Uncertainty and the slowdown of capital accumulation in Europe

The slowdown in the process of capital formation in continental Europe in the 1990s is analysed. Sector-level data from the OECD's International Sectoral Data Base (ISDB) are used. Econometric estimates of an investment function indicate structural instability in the early 1990s and, specifically, a break in the coefficient linking the growth of capital stock to demand. This result neither seems to be related to non-linearities in the relationship between capital formation and expected demand, nor to the sectoral composition of European economies. Evidence is found that the drop in the accelerator is at least partly attributable to greater demand uncertainty in the 1990s as compared with the earlier period.     

Firm Size Distribution and Growth*

Abstract Empirical documentation of the sectoral distribution of firm size for a set of European countries reveals substantial differences. We study the relationship between productivity growth at the industry level and size structure. A positive and robust relation is found between average firm size and growth. We ask why size should matter for growth by considering the role of innovation to construct a test based on the differential effect of size on growth according to various indicators of R&D intensity. Our results indicate that larger size fosters productivity growth because it allows firms to take advantage of all the increasing returns associated with R&D. We argue that our test can be interpreted as a test of reverse causality, which lends support to the view that firm size has a causal impact on growth.     

Corruption’s impact on foreign portfolio investment

Corruption has significant effects on a nation’s financial markets through its adverse impact on foreign portfolio investment (FPI). Yet, the effects of corruption on FPI are nonlinear and reverse J-shaped, with intermediate levels of corruption yielding the most negative effects. Highly transparent nations, where a “level playing field” exists between foreign and local investors due to lack of information asymmetries related to corruption, attract the most foreign investment. However, at the margin, very corrupt countries attract more investment than moderately corrupt countries because a “perverse level playing field” in the former countries may put foreigners and locals on an even footing in terms of resolving asymmetric information problems. This nonlinear pattern is consistent with foreign investors’ desire to trade in markets where they are not at an informational disadvantage.     

How have global financial institutions responded to the challenges of the post-crisis era?

The study examines the largely unexplored effect of changes in the competitive landscape for large, global financial institutions on their ability to take risks, as well as deploy capital and labour in an efficient manner based on a novel measure of inefficiency. The analysis shows during 2001–2013 that inefficiency peaked during the 2008 crisis period and has fallen back to levels close to pre-crisis periods. The model also performs well in out-of-sample forecasts of the financial firms’ future market values. These results suggest that large financial firms have been adjusting to the ‘new normal’ of the post-crisis period and thus are able to use capital and labour more efficiently within the constraints of current market conditions. In addition, a non-linear pattern between inefficiency and a firm’s asset size suggests that there might be an optimal scale for such firms in the $450–650 billion range.     

Endogenous communication among lenders and entrepreneurial incentives

If banks have an informational monopoly about their clients,borrowers may curtail their effort level for fear of being exploitedvia high interest rates in the future. Banks can correct thisincentive problem by committing to share private informationwith other lenders. The fiercer competition triggered by informationsharing lowers future interest rates and future profits of banks.But, provided banks retain an initial informational advantage,their current profits are raised by the borrowers' higher effort.This trade-off determines the banks' willingness to share information.Their decision affects credit market competition, interest rates,volume of lending, and social welfare.     

Why Do Companies Go Public? An Empirical Analysis

Using a large database of private firms in Italy, we analyze the determinants of initial public offerings (IPOs) by comparing the ex ante and ex post characteristics of IPOs with those of private firms. The likelihood of an IPO is increasing in the company's size and the industry's market-to-book ratio. Companies appear to go public not to finance future investments and growth, but to rebalance their accounts after high investment and growth. IPOs are also followed by lower cost of credit and increased turnover in control.     

Survival analysis software on MS/PC-DOS computers

There exist more than a dozen MS/PC-DOS programs with the capability of performing some form of survival analysis. Most of these are reviewed here, with respect to (1) the survival analysis methods they cover, (2) their ease of use and flexibility, (3) their user interface, (4) their graphics capabilities, and (5) their computational accuracy.     

Convergence and risk-return linkages across financial service firms

We examine the risk and return linkages across US commercial banks, securities firms, and life insurance companies during the 1991–2001 period. After controlling for changes in the broader stock market, interest rates, and foreign currency values, we find that return and risk interdependencies across these financial firms are significant and size-varying; larger institutions display stronger volatility transmission linkages, while smaller ones exhibit more prominent return-related linkages. The tighter link in risk among large financial institutions (FIs) suggests stronger convergence, employment of common models of risk measurement and risk management, and more intense inter-industry competition, particularly between large banks and large securities firms, compared to smaller institutions. Lack of risk spillover among smaller FIs confirms the intuition that they typically assume more localized and idiosyncratic risk. The co-movement of stock returns among smaller FIs has been helped by the effects of locally based factors, such as economic conditions and state regulations, on all such institutions, and a less diversified product set. Differences in spillover patterns between large and smaller institutions have implications on investment choices and mergers and acquisitions in the industry. Introduction of the Gramm-Leach-Bliley Act (1999) has had dissimilar effects on the riskiness of large versus smaller life insurance and securities firms, and an insignificant effect on commercial banks.     

The welfare effects of liquidity constraints

We analyze the welfare implications of liquidity constraintsfor households in an overlapping generations model with growth.In a closed economy with exogenous technical progress, liquidityconstraints reduce welfare if the economy is dynamically inefficient.But if it is dynamically efficient, some degree of financialrepression is required to maximize steady-state utility, eventhough some generations are hurt in the transition. With endogenoustechnical progress, financial repression may increase welfareeven along the transition path, thus leading to a Pareto improvement.In this case the optimal degree of financial repression increasesas the economy grows.     

On fitting distributed lag models subject to polynomial restrictions

A methodology is presented for fitting distributed lag models with polynomial restrictions on the lag coefficients. The model incorporates autoregressive residuals. Orthogonal methods are employed so that the procedures are numerically sound. Furthermore, these have the effect of allowing for inference to be made about the three integer parameters in the model: (i) the length of the lag, (ii) the degree of the polynomial, and (iii) the order of the autoregression. The methodology is applied to the extended Almon data. This analysis suggests that estimation of polynomial distributed lags is highly sensitive to autoregressive disturbances. This underlines the importance of modeling the disturbances.