In this paper, we investigate six channels through which population aging affects output growth per capita of 35 OECD countries where the old dependency ratio is already quite high. The six channels we consider are changes in: (i) physical capital; (ii) human capital; (iii) average working hours; (iv) labor participation rate; (v) age composition of 15–64 (the share of population aged between 15 and 64 years; and (vi) total factor productivity (TFP). We first confirm findings from previous studies that aging in OECD countries has negative effects on GDP growth per capita. We then find that the most important channel through which the negative effects of aging on economic growth operate is lowered TFP growth. Across our empirical specifications, lowered TFP growth associated with aging explains more than fully the lowered growth rate of GDP per capita. We also find evidence of demographic deficit (decreases in working age population share), but this negative effect of aging is more than nullified by compensating increases in the average working hours and the labor force participation rate. We conclude that because TFP growth rate can be permanently lowered, aging's negative effects on GDP growth per capita are expected to be permanent.

In recent years, most developed countries have been experiencing high rates of population aging. A number of researchers have found that population aging has negative effects on economic growth. For example, using a sample of 142 countries for the period 1960–2017, Lee and Shin (2019) find that population aging leads to lower growth of per capita GDP in both the short run and the long run, particularly in more aged countries, which are mostly developed countries. Similarly, Cooley and Henriksen (2018) find that population aging is a major driver of slower growth in most advanced countries. Utilizing state-level U.S. data for the period 1980–2010, Maestas, Powell, and Mullen (2016) also find that state-level population aging decreases the growth rate of the state output per capita.

Theoretically, the negative effects of population aging on economic growth operate through a number of channels. First, aging may lead to diminishing working population, which lowers labor input growth. Bloom and Williamson (1998) argue that the demographic dividend—the demographic transition resulting in much-faster growth of working-age population—was essential to East Asian countries’ so-called economic miracle: its extraordinary rapid growth. Hence, population aging that reverses this demographic transition is expected to lower economic growth by imposing a significant demographic deficit.1

Second, aging, by lowering the saving rate (Park and Shin 2012; Horioka and Niimi 2017), can lead to less capital accumulation and hence a slowdown of growth. The theoretical background for the relationship between aging and saving goes back to the life-cycle hypothesis pioneered by Modigliani and coauthors.2 According to them, because the aged rely on dissaving of wealth accumulated at earlier ages, an increase in the share of aged population lowers the saving rate.

Third, aging can also lower labor productivity growth. In a sample of 87 non-oil-exporting countries, Feyrer (2007) finds that productivity is highest for the workforce between the ages of 40 and 49 years. Aging, by decreasing this proportion of workers, lowers the aggregate productivity growth. Maestas, Powell, and Mullen (2016) and Aiyar, Ebeke, and Shao (2016) confirm that aging lowers the aggregate productivity growth based on state-level U.S. data and country-level European data, respectively.

Finally, aging can also have negative effects on the growth rate of total factor productivity (TFP). According to Jones (2010), aged people are less innovative and hence aged economies suffer from lower technological progress. Derrien, Kecskés, and Nguyen (2018) and Aksoy et al. (2019) provide supporting evidence based on the local labor force in the United States and OECD country samples, respectively. Engbom (2019) also develops a model that shows that a more aged labor force leads to a decline in firm and worker dynamics, which reduces economic growth due to less creative destruction.

However, there are also studies providing evidence that the impact of aging on economic growth is not so adverse. For example, using a panel data set for the period 1960–2005, Bloom, Canning, and Finlay (2008) find that the negative effect of old age on growth is minimal; they explain that the negative effects of diminishing working population can be mitigated by behavioral responses such as higher savings for longer lifetime, greater labor force participation, and increased immigration from foreign, relatively younger, countries. Acemoglu and Restrepo (2017) even further argue that population aging can promote higher growth by encouraging more active adoption of automation technology and provide some supporting empirical evidence.

Lee and Shin (2019) explain these contradictory findings by nonlinear effects of aging on growth. They argue that the negative effects of population aging on economic growth are not realized until the old dependency ratio reaches a certain high level; this is because when the rate of population aging is relatively low, an increase in the rate of population aging (in terms of old dependency ratio or old-age population share) does not coincide with a decrease in the share of working-age population. Only when the rate of population aging reaches a certain high level does increasing population aging coincide with a decrease in the share of working population, which then leads to the slowdown of economic growth rate. Eggertson, Lancastre, and Summers (2019) also show that in a more recent period (2008–15) and for different reasons, compared with the period 1990–2008, the impact of aging on output growth per capita became negative.3

In this paper, we identify the channels through which aging affects output growth per capita. We focus on 35 OECD countries in which population aging is more advanced and hence the negative effects of aging on output growth per capita are more pronounced.4 Another reason to focus on OECD countries is data restrictions: The labor force participation rate that is essential in our empirical study is not widely available for non-OECD countries.

There are researchers in the literature who decompose output growth into several channels and investigate how the determinants operate. For example, Wong (2007) decomposes output growth per capita into capital accumulation, human capital accumulation, and TFP growth and finds that TFP growth is the main channel through which determinants of output growth per capita operate. In particular, Maestas, Mullen, and Powell (2016) decompose state output growth per capita into growth in labor productivity and labor force growth and focus on the role of aging as a determinant of growth. They find that two-thirds of the negative effect of aging works through slower growth in the labor productivity and the remaining one-third arises through slower labor force growth. And, as noted earlier, whereas previous researchers had examined output growth per capita, Feyrer (2007) and Aiyar, Ebeke, and Shao (2016) focus on labor productivity growth and both find that aging of the workforce by decreasing the proportion of the workforce between the ages of 40 and 49 years lowers labor productivity growth mainly through TFP channel.

In this paper, we extend the previous studies in several dimensions. First, while most previous researchers consider three channels, we decompose GDP growth per capita into six: changes in (i) capital accumulation; (ii) human capital accumulation; (iii) average working hours; (iv) labor participation rate; (v) age composition; and (vi) TFP growth. By adding (iii), (iv), and (v), we can further investigate whether the negative effects of population aging on economic growth can be mitigated by increases in average working hours or labor force participation rate. We can also quantify the importance of demographic deficits by examining the impact on changes in age composition.

Second, while a number of previous researchers who focused on labor productivity were mostly interested in demographic changes in the workforce, we are more interested in the age distribution of the whole population, specifically, the old and youth dependency ratios (or the old and youth population shares). For example, Feyrer (2007) emphasizes that the share of cohorts in its 40s contributes most to the aggregate labor productivity, and Aiyar, Ebeke, and Shao (2016) find that the workforce share aged 55–64 years negatively affects labor productivity growth.

Finally, whereas most previous researchers on population aging's contemporaneous effects, we focus on its future effects. That is, previous researchers often measured the impact of aging by change in the old population share (or the old dependency ratio) in the same interval, that is, the change in the old population share from the beginning to the end of the period. Thus, they investigated the impact of rapid increase in the old population share on the growth rate of labor productivity during the same period; in their doing so, however, there was a high possibility of reverse causality.

In contrast, in the current study, we measure the extent of aging by the old population share (or the old dependency ratio) at the beginning of the period and examine its impact on future intermediate-run growth (i.e., 10-year average growth rate). In this way, we minimize the possibility of reverse causality. However, we also show the robustness of our results using instrumental-variable estimations.

We first confirm that aging in OECD countries has negative effects on GDP growth per capita. We then find that the most important channel through which the negative effects operate is lowered TFP growth; in many of our empirical specifications, the negative impact of aging on GDP per capita growth is explained solely by the channel of lowered TFP growth. We also find evidence of demographic deficit that works through lowered growth of age composition of the working population (ages 15 to 64 years), but this negative effect is more than neutralized by increases in the growth rates of average working hours and the labor force participation rate. In contrast, we find little evidence of a negative impact of aging on physical and human capital accumulations. While we examine intermediate-run growth, because TFP growth rate can be permanently lowered, population aging's negative effects on GDP growth per capita are expected to be permanent.

The remainder of this paper is organized as follows. Section 2 explains our empirical specification for the six channels through which population aging affects output growth per capita. Section 3 provides main empirical results. Section 4 concludes.

We assume that the aggregate output is determined by the following Cobb-Douglas production function:
$Y=AK1-α(hvpN15)α,$
(1)
where Y is GDP, A is the TFP level, K is physical capital, h is average human capital, v is average working hours, p is the labor force participation rate, and $N15$ is population aged 15 years and over. Note that $pN15$ is labor force, not employment. Although it is more appropriate to use employment, because we desire to analyze the role of the labor force participation rate as one of six channels, we instead use labor force by assuming that growth of labor force is approximately the same as growth of employment in the long run.5
We normalize equation (1) by dividing both sides by population, N, and thus obtain:
$y=A1/αk(1-α)/αhvpn15,$
(2)
where $y=YN$, $k=KY$ and $n15=N15N$. Note that this decomposition of output per capita in terms of the capital-output ratio rather than the capital-labor ratio follows Hall and Jones (1999) and other literature on the channel decomposition because, as emphasized by these studies, unlike the steady state of the capital-labor ratio, that of capital-output ratio is independent of the level of total factor productivity. This allows us to estimate the determinants of growth for each channel in separate equations. By taking the natural logarithm of both sides of equation (2) and taking the time difference, we obtain:
$Δlny=1-ααΔlnk+Δlnh+Δlnv+Δlnp+Δlnn15+1αΔlnA,$
(3)
where Δ represents the time difference. Equation (3) shows that output growth per capita is decomposed into six channels, which are changes in: (i) physical capital; (ii) human capital; (iii) average working hours; (iv) labor participation rate; (v) age composition 15+ (the share of population aged 15 years and over); and (vi) TFP. Note that the six channels can be divided into two groups depending on whether each component can grow perpetually. The first group comprises channels (i), (ii), and (vi), which can change the growth rate of per capita output permanently; that is, physical and human capital and TFP can grow without any limit, and their growth rates can also change permanently.6 In contrast, the other channels, (iii), (iv), and (v), cannot induce perpetual change in the growth of per capita output because there are limits to average hours, the labor participation rate, and age composition 15+. Hence demographic dividends or deficits that are related to age composition 15+ also cannot last forever. However, in the empirical specification, the time interval is ten years, and hence we are estimating the importance of these latter three channels as well in influencing the growth rate of per capita output in the intermediate run.
Note that age composition 15+ is not the same as the age composition of population ages from 15 to 64 years (working-age population) that is commonly used to determine demographic dividend or deficit. Hence it is desirable to further decompose age composition 15+ as follows:
$n15≡N15N=N15-64N*N15N15-64≡n15-64*n15-6415,$
where $N15-64,n15-64$, and $n15-6415$ are the number of working-age population, the share of working-age population, and the ratio of population ages 15+ to the number of working-age population, respectively. Then equation (3) turns to:
$Δlny=1-ααΔlnk+Δlnh+Δlnv+Δlnp+Δlnn15-64+Δlnn15-6415+1αΔlnA.$
(4)
Note that the growth of GDP per capita is decomposed into seven channels in equation (4).7 However, the fifth and sixth terms, $Δlnn15-64$ and $Δlnn15-6415$, can be regarded as one channel of age composition, and thus we continue to consider that there are only six channels.

We collect GDP, capital stock, human capital stock, average working hours, and TFP from the Penn World Table (PWT) 9.1.8 Population and its age distribution are collected from the United Nations population database. The old-age dependency ratio is retrieved from the World Bank's World Development Indicators. The labor force participation rates are obtained from ILOSTAT provided by International Labor Organization. The definitions and sources of these variables are summarized in Appendix Table A-1.

To minimize the influence of business cycle fluctuations, we calculate average annual growth rate of GDP per capita at 10-year intervals from 1960 to 2017: (Period 1: 1960–69); (Period 2: 1970–79); (Period 3: 1980–89); (Period 4: 1990–99); (Period 5: 2000–09); and (Period 6: 2010–17). We calculate other growth rates corresponding to the six channels in the same way. For calculating GDP growth, we use PWT's national-accounts real GDP (RGDPNA).9 We use the initial level of per capita GDP as a regressor, for which we use another PWT measure of real GDP, RGDPO, that is, output side real GDP at chained purchasing power parities, calculated using 2011 prices that are constant across countries and time.

Table 1 reports summary statistics of the variables considered in the empirical analysis. The average growth rate of per capita GDP is 2.3 percent. The average per capita GDP in 2011 constant prices is $21,410. The average old and youth dependency ratios are 0.18 and 0.37, respectively. The average share of working population is 0.65. The average labor force participation rate is 59.9. The average annual growth rate of TFP is 1.2 percent. Table 1. Summary statistics Variablescountmeansdminmax Annual GDP growth rate per capita, national accounts 193 2.33% 1.70% −3.41% 8.62% Real per capita GDP, output side 193 21,410 12,328 1,113 73,263 Youth dependency rate 210 0.18 0.05 0.06 0.35 Old dependency rate 210 0.37 0.14 0.20 0.94 Youth-aged population share 210 0.12 0.04 0.03 0.22 Old-age population share 210 0.23 0.07 0.13 0.47 Working aged population share 210 0.65 0.04 0.50 0.73 Share of 55–64 ages in total population 210 0.10 0.02 0.04 0.15 Labor force participation rate (15+, both sex) 112 59.88 6.01 47.10 78.37 Annual growth rate of capital-output ratio 186 0.51% 1.02% −2.51% 5.11% Annual growth rate of human capital 193 0.72% 0.39% −0.31% 2.32% Annual growth rate of average working hours 178 −0.37% 0.42% −1.57% 1.12% Growth of labor force participation rate (15+ population) 112 0.11% 0.51% −1.24% 1.72% Growth of ratio of 15–64 years old population 210 0.07% 0.39% −0.99% 1.26% Growth of ratio of +15 years old over 15–64 years old 210 0.20% 0.20% −0.21% 1.02% Annual growth rate of TFP 186 1.22% 2.12% −4.63% 11.43% Variablescountmeansdminmax Annual GDP growth rate per capita, national accounts 193 2.33% 1.70% −3.41% 8.62% Real per capita GDP, output side 193 21,410 12,328 1,113 73,263 Youth dependency rate 210 0.18 0.05 0.06 0.35 Old dependency rate 210 0.37 0.14 0.20 0.94 Youth-aged population share 210 0.12 0.04 0.03 0.22 Old-age population share 210 0.23 0.07 0.13 0.47 Working aged population share 210 0.65 0.04 0.50 0.73 Share of 55–64 ages in total population 210 0.10 0.02 0.04 0.15 Labor force participation rate (15+, both sex) 112 59.88 6.01 47.10 78.37 Annual growth rate of capital-output ratio 186 0.51% 1.02% −2.51% 5.11% Annual growth rate of human capital 193 0.72% 0.39% −0.31% 2.32% Annual growth rate of average working hours 178 −0.37% 0.42% −1.57% 1.12% Growth of labor force participation rate (15+ population) 112 0.11% 0.51% −1.24% 1.72% Growth of ratio of 15–64 years old population 210 0.07% 0.39% −0.99% 1.26% Growth of ratio of +15 years old over 15–64 years old 210 0.20% 0.20% −0.21% 1.02% Annual growth rate of TFP 186 1.22% 2.12% −4.63% 11.43% Source:Authors’ calculations. Note:Annual growth rates are calculated by averages of 10-year periods. Other variables are measured at the beginning of each period. The sample period is from 1960 to 2014: (Period 1: 1960–69); (Period 2: 1970–79); (Period 3: 1980–89); (Period 4: 1990–99); (Period 5: 2000–09); and (Period 6: 2010–14). Before we present regression results, we present Figure 1, which shows simple relationships of the old dependency ratio in the beginning year of each period with the growth rate of GDP per capita and six variables that represent the six channels. Figure 1-1 clearly shows a negative relationship between the old dependency ratio in the beginning year and the growth rate of GDP per capita thereafter. Figure 1. Correlates of the old dependency ratio Figure 1-1. Correlates with the GDP growth rate per capita Note:The vertical axis represents the annual average growth rate of GDP per capita in OECD countries for the 10-year period. The horizontal axis represents the old dependency ratio at the beginning of each period. Figure 1. Correlates of the old dependency ratio Figure 1-1. Correlates with the GDP growth rate per capita Note:The vertical axis represents the annual average growth rate of GDP per capita in OECD countries for the 10-year period. The horizontal axis represents the old dependency ratio at the beginning of each period. Figure 1-2. Correlates with the growth rate of capital-output ratio Note: The vertical axis represents the annual average growth rate of capital-output (K/Y) ratio in OECD countries for the 10-year period. The horizontal axis represents the old dependency ratio (A) and the per capita GDP at the beginning of each period. Figure 1-2. Correlates with the growth rate of capital-output ratio Note: The vertical axis represents the annual average growth rate of capital-output (K/Y) ratio in OECD countries for the 10-year period. The horizontal axis represents the old dependency ratio (A) and the per capita GDP at the beginning of each period. Figure 1-3. Correlates with the growth rate of human capital Note: The vertical axis represents the annual average growth rate of human capital in OECD countries for the 10-year period. The horizontal axis represents the old dependency ratio (A) and the per capita GDP at the beginning of each period. Figure 1-3. Correlates with the growth rate of human capital Note: The vertical axis represents the annual average growth rate of human capital in OECD countries for the 10-year period. The horizontal axis represents the old dependency ratio (A) and the per capita GDP at the beginning of each period. Figure 1-4. Correlates with the growth rate of average working hours Note: The vertical axis represents the annual average growth rate of average working hours in OECD countries for the 10-year period. The horizontal axis represents the old dependency ratio (A) and the per capita GDP at the beginning of each period. Figure 1-4. Correlates with the growth rate of average working hours Note: The vertical axis represents the annual average growth rate of average working hours in OECD countries for the 10-year period. The horizontal axis represents the old dependency ratio (A) and the per capita GDP at the beginning of each period. Figure 1-5. Correlates with the growth rate of labor force participation Note: The vertical axis represents the annual average growth rate of labor force participation in OECD countries for the 10-year period. The horizontal axis represents the old dependency ratio (A) and the per capita GDP at the beginning of each period. Figure 1-5. Correlates with the growth rate of labor force participation Note: The vertical axis represents the annual average growth rate of labor force participation in OECD countries for the 10-year period. The horizontal axis represents the old dependency ratio (A) and the per capita GDP at the beginning of each period. Figure 1-6. Correlates with the growth rate of age composition 15+ Note: The vertical axis represents the annual average growth rate of age 15+ composition in OECD countries for the 10-year period. The horizontal axis represents the old dependency ratio (A) and the per capita GDP at the beginning of each period. Figure 1-6. Correlates with the growth rate of age composition 15+ Note: The vertical axis represents the annual average growth rate of age 15+ composition in OECD countries for the 10-year period. The horizontal axis represents the old dependency ratio (A) and the per capita GDP at the beginning of each period. Figure 1-7. Correlates with the growth rate of total factor productivity, PWT measure Note: The vertical axis represents the annual average growth rate of total factor productivity in OECD countries for the 10-year period, calculated using the PWT 9.0 database. The horizontal axis represents the old dependency ratio (A) and the per capita GDP at the beginning of each period. Figure 1-7. Correlates with the growth rate of total factor productivity, PWT measure Note: The vertical axis represents the annual average growth rate of total factor productivity in OECD countries for the 10-year period, calculated using the PWT 9.0 database. The horizontal axis represents the old dependency ratio (A) and the per capita GDP at the beginning of each period. Figure 1 also presents how each component of GDP per capita growth relates to the old dependency ratio. Figure 1-2 shows no clear relationship between the rate of capital accumulation and the old dependency ratio; note that we measure capital accumulation based on capital-output ratio. Figure 1-3 illustrates that the relationship between the rate of human capital accumulation and population aging is negative. Figure 1-4 presents no clear relationship between the growth rate of average hours and the old dependency ratio. Figure 1-5 is for the growth rate of labor force participation. Again, there is no clear relationship of the growth rate of labor force participation with the old dependency ratio. Figure 1-6 presents a clear negative relationship between the age 15+ composition and the old dependency ratio. Lastly, Figure 1-7 shows a negative relationship between TFP growth and the old dependency ratio. ### 3.1 Regression results Table 2-1 presents the estimation results when we regress change in each of the six channels on the old and youth dependency ratios; to verify the effects of demographic deficit based on the share of working-age population, we estimate equation (4) rather than equation (3). In the first column, we report also the estimation results when the dependent variable is the growth rate of GDP per capita. As explained in Section 2, we use the old and youth dependency ratios at the beginning of each period—that is, in 1960 for period 1, and so on. In addition to the dependency ratios, we include country fixed effects; we also include period dummies so as to capture OECD-wide shocks. As stressed by the previous studies on channel regressions, the estimated coefficient of the old dependency ratio in column (1) is supposed to be equal to the sum the estimated coefficients in columns (2)–(8) that represent the six channels.10 In column (1) of the table, the estimated coefficient of the old dependency ratio is negative and statistically significant, suggesting that the impact of population aging on growth is negative. The impact is sizable in the sense that increasing the old dependency ratio by 0.01 lowers the growth rate of GDP per capita by 0.18 percentage points. Among the six channels, the estimated coefficients of the old dependency ratio are statistically significant in columns (5), (6), and (7), and the estimated coefficient of the old dependency ratio in column (1) is almost the same as the sum of those in columns (5), (6), and (7). According to the estimates, the negative effect of aging on GDP per capita growth rate operates mainly through the channel of lowered TFP growth. The estimated coefficients indicate that increasing the old dependency ratio by 0.01 decreases the GDP per capita growth rate by 0.18 percentage points, which is more than fully explained by the 0.20 percentage point decrease in the TFP growth rate. It also decreases the growth rate of the share of working-age population by 0.02 percentage points, but this is more than nullified by an increase in the labor force participation rate of 0.05 percentage points. In Table 2-2, we replace old and youth dependency ratios with old and youth population shares, and the coefficients of the old population share are statistically significant in columns (1), (4), (6), (7), and (8). The estimated coefficient in column (1) shows that increasing the old population share by 0.01 lowers the growth rate of GDP per capita by 0.32 percentage points; again, the sum of the estimated coefficients of the old dependency share in columns (4), (6), (7), and (8) is close to that in column (1). The table also shows that the negative impact of population aging is mostly explained by lowered TFP growth rate by 0.38 percentage points. While the effect of the demographic deficit in column (6) is negative, it is more than fully mitigated by increased average working hours in column (4). Overall, our estimates in Tables 2-1 and 2-2 suggest that population aging negatively affects GDP per capita growth rate mainly through the channel of lowered TFP growth rate. Aging also affects economic growth negatively through demographic deficit, but this purely demographic change is more than nullified by increased labor participation rate or average working hours. In Table 3, we add the initial per capita GDP as a regressor and report panel regression results with country as well as period fixed effects.11,12 In the regressions that use old and youth dependency ratios as regressors (Table 3-1), the coefficients of the initial GDP per capita are also statistically significant in columns (1), (2), (4), and (8). Overall, the results are similar to those reported in Table 2-1, where the initial per capita GDP is not included. In column (1) of Table 3-1, all the coefficients of the old and youth dependency ratios are statistically significant with negative signs; the estimated coefficients of the old dependency ratio are also statistically significant in columns (5), (6), and (8). The estimated coefficient is slightly smaller in absolute value in column (1): An increase in the old dependency ratio by 0.01 decreases the GDP per capita growth rate by 0.16 percentage points. Again, the coefficient of the old dependency ratio in column (1) is almost the same as the sum of the coefficients in columns (5), (6), and (8) where they are statistically significant. In fact, the negative effect of aging on GDP per capita growth is more than fully explained by its negative impact on TFP growth: An increase in the old dependency ratio by 0.01 decreases the TFP growth rate by 0.18 percentage points. It also decreases the share of working age population by 0.02 percentage points, but this is more than nullified by the 0.048 percentage point increase in the labor participation rate. Table 2. The effects of aging on GDP growth and its six channels when the initial per capita GDP not controlled Table 2-1. Dependency ratios (1)(2)(3)(4)(5)(6)(7)(8) VariablesGDP per capitaK/YHuman capitalWork hourLF participation15–64 population15+ populationTFP Old dependency rate −0.175*** −0.008 −0.011 0.004 0.048** −0.024** −0.006 −0.196*** [0.049] [0.032] [0.010] [0.013] [0.024] [0.009] [0.005] [0.062] Youth dependency rate −0.021 −0.015 0.010** 0.032*** −0.044*** 0.022*** −0.006*** −0.035 [0.021] [0.013] [0.004] [0.006] [0.016] [0.004] [0.002] [0.026] Observations 193 186 193 178 112 210 210 186 R2 0.362 0.240 0.315 0.332 0.229 0.524 0.544 0.364 Number of countries 35 35 35 35 35 35 35 35 (1)(2)(3)(4)(5)(6)(7)(8) VariablesGDP per capitaK/YHuman capitalWork hourLF participation15–64 population15+ populationTFP Old dependency rate −0.175*** −0.008 −0.011 0.004 0.048** −0.024** −0.006 −0.196*** [0.049] [0.032] [0.010] [0.013] [0.024] [0.009] [0.005] [0.062] Youth dependency rate −0.021 −0.015 0.010** 0.032*** −0.044*** 0.022*** −0.006*** −0.035 [0.021] [0.013] [0.004] [0.006] [0.016] [0.004] [0.002] [0.026] Observations 193 186 193 178 112 210 210 186 R2 0.362 0.240 0.315 0.332 0.229 0.524 0.544 0.364 Number of countries 35 35 35 35 35 35 35 35 Source:Authors’ calculations. Notes:The dependent variable is annualized growth rate of the variable listed in the first row. We report panel regression results with country fixed effects. Period dummies are included but their coefficients are not reported. Standard errors are in brackets. ***Statistically significant at the 1 percent level; **statistically significant at the 5 percent level. Table 2-2. Population shares (1)(2)(3)(4)(5)(6)(7)(8) VariablesGDP per capitaK/YHuman capitalWork hourLF participation15–64 population15+ populationTFP Old population share −0.323*** −0.030 −0.010 0.042* 0.057 −0.028* −0.014* −0.384*** [0.084] [0.054] [0.017] [0.023] [0.044] [0.015] [0.008] [0.106] Youth population share −0.074 −0.046 0.025** 0.081*** −0.081** 0.061*** −0.018*** −0.097 [0.051] [0.033] [0.010] [0.014] [0.035] [0.009] [0.005] [0.065] Observations 193 186 193 178 112 210 210 186 R2 0.362 0.244 0.321 0.331 0.229 0.579 0.552 0.365 Number of countries 35 35 35 35 35 35 35 35 (1)(2)(3)(4)(5)(6)(7)(8) VariablesGDP per capitaK/YHuman capitalWork hourLF participation15–64 population15+ populationTFP Old population share −0.323*** −0.030 −0.010 0.042* 0.057 −0.028* −0.014* −0.384*** [0.084] [0.054] [0.017] [0.023] [0.044] [0.015] [0.008] [0.106] Youth population share −0.074 −0.046 0.025** 0.081*** −0.081** 0.061*** −0.018*** −0.097 [0.051] [0.033] [0.010] [0.014] [0.035] [0.009] [0.005] [0.065] Observations 193 186 193 178 112 210 210 186 R2 0.362 0.244 0.321 0.331 0.229 0.579 0.552 0.365 Number of countries 35 35 35 35 35 35 35 35 Source:Authors’ calculations. Note:The dependent variable is listed in the first row. We report panel regression results with country fixed effects. Period dummies are included but their coefficients are not reported. Standard errors are in brackets. ***Statistically significant at the 1 percent level; **statistically significant at the 5 percent level; *statistically significant at the 10 percent level. In Table 3-2, when we replace old and youth dependency rates with old and youth population shares, the coefficients of the initial GDP per capita are statistically significant in columns (1), (4), and (8); the coefficients of the old dependency ratio are statistically significant in columns (1), (4), and (8), and the estimated coefficient in column (1) is approximately the same as the sum of those in columns (4) and (8). Again, the negative impact of aging on GDP per capita growth is mainly explained by its negative impact on TFP growth. There is no evidence of demographic deficit, but population aging increases the average working hours by 0.04 percentage points. Table 3. The effects of aging on GDP growth and its six channels when the initial per capita GDP controlled Table 3-1. Dependency ratios (1)(2)(3)(4)(5)(6)(7)(8) VariablesGDP per capitaK/YHuman capitalWork hourLF participation15–64 population15+ populationTFP Old dependency rate −0.155*** −0.014 −0.011 0.007 0.048** −0.021** −0.005 −0.176*** [0.045] [0.032] [0.010] [0.013] [0.024] [0.010] [0.005] [0.061] Youth dependency rate −0.055*** −0.006 0.010** 0.028*** −0.055*** 0.021*** −0.006*** −0.066** [0.020] [0.014] [0.004] [0.006] [0.018] [0.004] [0.002] [0.028] Initial GDP per capita −0.027*** 0.006* 0.000 −0.003* −0.005 −0.000 0.001 −0.021*** [0.005] [0.004] [0.001] [0.002] [0.003] [0.001] [0.001] [0.007] Observations 193 186 193 178 112 193 193 186 R2 0.468 0.256 0.315 0.350 0.252 0.546 0.549 0.402 Number of countries 35 35 35 35 35 35 35 35 (1)(2)(3)(4)(5)(6)(7)(8) VariablesGDP per capitaK/YHuman capitalWork hourLF participation15–64 population15+ populationTFP Old dependency rate −0.155*** −0.014 −0.011 0.007 0.048** −0.021** −0.005 −0.176*** [0.045] [0.032] [0.010] [0.013] [0.024] [0.010] [0.005] [0.061] Youth dependency rate −0.055*** −0.006 0.010** 0.028*** −0.055*** 0.021*** −0.006*** −0.066** [0.020] [0.014] [0.004] [0.006] [0.018] [0.004] [0.002] [0.028] Initial GDP per capita −0.027*** 0.006* 0.000 −0.003* −0.005 −0.000 0.001 −0.021*** [0.005] [0.004] [0.001] [0.002] [0.003] [0.001] [0.001] [0.007] Observations 193 186 193 178 112 193 193 186 R2 0.468 0.256 0.315 0.350 0.252 0.546 0.549 0.402 Number of countries 35 35 35 35 35 35 35 35 Source:Authors’ calculations. Note:The dependent variable is listed in the first row. We report panel regression results with country fixed effects. Period dummies are included but their coefficients are not reported. Standard errors are in brackets. ***Statistically significant at the 1 percent level; **statistically significant at the 5 percent level; *statistically significant at the 10 percent level. Table 3-2. Population shares (1)(2)(3)(4)(5)(6)(7)(8) VariablesGDP per capitaK/YHuman capitalWork hourLF participation15–64 population15+ populationTFP Old population share −0.324*** −0.031 −0.010 0.041* 0.048 −0.023 −0.012 −0.380*** [0.076] [0.054] [0.017] [0.023] [0.044] [0.016] [0.008] [0.103] Youth population share −0.169*** −0.022 0.026** 0.070*** −0.107*** 0.065*** −0.019*** −0.185*** [0.049] [0.036] [0.011] [0.015] [0.038] [0.010] [0.005] [0.070] Initial GDP per capita −0.028*** 0.006 0.000 −0.003* −0.005 0.001 0.001 −0.021*** [0.005] [0.004] [0.001] [0.002] [0.004] [0.001] [0.001] [0.007] Observations 193 186 193 178 112 193 193 186 R2 0.471 0.257 0.322 0.345 0.254 0.603 0.557 0.403 Number of countries 35 35 35 35 35 35 35 35 (1)(2)(3)(4)(5)(6)(7)(8) VariablesGDP per capitaK/YHuman capitalWork hourLF participation15–64 population15+ populationTFP Old population share −0.324*** −0.031 −0.010 0.041* 0.048 −0.023 −0.012 −0.380*** [0.076] [0.054] [0.017] [0.023] [0.044] [0.016] [0.008] [0.103] Youth population share −0.169*** −0.022 0.026** 0.070*** −0.107*** 0.065*** −0.019*** −0.185*** [0.049] [0.036] [0.011] [0.015] [0.038] [0.010] [0.005] [0.070] Initial GDP per capita −0.028*** 0.006 0.000 −0.003* −0.005 0.001 0.001 −0.021*** [0.005] [0.004] [0.001] [0.002] [0.004] [0.001] [0.001] [0.007] Observations 193 186 193 178 112 193 193 186 R2 0.471 0.257 0.322 0.345 0.254 0.603 0.557 0.403 Number of countries 35 35 35 35 35 35 35 35 Source:Authors’ calculations. Note:The dependent variable is listed in the first row. We report panel regression results with country fixed effects. Period dummies are included but their coefficients are not reported. Standard errors are in brackets. ***Statistically significant at the 1 percent level; **statistically significant at the 5 percent level; *statistically significant at the 10 percent level. In sum, our empirical evidence strongly suggests that population aging in terms of both higher old dependency ratio and old population share is negatively associated with lower GDP growth per capita in the next ten years. When we examine six channels that potentially drive this negative correlation, our evidence further suggests that this is mainly because population aging results in lower TFP growth. We also find that the negative association of high old dependency ratio and old dependency share with working age population is more than compensated by increases in the labor force participation rate and average working hours. ### 3.2 Robustness checks Although we make the old dependency ratio and the old population share a predetermined variable by choosing its value at the beginning of each period, there is still a possibility that the causality runs reversely. For example, if workers emigrate from countries that have more pessimistic prospects, both the old dependency ratio and the old population share can increase due to emigration of young workers expecting lower GDP growth in the future. In Table 4, to overcome this problem, we present instrumental-variable (IV) estimates. We use 10-year lagged values of the old and youth dependency ratios (population shares) and the birth rate as instruments. In both Tables 4-1 (old and youth dependency ratios) and 4-2 (old and youth population shares), the first-stage F-statistics and the overidentification-test p values indicate that the IVs are quite satisfactory in most columns. Table 4. The effects of aging on GDP growth and its six channels when the initial per capita GDP controlled: IV regressions Table 4-1. Dependency ratios (1)(2)(3)(4)(5)(6)(7)(8) VariablesGDP per capitaK/YHuman capitalWork hourLF participation15–64 population15+ populationTFP Old dependency rate −0.267*** −0.022 −0.008 0.005 0.001 −0.004 −0.013* −0.135* [0.071] [0.046] [0.015] [0.019] [0.035] [0.014] [0.007] [0.081] Youth dependency rate −0.055** 0.001 0.010** 0.028*** −0.071*** 0.019*** −0.003 −0.064** [0.024] [0.017] [0.005] [0.007] [0.020] [0.005] [0.002] [0.030] Initial GDP per capita −0.033*** 0.004 −0.000 −0.003* −0.008** −0.001 0.001 −0.019** [0.006] [0.004] [0.001] [0.002] [0.004] [0.001] [0.001] [0.008] Observations 165 158 165 155 110 165 165 158 R2 0.220 0.286 0.355 0.356 0.213 0.624 0.578 0.130 Number of countries 35 35 35 35 35 35 35 35 First stage F-statistic 45.73 53.37 45.73 53.84 19.46 45.73 45.73 53.37 Hansen's J-test (p value) 0.228 0.509 0.994 0.432 0.269 0.0488 0.162 0.450 (1)(2)(3)(4)(5)(6)(7)(8) VariablesGDP per capitaK/YHuman capitalWork hourLF participation15–64 population15+ populationTFP Old dependency rate −0.267*** −0.022 −0.008 0.005 0.001 −0.004 −0.013* −0.135* [0.071] [0.046] [0.015] [0.019] [0.035] [0.014] [0.007] [0.081] Youth dependency rate −0.055** 0.001 0.010** 0.028*** −0.071*** 0.019*** −0.003 −0.064** [0.024] [0.017] [0.005] [0.007] [0.020] [0.005] [0.002] [0.030] Initial GDP per capita −0.033*** 0.004 −0.000 −0.003* −0.008** −0.001 0.001 −0.019** [0.006] [0.004] [0.001] [0.002] [0.004] [0.001] [0.001] [0.008] Observations 165 158 165 155 110 165 165 158 R2 0.220 0.286 0.355 0.356 0.213 0.624 0.578 0.130 Number of countries 35 35 35 35 35 35 35 35 First stage F-statistic 45.73 53.37 45.73 53.84 19.46 45.73 45.73 53.37 Hansen's J-test (p value) 0.228 0.509 0.994 0.432 0.269 0.0488 0.162 0.450 Source:Authors’ calculations. Note:The dependent variable is listed in the first row. We report instrumental-variables panel regression results with country fixed effects. Period dummies are included but their coefficients are not reported. We use 10-year and 20-year lagged values of the old population share are instrumental variables for the current old population share. Standard errors are in brackets. ***Statistically significant at the 1 percent level; **statistically significant at the 5 percent level; *statistically significant at the 10 percent level. The IV estimation results of column (1) in Tables 4-1 and 4-2 are quite consistent with those in other tables; all three coefficients are statistically significant with a negative sign at the 1 percent or 5 percent level. The coefficient of the old dependency ratio is larger in absolute value than that of the youth dependency ratio. The estimated coefficients of the old dependency ratio and the old population shares are slightly larger in absolute value than they are in Table 3: The estimated coefficients indicate that a one percentage point increase in the old dependency ratio (the old population share) lowers annual GDP growth rate per capita by 0.27 (0.50) percent. Looking at the channels, in Table 4-1, the coefficient of the old dependency ratio is also statistically significant in columns (7) and (8). The sum of the estimated coefficients of the old dependency ratio in columns (7) and (8) explains only about a little more than half of the negative impact of aging in column (1); still, the negative impact of aging on the GDP per capita growth rate is mainly explained through the channel of lowered TFP growth. Interestingly the negative impact of aging on demography is through the ratio of population ages 15+ and the working age population in column (7). In contrast, in Table 4-2, the coefficient of the old dependency ratio is statistically significant only in columns (1) and (8), and more than 60 percent of the negative impact of aging on growth can be explained solely by lowered TFP growth. Overall, the IV estimation results confirm that aging affects economic growth negatively and that this negative effect operates mainly through lowered TFP growth. Table 4-2. Population shares (1)(2)(3)(4)(5)(6)(7)(8) VariablesGDP per capitaK/YHuman capitalWork hourLF participation15–64 population15+ populationTFP Old population share −0.498*** −0.043 −0.006 0.040 −0.045 −0.011 −0.016 −0.299** [0.118] [0.076] [0.024] [0.032] [0.064] [0.021] [0.012] [0.136] Youth population share −0.178*** −0.001 0.025** 0.074*** −0.150*** 0.049*** −0.008 −0.189** [0.062] [0.045] [0.013] [0.019] [0.043] [0.011] [0.006] [0.079] Initial GDP per capita −0.033*** 0.004 −0.000 −0.003 −0.008** −0.000 0.001 −0.020** [0.006] [0.004] [0.001] [0.002] [0.004] [0.001] [0.001] [0.008] Observations 165 158 165 155 110 165 165 158 R2 0.232 0.286 0.362 0.345 0.215 0.666 0.583 0.127 Number of countries 35 35 35 35 35 35 35 35 First stage F-statistic 54.59 66.18 54.59 66.40 21.36 54.59 54.59 66.18 Hansen's J-test (p value) 0.359 0.635 0.990 0.556 0.253 0.00360 0.0449 0.502 (1)(2)(3)(4)(5)(6)(7)(8) VariablesGDP per capitaK/YHuman capitalWork hourLF participation15–64 population15+ populationTFP Old population share −0.498*** −0.043 −0.006 0.040 −0.045 −0.011 −0.016 −0.299** [0.118] [0.076] [0.024] [0.032] [0.064] [0.021] [0.012] [0.136] Youth population share −0.178*** −0.001 0.025** 0.074*** −0.150*** 0.049*** −0.008 −0.189** [0.062] [0.045] [0.013] [0.019] [0.043] [0.011] [0.006] [0.079] Initial GDP per capita −0.033*** 0.004 −0.000 −0.003 −0.008** −0.000 0.001 −0.020** [0.006] [0.004] [0.001] [0.002] [0.004] [0.001] [0.001] [0.008] Observations 165 158 165 155 110 165 165 158 R2 0.232 0.286 0.362 0.345 0.215 0.666 0.583 0.127 Number of countries 35 35 35 35 35 35 35 35 First stage F-statistic 54.59 66.18 54.59 66.40 21.36 54.59 54.59 66.18 Hansen's J-test (p value) 0.359 0.635 0.990 0.556 0.253 0.00360 0.0449 0.502 Source:Authors’ calculations. Note:The dependent variable is listed in the first row. We report instrumental-variables panel regression results with country fixed effects. Period dummies are included but their coefficients are not reported. We use 10-year and 20-year lagged values of the old population share are instrumental variables for the current old population share. Standard errors are in brackets. ***Statistically significant at the 1 percent level; **statistically significant at the 5 percent level. Previous studies such as Feyrer (2007) and Aiyar, Ebeke, and Shao (2016) highlighted lowered TFP growth as the most important channel in explaining the negative effect of aging on labor productivity growth. Although with our study we focus on GDP per capita and not GDP per worker, we also find that TFP growth is the most important and statistically significant channel. These previous studies also emphasize the importance of the share of workers aged 55–64 years; the idea is that because the productivity of workers in this age group is lower, increase in its share contributes negatively to labor productivity growth. In Table 5, following Aiyar, Ebeke, and Shao (2016), we add the population share aged 55–64 years as an additional variable and report panel regression results with country/period fixed effects. As before, we use old and youth dependency ratios in Table 5-1 and old and youth population shares in Table 5-2. As expected, the coefficient of the population share aged 55–64 years in column (1) is negative and statistically significant at the 5 percent level in both tables.13 Regarding the coefficient of the old dependency ratio and the old population share, the results in Tables 5-1 and 5-2 are quite consistent with those in Tables 3-1 and 3-2. In Table 5-1, while the coefficient of the old dependency ratio is negative and statistically significant in columns (7) and (8), it is positive and statistically significant in column (5). The estimated coefficient of the old dependency ratio in column (1) is almost the same as the sum of its coefficients in columns (5), (7), and (8). In Table 5-2, it is negative and statistically significant at the 1 percent level in columns (7) and (8) and positive and statistically significant at the 10 percent level in column (4). Again, its estimated coefficient is almost the same as the sum of its coefficients in columns (4), (7), and (8); the only difference is that the coefficient in column (7) rather than column (6) is statistically significant in Tables 5-1 and 5-2. Table 5. The effects of aging on GDP growth and its six channels when the initial per capita GDP and age composition 55–64 years controlled Table 5-1. Dependency ratios (1)(2)(3)(4)(5)(6)(7)(8) VariablesGDP per capitaK/YHuman capitalWork hourLF participation15–64 population15+ populationTFP Old dependency rate −0.121** −0.028 −0.016 0.007 0.044* −0.007 −0.021*** −0.133** [0.047] [0.034] [0.010] [0.014] [0.024] [0.010] [0.003] [0.064] Youth dependency rate −0.074*** 0.002 0.013*** 0.028*** −0.055*** 0.014*** 0.002* −0.089*** [0.021] [0.016] [0.005] [0.007] [0.018] [0.004] [0.001] [0.030] 55–64 population share −0.259** 0.101 0.041 −0.003 0.060 −0.106*** 0.123*** −0.304** [0.115] [0.081] [0.025] [0.034] [0.059] [0.024] [0.007] [0.153] Initial GDP per capita −0.025*** 0.006 −0.000 −0.003* −0.006 0.001 0.000 −0.019*** [0.005] [0.004] [0.001] [0.002] [0.004] [0.001] [0.000] [0.007] Observations 193 186 193 178 112 193 193 186 R2 0.486 0.264 0.326 0.350 0.263 0.599 0.845 0.418 Number of countries 35 35 35 35 35 35 35 35 (1)(2)(3)(4)(5)(6)(7)(8) VariablesGDP per capitaK/YHuman capitalWork hourLF participation15–64 population15+ populationTFP Old dependency rate −0.121** −0.028 −0.016 0.007 0.044* −0.007 −0.021*** −0.133** [0.047] [0.034] [0.010] [0.014] [0.024] [0.010] [0.003] [0.064] Youth dependency rate −0.074*** 0.002 0.013*** 0.028*** −0.055*** 0.014*** 0.002* −0.089*** [0.021] [0.016] [0.005] [0.007] [0.018] [0.004] [0.001] [0.030] 55–64 population share −0.259** 0.101 0.041 −0.003 0.060 −0.106*** 0.123*** −0.304** [0.115] [0.081] [0.025] [0.034] [0.059] [0.024] [0.007] [0.153] Initial GDP per capita −0.025*** 0.006 −0.000 −0.003* −0.006 0.001 0.000 −0.019*** [0.005] [0.004] [0.001] [0.002] [0.004] [0.001] [0.000] [0.007] Observations 193 186 193 178 112 193 193 186 R2 0.486 0.264 0.326 0.350 0.263 0.599 0.845 0.418 Number of countries 35 35 35 35 35 35 35 35 Source:Authors’ calculations. Note:The dependent variable is listed in the first row. We report panel regression results with country fixed effects. Period dummies are included but their coefficients are not reported. We use 10-year and 20-year lagged values of the old dependency ratio are instrumental variables for the current old dependency ratio. Standard errors are in brackets. ***Statistically significant at the 1 percent level; **statistically significant at the 5 percent level; *statistically significant at the 10 percent level. Table 5-2. Population shares (1)(2)(3)(4)(5)(6)(7)(8) VariablesGDP per capitaK/YHuman capitalWork hourLF participation15–64 population15+ populationTFP Old population share −0.283*** −0.048 −0.017 0.042* 0.043 −0.010 −0.031*** −0.328*** [0.077] [0.055] [0.017] [0.024] [0.044] [0.015] [0.005] [0.105] Youth population share −0.213*** −0.005 0.034*** 0.070*** −0.106*** 0.051*** 0.000 −0.237*** [0.052] [0.039] [0.011] [0.017] [0.038] [0.010] [0.003] [0.074] 55–64 population share −0.271** 0.098 0.047* −0.003 0.054 −0.084*** 0.121*** −0.297* [0.115] [0.081] [0.025] [0.035] [0.060] [0.023] [0.008] [0.155] Initial GDP per capita −0.026*** 0.005 0.000 −0.003* −0.006* 0.001 0.000 −0.020*** [0.005] [0.004] [0.001] [0.002] [0.004] [0.001] [0.000] [0.007] Observations 193 186 193 178 112 193 193 186 R2 0.490 0.264 0.337 0.345 0.263 0.636 0.837 0.418 Number of countries 35 35 35 35 35 35 35 35 (1)(2)(3)(4)(5)(6)(7)(8) VariablesGDP per capitaK/YHuman capitalWork hourLF participation15–64 population15+ populationTFP Old population share −0.283*** −0.048 −0.017 0.042* 0.043 −0.010 −0.031*** −0.328*** [0.077] [0.055] [0.017] [0.024] [0.044] [0.015] [0.005] [0.105] Youth population share −0.213*** −0.005 0.034*** 0.070*** −0.106*** 0.051*** 0.000 −0.237*** [0.052] [0.039] [0.011] [0.017] [0.038] [0.010] [0.003] [0.074] 55–64 population share −0.271** 0.098 0.047* −0.003 0.054 −0.084*** 0.121*** −0.297* [0.115] [0.081] [0.025] [0.035] [0.060] [0.023] [0.008] [0.155] Initial GDP per capita −0.026*** 0.005 0.000 −0.003* −0.006* 0.001 0.000 −0.020*** [0.005] [0.004] [0.001] [0.002] [0.004] [0.001] [0.000] [0.007] Observations 193 186 193 178 112 193 193 186 R2 0.490 0.264 0.337 0.345 0.263 0.636 0.837 0.418 Number of countries 35 35 35 35 35 35 35 35 Source: Authors’ calculations. Note: The dependent variable is listed in the first row. We report panel regression results with country fixed effects. Period dummies are included but their coefficients are not reported. We use 10-year and 20-year lagged values of the old population share are instrumental variables for the current old population share. Standard errors are in brackets. ***Statistically significant at the 1 percent level; **statistically significant at the 5 percent level; *statistically significant at the 10 percent level. As explained, previous studies that examine the impact of aging on labor productivity growth ignore aging of the whole population and focus on aging of the workforce. However, if aging of the whole population affects TFP growth negatively, it will also negatively affect labor productivity growth. In Table 6,0 in order to evaluate the importance of the old dependency ratio on labor productivity, we adopt the growth rate of labor productivity (i.e., per worker GDP) as a dependent variable and show the panel regressions results with country fixed effects for various specifications. In columns (1) and (2) in Table 6-1, we use only the old and youth dependency ratios as a regression. In columns (3) and (4), we add the population share aged 55–64 years as an additional regressor. In columns (5) and (6), we instead add the initial per capita GDP as an additional regressor. In columns (7) and (8), we add both the population share aged 55–64 years and the initial per capita GDP as regressors. Finally, in columns (9) and (10), we report IV panel regression results. For each pair, the first one does not include period dummies and the second one does. In all the specifications from (1) to (10), the coefficient of the old dependency ratio is negative and statistically significant. In Table 6-2, we replace old and youth dependency ratios with old and youth population shares. Again, in all the specifications from (1) to (10), the coefficient of the old population share is negative and statistically significant. Our results in Table 6 suggest that aging of the whole population negatively affects not only on GDP per capita growth but also on labor productivity growth. Table 6. The effects of aging on per worker GDP growth Table 6-1. Dependency ratios (1)(2)(3)(4)(5)(6)(7)(8)(9)(10) Variables10-year GDP growth per worker Old dependency rate −0.262*** −0.178*** −0.260*** −0.139*** −0.135*** −0.159*** −0.129*** −0.132*** −0.093* −0.122* [0.035] [0.043] [0.036] [0.044] [0.035] [0.039] [0.037] [0.041] [0.050] [0.072] Youth dependency rate 0.012 −0.022 0.011 −0.045** −0.069*** −0.052*** −0.071*** −0.067*** −0.048** −0.021 [0.013] [0.018] [0.014] [0.020] [0.016] [0.017] [0.017] [0.019] [0.021] [0.038] 55–64 population share −0.024 −0.285*** −0.045 −0.213** 0.187 0.397 [0.091] [0.107] [0.080] [0.099] [0.285] [0.607] Initial GDP per capita −0.024*** −0.024*** −0.024*** −0.023*** −0.022*** −0.028*** [0.003] [0.004] [0.003] [0.004] [0.004] [0.006] Period dummies No Yes No Yes No Yes No Yes No Yes Observations 193 193 193 193 193 193 193 193 165 165 R2 0.419 0.515 0.420 0.536 0.560 0.599 0.561 0.611 0.330 0.269 Number of countries 35 35 35 35 35 35 35 35 35 35 First stage F-test 2.678 1.015 Hansen's J-test (p value) 0.346 0.002 (1)(2)(3)(4)(5)(6)(7)(8)(9)(10) Variables10-year GDP growth per worker Old dependency rate −0.262*** −0.178*** −0.260*** −0.139*** −0.135*** −0.159*** −0.129*** −0.132*** −0.093* −0.122* [0.035] [0.043] [0.036] [0.044] [0.035] [0.039] [0.037] [0.041] [0.050] [0.072] Youth dependency rate 0.012 −0.022 0.011 −0.045** −0.069*** −0.052*** −0.071*** −0.067*** −0.048** −0.021 [0.013] [0.018] [0.014] [0.020] [0.016] [0.017] [0.017] [0.019] [0.021] [0.038] 55–64 population share −0.024 −0.285*** −0.045 −0.213** 0.187 0.397 [0.091] [0.107] [0.080] [0.099] [0.285] [0.607] Initial GDP per capita −0.024*** −0.024*** −0.024*** −0.023*** −0.022*** −0.028*** [0.003] [0.004] [0.003] [0.004] [0.004] [0.006] Period dummies No Yes No Yes No Yes No Yes No Yes Observations 193 193 193 193 193 193 193 193 165 165 R2 0.419 0.515 0.420 0.536 0.560 0.599 0.561 0.611 0.330 0.269 Number of countries 35 35 35 35 35 35 35 35 35 35 First stage F-test 2.678 1.015 Hansen's J-test (p value) 0.346 0.002 Source:Authors’ calculations. Note:The dependent variable is the GDP growth rate per worker. We report panel regression results with country fixed effects in columns (1)–(8). Instrumental-variables regression results are reported in columns (9) and (10). When period dummies are included, their coefficients are not reported. Standard errors are in brackets. ***Statistically significant at the 1 percent level; **statistically significant at the 5 percent level; *statistically significant at the 10 percent level. Table 6-2. Population shares (1)(2)(3)(4)(5)(6)(7)(8)(9)(10) Variables10-year GDP growth per worker Old population share −0.430*** −0.331*** −0.427*** −0.288*** −0.295*** −0.331*** −0.286*** −0.298*** −0.197** −0.204** [0.067] [0.073] [0.069] [0.073] [0.062] [0.066] [0.063] [0.067] [0.080] [0.091] Youth population share −0.018 −0.073 −0.019 −0.123** −0.201*** −0.156*** −0.208*** −0.192*** −0.144*** −0.113 [0.036] [0.044] [0.037] [0.048] [0.041] [0.043] [0.042] [0.045] [0.051] [0.078] 55–64 population share −0.019 −0.276** −0.057 −0.216** 0.122 0.051 [0.092] [0.109] [0.080] [0.100] [0.260] [0.480] Initial GDP per capita −0.025*** −0.024*** −0.025*** −0.023*** −0.022*** −0.027*** [0.003] [0.004] [0.003] [0.004] [0.004] [0.005] Period dummies No Yes No Yes No Yes No Yes No Yes Observations 193 193 193 193 193 193 193 193 165 165 R2 0.422 0.516 0.422 0.536 0.563 0.601 0.564 0.613 0.352 0.393 Number of countries 35 35 35 35 35 35 35 35 35 35 First stage F-test 3.160 1.373 Hansen's J-test (p value) 0.333 0.000 (1)(2)(3)(4)(5)(6)(7)(8)(9)(10) Variables10-year GDP growth per worker Old population share −0.430*** −0.331*** −0.427*** −0.288*** −0.295*** −0.331*** −0.286*** −0.298*** −0.197** −0.204** [0.067] [0.073] [0.069] [0.073] [0.062] [0.066] [0.063] [0.067] [0.080] [0.091] Youth population share −0.018 −0.073 −0.019 −0.123** −0.201*** −0.156*** −0.208*** −0.192*** −0.144*** −0.113 [0.036] [0.044] [0.037] [0.048] [0.041] [0.043] [0.042] [0.045] [0.051] [0.078] 55–64 population share −0.019 −0.276** −0.057 −0.216** 0.122 0.051 [0.092] [0.109] [0.080] [0.100] [0.260] [0.480] Initial GDP per capita −0.025*** −0.024*** −0.025*** −0.023*** −0.022*** −0.027*** [0.003] [0.004] [0.003] [0.004] [0.004] [0.005] Period dummies No Yes No Yes No Yes No Yes No Yes Observations 193 193 193 193 193 193 193 193 165 165 R2 0.422 0.516 0.422 0.536 0.563 0.601 0.564 0.613 0.352 0.393 Number of countries 35 35 35 35 35 35 35 35 35 35 First stage F-test 3.160 1.373 Hansen's J-test (p value) 0.333 0.000 Source:Authors’ calculations. Note:The dependent variable is the GDP growth rate per worker. We report panel regression results with country fixed effects in columns (1)–(8). Instrumental-variables regression results are reported in columns (9) and (10). When period dummies are included, their coefficients are not reported. Standard errors are in brackets. ***Statistically significant at the 1 percent level; **statistically significant at the 5 percent level. In this paper, we investigate six channels through which aging affects per capita GDP growth based on 35 OECD countries where population aging has reached remarkably high levels. The six channels we considered are increases in: (i) capital accumulation; (ii) human capital accumulation; (iii) average working hours; (iv) the labor force participation rate; (v) the share of population aged 15 years and over; and (vi) TFP. We confirm the findings from previous studies that population aging in OECD countries has negative effects on GDP growth per capita. We find that the most important channel through which the negative effects operate is lowered TFP growth. In most of our empirical specifications, lowered TFP growth associated with aging exceeds the lowered growth rate GDP per capita. We also find evidence of demographic deficit, but this negative effect of aging is more than nullified by compensating increases in the labor force participation rate or average working hours. We conclude that because TFP growth rate can be permanently lowered, aging's negative effects on GDP growth per capita may last permanently. 1 Based on a deterministic computable OLG (overlapping generations) model, Choi and Shin (2015) find that population aging decreases labor supply substantially. 2 See, for example, Ando and Modigliani (1963). 3 Eggertson, Lancastre, and Summers (2019) argue that a negative real interest rate in the post-2008 period played a crucial role in inducing negative effect of aging on output growth per capita. 4 Lee and Shin (2019) indeed find that the negative effects of aging on output growth per capita are clearly observed in the sample of OECD countries, not in the non-OECD countries. 5 In Section 3, we verify the validity of this assumption by comparing our TFP measure with the measure provided by Penn World Table that uses employment rather than labor force. We find that our assumption holds quite accurately as the two TFP measures are adequately close. 6 Human capital accumulation may not belong to this group if we measure human capital by years of education because life is finite. However, if we assume that human capital accumulated by ancestors is embodied in the current generation, it may increase forever. 7 In the empirical specification, we also rely on equation (4). 8 We can calculate TFP growth from the residual of the identity equation (3). The only difference is that TFP growth in equation (3) uses labor force calculated from UN population data and the ILO labor force participation rate instead of from the employment data in PWT 9.1. When we compare 10-year growth of TFP measure from equation (3) with that of PWT's TFP measure, they are almost identical. 9 There are five types of real GDP reported in PWT 9.0. See https://www.rug.nl/ggdc/productivity/pwt/. They recommend using national-accounts real GDP (RGDPNA) for studies on cross-country growth regressions. 10 This is true only when the number of observations is the same across columns. Because the number of observations is not the same due to missing labor force data, this holds only approximately. 11 Because we included the initial per capita GDP as a regressor, some may argue that a dynamic panel specification is preferred. We do not estimate a dynamic specification because the initial per capita GDP is measured based on the output-side real GDP at chained PPPs (RGDPO), which is not the same as the real GDP measure used to calculate the dependent variable, i.e., the growth rate. 12 We also add trade openness and world governance index (polity IV) as additional explanatory variables, but the results do not change. 13 There are three differences between our specification and that of Aiyar, Ebeke, and Shao (2016). First, while we use 10-year growth, they use annual growth as a dependent variable. Second, we control the initial per capita GDP. Finally, we use the population age share, while they use the workforce age share. Acemoglu , Daron , and Pascal Restrepo . 2017 . Secular Stagnation? The Effects of Aging on Economic Growth in the Age of Automation . American Economic Review 107 ( 5 ): 174 179 . . Aiyar , Shekhar , Christian Ebeke , and Xiaobo Shao . 2016 . The Impact of Workforce Aging on European Productivity . IMF Working Paper No. WP/16/238 . Washington, DC : International Monetary Fund . Aksoy , Yunus , Henrique S. Basso , Ron P. Smith , and Tobias Grasl . 2019 . Demographic Structure and Macroeconomic Trends . American Economic Journal: Macroeconomics 11 ( 1 ): 193 222 . Ando , Albert , and Franco Modiglian . 1963 . The “Life Cycle” Hypothesis of Saving: Aggregate Implications and Tests . American Economic Review 53 ( 1 ): 55 84 . Bloom , David E. , David Canning , and Jocelyn Finlay . 2008 . Population Aging and Economic Growth in Asia . PGDA Working Paper No. 40 . Cambridge, MA : Harvard School of Public Health . Bloom , David E. , and Jeffrey G. Williamson . 1998 . Demographic Transitions and Economic Miracles in Emerging Asia . World Bank Economic Review 12 ( 30 ): 419 455 . . Choi , Ki-Hong , and Sungwhee Shin . 2015 . Population Aging, Economic Growth, and the Social Transmission of Human Capital: An Analysis with an Overlapping Generations Model . Economic Modelling 50 : 138 147 . . Cooley , Thomas , and Espen Henriksen . 2018 . The Demographic Deficit . Journal of Monetary Economics 93 : 45 62 . . Derrien , François , Ambrus Kecskés , and Phuong-anh Nguyen . 2018 . Labor Force Demographics and Corporate Innovation . HEC Paris Research Paper No. FIN-2017-1243 . Paris : HEC . Eggertson , Gauti B. , Manuel Lancastre , and Lawrence H. Summers . 2019 . Aging, Output per Capita and Secular Stagnation . American Economic Review: Insights 1 ( 3 ): 325 - 342 . Engbom , Niklas. 2019 . Firm and Worker Dynamics in an Aging Labor Market . Working Paper No. 756 . Minneapolis, MN : Federal Reserve Bank of Minneapolis . Feyrer , James. 2007 . Demographics and Productivity . Review of Economics and Statistics 89 ( 1 ): 100 109 . . Hall , Robert E. , and Charles I. Jones . 1999 . Why Do Some Countries Produce So Much More Output Per Worker than Others? Quarterly Journal of Economics 114 ( 1 ): 83 116 . . Horioka , Charles Y. , and Yoko Niimi . 2017 . Saving Behavior of Japanese Middle-aged and the Elderly . ESRI International Conference on Empirical Analysis on Population Decline and Aging in the Japanese Economy . Tokyo : ESRI . Jones , Benjamin F. 2010 . Age and Great Invention . Review of Economics and Statistics 92 ( 1 ): 1 14 . . Lee , Hyun-Hoon , and Kwanho Shin . 2019 . Nonlinear Effects of Population Aging on Economic Growth? Japan and the World Economy 51 : 100963 . . Maestas , Nicole , Kathleen J. Mullen , and David Powell . 2016 . The Effect of Population Aging on Economic Growth, the Labor Force and Productivity . NBER Working Paper No. 22452 . Cambridge, MA : NBER . Park , Donghyun , and Kwanho Shin . 2012 . Impact of Population Aging on Asia's Future Growth . In Aging, Economic Growth, and Old-Age Security in Asia , edited by Donghyun Park , Sang-Hyop Lee , and Andrew Mason , pp. 83 110 . Cheltenham, UK : Edward Elgar . Summers , Lawrence. 2015 . Demand Side Secular Stagnation . American Economic Review: Papers & Proceedings 105 ( 5 ): 60 65 . United Nations . 2017 . World Population Prospects The 2017 Revision: Key Findings and Advance Tables . New York, NY : Department of Economic and Social Affairs, United Nations . Wong , Wei-Kang . 2007 . Economic Growth: A Channel Decomposition Exercise. The B.E. Journal of Macroeconomics 7 (1):1--38. Appendix Table A-1. Definitions and sources of variables VariablesDescription and constructionData source Real GDP (National Account), 1960–2017 Per capita real GDP at constant 2011 national prices divided by population (in 2011US$) Penn World Table 9.1
Real GDP (Output side), 1960–2017 Output-side real GDP at chained PPPs (in 2011US$) Penn World Table 9.1 Employment, 1960–2017 Number of persons engage (in millions) Penn World Table 9.1 Labor Compensation Share, 1960–2017 Share of labor compensation in GDP at current national prices Penn World Table 9.1 Capital Accumulation, 1960–2017 Capital stock at constant 2011 national prices (in mil. 2011US$) Penn World Table 9.1
Human Capital Accumulation, 1960–2017 Human capital index: see notes below for details (rug.nl/ggdc/docs/human_capital_in_pwt_90.pdfPenn World Table 9.1
Average Working Hours, 1960–2017 Average annual hours worked by persons engage Penn World Table 9.1
Total Factor Productivity, 1950–2017 TFP at constant national prices (2011 = 1) Penn World Table 9.1
Labor Force Participation Rate, Official Data 1960–2017 Labor force participation rate for age group 15+, official data collected by ILO International Labour Organization, ILOSTAT database
Labor Force Participation Rate, ILO modeled Estimate 1990–2017 Labor force participation rate for age group 15+, ILO Modeled Estimate International Labour Organization, ILOSTAT database
Population Population of all ages UN, DESA. World Population Prospects 2017.
Old dependency ratio Annual old-age dependency ratio. (Population age 65+ / population age 15–64) UN, DESA. World Population Prospects 2017.
Youth dependency ratio Annual child dependency ratio. (Population age 0–14 / population age 15–64) UN, DESA. World Population Prospects 2017.
Working-age population ratio Annual working-age population ratio. (Population age 15–64 / total population) UN, DESA. World Population Prospects 2017.
OECD dummy 1 if the country's accession to OECD was before 2017 OECD
VariablesDescription and constructionData source

Real GDP (National Account), 1960–2017 Per capita real GDP at constant 2011 national prices divided by population (in 2011US$) Penn World Table 9.1 Real GDP (Output side), 1960–2017 Output-side real GDP at chained PPPs (in 2011US$) Penn World Table 9.1
Employment, 1960–2017 Number of persons engage (in millions) Penn World Table 9.1
Labor Compensation Share, 1960–2017 Share of labor compensation in GDP at current national prices Penn World Table 9.1
Capital Accumulation, 1960–2017 Capital stock at constant 2011 national prices (in mil. 2011US\$) Penn World Table 9.1
Human Capital Accumulation, 1960–2017 Human capital index: see notes below for details (rug.nl/ggdc/docs/human_capital_in_pwt_90.pdfPenn World Table 9.1
Average Working Hours, 1960–2017 Average annual hours worked by persons engage Penn World Table 9.1
Total Factor Productivity, 1950–2017 TFP at constant national prices (2011 = 1) Penn World Table 9.1
Labor Force Participation Rate, Official Data 1960–2017 Labor force participation rate for age group 15+, official data collected by ILO International Labour Organization, ILOSTAT database
Labor Force Participation Rate, ILO modeled Estimate 1990–2017 Labor force participation rate for age group 15+, ILO Modeled Estimate International Labour Organization, ILOSTAT database
Population Population of all ages UN, DESA. World Population Prospects 2017.
Old dependency ratio Annual old-age dependency ratio. (Population age 65+ / population age 15–64) UN, DESA. World Population Prospects 2017.
Youth dependency ratio Annual child dependency ratio. (Population age 0–14 / population age 15–64) UN, DESA. World Population Prospects 2017.
Working-age population ratio Annual working-age population ratio. (Population age 15–64 / total population) UN, DESA. World Population Prospects 2017.
OECD dummy 1 if the country's accession to OECD was before 2017 OECD

Source:Authors’ calculations.

## Author notes

*

We gratefully acknowledge the support by the National Research Foundation of Korea grant funded by the Korean government (NRF-2017S1A5A2A03069146). We thank Jae-young Yoo for excellent research assistance.