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We are interested in modeling the impact of spatial and interindustry dependence on firm-level innovation of Chinese firms The existence of network ties between cities imply that changes taking place in one city could influence innovation by firms in nearby cities (local spatial spillovers), or set in motion a series of spatial diffusion and feedback impacts across multiple cities (global spatial spillovers). We use the term local spatial spillovers to reflect a scenario where only immediately neighboring cities are impacted, whereas the term global spatial spillovers represent a situation where impacts fall on neighboring cities, as well as higher order neighbors (neighbors to the neighboring cities, neighbors to the neighbors of the neighbors, and so on). Global spatial spillovers also involve feedback impacts from neighboring cities, and imply the existence of a wider diffusion of impacts over space (higher order neighbors).

Similarly, the existence of national interindustry input-output ties implies that changes occurring in one industry could influence innovation by firms operating in directly related industries (local interindustry spillovers), or set in motion a series of in interindustry diffusion and feedback impacts across multiple industries (global interindustry spillovers).

Typical linear models of firm-level innovation based on knowledge production functions would rely on city- and industry-specific fixed effects to allow for differences in the level of innovation by firms located in different cities and operating in different industries. This approach however ignores the fact that, spatial dependence between cities and interindustry dependence arising from input-output relationships, may imply interaction, not simply heterogeneity across cities and industries.

We construct a Bayesian hierarchical model that allows for both city- and industry-level interaction (global spillovers) and subsumes other innovation scenarios such as: (1) heterogeneity that implies level differences (fixed effects) and (2) contextual effects that imply local spillovers as special cases.

This paper presents the second-generation estimates for the Italian engineering industry in 1911, a year documented both by the customary demographic census, and the first industrial census. The first part of this paper uses the census data to estimate the industry’s value added, sector by sector; the second further disaggregates each sector by activity, and estimates the value added, employment, physical product, and metal consumption of each one. A third, concluding section dwells on the dependence of cross-section estimates on time-series evidence. Three appendices detail the specific algorithms that generate the present estimates; a fourth, a useful sample of firm-specific data.

This chapter presents information regarding the significance of the Pacific Alliance in the commercial development in Mexico. Mexico’s integration with the countries of the Pacific Alliance (Peru, Colombia, and Chile) is highlighted through statistical analysis of the composition of Mexico’s exports, the future of the country’s exports, and the balance of trade. Some of these challenges are the growth and diversification of exports, internationalization of small business enterprises, and reduction of the balance of trade deficit with respect to some of its main export products. Results showed that Mexico’s three Latin American commercial partners together represented the third-highest destination for its exports, where Mexico had a favorable balance of trade, which suggests that a further opening of these countries’ economies would bring benefits to Mexican economy.

The decrease of Mexican exports since 2012 to countries such as Chile and Colombia are symptoms that the Pacific Alliance free trade agreement needs more support in order to represent an opportunity to increase export diversification, since most of the Mexico-exported goods go to the United States. This can bring economic and political risks because of the overreliance on one trading partner.

The results also showed that most of Mexico’s exports to the Pacific Alliance country members are hi-tech products. The principal exports in terms of sales come from the telecommunications industry, transportation industry, and the home appliance industry. The rest of the main products exported come from medium- or low-technology industries, with high levels of local added value.

Every year, tens of millions of the 1.4 billion cars on the world’s roads are decommissioned. While the ferrous and other metals that constitute about 75% of a vehicle by weight can be readily and profitably recycled, the remaining mix of plastics, glass, composites, complex materials, fragments and contaminants are mainly destined for landfill as automotive shredder residue (ASR). For every car, approximately 100–200 kg of ASR is disposed of in landfill, posing a growing technical and environmental challenge worldwide. The recovery of the ASR for high-end application is the focus of this study, aiming to optimise the use of these valuable resources and minimise the extractive pressure for raw materials, a future green manufacturing, contributing towards a zero waste circular economy. As the dissolution of carbon into iron is a key step in the manufacture of iron-carbon alloys, the feasibility of utilizing the waste polymers within ASR as sources of carbon in different areas of pyrometallurgical processing was investigated. Polypropylene and rubber, in a blend with metallurgical coke, were used as carbonaceous substrates and the slag-foaming phenomenon was investigated via the sessile drop technique in an argon environment at 1,550°C. The results indicated the rubber/coke blend achieved significantly better foaming behaviour, and the PP/coke blend exhibited a moderate improvement in slag foaming, in comparison to 100% metallurgical coke. The overall results indicated the incorporation of ASR had significant improvement in foaminess behaviour, increasing furnace efficiency.

Data, either in primary or secondary form, represent the core strength of quantitative research. However, there is significant difference between collected data and the final researchable data. The data collection is driven by objectives of the research. The data also could be in various formats at different sources. The collected data in its original form may contain systematic and random errors. Such errors need to be cleaned from the data which is termed as data cleaning process.

The present chapter discusses about the different methodologies and steps that may be helpful for fine tuning the data into researchable format. The discussions are instantiated with the applications of methodologies on a set of financial data of companies listed in Bombay Stock Exchange. Various steps involved in transformation of collected data to researchable data are presented. A schematic model including data collection, data cleaning, working with variables, outlier treatment, testing the assumption of statistical test, normality, and heteroscedasticity is presented for the benefit of research scholars. Beyond this generic model, this paper focuses exclusively on financial data of listed companies in the Bombay Stock Exchange. The challenges involved in various sources, data gathering and other pre-analysis stages are also considered. This is also applicable for research based on secondary data sources in other fields as well.

This course has made me observe the international business environment in a completely different light, and has made me think about whether a ‘win-win situation’ is ever possible in an international business context, where the organisations are involved in extremely complex supply chains. (Dianne from Estonia who studied in a UK business school)

Many jurisdictions fine illegal cartels using penalty guidelines that presume an arbitrary 10% overcharge. This article surveys more than 700 published economic studies and judicial decisions that contain 2,041 quantitative estimates of overcharges of hard-core cartels. The primary findings are: (1) the median average long-run overcharge for all types of cartels over all time periods is 23.0%; (2) the mean average is at least 49%; (3) overcharges reached their zenith in 1891–1945 and have trended downward ever since; (4) 6% of the cartel episodes are zero; (5) median overcharges of international-membership cartels are 38% higher than those of domestic cartels; (6) convicted cartels are on average 19% more effective at raising prices as unpunished cartels; (7) bid-rigging conduct displays 25% lower markups than price-fixing cartels; (8) contemporary cartels targeted by class actions have higher overcharges; and (9) when cartels operate at peak effectiveness, price changes are 60–80% higher than the whole episode. Historical penalty guidelines aimed at optimally deterring cartels are likely to be too low.