Finance and development: Is Schumpeter’s analysis still relevant?

In recent years, numerous studies have been published highlighting the role of financial structures in the development process of contemporary economies. In these recent studies, there is always a reference to the pioneering work of Schumpeter; in particular in the writings of Rajan and Zingales [Rajan, R., Zingales, L. 2003a. Banks and markets: The changing character of european finance, NBER Working Paper Series, No. 9595, March; Rajan, R., Zingales, L. 2003b. The great reversal: The politics of financial development in the twentieth century, Journal of Financial Economics 69, 5–50; Rajan, R., Zingales, L. 2003c. Saving Capitalism from Capitalist, Crown Business Division of Random House, New York], important elements of Schumpeter’s theoretical framework are used. These works afford us an interesting opportunity to re-evaluate the importance of Schumpeter’s contribution. The thesis put forward in this paper is that while they do indeed highlight important elements of Schumpeter’s theory, Rajan and Zingales do not take the implications thereof into account and, furthermore, they neglect certain fundamental aspects of the Schumpeterian analysis that are closely connected with the parts that they consider. This renders their work incomplete, and prevents their analysis from achieving the coherence of Schumpeter’s theory.     

Innovation and Market Structure in the Manufacturing Sector: An Application of Linear Feedback Models*

In recent years, there has been a renewed interest in the relationship between market structure and innovation activities at an industry level. The paper examines the relationship between these for the manufacturing sector in a sample of 33 countries. It applies a recently developed method, based on linear feedback models, and uses cross‐country time‐series data. Despite the relatively small number of observations, the results indicate that domestic competition, external market share, and research and development have a positive effect on innovation activities. The results also suggest that the introduction of the foreign competition may have an initial positive effect on innovation activities, but over time this becomes dissipated.     

Finite sample properties of maximum likelihood estimator in spatial models

We investigate the finite sample properties of the maximum likelihood estimator for the spatial autoregressive model. A stochastic expansion of the score function is used to develop the second-order bias and mean squared error of the maximum likelihood estimator. We show that the results can be expressed in terms of the expectations of cross products of quadratic forms, or ratios of quadratic forms in a normal vector which can be evaluated using the top order invariant polynomial. Our numerical calculations demonstrate that the second-order behaviors of the maximum likelihood estimator depend on the degree of sparseness of the weights matrix.     

STRUCTURE, CONDUCT AND PERFORMANCE ANALYSIS OF THE SOUTH AFRICAN AUTO INSURANCE MARKET: 1980-2000

The traditional structure-conduct-performance framework makes the explicit prediction that market structure determines market conduct, performance and power. The primary goal of this study is to evaluate this prediction using the South African (SA) short-term auto insurance market. The empirical evidence shows that a link between market structure and market conduct, performance and/or power is not present. "Prices" and profits are not statistically significantly related to various sellers' concentration measure and do not follow any explicit trend over time. By construction, profits are directly related to "prices" and follow a cyclical trend, as observed in other countries. A secondary goal of this paper is to compare the findings for the US auto insurance market with the empirical evidence on the SA auto insurance market. While there are few similarities, there is an underwriting cycle in the SA auto insurance market such as is observed for the US auto insurance market.     

A missing link in modeling : The future of time-use as an illustration

A Theory of Action is formulated based on the intentionality of actions. The transformation rules from the new theory to system dynamics were developed and the methodology was then applied to certain problems of future time-use. The modeling approach used is holistic and it requires a meta-system to system dynamics in order to produce quantitative results.     

Financing patterns around the world: Are small firms different?

Using a firm-level survey database covering 48 countries, we investigate how financial and institutional development affects financing of large and small firms. Our database is not limited to large firms but includes small and medium-size firms and data on a broad spectrum of financing sources, including leasing, supplier, development, and informal finance. Small firms and firms in countries with poor institutions use less external finance, especially bank finance. Protection of property rights increases external financing of small firms significantly more than of large firms, mainly due to its effect on bank finance. Small firms do not use disproportionately more leasing or trade finance compared with larger firms, so these financing sources do not compensate for lower access to bank financing of small firms. We also find that larger firms more easily expand external financing when they are constrained than small firms. Finally, we find suggestive evidence that the pecking order holds across countries.     

How do policy and information shocks impact co-movements of China’s T-bond and stock markets?

We investigate the impacts of policy and information shocks on the correlation of China’s T-bond and stock returns, using originally the asymmetric dynamic conditional correlation (DCC) model that allows for the coexistence of opposite-signed asymmetries. The co-movements of China’s capital markets react to large macroeconomic policy shocks as evidenced by structural breaks in the correlation following the drastic 2004 macroeconomic austerity. We show that the T-bond market and the bond–stock correlations bear more of the brunt of the macroeconomic contractions. We also find that the bond–stock correlations respond more strongly to joint negative than joint positive shocks, implying that investors tend to move both the T-bond and stock prices in the same direction when the two asset classes have been hit concurrently by bad news, but tend to shift funds from one asset class to the other when hit concurrently by good news. However, the stock–stock correlation is found to increase for joint positive shocks, indicating that investors tend to herd more for joint bullish than joint bearish stock markets in Shanghai and Shenzhen.     

Dispersion in beliefs among active mutual funds and the cross-section of stock returns

We propose a measure of dispersion in fund managers? beliefs about future stock returns based on their active holdings, i.e., deviations from benchmarks. We find that both the level of and the change in dispersion positively predict subsequent stock returns on a risk-adjusted basis. This effect is particularly pronounced among stocks with high information asymmetry and binding short-sale constraints. These results suggest that a subgroup of informed managers drives up the dispersion in active holdings when they place large bets after receiving positive private information. Binding short-sale constraints, however, prevent them from fully using their negative private information, leading to low dispersion in active holdings.     

A New Method to Measure the Performance of Leveraged Exchange‐Traded Funds

We examine the effects of daily return compounding, financing costs, and management factors on the performance of leveraged exchange‐traded funds (LETFs) over various holding periods. We propose a new method to measure LETFs’ tracking errors that allows us to disentangle these effects. Our results show that the compounding effect generally has more influence on tracking errors than other factors, especially for long holding periods and in a “sideways” market. The explicit costs (i.e., the expense ratios) and other factors (e.g., financing costs) can materially affect the performance of LETFs, especially for those with high leverage ratios and bear funds.