In a general equilibrium model of a labor economy, the size of government, measured by the share of income redistributed, is determined by majority rule. Voters rationally anticipate the disincentive effects of taxation on the labor-leisure choices of their fellow citizens and take the effect into account when voting. The share of earned income redistributed depends on the voting rule and on the distribution of productivity in the economy. Under majority rule, the equilibrium tax share balances the budget and pays for the voters' choices. The principal reasons for increased size of government implied by the model are extensions of the franchise that change the position of the decisive voter in the income distribution and changes in relative productivity. An increase in mean income relative to the income of the decisive voter increases the size of government.

A Rational Theory of the Size of Government

As GDP is a measure of market capacity and not economic well-being, this report has been commissioned to more accurately understand the social progress indicators of any given state. Gross domestic product (GDP) is the most widely used measure of economic activity. There are international standards for its calculation, and much thought has gone into its statistical and conceptual bases. But GDP mainly measures market production, though it has often been treated as if it were a measure of economic well-being. Conflating the two can lead to misleading indications about how well-off people are and entail the wrong policy decisions. One reason why money measures of economic performance and living standards have come to play such an important role in our societies is that the monetary valuation of goods and services makes it easy to add up quantities of a very different nature. When we know the prices of apple juice and DVD players, we can add up their values and make statements about production and consumption in a single figure. But market prices are more than an accounting device. Economic theory tells us that when markets are functioning properly, the ratio of one market price to another is reflective of the relative appreciation of the two products by those who purchase them. Moreover, GDP captures all final goods in the economy, whether they are consumed by households, firms or government. Valuing them with their prices would thus seem to be a good way of capturing, in a single number, how well-off society is at a particular moment. Furthermore, keeping prices unchanged while observing how quantities of goods and services that enter GDP move over time would seem like a reasonable way of making a statement about how society’s living standards are evolving in real terms. As it turns out, things are more complicated. First, prices may not exist for some goods and services (if for instance government provides free health insurance or if households are engaged in child care), raising the question of how these services should be valued. Second, even where there are market prices, they may deviate from society’s underlying valuation. In particular, when the consumption or production of particular products affects society as a whole, the price that individuals pay for those products will differ from their value to society at large. Environmental damage caused by production or consumption activities that is not reflected in market prices is a well-known example.

https://andrewoswald.com/docs/stiglitz20report_commission20final20200920sept.pdf

Report by the commission on the measurement of economic performance and social progress

Heteroskedasticity biases tests for contagion based on correlation coefficients. When contagion is defined as a significant increase in market comovement after a shock to one country, previous work suggests contagion occurred during recent crises. This paper shows that correlation coefficients are conditional on market volatility. Under certain assumptions, it is possible to adjust for this bias. Using this adjustment, there was virtually no increase in unconditional correlation coefficients (i.e., no contagion) during the 1997 Asian crisis, 1994 Mexican devaluation, and 1987 U.S. market crash. There is a high level of market comovement in all periods, however, which we call interdependence.

/pdf/no-contagion-only-interdependence-measuring-stock-market-2e4xwzrnsw.pdf

No Contagion, Only Interdependence: Measuring Stock Market Comovements

This paper examines stock market co-movements. It begins with a discussion of several conceptual issues involved in measuring these movements and how to test for contagion. Standard tests examine if cross-market correlation in stock market returns increase during a period of crisis. The measure of cross-market correlations central to this standard analysis, however, is biased. The unadjusted correlation coefficient is conditional on market movements over the time period under consideration, so that during a period of turmoil when stock market volatility increases, standard estimates of cross-market correlations will be biased upward. It is straightforward to adjust the correlation coefficient to correct for this bias. The remainder of the paper applies these concepts to test for stock market contagion during the 1997 East Asian crises, the 1994 Mexican peso collapse, and the 1987 U.S. stock market crash. In each of these cases, tests based on the unadjusted correlation coefficients find evidence of contagion in several countries, while tests based on the adjusted coefficients find virtually no contagion. This suggests that high market co-movements during these periods were a continuation of strong cross-market linkages. In other words, during these three crises there was no contagion, only interdependence.

No Contagion, Only Interdependence: Measuring Stock Market Co-Movements

This paper provides a general framework for integration of high-frequency intraday data into the measurement, modeling, and forecasting of daily and lower frequency volatility and return distributions. Most procedures for modeling and forecasting financial asset return volatilities, correlations, and distributions rely on restrictive and complicated parametric multivariate ARCH or stochastic volatility models, which often perform poorly at intraday frequencies. Use of realized volatility constructed from high-frequency intraday returns, in contrast, permits the use of traditional time series procedures for modeling and forecasting. Building on the theory of continuous-time arbitrage-free price processes and the theory of quadratic variation, we formally develop the links between the conditional covariance matrix and the concept of realized volatility. Next, using continuously recorded observations for the Deutschemark / Dollar and Yen / Dollar spot exchange rates covering more than a decade, we find that forecasts from a simple long-memory Gaussian vector autoregression for the logarithmic daily realized volatilities perform admirably compared to popular daily ARCH and related models. Moreover, the vector autoregressive volatility forecast, coupled with a parametric lognormal-normal mixture distribution implied by the theoretically and empirically grounded assumption of normally distributed standardized returns, gives rise to well-calibrated density forecasts of future returns, and correspondingly accurate quantile estimates. Our results hold promise for practical modeling and forecasting of the large covariance matrices relevant in asset pricing, asset allocation and financial risk management applications.

/pdf/modeling-and-forecasting-realized-volatility-2mqo5gygak.pdf

Modeling and forecasting realized volatility

This paper investigates why, in October 1987, almost all stock markets fell together despite widely differing economic circumstances. The idea is that "contagion" between markets occurs as the result of attempts by rational agents to infer information from price changes in other markets. This provides a channel through which a "mistake" in one market can be transmitted to other markets. Hourly stock price data from New York, Tokyo and London during an eight month period around the crash offer support for the contagion model. In addition, the magnitude of the contagion coefficients are found to increase with volatility.

Transmission of Volatility Between Stock Markets

/pdf/transmission-of-volatility-between-stock-markets-3808btgndi.pdf

Transmission of Volatility between Stock Markets

The authors attempt to account for the covariances between stock markets and to assess their integration. They estimate a factor model for sixteen national stock market returns whose volatility is induced by changing volatility in the factors. Unanticipated returns depend on innovations in economic variables and 'unobservable' factors. Assets risk premia are linear combinations of the factors risk premia. The authors find that idiosyncratic risk is priced and the 'price of risk' is different across stock markets. Besides, only a small proportion of their covariances can be accounted for by 'observable' economic variables. Correlation changes are driven primarily by movements in 'unobservables.' Copyright 1994 by The Econometric Society.

/pdf/volatiltiy-and-links-between-national-stock-markets-48v1sg7cgm.pdf

Mervyn A. King

Papers

Transmission of Volatility Between Stock Markets

Transmission of Volatility between Stock Markets

Volatiltiy and Links Between National Stock Markets

Public Policy and the Corporation

The Taxation of Income from Capital: A Comparative Study of the United States, the United Kingdom, Sweden and West Germany