Thursday, August 14, 2014

Assume a dangerous crowd and you get one: How unsophisticated policing can make things much much worse


Events in Ferguson – the suburb outside St. Louis, where last week 19-year-old Michael Brown was fatally shot by police – are changing fast, and currently for the better.  As I write this, police tactics have changed.  The riot gear, assault weapons, and faux tanks are gone.  Officers are out, mingling with the protestors, connecting, talking.  The protesters, in turn, are calm, expressing relief at the positive turn of events.  Observers around the country are also relieved, for not long ago, as tear gas canisters rained down and cameras were ordered off, one couldn’t help feeling that we were back in the worst, repressive days of the 1960s.

It was like the 1960s, except that the weapons and gadgetry sported by the police are very much 2014.  But what we have observed over the last few days is that none of this modern, sophisticated equipment makes a police force sophisticated.   What makes a police force sophisticated in protest situations is how well it understands the psychology of crowds.

Unsophisticated policing assumes that protestors are the same as a mob – unthinking, irrational and inherently dangerous. Unsophisticated policing assumes that all members of a crowd have the same goals, and treats them accordingly.  Unsophisticated policing assumes that the only check on anarchy is the police force itself – and that the best means of control is through fear and submission.

 All of these assumptions are wrong.  Further, they are counter-productive, exposing everyone, including police officers themselves, to unnecessary danger.  Outstanding research by social psychologists – including Steve Reicher, Clifford Stott and John Drury – has found that the assumptions made about crowds tend to be self-fulfilling.  Assume a unified crowd and you end up with a unified crowd.  Assume a dangerous crowd and treat it as dangerous – weapons drawn, orders shouted by megaphone, heads knocked – and you get a dangerous crowd. 


“If one believes that all crowd members are potentially if not actively dangerous, then one will (1) treat all crowd members alike and hence create unity amongst them, (2) react to the violence of some crowd members by imposing restraint on all, thus increasing the likelihood of violating ingroup conceptions of legitimacy and uniting the crowd in hostility and opposition to the police, and (3) increase the influence of those advocating conflict in the crowd and undermine self-policing amongst crowd members.”

So what are crowds actually like?  They rarely start out unified – but are instead made up of disparate subgroups with disparate goals.  They rarely contain many members intent on violence and destruction.  Certainly there may be some such individuals – who would like to escalate aggression, vandalize or loot – but most of the time, most protestors are there to peaceably express their frustration, anger or disappointment at something they consider unjust.  And in a democratic society, this is a most important right.

What sophisticated crowd management understands is that crowds will seek a unity – a common identity and sense of direction– and that it could go either way.  The group may solidify around peaceable goals and “crowd out” members advocating aggression and hooliganism.  These sorts of crowds are self-policing, setting and enforcing their own pro-social norms for behavior.  When this happens there is more than just a thin blue line between protest and anarchy.  Alternately, however, crowds may coalesce around more hostile and aggressive norms, pushing out advocates for moderation.  Which of these identities wins out has a great deal to do with the way the crowd is treated.

It is not, of course, inevitable that aggressive policing leads to an aggressive crowd.  Non-violent protest movements around the world consciously choose to respond to repression with non-aggression – and they gain moral authority and are often successful because of it.  However, when this happens it is usually due to strong moral leadership from within the group, fostering the strength to resist an oppositional identity. 

And in this sense, as much as events as Ferguson have felt reminiscent of the worst of the 1960s, they have also been all the more worrying because we are without the best of that era.  Without the non-violent ethos of leaders like the Rev. Martin Luther King, there is the very real danger that aggressive police tactics will lead to retaliation and violent escalation.

Fortunately – for now – it appears that things are getting a bit more sophisticated out there.  It’s about time.  In 2014, we should (and do) know better.

Monday, June 16, 2014

How Do World Cup and Economic Performance Relate? Historically Little Gain for Winners, But Bad News for Runners Up. Can't Claim Much About the Future.


A couple of weeks ago, Goldman Sachs published a World Cup and Economics Report.  They found, among other things, that winners have tended to experience a short ‘honeymoon bounce’ in equity market performance – outperforming the market by about +3.5% in the following month.  In contrast, runners up tend to experience a rough patch - underperforming by about -5.6% over the following 3 months. 

We were interested in whether World Cup winners might also experience broader economic gains.  Winning the World Cup is clearly a Good Thing to many people.  Might the morale boost, the surge in national identity and pride following a victory be associated with greater productivity, consumer spending, etc., etc., resulting in a boost to, say, Gross Domestic Product (GDP)?

To find out, we did a quick analysis and examined percent change in GDP per capita from the year prior to the World Cup to the year after for the 1st, 2nd, 3rd and 4th placing nations in every contest from 1930 through 2010*.  And the answer appears to be: No!  Average increases in GDP per capita for the winning nation have been no greater than increases for the 3rd and 4th placing countries.

So, no good news for the winner.  There has, however, typically been bad news for the runner up.  As shown in the Figure below, nations placing 2nd in the World Cup have tended to perform worse economically (in the following year) than countries placing 1st, 3rd and 4th – exhibiting gains in GDP per capita that have been, on average, less than half that of their rivals.


The next Figure illustrates that this pattern is fairly robust.  The 1930s were a bad decade for everyone, and results for contests since WWII (i.e., excluding 1930, 1934 and 1938) show the same configuration.  Further, if we break the contests down by time period – the 30s, 50-60s, 70-80s and 90-00s – we find the same pattern in each case.  In every period, the 2nd placing nation tended to exhibit the weakest economic gains, although this trend seems to have lessened in the recent past.


Taking a finer-grained look, the next Figure shows percent changes in GDP per capita following every World Cup.  The trend lines indicate rolling 5-contest averages, and it is notable that the line for 2nd placing nations falls below all of the others at all time points except two.


Descriptively, this seems to be a historically robust pattern.  Treating the Cups to date as the full population of World Cup contests, it is accurate to say that in the year following the  World Cup, second placing countries have exhibited only ~44% of the gains in GDP per capita shown by winning nations (and still less compared to the 3rd and 4th placing nations).   

Inferentially, however, we are on much less solid ground.  Treating the Cups to date as a sample of all possible World Cup contests (and the data as providing estimates for the future population), it is not valid to assume that this pattern will hold going forward.  The first Figure above shows the 95% confidence intervals for each estimate.  There is clearly a great deal of overlap in these intervals, and with the limited sample they do not differ statistically from one another (although the 2nd place estimate is the only one to overlap with zero growth).  For this reason, coupled with the fact that these data are correlational, we cannot claim that placing second in the World Cup hurts an economy.  We can simply say that 2nd placing nations have tended – to this point – to have smaller economic gains in the following year.

This pattern could well be an historical fluke.  But is there any plausible mechanism for why placing second might hurt an economy? Psychologically, this pattern is reminiscent of findings that individual athletes who place second (e.g., in Olympic contests) tend to be less happy than athletes who place third (Medvec, Madey & Gilovich, 1995).  The standard explanation for this has to do with the counterfactuals – the alternate histories – that these athletes can imagine.  The runner up can almost taste her victory, and compares her #2 spot to the glory of the victor.  The bronze medalist can imagine not having made it to the podium and is, as a result, happier with her lot.  Perhaps something similar could be going on at a national level – a large-scale deflation in morale with economically damaging consequences – although this would be remarkable.

Finally, we should note that the underperformance exhibited by 2nd placing nations has historically been reasonably short-term.  The final Figure plots percent change in GDP per capita from the year prior to the World Cup to three years after – and shows that by this point, second placing nations had, on average, caught up to their rivals.


*Economic data is from WorldEconomics.com (1990 GK$), except for Turkey in the 2002 WC and all nations in the 2010 WC, which is from IndexMundi.com (2011 GK$).  Thanks to Liana Mitteldorf for checking the figures.

And here it is - % Change in GDP from the year prior to World Cup to the year following…


Year
1st Place
GDP %  Change
2nd Place
GDP %  Change
3rd Place
GDP %  Change
4th Place
GDP %  Change
1930
Uruguay
-9.020
Argentina
-14.999
USA
-17.510
Yugoslavia
-7.551
1934
Italy
8.328
Czechoslovakia
-5.564
Germany
15.861
Austria
2.612
1938
Italy
6.086
Hungary
11.600
Brazil
1.040
Sweden
10.454
1950
Uruguay
10.013
Brazil
2.592
Sweden
6.225
Spain
10.719
1954
W. Germany
18.186
Hungary
10.194
Austria
22.142
Uruguay
4.145
1958
Brazil
11.384
Sweden
5.812
France
3.209
W. Germany
10.551
1962
Brazil
1.067
Czechoslovakia
-1.767
Chile
6.253
Yugoslavia
10.270
1966
UK
3.046
W. Germany
2.297
Portugal
12.249
USSR
7.100
1970
Brazil
14.615
Italy
2.854
W. Germany
6.102
Uruguay
2.785
1974
W. Germany
0.627
Netherlands
2.240
Poland
8.764
Brazil
7.912
1978
Argentina
-0.927
Netherlands
3.315
Brazil
7.119
Italy
9.016
1982
Italy
1.447
W. Germany
1.272
Poland
2.098
France
2.729
1986
Argentina
6.774
W. Germany
3.705
France
4.044
Belgium
3.766
1990
W. Germany
0.556
Argentina
7.009
Italy
3.569
UK
-1.566
1994
Brazil
7.272
Italy
4.740
Sweden
5.995
Bulgaria
7.117
1998
France
4.935
Brazil
-1.969
Croatia
2.421
Netherlands
9.383
2002
Brazil
0.199
Germany
-0.360
Turkey
1.419
South Korea
8.229
2006
Italy
3.553
France
3.487
Germany
5.820
Portugal
2.578
2010
Spain
0.266
Netherlands
2.654
Germany
4.791
Uruguay
7.669
AVERAGE
4.653
2.059
5.341
5.680