The contribution of population health and demographic change to
economic growth in China and India q
David E.Bloom a,*,David Canning a ,Linlin Hu b ,Yuanli Liu a ,Ajay Mahal a ,Winnie Yip c
a Harvard School of Public Health,Department of Global Health and Population,655Huntington Avenue,Boston,MA 02115,United States
b
Tsinghua University,School of Public Policy and Management,Beijing,China
c Oxfor
d University,Oxford,United Kingdom a r t i c l
e i n
f o Article history:Received 8November 2009Available online 11November 2009JEL classification:I18J10J11J21J23J22O40Keywords:Economic growth Reallocation of labor
Full income
China
红旗村India
a b s t r a c t
Bloom,David E.—The contribution of population health and demographic change to eco-
nomic growth in China and India
We find that a cross-country model of economic growth successfully tracks the growth
takeoffs in China and India.The major drivers of the predicted takeoffs are improved
health,incread openness to trade,and a rising labor force-to-population ratio due to fer-
tility decline.We also explore the effect of the reallocation of labor from low-productivity
agriculture to the higher-productivity industry and rvice ctors.Including the money
value of longevity improvements in a measure of full-income reduces the gap between
the magnitude of China’s takeoff relative to India’s due to the relative stagnation in life
expectancy in China since 1980.Journal of Comparative Economics 38(1)(2010)17–33.Har-
vard School of Public Health,Department of Global Health and Population,655Huntington
Avenue,Boston,MA 02115,United States;Tsinghua University,School of Public Policy and
Management,Beijing,China;Oxford University,Oxford,United Kingdom.
Ó2009Association for Comparative Economic Studies.Published by Elvier Inc.All rights
rerved.1.Introduction
Comparing China and India is a decades-old activity.Long known to the West as the world’s two population superpowers,China and India have been until fairly recently only small players on the international economic scene.The emergence of China as a major economic power was followed by a slower,but still important,economic transformation in India.
Before 1980,economic growth in both China and India,as measured by the growth rate of income per capita (in purchas-ing power parity terms),was relatively slow.After 1980,growth in both countries accelerated,dramatically in China and more modestly in India (e Fig.1).China rapidly overtook India,and now has substantially higher income per capita.Haus-mann et al.(2005)date the growth accelerations as starting in 1978in China and 1982in India.This growth has changed the nature of the West’s interest in China and India.Both countries,by virtue of their population size,have the potential to be dominant forces in the international economy.
This paper analyzes and compares the acceleration of economic growth in China and India.We start with a simple shift-share analysis in which we decompo the growth of income per worker into a portion attributable to the reallocation of 0147-5967/$-e front matter Ó2009Association for Comparative Economic Studies.Published by Elvier Inc.All rights rerved.
doi:10.1016/j.jce.2009.11.002
q An earlier version of this paper was prented at the annual meetings of the American Economic Association in Boston in January 2006.
*Corresponding author.
E-mail address:dbloom@hsph.harvard.edu (D.E.Bloom).
Journal of Comparative Economics 38(2010)17–33
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jo ur na l h o me pa ge :w w w.e ls ev ie r.c o m/lo c a t e/jce
18 D.E.Bloom et al./Journal of Comparative Economics38(2010)17–33
labor from low-to high-productivity ctors and a portion attributable to the growth of labor productivity within ctors.We then u cross-country panel data to estimate the parameters of an empirical growth model,which allows us to estimate the contribution of different variables to the recent growth trajectories of China and India.This approach lends itlf naturally to an examination of the influence on growth of variables such as openness to trade and institutional quality,a feature that distinguishes it from other common approaches to the study of economic growth such as calibrating a production function using parameters estimated from micro data(e Young,1994,1995).
The ability of models such as ours to explain takeoffs in economic growth is by no means assured.Easterly et al.(1993) emphasize that national rates of growth of income per capita are esntially uncorrelated across successive5-year periods. They point out that it is difficult to explain the esntially randomfluctuations using the fairly slow-moving variables ud to explain variations in economic growth in cross-country panels.Our explanation emphasizes demographic changes,which can occur relatively quickly and have the potential to explain takeoffs into rapid economic growth.The two demographic factors we emphasize are rapid increas in life expectancy and declines in fertility.The have occurred in both China and India,though for each factor the magnitude of the effects has been greater in China.
An increa in life expectancy,which we regard as a proxy for population health,has a number of potential economic effects.To the extent that health affects labor quality and productivity,one would expect improved health in China,and therefore the rising standard of labor inputs,to have an effect on GDP per worker.Fogel(1994)emphasizes the role played by better health and nutrition in the Industrial Revolution,and Fogel(2004)argues that the same changes led to a sub-stantial improvement in productivity in China.Bloom et al.(2004)estimate the effect of health as a form of human capital in a cross-country study of economic growth rates.1In addition to this effect of health
on worker productivity,increas in health and prospective longevity can also drive incread savings for retirement(Bloom et al.,2003a;Bloom et al.,2007),higher rates of foreign direct investment(Alsan et al.,2006),and higher rates of domestic investment,savings,and school enrollment (Lorentzen et al.,2005).
In the1970s,declines in mortality in China and India,which included large declines in infant and child mortality,led to large cohorts of young people.Subquent declines in fertility produced‘‘bulge”generations in each country,although the bulges occurred earlier and are more pronounced in China than in India.In general,when relatively large generations reach the prime ages for working and saving,a country will experience a demographically induced economic boost,provided this demographic cohort is productively employed.Bloom and Williamson(1998),Bloom et al.(2000),and Mason(2001)have investigated the role of this‘‘demographic dividend”in the successful East Asian‘‘Tiger”economies.Cai(2004)provides a similar study for China,and Bloom and Canning(2003)likewi examine through a demographic lens the recent economic boom in Ireland.
East Asia’s macroeconomic performance is tracked very cloly by its demographic transition and the resulting changes in age structure.Estimates indicate that the‘‘demographic dividend”accounts fo
r as much as one-third of the East Asian‘‘eco-nomic miracle”(Bloom and Williamson,1998;Bloom et al.,2000).By contrast,the abnce of demographic change also ac-counts for a large portion of Africa’s economic debacle(Bloom et al.,2003b;Bloom and Sachs,1998).The results of the analyzes have reduced the need to claim that factors exceptional to East Asia or Africa account for their different economic trajectories.Once age structure dynamics are introduced into the economic growth model,the regions appear to more clo-ly obey common principles of economic growth(Bloom et al.,2000).
In1975,the ratio of working-age(15–64)to non-working-age(0–14and65+)people in both China and India was around 1.3.This means that the number of working-age people was only modestly larger than the number of people who,by virtue of their age,were most likely dependents.In the1970s,China launched the‘‘later,longer,fewer”campaign(later marriage 1See also Bhargava et al.(2001).
D.E.Bloom et al./Journal of Comparative Economics38(2010)17–3319 and age atfirst birth,longer inter-birth intervals,and fewer ,two in the cities and three in the countryside)).This evolved into the one-child policy adopted in1980,which encouraged and very often required couples to limit their families to only one child.2The campaign and policy propelled a sharp decline in fertility that began in the1970s.This decline triggered a subquent sharp ri in the ratio of working-age to non-
working-age people,a ri that is expected to peak at2.5in2010. India’s demographics are changing similarly but more slowly,with an expected peak ratio of2.1in2035,a level that China reached in1995.In India,therefore,the greater portion of the potential demographic dividend lies ahead.China,by contrast, anticipates a very rapidly rising elderly population in the not-too-distant future,with over400million Chine–30%of the population–projected to be age60and over by2050.
It is clear,both theoretically and empirically,that there is nothing automatic about the effects of demographic change on economic growth(Bloom et al.,2003b;Bloom and Canning,2003).Age distribution changes create supply-side potential for economic growth.Whether this potential is captured depends on the policy environment,as reflected by the quality of gov-ernmental institutions,labor legislation,macroeconomic management,openness to trade,and education policy,among other factors.Additional factors have contributed to China’s and India’s remarkable economic growth.Both countries under-took economic reforms characterized by deregulation and liberalization,which incread the role of markets,opened up their economies to international trade,and attracted foreign investment.China reformed earlier and much more aggressively than India.Reform of communal farming in China began in the late1960s though it could be argued that it was only with the agricultural reforms of1978,followed by industrial ctor reforms in the early1980s,t
黄瓜汤的做法hat substantial productivity gains were realized.By contrast,India’s market-oriented economic policy reforms began in the early1990s in respon tofiscal and balance-of-payments cris.Rodrik and Subramanian(2004),however,argue that pro-business reforms in India in the1980s,which aided incumbentfirms rather than increasing market competition,had beneficial growth effects.数控论文
In both countries,the reforms led to incread volumes of international trade and large inflows of foreign direct invest-ment(FDI).The relationships among economic reform,the opening up of trade,and economic growth are discusd for the ca of China by Démurger et al.(2002),Shen and Geng(2001),Chen and Feng(2000),and Cai et al.(2002).For India,they are discusd by Chopra et al.(1995)and Sachs et al.(1999).We try to capture the effects in our empirical analysis with a measure of institutional quality and a measure of openness to trade though the are admittedly imperfect proxies for the policy reforms that took place.
China and India have also made improvements in education.Cai et al.(2002),Wang and Yao(2001),and Chopra et al. (1995)examine the contribution of human capital,proxied by education level,to economic growth.We include a measure of education in our analysis.
Labor reallocation from agriculture to other ctors has also been singled out for attention as a sourc
就你不知道e of economic growth in China and India.Prior to reform the two countries,like many others,3had surplus labor in agriculture with a large dif-ferential in labor productivity between agriculture and industry.As a conquence,the inter-ctoral shift of labor(away from agriculture)incread overall productivity and therefore aggregate output.The effect in China of labor reallocation has been investigated by Sachs and Woo(1994),Woo(1998),and Cai and Wang(1999).We report a new analysis of this effect for China and India,and also include a measure of ctoral change in our cross-country panel data analysis of economic growth.4 Lucas(1993)emphasizes productivity growth as a source of economic‘‘miracles.”We put more emphasis on demo-graphic factors that increa labor supply per capita,and on improvements in the health and productivity of labor.However, our measures of openness to trade,institutional quality,and ctoral reallocation can also be interpreted as proxies for pro-ductivity growth effects rather than effects associated with changes in the quantity or quality of factor inputs.
The rationale for this paper is not to give a detailed explanation of economic growth in India and China,which has de-pended on many factors we do not model.Rather we investigate if a simple cross-country growth model,including demo-graphic factors,can explain the takeoff in growth that the two countries experienced.The key idea is to compare their experience of growth with the exp
erience of rest of the world.We argue it is interesting that much of the growth that was experienced in India and China would be predicted by a model thatfits the rest of the world.
Our paper rests on two related premis.Thefirst premi is that influences on economic growth can be divided into fun-damental long-run factors(such as demographic change)and short-term cyclical or idiosyncratic factors(such as changes in fiscal policy).We capture the fundamental factors directly in an empirical growth model,while the short-term factors are reflected in an error term.The cond premi is that the fundamental determinants of economic growth are common across countries,implying that inferences about the fundamental determinants can be made from the analysis of cross-country pa-nel data.Within this framework,the experience of countries other than India and China is relevant to understanding the con-tribution to economic growth in China and India of fundamental factors like demographic change and population health.
In using a common model to explain different countries’growth experiences,we weight countries equally,taking each as an equally important obrvation of economic growth.Weighting large countries in such a regression more heavily is only appropriate if large countries contain more‘‘information”in the form of lower variance in growth rates.However the vol-atility of growth rates declines only very slightly with country size(Canning et al.,1998),meaning that the economic circum-
stances of individuals within countries tend to move together,rather than reprenting independent outcomes that are
2The policy was less rigorous in rural areas and among ethnic minorities.
3Bloom and Freeman(1986).
4See Bosworth and Collins(2006)for a similar decomposition analysis conducted over a similar time period,which reaches roughly the same conclusions as tho reported herein.
smoothed when we average over larger numbers.In addition,this issue is,in principle,addresd by the reporting of het-eroskedasticity-consistent standard errors,which assign more weight to obrvations with lower variance.
Becau we emphasize economic growth,we treat health as an input who value is purely instrumental.A broader no-tion of‘‘full income”would measure health as well as consumption.We follow the methodology ud in Becker et al.(2005) to monetize the value of gains in life expectancy and compare the gains to tho due to rising consumption.Continued health improvements in India mean that full-income has been rising faster than consumption.This is not the ca in China, w
here population health has improved rather sluggishly since1980.As a result,the comparative deficit in India’s progress in development in relation to that of China is smaller when measured in full-income terms than when measured in terms of economic growth.
This paper complements earlier work that focud on explaining rapid economic growth in East Asia over the whole per-iod1970–2000(Bloom et al.,2000).We provide a more detailed analysis of the contribution of health and demography to growth in India and China,in particular investigating if the timing of the health and demographic effects matched the timing of the takeoff in growth in India and China.Bloom and Canning(2003)undertake a similar study of the role of demography in the takeoff in the Irish economy after1985.We also provide estimates of the direct welfare benefits of health improvements experienced in India and China,as well as their effect on welfare through generating economic growth.
2.Sources of economic growth
Most empirical models of economic growth focus on the growth of income per capita,which is a convenient summary indicator of the standard of living.However,economic theories typically address the level of output per worker.In addition, growth models often do not consider the ctoral compositi
on of the economy.We therefore begin by considering the pos-sible role that changes in the number of workers per capita and in ctoral composition play in explaining economic growth in China and India.
We start with an accounting identity that links income per capita(Y/N)to income per worker(Y/L).
Y N ¼
Y
L
L
WA
WA
N
ð1Þ
如何提高工作效率
奉献的事例In this identity,WA reprents the population of working-age.The identity merely states that the level of income per ca-pita equals the level of income per worker times the labor participation rate(L/WA)times the ratio of working-age to total population(WA/N).Defining.
y¼log Y
N
;z¼log
Y
L
;q¼log
L
WA
小动物卡通;w¼log
WA
N
ð2Þ
and totally differentiating the identity,we e that the growth rate of income per capita equals the growth of income per worker plus the growth of labor participation plus the growth of the ratio of working-age to total population.That is: _y¼_zþ_qþ_wð3ÞTable1performs this decomposition of growth in China and India for the period1980–2000with a corresponding decom-position for the period1970–1980reported for comparison.Thefigures suggest that faster growth in output per worker ac-counts for most of the speed up in growth in China and India,with modest contributions from rising participation rates and increas in the working-age share of the total population.
Growth in income per worker can result from increas in worker productivity in each ctor,or from the reallocation of workers from low-productivity to high-productivity ctors.Table2reports the shares of employment in agriculture,indus-try,and rvices,and Table3reports labor productivity in each ctor for China and India since1970.5Both China and India have en a movement of workers out of agriculture and into industry and rvices.This effect has been more pronounced in China,cont
ributing to its higher growth rate.Table3shows that agricultural productivity is about a quarter of the level of work-er productivity found in industry and rvices.The most striking feature shown in Table3is the rapid growth of output per worker in industry in China since1980.
We can decompo the effect of changing ctoral shares on economic growth more formally by writing z¼z a b aþz i b ið4Þaccording to which GDP per worker is a weighted average of the output per worker in the agriculture and industry(which we take to include rvices),given by z a and z i,with the weights being the respective shares of each ctor in total employment, given by b a and b i.
5The data ud to construct the shift-share analyzes are drawn from the following sources:(1)World Development Indicators Databa of the World Bank;
(2)China Statistical Yearbook(various years),and(3)National Sample Employment–Unemployment Surveys(for India),various years(data obtained by communication with S.Sakthivel and Anup Karan).
20 D.E.Bloom et al./Journal of Comparative Economics38(2010)17–33
Totally differentiating and dividing by z ,we can write
dz z ¼z a z b a dz a z a þd b a b a þz i z b i dz i z i þd b i b i ð5Þ
so that the growth in output per worker depends on (1)the growth of worker productivity in each ctor and (2)the growth of each ctor’s share of total employment.We can parate out the two effects.Growth in output per worker due to pro-ductivity growth within each ctor is
dz producti v ity ¼Y a dz a a þY i dz i i ð6Þ
where each ctor’s productivity growth rate is weighted by the share of that ctor in total GDP (e.g.,Y a =Y is the share of agricultural output in total output).The increa in output per worker due to changes in ctoral composition is given by
dz z c toral ¼z a z Àz i z d b a ð7Þ
where we have ud the fact that d b a ¼Àd b i due to the adding up constraint that the share of the two ctors must sum to one.Table 4gives the results of this decomposition (expanded to include rvices).The effect of growth in ctoral produc-tivity is calculated by weighting each ctor according to its share in GDP at the beginning of the period.The effect of changing ctoral employment shares is weighted using productivity levels at the beginning of the period.Using beginning-of-period weights,rather than continuously updating the weights as they change over time,means that our decomposition is an approximation and not an identity.6
Our calculations suggest that over the period 1980–2000,most of the growth of GDP per capita in China and India was due to incread productivity within ctors (5.3%of the 8.1%points in China,and 3.0%of the 3.6%points in India).The re-sults in Tables 1and 4also imply that increas in labor force participation,a rising share of working-age people in the total
Table 1
Sources of growth in China and India annual average growth rate (percent).
Variable India
China 1970–1980
1980–20001970–19801980–2000Growth rate of real GDP per capita
1.4 3.6 3.28.1Decomposition of growth rate of GDP per capita
Growth rate of real GDP per worker
1.2 3.9 3.0 6.7Growth of participation rate
À0.1À0.7À0.40.7Growth rate of ratio of population aged 15–64to total population 0.30.30.70.6Table 3
Output per worker by Sector (1995US$).
Variable India
China 1970
19802000197019802000Average GDP per worker
45350010722963961454By ctor
Agriculture
264287429188219478Industry
92489318067388013405Services 1042106222167607601630Table 2
Share of employment by ctor.
Variable India
China 1970宝宝反复低烧
19802000197019802000Share in total employment (%)
Agriculture 74
7060816950Industry 11
1316101823Services 151723913276For a general decomposition over a finite period we have D xy ¼x 0D y þy 0D x þD x D y .As the period becomes short the final term becomes very small and can be ignored (as occurs in the limit when we differentiate as above).In practice,we find that our decomposition in Table 4,ignoring the final interactive term,provides a clo approximation to the total change.D.E.Bloom et al./Journal of Comparative Economics 38(2010)17–33
21
