economic growth, demographic change and rural-urban migration in china

Journal of Integrative Agriculture2013, 12(10): 1884-1895 October 2013RESEARCH ARTICLE

© 2013, CAAS. All rights reserved. Published by Elsevier Ltd.doi: 10.1016/S2095-3119(13)60597-3

Economic Growth, Demographic Change and Rural-Urban Migration in China

ZHONG Fu-ning1, LI Qing1, XIANG Jing2 and ZHU Jing1

1 Nanjing Agricultural University, Nanjing 210095, P.R.China2 Chinese Academy of Social Sciences, Beijing 100732, P.R.China

Abstract

Is China able to maintain fast growth after three decades? This paper tries to answer this question by: 1) arguing that factors contributed to sustained long-run growth at supply side; 2) focusing on contributions of demographic dividend especially that of rural-urban migration; and 3) analyzing rural demographic change with information collected through village-wide household survey. Policy alternatives to realize remaining potential demographic dividend are proposed based on the analysis of changing rural demographic structure.

Key words: demographic dividend, rural-urban migration, economic growth, China

INTRODUCTION

China has experienced fast economic growth for more than three decades, which is generally attributed to the reform and openness started in the late 1970s. The new leadership has recently announced an ambitious objective of doubling GDP and people’s income by 2020 compared those achieved in 2010. A natural question is if China is able to sustain the fast growth for another decade or even longer? As no other single country is able to maintain such a growth rate beyond three decades, where is the potential for China to do so?

Many existing studies argue that consumption will be a new engine of growth, as the potentials of boosting economy through increase of investment and expending of exports are declining. Although demographic dividend has been recognized as an important contributor to past economic growth, most studies have noticed the Lewis’ turning point in

Chinese labor market (Cai 2004, 2010; Ryoshi and Ma 2009). So the focus has been targeting demand side of the economy. The policy focusing on demand in order to boost economy seems quite effective, as evidenced by the positive outcome of 4 trillion CNY investment plan in combating the 2008 world economic crisis. Worldwide, fiscal and monetary stimuli, such as government expenditure and Quantitative Easing policies, are widely adopted by both developed and developing countries. However, if demand side factors are truly leading to sustained long-run growth?

This paper tries to demonstrate that the sustained long-run growth is more relied on supply side factors rather than demand side ones. Some demand side factors such as consumption and exports are at best effective to stimulate production to its maximum potential, in other words, to pull actual production up to its frontier instead of shift the production frontier outwards. Investment is different from consumption and exports, as it is not a pure demand side factor. If

Received 19 February, 2013 Accepted 15 April, 2013Correspondence ZHONG Fu-ning, Tel: +86-25-84395735, E-mail: [email protected]

Economic Growth, Demographic Change and Rural-Urban Migration in China 1885

© 2013, CAAS. All rights reserved. Published by Elsevier Ltd.

the investment is made on non-productive assets, it acts as purely expanding demand for current products; however, if it is used to expand production capacity as in most cases, it shifts production frontier outward for future growth while increases demand and pulls current production up towards the frontier.

As described above, investment is also a supply factor, i.e., incremental in capital, in economic growth, and its effectiveness largely depends on another supply factor, namely, labor. A pure expansion of capital through investment may increase output only when additional laborers available, and an upgrading of industry may contribute to growth only when human resource is improved for current labor force. Therefore long-run sustained growth is more relied on supply side factors.

The sustained long-run growth is essentially a continuous argument of production capacity, or out-shifting of the production frontier. Of cause, demand side factors may stimulate out-shifting of the production frontier indirectly, as they may stimulate technology advancement and capital investment to some extent. However, stimulating out-shifting of production frontier is different from pulling actual production up to the frontier; and some other supply side factors may have more direct and more effective impacts on expansion of production capacity, and hence on sustained long-run growth.

This paper focuses on one of the supply side factors, namely, change in population and its impact on economic growth; or in other words, the potential contribution of demographic dividend. After a systematic analysis of demographic dividend in the past three decades, the contribution of rural-urban immigration is believed to have contributed significantly to growth of the whole economy, and defined as a special form of demographic dividend.

Official statistics and most existing surveys are not able to reveal comprehensive change in rural population especially the demographic structure due to lack of information for those absent farmers, i.e., those who have already migrated or worked away from home. Therefore, this study has conducted a village-wide household survey, and then tries to draw rural demographic picture and make a preliminary analysis on information collected through the survey.

SOURCES OF ECONOMIC GROWTH

As mentioned above, most studies proposed to stimulate economic growth through either fiscal or monetary policy based on Keynesian theory of inadequate effective demand. However, if one examines Keynesian theory more carefully, he or she may find that, at best, either fiscal or monetary policy alone or two policies jointed together, could push the economy to the level permitted by full employment, but not to speed up growth. In other words, Keynesian theory is only applicable to reach existing potential in static terms, not a long-run dynamic growth, no matter how fast.

Full employment does not necessarily imply growth. If the total output is entirely used for current consumption with no accumulation in capital assets, and the current investment is made on non-productive assets such as presidential housing, there would be no growth at all. There might be a strong force to pull the current production up to the frontier, but no argument of production capacity. The effect of any stimulus moving production from inside frontier to the frontier would be short-lived and stopped by the frontier. Therefore, the long-run and sustainable growth should be explained by major factors in supply side, i.e., factors shifting the production frontier outwards.

All the conventional growth models rely on supply side factors, namely, labor and capital, in explaining the source of growth. Assuming equilibrium prices having reached in both labor and capital markets, the Neoclassical growth model suggests that increases in labor force and advancement in technology determine growth. As new investment is always available, any incentive for incremental investment should be at least a marginal increase in return to capital investment. Only if the number of laborers increases, and/or technology is improved, that the marginal return to capital will increase, and new investment will be stimulated. Following the above approaches, we may focus on supply side factors to analyze the sources of future growth in China. The economic growth could be defined as: Yt+1-Yt , where Y is usually representing GDP and t denotes time.

In real world, Y is expressed by actual GDP for a given time period. It may be on the production

1886 ZHONG Fu-ning et al.


frontier at that time, but more likely to be inside the frontier by unknown distance. Therefore, any revealed actual growth in GDP might be a combination of moving inside the frontier and shifting of the frontier itself, including realization of the existing potential and augmenting potential. In some cases, a decline in GDP might be observed as the actual output felt down further inside the frontier due to short term shocks.

A short term stimulus may bring the production up to the frontier, hence realizing the existing potential in technical terms; it may also move the actual output along the frontier or inside the frontier, and the changing of output composition may increase GDP in value terms given production frontier. Nonetheless, the long-run growth relies on continuing out-shifting of the production frontier itself, i.e., augmenting the production potential.

In order to analyze the source of growth in terms of augmenting production potential, it is convenient to define the production function as:

VLY ×= (1)

and dV

VYdL

LYdY

∂∂

+∂∂

= (2)

Where L is the total labor force and V is labor productivity.

What eq. (1) expresses is that the total output Y, or GDP, is the product of labor force and labor productivity. Of cause Y could be defined as either actual or potential output; accordingly, L and V could be defined as either actual (revealed) or potential labor force and productivity. For our purpose, only the potential output, labor force and productivity and their changes will be discussed. As eq. (2) indicates, the growth of GDP is determined by growth of labor force L and labor productivity V, given their marginal impact on Y.

According to Justin Yifu Lin, a continuous stream of technological innovation and industrial upgrading is the basis for sustained growth in any economy in any time. However, as technological innovation and industrial upgrading are costly and risky in developed and developing countries at global frontier, developing countries such as China, potentially have the advantage of backwardness in following the existing technology

and industrial upgrading, as well as institutions, at relatively low costs and higher speed (Lin 2012a, 2012b, 2013).

Lin has explained that the advantage of back-wardness is a major source of long-run and sustained growth in China and some other developing countries, as it allows relative fast increase in productivity for a long time until the productivity gap is closed, or largely reduced. However, eq. (2) suggests that there might be another important source of sustained growth: The increase in labor force during a time period, and the contribution of growth in labor force is to be discussed in more details below.

ECONOMIC GROWTH AND DEMOG-

RAPHIC DIVIDEND

It is easy to understand, holding productivity constant, the number of total labor force determines total output directly, so an increase in labor force will increase total output. Of cause, the increase in labor supply may increase total output only if additional investment in capital is ready to accommodate expansion of production capacity. As people’s living standard is determined by per capita income, age structure matters when economic growth is measured in per capita terms. It is widely observed that fast growth in some developing countries is associated with fast growth in labor force, including growth of population in total and in working age, the later could be expressed as labor participation rate.

The increase in total population and labor part icip-ation rate is usually defined in terms of demographic dividend, which permits relatively faster growth of the whole economy and/or in per capita terms (Caldwell 1976; Krugman 1994; Bloom and Williamson 1998; Bloom et al. 2000; Bloom et al. 2003; Lee and Mason 2006). Eq. (2) illustrated that increase in labor force dL is an important determinant of increase in total output dY, and this contribution could be defined as Demographic Dividend I. The existence of Demographic Dividend I implies that the economy of this country is able to grow faster, in terms of total output, compared with before, or compared with other countries. However, it does not necessarily imply



faster growth in per capital terms. On the contrary, if the total population grows faster than labor force, per capita income may decline while total output increases.

In order to explain the contribution of increase in labor force to per capita income, two more equations are defined and derived below:

VPL

PY ×= (3)

and ( )

( ) dVVY

PLd

PL

PY

PYd

∂∂

+

∂

∂=

(4)

Where, P is total population, Y/P is per capita income expressed as per capita GDP, and L/P is the ratio of labor force to population, i.e., the labor participation rate.

While eq. (3) indicates that per capita output is determined by labor participation rate and labor productivity, eq. (4) suggests that, together with increase in labor productivity, the incremental labor participation rate determines increase in per capita output. For convenience, the contribution of labor participation rate might be defined as Demographic Dividend II. The existence of Demographic Dividend II implies that per capita income in the country is able to grow faster compared with before, or compared with other countries. However, it does not necessarily imply faster growth in total output. On the contrary, if the total population declines faster than labor force, the resulted higher labor participation rate may bring higher per capita income while total output decreases.

However , the re migh t be ano the r fo rm of demographic dividend: labor moving from low productivity to high productivity industries. In many developing countries agriculture is dominated by huge numbers of small holders working on small-scale farms, with very low labor productivity and significant underemployment before economy take-off. As the transition of traditional agrarian to modern economy starts, a common phenomenon in these countries is a large number and continuous rural-urban immigration. The relocation of labor force in large scale makes significant contribution to the fast growth in non-agricultural sectors, and the fast growth in the whole economy as well.

At the same time, reduction in rural labor force allows the remaining farmers to work more hours and pull themselves out of underemployment. With help of development in mechanization, agricultural output may continue to increase during this process, though the speed is behind that in other sectors. The rural-urban migration is likely to turn a large number of underemployed rural laborers into effective labor force working in non-agricultural sectors, the effect is similar to an increase in actual labor force. As such, the increase in effective labor force could be defined as Demographic Dividend III, and approximately expressed by relative increase in non-agricultural employment.

A temporary reduction in unemployment due to short term stimulus may not be considered as demographic dividend. However, if a transitional economy has a huge number of under-employed laborers able to be transferred to high productivity sectors in a large scale and for a long time, there exists demographic dividend. The Demographic Dividend III does not require increase in total labor force or in labor participation rate; rather, it simply implies a large scale of laborers shifting from under employed sector to high productive ones for a long time given the statistics of total labor force and population constant. To materialize such demographic dividend, it may require policy measure different from short term stimulus.

The above defined Demographic Dividends could be used to partly explain China’s fast economic growth in the last three decades, and to analyze the potential source for future fast growth and the appropriate approach to materialize the potential. In general, the Demographic Dividend I indicates labor force in aggregate terms, the Demographic Dividend indicates labor force in relation to total population, and the Demographic Dividend indicates labor force in effective terms. If the population structure keeps constant, in terms of age and gender, and in terms of efficient distribution among sectors, the three Dividends would move in the same direction and no distinction necessary. However, if a country is undergoing rapid demographic change, the distinction of the demographic dividends might be useful in explaining the past growth, and in providing policy



alternatives for future growth. According to the National Bureau of Statistics of

China, the total employment in the whole economy grew at an annual rate of roughly 3% during the 1980s, which greatly contributed to fast growth of the economy at that time. The annual growth rate of total employment then declined to about 1% during the 1990s, and further to 0.5% in the first 11 yr of new century. At the same time, the growth rate of non-agricultural employment grew much faster: about 6% on average during the 1980s and 1990s, except a few years during and immediately after the Asian financial crisis. It declined rather slowly in the new century, still kept at around 3% per year recently (see Fig. 1 for details).

Fig. 1 indicates that annual fluctuations exist in the growth rates for both total and non-agricultural employment due to short term shocks from either inside or outside China. However, looking at the long-

as the base, this suggests an annual growth rate of 1% in labor participation rate, implying a contribution to per capita income of at least 1% a year (see Fig. 2 for details).

However, Fig. 2 also revealed the source of the increase in labor participation rate: declining propor-

Fig. 1 Growth rate of total & non-agricultural employment. Source: National Bureau of Statistics of China, 2012. The same as below.

0.002.004.006.008.00

10.0012.0014.0016.0018.0020.00

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

Total Non-farm

Year

Gro

wth

rate

(%)

run trend, Demographic Dividend I was quite high before, but declined to a very low level; in comparison, Demographic Dividend III might contributed much more to the growth of the whole economy, and still able to do so for a time period to a certain extent.

As defined before, Demographic Dividend II is an increase in the labor participation rate that will increase per capita output (or income) given other conditions constant. According to NBS, labor participation rate was relatively low and stable in the late 1990s, between 67 and 68%. It started to increase quite fast since 2000, reaching about 75% in 2010. Taking 67%

Fig. 2 Pre-working aged persons and labor force participation rate.

tion of young people before working age which is the result of long time reduction in birth rate. When birth rate declines, the immediate effect is a reduction in dependency rate and an increase in labor participation. If the birth rate continues to decline or maintained at a low level for a time long enough, when the people born at low birth rate enter working age and replace the elderly reaching requirement, the total number of persons in working age will start to decline, so does the labor participation rate. As the proportion of young people under age of 14 declined from a little bit less than 27% to under 17% in 16 yr, labor participation rate had increased during the first 15 yr, and started to decline since 2011.

According to a recent press release by the NBS, the number of population at the working age of 15-59 decreased by 3.45 million in 2012 (National Bureau of Statistics of China 2013). As the total population continues to grow, though at a quite low rate, the reduction in working population certainly implies not only a decrease in total labor force and Demographic Dividend I, but also a further decrease in labor participation rate and the Demographic Dividend II.

To check out if the reduction of 3.45 million persons in the working age is a temporary fluctuation or long-run trend, a more systematic calculation is

15.0

17.0

19.0

21.0

23.0

25.0

27.0

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011 66.0

67.068.069.070.071.072.073.074.075.0Age 0-14 Age 15-64

YearPr

e-w

orki

ng a

ged

pers

ons

parti

cipa

tion

rate

(%)

Lab

or fo

rce

parti

cipa

tion

rate

(%)



conducted based on 2010 population census data that give detailed number of persons at each age (see Fig. 3 for details). Assuming no death in each age group, if the number of persons at 14 is greater than that at 64 in any year, then the total number of economically active persons (age 15-64) will increase in the next year, and vice versa. Based on the number of persons at each age in 2010 collected in the population census, the potential changes in the number of economically active persons for the time period of 2011-2025 are calculated and presented in Fig. 3.

Fig. 3 suggests that, if no death at each age, the total number of economically active persons will

big famine during the early 1960s. As the birth rate was extremely low during 1960-1962, the number of persons reaching 64 would be significantly less than usual after 64 yr, i.e., around 2024-2026. As the potential increase around 2024 is due to the small number of existing population to be replaced, not a big number of young people to replace them, the trend of declining labor force is likely to be continue, until 15 yr after significant and continuous increases in birth rate.

As the total number of labor force starts to decline, the labor participation rate may decline faster as the total population will continue to increase for the next two decades. As a result, though China’s total labor force is still the largest in the world, and the labor participation rate is among the highest in the world, compared with the last three decades and in incremental terms, Demographic Dividend I and II are likely to have been exhausted and will turned into negative from now on.

How about Demographic Dividend III? Is the agricultural sector still able to provide additional laborers for other sectors? And if so, how long can it do so and how large is the scale in the future? The answers depend on the remaining population in the rural areas, especially the age structure of the remaining rural population which determines the mobility of the population and potential scale of rural-urban migration.

VILLAGE-WIDE SURVEY OF RURAL

POPULATION

The future of rural-urban immigration, i.e., Demo-graphic Dividend III, is largely determined by the remaining rural population and its demographic structure. It is understandable that, with the same total population, one aged rural society may have relatively smaller percentage of population in working age, and even less laborers looking for off-farm jobs as immigrants and able to do so, compared with another but much younger society. However, most existing data are not sufficient to provide a clear picture of the

Fig. 3 Potential changes in economically active persons. Laborers being replaced between 2023 and 25 were born in 1959-1961, which were far less than during normal years, so economically active population may temporally increase. Source: Population Census Office of the State Council and Department of Population and Employment Statistics, National Bureau of Statistics, China (2012).

-4-3-2-101234567

2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025

Year

Pote

ntia

l cha

nges

(mill

ion)

start to decline in 2016-2017. However, if the actual death rate for each age is added to the calculation, the decrease may start as early as since 2013-20141, and the total number of economically active persons should be reduced by some 3 million every year, the accumulated reduction in total labor force would be much bigger than the calculation with the discrepancy increasing with time.

A seemly irrational increase in the number of econ-omically active persons appears around 2023-2025, against the trend. This is the effect resulted from the

1 As the number of persons aged 14 is about 1 million less than that aged 59 in 2011, the above quoted reduction of working age population (between 15 and 59) includes about 2.5 million deaths in the working age population in a year. Therefore, the total death of the entire economic active persons aged 15-64 might be as high as 3 million.



rapid demographic transition. The population census conducted every 10 yr does

collect comprehensive information of demographic transition, but the 10 yr interval is rather too long, and only aggregated data are released to the public, which are not helpful for timely empirical study. Other official survey data, as well as survey conducted by various research teams, contain limited information and could do little good to draw a whole picture (Yue 2005; Liu 2006). The basic problem is the exclusion of households that have already immigrated or with no one at home during the survey. Therefore, traditional survey procedures are believed to yield only unsatisfactory information of rural demographic changes.

In order to provide a more comprehensive picture of demographic change in rural China, this study tries to adopt a new approach, i.e., to conduct a village-wide household survey, covering residence and employment status of all village members including both those having moved out and remaining at home (more detailed procedure and outcome of the survey in Zhong and Xiang (2012)). With detailed and complete information of residence and employment status, we are able to find out age and gender structure of all village members who are divided into groups according to how many months they are staying at home and what jobs they are taking. And we may be able to find the relationship between the number of migrants and exiting rural population given the changing demographic structure by analyzing our survey data.

The desired village-wide survey relies on the cooperation from villagers to provide necessary information regarding absent household members, and hence are best to be conducted by “insiders”. In our case, agricultural university students came from the sampled villages are selected to conduct the survey in their home villages. It is believed that such survey may provide relatively accurate information of demographic changes which are not sensitive to most farmers interviewed.

The survey pilot targeted five provinces with high rates of outward migration: Anhui, Hunan, Henan, Sichuan and Jiangsu. Twenty counties in each of the provinces were selected based on distribution of per

capita income, applying equal interval procedure. Balanced between availability of students who could collect the survey data and other considerations, two villages are selected in each of the counties. In the selection procedure, villages close to county centers and/or major towns are avoided to reduce possible bias of including over-proportional villages/laborers with better migration opportunities in the sample.

To ensure reliable information was collected, especially for absent villagers, the survey was restricted to demographic information which is non-sensitive and easily accessible. Demographic information of each individual was recorded, for example such as age, education, marital status, occupation, number of months staying at home in a year, the starting year working outside village either permanently or seasonally, and residential status outside village.

BASIC FEATURE OF DEMOGRAPHIC

STRUCTURE BASED ON SURVEY DATA

The survey was conducted during the winter of 2010/2011, covering 5 provinces, 121 counties, 203 villages, 7 317 households and 28 021 persons. On average, there are 36 households in a village, and 3.83 persons in a household. After checking completeness and consistency of the information collected, the basic statistics of the effective survey data are summarized in Table 1 below.

The reliability of the survey data was crosschecked with data contained in China Population and Emp-loyment Statistical Yearbook (Department of Popula-tion and Employment Statistics of NBS, China 2010) to ensure its accuracy. The two population pyramids are shown in Fig. 4 below, one from our survey at right and another from national population and employment statistics at left. The two population pyramids are

Table 1 Sample statisticsProvince Jiangsu Anhui Hunan Henan Sichuan TotalNo. of counties 24 20 21 36 20 121No. of villages 51 42 40 39 31 203No. of households 2 246 1 276 929 1 305 1 561 7 317No. of persons 8 950 4 833 3 506 5 230 5 502 28 021Source: field survey.



generally consistent. Considering the time difference in survey conduc-

ting, the similarity might increase if the right-panel pyramid generated from our survey moves down 1 or 2 yr. However, there are two features requiring further explanation. Firstly, both the pyramids are not smoothly shaped like a true pyramid, with three gaps each indicting significant lower birth rates during certain time periods. The first gap represents low birth rate during the big famine during the early 1960s, which is rather smaller in our survey compared to the official data. A plausible explanation is that all the five provinces have been major grain producing and exporting areas for long time and rural population might have relatively better access to food compared with those living in food deficit regions, this could lead to relatively a higher birth rate as a result, even during times of famine.

Secondly, the population reproduction cycle seems to extend longer over time. As the reproduction cycle is around 20-25 yr, any significant gap in the popula-tion pyramid is likely to repeat in 20-25 yr. As China started “one child” policy in the early 1980s, the effect of birth control was largely added to that of population recycle, so the second population gap was further wid-ened. The peak of birth after the second gap centers the age group around 21, about 20-25 yr after the peak

following the first gap as expected; but the third gap does not show clear sign of ending or bouncing back. One of the plausible explanations for this delay could be that rural young people have delayed their marriag-es and reproduction due to the uncertainty associated with migration.

As mentioned before, demographic change in ru-ral society is the key to explain the past rural-urban immigration and to project it in the future. Based on the village-wide household survey, all village mem-bers may be grouped according to their residence and employment status, i.e., how long they stay at home in a year, and what is the major employment of those villagers remaining at home. Different from official statistics which set only one criterion, i.e., 6 mon to determine residence status, i.e., who should be clari-fied as a permanent rural or urban resident, there are 5 groups in our survey and study: staying home more than 10 mon a year, between 6 and 10 mon, between 3 and 6 mon, less than 3 mon, and already having moved out. A villager staying at home for more than 10 mon a year is considered permanent rural resident, and his or her age, as well as employment status, are important factors to analyze the potential for future immigration.

Among 28 021 persons surveyed, 13 169 live at home for more than 10 mon a year and to be consid-ered as permanent rural residents, accounted for 47%

Fig. 4 Comparison between survey and official statistics. Left, source: China Population and EmploymentStatistical Yearbook, 2010. Right, source: field survey.

1.200 0.900 0.600 0.300 0.000 0.300 0.600 0.900 1.2001

11

21

31

41

51

61

71

81

91

Age

Male Female

1.4000 1.0000 0.6000 0.2000 0.2000 0.6000 1.0000 1.40001

11

21

31

41

51

61

71

81

91

101

Age

Number of persons (×10 million) Number of persons (×100)



of the total; 8 020 (28.6%) have left home; if those live at home for less than 3 mon (18.5%) added, the per-sons left home or mainly staying outside home village have reached 47% as well. The number of “commuting rural residents” staying at home between 3 and 10 mon is less than 6% of the total in our sample (see Table 2 for details). It is clear that the potential of future ru-ral-urban immigration will come from those permanent residents who currently stay at home for more than 10 mon a year.

rural residents”, especially those mainly engaged in agriculture. The types of jobs taken by permanent rural residents, i.e., those staying at home for more than 10 mon a year, are listed in Table 3 below.

There are 9 279 economically active persons, i.e., between 16 and 65 of age staying at home for more than 10 mon a year in our sample. They could be clarified as permanent rural residents in working age, and potential source of rural-urban immigration in the near future. Among the 9 279 persons, 3 196, or about 1/3, are mainly engaged in agriculture; 999, or a little above 10%, are mainly engaged in non-farm activities; and 4 340, or some 47%, are taking both farm and non-farm jobs.

As usually understood, the net income earned from agriculture is generally significantly below that earned elsewhere, and the major incentive for rural-urban immigration is to seek opportunities for higher income. As all rural laborers are considered rational person looking for better return to their efforts, those working in non-farm sectors at home may have already earned relatively good income, and so do some of the farmers taking both farm and non-farm jobs at home. For those mainly engaged in agricultural production, more than 85% of them are over 36 of age. It is quite likely that a lot of them have some difficulties in finding non-

Table 2 Residential status of whole rural population1)

Home staying time Persons Share (%)Non 8 020 28.62<3 mon 5 174 18.463-6 mon 899 3.216-10 mon 759 2.71>10 mon 13 169 47.00Total 28 021 100.001) Source: field survey.

Fig. 5 provides age distribution of the residence status in our sample. It shows that more than 80% of young people with age around 20 have left home, implying limited potential of future immigration from this age group. The absolute number of people with age around 30 remaining at home, as well as those below 15, is about the same as those around age of 20, though greater in terms of proportion, also implying limited potential for future immigration.

It is usually expected that the major source of rural-urban migration will be young generation who would leave agriculture when grown up. However, as the absolute number of children under 15 of age is significantly less than that around 20, so will be the total absolute number of rural-urban migration from young generation. At the moment and for the next one decade (or even two decades), a potential source of Demographic Dividend III might be the “permanent rural residents” with age above 40 and mainly engaged in agriculture currently.

As our survey data shown, 47% of rural population have already left home or mainly engaged in non-farm sectors outside their villages, with about 6% of rural population working outside their villages between 3 and 10 mon a year. Therefore, any major rural-urban immigration is likely to be from the “permanent

Fig. 5 Age and gender distribution of population survey. Source: field survey.

Age

400 350 300 250 200 150 100 50 0 50 100 150 200 250 300 350 4001

11

21

31

41

51

61

71

81

91

Male Female

Number of persons

Stay at home more than 10 mon Stay at home 3-10 monStay at home less than 3 mon Already immigrated



farm jobs, probably due to limited human resource and other constraints.

The past rural-urban immigrat ion has been absorbed by fast expansion of the industrial sectors, with majority of migrant workers engaged mainly in manufacturing and construction, both demanding high level of human resources. Despite three decades of large scale rural-urban immigration, the average size of Chinese farm is still very small, less than one hectare. At the same time, the development of farm mechanization has been accelerated, with service of machineries widely available to production of major field crops. If non-farm employment is accessible for farmers remaining in agriculture, their work could be easily done by machinery without reduction in production of the agricultural sector. The basic issue is to find non-farm jobs for those who have limited human resources.

PRELIMINARY CONCLUSIONS AND

POLICY IMPLICATIONS

In contrast to emphasizing demand-pulling growth, this paper argues that supply side factors are more important in long-run sustained growth. Increases of demand for final goods and services could pull production to its frontier, but not necessarily lead to expansion of production capacity unless further investment is stimulated. If existing investment is at an equilibrium level, i.e., the expected marginal return is equal to marginal cost, any new investment will be made only when more efficient technology or additional laborers are available to increase the expected return or reduce the cost. Adding supply side factors may help understanding of the potential growth in China in the next decade or two. As the

contribution of technology advancement has been well documented in literature, this paper focuses on contribution of labor supply, more specifically, on demographic dividends, to economic growth.

There is no doubt that growth of labor force, increase in labor participation, and rural-urban immigration all have contributed to fast economic growth in China. Of course they do not make the contribution alone, as they may do little good without additional investment accompanied. Capital investment not only materializes the potential productivity of the increased labor, but also raises labor productivity in terms of technology advancement and industrial upgrading. However, without increase in labor force, capital investment itself may not lead to real growth, and even may not be made due to insufficient incentives.

Based on Fig. 1 through Fig. 3, we may conclude that both Demographic Dividend I and Demographic Dividend II have exhausted and their contribution to economic growth will turn to zero or even negative. To ensure future growth at desirable pace, China will rely more on improved productivity, through R&D and adoption of advanced technology, industrial upgrading and optimization of economic structure, as well as institutional innovations. However, the remaining large number of rural laborers should not be forgotten, as Demographic Dividend III could still contribute to economic growth at low or zero cost.

As mentioned above, those rural laborers who mainly engage in agriculture are likely to have relatively less human resources: less educated and over age of 35, or have some other difficulties to be employed in manufacturing and construction sectors. However, they may capable to work in the service sector where a lot of jobs could be fulfilled by middle-aged and less educated workers. Therefore,

Table 3 Age and employment distribution of permanent rural workers1)

Age groupFarm Non-farm Both No work Total

No. % No. % No. % No. % No. %16-25 154 1.66 240 2.59 458 4.94 360 3.88 1212 13.0626-35 286 3.08 268 2.89 633 6.82 55 0.59 1242 13.3936-45 881 9.49 260 2.80 1322 14.25 65 0.70 2528 27.2446-55 967 10.42 164 1.77 1202 12.95 92 0.99 2425 26.1356-65 908 9.79 67 0.72 725 7.81 172 1.85 1872 20.17Total 3 196 34.44 999 10.77 4340 46.77 744 8.02 9279 100.001) Source: Field survey.



expansion of the service sector is likely to provide an opportunity to turn those underemployed rural laborers into effective workers and hence enlarges the stock of effective labor force. Similarly, any sub-sector or group of firms that capable to absorb relatively more mid-aged and less educated laborers should be encourage to grow faster, in order to materialize the potential Demographic Dividend III.

In order to achieve the above goal of realizing Demographic Dividend III, the development of the service sector should be given higher priority and development of small firms should be encourage. Public investment, especially those in infrastructures, education and training, and social security system, may greatly help expansion of the service sector and small firms, in turn, more laborers with less human resources could be employed and contributed more to the whole economy.

As those potential workers are likely to come from the stock of remaining rural labor force, development of farm machinery service should be encouraged with public support in both physical investment and institutional innovation. Appropriate organization of farm machinery service, and appropriate machines, should be found and developed to fit the need of changing farming system following demographic transition. Any further rural-urban immigration should not undermine the growth of the agricultural sector, especially the efforts to achieve food security.

It should be kept in mind that the government has launched a series program to develop rural economy in order to stimulate agricultural production and to improve farmers’ living. If the income and welfare gap between rural and urban residents is narrowed down, the rural-urban migration will slow down as well. Therefore, although Demographic Dividend III still exists, to a certain extent, it may shrink quite fast, and future growth will more and more depend on technology advancement. Restructuring industrial sectors in order to realize the remaining Demographic Dividend III may help China maintain fast growth in one decade or two, and more efforts in pushing technology advancement could be accumulated in this time period.

Though our survey covers only 5 provinces, it may still present the general trend in rural demographic

change to a large extent. Fig. 4 suggests that the age structure is very similar between our survey and the national statistics. Of cause there must be regional difference due to various natural, economic and social conditions and the resulted agricultural production patterns, as well as the different opportunities in rural-urban immigration. Because the survey was conducted in provinces with relatively larger numbers of out-migration, the remaining surplus rural laborers might be underestimated for other regions. Therefore, Demographic Dividend III might be a little bit greater than inferred directly from our survey, and larger survey covering various regions is needed to provide more accurate picture.

Our survey has shown some regional differences among the 5 provinces already, but they are not discussed here due to the main objective of this paper. The future study in this direction should enlarge the scale of survey, and find the general trends, and analyze regional differences along with their policy implications.

AcknowledgementsThe authors are grateful for the help of colleagues at Anhui Agricultural University, Henan Agricultural University, Hunan Agricultural University, and Xinan University, China, in the field survey.

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