Tolerance for uncertainty and the growth of informationally opaque industries

Hofstede's [Hofstede, Geert H. 1980. Culture's consequences: international differences in work-related values. Thousand Oaks, CA: Sage.] cross-country psychological survey of IBM employees finds that some countries (societies) are systematically less tolerant of uncertainty, while uncertainty-tolerance is shown by some theoretic models to be essential to the growth of emerging sectors about which less is known. This paper first uses Durnev, Morck, and Yeung's [Durnev, Art, Morck, Randall, and Yeung, Bernard. 2004. Value-enhancing capital budgeting and firm-specific stock return variation. The Journal of Finance. 59(1): 65–105.] methodology to identify these informationally opaque industries. The hypothesis is then made that countries characterized by high uncertainty aversion (measured by Hofstede's indicator, and two other alternative indicators) will grow disproportionately slower in industrial sectors where information is less available. Using the Rajan and Zingales [Rajan, Raghuram G. and Zingales, Luigi. 1998. Financial Dependence and Growth. American Economic Review. Vol. 88(3): 559–586.] “difference-in-differences” methodology, which is relatively free from the endogeneity problem, the study provides robust evidence of such an industrial growth pattern in 34 countries and 36 manufacturing industries. It also shows that national uncertainty aversion is not driven by underdevelopment of financial sector, inadaptability of civil law systems, lower level of economic or human capital development, labor market inflexibility, or any of many other institutional factors. The results remain robust when religious (Catholic vs. Protestant) composition is used as an instrumental variable for national uncertainty aversion. The international evidence presented helps explain why some countries are slower in embracing “new” (vs. traditional) industries.     

Inflation,interest and exchange rates in the transition1

Central and Eastern European economies have made extraordinary progress in their trade and exchange regimes. Surprisingly, instant convertibility was established for a great variety of exchange rate regimes. In spite of diversity, all these countries have followed a common pattern: severe initial undervaluation - the cost of speed and unrestricted trade - followed by rapid real revaluation and incipient protectionism. Since 1994 in many cases an embarrass de richesse has appeared: high capital inflows which are either inflationary or costly to sterilize. A major cause of these flows - or at any rate of the high cost of sterilization - is the presence of significant interest rate differentials higher than required to cover the risk of devaluation. These are the necessary consequence of a policy of positive real interest rates and of real revaluation from excessively undervalued exchange rates. Lower interest rates are recommended, both to stem financial capital inflows and to reduce the cost of their sterilization.     

What drives organic food consumption in Lebanon?

To increase the sparse knowledge about what drives organic food consumers in developing markets, Lebanese consumers' reasons and motives for buying organic food are studied in the framework of means‐end chain theory. A sample of N = 180 Lebanese consumers was interviewed in several retail outlets using a hard laddering questionnaire. The data were subjected to frequency analysis, cluster analysis, and structural equation modelling. Hierarchal value maps constructed from the laddering interviews revealed that “quality of life,” “pleasure,” and “peace of mind” are the main values driving organic food consumption in Lebanon. Frequent organic consumers displayed higher concern for their family's health and for the environment, whereas occasional organic consumers cared more about their own pleasure and were more likely to perceive organic food as a traditional and nostalgic product. Structural equation modelling revealed that “care for nature” and “care for children's health” significantly affect consumers' (self‐reported) purchasing behaviour. The found means‐end chains are a useful basis for marketing campaigns for organic food products in the Lebanese market. One focus of marketing campaigns should be raising consumers' awareness on the environmental benefits of organic production and the fact that it does not allow the use of harmful chemical products.     

EXTREME VALUE THEORY IN FINANCE: A SURVEY

Extreme value theory is concerned with the study of the asymptotic distribution of extreme events, that is to say events which are rare in frequency and huge in magnitude with respect to the majority of observations. Statistical methods derived from it have been employed increasingly in finance, especially for risk measurement. This paper surveys some of those main applications, namely for testing different distributional assumptions for the data, for Value‐at‐Risk and Expected Shortfall calculations, for asset allocation under safety‐first type constraints, and for the study of contagion and dependence across markets under conditions of stress.     

The effect of cash holdings on firm performance in large Italian companies

This paper examines the relationship between cash holdings and performance in Italy over 36 years. Specifically, in light of the presence of conflicting evidence concerning the worth of cash stock, which could lead to a positive effect rather than a negative one, the role of moderating factors that can shape the magnitude of this relationship is investigated. The results show that the value of cash holdings is affected by firm‐specific characteristics, as well as factors related to the institutional context. Although other studies have analyzed moderators one at a time, this is the first work to consider how they jointly work. When the moderators are considered together, some of them become no longer statistically significant while others become even more economically and statistically relevant.     

Issues in the analysis of co-authorship networks

Scientific collaboration is a complex phenomenon that improves the sharing of competences and the production of new scientific knowledge. Social Network Analysis is often used to describe the scientific collaboration patterns defined by co-authorship relationships. Different phases of the analysis of collaboration are related to: data collection, network boundary setting, relational data matrix definition, data analysis and interpretation of results. The aim of this paper is to point out some issues that arise in these different phases, highlighting: (i) the use of local archives versus international bibliographic databases; (ii) the use of different approaches for setting boundaries in a whole-network; (iii) the definition of a co-authorship data matrix (binary and weighted ties) and (iv) the analysis and the interpretation of network measures for co-authorship data. We discuss the different choices that can be made in these phases within an illustrative example on real data which is referred to scientific collaboration among researchers affiliated to an academic institution. In particular, we compare global and actor-level network measures computed from binary and weighted co-authorship networks in different disciplines.     

Hospital competition under pay‐for‐performance: Quality,mortality, and readmissions

Health outcomes, such as mortality and readmission rates, are commonly used as indicators of hospital quality and as a basis to design pay‐for‐performance (P4P) incentive schemes. We propose a model of hospital behavior under P4P where patients differ in severity and can choose hospital based on quality. We assume that risk‐adjustment is not fully accounted for and that unobserved dimensions of severity remain. We show that the introduction of P4P which rewards lower mortality and/or readmission rates can weaken or strengthen hospitals' incentive to provide quality. Since patients with higher severity have a different probability of exercising patient choice when quality varies, this introduces a selection bias (patient composition effect) which in turn alters quality incentives. We also show that this composition effect increases with the degree of competition. Critically, readmission rates suffer from one additional source of selection bias through mortality rates since quality affects the distribution of survived patients. This implies that the scope for counterproductive effects of P4P is larger when financial rewards are linked to readmission rates rather than mortality rates.     

Corporate social responsibility and earnings forecasting unbiasedness

We investigate the relationship between corporate social responsibility (CSR) and I/B/E/S analysts’ earnings per share (EPS) forecasts using a large sample of US firms for 1992–2011. Based on literature findings, we decompose the CSR effect into four factors: accounting opacity, corporate governance, stakeholder risk, and overinvestment. We find that all of them significantly affect both the absolute forecast error on EPS and its standard deviation controlling for forecast horizon; number of analysts and forecasts; and year, industry, and broker house effects. Consistently with our ex ante hypotheses, overinvestment, stakeholder risk, and accounting opacity have a positive effect, increasing both dependent variables, while corporate governance quality has a negative effect. A crucial aspect of our findings is that high CSR quality in terms of the four factors (i.e., accounting transparency, high corporate governance quality, stakeholder risk mitigation, and absence of overinvestment) contributes to making earnings forecasts unbiased as unbiasedness is generally met in the subsample of the Top CSR quality companies and markedly violated in the subsample of the Bottom CSR companies. We also document that overinvestment and stakeholder risk are sufficient to produce this effect.     

Combining forecasts: some results on exchange and interest rates

The aim of this work is to investigate whether the combination of forecasts plays an important role in the improvement of forecast accuracy Particular attention is paid to: (a) the methods of forecasting (the methods compared are neural networks, fuzzy logic, GARCH models, switching regime and chaotic dynamics); (b) combining the forecasts provided by the different methods. This work has also the aim of revising a short-term econometric forecast using a longer-term forecast. The revision process usually runs the opposite way (revision is made on a longer-term forecast using a short-term one to reflect the current available information, but it is not excluded that it is possible to proceed as described above. Daily data from the financial market is used. Some empirical applications on exchange and interest rates are given.