“Civil Wars beyond their Borders: The Human Capital and Health Consequences

of Hosting Refugees”

Javier E. BaezDepartment of Economics

Maxwell School of Public Affairs - Syracuse University

2nd Annual Research ConferenceDecember 7-9, 2007Arusha, Tanzania

Worldwide stock of refugees(numbers in thousands)

Source: United Nations High Commission for Refugees (UNHCR, 2001), taken from Hatton and Williamson (2004)

-2,0004,0006,0008,000

10,00012,00014,00016,00018,00020,000

1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

Worldwide stock of refugees& world population

Source: United Nations High Commission for Refugees (UNHCR, 2001), taken from Hatton and Williamson (2004) and Earth Policy Institute

0

100

200

300

400

500

600

1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

Refugees Population

(1970=100)

Motivation

• Assessing the socioeconomic impacts of wars on refugees is extremely important

• Yet, this study turns the attention on other individuals that don’t receive the same public coverage and are as important: host communities

• Question: what are the effects of hosting refugees on local populations, in particular on children?

Existing evidence

• The effects of civil conflicts on the well-being of the communities that receive their refugees still remain largely unexplored

Brief Overview

• Question: impact of the arrival of a massive influx of refugees on welfare outcomes of local children (under 5)?

• Context: a rural region in sub-Saharan Africa

• Scope: short and long run analysis

• Strategy: exploit a natural experiment → D-D & D-D-D

• Outcomes: anthropometrics, morbidity & infant mortality (short run), height, health and human capital accumulation (long run)

• Results: large negative effects in the S-R, some persistence in the L-R (and appear to be very robust)

How to empirically address this issue?

• Ideal for evaluation purposes: random allocation of refugees among communities. But, is it feasible?

• So need a big and exogenous population shock + an instrument for refugee intensity– Exploits population flows from the genocides in

Burundi (late 1993’s) and Rwanda (early 1994’s) as a natural experiment + geography

Population shocksBurundian genocide:

• October, 1993: Hutu president of Burundi was assassinated• Massive clashes between Hutus & Tutsis• Country entered a bloody period of civil strife: between

100,000-150,000 died and around 800,000 refugees

Rwandan genocide:

• April, 1994: airplane carrying the presidents of Rwanda and Burundi was shot down

• Extremist militia groups started the extermination of ethnic Tutsis and moderate Hutus

• A three-month killing campaign that left between 800,000 and one million people dead and about two million refugees

Flows of refugees into Tanzania

• Tanzania has historically been welcoming displaced people for decades. However, these population shocks produced a sharp increase in the number of refugees hosted

Source: United Nations High Commission for Refugees (UNHCR)

0

100200

300400

500

600700

800900

100019

91

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

Num

ber o

f ref

ugee

s (th

ousa

nds)

Civil conflict in RwandaCivil conflict in Burundi

Area under study: Kagera Region

Source: U.S. Central Intelligence Agency

Refugees within the Kagera Region (I)

• 250,000 Burundians fled into Kigoma & Kagera regions in the first wave of immigrants between late 1993 and early 1994

• But, largest flow began in April 1994: 250,000 Rwandans (forming lines of up to 12 km long) flooded into Kagera in less than 24 hours

• Kagera region hosted over 500,000 refugees (≈ 33% of local population, ≈ 80% of population in two main recipient provinces)

Source: www.post-gazette.com

Source: Reuters

Refugees within the Kagera Region (II)

• Refugee camps located along the border with Rwanda and Burundi

• But anecdotal evidence indicates that many of them blended with local populations

Identification Strategy

• Several restrictions to test a causal pathway

• Another big limitation:– no refugee census data to obtain the cross-sectional

variation in the number of forced migrants at the village level

• But perhaps can exploit geography ...

Topographic characteristics

Land use

Weather

The next natural question …

• Are there any data sources available to implement this strategy?

• Fortunately, yes

Data

• For short-run impacts:

– Tanzania Demographic and Health Surveys (TDHS)

– Pool of cross-sections (1992, 96), used same sample frame

• For long-run impacts:

– Kagera Health and Development Survey (KHDS)

Both datasets merged with their own GIS modules (restricted access) and 25 years of monthly rainfall data

Any evidence to support my IV story?

• Anecdotal evidence

– Case studies & reports– Personal talk with former Prime Minister of TZ

• Some quantitative evidence

– 5th round of KHDS asked community leaders to assess the intensity of the influx of refugees in their villages (in a scale from 1 to 4)

Districts of Kagera Region by the Intensity of the Influx of Refugees

Indicator Ngara Karagwe Biharamulo Muleba Bukoba Rural

Bukoba Urban

Refugee influx intensity 83.3% 60.0% 20.0% 14.3% 5.9% 0.0%

High Treatment Regions Low Treatment Regions

Notes: The indicator of intensity is calculated using retrospective information from the fifth round of the Kagera Health and Development Survey (2004) and shows the percentage of communities that reported having refugees’ resettlements close by in the village or the ward after 1993.

Administrativedivisions

Intensity of the Influx of Refugees and Distance from the Village to the Border with Rwanda

0.5

1

1.5

2

2.5

3

3.5

4

4.5

0 20 40 60 80 100 120 140Distance to the border with Rwanda (kms)

Inde

x of

inte

nsity

of r

efug

ees

rese

ttlem

ents

Research design: Shot run analysis

For short run impacts (D-D)

• Strategy 1: use intra-regional variation, i.e. treatment intensity

– Specification A: binary treatment indicator, 1=if western, 0 otherwise

– Specification B: continuous treatment indicator, distance to theborder with Rwanda

• Strategy 2: use inter-regional variation (Kagera, Mara and Mwanza)

– Specifications A & B

Kagera (T), Mara & Mwanza (C)

Short run empirical analysis (I)

dtiidtitd RXY 43201 βτβδββα ++++= =

Empirical Specification

Y = outcome variable (e.g. nutrition indicators) X = set of pre-shock covariates (individual & household)

, = pre-shock village covariates, village and year fixed effects= treatment indicator (both binary and continuous)

5 6( ) ( )t d t d itd itdR R Zβ τ β τ ε+ ∗ + ∗ ∗ +

τδR

Short run empirical analysis (II)

Baseline comparison in key covariates

• No significant differences within Kagera

• Some minor differences with Mara & Mwanza– hh. size, age of household head, access to clean water and

prenatal care

OutcomeHigh treat areas

Low treat areas (i) (ii) (i) (ii) N

Nutritional Indicators

Weight-for-height z-score -0.446 -0.490 -0.542 ** -0.596 ** 0.0072 ** 0.0077 ** 457[1.107] [1.203] [0.222] [0.247] [0.0025] [0.0034]

Height-for-age z-score -1.666 -1.746 -0.522 -0.660 ** 0.0075 * 0.0094 ** 457[1.356] [1.391] [0.307] [0.302] [0.0043] [0.0041]

Morbidity

Children with diarrhea 0.036 0.046 0.135 0.183 * -0.0024 * -0.0028 ** 504[0.186] [0.211] [0.090] [0.093] [0.0014] [0.0012]

Children with fever 0.250 0.267 0.217 0.270 ** -0.0037 * -0.0040 * 504[0.434] [0.443] [0.125] [0.100] [0.0017] [0.0021]

Infant Mortality

Death in the first 5 years of life 0.084 0.163 0.083 0.108 * -0.0016 ** -0.0018 ** 647[0.279] [0.370] [0.061] [0.054] [0.0007] [0.0007]

Controls? No Yes No Yes

D-D: Distance to the borderPre-shock mean D-D: West=1, East=0

Sample: Villages in Kagera Region

Reduced form regressions

Strategy 1: Differential Short Run Local Impacts of the Influx of Refugees on Nutrition, Morbidity and Infant Mortality 

Outcome T C (i) (ii) (i) (ii) N

Nutritional Indicators

Weight-for-height z-score -0.474 -0.252 -0.291 * -0.333 * 0.0009 * 0.0011 ** 1,619 [1.165] [1.188] [0.170] [0.175] [0.0005] [0.0005]

Height-for-age z-score -1.715 -1.695 -0.304 * -0.303 * 0.0011 ** 0.0011 ** 1,619 [1.376] [1.258] [0.183] [0.184] [0.0005] [0.0005]

Morbidity

Children with diarrhea 0.042 0.100 0.151 *** 0.171 *** -0.0004 *** -0.0005 *** 1,796 [0.202] [0.300] [0.044] [0.047] [0.0001] [0.0001]

Children with fever 0.260 0.373 0.179 ** 0.201 *** -0.0005 *** -0.0006 *** 1,784 [0.439] [0.483] [0.074] [0.079] [0.0002] [0.0002]

Infant Mortality

Death in the first 5 years of life 0.136 0.128 0.047 0.076 * -0.0002 *** -0.0003 *** 2,286 [0.343] [0.334] [0.039] [0.041] [0.0001] [0.0001]

Controls? No Yes No Yes

Sample: Villages in Kagera, Mara and Mwanza regions

Pre-shock mean D-D: Kagera=1, Kagera=0

D-D: Distance to the border

Reduced form regressions

Strategy 2: Aggregated Short Run Impacts of the Influx of Refugees on Nutrition, Morbidity and Infant Mortality 

Results: Short run analysis

• A worsening of children’s anthropometrics ≈ 0.3 s.d.

• A increase of 15-18 percentage points in the incidence of infectious diseases

• An increase in mortality for children under five (≈ 7 percentage points

Distributional changes0

.1.2

.3.4

dens

ity

-5 -4 -3 -2 -1 0 1 2 3 4 5wh_zscore

treatment control

Pre- and post-shock distributions of the W/H z-score between T and C groups (within Kagera)

0.1

.2.3

.4de

nsity

-5 -4 -3 -2 -1 0 1 2 3 4 5wh_zscore

treatment control

Distributional changes

Pre- and post-shock distributions of the H/A z-score between T and C groups (within Kagera)

0.1

.2.3

.4de

nsity

-6 -4 -2 0 2 4 6ha_zscore

treatment control

0.1

.2.3

.4de

nsity

-6 -4 -2 0 2 4 6ha_zscore

treatment control

Research design: Long run analysis

For long run impacts (D-D & D-D-D)

• Strategy: use intra-regional and intra- and inter-cohort variation in exposure to the shock

– Exposed cohort: children 0-5 years old in 01/1994, 10-15 years in 2004

– Less exposed cohorts: children 0-5 years old in 01/1994 & Western=0,

– Less responsive cohorts: people 15-20 years old in 01/1994 both at Western=1 & Western=0.

– Specifications A & B

dtiidtitd RXY 4321 βτβδββα ++++=

Empirical Specification: D-D

Empirical Specification: D-D-D

5 6( ) ( )t d t d itd itdR R Zβ τ β τ ε+ ∗ + ∗ ∗ +

1 2 3 4 5 6( ) ( )ijt idt i t d i t i dY X R Rα β β λ β τ β β λ τ β λ= + + + + + ∗ + ∗

7 8( ) ( )t d t i d itdR Rβ τ β τ λ ε+ ∗ + ∗ ∗ +

Long run empirical analysis (I)

Outcome West=1 East=0 (i) (ii) (i) (ii) (i) (ii) (i) (ii)

Anthropometrics

Height (centimeters) 84.90 86.31 -1.943 -1.719 ** -1.777 * -2.184 0.0208 0.0196 ** 0.0280 ** 0.0289 **[1.030] [0.639] [2.068] [0.680] [1.051] [1.135] [0.0229] [0.0094] [0.0131] [0.0142]

Observations 1,861 1,785 1,159 1,078 1,861 1,785 1159 1078

Morbidity

Chronic disease 0.088 0.110 -0.022 -0.005 -0.066 -0.069 0.0003 0.0001 0.0006 0.0007[0.034] [0.042] [0.041] [0.037] [0.130] [0.147] [0.0005] [0.0006] [0.0017] [0.0019]

Observations 1,548 1,340 855 722 1,548 1,340 855 722

Education

School attainment (years) --- --- -0.179 -0.172 -0.272 -0.200 0.0040 * 0.0045 *** 0.0064 *** 0.0051 **[0.163] [0.120] [0.193] [0.204] [0.0022] [0.0014] [0.0023] [0.0025]

Observations 2,125 2,026 1,281 1,190 2,125 2,026 1281 1190

Does read/write? --- --- -0.057 -0.068 -0.075 -0.065 0.0011 0.0013 ** 0.0013 ** 0.0012 **[0.067] [0.048] [0.0467] 0.051 [0.0007] [0.0005] [0.0005] [0.0006]

Observations 2,127 2,028 1,284 1,193 2,127 2,028 1284 1193

Controls? No Yes No Yes No Yes No Yes

Reduced form regressions

D-D: West=1, East=0 D-D: Distance to the border

Pre-shock mean Pooled Panel Pooled Panel

Strategy 1: Long Run Impacts on Health and Human Capital Accumulation Exploiting Intra‐cohort Variation in Exposure to the Shock

Outcome West=1 East=0 (i) (ii) (i) (ii) (i) (ii) (i) (ii)

Anthropometrics

Height (centimeters) 84.90 86.31 -2.266 -1.435 -1.429 -1.878 0.0316 0.0176 0.0334 0.0337 *[1.030] [0.639] [1.939] [1.533] [1.493] [1.630] [0.0212] [0.0166] [0.0187] [0.0202]

Observations 3,362 3,179 2,325 2,070 3,362 3,179 2,325 2,070

Morbidity

Chronic disease 0.088 0.110 0.052 0.060 0.010 0.084 -0.0008 -0.0007 -0.0010 -0.0014[0.034] [0.042] [0.066] [0.070] [0.169] [0.198] [0.0008] [0.0009] [0.0021] [0.0025]

Observations 2,963 2,544 1,956 1,561 2,963 2,544 1,956 1,561

Education

School attainment (years) --- --- -0.277 -0.083 -0.437 -0.347 0.0054 0.0039 0.0076 * 0.0074 *[0.454] [0.429] [0.379] [0.372] [0.0052] [0.0050] [0.0046] [0.0041]

Observations 3,824 3,743 2,584 2,523 3,824 3,743 2,584 2,523

Does read/write? --- --- -0.119 -0.111 * -0.084 -0.070 0.0018 ** 0.00195 ** 0.0016 ** 0.0014 *0.073 [0.066] [0.060] [0.060] [0.0008] [0.0008] [0.0007] [0.0007]

Observations 3,914 3,688 2,639 2,564 3,914 3,688 2,639 2,564

Controls? No Yes No Yes No Yes No Yes

Reduced form regressions

D-D-D: West=1, East=0 D-D-D: Distance to the border

Pre-shock mean Pooled Panel Pooled Panel

Strategy 2: Long Run Impacts on Health and Human Capital Accumulation Exploiting Intra‐ and Inter‐cohort Variation in Exposure to the Shock

Results: Long run analysis

Childhood exposure to this massive arrival of refugees reduced:

• Height in early adulthood by 1.8 cm (1.2%)

• Schooling by 0.2 years (7.1%) and literacy by 7 percentage points (8.6%).

Robustness Analysis: Pre-trends?

Placebo Test: Pre‐shock D‐D Models on Nutrition, Morbidity and Infant Mortality, 1992‐1994

OutcomeHigh

treatment areas

Low treatment

areas(i) (ii) (i) (ii) N

Nutritional Indicators

Weight-for-height z-score -0.109 -0.270 -0.121 -0.165 0.0005 0.0019 1,067 [0.174] [0.069] [0.263] [0.173] [0.0031] [0.0020]

{0.644} {0.342} {0.850} {0.329}

Height-for-age z-score -2.201 -1.540 0.075 0.134 0.0000 -0.0013 1,069 [0.143] [0.090] [0.273] [0.130] [0.0033] 0.0015

{0.783} {0.307} {0.997} {0.393}

Morbidity

Child sick? 0.232 0.248 0.022 0.058 0.0000 -0.0004 1,115 [0.036] [0.020] [0.061] [0.043] [0.0007] [0.0005]

{0.717} {0.185} {0.960} {0.478}

Infant Mortality

Death in the first five years 0.124 0.101 0.009 0.020 0.0001 0.0000 1,740 of life [0.0242] [0.011] [0.030] [0.022] [0.0003] [0.0002]

{0.778} {0.365} {0.637} {0.896}

Controls? No Yes No Yes

D-D: Distance to the borderPre-shock mean D-D: West=1, East=0

Sample: Villages in Kagera Region

Reduced form regressions

Robustness Analysis: Misspecification Bias?

D‐D Matching Estimates of Short Run Impacts on Nutrition, Morbidity and Infant Mortality 

NN(10) E LL NN(10) E LLbw = 0.01 bw = 0.01 bw = 0.01 bw = 0.01 bw = 0.01 bw = 0.01

Nutritional Indicators

Weight-for-height z-score -0.704 *** -0.729 *** -0.735 *** 4,463 99.1% -0.332 *** -0.318 *** -0.349 *** 4,463 100%[0.227] [0.220] [0.255] [0.086] [0.083] [0.010]

Height-for-age z-score -0.453 ** -0.458 ** -0.455 ** 4,277 99.1% -0.165 -0.060 -0.072 4,277 100%[0.250] [0.242] [0.247] [0.134] [0.097] [0.075]

Morbidity

Children with diarrhea 0.283 ** 0.282 ** 0.288 ** 4,904 100% 0.178 ** 0.173 ** 0.171 ** 4,904 100%[0.130] [0.132] [0.142] [0.081] [0.079] [0.082]

Children with fever 0.334 *** 0.340 *** 0.340 *** 4,900 100% 0.177 *** 0.169 *** 0.165 *** 4,900 100%[0.120] [0.118] [0.121] [0.054] [0.053] [0.053]

Infant Mortality

Death in the first five years of life 0.076 * 0.076 ** 0.075 ** 5,139 100% 0.042 ** 0.049 ** 0.050 ** 5,139 100%[0.050] [0.046] [0.048] [0.028] [0.029] [0.028]

D-D: West of Kagera=1, Rest of Tanzania=0 D-D: Kagera=1, Rest of Tanzania=0

Type of Kernel a

N CSCS

Type of Kernel a

OutcomeN

Robustness Analysis: Endogenous Migration and Attrition?

Migration and Attrition

• For Tanzania DHS-1996:– 806 out of 837 (96.3%) resided in same hh. for more than 3

years

– No evidence of endogenous migration

• For the KHDS-1991-2004:– 96.5% of the 788 households (760) reinterviewed in 2004

– 529 out of 774 (68.4%) children 0-5 years reinterviewed in 2004

– No evidence of endogenous migration/attrition

Effects of Refugee Intensity on Household Migration and Attrition 

Dependent variable LPM Probit N

Migratory status after the shock 0.0002 0.0001 837(=1 if child in household that moved, 0 otherwise) [0.0005] [0.0001]

{ 0.624} { 0.570}

Dependent variable LPM Probit N

Attrition status after the shock 0.0005 0.0004 774(=1 if child in untracked household, 0 otherwise) [0.0006] [0.0007]

{0.466} {0.620}

Migratory status after the shock 0.0001 * 0.0012 ** 529(=1 if child in household that moved, 0 otherwise) [0.0006] [0.0006]

{0.096} [0.037]

Controls? Yes Yes

Sample: Tanzania DHS 1996

Sample: Tanzania KHDS 1994

Robustness Analysis: Other Issues

• Selection bias for survival– Depends how we model innate healthiness (fixed vs. variable)

• Measurement error in real level of exposure

• Endogenous response of national and foreign programs of humanitarian assistance

>> Lower bound estimates

Interpretation: Why negative effects?

• Strategy cannot disentangle different channels, but possible mechanisms …

• Threats posed to public health– Emergence and spread of existing infectious diseases by food,

water and environmental contaminating factors

– Multiplicative effects in the transmission/infection of malaria

• Overpopulation– Competition for resources (food, land, wood)

– Collapse of health and educational services

Conclusions

• Evidence of a previously undocumented indirect effect of civil wars on the health and human capital of children in hosting communities.

• Generalization of the results?– Not less than 15 civil wars in Sub-Saharan Africa + internally

displaced + refugees from political oppression (e.g. Zimbabwe)

• Persistence of the effects– Health shocks in childhood correlated with cognitive

development deficits and attained body size later in life – Effects on future productivity (2.8% fall in their earnings)

• Policy implications: global assistance in the scale required

“Civil Wars beyond their Borders: The Human Capital and Health Consequences

of Hosting Refugees”

Javier E. BaezDepartment of Economics

Maxwell School of Public Affairs - Syracuse University

2nd Annual Research ConferenceDecember 7-9, 2007Arusha, Tanzania