The Rising Trend of China’s Inter-The Rising Trend of China’s Inter-regional Aging Gap, 2000-2040regional Aging Gap, 2000-2040

Wen Lang Li (Wen Lang Li (李文朗）李文朗）

Professor Emeritus of Sociology, Ohio State University, 1996-Professor Emeritus of Sociology, Ohio State University, 1996-presentpresent

ROC Legislator, 1996-2000ROC Legislator, 1996-2000

Director, Chinese Studies Center, Tunghai University, 2001-Director, Chinese Studies Center, Tunghai University, 2001-presentpresent

The Rising Trend of China’s Inter-The Rising Trend of China’s Inter-regional Aging Gap, 2000-2040regional Aging Gap, 2000-2040

The Concept of “Demographic Constriction”The Concept of “Demographic Constriction” Research ObjectivesResearch Objectives Method and DataMethod and Data The Coming Labor Shortage in ChinaThe Coming Labor Shortage in China Inevitable Rise of Inter-regional Aging GapInevitable Rise of Inter-regional Aging Gap Macro-simulation ResultsMacro-simulation Results An Analytical ExplanationAn Analytical Explanation

The Concept of “Demographic The Concept of “Demographic Constriction”Constriction”

A Theoretical PerspectiveA Theoretical Perspectiveo Political Climate Political Climate Population Policies Population Policies

Demographic Factorial Changes Demographic Factorial Changes Population Population Structural Change Structural Change Remedial Social Policies Remedial Social Policies ? ?

o Will Redistributing Internal Resource be able to Will Redistributing Internal Resource be able to resolve demographic dilemma? resolve demographic dilemma?

Drastic Change of Fertility Policy in One Drastic Change of Fertility Policy in One GenerationGenerationo Mao’s Pro-natalist population policy, 1950-60sMao’s Pro-natalist population policy, 1950-60so Deng’s Anti-natalist population policy, 1970-80sDeng’s Anti-natalist population policy, 1970-80s

Research ObjectivesResearch Objectives

Is China’s forthcoming aging issue a simple result of Is China’s forthcoming aging issue a simple result of reversing fertility policy within one generation?reversing fertility policy within one generation?

How serious China’s inevitable population aging is How serious China’s inevitable population aging is exasperated by inter-regional inequality?exasperated by inter-regional inequality?

To what extent China’s inter-regional aging gap To what extent China’s inter-regional aging gap becomes insurmountable in the next generation?becomes insurmountable in the next generation?

Which demographic factor accounts for the most Which demographic factor accounts for the most significant impact on inter-regional aging gap?significant impact on inter-regional aging gap?

What role does migration play on the accentuation What role does migration play on the accentuation of inter-regional aging gap?of inter-regional aging gap?

Method and DataMethod and Data

An Open Model of Population ProjectionAn Open Model of Population Projectiono Cohort component method applied to provincial dataCohort component method applied to provincial datao Bottom-up approach of population projectionBottom-up approach of population projection

Stringent AssumptionsStringent Assumptionso Age-sex specific fertility and mortality rates in 2000 Age-sex specific fertility and mortality rates in 2000

are assumed to be constantare assumed to be constanto Age-sex specific migration and employment rates in Age-sex specific migration and employment rates in

1990 are assumed to be constant 1990 are assumed to be constant Macro-simulation to assess the effects of Macro-simulation to assess the effects of

demographic factors on inter-regional aging gapdemographic factors on inter-regional aging gap

Cohort-component Projection ModelCohort-component Projection Model

FF00SS00 F F11SS00 ------- P ------- P00(1+M(1+M00) P’) P’00

0 S0 S1 1 ------- P ------- P11(1+M(1+M11) = P’) = P’11

----------------------------- ------------ --------------------------------- ------------ ----

------------------------------ P------------------------------ Pnn(1+M(1+Mnn) P’) P’nn

The Coming Labor Shortage in The Coming Labor Shortage in ChinaChina

The Myth of China has unlimited labor supplyThe Myth of China has unlimited labor supplyo Total labor size vs. internal labor distributionTotal labor size vs. internal labor distributiono Can redistribution compensate for labor shortage?Can redistribution compensate for labor shortage?

China’s labor supply begins to decline around China’s labor supply begins to decline around 2010, after reaching a climax of 834 million.2010, after reaching a climax of 834 million.

China’s elderly will increase from 88 million in China’s elderly will increase from 88 million in 2000 to 289 million in 2040, about 228%.2000 to 289 million in 2040, about 228%.

The R/W ratio drastically increase from 11.7 The R/W ratio drastically increase from 11.7 % in 2000 to 48.1% in 2040, an increment of % in 2000 to 48.1% in 2040, an increment of 36.4 points.36.4 points.

Chinese Elderly, Workers, and R/W Ratio, 2000-2040

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Working 65+ R/W

Inevitable Rise of Inter-regional Aging GapInevitable Rise of Inter-regional Aging Gap

Inter-regional variation of R/W ratio drastically Inter-regional variation of R/W ratio drastically increases from 2000 to 2040.increases from 2000 to 2040.o Provincial absolute deviations from the national ratio Provincial absolute deviations from the national ratio

has an average of only 2.0% in 2000; it escalates to has an average of only 2.0% in 2000; it escalates to 13.1% in 2040.13.1% in 2040.

o The level of provincial aging (R/W ratio) in 2040 The level of provincial aging (R/W ratio) in 2040 appears unrelated to the level of R/W ratio in 2000.appears unrelated to the level of R/W ratio in 2000.

The aging increment is the highest in The aging increment is the highest in Heilongjian (79.5 pt),Jilin (69.5 pt), and Heilongjian (79.5 pt),Jilin (69.5 pt), and Liaoning (60.8 pt); lowest in Inner Mongolia Liaoning (60.8 pt); lowest in Inner Mongolia (2.3 pt). Why?(2.3 pt). Why?

R/W Ratio in 2000 and in 2040

y = 4.1028x

R2 = 0.2682

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退勞比之地區差異，2000-2040

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東北

北海

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退勞比之地帶差異，2000-2040

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00 05 10 15 20 25 30 35 40

沿海 華中 大西北

Macro-simulation ResultsMacro-simulation Results

An analytical question: Which of the three An analytical question: Which of the three demographic factors, fertility, mortality, and demographic factors, fertility, mortality, and migration, exerts the most significant impact on migration, exerts the most significant impact on provincial aging increments from 2000 to 2040?provincial aging increments from 2000 to 2040?

Experimental design through “Controlling the Experimental design through “Controlling the intervening factor.”intervening factor.”o If provincial age-specific fertility rates were the same as If provincial age-specific fertility rates were the same as

those of the nation;those of the nation;o If provincial age-sex-specific mortality rates were the same If provincial age-sex-specific mortality rates were the same

as those of the nation;as those of the nation;o If there were no inter-provincial migration.If there were no inter-provincial migration.

ACTUAL AND SIMULATED ESTIMATES OF AGING GAP

Province Actural Estimation Same Fertility Same MortalityNo Migration

Beijing 47.63 34.64 43.99 80.87Tianjin 22.34 17.08 22.72 57.91Hebei 30.50 31.77 34.51 28.97Shanxi 30.09 33.16 34.34 30.00Inner Mong -2.27 -2.48 -1.81 -2.27Liaoning 60.77 53.95 61.42 62.27Jilin 69.55 57.29 73.79 67.63Heilongjiang 79.49 65.96 82.65 78.83Shanghai 45.58 32.08 36.11 86.13Jiangsu 49.80 43.67 46.05 49.40Zhejiang 56.56 52.38 52.70 52.38Anhui 30.49 31.87 29.33 30.31Fujian 45.60 41.52 43.14 46.17Jiangxi 31.64 36.78 32.17 30.07Shandong 38.02 37.75 37.93 37.67Henan 22.03 24.64 22.88 21.64

Hubei 36.95 34.24 39.58 37.76Hunan 39.34 40.22 38.96 37.21Guangdong 31.81 28.05 29.87 36.72Guangxi 26.31 30.11 23.39 23.24Hainan 25.10 28.77 21.37 26.27Sichuan 42.19 41.94 42.53 38.81Guizhou 17.73 25.49 17.76 16.16Yunan 22.83 28.79 24.25 22.22Tibet 14.33 18.79 17.70 12.58Shaanxi 22.51 21.55 25.56 23.12Gansu 37.40 38.68 42.14 36.98Qinghai 28.08 31.83 30.92 30.49Ningxia 20.71 25.63 21.97 17.65Xingjiang 25.99 29.57 24.93 28.97

AVEDEV 12.95 9.26 12.32 15.89

Actual Estimate and Percent Deviation of Non-migration Simulation

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Average of Provincial Percent Average of Provincial Percent Deviations from Actual EstimatesDeviations from Actual Estimates

If provincial fertility same as national If provincial fertility same as national fertility:fertility:

13.47%13.47% If provincial mortality same as national If provincial mortality same as national

mortality:mortality:

6.50%6.50% If no inter-provincial migration:If no inter-provincial migration:

15.27%15.27%

An Analytical ExplanationAn Analytical Explanation

How does the migration factor affect the inter-How does the migration factor affect the inter-regional aging gap?regional aging gap?

Standard of living Standard of living In-migration In-migration Expansion of Expansion of working population working population Reducing the aging process Reducing the aging processo A region’s standard of living, or the level of in-migration, has A region’s standard of living, or the level of in-migration, has

no direct effect on reducing the aging processno direct effect on reducing the aging processo The most direct and significant variable is the work factorThe most direct and significant variable is the work factor

Implications for Social PoliciesImplications for Social Policieso Expanding the working population should be the most Expanding the working population should be the most

important policy consideration to reducing the aging gap.important policy consideration to reducing the aging gap.o Work incentives (e.g., female labor supply) should be major Work incentives (e.g., female labor supply) should be major

policy concerns; De-working incentives (early retirement or policy concerns; De-working incentives (early retirement or even massive education enrollments) should be discouraged.even massive education enrollments) should be discouraged.

GDP/P and Aging Increment among 30 Provinces

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Bivariate CorrelationsBivariate Correlations

AGING1 GDP/P

GROWTH LIFE TFR NMR EMP GAP

AGING1 1.000 0.678 0.002 0.722 -0.460 0.517 -0.320 0.406

GDP/P 0.678 1.000 0.422 0.741 -0.714 0.804 -0.090 0.370

GROWTH 0.002 0.422 1.000 0.075 -0.233 0.284 0.053 -0.011

LIFE 0.722 0.741 0.075 1.000 -0.777 0.533 -0.279 0.488

TFR -0.460 -0.714 -0.233 -0.777 1.000 -0.534 0.393 -0.596

NMR 0.517 0.804 0.284 0.533 -0.534 1.000 0.261 0.059

EMP -0.320 -0.090 0.053 -0.279 0.393 0.261 1.000 -0.883

GAP 0.406 0.370 -0.011 0.488 -0.596 0.059 -0.883 1.000

Regression Analysis of Inter-regional Aging Regression Analysis of Inter-regional Aging GapGap

47.554 58.719 .810 .427

-2.246 .833 -.345 -2.697 .013

9.226E-04 .001 .317 1.822 .082

-1.302 .703 -.149 -1.851 .078

1.000 .790 .183 1.266 .219

5.408 6.839 .115 .791 .438

1.848 1.044 .254 1.770 .091

-1.307 .138 -1.018 -9.502 .000

(Constant)

AGING1

GDP

GROWTH

LIFE

TFR

NMR

EMP

B Std. Error

UnstandardizedCoefficients

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Standardized

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t Sig.