The power of improvisation. Directed improvisation.

There is no shortage of ‘best practices’ or ‘one-size-fits-all’ solutions in a development economy. Free trade, democracy, institution building, you name it. The Washington Consensus has reigned in the decades since the 1990s. Now the Beijing consensus is emerging.

Yuen Yuen Ang dismisses such solutions and suggests that there is no universal prescription. Her one-sentence summary is “Poor and weak countries can escape the poverty trap by first building markets with weak institutions and, more fundamentally, by crafting environments that facilitate improvisation among the relevant players.” She explores this idea through studying how China managed to achieve and sustain economic development after Mao. The country employed this approach, which allowed the to achievement of economic growth in complex circumstances. Yuen Yuen Ang call this approach ‘directed improvisation’ where central reformers direct and local state agents improvise. It taps local knowledge and adapts to the local circumstances, while aiming at an overarching goal. The resulting transformative process has displayed three distinct patterns. It is broad, bringing systemic changes despite incremental reforms. It is bold, unusually entrepreneurial but also attracts corruption-prone bureaucrats. Finally, it is uneven, with wide regional disparities coexisting with national prosperity.

The Government nurtured what Yuen Yuen Ang calls ‘directed innovation’ through variation, selection and niche creation. To promote variation, central reformers allowed local agents to flexibly implement central mandates according to local conditions. This has been done through deliberate creation of grey zones, as too much leeway could create chaos. Hence, Central bodies clearly delineated these zones of local improvisation. They imposed red lines around local administration, denoting things which are prohibited and risk very severe punishments; and black lines for things which must be delivered, again at the risk of severe punishment in the case of non-performance. The rest was in a grey zone, open for innovation and adaptation to local conditions. Selection was promoted by clearly defining and rewarding success within bureaucracy, of the type in the black lines discussed above. Central reformers clearly communicated the criteria for success to lower levels and ranked localities, and closely looked for what has worked and what didn’t. Successful models and approaches then became central policy, scaled up and replicated throughout China. For instance, the famous Township and Village Enterprises (TVEs) were neither prescribed, nor anticipated by central reformers, as Deng Xiaoping himself admitted. They grew up out of local experimentation at that stage of the reforms, as best fit to local needs and conditions, to produce growth spurts, and centrally imposed restrictions for the of non-acceptability of private property. The diversity of China provided the raw material for innovation, resulting in niche creation for different localities. Regional diversity thus turned from liability into a collective advantage.

The methodology Yuen Yuen Ang used is a mapping of the ‘coevolutionary process’, with whole Chapter (1) and Annex (A) devoted to the description of the methodology. She does not engage into construction of sophisticated regression models, torturing data in the elusive quest for causality out of correlation. Neither does she stick to small N approach, looking through messy and overcomplicated set of variable for a single case (By the way, enquiring reader could find great discussion of cultural differences between small N versus big N approaches in “A Tale of Two Cultures: Qualitative and Quantitative Research in the Social Sciences” by Gary Goertz and James Mahoney). Hence, no oversimplification and no messy non-reductionist approach to complexity. Rather, a complex approach, which captures a non-linear, co-evolutionary process, in reduced form. She tracks changes in the related systems of economy and institutions, over the time. Throughout the book, she dives into examples of Forest Hill, Blessed County and Humble County in China, which are archetypes of various types of localities in China. In her methodological annex she extends this approach to two additional cases of tax-less finance in United States of America in the 1880s and raise of Nollywood in Nigeria.

The three ‘I’s haunting development economics, according to Esther Duflo, are a conceiving Ideology, often derived out of Ignorance, that is perpetuated as a result of Inertia (see also “Poor Economics: A Radical Rethinking of the Way to Fight Global Poverty” by Abhijit Banerjee and Esther Duflo). More and more we do recognize the complex nature of issues we face with, the inadequacy of opaque models pushing correlation for causality, and the lack of Silver Bullet solutions. However, we are yet to find the instruments for handling complexity in a meaningful way. The book by Yuen Yuen Ang is an excellent starting point for this intellectual inquiry.

Keep it simple: Complexity and the SDGs

As Einstein says, “Everything should be made as simple as possible, but not simpler.”

Grey wolves in Lamar Valley. Photo: Yellowstone National Park

Grey wolves in Lamar Valley. Photo: Yellowstone National Park

Can wolves change a river flow? Hard to believe, but yes.

In 1995, grey wolf packs were reintroduced in Yellowstone National Park, with remarkable consequences for the entire ecosystem. Elks, afraid of being eating by the wolves, began avoiding open regions such as open meadows and gorges. New plant growth increased, provoking subtle changes in the park’s waterways. An increased number of trees attracted various bird species. The beaver, previously extinct in the region, returned and their dams attracted otters, muskrats and reptiles. The rivers meandered less, reducing erosion and forming more pools. So ultimately, the wolves helped stabilize river banks and fix the rivers in their courses.

This story resonates strongly with my experience with the Sustainable Development Goals, especially as we at UNDP help countries to embrace and domesticate Agenda 2030. We are living in ecosystems, not hierarchies. Our relationships in the world are circular, not linear.

Linkages are more important than the elements of the system themselves.

For example, in Bosnia and Herzegovina some 40 percent of companies have vacancies hard to fill due to applicants’ lack of skills. But at the same time, nearly 25 percent of its youth aren’t working or in school or training. People complain that not knowing the right people is a major obstacle in getting good job (56%), not just lack of jobs (35%) or inadequate or irrelevant education (14%). These relationships are clearly non-linear and cannot be solved by traditional approaches, assuming that a good education will find you a job.

In this instance, UNDP looked at Bosnia and Herzegovina’s local employment “ecosystem” and saw that one of the big issues is a disconnection between social assistance and job placement systems. UNDP supported establishing links between the two, so people who are receiving social assistance also have chances at job offers and are helped transition to jobs. And UNDP brokered partnerships with private companies to provide internships and apprenticeships to provide practical skills demanded by the labor market.

This is just one example of the broader SDG issue. We need tools to make this type of complexity manageable, and we are actively designing and testing them:

1) Tools to zoom out and zoom in

System Dynamic Map maps out all problems and interactions between them. For instance, youth employment is linked not only with enrollment to universities, but also with skills for employment, economic development policies, and accessibility of transport. The whole system map is a spaghetti of interactions. You can easily spot “economic”, “social” or “environmental” corners and dig in for more details, or you can zoom out and see the overall picture. Such maps are also a great tool for building partnerships. A similar map in Tajikistan revealed one village self-support group of migrant wives left behind organized a community kindergarten to free up time for business.

2) Better tools for Sense Making and Foresight

In a complex system, you cannot really categorize things into neat boxes and use “best practices”. Regular indicators only tell part of the story. You need triangulation, i.e. looking at the same issue from different perspectives, combining qualitative and quantitative data.  Micronarratives, which combines narrative with quantitative description, is one example of sense-making tools we are using. As testified by stories told by the Roma, the main reason to embark on an uncertain – and often unregulated – migration to the EU is the search for mere survival in terms of income and physical and emotional security. Those who returned from the EU feel rejected and alienated with limited support for reintegration. Mere data from our recent study could not tell us these nuances.

Equipped with these tools, we can better handle the enormous complexity of SDGs. We could go from unmanageable complexity—everything is related to everything—to well-informed Theories of Change.

 

Originally posted at UNDP Europe and Central Asia blog.

0. Making sense of Elephant

Hokusai’s Blind Men Examining an ElephantAn Elephant in the Dark

Some Hindus have an elephant to show.
No one here has ever seen an elephant.
They bring it at night to a dark room.

One by one, we go in the dark and come out
saying how we experience the animal.
One of us happens to touch the trunk.
A water-pipe kind of creature.

Another, the ear. A very strong, always moving
back and forth, fan-animal. Another, the leg.
I find it still, like a column on a temple.

Another touches the curved back.
A leathery throne. Another the cleverest,
feels the tusk. A rounded sword made of porcelain.
He is proud of his description.

Each of us touches one place
and understands the whole that way.
The palm and the fingers feeling in the dark
are how the senses explore the reality of the elephant.

If each of us held a candle there,
and if we went in together, we could see it.

–Rumi

 

 

The power of weak bonds

Animation of a rotating DNA structure.DNA, as well as proteins, are the most complex and most amazing molecules in the world. They are live, with many functions, which makes life itself possible. However, their primary structure are not live, they are simply big molecules. What makes them live and functional is secondary and tertiary structures, which twist and bend them into complex 3D structures, necessary for performing functions. And here is interesting thing, these secondary and tertiary structures are formed by “hydrogen bonds”—special type of bonds, involving hydrogen atom. These bonds are flexible, easy to form and easy to break, and are weak, some 20 times weaker than regular bonds of primary structure.

This pretty much remind me the development work—how could we nurture these weak but lean bonds to perform societal function necessary for achieving better life?

 

Economics of Good and Evil: The Quest for Economic Meaning from Gilgamesh to Wall Street by Tomáš Sedláček

Economics of Good and Evil: The Quest for Economic Meaning from Gilgamesh to Wall Street by Tomáš Sedláček

The guy drives me crazy trying to persuade that gender equality is much higher at distant districts of that (quite patriarchal) country, than in capital. The best argument he uses “econometrics shows this, and you know, math doesn’t lie”. When we run down devils in details, it turned out that the guy used share if girls among higher education students as a metrics of gender equality. In distant districts higher education facilities are limited to medical and pedagogical ones, overpopulated by girls. Contrary, in the capital there is much broader set of education institutions, including technical ones preferred by boys, and share of girls is naturally lower. Wrong implicit assumptions lead to wrong results, despite of all that ubersophisticated math.

Tomáš Sedláček tells that story, but on a bigger scale. Currently, we hide implicit assumptions behind sophisticated formulas of economics (which more and more is limited to econometrics). Math replaced ethics in economic debates, based on assumption that math is value-neutral. However, this is very recent development. Over centuries economic though was inseparable from ethics, moral philosophy. In this book author walk through the long history, analyzing sources as old as Gilgamesh and the Old Testament, coming to the Greek philosophers, continuing to Christian economics, and then to Enlightenment ages, and finally the Wall Street. The book is well written and easy to read. While I don’t agree with several arguments, it is thought provoking and very useful.

To my surprise, there is not much Wall Street in the book, while Crash 2008 could be a very good case study. Intricate econometrics and math models simply hide the basic assumption that property prices will rise forever. As soon as this assumption turned out be false, and prices stagnated and slightly went down, all models went crazy and market crashed. On the other hand, author pay some attention to Debt, which is a great issue going well beyond Public Debt.

Overall—nicely written, thought provoking, well referenced book.

http://amzn.to/2wQr60Q

 

Disaggregation Dizziness

Still Life with Apples and Oranges, 1895 by Paul Cezanne

Recently I had a great pleasure and honor to attend the International conference on the implementation of a national system for monitoring the Sustainable Development Goals. The conference brought together statisticians, policy makers, and international organizations to discuss how to challenges in measuring sustainable development, in monitoring of SDGs. Everybody asked for better data disaggregation, for each indicator (let me remind you that current SDG monitoring framework contains 230 indicators). Statisticians listened this with caution, then did back of the envelope calculations and discovered that they would need to calculate each of 230 indicators for some 40+ groups, if take into account all break downs proposed (by sex, poverty status, disability status, and so on and so on, and this does not include territorial division, which could be different for each country). These number make you dizzy. Without doubts, it will put enormous pressure on Statistical Services, who still are looking for ways to produce all required SDG indicators (as significant part of them are still in Tier II or III, i.e. “established methodology and standards available but data are not regularly produced” and “no established methodology and standards“).

Without doubts, disaggregation of indicators is indispensable, especially to ensure that Sustainable Development Goals leave no one behind.  The issue is how to balance costs and opportunities?

First, it should be clarity on why, how, and what we disaggregate. The groups we identify—”women”, “rural”, “children”—are not that heterogeneous as we tend to think. These identities are not unique, often combined, resulting in very different people being counted under the same category—compare, for instance, situation of “Roma low educated woman with disabilities living in rural settlement” and “highly-educated woman living in economic center“. Trying to imaging all possible combinations makes you dizzy and inevitably ruins your friendship with statistician. In many cases the line between groups is also very blurry, while statisticians prefer crisp definitions. Possible solution is proposed by Todd Rose in “The End of Average: How We Succeed in a World That Values Sameness“. Instead of mundane “aggregate then analyze” you should “analyze then aggregate“. Being applied wisely, this approach could bring great results—it allowed Mexico to estimate Human Development Index for very diverse groups, including internal migrants; and us to compare and understand social exclusion of different people in Europe and Central Asia.

Second, we should ensure that data collection instruments include all necessary information for future disaggregation. This issues is not that trivial as it seems. For instance, many indicators calculated on the basis of household surveys—like poverty rates or calories consumption—could be gender-blind (or, more precisely gender-myopic) as these surveys do not catch intra-household distributions and inequalities. Collecting ethnic-related data could be challenging not only due to fluid and double identities, but also due to national regulations. Some hard to reach groups—who tend to be left behind—could be simply left out of statistics viewpoint. At the same time, providing geographical information offers great opportunities in linking with other data sources and getting more disaggregated, nuanced picture.

Last but not the least, we should promote practice and culture of data use. Statistical offices are very busy with data collection, production, and dissemination. If you peek into statistical yearbooks, you will find indicators tabulated for dozens of different groups. However, this is a role of scholars to do additional analysis, explore possible disaggregations, and perhaps suggesting statistical services useful revisions of regular tables. In Serbia collaboration of  Social Inclusion and Poverty Reduction Unit of the Government, Statistical Office with UNDP resulted in set of studies, which help formulating efficient public policies for social inclusion.

 

Blink: The Power of Thinking Without Thinking by Malcolm Gladwell


Blink: The Power of Thinking Without Thinking by Malcolm Gladwell
My rating: 4 of 5 stars

How did gender discrimination has been radically eliminated from classic music world, with the percentage of women in major symphony orchestras in the United States skyrocketed from meager 5 percent to close to 50 percent over twenty-five years? Affirmative actions, boycotting, public awareness campaign? No, just simple procedural change–introduction of blind auditions, which radically changed decision making (and associated biases)

The book by Malcolm Gladwell tells the stories how our brain make intuitive decisions, how these decisions shape our life for good or for bad. How simple quiz with words associated with elderly make you to walk slower. How people in Emergency Room radically improved heart attack diagnostics and saved many lives (and money for hospital). How New Cola miserably failed despite all marketing researches. How rough commander with outdated military equipment managed to beat state-of-the-art Blue Team (resembling movie “Down Periscope”). How unconscious racial bias affect hiring decisions. How police end up with brutality (and how they could avoid it trough very simple changes in procedures).

The book addresses three questions: Why and how our brain take these fast, intuitive decisions (thinking without thinking)? When these decisions are good and when are bad? When we should rely on these intuitive decisions, and when we should deliberately take time to make rational decision?

Gladwell is a great journalist–he collects and references interesting researches and compliment them with real life accounts, thus telling the whole story. The book is very well written and easy to read. However, if you are more interested in substance, rather than whole story, you could read just introduction, as author laid out whole argument there. If you are more interested in subject of brain work, you could read thick volume “Thinking, Fast and Slow” by Daniel Kahneman (he got the Nobel prize in 2002 for “having integrated insights from psychological research into economic science, especially concerning human judgment and decision-making under uncertainty”), which goes into details of thinking and decision making.

View all my reviews

Sustainable Development Goals as a Network of Targets

12009662_882179671871702_8487110901368455942_n[1]Sustainable Development Goals, to be adopted by the United Nations summit at the end of September 2015, will set up international development agenda for next 15 year till 2030. SDGs are making a serious step forward from their ancestor, Millennium Development Goals. Lack of integration across sectors in terms of strategies, policies and implementation has long been perceived as one of the main pitfall of previous approaches to sustainable development. SDGs offer more comprehensive and more integrated approach sustainable development. The backside of this more complex agenda is necessity to understand internal links and trade-offs, both explicit and implicit.

One way to embrace complexity is too look on SDGs as a network of targets:

 

Another way to embrace complexity is to consider extended SDG Goals, which include not only targets listed under each goal (which reflect long negotiation and consultation process rather than internal logic), but also those from other Goals logically linked to the current goal. Here area a couple of examples:

SGD1 Poverty reduction
SDG1: Core
SDG1: Extended

SGD8 Growth and Jobs
SDG8: Core
SDG8: Extended

also see Goals 1, 10 and 8 highlighted
Cluster of 1, 8, 10, and 16 (extended)

Some themes—like migration—do not have a specific goal, they are related to a number of  targets, linked to different goals.

 
Indicators to measure SDGs should also take into accounting this complexity

 
 
 

See also:

 
 
 
 
Creative Commons License
SDGs as a network of targets by Mike Peleah is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Based on a work at http://peleah.me/sdgs-as-network/.

All inequalities are unequal, but some are more unequal than others

Not all inequalities are created equal.” So goes one of the main takeaway messages from the Dialogue on Inequalities recently held in Istanbul.

Yet we still use only one indicator—the Gini coefficient of income inequality—to judge them all.

Back in 1968 Robert F. Kennedy said GDP “measures everything. . . except that which makes life worthwhile.” This holds true for the Gini coefficient as well—it measures all income inequalities, except that which make inequalities important for us.

 

All men, brother Gallio, wish to live happily, but are dull at perceiving exactly what it is that makes life happy (Seneca)
Life cannot be defined by income, just as quality of life cannot be measured with how much one get.

Yes, access to a good education and health care does matter. And different countries in the world have very different models of provision for these things. On the one side of the spectrum are heavily market-oriented countries, like the USA, Singapore and Hong Kong. On the other side, one could find such countries relying on state in public goods provision, as Sweden, France and Germany. Therefore, social inequalities are as worthy of discussion as the Gini.

Just look at Belgium and Bangladesh: They share, besides their first letter, a similar level of income inequality with a Gini index of 33. But when it comes to social inequalities, in education and health, Bangladesh performs four times worse than Belgium.

Dhaka, Bangladesh

Income Inequality Gini 32

gini-bangladesh

Social inequality 29

ghent-belgium

Ghent, Belgium

Income Inequality Gini 33

Social inequality 7

 

Shared society
Perhaps what matters even more than income inequalities is the sense of shared society.

Singapore is a free market society, number one in “Doing Business” ranking, second in “Economic Freedom” score, with a very low tax rate. Not surprisingly, income inequality is quite high there, with a Gini score around 45.

However, Lee Kwan Yew, the founding father of modern Singapore seems to have managed “to give every citizen a stake in the country and its future.” People in Singapore trust each other and state institutions, and only a few would call for more equal income redistribution.

In contrast, countries in our region, despite relatively low income inequalities, do not perform well on this front. Recent findings highlight that in the most of the countries surveyed, the majority of people do not think that their interests are represented by the National Parliament or the regional and local administrations. Hence, one could suggest that this lack of shared society perhaps hurts people much more than differences in incomes.

Source: Own calculations based on Regional Human Development Report, 2011

 

So how do we measure inequalities?

These days, inequalities are quite high on the development agenda.

Just look at the Open Working Group proposal for Sustainable Development Goals, which includes two goals on inequality.

So far, the proposal does not offer indicators for goals and targets. This could be an opportune moment to say goodbye to Gini and welcome some newcomers. In that case, keeping in mind what I’ve discussed above, let’s review some alternatives:

  • The Palma ratio has recently been proposed as a more meaningful measure of inequality. It proposes to look at the income share of the top 10% divided by the income share of the poorest 40%. (Assumption confirmed by statistics is that middle income groups between the ‘rich’ and the ‘poor’ capture around half of the Gross National Income). In this way, Palma may be much better at capturing excessive inequalities, or as we call it, “the bad and the ugly”.
  • To capture Human Inequalities, UNDP proposed the Inequality-adjusted Human Development Index (IHDI) back in 2010. The index takes into account not only the average achievements of a country on health, education and income, but also how those achievements are distributed among its population by “discounting” each dimension’s average value according to its level of inequality.

Finally, the World Bank offers a similar indicator, the Human Opportunities Index (HOI), which looks at how access to different opportunities—education, water, sanitation, etc—is distributed in a society. This could help us uncover how access to a particular right may be quite unequal across groups of children (urban boys vis-à-vis rural girls, for example).

There is only weak connection between income inequalities and achieved level of development as measured by HDI…

 …but development and social inequalities are going together much more closer.

 

Bottom line

All models are wrong, but some are useful. It is time to move away from relying on the Gini coefficient – and towards more useful indicators of inequality to distinguish between the good, the bad and the ugly inequalities. More equitable world we all want should not end up as the kingdom of uravnilovka and suppressing people desire and ability to take part in development.

 

 

Join #TalkInequality conversation at Twitter. Have look on slides from presentation at “Dialogue on Inequality” meeting.

Demographic history and Mortality heatmaps

Turbulent events of history leave sharp marks in demographic structure. Demographic history could tell us a lot about historical events…providing we could get necessary data. Demographic portal recently start offering access to relatively long time series for a broad range of countries. It also offers possibility to construct heatmaps of mortality changes (detailed description is available in Russian), which is an excellent tool for tracking historical changes.
Chart for Russia 1959-2010 (male) is clearly shows heavy impact of 1990s. The blood-red spot shows increased mortality in all ages, especially in working age–consequences of transitional shock.  One could also note positive impact of Gorbachev’s anti-alcohol campaign, a blue spot around 1985. It shows declining mortality of working age men. Unfortunately campaign was not long enough (and not very well implemented).


russia-mortality-map

France 1900-2010 clearly shows two red cradles of mortality hikes during WWI and WWII and more or less monotonous decline of mortality for the rest of period.

france-mortality-map