1 Pariahs or Pals: Understanding peer in uences on the bankruptcy decision Ethan Cohen-Cole and Burcu Duygan-Bump Federal Reserve Bank of Boston Presentation at the Chicago Bank Structure Conference 8 May 2009
3 Existing Explanations: Commonly discussed explanations for rising bankruptcy rates: Adverse events, such as unemployment, health shocks, and divorce. Changes in the credit market environment: 1 Decreased transaction costs, and expansion of credit including to riskier households (supply side); 2 Decreased costs of ling for bankruptcy, including legal and information costs, as well as social stigma (demand side). Our focus is this last item the role of social factors.
4 Why focus on these social phenomena? Much discussion and anecdotal evidence about the role of stigma and changes in social attitudes: A recent Wall Street Journal article: Now, Even Borrowers With Good Credit Pose Risks. Bank of America CEO: There s been a change in social attitudes toward default. Sociologists continue to provide quotes showing that stigma is still strong and alive: I thought of [bankruptcy] as a mark against my name... It was too embarrassing... I feel like I failed. You know, to go bankrupt, that s a sign of failure. And also among economists: Gross and Souleles (2002), Fay et al. (2002), Athreya (2004), Chatterjee et al. (2007), Livshits et al. (2007)...
5 How do these social factors matter? Interacting with others who have gone bankrupt or are in the process may increase the likelihood of an individual going bankrupt herself, because: The idea that everybody does it reduces the associated embarrassment, or social stigma associated with bankruptcy; People share information and learn from one-another about eligibility, application procedures, and other bureaucratic details of ling.
6 New York/New Jersey Bankruptcy Rates Bankruptcies appear to cluster - even in rich areas. Evidence that more than prices may play a role.
7 Miami Bankruptcy Rates
8 Chicago Bankruptcy Rates
9 This Paper Main Questions: To what extent can we explain the two patterns? To what extent can de-coupling information and stigma e ects help us understand the patterns? Using (a lot of) data from a credit bureau and a new methodology based on the social psychology literature, we analyze the empirical relevance of aggregate in uences on individual decisions, disentangle the role of separate channels stigma and informational factors; and comment on their role in explaining the observed bankruptcy trends.
10 Summary of Findings Social Factors (3-11%) matter more than other controls including risk factors (<<.1%): On average societal stigma dominates the role of information, especially post 2005; Both information costs and stigma seems to have indeed decreased in this period; But changes in information sharing, not stigma, is the more likely factor behind the observed bankruptcy patterns.
11 Summary of Findings More interestingly, these aggregate patterns hide a very high degree of heterogeneity: Social factors are stronger amongst the poorer and less-well educated communities; Stigma has declined principally amongst all but the poorest and least well educated; Information costs decreased uniformly across socioeconomic groups.
12 Personal Bankruptcy in the US: An overview Example Historically, bankruptcy was seen as anything but an individual choice: Creditors would le and show that the debtor committed an act of bankruptcy akin to fraud; Associated with truly draconian penalties. To disgrace the bankrupt, [his] hair would be cut o..his palm would be branded with the T for thief and he would be mandated to stand in a public square for two hours with an ear nailed to the pillory and then cut o. Currently, the US has two di erent personal bankruptcy procedures, Chapter 7 and 13.
13 Social Interactions models SI analyze interplay between individual decisions and social processes: How do characteristics and choices of others impact individual decision making? How are social in uences re ected in equilibrium group behavior? Evaluate interdependencies that are not price mediated. Otherwise, follows classic rational analysis. ω i = arg max ω2ω i V (ω i, X i, Y g, ω i ) ω i : individual action of agent i (e.g. bankruptcy); X i : individual characteristics of agent i (e.g. credit quality); Y g : neighborhood-speci c characteristics.
14 Basic social e ect modeling De ne g as some neighborhood or reference group that impacts individual decisions: through price externalities; through social e ects. A price-only mediation of the bankruptcy decision does not capture the variation (clustering) of bankruptcies shown above. A portion of the residual variation appears to be explained by social phenomena, modeled for example by including the mean bankruptcy rate in the state. Fay et al. (2002), Gross and Souleles (2002). B ig = b + cx i + dy g + J g m g + ε ig Find a signi cant and positive coe cient (J g ).
15 Social In uence, continued Recall that we are interested in understanding the social components of the bankruptcy decision. Social Psychologists have long studied two forms of social in uence: Informational "in uence to accept information obtained from another as evidence about reality" Normative (stigma) "in uence to conform to the positive expectation of behavior" Why does this help? Individual response depends on type of in uence.
16 Social in uence Informational in uence: Social Influence Probability Of Action Growth Curve: Informational Social Influence Number of People Influencing More people providing information does not necessarily increase probability, even if resolve some uncertainty.
17 Social in uence, continued Normative social in uence: Social Influence Probability Of Action Growth Curve: Normative Social Influence Number of People Influencing More people providing pressure does increase probability.
18 Social In uence and identi cation Wedge provides identi cation method Social Influence Probability Of Action Normative Social Influence Informational and Normative Social Influence Local Non local Number of People Influencing
19 Modeling with composite social e ects We ll look at both local (1 mile) and non-local (1 4 mile) e ects. For each, we include the average bankruptcy rate (primary model): Auxiliary model B ig = b + cx i + dy g + J SI g m g + ~J S o m o + ε ig B ig = b + cx i + dy g + J I g m g + J S go (α g m g + (1 α g ) m o ) + ε ig Note α g : the marginal rate of substitution between stigma from local vs. non-local groups, which is proportional to population shares in each group (frequency of contact).
20 Credit Bureau Data US credit bureau 4 national samples: June 2003, December 2004, June 2006, December 2007; First two are large samples of about 285,780 individuals each; Latter two are HUGE samples of 27,000,000 individuals each. 1 in 9 sample of all US households with credit. Includes typical information available in a credit history on all open credit accounts. Many advantages w.r.t. measurement error; Detailed geo-code info; No individual level income and employment information.
21 Baseline Speci cation Re-estimate basic regressions using state-level social interactions only: B ig = b + cx i + dy g + J g m g + ε ig X i : individual-speci c credit risk controls, e.g. age, total outstanding debt, etc.; Y g : community level controls for the economic environment, e.g. unemployment rate, poverty rate; m g : average bankruptcy rate in the state.
23 Stigma vs. Information: national level results and trends TABLE 3: TOTAL STIGMA AND INFORMATION Stigma ** ** 0.118*** 0.106*** (0.0141) (0.0157) (0.0018) (0.0016) Information *** *** *** *** ( ) ( ) (0.0014) (0.0013) Number of Observations: 131, ,046 12,300,000 12,300,000 Interestingly, the changes in stigma coe cients move against the bankruptcy trends.
24 Stigma and Information by Social Context The role of education and Income Variation suggests a link between social context and the strength of social interactions: Do higher income groups share less information? Does bankruptcy stigma di er by education level? An individual s bankruptcy decision depends on: 1 Changes in price information, including shocks to income and employment, 2 The decisions of others in one s network, e.g. the perception of stigma, 3 And the interaction between the rst two: an economic shock may in uence social stigma, e.g. increasing wealth undermines social fabric.
25 Stigma and Information by social context (cont d) We evaluate these three possibilities by exploiting the richness of the data-set. We subdivide the data into 25 groups ( ve education and income quintiles) and re-estimate social interactions e ects for each. We nd that: Social factors are stronger amongst the poorer and less-well educated communities; Changes in these factors are dependent on context as well. (see social multiplier)
26 Table 4b. TABLE 4b: STIGMA AND INFORMATION ACROSS EDUCATION AND INCOME QUINTILES 2007 Stigma: Income Quintile Education *** *** 0.116*** 0.129*** *** 0.212*** 0.166*** 0.119*** * *** 0.169*** 0.146*** 0.130*** *** *** 0.191*** 0.193*** 0.130*** *** *** 0.170*** 0.173*** 0.136*** *** 2007 Information: Income Quintile Education *** 0.116*** * *** *** *** *** * *** *** *** *** *** ** *** *** *** *** ** * *** ***
27 Table 4c. Also, stigma shows increases in upper left of table, where most number of bankruptcies occur! TABLE 4c: STIGMA AND INFORMATION ACROSS EDUCATION AND INCOME QUINTILES Change in Stigma: Stigma: Income Quintile Education 1 (0.146) (0.107) (0.060) (0.033) (0.031) (0.042) (0.104) (0.008) (0.035) 4 (0.008) (0.004) Change in Information: Information: Income Quintile Education (0.006) (0.053) (0.037) (0.072) (0.029) (0.005) (0.007) (0.031) (0.042) (0.031) (0.037) (0.023) (0.024) (0.033) (0.019) 5 (0.003) (0.023) (0.048) (0.033) (0.014)
28 Sensitivity Analysis To provide further support our for identi cation/interpretation of the estimated social e ects, we check the sensitivity of our results: Look at the behavior of movers, using prior place of residence to measure information sharing and current to measure stigma. Di erent radii de nitions
29 Summary and what s next We ve investigated the role of social in uences on bankruptcy decisions and found: an important distinction between stigma and information e ects; a non-trivial time trend in stigma that does not match the secular increases in the overall rate; a relationship between economic factors and these social in uences. Feature that con rms social psychology and current economic research Stigma more transient (Dick et al 2008) (Zitek and Hebl 2007) Future investigation: other economic drivers of social e ects; further evaluation of the economic risk component; true cost of bankruptcy, e.g. access to credit.
30 Movers TABLE 7: MOVERS Baseline Movers Stigma 0.155*** 0.167*** (0.0250) (0.015) Information *** 0.263*** (0.0190) (0.017) Number of Observations 108, ,023
31 Di erent radii de nitions TABLE 8: ALTERNATIVE STIGMA DEFINITIONS 2003 Information Stigma Stigma1 Stigma2 Stigma3 Baseline *** * Stigma (Multiple) *** *** 0.293*** 2004 Information Stigma Stigma1 Stigma2 Stigma3 Baseline *** ** Stigma (Multiple) *** *** 0.376*** 2006 Information Stigma Stigma1 Stigma2 Stigma3 Baseline *** 0.118*** Stigma (Multiple) 0.116*** *** *** 0.110*** 2007 Information Stigma Stigma1 Stigma2 Stigma3 Baseline *** 0.106*** Stigma (Multiple) *** *** *** ***
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