Complementary effects of clusters and networks on firm innovation: A conceptual model

We develop a conceptual model that explains how a firm's cluster and network complement each other in enhancing the firm's likelihood of technological innovations. We identify critical innovation catalysts-awareness and motivation—and innovation barriers—resource constraints, organizational rigidity, and uncertainty. Our conceptual model explains how various factors in the cluster such as competitive intensity, social interaction intensity, and cluster vitality and network factors such as resource potential, acquisition orientation, co-development orientation, and network vitality impact innovation catalysts and barriers and subsequently the firm's likelihood of generating incremental and breakthrough innovations. We discuss several promising avenues for future research.     

THE AGRICULTURAL PRODUCTIVITY GAP IN EUROPE

For 15 European countries over the 1970–2004 period we find large and persistent agricultural productivity gaps, the ratio of value added per hour in nonagriculture to that in agriculture. Comparing the gap in value added per hour to the wage gap between the two sectors suggests that value added in the data is mismeasured. We further find that, controlling for differences in gross domestic product per capita and institutions, the mismeasurement is positively related to self‐employed share of hours in agriculture. Correcting for underreporting of self‐employment income using our preferred correction factor reduces the measured agricultural productivity gap by 38%. These findings suggest that underreporting can account for a significant portion of the measured agricultural productivity gap. (JEL E01, O47, O52, Q10)     

A general class of chain estimators for ratio and product of two means of a finite population

This paper proposes a class of estimators for estimating ratio and product of two means of a finite population using information on two auxiliary characters. Asymptotic expression to terms of order 0(n^-1) for bias and mean square error (MSE) of the proposed class of estimators are derived. Optimum conditions are obtained under which the proposed class of estimators has the minimum MSE. An empirical study is carried out to compare the performance of various estimators of ratio with the conventional estimators.     

Prediction under uncertainty of degree of polynomial in growth curve models

Random coefficient polynomial regression model has been considered for prediction purpose when there is uncertainty about the degree of the polynomialo Expressions for mean square errors of two predictors based on simple estimators have been derived and their perfomaiices have been compared when parameters are estimated from the sample. A modified predictor has also been suggested when parameters in the predicting equations are to be estimated from the sample. Perform-ance ofseveral predictors haife been compared by cross validation technique from a real set of data.     

Optimal change-over designs for correlated observations

Design implications of an autoregressive model for change-over experiments are investigated. In this model, the residual effect due to the previous treatment is assumed to be proportional to the response in the previous period. In addition, the errors from the same experimental subject are assumed to be correlated according to a first-order autoregressive model. Models with fixed and random subject effects are discussed separately. An attempt has been made to identify and construct optimal or nearly optimal designs in various situations. Empirical conclusions of Taka and Armitage [Commun. Statist. Theor. Meth. (1983)12, 865-876] regarding the efficiency of some designs have also been confirmed.     

Simple bounds on availability in a model with unknown life and repair distributions

We take a fresh look at the classic model of a device supported by a single statistically identical spare and provision for repairs, with system failure resulting whenever the currently operating unit fails before the repair of the previously failed unit is completed to allow it to become a spare. The limiting availability A(F,G) of this system depends on the life distribution F and repair time distribution G through α=∫GdF and the expected downtime. In this paper we derive several computable and sharp bounds on A(F,G) when F,G have suitable life distribution characteristics in the sense of reliability theory but are otherwise unknown except for at most two moments. Among other results, we find a sharp bound which involves the MTBF, MTTR and the second moment of the life-distribution of the device through its coefficient of variation. This leads to a maximin result for DFR repairs and DMRL lives.     

Preliminary Test Regression Estimator When two Prior Values of Population Mean (μx) of an Auxiliary Variable (x) are Available

A regression estimator using two prior values of population mean (μ_x) of an auxiliary variable (x) is proposed after a preliminary test of closeness of these prior values to the true valueμ_x. The proposed preliminary test regression estimator has been found to be more efficient in general than the usual regression estimator when prior values are used in place of μ_xwithout preliminary test of significance. The efficiency of the proposed estimator over the usual regression estimator has also been computed for different values of Δ₀, Δ₁, n, and ρ, which showed considerable gain in precision.     

Models and algorithms for the design of survivable multicommodity flow networks with general failure scenarios

We consider the design of a multicommodity flow network, in which point-to-point demands are routed across the network subject to link capacity restrictions. Such a design must build enough capacity and diverse routing paths through the network to ensure that feasible multicommodity flows continue to exist, even when components of the network fail. In this paper, we examine several methodologies to optimally design a minimum-cost survivable network that continues to support a multicommodity flow under any of a given set of failure scenarios, where each failure scenario consists of the simultaneous failure of multiple arcs. We begin by providing a single extensive form mixed-integer programming formulation for this problem, along with a Benders decomposition algorithm as an alternative to the extensive form approach. We next investigate strategies to improve the performance of the algorithm by augmenting the master problem with several valid inequalities such as cover constraints, connectivity constraints, and path constraints. For the smallest instances (eight nodes, 10 origin–destination pairs, and 10 failure scenarios), the Benders implementation consumes only 10% of the time required by the mixed-integer programming formulation, and our best augmentation strategy reduces the solution time by another 50%. For medium- and large-sized instances, the extensive form problem fails to terminate within 2 h on any instance, while our decomposition algorithms provide optimal solutions on all but two problem instances.     

Betweenness computation in the single graph representation of hypergraphs

Many real-world social networks are hypergraphs because they either explicitly support membership in groups or implicitly include communities. We present the HyperBC algorithm that exactly computes betweenness centrality (or BC) in hypergraphs. The forward phase of HyperBC and the backpropagation phase are specifically tailored for BC computation on hypergraphs. In addition, we present an efficient method for pruning networks through the notion of “non-bridging” vertices. We experimentally evaluate our algorithm on a variety of real and artificial networks and show that it significantly speeds up the computation of BC on both real and artificial hypergraphs, while at the same time, being very memory efficient.     

Perceptions of Recreational Fishing Boat Captains: Knowledge and Effects of Fish Consumption Advisories

The impacts of fish consumption advisories on recreational and subsistence fishing, particularly in fresh waters, have been examined extensively. By contrast, little attention has focused on organized recreational fishing, such as from party and charter boats, and particularly for salt water fish. We interviewed 93 New Jersey boat captains to determine their knowledge about fish consumption advisories, and whether, in their opinion, clients knew of fish consumption advisories, and whether they thought advisories had an effect on recreational fishing and their businesses. Advisories were ranked by captains as a moderate influence on the success of their business, less so than number of fish caught, strength of the economy, overfishing by commercial boats, and management regulations. Only one boat captain had not heard warnings about eating fish, but what captains said they had heard was mixed in its accuracy and completeness. Clients expect captains to know about fish, and about half of boat captains said clients had asked about the safety of eating fish. Captains who felt advisories were affecting their businesses tended to fish for species without high levels of mercury (except for bluefish) or PCBs, the primary contaminants of concern for state advisories and federal advice. However, these captains worked closer to areas (e.g., Raritan Bay complex and New York Harbor) subject to advisories than did other captains, and were more prone to say that management regulations (e.g., fish size, creel limits, seasons) and marketing and advertising by the industry or state were strong influences on the success of their seasons. Comparing captains who thought advisories had some or great effect (60%) versus those reporting "no effect" (40%), there was no difference in the mean percentage of trips targeting high mercury species such as swordfish and shark. Many captains said they would or might post advisories, but 42% of the boat captains said they would not post consumption warnings if the state provided them. The significant portion (at least 15%) of saltwater fishing supported by these businesses suggests that these captains are an important conduit for future risk communication.