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The Impact of Social Capital on Vaccinations

The Impact of Social Capital on Vaccinations. Fatima Aqeel. Does social capital factor into the demand function for vaccinations?. Why is this an interesting area of study? Number of children impacted: In 2008 1.5 million deaths of children under 5 caused by immunization preventable deaths

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The Impact of Social Capital on Vaccinations

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  1. The Impact of Social Capital on Vaccinations Fatima Aqeel

  2. Does social capital factor into the demand function for vaccinations? Why is this an interesting area of study? • Number of children impacted: In 2008 1.5 million deaths of children under 5 caused by immunization preventable deaths • Cost effective and tangible way of reducing child mortality, has policy implications • Unvaccinated children pose a large cost for the economy, especially in terms of future labor force and health bills • Evidence exists that social capital factors into other interventions: de-worming drugs, insecticide sprayed mosquito nets, health clinic visits (Shonchoy, 2012)

  3. Social Capital: Definition and Measurement • What is social capital? Social networks and the norms of reciprocity and trustworthiness that define them, popularized by Robert Putnam, HKS. Inter- and intra- social capital. • Positive or negative: Sometimes networks have made civic virtue more effective => could improve vaccinations, either through peer effects, increased information etc. But, sometimes could restrict individuals within communities => could decease vaccination coverage

  4. Continued • Measures: Direct measures include survey data on the level of participation in community activities such as voting, and level of altruism An indirect measure often used is community diversity (Alesina et al. (2003), Easterly and Levine (1997)) • Advantage of indirect measure: Data availability • Disadvantage of indirect measure: Indirect measure, the link could be weak or could not hold at all Inverse relationship found in most literature, although some papers find a positive link or no link

  5. Brief Literature Review • Field et al. (2008) – Ahmedabad violence study • Khwaja (2006) – Himalayan region infrastructure maintenance study • Alesina et al. (2003) – Cross country study • Speizer et al. (2001) – Peer educator and contraceptive use • Rao et al. (2007) - Vaccination and peer effects My contribution : • I link social capital to vaccinations, which is an important area of study • I employ a new technique in isolating diversity – the Partition of India in 1947

  6. Data • The data set is a household level survey from Pakistan from 2006-2007 • Conducted by Measure DHS in collaboration with a Pakistani government body (the National Institute of Population Studies) • Why Pakistan? Country characterized by strong community and familial ties, and vaccination coverage is very low, so more to gain Only 47% of children between 12-23 months have received all recommended vaccines • Data strengths: Relatively large and very reliable • Data weaknesses: Cross-sectional, missing some variables

  7. Summary Statistics I

  8. Regression and Variables I • Vaccination Scoreij = β0ij + β1(Diversity)ij+ εij • Diversity Index based on the Herfindahl Index Diversity Index = 1-HHI Herfindahl Index (HHI) = ∑Ii=1 ni2 • Vaccination Scoreij= β0ij + β1(Diversity)ij + β2(majority ethnicity)ij + β3(woman belongs to majority)ij + β3(controls) +εij • Vaccination Scoreij= β0ij + β1(diversity)ij + β2(proportion of ethnicity)ij + β3(controls) + εij

  9. Identification Problem and Potential Solution – Partition of the Punjab, 1947 • Random assignment of people needed to make diversity exogenous • What was the Partition? • Why can it be used? i.Variation in social capital created by settlement patterns ii. High relative replacement effect (Bharadwaj et al., 2008). (Destination also decided by distance from border, urban) iii. The Pakistani government re-distributed land and property of abandoned non-Muslims to refugees (farmers in the East compensated with farms in the West), muhajirsand human capital variation

  10. Evidence – Settlement Patterns

  11. Evidence – Human Capital Distribution

  12. Summary Statistics II

  13. Regression and Variables II • Vaccination Scoreij =β0ij + βi(Muhajir%1951)ij+ β2(controls)ij+ εij • Vaccination data is for 2006-2007 • Data set is restricted to Punjab only • Muhajir%1951 is the percentage of immigrants in West Punjab in 1951 • Mean muhajirpercentage is 21.8%, s.d is 12.1%.

  14. Results • Coefficient on Diversity is positive and significant initially, when controls are added its variance grows very large and it loses significance • Individual ethnicity is a strong predictor of vaccinations, even with clustered standard errors – Urdu speakers and Punjabi speakers are the best off, possibly because of custom, or better access etc. • The coefficient on ethnic proportions is significant, but the coefficient on ethnic majority is not. After clustering, neither is significant • The percentage of muhajirsin a district in 1951 has a positive and significant impact on vaccinations in 2007

  15. Implications • Simple regression does not allow us to reject the null hypothesis => social capital has zero impact on vaccination • The result using muhajirpercentage as an exogenous source of diversity shows social capital is linked to vaccinations, and the link is positive • Other factors such as education, age, wealth, sex, birth order and urban rural have the expected significant impact

  16. Caveats • 60 year time lag between muhajirdata and the vaccination data, initial low social capital districts could catch up (but psychological effects died down) • Internal migration data not incorporated into study yet – muhajir-local mix could be different => next step • Indirect measure of social capital, better data would allow for a direct measure • Muhajirsthat migrated could have had higher levels of social capital, or they could have only reached West Punjab because they had connections there. Use human capital to check this Anecdotal evidence to suggest that muhajirswere generally unwelcome

  17. Using human capital to check social capital levels

  18. Sub-group Analyses – Preliminary results • Justification: Sub-groups too different to simply control for, for example in terms of supply • Partial sub-group analysis: interaction of rural and diversity and a separate regression interacting wealth and diversity • Complete sub-group analysis: Restricting data to rural areas and certain wealth quintiles only • Results: Partial sub-group analysis with rural yields no significant results, but with wealth, the coefficient on the interaction is positive and the coefficient on Diversity is negative • No significant results with complete sub-group analyses

  19. Where to go from here... • Diversity impacts vaccinations => social capital impacts vaccinations, but I would want to conduct more analyses before I say what the direction is • Both are very difficult to measure, but here are the next steps I would take to find answers, given the time and resources: • Further sub-group analyses • Find data that includes direct social capital measures, for example on voting • Try using muhajirpercentage as an instrument

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