Veblen has been claimed and rejected both by sociologists and economists as being one of theirs. He enriched and attacked both disciplines, as he did so many others: philosophy, history, social psychology, politics, and linguistics. Because he took all knowledge as necessary and relevant to adequate understanding, Veblen was a holistic analyst of the social process. First published in 1904, this classic analysis of the U.S. economy has enduring value today. In it, Veblen posited a theory of business fluctuations and economic growth which included chronic depression and inflation. He predicted the socioeconomic changes that would occur as a result: militarism, imperialism, fascism, consumerism, and the development of the mass media as well as the corporate bureaucracy. Douglas Dowd's introduction places the volume within the traditions of both macroeconomics and microeconomics, tracing Veblen's place among social thinkers, and the place of this volume in the body of his work.

Theory of business enterprise

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

A Very Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix

We revisit the relationship between financial development and economic growth in a panel of 52 middle income countries over the 1980-2008 period, using pooled mean group estimator in a dynamic heterogeneous panel setting. We show that financial development does not have a linear positive long-run impact on economic growth in this sample. When we consider a non-linear relationship between financial development and growth, we find an inverted U-shaped relationship between finance and growth in the long run. In the short-run, the relationship is insignificant. This finding suggests that middle income countries face a threshold point after which financial development no longer contributes to economic growth.

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID2434445_code459177.pdf?abstractid=2434445&mirid=5&type=2

Is the Relationship between Financial Development and Economic Growth Monotonic? Evidence from a Sample of Middle Income Countries

Can It Happen Again

In newly collected data on 46 economies over 1990–2011, we show that financial development since 1990 was mostly due to growth in credit to real estate and other asset markets, which has a negative growth coefficient. We also distinguish between growth effects of stocks and flows of credit. We find positive growth effects for credit flows to nonfinancial business but not for mortgage and other asset market credit flows. By accounting for the composition of credit stocks and for the effect of credit flows, we explain the insignificant or negative growth effects of financial development in recent times. What was true in the 1960s, 1970s, and 1980s when the field of empirical credit-growth studies blossomed, is no longer true in the 1990s and 2000s. New bank lending is not primarily to nonfinancial business and financial development may no longer be good for growth. These trends predate the 2008 crisis. They prompt a rethink of the role of banks in the process of economic growth. (JEL E44, O16, O40, C33)

/pdf/more-mortgages-lower-growth-37f57k9qcd.pdf

More Mortgages, Lower Growth?

During the Great Moderation, financial innovation in the US increased the size and scope of credit flows supporting the growth of wealth. We hypothesize that spending out of wealth came to finance a wider range of GDP components such that it smoothed GDP. Both these trends combined would be consistent with a decrease in the volatility of output. We suggest testable implications in terms of both growth of credit and output and volatility of growth. In a multivariate GARCH framework, we test this view for home mortgages and residential investment. We observe unidirectional causality in variance from total output, residential investment and non-residential output to mortgage lending before, but not during the Great Moderation. These findings are consistent with a role for credit dynamics in explaining the Great Moderation.

/pdf/the-role-of-credit-in-the-great-moderation-a-multivariate-40ygr5fqkq.pdf

The role of credit in the Great Moderation: A multivariate GARCH approach

Is financial development good for growth? In new data, we find that the growth effect of bank credit-toGDP ratios the traditional measure for financial development is on average negative for 46 economies over 1990-2011. We explain this by the changing composition of credit. Financial development since 1990 was mostly due to growth in credit to real estate and other asset markets. The share of credit to nonfinancial business in total credit decreased sharply. We find negative growth coefficients for credit-to-GDP stocks supporting asset markets. In contrast, we estimate robustly positive growth effects of credit flows to nonfinancial business and insignificant effects of credit flows to asset markets, including real estate. The positive growth effect of credit flows diminishes at higher levels of financial development. Our findings are in line with recent suggestions that high ratios of financial capital to GDP since the 1990s may depress growth, through real negative returns to capital (Summers, 2013; Piketty, 2014). Even though credit flows may give a short-term stimulus to growth, the longer term effect of financial development is negative.

/pdf/is-financial-development-bad-for-growth-6ug4tnq4gu.pdf

Is financial development bad for growth

During the Great Moderation, financial innovation in the U.S. increased the size and scope of credit flows supporting the growth of wealth. We hypothesize that spending out of wealth came to finance a wider range of GDP components such that it smoothed GDP. Both these trends combined would be consistent with a decrease in the volatility of output. We suggest testable implications in terms of both growth of credit and output and volatility of growth. In a multivariate GARCH framework, we test this view for home mortgages and residential investment. We observe unidirectional causality in variance from total output, residential investment and non-residential output to mortgage lending before, but not during the Great Moderation. These findings are consistent with a role for credit dynamics in explaining the Great Moderation.

The Role of Credit in Great Moderation: a Multivariate GARCH Approach

A nascent literature explores the measurement of financial fragility. This paper considers evidence for rising financial fragility during the 1984–2007 Great Moderation in the U.S. The literature suggests that macroeconomic stability combined with strong growth of credit to asset markets, in asset prices and in credit relative to output are all indicators of rising financial fragility. We show each of these trends in the Great Moderation. We derive the testable implication that in the Great Moderation credit growth is driven more by past credit growth and less by output growth (Allen and Gale, 2000), relative to pre-Great Moderation years. Results from a VAR model estimated on quarterly data for 1955–2007 are consistent with the hypothesis. This invites a reinterpretation of the Great Moderation. Our methodology may help understand when a credit boom turns into a credit bubble, and contributes to the development of methods of measuring financial fragility.

/pdf/financial-fragility-in-the-great-moderation-brauuyi021.pdf

Maria Grydaki

Papers

More Mortgages, Lower Growth?

The role of credit in the Great Moderation: A multivariate GARCH approach

Is financial development bad for growth

The Role of Credit in Great Moderation: a Multivariate GARCH Approach

Financial fragility in the Great Moderation