Institutions, unionization and voice: the relative impact of context and actors on firm level practice

Trade unions provide a voice in the way firms are run, an input into reward systems and increased security of employment. But these vary with national context. Using transnational survey evidence, this article explores the relative impact of setting, and of unions and collective bargaining, on these issues. It is found that, irrespective of context, organizations are significantly more likely to make use of compulsory redundancies in the absence of unions and collective bargaining. However, in other areas, the impact of unions appeared less pronounced than that of the wider context. The article explores the reasons behind this, and the broader policy implications thereof.

How does investing in cheap labour countries affect performance at home? Firm-level evidence from France and Italy

Transferring low tech manufacturing jobs to cheap labour countries is often seen by part of the general public and policy makers as a step into the de-industrialization of the European economies. However, recent contributions have shown that the effects on home economies are rarely negative. Our paper contributes to this literature by examining how outward investments to developing and less developed countries (LDCs) affect home activities of French and Italian firms that turn multinational in the period analysed. The effects of these investments are also compared to the effects of investments to developed economies (DCs). The analysis is carried out by using propensity score matching. We find no evidence of a negative effect of outward investments to LDCs. In Italy they have a positive long term effect on value added and employment. For France we find a positive effect on the size of domestic output and employment.

The brand likeability scale: an exploratory study of likeability in firm-level brands

We develop a new measurement scale to assess consumers’ brand likeability in firm-level brands. We present brand likeability as a multidimensional construct. In the context of service experience purchases, we find that increased likeability in brands results in: (1) greater amount of positive association; (2) increased interaction interest; (3) more personified quality; and (4) increased brand contentment. The four-dimensional multiple-item scale demonstrates good psychometric properties, showing strong evidence of reliability as well as convergent, discriminant and nomological validity. Our findings reveal that brand likeability is positively associated with satisfaction and positive word of mouth. The scale extends existing branding research, providing brand managers with a metric so that likeability can be managed strategically. It addresses the need for firms to act more likeably in an interaction-dominated economy. Focusing on likeability acts as a differentiator and encourages likeable brand personality traits. We present theoretical implications and future research directions on the holistic brand likeability concept.

Debt composition and exchange rate balance sheet effects in Brazil: A firm level analysis

In this paper we study the interaction between macroeconomic environment and firms’ balance sheet effects in Brazil during the 1990’s. We start by assessing the influence of macroeconomic conditions on firms’ debt composition in Brazil. We found that larger firms tend to change debt currency composition more in response to a change in the exchange rate risk than small firms. We then proceed to investigate if and how exchange rate balance sheet effects affected the firms’ investment decisions. We test directly the exchange rate balance sheet effect on investment. Contrary to earlier findings (Bleakley and Cowan, 2002), we found that firms more indebted in foreign currency tend to invest less when there is an exchange rate devaluation. We tried different controls for the competitiveness effect. First, we control directly for the effect of the exchange rate on exports and imported inputs. We then pursue an alternative investigation strategy, inspired by the credit channel literature. According to this perspective, Tobin’s q can provide an adequate control for the competitiveness effect on investment. Our results provide supporting evidence for imperfect capital markets, and for a negative exchange rate balance sheet effect in Brazil. The results concerning the exchange rate balance sheet effect on investment are statistically significant and robust across the different specifications. We tested the results across different periods, classified according to the macroeconomic environment. Our findings suggest that the negative exchange rate balance sheet effect we found in the whole sample is due to the floating exchange rate period. We also found that exchange rate devaluations have important negative impact on both cash flows and sales of indebted firms. Furthermore, the impact of exchange rate variations is asymmetric, and the significant effect detected when no asymmetry is imposed is engendered by exchange rate devaluations.

A machine learning approach for genome-wide prediction of morbid and druggable human genes based on systems-level data

Background: The genome-wide identification of both morbid genes, i.e., those genes whose mutations cause hereditary human diseases, and druggable genes, i.e., genes coding for proteins whose modulation by small molecules elicits phenotypic effects, requires experimental approaches that are time-consuming and laborious. Thus, a computational approach which could accurately predict such genes on a genome-wide scale would be invaluable for accelerating the pace of discovery of causal relationships between genes and diseases as well as the determination of druggability of gene products.Results: In this paper we propose a machine learning-based computational approach to predict morbid and druggable genes on a genome-wide scale. For this purpose, we constructed a decision tree-based meta-classifier and trained it on datasets containing, for each morbid and druggable gene, network topological features, tissue expression profile and subcellular localization data as learning attributes. This meta-classifier correctly recovered 65% of known morbid genes with a precision of 66% and correctly recovered 78% of known druggable genes with a precision of 75%. It was than used to assign morbidity and druggability scores to genes not known to be morbid and druggable and we showed a good match between these scores and literature data. Finally, we generated decision trees by training the J48 algorithm on the morbidity and druggability datasets to discover cellular rules for morbidity and druggability and, among the rules, we found that the number of regulating transcription factors and plasma membrane localization are the most important factors to morbidity and druggability, respectively.Conclusions: We were able to demonstrate that network topological features along with tissue expression profile and subcellular localization can reliably predict human morbid and druggable genes on a genome-wide scale. Moreover, by constructing decision trees based on these data, we could discover cellular rules governing morbidity and druggability.

Network-based high level data classification

Traditional supervised data classification considers only physical features (e. g., distance or similarity) of the input data. Here, this type of learning is called low level classification. On the other hand, the human (animal) brain performs both low and high orders of learning and it has facility in identifying patterns according to the semantic meaning of the input data. Data classification that considers not only physical attributes but also the pattern formation is, here, referred to as high level classification. In this paper, we propose a hybrid classification technique that combines both types of learning. The low level term can be implemented by any classification technique, while the high level term is realized by the extraction of features of the underlying network constructed from the input data. Thus, the former classifies the test instances by their physical features or class topologies, while the latter measures the compliance of the test instances to the pattern formation of the data. Our study shows that the proposed technique not only can realize classification according to the pattern formation, but also is able to improve the performance of traditional classification techniques. Furthermore, as the class configuration's complexity increases, such as the mixture among different classes, a larger portion of the high level term is required to get correct classification. This feature confirms that the high level classification has a special importance in complex situations of classification. Finally, we show how the proposed technique can be employed in a real-world application, where it is capable of identifying variations and distortions of handwritten digit images. As a result, it supplies an improvement in the overall pattern recognition rate.

Differences in coronary risk factors, procedural characteristics, mortality and stent thrombosis between two all-comers percutaneous coronary intervention registries from Europe and Japan: a patient-level data analysis of the Bern-Rotterdam and j-Cypher registries

Aims: The reported rate of stent thrombosis (ST) after drug-eluting stent (DES) implantation varies among registries. To investigate differences in baseline characteristics and clinical outcome in European and Japanese all-comers registries, we performed a pooled analysis of patient-level data. Methods and results: The j-Cypher registry (JC) is a multicentre observational study conducted in Japan, including 12,824 patients undergoing SES implantation. From the Bern-Rotterdam registry (BR) enrolled at two academic hospitals in Switzerland and the Netherlands, 3,823 patients with SES were included in the current analysis. Patients in BR were younger, more frequently smokers and presented more frequently with ST-elevation myocardial infarction (MI). Conversely, JC patients more frequently had diabetes and hypertension. At five years, the definite ST rate was significantly lower in JC than BR (JC 1.6% vs. BR 3.3%, p<0.001), while the unadjusted mortality tended to be lower in BR than in JC (BR 13.2% vs. JC 14.4%, log-rank p=0.052). After adjustment, the j-Cypher registry was associated with a significantly lower risk of all-cause mortality (HR 0.56, 95% CI: 0.49-0.64) as well as definite stent thrombosis (HR 0.46, 95% CI: 0.35-0.61). Conclusions: The baseline characteristics of the two large registries were different. After statistical adjustment, JC was associated with lower mortality and ST.

Predicting 3-year mortality after percutaneous coronary intervention: updated logistic clinical SYNTAX score based on patient-level data from 7 contemporary stent trials

OBJECTIVES This study aimed to update the Logistic Clinical SYNTAX score to predict 3-year survival after percutaneous coronary intervention (PCI) and compare the performance with the SYNTAX score alone. BACKGROUND The SYNTAX score is a well-established angiographic tool to predict long-term outcomes after PCI. The Logistic Clinical SYNTAX score, developed by combining clinical variables with the anatomic SYNTAX score, has been shown to perform better than the SYNTAX score alone in predicting 1-year outcomes after PCI. However, the ability of this score to predict long-term survival is unknown. METHODS Patient-level data (N = 6,304, 399 deaths within 3 years) from 7 contemporary PCI trials were analyzed. We revised the overall risk and the predictor effects in the core model (SYNTAX score, age, creatinine clearance, and left ventricular ejection fraction) using Cox regression analysis to predict mortality at 3 years. We also updated the extended model by combining the core model with additional independent predictors of 3-year mortality (i.e., diabetes mellitus, peripheral vascular disease, and body mass index). RESULTS The revised Logistic Clinical SYNTAX models showed better discriminative ability than the anatomic SYNTAX score for the prediction of 3-year mortality after PCI (c-index: SYNTAX score, 0.61; core model, 0.71; and extended model, 0.73 in a cross-validation procedure). The extended model in particular performed better in differentiating low- and intermediate-risk groups. CONCLUSIONS Risk scores combining clinical characteristics with the anatomic SYNTAX score substantially better predict 3-year mortality than the SYNTAX score alone and should be used for long-term risk stratification of patients undergoing PCI.

Determinants and Effects of Maturity Mismatches in Emerging Markets: Evidence from Bank Level Data

Despite the extensive work on currency mismatches, research on the determinants and effects of maturity mismatches is scarce. In this paper I show that emerging market maturity mismatches are negatively affected by capital inflows and price volatilities. Furthermore, I find that banks with low maturity mismatches are more profitable during crisis periods but less profitable otherwise. The later result implies that banks face a tradeoff between higher returns and risk, hence channeling short term capital into long term loans is caused by cronyism and implicit guarantees rather than the depth of the financial market. The positive relationship between maturity mismatches and price volatility, on the other hand, shows that the banks of countries with high exchange rate and interest rate volatilities can not, or choose not to hedge themselves. These results follow from a panel regression on a data set I constructed by merging bank level data with aggregate data. This is advantageous over traditional studies which focus only on aggregate data.

The role of home and host country characteristics in FDI: firm-level evidence from Japan, Korea and Taiwan

There is a large and growing empirical literature that investigates the determinants of outward foreign direct investment (FDI). This literature examines primarily the effect of host country characteristics on FDI even though home country characteristics also influence the decision of firms to invest abroad. In this paper, we examine the role of both host and home country characteristics in FDI. To do so, we constructed a firm-level database of outward FDI from Japan, Korea, and Taiwan. Our empirical analysis yields two main findings. First, host countries with better environment for FDI, in terms of larger market size, smaller fixed entry costs, and lower wages, attract more foreign investors. Second, firms from home countries with higher wages are more likely to invest abroad. An interesting and significant policy implication of our empirical evidence is that policymakers seeking to promote FDI inflows should prioritize countries with higher wages.

Chapter 2 On the usage of the measurements of geographical concentration and specialization with areal data

Measures have been developed to understand tendencies in the distribution of economic activity. The merits of these measures are in the convenience of data collection and processing. In this interim report, investigating the property of such measures to determine the geographical spread of economic activities, we summarize the merits and limitations of measures, and make clear that we must apply caution in their usage. As a first trial to access areal data, this project focus on administrative areas, not on point data and input-output data. Firm level data is not within the scope of this article. The rest of this article is organized as follows. In Section 2, we touch on the the limitations and problems associated with the measures and areal data. Specific measures are introduced in Section 3, and applied in Section 4. The conclusion summarizes the findings and discusses future work.

Transport modal choice by multinational firms : firm-level evidence from Southeast Asia

We examine transport modal decision by multinational firms to shed light on the role of freight logistics in multinational activity. Using a firm-level survey in Southeast Asia, we show that foreign ownership has a significantly positive and quantitatively large impact on the likelihood that air/sea transportation is chosen relative to truck shipping. This result is robust to the shipping distance, cross-border freight, and transport infrastructure. Both foreign-owned exporters and importers also tend to use air/sea transportation. Thus, our analysis presents a new distinction between multinational and domestic firms in their decision over transport modes.

Political limits on the world oil trade : firm-level evidence from US firms

International politics affect trade patterns, especially for firms in extractive industries. We construct the firm-level dataset for the U.S. oil-importing companies over 1986-2010 to test whether the state of international relations with the trading partners of the U.S. affect importing behavior of the U.S. firms. To measure "political distance" between the U.S. and her trading partners we use voting records for the UN General Assembly. We find that the U.S. firms, in fact, import significantly less oil from the political opponents of the U.S. Our conjecture is that the decrease in oil imports is mainly driven by large, vertically-integrated U.S. firms that engage in foreign direct investment (FDI) overseas.