near and dear? evaluating the impact of ... - economics

Near and Dear?Evaluating the Impact of Neighbor Diversity on

Inter-Religious Attitudes

Sharon Barnhardt, Institute for Financial Management & Research

UNSW16 September, 2011

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Motivation

• Growing evidence ethnic diversity reduces trust and public goods; related to violence. (Alesina, Baqir, and Easterly 1999; Alesina and La Ferrara 2002; 2005; Banerjee, Iyer, and Somanathan 2005; Easterly and Levine 1997; Khwaja 2009; Miguel and Gugerty 2005)

• How do we design policies to manage ethnic differences?- Opposite approaches: Integration or Separation

• What are the consequences of “mixing” members of different ethnic communities into sustained, close proximity on their attitudes about each other?

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Introduction Literature Setting Outcomes Results Conclusion

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Motivation continued

• Massive urbanization of poor in developing countries requires attention to prevent growth of slums

‣ Relocation programs bring low-income populations together in new buildings

‣ Potentially relevant for millions of households

• Similar questions in US and Europe

‣ 1950s: mixed-race housing

‣ Currently: mixed-income and native-immigrant neighborhoods

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Issues & Contribution

• Issue 1: Self-selection into neighborhoods

‣ Use randomly-allocated neighbors in government housing in Hyderabad

• Issue 2: Explicit attitudes may suffer from self-presentation bias

‣ Add second measure: “implicit associations”

• Issue 3: Evidence we have is on college students in the US

‣ Extend into new population (Hindus & Muslims, lower income, less educated, adults, environment lacking institutions to manage conflict)

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Preview of Results

• Hindus’ attitudes become more favorable with greater exposure to Muslims

‣ Explicit attitudes are more positive by 0.25 to 0.40 standard deviations

‣ Hindu Children’s implicit associations are less negative by 0.20 to 0.57 standard deviations

• Muslims’ attitudes not consistently affected

• Why the difference?

‣ Suggest information available about each other differs in a Hindu-dominated society

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Roadmap

1.Introduction

2.Related Literature

3.Setting and Identification Strategy

4.Outcome Variables

5.Estimation and Results

6.Conclusion

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Literature - framework

• Mechanisms through which attitudes may become more or less favorable

‣ Preferences (taste for interaction; Becker, 1957)

- Become more/less comfortable with inter-ethnic interaction

‣ Beliefs (statistics about other group; Aigner & Cain, 1977)

- Information gained depends on initial priors, nature of interactions

• “Contact” Theory (Allport, 1954)

‣ 1) equal status 2) cooperation 3) common goals, and 4) institutional support - all likely to make attitudes improve

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Literature - empirical

• Many laboratory experiments, result of contact tends to improve attitudes

‣ Lack of competition is key to positive result

• We have some experimental field evidence that integration increases empathy for white college students randomly assigned an African American roommate (Boisjoly, Duncan, Kremer et al., 2006)

• Also, evidence of links between attitudes and other outcomes

‣ HR managers with worse attitudes toward Muslims less likely to interview Muslims (Rooth, 2008)

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• India has history of Hindu-Muslim conflict

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From slum to “condos”Introduction Literature Setting Outcomes Results Conclusion

Religious Restrictions in the 25 most Populous Countries

Pew Forum on Religion & Public Life - Global Restrictions on Religion, December 2009

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• Hyderabad is “riot prone” (Varshney)

‣ Large slum on southern side of city. Interviews indicate sorting

- February 2005: Fire- April 2007: New houses completed, lottery- October 2008 - January 2009: Survey- February - April 2009: Implicit Associations Test

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• Hyderabad is “riot prone” (Varshney)

‣ Large slum on southern side of city. Interviews indicate sorting

- February 2005: Fire- April 2007: New houses completed, lottery- October 2008 - January 2009: Survey- February - April 2009: Implicit Associations Test

• Weak property rights help maintain random assignment

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Architecture creates proximity

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112 four-story buildings

448 “clusters” of four households


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Cluster types (Muslim perspective)

Orange = Hindus, Green= Muslims, White=Christians

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Muslim Majority Equal Muslim Minority


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Allocation

• 377 clusters/ 1508 units / 1441 Hindus and Muslims• 1363 responded (95%)• 97% Female; Average age 37• Household income Rs. 3817 ($90 in 2009) per month

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Hindus MuslimsOwn majority 584 185Equal 199 199Own religion is minority 57 139Total 840 523


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Public Lottery

• Interviewed Housing Corporation and Revenue Divisional Officer

‣ Government made lists of affected households on-site the day after the fire.

‣ Private company scanned irises and authenticated BPL status

‣ Stratified by ground floor

‣ Over-subscribed

• Randomization checks

‣ Sorting into clusters

‣ Balance by type

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Randomization Check 1 (sorting)• Run a regression like the following on administrative data:

‣ The 1 is a reference household in cluster of 4, γ is a ground-floor fixed effect because the lottery was stratified

‣ Age, Muslim, Hindu, Female, Widow, Christian, Unknown Religion, Backward Caste, Scheduled Caste/Tribe

• Following Kremer and Levy (2003), use administrative data and simulate a fair draw by randomly allocating names from list to 1792 slots

• Run the same regression on each simulated draw• All coefficients from such regressions using “actuals” fall within ±2

standard deviation of mean of coefficients from regressions using simulated draws

Hindu in unit 1c=β(Number Hindus in 2-4)c + γg +εc

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Distributions of Coefficients from Simulated Lotteries

0

0

02

2

24

4

46

6

68

8

810

10

10Percent

Perc

ent

Percent-.1

-.1

-.1-.05

-.05

-.050

0

0.05

.05

.05.1

.1

.1Coefficient

Coefficient

CoefficientOLS Regression Coefficients Using Simulated Lottery Data. n=448

OLS Regression Coefficients Using Simulated Lottery Data. n=448

OLS Regression Coefficients Using Simulated Lottery Data. n=448Reference Household Hindu on Number Neighbors Hindu

Reference Household Hindu on Number Neighbors Hindu

Reference Household Hindu on Number Neighbors Hindu

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Randomization Check 2 (balance)

OLS regressions. Robust standard errors in parentheses adjusted for clustering at level of 4 unit clusters. All columns contain a fixed effect for the ground floor and for the number of Christians randomly allocated to the cluster.

Hindus MuslimsMean: Hindus

in Hindu Majority Cluster

Difference (Equal− Majority)

Difference (Minority− Majority)

Mean: Muslims in Muslim Majority Cluster



(1) (2) (3) (4) (5) (6)Age of beneficiary 32.144 0.094 0.580 35.297 -0.525 -0.859

(0.359) (0.733) (1.083) (0.863) (1.118) (1.243)Female 0.947 0.021 0.020 0.962 0.020 0.031**

(0.009) (0.015) (0.025) (0.014) (0.017) (0.015)Widow 0.027 0.014 0.042 0.055 -0.013 -0.011

(0.007) (0.015) (0.034) (0.016) (0.022) (0.024)Backward Class/ Caste 0.398 -0.005 0.036

(0.021) (0.044) (0.070)Scheduled Caste 0.421 0.016 0.018

(0.023) (0.044) (0.070)Scheduled Tribe 0.158 -0.005 -0.032

(0.016) (0.030) (0.046)

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Administrative Data


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Randomization Check 2 (continued)

OLS regressions. Robust standard errors in parentheses adjusted for clustering at level of 4 unit clusters. All columns contain a fixed effect for the ground floor and for the number of Christians randomly allocated to the cluster. ** p<.05 * p<0.1 The p-value on a chi-square test of the joint significance in predicting groups are p=.2133 for Hindus and p=0.665 for Muslims.

Hindus MuslimsMean:

Hindus in Hindu

Majority Cluster



Mean: Muslims in

Muslim Majority Cluster



(1) (2) (3) (4) (5) (6)Surveyed 0.951 0.011 -0.090** 0.974 -0.016 -0.015

(0.009) (0.017) (0.043) (0.011) (0.019) (0.020)Years lived in Hyderabad 19.261 0.678 3.957* 27.798 -0.950 -1.412

(0.522) (1.114) (2.011) (1.140) (1.656) (1.640)Moved to Hyderabad to earn a living 0.443 -0.308 -0.032 0.389 -0.004 -0.026

(0.166) (0.507) (0.167) (0.039) (0.052) (0.055)Years Education 1.433 -0.008 -0.181 1.816 0.018 0.225

(0.123) (0.251) (0.370) (0.222) (0.345) (0.372)Grew up in a village 0.719 -0.022 -0.123* 0.497 0.007 -0.022

(0.019) (0.039) (0.068) (0.041) (0.054) (0.059)Knew any cluster neighbor before 0.089 0.027 0.017 0.222 -0.083 -0.113**

(0.014) (0.033) (0.043) (0.046) (0.054) (0.053)

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Survey Data


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Outcome Variables

1.Explicit attitudes (index of survey questions)2.Implicit associations (test) 3.Willingness to live together (mean of survey

questions)

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Outcome 1: Explicit Attitudes

• Questions‣ How trustworthy are group X?‣ How brave are group X?‣ How much do Xs cheat?‣ How peace-loving are Xs?‣ How much do you trust Xs?

• Scaled from 1 (most unfavorable) to 5 (most favorable)• Two approaches for aggregating explicit attitudes:

‣ Index: Standardize scores using mean & SD of other group homogenous clusters - ie, mean and SD for Muslims living in all-Muslim clusters used for “How brave are

Muslims?” - Larger negative value means less favorable attitudes. Mean = -1.9 SD = 1.2

‣ Average effect sizes (as in Kling et al. 2004, & Clingingsmith, Khwaja, and Kremer, 2008)

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Outcome 2: IAT

• IAT measures cognitive associations• Put two lists of words into categories at the same

time‣ Names: Hindu or Muslim‣ Concepts: Good or Bad

• Instructions change

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Outcome 2: IAT 1st Double Categorization

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• When the task is harder (incongruent with associations in the mind), reaction time is longer. Difference will be

‣ Negative when mental association is Hindus are “good”

‣ Positive when mental association is Muslims are “good”

• Use D-measure, standard in the IAT literature (Greenwald, Banaji & Nosek, 2003) D= (Mean Good H1 - Mean Good M1)/σ1 + (Mean Good H2 - Mean Good M2)/σ2

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• Participation fee incentivized to go as fast as possible without making mistakes

• Sub-sample: under 50 years old, 200 no exposure and 200 from Hindu-Muslim clusters and oldest child age 10 - 14, if there was one

‣ Response rate 341 adults (85%); 129 / 165 kids (78%)

Outcome 2: IAT Measurement

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Difference= (Mean reaction good with Hindu) - (Mean reaction good with Muslim)


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Outcome 3: Living Together

• Two questions about preferences for inter-religious living

‣ Do you mind living next to someone from group X?

- 1= “Don’t mind, 0 otherwise”

‣ What is the best way for Hindus and Muslims to co-exist?

- 1= “Live together and become friends”, 0 otherwise• Higher value indicates greater preference for inter-

religious living.

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Basic Empirical Strategy

• Where O is an outcome, i indexes the respondent, c indexes a cluster of houses.

• equal is an indicator variable equaling 1 if the cluster type is equal Hindus and Muslims.

• Respondent's Group is the minority is an indicator equal to one if the respondent is the only one of her religion in the cluster.

• Fixed effects for number of Christians allocated to the cluster (γc) and for the ground-floor lottery strata (αg). Standard errors adjusted for clustering at the 4-unit level.

• Religion is defined by lottery assignment. ITT estimates.

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Oic=β1(equalc) +β2(Respondent's Group is minorityc) + β3(Muslimi) + β4(Muslimi×equalc) + β5(Muslimi×minorityc) +αg +γc +εic


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Explicit attitudes• Bar chart: attitudes by cluster type (from regressions)

• Bigger negative value is less favorable (worse) attitude

Hindu Respondent in Hindu Majority Cluster

Hindu Respondent in Equal Cluster***

Hindu Respondent in Hindu Minority Cluster**

Muslim Respondent in Muslim Majority Cluster

Muslim Respondent in Equal Cluster

Muslim Respondent in Muslim Minority Cluster

-3.0 -2.3 -1.5 -0.8 0

-0.741

-0.768

-0.757

-2.285

-2.420

-2.662

Estimates from OLS regressions. FE ground floor & Number Christians. SE clustered at level of 4-housing units.

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Explicit attitudes Robustness

Average Effect Sizes are with SD weights.n=840 Hindus and 523 Muslims. See text section 5.1.1 for a description of average effect sizes.

Treatment Effects

Index

Index with covariates

Ave. Effect Size

Bound 1 on Hindu Minority

Bound 2 on Hindu Minority

(1) (2) (3) (4) (5)HindusEqual versus majority 0.242** 0.230** 0.225** 0.242** 0.242**Minority versus majorityMinority versus majority 0.377*** 0.360** 0.346** 0.340*** 0.336***

MuslimsEqual versus majority -0.011 0.001 -0.028Minority versus majorityMinority versus majority 0.016 0.027 -0.041

Covariates no yes no no noN 1363 1363 1363 1369 1369

• “Bound 1” replaces 6 un-surveyed Hindus in Hindu minority with mean index score for Hindus in Hindu majority (-2.66)• “Bound 2” replaces them with mean in Hindu-only clusters (-2.71)

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Implicit attitudes - Adults• Negative D-Measure means “good” & Hindu

associatedHindu Respondent in Hindu Majority Cluster

Hindu Respondent in Equal Cluster

Hindu Respondent in Hindu Minority Cluster



Muslim Respondent in Muslim Minority Cluster~

-0.45 -0.30 -0.15 0 0.15 0.30 0.45 0.60

0.538

0.432

0.301

-0.185

-0.311

-0.298

N= 256 Hindus and 83 Muslims. Estimates from OLS regressions. FE ground floor. SE clustered at level of 4-housing units.

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Implicit attitudes - Children

• Negative D-Measure means “good” and “Hindu” are associated• Only Hindu children affected by cluster type

N= 83 Hindu children & 46 Muslim Children. Estimates from OLS regressions. FE ground floor. SE clustered at level of 4-housing units.Note; Hindu in Hindu Minority Effect is robust to weighting, but when covariates are included the effect becomes smaller and is not significant.

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Hindu Respondent in Equal Cluster**

Hindu Respondent in Hindu Minority Cluster**

Muslim Respoindent in Muslim Majority Cluster


Muslim Respondnet in Muslim Minority Cluster

-0.4 -0.3 -0.1 0 0.1 0.3 0.4

0.14

0.36

0.371

-0.14

-0.02

-0.278


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Living together

• Only significant difference is between Hindus in Hindu Majority and Hindu Minority Clusters


Hindu Respondent in Equal Cluster

Hindu Respondent in Hindu Minority Cluster***



Muslim Respondent in Muslim Minority Cluster

0.85 0.90 0.95 1.00

0.994

0.978

0.985

0.999

0.965

0.958

N=1363. Estimates from OLS regressions. FE ground floor & Number Christians. SE clustered at level of 4-housing units.

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Why this pattern?• Hindu effect does not appear to be a change in tastes for interaction

‣ Can look at behaviors - do they change along with attitudes?

- Do they talk to each other? Over 90% of neighbor-pairs say they talk daily.

- Do they eat in each other’s houses? About 20% have eaten in neighbor’s house in past month. For Hindu respondents, no effect of neighbor religion or cluster type on eating together.

- Do they provide “neighborly” help? (index) High scores for Hindu respondents, no impact of neighbor religion or cluster type.

- Who do they spend time with in the housing complex? Hindus include more Muslims on their lists of who they spend the most time with when they have more Muslim neighbors.

• Potential reason Muslims’ attitudes do not respond to more neighbors

‣ Interaction is different by social status

‣ Not getting “new” information since they’re surrounded by a dominant Hindu culture

- 70% of Hindus grew up in a village, only 50% of Muslims did

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Summary

• Hindu improvement in attitudes is robust to background characteristics, index creation, inclusion of clusters with incomplete government data

‣ Consistent with results from college students in US

• Taste for discrimination seems unlikely channel through which Hindu attitudes change

• Externally valid for those who would take up highly-subsidized housing; for areas where hostilities latent

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Thank you

[email protected]


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mailto:[email protected]

mailto:[email protected]

near and dear? evaluating the impact of ... - economics

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