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Introduction

  • Kankesu JayanthakumaranEmail author
  • Reetu Verma
  • Guanghua Wan
  • Edgar Wilson
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Abstract

The purpose of this book is to provide a dynamic portrayal of internal migration, urbanization, and poverty in Asia. It comprises papers presented and critically reviewed at an Asian Development Bank workshop held in Siem Reap, Cambodia, on November 5–7, 2014. The issues addressed in this volume are important as unprecedented demographic transitions and structural transformations are taking place in Asia. While these changes have the potential to improve the well-being of many households, the complexities involved represent significant challenges to policymakers and other stakeholders. Also, there is an apparent lack of attention to the interrelated and dynamic nature of these issues.

The purpose of this book is to provide a dynamic portrayal of internal migration, urbanization, and poverty in Asia. It comprises papers presented and critically reviewed at an Asian Development Bank workshop held in Siem Reap, Cambodia, on November 5–7, 2014. The issues addressed in this volume are important as unprecedented demographic transitions and structural transformations are taking place in Asia. While these changes have the potential to improve the well-being of many households, the complexities involved represent significant challenges to policymakers and other stakeholders. Also, there is an apparent lack of attention to the interrelated and dynamic nature of these issues.

Asia deserves special attention since it is home to over 50% of the world’s urban population.1 The People’s Republic of China (PRC) has the largest urban population of 758 million, followed in second place by India with 410 million, while Indonesia has the world’s fifth largest urban population of 134 million. These three countries account for around one-third of the world’s urban population.2 Further, Asia is fast urbanizing, and by 2050, the urban population of the region may increase by one billion or more. The largest increases are projected to be in India (over 400 million), the PRC (300 million), and Indonesia (100 million). More than one-third of the increase in the world’s urban population by 2050 will occur in India and the PRC alone.

Rural to urban migration is estimated to contribute about one-third of this urban expansion in Asia. In the PRC, around 150 million people have moved from rural to urban regions since the start of the 1990s (Freeman 2006), while in India there are almost 100 million transient migrants (Deshingkar and Akter 2009). It is expected that these contributions to the predicted 2.4% annual growth in Asian urbanites will certainly help promote regional growth. However, these factors may also contribute to the problem of aging.3 In general, migrants to urban areas are younger, but the fertility of migrants tends to decline relative to rural counterparts, mainly because of the higher costs of raising children, better education, higher age at marriage, and greater access to contraception.

Turning to poverty, although urban poverty has been falling and is typically less prevalent than rural poverty, urban inequality has been rising. Urban gaps between the formal and informal sectors are widening, and there is also evidence of increasing polarizations. A large proportion of urban migrants have to survive in slums. For example, in 2009, the percentage of slum dwellers in the urban population was 62% in Bangladesh, 47% in Pakistan, 41% in the Philippines, 36% in Viet Nam, and 29% in both the PRC and India (UN Habitat 2012).

There are other important issues related to internal migration, urbanization, and poverty. Rapid urbanization will continue to place pressure on the provision of infrastructure, utilities, health care, and education services.4 It will also stimulate the demand for energy, thus increasing air, water, and land pollution.5

Given this background, it is important to examine the complex and evolving dynamic interrelationships between internal migration, urbanization, and poverty. The studies presented in Part I form the thematic epistemological contribution of these interdependencies, and the new evidence presented covers a wide range of possibilities. Part II focuses on the better-known positive effects of migration and urbanization in reducing urban poverty. This is then balanced in Part III with studies showing worsening multidimensional poverty and widening relative poverty gaps.

1 Part I: The Dynamic Interplay of Internal Migration, Urbanization, and Poverty

Chapter  2 by Graeme Hugo comprehensively reviews the recent demographic patterns of urbanization in the Asia region. He distinguishes between two dimensions—urbanization, which refers to the increasing proportion of the population living in urban areas as opposed to urban growth, which is measured as the increase in the absolute numbers living in urban areas. Urbanization is highest for the countries of East Asia (the PRC’s proportion of urbanized population was 54% in 2014), followed by Southeast Asia, with South Asian countries having lower ratios (India’s urbanization is 32%). These proportions have been increasing over time, with the number of people in urban areas steadily increasing to nearly 1.7 billion in 2010. While the more recent focus has been on issues relating to megacities, Hugo acknowledges that small- to medium-sized cities are also contributing to urban growth, particularly in the PRC, India, and Indonesia. The growth is due to natural population increases, internal and international migration, and the reclassification of rural areas due to expanding urban zones. Hugo argues there is a clear link between urbanization, economic growth, and poverty reduction, although wide variations are experienced across the Asia region. He claims that while poverty rates are falling, the sizeable growth in urban populations means that urban poverty is becoming an important issue in Asia.

Riyana Miranti examines possible interdependencies between internal migration, urbanization, urban poverty, and inequality in Indonesia in Chap.  3. Indonesia has a high urbanization rate (over 50%), large intra-provincial migration, and a relatively low urban poverty rate, but it has relatively high urban inequality. Regressions are run on the 2008 wave of longitudinal microeconomic rural to urban migration in Indonesia (RUMiI) data. Migration status is used to proxy migration, and demographic characteristics of households (including labor market details of the household head) are used as controls. The estimates provide strong support for recent rural to urban migrants being more likely to be in the top quintile of the household per capita expenditure distribution and less likely to be below the poverty line expenditure level. Education, age, housing infrastructure, and job status are found to reduce poverty, while household size has a negative effect.

Four waves of Indonesian interprovincial migration data for the 5 yearly periods during 1995 to 2010 are then examined. The random effects estimates show that urbanization reduces urban poverty. Dual causality is also found with a positive relationship between urban poverty and urban inequality (this is further considered for India in Chap.  5). The study concludes that rural to urban migration reduces poverty in Indonesia with the implication that the authorities should formulate coordinated policies to reduce poverty and inequality by promoting access to urban infrastructure and education and reducing labor market barriers.

In Chap.  4, Xin Meng reports migration dynamics for the PRC where over 130 million people have moved to cities in the last 15 years. This migration is much larger and faster than that experienced in Europe and the United States during their industrial revolutions. Ten to 20 million migrants with rural hukou migrated each year from 1998 to 2004. These increases, coupled with sustained strong economic growth, seem to indicate that the PRC was running out of surplus unskilled labor. However, Xin Meng disagrees with this deduction because unskilled migration represents 25% of the hukou labor force, which is less than 20% of the total labor force in the PRC. She argues that the significant official migration restrictions are the cause, making it more costly and risky for individuals to migrate, restricting family members to follow them, and increasing the likelihood of the migrants returning to their rural homes. These institutional restrictions to rural–urban migration, by reducing migration numbers and shortening the migration duration, have reduced the unskilled labor supply in urban areas. The resulting upward pressure on wages creates a bias away from labor toward capital-intensive industries. Ming argues that it is therefore necessary to increase employment opportunities in smaller cities and local towns and improve education in rural areas in order to encourage rural workers to migrate.

A linear probit model is estimated using the rural–urban migration in the PRC (RUMiC) survey data for the 3 years 2008 to 2010 (similar to the longitudinal survey data used by Riyana Miranti for Indonesia in Chap.  3). Using poverty measured in per capita income terms for migrant households, the regressions show they are less likely to be poor. However, using per capita expenditure as the poverty measure, the estimates show the reverse effect—poverty is approximately 1.5% higher for migrant households. This difference may be due to migrants working very hard to save for the short duration they are in the city. Since migrants are generally without their families (the average urban migrant household size is only around 1.5 people), savings may be remitted back home. Their expenditure is therefore expected to be lower than income. These positive findings between migration and poverty using expenditure measures contrast with Riyana Miranti’s findings of reducing poverty for Indonesia using per capita expenditure data. The dynamic evidence relating poverty and migration is therefore ambiguous and influenced by the official policies restricting migration numbers and the duration of migration.

Wilson, Jayanthakumaran, and Verma’s analysis in Chap.  5 focuses on urban migration, urban poverty (measured by the expenditure-based urban headcount ratio), and inequality in India. The time series analysis for four decades from 1982 to 2012 shows that migration to urban areas increases urban poverty nationally. The spatial estimates for 16 Indian states for the shorter period 2006–2011 reinforce the time series results. Migrant urbanization is found to increase urban poverty with a significant elasticity of around 0.7 or more.

The results also show that additional feedback effects are occurring between urban poverty and inequality, indicating an upward/downward spiral and, as was found for Indonesia in Chap.  2, the necessity to provide coordinated policies to reduce both urban poverty and inequality. These results are consistent with the expenditure findings for the PRC in Chap.  3.

To summarize, the conclusion from Part I is that there are strong dynamic links between internal migration, urbanization, urban poverty, and inequality, but these links differ across the three countries. The mostly shorter-range internal migration and smaller rural to urban movements in Indonesia have helped reduce urban poverty. However, the official restrictions to internal migration in the PRC have had ambiguous effects on urban poverty. For India, internal migration to cities and towns that are relatively less urbanized compared to those of Indonesia and the PRC is associated with increasing urban poverty and inequality. The lessons here are that the dynamic interplays are important in Asia and that rural to urban migration is a necessary but not sufficient condition for reducing urban poverty.

2 Part II: Migration, Urbanization, and Poverty Alleviation

Given the complicated dynamics involved, the chapters in this section focus on the better-known positive effects of migration and urbanization in reducing urban poverty. The World Bank and the IMF (2013) argue that internal migration and urbanization are important to support efforts in reducing poverty and achieving the Millennium Development Goals (MDGs). With internal migration, many workers move from low-skilled jobs to working in higher value-added industries. These movements create new opportunities for skilled migrants, increasing wages and reducing poverty. Part II supports these traditional theories, showing that internal migration and urbanization have been mostly poverty reducing (Chaps.  6, and  7) and skilled migrants receive higher wages (Chaps.  8, and  9).

In Chap.  6, Endang Sugiyarto, Priya Deshingkar, and Andy McKay examine internal migration and poverty in Indonesia using the Indonesian Family Life Survey (IFLS) panel data for 2000 and 2008. They show that 28% of the population has migrated over a 7-year period, with the majority moving by themselves and locally within provinces. The most common causes of migration are for family reasons, followed by work and then school. Migration is more likely for older household members with higher education, while gender is not found to be a determining factor. Costs, distance, and locations are important determinants of internal migration.

Contrary to the common view, the authors find that only 8% of all migrants move from rural to urban areas, 40% rural to rural, 37% urban to urban, and 15% urban to rural. No matter what the movement type, poverty reduction among return migrants is always higher compared to current migrants. The authors find that 35% of “currently away” migrants are in the top per capita expenditure quintile compared to 19% of nonmigrants. This agrees with the findings for Indonesia in Chap.  3 of Part I. However, the poorer migrants move from rural to urban areas and are found to experience the least, if any, improvement in poverty. Chapter  7 by Nandini Mukherjee and Biswajit Chatterjee also shows a decline in poverty for India. The National Sample Survey (NSS) data for six rounds shows that urban poverty has fallen both at the national and state level in India since the 1990s. However, the authors find there are substantial differences across states and time, and the results do vary depending on the type of methodology used in estimating the urban poverty line. Orissa (Odisha) was the only state that experienced no fall in poverty during these years. In comparison, the large and increasingly urbanized state of West Bengal experienced large falls in poverty, although there was an increase in inequality during this time, consistent with the findings on India in Chap.  5 of Part I. The fixed and random effects panel regressions reveal that the decline in urban poverty is significantly associated with increased urbanization, per capita public expenditure on education and health, and per capita industrial income.

Of the other determinants of urban income and poverty, the effects of urban–rural wages and their differentials are major. Collective bargaining, minimum wage laws, and efficiency wages in the urban formal sector widen income disparities between the urban formal–informal and rural–urban sectors and skilled–unskilled workers. In Chap.  8, Jajati Keshari Parida analyzes the migration-specific National Sample Survey (NSS) data for India for the years 2000 and 2008. The share of migrants in urban population increased from 33.3% in 1999–2000 to 35.5% in 2007–2008. This share is more than 40% in Maharashtra, Delhi, Haryana, Andhra Pradesh, Orissa, Chhattisgarh, and Uttarakhand. Small and medium cities are growing faster than the big cities. Chapter  11 identifies top 10 urban areas (cities) which received the highest rural to urban migration in order in 2001: Surat, Dhanbad, Nashik, Greater Mumbai, Kochi, Asansol, Jamshedpur, Delhi, Rajkot, and Patna. Bivariate probit regressions are used to simultaneously estimate the dual migration and workforce participation decisions. Labor force participation in India is affected by the level of technical education and is found to be the main determinant of rural to urban migration. The average wage of migrants is higher than that of nonmigrants across industries and occupations for regular salaried employment. This difference also applies to migrants in the higher wage distribution quintiles who are engaged in casual or informal employment, but the difference is not consistently higher across industries and occupations. All industries have average wages higher than in agriculture, which confirms the pull of workers from agriculture to other sectors. Decomposing the wage gap between migrants and nonmigrants shows that differences in productivity endowments like age, sex, and education levels are significant, explaining over 90% of the wage differentials between the two groups. These results are consistent with the analysis in Chap.  4 finding that migrants in the PRC work harder and obtain higher wage incomes.

The high incidence of poverty; increasing mean years of schooling; growing enrollments at higher, technical, and vocational education; and increasing number of migrant’s labor force participation have implications on urban infrastructural facilities especially on urban housing/slums. Chapter  8 has some limitations by not explicitly analyzing the impact of rural–urban migration, with the implications on urban infrastructural facilities especially on urban housing/slums. Chapter  11 addresses this issue, indicating that about 18.78 million urban households are facing housing shortage and around 17.4% of urban households are living in slums in 2011.

In Chap.  9, Mohamed Marouani and Björn Nilsson examine the role of skills in increasing productivity. They show that the evolution of educational attainment among Malaysians, as a measure of human capital skills, has increased substantially in the last two decades. They highlight the large drop in numbers with only a primary education or less, coupled with an increase in the number of secondary and tertiary educated. This has coincided with a sixfold increase in the number of universities from 7 in 1990 to 42 in 2009 and the increase in vocational education polytechnics and community colleges.

The authors then examine the impact of education by developing a dynamic general equilibrium model. Detailed labor market characteristics include jobs across sectors and workers with different ages and skills defined according to education and fields of study. A microdata social accounting matrix with social security contributions and transfers is developed using an available 2005 input–output matrix and the 2007 Labor Force Survey (LFS). The model is simulated to consider, first, the possible effects of skill-biased technological change on wages and unemployment and, second, the consequences of affecting the supply of education in Malaysia. The counterfactual simulations show that skill-biased technological change increases skilled wages and reduces skilled unemployment, with the unskilled facing lower wages and higher unemployment. However, substantial expansion of higher education significantly reduces wage inequalities by limiting the increases in skilled wages. The simulations show that skill-biased technological change benefits the skilled labor sectors, provided it is coupled with open-door higher educational policies. Again, the findings here are in line with those of Chap.  4 for the PRC and Chap.  8 for India that migrants are better off because they tend to obtain higher wages.

The chapters in Part II, therefore, collectively indicate that internal migration and urbanization have led to declines in urban poverty mostly due to the traditional arguments that skilled migrants receive higher wages and income in formal and, to a lesser extent, informal employment. However, there is evidence for Indonesia that poorer, less skilled rural workers do not receive the same benefits from migrating to urban areas. This will be further considered, along with the case for the PRC, in the next section.

3 Part III: Polarization and Poverty Gaps

The chapters in Part III focus on the complications arising from internal migration and urbanization, particularly in terms of increasing multidimensional poverty and widening poverty gaps.

The Harris–Todaro model predicts that higher wages in urban areas induce rural–urban migration, which helps close the urban–rural wage gap. But such migration may lead to rising urban inequality when labor heterogeneity is taken into account and skilled migrants move to cities. The impact of migration on the wage of the unskilled migrants depends almost entirely on the magnitude to which skilled and unskilled workers are complements or substitutes. Such wage divergences are only a part of the story because urban migrants may invest in physical and riskier investments, and this will eventually influence on real average income and income inequality of urban sector (Lucas, 1997). In reality, the effect of urban migration on income inequality is ambiguous.

Jing Yang and Pundarik Mukhopadhaya examine the dimensions of poverty in the PRC in Chap.  10. They use the China Health and Nutrition Survey (CHNS) longitudinal data for the years 2000 to 2011 to incorporate capability and social inclusion as additional poverty indicators. The four dimensions they take into account are income, health, education, and living standards, and the income poverty line is adjusted to include economic vulnerability and food insecurity. Until now, measures of poverty have been based on income in Chaps.  4,  8, and  9 or on consumption expenditure in Chaps.  3,  5,  6, and  7. This method helps identify not only different categories of the poor but also target resources and policies of poverty alleviation more accurately. The authors find that multidimensional poverty declined over the decade, but the decline has slowed since 2009. Including economic vulnerability and food insecurity reduces these falls, and using the $1.51 cutoff even increases the index. The rural–urban disparity for moderate poverty decreased prior to 2009 but has increased since then. The disparity for severe poverty is high for all the sample years.

Per capita income, health insurance, and the highest level of education are the major contributors to decreasing multidimensional poverty for urban dwellers. It is more difficult to determine the main contributors to reducing rural poverty, although improved toilet facilities and cooking fuels as well as per capita income and education appear important. For the rural poor, vulnerability to risk, particularly with income fluctuations, is very important. The analysis concludes that the rural–urban gap has narrowed in terms of the severity of multidimensional poverty but less so in terms of its intensity.

In Chap.  11, Sabyasachi Tripathi tests whether urban economic growth has been absolutely or relatively pro-poor in India. “Absolute pro-poor” is defined as the income of the poor increasing in absolute terms, while “relative pro-poor” is defined as the increase in income being at least the increase in mean expenditure. The data used to calculate the indices comes from the urban household monthly per capita consumer expenditure (MPCE) figures of the NSS for 2004, 2009, and 2011. The statistical evidence supports that India’s urban economic growth has been absolutely pro-poor but relatively anti-poor in this period.

This conclusion can be linked to Chap.  5, which shows evidence of increasing urban inequality in India. Given that most of the poverty reduction policies in India and the PRC are designed to target rural rather than urban poverty, these findings indicate a need to reorient policies to reduce poverty.

The final chapter is a study of the unskilled rural poor migrating to urban areas only to become part of the urban poor. Abu Hena Reza Hasan studies migrants who become rickshaw pullers in urban Dhaka, Bangladesh, and this can be considered as a case study for Chaps.  10, and  11 of Part III. Dhaka is one of the largest cities in the world. Since it lacks motorized public transport, human-pulled pedicabs are the primary mode of transport. These human rickshaws provide over half of the estimated daily trips in the city for its 15 million inhabitants. The lack of any required skills reduces barriers to entry for workers from the rural sector, and there has been a large increase in these urban workers.

The researcher completed 127 survey questionnaires with the rickshaw pullers in Dhaka during 2014. Nearly all of those interviewed migrated to Dhaka to become rickshaw pullers—with two-thirds previously agricultural workers—and came without their families. Regression analysis shows their expected income is two-thirds higher than for employment at home outside Dhaka and marginally higher than that for other employment in Dhaka. The calculated present value benefit–cost ratio is 1.37 for a rickshaw puller who migrated with his family and only 1.19 for migration without family. The survey found that one-third of the rickshaw pullers were not able to increase wealth, and a quarter had only cash savings. The lack of ability to accumulate assets over the short physically arduous working period diminishes their ability to get out of poverty.

The central thread of the chapters in Part III is the complexities involved in examining urban poverty in the PRC, India, and Bangladesh. Multidimensional poverty has increased since the global financial crisis (GFC). The rural–urban gap for severe poverty also remained high for this period, and the rural poor remain vulnerable to risk. India’s urban economic growth is found to be pro-poor in absolute income changes but anti-poor in relative income terms for the same period. For the case study of Bangladesh, the induced migration to the big city of Dhaka transforms the rural poor into urban poor, caught in a poverty trap with worsening urban working and living conditions.

4 Concluding Remarks

The recent demographic transitions in Asia in the form of spectacularly increasing internal migration and urbanization are unprecedented in history, and as Hugo says in Chap.  2, poverty is fast becoming an urban issue. Skilled workers in urban areas and migrants returning home are quickly moving out of poverty. So while poverty is falling and winners are now being identified, there are those in urban areas who are being left behind. The new challenge is for research to identify the newly emerging urban disadvantaged and provide policies to assist them out of poverty. Data remains a problem, but more importantly there is a need for new methodologies relating to the complex and evolving dynamic interrelationships in urban areas. The examination of one or two issues in isolation must give way to a system-wide approach based on innovative concepts and measures of poverty. The chapters presented here are an attempt to start this process of enquiry.

Footnotes

  1. 1.

    This compares with Europe comprising only 14% and Latin America and the Caribbean 13% of the world’s urban population (UN DESA World Urbanization Prospects: The 2014 Revision).

  2. 2.

    The other countries with large urban populations are the United States with 263 million, Brazil 173 million, Japan 118 million, and the Russian Federation 105 million.

  3. 3.

    This very positive outcome contrasts with the predicted decline in the Asian rural sector population of 0.2% per annum over the same period and dominates the slower forecast urban population growth of 0.7% per annum in the more developed regions of the world.

  4. 4.

    While global spending on infrastructure and capital projects is expected to increase from US$ 4 tn in 2012 to US$ 9 tn by 2025, Asia’s emerging economies’ proportional share of global spending on infrastructure is expected to increase from 30% of global spending in 2012 to 48% by 2025 (Beyondbrics, 2014).

  5. 5.

    Rapid urbanization places tremendous pressure on the environment, especially due to increase in particulate matter (PM) and carbon monoxide levels because of rapidly increasing industrial products and road transport. Of the world’s most polluted 57 cities, around 60% are located in Asia. If European air quality standards are used as the benchmark, 67% of Asian cities fail to meet those standards compared to less than 11% of non-Asian cities (Wan and Wang, 2014).

References

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Authors and Affiliations

  • Kankesu Jayanthakumaran
    • 1
    Email author
  • Reetu Verma
    • 1
  • Guanghua Wan
    • 2
  • Edgar Wilson
    • 1
  1. 1.Faculty of Business, School of Accounting, Economics and FinanceUniversity of WollongongWollongongAustralia
  2. 2.Institute of World Economy, Fudan UniversityShanghaiPeople’s Republic of China

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