1、ASIAN DEVELOPMENT BANKASIAN DEVELOPMENT BANK6 ADB Avenue,Mandaluyong City1550 Metro Manila,Philippineswww.adb.orgPopulation Aging,Silver Dividend,and Economic GrowthThe silver dividend refers to increased longevity and longer working life becoming potential sources of growth in an aging society.The

2、authors examine the potential for a silver dividend by empirically investigating the channels through which population aging affects economic growth.They find that lower total factor productivity growth is the main mechanism through which population aging harms economic growth.Labor shortage caused

3、by aging is mostly offset by higher labor force participation rates of the elderly.About the Asian Development BankADB is committed to achieving a prosperous,inclusive,resilient,and sustainable Asia and the Pacific,while sustaining its efforts to eradicate extreme poverty.Established in 1966,it is o

4、wned by 68 members 49 from the region.Its main instruments for helping its developing member countries are policy dialogue,loans,equity investments,guarantees,grants,and technical assistance.POPULATION AGING,SILVER DIVIDEND,AND ECONOMIC GROWTHDonghyun Park and Kwanho ShinADB ECONOMICSWORKING PAPER S

5、ERIESNO.678March 2023ASIAN DEVELOPMENT BANKADB Economics Working Paper SeriesPopulation Aging,Silver Dividend,and Economic GrowthDonghyun Park and Kwanho ShinNo.678|March 2023Donghyun Park(dparkadb.org)is an economic advisor at the Economic Research and Regional Cooperation Department,Asian Developm

6、ent Bank.Kwanho Shin(khshinkorea.ac.kr)is a professor at the Department of Economics,Korea University.The ADB Economics Working Paper Series presents research in progress to elicit comments and encourage debate on development issues in Asia and the Pacific.The views expressed are those of the author

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8、 2444www.adb.orgSome rights reserved.Published in 2023.ISSN 2313-6537(print),2313-6545(electronic)Publication Stock No.WPS230070-2DOI:http:/dx.doi.org/10.22617/WPS230070-2The views expressed in this publication are those of the authors and do not necessarily reflect the views and policies ofthe Asia

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16、on.ABSTRACT While there are growing concerns about population aging,some studies explore the possibility that population aging can give rise to a silver dividend that contributes to economic growth(ADB 2019).While the demographic dividend refers to the increase of the working-age population,the silv

17、er dividend points to increased longevity and longer working life as potential sources of growth in an aging society.Extending Lee and Shin(2021)to include developing countries,we examine the potential for a silver dividend by investigating the channels through which population aging affects economi

18、c growth.We find that lower total factor productivity growth is the main mechanism through which population aging harms economic growth.Labor shortage caused by population aging is mostly offset by higher labor force participation rates of males,females,and older workers.In particular,the labor forc

19、e participation rate of the older people increases the most.1 Keywords:aging,growth,labor force participation,total factor productivity,silver dividend JEL codes:J10,O40,J21,O47,E2 The authors are grateful for the financial support from the Asian Development Bank and would like to thank Charles Hori

20、oka and other seminar participants at Kobe University;Sang-Hyop Lee,Andrew Mason,Aiko Kikkawa,and other seminar participants at the Asian Development Bank;and Hye-In Han for her excellent research assistance.1.Introduction It has been argued that the demographic dividendthe expansion of working-age

21、population during the demographic transitionwas essential for the fast growth of East Asian economies(Bloom and Williamson 1998).However,a number of Asian economies are experiencing rapid population aging,slower growth or even contraction of workforce,and slower economic growth(Park and Shin 2012,20

22、22;Mason and Lee 2012).However,there is also some optimism that population aging can yield a silver dividend which can offset the reduction of the demographic dividend(Ogawa et al.2021,ADB 2019).While the demographic dividend refers to the increase in the working-age population,the silver dividend p

23、oints to longevity and longer working life as potential sources of growth in an aging society.In particular,encouraging older people to continue to learn can motivate them to participate in the labor market.1 The estimation of both demographic dividend and the silver dividend in most existing studie

24、s assume that population aging affects economic growth mainly through its effect on the workforce.Theoretically,however,the negative growth effects of aging operate through other channels as well.An aging population lowers the saving rate(Park,Shin,and Whang 2010;Horioka and Niimi 2017),slowing capi

25、tal accumulation and consequently lowering economic growth.The decline in the number of children also affects the accumulation of human capital by affecting the motivation to invest in their human capital(Becker and Nigel 1973).Finally,aging 1 The recent development of new technologies such as robot

26、s and artificial intelligence can be friendlier toward old workers and help them become more productive.(Park,Shin,and Kikkawa 2021;2022).2 has a negative effect on the growth rate of total factor productivity(TFP)since older people tend to be less innovative,leading to lower technological progress(

27、Jones 2010).2 Empirical studies on the various channels through which aging affects economic growth find that lower TFP growth is the most important channel.3 For example,Maestas,Mullen,and Powell(2022)find that two-thirds of the negative effect of aging is explained by slower productivity growth.Mo

28、re recently,based on data from 35 Organisation for Economic Co-operation and Development(OECD)countries,Lee and Shin(2021)investigated six channels through which population aging affects the growth rate of per capita gross domestic product(GDP).The six channels are changes in:(i)physical capital;(ii

29、)human capital;(iii)average working hours;(iv)labor participation rate;(v)the share of population aged 15 and over;and(vi)TFP.They find that population aging harms economic growth primarily through slower TFP growth.We extend Lee and Shin(2021)to include developing countries and examine whether popu

30、lation aging has a different impact on economic growth depending on the characteristics of each economy.Based on our panel data set of countries,we investigate how countries differ in the relative importance of the different channels depending on the value of the following characteristics:(i)old dep

31、endency ratio,(ii)human capital,(iii)life expectancy,(iv)labor market flexibility,(v)government size,(vi)trade openness,and(vii)capital market openness.We find that the main channel of the negative growth effect of 2 Liang,Wang,and Lazear(2018)argue that as an economy gets aged,older workers occupy

32、high-level positions and block younger workers from acquiring skills,which eventually impedes innovation.Derrien,Kecsks,and Nguyen(2018);Aksoy et al.(2019);and Lee and Shin(2021)provide evidence that aging lowers the growth rate of TFP based on advanced-economy data.3 More generally,even for the oth

33、er determinants of economic growth,Wong(2007)shows that TFP growth is the main channel.3 population aging is reduced TFP growth.Previous studies find these results based mostly on data from advanced countries.However,we confirm this finding even using a much broader sample of 166 countries encompass

34、ing both advanced and developing economies.Labor shortage caused by population aging is mostly offset by higher labor force participation rates of males,females,and old workers.In particular,the shortage seems to cause a remarkable increase in the labor force participation rate among older people.Si

35、gnificantly,labor shortage due to aging does not seem to be a problem in most countries due to higher labor force participation.We find that higher life expectancy,human capital,and trade openness amplify the mitigating effect of the increased labor force participation rate among older people.Groupi

36、ng countries according to the values of the seven characteristics listed previously,we find nonlinear effects of population aging.In particular,the effect of population aging is not even negative for countries with low values of some characteristics.In addition,we find that the mitigating effect of

37、higher labor force participation rate is not enough to offset the negative growth effect of population aging.Although the shortage of labor force can be completely offset by higher labor force participation,the primary channel for the negative growth effect of aging is reduced TFP growth,which is di

38、fficult to offset.This is especially true for countries with high-value characteristics,which are mostly advanced countries.The rest of the paper is organized as follows:Section 2 explains the empirical specification,Section 3 reports our main empirical results,and Section 4 concludes.4 2.Empirical

39、Specification and Data In this section,we describe our empirical framework and data.The empirical specification follows Lee and Shin(2021).Assuming a Cobb-Douglas production function,output per capita is represented as:y=?/?(?)/?(1)where y=?,=?,?=?,A is the TFP level,is labor income share,h is avera

40、ge human capital,v is average working hours,p is the labor force participation rate,?is population aged 15 and over,and is the total population.As emphasized by Lee and Shin(2021),is the capital-output ratio rather than the capitallabor ratio.Here we follow Hall and Jones(1999)to allow the steady st

41、ate of capitaloutput ratio to be independent of the level of TFP.By taking log difference of equation(1),we obtain the following equation:lny=?ln+ln+ln+ln+ln?+?ln (2)where represents the time difference.Equation(2)implies that any determinant of output growth per capita works through six channels:ch

42、anges in(i)physical capital-output ratio,(ii)per capita human capital,(iii)average working hours,(iv)labor participation rate,(v)the share of 15 and above(the share of population aged 15 and over),and(vi)TFP.Lee and Shin(2021)noted that the six channels can be divided into two groups depending on wh

43、ether or not the channel can affect growth permanently.The first group,which can change the growth rate of per capita output permanently,comprises channels(i),(ii),and(iv).The second 5 group,which does not have a permanent growth effect,includes channels,(iii),(iv),and(v).The key difference between

44、the two groups is whether each component can grow without any limit.For example,average hours,the labor participation rate,and the share of 15 and above,which constitute the second group,cannot grow forever.However,since the time interval in the empirical specification is either five or ten years,we

45、 believe that even group 2 channels can affect the growth rate of per capita output in the intermediate run.The share of 15 and above is not the same as the conventional working-age population that is defined as the share of population aged between 15 and 64.Hence,Lee and Shin(2021)further decompose

46、 the share of 15 and above into two parts.Then the final equation for the estimation becomes:lny=?ln+ln+ln+ln+ln?+ln?+?ln (3)Note that ln?is decomposed into ln?and ln?,where the first component is the change in the share of working-age population and the second,the change in the share of 15 and abov

47、e to the working-age population.While these two components are estimated separately,they will be combined and regarded as one channel when we interpret our empirical results later.We collect data from various sources,as summarized in the Appendix.Output,population,capital stock,human capital stock,a

48、verage working hours,and TFP are collected from the Penn World Table(PWT)10.0 update(18 June 2021).Output growth per capita is calculated using the PWTs national-accounts real GDP(RGDPNA).4 The country sample includes 166 countries.The 4 There are five different GDP variables calculated in PWT 10.0.

49、See https:/www.rug.nl/ggdc/productivity/pwt/.Among these,the national-accounts real GDP(RGDPNA)is recommended for cross-country growth regressions.6 sample period is 1960 to 2019 and the growth rate is calculated by using 5-year averages:(Period 1:19601964),(Period 2:19651969),and(Period 12:20152019

50、).5 The old-age dependency and youth dependency ratios are retrieved from the World Banks World Development Indicators.The labor force participation rates are modelled estimates from the statistics database of the International Labour Organization(ILO),ILOStat.3.Empirical Findings In this section,we

51、 report and discuss our empirical findings.Table 1 presents the summary statistics of the variables we used in this study.The average growth rate of per capita output is 2%.The average per capita GDP in 2017 constant United States dollars is$13,387.The average old-age ratio is 0.11 and 0.61 for yout

52、h dependency.The average old-aged population share is 0.07 and 0.34 for the youth-aged population.The average share of the working age population is 0.59.The average labor force participation rate for population aged 15+is 0.62.The average annual growth rates of capitaloutput ratio is 0.55%and 0.91%

53、for human capital.The average annual growth rate of TFP is 0.48%.Table 1:Summary Statistics 5 Calculating the growth rate between the averages after calculating the 5-year average reduces the randomness associated with setting arbitrary intervals.Other growth rates are calculated similarly.Variables

54、 Count Mean SD Minimum Maximum Annual GDP growth rate per capita,national accounts 1,649 2.02%3.20%-22.08%21.47%Real per capita GDP,output side 1,818 13,387 20,279 457 277,563 Youth dependency ratio 1,746 0.61 0.25 0.15 1.12 Old-age dependency ratio 1,746 0.11 0.07 0.02 0.45 Youth-aged population sh

55、are 1,746 0.34 0.11 0.11 0.51 Old-age population share 1,746 0.07 0.05 0.01 0.27 Working aged population share 1,746 0.59 0.07 0.46 0.80 Share of 5564 ages in total population 1,746 0.07 0.03 0.02 0.16 7 GDP=gross domestic product,SD=standard deviation,TFP=total factor productivity.Note:Definitions

56、of variables and data sources are in the Appendix.The 5-year average growth rates are calculated and then annualized.Other variables are measured at the beginning of each period.The sample period is from 1960 to 2019:(Period 1:19601964),(Period 2:19651969),(Period 12:20152019).Source:Authors calcula

57、tions.Table 2.1 presents the panel estimation results with country fixed effects when we regress the growth rates of per capita output and the variable representing each of the six channels on the old-age and youth dependency ratios.6 We include period dummies,but their coefficients are not reported

58、.Robust standard errors are in brackets.*,*,and*represent statistical significance at the 1%,5%,and 10%levels,respectively.As shown in equation(3),since?and?are multiplied ln and ln,respectively,we report the estimated coefficients in columns(2)and(8)after multiplying these values by the dependent v

59、ariables.The sum of the coefficients of the old-age dependency ratio in columns(2)to(8)should be identically equal to that the coefficient in column(1).However,since the number of observations differs across columns due to data availability,this 6 Since we estimate equation(3)rather than equation(2)

60、,there are seven channels in the table.However,the fifth and sixth channels are combined as ln?.Variables Count Mean SD Minimum Maximum Labor force participation rate(15+,both sex)942 0.62 0.10 0.32 0.89 Annual growth rate of capitaloutput ratio 1,187 0.55%2.66%-11.12%17.61%Annual growth rate of hum

61、an capital 1,317 0.91%0.60%-2.32%4.34%Annual growth rate of average working hours 565-0.29%0.49%-1.98%1.12%Annual growth of labor force participation rate(15+population)785-0.04%0.36%-1.95%1.50%Annual growth of ratio of 1564 years old population 1,584 0.25%0.49%-1.32%2.49%Annual growth of ratio of+1

62、5 years old over 1564 years old 1,584 0.07%0.15%-0.34%0.79%Annual growth rate of TFP 1,649 0.48%2.16%-17.46%11.29%Annual growth rate of life expectancy at birth(Total)1,591 0.53%0.78%-10.57%12.04%8 identity does not hold exactly.In line with Lee and Shin(2021),the coefficient of the old-age dependen

63、cy ratio is negative and statistically significant in column(1)where the dependent variable is the growth rate of per capita output.This negative effect of aging on economic growth is explained by the six channels reported in columns(2)to(8).Again,in line with Lee and Shin(2021),the negative effect

64、is mostly explained by the decrease in TFP growth in column(8).7 Note that aging has a negative impact on the share of working age population column(6),but more than two-thirds of the impact is offset by the increase in the labor force participation column(5).Aging also has a negative and statistica

65、lly significant impact on human capital accumulation column(3).In Table 2.2,we report the same panel estimation results when we use old and youth population shares instead of old-age and youth dependency ratios as explanatory variables.The results are consistent with those in Table 2.1.In particular

66、,the coefficient of the old population share is negative and statistically significant in column(1)and the negative impact of aging is more than fully explained by lowered TFP growth.Aging also has a negative impact on the share of working age population column(6),but more than three-fourths of the

67、impact is offset by an increase in the labor force participation column(5).In addition,we find a negative and statistically significant impact of aging on human capital accumulation column(3).7 In fact,the coefficient reported in column(8)is greater than that in column(1),indicating that lowered TFP

68、 is more than enough to explain the negative effect of aging on economic growth.9 Table 2:The Effects of Aging on GDP Growth and its Eight Channels When the Initial Per Capita GDP is Not Controlled Table 2.1:Dependency Ratios (1)(2)(3)(4)(5)(6)(7)(8)Variables GDP per capita K/Y Human capital Work ho

69、ur LF participation 1564 population Share of 15 and above TFP Old-age dependency ratio-0.170*0.005-0.045*-0.004 0.044*-0.061*0.003-0.263*0.053 0.075 0.012 0.012 0.011 0.006 0.004 0.097 Youth dependency ratio 0.012-0.002 0.009*0.016*-0.009*0.011*-0.002*-0.024 0.014 0.029 0.005 0.007 0.003 0.002 0.001

70、 0.040 Observations 1,584 1,165 1,306 554 780 1,584 1,584 1,055 R-squared 0.093 0.061 0.106 0.152 0.050 0.260 0.155 0.106 Number of countries 162 125 129 66 156 162 162 109 GDP=gross domestic product,K/Y=capitaloutput ratio,LF=labor force,TFP=total factor productivity.Notes:The dependent variable is

71、 annualized log-difference of 5-year periods of the variable listed in the first row.Panel regression results with country fixed effects are reported.Period dummies are included but their coefficients are not reported.Robust standard errors are in brackets.*,*,and*represent statistical significance

72、at the 1%,5%,and 10%levels,respectively.Source:Authors calculations.Table 2.2:Population Shares (1)(2)(3)(4)(5)(6)(7)(8)Variables GDP per capita K/Y Human capital Work hour LF participation 1564 population Share of 15 and above TFP Old population share-0.293*0.036-0.066*0.015 0.066*-0.085*0.002-0.53

73、4*0.094 0.179 0.025 0.020 0.019 0.011 0.007 0.217 Youth population share 0.018-0.009 0.018 0.042*-0.019*0.031*-0.005*-0.095 0.039 0.091 0.015 0.016 0.008 0.005 0.002 0.114 Observations 1,584 1,165 1,306 554 780 1,584 1,584 1,055 R-squared 0.097 0.061 0.109 0.155 0.058 0.301 0.160 0.110 Number of cou

74、ntries 162 125 129 66 156 162 162 109 GDP=gross domestic product,K/Y=capitaloutput ratio,LF=labor force,TFP=total factor productivity.Notes:The dependent variable is annualized log-difference of 5-year periods of the variable listed in the first row.Panel regression results with country fixed effect

75、s are reported.We include period dummies but their coefficients are not reported.Robust standard errors are in brackets.*and*represent statistical significance at the 1%,5%,and 10%levels,respectively.Source:Authors calculations.10 In Table 3.1,we add the initial level of GDP per capita as an additio

76、nal variable and report panel estimation results with fixed effects.While it makes sense to add the initial level of GDP per capita in column(1)and possibly in column(8),it may not be entirely appropriate to add it in other columns.However,to preserve the identity that the sum of the coefficients of

77、 columns(2)to(8)is equal to the coefficient of column(1),we added it to other columns as well.We use output-side real GDP at chained purchasing power parity(PPPs)as the initial level of GDP per capita.8 Although the coefficient of the old-age dependency ratio is negative,it is no longer statisticall

78、y significant column(8).9 However,we still observe that the negative impact of aging on the working age population is substantially offset by an increase in the labor for participation rate.Note that the coefficient of the old-age dependency ratio is negative and largest in absolute value in column(

79、2).This suggests that in this specification,reduced capital accumulation is the largest channel for the negative impact of aging.In Table 3.2,when we use the old and youth population shares and the initial level of per capita GDP as explanatory variables,the coefficient of the old population share i

80、s negative and statistically significant in column(1).Again,the negative impact of aging on economic growth is more than fully explained by reduced TFP in column(8).In addition,the negative impact of aging on the working age population is substantially offset by an 8 Note that we use national-accoun

81、ts real GDP per capita(RGDPNA)when calculating the growth rate.Since we use output-side real GDP per capita(RGDPO)as the initial level of real GDP per capita,our panel specification in column(1)is not suitable for dynamic panel estimations.PWT recommends RGDPO comparing per capita GDP across countri

82、es Hence,it is appropriate to use it as the initial level of per capita GDP.9 While the coefficient of the old-age dependency ratio is not statistically significant,we find evidence of the negative impact of aging elsewhere.As shown in Table 3.2,the coefficient of the old population share is negativ

83、e and statistically significant.When we use 10-year period instead of 5-year period(not reported),even the coefficient of the old-age dependency ratio is negative and statistically significant in the same specification.More importantly we find the coefficient of the old-age dependency ratio to be ne

84、gative and statistically significant in the instrumental-variable(IV)estimation reported in Table 4.1.11 increase in the labor for participation rate.Interestingly,the coefficient of the old population share is negative and large in magnitude but it is not statistically significant.12 Table 3:The Ef

85、fects of Aging on GDP Growth and its Eight Channels when the Initial Per Capita GDP is Controlled Table 3.1:Dependency Ratios (1)(2)(3)(4)(5)(6)(7)(8)Variables GDP per capita K/Y Human capital Work hour LF participation 1564 population Share of 15 and above TFP Old-age dependency ratio-0.047-0.132*-

86、0.048*-0.001 0.044*-0.059*0.002-0.082 0.053 0.062 0.012 0.012 0.011 0.007 0.004 0.072 Youth dependency ratio-0.035*0.054 0.010*0.011*-0.008*0.010*-0.002*-0.101*0.016 0.042 0.005 0.006 0.003 0.002 0.001 0.052 Initial GDP per capita-0.033*0.042*0.001-0.003*0.001-0.000 0.000-0.057*0.004 0.014 0.001 0.0

87、02 0.001 0.001 0.000 0.016 Observations 1,584 1,165 1,306 554 780 1,584 1,584 1,055 R-squared 0.216 0.169 0.107 0.170 0.056 0.262 0.156 0.220 Number of countries 162 125 129 66 156 162 162 109 GDP=gross domestic product,K/Y=capitaloutput ratio,LF=labor force,TFP=total factor productivity.Notes:The d

88、ependent variable is annualized log-difference of 5-year periods of the variable listed in the first row.We add the initial level of GDP per capita,calculated from output-side real GDP per capita,as an additional regressor.Panel regression results with country fixed effects are reported.We include p

89、eriod dummies but their coefficients are not reported.Robust standard errors are in brackets.*,*,and*represent statistical significance at the 1%,5%,and 10%levels,respectively.Source:Authors calculations.13 Table 3.2:Population Shares (1)(2)(3)(4)(5)(6)(7)(8)Variables GDP per capita K/Y Human capita

90、l Work hour LF participation 1564 population Share of 15 and above TFP Old population share-0.160*-0.111-0.070*0.014 0.067*-0.086*0.002-0.342*0.086 0.132 0.025 0.019 0.019 0.011 0.007 0.150 Youth population share-0.116*0.159 0.021 0.029*-0.017*0.032*-0.005*-0.323*0.046 0.126 0.015 0.015 0.008 0.004

91、0.002 0.151 Initial GDP per capita-0.034*0.043*0.001-0.003*0.001 0.000 0.000-0.059*0.004 0.014 0.001 0.002 0.001 0.001 0.000 0.016 Observations 1,584 1,165 1,306 554 780 1,584 1,584 1,055 R-squared 0.218 0.172 0.111 0.169 0.063 0.301 0.160 0.225 Number of countries 162 125 129 66 156 162 162 109 GDP

92、=gross domestic product,K/Y=capitaloutput ratio,LF=labor force,TFP=total factor productivity.Notes:The dependent variable is annualized log-difference of 5-year periods of the variable listed in the first row.We add the initial level of GDP per capita,calculated from output-side real GDP per capita,

93、as an additional regressor.Panel regression results with country fixed effects are reported.We include period dummies but their coefficients are not reported.Robust standard errors are in brackets.*,*,and*represent statistical significance at the 1%,5%,and 10%levels,respectively.Source:Authors calcu

94、lations.14 While the results in Tables 2 and 3 are suggestive,they may suffer from endogeneity.For instance,as the economy matures and economic growth rate stagnates,the demographic structure also matures,and the share of older population tends to increase.Another possibility is that if young worker

95、s who feel pessimistic about economic prospects emigrate,expectations of lower future GDP growth can induce the old dependency ratio and the older population share to increase.Hence,we cannot be sure about the direction of causality of the results in Tables 2 and 3.In Table 4.1,by using the same emp

96、irical specification as in Table 3.1,we report instrumental-variables(IV)panel regression results with country fixed effects.We use 10-year lagged values of the old and youth dependency ratios and the birth rate as instruments for the current old dependency ratio.We include period dummies but their

97、coefficients are not reported.In most columns,the first stage F statistics indicate that our instrumental variables are appropriate.However,some caution is warranted since the regression does not pass the Hansens J-test in columns(1),(3),(6),and(7).In column(1)of Table 4.1,unlike in Table 3.1,the co

98、efficient of the old-age dependency is highly statistically significant,indicating that aging adversely affects economic growth.However,in column(8),the coefficient of the old-age dependency ratio is negative and large in magnitude but not statistically significant.The negative impact of aging on th

99、e working age population in column(6)is almost entirely offset by the increase in the labor force participation rate in column(5).We also observe that aging has a negative impact on human capital accumulation in column(3).In Table 4.2,we report the same IV panel regression results with fixed effects

100、 but with the old and young population shares replacing the old-age and youth dependency ratios as regressors.The results are consistent with those in Table 4.1 except that the 15 coefficient of the older population share is highly statistically significant in column(8).The estimated coefficient in

101、column(8)more than fully explains the negative growth effect of aging in column(1).Again,the negative impact of aging on the working age population in column(6)is almost entirely offset by the increase in the labor force participation rate in column(5).Aging also negatively affects human capital acc

102、umulation.16 Table 4:The Effects of Aging on GDP Growth and its Eight Channels When the Initial Per Capita GDP is Controlled:IV Regressions Table 4.1:Dependency Ratios (1)(2)(3)(4)(5)(6)(7)(8)Variables GDP per capita K/Y Human capital Work hour LF participation 1564 population Share of 15 and above

103、TFP Old-age dependency ratio-0.094*-0.068-0.035*-0.007 0.054*-0.059*-0.006*-0.057 0.043 0.058 0.008 0.012 0.011 0.007 0.003 0.082 Youth dependency ratio-0.022*0.034 0.007*0.014*-0.009*0.015*-0.002*-0.117*0.011 0.033 0.003 0.004 0.003 0.002 0.000 0.047 Initial GDP per capita-0.032*0.034*-0.000-0.003*

104、0.001-0.000 0.000*-0.062*0.003 0.009 0.001 0.001 0.001 0.000 0.000 0.012 Observations 1,417 1,060 1,173 521 758 1,417 1,417 961 R-squared 0.222 0.140 0.083 0.174 0.044 0.249 0.154 0.212 Number of countries 161 125 129 66 156 161 161 109 First stage F-statistic 721.2 600.2 700.8 291.2 225.3 721.2 721

105、.2 609.2 Hansens J-test (P-value)0.000353 0.240 4.25e-07 0.248 0.896 0 4.56e-09 0.0662 GDP=gross domestic product,K/Y=capitaloutput ratio,LF=labor force,TFP=total factor productivity.Notes:The dependent variable is annualized log-difference of 5-year periods of the variable listed in the first row.W

106、e add the initial level of GDP per capita,calculated from output-side real GDP per capita,as an additional regressor.We report instrumental-variables panel regression results with country fixed effects by using 10-year lagged values of the old and youth dependency ratios and the birth rate as instru

107、ments for the current old dependency ratio.We include period dummies but their coefficients are not reported.Robust standard errors are in brackets.*,*,and*represent statistical significance at the 1%,5%,and 10%levels,respectively.Source:Authors calculations.17 Table 4.2:Population Shares (1)(2)(3)(

108、4)(5)(6)(7)(8)Variables GDP per capita K/Y Human capital Work hour LF participation 1564 population Share of 15 and above TFP Older population share-0.227*-0.045-0.051*0.011 0.080*-0.081*-0.010*-0.319*0.067 0.117 0.015 0.018 0.018 0.010 0.005 0.167 Youth population share-0.071*0.090 0.016 0.035*-0.0

109、18*0.045*-0.007*-0.338*0.032 0.100 0.010 0.012 0.007 0.004 0.001 0.139 Initial GDP per capita-0.032*0.034*0.000-0.003*0.000 0.000 0.000-0.064*0.004 0.009 0.001 0.001 0.001 0.000 0.000 0.013 Observations 1,417 1,060 1,173 521 758 1,417 1,417 961 R-squared 0.224 0.140 0.084 0.171 0.053 0.297 0.168 0.2

110、17 Number of countries 161 125 129 66 156 161 161 109 First stage F-statistic 1092 937.6 1094 550.0 330.6 1092 1092 973.8 Hansens J-test (P-value)0.000568 0.215 2.42e-05 0.137 0.705 0 4.12e-10 0.0668 GDP=gross domestic product,K/Y=capitaloutput ratio,LF=labor force,TFP=total factor productivity.Note

111、s:The dependent variable is annualized log-difference of 5-year periods of the variable listed in the first row.We add the initial level of GDP per capita,calculated from output-side real GDP per capita,as an additional regressor.We report instrumental-variables panel regression results with country

112、 fixed effects by using 10-year lagged values of the old and youth population shares and the birth rate as instruments for the current older population share.We include period dummies but their coefficients are not reported.Robust standard errors are in brackets.*,*,and*represent statistical signifi

113、cance at the 1%,5%,and 10%levels,respectively.Source:Authors calculations.18 This section summarizes our findings thus far.Aging has a negative impact on economic growth but this negative impact is not due to a decrease in the labor force.The decline of the working-age population is mostly offset by

114、 the increase in the labor market participation rate.Instead,the negative growth effect of aging was mainly driven by the decline in TFP growth.Most existing studies of silver dividend took labor shortage for granted and focused on how to mitigate the labor shortage.However,our study shows that redu

115、cing the negative effects of aging on TFP growth matters more for reducing the negative effect of aging on economic growth.The economy can offset the labor shortage caused by population aging by increasing the labor force participation rate of three groups,namely working-age males,working-age female

116、s,and among older people.In Table 5,we estimate the impact of aging on the three groups labor force participation rates.In Table 5.1,we use old-age dependency ratio as a proxy of aging and report the ordinary least squares(OLS)panel regression results with fixed effects in columns(1),(2),and(3)where

117、 the dependent variable is the labor force participation rate of males,females,and old-age population,respectively.10 The equation specification follows those in Tables 3 and 4,and includes the youth dependency ratio and initial GDP per capita as additional control variables.We find that the coeffic

118、ient of old-age dependency ratio is positive and highly statistically significant in all three columns.This indicates that the shortage of labor due to aging is offset by higher force participation rate.The estimated coefficient in column(3)is three to 10 While it is desirable to use the labor force

119、 participation rate of males and females of the working age population,the ILO statistics report the labor force participation of males and females of the whole population aged 15+.Hence our estimates are likely to overstate the impact of aging on the labor force participation rate of working-age ma

120、les and females.However,the coefficient of the old-age population still remains by far the largest.19 four times larger than the corresponding figures for column(1)or(2),suggesting that higher labor force participation among older people is the strongest antidote to labor shortage.In columns(4)to(6)

121、,we report the IV panel regression results with fixed effects.The results are consistent with those reported in columns(1)to(3).The coefficient of old-age dependency ratio is positive and highly statistically significant in all three columns and the estimated coefficient reported in column(3)is five

122、 to six times as large as those in columns(4)and(5).In Table 5.2,we report the same set of regression results as in Table 5.1,but with old-age population share replacing old-age dependency ratio.The estimation results are consistent with those in Table 5.1.The coefficient of old-age population share

123、 is positive and highly statistically significant in both OLS and IV panel regressions.Again,the coefficient of old-age population share,shown in columns(3)and(6),is much larger than that of working-age males and females,shown in columns(1),(2),(4),and(5).Again,higher labor force participation rate

124、among older people plays the most important role in offsetting labor shortage.20 Table 5:The Impact of Population Aging on the Labor Force Participation Rate Table 5.1:Dependency Ratios (1)(2)(3)IV(4)IV(5)IV(6)Variables Female Male Older people Female Male Older people Old-age dependency ratio 0.089

125、*0.067*0.243*0.100*0.086*0.511*0.028 0.018 0.111 0.026 0.019 0.124 Youth dependency ratio 0.012-0.014*0.067*0.005-0.016*0.054*0.016 0.004 0.032 0.012 0.004 0.031 Initial GDP per capita-0.000 0.002*0.011-0.000 0.001 0.004 0.003 0.001 0.007 0.002 0.001 0.004 Observations 780 780 780 758 758 758 R-squa

126、red 0.034 0.074 0.062 0.015 0.066 0.073 Number of countries 156 156 156 156 156 156 First stage F-statistic 225.3 225.3 225.3 Hansens J-test (P-value)0.972 0.153 0.0131 GDP=gross domestic product,IV=instrumental-variable.Notes:The dependent variable is annualized log-difference of 5-year periods of

127、the labor force participation rate for the group denoted in the first row.We add the initial level of GDP per capita,calculated from output-side real GDP per capita,as an additional regressor.We report instrumental-variables panel regression results with country fixed effects by using 10-year lagged

128、 values of the old and youth dependency ratios and the birth rate as instruments for the current old dependency ratio.We include period dummies but their coefficients are not reported.Robust standard errors are in brackets.*,*,and*represent statistical significance at the 1%,5%,and 10%levels,respect

129、ively.Source:Authors calculations.21 Table 5.2:Population Shares (1)(2)(3)IV(4)IV(5)IV(6)Variables Female Male Older people Female Male Older people Old-age population share 0.178*0.100*0.532*0.181*0.128*0.983*0.050 0.030 0.197 0.043 0.031 0.220 Youth population share 0.025-0.030*0.150 0.023-0.032*0

130、.158*0.037 0.011 0.093 0.028 0.010 0.088 Initial GDP per capita-0.001 0.002 0.010-0.000 0.001 0.003 0.003 0.001 0.008 0.002 0.001 0.005 Observations 780 780 780 758 758 758 R-squared 0.035 0.081 0.060 0.018 0.078 0.075 Number of countries 156 156 156 156 156 156 First stage F-statistic 330.6 330.6 3

131、30.6 Hansens J-test (P-value)0.988 0.423 0.00422 GDP=gross domestic product,IV=instrumental-variable.Notes:The dependent variable is annualized log-difference of 5-year periods of the labor force participation rate for the group denoted in the first row.We add the initial level of GDP per capita,cal

132、culated from output-side real GDP per capita,as an additional regressor.We report instrumental-variables panel regression results with country fixed effects by using 10-year lagged values of the old and youth dependency ratios and the birth rate as instruments for the current old dependency ratio.We

133、 include period dummies but their coefficients are not reported.Robust standard errors are in brackets.*,*,and*represent statistical significance at the 1%,5%,and 10%levels,respectively.Source:Authors calculations.22 In Tables 6 to 8,we investigate the determinants of the increase in the labor force

134、 participation rate in response to aging.Tables 6.1 and 6.2 show the role of life expectancy.We expect that as life expectancy increases,individuals work more because they are healthier.In Table 6.1,we report both OLS and IV panel regression results for the same set of equations as in Table 5.1,exce

135、pt we add life expectancy and its interaction term with old-age dependency ratio as additional explanatory variables.The first stage F statistics and Hansens J test indicate that our instrumental variables are appropriate.Both coefficients of the interaction term in the OLS estimation of column(3)an

136、d the IV estimation of column(6)are positive and highly statistically significant.This suggests that in countries with higher life expectancy,labor force participation rate among older people increases more in response to population aging.In Table 6.2,we replace old-age dependency ratio with old-age

137、 population share and find qualitatively similar results.23 Table 6:Life Expectancy and the Impact of Population Aging on the Labor Force Participation Rate Table 6.1:Dependency Ratios OLS IV (1)(2)(3)(4)(5)(6)Variables Female Male Older people Female Male Older people Old-age dependency ratio 0.226

138、-0.173-3.371*-0.139-0.250-2.905*0.250 0.129 1.045 0.398 0.224 1.545 Youth dependency ratio 0.015-0.017*0.018 0.000-0.022*-0.006 0.018 0.004 0.031 0.015 0.005 0.033 Initial GDP per capita 0.000 0.002*0.011-0.000 0.001 0.003 0.003 0.001 0.008 0.002 0.001 0.004 Life expectancy x old dependency ratio-0.

139、002 0.003*0.045*0.003 0.004 0.042*0.003 0.002 0.013 0.005 0.003 0.019 Life expectancy-0.000-0.000*-0.003*-0.000-0.000*-0.003*0.000 0.000 0.001 0.000 0.000 0.001 Observations 780 780 780 758 758 758 R-squared 0.037 0.084 0.099 0.015 0.078 0.104 Number of countries 156 156 156 156 156 156 First stage

140、F-statistic 26.58 26.58 26.58 Hansens J-test (P-value)0.706 0.491 0.0839 GDP=gross domestic product,IV=instrumental-variable,OLS=ordinary least squares.Note:The dependent variable is annualized log-difference of 5-year periods of the labor force participation rate for the group denoted in the first

141、row.We add the initial level of GDP per capita,calculated from output-side real GDP per capita,as an additional regressor.We report instrumental-variables panel regression results with country fixed effects by using 10-year lagged values of the old and youth dependency ratios and the birth rate as i

142、nstruments for the current old dependency ratio and the interaction term with life expectancy.We include period dummies but their coefficients are not reported.Robust standard errors are in brackets.*,*,and*represent statistical significance at the 1%,5%,and 10%levels,respectively.Source:Authors cal

143、culations.24 Table 6.2:Population Shares OLS IV (1)(2)(3)(4)(5)(6)Variables Female Male Older people Female Male Older people Old-age population share 0.346-0.231-6.320*-0.465-0.554-7.993*0.361 0.208 1.683 1.021 0.449 3.218 Youth population share 0.034-0.039*-0.070-0.004-0.059*-0.183 0.043 0.012 0.0

144、97 0.046 0.019 0.128 Initial GDP per capita-0.000 0.002 0.009-0.000 0.001-0.001 0.003 0.001 0.008 0.002 0.001 0.005 Life expectancy x old population share-0.002 0.004 0.082*0.007 0.008 0.105*0.004 0.002 0.021 0.012 0.005 0.038 Life expectancy-0.000-0.000*-0.003*-0.000-0.000*-0.004*0.000 0.000 0.001

145、0.000 0.000 0.001 Observations 780 780 780 758 758 758 R-squared 0.038 0.089 0.113 0.012 0.086 0.117 Number of countries 156 156 156 156 156 156 First stage F-statistic 18.79 18.79 18.79 Hansens J-test (P-value)0.566 0.888 0.104 GDP=gross domestic product,IV=instrumental-variable,OLS=ordinary least

146、squares.Notes:The dependent variable is annualized log-difference of 5-year periods of the labor force participation rate for the group denoted in the first row.We add the initial level of GDP per capita,calculated from output-side real GDP per capita,as an additional regressor.We report instrumenta

147、l-variables panel regression results with country fixed effects by using 10-year lagged values of the old and youth dependency ratios and the birth rate as instruments for the current old dependency ratio and the interaction term with life expectancy.We include period dummies but their coefficients

148、are not reported.Robust standard errors are in brackets.*,*,and*represent statistical significance at the 1%,5%,and 10%levels,respectively.Source:Authors calculations.25 We investigate the role of human capital in Tables 7.1 and 7.2.We expect workers with more human capital to have stronger incentiv

149、e to participate in the labor market.Again,we report both OLS and IV panel regression results for the same set of equations as in Tables 5.1 and 5.2,except we add human capital and its interaction term with old-age dependency ratio or older population share as additional explanatory variables.The IV

150、 estimation results pass the first stage F test and the Hansens J test at the conventional level.We find that higher human capital helps to offset labor shortage by boosting the labor force participation rates of both males and older workers.The coefficients of the interaction term are positive and

151、highly statistically significant in columns(2),(3),(5),and(6).In Table 7.2,we replace old-age dependency ratio with old-age population share and find qualitatively similar results.Interestingly,however,we do not observe the same effect for females in either Table 7.1 or 7.2.26 Table 7:Human Capital

152、and the Impact of Population Aging on Labor Force Participation Rate Table 7.1:Dependency Ratios OLS IV (1)(2)(3)(4)(5)(6)Variables Female Male Older people Female Male Older people Old-age dependency ratio-0.010-0.293*-1.918*-0.106-0.206*-1.633*0.232 0.086 0.679 0.221 0.113 0.925 Youth dependency r

153、atio 0.012-0.025*0.010 0.000-0.025*-0.008 0.026 0.005 0.040 0.019 0.005 0.038 Initial GDP per capita-0.004 0.001 0.008-0.003 0.000-0.002 0.004 0.001 0.008 0.002 0.001 0.005 Human capital x old dependency ratio 0.028 0.107*0.646*0.058 0.087*0.622*0.066 0.025 0.199 0.064 0.033 0.274 Human capital 0.01

154、2-0.003-0.043*0.006-0.001-0.033 0.013 0.004 0.024 0.010 0.004 0.028 Observations 640 640 640 626 626 626 R-squared 0.049 0.140 0.107 0.031 0.127 0.136 Number of countries 128 128 128 128 128 128 First stage F-statistic 21.89 21.89 21.89 Hansens J-test (P-value)0.897 0.884 0.113 GDP=gross domestic pr

155、oduct,IV=instrumental-variable,OLS=ordinary least squares.Notes:The dependent variable is annualized log-difference of 5-year periods of the labor force participation rate for the group denoted in the first row.We add the initial level of GDP per capita,calculated from output-side real GDP per capit

156、a,as an additional regressor.We report instrumental-variables panel regression results with country fixed effects by using 10-year lagged values of the old and youth dependency ratios and the birth rate as instruments for the current old dependency ratio and the interaction term with human capital.W

157、e include period dummies but their coefficients are not reported.Robust standard errors are in brackets.*,*,and*represent statistical significance at the 1%,5%,and 10%levels,respectively.Source:Authors calculations.27 Table 7.2:Population Shares OLS IV (1)(2)(3)(4)(5)(6)Variables Female Male Older p

158、eople Female Male Older people Old-age population share-0.068-0.437*-3.565*-0.440-0.356*-3.816*0.331 0.144 1.191 0.545 0.214 1.804 Youth population share 0.012-0.067*-0.133-0.025-0.069*-0.204 0.063 0.016 0.148 0.054 0.020 0.145 Initial GDP per capita-0.004 0.001 0.007-0.003-0.000-0.004 0.004 0.001 0

159、.008 0.002 0.001 0.005 Human capital x old population share 0.067 0.148*1.135*0.164 0.132*1.285*0.085 0.039 0.325 0.148 0.056 0.485 Human capital 0.009-0.003-0.060*0.001-0.002-0.060*0.013 0.004 0.027 0.011 0.005 0.030 Observations 640 640 640 626 626 626 R-squared 0.050 0.138 0.119 0.029 0.137 0.158

160、 Number of countries 128 128 128 128 128 128 First stage F-statistic 17.76 17.76 17.76 Hansens J-test (P-value)0.731 0.557 0.120 GDP=gross domestic product,IV=instrumental-variable,OLS=ordinary least squares.Notes:The dependent variable is annualized log-difference of 5-year periods of the labor for

161、ce participation rate for the group denoted in the first row.We add the initial level of GDP per capita,calculated from output-side real GDP per capita,as an additional regressor.We report instrumental-variables panel regression results with country fixed effects by using 10-year lagged values of th

162、e old and youth dependency ratios and the birth rate as instruments for the current old dependency ratio and the interaction term with human capital.We include period dummies but their coefficients are not reported.Robust standard errors are in brackets.*,*,and*represent statistical significance at

163、the 1%,5%,and 10%levels,respectively.Source:Authors calculations.28 Finally,we investigate the role of trade openness in Tables 8.1 and 8.2.We add trade openness and its interaction term with old-age dependency ratio or old population share as additional explanatory variables.We find that higher tra

164、de openness increases the labor force participation response among older people.We find the same effect for working age males in OLS estimation in both Tables 8.1 and 8.2 but not in the IV estimation.For working-age females,we do not observe such effect.29 Table 8:Trade Openness and the Impact of Po

165、pulation Aging on Labor Force Participation Rate Table 8.1:Dependency Ratios OLS IV (1)(2)(3)(4)(5)(6)Variables Female Male Older people Female Male Older people Old-age dependency ratio 0.060 0.015-0.271 0.026-0.010-0.331 0.039 0.021 0.176 0.107 0.059 0.374 Youth dependency ratio 0.010-0.017*0.036-

166、0.001-0.024*-0.012 0.016 0.004 0.029 0.015 0.005 0.035 Initial GDP per capita 0.000 0.002*0.013*-0.000 0.001 0.002 0.003 0.001 0.007 0.002 0.001 0.005 Trade openness x old dependency ratio 0.022 0.045*0.438*0.057 0.082 0.688*0.027 0.016 0.130 0.096 0.054 0.353 Trade openness 0.001-0.003*-0.026*-0.00

167、4-0.008-0.060 0.004 0.002 0.014 0.013 0.007 0.047 Observations 780 780 780 758 758 758 R-squared 0.039 0.108 0.138 0.016 0.089 0.142 Number of countries 156 156 156 156 156 156 First stage F-statistic 7.802 7.802 7.802 Hansens J-test (P-value)0.835 0.393 0.0832 GDP=gross domestic product,IV=instrume

168、ntal-variable,OLS=ordinary least squares.Notes:The dependent variable is annualized log-difference of 5-year periods of the labor force participation rate for the group denoted in the first row.We add the initial level of GDP per capita,calculated from output-side real GDP per capita,as an additiona

169、l regressor.We report instrumental-variables panel regression results with country fixed effects by using 10-year lagged values of the old and youth dependency ratios and the birth rate as instruments for the current old dependency ratio and the interaction term with trade openness.We include period

170、 dummies but their coefficients are not reported.Robust standard errors are in brackets.*,*,and*represent statistical significance at the 1%,5%,and 10%levels,respectively.Source:Authors calculations.30 Table 8.2:Population Shares OLS IV (1)(2)(3)(4)(5)(6)Variables Female Male Older people Female Mal

171、e Older people Old-age population share 0.133*0.024-0.380-0.016-0.056-1.104 0.065 0.035 0.279 0.262 0.110 0.758 Youth population share 0.022-0.038*0.056-0.008-0.060*-0.132 0.038 0.011 0.088 0.042 0.017 0.119 Initial GDP per capita-0.000 0.002*0.012*-0.001 0.001-0.000 0.003 0.001 0.007 0.002 0.001 0.

172、005 Trade openness x old population share 0.027 0.054*0.647*0.133 0.130 1.440*0.037 0.022 0.168 0.196 0.079 0.553 Trade openness 0.001-0.002-0.026*-0.008-0.009-0.098*0.003 0.002 0.012 0.017 0.007 0.050 Observations 780 780 780 758 758 758 R-squared 0.040 0.107 0.141(0.001)0.074 0.081 Number of count

173、ries 156 156 156 156 156 156 First stage F-statistic 6.265 6.265 6.265 Hansens J-test (P-value)0.650 0.955 0.110 GDP=gross domestic product,IV=instrumental-variable,OLS=ordinary least squares.Notes:The dependent variable is annualized log-difference of 5-year periods of the labor force participation

174、 rate for the group denoted in the first row.We add the initial level of GDP per capita,calculated from output-side real GDP per capita,as an additional regressor.We report instrumental-variables panel regression results with country fixed effects by using 10-year lagged values of the old and youth

175、dependency ratios and the birth rate as instruments for the current old dependency ratio and the interaction term with trade openness.We include period dummies but their coefficients are not reported.Robust standard errors are in brackets.*,*,and*represent statistical significance at the 1%,5%,and 1

176、0%levels,respectively.Source:Authors calculations.31 In Tables 2 to 4,we assumed that the decomposition of the channels is identical across countries.In Tables 6 to 8,we investigated the possibility that countries differ in the degree to which labor participation rates change in response to populati

177、on aging.However,it is expected that the relative importance of the channels varies depending on how each country responds to population aging.Investigating how each country responds to population aging is beyond the scope of this paper.Instead,we will examine how the relative importance of each cha

178、nnel differs as country characteristics vary.We select seven country characteristics,which are(i)old dependency ratio,(ii)human capital,(iii)life expectancy,(iv)labor market flexibility,(v)government size,(vi)trade openness,and(vii)capital market openness.11 The definition and source of the characte

179、ristics are listed in the Appendix.For each characteristic,we divide the entire sample into three groups and examine how the decomposition of channels varies as the value of the characteristic changes.12 For example,for the first characteristic,which is the old-age dependency ratio,we divided the sa

180、mple into three groups based on average magnitude.One-third of the countries have high values,another third of the countries have low values,and the remaining third have middle values.We estimate IV panel regressions with fixed effects as in Table 4.1 for each group and report the coefficients of ol

181、d-age dependency ratio in Table 9.1.13 To save space,we do not report the estimated coefficients of other variables.An important caveat of our analysis is that it does not gauge 11 We also divided the sample by income level.While the negative impact of aging is found only in advanced economies,the o

182、ffsetting effect of the labor participation rate is observed in both advanced and developing economies.When we divide the sample by time period,the compensating effect of the labor participation rate is more pronounced in more recent periods.12 We use the average value over the entire sample period

183、in classifying countries into the three groups.13 The OLS panel regression results are consistent with the IV panel regression results and hence not reported.The results are available upon request.32 causality.Instead,our analysis simply shows that the effect of aging differs across countries with d

184、ifferent characteristics.Determining whether such differences are due to country characteristics requires more in-depth analysis.33 Table 9:The Effects of Aging on GDP Growth and its Eight Channels Instrumental Variable Regressions for Three Sub-samples Table 9.1:Old-age Dependency Ratio Factors Gro

185、ups(1)(2)(3)(4)(5)(6)(7)(8)GDP per capita K/Y Human capital Work hour LF participation 1564 population Share of 15 and above TFP Old dependency ratio Low 0.636*0.549 0.261*-0.706*-0.098*0.003-0.045*-0.774 0.229 0.229 0.593 0.054 0.421 0.041 0.030 0.004 Middle 0.04 0.048 0.021-0.033-0.006-0.040*0.008

186、*-0.093 0.118 0.177 0.031 0.045 0.026 0.017 0.004 0.228 High-0.092*-0.043-0.013-0.012 0.075*0.003-0.029*-0.082 0.052 0.046 0.010 0.013 0.018 0.009 0.005 0.078 Human capital Low 0.719*-0.538 0.082*0.074 0.025-0.018-0.034*0.842*0.199 0.337 0.032 0.103 0.041 0.024 0.004 0.478 Middle 0.147 0.019 0.015 0

187、.013-0.063*-0.069*-0.006 0.19 0.106 0.207 0.031 0.037 0.033 0.018 0.004 0.277 High-0.159*0.017-0.01-0.014 0.089*-0.003-0.023*-0.198*0.050 0.051 0.010 0.013 0.018 0.008 0.004 0.082 Life expectancy Low 0.843*-0.379 0.107*0.047-0.083*-0.011-0.022*1.345*0.191 0.325 0.039 0.211 0.028 0.021 0.003 0.467 Mi

188、ddle 0.157 0.203-0.001-0.097*0.051-0.077*-0.006-0.371*0.111 0.151 0.029 0.042 0.035 0.020 0.005 0.208 High-0.120*-0.008-0.007-0.012 0.076*-0.002-0.027*-0.181 0.053 0.096 0.011 0.012 0.018 0.008 0.004 0.129 Labor market efficiency Low-0.153*-0.022-0.030*0.02 0.042*-0.060*-0.013*-0.257*0.092 0.092 0.0

189、13 0.023 0.022 0.012 0.004 0.156 Middle-0.012-0.129-0.030-0.027 0.098*-0.076*0.007*-0.177 0.091 0.147 0.019 0.024 0.020 0.013 0.004 0.235 High-0.145*-0.009-0.036*-0.007 0.045*-0.055*-0.012*-0.053 0.047 0.069 0.011 0.015 0.017 0.011 0.006 0.078 Government size Low-0.220*-0.105-0.052*0.008 0.043*-0.06

190、2*-0.014*0.005 0.082 0.093 0.017 0.018 0.019 0.012 0.004 0.130 Middle-0.127*-0.12-0.021-0.024*0.015-0.075*0.004 0.013 0.056 0.104 0.016 0.014 0.015 0.010 0.003 0.145 34 Factors Groups(1)(2)(3)(4)(5)(6)(7)(8)GDP per capita K/Y Human capital Work hour LF participation 1564 population Share of 15 and a

191、bove TFP High 0.007-0.167-0.048*-0.193*0.115*-0.040*-0.020*0.046 0.106 0.143 0.019 0.078 0.021 0.013 0.005 0.198 Trade openness Low-0.090-0.079-0.033*0 0.053*-0.046*0.005*0.038 0.064 0.076 0.014 0.016 0.016 0.010 0.002 0.111 Middle-0.131*-0.129-0.073*-0.023 0.041*-0.074*0-0.202 0.079 0.098 0.017 0.0

192、28 0.022 0.011 0.003 0.143 High-0.012 0.155 0.004 0.001 0.086*-0.072*-0.028*-0.221 0.086 0.157 0.018 0.021 0.021 0.013 0.005 0.211 Capital market openness Low 0.133-0.371 0.124*0.057-0.049-0.032-0.012*0.576 0.196 0.311 0.046 0.063 0.031 0.025 0.006 0.537 Middle 0.021-0.125-0.044*-0.053 0.059*-0.065*

193、-0.007*0.126 0.077 0.091 0.016 0.036 0.019 0.011 0.004 0.125 High-0.086 0.007-0.012-0.006 0.065*-0.038*-0.017*-0.203 0.060 0.106 0.014 0.012 0.018 0.010 0.004 0.136 GDP=gross domestic product,K/Y=capitaloutput ratio,LF=labor force,TFP=total factor productivity.Notes:The dependent variable is annuali

194、zed growth rate of the variable listed in the first row.We divide the entire sample into three groups based on the magnitude of each factor listed in the first column:Low,middle and High groups.We report the same IV regression results as in Table 4.1 for each group separately.To save space,we report

195、 the coefficient of the old-age dependency ratio only.Robust standard errors are in brackets.*,*,and*represent statistical significance at the 1%,5%,and 10%levels,respectively.Source:Authors calculations.35 Table 9.2:Older Population Share Variables Groups(1)(2)(3)(4)(5)(6)(7)(8)GDP per capita K/Y H

196、uman capital Work hour LF participation 1564 population Share of 15 and above TFP Old dependency ratio Low 1.213*1.919*0.592*-1.092-0.261*0.059-0.094*-3.085*0.446 1.147 0.106 0.710 0.078 0.057 0.009 1.710 Middle-0.039 0.257 0.05-0.052-0.012 0.007 0.007-0.547 0.224 0.321 0.057 0.076 0.042 0.031 0.007

197、 0.408 High-0.144*-0.120-0.017-0.006 0.118*0.004-0.043*-0.146 0.087 0.081 0.017 0.024 0.032 0.014 0.008 0.138 Human capital Low 1.232*-1.125 0.187*0.088 0.027-0.008-0.076*1.536 0.401 0.695 0.065 0.162 0.079 0.048 0.008 0.988 Middle 0.1 0.279 0.006 0.035-0.112*-0.050*-0.013*-0.206 0.177 0.347 0.051 0

198、.052 0.052 0.028 0.007 0.463 High-0.364*0.047-0.008-0.004 0.141*0.006-0.038*-0.471*0.084 0.087 0.016 0.024 0.030 0.014 0.007 0.141 Life expectancy Low 1.632*-0.757 0.271*0.096-0.194*0.023-0.050*2.851*0.382 0.652 0.078 0.376 0.056 0.041 0.006 0.930 Middle 0.166 0.217-0.071-0.136*0.063-0.093*-0.011-0.

199、686*0.170 0.229 0.045 0.063 0.048 0.029 0.007 0.318 High-0.212*0.109-0.013-0.007 0.123*-0.001-0.038*-0.518*0.085 0.157 0.019 0.022 0.031 0.013 0.007 0.212 Labor market efficiency Low-0.222-0.027-0.044*0.021 0.047-0.076*-0.026*-0.324 0.149 0.128 0.020 0.035 0.040 0.020 0.007 0.214 Middle-0.182-0.099-

200、0.008 0 0.134*-0.099*0.011-0.631 0.143 0.349 0.031 0.034 0.032 0.020 0.007 0.530 High-0.342*0.022-0.052*0.015 0.074*-0.079*-0.016*-0.268*0.076 0.110 0.020 0.025 0.028 0.017 0.009 0.138 Government size Low-0.439*-0.148-0.087*0.022 0.038-0.090*-0.021*-0.097 0.137 0.157 0.028 0.029 0.031 0.019 0.006 0.

201、217 Middle-0.304*-0.076-0.034-0.001 0.009-0.109*0.008*-0.285 0.090 0.166 0.025 0.024 0.026 0.016 0.005 0.232 High 0.019-0.147-0.046-0.325*0.189*-0.042*-0.032*-0.213 0.172 0.215 0.029 0.139 0.032 0.020 0.007 0.297 36 Variables Groups(1)(2)(3)(4)(5)(6)(7)(8)GDP per capita K/Y Human capital Work hour L

202、F participation 1564 population Share of 15 and above TFP Trade openness Low-0.230*-0.104-0.038-0.004 0.091*-0.056*0.005-0.066 0.109 0.129 0.023 0.025 0.028 0.016 0.004 0.187 Middle-0.229*-0.149-0.135*0.032 0.054-0.103*-0.002-0.435*0.130 0.153 0.026 0.042 0.033 0.018 0.005 0.221 High-0.109 0.343 0.0

203、23 0.022 0.115*-0.111*-0.033*-0.679*0.134 0.244 0.029 0.038 0.032 0.020 0.008 0.333 Capital market openness Low-0.016-0.550 0.198*0.061-0.093*-0.021-0.029*0.602 0.364 0.563 0.084 0.117 0.054 0.046 0.011 0.988 Middle-0.059-0.227-0.034-0.038 0.080*-0.068*-0.016*0.049 0.131 0.153 0.027 0.054 0.032 0.01

204、9 0.006 0.210 High-0.205*0.141-0.034 0.002 0.094*-0.077*-0.018*-0.656*0.092 0.161 0.021 0.021 0.030 0.015 0.006 0.209 GDP=gross domestic product,K/Y=capitaloutput ratio,LF=labor force,TFP=total factor productivity.Notes:The dependent variable is annualized growth rate of the variable listed in the f

205、irst row.We divide the entire sample into three groups based on the magnitude of each factor listed in the first column:Low,middle and High groups.We report the same IV regression results as in Table 4.2 for each group separately.To save space,we report the coefficient of the older population share

206、only.Robust standard errors are in brackets.*,*,and*represent statistical significance at the 1%,5%,and 10%levels,respectively.Source:Authors calculations.37 In the first panel of Table 9.1,we report the coefficients of old-age dependency ratio for low,middle and high old-age dependency ratio groups

207、.The dependent variable is denoted in the first row.For the low old-age dependency group,the coefficient of old-age dependency ratio is positive and statistically significant in column(1).On the other hand,the coefficient is not significant for the middle group and negative and significant for the h

208、igh group.This implies that the impact of aging on economic growth may be nonlinear as argued by Lee and Shin(2019),i.e.the negative effect of aging is more pronounced in more aged economies.In addition,we find that the coefficient of the labor force participation rate is positive and significant on

209、ly for the high group,which suggests that the offsetting role of the labor force participation rate is more evident in more aged economies.On the other hand,the positive impact of human capital accumulation is visible only in the low group.In the second panel,we report the coefficients of the old-ag

210、e dependency ratio for the low,middle,and high human capital groups.Again,we observe a nonlinear effect in the sense that the negative effect of aging on economic growth is more pronounced for economies with high human capital.Further,the negative effect of aging on labor shortage and the offsetting

211、 role of the labor force participation rate are more pronounced in the high group.On the other hand,the positive impact of human capital accumulation is visible only in the low group.In the third panel,related to life expectancy,we again find a nonlinear effect of population aging on economic growth

212、.The coefficient of old-age dependency ratio is positive and statistically significant in the low life expectancy group but negative and significant in the high life expectancy group.The negative effect of aging on labor shortage and the mitigating role of the labor force participation rate is visib

213、le only in the high group.We observe a negative effect of aging on TFP growth only in the middle 38 group.The coefficient for TFP growth is also negative in the high group although it is not precisely estimated.The evidence in the second and third panels suggest that lowered TFP growth is the main c

214、hannel through which population aging harms economic growth,especially in the high group.In the fourth panel,we report the coefficients of the old-age dependency ratio for countries with low,middle,and high labor market efficiency or flexibility.The negative effect of aging on economic growth in col

215、umn(1)does not differ substantially across groups.The negative effect of aging and the offsetting role of labor force participation rate are equally visible in all three groups.Interestingly,the effect of aging on TFP growth is negative and statistically significant only in the low group.In the fift

216、h panel,the size of government,defined as the ratio of government expenditures to GDP,is the defining country characteristic.We find that the negative effect of population aging on economic growth is highest in the low group.The effect is almost zero in the high group.We find a negative effect of ag

217、ing on labor shortage and a mitigating role of the labor force participation rate only in the high group.Our results suggest that the negative effect of population aging on economic growth is smallest in countries with the largest governments.The sixth characteristic is trade openness.We do not see

218、much difference across groups.The coefficient of the old-age dependency ratio is negative and large only in the middle group.In line with Table 8,we find that the mitigating effect of increasing labor force participation rate is largest in the high group.The seventh panel reports the results for cou

219、ntry groups with different degrees of capital openness.While not precisely estimated,the coefficient of old-age dependency ratio is negative in columns(1)and(8)only in the high group.At the same time,the mitigating effect of labor force participation rate is also largest in the high group.39 Table 9

220、.2 reports the same results as in Table 9.1,except we replace old-age dependency ratio with old-age population share.The results are consistent.In general,we find even stronger results.Some coefficients that were not statistically significant in Table 9.1 become statistically significant.For example

221、,for trade openness,the coefficient of old-age population share is negative and statistically significant in the low group.For capital market openness,it is negative and statistically significant only in the high group.The results in Table 9.2 suggest that the negative growth effect of population ag

222、ing is larger if trade is less open and capital market is more open.However,the coefficient of old-age population share is negative,large,and highly statistically significant in the high trade group and high capital market openness group.The mitigating effect of higher labor force participation rate

223、 is also strongest in those two groups.Overall,our findings in Tables 9.1 and 9.2 suggest that the mitigating effect of higher labor force participation rate is not enough to offset the negative effect of population aging.The shortage of labor can be completely nullified by higher labor force partic

224、ipation.But the primary channel for the negative growth effect of aging is lowered TFP growth,which is difficult to offset.This is especially true in countries with high values of country characteristics,which are mostly advanced countries.40 4.Conclusion There are growing concerns about the negativ

225、e impact of population aging on economic growth.Such concerns are especially pronounced in advanced economies and some Asian economies that are experiencing rapid aging.They are also relevant to many developing economies that are still relatively young but are already experiencing a demographic tran

226、sition.The one ray of hope in this gloomy demographic landscape is the silver dividend,or increased longevity and longer working life.That is,older workers working longer can augment the labor supply and thus boost growth,offsetting the negative growth effects of a smaller working-age population.In

227、this paper,we investigated the extent to which the silver dividend can support economic growth in the face of population aging.To do so,we followed the framework of Lee and Shin(2021)and investigated six channels through which population aging potentially affects the growth rate of per capita GDP.Th

228、e six channels are changes in:(i)physical capital,(ii)human capital,(iii)average working hours,(iv)labor participation rate,(v)the share of population aged 15 and over,and(vi)TFP.It is important to note that changes in the working-age population is only one of several economic effects of population

229、aging.Our analysis yielded some interesting findings.Above all,we found that the primary channel through which population aging harms economic growth is lowered TFP growth.Labor shortage caused by aging is mostly offset by higher labor force participation rates of males,females,and especially older

230、workers.Higher life expectancy,human capital,and trade openness amplify the mitigating effect of the labor force participation rate among older people.While most of the concern about the 41 economic impact of aging centers on shortage of workers,our analysis suggested that more workers entering the

231、workforce eliminates the shortage in most countries.However,the increase in labor force participation is not enough to completely offset the negative effect of aging on growth,which is largely driven by a decline in TFP growth.To investigate how country characteristics affect the impact of populatio

232、n aging on economic growth,we divided countries into three groupslow value,medium value,and high value.The country characteristics are(i)old dependency ratio,(ii)human capital,(iii)life expectancy,(iv)labor market flexibility,(v)government size,(vi)trade openness,and(vii)capital market openness.For

233、instance,low value of human capital refers to countries with relatively little human capital.Our analysis indicated that population aging has a nonlinear effect on economic growth,i.e.,the negative effect of aging is more pronounced in more aged economies.To conclude,our analysis indicated that cont

234、rary to conventional wisdom,the primary channel through which population aging harms economic growth is through lower TFP growth rather than a shortage of workers.We found that there is a substantial silver dividendi.e.,more older workers entering the labor marketin the face of population aging.In f

235、act,this silver dividend is the driving force behind the increase in labor force participation that offsets the labor shortage due to aging in most countries.However,the silver dividend and the broader increase in labor force participation is not enough to nullify the negative impact of population a

236、ging on growth.This is because reducing the negative effects of aging on TFP growth matters more for reducing the negative effect of aging on economic growth.42 Appendix:Definitions of Variables and Data Sources Variables Description and Construction Data Source Aggregate GDP(national price)Real GDP

237、 at constant 2017 national prices(in million 2017 US$)Penn World Table 10.0 Aggregate GDP(output side)Output-side real GDP at chained PPPs(in million 2017 US$)Penn World Table 10.0 Population Population in millions Penn World Table 10.0 Average hours worked Average annual hours worked by persons eng

238、aged in employment Penn World Table 10.0 Total factor productivity(national price)Total factor productivity at constant national prices(2017=1)Penn World Table 10.0 Education/human capital Human Capital Index Penn World Table 10.0 Labor compensation share Share of labor compensation in GDP at curren

239、t national prices Penn World Table 10.0 Trade openness(at current PPPs)Share of merchandise exports Share of merchandise imports Penn World Table 10.0 Government size Share of government consumption at current PPPs Penn World Table 10.0 Labor force participation rate,official data Labor force partic

240、ipation rate for age group 15+,official data collected by ILO ILO.ILOStat.https:/ilostat.ilo.org/data/Labor force participation rate,ILO modeled estimate Labor force participation rate for age group 15+,ILO modeled estimate ILO.ILOStat.https:/ilostat.ilo.org/data/Labor force participation rate,males

241、,ILO modeled estimate Labor force participation rate for males of age group 15+,ILO modeled estimate ILO.ILOStat.https:/ilostat.ilo.org/data/Labor force participation rate,females,ILO modeled estimate Labor force participation rate for females of age group 15+,ILO modeled estimate ILO.ILOStat.https:

242、/ilostat.ilo.org/data/Older people participation Labor force participation rate,official data,65+The International Labour Organizations Office of Statistics LABORSTA database Capital Market Openness ChinnIto Index;a countrys degree of capital account openness(normalized to one)Chinn and Ito.2006.“Wh

243、at Matters for Financial Development?Capital Controls,Institutions,and Interactions.”Journal of Development Economics 81(1):163192.43 Variables Description and Construction Data Source Old-age dependency Annual old-age dependency ratio.(Population age 65+/population age 1564)UN DESA.2017.World Popul

244、ation Prospects:The 2017 Revision.Youth dependency Annual child dependency ratio.(Population age 014/population age 1564)UN DESA.2017.World Population Prospects:The 2017 Revision.Working-age population ratio Annual working-age population ratio.(Population age 1564/total population)UN DESA.2017.World

245、 Population Prospects:The 2017 Revision.Life expectancy Life expectancy at birth,total(years)World Banks World Development Indicators Labor market efficiency Labor Market Efficiency,Index(17)World Economic Forum,Global Competitiveness Index(GCI)GDP=gross domestic product,ILO=International Labour Org

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257、nomic Growth:A Channel Decomposition Exercise.”The B.E.Journal of Macroeconomics 7(1):138.ASIAN DEVELOPMENT BANKASIAN DEVELOPMENT BANK6 ADB Avenue,Mandaluyong City1550 Metro Manila,Philippineswww.adb.orgPopulation Aging,Silver Dividend,and Economic GrowthThe silver dividend refers to increased longe

258、vity and longer working life becoming potential sources of growth in an aging society.The authors examine the potential for a silver dividend by empirically investigating the channels through which population aging affects economic growth.They find that lower total factor productivity growth is the

259、main mechanism through which population aging harms economic growth.Labor shortage caused by aging is mostly offset by higher labor force participation rates among older people.About the Asian Development BankADB is committed to achieving a prosperous,inclusive,resilient,and sustainable Asia and the

260、 Pacific,while sustaining its efforts to eradicate extreme poverty.Established in 1966,it is owned by 68 members 49 from the region.Its main instruments for helping its developing member countries are policy dialogue,loans,equity investments,guarantees,grants,and technical assistance.POPULATION AGING,SILVER DIVIDEND,AND ECONOMIC GROWTHDonghyun Park and Kwanho ShinADB ECONOMICSWORKING PAPER SERIESNO.678March 2023