TPOVs @F-L-O-W
Enter Social Capital
I ran across this in HBR [Harvard Business Review] Daily
Stats:
MARCH 22, 2013
Higher Levels of Social Capital Lead to Fewer Traffic
Fatalities.
A 5% increase in the average level of agreement with the
statement "Most people are honest" within a U.S. state results
in a decline in traffic fatalities in that state by about 11%,
says Matthew G. Nagler of the City College of New York. This
measure of trust in others is an indicator of the state’s
level of social capital, sometimes defined as a willingness to
engage in community activities. Less-conscientious people who
reject civic engagement presumably drive more recklessly.
—
Source: Does Social Capital Promote Safety on the Roads?
Since this is full of pattern
information, I sought to find out more:
I attached the pdf I found, FYI,
and copied some of it here so you don’t have to open it, because
this piece is loaded with information at the meta level:
1) Social capital as a
metaconstruct
2) How to skirt previously direct links looking for indirect causal links (something in this, I don’t fully understand, but I noticed it’s there)
3) Recognition that something besides war kills so many people, and how it’s escalating exponentially
4) If this kind of noise/signal finding works, then we can expect it to become much more prevalent…?
2) How to skirt previously direct links looking for indirect causal links (something in this, I don’t fully understand, but I noticed it’s there)
3) Recognition that something besides war kills so many people, and how it’s escalating exponentially
4) If this kind of noise/signal finding works, then we can expect it to become much more prevalent…?
Helpful Hint: I don’t know why HBR chose something like this, as I don’t know their editorial guidelines… it could be for the same reasons I mentioned I was attracted to it… It would be interesting to see how many people in their database actually clicked on some part of this for more research.
I suppose (for the curious)… and for the practical, is "social capital" really an important prime in correlating cause and effect issues, in something as simple as traffic fatalities and if so, then what else can we predict/explain with social capital?
Action Step: And if so… will social capital start appearing more widely in our policy making decisions/design?
In sociology, social capital is the expected collective or economic benefits derived from the preferential treatment and cooperation between individuals and groups. Although different social sciences emphasize different aspects of social capital, they tend to share the core idea "that social networks have value". Just as a screwdriver (physical capital) or a university education (cultural capital or human capital) can increase productivity (both individual and collective), so do social contacts affect the productivity of individuals and groups.[1]
I found this expose on the definition of social capital because I had not seen social capital used in the manner in which the HBR Stats presented it before, another reason it caught my mind, not just my eye…
http://www.socialcapitalresearch.com/literature/definition.html
Now, here is the piece on Social Capital:
MATTHEW G. NAGLER The City College of New York, mnagler@….
I present evidence that social capital reduces traffic accidents and related death and injury, using data from a ten-year panel of 48 U.S. states. The econometric challenge is to distinguish the causal effects of social capital from bias resulting from its correlation with unobservable characteristics by state that influence road risks.
I accomplish this by employing snow depth as an instrument, and by restricting attention to summertime accidents. My results show that social capital has a statistically significant and sizable negative effect on crashes, traffic fatalities, serious traffic injuries, and pedestrian fatalities that holds up across a range of specifications.
JEL Classification: R41, I18, Z13 Keywords: Highway safety; Panel data; Instrumental variables; Social factors; Trust
INTRODUCTION
Social capital has generated substantial academic interest over the past two decades in the wake of two influential books by Robert D. Putnam, Making Democracy Work (1993) and Bowling Alone (2000). A large number of recent studies suggest that interpersonal trust and civic engagement have positive economic impacts (e.g., Narayan and Pritchett 1999, Knack 2001, Zak and Knack 2001, Grootaert et al. 2002, Karlan et al. 2009).
Recent work also indicates that social capital, measured variously, has beneficial impacts on health and well-being. The research attention has, however, brought with it a measure of controversy. While there is much evidence of association between social capital and outcomes relevant to economics, causal links have been hard to establish conclusively (Helliwell 2001).
The lack of evidence of causation flowing from social capital to putative outcomes has been one of the central critiques of this literature (see, e.g., Sobel 2002). This paper examines how social capital relates to highway safety. It uses an aggregate measure of generalized interpersonal trust to explain variations in the level of traffic fatalities and three other measures of highway safety across a panel of U.S. states over the years 1997- 2006. The focal point of my approach is an innovative identification strategy that solves the problem of establishing causation.
The main difficulty with estimating the effect of social capital on highway safety at an aggregate level is that selection on unobserved characteristics of the population may introduce spurious effects. For example, if less conscientious individuals who both eschew civic engagement and drive more recklessly tend to sort disproportionately across states, one might observe higher rates of fatal accident and other highway risk measures in states with lower levels of social capital. This, of course, would prove nothing about the effects of social capital per se.
The paper addresses this problem by exploiting variation in winter snow depth across states as an exogenous source of variation to social capital formation. Snow depth offers a relevant instrument because a snowy climate impacts the long-term movement patterns of individuals. These in turn are relevant to the extent to which individuals form strong ties with each other.
But for this instrument to be valid, it must also be orthogonal to unobservable determinants of highway risks. As snow accumulation likely contributes directly to the incidence of accidents during the winter, I restrict the dependent variable to safety-related incidents occurring during the summer. Conceptually, while variation in snow depth plausibly explains variations in social capital from state to state, it does not directly affect the rate of accidents in the summer (non-snow) months of the year.
Thus I am able to investigate how relative differences in social capital – for which variation is induced through exogenous snow depth variation – influence non-snow-related rates of traffic incidents.
I find that social capital has a significant negative impact on the incidence of crashes, injuries and deaths on the roads. For example, a one-standard-deviation increase in my main social capital variable – the average individual agreement level with the statement, “Most people are honest” – results in a decline in state crashes of between 9 and 18%, a decline in traffic fatalities of between 11 and 19%, a decline in serious injuries of between 11 and 22%, and a decline in pedestrian fatalities of between 20 and 39%.
A number of previous studies have used ordinary least squares or fixed effects regression to estimate the economic effects of social capital (e.g., Helliwell 1996, 2007; Narayan and Pritchett 1999; Maluccio et al. 2000; Grootaert et al. 2002; Yamamura 2008).
While some studies have attempted to account for selection bias by employing instrumental variables techniques (e.g., Knack and Keefer 1997, Narayan and Pritchett 1999, Maluccio et al. 2000, Zak and Knack 2001, Easterly et al. 2006), the instruments have largely consisted of other sociopolitical variables.
Given the nature of relationships among social science variables, questions remain about the endogenity of such instruments and whether they may be reasonably perceived as meeting the exclusion criteria. With its “natural” (i.e., climatic) experimental design, the present study offers an important innovation in conclusively demonstrating the effects of social capital.
An important motivation for this study is that traffic fatalities pose a serious public health problem worldwide. Over one million deaths are attributed each year to automobile accidents (Bishai et al., 2006). If current trends are not reversed, traffic injuries are projected to grow from the ninth leading cause of disability-adjusted life years lost in 1990 to the third by 2020 (Murray and Lopez, 1996).
In the United States, a trend of declining traffic deaths during the 1970s and 1980s, attributable to the use of seat belts and adoption of safety equipment such as antilock brakes, has yielded to stagnation since the 1990s; the toll in the U.S. remains near 43,000 deaths annually (White, 2004). Traffic fatalities remain a major cause of death at all ages and the leading cause for persons under the age of 44 (Heron et al., 2009).
The rest of the paper is structured as follows. Section II considers conceptually why social capital should affect highway safety. Section III lays out my empirical strategy. Section IV describes the data used in the study. Section V evaluates the instrumental variables mechanism. Section VI presents results and robustness checks, and Section VII discusses public policy implications.
I present evidence that social capital reduces traffic accidents and related death and injury, using data from a ten-year panel of 48 U.S. states. The econometric challenge is to distinguish the causal effects of social capital from bias resulting from its correlation with unobservable characteristics by state that influence road risks.
I accomplish this by employing snow depth as an instrument, and by restricting attention to summertime accidents. My results show that social capital has a statistically significant and sizable negative effect on crashes, traffic fatalities, serious traffic injuries, and pedestrian fatalities that holds up across a range of specifications.
JEL Classification: R41, I18, Z13 Keywords: Highway safety; Panel data; Instrumental variables; Social factors; Trust
INTRODUCTION
Social capital has generated substantial academic interest over the past two decades in the wake of two influential books by Robert D. Putnam, Making Democracy Work (1993) and Bowling Alone (2000). A large number of recent studies suggest that interpersonal trust and civic engagement have positive economic impacts (e.g., Narayan and Pritchett 1999, Knack 2001, Zak and Knack 2001, Grootaert et al. 2002, Karlan et al. 2009).
Recent work also indicates that social capital, measured variously, has beneficial impacts on health and well-being. The research attention has, however, brought with it a measure of controversy. While there is much evidence of association between social capital and outcomes relevant to economics, causal links have been hard to establish conclusively (Helliwell 2001).
The lack of evidence of causation flowing from social capital to putative outcomes has been one of the central critiques of this literature (see, e.g., Sobel 2002). This paper examines how social capital relates to highway safety. It uses an aggregate measure of generalized interpersonal trust to explain variations in the level of traffic fatalities and three other measures of highway safety across a panel of U.S. states over the years 1997- 2006. The focal point of my approach is an innovative identification strategy that solves the problem of establishing causation.
The main difficulty with estimating the effect of social capital on highway safety at an aggregate level is that selection on unobserved characteristics of the population may introduce spurious effects. For example, if less conscientious individuals who both eschew civic engagement and drive more recklessly tend to sort disproportionately across states, one might observe higher rates of fatal accident and other highway risk measures in states with lower levels of social capital. This, of course, would prove nothing about the effects of social capital per se.
The paper addresses this problem by exploiting variation in winter snow depth across states as an exogenous source of variation to social capital formation. Snow depth offers a relevant instrument because a snowy climate impacts the long-term movement patterns of individuals. These in turn are relevant to the extent to which individuals form strong ties with each other.
But for this instrument to be valid, it must also be orthogonal to unobservable determinants of highway risks. As snow accumulation likely contributes directly to the incidence of accidents during the winter, I restrict the dependent variable to safety-related incidents occurring during the summer. Conceptually, while variation in snow depth plausibly explains variations in social capital from state to state, it does not directly affect the rate of accidents in the summer (non-snow) months of the year.
Thus I am able to investigate how relative differences in social capital – for which variation is induced through exogenous snow depth variation – influence non-snow-related rates of traffic incidents.
I find that social capital has a significant negative impact on the incidence of crashes, injuries and deaths on the roads. For example, a one-standard-deviation increase in my main social capital variable – the average individual agreement level with the statement, “Most people are honest” – results in a decline in state crashes of between 9 and 18%, a decline in traffic fatalities of between 11 and 19%, a decline in serious injuries of between 11 and 22%, and a decline in pedestrian fatalities of between 20 and 39%.
A number of previous studies have used ordinary least squares or fixed effects regression to estimate the economic effects of social capital (e.g., Helliwell 1996, 2007; Narayan and Pritchett 1999; Maluccio et al. 2000; Grootaert et al. 2002; Yamamura 2008).
While some studies have attempted to account for selection bias by employing instrumental variables techniques (e.g., Knack and Keefer 1997, Narayan and Pritchett 1999, Maluccio et al. 2000, Zak and Knack 2001, Easterly et al. 2006), the instruments have largely consisted of other sociopolitical variables.
Given the nature of relationships among social science variables, questions remain about the endogenity of such instruments and whether they may be reasonably perceived as meeting the exclusion criteria. With its “natural” (i.e., climatic) experimental design, the present study offers an important innovation in conclusively demonstrating the effects of social capital.
An important motivation for this study is that traffic fatalities pose a serious public health problem worldwide. Over one million deaths are attributed each year to automobile accidents (Bishai et al., 2006). If current trends are not reversed, traffic injuries are projected to grow from the ninth leading cause of disability-adjusted life years lost in 1990 to the third by 2020 (Murray and Lopez, 1996).
In the United States, a trend of declining traffic deaths during the 1970s and 1980s, attributable to the use of seat belts and adoption of safety equipment such as antilock brakes, has yielded to stagnation since the 1990s; the toll in the U.S. remains near 43,000 deaths annually (White, 2004). Traffic fatalities remain a major cause of death at all ages and the leading cause for persons under the age of 44 (Heron et al., 2009).
The rest of the paper is structured as follows. Section II considers conceptually why social capital should affect highway safety. Section III lays out my empirical strategy. Section IV describes the data used in the study. Section V evaluates the instrumental variables mechanism. Section VI presents results and robustness checks, and Section VII discusses public policy implications.
If you have any comments, questions, suggestions, or need some additional help, please use the form below to submit them. Someone will get back to you within 48 hours.
We hope you pick up valuable insights, ideas, and
tools during this process, which you can use for your own development as
well as your work and leadership with others.
You, Me, and We @F-L-O-W
Mike R. Jay is a developmentalist utilizing consulting, coaching, mentoring and advising as methods to offer developmental scaffolding for aspiring leaders who are interested in being, doing, having, becoming, and contributing… to helping people have lives.
© Generati
Learn how you may become a member of our Inner Circle and receive the cutting edge on the most current thinking in Leader Development. Visit Inner Circle Membership.