Osservatorio sulle economie emergenti- Torino (OEET) Turin Center on Emerging Economies Workshop (Turin, March 12-13, 2015) “Asian emerging economies in

Osservatorio sulle economie emergenti- Torino (OEET) Turin Center on Emerging Economies

Workshop (Turin, March 12-13, 2015)“Asian emerging economies in the post crisis era: growth trajectories, challenges and perspectives”

Age Dependency and Labour Productivityin Developing and Developed Economies

Misbah T. Choudhry (Lahore University) Enrico Marelli (University of Brescia)

Marcello Signorelli (University of Perugia)

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Summary

- This study finds empirical evidence in favor of the hypothesis that the age composition of population matters for labour productivity growth.

- We applied the fixed effects panel model using data on a large number of countries over the period 1980-2010.

- Our results suggest that higher age dependency not only directly impacts negatively on labour productivity but also modifies the impact of other determinants of labour productivity.

- The impact of age dependency varies between developed and developing economies.

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Age composition

The relationship between the age composition of the population and labour productivity is important because countries pass through different demographic phases. While developed nations are characterized by a rapidly increasing aging population share, youth dependency in developing and emerging economies is still quite high, despite a declining trend.

A better understanding of how these changes in age composition affect labour productivity growth is important for a meaningful analysis of economic growth potential.

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Database

We use a panel of up to 108 (from 102 to 108) countries to examine the determinants of labour productivity (output per worker) growth over the period 1980-2010.

Along with other socio-economic determinants of labour productivity growth, our main focus is on the role played by changes in the demographic structure of a country.

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Key (short) Literature review

There is a large body of literature on demographic transition and economic growth, suggesting that an increase in the working age population share or a decline in the dependent population can promote economic growth (e.g. Sarel (1995), Bloom and Williamson (1998), IMF (2004), Kelley and Schmidt (2005) and Choudhry and Elhorst (2010).

Valli and Saccone (2011) find and explain an inverted-U curve between the growth rate of population and the growth rate of the per-capita GDP.

The effects of participation and of the age composition of the labour force on labour productivity have also been analyzed: see Kogel (2005), Feyrer (2007) and Choudhry and Van Ark (2010).

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Our main research question

The hypothesis that we test is whether the age structure of the total population not only directly affects cross-country labour productivity growth differentials but also indirectly affects the effectiveness of other determinants of labor productivity growth.

With a few exceptions (see e.g. Islam, 2008), most studies on productivity growth have focused on high-income OECD countries. Apart from considering both advanced and developing economies, we also analyze whether the effects are different in the two group of countries.

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Key results

Our results show that child dependency has a stronger adverse effect on labour productivity than old age dependency.

Moreover, higher age dependency negatively impacts on labour productivity growth not only directly but also indirectly by modifying the impact of other determinants. More specifically, we find that the marginal effects of gross capital formation, labor market reforms, and information and communication technology investment are statistically significant and higher at lower levels of age dependency. These findings hold for both developed and developing economies. The marginal effect of saving at higher levels of age dependency in high income countries appears to be greater than in other countries.

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Descriptive evidence

Figure 1 - Labor productivity index (1990-2013), output per person employed (1990=100)

Note: we set labor productivity to 100 in year 1990.

Source: KILM 8th edition

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Labor Productivity Index by Region,1990-2013 where (1990=100)

Central & South-Eastern Europe (non-EU) & CIS East AsiaSouth-East Asia & the Pacific South AsiaLatin America Middle EastNorth Africa Sub Sahara Africa

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Figure 1 shows that, in sub-Sahara Africa, the Middle East, North Africa and Latin America, labor productivity remained more or less the same during the period under analysis.

The best performing region was East Asia, where all economies followed more or less the same growth path until 1993, but thereafter China’s productivity growth increased more rapidly.

The South Asian region also recorded improvements in terms of productivity growth, especially after 1990, because of India’s impressive labor productivity growth figures. India’s output per person employed increased by more than 130 percent during 1990-2010, which was a faster and far better performance than those of its neighboring countries.

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Figure 1A - Child dependency and old age dependency by income group

Data Source: World Development Indicators

0% 20% 40% 60% 80% 100%

HYE

UMYE

LMYE

LYE

HYE

UMYE

LMYE

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YD OD

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Determinants of labour productivity:literature review

The literature has mainly used two methods of analysis.

The first one is a short term business cycle approach in which productivity growth is mostly related to employment growth (e.g. Beaudry and Collard, 2002; Becker and Gordon, 2008 and Choudhry and Van Ark, 2010).

The second method can be described as the ‘long term growth approach’. Starting from Solow’s neoclassical growth model, these studies focus on the main determinants of labor productivity growth (or per capita income growth, as a proxy) in both the steady state and the transition state. In conditional convergence versions of the Solow model, other exogenous variables have also been considered in this literature (Barro, 1991).

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There are several studies, both theoretical and empirical, on productivity determinants and their dynamics. The key determinants are identified as economic variables (e.g. those considered in economic growth models: physical and human capital, R&D and innovations, etc.), structural variables, institutional determinants, and the role of policies.

Empirical studies also conduct comparisons across countries or in specific world regions (e.g. Marelli and Signorelli, 2010).

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Studies investigating the effect of the age structure on aggregate productivity growth are limited in number. Some of them focus on the impact of the age structure on economic growth (Bloom and Williamson, 1998; Choudhry and Elhorst, 2010), or on firm (plant) productivity (Skirbekk, 2003; IImakunnas and Maliranta, 2005). Others examine the effect of the age structure of the labor force on labor productivity (Feyrer, 2007; Choudhry and Van Ark, 2010). In addition, Kogel (2005) finds a negative relationship between youth dependency and total factor productivity. Finally, some studies find that the age structure of the entire population affects output (Sarel, 1995; Persson, 2002).

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This paper differs from the existing literature because it focuses on the impact of the age composition of the entire population on labour productivity and because it considers an interactive model of age composition with other determinants of labour productivity. We will show that these determinants have not only a direct effect on labour productivity but, due to these interactions, also indirect effects conditional on the age composition of the total population.

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In this study, we evaluate the impact of gross capital formation, labour market reforms, savings, and ICT development on labor productivity growth. However, since other socioeconomic indicators (Human capital development, infrastructure development, role of R&D and employment in different sectors) have also played an important role in recent growth studies (Barro, 1995; Bourlès and Cette, 2007; Islam, 2008; Marelli and Signorelli, 2010), we consider several additional control variables, such as the labour force participation rate, investment, financial development, human capital, labour market reform, openness, and the inflation rate as a measure of macroeconomic instability.

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The variable most frequently discussed in recent studies as a potential determinant of labor productivity is ICT investment (Van Ark et al., 2003; Belorgey et al., 2006; Jorgenson, 2007). These studies find that ICT investment and diffusion can explain part of productivity differential between Europe and the U.S. We therefore incorporate ICT goods imports as our proxy for ICT development as an explanatory variable. The positive impact of new technologies on developing economies also depends on the availability of skilled labor (Acemoglu and Zilibotti, 2001).

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It has been argued that many of the technologies adopted by developing economies – technologies which have been developed in the OECD economies – are complementary to the skill set of these richer countries’ labor forces. Low income developing economies often lack the skilled workers necessary for the efficient utilization of these technologies, which reduces the positive impact that these countries might obtain from a relatively young labor force. The technology/skill mismatch could account for large part of the output-per-worker differences observed around the world.

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Furthermore, there are numerous studies on the association between longevity and savings (Bloom, Canning & Graham, 2003; Wong & Tang, 2013); and longevity and economic growth (Fougere & Merette, 1999; Lindh & Malmberg, 1999). However, studies focused on the impact of old age on savings and economic growth jointly are few in number. Deaton and Paxson (1994) show that savings behavior is positively correlated with age in Taiwan. Furthermore, Zhang & Zhang (2005) examine the impact of longevity on savings, education, fertility and growth in 76 countries. Our hypothesis is that savings can promote labour productivity and longevity whilst old age dependency can increase labour productivity indirectly by promoting savings in the economy.

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Model specification

The dependent variable in our model is labour productivity growth, measured as the change in output per employed person. Alternatively, as a part of the sensitivity analysis, we consider output per hour as the dependent variable.

In our baseline model, we assume that labour productivity is dependent on several economic indicators, labour market institutions, age composition of population, savings, and the growth in information and communication technology (ICT) in an economy. We test whether these determinants have a direct impact on labour productivity, as well as an indirect impact conditional on the age composition of the total population.

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Model specification

Our baseline model is

where i represents a country and t a time period. LP denotes labor productivity growth; ER is the change in the broad participation rate measured as total employment divided by total population, IPEY is the log of the initial level of labour productivity in an economy at time t-1, INF is the inflation rate, and AD is the ratio between the dependent population and the working age population. Finally we have a vector of variables including gross capital formation, ICT development, labour market reforms, and savings in the economy.

ittiitititit ADADINFIPEYER 62

54321iit = LP

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Model specification

Country fixed effects control for all country-specific, time-invariant variables whose omission could bias the estimates in a typical cross-sectional study. For example, these country-specific fixed effects allow for different degrees of competitiveness, liberalization, efficiency of public administration and public services, infrastructures, as well as cultural and social norms across countries.

The reason for adding time-period fixed effects is that they control for all time-specific, country-invariant variables whose omission could bias the estimation in a typical time-series study (see Baltagi, 2005).

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Model specification

To check whether the impacts of other productivity determinants are conditional on the age structure of the population, we introduce an interaction term between age dependency and one of the variables of interest. Consequently, our model extends to

where *AD is the interaction term of one of our variables of interest with age dependency. Since we are primarily interested in evaluating the effect of four explanatory variables and their interaction with age dependency, we will estimate four separate models extended to include: 1) age dependency * gross capital formation; 2) age dependency * ICT ; 3) age dependency * labour market reforms and 4) age dependency * savings.

ittiititititititit ADADADINFIPEYER * = LP 762

54321iti,

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Model specification

We estimate four separate models for the following reasons:

1) to have a sufficient number of observations. For example, we have around 1000 observations for LMR while we have more than 2000 observations for gross capital formation and savings;

2) we have also carried out a regression analysis including all linear and interaction terms in one single equation. Although the number of observations declined significantly, the results were comparable.

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Model specification

To determine the effect of gross capital formation on labour productivity at different levels of age dependency, we estimate the model

We also calculate the marginal effect of each variable of interest at different levels of age dependency. For this purpose we differentiate second (2) equation with respect to that particular variable. Thus for the calculation of the marginal effect of change in gross capital formation conditional on age dependency, we take the derivative of equation (3) with respect to the financial development variable to obtain

ittiititititititit ADGCFGCFADADINFIPEYER * = LP 762

54321iti,

ADGCFLP

76

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Data description and analysis

We use data on labor productivity (output per worker, measured as GDP per person employed) taken from The Conference Board and Groningen Growth and Development Centre. Labour productivity levels vary significantly across countries. In our sample, the average labor productivity level of high income economies was more than 11 times higher than that of low income economies in 1990. This difference in labor productivity increased further to 13.7 in 2000. However, in 2010 the gap narrowed (labor productivity was 10.7 times higher in developed countries compared with low income countries). We use the employment to population ratio as a measure of the labor force participation rate (TCB/GGDC database).

GDP per capita in 1990 US$ (converted at Geary Khamis PPPs).

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Age dependency is measured as the ratio between the dependent population and the working age population, which is obtained from the World Development Indicators. The dependent population is defined as the sum of the young population (0-14 years) and the aged population (65+ years). The working age population is measured by the population aged 15-64 years. To evaluate the relationship between labour productivity and age dependency, presented in Figure 2 is a scatter plot which suggests a negative and non-linear relationship between these two variables. We therefore included a square term of age dependency as an additional explanatory variable in our empirical model.

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Figure 2 - Relationship (scatter plot) between labor productivity and age dependency

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Figure 2 - Relationship (scatter plot) between labor productivity and age dependency

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A comparison between age dependency and labour productivity performance for different income groups is presented in Figure 3. Age dependency in low income economies is nearly twice as high as in high income economies. At the same time, labour productivity is relatively low compared to high income and upper middle income economies. Age dependency seems to have diminished most rapidly in the upper and lower middle income countries.

We have 27 countries in the high income group, 25 in the upper middle income group, 27 in the lower middle income group, and 17 in the low income group.

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Figure 3 - Labor productivity and age dependency by country groups ranked according to average income (1980-2010)

Notes: LP is labor productivity measured as output per employed worker and AD is age dependency measured as the ratio between the dependent population and the working age population. HYE= High Income Economies, UMYE = Upper Middle Income Economies, LMYE = Lower Middle Income Economies, and LYE= Lower Income Economies

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Other potential determinants of labour productivity are macroeconomic indicators like labour market institutions and ICT development.

Two macroeconomic variables will be included: (i) adjusted inflation as a proxy for changes in the price level and macro-economic instability of a country; and (ii) gross capital formation growth as a key driver of economic growth and to capture macroeconomic developments. Data on inflation and gross capital formation are taken from the World Development Indicators (WDI).

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To evaluate the role of infrastructure development, we incorporate the total network of roads (KM) as a proxy for infrastructure development.

LMR is a composite index based on six measures of labor market institutions (minimum wage, hiring and firing regulations, centralized collective bargaining, mandated cost of hiring, mandated cost of worker dismissal and conscription). The LMR index is an un-weighted average of these six measures, and its value varies from 1 to 10. Data on the LMR index are taken from the Fraser Institute.

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Moreover, we use imported ICT goods as a percentage of total imported goods as a proxy for ICT development; and we use gross savings as a percentage of GDP to evaluate their impact on labor productivity directly and also conditional on age dependency. Data for both variables are taken from WDI.

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We also use a set of control variables suggested in previous studies on the determinants of labour productivity to check the robustness of our empirical model. We include the lagged log level of GDP per person employed to reflect the different levels of development that characterize each of the countries. Other control variables include employment in the agricultural sector, openness, foreign direct investment, urbanization, and infrastructure development. Data on these control variables are taken from the historical database of World Bank Development Indicators (WDI).

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Empirical results

Table 2 - Impact of demographics on labor productivity Direct Model Interactive model

1 2 3 4 5 6 7 8

Age Dependency -0.320*** -0.325* -0.095 -0.381*** -0.291*** -0.278 -0.021 -0.500***

0.103 0.165 0.185 0.097 0.107 0.169 0.210 0.129

Age Dependency*Age Dependency 0.171** 0.070 -0.079 0.192*** 0.154*** 0.044 0.085 0.239***

0.066 0.134 0.129 0.062 0.068 0.134 0.129 0.071

Change in Participation -0.622*** -0.396*** -0.277*** -0.385*** -0.649*** -0.396*** -0.277*** -0.392***

0.077 0.093 0.109 0.084 0.078 0.093 0.11 0.082

Initial level of labor productivity -0.030*** -0.068*** -0.105*** -0.061*** -0.029*** -0.067*** -0.405*** -0.062***

0.007 0.015 0.023 0.008 0.006 0.015 0.023 0.009

Inflation -0.091*** -0.065** -0.090*** -0.115*** -0.089*** -0.065** -0.092*** -0.111***

0.024 0.029 0.026 0.019 0.024 0.029 0.026 0.019

Change in Gross Capital Formation 0.001*** 0.002***

0.000 0.001

Gross capital Formation*Age Dependency -0.001*

0.001

ICT Expenditure 0.003*** 0.004*

0.001 0.002

ICT expenditure*Age Dependency -0.002

0.003

Labor Market Reforms 0.001 0.006

0.002 0.007

Labor Market Reforms*Age Dependency -0.011***

0.00

Savings (% of GDP) 0.001 -0.001

0.001 0.001

Savings*Age Dependency 0.002

0.002

Constant 0.436*** 0.816*** 1.122*** 0.748*** 0.415*** 0.794*** 1.089*** 0.816***

0.086 0.169 0.241 0.104 0.085 0.176 0.232 0.133

Significance of Fixed Effects

P-value of F-statistics 0.000 0.000 0.002 0.001 0.003 0.002 0.005 0.001

Hausman Test 61.31 0.00

61.98 0.00

157.18 0.00

132.86 0.00

56.57 0.00

63.41 0.00

206.99 0.00

128.24 0.00

Joint significance of Interaction and Constituent Term

200.63 0.000

20.55 0.000

5.69 0.00

28.68 0.00

Number of observations 2362 1243 1010 2563 2362 1243 1010 2563

Number of Countries 102 108 102 107 102 108 102 107

R2 0.39 0.16 0.17 0.23 0.40 0.17 0.17 0.24

F Statistic 43.037*** 14.448*** 12.104*** 24.557*** 39.02*** 12.256**

* 10.756**

* 10.647**

* Robust standard errors are reported below the coefficients. Coefficient of financial depth is multiplied by 100 to make the results presentable. *** indicates significance at 1 percent level, ** indicates significance at 5 percent and * indicates significance at 1 perce nt level

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Empirical results

Figure 4 - Marginal impact of gross capital formation, ICT expenditure, labor market reforms, and savings on labor productivity at different levels of age dependency - complete sample

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This figure shows the impact of gross capital formation, ICT expenditure, labor market reforms and savings on labor productivity growth at different levels of age dependency. These figures correspond to our main results as set out in columns 5-8 of Table 2. The upper panels show the marginal effect of Gross Capital Formation (GCF) and ICT expenditure (ICT) and the lower panels show the marginal effect of Labor Market Reforms (LMR) and Savings (Sav) at different levels of age dependency.

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Robustness and extensions

We carried out a number of robustness checks to determine whether our results held when we restricted our sample to only developed or only developing economies.

Our findings remain almost the same for both groups of economies with the exception of the marginal effect of Savings.

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We find that the marginal effect of gross capital formation, ICT expenditure, and labour market reforms declines at higher levels of age dependency.

Separate analyses for developed and developing economies also yield very similar marginal effects of GCF on labour productivity conditional on age dependency. However, in the case of developed countries, the slope is somewhat flatter, indicating a slower decline in labor productivity as compared to developing countries. The same applies to LMR.

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In the case of ICT, its role in labour productivity growth is high at lower level of age dependency in developing countries and in the complete sample. However, in the case of the sample of only developed countries, the role of ICT increases at high age dependency but is statistically not significant. This may be due to the different nature of age dependency in these countries (child dependency in developing economies and old age dependency in developed ones).

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The marginal effect of savings on labor productivity growth at old age dependency is larger in high income OECD countries, but it is only statistically significant at low levels of age dependency. For developing economies, the savings marginal impact curve is steeper and insignificant for labour productivity at different levels of age dependency.

These differences in the marginal impact of savings may be due to the different nature of age dependency in developed and developing countries. Developed countries are characterized by high old age dependency, contrary to developing countries, where high child dependency mainly represents age dependency.

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We have also split age dependency between child dependency and old age dependency.

The results show that child dependency affects labour productivity adversely both in the complete sample and in the sub-samples of developed and developing countries.

However, in the case of high income developed economies, the impact of gross capital formation remains positive and significant at higher levels of child dependency. The marginal effects curves for developing countries are slightly steeper than those for the high income economies.

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The impact of old age dependency is not negative in the complete sample and in the sample of developing countries. This can be explained by the low level of old age dependency in developing countries (in low income developing economies, the higher share of age dependency is due to child dependency; moreover, due to poor health conditions, life expectancy is also lower than in developed economies).

It is only the marginal effect curve of savings that is upward sloping, though it is not significant at higher levels of age dependency.

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In the sample of developed countries, the marginal effect of GCF is slightly lower at different levels of old age dependency, while the impact of ICT on labor productivity remains almost the same at different levels of age dependency.

The impact of labour market reforms is positive in the presence of old age dependency. Altogether, the analysis by child and old age dependency burdens reveals that child dependency has a stronger adverse effect on labour productivity than old age dependency. However, the long-run effects of children may still be positive because they receive schooling and develop skills for higher productivity in the future.

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We also determined the impact of other control variables suggested by previous studies in the literature along with our preferred interactive model. We add several economic, human capital and infrastructural variables. We find that openness, remittances and infrastructure development are significantly related to labor productivity. But the sign and significance of the variables of interest remain unchanged.

Education is measured as years of schooling for the population aged 25 and above, Financial depth is measured as domestic credit to the private sector as a % of GDP, FDI represents foreign direct investment (net inflow as % of GDP), Infrastructure is the total road network (km), Urbanization is urban population (as a percentage of the total population), and openness is measured as trade as % of GDP.

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As a sensitivity analysis, we also changed our period of analysis to 1995-2005. The results showed that the marginal effects of various determinants, conditional on age dependency, changed little, and thus may be said to be robust to the period of analysis. Finally, we changed our dependent variable from output per worker to output per hour. This led to a significant decline in the number of observations (this data are not available for a large number of developing countries).

On the basis of these sensitivity analyses, we may conclude that our main finding that age dependency has a negative effect on labour productivity growth and reduces the impact of other determinants of labor productivity growth holds.

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Conclusion

We have examined the effect of gross capital formation, labor market institutions, ICT expenditure, and savings on labor productivity (measured by output per worker) conditional on different levels of age dependency for up to 108 countries over the period 1980-2010.

We have found that age dependency matters for labour productivity growth.

Our results show that higher age dependency impacts negatively on labour productivity growth not only directly but also indirectly by modifying the impact of other social, economic and infrastructural determinants.

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Conclusion

In our sensitivity tests, we used a number of variations in the definitions of the variables used, in the sample of countries and the period of analysis.

It turned out that our results were generally very robust and held for both sub-samples of developed and developing countries. We also found that child dependency has a stronger adverse impact on labour productivity than old age dependency.

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Conclusion

Age dependency in low income economies is 1.8 times higher than in high income developed economies. However, the nature of age dependency is different in high income economies. Rising old age dependency and the consequent social benefits burden is a matter of concern for developed economies. Our finding that age dependency negatively affects labour productivity helps to explain the productivity growth differentials across country groups at different income levels.

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Conclusion

Furthermore, our finding suggests that, in the future, when child dependency in developing and emerging economies declines, there may be an increase in labour productivity not only directly but also through the improved performance of other determinants of labour productivity.

In developing and emerging economies, the greatest priority should therefore be given to an increase in the education and skill training of the working age population, so that these economies can properly utilize the favorable demographic changes to increase labour productivity and economic growth.

Documents

Osservatorio sulle economie emergenti- Torino (OEET) Turin Center on Emerging Economies Workshop (Turin, March 12-13, 2015) “Asian emerging economies in