Determinants of Internal Migration Flows in Thailand
Assoc.Prof. Montree Piriyakul
Department of Statistics, Ramkhamheang University, Bangkok, Thailand
2006
Abstract
The study is aimed to illustrate two aspects of internal migration in Thailand:
(i) spatial focused location characteristics and classification of location choices on
migra-tion decision and (ii) the determinants of inter-provincial migration, inter-regional
migra- tion and spatial focused migration flows
In this study, the analysis of spatial concentrated location is based on migration
effectiveness statistics while the statistical processing of migration stream is based on
multiple regression analysis and ridge regression analysis with Box-Cox transformation.
The main findings have been reported in location specific concentration characteristics and
factors affecting population movements.
Research questions
There are 73 provinces in Thailand in 1990 census year and became 76 provinces
in 10 years later. There were 5% to 10% of us migrate internally each year. The questions
frequently asked are:
1. Where are the main streams of migrants come from? , and, where are the specific
destinations?
2. What are the determinants of the migration, both in general stream and main
streams?
In order to illustrate the spatial focused location characteristics and classification
of location choices on migration decision and to examine the determinants of provincial
migration flows as asked, data were collected from several official sources but mainly
retrieved from 1990 and 2000 Thailand census data collected by the National Statistics
Office of Thailand (NSO). Data manipulation was organized by using origin-destination
algorithm.
Methodology
Dependent variables are number of migrants moved into destination areas and
from original areas. These figures appeared in array form and need to be rearranged in
columnar fashion, i.e., they must be organized column wised for in-migration and row
wised for out migration. Independents variables listed below formulated primarily from
gravity model (Andrienko & Guriev, 2003; Brown, 1997; Goetz, 1999; Mare & Timmins,
2000; Vermeulen, 2003) i.e. DIST, POPi and POPj , and the others are added systematically from those found in relevance migration theories and contemporary migration
models as shown in the table 1.
Table 1 Definition of independent variables with their references and sources of data
independent
variables
DIST
BRDR
definition and reference
source
distance between provinces
(Andrienko & Guriev, 2003; Brown, 1997; Goetz,
1999; Mare & Timmins, 2000; Vermeulen, 2003)
border between provinces 1=contagious 0 = otherwise
(Wall, 2001)
Dept. of Land Transport
map of Thailand
Table 1 (continue)
independent
variables
definition and reference
source
population size of original provinces
(Andrienko & Guriev, 2003; Brown, 1997; Goetz,
1999; Mare & Timmins, 2000; Vermeulen, 2003)
population size of destination provinces
(Andrienko & Guriev, 2003; Brown, 1997; Goetz, 1999;
Mare & Timmins, 2000; Vermeulen, 2003)
previous migration at destination provinces
(Cushing, 2002 a; Preush, 1999; Rebhun, 2003)
percentage of youth age 15-24 yr. at original
provinces (Mare & Timmins, 2000; Vermeulen, 2003)
NSO
P35T34
percentage of youth age 25-34 yr. at original provinces
(Rosenbluth, 1996; Mare & Timmins, 2000)
NSO
PVTY
percentage of people earned under poverty line
(Andrienko & Guriev, 2003; De Jong & Graefe, 2002;
Skeldon, 2003; De Haan, 2000)
NESDB
INC
GPP per capita
(Cebula & Belton, 1994; Rosenbluth, 1996;
NSO
POPi
POPj
PMGT
P15T24
COL
URBAN
AREAi
AREAj
HOUSE
EMP
LFDT
NJB
EDUC
DOC
BED
ACRM
ROADDENS
NSO
NSO
NSO
Preuhs, 1999; Gurak & Kritz, 2000)
cost of living (using population density as proxy)
NSO
(Blanciforti & Kranner, 1997;Giannetti, 2001; Wall, 2001)
number of people in municipal area
NSO
(Andrienko & Guriev, 2003; Dang et al., 1996)
area in sq. km. of original provinces
NSO
(Mare & Timmins, 2000)
area in sq. km. of destination provinces
(Mare & Timmins, 2000)
percentage of private house owned
(Beenstock, 1997; Brown, 1997)
NSO
employment rate
(Cushing, 2002b)
percentage of labor force in non-agriculture industry
(Dang, et al., 1996;Gurak & Kritz, 2000)
NSO
NSO
NSO
number of new job created
Dept. of Employment
(Breakley & Fuhrer, 1997;Brown, 1997; Shield, 1999)
average number of years in school
NSO
(Brown, 1997; Kritz, 2000; Kerr, Mare, Power & Timmins
, 2001; Andrienko & Guriev, 2003)
doctor population ratio
Dept. of Medical Services
(Andrienko & Guriev, 2003)
bed population ratio
Dept. of Medical Services
(Andrienko & Guriev, 2003)
arrested crime rate
Royal Thai Police
(Andrienko & Guriev, 2003)
road density (road length per sq. km.)
Geo-Informatic System
(Andrienko & Guriev, 2003)
NSO is National Statistics Office of Thailand
NESDB is National Economic and Social Development Board of Thailand
Statistical tools used are multiple regression and ridge regression Hoerl & Kennard,
1970) with Box-Cox transformation for overcoming multicollinearity and non-normality
difficulty (Judge, Griffiths, Hill & Lee, 1980). Chow test (Chow, 1960).was employed
for testing the stability of models across two census years and between provincial models
and spatial focused models. Several statistical methodologies - Goldfeld-Quandt test,
Durbin-Watson and Score test (Ravishanker & Tsai, 2002)., VIF and correlation analysis,
P-P plot (Judge, Griffiths, Hill & Lee, 1980) - were analyzed in order to determine the
appropriateness of regression assumptions. And, for classifying the locations specific of
migrants and for knowing how balance the population movement in Thailand is, the
migration effectiveness coefficient (r) and migration efficiency index (MEI) that have
been using by U.S. Census Bureau were calculated accordingly (Stillwell, Bell, Blake,
Duke-Williams, & Rees, 2000; Perry, 2003)
Findings
1. It was found that Thailand has MEI of 25% and average r of about -7%, these
figures explained that there was rather strong imbalanced population redistribution.
2. Using r ≤ 25%, there are 14 and 24 sending provinces in year 1990 and 2000
respectively.
3. Using r  25%, there are 7 and 8 receiving provinces in year 1990 and 2000
respectively.
Figure1. Spatial focused out migration provinces (yellow and green area) and spatial
focused in-migration provinces (pink area) in Thailand for census year 1990.
4. Using chloropleth mapping technique as in GIS between areas in 2. and 3.
above, it was found that in 1990 large migration flows flooded into Bangkok vicinity;
Bangkok-SamutPrakan-PathumThani region; depicted in figure 1 while in 2000, also
moved mainly into Bangkok vicinity but excluded Bangkok itself; SamutPrakanSamutSakon-Chonburi-PathumThani; as shown in figure 2.
Figure 2. Spatial focused out migration provinces (yellow and green area) and spatial
focused in-migration provinces (pink area) in Thailand for census year 2000.
5. Key determinant of migration in Thailand (see table 2 and table 3) include
distance, boundary, population size, previous migration size, and the proportion of
working-age population, specifically the youth. Other factors that also affecting current
migration include personal income, cost of living (using population density as its proxy
variable), poverty, urbanization, area size, employment rates, proportion of labor forces
in industrial sector, new job availability, and the government’s health and social services
and facilities i.e. education, doctor-population ratio, bed-population ratio, arrested
crime rate and road density.
Added Material
Table 2 Regression coefficient for out migration in Thailand
Regression coefficient
year 1990
Independent variables
provincial
% population age 15-24 yr (log)
7.767**
-1.085***
-3.234**
4.924**
6.271**
-2.585**
-2.636**
-1.126**
-0.911**
-2.558**
0.088**
0.133**
5.270**
-0.007**
0.188***
5.060**
0.740**
-0.367
0.040
-56.346**
5,329
0.798
1,102.73**
(19,5309)
3.050
0.048
0.958
yes
9.337
0.529
1.836
0.082
0.24
-
spatial
year 2000
regional
provincial
focused
% population age 25-34 yr (log)
distance (log)
border
population size (log)
income ratio(log)
COL (log. population density ratio)
% poor
urbanization ratio (log)
area ratio (log)
% private house
employment ratio (log)
% labor force in industry
new job ratio
education (average year. in school.)
doctor population ratio
bed population ratio
arrested crime rate
road density ratio
Constant
n
R2
F (p-value)
(df)
SEE
K-S extreme difference
Kolmogorov-Smirnov Z test
P-P plot (normal residual)
Maximum VIF
Goldfeld & Quandt’s R test
DW
Score test
Chow test (between models)
(df)
Chow test (between census time)
(df)
 (Box-Cox)
C (ridge constant)
*=p
 0.05 ** =p  0.01 *** = p  0.10
-0.358
-4.229***
-2.938**
4.813**
4.807**
-2.139**
-0.718**
-1.763
-1.344**
-0.112*
-0.027
-0.964**
2.859*
-0.034*
0.138
-5.600
0.362
-3.011
0.123
-41.867**
1,022
0.721
136.14**
(19,1002)
3.517
0.022
1.451
yes
6.118
0.456
1.810
0.093
16.621**
(19,6313)
0.24
0.10
spatial
regional
focused
-644.666
-371.755
-40.957**
-12.239
161.411*
-51.846
6.073*
-0.097
0.941
-186.015*
189.388**
-33.111
26.746
3,857.2**
25
0.858
2.813*
(13,11)
6.392
0.158
1.213
yes
18.386
0.515
2.071
0.022
0.24
-
-2.174**
3.948**
-3.190**
4.993**
6.722**
-2.979**
-2.662**
-1.481**
-1.138**
-2.093**
-0.106**
0.055***
3.334**
-0.002
0.054**
6.825**
-0.539**
-0.389
0.076**
51.198**
5,776
0.811
1,296.90**
(19,5756)
3.110
0.033
0.898
yes
19.064
0.616
1.732
0.134
33.81**
(19,11,067)
0.25
-
1.535**
1.437**
-2.739**
3.996**
1.411**
-3.136**
-0.991**
-2.977**
-1.035**
-0.071**
0.0001**
-0.693**
2.259**
0.032
0.011
0.204**
-0.033
3.735**
-0.069
-34.500**
1,824
0.755
292.52**
(19,1804)
3.110
0.125
0.829
yes
6.823
0.761
1.752
0.123
17.097 **
(19,7562)
0.24
0.10
95.379**
-1.422***
-0.187
0.195
0.705*
0.008
0.040
-4.358
-43.631*
14.531
186.165*
-4.765
52.828**
25
0.704
2.382**
(12,12)
6.913
0.123
1.464
yes
5.812
0.861
1.918
0.021
0.15
-
Table 3 Regression coefficient for in migration in Thailand
Regression coefficient
year 1990
Independent variables
distance (log)
border
provincial
year 1990
spatial
focused
-1.493**
-0.385**
3.059**
1.553**
**
**
regional
-2.156**
provinci
al
spatial
focused
regional
-1.441**
-2.684**
-2.356**
-
2.607**
5.450**
-
**
**
-
population size (log)
1.655
0.531
-
1.802
2.933
previous migration (log)
1.877**
0.732**
4.947**
2.418**
0.447**
4.654**
income ratio(log)
0.490
**
**
1.268
1.173
**
**
0.365
1.323
**
1.625**
COL (log. population density ratio)
0.154**
0.125
-
0.211**
0.205
urbanization ratio (log)
0.727
**
0.110
-
0.615**
0.826**
-3.092**
area ratio (log)
0.054**
0.036
-
0.026*
0.006
0.064
**
-0.028
-
-0.038*
0.001**
employment ratio (log)
0.642
*
-
*
% labor force in industry)
1.738**
% private house
**
new job ratio
0.008
education (average year. in school.)
0.010
doctor population ratio
6.732
-0.205
arrested crime rate
-0.589
Constant
2.186***
-18.519
0.778*
0.299
-0.089
-0.317
-
0.026
-
0.033**
-
**
**
1.974
*
**
3.902
-0.068
0.3919.371
0.585
-
-0.340
-
-3.080**
-
**
0.093
**
**
20.596
-0.540
0.089
**
3.114
**
-0.391
-11.230
**
-
***
-
8.112**
**
-0.089
0.011
-19.661
-1.165
-1.519**
-0.066
**
bed population ratio
road density ratio
-23.685
***
-
- -0.364
***
-
0.395
-11.431
-
*
*
**
3.532**
664**
-3.
5,329
511
25
5,776
684
25
0.888
0.761
0.984
0.901
0.812
0.967
F (p-value)
2481.65**
92.64**
4.241**
3,094.73**
169.33**
110.179**
(df)
(17,5311)
(17,493)
(4,20)
(17,5758)
(17,666)
(5,19)
SEE
K-S extreme difference
2.260
0.091
1.770
0.045
2.059
0.058
2.250
0.046
2.91
0.038
3.034
0.023
Kolmogorov-Smirnoz Z test
0.651
0.861
1.021
0.961
1.315
1.211
yes
yes
yes
yes
yes
yes
Maximum VIF
7.567
6.887
2.265
3.954
5.624
3.090
Goldfeld & Quandt’s R test
0.129
0.626
0.255
0.569
0.776
0.125
DW
1.709
2.058
1.285
1.694
2.110
1.968
Score test
0.145
0.028
0.298
n
R
2
P-P plot (normal residual)
0.153
0.054
0.013
Chow test (between models)
-
15.806**
-
-
17.639**
-
(df)
-
(17,5806)
-
-
(17,6426)
-
Chow test (between census time)
-
-
(df)
-
-
 (Box-Cox)
C (ridge constant)
*=p
 0.05 ** = p  0.01 *** = p  0.10
376.92
-
(17,11,079)
**
-
-
-
-
0.24
0.10
0.20
0.25
0.24
0.20
-
0.10
-
-
0.10
-
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