Search

This article explores the determinants of changes to the range of exports at the firm level with a focus on the role played by the firm’s local environment. It extends the model developed by Bernard et al. (2010) to a multiregional setting to account for localized externalities. The model is tested using French micro-data on monoregional manufacturing firms covering the period 2002–2007. Our main finding is that the local product space has an impact on exporters’ product-market entry and exit decisions. Firms tend to modify their exported product mix to achieve congruence with the core products of the locality. Also, firms receive higher revenue from the export of products that are more related to the core capabilities of the locality.

The standard approaches to studying industrial agglomeration have been in terms of summary measures of the “degree of agglomeration” within each industry. But such measures often fail to distinguish between industries that exhibit substantially different spatial scales of agglomeration. In a previous paper, Mori and Smith [45] proposed a new pair of quantitative measures for distinguishing both the scale and degree of industrial agglomeration based on an explicit method for detecting spatial clusters. The first, designated as the global extent (GE) of industrial clusters, measures the spatial spread of these clusters (within a given country) in terms of the areal size of their essential containment, defined to be the (convex-solid) region containing the most significant subset of these clusters. The second, designated as the local density (LD) of industrial clusters, measures the spatial extent of individual clusters within their essential containment in terms of the areal share of that containment occupied by clusters. The central purpose of the present paper is to apply these two measures to the manufacturing industries in Japan, and to demonstrate how they can be used in combination to distinguish both the relative scale and degree of agglomeration exhibited by cluster patterns for each industry. In addition, the information provided by this pair of measures (GE, LD) is systematically compared to that of the most prominent summary measures currently in use. Finally, it is shown that these measures also support certain predictions of new economic geography models in the sense that shipping distances for establishments in each industry tend to be negatively (positively) correlated with the GE (LD) measures of agglomeration in these industries.

What can we, as users of microdata, formally guarantee to the individuals (or firms) in our dataset, regarding their privacy? We retell a few stories, well-known in data-privacy circles, of failed anonymization attempts in publicly released datasets. We then provide a mostly informal introduction to several ideas from the literature on differential privacy, an active literature in computer science that studies formal approaches to preserving the privacy of individuals in statistical databases. We apply some of its insights to situations routinely faced by applied economists, emphasizing big-data contexts.

The findings presented in this article improve the existing methods for estimating financial models of returns and especially for estimating the parameters that are relevant for portfolio managers, such as the Jensen alpha, a popular measure for stock selection, and beta, a well-known systemic risk measure. The authors focus on the presence of measurement errors in these models. For instance, the risk factors in the Fama and French models or the market model are biased by measurement, or specification, errors. These measurement errors are related to the use of proxies for measuring risk factors, such as the risk premium, and other risk factors, which are approximated by mimicking portfolios. To tackle these specification problems, the authors propose new Hausman-based estimators lying on cumulants optimal instruments. Using these newly generated strong instruments in a generalized method of moments (GMM) setting, the authors obtain new GMM estimators that they call GMM-C and its homologue GMM-hm. They also extend the methodology to the standard two-stage least squares (TSLS) framework using new optimally generated instruments. These procedures improve the existing method of moments for estimating, or calibrating, the parameters of a financial model and, more generally, for treating the problem of endogeneity often encountered in financial studies. Moreover, this study leads to a new indicator that signals the presence of specification errors in financial models. The authors apply a battery of tests and estimators to a sample of 22 HFR hedge fund indices observed monthly over the period 1990–2005. Their tests reveal that specification errors bias parameter estimation of financial models of returns and that the ranking of hedge funds is very sensitive to the choice of estimators.

In this paper, a Kalman filter-type model is used to extract a global stochastic trend from discrete non-synchronous data on daily stock market index returns from different markets. The model allows for the autocorrelation in the global stochastic trend, which means that its increments are predictable. It does not necessarily mean the predictability of market returns, since the global trend is unobservable. The performance of the model for the forecast of market returns is explored for three markets: Japan, UK, USA.

We use a Kalman filter type model of financial markets to extract a global stochastic trend from the discrete non-synchronous data on daily stock market index returns of different stock exchanges. The model is tested for robustness. In addition, we derive “most important” hours of world financial market and estimate the relative importance of local versus global news for different stock markets. The model generates results that are consistent with intuition.

This paper tests the hypothesis that the links and leadership/dependency relationships between the People’s Republic of China (PRC), the United States (US), and the other large Asian economies have changed over the past 20 years with the industrialization of the PRC economy. We use time-varying spectral methods to decompose the links between seven advanced Asian economies and the US. We find : (a) the links with the US have been weakening, while those within a bloc based on the PRC have strengthened; (b) that this is not new—it has been happening since the 1980s, but has been partly reversed by the recent surge in trade; (c) that there are two blocs within the Asian economic area : one based on Japan and the Republic of Korea and the other on the PRC and her satellites; (d) that product composition is responsible for this division (and for some movement between the blocs); and (e) that the links between the PRC and the US are rather complex, with the US able to shape the cycles elsewhere through her control of monetary conditions, but the PRC able to control the size of the cycles at home and (to some extent) abroad.

We present a statistical investigation on the domain of sex-related homicides. As general sociological and psychological theory on this specific type of crime is incomplete or even lacking, a data-driven approach is implemented. In detail, graphical modelling is applied to learn the dependency structure and several structure learning algorithms are combined to yield a skeleton corresponding to distinct Bayesian Networks. This graph is subsequently analysed and presents a distinction between an offender and a situation driven crime.

Social security entitlements are a substantial source of wealth that grows in importance over the individual's lifecycle. Despite its quantitative relevance, social security wealth has been thus far omitted from wealth inequality analyses. In Germany, it is the lack of adequate micro data that accounts for this shortcoming. The two main contributions of this paper are: First, to elaborate a statistical matching approach that complements information on net worth as surveyed in the German Socio-Economic Panel (SOEP), a population representative panel study, with information on social security wealth from the Sample of Active Pension Accounts (SAPA), a large-scale administrative dataset maintained by the German Statutory Pension Insurance. Second, we show to what extent the inclusion of social security wealth affects the level and the distribution of individual net worth as well as overall inequality. The present value of pension entitlements (including entitlements from the statutory pension system as well as from the separate system for civil servants) amounts to 5.6 trillion Euros, which corresponds to an average of 78,500 Euros per person - thus almost doubling the level of net worth. Compared to results based on net worth only, inequality of our amended wealth measure is about 25 percent less. Moreover, we present significant differences in pension entitlements across occupational groups with civil servants gaining most from the inclusion of public pension wealth in the extended wealth meas-ure and self-employed benefiting the least. Overall, our results provide clear indication for the relevance of including the notional wealth held in pension entitlements providing a less biased picture of the level and the socio-economic structure of wealth in Germany. Above and beyond such within-country variation, our findings may also be most relevant for comparative analyses across welfare-regimes.

Shift-Share Analysis aims to break down total change of economic indicators into various components to identify underlying sources of growth or decline. A key feature is that the unit of analysis (e.g. a city, a region or a country) exists within a broader frame of reference that strongly influences it (e.g. a national productive system or the world economy). It is based on the principle that total change can be disaggregated into contributing factors and any change that can not be accounted for by these factors can be interpreted as the 'local contribution' to that total change. This method has been subject to many refinements. Because the objectives of this paper are both didactic and analytic, traditional Shift-Share Analysis is applied to international trade. It uses the 'constant market share' assumption by decomposing the growth of exports into four separate components: a global component (GLOBO) indicating changes due to overall growth of world trade, a geographical component (GEO) indicating changes due to the country's distribution of trading partners, a product composition component (COMPO) indicating growth due to the mix of products exported, and a residual term (the 'local' contribution) indicating changes in competitiveness, or performance (PERFO). The first 3 components, GLOBO, COMPO and GEO all relate to the 'expected change in trade' should trade change proportionally. The fourth and residual component, PERFO, refers to that part of the change in trade that 'shifts away' from expected proportional changes, hence the term 'Shift-Share Analysis'. This paper will analyse a change or 'shift' in shares in trade (particularly exports) of different economies. By focusing on selected time periods and using the PERFO indicator, the method will show what industries shift away from the expected change in trade, which economies have experienced such shifts in their industries, and to which regions.

A common problem with spatial economic concentration measures based on areal data (e.g., Gini, Herfindhal, entropy, and Ellison-Glaeser indices) is accounting for the position of regions in space. While they purport to measure spatial clustering, these statistics are confined to calculations within individual areal units. They are insensitive to the proximity of regions or to neighboring effects. Clearly, since spillovers do not recognize areal units, economic clusters may cross regional boundaries. Yet with current measures, any industrial agglomeration that traverses boundaries will be chopped into two or more pieces. Activity in adjacent spatial units is treated in exactly the same way as activity in far-flung, nonadjacent areas. This paper shows how popular measures of spatial concentration relying on areal data can be modified to account for neighboring effects. With a U.S. application, we also demonstrate that the new instruments we propose are easy to implement and can be valuable in regional analysis.

Segmentation in PLS path modeling framework results is a critical issue in social sciences. The assumption that data is collected from a single homogeneous population is often unrealistic. Sequential clustering techniques on the manifest variables level are ineffective to account for heterogeneity in path model estimates. Three PLS path model related statistical approaches have been developed as solutions for this problem. The purpose of this paper is to present a study on sets of simulated data with different characteristics that allows a primary assessment of these methodologies.

The aims of this paper are twofold: first, we attempt to express the threshold of a single “A” rating as issued by major international rating agencies in terms of annualised probabilities of default. We use data from Standard & Poor’s and Moody’s publicly available rating histories to construct confidence intervals for the level of probability of default to be associated with the single “A” rating. The focus on the single “A” rating level is not accidental, as this is the credit quality level at which the Eurosystem considers financial assets to be eligible collateral for its monetary policy operations. The second aim is to review various existing validation models for the probability of default which enable the analyst to check the ability of credit assessment systems to forecast future default events. Within this context the paper proposes a simple mechanism for the comparison of the performance of major rating agencies and that of other credit assessment systems, such as the internal ratings-based systems of commercial banks under the Basel II regime. This is done to provide a simple validation yardstick to help in the monitoring of the performance of the different credit assessment systems participating in the assessment of eligible collateral underlying Eurosystem monetary policy operations. Contrary to the widely used confidence interval approach, our proposal, based on an interpretation of p-values as frequencies, guarantees a convergence to an ex ante fixed probability of default (PD) value. Given the general characteristics of the problem considered, we consider this simple mechanism to also be applicable in other contexts.

In high-dimensional data, one often seeks a few interesting low-dimensional projections that reveal important features of the data. Projection pursuit is a procedure for searching high-dimensional data for interesting low-dimensional projections via the optimization of a criterion function called the projection pursuit index. Very few projection pursuit indices incorporate class or group information in the calculation. Hence, they cannot be adequately applied in supervised classification problems to provide low-dimensional projections revealing class differences in the data. This article introduces new indices derived from linear discriminant analysis that can be used for exploratory supervised classification.

In the present study, CDM host countries are classified according to their attractiveness for CDM non-sink projects. A cluster analysis is conducted based on three different factors determining host country attractiveness (mitigation potential, institutional CDM capacity and general investment climate) in order to elaborate a CDM host country classification. The results suggest that only a small proportion of potential host countries will attract most of the CDM investment. The CDM (non-sink) stars are China, India, Brazil, Argentina, Mexico, South Africa, Indonesia and Thailand. They are followed by attractive countries like Costa Rica, Trinidad and Tobago, Mongolia, Panama, and Chile. While most of the promising CDM host countries are located in Latin America and Asia, the general attractiveness of African host countries is relatively low (with the exception of South Africa). Policy implications of this rather inequitable geographical distribution of CDM project activities are discussed briefly.

Distributions for returns are used to compute the capital charge for portfolios in investment banks. The mainstream definition of returns is based on closing prices and neglects the important effects of intraday trading activity on the losses . In this paper we introduce ''minimal returns'', a definition of returns that accounts for intraday trading and gives a worst-case approach on losses. We suggest an appropriate distribution for minimal returns that can be used to compute Value at Risk and coherent risk measures, as suggested by Artzner et al. (1997).

Highly competitive environments are leading companies to implement Supply Chain Management (SCM) to improve performance and gain a competitive advantage. SCM involves integration, co-ordination and collaboration across organisations and throughout the supply chain. It means that SCM requires internal (intraorganisational) and external (interorganisational) integration. This paper examines the Logistics-Production and Logistics- Marketing interfaces and their relation with the external integration process. The study also investigates the causal impact of these internal and external relationships on the company’s logistical service performance. To analyse this, an empirical study was conducted in the Spanish Fast Moving Consumer Goods (FMCG) sector.

Adversarial relationships have long dominated business relationships, but Supply Chain Management (SCM) entails a new perspective. SCM requires a movement away from arms-length relationships toward partnership style relations. SCM involves integration, co-ordination and collaboration across organisations and throughout the supply chain. It means that SCM requires internal (intraorganisational) and external (interorganisational) integration. This paper analyses the relationship between internal and external integration processes, their effect on firms’ performance and their contribution to the achievement of a competitive advantage. Performance improvements are analysed through costs, stock out and lead time reductions. And, the achievement of a better competitive position is measured by comparing the firm’s performance with its competitors’ performance. To analyse this, an empirical study has been conducted in the Spanish grocery sector.

We propose a continuum model for the description of buyer and seller dynamics in an Internet market. The relevant variables are the research effort of buyers and the sellers' reputation building process. We show that, if a commercial web-site gives consumers the possibility to rate credibly sellers they bargained with, vendors are forced to be more honest. This leads to mutual beneficial symbiosis between buyers and sellers; the overall enhanced volume of transactions contributes ultimately to the web-site, which facilitates the matchmaking service., Comment: 15 pages, 8 figures

This paper examines costs in the Swiss nursing home industry, an issue of concern to Swiss policy makers because of the explosion of elderly care costs and the aging of the population. The paper considers estimation of a translog cost function employing panel data for a sample of 36 nonprofit nursing homes operating in Ticino, a canton of Switzerland, over the period 1993-1995. Using a set of dummy variables quality differences are considered. The results of this analysis indicate the existence of economies of scale for most output levels.

The trade collapse that followed the global financial crisis of 2008–2009 has led to a renewed interest in measurement issues affecting international merchandise trade statistics in the new globalized economy. The international fragmentation of industrial production blurs the concept of country of origin and calls for the production of new statistics on the domestic content of exports, with a view of estimating trade in value added. In 2010, the international statistical community revised the concepts and definitions on both international merchandise trade and trade in services statistics. This paper discusses the various issues related to the concepts of "goods for processing" and "intra-firm trade" in trade statistics, and provides an overview of the method of analyzing the impact of the fragmentation of production in international value chains.