DP2016-29

Asia's Rural-urban Disparity in theContext of G rowing Inequality*
Katsushi S. IMAI
Bilal MALAEB
August 18, 2016

* The Discussion Papers are a series of research papers in their draft form, circulated to encourage
discussion and comment. Citation and use of such a paper should take account of its provisional character.
In some cases, a written consent of the author may be required.

Asia's rural-urban disparity in the context of growing inequality

Katsushi S. Imai *Economics, School of Social Sciences, University of Manchester, UK & RIEB, Kobe
University, Japan
Bilal Malaeb
Oxford Department of International Development, University of Oxford, UK
This Version: 18th August 2016

Abstract
This study offers empirical evidence on the rural-urban gap in the context of growing
inequality in Asia. First, China and India explain the trends of regional inequality given
their large population, signifying their importance as major contributors. Overall, China’s
income inequality is characterised by rural-urban disparity, but the inequality withinrural and/or within urban areas has worsened, although it experienced very higheconomic growth. India is mainly characterised by high inequality within urban areas
despite a sharp reduction in urban poverty. Rural-urban income gap has narrowed inrecent years. We also find that the rural and urban income gap has narrowed in many
countries, such as, India, Vietnam and Thailand. Second, our econometric results on the
agricultural and non-agricultural income gap suggest that higher non-agricultural growthrate tends to widen the urban-rural gap over time, while agricultural growth is unrelated
to the rural-urban gap. Third, the rural-urban human resources gaps in terms ofeducational attainment have increased in both India and China. Fourth, remittances arelikely to reduce poverty in many countries. Policies which would promote agricultural
growth and rural education are deemed important not only for reducing rural poverty, butalso for narrowing the rural-urban gap of human resources.

*Corresponding Author:
Economics, School of Social Sciences,
University of Manchester,
Arthur Lewis Building, Oxford Road,
Manchester M13 9PL, UK
Email: Katsushi.Imai@manchester.ac.uk

Acknowledgements
This study is funded by Asia and the Pacific Division (APR), IFAD (International Fund for
Agricultural Development). The authors thank Fabrizio Bresciani, Raghav Gaiha and one reviewer for
their advice. The views expressed are, however, personal and not necessarily of the organisations towhich we are affiliated or of IFAD.

1

Asia's Rural-Urban disparity in the context of growing inequality
1.

Introduction

Structural transformation of the rural economy involves (i) urbanization, (ii) growth of therural non-farm economy, (iii) dietary diversification, (iv) a revolution in supply chains andretailing; and (v) transformation of the agricultural sector (Reardon and Timmer, 2014; Imai,
Gaiha, and Bresciani, 2016). As the country experiences structural transformation, rural andurban disparity tends to increase. If, for example, labour productivity in rural areas rises at a
slower rate than in urban areas, the disparity between rural and urban areas will widen even if
the rural and urban population shares remains constant. If this comes with an increase in theshare of population or labour force in urban areas, overall inequality tends to increase much
faster. Other aspects of structural transformation may be associated with inequality and ruralurban disparity in more complex way. Growth of the rural-nonfarm economy tends topromote the growth of rural economy and reduce poverty significantly, as shown by Imai,
Gaiha and Thapa (2015) for Vietnam and India. However, whether this reduces rural-urban
disparity is unclear as growth of rural non-farm sector may be associated with an interaction
between farm and non-farm sectors, industrialisation and/or the growth of service and retailsectors at the national level.1 2
The focus of this paper is on whether disparity between rural and urban areas has
increased and the underlying reasons for the change. Of particular importance are farm andImai, Gaiha and Cheng (2015) found that agricultural value added per capita – specified as an
endogenous variable in the model – significantly reduced poverty headcount ratio, poverty gap andpoverty gap squared defined at the international poverty thresholds, US$1.25 and US$2.00, as well as
the Gini coefficient calculated by LSMS household datasets.
2
Dietary diversification - which is associated with nutritional improvement - may take place much
faster in urban than in rural areas and may expand the rural-urban disparity in nutritional conditions.
Nutritional disparity is more complex than income disparity as higher intakes of calories and fats leadto obesity (Gaiha et al. 2014, You, Imai and Gaiha, 2016). While the rural-urban disparity in nonincome aspects of welfare, such as nutrition, is important, the focus of this paper is restricted toincome disparity between urban and rural areas.
12

non-farm linkages and whether higher rural incomes are in part due to more diversified
livelihoods and emergence of high value chains and the extent to which these have reduced
rural-urban disparities and dampened migration. Apart from easier access to credit in order tostrengthen farm and non-farm linkages as well as participation of smallholders in high value
chains, other major policy concerns relate to whether remittances could be allocated to moreproductive uses in rural areas, through higher risk-weighted returns-specifically, whether
returns could be enhanced in agriculture and rural non-farm sector while risks are reduced.
This study will also discuss the policy implications of growing farm and nonfarm disparities.
Given the objectives of this study, it carries out analyses of cross-country panel data andhousehold data.
The rest of this study is organised as follows. The next section reviews the statistics todiscuss the shifts in Asia’s and sub-regional income distributions with a focus on whether
India and China are largely responsible for these shifts. Section 3 further analyses the crosscountry data to see whether rural and urban income gaps have narrowed; what sort of factors
are associated with the narrowing gap; whether dispersion of earnings within rural non-farmactivities has narrowed or widened. Section 4 identifies factors associated with narrowing theincome gaps and migration. Section 5 reviews the human resource gaps between urban andrural areas using descriptive statistics. Patterns in demographic transitions, urbanization,
labour force growth, and demographic dividend are reviewed and summarised in Section 6.
Section 7 analyses impacts of remittances on poverty, inequality and growth at the national
level, and sectoral growth rates. The final section offers concluding observations with policy
implications.

32. Changes of Income Distributions in Asia - Overview
The overall change in Asia’s income distributions is intricately associated with those in subgeographical categories, such as sub-regional (e.g. South Asia), national (e.g. India) and
subnational levels (e.g. state level). In the context of developing countries, the income gap
between rural and urban areas and its changes will influence income distributions at all thesegeographical levels.
A recent ODI report has suggested that rural wages have recently increased substantiallyin most Asian countries with some acceleration from the mid-2000s (e.g. Bangladesh, China
and India) (Wiggins and Keats, 2014) This may have been partly due to a decrease inpopulation - as a result of decline in fertility rates - and an increase in manufacturing growthrate, which has accelerated the rise in rural non-farm wage (ibid., 2014). Whether the rise inrural wages has decreased the rural-urban wage gap is an empirical question, but using the
National Sample Survey Data for India, Hnatkovskay and Lahiriz (2014) found a significant
decline in the wage differences between individuals in rural and urban India during the period
1983 to 2010. The increase in rural wages may not necessarily imply increase in rural output
or income, as it will increase production costs in both agricultural and non-agricultural
sectors. Increase in rural wages may also be linked with higher food price, which may limit
the benefit for poor households.
Our main interest lies in the rural-urban gap of the overall income, not wage levels, andso this section provides evidence on the changes in Asia’s income distribution. We firstevaluate whether large countries (for instance, India and China) bear the main responsibility
for the shifts in income distribution of Asia. We will review the trends in selected measures,such as the Gini coefficient, mean income, poverty headcount, and the poverty gap inselected Asian countries.

4

The Gini Coefficient (%)
50

China*
China--Rural
China--Urban

45

India*
India--Rural

40

India--Urban
Indonesia*
Malaysia

35

Pakistan
Philippines
30

Sri Lanka
Thailand
Vietnam25
1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012

Figure 1 – The Gini Coefficient for different Asian Countries from 1988 to 2013, based on
povcal data from World Bank (Authors' computations)Figure 1 illustrates the time trends of the Gini coefficient during the last three decades acrossdifferent Asian countries to see whether the trends of the Gini coefficient for India and China- disaggregated into rural and urban areas - are similar, or dissimilar to those of othercountries.
We observe that the national Gini coefficient of China increased sharply from 33% to 42%
during 1998-2001 and remained at a high level, around 42-43%, with a small decline in 20082012. It is striking to find that the national Gini remained higher than the rural Gini and theurban Gini, implying that rural-urban disparity remained high in China. 3 It is noted that
inequality was much higher in rural than urban areas. The rural income Gini coefficient in

3

More rigorously, the Theil indices should be used for the national household data to decompose
them into sub-national components, as in Kang and Imai (2012). Decomposing the Gini coefficient isfeasible, but it is not a simple procedure since the functional form of inequality indices is not
additively separable in incomes (Araar, 2006). Under some assumptions, Yang (1999) decomposed
the Gini coefficient of household income in two provinces in China (Sichuan and Jiangsu) and foundthat the rural-urban inequality dominates within-rural or within-urban inequality components.

5

China increased from 31% to 41% from 1989 to 2009 with some fluctuations, but it recorded
a significant decrease in 2009-2011. This is important as rural poverty has constantly
declined over time in China (Imai and You, 2014). It is inferred that economic growth has
resulted in reduction of rural poverty over the years, but the growth was not pro-poor in rural
China. On the other hand, the urban Gini coefficient, although lower than the rural Gini,
constantly and significantly increased from 25-26% in 1989 to 35-36% in 2011. Overall,
China’s inequality is characterised by rural-urban disparity, and inequality within-rural orwithin urban areas has constantly worsened, while the country experienced very higheconomic growth.
India’s Gini coefficient of expenditure at the national level has also increased steadily
from 31% to 36% from 1993 to 2011, but the annual average increase in the Gini coefficient(0.28%) is smaller than that of China (0.39%, where the national Gini of expenditure
increased from 35.5% to 42.5% in the same period). Indian inequality is characterised by thehigh Gini coefficient in urban area, rather than the rural-urban disparity because the urban
Gini is higher than the national Gini, which is higher than the rural Gini with the order of thethree unchanged over time. The urban Gini coefficient in India increased from 34% to 39%,
while the rural Gini marginally increased 28% to 31% in 1993-2011. This is in sharp contrast
to poverty trends in India in the same period 1993-2001 in which national poverty head countdeclined from 45.3% to 29.8% (-15.5%), urban poverty headcount from 31.8% to 20.9% (10.9%), and rural poverty from 50.1% to 33.8% (-16.3%) with broadly similar trends
observed for poverty gap and poverty gap squared (Himanshu, and Sen, 2014). Overall, India
is characterised by high inequality within urban areas despite a sharp reduction in urban
poverty. Indian economic growth reduced urban poverty, but the urban rich benefited more in
relative terms.

6Given the huge populations of India and China, the steady increase in income inequality ofthese countries is likely to be a leading cause of the overall shift in income distribution in
Asia, while the rural-urban disparity - as well as the high and increasing inequality in rural
China and the high and increasing inequality in urban India characterise overall inequality atnational levels.
This does not discount altogether the roles played by Malaysia, Philippines, Thailand, andVietnam showing comparatively higher levels of inequality with overall increasing trends
during the period between 1990 and 2010. For instance, Malaysia’s national Gini remained
very high in the range from 46 to 48%. The urban Gini coefficient reduced from 44.0% to43.9% and the rural Gini rose from 40.9% to 42.6% in 2004-2009, implying that ‘within
urban’ and ‘within rural’ inequality explain the high national Gini coefficient.4 Thailand has
experienced an overall decreasing trend in the national Gini coefficient (44% to 39%) withsome fluctuations. As in Malaysia, both the urban Gini and the rural Gini remained high in
Thailand with the former at slightly higher levels than the latter (urban: 40.5% to 38.0%;
rural 35.8% to 36.2% in 1999-2012). The Philippines also recorded a relatively high Gini
coefficient (39%-46%) with fluctuations in 1990-2010, with a similar pattern in the
disaggregated Gini coefficients (urban 44.4% to 41.1%; rural 36.9% to 39.1% in 2000-2011).
The national Gini of Sri Lanka increased from 33% to 38% with the urban rising from 38.4%
to 39.9% and the rural Gini from 33.1% to 37.4% in 1996-2013. Indonesia has seen an
increase in the national Gini coefficient from 29% to 35%, with both the urban and the rural
Gini increasing between 1987-2011 (urban 32.8% to 42.5%; rural 27.7% to 34.2%), and witha broadly similar pattern to India’s. The Gini in Pakistan was relatively low in the range

4

Disaggregated results of the countries other than China and India are not shown in Figure 1 to avoid
cluttering it.

7

between 29% and 33% with a much higher inequality in urban areas (urban 32.0% to 34.0%;
rural 24.5% to 24.5% in 1997-2011).

700
600

Mean household income (US$)

China*
China--Rural
China--Urban
India*

500

India--Rural
India--Urban

400

Indonesia*
Malaysia

300

Pakistan
Philippines
Sri Lanka

200

Thailand
Vietnam

100

1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013

0

Figure 2 –Annual Mean Household Income (at PPP in 2011, US$) for different Asian Countries
from 1988 to 2013, based on Povcal data/World Development Indicator from World Bank
(Authors' own computations)Figure 2 plots the trends of annual mean household income during the period 1988-2013. The
trends of national, urban and rural mean incomes in China suggest that household income has
increased more rapidly in urban than in rural areas, which explains the increasing rural-urban
disparity in China. On the other hand, mean household income in India has increased at
slower rates in both urban and rural areas. 5 In recent years (after 2000), mean household

5

In Appendix 1 (Figure A-1), we have compared the trends of GDP per capita (at 2011 PPP) for
China and India with regional and population-weighted averages of East Asia and South Asia (in bothof which developing countries are excluded). As China and India are the two most populous countries
in the world, China’s trend coincides with East Asia’s, while India’s trend coincides with South
Asia’s. China and India, however, surpassed the regional average after 2005-2006 (to a much larger
extent in China). We have also compared poverty headcount ratios and poverty gaps for China, India

8

income in rural India appears to increase faster than mean household income in urban India,
suggesting the narrowing of rural-urban income gap.6 Mean household income, however, wasfluctuating in Malaysia. Other countries have experienced more or less steady growth ofmean income in the same period (e.g. Thailand and Sri Lanka).
90
80

Poverty Headcount (%)

70
60
50
40
30
20
10
0

China*
China--Rural
China--Urban
India*
India--Rural
India--Urban
Indonesia*
Malaysia
Pakistan
Philippines
Sri Lanka
Thailand
Vietnam1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012

Figure 3 - Poverty headcount trends for different Asian Countries from 1988 to 2013, based on
povcal data from World Bank (Authors' own computations).

and their regional population-weighted averages and have found that China’s poverty figures match
East Asia’s and India’s are broadly same as South Asia’s (Figures A-2 and A-3). It is noted that while
China’s poverty figures lower than East Asia’s after 2004, India’s poverty estimates are higher thanSouth Asia’s after 2002-2003. Overall, China and India’s trends explain regional trends of GDP per
capita and poverty. Regional averages of the Gini coefficients are not available from WDI.
6
This is investigated in greater detail in the next section.

9

35

Poverty Gap (%)

30

China*
China--Rural
China--Urban
India*

25

India--Rural
India--Urban

20

Indonesia*
15

Malaysia
Pakistan

10

Philippines
Sri Lanka

5

Thailand
Vietnam01988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012

Figure 4 - Poverty gap trends for different Asian Countries from 1988 to 2013, based on povcal
data from World Bank (Authors' own computations)

It is observed in Figures 3 and 4 that both poverty headcount ratio and poverty gap havedeclined over the years for most countries. In China, rural poverty declined dramatically in
1989-2009, which resulted in a sharp decline in national poverty, while urban poverty
declined but moderately. This is in contrast with increasing trends in the national Gini, therural Gini and the urban Gini (Figure 1). That is, economic growth has resulted in substantial
poverty reduction in both urban and rural areas, but rural-urban disparity as well as inequality
within rural or urban areas increased during the period when China experienced economic
growth. India shows similar patterns of poverty reduction, that is, both rural and urban
poverty declined, while the rate of reduction has been higher in rural than in urban areas forboth poverty headcount ratio and poverty gap. However, the rate of poverty reduction ismuch slower in India than in China regardless of definitions of poverty or rural-urban
distinctions. Other countries have also experienced overall poverty reduction during theperiod 1988 - 2012.
10

Figure 3 – Average annual change in the Gini coefficient in developing Asia in the 1990s and the2000s (%). Source: Inequality in Asia and the Pacific (ADB, 2014)

Figure 3 illustrates the average annual change of the Gini coefficient in different Asian
countries. The left panel overviews the average annual changes in the Gini of Asiandeveloping countries, and the right panel identifies the countries with positive changes. It isclear from the left panel that China had the highest increase in the Gini coefficient in the
1990s and 2000s. India also had experienced an increase in the Gini coefficient in the same
period.
We can conclude that, while India and China experienced high economic growth andpoverty reduction in both urban and rural areas, the rural-urban disparity became larger in
China. On the contrary, both ‘within-rural’ and ‘within’ urban inequality also increased inboth India and China. In India, the rural-urban income gap decreased in recent years.

3. Rural and Urban Income Gaps
Another important question that may arise from the graphical analyses in Section 2 is whether
the rural and urban income gaps have narrowed or widened. Given the data limitations on
11rural and urban income, we also examine agricultural and non-agricultural income gaps as
related evidence. Recent studies suggest that the share of income from farming activities has
decreased, while the share of non-farm activities has risen as the wealth of households
increased (Covarrubias et al., 2013). The authors emphasise the relative importance ofagriculture and farm activities for poorer countries. The variation in non-farm income,however, reveals that considerable differences exist between non-farm sources of income
within each country (see Figure 4). Covarrubias et al. (2013) highlight the increasing
diversity in income generation portfolios among households across countries in Asia.

12Figure 4 - Composition of non-farm, non-agricultural, and self-employment by sector. Source: Covarrubias et al. (2013)13

Wiggins and Keats (2014) suggest that an increase in rural wages in the 2000s has been
observed in almost all the Asian countries. In China and India, in particular, the increase hastaken an accelerated avenue in the second half of the 2000s. The increases between 2005 and2012 in India ranged between 35% and 92% in China. As shown in Figure 6, similar trends
are observed in other Asian countries (Wiggins and Keats, 2014).

Figure 5 - Changes in real daily wages for agricultural labour in four Asian Countries (USDollar constant 2010 PPP) - Source: Wiggins and Keats (2014).

If we examine the case of Bangladesh more closely, the gap between urban and rural wages
has narrowed as a result of rise in rural wages (Zhang et al., 2013, see Figure 6 below). Herethe average rural wage for a male worker has increased by around 45% during 2005-2010.

Figure 6 - Urban and rural wages for unskilled workers trends in Bangladesh 2001-2011 Source:
Zhang et al. (2013, p.5)

14

We have processed and analysed the Indian Human Development Survey (IHDS)7 household
data (2005 to 2012) as well as VHLSS data (2002, 2004 and 2006) to examine closely the
distribution of income per capita. A number of graphs are reported in Appendix 2. For
instance, in India we have found that (i) The gap showing the degree of inequality acrossdifferent distributional points - narrowed between 2005 and 2012 and (ii) both ruralinequality and urban inequality rose in the same period (Figure A4 in Appendix 2). It is foundthat the rural-urban gap in income inequality (in terms of real income per capita) marginally
narrowed over the period 2002-2006 in Vietnam (Figure A7 in Appendix 2).
We have also used data from the ILOSTAT for mean monthly employment related income
from all self-employed persons in the working age population for Thailand. We observe an
increase from 2011 to 2014 in mean income in rural areas, an increase of urban income from2011 to 2013 followed by a slight decline in 2014, and an overall increase in average income.
From 2011 to 2013, rural and urban income moved almost in parallel, but the difference
reduced in 2014. So overall, the gap between urban and rural income has narrowed in
Thailand.
16000
15000
14000
13000
12000

Total

11000

Rural

10000

Urban

9000
8000
7000
2011

2012

2013

2014

Figure 7 - Data on income of the self-employed on the basis of the mean monthly employmentrelated income of all self-employed persons in the working age population (Source ILO-STAT)

7

Details of IHDS are found on http://www.ihds.umd.edu/.

15

It is generally inferred that the rural-urban gaps of households at different distributional
points have been narrowing in a number of Asian countries over time, for instance, due to the
reduction of the rural-urban wage gap over time.
4.

Cross-country evidence

We will now turn our attention to the underlying factors which explain the gap between
agricultural and non-agricultural incomes. Ideally, we should examine the causes for therural-urban income gap using the cross-sectional data, but to our knowledge such data are notavailable. Because rural non-agricultural income is growing while urban agricultural income
is low, it is inferred that the estimate of the gap between agricultural and non-agricultural
income provides the upper limit of the urban-rural income gap. Also, the share of the ruralnonfarm sector varies considerably across countries. We would thus use the gap between the
non-agricultural and agricultural income gap only as a crude estimate of urban-rural income
gap. In this section, we will also examine the determinants of rural inequality and the rural urban migration.
The factors which narrowed the farm and non-fame income gap are estimated by thefollowing econometric model applied to the cross-country panel data for Asian countries.
𝑨𝑫𝑖𝑡 = ∑𝑃𝑗=1 𝛼𝑗 ∆𝑨𝑮𝑖𝑡−1 + ∑𝑄𝑘=0 𝛽𝑘 ∆𝑵𝑨𝑮𝑖𝑡−1 + 𝑿′ 𝑖𝑡 𝛾 + 𝜂𝑖 +𝜀𝑖𝑡(1)

where i and t denote country and year (1963, … , 2012), 𝑨𝑫𝑖𝑡 is the difference between nonagricultural value added per capita and agricultural value added per capita, which serves as aproxy for agricultural and non-agricultural income disparity, ∆𝑨𝑮𝑖𝑡−1 is the lagged firstdifference of log of agricultural value added per capita (or lagged agricultural growth), and∆𝑵𝑨𝑮𝑖𝑡−1 is the lagged first difference of log of non-agricultural value added per capita (orlagged non-agricultural growth). Agricultural growth and non-agricultural growth are lagged
to consider, at least partially, the endogeneity of these terms. 𝑿′ 𝑖𝑡 is a vector of control
variables including, macro institutional quality, land area, population density, fragility index,
16

trade openness, and labour force with secondary index (see Appendix 3 for definitions anddescriptive statistics of the variables). 𝜂𝑖 is an unobservable individual effect and 𝜀𝑖𝑡 is anerror term. Using the Hausman test, we reject the hypothesis that the household-level effects
are uncorrelated with the covariates we control for. We therefore choose the fixed-effects
model as our preferred specification.
Table 1 - Fixed Effects and Random Effects results of the Farm-Non Farm Income Disparity
(1)

(2)

FE

RE

0.03

0.0344(0.675)(0.685)6.492***

9.230***(2.248)(2.021)0.874***

0.872***(0.131)(0.135)-0.670***-0.678***(0.136)(0.144)0.164***

0.0957(0.0595)(0.0616)0.165***

0.0678(0.0490)(0.0423)-0.0496-0.0909(0.0601)(0.0599)-0.575***-0.641***(0.128)(0.132)0.152***

0.148***(0.0338)(0.0341)

6.998

8.299

(0.839)(0.851)

Observations

125

125

R-squared

0.824

0.824

Number of years

17

17

VARIABLES
L.dlogagrivapc(lagged agricultural growth)

L.dlognoagrivapc(lagged non-agricultural growth)Institutional Quality

land

population_density

fragility_index

Openness

Ethnic fractionalization

lab_with_second

Constant

Notes. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Statistically significant
coefficient estimates are shown in bold.

17

The result in Table 1 indicates that the higher lagged non-agricultural growth tends to widen
the sectoral income gap, while the lagged agricultural growth does not. If sectoral growth ispersistent over time, there still remains the issue of the endogeneity of these lagged sectoral
growth terms, but given this caveat, our results imply that a higher level of non-agricultural
growth tends to widen the non-agricultural and agricultural income gap over the years. As
non-agricultural growth can be associated with both urban and rural non-agricultural growth,
but given that the urban non-agricultural growth is likely to be much larger than rural nonagricultural growth, we can infer that higher non-agricultural growth rate tends to widen theurban-rural gap over time, while agricultural growth is unrelated to it.
If a country has better macro institutional quality, the gap between non-agricultural andagricultural income tends to be larger. That is, after controlling for (lagged) agricultural andnon-agricultural growth, better institutional quality at the country level is associated with alarger sectoral income gap. This is consistent with urban institutional quality favouring
investment in urban areas and thus contributing to non-agricultural growth. Larger land areas,on the other hand, tend to narrow the income gap in favour of agricultural sector, while thehigher population density - which tends to be correlated with urbanisation - is associated withlarger income gap.
If the country’s macro situation is more fragile, the non-agricultural and agricultural
income gap tends to be larger. The higher ethnic fractionalization index - which reflects theprobability that two randomly selected people will not belong to the same ethnolinguistic
group - , the smaller is the agricultural and non-agricultural income gap. It is plausible that
ethnic fractionalisation dampens both agricultural and non-agricultural growth. Also, better
education in terms of the share of labour force with secondary education is associated withhigher sectoral income gap.

18

In Equation (2), we specify a fixed effects model to estimate the effect of agricultural andnon-agricultural growth on the rural Gini coefficient in rural areas. That is, we examine how
sectoral growth affects inequality in the rural areas.
𝑮𝒊𝒏𝒊_𝑹𝒖𝒓𝒂𝒍𝑖𝑡 = ∑𝑃𝑗=1 𝛼𝑗 ∆𝑨𝑮𝑖𝑡−1 + ∑𝑄𝑘=0 𝛽𝑘 ∆𝑨𝑻𝑖𝑡−1 + 𝜂𝑖 +𝜀𝑖𝑡

(2)

Table 2 - Fixed Effects model of the effect of agricultural and non-agricultural value added onrural Gini coefficient (Cross- country analysis)
The Gini coefficient in

Net rural-urban Migration

rural areas
lagged log Non Agri Value Added PC

0.703*

123.5(lagged non-agricultural growth)(0.368)(100.8)Lagged log Agri Value Added PC-0.823**-75.20(lagged agricultural growth)(0.363)(110.8)Constant

3.462

0.0327(0.0203)(7.438)81705

0.703*

123.5

N
R

2

Notes: Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Statistically significant
coefficient estimates are shown in bold.

The first column of Table 2 suggests that the acceleration of (lagged) agricultural growthtends to reduce the Gini coefficient in rural areas (rural Gini) significantly over time, while
the acceleration of (lagged) non-agricultural growth tends to increase rural Gini significantly.
This is understandable as non-agricultural growth tends to benefit relatively rich households
in rural areas (either through rural-to-urban migration or rural non-agricultural business),while agricultural growth directly benefits the relatively poor households.
We have also examined whether these factors had a dampening effect on rural to urban
migration (the second column of Table 2). We find no significant effect of agricultural andnon-agricultural incomes on internal migration in Asia. Here the non-significant result could
be associated with our proxy for rural- urban migration. Because the cross-country data ofrural - urban migration is not available, we measured it as difference between the net urban

19population growth and the net rural population growth by ignoring the natural population
growth in urban or rural areas and international migrations.
The empirical literature suggests that rural-urban migration in itself has a significant effecton inequality in China, as shown by Ha et al. (2016). The authors suggest that inequality andmigration have a reciprocally intertwined relationship and find that contemporary migration
increases income inequality, while migration from previous periods has a strong income
inequality reducing effect. Ha et al. (2016) have estimated a system GMM model and havefound that rural-urban migration benefited the communities where households with migrants
are dominant. Migration improved educational attainments, increased income andconsumption per capita, and promoted faster economic growth of the rural communities.
While migration tends to increase the gender wage gap in general, it decreases the gender gap
in the migrant’s village of origin.

5.

Human resource gaps between urban and rural areas

We now turn our attention to the human resource gap between urban and rural areas.
Although the urban-rural income gap appears to be narrowing in several Asian countries
except China (see Sections 2 and 3), we observe a clear rural-urban gap in human resourcesin terms of educational levels. In this section, to track the evolution of the rural-urban humanresource gap, we use household data from India - the India Human Development Survey(IHDS) data in 2005 and 2012.
Table 3 reviews the proportion of households in terms of the highest educational
attainment of all household members in each household, based on the four categories - noschooling, primary education, secondary education, and higher education. We observe asignificantly higher proportion of households with no schooling in rural areas (26.9% in 2005,
22.1% in 2012) than in urban areas (9.7% in 2005, 7.4% in 2012), while the proportion20

decreases between 2005-2012 in both areas. The shares of households with primary,secondary, and higher education have consistently risen during 2005-2012 in both rural andurban areas (columns (e) and (f)). The gap in human resources between the two areas
decreased for ‘no-schooling’, primary education and secondary education, but slightly
increased at the higher education level (columns (g) and (h)), which is expected given theconcentration of higher education institutions in urban areas.
Table 3 - Educational attainment in rural and urban areas in India 2005 and 20122005

No SchoolingPrimary Ed.
Secondary Ed.
Higher Ed.
No. Obs.

Rural
(a)

Urban
(b)

26.88%
72.96%
55.78%
8.20%
26734

9.74%
90.18%
80.49%
27.87%
14820

India
2012
Change (within)from 2005 to 2012
Rural
Urban
Rural
Urban
(c)
(d)
(e)
(f)
=(c)-(a) =(d)-(b)22.06%
7.42%-4.82% -2.32%
77.94%
92.58% 4.98%
2.40%
62.25%
84.13% 6.47%
3.64%
10.15%
30.80% 1.95%
2.93%
27579
14573

Gap Across rural andurban areas
2005
2012
(g)
(h)
=|(b)-(a)|=|(d)-(c)|17.14%
14.64%
17.22%
14.64%
24.71%
21.88%
19.67%
20.65%(Source: IHDS data, authors' calculations )6.
Demographic Transitions, Urbanization, Labour Force Growth, Demographic
Dividend
In this section we carry out analyses to illustrate demographic transitions in Asia byreviewing demographic changes in different age groups in rural and urban areas during theperiod 1960-2014, and by analysing the trends in labour force for selected Asian countries inthe same period.
We find that the population aged between 0 and 14 has declined from the mid-1990s
onwards for all Asian countries. This is consistent with evidence in the literature
documenting decrease in fertility rates across Asia (Imai and Sato, 2014). China - along with
India (ibid, 2014) - has experienced a sharp decline as an outcome of the one-child policy (asshown by the dark red line in panel 1).

21

On the other hand, the population aged between 15 and 64 has continued to increase,resulting in an increase in the working-age population. This suggests a positive demographic
dividend, used as a proxy for the future economic growth potential.8 On the other hand, thepopulation above 65 years shows an increasing trend, particularly in China and India,
suggesting the ageing of these countries.
We then turn our attention to the change in rural and urban population structure. We
observe that across Asia the trends suggest dramatic increases in urban population, which is a
result of growing urbanization in Asia, and small increases in most Asian rural population(except for India and China, which exhibit increasing trends).

8

Strictly speaking, the younger workforce population should be analysed, but the graphs suggest thatmany Asian countries have experienced a decline in fertility and an increase in workforce at the sametime. UNFPA (2016) argues that “(a) country with both increasing numbers of young people anddeclining fertility has the potential to reap a ‘demographic dividend’ – a boost in economic
productivity that occurs when there are growing numbers of people in the workforce relative to thenumber of dependents” (http://www.unfpa.org/demographic-dividend). This situation, however, ischanging swiftly in China where the younger working-age population has declined due to the one
child policy which was implemented in 1979, while old-age population has increased significantly(Zhong, 2011).

22

Figure 8 - Changes in population by age groups (Authors' Calculations using cross-country data)

For a subset of the countries and years displayed in Figure 16, the International Labour
Organization has some data for selected countries from 2009 to 2012. Although Figure 17 is asnapshot of the long-term trend shown in Figure 16, it is still useful to draw some inferences
about the urbanization in labour force in Asia. Although the total labor force population (bothrural and urban) shows no statistically significant changes during the period, this can be
attributed to the growing urban labour force and the decline in rural labour force as observedin panels 2 and 3 of Figure 17. This is an indication of the growing urbanization of Asia. Thismight also be due to an increase in rural-urban migration.

23

Figure 9 - Employment changes in Select Asian Countries (Source: ILO Statistical Database Labour Force Surveys) Authors' Calculations

To analyse the extent to which Asian countries have benefited from the demographic
dividend, we estimate the following fixed effects model.
𝑮𝑫𝑷𝑮𝒓𝒐𝒘𝒕𝒉 𝑖𝑡 = 𝛼1 + 𝛼2 𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛15𝑡𝑜64𝑖𝑡 + 𝛼3 𝑆𝑒𝑐𝐸𝑑𝑢𝑐 + 𝛼4 𝑆𝑒𝑐𝐸𝑑𝑢𝑐 ∗ 𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛15𝑡𝑜64𝑖𝑡 +𝑋𝑖𝑡 ∙ 𝛼5 + 𝜂𝑖 +𝜀𝑖𝑡(3)where ‘Population15to 64’ (years) denotes the population in the working age group (15-64
years) and ‘SecEduc’ is the share of the working age population that is educated to asecondary education level. ‘Population15to 64’ and ‘SecEduc’ are interacted to examine
whether the effect of the population dividend varies depending on the country’s educational
level. Xit is a vector including several control variables, namely, FDI share of GDP, Services
Value Added share of GDP, Trade share of GDP, Agricultural Value Added share of GDP,and Industry’s value added share of GDP. The coefficient estimate of population from 15 to24

64 (𝛼̂2 ), and the interaction of secondary education and working age population (𝛼̂2 ) are ofinterest as they represent the demographic dividend’s effect on GDP growth. 𝛼̂2 shows an
overall demographic dividend, while 𝛼̂3 measures the extent to which the effect ofdemographic devident is changed according to the level of education. The results are reported
in Table 4.
Table 4 - Results of fixed and random effects models of the Demographic Dividend(1)
Fixed Effects
GDP growth0.349***(0.122)(2)
Random Effects
GDP growth0.417***(0.108)

SecEdu4.475*(2.633)5.615**(2.553)

Pop15to64*SecEduc-0.0752*(0.0407)-0.0909**(0.0396)

FDI share of GDP (t-1)

0.332***

0.283***(0.104)(0.0881)0.01320.0257

Trade share of GDP (t-1)(0.0250)(0.0231)Agri VA share of GDP (t-1)-0.0170**-0.0121*(0.00603)(0.00625)

agriva_share

0.0997***(0.0316)0.115***(0.0315)

indva_share

0.0184(0.0427)

0.0139(0.0411)

Pop15to64

Services VA share of GDP (t-1)

Initial industry VA share of GDP

Constant-18.80-23.97(7.741)(7.063)N364
364
2
Hausman testchi (8)= 15.66
Prob>chi2 =0.0475
Notes: Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Statistically significant
coefficient estimates are shown in bold.

25

We have estimated both fixed-effects and random-effects models in Table 4. As Hausman
test result is statistically significant and in favour of fixed-effects model, our discussion isbased on it (Column (2)). We find a positive and significant effect of the working age
population, suggesting that there is a positive ‘demographic dividend’ effect on economic
growth. However, the interaction term shows that as the demographic dividend tends toweaken as the share of labour force with secondary education increases. Secondary educationis positive and statistically significant. It is inferred that the demographic dividend is mainly
associated with the uptake of non-skilled urban jobs in Asia.

7.
Remittance flows, impacts on poverty, inequality and national and sectoral
growthWe illustrate the movement of international remittance flows to selected Asian countries in
Figure 18. This shows increases in the levels and the growth rates of the remittance flows
across the years for the Asian countries. In Figure 19, we find that the poverty headcountratios both at the rural and urban level have decreased. A negative statistical correlation
between poverty and remittance flows is implied by these diagrams.
Imai et al. (2014) examine how remittances affect poverty in Asian countries where theeffect of remittances on economic growth is also taken into account. They find that in India
for instance, a 50% increase in the share of remittances in GDP (3.59–5.39%) accelerates
economic growth (from 7.65% to 8.84%) and reduces the US$1.25 poverty headcount from
41.6% to 38.9%, and the US$2 poverty headcount from 75.6% to 72.5%. Similar results are
obtained for Sri Lanka, the Philippines, and Nepal. This suggests that remittances have asubstantial poverty-reducing effect as well as a growth enhancing effect. They conclude thatboth migration and remittances are likely to have a positive and substantial effect on
development efforts. They, however, note that this does not necessarily imply the best option.

26

For instance, as migration and remittances increase, the country of origin may suffer negative
externalities such as brain drain, lower work effort, and the Dutch disease.
More significantly, Imai et al. (2014) found that most of the migrants are not from very
poor households, suggesting that access to credit is a viable strategy. But the use of
conventional financial services is very limited – to avoid the risk of money laundering andfinancial terrorism. This leaves households with the need to rely on costly money-transfer
services or other informal modes of transferring money. This has important policy
implications on how remittances can be steered in a way to enhance the development process
and help in building capacities (financial and by knowledge transfer) for farm and non-farm

10
50

remittancewd

15

investments to flourish in poor countries.

1960

1980

2000
year

cname = Cambodia
cname = India
cname = Malaysia
cname = Thailand

cname = China
cname = Indonesia
cname = Philippines
cname = Vietnam

Figure 10 - Remittance flows by country (Authors’ own computations)

27

2020

80
60

40

40

30

20

20

0

10
0
1960

1980

2000

2020

1960

year

1980

2000

2020

year

cname = Cambodia
cname = India
cname = Malaysia
cname = Thailand

cname = China
cname = Indonesia
cname = Philippines
cname = Vietnam

Figure 11 - Poverty Headcount by rural and urban areas of selected Asian countries (N.B. thisseries includes many missing values)

Based on the analysis above, we could draw policy implications regarding remittance towards
farm/nonfarm investments (e.g. premium exchange rates as in Bangladesh, development
bonds, Foreign Exchange Bearer Certificate (Pakistan), lower import duties on machineryand equipment by migrants (depending on target sectoral investment increase), granting
preferential access to capital goods and raw material imports (India), offering an advisory andinvestment service for returning migrant-worker customers and supplementary loans(Thailand)).

8.

Conclusions and Policy Implications

This study offers evidence on the rural-urban gap in the context of growing inequality in Asia.
Our findings are summarised as follows.
First, China and India explain the trends of regional inequality given their large population,as major contributors. Overall, China’s income inequality is characterised by rural-urban
28

disparity, but the inequality within-rural and within urban areas has constantly worsened,
while it experienced very high economic growth. India is mainly characterised by highinequality within urban areas despite a sharp reduction in urban poverty. Rural-urban income
gap has narrowed in recent years. Given the huge populations in India and China, the steadyincrease in income inequality of these countries is likely to be a leading cause of the overall
shift in income distribution in Asia. The rural-urban disparity in GDP per capita- as well as
the high and increasing inequality in rural China and the high and increasing inequality inurban India- characterise overall inequality at national levels. If we examine the recentchanges in household income distributions in both rural and urban areas, we find that therural and urban income gap has narrowed in many countries, such as, India, Vietnam andThailand. This could be associated at least in part with the narrowing of the rural and urban
wage gap in recent years.
Second, our econometric results on the agricultural and non-agricultural income gap
suggest that higher non-agricultural growth rate tends to widen the urban-rural gap over time,
while agricultural growth is unrelated to the rural-urban gap. If the country’s macro situation
is more fragile, the gap tends to be larger. On the other hand, the acceleration of agricultural
growth tends to reduce inequality in rural areas significantly over time, while the accelerationof non-agricultural growth tends to increase rural inequality significantly. Rural-to-urban
migration will increase income and consumption and reduce the gender gap (Ha et al., 2016).
Third, the rural-urban human resources gaps in terms of educational attainment haveincreased in both India and China. This will have to be interpreted in the growing literature
on demographic dividend in Asia. While the working-age population has increased and the
population of 0-14 years old has decreased, an overall positive effect of the former on
economic growth is reduced if the share of educated labour force is higher. Promotion of
education in rural areas without undermining the positive benefit from demographic dividend29

is likely to be important in many Asian countries. It should be noted that secondary education
alone has a positive and significant effect on economic growth.
Fourth, remittances reduce poverty in many countries. But given that poor households
may not benefit enough from remittances or migration (Imai et al., 2014), these households
need to be supported by policy measures, such as, policies facilitating their access to credit or
skill development by which they can work in the non-agricultural sector. It has also beensuggested that use of remittances for the productive purposes is important for poverty
reduction.
In term of policy implications, policies which would promote agricultural growth and/or
those facilitating the access of the poor or disadvantaged households to remittances or credit
are important for reducing rural poverty. Given that higher non-agricultural growth tends towiden the rural-urban income gap, the acceleration of agricultural growth rate (towards nonagricultural growth rate) would reduce the income gap. Policies to promote education in rural
areas are deemed important to narrow the rural-urban gap of human resources. Policies which
would stabilise the macro economic conditions and reduce fragility are likely to fill the ruraland urban income gap.

30

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0

5000

GDPpc
10000

15000

Appendix 1. Positions of India and China in the changes of Poverty and GDP per capita in the region

1990

1995

2000

2005

2010

2015

Year
Pacific Islands
East Europe & Central Asia (developing)
India

East Asia
South Asia
China

60
40
20
0

Pov_31080

100

Figure A1. Changes of real GDP per capita (at 2011PPP)

1990

1995

2000

2005

2010

2015

Year
Pacific Island
East Europe & Central Asia
India

East Asia
South Asia
China

Figure A2. Changes of Poverty Head Count (based on $1.90 a day at 2011PPP)

34

100
80
60

Pov_310

40
20
0
1990

1995

2000

2005

2010

2015

Year
Pacific Island
East Europe & Central Asia
India

East Asia
South Asia
China

Figure A3. Changes of Poverty Head Count (based on $3.10 a day at 2011PPP)

35

Appendix 2 Graphs showing the distribution of income per capita
Figure A4 illustrates the Lorenz curves of inequality for rural and urban areas. These figures
clearly show that (i) the gap between the rural and urban Lorenz curves - that is, the gap
showing the degree of inequality across different distributional points - narrowed between
2005 and 2012 and (ii) both rural and urban Lorenz curves shifted towards the right during
2005-2012, suggesting that both rural inequality and urban inequality rose in the same period.
This is consistent with Figure 1, which shows that (i) both the urban Gini index and the rural
Gini indexed increased in India, and (ii) the difference between the urban Gini and the rural
Gini became larger between 2005 and 2012 because the urban Gini increased substantially,
while the rural Gini increased only marginally during the same period.

Figure A4 - Graphs showing the distribution of income per capita between rural and urban
areas using the IHDS (2005, left) and (2012, right). Authors’ calculations.

Figure A5 - Graphs showing the distribution of rural non-agricultural wage using the IHDS
(2005, left) and (2012, right)

36

In Figure A5 we show the distribution of rural non-agricultural wage which has shifted
further to the right during 2005 - 2012. This suggests that the inequality of rural nonagricultural wages rose in India from 2005 to 2012.
Furthermore, we plot the Lorenz curves for agricultural and non-agricultural incomes per
capita using the Indian IHDS data in Figure A6. We observe a similar pattern, that is,inequality in both agricultural and non-agricultural income rose in the same period. While theLorenz curves in the two sectors almost overlap in each year, the Lorenz curve of nonagricultural income lies below that of agricultural income in the middle range of cumulative
distributions (or 0.2-0.6). That is, non-agricultural income became more unequally distributed
in 2012.

Figure A6 - Graphs showing the distribution of agricultural versus non-agricultural income percapita using the IHDS (2005, left) and (2012, right)

We have also repeated the same analyses using the Vietnam Households Living Standard
Survey data9 in 2002, 2004, and 2006. In Figure A7, we have plotted the Lorenz curve foreach year. The rural-urban gap in income inequality (in terms of real income per capita)
marginally narrowed over the period 2002-2006 in Vietnam.

9

See http://www.gso.gov.vn/default_en.aspx?tabid=483&idmid=5 for details.

37

Figure A7 - Graphs showing the distribution of income per capita in Vietnam between ruraland urban areas using the VHLSS data 2002-2006

In Figure A8, the Lorenz curves of rural non-agricultural income are plotted for each year.
The distribution of rural non-agricultural income remained stable with no observable changes.

38

Figure A8 - Graphs showing the distribution of rural non-agricultural wage per capita in
Vietnam using the VHLSS data 2002-2006. Authors’ calculations.

In Figure A9 we have plotted the Lorentz curves for agricultural income per capita as well as
non-agricultural income per capita in Vietnam using the VHLSS data 2002-2006. It isobserved that the gap between agricultural and non-agricultural income has slightly narrowed
over the period.

Figure A9 - Graphs showing the distribution of agricultural versus non-agricultural income percapita in Vietnam using the VHLSS data 2002-2006. Authors’ calculations.

39

Appendix 3. Data used for the cross-country regression
Variable

Definition (Data source)

Obs

Mean

Std. Dev.

Min

Max

quality, rule of law and control of corruption

395-0.5240.464-1.6600.940

Land area is a country's total area (WDI).

1,155

3.815

1.166

0.000

5.118

1,155

6.302

1.641

0.000

7.636

and coping with fragility (1=low to 6=high)

1,176

7.618

1.193

1.000

8.000

Imports and exports (value added)/GDP (WDI).

1,155

4.543

2.846

0.000

7.146

Ethnic fractionalization Index *1

697-1.0590.706-3.091-0.308

Labour force with secondary education

1,155

0.150

0.706

0.000

5.209

populati~_14

Population below 14 years old

1,1551495.045

405.122205.000

2116.000

populati~65_

Population above 14 years old

1,1551225.122

366.004

2.000

2080.000

Aggregate institutional quality (average of voice andaccountability, government effectiveness, regulatory

Macro Institution
Land

Population density (people per sq. km of land
Population_densityarea).
CPIA rating of macroeconomic management

Fragility Index
Openness
Ethnicfractionalization

lab_with_secondary

Notes: *1. Ethnic fractionalization Index reflects probability that two randomly selected people from a given country will notbelong to the same ethnolinguistic group. The higher the number, the more fractionalized society. The definition of ethnicity
involves a combination of racial and linguistic characteristics. The result is a higher degree of fractionalization than thecommonly used ELF-index (see el_elf60) in for example Latin America, where people of many races speak the same language.

40