Who Can Answer the Ghanaian Schoolboy’s Question?

“Why is America so rich and we are so poor?” The question came from a schoolboy in Ghana. It caught me by surprise. I hesitated before answering.

I didn’t have the knowledge to give him a solid answer. I was not an economist. I knew nothing about Ghana’s business enabling environment or its financial markets system. I didn’t know about the causes of poverty in Ghana, and was totally unaware of what the international donor community was doing about it. My knowledge of politics, colonialism and history was limited, although I was aware that Ghana had become independent from Great Britain on March 6, 1957.

Every schoolboy in Ghana knew that. Even me. The boy who asked the question was my classmate at Prempeh College in Kumasi. It was 1975. We were both 11 years old, and we wore the same uniform: khaki shorts and a short-sleeved green shirt. Neither of us understood that donor aid was already streaming into Africa to alleviate poverty, an effort that would later come under heavy fire.

In hindsight, it’s easy to criticize traditional poverty-fighting assistance. One of the sharpest critics is Dr. Dambisa Moyo, an international economist and author of “Dead Aid: Why Aid Is Not Working and How There Is a Better Way for Africa.” Dr. Moyo notes that development aid has fueled corruption, has removed incentives for governments to become efficient, has created a culture of aid dependency, and has distorted markets. In a 2009 article in the Wall Street Journal, she points out that in spite of $1 trillion of aid delivered over 60 years, real per capita income in Africa has fallen. She argues that countries that rely on markets rather than aid are more successful, citing Ghana as an example.  “Governments need to attract more foreign direct investment by creating attractive tax structures and reducing the red tape and complex regulations for businesses,” she writes.

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Stretching Your Global Mindset

How well do you really understand the world beyond your own country’s borders? Do you value all human beings equally or are you more sympathetic toward people who are closer and more similar to you? What does it matter for business and for society?

Several readers of my previous post have commented that mindsets or perceptions about globalization can be at least as important as actual levels of globalization. I agree. Mindsets matter. They have big real world consequences, particularly as is the case now with Europe’s debt crises and bailouts, when collective prosperity depends on the extension — as opposed to contraction — of cooperation, trust, and sympathy across borders and distances.

Consider the following data on trust levels based on surveys conducted in various Western European countries. As the graph below shows, more than twice as many Western Europeans trust their own fellow citizens “a lot” as compared to citizens of other Western European countries (48% vs. 20%). And only 13% place the same level of trust in citizens of countries outside of Western Europe.

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More systematic research shows that bilateral trust decreases with geographic, linguistic, religious, genetic, and somatic distance (measured by an index of body type differences) as well as with income differences and a history of wars — findings that hit on all four dimensions of my CAGE framework (Cultural, Administrative, Geographic, and Economic), with particular focus on the cultural dimension. And levels of trust influence more than just whom you’re inclined to lend to, to bail out in a crisis. Moving from lower to higher levels of bilateral trust has been shown to increase trade, direct investment, portfolio investment, and venture capital investment by 100% or more, even after controlling for other characteristics of a pair of countries.

It’s easy to say you trust foreign people on a survey, but what about actually doing something that reflects how much you care about distant and different people? Data on foreign news coverage and foreign aid provide some indication of how much human sympathy declines with distance. As it turns out, trust is relatively insensitive to distance in comparison to sympathy.

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First, some disturbing data about news coverage, which given the importance of ratings probably provide a reasonable reflection of what the general public cares about. A study of more than 5,000 natural disasters suggests that from the standpoint of U.S. media coverage, each dead European is “worth” three South Americans, 43 Asians, 45 Africans, or 91 Pacific Islanders. To summarize in rough terms, as shown on the graph above, news coverage indicates we care about people in neighboring countries about 10% as much as we care about our own fellow citizens, and this figure declines to 1% as you move to countries farther from home.

Then, consider actual willingness to help distant people in need. Compare aid to the domestic poor versus official development assistance (ODA) to the rest of the world’s poor, using the approach outlined by Branko Milanovic. Based on weighted per-person averages for fourteen developed OECD economies, national governments spend 30,000 times as much helping each domestic poor person as each poor foreigner. Or put differently, when it comes to aid, the foreign poor are valued at 0.003 percent (1/30,000th) as much as the domestic poor. And if you’re wondering where this would stand if the rich countries all gave 0.7% of their GDP in foreign aid, as they promised to do in 1992 at Rio de Janeiro, that would only mean the foreign poor would receive 1/15,000th as much as the domestic poor. Hardly a promise to value every human being equally.

A host of business and social problems could be solved more easily if people broaden their mindsets and shift their own personal sympathy decay curves a little closer to horizontal. In business, imagine how much better multinational corporations could function if there was more trust between headquarters and far flung country operations. In the social sphere, an interesting study by David Anthof and Richard Tol relates carbon prices to levels of sympathy, showing that it would be far easier to address climate change if we placed more weight on the harms inflicted on others.

What do your own trust and sympathy decay curves look like? What curves are embodied in the values of your organization and its workforce?

Please click the button below and answer the survey questions. In a future post, I’ll report back the results.

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Or try out the full Global Attitude Protocol (GAP) from which these questions were drawn. For more suggestions on how to change individual mindsets, see the last chapter of my new book World 3.0: Global Prosperity and How to Achieve It, and for ideas for corporations on how to foster this mindset shift within their organizations, refer to my most recent HBR article, The Cosmopolitan Corporation.


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8 Steps to Publish Open Data and Prepare for IATI

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Siobhan Green, the founder of Sonjara attended the Technology Salon exploring How Can USAID Development Partners Implement IATI? and was inspired to define 8 steps to publish Open Data to prepare for the day when we all will need to be compliant with the International Aid Transparency Initiative standards. Here is her list:

1. Stop the bleeding

Organizations often get caught up in backlog of their information trapped in word and PDFs, and freak out due to the cost of migrating these into an open data format. Often this results in firms saying nothing can be done, even for data they are starting to capture NOW.

Our strongest recommendation is to identify where data is currently being captured in these non-open data formats and address that immediately. For example, if your staff submit reports in word, creating a form that captures IATI (International Aid Transparency Initiative) type meta data, and then attach the word document to that form already starts you down the path of IATI data.

Find out where your raw data for evaluations and analysis are housed and make sure the structured data is not lost (many an excel spreadsheet or database are converted to a PDF or website, and then the source data is lost, leaving only the aggregated data).

2. Look for existing standard structures

In international development, for example, many donors are using IATI as the structure for project descriptions. Many fields also have standard taxonomies or data structures for geolocation. Look around at other data sets that are open and borrow from them as appropriate and compare with what you are already capturing.

3. Design rather than retrofit

It is MUCH cheaper to design for open data before you capture it than to retrofit your data. In addition to making sure your data is structured, IATI also helps identify data points you may not be capturing in a structured way, such as geolocation or sector. Applying this information after the fact can be very hard if those who have the knowledge of the project are no longer available.

4. Can aggregate up, but cannot disaggregate down

Figure out the lowest reporting level needed, (especially geolocation) and start there. Disaggregate by gender whenever feasible/appropriate, and by other factors.

5. Structure is always better than unstructured

A pattern I see a lot is teams build structured data for analysis, like a spreadsheet or database, and then export that data (or a summary of it) into a PDF for the final report – and then the raw data disappears. What has happened is data has gone from structured (Database) to unstructured (PDF), which is a real loss for open data/interoperability.

For data management purposes, it is significantly easier, cheaper, faster to migrate data from one structure to another structure than unstructured to structured. For that reason, if your data is structured, protect it! Keep the raw data somewhere, and don’t rely on the PDF as the archive of the data.

6. Structure is better than unstructured, Part II

Structured data allows you 1000s more options than unstructured data. With structured data, you can aggregate up/summarize. You can slice and dice the data to compare within it. you can pull it together with other data sets and compare/contrast/analyze. You can use it to feed into dashboards and automatic graphic and tracking tools, and even export it to PDF.

7. Prioritize data, not platform

Technology is changing so rapidly that software platforms go obsolete pretty quickly. If you are lucky, you will have five years with your platform – maybe ten with an entire platform approach. But your data should outlast your platform. It is incredibly important that whatever platform you select is open-data friendly (even if none of the data ever is shared outside your organization). Open data friendly means that it is migrate into another platforms and can be aggregated and mixed with other data.

Sonjara’s rapid prototyping CMS and web application framework, Fakoli, is open data friendly by design. We built it so that it respects the data model, meaning it is easy to pull the data out of the system in a variety of formats.

8. Politics will thwart you before the technology does

The major barriers we have seen to open data structures are battles over taxonomy, right of access, concerns over privacy/security, and a concern about cost. These are all important questions that need to be tackled but none of them are deal-breakers. Not all data needs to be open to the world, or to even everyone in the same organization. Taxonomies need to be fluid enough to capture emerging trends, but meaningful enough to be used. And open data does not have to cost a fortune, if done thoughtfully.

Now that you’ve read this far, sign up to get invited to future events.


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Innovation for Sustainable Intensification in Africa

The Montpellier Panel is a panel of international experts from the fields of agriculture, sustainable development, trade, policy, and global development chaired by Sir Gordon Conway of Imperial College London. The Panel is working together to make recommendations to enable better European government support of national and regional agricultural development and food security priorities in Sub-Saharan Africa. This report looks at the role of innovation in sustainable intensification for food and nutrition security in Africa.


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How to make markets work for the poor–and measure the results

Despite local demand for hay, Kenyan dairy farmers face milk shortages due to lack of quality supply. But instead of handing over the hay, why not bring the hay market to the farmers?

There is little hope of escaping poverty if markets don’t provide the poor with opportunities to increase their income through access to goods, services and employment opportunities. Making Markets Work for the Poor (M4P) is a facilitative approach to poverty reduction that seeks to understand where market systems are failing to benefit the poor, and how to take action to set them right.

Rather than having aid organizations supply hay directly to the Kenyan dairy farmers, a M4P project might support local cooperatives to meet their demand with quality supply, while establishing a local and sustainable market system.

But how do you measure the impact of M4P projects? The point was raised last week in an online forum hosted by the Guardian. Experts who joined the chat noted that one of the key challenges of the M4P approach has been finding metrics that can capture both the short- and long-term effects of these approaches.

“The impact chain of M4P programs is indirect, so it is much more difficult to measure these results,” said Nabanita Sen, from the Donor Committee for Enterprise Development (DCED).

Finding ways to better measure long-term, sustainable and transformational change is crucial for aid organizations looking to convey to their donors that these programs work.

But development practitioners believe there are ways to create a new measurement standard.

“There is a need to adopt a long-term vision, but M4P programs can show structural changes and trends toward long-lasting change in the first years of implementation,” said Luis E. Osorio-Cortes, an international market systems specialist at Practical Action Consulting.

“The problem is that many standard M&E [monitoring and evaluation] frameworks are still blind to those signals,” Osorio-Cortes added. “This must, and can, be improved.”

But traditional charitable approaches have dominated the development scene since its beginning, just after World War II, and largely shape today’s standard measurement framework.

Aid organizations traditionally count the number of vaccinations, food rations or emergency supplies distributed and share those counts and results with donors.

On the other hand, creating links between supply and demand for hay with a M4P approach produces a number of indirect effects that are harder to “count,” and donors accustomed to annual giving cycles may not have the patience to wait for long-term results.

For example, the hay cooperatives might open and expand operations, creating income and employment opportunities, and boost business for companies that provide farming machinery and production equipment. Quality hay is made accessible to farmers who can then feed their livestock, produce more milk and increase their incomes. The impact of these results can be easily miscalculated if it’s forced into the standard, short-term measurement framework.

Yet some of the leading development organizations are finding more efficient measurement strategies to fit these unconventional programs.

The Donor Committee for International Development has applied a monitoring and results measurement system to better capture the qualitative factors of systemic change. “We have developed measurements based on result chains to explain how activities will lead to different levels of long-term change,” Sen said.

The UK’s Department for International Development (DFID) has taken a similar approach by designing a “value for money” framework (pdf) that will satisfy the needs of donors and provide qualitative measurements.

“We call it a ‘weightings and ratings’ approach,” said Julian Barr of DFID. “Its basis is to select key processes in the intervention, and develop specific criteria against which to rate the ‘quality’ of these processes. Different processes are weighted according to their importance in achieving outcomes and impact.”

What does that mean? In the case of the Kenyan hay market, it might mean project managers work to determine which interventions are most valuable in creating adequate supply, such as working with cooperatives to educate hay farmers on quality standards, and weight those more heavily than other activities in order to illustrate the project’s overall value to its donors.

Despite its challenges, the M4P approach has gained substantial traction in recent years as some of the largest aid organizations and businesses generate interest in market development strategies. And businesses have come to incorporate similar practices through social investment and fair trade, increasing engagement with the bottom of the pyramid as valued customers.

While capturing the full impact of these programs may prove a hefty challenge for aid organizations worldwide, it seems the opportunity to approach poverty alleviation from an entirely new angle may be enough to convince them to take on the task. And that’s certainly something worth measuring.

Development practitioners may be shifting away from traditional approaches to poverty relief to explore ways to make markets work better for the worst off. Photo: Cassandra Nelson/Mercy Corps.

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A Computer Can Do Your International Development Job Thanks to Open Data

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At a recent Technology Salon on “How Can We Make Data Useful for Development,” one of the participants put forth an interesting question to the group:

Could computers make better international development decisions than humans?

Now at first, those present laughed off this question. It borders on fantasy to think a computer could take in the many social and cultural histories, divine the subtleties of donors and the parliaments behind them, and introduce innovations that have long-term impact on notoriously unpredictable humans. Or that’s what we thought until someone brought up the impact of computer algorithms on stock market activity. Read more

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ID Hack for International Development… Would you?

Technology is powerful. Powerful enough to make a tremendous impact on international development. The rapid proliferation of mobile phones across the world has enabled mobile health technology to improve health care for people living in rural areas of developing countries. Crowd-sourcing data has forever changed the impact and organization of disaster relief. People with computer science skills have an incredibly powerful toolset for making a difference in the world. Developers for Development (D4D), a student group at Harvard University, aims to inspire students in computer science to work on projects with a social impact. This upcoming weekend, D4D has teamed up with MIT’s Global Poverty Initiative (GPI) to hold ID Hack, the first hackathon in the Boston area focused around international development. Read more

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