new technology based firms and the creation of regional growth potential this paper focuses on the role of new technology based firms ntbfs as a vital determinant of regional growth potential the theoretical framework starts from the relation between innovation and economic development macro approach to show how ntbfs can contribute to the creation of a regional growth potential micro approach empirical data for belgium illustrate how ntbfs differ significantly from common starters on a number of fundamental characteristics such as educational level product market orientation socio economic networking delegation growth strategy and research and development taking these findings into account we concentrate on the region specific technology policies in belgium the case of the emerging biotechnology industry is used as an illustration

estimating price elasticities with theory based priors the authors show how price elasticity estimates can be improved in demand systems that involve multiple brands and stores they treat these demand models in a hierarchical bayesian framework unlik

employment generation potential of new technology based firms during a recessionary period the case of finland this article analyzes the employment generation potential of new technology based firms during a recessionary period the empirical data is from finland a country that was hit particularly hard by the economic recession between years 1990 and 1993 in spite of the recession the number of ntbfs new technology based firms rose by 17 from 1986 to 1993 in traditional manufacturing industry branches the overall employment fell by as much as 33 during the same period alternative hypotheses to explain the increase in the importance of ntbfs are presented and discussed in the light of research findings both flexible specialization and recession push hypotheses receive support in the empirical data

how effective are policies to reduce gasoline consumption evaluating a set of measures in spain abstract using a panel of 48 provinces for four years we empirically analyze a series of temporary and permanent policies aimed at curbing fuel consumption implemented in spain between march and june 2011 the first policy was a reduction in the speed limit in highways the second policy was an increase in the biofuel content of fuels used in the transport sector the third measure was a decrease of 5 in commuting and regional train fares that resulted in two major metropolitan areas reducing their overall fare for public transit the results indicate that the speed limit reduction in highways lowered gasoline consumption by 2 to 3 while an increase in the biofuel content of gasoline increased this consumption this last result is consistent with experimental evidence that indicates that mileage per liter falls with an increase in the biofuel content in gasolines as for the reduction in transit fares we do not find a significant effect for this policy however in specifications including the urban transit fare for the major cities in each province the estimated cross price elasticity of the demand for gasoline used as a proxy for car use with respect to the price of transit is within the range reported in the literature this is important since one of the main efficiency justifications for subsidizing public transit rests on the positive value of this parameter and most of the estimates reported in the literature are quite dated

the association between venture capitalists selection and value adding behavior evidence from early stage high tech venture capitalists building upon self efficacy and collective effort theories we study the association between the selection behavior of venture capitalists and their involvement in value adding activities we argue that investors who prioritize different characteristics of a business proposal during selection will be more or less confident of their own abilities and the abilities of entrepreneurial teams to effectively add value to portfolio companies and hence will be more or less involved in providing value adding activities in order to test this claim we use a stratified sample comprising 68 european early stage high tech venture capitalists results show that venture capitalists who focus on entrepreneurial team characteristics or financial criteria during selection are less involved in value adding activities compared to their peers who focus on technological criteria we discuss these findings from a theoretical and practical perspective

job creation process in a transition economy in many european countries sme policy is close to being a synonym for job creation most empirical research on the job generation potential of small firms over the last decade has been done in the stable and favorable economic conditions of market economies the paper investigates the role of small firms in more difficult circumstances on an empirical data set for slovenia the paper tries to show that small firms have been the most important employer during the transition period a finding that very likely can be extrapolated for other european transition economies over the last decade slovenia has transformed from a labor managed economic system to a market oriented economy if we compare the firm size structure in a socialist like economy to the firm size structure in a mature market economy close to equilibrium we notice a typical absence of small and medium sized firms with the exception of craft shops that was how slovenia appeared in the early nineties charac terized by the removal of administrative barriers transition encouraged a spontaneous entrepreneurial wave through an expanding small business sector the ensuing changes in employment distribution changed the firm size structure

asymmetric and neighborhood cross price effects some empirical generalizations this paper provides some empirical generalizations regarding how the relative prices of competing brands affect the cross price effects among them particular focus is on the asymmetric price effect and the neighborhood price effect the asymmetric price effect states that a price promotion by a higher priced brand affects the market share of a lower priced brand more so than the reverse the neighborhood price effect states that brands that are closer to each other in price have larger cross price effects than brands that are priced farther apart the main objective of this paper is to test if these two effects are generalizable across product categories and to assess which of these two effects is stronger while the neighborhood price effect has not been rigorously tested in past research the asymmetric price effect has been validated by several researchers however these tests of asymmetric price effect have predominantly used elasticity as the measure of cross price effect the cross price elasticity measures the percentage change in market share or sales of a brand for 1 change in price of a competing brand we show that asymmetries in cross price elasticities tend to favor the higher priced brand simply because of scaling effects due to considering percentage changes furthermore several researchers have used logit models to infer asymmetric patterns we also show that inferring asymmetries from conventional logit models is incorrect to account for potential scaling effects we consider the absolute cross price effect defined as the change in market share percentage points of a target brand when a competing brand s price changes by one percent of the product category price the advantage of this measure is that it is dimensionless hence comparable across categories and it avoids scaling effects we show that in the logit model with arbitrary heterogeneity in brand preferences and price sensitivities the absolute cross price effect is symmetric we develop an econometric model for simultaneously estimating the asymmetric and neighborhood price effects and assess their relative strengths we also estimate two alternate models that address the following questions i if i were managing the ith highest priced brand which brand do i impact the most by discounting and which brand hurts me the most through price discounts ii who hurts whom in national brand vs store brand competition based on a meta analysis of 1 060 cross price effects on 280 brands from 19 different grocery product categories we provide the following empirical generalizations 1 the asymmetric price effect holds with cross price elasticities but tends to disappear with absolute cross price effects 2 the neighborhood price effect holds with both cross price elasticities and absolute cross price effects and is significantly stronger than the asymmetric price effect on both measures of cross price effects 3 a brand is affected the most by discounts of its immediately higher priced brand followed closely by discounts of its immediately lower priced brand 4 national brands impact store brands more so than the reverse when the cross effect is measured in elasticities but the asymmetric effect does not hold with absolute effects store brands hurt and are in turn hurt the most by the lower priced national brands that are adjacent in price to the store brands 5 cross price effects are greater when there are fewer competing brands in the product category and among brands in nonfood household products than among brands in food products the implications of these findings are discussed

the origins and dynamics of production networks in silicon valley abstract computer systems firms in silicon valley are responding to rising costs of product development shorter product cycles and rapid technological change by focusing and building partnerships with suppliers both within and outside of the region well known firms like hewlett packard and apple computers and lesser known ones like silicon graphics and pyramid technology are organized to combine the components and sub systems made by specialist suppliers into new computer systems as these firms collaborate to both define and manufacture new systems they are institutionalizing their capacity to learn from one another three cases a contract manufacturer a silicon foundry and the joint development of a microprocessor illustrate how inter firm networks help account for the sustained technological dynamism of the regional economy

accommodating heterogeneity and nonlinearity in price effects for predicting brand sales and profits abstract we propose a hierarchical bayesian semiparametric approach to account simultaneously for heterogeneity and functional flexibility in store sales models to estimate own and cross price response flexibly a bayesian version of p splines is used heterogeneity across stores is accommodated by embedding the semiparametric model into a hierarchical bayesian framework that yields store specific own and cross price response curves more specifically we propose multiplicative store specific random effects that scale the nonlinear price curves while their overall shape is preserved estimation is fully bayesian and based on novel mcmc techniques in an empirical study we demonstrate a higher predictive performance of our new flexible heterogeneous model over competing models that capture heterogeneity or functional flexibility only or neither of them for nearly all brands analyzed in particular allowing for heterogeneity in addition to functional flexibility can improve the predictive performance of a store sales model considerably while incorporating heterogeneity alone only moderately improved or even decreased predictive validity taking into account model uncertainty we show that the proposed model leads to higher expected profits as well as to materially different pricing recommendations

valuing rail access using transport innovations this paper uses a powerful empirical method to evaluate the benefits of rail access to consumers we study the effects on house prices of a transport innovation that altered the distance to the nearest station for some households but left others unaffected the transport innovation we study is the construction of new stations in london in the late 1990s using this innovation we implement a quasi experimental approach that allows us to avoid the biases inherent in cross sectional valuation work our evidence on distance station effects on local prices identified through distance changes induced by the transport innovation suggests that households value rail access and that these valuations are large compared to the valuations of other local amenities

transport mode choice by commuters to barcelona s cbd this paper analyses the determinants of travel mode choice for suburbanised commuters in barcelona spain after describing the importance of residential suburbanisation and the parallel increase in the share of private car use by commuters into the city centre a nested logit model of transport mode choice is estimated the results reveal low elasticities particularly in the case of car commuters values of travel time savings are high although within the ranges obtained in the literature it is observed that continuing suburbanisation will result in increasing the market share of rail at the expense of the private car use and mainly the bus provided that accessibility to the public transport network is maintained at present levels

information assets technology and organization although there is good reason to expect that the growth of information work and information technology will significantly affect the trade offs inherent in different structures for organizing work the theoretical basis for these changes remains poorly understood this paper seeks to address this gap by analyzing the incentive effects of different ownership arrangement in the spirit of the grossman hart moore ghm incomplete contracts theory of the firm a key departure from earlier approaches is the inclusion of a role for an information asset analogous to the ghm treatment of property this approach highlights the organizational significance of information ownership and information technology for instance using this framework one can determine when 1 informed workers are more likely to be owners than employees of firms 2 increased flexibility of assets will facilitate decentralization and 3 the need for centralized coordination will lead to centralized ownership the framework developed shed

urban commuters mode switching behaviour in taipai with an application of the bounded rationality principle this paper applies the bounded rationality rule to analyse commuters mode switching behaviour from private transport i e automobile and motorbike to public transport i e rail transit and bus transit in the taipei metropolitan area a stated choice approach was used to collect the data and to investigate how different attributes such as parking fees travel time and cost influence commuters mode switching behaviour a multinomial probit modelling framework was developed to capture the correlation effects when commuters switch from private to public modes the estimation results indicate that the socioeconomic characteristics and trip characteristics significantly influence commuters mode switching behaviour accounting for the structure of unobserved effects suggests that switching behaviour from private to public modes was significantly positively correlated an elasticity analysis shows that private commuters were more likely to switch to mrt than to bus and that auto commuters are generally m

monotonic regression based on bayesian p splines an application to estimating price response functions from store level scanner data generalized additive models have become a widely used instrument for flexible regression analysis in many practical situations however it is desirable to restrict the flexibility of nonparametric estimation in order to accommodate a presumed monotonic relationship between a covariate and the response variable for example consumers usually will buy less of a brand if its price increases and therefore one expects a brand s unit sales to be a decreasing function in own price we follow a bayesian approach using penalized b splines and incorporate the assumption of monotonicity in a natural way by an appropriate specification of the respective prior distributions we illustrate the methodology in an empirical application modeling demand for a brand of orange juice and show that imposing monotonicity constraints for own and cross item price effects improves the predictive validity of the estimated sales response function considerably

forecasting daily supermarket sales using exponentially weighted quantile regression inventory control systems typically require the frequent updating of forecasts for many different products in addition to point predictions interval forecasts are needed to set appropriate levels of safety stock the series considered in this paper are characterised by high volatility and skewness which are both time varying these features motivate the consideration of forecasting methods that are robust with regard to distributional assumptions the widespread use of exponential smoothing for point forecasting in inventory control motivates the development of the approach for interval forecasting in this paper we construct interval forecasts from quantile predictions generated using exponentially weighted quantile regression the approach amounts to exponential smoothing of the cumulative distribution function and can be viewed as an extension of generalised exponential smoothing to quantile forecasting empirical results are encouraging with improvements over traditional methods being particularly apparent when the approach is used as the basis for robust point forecasting

semiparametric analysis to estimate the deal effect curve abstract the marketing literature suggests several phenomena that may contribute to the nature of the relationship between sales and price discounts these phenomena can produce complex nonlinearities and interactions in the deal effect curve that are best captured with a flexible approach because a fully nonparametric regression model suffers from the curse of dimensionality the authors propose a semiparametric regression model store level sales over time are modeled as a nonparametric function of own and cross item price discounts and a parametric function of other predictors the authors compare the predictive validity of the semiparametric model with that of two parametric benchmark models and obtain superior performance the results for three product categories indicate threshold and saturation effects for both ownand cross item temporary price cuts the authors also find that the nature of the own item curve depends on other items price discounts comparisons with restricted model specification

creating micro marketing pricing strategies using supermarket scanner data micro marketing refers to the customization of marketing mix variables to the store level this paper shows how prices can be profitably customized at the store level rather than adopting a uniform pricing policy across all stores historically there has been a trend by retailers to consolidate independent stores into large national and regional chains this move toward consolidation has been driven by the economies of scale associated with these larger operations however some of these large chains have lost the adaptability of independent neighborhood stores micro marketing represents an interest on the part of managers to combine the advantages of these large operations with the flexibility of independent neighborhood stores a basis for these customized pricing strategies is the result of differences in interbrand competition across stores these changes in interbrand competition are measured using weekly store level scanner data at the product level obviously this presents a huge estimation problem since we wish to measure substitution between each product at a store level for a chain with 100 stores and 10 products in a category we would need to estimate over 100 000 parameters to reliably estimate these individual store differences we phrase our problem in a hierarchical bayesian framework essentially each store level parameter can be thought of as a combination of chain level and random store specific effects the improvement in estimating this model comes from exploiting the common chain level component in addition we relate these store specific changes to demographic and competitive characteristics of the store s trading area which helps explain why these differences are present these estimated differences in price response are in turn used to set store level prices to simplify and focus the problem we limit our attention to everyday price changes i e the prices of products that are not advertised there are many marketing variables that can be adjusted at a storelevel e g promotions and product assortments the reason we concentrate upon everyday pricing is driven by its importance in the marketing mix that most profits are earned on products sold at their everyday price and the amenability of everyday prices to store level customizations a limitation of this approach is that it yields only a partial solution to the retailer s global optimization problem a challenge for the retailer in implementing micro marketing pricing strategies is to retain a consistent image while altering prices that adapt to neighborhood differences in demand our approach is to search for price changes that leave image unchanged we argue that a sufficient condition for holding the input to store image constant from everyday pricing is to hold average price and revenues at their current levels we implement this condition by introducing constraints into the profit maximization problem future research into store choice may yield more efficient conditions a benefit of holding the retailer s image constant is that it does not require costly new information about competitors and promotional activity instead retailers are able to derive these store level customizations based largely upon scanner data this is very advantageous since this information is already being collected and is readily available our results indicate that micro marketing pricing strategies would be profitable and could increase gross profit margins by 4 percent to 10 percent when these gross profit gains are considered after administrative and operating costs are taken into account they could increase operating profit margins by 33 percent to 83 percent these gains come from encouraging consumers through everyday price changes to switch to more profitable bundles of products and not through overall price changes at the chain level these results show that the information contained in the retailer s store level scanner data is an under utilized resource by exploiting this information using newer and more powerful computational techniques managers can better appreciate its value the implication is that profits could be increased and gains can be made by using this information as the basis for micro marketing

improving behavioral realism in hybrid energy economy models using discrete choice studies of personal transportation decisions hybrid energy economy models combine top down and bottom up approaches to explore behaviorally realistic responses to technology focused policies this research uses empirically derived discrete choice models to inform key behavioral parameters in cims a hybrid model the discrete choice models are estimated for vehicle and commuting decisions from a survey of 1150 canadians with the choice models integrated into cims we simulate carbon taxes gasoline vehicle disincentives and single occupancy vehicle disincentives to show how different policy levers can motivate technological change we also use the empirical basis for the choice models to portray uncertainty in technological change costs and emissions

new technology based firms in innovation networks symplectic and generative impacts abstract the traditional body of research on new technology based firms reflects the linear sequential view of the technological innovation process the modern innovation theory views the innovation process as a complex interative process that is essentially systemic in character the systemic view of the technological innovation process largely remains to be implemented in research on new technology based firms the present study represents an attempt to bridge this gap by applying a systemic approach to research on new technology based firms the empirical part of the present study develops and empirically tests a model that classifies new technology based firms into science based firms and engineering based firms the two categories are defined in terms of the functional relationship between the new technology based firm and the articulation process of basic technologies in the model new technology based firms are analyzed in terms of the systemic knowledge conversion process to which they are attached the model receives support in three empirical databases compiled among new technology based firms in finland the united kingdom and silicon valley the analysis of the silicon valley database is the first ever systematic survey of the spin off firms of stanford university

tactical sales forecasting using a very large set of macroeconomic indicators abstract tactical forecasting in supply chain management supports planning for inventory scheduling production and raw material purchase amongst other functions it typically refers to forecasts up to 12 months ahead traditional forecasting models take into account univariate information extrapolating from the past but cannot anticipate macroeconomic events such as steep increases or declines in national economic activity in practice this is countered by using managerial expert judgement which is well known to suffer from various biases is expensive and not scalable this paper evaluates multiple approaches to improve tactical sales forecasting using macro economic leading indicators the proposed statistical forecast selects automatically both the type of leading indicators as well as the order of the lead for each of the selected indicators however as the future values of the leading indicators are unknown an additional uncertainty is introduced this uncertainty is controlled in our methodology by restricting inputs to an unconditional forecasting setup we compare this with the conditional setup where future indicator values are assumed to be known and assess the theoretical loss of forecast accuracy we also evaluate purely statistical model building against judgement aided models where potential leading indicators are pre filtered by experts quantifying the accuracy cost trade off the proposed framework improves on forecasting accuracy over established time series benchmarks while providing useful insights about the key leading indicators we evaluate the proposed approach on a real case study and find 18 8 accuracy gains over the current forecasting process

product differentiation and oligopoly in international markets the case of the u s automobile industry this paper develops and estimates a model of the u s automobile industry on the demand side a discrete choice model is adopted that is estimated using micro data from the consumer expenditure survey the estimation results are used in conjunction with population weights to derive aggregate demand on the supply side the automobile industry is modelled as an oligopoly with product differentiation equilibrium is characterized by the first order conditions of the profit maximizing firms the estimation results are used in counterfactual simulations to investigate two trade policy issues the effects of the ver and exchange rate pass through

smooth quantile based modeling of brand sales price and promotional effects from retail scanner panels semiparametric quantile regression is employed to flexibly estimate sales response for frequently purchased consumer goods using retail store level data we compare the performance of models with and without monotonic smoothing for fit and prediction accuracy we find that a flexible models with monotonicity constraints imposed on price effects dominate both in sample and out of sample comparisons while being robust even at the boundaries of the price distribution when data is sparse b quantile based confidence intervals are much more accurate compared to least squares based intervals c specifications reflecting that managers may not have exact knowledge about future competitive pricing perform extremely well