléphonie mobile comprend trois catégories de services. Il s'agit des services qui impliquent l'usage des espèces (monnaie fiduciaire), de ceux qui sont effectués en monnaie électronique et des services dits de « deuxième génération ».

Le premier type de services concerne essentiellement les dépôts d'espèces ou rechargements de porte-monnaies électroniques, ainsi que les retraits. Ils représentent 24% des transactions effectuées par les utilisateurs. Les dépôts d'espèces sont prédominants et permettent aux clients d'approvisionner leurs comptes de monnaie électronique.

La monnaie électronique rechargée est utilisée à hauteur de 76%, prioritairement pour les achats de crédit téléphonique, les paiements de factures, l'exécution de transferts de personne à personne, de personne à entreprise et aux Administrations publiques. Les principaux services de paiement dans l'UEMOA sont liés au règlement des factures relatives à la consommation d'eau, d'électricité, l'abonnement à des chaînes de télévision satellitaires, l'achat de marchandises dans les grandes surfaces ou de carburant dans les stations-service.

Des paiements d'impôts et taxes auprès des Administrations publiques et le remboursement des échéances de microcrédit sont également effectués, mais de façon très marginale.

Dans l'UEMOA les services dits de « deuxième génération », à savoir la micro-assurance, la micro-épargne et le micro-crédit, font leur apparition. Leur développement pourrait constituer une opportunité de bancarisation des utilisateurs de ces services.

Enfin, un début d'interopérabilité est mis en œuvre sur la base de conventions bilatérales entre les acteurs, notamment en vue d’offrir des services de paiement transfrontaliers entre les Etats membres de l'Union.

Les défis à relever

L'examen de l’évolution des services financiers via la téléphonie mobile dans l'UEMOA fait ressortir quelques obstacles à un développement plus rapide de ces services financiers au sein de l'UEMOA. Il s'agit de:

• la faiblesse du taux d'utilisateurs actifs, en raison du coût élevé des services;
• la méconnaissance des services, du fait d'une éducation financière insuffisante;
• la faible digitalisation des circuits de paiement des Administrations publiques;
• l'insuffisance des partenariats entre les émetteurs bancaires et non-bancaires pour le développement d'une offre de services plus inclusifs, dits de « seconde génération »

En collaboration avec toutes les parties prenantes, la Banque Centrale a développé une stratégie d’inclusion financière visant à améliorer l’accès et l’utilisation de divers services financiers personnalisés et aux prix abordables. La mise en place de ces actions, comme décrite dans la stratégie d’inclusion financière conçue par la BCEAO, devrait résoudre les défis mentionnés ci-dessus.

Lire en anglais »

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[1] EME: toute personne morale, autre que les banques, les établissements financiers de paiement, les systèmes financiers décentralisés, habilitée à émettre des moyens de paiement sous forme de monnaie électronique et dont les activités se limitent à l'émission et la distribution de monnaie électronique.

       

       

       

       

       

       

       

       

       

       

DC KIDS COUNT, housed at the nonprofit DC Action for Children, is the DC chapter of a nationwide network of local-level organizations aiming to provide a community-by-community picture of the conditions of children. The 26 year-old project is funded by the Annie E. Casey Foundation and its aim is to provide high-quality data and trend analysis as well as help local governments monitor budget and legislative decisions based on evidence of what works for children and families. As we pointed out in our recent papers and a blog, developing reliable and comprehensive data is a critical step to building effective community partnerships and producing outcomes that improve economic mobility and health in a neighborhood.

We discussed these issues with HyeSook Chung, Executive Director of DC Action for Children.

Q. Please summarize the history of the DC Kids Count project. What motivated it, and how it has evolved over the last years?

A. As part of the nationwide Kids Count network, each chapter tracks a number of indicators on child and family well-being through an online database called Kids Count Data Center. Each chapter also releases a yearly data book which summarizes the state of child well-being within their state or locality. When DC Action for Children became the host of DC Kids Count in 2012, I wanted to rethink the way we presented our data to move beyond the traditional print format into the exciting realm of visualizing data. This led to the beginning of our partnership with DataKind, a group of dedicated pro-bono data scientists who worked with us to create an interactive, web-based data tool that maps out indicators of child well-being across DC’s 39 neighborhood clusters.

We know that the neighborhood children grow up in, and the resources they have access to, plays a huge role in shaping children’s future opportunities. The maps we created with our Data Tool 2.0  reveal sharp disparities in DC neighborhoods: some DC neighborhoods are wealthy and have many assets, while others are characterized by high levels of poverty. The many challenges that come with high poverty neighborhoods include: poorer performing schools, more crime, and less access to libraries, parks, and healthy foods.  

Q. What type of indicators do you gather? How many years does the data cover? What level of granularity does the data have?

A. We track a variety of indicators of child well-being, including demographics, economic well-being, health and safety. The data is housed online in two places: The KIDS COUNT Data Center and our Data Tool 2.0. The Data Tool 2.0 maps the most recent available data at the neighborhood cluster, while the Data Center allows for a wider range of geographies (citywide and ward level) and different timeframes.  Many of the indicators have data from 1990 to the present.

Q. How do you measure the data tool’s impact on policy and legislation?

A. We have made it a priority to conduct internal evaluations to assess the utilization of the online tool, but we also believe that measuring the tool’s impact must go beyond traditional web analytics. We regularly use the Data Tool 2.0 in our work with city officials and direct service providers to offer an overview of the social context in the city’s different neighborhoods.     

In a city where the allocation of resources is often guided by personal relationships and old-school politics, it is important to show clearly whether budget decisions are aligned with the needs of our children. We believe that our Data Tool 2.0 project can bring much needed transparency to the allocation of the DC government budget and help achieve agreement.

Q. The DC Kids Count project is helping build data capacity across organizations, with the aim of creating a more “data-literate” city. Could you tell us about some of these initiatives? 

A. Businesses like Amazon and Netflix increasingly focus on finding “actionable” insights from their data. For them, “big data” analytics can help answer tough business questions. With the right platforms for analytics, they can increase efficiency or even improve operations and sales.

In a similar manner, we at DC Action for Children believe that big data opens up the opportunity for us to improve and reshape our strategy and decision making process to better align services with the needs of DC children in the same way Amazon or Netflix does with their customers.

For instance, we are offering the Child and Family Services Agency technical and data analysis support for their Healthy Families Thriving Communities Collaboratives, which are a citywide network of community-based organizations designed to embed family supports in their communities. Their mission is to strengthen and stabilize families and to prevent child abuse and neglect by offering services in the form of case management and support. We use KIDS COUNT data at the ward and neighborhood levels to highlight needs in the community and inform their planning. This encourages the Collaboratives’ staff to look at data differently—integrating it as a vital part of their program planning and strategy.

Q. What are some of the obstacles and challenges you face in integrating the data, and updating it?

A. Historically, our data analysis looked at more traditional indicators, such as program enrollment and the number of child welfare cases. But now we think we can use our access to big data to pull out patterns within our datasets and help guide the decisions of the city administrators. For example, if we are trying to prevent future child abuse cases, we can look at patterns analyzing family and child data in specific neighborhoods. We can use the type of predictive analysis practiced in the for-profit business to help us serve DC children more efficiently and effectively.

One of the most significant obstacles we face is ensuring that the indicators are up-to-date. This can be an issue with government agencies since some of them are slow in their release of new data.  Moreover, there is also no standard format across local agencies for how data is collected and released. Furthermore, data is often aggregated at different geographical units, like zip codes or census tracts. To get the data ready to upload to our Data Tool, we must recalculate the data into neighborhood clusters.  

Q. What policy changes would help produce better data-sharing ecosystems? 

A. DC has in many ways demonstrated leadership in data sharing. The Office of the Chief Technology Officer works to make a large variety of datasets publicly available. We have also seen large investments over the years to create new data systems that track progress and service delivery for different agencies. But our city can do more to promote a data-sharing ecosystem. So can other cities.

While multiple agencies are adopting innovative data systems, the systems are often siloed and do not speak to each other. Moreover, since data is tracked differently across agencies, based on needs and requirements for reporting, it is difficult for agencies to share data both publicly and internally. It is also often difficult to get access to de-identified disaggregated data for richer analysis. We are glad that many agencies recognize the value of robust data collection, but more data transparency policies would give us a better understanding of the challenges that lie behind improving the wellbeing of children in the city.

Q. What are the next steps for the DC Kids Count project, and how do you expect it to grow over the next few years?

A. We just finished wrapping up some of the final work on our DataTool 2.0. In terms of next steps, we are working on a handbook that explains how we created our Data Tool so that other Kids Count chapters and organizations can replicate and adapt our tool.

We would also like to add local budget data to the asset maps to see if public investments align with the neighborhoods that need it the most. This would give us a more nuanced understanding of the geography of DC budget investments, including inequities in investments by geography and demographics.

Big data analytics has changed the way we focus our priorities and engage in business practices. I’m committed to this movement. I think that, through big data, we can also revolutionize the way we do policy.

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In conclusion, DC Kids Count, housed at the nonprofit DC Action for Children, belongs to a larger, nationwide group of organizations helping to better coordinate regional development through data-driven decision making. By centralizing different government databases, and providing real-time, community level data, DC Kids Count can help local government entities allocate their resources more efficiently and creatively and help foster place-conscious strategies. The process behind compiling the data also illustrates many of the challenges—data sharing, interoperability of data systems, access to real-time data involved in building “data- sharing ecosystems.”