This paper addresses the ongoing debate on cross-country convergence from the perspective of labor markets. It constructs and analyzes a new data set on purchasing-power-parity (PPP) adjusted wages for skilled and unskilled labor in a large sample of countries from 1968-1998. Up till now, research on growth and convergence has concentrated almost exclusively on the behavior of aggregate variables such as GDP per capita or per worker; Baumol (1986), Abramovitz (1986), Barro and Sala-i-Martin (1995), Caselli, Esquivel and Lefort (1996), and others have established many important results on the long run evolution of aggregate incomes.
This paper, however, shares the basic contention of Williamson (1995) and O’Rourke and Williamson (1999) that factor incomes ought to be studied for the additional leverage that they give to our understanding of convergence processes, illustrated so well by studies on Heckscher-Ohlin-Samuelson (HOS) effects. There are at least three good reasons for studying factor incomes.
Understanding the nature of systemic risk is key to understanding the occurrence and propagation of financial crises. Traditionally the term "systemic risk" describes a situation where many (if not all) financial institutions fail as a result of a common shock or a contagion process. A typical common shock leading to systemic failures is a collapse of residential or commercial real estate values (see Reinhart and Rogoff, 2009). Contagion refers to the risk that the failure of one financial institution leads to the default of others through a domino e¤ect in the interbank market, the payment system or though asset prices (see, for example, the survey in Allen, Babus and Carletti, 2009).
Technical analysis uses past prices and perhaps other past statistics to make investment decisions. Proponents of technical analysis believe that these data contain important information about future movements of the stock market. In practice, all major brokerage firms publish technical commentary on the market and many of the advisory services are based on technical analysis. In his interviews with them, Schwager (1993, 1995) finds that many top traders and fund managers use it. Moreover, Covel (2005), citing examples of large and successful hedge funds, advocates the use of technical analysis exclusively without learning any fundamental information on the market.
Academics, on the other hand, have long been skeptical about the usefulness of technical analysis, despite its widespread acceptance and adoption by practitioners. There are perhaps three reasons. The first reason is that there is no theoretical basis for it, which this paper attempts to provide. The second reason is that earlier theoretical studies often assume a random walk model for the stock price, which completely rules out any profitability from technical trading. The third reason is that earlier empirical findings, such as Cowles (1933) and Fama and Blume (1966), are mixed and inconclusive. Recently, however, Brock, Lakonishok, and LeBaron (1992), and especially Lo, Mamaysky, and Wang (2000), find strong evidence of profitability in technical trading based on more data and more elaborate strategies. These studies stimulated many subsequent academic research on technical analysis, but these later studies focus primarily on the statistical validity of the earlier results (reviewed in more detail in the next section).
