ECONOMETRIC ESTIMATION OF ARMINGTON IMPORT ELASTICITIES FOR A REGIONAL CGE MODEL OF THE ILLINOIS ECONOMY

One of the main concerns associated with the development and use of regional CGE models is the determination of key parameter values, particularly substitution and other price elasticities. A common problem is the lack of appropriate regional data for econometric estimation. Consequently, it is important to identify key parameters that are likely to be important in determining quantitative results and then to prioritize these for estimation where appropriate data are available. In this paper, the focus is on the estimation of the regional trade (import) substitution parameters, which tend to be important in analysis for regional economies (given their openness to trade). Here, commodity import elasticities for the Illinois economy are estimated and tested in a single region CGE model of the Illinois economy. In our econometric estimation, we apply a model that takes account of market size and distance in estimating the substitutability between commodities produced in Illinois and other US states.     

FROM INPUT–OUTPUT TABLES TO SUPPLY-AND-USE TABLES

In 1991, Statistics Netherlands introduced the supply-and-use tables as part of the national accounts. Since then, the supply-and-use tables have been the main statistics on the production structure of the Dutch economy. They form the basis from which input–output tables are derived. The time series of supply-and-use tables starts in 1987. However, there is a need for a time series since 1970 because benchmark revisions of the Dutch national accounts would become far easier if such time series were available. Therefore, a method has been developed to derive supply-and-use tables from existing input–output tables. This article presents the algorithm.     

Ordering quantity decisions considering uncertainty in supply-chain logistics operations

This research seeks to determine the optimal order amount for a retailer given uncertainty in a supply-chain's logistics network due to unforeseeable disruption or various types of defects (e.g., shipping damage, missing parts and misplacing products). Mixture distribution models characterize problems from solitary failures and contingent events causing network to function ineffectively. The uncertainty in the number of good products successfully reaching distribution centers (DCs) and retail stores poses a challenge in deciding product-order amounts. Because the commonly used ordering plan developed for maximizing expected profits does not allow retailers to address concerns about contingencies; this research proposes two improved procedures with risk-averse characteristics towards low probability and high impact events. Several examples illustrate the impact of a DC's operation policies and model assumptions on a retailer's product-ordering plan and resulting sales profit.     

The influence of modularity and industry clockspeed on reverse logistics strategy: Implications for the purchasing function

Environmental policies and recent take-back legislation pose new management challenges and require many companies to come up with new strategies and corresponding processes for recovering their products. Some leading firms have already succeeded in a field marked by ever-greater levels of uncertainty and material flows. However, simply mimicking the example of these leading companies does not seem to be suitable for all companies. A configurational approach is needed to understand why this is the case. This paper therefore considers two case studies to analyze the effects of industry clockspeed and product architecture on recovery options involving repair, refurbishing, or reuse. Its findings suggest that the recovery and reuse of key components is an economically viable reverse logistics strategy in a high-modularity case; in contrast, scrapping or recycling of materials is often the most viable strategy for integrated architectures. Different strategies must also be employed for high and low clockspeed situations. The demands that recovery activities place on a company's purchasing function is also illustrated in the second part of the paper.     

When does inward technology licensing facilitate firms' NPD performance? A contingency perspective

Many firms find inward technology licensing (ITL), as a means to access external technological knowledge, an effective and relatively inexpensive way for new product development (NPD). However, although the literature has suggested some advantages and disadvantages of ITL with respect to NPD, the relationship between ITL and licensee firms' subsequent NPD performance has not yet been found convincingly evident. Sharing with many other likeminded scholars and practitioners, we believe that the dynamics between external knowledge, internal capability, external environment, and firm performance should be investigated through a contingency perspective. Thus, this study posits that a firm's propensity to develop new products through ITL is contingent upon two categories of contingency factors that are internal and external to firms. Using a dataset containing information about Chinese firms' licensing activities, we find support for our hypotheses: the positive relationship between ITL and NPD performance of a licensee firm is moderated by firms' absolute and relative absorptive capacity and the knowledge endowment in the region where the licensee firm operates.     

Multifactor CES general equilibrium: Models and applications

Sector-specific multifactor CES elasticities of substitution and the corresponding productivity growth are jointly measured by regressing the growth of per-factor cost shares against the growth of factor prices. We use linked input-output tables for Japan and the Republic of Korea as the data source for factor price and cost shares in two temporally distant states. We then construct a multisectoral general equilibrium model using the system of estimated CES unit cost functions and evaluate the economy-wide propagation of an exogenous productivity stimulus in terms of welfare. Further, we examine the differences between models based on a priori elasticities such as Leontief and Cobb-Douglas.     

The contingency effects of environmental uncertainty on the relationship between supply chain integration and operational performance

This paper extends prior supply chain research by building and empirically testing a theoretical model of the contingency effects of environmental uncertainty (EU) on the relationships between three dimensions of supply chain integration and four dimensions of operational performance. Based on the contingency and organizational information processing theories, we argue that under a high EU, the associations between supplier/customer integration, and delivery and flexibility performance, and those between internal integration, and product quality and production cost, will be strengthened. These theoretical propositions are largely confirmed by multi-group and structural path analyses of survey responses collected from 151 of Thailand's automotive manufacturing plants. This paper contributes to operations management contingency research and provides theory-driven and empirically proven explanations for managers to differentiate the effects of internal and external integration efforts under different environmental conditions.     

ECONOMETRIC ANALYSIS OF EUROPEAN CARBON DIOXIDE EMISSIONS BASED ON RECTANGULAR SUPPLY-USE TABLES

This paper formalises the so-called Supply-Use Based Econometric (SUBE) approach that allows for the introduction of econometric analysis in the calculation of backward input–output multipliers of the Leontief-type quantity model, using rectangular supply and use tables. The SUBE approach does not require any kind of inverse matrix and incorporates the traditional approach (with square supply-use tables) as a particular case. The empirical analysis shows that the SUBE carbon dioxide multipliers for the EU27 are considerably lower than those obtained by the traditional Leontief inverse. In an application of the SUBE approach, the European economy appears to emit about 10% less carbon dioxide than in a situation in which it would not import any intermediate inputs from outside the EU27.     

Unconditional tests for association in 2 × 2 contingency tables in the total sum fixed design

The asymptotic approach and Fisher's exact approach have often been used for testing the association between two dichotomous variables. The asymptotic approach may be appropriate to use in large samples but is often criticized for being associated with unacceptable high actual type I error rates for small to medium sample sizes. Fisher's exact approach suffers from conservative type I error rates and low power. For these reasons, a number of exact unconditional approaches have been proposed, which have been seen to be generally more powerful than exact conditional counterparts. We consider the traditional unconditional approach based on maximization and compare it to our presented approach, which is based on estimation and maximization. We extend the unconditional approach based on estimation and maximization to designs with the total sum fixed. The procedures based on the Pearson chi‐square, Yates's corrected, and likelihood ratio test statistics are evaluated with regard to actual type I error rates and powers. A real example is used to illustrate the various testing procedures. The unconditional approach based on estimation and maximization performs well, having an actual level much closer to the nominal level. The Pearson chi‐square and likelihood ratio test statistics work well with this efficient unconditional approach. This approach is generally more powerful than the other p‐value calculation methods in the scenarios considered.     

Disaggregating input–output tables in time: the temporal input–output framework

The input–output framework has evolved dramatically since its initial formulation. New analytical techniques and extensions have allowed a more comprehensive assessment of the economy and expanded its applicability. Nonetheless, the core of the framework has remained unchanged: an annually compiled input–output table, which conveys monetary flows between sectors in a region in a particular year. Hence, the technical coefficients derived from it are ‘average’ input compositions, neglecting fluctuations in production capacity, seasonality and temporal shocks within that period. This paper develops a consistent methodology to disaggregate the annual input–output table in its time dimension in order to estimate intra-year input–output matrices with distinct technical structures for a particular year. The main advantages in relation to the annual model are to allow seasonal effects to be studied within the input–output framework, to better understand the process of coefficient change and to offer a more comprehensive dynamic view of production.