FIGHTING AGAINST MALARIA: PREVENT WARS WHILE ... - Ivie

FIGHTING AGAINST MALARIA:
PREVENT WARS WHILE WAITING FOR THE
“MIRACULOUS” VACCINE *
José García Montalvo and Marta Reynal-Querol **
WP-EC 2002-31
Correspondence to: José García Montalvo, Departament d'Economia, Universitat Pompeu
Fabra, Calle Ramón Trias Fargas, 25-27, 08005 BARCELONA, Spain, Tel. +34 93 542 25 09,
e-mail: jose.garcia-montalvo@econ.upf.es.
Editor: Instituto Valenciano de Investigaciones Económicas, S.A.
Primera Edición Noviembre 2002
Depósito Legal: V-4549-2002
IVIE working papers offer in advance the results of economic research under way in order to
encourage a discussion process before sending them to scientific journals for their final
publication.
* We thank J. Caballé and M. Reiter and participants in the European Economic Association Meetings
(Venice 2002) for many useful comments. The authors are greatful for financial support from the IVIE.
** J. García-Montalvo: Ivie and Universitat Pompeu Fabra; M. Reynal-Querol: Instituto de Análisis
Económico (IAE), CSIC, Barcelona.

FIGHTING AGAINST MALARIA:
PREVENT WARS WHILE WAITING FOR THE “MIRACULOUS” VACCINE
José García Montalvo and Marta Reynal-Querol
ABSTRACT
The World Health Organization estimates that 300 million clinical cases of
malaria occur annually and its incidence increased during the 90’s. There are basically
two factors behind the incidence of malaria: “geographical destiny”, or ecological
conditions, and social conditions, which are related with unstable populations or
movements of people. In this paper we explore the influence of civil wars and refugees
from wars on the incidence of malaria in the asylum countries using a large panel data.
The panel structure helps to separate “geographical destiny” from other social
conditions. The results of the estimation show the importance of civil wars’ refugees on
the increase of malaria during recent years.
Keywords: Malaria, migrations, ecological conditions.
RESUMEN
La Organización Mundial de la Salud estima que cada año se producen 300
millones de nuevos casos de malaria y su incidencia ha aumentado durante los años 90.
Hay básicamente dos factores explicativos de la incidencia de la malaria: “destino
geográfico”, o condiciones ecológicas, y el contacto social, que está relacionado con
movimientos de la población. En este trabajo estudiamos la influencia de las guerras
civiles y los refugiados generados por dichas guerras sobre la incidencia de la malaria en
los países de asilo usando datos de panel. Esta estructura ayuda a separar el “destino
geográfico” de las condiciones sociales. Los resultados de la estimación muestran la
importancia de los refugiados de guerras civiles sobre el incremento de la incidencia de
la malaria.
Palabras clave: malaria, migraciones, condiciones ecológicas.
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Table 1. Determinants of the flow of refugees: Fixed effects model.
Dependent variable: flow of refugees
Model 1 2 3 4 5 6
LNGDP 0.0000
(0.36)
0.0002
(1.59)
0.0002
(1.45)
0.0002
(1.44)
0.0002
(1.49)
0.0002
(1.54)
CW 0.004
(5.75)
0.104
(20.53)
0.1211
(23.27)
0.1274
(23.80)
0.1330
(24.53)
0.1389
(24.39)
CW*STLIV -0.0012
(-20.14)
-0.0012
(-21.65)
-0.0013
(-22.04)
-0.0014
(-22.78)
0.0015
(-2255)
STLIV 0.00002
(0.92)
0.0000
(0.51)
0.0000
(0.76)
0.000
(1.15)
0.000
(1.66)
CW*URB -0.0004
(-9.36)
-0.0003
(-5.97)
-0.0003
(-5.18)
-0.0002
(-4.54)
URB 0.00005
(1.04)
0.00005
(1.08)
0.00006
(1.24)
0.000
(1.02)
CW*IRRIG -0.1733
(-5.50)
-0.1698
(-5.35)
-0.1622
(-4.90)
IRRIG -0.025
(-0.80)
-0.0252
(-0.81)
-0.024
(-0.75)
URBDEATH 5.810
(1.75)
7.27
(2.10)
OPEN 0.000
(0.91)
Drought -0.002
(-0.70)
NATDIS
Constant 0.00009
(0.09)
-0.0038
(-1.51)
-0.0043
(-1.47)
-0.0042
(-1.34)
-0.0054
(-1.71)
Yes
-0.007
(-2.03)
R-squared 0.0123 0.1107 0.2073 0.2250 0.2191 0.2187
N 3366 1136 1136 1075 1066 997
Numbers in parentheses are t-statistics

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Iru d klvwrulfdo dqg jhrjudsklfdo shuvshfwlyh vhh Kdprxgl dqg Vdfkv +4

Figure 1. Cases of malaria and civil wars.
Source: WHO Weekly Epidemiological Record (1999) and Doyle and Sambanis (2000).
35
45000000
30
40000000
35000000
25
30000000
Civil wars
20
15
10
5
0
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
Number of civil wars Total Without India and China
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
25000000
20000000
15000000
10000000
5000000
0
Cases of malaria

Figura 2: Number of African countries reporting and number of cases.
Source: WHO Weekly Epidemiological Record (1999).
50
40000000
45
35000000
40
30000000
35
30
25000000
25
20000000
20
15000000
15
10
5
0
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
Number of reporting countries
1980
1981
1982
1983
1984
1985
1986
Cases without China and India
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
10000000
5000000
0

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Vhh Eorrp dqg Vdfkv +4

Figure 3. Refugees and cases of malaria.
Sources: UNHCR and Weekly Epidemiological Record (1999).
45000000
16000000
40000000
14000000
Interpolated number of cases (without China and India)
35000000
30000000
25000000
20000000
15000000
10000000
5000000
0
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
Cases of malaria
1981
1982
1983
Refugees
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
12000000
10000000
8000000
6000000
4000000
2000000
0
Refugees

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Table 2A: Regression results: All refugees (all asylum countries)
Dependent variable: malaria incidence
Model 1 2 3 4 5 6
REF 0.055 0.189 0.052 0.023 0.019 0.016
(1.37) (2.34) (1.23)
RURAL -100.91 -224.33 -142.86
(-7.31) (-3.72) (-8.84)
PHYS -202.49
(-2.55)
IPHYS -258.24
(-4.96)
(0.51)
-144.09
(-8.25)
(0.44)
-146.21
(-8.34)
(0.36)
-162.25
(-8.65)
-317.40
(-5.43)
-317.67
(-5.43)
-321.33
(-5.25)
IRRIG 0.040
(4.19)
0.040
(4.27)
0.038
(3.94)
DR Yes Yes
CW Yes Yes
MCID
Yes*
R-sq 0.0587 0.0351 0.0494 0.1154 0.1200 0.1242
N 3214 789 3194 2863 2863 2722
The explanatory variables include time dummies.
Numbers in parentheses are t-statistics.
*significant
Table 2B: Regression results: All Refugees (only asylum tropical countries)
Dependent variable: malaria incidence
Model 1 2 3 4 5 6
REF 0.887 1.448 0.865 0.871 0.875 0.865
(6.71) (7.18) (6.52)
RURAL -123.45 -121.54 -156.13
(-7.18) (-3.35) (-7.83)
PHYS -142.53
(-1.41)
IPHYS -233.43
(-3.28)
(6.06)
-161.03
(-7.39)
(6.08)
-163.22
(-7.46)
(5.90)
-175.58
(-7.58)
-249.08
(-3.06)
-246.79
(-3.03)
-265.29
(-3.15)
IRRIG 0.004
(0.08)
-0.003
(-0.06)
-0.008
(-0.15)
DR Yes Yes
CW Yes Yes
MCID
Yes
R-sq 0.0936 0.1518 0.1221 0.1280 0.1296 0.1269
N 2295 539 2275 1991 1991 1919
The explanatory variables include time dummies.
Numbers in parentheses are t-statistics

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UL?t|h@? |i t@4T*i |L |i @t)*4 |hLTU@* UL?|hitc |@M*i ec |i |L|@* ?4Mih Lu

Table3A: Regression results: Refugees from tropical countries (all asylum countries)
Dependent variable: malaria incidence
Model 1 2 3 4 5 6
REF -0.042
(-0.97)
-0.045
(-0.57)
-0.045
(-1.04)
-0.071
(-1.53)
-0.076
(-1.63)
-0.078
(-1.63)
REF1 1.176 2.354 1.147 1.146 1.159 1.146
(8.03) (9.48) (7.82)
RURAL -87.56 -83.26 -127.06
(-2.87) (-7.88)
PHYS -168.19
(-2.25)
IPHYS -241.25
(-4.68)
(7.34)
-128.12
(-7.35)
(7.41)
-130.67
(-7.47)
(7.15)
-145.64
(-7.77)
-297.26
(-5.12)
297.15
(-5.12)
-299.93
(-4.94)
IRRIG 0.038
(4.09)
0.039
(4.17)
0.037
(3.86)
DR Yes Yes
CW Yes Yes
MCID
Yes*
R-sq 0.1060 0.1646 0.0808 0.1485 0.1533 0.1584
N 3214 789 3194 2863 2863 2722
The explanatory variables include time dummies.
Numbers in parentheses are t-statistics
*significant
Table 3B: Regression results: Refugees from tropical countries (only asylum
tropical countries)
Dependent variable: malaria incidence
Model 1 2 3 4 5 6
REF -0.071
(-0.29)
-0.052
(-0.16)
-0.069
(-0.28)
-0.067
(-0.26)
-0.058
(-0.22)
-0.060
(-0.22)
REF1 1.334 2.372 1.303 1.317 1.312 1.299
(4.57) (5.82) (4.46)
RURAL -117.80 -101.31 -149.17
(-6.87) (-2.88) (-7.53)
PHYS -115.20
(-1.17)
IPHYS -223.11
(-3.15)
(4.20)
-153.65
(-7.06)
(4.18)
-155.85
(-7.13)
(4.06)
-168.00
(-7.26)
-236.32
(-2.91)
-234.11
(-2.89)
-249.94
(-2.97)
IRRIG 0.004
(0.07)
-0.003
(-0.06)
-0.008
(-0.14)
DR Yes Yes
CW Yes Yes
MCID
Yes
R-sq 0.1109 0.2139 0.1353 0.1406 0.1427 0.1412
N 2295 539 2275 1991 1991 1919
The explanatory variables include time dummies.
Numbers in parentheses are t-statistics

Table 4A: Regression results: Refugees from civil war (all asylum countries)
Dependent variable: malaria incidence
Model 1 2 3 4 5 6
REF -0.212
(-1.05)
-0.151
(-0.56)
-0.229
(-1.14)
-0.255
(-1.20)
-0.255
(-1.19)
-0.264
(-1.21)
REF2 0.279 0.368 0.290 0.287 0.283 0.289
(1.37) (1.31) (1.43)
RURAL -101.03 -113.65 -143.12
(-7.32) (-3.70) (-8.86)
PHYS -202.92
(-2.56)
IPHYS -259.07
(-4.98)
(1.34)
-144.36
(-8.27)
(1.32)
-146.43
(-8.35)
(1.32)
-162.52
(-8.66)
-318.23
(-5.44)
-318.51
(-5.44)
-322.10
(-5.26)
IRRIG 0.039
(4.18)
0.040
(4.25)
0.038
(3.92)
DR Yes Yes
CW Yes Yes
MCID
Yes*
R-sq 0.0581 0.0352 0.0493 0.1147 0.1193 0.1235
N 3214 789 3194 2863 2863 2722
The explanatory variables include time dummies.
Numbers in parentheses are t-statistics
*significant
Table 4B: : Regression results: Refugees from civil war (only asylum tropical countries)
Dependent variable: malaria incidence
Model 1 2 3 4 5 6
REF 0.182
(0.82)
0.192
(0.72)
0.158
(0.71)
0.157
(0.66)
0.156
(0.65)
0.150
(0.61)
REF2 0.938 2.283 0.940 0.954 0.960 0.955
(3.89) (6.68) (3.90)
RURAL -120.74 -102.83 -153.54
(-7.04) (-2.96) (-7.77)
PHYS -150.17
(-1.55)
IPHYS -234.48
(-3.31)
(3.68)
-158.30
(-7.28)
(3.71)
-160.46
(-7.35)
(3.62)
-172.55
(-7.47)
-250.12
(-3.08)
-247.84
(-3.05)
-266.23
(-3.17)
IRRIG 0.004
(0.08)
-0.003
(-0.07)
-0.008
(-0.16)
DR Yes Yes
CW Yes Yes
MCID
Yes
R-sq 0.1093 0.2402 0.1362 0.1407 0.1420 0.1396
N 2294 539 2275 1991 1991 1919
The explanatory variables include time dummies.
Numbers in parentheses are t-statistics

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Table5A: Regression results: Refugees from civil war of tropical countries
(all asylum countries)
Dependent variable: malaria incidence
Model 1 2 3 4 5 6
REF -0.034
(-0.80)
-0.015
(-0.21)
-0.038
(-0.88)
-0.063
(-1.38)
-0.068
(-1.48)
-0.070
(-1.49)
REF3 1.408 2.997 1.378 1.382 1.391 1.378
(9.33) (11.52) (9.12)
RURAL -86.46 -74.31 -125.44
(-6.31) (-2.63) (-7.81)
PHYS -144.59
(-1.99)
IPHYS -238.24
(-4.64)
(8.59)
-126.41
(-7.28)
(8.64)
-128.91
(-7.40)
(8.35)
-143.69
(-7.70)
-293.46
(-5.08)
-293.60
(-5.08)
-296.14
(-4.90)
IRRIG 0.038
(4.04)
0.039
(4.14)
0.037
(3.83)
DR Yes Yes
CW Yes Yes
MCID
Yes*
R-sq 0.1234 0.2036 0.0916 0.1587 0.1634 0.1691
N 3214 789 3194 2863 2863 2722
The explanatory variables include time dummies.
Numbers in parentheses are t-statistics
*significant
Table 5B: Regression results: Refugees from civil war of tropical countries
(only asylum tropical countries)
Dependent variable: malaria incidence
Model 1 2 3 4 5 6
REF 0.113
(0.63)
0.182
(0.77)
0.104
(0.58)
0.106
(0.55)
0.105
(0.55)
0.101
(0.51)
REF3 1.408 2.888 1.387 1.410 1.417 1.406
(6.29) (8.59) (6.18)
RURAL -116.12 -90.30 -147.08
(-6.80) (-2.66) (-7.46)
PHYS -99.92
(-1.06)
IPHYS -220.32
(-3.12)
(5.84)
-151.26
(-6.98)
(5.86)
-153.32
(-7.04)
(5.71)
-165.12
(-7.17)
-234.43
(-2.90)
-232.15
(-2.87)
-248.22
(-2.97)
IRRIG 0.006
(0.11)
-0.002
(-0.04)
-0.007
(-0.13)
DR Yes Yes
CW Yes Yes
MCID
Yes
R-sq 0.1330 0.2768 0.1544 0.1578 0.1594 0.1584
N 2295 539 2275 1991 1991 1919
The explanatory variables include time dummies.
Numbers in parentheses are t-statistics

Table 6A: Regression results: Refugees from civil war of tropical countries
(all asylum countries). Per capita fixed effects and glogit
Dependent variable: malaria incidence per capita
Model 1 2 3 4 5 6
CapREF -0.0189
(-0.47)
-0.0027
(-0.04)
-0.036
(-0.89)
-0.037
(-0.70)
-0.0383
(-0.71)
-0.031
(-0.58)
CapREF3 0.2300 0.9788 0.2196 0.239 0.2379 0.2267
(2.75) (5.61) (2.63)
RURAL 0.0008 0.0004 0.0006
(4.38) (1.43) (3.46)
PHYS -0.014
(-2.26)
IPHYS -0.0265
(-6.04)
(2.75)
0.0005
(3.10)
(2.73)
0.0005
(3.11)
(2.66)
0.0005
(3.06)
-0.0258
(-5.71)
-0.026
(-5.74)
-0.026
(-5.87)
CapIRRIG 0.0498
(0.65)
0.0453
(0.59)
0.1314
(1.75)
DR Yes Yes
CW Yes Yes
MCID 0.0017
(0.47)
Year Yes Yes Yes Yes Yes Yes
Country
R-squared 0.1155 0.1236 0.1206 0.1110 0.1161 0.1002
N 3214 789 3194 2863 2863 2722
Numbers in parentheses are t-statistics. *significant
Table 6B: Regression results: Refugees from civil war of tropical countries
(only asylum tropical countries). Per capita fixed effects
Dependent variable: malaria incidence per capita
Model 1 2 3 4 5 6
CapREF -0.0147
(-0.31)
0.0137
(0.16)
-0.037
(-0.77)
-0.0436
(-0.66)
-0.044
(-0.67)
-0.044
(-0.65)
CapREF3 0.2049 0.941 0.1979 0.2261 0.2241 0.223
(2.15) (4.48) (2.08)
RURAL 0.001 0.000 0.0006
(3.76) (0.96) (2.86))
PHYS -0.022
(-2.24)
IPHYS -0.039
(-6.42)
(2.24)
0.0005
(2.45)
(2.22)
0.0005
(2.48)
(2.16)
0.0005
(2.11)
-0.037
(-6.07)
-0.0377
(-6.09)
-0.0383
(-5.94)
CapIRRIG 0.214
(1.64)
0.1958
(1.48)
0.199
(1.47)
DR Yes Yes
CW Yes Yes
MCID 0.00013
(0.03)
Year Yes Yes Yes Yes Yes Yes
Country
R-squared 0.1331 0.1681 0.1558 0.1313 0.1416 0.1411
N 2295 539 2275 1991 1991 1919
Numbers in parentheses are t-statistics

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Table 7: Analysis of Robustness.
Dependent variable: Incidence of Malaria
Model All countries Only tropical
countries
Without Africa,
China, India
REF 0.111
(0.50)
0.218
(0.89)
0.262
(0.50)
REF3 1.414
(8.29)
1.623
(4.48)
0.340
(4.03)
REF2 -0.189
(-0.84)
-0.314
(-0.81)
-0.055
(-1.05)
RURAL -143.01
(-7.66)
-164.51
(-7.14)
6.23
(-1.65)
IPHYS -294.96
(-4.87)
-245.28
(-2.93)
9.38
(1.02)
IRRIG 0.037
(3.83)
-0.006
(-0.12)
0.025
(5.85)
DR Yes Yes Yes
CW Yes Yes Yes*
MCID Yes* Yes Yes
R-sq 0.1694 0.1588 0.1724
N 2722 1919 1643
Numbers in parentheses are t-statistics
*significant

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