Solow Growth Model and Beyond - Essay Example

Development of Problems and Limitations of the Solow Growth Model Using Extensive Theoretical Approaches and More Extensive Application of the Theoryto the Real World Name Institution Course Date The Solow model is a growth model that is exogenous in nature. Solow (1956) recognizes it is an economic framework that focuses on long-run economic growth that is in the neoclassical economics framework. In expounding in his model, Solow (1956) explains the long-run economic growth by focusing on two major factors that contribute to growth that are factor accumulation and productivity factors. The factor accumulation entails the accumulation of capital, labor, as well as the population growth while the productivity factor entails progress in technology and efficiency. At the core of this model, it is a neoclassical aggregate production function that in most cases is similar to Cobb – Douglas model and this makes it possible for this model to be in contact with microeconomics. This model was established by Robert Solow and Trevor Swan in the year 1956 (Dimand and Spencer, 2008). Dimand and Spencer (2008) say that the version of Solow’s Growth model where savings are chosen optimally by utility maximization of the households is known as the Neoclassical Growth Model. The neoclassical model was an extension of Harrod – Domar model that was developed in 1946. It superseded Harrod – Domar model due to the characteristic mathematical attractiveness. In that sense, the model was a convenient start point for various extensions (Hendrik and Lewer, 2015). David Cass developed a solution for the growth model in 1965 with technological change and the growth in population (Jones, 1997). Jones (1997) observed that Solow and Swan did an extension of Harrod - Domar model, first, by the addition of labor as a factor of production. Secondly, they ensured that the capital – labor ratios were not in a fixed position like in Harrod–Domar model’s case. In a study carried out by Jones he recognized the modification that provided for a continual increase in capital intensity which could be distinguished from progress in technology (Jones, 1997). In this sense, they independently simplified the growth model. Solow’s model fitted the economic growth data that was available with some level of success. In the present day, his model is used by economists in the estimation of the separate effects on the economic growth of capital, labor as well as the technological change. The previous models that include the closed economy and small open economy models give a static observation of the economy at a given point in time. The Solow growth model allows us a dynamic view of how savings affects the economy over time. The following gives a general equation of Solows growth model. y = A*f (K, L), where y = output, A = productivity, K = physical capital, and L = labor. In this case, the model can result in the following implications (Solow, 1956). In the short run, determination of growth is done by its movement to the new steady state (Romer, 1986). Growth is, therefore, faster for countries far away from their steady state. Hence, all else being equal, the poor should grow faster than the rich. This state is attained only by the change in the capital investment, growth of the labor force and the rate of depreciation. The capital investment change is as a result of a change in the rate of saving. There is a prediction by the standard Solow model that there shall be an equal growth in relation to the new steady state considering the long-run implications. As Romer (1986) observes this is the most outstanding weakness of the Solow model. It implies that there shall be no growth in the long run hence there is no sustainability of the model. The idea of a country reaching a state of stability and staying there forever is considered to be unrealistic by the economists. Growth in income per person is driven by total factor productivity that increases in an exogenic manner (Bergh and Henrekson, 2011). Consequently, long-run growth has nothing to do with the demand side. Bergh and Henrekson (2011) also mention that in the long-run, capital accumulation is a consequence of growth in technology, not a cause. In addition, to ensure that there is a continuity of growth in the long-run, the Solow Romer model is employed. The combination of the Solow and Romer models aids the economists in the prediction of long-run situations including the growth sustainability. However, the Solow Romer approach does not address the natural limits of the planetary boundaries that hinder economic growth (Solow, 2000). Even though the Solow model may be original, there are some limitations that have been identified in it. First, it is built basing on the closed economy assumption ((Barro and Sala-I-Martin, 1992). That is, the convergence hypothesis assumes no type of interrelationship between groups of countries. However, Barro and Sala-I-Martin (1992) offers a solution to the lapse as they mention that the difficulty can be evaded by arguing in unison with Solow. It is in the sense that each model has some assumptions that are untrue but may be successful if the final results are affected by the used simplifications. The assumptions of the model include; continuous time, single well produced with a constant technology, no government or international trade. In addition, all factors of production are fully employed Labor force grows at constant rate n such that Ĺ/L = n where Initial values for capital, K 0 and labor, L 0 are given. Apart from the proposed model by Solow, there have been attempts to develop a growth model that caters for an open economy. Secondly, the model can only explain positive long-term growth in per-capita variables only through some growth in productivity (Lucas, 1988). It is in that sense that the model be made consistent with some regularity observed in actual data. Another limitation is that the inherent income shares emanating from the capital, obtained from the model estimates) does not match the accounting information of the government. An attempt was carried out by Lucas (1988) to eliminate this problem involved enlargement of the capital concept. This was done with an aim of including that which is physical and human (the latter of which includes education, as well as health). The third limitation of the model is that the convergence rate estimated was too low even though modification attempts of the model have impacts on this rate. For instance, both the Diamond model and versions of an open economy of the Ramsey-Cass-Koopmans model have large convergence rates. Finally, the growth rate equilibrium of relevant variables depends on technological progress rates, which is an exogenous factor (Dimand and Spencer, 2008). Additionally, the individuals in the Solow model as well as in some of his successors, lack motivation for the invention of new goods. Additionally, Dimand and Spencer (2008) mentions that one of the limitations of the model entail inability of the model to take into account entrepreneurship as well as the institution strength. Entrepreneurship can catalyze economic growth. Additionally, there is no explanation of how and why the progress in technology happens. This weakness has resulted to the developing of endogenous growth theory. The theory androgenizes the progress of technology and accumulation of knowledge. However, the critics held that Schumpeter’s 1939 modern Institutionalism, as well as Austrian economics, offers a chance of explaining how long run economic growth occur then the later Lucas/Romer models. From the far left, Marxist critics of growth theory have questioned the models underlying assertion that economic growth is necessarily a good thing. While the model maximizes in welfare, the use of a representative agent hides equity issues. In the mid-1980s, there was a group of theorists who dealt with growth economics and they were completely dissatisfied with the exogenous growth factors that had the long run implications. They considered a model that was a replacement for the exogenous variable together with a model in which the main growth determinants were precise in the model. The work that was developed by Arrow and Schwartz (1962), as well as Uzawa (1965), was a foundation for this research. Lucas (1988) did an omission of technological change. As a substitute, the growth in these models maximizes in infinite investment in human capital. These growths have an effect of spill over on the economy and leads to the reduction of diminishing return to capital accumulation. The simplest endogenous model is the AK model. It provides an endogenous growth saving rate that is constant. It models progress in technology with a single parameter. In addition, it operates on the basic assumption of a production function that does not portray diminishing returns to scale that leads to endogenous growth. There are different rationales that have been given for this assumption. They include positive spill-over emanating from capital investments in the economy as a whole. Alternatively, it entails technological improvements such as learning by doing. The endogenous growth theory also receives support from models in which agents maximally determined the consumption and saving, optimization of resources allocation to research and development resulting in the progress in technology. The incorporation of imperfect markets and R& D of the rates to the growth models were carried out by Romer (1986, 1990). He received significant contributions from Aghion and Howitt (1992). As a result of the shortcomings mentioned above, the Solow Growth model was taken as a basis of economic literature that focuses on the income growth behavior across countries (Benard and Durlauf, 1996). In addition, the conditional convergence of the income among countries drew a negative implication between the initial real per capita GDP level and the following growth rates of the same variable. Indeed, this result comes from the assumption of diminishing returns in each input. It ensures that a less capital-intensive country gains higher return rates and as a consequence, higher GDP growth rates are realized. The convergence hypothesis that is detailed and its validity in particular across various techniques of estimation are established, among other things, Benard and Durlauf (1996), and others. The neoclassical growth model by Solow was rigorously put to a test by Mankiw, Romer and Weil (1992). They speculated that for Solow model to fit in a better manner, an additional variable had to be introduced. This improves the original capability to explain the differences in income across the countries. In the recent past, there have been noteworthy advances in econometric methods which have developed a new set of empirical tests economic growth theories. In order to keep up with the trend, an essential contribution was put forward by Islam and Zanini (2008). His paper reports give an estimation of the neoclassical model parameters in a panel data approach. In this case, the author acknowledges on leveling effects for each country as heterogeneously fixed intercepts in a dynamic panel. Even though Mankiw, Romer and Weil (1992) results enables us to come up with conclusions human capital performs and essential responsibility in the production function, a contrasting conclusion is brought forward by Islam and Zanini (2008). It becomes the case introduction of the specific technological progress of a country in the model. There was a presentation of an individual random effect version of the model. Lee did this presentation, together with Pesaran and Smith (1997), and the model was developed by Islam and Zanini (2008). They introduced heterogeneities in the intercepts and the production slopes function in a heterogeneous, dynamic panel data approach. There was a conclusion by the authors that the hypothesis of parameter homogeneity can be rejected definitely. In addition, they state that different rates of growth declare that the economic convergence has no economic meaning. It is because the knowledge of convergence rate gives no provision for insights into the evolution of the variance of cross country output over time. However, there have been predictions of classical economic theory by assuming that the observed data emanates from the stationary processes. A preliminary look at most of the economic time series graphs or even the track records of the past of economic forecasting is enough to make the assumption invalid. It is because if the evolving, growing and dynamic nature of the economy in both real and nominal terms. The economic literature that gives the relationship between the government sizes to its economic growth gives the findings that are contradicting. Jones (1997) says that these conflicting findings have come to be attributed to the variations in definitions and the countries under study. Nevertheless, there is an alternative approach that overcomes study limitations so that much focus can be put on the relationship in developed countries. It is done by taking the measurement of the size of government as total taxes or the total expenditure in relation to GDP. In addition, reliance on estimations of panel data with variations over time is also measured revealing a more consistent picture. The recently carried out studies reveal a negative correlation. It shows that an increase in the size of government by ten percentage points is related to a 0.5 to 1.0% lower growth rate annually. In conclusion, there are discussions conducted to come up with efforts to enable the correlation to make sense. Additionally, they aid in the identification of several noted pitfalls involved in giving it a casual interpretation. The discussion typically comes up with two possible explanations as to why many countries that have high taxes seem to be in a position to enjoy growth rate above average. First, those countries with high levels of social trust are capable of developing larger government sectors without causing harm to the economy. Secondly, the countries having large institutions of governance compensate for higher taxes as well as high expenditure by carrying out the implementation of policies that are market friendly in other areas. Both explanations obtain a backup by the current research. References Aghion, P. and Howitt, P. (1992). A Model of Growth through Creative Destruction. Econometric, 60(2), p.323. Arrow, K. and Schwartz, J. (1962). Lectures on the Mathematical Method in Analytical Economics. Econometrica, 30(4), p.833. Barro, R.J. (2000). Inequality and Growth in a Panel of Countries. Journal of Economic Growth, Vol. 5, pp. 532. Barro, R.J. and McCleary, R.M. (2003). Religion and Economic Growth across Countries. American Sociological Review, Vol. 68, No. 5, pp. 760-781. Barro, R. and Sala-I-Martin, X. (1992). Public Finance in Models of Economic Growth. The Review of Economic Studies, 59(4), p.645. Bergh, A. and Henrekson, M. (2011). Government Size and Growth: A Survey and Interpretation of the Evidence. Journal of Economic Surveys, Vol. 25, No. 5, pp. 872-897. Bernard, A. and Durlauf, S. (1996). Interpreting tests of the convergence hypothesis. Journal of Econometrics, 71(1-2), pp.161-173. Boianovsky, M. and Hoover, K. (2009). Robert Solow and the development of growth economics. Durham: Duke University Press. Dimand, R. and Spencer, B. (2008). Trevor Swan and the neoclassical growth model. Cambridge, Mass.: National Bureau of Economic Research. Feiwel, G. (1982). Samuelson and neoclassical economics. Boston: Kluwer Nijhoff. Hendrik, V. and Lewer, J. (2015). International Trade and Economic Growth. Hoboken: Taylor and Francis. Islam, R. and Zanini, G. (2008). World trade indicators 2008. Washington, DC: World Bank. Jones, C. (1997). On the evolution of the world income distribution. Jones, C.I. (1997). On the Evolution of the World Income Distribution. Journal of Economic Perspectives, Vol. 11, No. 3, pp. 19-36. Jones, C.I. (1995). R&D Models of Economic Growth. Journal of Political Economy, Vol. 103, No. 4, pp. 759-784. Jones, C.I. (2015). Pareto and Piketty: The Macroeconomics of Top Income and Wealth Inequality. Journal of Economic Perspectives, Vol. 29, No. 1, pp. 2946. Lee, K., Pesaran, M. and Smith, R. (1997). Growth and convergence in a multi‐country empirical stochastic Solow model. Journal of Applied Econometrics, 12(4), pp.357-392. Lucas, R. (1988). Models of Business Cycles. Economics, 55(218), p.283. Mankiw, N., Romer, D. and Weil, D. (1992). A Contribution to the Empirics of Economic Growth. The Quarterly Journal of Economics, 107(2), pp.407-437. Romer, P.M. (1986). Increasing Returns and Long-Run Growth. Journal of Political Economy, Vol. 94, No. 5, pp. 1002-1037. Romer, P.M. (1990). Endogenous Technological Change. Journal of Political Economy, Vol. 98, No. 5, pp. S71-S102. Sala-i-Martin, X. (1997). I Just Ran Two Million Regressions. American Economic Review, Vol. 87, No.2, pp. 178-183. Solow, R. (2000). Growth theory. New York: Oxford University Press. Solow, R.M. (1956). A Contribution to the Theory of Economic Growth. Quarterly Journal of Economics, Vol. 70, No. 1, pp. 65-94. Uzawa, H. (1965). Optimum Technical Change in an Aggregative Model of Economic Growth. International Economic Review, 6(1), p.18. Read More