OECD Life Satisfaction Index - What makes people happy?

Recently, OECD published its life satisfaction index across its member countries. How's Life?: Measuring Well Being gives an overview of the OECD Better Life Index methodology, which is focused on households and individuals rather than aggregate economic conditions, and on well-being outcomes as opposed to well-being drivers. In particular, life satisfaction measures how people rate their general satisfaction with life on a scale from 0 to 10. The surveys show that Hungary, Portugal, Russia, Turkey and Greece have a relatively low level of overall life satisfaction while  Denmark, Norway, the Netherlands and Switzerland have a high level of overall life satisfaction. 201205242132.jpg

In addition to life satisfaction, OECD survey also captures various indicators under the topics of housing, income, jobs, community, education, environment, civic engagement, health, safety, and work-life balance. For instance, the housing topic includes the rooms per person (average number of rooms shared by person in a dwelling), dwellings with basic facilities (percentage of people with indoor flushing toilets), and housing expenditure (housing expenditure as a percentage of disposable household income) indicators. In contrast, the jobs topic includes the employment rate (percentage of people currently employed in a paid job), long term unemployment rate (percentage of unemployed people who have been actively looking for a job for over year), personal earnings (average annual earnings for a full-time employee), and job security (share of employment with a tenure less than 6 months) indicators. 201205242205.jpg The chart on the left shows the indicator values for Hungary. In general, Hungarians are less satisfied with their lives than the OECD average. 65% of Hungarians have more positive experiences (feelings of rest, pride in accomplishment, enjoyment, etc) than negative ones (pain, worry, sadness, boredom, etc) on a daily basis in contrast to the OECD average of 72%.

As the chart shows Hungarians feel safe, are happy with the water and air quality, find their work-life balance acceptable, have a strong sense of community, and are happy with the general quality of the education. For instance, 89% of Hungarians believe that they know someone they could rely on in time of need. Yet in life satisfaction, Hungary scores the lowest.

Referring back to the chart, the scores for housing, jobs, civic engagement, and health for Hungary are lower than the OECD average while income is considerably lower than the OECD average. In Hungary, the average person earns about $13K a year, less than the OECD average of $22K a year. It seems like the low life satisfaction score for Hungary is connected to low living standards stemming from sub-par income levels coupled with a lack of jobs. For instance,  around 55% of Hungarians aged 15 to 64 have a paid job, well below the OECD employment average of 66%.

In contrast, sense of community is the lowest score for Turkey. For instance, 69% of Turks believe that they know someone they could rely on in time of need, lower than the OECD average of 91%. Is there a correlation between life satisfaction and indicators for living conditions and quality of life? If yes, does the correlation hold across the OECD countries?

201205242219.jpgThe OECD Web site has a mixer tool that lets a user to select the relative rankings of the indicators and analyze the ranked list of countries based on these preferences. The customized index enables the comparison of well-being across countries based on personal preference of the importance of 11 topics the OECD has identified as essential, in the areas of material living conditions and quality of life.

While the OECD mixer is a nice tool for engaging readers, as a modeler, we see the life satisfaction sentiment indicator as an output while the rest of the indicators (housing, income, jobs, etc.) as inputs. In other words, we believe that the input indicators drive people how people feel about their life experiences. To test this hypothesis, we performed a correlation analysis between the life satisfaction index and the rest of the indicators in order to understand which factors contribute the most or the least to the life satisfaction sentiment. The correlation analysis is shown on the right.

In terms of topics, life satisfaction has high correlation with income, jobs, housing, health, low correlation with education and no correlation with safety. In terms of individual indicators, room per person has the highest correlation with life satisfaction while job security, housing expenditure, employees working long hours, educational attainment, years in education, student skills, consultation on rule making, air pollution, homicide and assault rate indicators have very low correlation with life satisfaction.

The indicators under each topic show some interesting results. For the jobs topic, while employment rate, personal earnings, long term unemployment rate indicators are correlated with life satisfaction whereas job security is not. Similarly, for the environment topic, while water quality has a high correlation with life satisfaction, air pollution does not.

It would be interesting of comparison if there was a similar survey for non-OECD countries. Perhaps the OECD country values are dominated by the population's desire for the ability to collect as many material possessions as possible. Relatively poorer country values may not follow this correlation.

Countries with Increased Political Violence Forecast: 2011 - 2015

In our companion Domestic Political Violence Model blog, we published yesterday the list of countries predicted to have increases in political violence for 2011 to 2015. The map below shows the countries with expected increase in political violence grouped by Very High Risk, High Risk, and Medium Risk. Our forecast is based on four different models. In the Very High Risk category, all four models predicted an increase. In the High Risk category, three models predicted an increase. In the Medium Risk category, half of the models predict an increase in violence. The countries in each category are sorted based on the size of the mean residual, so the states with the most pent-up demand for violence are listed at the top. The residuals imply that these are states that we expect to observe increases in violence although not necessarily high levels of violence. So United Kingdom and Israel are not expected to have the same level of violence but are expected to have the same magnitude increase in political violence.


United Kingdom, Israel, Sri Lanka, Iran, Colombia, Zimbabwe, South Africa, Haiti, Egypt, Philippines, Guinea-Bissau, Venezuela, Chile, Syria, Chad, Belarus, Guinea, Kyrgyzstan, Greece make up the very high risk list. Israel, Sri Lanka, Iran, Colombia, South Africa, Egypt, Chile, Syria, Chad, Belarus, and Kyrgyzstan are returning countries from our 2010-2014 forecast. Of our 2010-2014 forecast, Syria, Egypt, and Libya saw the most violent protest in the Arab Spring of 2011. United Kingdom, Zimbabwe, Haiti, Philippines, Guinea-Bissau, Venezuela, Guinea, and Greece are the new additions to our very high risk list. United Kingdom tops the list as the pent-up demand for increased violence was certainly evident in the London Riots over the Summer of 2011. Greece saw substantial increase in political violence due to the measures introduced by the Greek government to address the debt crisis.

It is worth noting that our 2011-2015 forecast model is based on events dataset which captures both the frequency and the intensity of political violence from 1990 to 2010. Similarly, our 2010-2014 forecast model is based on events dataset which captures both the frequency and the intensity of political violence from 1990 to 2009. We publish our forecast based on our acquisition date of the event dataset. As the event dataset is available on a real-time basis - albeit at a higher cost, we can publish our forecast in real-time if needed.

Using a regression model applied to a large number of drivers of conflict variables spanning numerous open source social science datasets, our model uses a novel Negative Residuals technique. Negative Residuals result from the model predicting higher levels of violence than actually experienced, indicating nation states that are pre-disposed to increasing levels of violence based on the presence of environmental conditions and drivers of conflict with demonstrated correlation with measured political violence. In our model, the magnitude of future political violence directed towards the state is heightened by coercion, often thought of as violations of physical integrity rights, and by coordination, or the tools by which groups can associate and organize against the state. Conversely, the magnitude of political violence is lessened by capacity, defined as the ability of the state to project itself throughout its territory.

For the event dataset, we use the Integrated Data for Event Analysis (IDEA) framework. IDEA event dataset is based on the Reuters Global News Service, and organized in a “who” did “what” to “whom” manner for each particular event. This framework allows researchers to isolate events of interest for their particular project. Using this framework allows us to capture and isolate domestic anti-government violence. For the dependent variable, our model uses the Goldstein scores that captures the overall level and intensity of domestic antigovernment violence within a state in a given year.

Wiki Surveys for Social Science Research

Surveys and interviews form the central methodology for analyzing and discovering attitudes and opinions in social science research. With the advent of Web, online surveys have become an efficient way for researchers to collect and analyze large amounts of data. The popularity of the online survey tools like SurveyMonkey , Zoomerang, SurveyGizmo , etc. are testament to the productivity enabled by surveys. However, surveys represent a top-down rigid methodology forcing the survey designer to account for all possible answers up front, which is an impossible feat. In contrast, interviews allow the unanticipated information to bubble up bottoms up from the respondents. For instance, Integrity Watch Afghanistan (IWA), Afghan Perceptions and Experiences with Corruption: A National Survey 2010 primary data, involves interviewing randomly selected 6,500 respondents in 32 provinces on over 100 questions that deal with sectors where people experienced corruption; levels of bribes people paid to obtain services; what type of access people had to essential services; who people trusted to combat corruption; and experiences with corruption in the judiciary, police, and land management. However, the interview methodology is expensive and time-consuming as it requires implementation by research companies with expertise in effective research design, and precise management of data collection over several months.

Is there an alternative to surveys and interviews in social science research? Prof. Salganik's team at Princeton came up with a hybrid approach, "wiki surveys", that combines the structure of a survey with the open-endedness of an interview. To date, various organizations have created more than 1000 wiki surveys on the project Web site - All Our Ideas, generating in 45,000 ideas with 2 million votes. Wiki surveys range from the New York City Mayor's Office's engagement with citizens in shaping the city’s long term sustainability plan to the Catholic Relief Services surveying their 4000 employees to find out what makes an ideal relief worker. The figure below shows how the third question in Tactical Conflict Assessment Planning Framework (TCAPF) would be be implemented as a wiki survey:

tcapf wiki survey.jpg

Inspired by extending the kittenwar concept to ideas, the user interface guides the respondent to choose between two random alternatives, while encouraging the respondents to add their ideas into the mix of alternative responses. The additional ideas are added into the survey’s marketplace and voted up or down by the other survey-takers. Prof. Salganik says that “One of the patterns we see consistently is that ideas that are uploaded by users sometimes score better than the best ideas that started it off. Because no matter how hard you try, there are just ideas out there that you don’t know.”

All Our ideas have some basic visualization features to make sense of the wiki survey responses. Here is the visualization for the responses - "What do you think the Digital Public Library of America (DPLA) should be like?":

DPLA Survey Reponse.jpg

It is worth noting that the top scoring 15 ideas starting with DPLA interoperability with Government Printing Office (GPO), Defense Technical Information Center (DTIC), an National Records Archive Administration (NARA) are all uploaded ideas not in the original set of alternatives. A powerful argument for crowd sourcing!

Admittedly, we still need boots on the ground to collect TCAPF data in Afghanistan given the demographics of the people we want to reach. On the other hand, wiki surveys hold great potential in reaching the younger generation fueling the Arab spring and the like.

Bin Laden Hideout vs. Al Qaeda Training Manual

In our Building Intent project, we developed a geoprofiling algorithm that predicts the location of facilities that support adversary operations in the urban environment. Geoprofiling is a technique that is widely used in serial crime investigations. In our project, we researched and developed a building intent inference system based on terrorist preferences, building characteristics, and social network behavior. Our approach learns the utility function that the adversaries are using, and classifies and predicts the potential utility of a facility to the adversaries based on the derived metadata of each facility using influence networks. For terrorist preferences, we have studied Military Studies in the Jihad Against the Tyrants: The Al-Qaeda Training Manual in order to find building use tactics that the adversary is training its recruits, and found a significant number of building use related tactics and procedures embodied in these manuals. In collaboration with the Terrorism Research Center in Fulbright College, University of Arkansas, we then studied the international terrorism cases in the American Terrorism Study, and found empirical evidence that shows the practice of terrorism manual tactics in the observed data. Based on these findings, we developed a baseline set of indicators for modeling building intent, and researched the likely causal connections among these variables. We then built extractors to derive a set of metadata for these indicators, and used machine learning algorithms to find the causal connections between the incidents or events and building attributes, and model parameters, and build classifiers based terrorist process preferences , building characteristics, and guilt by association data.

As shown in the figure below, our geoprofiling algorithm does a nice job in predicting the Japanese Red Army terrorist Yu Kikumura's residence in New York based on the American Terrorism study. Here the blue markers signify police stations and white arrows signify the egress points. As shown in the figure, Yu Kikumura's residence at 327 East 34th Street, NY is in the red hotspot area predicted by our algorithm. Avoiding police stations and ease of egress were two of the primary factors in Kikumura’s choice of housing. Not only is his apartment equidistant from the nearest police departments – all of which are over one kilometer away – it’s back-alley access road to the underground Queens Memorial Tunnel provides a quick get-away by car. In addition, the examination of the residence floor plan reveals that the apartment building had numerous staircases (one of which is private to the unit) to the basement level with a rear exit.

Japanese Red Army.png

The Al Qaeda Training Manual gives several instructions for renting a residence as shown in the table below. For instance, it is preferable to rent apartments on the first floor for ease of egress, avoid apartments near police stations and government buildings, and in isolated or deserted locations, rent in newly developed areas, and the like. In particular, the Al Qaeda Training Manual calls for the use if the following tactics in renting an apartment:

Al Qaeda Tactics.jpg

So how does the location of Bin Laden's secret hideout in in Abbottabad follow the advice of the Al Qaeda Training Manual? Not that closely. Bin Laden clearly did not follow the tactics for selecting a ground floor location by living on the third floor, for avoiding police stations and government buildings by selecting a location near the Pakistan Military Academy, for finding an apartment in newly developed areas where people do not know each other by choosing a neighborhood with retired Army Generals, and for preparing ways of vacating the premises in case of a surprise attack by not building exit stairs. The only tactic that Bin Laden has used from the list above is avoiding an isolated location. One wonders if Bin Laden made a concerted effort to avoid his own tactical advice in order to thwart geoprofiling techniques. Perhaps another consideration that will need to be taken into account in future geoprofiling is the assistance from outside forces, given the possible connection to a support network that included elements of the Pakistani military or intelligence services in the Abottabad area.

Increased political violence in store for Italy and Czech Republic?

In collaboration with our academic partners Prof. Cingranelli at the Political Science Department, SUNY Binghamton University and Profs. Sam Bell and Amanda Murdie at the Department of Political Science, Kansas State University, we developed a Domestic Political Violence Model that forecasts political violence levels five years into the future. The model enables policymakers, particularly in the COCOMs, to proactively plan for instances of increased domestic political violence, with implications for resource allocation and intelligence asset assignment. Our model uses the IDEA dataset for political event coding, plus numerous indicators from the CIRI Human Rights Dataset, Polity IV Dataset, World Bank, OECD, Correlates of War project, and Fearon and Laitin datasets. Here is our model's forecast for 2010 - 2014 as a ranked list:

  1. Iran
  2. Sri Lanka
  3. Russia
  4. Georgia
  5. Israel
  6. Turkey
  7. Burundi
  8. Chad
  9. Honduras
  10. Czech Republic
  11. China
  12. Italy
  13. Colombia
  14. Ukraine
  15. Indonesia
  16. Malaysia
  17. Jordan
  18. Mexico
  19. Kenya
  20. South Africa
  21. Ireland
  22. Peru
  23. Chile
  24. Armenia
  25. Tunisia
  26. Democratic Republic of the Congo
  27. Belarus
  28. Argentina
  29. Albania
  30. Ecuador
  31. Sudan
  32. Austria
  33. Nigeria
  34. Syria
  35. Kyrgyz Republic
  36. Egypt
  37. Belgium

Using a regression model applied to a large number of drivers of conflict variables spanning numerous open source social science datasets, our model uses a novel Negative Residuals technique. Negative Residuals result from the model predicting higher levels of violence than actually experienced, indicating nation states that are pre-disposed to increasing levels of violence based on the presence of environmental conditions and drivers of conflict with demonstrated correlation with measured political violence. The residuals imply that these are states that we expect to observe increases in violence although not necessarily high levels of violence. So Iran and Sri Lanka are not expected to have the same level of violence but are expected to have the same magnitude increase in violence.

There some unexpected countries on our list like Czech Republic and Italy. Time will tell the accuracy of our model's predictions although recent political violence in Ecuador is an early indicator of the model's effective performance. The model uses nuanced measures of repression and captures variables that can be manipulated by policy makers. Our project page has further details on the model.

Tribal Human Terrain of Afghanistan

Under the sponsorship of the OSD Human Social Culture Behavior (HSCB) program, we are developing a semantic wiki for Complex Operations. The envisioned operational impact of our effort is to foster collaboration and sharing of knowledge for whole-of-government approach, and to improve COIN/SSTR operations analysis and execution by focusing on population as center of gravity. The development of such a wiki presents several challenges that include the broad domain area of knowledge complex operations require, a large number of doctrine publications to wikify and semantify, several out of print key references, etc. With these challenges, we saw an opportunity to develop an open source culturepedia for Afghan and Pakistan human terrain as such knowledge is not aggregated and not readily available.

The Complex Operations wiki currently contains more than 1,000 articles on the various tribal dynamics and locational knowledge for the Afghanistan and Pakistan region, outlining tribal meta-knowledge such as the sub-groups, primary locations, traditional alliances, and traditional disputes of various groups to support situational awareness about the human terrain. Here is the wiki page for the covered Afghanistan Organizational Groups. We have created over 150 concept maps (an example shown below) to capture the knowledge about 1,000 ethnic groups, tribes, sub-tribes, clans within Afghanistan and Pakistan region to make this human terrain knowledge readily accessible to the complex operations practitioner.

tribal concept map.png

Our use of a semantic wiki platform enables the representation of the human terrain knowledge as facts and relationships. For instance, the wiki page for the Achakzai tribal group lists the the known facts and relationships about this ethnic group both a human consumable form using semantic forms:

Achakzai Semantic Form.tiff

, and a machine consumable form as semantic RDF relationships:

Achakzai RDF.tiff

By inspecting the semantic form, the reader can deduce that Achakzai is a sub-tribe of Zirak, which is a sub-tribe of the Durrani super-tribe, primarily located in the Chora and Khas Uruzgan districts, and traditionally have disputes with the Nurzai, Panjpai and Kakar tribes. The representation of this knowledge in a semantic wiki has the additional advantage for faceted browsing and answers engine queries. For instance, the semantic wiki can answer questions like "What are the tribes in Kandahar Province and their traditional disputes?" as a table which gets automatically updated every time a new tribe in this province is added to the wiki: Tribes in Kandahar.tiff There are also several groups in Afghanistan that do not organize around tribal kinship ties, including Uzbeks, Tajiks, and Hazaras. In addition to tribal affiliation, social organizations such as solidarity groups - a group of people that acts as a single unit and organizes on the basis of some shared identity, and patronage networks - led by local warlord or khan - play an important role in understanding of the human terrain. Afghan and Pakistan human terrain and situational awareness knowledge base can be extended to include other populations of interest to the community, such as Yemen or Somalia.