Category Archives: survey research

Measuring happiness comes close to home

Flickr photo by seq

We’ve reported earlier on the UK government’s recent decision to measure the happiness of its citizens.  The latest government to do so is neighboring Somerville, MA.  Somerville, which went by the nickname of “Slummerville” in the 1980s for cheap and affordable 3-decker housing and the highest residential concentration of any community in New England, has recently become more hip and gentrified thanks to the revitalization of places like Inman Square and Davis Square.

Somerville Mayor Joseph A. Curtatone is a recent graduate of the mid-career program Harvard Kennedy School (HKS) and a visionary who has worked with HKS on many other local government measurement projects (SomerStat). The NY Times quotes Curtatone as saying that the project was a “no-brainer” and he noted that “cities keep careful track of their finances, but a bond rating doesn’t tell us how people feel or why they want to raise a family here or relocate a business here.”

The city is collaborating with happiness expert Dan Gilbert at Harvard and ultimately hopes to use these data to see how things like the extension of the subway green line affect happiness or how Somerville’s happiness compares with neighboring towns.

The voluntary survey asks such questions like:

  • How happy do you feel right now? (1-10 scale)How satisfied are you with your life in general? (1-10 scale)
    In general, how similar are you to other people you know? (1-10 scale)
    When making decisions, are you more likely to seek advice or decide for yourself? (1-10 scale)
  • Taking everything into account, how satisfied are you with Somerville as a place to live? (1-10 scale)

The survey also asks residents to rate Somerville’s “beauty or physical setting” [likely fairly low for anyone who has spent time in Somerville], “availability of affordable housing”, quality of local public schools, and effectiveness of local police.

Researchers hope to correlate ratings of well-being, demographics, satisfaction with Somerville amenities, and proximity to various parts of Somerville to unpack what makes residents more or less satisfied.

As the NY Times observes: “Monitoring the citizenry’s happiness has been advocated by prominent psychologists and economists, but not without debate over how to do it and whether happiness is even the right thing for politicians to be promoting. The pursuit of happiness may be an inalienable right, but that is not the same as reporting blissful feelings on a questionnaire. ”

See “How Happy Are You? A Census Wants to Know” (NY Times, 4/30/11 by John Tierney)

See Somerville’s voluntary “Wellbeing and Community Survey

See “Somerville, Mass., aims to boost happiness. Can it?” (CS Monitor, 4/4/11 by Mary Helen Miller)

Workers of the World Connect?

Interesting talk by Richard Freeman on how the Internet might influence what unions look like and what such e-unions might play or services that they could provide.  He notes the sharp declines of private sector unions in the United States and the difficulty of getting individuals in U.K. unions to pay dues (approximately free-ride since there are no requirements of being of a union member in companies represented by unions — so they get the benefit of higher wages or services without having to pay dues).

Freeman notes that a majority (53%) in recent Harris polls in the U.S. suggest that they would join a union if there was one in their workplace, but Freeman indicates that unions have in most cases not been able to win out against intransigent employers or it uses up so much in resources that doing this repeatedly with a wide number of firms is not feasible.  It is one reason that Andy Stern (of SEIU) has tried to win union battles at the wholesale level, converting multi-site employers at the corporate level through proxy battles and adverse press.  Nevertheless, Freeman thinks that even Stern’s strategy has not been all that effective.

Freeman is doubtful that the Stalinist work structure of unions can continue, but thinks that many (although probably not all) services that unions provide can be offered more cheaply through Internet-based versions, although he has yet to see any one example that does all things optimally.

He highlighted the Working Family Network) that has 2-3 million e-mails in their network and can often mobilize tens of thousands of members to sign petitions.  They were successful at getting Enron and Worldcom employees to get severance pay when those organizations were in bankruptcy.    A weakness of them is that there is no interaction/feedback with these individuals since the e-mail directories are controlled by individual unions who are not willing to share these with  They have 1.6 million members and signed up hundreds of thousands of members in summer 2004 in 5 political battleground states (OH, FL, MO, WA, and OR) through a large door-to-door canvassing effort.  They are likely to hit 2M members this year.  The members vote online to determine priorities, although at the moment, they only get to choose among 4 priority areas of John Sweeney’s.  They are expanding to new states and have signed up a lot of non-traditional members (Republicans, evangelicals, etc.).  Their weakness is that they are run from DC and they don’t ask who the member’s employer is (since they worry that these members might fear it would get back to their employer).    They asked for $5 in voluntary membership dues but don’t actually collect it since the transaction costs are greater than the benefit. 

The third example was which is something of a UK equivalent of WorkingAmerica.  They provide information on things like childcare provision, rights of privacy, etc.  They have 5000 visitors a month and this number is fairly stable.  Their weakness is that they only provide information.  This site gives associates of prestigious law firms a chance to publicly air if they feel they have been mistreated.  Given the furious competition among law firms for top legal talent coming out of law school, this outlet had the impact of raising associates’ salaries by 20% in the year after the site become well known.  Such publicity can work in an industry with a scarcity of specific kinds of workers.  This is a site that Richard Freeman is working on at Harvard.   It currently has 5000 users who, after filling out a social science survey, get access to a Harvard expert on a range of work topics.  They are in the process of building an artificial intelligence system to answer user questions as a partnership with may bring hundreds of thousands of users.

Finally, Freeman described is a site to connect union reps (who are the frontline providers, usually elected by workers, sometimes paid by firms, who deal with disputes, organizing, training, and are the public face of the union to workers).  Freeman has done repeated cross-sectional surveys with they, monitored e-mail threads, and conducted follow-up surveys.  Their work suggested that this site was effective at disseminating information and demonstrating the wisdom of crowds.  And the site actually helped to build a community of activists:  about 20% of workers suggested that e-mail discussions started online get continued offline to resolve differences or take action on something.  And union reps surveyed confirmed that they had met offline.

Freeman concluded that there is evidence that labor can be successful at doing things online if they can combine individual successes: e.g., like WorkingAmerica’s success at signing up large numbers of individuals, or WorkingFamilies success in getting individuals to act, or’s success in building a learning community that shares expertise.  But Freeman said it was unclear what the financial model will be since these organizations will be financially small since employees will not pay much for an organization that doesn’t do collective bargaining for them.  Moreover, he wondered if these e-unions actually supported minority activists, whether firms would retaliate, which in turn would force such entities to keep the names of their supporters secret.  (He said there is historical precedent for this, with the Knights of Columbus needing to maintain the secrecy of their members.)

And Freeman thinks that in order to gain clout, such e-unions may have to ally regionally.  So even if there were not enough members in one workplace in Boston, you might have all the Boston members in a group threatening to boycott a product unless the offending company changed their policies.  (To the extent that a company sold nationwide or over the Internet, one might be able to mobilize boycotters across the country.)

Some interesting questions:

1) Rosabeth Moss Kanter said that Internet is a disintermediating tool and the unions are prime examples of intermediators.  She thinks the focus should be on achieving change not on whether one can save these organizational by-products of an earlier industrial era.  One example of Internet pushing for such change is www.walmartsucks.comto focus on Walmart’s labor abuses.  She thinks that unions are too top-down, bureaucratic and slow to move and won’t be able to thrive in this new environment.  [Freeman acknowledged that for example, the large unions could never have orchestrated the May 2006 large immigrant rallies in the U.S.]

2) another question pointed out that unlike the typical open source structure that creates a common-based peer production (see Yoachi Benkler) and is bottom up, these initiatives are largely top-down and sometimes spearheaded by the union.  Freeman seemed to agree and pointed out that there may be inherent conflicts that limit the success of these entities through this conflict.

Some observations on this:

a) these Internet-based unions (or e-unions) are largely not going to be successful at the bread and butter of organization — collective bargaining to get better wages and benefits — unless firm conditions are so bad that they are an affront to our standards of justice.  So trying to mobilize the broader community to build support for raising middle class steel workers’ pay is likely to be far less effective than trying to get basic health care for some very poor employees, putting pressure on a company not to fire employees just before they get benefits, or raising pay of janitors from minimum wage to a working wage.

b) these efforts face the challenge that the new e-unions are going to have a hard time raising funds for two reasons beyond the one Freeman mentioned (that workers will not contribute significant funds unless the e-union is providing collective bargaining).  First workers are unlikely to contribute a lot to e-unions that provide services because they get 100% of the benefits if they don’t contribute and in a low social capital era, people are more inclined to free ride.  And second, users have come to expect free content on the Internet and are loathe to pay for other things that they pay for in real space (whether it is magazines or newspapers or communication).

c) It is easy to see that unions still have a lot of resources to invest (7.5% of workers providing check-off dues in America still produces a lot of resources).  And it is easyto see how they might deploy some of these resources through efforts like WorkingAmerica to build a lot of new members.  Through the right structure, they might build a sense of commitment and community among these workers (see that would make them willing to provide funds.  But there is a fundamental conflict between the command-and-control, top-down old line unions and the more decentralized, “power to the people” approach of the new e-unions (that provide some services).  Until the old unions are willing to bend to the new models, change seems unlikely.  And like any larger entities that reap large profits from the status quo, they are reluctant to endorse a new model of doing business that generates more members but at far slimmer revenue margins, and with unsure prospects about how effectively they can collectively bargain for their workers.

Surveiling ourselves

There’s an interesting article in the Utne Reader describing how citizens unwittingly reveal lots of information about themselves, in Invading Our Own Privacy. Tell-all blogs, digital surveillance, online profiling: Who needs Big Brother? (May/June 2007,  David Schimke)

The article points out that “On February 22, reported that Fox Interactive Media, a division of Rupert Murdoch’s News Corp., which owns MySpace, had hired a high-tech ad firm to mine user profiles, blog posts, and bulletins to ‘allow for highly refined audience segmentation and contextual microtargeting . . . which might put it in more direct competition with the likes of Yahoo, AOL, and MSN.'”

The article also mentions a Chronicle of Higher Education (Jan. 12, 2007) piece that notes that “two professors at Drake University’s law school, worried that their students’ casual approach to digital correspondence could hinder their careers, started a class stressing online discretion. The lesson, according to one student, is simple: ‘If you are not comfortable with shouting your comments from a street corner, you probably shouldn’t convey them via electronic print.'”

Finally, the article also refers to a New York article “Say Anything” (2/21/07) on the digital exhibitionism of youth today, willing to reveal lots of personal information about themselves on blogs, through e-mails, etc.

Schools of choice boost civic values

In a meta-analysis of 21 quantitative studies, Patrick J. Wolf (Univ. of Arkansas) found that schools of choice (private and public) better inculcate students in 7 civic values  necessary for democratic citizenship: political tolerance, voluntarism, political knowledge, political participation, social capital, civic skills, and patriotism.  Study called “Civics Exam: Schools of Choice Boost Civic Values” in EducationNext journal (Summer 2007). Among the more rigorous studies analyzed, 23 of 59 findings (52%) show school choice or private schooling as having statistically significant positive effects on civic values. [Ten findings show a neutral effect and only one finding showed a negative effect of school choice on civic values.]

All these studies control for selection bias in addition to differences in student backgrounds in the various schools. Most of the studies  compared students in private schools with those in public schools, but the effects were found in Catholic and non-Catholic private schools.

Wolf concluded that “These results suggest that the expansion of school choice is more likely to enhance than diminish the civic values of our next generation of citizens.”

Life in the Network (II)

This is a postscript to the May 7th post about David Lazer’s quite interesting talk on using digital traces to uncover human behavior..  [Read that post first.] The presentation of David Lazer I mentioned in the earlier post is available here.  Videos of the presentation are available in two parts:  part 1 and part 2.

There is also an interesting  post by Ben Waber on the Kennedy School of Government Complexity blog on the instrumentation of human behavior-trying to discern human behavior like friendship from their proximity and call logs.

Also, The Economist in their April 28, 2007 edition has a special report on telecoms.  And in one story called “The Hidden Revolution” (p. 58) they highlight that a patented technique of American Express enables the use of RFID chips to track the flow of people in public places from the RFID tags in their clothing and carried products.  The Economist notes that they have “agreed not to use it without disclosing the fact, after pressure from privacy advocates.”  But already the article notes that “Prisons in America are experimenting with bracelets that have wireless chips embedded in them to keep track of inmates….Guards are also tagged, so prisoners may feel safer from abuse.”  They note that the new wireless communication will be virtually invisible to humans and the only sure bet is that how it will be used will surprise us.”

Life In The Network: Possibilities of dynamically monitoring social networks

Fascinating talk by David Lazer at the Kennedy School of Government.  He talked about how new computational power, and new digital traces of our comings/goings, communication, etc. is creating and can create vast data troves even on a minute-by-minute basis that can be mined to understand the structure of social networks at an individual or collective level (organization, town, etc.). 

 The new data get over some earlier limitations in the data: 1) they are much larger scale than one could capture with survey data — they can be millions of observations not thousands; 2) they are dynamic so one can see how networks evolve over time; 3) they overcome response problems with survey (no problem of memory recall and much reduced response rate bias from who responds to surveys).  These new data enable us to understand understudied problems and properties of networks and get a better handle on inferential conclusions.

 He highlighted 4 examples of such computational studies: 1) call log analysis; 2) instrumentation of human behavior; 3) natural language processing; and 4) virtual worlds.

Call Log Analysis:  this study is summarized in National Academy of Sciences Proceeding paper with 4 international co-authors.  They analyzed cellphone call log information over a 9 month period of a medium sized European country cellphone provider with 7 million users and 49 trillion directed dyadic relationships.  [Picture of the network here.]  Analysis of these data showed that it did exhibit scale-free, power law properties.  It did not show “6 degrees of separation“; some nodes in the network were actually 13 degrees of separation away.  And the network did not quite show the strength of weak ties;  it looked more like dirt roads (infrequent communication) connected the hubs and then superhighways (very frequent communication) connected within the local clusters.  Much as a road system constructed in this way would not facilitate the quick dissemination materials across the network, the structure of the social networks was suboptimal for information dissemination.  They found in the cellphone network data that it was actually the moderate-frequency ties (rather than weak ties) that most enabled information to be shared (since the weak ties were too infrequent to get the information out.)  [Lazer admitted that the cellphone log data said very little about the content of these relationships.  A handyman might look like a hub of the network because he used a cellphone for his business and everyone with problems contacted him; or it doesn’t differentiate a short actual call from a wrong number, etc.]

A second study “Revealing Social Relationships Using Contextual Proximity and Communication Data” (Lazer, with Nathan Eagle and Sandy Pentland) monitored about 100 MIT students over 9 months using call log data, locational proximity (bluetooth monitors that detected what other subjects of the study they were near and when) and self reports on proximity, friends and satisfaction.   They found a substantial recency effect (subjects overweight who they’ve been near in the last 5 minutes rather over a longer-term basis) — this suggests why background always-on monitors produce more reliable data.  The subjects remembered proximity with reciprocal non-friends with 99.5% accuracy, but on reciprocal friends were only 35% accurate at reporting non-proximity (since one’s mind infers that you must have been proximate the friend even if you weren’t).    Interestingly, the researchers were able to predict reciprocal non-friends and reciprocal friendships (just using phone call log data and proximity) with 95% accuracy.    [They used elements like the frequency of phone calls, the proximity of A and B at home, the proximity of A and B at work, the proximity of A and B outside of work, the proximity of A and B on Sat. night, etc. ] And in some ways the call log and proximity data better captured the nuanced element of our social ties.  For example, there is strong literature that shows the relation between social ties and life satisfaction.  Interestingly, friendships inferred from the call log and proximity data better predicted life satisfaction than self-reported friendship data.  [David Lazer has also very recently used sociometers, devices developed by the MIT Media Lab and hung like a badge around one’s neck, that track things like the proximity of A and B, whether A or B is speaking and in what tone and modulation; whether A is facing B; the movements of A and B (standing, sitting, walking, etc.).  Kennedy School of Government students wore these sociometers during the Spring public policy exercise and the data will be analyzed with Nancy Katz to determine the effectiveness of teams.]

A third study was on natural language analysis. Lazer was involved in a study analyzing the content of Congressional representatives’ webpages. [Some of this project is summarized here.] These pages are all trying to do a similar thing: communicate to the constituents in a strategic way the representatives’ positions on various issue that the representative thinks it is advantageous to emphasize. For the moment, they have merely tried to analyze the content of certain words or phrases and found using natural language processors that the presence of certain phrases on a House Members’ website predict his/her party affiliation (more uses of “terror” in late 2001 was the best predictor of Republican affiliation and more uses of “Iraq” in 2006 was the best predictor of Democratic affiliation). In the future, they would like to use this to try to determine the dynamic evolution of these words or how they disseminate or what the social mapping is — what words are how closely affiliated with what other words.

The final project that Lazer highlighted is natural experiments.  So in his Connecting To Congress project with Kevin Esterling, Curt Ziniel and and Michael Neblo, they conducted 20 deliberative on-line sessions with Congressional members and their constituents.  Subjects engaged in pre-test and post-test surveys, follow-up surveys after the election to see how they voted, and demographics were gathered for all on-line participants.  They could manipulate the features of this on-line discussion.  They are analyzing these data but they did find that participation in these sessions for constituents was most frequent when they generally knew a lot about politics but not the specific topic of the on-line discussion.  And they found that participation in the session had a big impact on a favorable view of their representative and their level of general political participation.

In summary, Lazer predicted that these technologies (and others like it) will have a quantum leap in orders of magnitude in what’s known about human social behavior.  And the use of these data is likely to increase in an increasingly digitized environment and with increasing computational power to analyze such enormous datasets.  Lazer thinks that sociologic academics have lived in a Flatland and we are just emerging to see new dimensions beyond the squares and triangles we have observed all our lives.  We don’t yet know what the new paradigms will be and how to effectively use the new dimension.   But these technologies may permit us to observe properties like the evolution of social networks, or dynamically observe what predicts the spread of avian flu or a cold, or to see how an intervention or policy in real time changes social interaction in the way envisioned or an unexpected way.

Finally, Lazer cautioned that there are some clear obstacles. 1) overcoming academic silos — social scientists and scientists and computer scientists are not used to collaborating but these data will require cross-silo collaboration; 2) we will need new infrastructures to gather and analyze these data; 3) there are substantial human subjects and privacy issues — these data are most interesting the more one knows about the demographics of social network members and the content of their communications, but the more one knows about these, the less possible it is to protect the anonymity of people within these networks; 4) much of the data gathered is held by the government or private companies so there is much to be worked out about whether these data will be shared and under what conditions that don’t violate privacy issues or give up corporation competitively prized information.  Lazer thinks that this will require shifted paradigms, but we don’t yet know what those paradigms will be.

Some questions that were generated included asking whether if we could effectively predict friendship using such information, could we predict things like power or influence in a network?

I said it reminded me of the early days of ‘artificial intelligence.’  There was much promise of what machines might be able to accomplish, but a sense of how crude the instruments were relative to the nuances of human thinking.  Similarly here, while the network data is stunningly large, it is also blunt and simplistic, so depending on the data you may not be able to tell whether two people are talking, or what the content, or emotional level of the exchange is, or body language, or….    It may be that these things change over time and improve.

Nevertheless, I think the new data is quite interesting for understanding some of the dynamics of social networks that have always been studied in a static form (like looking at a one-time snapshot of a social network).  For example, how do hubs form?  Are the people who start to become hubs hubs because of power or extroversion?  Are hubs more likely to initiate new ties with new friends or are those ties most likely to be initiated by others who want to be friends with that “hub.”  Do hubs do more to strengthen weak ties than others?    Or many have commented that dyads tend to close into triads, in other words if A knows B and A knows C, B and C are more likely to become friends over time.  Such dynamic networks might help explain how this happens (is it proximity, or shared interests, does A tend to close the loop or do B and C, what factors distinguish under what conditions dyads are likely to close into triads)?  There are lots of other similarly interesting questions to this.

Moreover, such data could be valuable at the individual or organizational level to see if one could consciously strengthen a network, similar to what some call netweaving.  One could imagine that analyzing the structure of an organization’s networks could be really valuable at understanding that there need to be more links between cluster A and cluster B (where clusters might be defined by race or office location or functional group within an organization or age or educational background).  And an organization might consciously try certain interventions to graft these ties through the structuring of work groups or social events or office location or… and then could monitor how effective this was at building and sustaining a link and increasing flows of information across these sociologic silos.

If you knew the races/ethnicities of folks in the network it would be interesting to understand whether building bridging links across race (or it could be across other dimensions) helps increase the number of bridges between two clusters.  In other words assume A1 is in cluster A (largely composed of people like A) and forms a tie with B1 in cluster B (largely composed of people like B) or is encouraged to form a tie.  Does this increasingly make it more likely that others in cluster A will form ties with others in cluster B and under what conditions.

Anyway, you get a flavor of the types of interesting questions raised by this talk.