Assessing Systemic Importance With a Fuzzy Logic Inference System

Three metrics are designed to assess Colombian financial institutions' size, connectedness and non-substitutability as the main drivers of systemic importance: (i) centrality as net borrower in the money market network; (ii) centrality as payments originator in the large-value payment system network; and (iii) asset value of core financial services. An aggregated systemic importance index is calculated based on expert knowledge by using a fuzzy logic inference system. We use principal component analysis to calculate a benchmark index for comparison purposes. Overall similarities between both indexes put forward that expert knowledge aggregation is consistent with that based on a purely quantitative standard approach. Specific non-negligible differences concur with the nonlinear features of an approach whose intention is to replicate human reasoning. Both indexes are complementary and provide a comprehensive relative assessment of each financial institution's systemic importance in the Colombian case, in which the choice of metrics pursues the macroprudential perspective of financial stability. Copyright © 2015 John Wiley & Sons, Ltd.     

Information on sellers and buyers characteristics: added value to explain price formation at primary fish markets in managed French scallop fisheries

The aim of this article is to explore, through a hedonic approach, the factors that might explain the price variability for the French-managed fishery of scallop at primary fish markets. In addition to factors classically identified in the current literature like intrinsic product characteristics or markets situation, the characteristics of operators are tested. The relationships of loyalty between sellers and buyers, and market assiduity are notably considered.     

On the test score gap between Roma and non‐Roma students in Hungary and its potential causes

Using unique data from Hungary, the gap in reading and mathematics test scores between Roma and non‐Roma 8th grade students is assessed and a substantial gap between them revealed. Standardized test scores as well as the fraction of students with competences considered inadequate are examined. Regardless of measurement and subject area, the bulk of the gap is explained by social differences in income, wealth and parental education. Using reduced‐form regressions, two major mediating mechanisms are identified: first, on average the home environment of Roma children is less favourable for cognitive development; second, the educational environment of the average Roma student is different from the average non‐Roma student. Comparing students with similar home environments from the same school and class, the ethnic gap in test scores is found to be insignificant. Ethnic differences in the home environment are explained by social disparity, and ethnicity seems to play no additional role in that regard. The unequal distribution of Roma students in schools and classes is found to be explained predominantly by social difference, too, with a significant residual portion, indicating the effect of ethnic segregation.     

Two-stage DEA: <Emphasis Type="Italic">caveat emptor</Emphasis>

This paper examines the wide-spread practice where data envelopment analysis (DEA) efficiency estimates are regressed on some environmental variables in a second-stage analysis. In the literature, only two statistical models have been proposed in which second-stage regressions are well-defined and meaningful. In the model considered by Simar and Wilson (J Prod Anal 13:49–78, 2007), truncated regression provides consistent estimation in the second stage, where as in the model proposed by Banker and Natarajan (Oper Res 56: 48–58, 2008a), ordinary least squares (OLS) provides consistent estimation. This paper examines, compares, and contrasts the very different assumptions underlying these two models, and makes clear that second-stage OLS estimation is consistent only under very peculiar and unusual assumptions on the data-generating process that limit its applicability. In addition, we show that in either case, bootstrap methods provide the only feasible means for inference in the second stage. We also comment on ad hoc specifications of second-stage regression equations that ignore the part of the data-generating process that yields data used to obtain the initial DEA estimates.     

Do professional forecasters behave as if they believed in the New Keynesian Phillips Curve for the euro area?

This paper finds that participants in the European Central Bank’s Survey of Professional Forecasters have submitted forecasts that are consistent with a (mostly forward-looking) empirical version of the New Keynesian Phillips Curve for the euro area. The estimation technique takes advantage of the panel nature of the Survey of Professional Forecasters’ dataset to exploit both its time series and cross-section dimensions, and to control for unobservable individual heterogeneity across forecasters. The estimation results suggest that euro-area inflation forecasts have reacted less to unemployment forecasts after the start of the financial crisis but another cost measure (energy inflation) remains significant. This finding is consistent with a flatter Phillips Curve in the euro area after 2007. However, the reasons suggested by the International Monetary Fund for this finding, namely a better anchoring of inflation expectations and increases in structural unemployment do not seem to find support in the survey data. Instead, the expectations for compensation per employee submitted by professional forecasters are consistent with the existence of downward real-wage rigidities in euro-area labour markets.     

Antecedents of Purchase Intention in the Online Context

The understanding of the determinant factors of customer purchase intention is necessary, and it is equally important to study the online purchase context, since this context is disseminated among customers. A theoretical model has been elaborated on and tested, considering the constructs of information quality, distrust, and perceived risk as antecedents of purchase intention, and aiming to analyze the relationship among these constructs in the online purchase context. A quantitative research study has been performed by means of the application of a survey. Multivariate statistics techniques have been applied for data analyses, including structural equation modeling. This study contributes to the evolution of the empirically tested concepts by providing a greater individual understanding of each construct presented in the theoretical model, as well as the relationship among them as determinants of purchase intention; the indication is that meaningful relationships were found which may impact greater profitability and, consequently, greater competition for online retailers.     

Export survival and the dynamics of experience

This paper focuses on developing countries’ pioneer exports to the OECD and obtains several important results on export dynamics, linking export experience and export survival. Using product level data at the SITC 5-digit level for 114 developing countries over the 1962–2009 period, we show that prior export experience obtained in non-OECD markets significantly increases survival of pioneer exports toward the OECD. The experience does not need to last long, as gaining experience for more than two years does not confer any additional benefit. The effect of experience depreciates rapidly with time: a break in export experience prior to entering the OECD reduces the advantage on survival. Finally, the role of prior export experience is particularly relevant for survival in the first two years upon entry into the OECD. The geographic dynamic of export experience reveals that experience is acquired in neighboring, easy to access markets before reaching more distant, richer partners and ultimately serving the OECD with a higher probability of survival.