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

Food basket a century ago: Great Britain vs Russia

Infographics was born in the very exact moment when prehistoric man drew on the wall of the cave a buffalo and hunters, explaining something to his fellow tribesmen. Most probably, they have now proper language, but inforgraphics was already there. The years passed. At the turn of the last century, in 1912, the publishing house «Vestnik Zaninija» in St. Petersburg has published the book «Rossija v cifrah. Strana. Narod. Soslovija. Klassy» (i.e. «Russia in the figures. Country. People. Estates. Classes») authored by Nikolai Alexandrovich Rubakin. The book contains various statistical data on what was then the Russian Empire, as well as comparisons with other countries of the then World. It provides in particular revealing picture on weekly family budgets of English manual laborer (a family of 3 persons and an annual budget of 450 rubles) and locksmith from Nizhny Novgorod (family of 3 persons and an annual budget of 400 rubles).

Chart is in Russian, but it is easily understandable. English manual laborer is on left side and locksmith from Nizhny Novgorod is on right side. Labels from top to bottom reads as the following:

  • Tea 1/2 lb vs 1/10 lb
  • Butter 1 lb vs 1/2 lb
  • Sugar 4 1/2 lb vs 2 1/2 lb
  • Vegetable oil nil vs 3 lb
  • Meat and lard 4 1/2 lb vs 3 1/2 lb
  • Potatoes 8 lb vs 10 lb
  • Vegetables (cost) 4 kop. vs 10 kop. (100 kop. = 1 ruble)
  • White bread and flour 19 1/2 lb vs 19 lb
  • Back bread  nil vs 14 lb

“When Nature Helps Scientists: Natural Experiments of History” ed Jared Diamond

Human history and societies left many question open—why Haiti and the Dominican Republic, which share the very same island of Hispaniola, are so radically different in their level of development? Why expansion of Western Territories in USA in 19th century was so explosive? Is it unique? What condition level of political development in Polynesian societies? How slave trade has affected long-term development perspectives of Africa?

Social scientists find themselves in disadvantaged position, comparing to natural scientists, physicians, or chemists—they have no luxury to run a controlled laboratory experiments, often considered to be the hallmark of the scientific method. On the one hand, they often deal with past. On the other hand, even if they could design such an experiment, it would be immoral and illegal. To make things even worse, social phenomena often are hard to measure (for instance, what are the measures for “happiness”, “development” or “stability”?) and involve many variables, which affect outcome. (Back in 1987 Jared Diamond wrote an excellent article “Soft sciences are often harder than hard sciences”, where he touch upon some of the issues).

However, Mother Nature often times offers her helping hand, in the form of “natural experiments”—serendipitous situations, when systems or groups are similar in many respects, but are affected differently by a treatment, random or quasi-random. This allows comparing two systems or groups and studying influence of the treatment factor. “Natural Experiments of History” is a collection of eight comparative studies drawn from history, archeology, business studies, economics, economic history, geography, and political science.

Book is easy to read and it is extremely thought-provoking. It offers broad sample of approaches to comparative history, using range of methods—from nonquantitative to statistical, range of compared subjects—from two in development Hispaniola island case to 233 areas in India, range of temporal comparisons—from past to contemporary societies, and wide geographic coverage. Behind all cases there is one simple idea—comparative analysis of natural experiments can be applied to the messy realities of human history, politics, culture, economics and the environment.

Short summary of all chapters is available on-line. Many chapters in this book are based on research papers, which could provide additional information about research methods used.

 

On a deserted, wave-swept shore, He stood – in his mind great thoughts grow


 

While sitting on a beautiful hill and overlooking the tranquil expanse of water, it is difficult to notice the pulse of life there, in the depths. Sometimes on the surface appear ripple-like patterns from whales’ tails or submarine periscopes, which could provide only a sketchy idea of the life in depths. Over time, scientists have created a number of tools to explore the depths, which fall into one of two large groups. In the first case, we catch a particular instance from the abysmal depths and study it in details. However, we do not care how numerous are such specimens, how they interact in the ecosystem and so on. In the second case, we consider the system as a whole — we track shoals of fish, water flow or distribution of volcanic emissions. In that case, we care little to none what happens to specific instances, we are interested in macro-phenomena.

In the social sciences, we use exactly the same tools — roughly speaking, case studies and statistics, each having their own pros and cons.
Case studies (focus groups, in-depth interviews and other similar methods) allow looking deeper into the problem, describing it in detail and in colors, highlighting some features that are difficult to see otherwise. However, such stories are not representative, and reflect the particular specific case. We have too many variables in our society, and it is too hard to pick a «typical representative» (try to find «a typical representative of your country» or «a typical country in Central Asia»), and there is no guarantee that that his or her experience would be typical.

On the other hand, namely statistics, operating with large numbers, can highlight the typical cases, trends and other average values, by which you can judge a society as a whole. The trouble is that most of these indicators gives an understanding of underwater life, roughly speaking, by ripple-like patterns from whales’ tails or submarine periscopes. Razor of research hypotheses completely cuts out the flesh of meaning from the bones of numbers.

There are numerous and repeated attempts to befriend a variety of tools that would give us understanding what’s going on in the depths of society. For example, the article «Managing Yourself: Zoom In, Zoom Out», published in the Harvard Business Review, offers a very simple approach — zoom in or out of the problem as a map in Google Maps. When the map is zoomed out, one can see the mountain ridges, state borders and big highways. When the map is zoomed in, these are dropped out of sight, but one can distinguish individual neighborhoods, streets, and houses. At zoom out one can see the problem in context, while zooming in allow to see important details that are blurred in zoom out.

Cognitive Edge offers a similar tool, which brings together stories, «micro-narratives» and the meta-data about these stories. In this case, research hypotheses do not play a major role. Certain «patterns» of stories begin to emerge when a large number of stories is collected and plotted around certain metadata options — whether the story about the past, present, or future? Is the story about corruption, cooperation or competition? In this case, accuracy of the sample is not so important — whether in the cluster 400 or 401 story does not matter at all. What is more important is appearance of such a cluster. It is possible to go in more deeply analysis, using the layers of clusters by adding variables — demographic characteristics of the storytellers, the emotional background of stories, and so on. Moreover, the tool allows you to «dive» deep into the cluster and catch the specific history, thus merging the statistics and personal experience .

This combination is very useful — politicians and decision makers rarely hear the voice of the people, relying on public opinion studies, and other average values. Using this tool allow one, sitting on the hill, to observe the beat of life at all stages of program or project — analysis, design , implementation, monitoring and evaluation.
This article is also featured in Voices from Eurasia, available in Russian.

I like the environment so much! (Please, don’t ask me to pay for it)

Every morning I open my Facebook news feed and between kitty kitty photos I usually see some pictures attracting my attention to ecological issues, degradation of environment, or articles calling me to ‘go green’. The same situation is in my email box. It seems that global ecological issues caught global attention. The question is what can we do with them?
There are long-standing debates about ‘environmental Kuznets curve’, which suggest inverted U-shaped relationship between development and environment—environmental degradation tends to get worse as economy grows, until some income level is reached and the trend is reverted, as countries start valuing clean environment and have money to invest in it. However, attempts to find environmental Kuznets curve in vivo did not bring conclusive results.
World Values Survey is a multinational poll, asking people about their values and attitudes toward different issues. Survey conducted in waves and we used most recent, 2005-2008 wave. Among other questions, it asked people what is more important for them—economic growth or protecting environment? People also responded if they are ready to give a part of their income for environment protection. We also complemented these perception data with GDP per capita numbers from WorldBank database.

Environment+vs+Money_12813_image005[1]

First results were not surprising—the share of those who prefer protecting environment over economic growth had positive correlation with income level. In other words, the richer is a country, the more attention people tend to pay to environmental protection. (Of course, correlation does not imply causality). That the sort of things we expected. However, distribution seems to be rather ‘flat’ and, moreover, there is huge split among rich countries in their growth vs environment attitude. By the way, the picture is for more economically cheerful period of 2005-2008. With economic crisis hitting countries, especially developed ones, attention could gravitate from environmental protection toward economic growth.

Environment+vs+Money_12813_image006[1]

However, if we plot desire to give part of income for environmental protection against income level, we will find negative correlations. In other words, the richer is a country, the less desire have people to pay for environmental protection. (Again, this is correlation, not causality.) This finding is quite striking for me. I could see two reasons for this. First, the majority of people in richer (or more developed) countries do not face immediate impact of environmental problems, due to higher urbanization and bigger share of industry and services in economy. In richer country ‘deforestation’ could mean lack of nice parks for a walk. In less developed country deforestation could mean lack of fuel to cook and products to maintain livelihood. Second, more developed countries typically have stronger governance and higher taxation, consequently expectations could be that the Government should sort out environment issues without additional contributions.

Environment+vs+Money_12813_image003[1]

The picture gets even more intriguing if we look on preferring environment over economic growth and desire to pay for it—it seems that there is no correlation at all between them. In other words, people seem to like environment, however expect that someone else would pay for it.
Clicking ‘Like’ on yet another Save the Planet picture doesn’t make much sense. The big issue is how to include environmental concerns into a bigger economic picture and how to finance environment protection? On a personal level I switched to public transport, walking to work, and double side printing. What could we offer for societies at large?

This blog post is also available in Russian.