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.

 

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

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.

 

Sustainable Human Development Index: What exactly we try to measure?

Twenty years ago Rio Summit raised global issues of ecological concerns and sustainability. This year Rio+20 conference will take an overview of past two decades and suggest program for the future we want.

One of the issues related to ecology and sustainability is measurement. This is not just a matter of curiosity, to establish «sustainable development goals» we should have good enough metrics and indicators of sustainable development (as someone nicely put it «You never have to be ‘absolutely sure’ of something. Being ‘reasonably certain’ is enough»). Unfortunately, until now we have no commonly agreed set of indicators or one «sustainable human development index», despite of many efforts in this area—WorldBank Adjusted Net Savings, Yale Environmental Sustainability Index, UNU Human Sustainable Development Index, just to mention few.

One principal issue in «sustainable development» measurement, in my mind, is conceptual non-clarity what exactly are measuring: development or sustainability? And what sustainability means? If we would like to have «sustainable [human] development index», it should take both—achieved level of development and ability to sustain achieved level. Looking to either of these aspects is not enough. One could get high on «development» scale, but it says nothing about possibility to go on with this achievements (resemble Greece current crisis, isn’t it?). On the other hand, lower level of «development» could lay on more solid ground, which make it more sustainable.

 

DEV+vs+SUS+ENG+www[1]

Soon after Rio Summit Armenia start working on incorporation of sustainability aspects in Human Development Index. Resulting proposal with examples for two countries (Armenia and Georgia) was published in 1995. Proposed «Sustainable Human Development Index» adds fourth environmental component to three initial dimensions of human development, health, education, and living standards. Environmental component itself includes two sub-components—environmental state of a territory (4 indicators), and the environmental evaluation of human activities (7 indicators).

In the framework of Rio+20, Armenia is organizing side event to discuss possible options for Sustainable Development Index Methodology. We helped armenian team in structuring the approach to sustainable HDI in the light of recent development—changes in HDI index introduced in 2010, and extended approach to sustainability, which goes beyond ecological aspects only.

We started work with stepping back and asking a number of big questions:

  • What is the purpose of the index: Global or National?
  • What is the way of integrating the environmental aspects?
  • What is approach to sustainability: narrow (environment only) or broad (in all areas)?
  • What is the type of sustainability: weak (e.g. WorldBank Adjusted Net Savings) or strong (e.g. Ecological Footprint)?
  • What are the links to other human development indexes and indicators?
  • What is weighting procedure: for dimensions and indicators?
  • How we do attribution of the ecological damage—by place of production or by consumption?

Without properly addressing these questions, one could risk to end up with strange figures in hands and unexpected interpretation of index. The resulting proposal for Sustainable Human Development Index answer all of them.

In design of Sustainable Human Development Index we decided to give priority to national relevance, while keeping in mind necessity for international (or at least regional) comparisons. We decided to take a broad approach to sustainability, looking on ability to sustain not only natural environment, but also economic and social ones. Fourth dimension was added to the Human Development Index, to incorporate state of environment, as we value «ability to live in clean and balanced natural environment», in the same way we value abilities to «live long and healthy life», «being educated and having access to knowledge», and «enjoying decent standards of living». Resulting Extended Human Development Index (EHDI) includes only indicators of status of natural environment in five areas: quality of water, quality of air, state of soils, state of biodiversity, and habitat. This index says us what we achieved, without saying how it was achieved.

To answer the question how it was achieved and if we could maintain achievements, we added separate tier to the index—sustainability. Indicators in this tier says about ability to sustain achieved level (i.e. do we have debt big enough to threat our current wealth?) in all four dimensions of human development (including natural environment). These indicators penalize or reduce achieved level—if you have extremely high water withdrawal level your currently nice environment will disappear, as it happened with the Aral Sea. Resulting index is Affordable Human Development Index (AHDI), which should be judged against Extended Human Development Index (EHDI), looking how big is part of development (what we achieved), which is lost due to non-sustainability (how we achieved). The same approach is used, by the way, in Inequality-adjusted Human Development Index (IHDI), where HDI achievements (what) are penalized for inequalities (how).

«Yes, but…»—the question you will hear all the time you present some indicator or index. Indeed the broad picture of the world cannot be captured in one indicators or index, however perfect it could be. To put indexes in broader context, we included a broader set of «context» indicators—how much Government spend on education, health, environment? Do country have environmental protection institutions? What is the quality of education (as judged by PISA test results)? These things are very important, but are not hard enough to make their way into index.

Clearly, there are many questions to address to be ‘reasonably certain’ about the index. Trends in index, cross-country comparisons, approach to CO2 emission and energy efficiency of economies are just few of them.

SHDI+Construction+ENG+www[1]

This is a short outline of approach to sustainable human development measurement, which will be presented at Armenia side event at Rio+20 conference, alongside with calculations for a number of countries in Europe and Central Asia Region. I hope to see more developments in this area after the Rio+20 conference.

This article is also available in Russian.

Human and Development: What we are talking about?

Two decades ago, in 1990, the first Global Human Development Report gave the birth to new concept of development—human development concept. English was main language of the report, as well as the working language for the most of authors. Translation of the concept in other languages, mostly Slavic ones, has faced difficulties. Mechanical translation of the term human development cannot translate the essence of the concept. It is important to avoid terminological misunderstanding, taking into account that during these two decades three terms appeared in Russian literature—«человеческое развитие», «развитие человеческого потенциала» and «развитие человека». This becoming especially relevant when one start thinking what are we measuring: development of society? personal development? process of development?

The most correct and most frequently used term is «человеческое развитие», which more correctly transfer the essence of the human development concept—type of development aimed on people, or, in other words “development of people, for people and by the people”. In the very first report Mahbub ul-Haq put it in the following way: “The objective of development is to create an enabling environment for people to enjoy long, healthy and creative lives. People are real wealth of nations.” Namely by its orientation on people this concept differs from other development concepts. Actually, exactly in this way, by using adjective to the word “development”, the term is translated into French (développement humain), Spanish (desarrollo humano), Romanian (dezvoltarea umană) Czech (lidský rozvoj) and many other languages. Namely in this form one could find in dictionaries. Namely by this term one could find in search engines articles, manuals and other materials. Namely in this way was entitled human development manual, produced by Moscow State University.

Expression «развитие человеческого потенциала» is linked with the roots of the human development concept, namely the “human capital” theory of Schultz, which appeared in 1960s. Formation of human capital included not only expenditures for education, but also research and development expenses, health care services, family planning. In other words, all investments in people were considered as productive ones, regardless the aim—either to increase gross national product, or to increase human capabilities. Still, people were treated as a resource for development, the aim of development per se was economic development, and the measure of development was GDP growth. Also, human capital concept did not take into account empowerment of people and their participation in the development process. This term is most frequently used in the national and regional Human Development Reports in Russian Federation and for translation of “human development index into Russian. If you google it out, you will get mostly economic articles and Human Development Reports for Russia and its regions.

«Развитие человека» is related to biology, psychology, and anthropology. It could be used in very broad philosophical sense as “humankind development”. If you look for this term in search engines, you will get papers from anthropology, biology, psychology, and philosophy, but not ones related to the human development concept.
To sum up, the most appropriate translation of the human development term in Russian is «человеческое развитие». It most adequately translate the essence of the concept and is most frequently used in Russian papers. In future one should stick to this term to avoid terminological confusions.

Related materials:
Andrey Ivanov. “Internalizing the human development paradigm: reflections of a witness