Estimating Internally Forced Displacement (IFD) Flows in Colombia using Mark and Recapture methods, 1996 – 2004

Soledad GranadaUniversidad del Rosario and CERAC

Mauricio Sadinle García-RuízUniversidad Nacional and CERAC

Jorge A. Restrepo Pontificia Universidad Javeriana and CERAC

September 2nd, 2007

Content

1. Difficulties with displacement flows in Colombia

2. Objectives

3. IFD according to the available information

4. Sources

5. Methods

6. Results

7. Further planned work

Caveats/Acknowledgement

• Work still in progress– We are waiting for a major third source of data

to become available and to integrate it in order to improve on the estimation quality

– We are testing further improvements to extract more information from the data

• Project developed thanks to the support of Foreign Affairs-CANADA (DFAIT)

Difficulties with displacement flows in Colombia

• Multiple complementary sources• Measurement:

– Government– Catholic Church’s Conference of Bishops– NGO Codhes– ICRC

• Surveys– Ibáñez (2006)– Household survey (13 cities)– Catholic Church’s Conference of Bishops

• Estimations– NGO Codhes

Difficulties with displacement flows in Colombia

• In general, these sources offer similar trends, different levels, but they have differential coverage, methods and purposes

• As a results, there is no information for humanitarian attention and planning

• Inefficiencies in humanitarian attention• Victims attention is also made mostly on a ad-

hoc basis, both by government and other assistance agencies

• Most attention is emergency-based and not long-term oriented

Objectives

• To estimate the level of IFD using several complementary sources, starting with two

• To estimate dynamics of IFD

• To provide a measure of the reliability of the estimates

• To provide a measure of coverage/undercount of sources

• To provide a “humanitarian” IFD risk map

IFD available information

Internally Forced Displaced Population (Expulsion Rates) recorded by SIPOD of Acción Social

0

100000

200000

300000

400000

500000

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

Source: Sistema de Informaciòn para Poblaciòn Desplazada - SIPOD - Agencia Presidencial para la Acciòn Social

Internally Forced Displaced Population (Expulsion Rates) recorded by RUT of Pastoral Social

0

10000

20000

30000

40000

50000

60000

70000

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

Source: RUT from Pastoral

Sources

Secretariado Nacional de Pastoral Social –RUT-

• The RUT database has 242.565 records, between1996 y 2004; after cleaning it up we had 236.795 usable records, only 2.37% had incomplete fields.

• Several variables, including, victim information, family relatives, and regarding the displacement event.

• Voluntary, not compulsory, survey at humanitarian attention and Church attention posts.

• Low coverage, self-selected, good coverage in isolated areas

• Potential problematic biases

Acción Social –SUR-

• Acción Social database holds 2.278.978 records covering the 1995 2006 period.

• Several variables including family, individual, and event of displacement-related information.

• Low “quality”. After cleaning it up, only 1.616.743 records were being able to be used.

• Compulsory registry to obtain humanitarian emergency aid from the government and to be able to apply for further assistance

• Self-selected

• Large coverage, mostly on urban areas and large cities

Methods

• We use RUT and AS for the 1996-2004 period to estimate a level of displacement at day-municipality pairs, using Capture-recapture (C-R) census correction models. (Pollock, 2002, JASA). (Also known as mark-and recapture, by Wikipedia)

• C-R permits an estimation of population levels based on incomplete samples of it by different overlapping sources

• It assumes :– Two samples (at record level)– Equal probability of inclusion for every individual in every sample– Independency between samples– It is possible to identify the intersection– Population is closed

Methods

• Under those assumptions, estimation is unbiased (consistent) and efficient.

• Applications in natural sciences realms are countless

• In social sciences, Ball et al have been a pioneer of using these techniques to estimate the level of HR violation in civil war contexts (Guatemala, Perú, Timor Leste) (see Ball et al, 2002).

Methods

Estimators used

• Lincoln-Petersen:

• Chapman:

m

nnN 21ˆ

11

)1)(1(ˆ 21

m

nnN

1n

2n

m

Records at SUR

Records at RUT

Recaptured

Estimators

• Chapman (1951) improves on Lincoln-Petersen

• Chapman is unbiased if (Wittes, 1972):

• Chapman (1951) show its estimator has negative bias if:

Nnn 21

Nnn 21

• The most problematic of all assumptions (in terms of the quality of the estimate) is the equi-probability of capture across individuals

• Sekar and Deming (1949) propose a stratified estimation procedure in order to account for the effect of differential probability of capture. The procedure will yield thus an estimate:

• It assumes differential probability of capture across strata, same probability within strata, independence between samples and close population.

• This way the (upward) bias created falls dramatically

• The quality of stratification determines the quality of the estimate

iNN ˆˆ

Methods

• We use then both Lincoln Petersen and Chapman (preferred) stratified estimators

• When m=0,

• This is, we use the simple sum of the two observed samples.

iii nnN 21

ˆ

• There are several applications that take the strata as defined exogenously (communities, regions, etc.)

• We want to obtain k statistically different groups of records that will have a statistically similar probability of being included in the group:

• We test the null hypothesis:

Against

Where

How to define the strata?

jjjjjj nmpnmp 1221 ˆ,ˆ

ekee pppH ˆˆˆ: 210

jippH ejei somefor ˆˆ:1

2,1e

How to define the strata?

• We test using:

• That distributes

k

i i

ii

n

nmQ

1 00

20

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)(

k

i i

k

i i

n

m

1

10

21 kQ

Procedure for stratification

• We test for stratification by municipality (admin unit) of arrival, and day of arrival

• We further jointly test for strata by day and municipality of arrival, creating strata for each day and municipality pairs.

• We further test within each municipality in consecutive days, in order to discard over-stratification within municipality, that will lead to an upward bias.

• (We do not do this by municipality as these are more “natural” strata).

• The rejection level was set at 5% for all tests

Matching criteria

• We define the matching criteria according to date of expulsion and arrival, place of expulsion and arrival and gender. Other more strict applications offered no substantial estimated difference.

• We create an indicator for each record and compare them across sources using a computer algorithm

• We do not have access to more specific characteristics (name, id) of the individual that will help us to improve the matching

Confidence: Bootstrapping methods

• In order to obtain confidence intervals, and given that we do not know the theoretical distribution of the estimator, we use bootstrapping methods in order to approach the distribution of the estimator. We follow Buckland y Garthwaite (1991).

• We perform 10000 replications of sub-samples with replacement in order to obtain CI and an estimate of the variance of the estimator.

• We perform a correction of the bias of the estimator according to the results of the bootstrap obtaining:

NNR ˆ*ˆˆ RNN B

Results

• The following are the point estimates bias-corrected and uncorrected Lincoln-Petersen and Chapman estimates.

• In graphs and maps we present the Chapman estimate

Estimador Estimación Puntual Estimación Corregida* Intervalo de confianzaChapman 2.440.207 2.504.760 2.307.363 - 2.447.239Lincoln-Petersen 2.732.908 2.843.858 2.495.533 - 2.761.800

n1 (Acción Social) 1.616.743

*Estimación corregida por el sesgo estimado bootstrap n2 (RUT) 236.795

m (Intersección) 28.888

2250000 2300000 2350000 2400000 2450000 2500000 2550000

0e

+0

02

e-0

64

e-0

66

e-0

68

e-0

61

e-0

5

Distribución Estimada

ChapmanEstimación

De

nsi

da

d

0

500000

1000000

1500000

2000000

2500000

3000000

1996 1997 1998 1999 2000 2001 2002 2003 2004

Número de Desplazados Estimado, SUR, RUT e Intersección Acumulados

SUR RUT Estimación Corregida Intersección Per 97,5 Per 2,5 EstimaciónFuente: Conferencia Episcopal (RUT) y Acción Social (SUR).Procesado por: CERAC.

0

100000

200000

300000

400000

500000

600000

700000

800000

1996 1997 1998 1999 2000 2001 2002 2003 2004

Número de Desplazados Estimado, SUR, RUT e Intersección

SUR RUT Estimación Corregida Intersección Per 97,5 Per 2,5 EstimaciónFuente: Conferencia Episcopal (RUT) y Acción Social (SUR).Procesado por: CERAC.

Departmental Results

Estimaciones 1996-1999 2000-2002 2003-2004 Total generalAntioquia 38.422 144.367 53.672 236.461Atlántico 5.137 58.295 23.254 86.686Bogotá D.C 16.566 90.136 242.996 349.697Bolívar 35.495 151.062 21.479 208.036Boyacá 228 4.825 3.375 8.428Caldas 211 30.707 10.921 41.839Caquetá 6.066 71.686 80.031 157.783Cauca 145 25.727 10.391 36.263Cesar 3.938 69.803 34.987 108.729Córdoba 13.874 56.784 9.777 80.435Cundinamarca 1.916 24.178 18.475 44.569Chocó 16.174 101.594 7.017 124.786Huila 2.630 29.341 24.345 56.316La Guajira 703 20.073 16.222 36.998Magdalena 4.319 59.918 19.723 83.959Meta 13.957 28.549 22.555 65.061Nariño 1.996 66.058 31.537 99.590Norte de Santander 6.735 40.712 20.228 67.675Quindio 173 7.826 6.438 14.437Risaralda 753 30.369 13.001 44.123Santander 8.263 47.647 15.703 71.613Sucre 8.890 78.906 18.230 106.025Tolima 2.804 71.211 20.868 94.883Valle del Cauca 3.280 78.717 42.681 124.678Arauca 609 4.626 5.806 11.041Casanare 902 6.780 4.774 12.456Putumayo 316 27.758 16.386 44.460San Andrés y Providencia 11 22 13 46Amazonas 1 225 487 713Guainía 6 348 856 1.210Guaviare 681 11.220 7.043 18.944Vaupés 17 20 698 735Vichada 81 804 649 1.534Total general 195.299 1.440.294 804.614 2.440.207Fuente: SUR y RUT Estimaciones: CERAC

Recorded by Secretariado Nacional de Pastoral Social

– RUT -

Recorded by

Acción Social SUR

Recaptured

Estimated Arrival IFD

1996 – 2004

Recorded Exit IFD by the two systems (Projected)

Probability of being displaced by municipality

(exit)

Estimated arrival IFD population per 100.000

inhabitants

Comparison with other displacement figures

o CODHES: o Estimation (Jan 1985 - June 2006): 3.832.527*

o RUT: o Estimation (1985 – 1994): 720.000o Records (Jan 1995 - June 2004): 237.614

o SUR: o Records (Jan 1995 - June 2005): 1.877.328

o CEDE:o Estimation (Jan 1995 - June 2005): 2.576.610

o CERAC: o Estimation (1996 - 2004): 2.440.207

* Incluye cifra Conferencia Episcopal periodo 1985 – 1994.

Further Planned Work

• We just received data until mid-2007 and will consequently update the estimation

• We are devising a method to improve the estimation in order to incorporate the large number of un-matchable observations using a window-based recursive procedure with those observations.

• We are researching the properties of the estimator using Monte Carlo methods.