Absolute and Comparative Advantage, Reconsidered: The Pattern of International Trade with Optimal Saving

The paper obtains new results about absolute and comparative advantage, by introducing international technological differences into the three–sector Findlay–Komiya and two–sector Oniki–Uzawa–Stiglitz models of open–economy growth with optimal saving. For example, if a country has the same Hicks–neutral advantage in all industries, it exports the capital–intensive tradable, even though the technological advantage is only absolute rather than comparative. Alternatively, even a small comparative advantage in some good is sufficient for the advanced country to export this product, regardless of relative factor supplies. In either case, the fundamental reason for trade is technological superiority rather than factor abundance.     

Unemployment and Growth in the Long Run: an Efficiency‐Wage Model With Optimal Savings*

An efficiency‐wage model of steady‐state equilibrium with labor‐augmenting technical progress is developed to explore the long‐run relationship between unemployment and growth. The rate of productivity growth is either specified exogenously or determined endogenously. In both cases, we preserve key results of the Shapiro–Stiglitz efficiency‐wage analysis without growth. Our model, however, also yields some striking new results. For instance, an exogenous increase in the growth rate may raise the rate of efficiency‐wage unemployment, and a once‐for‐all rise in the labor force may reduce the unemployment rate in the endogenous‐growth case.     

The transmission of inflation in a small economy: an empirical analysis of the influence of US monetary disturbances on Canadian inflation, 1962–1980

The paper examines the transmission of inflation in Canada during 1962–1980 using an empirical methodology which is very flexible in determining the direction of casuality and the shape of distributed lag effects. The paper finds that US monetary growth exerted an important effect on Canadian inflation during both fixed and flexible exchange-rate periods. The evidence also shows that Canadian monetary growth has remained linked to US monetary growth under the present flexible exchange-rate regime.     

Bank ownership structure,regulations and risk-taking: evidence from commercial banks in Pakistan

This paper conducts the first empirical assessment of the theories concerning the influence of ownership structure on bank risk-taking in the presence of regulations in Pakistan. The sample used in this paper comprises a panel data of 26 banks in Pakistan, for the period from 2000 to 2014. The analysis provides evidence that increase in ownership concentration leads to an increase in bank risk-taking. Managerial ownership is associated with high risk-taking at low and high levels of managerial ownership while at intermediate level, managerial ownership has negative impact on bank risk-taking. Different types of ownership of banks in Pakistan have different impact on risk-taking. While government, family and institutional ownership have a positive impact on bank risk-taking, foreign ownership has a negative impact on bank risk-taking. Furthermore, the results show that capital regulations are important in influencing bank risk-taking with regard to higher ownership concentration. The findings of this paper suggest that the relation between bank risk-taking and capital regulations typically depends on the type of ownership.     

The efficacy of red flags in predicting the SEC's targets: an artificial neural networks approach

This paper illustrates the application of artificial neural networks (ANNs) to test the ability of selected SAS No. 53 red flags to predict the targets of the SEC investigations. Investors and auditors desire to predict SEC targets because substantial losses in equity value are associated with SEC investigations. The ANN models classify the membership in target (investigated) versus control (non-investigated) firms with an average accuracy of 81%. One reason for the relative success of the ANN models is that ANNs have the ability to ‘learn’ what is important. The participants in financial reporting frauds have incentives to appear prosperous as evidenced by high profitability. In contrast to conventional statistical models with static assumptions, the ANNs use adaptive learning processes to determine what is important in predicting targets. Thus, the ANN approach is less likely to be affected by accounting manipulations. Our ANN models are biased against achieving predictive success because we use only publicly available information. The results confirm the value of red flags, i.e. financial ratios available from trial balance in conjunction with non-financial red flags such as the turnover of CEO, CFO and auditors do have predictive value. © 2000 John Wiley & Sons, Ltd.     

Determinants of housing price volatility in Canada: a dynamic analysis

This article tries to identify the determinants of housing price volatility and to examine the dynamic effects of these determinants on volatility using quarterly data for Canada. The Generalized Autoregressive Conditional Heteroskedastic (GARCH) and the Vector Autoregressive (VAR) models have been employed to analyse possible time variation of the housing price volatility and the interactions between the volatility and the key macroeconomic variables. We find the evidence of time varying housing price volatility for Canada. Our VAR, Granger causality and variance decomposition (VDC) analyses demonstrate that housing price volatility is affected significantly by gross domestic product (GDP) growth rate, housing price appreciation rate and inflation. On the other hand, volatility affects GDP growth rate, housing price appreciation and volatility itself. The impulse response analysis reveals the asymmetric of the positive and negative shocks. The findings of this article have important implications, particularly for those seeking to develop derivatives for housing market prices.     

The Effects of Labor Markets and Income Inequality on Crime: Evidence from Panel Data

We estimate a model of crime using panel data for the U.S. We focus on the role of labor markets, income distribution, and demographics on property crime. We find strong evidence that favorable labor market conditions have a significant negative effect on property crime. We further test this result using sector-specific wages and find that crime is most elastic with respect to wages in sectors that use low-skilled labor. We also find that income inequality has no significant effect on crime and that the proportion of young males in the population has a significant positive effect on crime.