世界経済・社会統計 = World development indicators

We investigate whether income inequality affects subsequent growth in a cross-country sample for 1965-90, using the models of Barro (1997), Bleaney and Nishiyama (2002) and Sachs and Warner (1997), with negative results. We then investigate the evolution of income inequality over the same period and its correlation with growth. The dominating feature is inequality convergence across countries. This convergence has been significantly faster amongst developed countries. Growth does not appear to influence the evolution of inequality over time. Outline

https://www.econstor.eu/bitstream/10419/81822/1/02-28.pdf

Economic growth and income inequality

We examine the empirical role of difierent explanations for the lack of ∞ows of capital from rich to poor countries|the \Lucas Paradox." The theoretical explanations include difierences in fundamentals across countries and capital market imperfections. We show that during 1970i2000 low institutional quality is the leading explanation. For example, improving Peru’s institutional quality to Australia’s level, implies a quadrupling of foreign investment. Recent studies emphasize the role of institutions for achieving higher levels of income, but remain silent on the speciflc mechanisms. Our results indicate that foreign investment might be a channel through which institutions afiect long-run development.

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Why Doesn't Capital Flow from Rich to Poor Countries? An Empirical Investigation

This paper describes the correlations between inequality and the growth rates in cross-country data. Using non-parametric methods, we show that the growth rate is an inverted U-shaped function of net changes in inequality: Changes in inequality (in any direction) are associated with reduced growth in the next period. The estimated relationship is robust to variations in control variables and estimation methods. This inverted U-curve is consistent with a simple political economy model, although, as we point out, efforts to interpret this model causally run into difficult identification problems. We show that this non-linearity is sufficient to explain why previous estimates of the relationship between the level of inequality and growth are so different from one another.

Inequality and Growth: What Can the Data Say?

Business cycles are both less volatile and more synchronized with the world cycle in rich countries than in poor ones. We develop two alternative explanations based on the idea that comparative advantage causes rich countries to specialize in industries that use new technologies operated by skilled workers, while poor countries specialize in industries that use traditional technologies operated by unskilled workers. Since new technologies are difficult to imitate, the industries of rich countries enjoy more market power and face more inelastic product demands than those of poor countries. Since skilled workers are less likely to exit employment as a result of changes in economic conditions, industries in rich countries face more inelastic labour supplies than those of poor countries. We show that either asymmetry in industry characteristics can generate cross-country differences in business cycles that resemble those we observe in the data. We are grateful to Daron Acemoglu and Fabrizio Perri for useful comments. The views expressed here are the authors' and do not necessarily reflect those of The World Bank. Business cycles are not the same in rich and poor countries. A first difference is that fluctuations in per capita income growth are smaller in rich countries than in poor ones, in the top panel of Figure 1 , we plot the standard deviation of per capita income growth against the level of (log) per capita income for a large sample of countries. We refer to this relationship as the volatility graph and note that it slopes downwards. A second difference is that fluctuations in per capita income growth are more synchronized with the world cycle in rich countries than in poor ones. In the bottom panel of Figure 1 , we plot the correlation of per capita income growth rates with world average per capita income growth, excluding the country in question, against the level of (log) per capita income for the same set of countries. We refer to this relationship as the comovement graph and note that it slopes upwards. Table 1 , which is self-explanatory, shows that these facts apply within different sub-samples of countries and years. 1 Why are business cycles less volatile and more synchronized with the world cycle in rich countries than in poor ones? Part of the answer must be that poor countries exhibit more political and policy instability, they are less open or more distant from the geographical center, and they also have a higher share of their economy devoted to the production of agricultural products and the extraction of minerals. Table 1 shows that, in a statistical sense, these factors explain a substantial fraction of the variation in the volatility of income growth, although they do not explain much of the variation in the comovement of income growth. More important for our purposes, the strong relationship between income and the properties of business cycles reported in Table 1 is still present after we control for these variables. In short, there must be other factors behind the strong patterns depicted in Figure 1 beyond differences in political instability, remoteness and the importance of natural resources. With the exception that the comovement graph seems to be driven by differences between rich and poor countries and not within each group. Acemoglu and Zilibotti (1997) also present the volatility graph. They provide an explanation for it based on the observation that rich countries have more diversified production structures. We are unaware of any previous reference to the comovement graph. In this paper, we develop two alternative but non-competing explanations for why business cycles are less volatile and more synchronized with the world in rich countries than in poor ones. Both explanations rely on the idea that comparative advantage causes rich countries to specialize in industries that require new technologies operated by skilled workers, while poor countries specialize in industries that require traditional technologies operated by unskilled workers. This pattern of specialization opens up the possibility that cross-country differences in business cycles are the result of asymmetries between these types of industries. In particular, both of the explanations advanced here predict that industries that use traditional technologies operated by unskilled workers will be more sensitive to country-specific shocks. Ceteris paribus, these industries will not only be more volatile but also less synchronized with the world cycle since the relative importance of global shocks is lower. To the extent that the business cycles of countries reflect those of their industries, differences in industrial structure could potentially explain the patterns in Figure 1 . One explanation of why industries react differently to shocks is based on the idea that firms using new technologies face more inelastic product demands than those using traditional technologies. New technologies are difficult to imitate quickly for technical reasons and also because of legal patents. This difficulty confers a cost advantage on technological leaders that shelters them from potential entrants and gives them monopoly power in world markets. Traditional technologies are easier to imitate because enough time has passed since their adoption and also because patents have expired or have been circumvented. This implies that incumbent firms face tough competition from potential entrants and enjoy little or no monopoly power in world markets. The price-elasticity of product demand affects how industries react to shocks. Consider, for instance, the effects of country-specific shocks that encourage production in all industries. In industries that use new technologies, firms have monopoly power and face inelastic demands for their products. As a result, fluctuations in supply lead to opposing changes in prices that tend to stabilize industry income. In industries that use traditional technologies, firms face stiff competition from abroad and therefore face elastic demands for their products. As a result, fluctuations in supply have little or no effect on their prices and industry income is more volatile. To the extent that this asymmetry in the degree of product-market competition is important, incomes of industries that use new technologies are likely to be less sensitive to country-specific shocks than those of industries that use traditional technologies. Another explanation for why industries react differently to shocks is based on the idea that the supply of unskilled workers is more elastic than the supply of skilled workers. A first reason for this asymmetry is that non-market activities are relatively more attractive to unskilled workers whose market wage is lower than that of skilled ones. Changes in labour demand might induce some unskilled workers to enter or abandon the labour force, but are not likely to affect the participation of skilled workers. A second reason for the asymmetry in labour supply across skill categories is the imposition of a minimum wage. Changes in labour demand might force some unskilled workers in and out of unemployment, but are not likely to affect the employment of skilled workers. The wage-elasticity of the labour supply also has implications for how industries react to shocks. Consider again the effects of country-specific shocks that encourage production in all industries and therefore raise the labour demand. Since the supply of unskilled workers is elastic, these shocks lead to large fluctuations in employment of unskilled workers. In industries that use them, fluctuations in supply are therefore magnified by increases in employment that make industry income more volatile. Since the supply of skilled workers is inelastic, the same shocks have little or no effects on the employment of skilled workers. In industries that use them, fluctuations in supply are not magnified and industry income is less volatile. To the extent that this asymmetry in the elasticity of labour supply is important, incomes of industries that use unskilled workers are likely to be more sensitive to country-specific shocks than those of industries that use skilled workers To study these hypotheses we construct a stylized world equilibrium model of the cross-section of business cycles. Inspired by the work of Davis (1995), we consider in section one a world in which differences in both factor endowments a la Heckscher-Ohlin and industry technologies a la Ricardo combine to determine a country's comparative advantage and, therefore, the patterns of specialization and trade. To generate business cycles, we subject this world economy to the sort of productivity fluctuations that have been emphasized by Kydland and Prescott (1982). 2 In section two, we characterize the cross-section of business cycles and show how asymmetries in the elasticity of product demand and/or labour supply can be used to explain the evidence in Figure 1 . Using available microeconomic estimates of the key parameters, we calibrate the model and find that: (i) The model exhibits slightly less than two-thirds and one-third of the observed cross-country variation in volatility and comovement, respectively; and (ii) The asymmetry in the elasticity of product demand seems to have a quantitatively stronger effect on the slopes of the volatility and comovement graphs, than the elasticity in the labour supply. We explore these results further in sections three and four. In section three, we extend the model to allow for monetary shocks that have real effects since firms face cash-in-advance constraints. We use the model to study how cross-country variation in monetary policy and financial development affect the cross-section of business cycles. Once these factors are considered, the calibrated version of the model exhibits roughly the same cross-country variation in volatility and about 40 percent of the variation in comovement as the data. In section four, we show th

http://link.springer.com/content/pdf/10.1007/BF01322868.pdf

Business cycles: A methodological approach

Debt decomposition and the role of inflation: A security level analysis for India

Monetary Policy in India

We construct a model of endogenous investment specific techological change in which the stock of public capital influences the real price of capital goods. We show that the growth and welfare maximizing tax rates coincide in the planned economy. When factor income taxes finance public investment infintely many tax-subsidy combinations can decentralize the planner's allocations. The optimal capital income tax can be positive in this environment. We then augment the model to incorporate administrative costs. A unique combination of factor income taxes now decentralizes the planner's allocations. A simple calibration exercise suggests that changes in factor income taxes does not cause a significant change in the optimal growth rate or welfare. Our framework broadens the environment in which investment specific technological change occurs, and characterizes the role of optimal factor income taxation in raising long run growth and welfare.

https://mpra.ub.uni-muenchen.de/38182/1/MPRA_paper_38182.pdf

Distortionary Taxes and Public Investment in a Model of Endogenous Investment Specific Technological Change

Since the unfolding of the 2008 global financial crisis, the G20 has played a major role in coordinating macroeconomic policies of major economies and reviving the world economy. As the world’s primary forum for international economic cooperation, its objectives have been to ensure more sustainable and balanced growth, achieve economic and financial stability and reform the prevailing international financial architecture. In the wake of the crisis, there was a sense of urgency and strong agreement to enact extraordinary policy measures to fend off the collapse of the real sector because of the “collapse of confidence” in the financial sector. The G20 performed spectacularly in this regard: global gross domestic product (GDP) contracted less than expected in 2009 and rebounded faster than expected in 2010. These coordinated actions were widely credited for forestalling a second Great Depression, with the G20 declaring victory at their third summit at Pittsburgh in September 2009 (“It worked”).

Global Cooperation Among G20 Countries: Responding to the Crisis and Restoring Growth

This paper constructs a one sector growth model to examine the impact of political lobbying on the formation of fiscal policy. The model predicts that lobbying can induce endogenous regime switches, development traps, and a sub-optimal allocation of government expenditures between productive and unproductive ends, leading to long run income losses in the economy. A calibrated version of the model is used to generate estimates of the dynamic social costs of lobbying by estimating the optimal savings rates necessary to induce balanced growth in the economy. Finally, the model predicts that lobbying may influence the growth of government.

Chetan Ghate

Papers

Debt decomposition and the role of inflation: A security level analysis for India

Monetary Policy in India

Distortionary Taxes and Public Investment in a Model of Endogenous Investment Specific Technological Change

Global Cooperation Among G20 Countries: Responding to the Crisis and Restoring Growth

Lobbying, the Composition of Government Expenditures, and the Politics of Fiscal Policy