INTERNAL MIGRATION IN THE COUNTRIES OF ASIA: LEVELS, AGES, AND SPATIAL IMPACTS

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Elin Charles-Edwards1,2,  Martin Bell1,2, Aude Bernard1,2 and Yu Zhu1,3

1 Asian Demographic Research Institute, Shanghai University, China

2 University of Queensland, Brisbane, Australia

3 Center for Population and Development Research, Fujian Normal University, China

Corresponding Author: Elin Charles-Edwards, e.charles-edwards@uq.edu.au

INTERNAL MIGRATION IN THE COUNTRIES OF ASIA: LEVELS, AGES, AND SPATIAL IMPACTS

 

ABSTRACT

The countries of Asia have undergone an epoch of rapid demographic change. While considerable effort has been exerted in the study of fertility and mortality, studies of internal migration are comparatively few, despite its major role in redistributing populations within countries.  This paper reports on a comparative study of internal migration for 30 countries in Asia, drawing on a common quantitative framework developed in the IMAGE project (www.imageproject.com.au). Three aspects of internal migration are explored: intensity, age profile, and spatial impact, drawing on both recent and lifetime data to explore current patterns and historical trends. Comparisons reveal that internal migration intensities, while on average lower than in other parts of the world, are highly variable across countries. This is connected to key indicators of development but also to individual countries’ progression through the urban transition. Migration intensities in Asia peak at an earlier age and are more concentrated than in other parts of the world. Analysis of spatial impacts highlights the contribution of migration to urbanisation throughout Asia, but also the enduring impacts of conflict, forced displacements, and government policies on national migration systems.

 

1. Introduction

Asia is the largest and most populous of the seven continents, home to more than three-fifths of the world’s population. Stretching across almost half the globe, the continent is home to diverse populations, cultures, political systems and economies, reflecting the combined forces of geography and history. The progress of demographic transition in Asia has been rapid. Across the continent as a whole, fertility declined from 5.8 children per women in 1950-1955 to 2.2 in 2010-2015 (United Nations, 2015a). Life expectancy increased from 42.1 years in 1950-1955 to 71.6 years in 2010-2015 (United Nations, 2015a). This shift has been accompanied by rapid urbanisation of the population from 17.5 per cent urban in 1950 to almost 50 per cent urban in 2015 (United Nations, 2015b), and mass international migration, with an estimated 78 million Asians living outside their country of birth in 2010 (Bell and Charles-Edwards, 2013). Considerable effort has been exerted in study of the patterns and processes of fertility, mortality and international migration, both within and across the countries of Asia. By contrast, studies of internal migration are comparatively few. This is despite the fact that 3.5 times as many Asians are internal migrants (280 million) as international migrants (78 million) (Bell and Charles-Edwards, 2013).

 

This paper seeks to address the deficit of comparative studies of internal migration in Asia, using a quantitative framework that draws on the data and methods developed under the IMAGE project (Comparing Internal Migration Around the GlobE – https://imageproject.com.au). The study seeks to go beyond a simple description of contemporary patterns to provide a rigorous comparison using robust quantitative measures that capture key aspects of internal migration. It also seeks to provide insights into the way internal migration has evolved over time. The paper begins in Section 2 by summarising relevant prior work, identifying key themes in the literature, and the extent of geographic coverage. In Section 3, we provide a brief overview of the IMAGE project. A major constraint to comparative research on internal migration is variation between countries in the type of data that are collected, the temporal interval over which migration is measured, and the spatial resolution at which migration is captured. We review the internal migration data available for Asian countries in Section 4 and discuss their strengths and limitations.  Sections 5 through 7 present the results of our analysis, focusing on three discrete, but inter-related, dimensions of migration: the overall intensity or level of movement in the country (Section 5), the migration age profile (Section 6), and the impact of migration on settlement systems (Section 7). Each of these dimensions provides a unique perspective on the nature and implications of the migration process and together they offer a complementary picture of the migration system. In Section 8, we compare results from the analysis of recent and lifetime migration data to explore the evolution of internal migration in the countries of Asia, and set out an agenda for future research.

 

  1. Internal Migration in Asia: An Overview

Internal migration in the countries of Asia remains underexplored with respect to key aspects of mobility, and studies are limited in their geographic coverage.  Cross-national studies of internal migration are especially rare, reflecting a lack of data availability and issues of data comparability. Amrith (2011) provided a comprehensive history tracing the evolution of migration in Asia since the mid-19th Century. Drawing on historic records and published case studies, this descriptive work challenged the notion of a traditionally sedentary society, and identified longstanding linkages between internal and international migration in the countries of Asia: a theme also highlighted in contributions by Skeldon (2006) and Hugo (2016). More recently, Fielding (2015) has described contemporary migration systems in the countries of North-East, East and South-East Asia, revealing significant regional variations in the patterns of internal migration and their changing drivers over time. In North-East Asia, high levels of internal migration coincided with rapid urbanisation and industrialisation in the decades following the Second World War (and the Korean War). Later decades were marked by a migration turnaround, characterised by net flows from urban to regional centres, following shifts in the location of manufacturing. The urban transition commenced later in East Asia, with rural-urban flows remaining a dominant feature of contemporary migration systems. In China, rural-urban flows are dominated by large scale migration of the “floating population” from western, inland provinces to the eastern, coastal provinces (Zhu, 2007), and partly offset by frontierward migration. In the countries of South-East Asia, internal migration has been dominated by two countervailing processes: the migration of rural populations to Asian megacities and frontierward migration to remote regions for the purposes of primary production, often facilitated by large scale government programs. The works of Amrith (2011), Fielding (2015) and others (see also Deshingkar, 2006) underscore the links between economic development and migration but also point to the impact of government intervention on Asian migration systems. These studies provide extremely valuable insights into migration processes for selected countries, but include limited quantification of internal migration intensity and impact. They are also restricted in geographic coverage, concentrating on the eastern half of the continent.

 

While comparative studies remain rare, a large and diverse body of country-specific research has developed since the middle of the 20th Century.  The subject and geographic locus of research has shifted over time. Early scholarship was preoccupied with rural to urban migration as countries underwent rapid urbanisation, and was focused on the countries of East and South Asia. The 1980s saw a broadening of interest to include the selectivity of migration, for example the feminisation of internal migration flows in South East Asia (see e.g. Phongpaichit, 1992, Thadani and Todaro, 1984), echoing trends in international migration research. Links to development became an important subject of inquiry in the 1990s, coterminous with the Asian economic miracle, and this continues to be an important topic (DeWind et al., 2012, Chan, 2012, Mendola, 2012). The first references to environment-driven migration emerged in the 1990s (Subedi, 1997) and have continued to grow, particularly in regions vulnerable to the effects of climate change, for example in the countries of South Asia including Bangladesh (Hassani-Mahmooei and Parris,2012, Hugo, 2011). The first decades of the new millennium have seen a significant expansion in both the volume and diversity of research. Three broad clusters of contemporary research can be identified: studies of migration impacts at origins and destinations; studies of different migration forms; and studies of spatial patterns. Studies of impacts at origins and destinations are the largest group. These deal with subjects ranging from the impacts on family left behind (see e.g. Abas et al., 2009, Chang et al., 2011, Adhikari et al., 2011) to the impacts on the labour market at the destination (see e.g.  El Badaoui et al., 2014, Meng and Zhang, 2010, Phongpaichit, 1993). The collection of papers edited by Zhu et al. (2013) provide a concise synthesis. A second cluster comprises studies of migrant characteristics. For example, migrant selection and health has emerged as an important topic of inquiry in China (see e.g. Chen, 2011, Chatterjee, 2006, Xiang, 2003, Mou et al., 2011), while marriage migration is prominent in the literature, especially in the countries of South and East Asia (Nedoluzhko and Agadjanian, 2010, Davin, 2007, Fan, 2008, Fulford, 2015). A final cluster of research relates to the spatial patterns of internal migration. Interest in this topic has grown in recent years, in part reflecting increased data availability. Studies of migration patterns, including both rural to urban and interprovincial migration, have been undertaken in a number of countries including China (Chan, 2013, Gu, 2014, Liu and Shen, 2014, Poncet, 2006,), Vietnam (Phan and Coxhead, 2010), Indonesia (van Lottum and Marks, 2012), Malaysia (Mohd Razani Mohd, 2009), Myanmar (Department of Population 2014) and Kyrgyzstan (Alymbaeva, 2013). There has also been a renewal of interest in the role of temporary and circular mobility, most notably in reference to China’s floating population (Zhu and Chen, 2010, Zhu, 2007).

 

Past research provides some insights into the role of migration with respect to livelihoods, economic development and the growth of cities and regions. Despite this, our overall understanding of the patterns and process of internal migration in Asia remains descriptive, fragmented and limited in geographic scope. The timing, patterns and drivers of migration clearly vary across the countries of Asia, but there remains a large gap in our understanding, particularly in Central and West Asia. The assembled evidence from the IMAGE project suggests that internal migration intensities in Asia are lower than in other parts of the world, although there are pockets of high mobility (Charles-Edwards et al., 2016), and that migrants are younger than in other continents (Bernard et al., 2014b). In some countries, too, the impact of migration on urbanisation (i.e. through urban-rural migration) may be less than in other parts of the world due to “in situ” urbanisation (Zhu, 2000, Jones, 1997), although this is yet to be systematically tested across multiple countries. Temporary migration also appears to be an important complement to, and in some countries perhaps a substitute for, permanent migration. The diversity of human mobility in Asia, and the variety of research, is therefore extensive, but as yet somewhat disparate and fragmentary.

 

Building on the work of the IMAGE project, which provided the first global synthesis, the current study seeks to provide an integrated account of internal migration in the countries of Asia using a common analytic framework and standard metrics. It also seeks to add a new dimension to the global IMAGE project by extending the analysis to incorporate a temporal perspective through the use of lifetime migration data, collected more widely in Asia than in most other world regions.

 

3.A comparative framework: IMAGE-Asia

The IMAGE project was a multiyear, international collaborative program of research which sought to provide wide-ranging, cross-national comparisons of internal migration for countries around the globe. The project coalesced in four discrete modules:

  1. A global inventory identifying the types of internal migration data collected by national statistical offices around the world (Bell et al., 2015a)
  2. A repository containing key sets of internal migration data for selected countries in a standardised format, together with digital boundaries (Bell et al., 2014);
  3. Specialised analytical software developed to compute a suite of robust migration indicators (IMAGE Studio) (Stillwell et al., 2014);
  4. A series of papers detailing analytical methods and comparing countries on various aspects of population mobility. (Bernard et al., 2017, Charles-Edwards et al., 2016).

 

The IMAGE project focused on four discrete dimensions of migration that were considered key to understanding the multifaceted nature of population movement (Bell et al 2002). The first was migration intensity, that is the overall level or rate of movement within a country (Bell et al., 2015b). The second dimension was age: one of the key characteristics that shapes the propensity to move. Migration is a highly selective process, peaking in early adulthood, and declining at older ages, but with evidence of marked variations between countries. The third dimension identified was migration impact. Migration is singularly significant among demographic processes in its ability to rapidly redistribute populations. This redistributive potential is captured in measures of migration impact and is especially pertinent to the process of urbanisation. Migration distance was the final dimension examined, reflecting the underlying spatiality of the migration process, and the frictional effect of distance in constraining population movement. Results of the analysis comparing countries globally on each of these dimensions were reported in a series of papers (Stillwell et al., 2016, Rees et al., 2016, Bernard et al., 2014a, Bell et al., 2015b).

 

Drawing on that body of work, the current paper seeks to provide a detailed synthesis aimed at advancing understanding of internal migration in the countries of Asia, paralleling a similar regional study of Latin America and the Caribbean (Bernard et al., 2017).  Here we focus on three dimensions of mobility: intensity, age and impact, setting aside migration distance, but adding a temporal perspective which exploits the lifetime migration data available in many Asia countries. As well as extending earlier work that considered both internal and international movements, the paper aims to provide the framework for a coordinated series of country-specific studies of migration being conducted under the auspices of the Asian Demographic Research Institute, IMAGE-Asia.

4. Internal Migration Data in the Countries of Asia

Who collects what?

The lack of comparable data has been a key impediment to cross-national studies of internal migration (Bell et al., 2015a). Issues arise with respect to: 1) differences in the types of internal migration data collected (e.g. events, transitions, lifetime or last move); 2) the interval over which migration is measured (e.g. one or five years); and 3) the spatial units into which countries are divided, giving rise to the Modifiable Areal Unit Problem (MAUP) (Bell et al., 2015a). While not strictly equivalent, different types of migration data (1) can be compared given a short enough temporal interval (e.g. for example the comparison of one year event and one year transition data), as well as judicious use of data on duration of residence to filter data on previous move (e.g. to compare five year transition with five year duration data (Bell et al., 2015b). The comparison of migration data measured for different intervals (2) is less tractable due to the differential impact of return and repeat migration on one and five year migration measures (Long and Boertlein, 1990). Issues of spatial comparability and the MAUP (3) are another barrier to cross-national studies (Openshaw, 1977). The IMAGE Project sought to address this aspect of comparability through the development of scale free metrics for different dimensions of internal migration including intensity (Bell et al., 2015b), age (Bernard et al., 2014b)  and impact (Rees et al., 2016), and also made considerable progress in  identifying the effect of the MAUP on migration metrics. The calculation of these metrics requires data at a high level of spatial resolution which is regrettably lacking for many countries in Asia. Nevertheless, it is clear that understanding the type, interval and spatial resolution of migration data is a key first step in undertaking any cross national comparisons.

 

Of the 47 UN Member States in Asia, all but four[1] have collected data on internal migration in the past two decades (Table 1). The Census is the most common source of internal migration data in Asia, with 41 countries collecting data in the 2000 or 2010 UN Census Round.  Sixteen countries collected internal migration data in a Population Register, while 26 countries have collected data via a nationally representative population survey such as USAID’s Demographic and Health Survey (DHS). While Censuses are widely implemented across Asia, Population Registers are concentrated in the countries of East and Central Asia. Population Surveys tend to be more important in the less developed countries of Asia, including parts of South, Central and Western Asia, where statistical systems are not yet so well developed.

 

Table 1 Internal migration data collection, Countries of Asia, from 1995

   Region of Asia Census Register Survey (DHS) Any data held in IMAGE Repository Total countries in region
1year 5YR Lifetime Last move
Central 0 0 4 3 5 5 3 5
East 0 5 2 1 4 1 5 5
South-East 2 4 7 4 2 7 7 11
South 1 4 8 6 0 5 7 9
Western 4 2 10 8 5 8 8 17
Total 7 15 31 22 16 26 30 47

Notes: A country specific listing is provided in Appendix A.

 

Different collection instruments capture different types of migration data. Population Registers measure migration events (i.e. the number of moves), whereas Censuses and Surveys usually collect information on migration transitions, (i.e. the number of migrants) measured either over a discrete period (generally one year, five years, or since birth), or with respect to the last move. Table 1 shows the frequency with which different data types are collected across the five regions of Asia. Details for individual countries are provided in Appendix A.

 

Lifetime data, which compare individuals’ place of current residence with their place of birth, are the most common, collected by 31 of 47 countries in Asia. Next are data on place of previous residence, regardless of the timing of move (referred to in Table 1 as ‘last move’ data), which are collected by 28 countries. In 22 countries these data can be combined with information on duration of residence to generate a measure of migration over a defined interval. These data are most commonly collected in South, Central and Western Asia. Fixed interval data are less common in Asia than in other parts of the world. Five year data, which compare respondents’ current place of residence with their place of residence five years earlier are collected in around a third of countries (15/47), while one year transition data are collected in just seven countries spanning Western and South East Asia.

 

A number of national migration surveys are conducted in Asia including the Malaysian National Migration Survey, the Indian National Social Survey and the Pakistan Labour Force Survey. The most ubiquitous is USAID’s Demographic and Health Survey (DHS) which collects comparable information on internal migration for a number of countries across Asia. Up to and including Wave 5 of the DHS, a standard question on place of previous residence and duration of current residence was included in all surveys. DHS generally only capture the mobility of women aged 15-49 and lack spatial detail beyond a broad rural/urban classification. These data do however fill gaps in geographic coverage where Census and Register data are not available, particularly in Central and Western Asia.

 

What data are available?

Differences in data collection practice are complicated by a lack of data availability, with detailed migration statistics rarely included in standard statistical releases. Furthermore, unlike births and deaths, data on internal migration are not produced with a view to generating internationally comparable statistics. Internal migration statistics are conspicuous by their absence from central repositories hosted by the United Nations and other international organisations. In response, the IMAGE project assembled a repository of internal migration data for 135 of 193 UN member states, including for 30 countries of Asia. Data were drawn from national statistical offices, custom tabulations from the IPUMS-International database (Minnesota Population Centre, 2017) and USAID’s DHS Survey (ICF International. 2012) There is wide variation in the types of data these countries collect, and in the level of detail available (Bell et al., 2015a). Figure 1 indicates the overall geographic coverage of the internal migration data in the IMAGE repository available for this study.

 

Figure 1 Summary of data holdings, IMAGE-Asia 

With respect to recent migration, one year transition data are only available for four of the 47 countries: Cyprus, Israel, Japan and Turkey.  Five year transition data provide wider coverage, with data available for 10 countries. These can be coupled with data on last move filtered by five year duration of residence to deliver data on migration over a five year interval for a further seven countries. Together these 17 countries encompass more than 80 per cent of the population of Asia[2]. Coverage is most complete in East and South Asia but decreases moving westward. Significant gaps in data holdings exist in Central and Western Asia.

 

The IMAGE Repository holds lifetime migration data for 19 of the 31 Asian countries which collect this type of data. The major gap is in Central Asia but lifetime data are also missing from the Repository for a number of countries in East Asia (Republic of Korea, Japan) and West Asia, most notably the Gulf States. Although lifetime data are the most ubiquitous, they are generally regarded as less useful for comparative purposes because they measure the cumulative migration history of national populations and mask more recent trends. This is particularly problematic for measuring migration intensity and for the analysis of migration age patterns because countries differ widely in age composition.  On the other hand lifetime data can provide valuable insights into the longer term effects of migration by showing the extent to which populations have left, or been displaced from, their places of birth: a useful complement to the one or five year data which measure patterns over a recent interval.

 

Differences in migration data type and interval are not the only barriers to cross-national comparisons. Because different migration metrics call for different data inputs, data must be available in a consistent and appropriate format.  Table 2 shows the number of countries for which appropriate data are available in the IMAGE Repository for the measurement of the three dimensions of migration examined in this paper: Migration Intensity; Age at Peak Migration, and Migration Impact.

 

Table 2 Summary of data availability for analysis of discrete dimensions of internal migration 

Region Intensity Age Impact Any measure Total countries
Recent (5 year)

ACMIs

Lifetime DHS (5YR) Recent

(5 year + 1 year)

Lifetime Rural-Urban
Central 1 2 3 0 1 0 1 3 5
East 5 2 0 1 4 2 0 5 5
South 3 4 3 3 3 4 1 7 11
South-East 6 6 4 6 6 6 6 8 9
West 2 5 4 3 2 5 5 7 17
Total 17 19 14 13 16 17 13 30 47

Note: the discrepancy between the counts for recent intensity and impact measures is due to differences in data format. DHS data are identified separately due to its incomplete population coverage, limited to women aged 25-49. See Appendix B for full table of countries.

 

As indicated, the data enable some aspect of migration intensity to be calculated for all regions of Asia, although coverage decreases steadily moving westward. While data are available for the whole of East Asia, estimates of migration intensities are possible for fewer than half of the countries in South, Central and West Asia. Geographic coverage for data on age at migration is more limited, with information for only 13 countries, principally in South East, South and West Asia. Broader geographic coverage is available for migration impact, reflecting the wide availability of lifetime migration data, but is a little more limited than for migration intensity as its computation requires detailed flow matrices, showing movements between origins and destinations. The central challenge for this study is the identification of regional patterns and processes from this patchwork of data. In the following sections we examine each of these dimensions in turn, first outlining the indicators we use, and showing how these measures are computed, then presenting the results.

 

5. Migration Intensity: How much movement?

The level of migration apparent within a country, or migration intensity, depends on the type of migration data collected (i.e. events; transitions; last move); the interval over which migration is measured (i.e. one year; five year; lifetime) and the spatial framework. Migration intensity is particularly sensitive to the number of spatial units used in the analysis, rising steadily as the number of spatial units increases, lifting the potential for any given residential relocation to cross a zonal boundary (Bell et al., 2015d). Following the early work of Long (1991), Bell et al. (2002) argue that the only reliable basis on which cross-national comparisons can be made is to utilise a measure that captures all permanent changes of residential address within a country, irrespective of the distance moved (see also Rees et al., 2000). This is measured by the Aggregate Crude Migration Intensity (ACMI), computed as:

 

ACMI=M/P*100

where M is the total number of internal migrants (transition/last move data) or migrations (event data) in a given time period and is expressed as a percentage of P, the national population at risk of moving.

 

Unfortunately, very few countries collect information on all changes of address. Only four of the 30 countries examined in this paper collect information on all changes of residence. To address this problem, we adopt the approach developed by Courgeau, Bell and Muhidin (2012) to estimate the ACMI by fitting a regression equation to Crude Migration Intensities[3] generated for each country at differing levels of spatial scale using the IMAGE Studio.  The method is elaborated in Bell et al. (2015b) who adopted the same approach to generate comparable estimates for a global sample of 92 countries covering 80% of the global population.  This raises the number of Asian countries for which we have estimates of recent migration from 4 to 17, comprising a third of all countries in Asia.

 

The results (Table 3) reveal considerable variability in the level of mobility. ACMIs range from a low of 5.2 per cent in India to 52.8 per cent in South Korea. This upper value for South Korea is an outlier in the sample, sitting more than two standard deviations above the sample mean. It is important to note that the ACMI for South Korea is based on observed data rather, and cannot therefore be explained away as some anomaly arising from the estimation process. The average five year ACMI for countries in the sample is 17.9% (15.5% with South Korea excluded). This is lower than the global mean of 21.0% calculated for the full 61 countries for which the requisite data are held in the IMAGE repository (Bell et al., 2015b), and suggests that, at least over the most recent period, Asian populations are relatively sedentary when compared with other parts of the world. Given, the high levels of emigration from many Asian countries, it is possible that some substitution by international movements is occurring. In contrast to other parts of the world, such as Europe and Latin America, there is no clear regionalisation in the spatial pattern of migration intensities (Bell et al., 2015b), although countries in South East Asia appear to record intensities below the average. Other regions display a mix of low intensities in some countries alongside high intensities elsewhere.

 

Table 3 Five year ACMI for selected countries of Asia

Year Type ACMI Method HDI (2015)
India 2001 5DR 5.2 ESTIMATED 0.62
North Korea 2008 5Y 6.3 ESTIMATED na
Nepal 2001 5Y 8.3 ESTIMATED 0.56
Iraq 1997 5DR 8.5 ESTIMATED 0.65
Philippines 2000 5Y 9.3 ESTIMATED 0.68
Iran 2011 5DR 11.0 ESTIMATED 0.77
Thailand 2000 5DR 11.2 ESTIMATED 0.74
Indonesia 2010 5Y 12.4 ESTIMATED 0.69
Vietnam 2009 5Y 12.6 ESTIMATED 0.68
China 2000 5Y 12.8 ESTIMATED 0.74
Malaysia 2000 5Y 16.4 OBSERVED 0.79
Cambodia 1998 5DR 18.4 ESTIMATED 0.56
Kyrgyzstan 1999 5DR 22.4 ESTIMATED 0.66
Mongolia 2000 5Y 27.4 ESTIMATED 0.73
Japan 2000 5Y 27.8 OBSERVED 0.90
Israel 1995 5Y 28.2 OBSERVED 0.90
South Korea 2000 5Y 52.8 OBSERVED 0.90
Asian Mean     17.9  
Global Mean     21.0    

Global mean across sample of 61 countries held in the IMAGE Repository (Source: Bell et al., 2015d)

 

The level of human development may provide part of the explanation for these differences, with South Korea, Israel and Japan recording high ACMIs and correspondingly high levels of human development, as captured by the Human Development Index (HDI), while countries such as India and Nepal record both low ACMIs and low HDI scores.  A simple regression of 2015 HDIs against ACMI yields an R2 of 0.46. Countries such as Mongolia record higher ACMIs than might be expected given the level of human development. The high ACMI in Mongolia might reflect a culture of mobility, similar to what has been observed for the new world countries of the USA, Australia and Canada (Long, 1991) but equally might be attributed to a dramatic shift in livelihoods away from transhumance and  nomadism which manifest as rural to urban migration (Fielding, 2015).

DHS data provide a potential mechanism to widen the geographic coverage to encompass more countries in West and Central Asia. DHS data are not directly comparable to the five year ACMIs shown in Table 3 for two reasons. First, the ACMI is a measure of all changes of address, while the DHS simply captures length of residence in a locality or place. Secondly, the 5 year ACMI measures mobility for the entire population aged 5 and over, whereas the DHS is confined to women aged 15 to 49. Table 4 shows intensities based on DHS data for 14 countries. The lowest migration intensity is recorded in Armenia (6.9%) and the highest in the Philippines (27.6%). The average CMI for the sample is 16.5%. As with the ACMI estimates, no clear regionalisation of migration intensities is evident. More concerning is the lack of agreement between ACMIs and DHS data for the four countries which appear in both samples. Nepal and the Philippines, low mobility countries according to the census based ACMIs, record the highest DHS CMIs. In contrast, Cambodia records a low CMI based on DHS data, but a relatively high ACMI from census data. It is not possible to reconcile these differences but the variation almost certainly arises from issues of population coverage, particularly with respect to sex differentials, but also the vague wording of the migration question in the DHS which simply asks respondents for their duration of residence in their current “place” of residence. While DHS data have previously been used by several commentators to explore internal migration, these results cast considerable doubts on their validity as a measure of overall migration intensity.

 

Table 4 Five year CMIs (DHS) and ACMIs (Census data) for selected countries of Asia

Year CMI (DHS) ACMI
Armenia 2000 6.9
Cambodia 2000 8.5 18.4
Timor-Leste 2009-10 9.1
Uzbekistan 1996 10.4
Viet Nam 2002 10.6
Kazakhstan 1999 15.4
Azerbaijan 2006 15.9
Jordan 2002 18.1
Turkey 2003 19.5
Sri Lanka 2006-7 20.1
Kyrgyzstan 1997 24.0 22.4
Bangladesh 1999-2000 24.2
Nepal 2001 24.7 8.3
Philippines 2003 27.6 9.3
Sample mean 16.5

 

Lifetime data provide an alternative lens through which to assess cross-national differences in migration intensity. Data are available for 19 countries, but comparisons are difficult because countries vary widely with respect to the size and number of units over which lifetime migration is measured. Moreover, place of birth is generally collected at a relatively coarse geographic scale (eg states or provinces, rather than municipalities or counties), which precludes use of the IMAGE Studio to generate lifetime estimate of ACMIs. Despite this, lifetime migration intensities do provide intriguing insights into the extent to which individuals within a population have made a significant shift away from their region of birth.

 

As shown in Table 5 the proportions who have relocated range from a low of 4.1 per cent between the 35 States of India to a high of 32.7 per cent between the 20 provinces of Bhutan. Countries which appear to have relatively high lifetime intensities include Bhutan, Kazakhstan, Malaysia, Mongolia and Turkey. Countries with relatively low intensities include Timor-Leste and Jordan. For the former group, these figures provide stark evidence of significant redistributions of population within the national territory, implying marked shifts in the pattern of human settlement. For the remaining countries, the results suggest greater stability in the settlement pattern, at least at this spatial scale. We examine patterns of redistribution within countries in greater detail below, but it is notable that comparing lifetime migration intensities against the current (five year) migration intensities at the same spatial scale delivers a positive correlation (R2 = 0.69) across the 12 countries for which both types of data are available.

Table 5 Lifetime CMIs for selected countries of Asia

Year No. of regions Lifetime Intensity
India 2001 35 4.1
China 2000 31 6.2
Iraq 1997 15 8.3
Indonesia 2000 26 8.4
Jordan 2004 11 9.0
Saudi Arabia 2004 13 9.2
Timor Leste 2004 13 11.9
Cambodia 2008 24 13.6
Armenia 2001 11 13.7
Nepal 2001 74 14.1
Myanmar 2014 74 14.6
Sri Lanka 2012 25 16.8
Thailand 2000 76 17.0
Kyrgyz Republic 2000 52 19.2
Mongolia 2000 21 20.2
Malaysia 2000 15 20.7
Turkey 1990 61 23.5
Kazakhstan 2009 13 26.4
Bhutan 2005 20 32.7

 

How do we reconcile these differences between the two measures? Migration theory suggests that the level of internal migration within a country rises then falls with progress through the mobility transition (Zelinsky, 1971). Time series data on internal migration are rare, and usually cover a limited time span, but a simple ratio of lifetime intensity against recent migration intensity provides some useful insight into temporal trends in a form that is directly comparable across countries (Figure 2).

Figure 2 Ratio of Lifetime to Five Year Migration Intensities

 

The results suggest that five Asian countries – China, Mongolia, Kyrgyz Republic, Myanmar and Cambodia – are currently undergoing a historically intense period of internal migration (i.e. current intensities are high compared with historical experience, resulting in low ratios). High migration intensities are characteristics of the middle stages of the mobility transition (and hence development), driven by large scale movements from rural to urban areas. Somewhat lower ratios of around four are observed in six countries: Indonesia, Malaysia, Thailand, India, Nepal and Iraq. Moderate ratios are expected both in early stages of the mobility transition, where internal migration intensities are increasing but are yet to reach peak levels, and at later stages, when mobility is once again moderating as space economies and settlements systems again stabilise. The highest ratio is found in Armenia, where lifetime migration intensity is more than six times greater than the current value. Contemporary migration intensity in Armenia is therefore at a relatively low level when set against historical norms. Such a result is not unexpected for countries at a late stage in the mobility transition, where significant historical shifts have taken place, or there have been major disruptions in the migration system. In Armenia, for example, high lifetime migration might be tied to relocations following dissolution of the USSR. Historically low migration may also be a product of rapid population ageing, as more people move into older age brackets, where mobility is comparatively low.

 

6.Age at Migration

Migration is an age-selective process, with young adults being the most mobile group. Irrespective of aggregate levels of mobility, the propensity to move typically peaks at young adult ages, then steadily declines with increasing age, sometimes rising again around the age of retirement. This broad age profile is replicated, with some variations, at various spatial scales and in a variety of countries (Castro and Rogers, 1983), including Japan, Korea and Thailand (Kawabe and Liaw, 1992, Ishikawa, 1978). Despite these persistent regularities, there is increasing evidence of systematic variations in the ages at which migration occurs, particularly at young adult ages.

 

Figures 3a and 3b report, for a few selected countries, age-specific migration intensities normalised to unity so that migration age profiles are independent from variations in overall intensities. It is important to bear in mind that migration is measured over a five-year interval. Since age is recorded at the end of the observation period, migrants will have moved on average 2.5 years earlier than the age which is recorded, assuming that migration is evenly distributed over the five-year interval. Figure 3a reveals marked variations between three Asian countries with migration reaching its peak before the age of 22 in India compared to 23 in Armenia and 24.5 in the Philippines. Broad variations are also apparent with respect to the degree of concentration of migration activity at young adult ages, with a stronger concentration of migration in young adulthood in India than in Armenia or in the Philippines.

 

These differences reflect similar variations in migration age patterns between world regions, though at a world scale the differences are even more pronounced. Figure 3b shows that migration within China peaks at age 21 and is strongly concentrated around the peak, whereas migration in Brazil and Portugal is dispersed across a broader age range and peaks later in adulthood, at 25 and 29 years, respectively. This result closely conforms to a previously identified pattern of a strong concentration of migration in the early 20s in China and South-East Asia that stands in contrast with late and dispersed migration peaks in Europe and North America (Bernard et al., 2014a, Bell and Muhidin, 2009).

3a. Across Asia 3b. Across the World

Figure 3     Age-specific Migration Intensities, selected countries

Source: Authors’ calculations based on five-year-interval migration data reported by single-year age groups. Migration data were normalised to sum to unity and smoothed using kernel regression (Bernard and Bell, 2015)

 

To systematically establish the extent of variation in the age profile of migration for a larger sample of Asian countries, it is possible to use two indicators that summarise migration age patterns: the age at which migration peaks, and the intensity of migration at the peak. These two indicators capture two thirds of the inter-country variance in migration age profiles (Bernard et al., 2014b) and, unlike the conventional Rogers parameters, have the significant benefit of being intrinsically meaningful. The age at which migration peaks captures how early in life migration occurs, while the intensity of migration at the peaks gauges the degree of concentration of migration activity at young adult ages.

 

Figure 4 plots the age at which migration peaks against normalised migration intensity at the peak. To interpret the results against countries in other regions, the data have been normalised across a global sample of 33 countries from all world regions so that the mean is zero and the standard deviation from the global mean is equal to one. With this normalisation, a unit on the graph represents one standard deviation from the global mean, which reveals how Asian countries compare to the rest of the world. Figure 4 shows that in all countries except the Philippines, Turkey and Iran, migration peaks at an age younger than the global mean. This difference is particularly pronounced in Vietnam and Indonesia where the age at the peak lies more than one standard deviation from the global mean, with peaks around 21 years of age. Figure 4 also shows a strong concentration of migration at young adult ages, with all countries except Iraq, the Philippines, Turkey and Iran displaying intensities at the peak above the global mean. In fact, no country outside Asia falls into the upper left quadrant that corresponds to early and concentrated migration activity. This confirms the distinctive age structure of migration in most Asian countries, best characterised as ‘early and concentrated’. A closer inspection of Figure 4, however, reveals important variations within Asia. The intensity at the peak is just one standard deviation from the global mean in China and Armenia, whereas it falls more than two standard deviations from the mean in Vietnam and India, indicating a very high concentration of migration activity at young adult ages.

 

Figure 4    Age at migration peak against normalised migration intensity at peak

Source: IMAGE Repository

Note: Measures were derived from migration data disaggregated by single years of age, normalised to unity, and smoothed using Kernel regression (Bernard and Bell, 2015). The global mean was estimated for a sample of 33 countries encompassing all world regions. Gridlines are located 1 standard deviation from the global mean.

 

Across the world, the age pattern of migration has been shown to closely mirror the age structure of key life-course transitions, in particular the completion of education, labour market entry, union formation and family formation (Bernard et al., 2014a). In many Asian societies, the process of becoming an adult is guided by social structures and norms that support early and rapid transitions into adult statuses (Yeung and Alipio, 2013). Thus, it is the concentration of life-course transitions in early adult life that underpins the pronounced concentration of migration activity in the early twenties, as shown in Figure 4.

 

Across the world, women tend to progress to adult roles earlier than men (Lloyd, 2005) and it is the gendered pattern of transitioning to adulthood that underpins the younger migration age profile that is commonly found among women. In all regions, including Asia, migration peaks on average 2.5 years earlier for women than for men (Bernard et al., 2014a). Of course, not all moves are triggered by life-course transitions, as young adults move in responses to a wide range of opportunities and constraints. Thus, contextual factors may, on occasion, trigger migration directly, as in the case of changes in economic conditions (Molloy et al., 2014) or in the level of social and political openness. This is one possible explanation for Iraq’s dispersed migration patterns.

 

  1. Migration Impact

Academic and policy interest in internal migration is driven in large part by its ability to transform national population distributions, particularly its contribution to urbanisation. Indeed, the urban transition is one of the great dynamics of our time and has been particularly pronounced in Asia. Despite the significance of internal migration to urbanisation globally, its actual contribution to population redistribution, has proven difficult to measure. Most commonly, net migration is computed simply as the residual component of population change, once natural increase has been taken into account, but this confounds internal and international migration, and inherits all the enumeration errors in the other components of change. Data on rural to urban migration provide a more direct measure of the way internal migration contributes to urbanisation, but such data are not widely collected, and cross-national comparisons are plagued by inconsistencies in the definition of urban and rural regions. The available data for Asian countries have been assembled in Figure 5, which shows the balance of flows between rural and urban areas. Countries are ordered by the share of their population living in urban areas in 2015. Migration within countries at early stages of the urban transition (Cambodia, Timor-Leste) is predominately between rural areas. The share of rural-urban migration increases as the level of urbanisation rises (see Vietnam, Thailand, Indonesia, Kyrgyzstan), before declining again as urban to urban migration becomes the dominant migration form (e.g. Malaysia, Israel).

 

Figure 5    Share of Rural-rural; rural-urban; urban-rural, urban-urban, recent migration flows, Asia

Note: The percentage of the population living in urban areas in 2015 and the MERRU are shown in brackets. Countries are ranked by the percentage of the population living in urban area in 2015.

 

The Rural to Urban Migration Effectiveness Ratio (MERRU shown in parentheses in Figure 5) provides a simple summary metric capturing the balance between rural to urban flows and counter flows:

MERRU = 100×(MRU MUR)/ (MRU+MUR)

 

where MRU are migration flows from rural to urban areas and MUR are migration flows from urban to rural.

Values for the MERRU vary between -100 and +100 with positive values signifying a net balance in favour of urban areas, and the magnitude of the indicator showing the strength of the redistribution for the given volume of movement. From Figure 5, the MERUR is highest for countries at relatively early stages of the urban transition (e.g. Timor Leste and India where the population is less than 30 per cent urban). Developed countries with highly urbanised populations (e.g. Israel and Malaysia) have negative values of MERUR, suggesting counter-urbanisation processes traditionally associated with late stages of the urban transition. Both the share of overall flows which are rural to urban flows, and the MERRU, reveal that the direction of flows, and therefore the redistributive impact of migration, change as countries urbanise.

 

Even where data on rural to urban migration are available, they provide only a crude measure of population redistribution, based on a coarse dichotomy between rural and urban areas. A more robust approach to the analysis of migration impact was implemented by Rees and Kupiszewski (1999) in their study of European countries, and subsequently refined by Rees et al (2016) using population density as a proxy for urbanisation. Rees et al. (2016) proposed a theoretical relationship between migration and the population density of regions, as depicted in Figure 6. The individual graphs embedded in the larger graph plot the net migration rate against the log of population density for all regions of a country, with the solid line indicating the hypothesised relationship across regions, captured empirically via linear regression. Population density is effectively adopted as a surrogate for the level of urbanisation within individual regions. Positive slopes indicate that more densely populated regions are gaining through net internal migration, while less densely populated areas are losing. The steeper the slope, the greater the rate of redistribution. The logistic curve on the larger graph traces the shift from low to high levels of urbanisation (the y axis) as development proceeds  through a series of phases (the x axis).  The conceptual model indicates that migration from low to high density regions, proceeds in a progressive sequence indicated by the changing steepness of the slope, accelerating as development takes off (Stages 1 to 2), reaching a peak as the rate of development peaks (Stage 2), then slowing at later stages of development (Stage 3) when countries become predominantly urban. In Stage 4 and beyond, migration flows become more closely balanced with net flows potentially oscillating between gains or losses in more urban areas, the latter corresponding to the classic process of counter-urbanisation.

Figure 6 A theoretical framework linking development to population redistribution through net migration  

Source: After  Rees et al (2016)

 

In practice, of course, the relationship between population density and the rate of net migration is not as clear cut as the model would suggest. Nevertheless, Rees et al (2016) found empirical support for the model across a global sample of 67 countries. Here we seek to further test the theorised relationship between internal migration impact and the urban transition for 22 Asian countries for which suitable data(recent and/or lifetime) are available. Linear regressions have been estimated using data for regions weighted by population to reduce the influence of regions with small populations on the overall line fit. The results are shown in Figure 7, the left hand panel depicting countries with relatively steep slopes, indicating high levels of migration from low density to high density regions, the right hand panel depicting countries where the slope, and the level of migration, is more moderate.

 

 

Figure 7  Fitted slopes capturing the relationship between NMR and population density for 17 countries based on recent migration data

 

Four distinct clusters of countries can be identified, two from each panel. A first cluster with steep slopes is comprised of Mongolia and Kyrgyzstan. Steep positive slopes suggest that the largest net migration gains are occurring in the most densely populated regions, while the largest net migration losses are occurring in the most sparsely settled regions. A second group of countries with more moderate, but still strongly positive, slopes is comprised of Nepal, Vietnam, Thailand, China and Cambodia. Here, net movements from low to high density regions continue, but at a more moderate pace.  The third and fourth clusters encompass the remaining ten countries, all with relatively flat slopes, that are characteristic of late stages of the urban transition. This group is sub-divided simply by the horizontal axis, with just seven countries displaying  modest positive slopes (Malaysia, India, Japan, Turkey, Japan and Iraq) while in three, (Iran, Indonesia and South Korea), the modest negative slopes indicate that the direction of migration has reversed, such that net gains now favour less densely settled areas.

 

To contextualise the relationship between migration and urbanisation we plot these recent migration-density slopes against the percentage of the population living in urban areas (Figure 7). The results provide solid support for the relationship hypothesised by Rees et al (2016). The steepest positive slopes are recorded among countries midway through the urban transition (G1). Moderate slopes are recorded in countries at early stages of the transition (G2), while countries at late stages of the urban transition record moderate positive or negative slopes (G3).  India, Myanmar and Indonesia departing from the theorised relationship, all recording slopes lower than anticipated by their level of urbanisation. In India, the moderate slope is likely an outcome of having a very large population distributed across relatively few spatial zones (35 states). Rural to urban flows occurring within Indian States are simply not captured by the relatively coarse geographic framework on which migration data are available. A second contributing factor may be the high levels of reciprocity in rural-urban migration flows in India. Rural-urban migration in India is highly masculinised, whereby young men migrate to urban areas at to accumulate wealth before returning home (Tumbe, 2016). The circularity of rural-urban flows lessens the overall effectiveness of migration, as young cohorts arriving in urban areas are offset by older cohorts returning to rural homes. By contrast, the modest slope recorded for Indonesia likely reflects the diversity of internal migration in that country. Large rural to urban flows sit alongside customary modes of circulation, migration to frontier regions (both independent and state sponsored) and significant populations of internally displaced persons (Fielding, 2015).   In Myanmar, the weak positive slopes reflects large losses from densely populated States such as Ayeyarwady  alongside large net gains to other densely populated states such as Yangon.

Figure 8  Recent slope against % population in Urban Area (various years)

Notes: % urban refers to the date closest to the Census data. This is 2000 in all countries except Iran (2005) and Vietnam (2010)

 

To explore the link between urbanisation and internal migration over a longer time scale and for more countries we fitted population-weighted regressions linking lifetime net migration rates against the log of regional population densities.

 

Lifetime data capture the cumulative migration history of a country, and while they bias recent migration movements, particularly in very young populations, they also reflect past movement patterns aggregated over many decades. Figure 9 shows lifetime slopes plotted for 14 countries. Clusters are not as distinct as for the recent migration data described above, but four natural grouping are evident. Large positive slopes are observed for Bhutan, Turkey, East Timor and Mongolia, implying that over the past half century or so, the dominant pattern of redistribution has been from rural to urban areas. The next group of countries (Cambodia, Nepal, Armenia, Thailand and Malaysia) have more moderate positive slopes. While the overall direction of flows has been from rural to urban areas, these may have been offset by recent processes of counter-urbanisation (e.g. Malaysia) and frontierward migration (e.g. Thailand). Only modest positive slopes are found for India, Iraq and China, probably because of the coarse geographic units for which lifetime data are available, masking rural-urban flows within regions which make a significant contribution to urbanisation (Provinces in China and States in India). Indonesia and Saudi Arabia comprise a final group recording negative slopes. For Saudi Arabia this is a product of flows to settlements adjacent to oil fields at the expense of other more densely populated regions with non-resource related economic bases (Al Bassam, 2011). In Indonesia, the negative slope is likely a product of longstanding population shifts away from Central Java to Indonesia’s outer provinces supported in part by the government’s Transmigrasi program (Fielding 2015).

 

Figure 9  Fitted slopes capturing the relationship between NMR and population density for 17 countries based on lifetime migration data  

 

To contextualise recent migration impacts against longer term migration processes we plot recent slopes against lifetime slopes for 8 countries for which both lifetime and recent data are available (Figure 10). The regression lines capturing recent migration impact are closely correlated with the lifetime slopes (R2 =0.70), suggesting that there is considerable inertia in the internal migration system, but there are clearly anomalies. In China and Mongolia the redistributive impact of recent rural to urban migration is higher than would be expected given historical flows, suggesting that both countries are currently undergoing an epoch of rapid redistribution. In Cambodia and Malaysia, on the other hand, the slope for recent migration is lower than expected, suggesting that rural to urban migration is increasingly offset by flows to less densely population regions, either through counterubanisation, or movement to resource frontiers, as suggested above.

Figure 10  Fitted lifetime slopes plotted against recent slopes, selected countries

 

This brief exploration of migration-density slopes indicates that internal migration in many countries of Asia has been driven by a much more complex set of forces than the straightforward pathway through the urban transition suggested by the Rees et al (2016) model. As so cogently demonstrated by Fielding (2015), rural to urban flows are just one element of more complex migratory systems in the countries of Asia which include frontierward migration associated with the exploitation of primary resources (e.g. Viet Nam, Philippines), migration arising from conflict (e.g. Myanmar, Laos) and government policies on internal migration (e.g. Malaysia, Indonesia). In such a complex setting, normative models based on a simple rural to urban transition will not suffice. Understanding the historical and cultural setting in individual countries is critical to teasing out the interplay between different forms of internal population movement which have shaped past and contemporary migration patterns.

 

  1. Conclusion

In a diverse region spanning 195° of longitude and 77° of latitude it is hardly surprising to find considerable diversity in the migration experience of individual countries. Indeed, prior work has brought to the fore the marked variability that exists between countries even within the same geographic region of the continent (see e.g. Fielding, 2015, Amrith, 2011). This paper has endeavoured to set aside these differences in the search for more fundamental underlying similarities, and to do so using a standard analytical framework and common statistical measures. This goal has inevitably faced impediments posed by wide-ranging differences in the way migration data are collected, the timeframes and spatial frameworks used and the availability of data. Despite these impediments, a number of consistent patterns in the mobility experience of countries in the Asian region have been identified. First, it is apparent that migration intensity is lower than the global average, but that there is also widespread variability connected in part to key indicators of development. Secondly there is a consistent pattern with respect to age, with peak migration intensities highly concentrated and early. The spatial patterning of recent migration flows provides strong supportive evidence that internal migration is performing a key role in the urban transition across the Asian region, with most countries displaying movements consistent with their progress through the urban transition. These findings are broadly consistent with those from the global sample of countries analysed elsewhere using similar techniques (Bell et al., 2015b, Rees et al., 2016, Stillwell et al., 2016) and with results for individual regions such as Latin America (Bernard et al., 2017). What is particularly novel from the current set of results is the diversity of spatial impacts apparent from the lifetime migration data which appear to inherent a panoply of migration streams driven by other forces including forced resettlement, conflict and primary resource exploitation. These in turn reflect the cultural, economic and political histories of individual countries in the Asian region and underline the need for more nuanced investigation of at a country specific level.

 

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Appendix A: Internal migration data collection and availability, 2000 and 2010 Census Round

Census Register Survey
1YR 5YR Lifetime Duration+ PPR   DHS or Other Survey IMAGE data holdings
Central Asia Kazakhstan ü ü ü ü ü
Kyrgyzstan ü ü ü ü ü
Tajikistan ü ü ü  
Turkmenistan ü ü ü ü  
Uzbekistan ü ü ü
East Asia China ü ü ü ü
Japan ü ü ü
DPR of Korea ü ü
Republic of Korea ü ü ü ü
Mongolia ü ü ü ü ü
South East Asia Brunei Darussalam
Cambodia ü ü ü ü
Indonesia ü ü ü ü
Lao PDR ü ü
Malaysia ü ü ü ü ü
Myanmar ü ü ü
Philippines ü ü ü
Singapore ü
Thailand ü ü   ü ü
Timor-Leste ü ü   ü ü
Vietnam ü ü ü ü
South Asia Afghanistan ü ü ü
Bangladesh ü ü ü ü
Bhutan ü ü ü ü
India ü ü ü ü
Iran ü ü
Maldives ü ü ü
Nepal ü ü ü ü
Pakistan ü ü ü
Sri Lanka ü ü      ü  ü
Western Asia Armenia ü ü ü ü ü
Azerbaijan ü ü ü ü ü ü
Bahrain ü
Cyprus ü ü ü ü
Georgia ü ü
Iraq ü ü ü ü
Israel ü ü ü ü ü
Jordan ü ü ü ü
Kuwait
Lebanon
Oman ü
Qatar ü
Saudi Arabia ü ü
Syria ü ü
Turkey ü ü ü ü ü ü
United Arab Emirates
Yemen ü ü
Data held 3 10 19 8 2 14 30
Collected () 7 15 31 22 16 26 42

 

Appendix B: Summary of Results

Region Country Census Year Intensity Age Impact (Slope) Rural- # Spatial Units
Recent

(ACMI)

Lifetime (Variable # units) DHS5 Recent Lifetime Urban MER Recent Lifetime
Central Kazakhstan 2009/

1999 (DHS)

22.5 12.9 16
Central Kyrgyzstan 1999/

1997 (DHS)

22.4 19.2 19.9 7.5 46.4 52 52
Central Tajikistan
Central Turkmenistan
Central Uzbekistan 1996 (DHS) 8.6
East China 2000 12.8 6.2 21.5 2.6 0.2 31 31
East Japan 2000 27.8 0.4 47
East DPR of Korea 2008 6.3 10
East Republic of Korea 2006 52.8 -0.7 242
East Mongolia 2000 27.4 20.2 8.5 37.2 21 21
South East Brunei Darussalam
South East Cambodia 2008/

2000 (DHS)

18.4 13.6 6.7 23.3 1.9 4.3 40.9 149 24
South East Indonesia 2000 12.4 11.0 21.0 -0.4 -0.3 30.8 494 26
South East Lao PDR
South East Malaysia 2000 16.4 20.7 22.5 0.6 4.3 -58.4 136 15
South East Myanmar 2014 14.6 0.0 0.1  15 15
South East Philippines 2000/

2003 (DHS)

9.3 24.0 24.5 1620
South East Singapore
South East Thailand 2000 11.2 17.0 23.0 3.0 4.4 41.0 76 76
South East Timor-Leste 2004/

2009-10 (DHS)

8.9 7.8 3.9 62.1 13
South East Vietnam 2009/

2002 (DHS)

12.6 8.9 20.8 3.6 58.0 63
South Afghanistan
South Bangladesh 1999-00 (DHS) 20.6
South Bhutan 2005 32.7 10.3 20
South India 2001 5.2 4.1 21.8 0.4 0.2 50.0 35 35
South Iran 2006  11.0 27.5 -0.3 -7.5
South Maldives
South Nepal 2001/ 2001 (DHS) 8.3 14.1 20.6 21.5 4.4 2.4 63 74
South Pakistan        
South Sri Lanka 2012/ 2006-7 (DHS) 19.9 20.1 0.0 25
West Armenia 2001/ 2000 (DHS) 13.7 5.6 23.3 2.4 -19.0 11
West Azerbaijan 2006 (DHS) 13.2
West Bahrain
West Cyprus
West Georgia
West Iraq 1997 8.5 8.3 22.3 0.0 1.1 -16.5 15 15
West Israel 1995 28.2 -7.5 Counts
West Jordan 2004/ 2002 (DHS) 9.0 15.2 -0.1 11
West Kuwait
West Lebanon
West Oman
West Qatar
West Saudi Arabia 2004 9.2 -0.3 13
West Syria
West Turkey 1990 27.0 16.4 25.8 0.3 9.5 -6.9 61
West United Arab Emirates
West Yemen
TOTAL   17 19 14 13 16 17 13     

 

[1] Brunei Darussalam; Kuwait; United Arab Emirates; Lebanon

[2] In comparison, one year transition and/or event data are only available for five Asian countries.

[3] CMI = M/P*100 for any number of regions

 

INTERNAL MIGRATION IN THE COUNTRIES OF ASIA: LEVELS, AGES, AND SPATIAL IMPACTS