1. Network Effects & Welfare Culture Marianne Bertrand, Erzo Luttmer, and Sendhil Mullainathan Oct. 29, 2004

2. Background William Julius Wilson: Poverty reinforces itself through social networks. Recent research in economics has become concerned with social networks…. Game theorists have modeled the importance of learning from neighbors and information spillovers Macroeconomists have used human capital spillovers to model economic growth Labor and public economists have used stigma and information spillovers to explain welfare participation, crime, fertility, and education.

3. How do Network Effects Operate? Two Channels: 1.The Information Channel emphasizes how a person’s knowledge depends on the behavior of others -The more contacts one has who are knowledgable about the welfare system, the easier it is to exploit or make use of the welfare system 2.The Social Norm Channel emphasizes how a person’s preferences themselves may depend on the behavior of others, either directly by affecting tastes or indirectly via social pressure. -Inner city norms may directly influence welfare participation and others (relating to sexual activity, education, and marriage) may influence welfare participation indirectly.

4. Finding evidence of Network Effects is difficult…. Much of the previous literature has used mean neighborhood characteristics to proxy for networks…. Potential Omitted Variable Biases: Omitted personal characteristics correlated with Omitted neighborhood characteristics correlated with Is individual behavior influenced by network characteristics or are members of the same network influenced by the same unobservable characteristics and/or shocks?

5. BLM Model Assume the true model governing welfare participation is given by: indicates whether person “i” from neighborhood “j” and language group “k” participates in welfare. are observed and unobserved individual characteristics. are observed and unobserved language group characteristics. are observed and unobserved local area characteristics.

6. BLM Proxy for Networks BLM exploit language spoken at home to construct a proxy for network contact…. Ample evidence suggests that people in the United States who speak a non-English language at home interact mainly with others who speak that language. Alba(1990) Bakalian(1993) Ideally, would contain information on the number of social contacts individual “i” (quantity) has as well as the level of knowledge these contacts (quality) have about the welfare system.

7. BLM Proxy for Networks One can argue that as increases, effects from the Norm channel and the Information channel intensify.

8. BLM Specification is a measure of the contacts available to an individual from language group “k” and area “j”. is a measure of the “quality” (with respect to welfare) of the contacts available to an invidual from language group “k” Is correlated with b/c of some omitted variable? Including area and language group fixed effects controls for unobserved differences across areas and language groups that are correlated with Including a language group fixed effect and controls for any unobserved individual effects correlated with

9. BLM Data BLM use 5% 1990 Census Public Use Micro Sample Geographical area indexed by Public Use Microdata Areas (100,000 people) or Metropolitan Statistical Areas (Extended cities) Language group variables constructed from question: “Does this person speak a language other than English at home? What is it?” (End up with 42 language groups, 271 MSAs, and 1196 PUMAs) equals “1” if the individual received any public assistance besides SS income. = and Demographic variables capture the marital status, age, educational attainment, race, and other family characteristics of individual “i”

10. Summary Statistics

11. Summary Statistics by Language Group

13. Regression (OLS) Results

15. In all specifications, estimated coefficients on are approximately equal and highly significant. What do these estimated coefficients imply about network effects? Consider a policy shock,,that increases welfare participation: A one percent increase in leads to a one percent increase in welfare participation in the absence of welfare effects, which implies….. = > 1 These computations suggest that networks may raise the responsiveness of Welfare use to policy by 15-27% (depending on the specification)

16. Specification Checks Probit and Logit regressions yield positive and significant coefficients on So do regressions with different measures of contact availability or different sets of controls. So do regressions that omit Spanish speaking households (55% of sample) or high-welfare language groups (Miao & Mon- Khmer). So do regressions that omit portions of the household sample on the basis of age and family characteristics.

17. Distribution of Network Effects Results suggest that strength of network effects depend on immigration status, length of stay in the United States, and English ability:

18. The Bureaucratic Channel Despite authors attempts to control for omitted variable biases, there is one mechanism not yet addressed: A heavy concentration of a high welfare using language group in an area may lead the welfare office in that area to hire a social worker speaking that language….. Possibly leading to a positive coefficient on. Test for this channel: Exclude non-Spanish speakers and use country of origin data to construct. Any effect due to the bureaucratic channel will be captured by the local area fixed effect

19. Bureaucratic Channel Results Estimated coefficients are slightly smaller but still highly significant

20. In conclusion…. Unlike previous research, authors find a creative way to distinguish between network effects and omitted variables. Results strongly suggestive of network effects….. –Importance to policy makers –Importance to theorists