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Explaining Economic Growth the Macro Level

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Explaining Economic Growth the Macro Level Chapter 8 Explaining economic growth The macro level TAKE-HOME MESSAGES FOR CHAPTER 8 1. GDPpc growth is the cornerstone of development. Yet it is one of the most difficult economic outcomes to explain and predict, with many competing theories, but also many different potentially effective strategies to accelerate growth. 2. The standard growth model is based on a structural form with five key parameters: the rate of population growth, the rate of saving, the rate of depreciation of capital, the rate of technological change, and the parameters of the production function. The effect of the rate of population growth on the growth of per capita income is ambiguous. 3. The Harrod-Domar model identifies the rate of saving and the choice of technique (the incremental capital"output ratio, or ICOR) as the two determinants of a country's growth rate. By contrast, the Solow model focuses on the relative roles of technolog­ ical change and factor deepening in explaining growth, leaving, however, technology, which is the main determinant of long-term growth in per capita income, unexplained by the model. Predictions of universal convergence in per capita income, as technology is considered to be an international public good, are also questionable. 4. The technologies available as an international public good for developing countries may not be best for their current factor-price ratio. However, in accordance with Solow, they can help these countries leapfrog toward the use of advanced technologies. 5. Dual-economy models stress the importance of labor transfers out of agriculture and of cheap food to keep nominal industrial wages low for growth in employment in industry. In the Lewis classical model, there is surplus labor in agriculture and labor transfers have zero opportunity cost for food production. In the Jorgenson neoclassical model, there is full employment, and productivity growth in agriculture is necessary for labor transfers without rising food prices. In the Lele-Mellor model, there is surplus labor in ag'riculture, but productivity growth in agriculture is necessary to keep industrial wages low. 6. · Other models such as ADU (agriculture demand-led industrialization), sectoral linkages, and competitive advantages based on institutional capacity explain why investing in agriculture is important for industrialization as a source of effective demand of multi­ plier effects and of foreign-exchange earnings. 326 EXPLAINING ECONOMIC GROWTH THE GROWTH PUZZLE We have seen that economic growth is the cornerstone on which economic devel­ opment is built (Chapter 1). We have also seen that growth performances across nations have been highly unequal, with some developing countries converging in per capita income with industrialized countries while others have been lagging behind, leading to the concept of conditional convergence (Chapter 2). And we have seen that finding ways to catch up with industrialized countries has been a primary objective in the long history of thought in development economics (Chapter 3). Yet our ability to explain economic growth, a central purpose of economics as a sci­ ence, is highly deficient, leaving us with what has been called the "mystery of eco­ nomic growth" (Helpman, 2004) and the "elusive quest for growth" (Easterly, 2002). What we know is that output growth basically depends on two determinants: (1) factor accumulation (labor, capital, materials, land) and the way these factors are combined in production for a given technology, as represented by the production function; and (2) gains in factor productivity deriving from how technology, institu­ tions, and factor quality change over time. However, what drives factor accumulation and especially gains in factor productivity remains highly contentious. Different models offer different explanations with different policy implications for what to do to accelerate growth. The truth is that each of these models offers particular insights into the growth process under particular circumstances, without exhausting potential explanations. For that reason, there are lessons to be derived from each of the main models that have been proposed to explain growth. With growth, as with the other dimensions of economic development, we are interested in both the positive analysis of growth (what explains· economic growth) and its normative analysis (what can be done to accelerate growth). To answer these questions, economists use economic models and take these models to the data for estimation, simulation, and predictions. In the quest to explain income growth, we will meet two families of models: classical and neoclassical growth models with exogenous technological change (treated in this chapter), and endogenous-growth models with technological change an outcome of economic decision-making (Chapter 9). In both families of models, technological change plays a key role in explaining growth, yet with very different outcomes and policy implications. The first family of models predicts income con­ vergence across countries, while the second predicts conditional convergence or no convergence at all. The verdict on the validity of one family of models or the other thus requires empirical verification. GENERIC MODELING OF INCOME GROWTH Structure of economic models An economic model is a simplification of reality that captures a complex process to explain an outcome, here economic growth. Because it has to be a simplification of reality, a model is always "reductionist," i.e. it omits details and has a large ceteris paribus 327 EXPLAINING ECONOMIC GROWTH Exogenous variables Model .,.__.... ;:.~ Endogenous variables Uncontrolled X Structural form h( Y, X1, Xzl 1 Outcomes Y Reduced form Y == fCX 1, X2 ) Policy instruments X2 Figure 8.1 Structural and reduced forms of an economic model clause, whereby other economic processes beyond the one under study are held constant. The model will be useful or not depending on the realism of the assump­ tions made about the processes at work and the capacity to replicate observed growth and predict future growth. Hence all models need to be carefully assessed in terms of the validity of their assumptions relative to the field of application in which we would like the model to hold (its internal validity), and in terms of whether their predicted outcomes match reality. As shown in Figure 8 .1, a model includes exogenous variables (X) that fall into two categories: uncontrolled variables such as weather, the world economic con­ text, and past events (X1); and policy instruments such as taxes and tariffs, public investment, technology, new institutions, and transfers (X2). It includes endoge­ nous variables that represent the outcomes of interest (Y). And it includes a set of technological, institutional, and behavioral functions that relate Y, Xi. and X 2. These functions can be written in structural form as h(Y, X1, X2), representing the assumptions of the model. They can also be written in reduced form as the solu­ tion to the model: namely, Y = f (X1, X2 ), where J also derives from h. In positive analysis, the first purpose of models is to check the logical structure of the reasoning and derive unexpected predictions; and the second is to estimate the parameters in the h andf functions.The first purpose is important because it is when a model is fully constructed that we know that the logic of the reasoning is complete and rigorous. Theory and theoretical derivations thus play a fundamental role in securing rigor in economic reasoning and in tracking the implications of the assump­ tions made. The second purpose is important in taking ideas to the data, enabling links between theory and empiricism. The model helps us formulate the equations that we want to estimate to either test the validity of the model or use it for norma­ tive purposes. Since there are many competing equations we could take to the data, having an economic model from which these equations derive helps us know exactly what it is that we are estimating. In normative analysis, models are used for ~o purposes: policy simulations and predictions. Policy simulations have the purpose of answering the following questions: How does a change in X2 lead to a change in Y for given h and X1? How does a change in h lead to a change in Y for given X1 and Xi Predictions, or forecasts, have the purpose of answering the following question: For a future Xt+ 1, w_hat is the corresponding predicted Y1+1 for given h? 328 EXPLAINING ECONOMIC GROWTH Theoretical rigor, a link to empiricism, simulation, and prediction are thus the four purposes of modeling in economics. Generic structure of a growth model The generic structure of a per capita income growth model is as follows. Initial conditions at time t Say that output is a function of capital, labor, technology, and a way of organizing production. This can be represented as follows. Initial factor endowments: Kt (stock of capital), Lt (labor force). Initial total factor productivity (TFP): At (technology). Initial income (or output level): Yt = J (Au Kt, Lt), where Yt is aggregate income andf is the production function that transforms technology, capital, and labor into output.· Growth process Say that income growth originates in three sources: growth in the labor force accom­ panying population growth, growth in the stock of capital as part of income is saved and invested, and growth in TFP representing technological change. 1. Demographic behavior: 6.P! P = 6.L! L = L is the rate of population (P) and labor force (L) growth. A dot on top of a variable indicates the rate of growth of that variable. Here the rate of labor-force participation in the population is constant as there is full employment. As a consequence, the rate of labor-force growth is equal to the rate of population growth. This rate of population growth n may be a function of initial income Yt. The population-growth equation is thl.1s Lt+1= Li(l + n).
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