Self-selection into export markets by business services firms:evidence from France, Germany and the United Kingdom

This study investigates business services firms that (start to) export, comparing exporters to firms that serve the national market only. We estimate identically specified empirical models using comparable enterprise data from France, Germany, and the UK. Our findings show that exporters are on average more productive and pay higher wages in all three countries. However, results for profitability differ across borders, where profitability of exporters is significantly smaller in Germany, significantly larger in France, and does not differ significantly in the UK. The results for wages and productivity hold in the years before firms start exporting, which indicates self-selection into exporting of more productive services firms that pay higher wages. The surprising finding of self-selection of less profitable German services firms into exporting does not show up among firms from France and the UK. In all three countries we do not find evidence for positive effects of exporting on firm performance. © 2012 Elsevier B.V.

Comparing methods to separate treatment from self-selection effects in an online banking setting

The literature discusses several methods to control for self-selection effects but provides little guidance on which method to use in a setting with a limited number of variables. The authors theoretically compare and empirically assess the performance of different matching methods and instrumental variable and control function methods in this type of setting by investigating the effect of online banking on product usage. Hybrid matching in combination with the Gaussian kernel algorithm outperforms the other methods with respect to predictive validity. The empirical finding of large self-selection effects indicates the importance of controlling for these effects when assessing the effectiveness of marketing activities.

Impact of online channel use on customer revenues and costs to serve:considering product portfolios and self-selection

Developing a strategy for online channels requires knowledge of the effects of customers' online use on their revenue and cost to serve, which ultimately influence customer profitability. The authors theoretically discuss and empirically examine these effects. An empirical study of retail banking customers reveals that online use improves customer profitability by increasing customer revenue and decreasing cost to serve. Moreover, the revenue effects of online use are substantially larger than the cost-to-serve effects, although the effects of online use on customer revenue and cost to serve vary by product portfolio. Self-selection effects also emerge and can be even greater than online use effects. Ignoring self-selection effects thus can lead to poor managerial decision-making.

Performance of small and medium enterprises and the impact of environmental variables: evidence from Vietnam

thesis is developed from a real life application of performance evaluation of small and medium-sized enterprises (SMEs) in Vietnam. The thesis presents two main methodological developments on evaluation of dichotomous environment variable impacts on technical efficiency. Taking into account the selection bias the thesis proposes a revised frontier separation approach for the seminal Data Envelopment Analysis (DEA) model which was developed by Charnes, Cooper, and Rhodes (1981). The revised frontier separation approach is based on a nearest neighbour propensity score matching pairing treated SMEs with their counterfactuals on the propensity score. The thesis develops order-m frontier conditioning on propensity score from the conditional order-m approach proposed by Cazals, Florens, and Simar (2002), advocated by Daraio and Simar (2005). By this development, the thesis allows the application of the conditional order-m approach with a dichotomous environment variable taking into account the existence of the self-selection problem of impact evaluation. Monte Carlo style simulations have been built to examine the effectiveness of the aforementioned developments. Methodological developments of the thesis are applied in empirical studies to evaluate the impact of training programmes on the performance of food processing SMEs and the impact of exporting on technical efficiency of textile and garment SMEs of Vietnam. The analysis shows that training programmes have no significant impact on the technical efficiency of food processing SMEs. Moreover, the analysis confirms the conclusion of the export literature that exporters are self selected into the sector. The thesis finds no significant impact from exporting activities on technical efficiency of textile and garment SMEs. However, large bias has been eliminated by the proposed approach. Results of empirical studies contribute to the understanding of the impact of different environmental variables on the performance of SMEs. It helps policy makers to design proper policy supporting the development of Vietnamese SMEs.

Exporting and productivity in business services:evidence from the United States

Does exporting make firms more productive, or do more productive firms choose to become exporters? This paper considers the link between exporting and productivity for a sample of firms in US business services. We find that larger, more productive firms are more likely to become exporters, but that these factors do not necessarily influence the extent of exporting. This conforms with previous literature that there is a self-selection effect into exporting. We then test for the effect of exporting on productivity levels after allowing for this selection effect. We model both the relationship between exporting and productivity, and a simultaneous relationship between export intensity and productivity after allowing for selection bias. In both cases we find an association, indicating that productivity is positively linked both to exporting and to increased exposure to international markets.

Exporting and productivity in business services:evidence from the United States

Does exporting make firms more productive, or do more productive firms choose to become exporters? Given the amount of resources devoted by governments to supporting exporters, this is an important question. There are reasons to expect exporting to boost productivity, both through the exposure to foreign competition which exporting brings, and through ‘learning by exporting’. However, the broad thrust of previous research is that more productive firms self-select into export markets, with relatively little evidence that exporting leads to higher productivity thereafter. This paper considers the link between exporting and productivity for a sample of firms in US business services. We find that larger, more productive firms are more likely to become exporters, but that these factors do not necessarily influence the extent of exporting. This conforms with previous literature that there is a self-selection effect into exporting. We then test for the effect of exporting on productivity levels after allowing for this selection effect. We model both the relationship between exporting and productivity, and a simultaneous relationship between export intensity and productivity after allowing for selection bias. In both cases we find a clear association, indicating that productivity is positively linked both to exporting and to increased exposure to international markets.

Evaluating the impact of a web-based resource on student learning

This paper reports on an experiment of using a publisher provided web-based resource to make available a series of optional practice quizzes and other supplementary material to all students taking a first year introductory microeconomics module. The empirical analysis evaluates the impact these supplementary resources had on student learning. First, we investigate which students decided to make use of the resources. Then, we analyse the impact this decision has on their subsequent performance in the examination at the end of the module. The results show that, even after taking into account the possibility of self-selection bias, using the web-based resource had a significant positive effect on student learning.

Spillovers from FDI and local networks:the importance of transactional linkages and vertical keiretsu in Japan

Purpose – The purpose of this paper is to explore the importance of host country networks and organisation of production in the context of international technology transfer that accompanies foreign direct investment (FDI). Design/methodology/approach – The empirical analysis is based on unbalanced panel data covering Japanese firms active in two-digit manufacturing sectors over a seven-year period. Given the self-selection problem affecting past sectoral-level studies, using firm-level panel data is a prerequisite to provide robust empirical evidence. Findings – While Japan is thought of as being a technologically advanced country, the results show that vertical productivity spillovers from FDI occur in Japan, but they are sensitive to technological differences between domestic firms and the idiosyncratic Japanese institutional network. FDI in vertically organised keiretsu sectors generates inter-industry spillovers through backward and forward linkages, while FDI within sectors linked to vertical keiretsu activities adversely affects domestic productivity. Overall, our results suggest that the role of vertical keiretsu is more prevalent than that of horizontal keiretsu. Originality/value – Japan’s industrial landscape has been dominated by institutional clusters or networks of inter-firm organisations through reciprocated, direct and indirect ties. However, interactions between inward investors and such institutionalised networks in the host economy are seldom explored. The role and characteristics of local business groups, in the form of keiretsu networks, have been investigated to determine the scale and scope of spillovers from inward FDI to Japanese establishments. This conceptualisation depends on the institutional mechanism and the market structure through which host economies absorb and exploit FDI.

Do work placements improve final year academic performance or do high-calibre students choose to do work placements?

This study investigates whether the completion of an optional sandwich work placement enhances student performance in final year examinations. Using Propensity Score Matching, our analysis departs from the literature by controlling for self-selection. Previous studies may have overestimated the impact of sandwich work placements on performance because it might be the case that high-calibre students choose to go on placement. Our results, utilising a large student data set, indicate that self-selection is present, but the effects of a placement on student performance still have an impact. This robust finding is found to be of a remarkably similar magnitude across two UK universities.

Data visualization with simultaneous feature selection

Data visualization algorithms and feature selection techniques are both widely used in bioinformatics but as distinct analytical approaches. Until now there has been no method of measuring feature saliency while training a data visualization model. We derive a generative topographic mapping (GTM) based data visualization approach which estimates feature saliency simultaneously with the training of the visualization model. The approach not only provides a better projection by modeling irrelevant features with a separate noise model but also gives feature saliency values which help the user to assess the significance of each feature. We compare the quality of projection obtained using the new approach with the projections from traditional GTM and self-organizing maps (SOM) algorithms. The results obtained on a synthetic and a real-life chemoinformatics dataset demonstrate that the proposed approach successfully identifies feature significance and provides coherent (compact) projections. © 2006 IEEE.

Stock selection with full-scale optimization and differential evolution

When composing stock portfolios, managers frequently choose among hundreds of stocks. The stocks' risk properties are analyzed with statistical tools, and managers try to combine these to meet the investors' risk profiles. A recently developed tool for performing such optimization is called full-scale optimization (FSO). This methodology is very flexible for investor preferences, but because of computational limitations it has until now been infeasible to use when many stocks are considered. We apply the artificial intelligence technique of differential evolution to solve FSO-type stock selection problems of 97 assets. Differential evolution finds the optimal solutions by self-learning from randomly drawn candidate solutions. We show that this search technique makes large scale problem computationally feasible and that the solutions retrieved are stable. The study also gives further merit to the FSO technique, as it shows that the solutions suit investor risk profiles better than portfolios retrieved from traditional methods.

A novel adaptive weight selection algorithm for multi-objective multi-agent reinforcement learning

To solve multi-objective problems, multiple reward signals are often scalarized into a single value and further processed using established single-objective problem solving techniques. While the field of multi-objective optimization has made many advances in applying scalarization techniques to obtain good solution trade-offs, the utility of applying these techniques in the multi-objective multi-agent learning domain has not yet been thoroughly investigated. Agents learn the value of their decisions by linearly scalarizing their reward signals at the local level, while acceptable system wide behaviour results. However, the non-linear relationship between weighting parameters of the scalarization function and the learned policy makes the discovery of system wide trade-offs time consuming. Our first contribution is a thorough analysis of well known scalarization schemes within the multi-objective multi-agent reinforcement learning setup. The analysed approaches intelligently explore the weight-space in order to find a wider range of system trade-offs. In our second contribution, we propose a novel adaptive weight algorithm which interacts with the underlying local multi-objective solvers and allows for a better coverage of the Pareto front. Our third contribution is the experimental validation of our approach by learning bi-objective policies in self-organising smart camera networks. We note that our algorithm (i) explores the objective space faster on many problem instances, (ii) obtained solutions that exhibit a larger hypervolume, while (iii) acquiring a greater spread in the objective space.

Self-adaptive probabilistic roadmap generation for intelligent virtual agents

Agents inhabiting large scale environments are faced with the problem of generating maps by which they can navigate. One solution to this problem is to use probabilistic roadmaps which rely on selecting and connecting a set of points that describe the interconnectivity of free space. However, the time required to generate these maps can be prohibitive, and agents do not typically know the environment in advance. In this paper we show that the optimal combination of different point selection methods used to create the map is dependent on the environment, no point selection method dominates. This motivates a novel self-adaptive approach for an agent to combine several point selection methods. The success rate of our approach is comparable to the state of the art and the generation cost is substantially reduced. Self-adaptation therefore enables a more efficient use of the agent's resources. Results are presented for both a set of archetypal scenarios and large scale virtual environments based in Second Life, representing real locations in London.

Self-adaptive volunteered services composition through stimulus-and time-awareness

Volunteered Service Composition (VSC) refers to the process of composing volunteered services and resources. These services are typically published to a pool of voluntary resources. Selection and composition decisions tend to encounter numerous uncertainties: service consumers and applications have little control of these services and tend to be uncertain about their level of support for the desired functionalities and non-functionalities. In this paper, we contribute to a self-awareness framework that implements two levels of awareness, Stimulus-awareness and Time-awareness. The former responds to basic changes in the environment while the latter takes into consideration the historical performance of the services. We have used volunteer service computing as an example to demonstrate the benefits that self-awareness can introduce to self-adaptation. We have compared the Stimulus-and Time-awareness approaches with a recent Ranking approach from the literature. The results show that the Time-awareness level has the advantage of satisfying higher number of requests with lower time cost.