Drivers and Citizens: The Impact of Social Capital
on Traffic Conditions in Russian Cities
Daria Zubareva, Leonid Polishchuk
National Research University – Higher School of Economics, Moscow
Abstract
Social capital, broadly understood as the capacity for collective action based on trust, norms,
and networks, is shown to be instrumental for the quality of public sector governance and
service delivery, and for the efficient use of open access resources (“commons”). Both of
these reasons make one to expect that social capital should have a tangible impact on safety
and comfort of urban driving: drivers’ courtesy, mutual respect and compliance with traffic
rules relieve congestion and prevent accidents, whereas civic culture and political activism
improve accountability of municipal governments and hence bode well for urban planning,
road maintenance, and traffic management and control.
These hypotheses are tested in the paper for a sample of twenty Russian cities, all of which
show signs of increased traffic congestion and accident rates caused by steep rise of car
ownership in the country. We find evidences that higher stocks of social capital are indeed
associated with lesser traffic jams and fewer accidents, and hence with cheaper vehicle
insurance. Pro-social norms and values have a tangible impact on drivers’ behavior on the
roads, putting the existing transportation networks – “urban commons” – into better use.
Accident rate shows significant sensitivity to social capital, whereas traffic congestion’
elasticity to social capital is less pronounced. Traffic jams in Russian cities result primarily
from the capacity bottlenecks, rather than from how this capacity is used by motorists.
Therefore an answer to congestion problems should be sought in improved public policies,
infrastructure investments, and road planning, and key to such problems is therefore in the
hands of citizens, rather than drivers. Our data show however that the Russian society has yet
to made its voice heard in addressing urban transportation problems – we found no significant
relationship between civic culture and awareness in Russian cities, on the one hand, and
driving conditions in these cities, on the other.
The paper shows that social capital is indeed a valuable resource in resolving increasingly
severe transportation problems, and that both “transmission channels” – horizontal and
vertical – between social capital and economic outcomes need to be engaged. The horizontal
channel improves the situation at the grassroots through better use of “urban commons”,
whereas the vertical one requires political collective action to ensure proper public policy
responses to mounting road congestion.
1. Introduction
Proverbial problems of Russian roads have become more acute in recent years, when steeply
increasing traffic and car ownership stretched to the limit the capacity of inter- and intra-city
road networks. Presently roads are one of the most severe bottlenecks to urban development
in Russia, causing massive air pollution, losses of time and increasingly of human life.
Traffic jams are now common features of major Russian cities, and with the number of
victims of car accidents over 70 per 100,000 cars a year, Russia is in a tie with countries
notorious for deplorable and unsafe traffic conditions.
Measures proposed to deal with this problem include expansion of road networks, toll roads,
greater reliance on public transit, various investments in road infrastructure, stricter
enforcement of traffic rules, etc. In this paper we’re interested in the role of the ‘human
factor’ in traffic jams and road accidents. More precisely, our goal is find out to what degree
Russian road problems are associated with and could be explained by norms, values and
behavioral patterns in the Russian society, comprising what is known as social capital.
Social capital’s main function is to facilitate collective action when individual choices and
behavior need to be coordinated for the sake of common good. Two types of collective action
are relevant for road conditions. The first is proper use of open access collective resources
known as the commons. Lack of coordination in commons leads to their congestion when
individual users crowd out each other; traffic jams and accidents illustrate this general pattern
in the case of urban commons – inter-city road networks. The second type is participation in
democratic processes to ensure government accountability and proper delivery of public
services, in the present context – urban planning and management, including construction and
maintenance of roads, and effective enforcement of traffic rules. Political awareness and
activism are powered by a special type of social capital known as civic culture.
The first type of social capital is essential for the proper use of the existing road networks and
operates on the demand side of urban traffic systems; it affects behavior of drivers. One
should expect that pro-social norms result in responsible driving, consideration for others,
mutual help and respect among motorists, compliance with traffic rules and joint efforts to
resolve traffic problems if they should occur. The second type is required for adequate supply
of urban traffic infrastructure, and shapes behavior of citizens.
In this paper we analyze and measure the impact of social capital on road conditions in
Russian cities. Main outcomes of social capital are convenience and safety of urban road
transportation; opposite measures of these outcomes are resp. severity of traffic jams and
various indicators reflecting accident rates. Social capital of drivers and citizens could be a
priory relevant for both of those outcomes; our task hence is to establish whether this is
indeed the case.
The paper contributes to the growing strand of literature studying the impact of social capital
on economic development and welfare, government performance, social service delivery, etc.
Surprisingly, urban traffic so far has not been properly represented in such studies, and our
paper partly fills this gap. One of the very few contributions on this subject is by Fishman and
Miguel (2007) who established an association between violations of parking rules by
diplomats based in New York City with corruption levels and ultimately cultural traits in their
home countries. In another paper Incla et al. (2005) reveal a link between traffic accidents
and casualties thereof, on the one hand, and propensity for cooperation in local communities
in Mexico, on the other. It is argued in the paper that due to a lack of reciprocity and capacity
for collective action residents fail to perceive high traffic mortality as a common problem and
hence are unable to jointly take measures to resolve it.
The main findings of our paper are as follows. Based on empirical data, we show that social
capital based on pro-social norms and values indeed reduces accident rates in Russian cities,
and that such conclusion is robust to different accident measures and estimation strategies.
Here social capital works at the grassroots through a ‘horizontal channel’ At the same time
the impact of such social capital on traffic jams is much milder and often insignificant. This
leads us to conclude that while drivers’ behavior (and norms and values that it reflects) is a
major cause of accidents, the roots of traffic jams are primarily on the supply side of urban
transportation in Russia, and keys to this problem are in improved urban management and
planning, i.e. better municipal governance. Solving this problem thus requires a different type
of social capital, which is based on civic culture and operates through a ‘vertical channel’
linking social capital and governance. While a sister paper (Menyashev, Polishchuk, 2011)
shows that such traits exhibit significant variations from one Russian city to another and have
substantial impact on life satisfaction and governance in Russia cities, in this particular
instance we do not observe a statistically significant relation between civic culture and
behavior on the one hand, and traffic jams, on the other. A plausible explanation is that
immature Russian democracy does not generate pressure on municipal governments strong
enough to counteract vested interests that push for lucrative development projects and neglect
everyday needs of ordinary citizens. To show that under more democratic conditions the
expected link exists, we use US data and observe a strong relationship between traffic jams in
US cities and participation in elections of these cities’ population, which is a telltale sign of
civic culture.
2. Data
The main source of empirical information for this study was a survey of drivers in 20 Russian
cities, 80 respondents per city, conducted in the summer 2010. City level is an appropriate
unit for our analysis, since this is a natural confine for urban traffic. Furthermore, since road
maintenance and development is the responsibility of urban governments, cities internalize
the link between citizens and governments with regard to road conditions. Finally, using
cities rather than countries in this sort of analysis makes it easier to meet the ‘ceteris paribus’
requirement. Cross-country studies would suffer from an obvious omitted variable bias, for
which control variables would offer only partial remedy. Cross-country differences in road
outcomes are obviously driven by road institutions, traffic rules and their enforcement,
composition of the traffic, role of public transit, etc. Studying various cities in a single
country alleviates this problem.
Traffic outcomes
We measure two traffic outcomes, accidents and road congestion. Our measure of road
congestion is based on survey questions where we ask drivers about relative increases of their
travel time from work to home in rush hours as opposed to off-pick hours.
We employ several measures of road accidents. First, we collect official statistics of road
accidents and road injuries from the State Inspectorate for Road Traffic Safety (GIBDD).
Specifically, we collect regional data on the number of car accidents per 10000 cars
(ac_10_cars) and the number of people killed in car accidents per 100000 people
(ac_100_pop). The potential problem with these official statistics is that they may be biased
because of incomplete reporting by police officers who might be trying to improve their
performance records by concealing accidents and/or underestimating their severity. To
complement official data by an independent and potentially more reliable source, we also
used regional car insurance premiums which reflect market assessment of the real risk of road
accidents. Both the official statistical data and the automobile insurance premiums are
available only for regions (Russia’s subnational units) rather than cities, but this does not
pose a serious problem, since most Russian motorists live in the capital cities of their region
and the lion’s share of road trips takes place within the confines of city borders. As a yet
another measure, we also use individual assessments by survey respondents who were asked
about the number of accidents they personally witnessed over a period of time. While being
subjective and prone to potential biases, this is the only measure that we have which is
available at the city level.
Table 1: Summary statistics for the traffic outcomes
Variable
Obs
Mean
Std. Dev
Min
Max
ac_10_cars
20
25.2
6.078608
15.7
39.3
ac_100_pop
20
100.57 24.11368
55.9
142.4
Kasko
20 31018.75 4319.777
25060
41895
Observed accidents 20 3.218855 0.25202 2.833333 3.6875
congestion
20 1.687454 0.415723 1.180132 2.596372
The above table shows that there is a wide variation across cities in traffic outcomes. Market
prices of car insurances reflect these variations and the highest premium is almost twice as
high as the lowest one. Overall the table reveals highly unsatisfactory traffic conditions in
Russian cities; the number of car accidents and of accident victims are very high by
international standards.
Traffic behavior
We measure traffic behavior by asking drivers to assess the behavior of other drivers in the
same city and averaging their answers at the city level. Here’s a sample of driving behavior
patterns registered by the survey

running yellow and red lights (s_slip_yell)

illegal parking obstructing traffic (s_park_proh);

bypassing traffic jam on sidewalk or road shoulder (s_jam_side);

hit and run (s_leave_acc)

causing traffic jams by blocking intersection (s_crossr_m~e)

yield to a pedestrian (s_skip_ped~r)

yield to a car which changes lines or entering highway traffic (r s_skip_byway)

help other drivers when their car is stuck in snow or mud (s_help_pull)

help other drivers when they experience technical problems (s_help_rep)
A summary statistics for city averages is presented below.
Table 2: Summary statistics of driving culture
Variable
s_slip_yell
s_park_proh
s_jam_side
s_leave_acc
s_crossr_m~e
s_skip_ped~r
s_skip_byway
s_help_pull
s_help_rep
Obs
20
20
20
20
20
20
20
20
20
Mean
0.480393
0.498956
0.415522
0.375204
0.424189
0.590299
0.438041
0.368918
0.297188
Std. Dev
0.085113
0.099947
0.089955
0.081408
0.094001
0.087597
0.063901
0.056382
0.056255
Min
0.225309
0.16358
0.138889
0.109155
0.166667
0.444767
0.324074
0.289474
0.212025
Max
0.589506
0.604651
0.625
0.506329
0.587209
0.777778
0.63141
0.503165
0.42284
Variations of city averages of the above indexes is surprisingly large; for some measures the
ratio of maximum to minimum could be several hundred percent. Some of the measures are
significantly correlated with each other, and we perform factor analysis to compress the list.
The first two factors (principal components) explain 84% of the total variation and are as
follows.
Table 3: Factor analysis of driving culture
Variable
Factor1 Factor2 Uniqueness
s_slip_yell
0.8975
0.2386
0.1376
s_park_proh
0.8914
0.1003
0.1953
s_jam_side
0.8268
0.2947
0.2295
s_leave_acc
0.8728
0.2821
0.1587
s_crossr_m~e
0.9204
0.0911
0.1446
s_skip_ped~r -0.4612
0.551
0.4837
s_skip_byway -0.4595
0.3674
0.6538
s_help_pull
-0.2223
0.9359
0.0747
s_help_rep
-0.3094
0.7961
0.2706
The loadings of the first factor suggest that it is a proper measure of ‘free-riding’ (pun
intended) behavior among drivers. Indeed, it loads strongly and positively on questions that
capture disregard of interests of fellow motorists and damping costs of such behavior on
others.
In Figure 1 we plot the answers to the questions that load strongly on the first factor. It shows
considerable differences in the incidence of ‘free-riding’ across cities in our sample.
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Vo ogr
ro ad
Ya ne
ro zh
sl
av
l
.2
.4
.6
Figure 1: Motorists’ free-riding
run yellow or red lights
illegal parking obstructing traffic
by passing traffic jam on sidewalk
hit and run
blocking road intersection
The second factor in table 2, according to its loading, can be interpreted as road courtesy; it
also varies in a broad range in our sample.
Figure 2: Motorists’ courtesy
.8
.6
.4
.2
0
l
r k
k
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k l
ls au urg rad zan ovo da ars etsk irs erm Don tov yat ms ve stok rad ezh lav
e
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a
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e n y ip i P n ar To To
vo o o ro
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Vl V
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os
R
yield to a car which changes
yield to a pedestrian
help other drivers when their car is stuck
help other drivers when they experience technical problems
Personal values
Behavioral patterns revealed by our survey could be expected to reflect drivers’ values. The
survey does not register respondents’ values, and we proxy those by data from another survey
(GeoRating2007) with a focus on values and attitudes of residents of Russian cities. It
appears that values which are commonly associated with social capital, such as trust, mutual
respect, help to each other and solidarity in the society have significant correlations of the
expected positive sign with the factor representing motorists’ courtesy, while their correlation
with the free riding factor, although also of the expected (this time negative) sign, is much
less pronounced.
Table 4: Values and road behavior
Respect1
Consensus and
cohesion 2
1
Courtesy traffic
behavior
0.38
Free riding traffic
behavior
-0.16
0.28
-0.11
Respect was measured by the question “How common is respect towards people in your city?”
Willingness to help3
0.25
-0.03
The table points out to normative roots of motorists’ behavior, although more research is
required to better visualize such links.
Political attitudes and associational activities
To find evidence of a vertical channel at work linking social capital with traffic outcomes in
Russian cities, we used the following data characterizing political attitudes and behavior:

participation in elections;

membership in automobile associations ;

willingness to participate in protest rallies against construction of a new shopping mall at a
place which already suffers from acute traffic problems;

willingness to go to court in case of violation of motorists’ rights by traffic police or road
service providers
The first two measures are drawn resp. from official electoral statistics and automobile
associations sourcses, whereas the two remaining ones are based on our survey data.
The above measures reveal low levels of political activities and association membership in
Russia, which are consistent with the general perception of the modern Russian society as a
largely apolitical one. Thus, 50% of respondents are not willing to take part in any protest
action, and only 15% express willingness to take part in public rallies (Figure 3). Less than
3% of respondents are members of automobile associations (although associational activity
among Russian car owners is presently on the rise, in response to worsening road conditions,
abuse by road police , and driving privileges of bureaucracy).
2
Consensus and cohesion was measured by the question “Is there more consensus and cohesion or more
disagreement and disunity in our country nowadays?”
3
Willingness to help was measured by the question “How often one can observe the willingness to help people
around?”
Figure 3: Political attitudes
3. Empirical results
In this section we present estimations of econometric models which relate traffic outcomes to
the above described indicators of social capital. Unless explicitly stated otherwise,
observation units in our sample are cities. Small size of the sample (20 cities) poses an
estimation problem; cognizant of this problem, we include in our regressions not more than
three explanatory and control variables, changing the composition of those to check
robustness.
Road accidents and the horizontal channel
We begin with estimation of regression equations
𝑡𝑟𝑎𝑓𝑓𝑖𝑐 𝑜𝑢𝑡𝑐𝑜𝑚𝑒𝑠 = 𝛼 + 𝛽1 𝐻𝑜𝑟𝑖𝑧𝑜𝑛𝑡𝑎𝑙 + 𝛾𝑖 𝑐𝑜𝑛𝑡𝑟𝑜𝑙𝑖 + 𝜀𝑖
which relate traffic outcomes to road behavior. We use three indexes of road accidents, the
first – automobile insurance premiums (Table 4), the second – official accidents statistics
(Table 5), and the third – accident rates reported by survey respondents (Table 6). Our main
explanatory variable is the free riding indicator as the prime ‘suspect’ in automobile
accidents. For measuring free riding traffic behavior we use the variable “free_ride”
constructed by factor analysis (higher value of the variable indicates more free riding on the
road). For measuring courtesy we use the second factor derived by factor analysis (higher
value of the variable indicates grater incidence of pro-social behavior on the roads).
We use in regressions various control variables. Some of them are obtained from our survey,
such as drivers’ assessment of road surface quality (est_pavmnt), urban planning
(est_plan_inters) and the quality of road police work (gai_viol). Other control variables are
obtained from official statistical sources (GosKomStat for 2009). We use bank deposits per
capita (depos) and budgetary funding per 1000 people (budg_1000) as proxies for economic
well-being. The last control variables are the number of cars in the city (cars) and the number
of cars per 1000 people (cars_on_1000_pop); these date are provided by the Autostat agency.
Table 4: Impact of driving behavior on car accidents (dependent variable – car insurance premium)
free_ride
respect
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
2,672***
2,108***
2,863***
2,706***
2,670***
2,715***
2,584***
2,397**
(885.0)
(699.8)
(903.7)
(877.0)
(893.1)
(901.3)
(855.8)
(851.3)
-1.271
-1.478
-2.341
-0.423
-1.504
-1.139
1.345
1.316
(9.866)
(7.606)
(9.771)
(9.856)
(9.938)
(9.973)
(9.793)
(9.451)
-442.6
(896.5)
cars_on_1000_pop
depos
0.455
(0.265)
budg_1000
-0.151
(0.183)
child_high_1000
-22.24
(29.47)
est_pavmnt
-24.54
(2,079)
est_plan_inters
-780.6
(2,532)
gai_viol
5,559
(4,671)
bus_1000
42,227
(28,115)
Constant
31,295***
26,665***
32,295***
33,229***
31,420***
33,297***
18,757
28,568***
(2,807)
Observations
R-squared
(3,138)
(2,982)
(3,708)
(4,941)
(6,846)
(10,939)
(3,229)
19
18
19
19
19
19
19
19
0.385
0.476
0.402
0.397
0.375
0.379
0.429
0.456
Table 5: Impact of driving behavior on car accidents (dependent variable – number of car accidents
per 1,000 cars)
free_ride
respect
(17)
(18)
(19)
(20)
(21)
(22)
(23)
(24)
2.792**
2.735**
2.529**
2.903***
2.766**
2.508**
2.816**
2.408**
(1.050)
(1.124)
(1.087)
(0.961)
(1.068)
(0.994)
(1.083)
(1.000)
0.991
(1.064)
cars_on_1000_pop
0.0120
0.0115
0.0136
0.0156
0.0127
0.0102
0.0119
0.0165
(0.0117)
(0.0122)
(0.0117)
(0.0108)
(0.0119)
(0.0110)
(0.0124)
(0.0111)
depos
0.000361
(0.000425)
budg_1000
0.000209
(0.000220)
child_high_1000
-0.0646*
(0.0323)
est_pavmnt
1.559
(2.485)
est_plan_inters
5.003*
(2.791)
gai_viol
-1.198
(5.910)
bus_1000
60.39*
(33.02)
Cars
Constant
Observations
R-squared
21.23***
17.96***
19.78***
26.46***
18.02***
8.730
23.78
17.05***
(3.332)
(5.039)
(3.585)
(4.065)
(5.906)
(7.548)
(13.84)
(3.792)
19
18
19
19
19
19
19
19
0.377
0.364
0.378
0.479
0.357
0.457
0.342
0.461
Table 6: Impact of driving behavior on car accidents (dependent variable – accidents reported by
respondents)
(25)
free_ride
respect
(26)
(28)
(29)
(30)
(31)
(32)
0.135**
0.133**
0.153**
0.129**
0.134**
0.140**
0.137**
0.145**
(0.0542)
(0.0566)
(0.0540)
(0.0527)
(0.0512)
(0.0542)
(0.0536)
(0.0539)
0.00136
(0.0584)
depos
(27)
-0.068
(0.0209)
budg_1000
-0.0125
(0.0108)
child_high_1000
0.00185
(0.00182)
est_pavmnt
-0.170
(0.128)
est_plan_inters
-0.0777
(0.150)
gai_viol
-0.168
(0.284)
bus_1000
-1.470
(1.731)
cars
Constant
Observations
R-squared
5.51e-07
5.15e-07
6.55e-07
3.32e-07
2.06e-07
5.30e-07
4.89e-07
5.16e-07
(6.70e-07)
(6.37e-07)
(6.01e-07)
(6.35e-07)
(6.40e-07)
(6.15e-07)
(6.20e-07)
(6.07e-07)
3.117***
3.275***
3.153***
2.983***
3.514***
3.322***
3.488***
3.195***
(0.148)
(0.237)
(0.137)
(0.189)
(0.327)
(0.417)
(0.639)
(0.164)
19
18
19
19
19
19
19
19
0.302
0.301
0.360
0.347
0.375
0.315
0.318
0.334
In all estimations free riding has the expected positive coefficient and is almost invariably
significant at 0.01-0.05 level; given small size of the sample, this effect is pronounced quite
clearly. It is particularly strong when we use the market-based accident index, i.e. car
insurance premium (Table 4), when free riding in all but one specification is significant at
the 0.01 level. Various controls do not diminish this significance and only modestly change
the coefficient for free riding, which attests to robustness of the established effect (Figure 4).
Figure 4: Automobile insurance premiums and road behavior
10000
0
5000
-5000
-3
-2
-1
0
1
free riding traffic behavior
coef = 2108.3457, se = 699.83445, t = 3.01
*) Control variables – number of cars per 1,000 people and bank deposits per capita
We obtain qualitatively similar results for other measures of accidents (Tables 5 and 6),
albeit with lower significance, which could be ascribed to noise in used measures due to
either imprecision and/or incompleteness of survey data, or falsification in official data.
The second factor (principal component) of road behavior – respect – has in our estimations
the expected sign, but its significance is considerably smaller than the first one. More
research is required to explain this observation.
Given the importance of the link between accidents and driving behavior powered by social
capital, we have performed a yet another robustness check, this time using individual data
rather than city averages. An important advantage of this specification strategy is a vastly
larger number of observations that considerably improves the odds to get statistically
significant estimators. We can also include in regression equations a much larger number of
control variables. Furthermore, there could be inter-city variations of driving patterns and
accident rates that would not be captured in regressions based on city averages. In the
regressions reported below standard errors are clustered per region. Instead of using the
aggregate free riding index, we enter in regression equations as dependent variables specific
indicators of ‘free riding’.
Table 7: Impact of driving behavior on car accidents (individual responses)
VARIABLES
s_crossr_move
(1)
Accidents
(2)
Accidents
(3)
Accidents
0.526**
(0.196)
s_slip_yell
0.433***
(0.139)
s_park_proh
0.543**
(0.205)
s_jam_side
elect_yes
sc_respect
est_pavmnt
est_plan_inters
gai_viol
c_sex
c_age
c_educ
c_inc
c_expr
c_freq
car_price
c_need
Constant
Observations
R-squared
Robust standard errors in
parentheses
(4)
Accidents
0.109
(0.0696)
-0.0470
(0.0575)
-0.0932*
(0.0494)
-0.0214
(0.0552)
0.0973**
(0.0464)
-0.0398
(0.0579)
0.000347
(0.00504)
-0.0361**
(0.0146)
0.0351
(0.0250)
0.302
(0.217)
0.0930
(0.0767)
-0.0654
(0.0548)
-0.0851
(0.0495)
-0.0266
(0.0559)
0.0865*
(0.0471)
-0.0779
(0.0667)
0.0939
(0.0756)
-0.0532
(0.0557)
-0.0897*
(0.0480)
-0.0163
(0.0534)
0.0831*
(0.0479)
-0.0757
(0.0675)
0.000607
(0.00474)
-0.0299*
(0.0152)
0.0381
(0.0248)
0.000430
(0.00451)
-0.0303*
(0.0151)
0.0385
(0.0258)
0.000665
(0.00480)
-0.140**
(0.0575)
0.0312
(0.0228)
0.0441*
(0.0242)
3.137***
(0.449)
0.0924
(0.0748)
-0.0493
(0.0564)
-0.0880*
(0.0465)
-0.0235
(0.0548)
0.0952*
(0.0495)
-0.0747
(0.0631)
0.000234
(0.00454)
-0.0332**
(0.0157)
0.0377
(0.0248)
0.000111
(0.00451)
-0.149**
(0.0559)
0.0414
(0.0240)
0.0492*
(0.0247)
3.160***
(0.442)
0.000381
(0.00456)
-0.153**
(0.0538)
0.0347
(0.0223)
0.0456*
(0.0233)
3.143***
(0.432)
-0.00129
(0.00444)
-0.146**
(0.0582)
0.0326
(0.0235)
0.0539**
(0.0252)
3.283***
(0.461)
1,180
0.079
1,217
0.074
1,221
0.084
1,214
0.067
*** p<0.01, ** p<0.05, * p<0.1
Once again free riding indexes have the expected positive sign, and in three out of four
specifications are significant at the 0.01-0.05 levels. Control variables also have expected
signs. It is noteworthy that some of these controls become significant, such as quality of the
road surface (est_pavmnt) and the performance of road police.
Traffic jams and the horizontal channel
While driving behavior has a strong impact on road accidents which is consistently robust
across all o our specification, its influence, if any, on traffic jams is harder to discern. In our
estimations (which we do not report here) with traffic jams as the dependent variable driving
behavior (free-riding and respect measured as results of the factor analysis fact) has the
expected sign but is never significant.
Our analysis thus indicates that driving behaviour, erratic and uncooperative as it may be, is
not a major cause of traffic jams plaguing Russian cities. This finding agrees with the
general dictum of the urban and transportation sciences that traffic jams require government
intervention through bottleneck removal, improved planning, maintenance, control, etc. (see
e.g. Downs, 2004). Road usage pales in its significance in comparison with the above
factors, and driving behaviour cannot be blamed for traffic jams when roads are in poor
conditions and urban transportation networks do not match traffic volumes and patterns.
As it was argued earlier in the paper, social capital can still be instrumental in such cases, but
this time it should work through the vertical channel and power not so much driving, but first
and foremost political behaviour. We now turn to search for evidence that such channel is
indeed at work in Russian cities.
Traffic accidents and the vertical channel
To find out whether civic culture and associational activity have an impact on Russian road
conditions, first and foremost traffic jams, we estimate regression models
𝑡𝑟𝑎𝑓𝑓𝑖𝑐 𝑜𝑢𝑡𝑐𝑜𝑚𝑒𝑠 = 𝛼 + 𝛽1 𝐻𝑜𝑟𝑖𝑧𝑜𝑛𝑡𝑎𝑙 + 𝛽2 𝑉𝑒𝑟𝑡𝑖𝑐𝑎𝑙 + 𝛾𝑖 𝑐𝑜𝑛𝑡𝑟𝑜𝑙𝑖 + 𝜀𝑖 ,
where traffic outcome as dependent variable is measured by road congestion. We allow in
the model both types of social capital – those affecting driving behaviour and those
underlying political actions – to find out if there is any substitution between the two. As
before, we use in our specifications various control variables that could be relevant for traffic
jams, including geographic location of the city (latitude and longitude) and the year the city
was established (one could argue that in older cities road networks could be less suitable for
modern traffic needs and this could be a factor contributing to traffic jams).
Estimation results for various specifications of the above model are presented below.
Table 8: Road congestion and vertical channel
free_ride
cars_on_1000_pop
est_pavmnt
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
0.116
(0.0886)
0.00170
(0.000986)
-0.287
(0.206)
0.114
(0.108)
0.00175
(0.00106)
0.106
(0.0948)
0.00179
(0.00106)
0.111
(0.0922)
0.00190*
(0.00105)
0.138
(0.103)
0.00165
(0.00104)
0.106
(0.0938)
0.00188
(0.00109)
0.123
(0.0790)
0.00110
(0.000914)
0.132
(0.0885)
0.00192*
(0.000980)
associations
0.0831
(1.220)
sit_court_gai
0.127
(0.476)
sit_court_serv
0.263
(0.334)
elections_yes
-0.528
(0.835)
moll_const
0.179
(0.399)
est_plan_park
-0.567**
(0.225)
est_plan_inters
Constant
1.797***
(0.490)
0.916
(4.724)
0.971
(1.040)
0.655
(0.795)
1.512**
(0.525)
0.453
(1.774)
2.636***
(0.608)
-0.385
(0.249)
2.185***
(0.673)
Observations
R-squared
19
0.304
19
0.214
19
0.218
19
0.245
19
0.235
19
0.225
19
0.448
In specifications 2 to 6 we introduce all measures of social capital that is expected to be
transmitted through the vertical channel. Their coefficients are never significantly different
from zero. Therefore we can reject the hypothesis the social capital through vertical channel
alleviates urban traffic problems in Russia. The above regressions however once again
confirm that more accountable municipal governance could be a potent resource of
improvement of urban traffic conditions: in specification (7) availability of parking is shown
to significantly (at 0.05 level) contribute to the alleviation of traffic jams. If social capital (in
the form of civic culture and political participation) is in sufficient supply, municipal
authorities would be more visibly and effectively involved in improving traffic conditions in
their cities.
The likely reason that we do not observe such relationship on our data is the low level of
civic culture in the Russian society, which could be ascribed to Russia’s political history
(Tabellini, 2008) and also perhaps to the recent political trends in the country which put
direct subordination of regional and municipal administrations to governments of higher tiers
ahead of direct accountability of subnational governments to people.
To show that the vertical channel could be working under more democratic conditions and
stronger civic culture we turn to US data and find a strong and robust relationship between
traffic jams in US’s major metropolitan areas4 and political activity and mobilization of local
population measured by election turnout. The latter exhibits strong statistically significant
negative association with incidence and severity of traffic jams, even after controlling for
4
We use road congestion measures of the Texas Transportation Institute (http://tti.tamu.edu/)
19
0.322
population, employment rates, income, inequality and educational attainments5. Vibrant
democracy indeed converts higher stocks of civic culture into better road conditions.
Table 9: Political participation and traffic jams in US metropolitan areas
VOTE
(1)
-0.00941***
(0.00335)
GROUP
0.000397***
(0.000139)
5.22e-06
(3.67e-06)
-0.00208
(0.00317)
0.000511
(0.00140)
-0.0119**
(0.00500)
1.617***
(0.232)
-0.00666*
(0.00358)
-9.27e05***
(2.73e-05)
0.00210***
(0.000490)
9.98e-06**
(3.83e-06)
-0.00236
(0.00301)
-0.000270
(0.00127)
-0.0121**
(0.00483)
1.097***
(0.197)
51
0.504
51
0.563
POPUL
unemployed
income_p_c
ed_bachelor
etn_black
ineq_below_poverty
Constant
Observations
R-squared
Figure 5: Vertical channel at work in the US
5
Source: US Census Bureau
(2)
.3
.2
.1
0
-.1
-.2
-10
-5
0
5
10
15
actual vote rate
coef = -.00721923, se = .00347157, t = -2.08
Endogeneity concerns
Our main results that free riding behavior is related to road accidents may be subject to an
endogeneity problem: it is plausible that on less safe roads people behave in a more
aggressive and self-interested way. The same argument can conceivably be applied to road
congestion: it cannot be ruled out that in cities with longer traffic jams local driving culture
deteriorates since incentives for co-operative behavior are greatly diminished. We observe
however no statistical relation between free riding and road congestion, which makes the
hypothesized reverse causality less likely for road congestion and therefore a fortiori also for
road accidents that are much less frequent and observable and hence less likely to affect the
local driving culture. Furthermore the quality of roads which in theory could affect driving
norms and behavior is found to be not related to the free riding behavior. All of the above
suggests that local driving cultures are exogenous to traffic outcomes and road quality and
have other, perhaps deeper, roots.
4. Conclusion
The paper confirms that social capital matters for Russian road conditions, although its
performance is uneven across traffic outcome and transmission channels.
We posited that traffic outcomes are influenced by drivers through their behavior (the
horizontal channel) or by citizens through their ability to hold local governments accountable.
We find that free riding behavior on the roads is indeed related to traffic accidents. We have
several indications that this finding is not driven by reverse causality. This lends support to
the hypothesis that the horizontal channel is indeed at work in affecting traffic outcomes. The
vertical channel is however is not manifesting itself in our data, but we find strong evidence
of its presence in a sample of US cities. The observed difference should be ascribed to
different levels of civic culture and democratic participation in the two countries.
Our study shows that improved road behavior of drivers (through internalization of pro-social
norms or perhaps stricter enforcement of traffic rules) could significantly improve road safety
in Russia, but is unlikely to alleviate mounting traffic jams. Improved urban governance and
planning seems to be the key to this second problem, which requires more civic culture and
involvement that is currently observed in Russia.
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