The Effect of Population Age Structure on Economic Growth in Iran

International Research Journal of Finance and Economics ISSN 1450-2887 Issue 72 (2011) EuroJournals Publishing, Inc. 2011 http://www.eurojournals.com/finance.htm The Effect of Population Age Structure on Economic Growth in Iran Mehdi Safdari Assistant Professor of Faculty of Economics University of Sistan and Baluchestan, Iran Masoud Abouie Mehrizi Islamic Azad University, Mehriz Branch, Iran Marzie elahi Islamic Azad University, Mehriz Branch, Iran Abstract In this paper, balance relation and long term of eight variables Gross domestic product growth rate, Gross domestic investment, Government consumption expenditure, Inflation, Trade, Age group 0 to 14 years, Age group 15 to 64 years and Age group 65 to top and also their influences on each other in Iran and for years 1973-2008 has been analyzed. In this purpose vector autoregressive model (VAR) has been used. First, stability of variables by the use of dickey-fuller test has been examined. Next, analysis of Johnson test for considering the convergence among eight variables has been used. The results of this research show that variables of Gross domestic investment, Government consumption expenditure, Trade, Age group 15 to 64 years have positive effect on economic growth. Also variables Age group 0 to 14 years, Age group 65 to top and inflation have a negative effect on economic growth. Keywords: Population aging Gross domestic investment economic growth Vector Autoregressive Model (VAR). JEL Classification Codes: E21, O47 1. Introduction Human resources of each country play a significant role in country s socio-economic development. So that it will be remembered as an important factor in economic growth. In the meantime it is important consider to population issues, particularly the age structure of population to achieve economic growth. So that if changes in population age structure knowingly and with the planning, can lead to prosperity and progress of societies also, if that was without planning can cause many negative effects of social and economic. Hence Understanding this structure and attention to the changes it can have an important role in decision-making and planning of Societies and no attention to it, can cause many problems. According to importance of subject, in this paper we examined the effect of population age structure on economic growth. Population age structure has been used in many articles. Prskawetz, Kögel, Sanderson and Scherbov (2007) in their article titled the effects of age structure on economic growth: An application of probabilistic forecasting to India estimated a new

International Research Journal of Finance and Economics - Issue (72) 63 cross-country regression model of the effects of age structure change on economic growth. They used the new model and recent probabilistic demographic forecasts for India to derive the uncertainty of predicted economic growth rates caused by the uncertainty in demographic developments. Dalton, O'Neill, Prskawetz, Jiang, pitkin (2006) in their article with this title Population aging and future carbon emissions in the United States incorporated population age structure into an energy economic growth model with multiple dynasties of heterogeneous households. The model is used to estimate and compare effects of population aging and technical change on baseline paths of U.S. energy use, and CO 2 emissions. Results showed that population aging reduces long-term emissions, by almost 40% in a low population scenario, and effects of aging on emissions can be as large, or larger than, effects of technical change in some cases. These results are derived under standard assumptions and functional forms that are used in economic growth models. The model also assumes a closed economy, substitution elasticities that are fixed and identical across age groups, and patterns of labor supply that vary by age group, but are fixed over time. Guest (2006) in his article with this title Innovations in the macroeconomic modelling of population ageing applied several innovations in the modelling of population ageing. Particular attention is given to two of the innovations which address shortcomings of the standard models that affect the assessment of the impact of ageing on average labour productivity. One is the assumption that workers of different ages are perfectly substitutable, which is implied by the assumption that aggregate labour is an additive function of labour inputs by age. The other is the assumption that all goods have equal capital intensities in a steady state. Simulations are undertaken which suggested that these effects are probably not large, but possibly not trivial either. Bloom, Canning, Fink and Finlay (2007) in their article titled Does age structure forecast economic growth? estimated the parameters of an economic growth model using a cross section of countries over the period 1960 to 1980, and investigated whether the inclusion of age structure improved the model's forecasts for the period 1980 to 2000. They found that including the age structure improved the forecast, although there is evidence of parameter instability between periods with an unexplained growth slowdown in the second period. They used the model to generate growth forecasts for the period 2000 to 2020. Wei and Hao (2010) in their article titled Demographic structure and economic growth: Evidence from China examined the economic implications of demographic change in the Chinese context. They extend the growth equation by incorporating age structure dynamics and applied it to China s provincial-level data during the period 1989 2004. They found that changes in demographic structure especially the contribution of fertility decline to lower youth dependency, have helped fuel China s economic growth since 1989. The effect of demographic change on income growth operates mainly through its impact on steady state income levels and the effect of age structure is more pronounced in provinces that are more open to market forces. They also found a significant feedback effect of economic growth on demographic behaviors through the mechanisms of birth rates, marriage age and life expectancy. Fougère, Mercenier, Mérette (2007) in their article titled A sectoral and occupational analysis of population ageing in Canada using a dynamic CGE overlapping generations model quantified the sectoral and transitional-dynamic impact of population ageing arising from the combination of two important structural changes that will affect the Canadian economy and its labour market. The first is the negative labour supply shock that will arise from slower labour force growth. The second is the change in the composition of consumption demands due to an increase in the proportion of older consumers. This situation is not unique to Canada and will applied to most industrialized countries. The analysis is done using a sectoral and occupational computable general equilibrium model with overlapping generations. The paper's key founding is that, though the negative labor supply shock will largely dominate; there will also be some significant sectoral compositional shifts due to final-demand changes. In particular, the sectoral share of health services in total GDP may increased by nearly 50%, from 4.8% of GDP in 2000 to 7% in 2050. It is also estimated that real wages may need to increase twice faster in health occupations than in the rest of the economy to prevent shortages in health

64 International Research Journal of Finance and Economics - Issue (72) services supply. In addition, finance, insurance and real estate activities will also benefit to a certain degree over the next 20 years from the restructuring of final demands. On the other hand, the combined sectoral contributions to GDP of education, construction, manufacturing, and wholesaling and retailing will fall by 3.3% of GDP between 2000 and 2050. This in turn would reduce wage pressures in processing and manufacturing occupations and in trades. Creedy, Guest (2007) in their article with this title Population ageing and intertemporal consumption: Representative agent versus social planner examined the optimal path of consumption over time in the context of population ageing. Older age groups are considered to have relatively greater needs, resulting for example from additional health costs. These differences give rise to the concept of the equivalent number of persons, as distinct from the population size. Emphasis is given to the difference between a framework involving a representative agent and one in which plans are made by a social planner. The precise conditions under which consumption growth paths are the same under the representative agent and the social planner are established. This equivalence is found to hold only in the case where the social planner's value judgements are such that individuals are considered to be the appropriate unit of analysis. An alternative assumption, in which equivalent persons are regarded as the appropriate units, is found to give rise to a different optimal consumption path. Numerical examples demonstrate the relative orders of magnitude for a range of parameter values. The differences are found to be potentially important. The choice of appropriate consumption units individuals or equivalent persons is far from arbitrary since it involves possibly conflicting value judgements. This choice has implications for policies designed to influence the optimal saving rate, such as superannuation policy and the fiscal balance. Fougère, Harvey, Mercenier and Mérette (2008) in their article titled Population ageing, time allocation and human capital: A general equilibrium analysis for Canada explored the long-term impact of population ageing on labor supply and human capital investment in Canada, as well as the induced effects on productive capacity. The analysis is conducted with a dynamic computable overlapping generation s model where in the spirit of Becker and Heckman, leisure had a quality-time feature and labor supply and human capital investment decisions are endogenous. The role of human capital in the growth process is based on the framework used by Mankiw et al. This paper indicated that population ageing creates more opportunities for young individuals to invest in human capital and supply more skilled labor at middle age. Consequently, the reduction in labor supply of young adults initially lowers productive capacity and exacerbates the economic costs of population ageing. However, current and future middle-age cohorts are more skilled and work more, which eventually raises productive capacity and significantly lowers the cost of population ageing. Finally, these results suggested that the recent increase in the participation rate of older workers might be the beginning of a new trend that will amplify over the next decades. The rest of the paper is organized into four main sections: Section 1 analyses previous studies. Section 2 describes the data and the econometric methodology. Section 3 discusses the results that emerge from the estimations. The conclusions of this paper are then presented in section 4. 2. Data and Methodology We use this data from 1973 to 2008 of Iran. We found them in Central Bank of Iran. One vector autoregressive (VAR) model which possess k as exogenous variable. And p as time`s inhibition for each variable, in shape matrix is shown as following: Yt = A t Yt 1 + A 2Yt 2 + + A pyt p + U t IN( 0, ) In this relation, and it`s lags, k 1 vectors are related to models variables., i= 1, 2,, p are model`s coefficients for k k matrix. And, k 1 vector is related to terms of model`s error. Now for linking short term behavior of to long term balance values, we can bring above relation as vector error correction model as following: Y β Y + β Y + + β Y + Y + U t = t t 1 2 t 2 p 1 t p 1 t p t

International Research Journal of Finance and Economics - Issue (72) 65 Where: B i = (I A A A 1 Π = (I A1 A 2 A p ) 2 P ) i = 1, 2,, p 1 Matrix π contains of information of long term balance variables. We follow the Johansen approach in determining long-run relationships. Patterson (2000) and Doornik and Hendry (2001) provide a full treatment of the issues involved in this method. The first step is to estimate the VAR in levels with an appropriate lag structure. The next stage involves determining the co integrating rank, i.e. the number of long-run equilibrium relationships or co integration vectors among the variables. Finally, to allow a reasonable interpretation of the results, co integration vectors are identified (Abouie, 2011). 2.1. Theoretical Principles The model which is used for investigate the effect of population Age Structure on economic growth in Iran inspired from the propounded model in article of Barro (1991). This model is defined as following: G=F (INV, GC, INF, TR, PA14, PA15, PA65) Where: G: Gross domestic product growth rate INV: Gross domestic investment GC: Government consumption expenditure INF: Inflation TR: Trade PA14: Age group 0 to 14 years PA15: Age group 15 to 64 years PA65: Age group 65 to top 3. Findings/Discussion We use the above formulation to estimate a VAR model containing sex variables. Table 1: Variable G INV GC INF TR PA14 PA15 PA65 Variable Definitions and Descriptions DESCRIPTION Gross domestic product growth rate Gross domestic investment Government consumption expenditure Inflation Trade Age group 0 to 14 years Age group 15 to 64 years Age group 65 to top As the time of accumulation analysis, statistical properties of variables are very important. In fact, the accumulation method tests with theory the compatibility among statistical properties of the model s variables. Economic variables are generally non stationary and they are a random process. Linear combination of non stationary series in general is a non stationary series and closely associated with economic theory. Because economic theory guarantee stagnation of combination of economic variables so in this study Dickey Fuller s generalized Test for investigation of variables stationary is used. The results of the test for the variables in levels are presented in Table2, 3. The results reported in Table 2, 3 showed that all variables except G, INF are I (1).

66 International Research Journal of Finance and Economics - Issue (72) Table 2: ADF tests for unit roots Variable ADF Critical Value Lag 1% 5% 10% G -3.72-3.57-2.93-2.60 1 INV -2.19-3.57-2.93-2.60 0 GC -1.10-3.57-2.93-2.60 0 INF -3.99-3.57-2.93-2.60 0 TR -1.12-3.57-2.93-2.60 0 PA14-1.18-2.61-1.95-1.61 1 PA15-1.91-2.61-1.95-1.61 2 PA65-0.97-4.21-3.52-3.19 1 Table 3: ADF tests for unit roots Variable ADF Critical Value Lag 1% 5% 10% G -3.72-3.57-2.93-2.60 1 DINV -5.99-3.57-2.93-2.60 0 DGC -5.16-3.57-2.93-2.60 0 INF -3.99-3.57-2.93-2.60 0 DTR -5.82-3.57-2.93-2.60 0 DPA14-1.95-2.61-1.95-1.61 0 DPA15-1.25-2.61-1.95-1.61 1 DPA65-4.08-4.21-3.52-3.19 0 After investigation of persistent of variables, one of the important stages in evaluation of vector regression model is choosing rank of pattern. For choosing optimum rank of pattern, we can use criterion of Akaike or Schwarz. The most lag which are given to models is 2, and considering tables 4, 5 the least quantity of Akaike and Schwarz statistic in two models are prepared in second lag, we can indicate that the optimum lag of VAR models is equal to 2. Table 4: Determination of magnitude of lag of VAR model (vector1) Schwarz information Akaike information Lag -8.95-9.51 0-19.3-21.8 1-23.3* -27.8* 2 Table 5: Determination of magnitude of lag of VAR model (vector2) Schwarz information Akaike information Lag -7.37-7.81 0-15.24-17.19 1-18.21* -21.61* 2 In this article we follows vectors and accumulated vector among variables of Gross domestic product growth rate, Gross domestic investment, Government consumption expenditure, Inflation, Trade, Age group 0 to 14 years, Age group 15 to 64 years and Age group 65 to top by the use of Johansson s method. In Johnson s method after doing necessary calculations for studding existence of convergence we use two criterions consist of and. If is verified existence of convergence among variables, we can say that is established balance and long term relation among variables.

International Research Journal of Finance and Economics - Issue (72) 67 Results which are concluded from effect's examination and examination of maximum specific values for determination of accumulated vectors among model's variables for two models are presented in tables 6-9. Table 6: Test Statistics for Co integrating rank (Max tests- vector1) Null alt Critical value Probe r=0 r 1 55.26 65.35 0.0000 r 1 r 2 48.18 43.62 0.0041 r 2 r 3 34.62 30.18 0.0145 r 3 r 4 28.98 21.34 0.1236 Table 7: Test Statistics for Co integrating rank (Trace tests- vector1) Null alt Critical value Probe r=0 r 1 86.15 102.23 0.0001 r 1 r 2 72.35 98.25 0.0140 r 2 r 3 45.25 48.12 0.1247 r 3 r 4 36.15 35.82 0.5369 Table 8: Test Statistics for Co integrating rank (Max tests- vector2) Null alt Critical value Probe r=0 r 1 76.87 84.52 0.0000 r 1 r 2 57.15 53.47 0.0875 r 2 r 3 41.31 40.57 0.0632 r 3 r 4 29.14 28.27 0.4258 Table 9: Test Statistics for Co integrating rank (Trace tests- vector2) Null alt Critical value Probe r=0 r 1 96.36 102.66 0.0000 r 1 r 2 89.25 90.25 0.0256 r 2 r 3 80.14 72.15 0.1123 r 3 r 4 65.91 63.14 0.4932 The magnitudes of vectors which are prepared by statistic of examination effect matrix in vector1 are equal to 3 vector and magnitudes of vectors which are prepared statistic of maximum specific values are equal to 1. And the magnitudes of vectors which are prepared by statistic of examination effect matrix in vector2 are equal to 2 vector and magnitudes of vectors which are prepared statistic of maximum specific values are equal to 1. Considering that examination of maximum specific values is stronger than examination of effect matrix. Therefore for determination of magnitude of accumulated vector, examination of maximum specific values is used. considering results of above tables in level of probability of 90 percent magnitude of long term relations among variables compatible pattern with economic theory is equal to (r=1)1 for vector1 and for vector2 is equal to (r=1)1 are determined.

68 International Research Journal of Finance and Economics - Issue (72) To examine the impact of demographic variables on growth, we take these variables into the model separately. Hence we use of two vectors. Table 10: Co integrating vectors Variables Vector 1 Vector 1 G(-1) 1.00 1.00 INV(-1) -3.41(3.50) -0.51(-1.06) GC(-1) -1.59(1.49) -2.94(-5.52) INF(-1) 0.15(-0.41) 1.37(7.75) TR(-1) -0.80(-2.40) -1.64(9.78) PA14(-1) 0.18(11.78) 0 PA15(-1) 2.97(18.51) 0 PA65(-1) 0-0.001(-0.23) C -19.78-0.41 In table10, Numbers in parentheses are statistic of accounting t. estimated coefficients of all variables in a meaning full level, 5 percent are significant from statistical aspect. Considering prepared results within investigated period, variables of Gross domestic investment, Government consumption expenditure, Trade, Age group 15 to 64 years have positive effect on economic growth. Also variables Age group 0 to 14 years, Age group 65 to top and inflation have a negative effect on economic growth. According to results, with an increase of one percent in the ratio of variable of Age group 15 to 64 in order growth rate will be increase 0.001 percent and with an increase of one percent in Age group 0 to 14 years, Age group 65 to top order Growth rate will be decreased 0.18 and 2.97 percent. 4. Conclusion Generally, in this article linkage between population age structures on economic growth in Iran investigated. In this article, first we presented a model and estimated this model, in order to fitness of VAR pattern we used unit root test, then the magnitude of inhibition of VAR model was determined after that by using of Johansson s and existence of accumulated vectors showed long term relations among variables. After certainty about existence of long term relation, we estimated this relation and then interpreted these coefficients. The results of this research show that variables of Gross domestic investment, Government consumption expenditure, Trade, Age group 15 to 64 years have positive effect on economic growth. Also variables Age group 0 to 14 years, Age group 65 to top and inflation have a negative effect on economic growth. Age group 15 to 64 years are part of the country's working population and this group have a higher income than other age groups. Thus saving in this group (15-64) is higher than other group of society. With the increase people in this group the amount of savings will increase and Possibility of increased investment from these savings is formed and Causes job creation and also economic growth. Thus age group 15 to 64 plays a positive role in economic growth. Similarly increase people in Age group 0 to 14 years and Age group 65 to top Causes decrease in savings of society and in this way will reduce economic growth in country. References [1] Abouie M, Safdari M (2011). External debt and economic growth in Iran, Journal of Economics and International Finance, Vol. 3(5), pp. 322-327 [2] Bloom D, Canning D, Fink G, Finlay J (2007). Does age structure forecast economic growth?, International Journal of Forecasting, Volume 23, Issue 4, pp. 569-585.

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