Projection of input–output tables by means of mathematical programming based on the hypothesis of stable structural evolution

The high costs involved in the construction of input–output tables (IOTs) using survey methods, makes the development of mathematical projection techniques attractive. An adjustment and projection method of IOTs, based on mathematical programming techniques, is proposed here. The flexibility and ability to include information on elements and aggregates is one of this method's main advantages over alternative adjustment methods. Among the information included in the adjustment, the most relevant is related to the evolution hypothesis of the production structure under stable conditions. This leads to the inclusion of intervals for coefficients. A set of adjusted tables, consistent with their own internal structure, is obtained after an interactive and iterative process that reconciles all information sources. A projection of IOTs in Spain for the period 1995–1998 is undertaken to examine the accuracy of the method.     

Breaking Bad in Bourbon Country: Does Alcohol Prohibition Encourage Methamphetamine Production?

This article examines the influence of local alcohol prohibition on the prevalence of methamphetamine labs. Using multiple sources of data for counties in Kentucky, we compare various measures of meth manufacturing in wet, moist, and dry counties. Our preferred estimates address the endogeneity of local alcohol policies by exploiting differences in counties' religious compositions between the 1930s, when most local-option votes took place, and recent years. Even controlling for current religious affiliations, religious composition following the end of national Prohibition strongly predicts current alcohol restrictions. We carefully examine the validity of our identifying assumptions, and consider identification under alternative assumptions. Our results suggest that the number of meth lab seizures in Kentucky would decrease by 35% if all counties became wet.     

Proposal of a model to measure competitiveness through factor analysis

This article presents a proposal for a simultaneous competitiveness measurement model for the three geographical levels: country, states, and municipalities. For this, a multivariate factor analysis method is used to help identify five factors, seven sub-factors, and thirty variables, which will be used for the measurement and to present the results of an empirical study on eleven entities: the country, the State of Sonora, and nine municipalities, which represent 80% of the population and 80% of their TGP. The results indicate that, in 2010, the municipality of Hermosillo was the most competitive.     

Designing a fair,financially sustainable pay rate for owner-operator truck drivers. Modeling and case study

Owner operator truck drivers have been dealing with a long-standing problem: compensation per distance. Owner operators who get paid according to these criteria get a fixed payment per distance traveled regardless of how long it takes to actually cover the distance. This means that there are numerous situations that truck drivers are working; yet they might be unpaid because the truck is not moving. To compensate for the unfairness of the pay rate models, owner operators have continuously increased their working hours. In addition, many studies have confirmed that a fair payment is among the most important factors that truck drivers take into consideration when deciding to leave a company. Consequently, an unfair pay rate, along with the hard labor conditions truck drivers suffer from, inevitably leads to high turnover rates. For all these reasons, our study aims at developing a fair, financially sustainable pay rate for owner operators that will help companies ensure a stable and highly experienced workforce by making sure that owner operators can cover the real expenses of their working activity. Finally, in order to prove that our pay rate was of practical use, we test the model on one of the largest Spanish agro-food companies.     

Reducing the level of switching costs in mobile communications: The case of Mobile Number Portability

Switching costs are one of the most important economic forces that affect market competition in mobile communications. Both theoretical and empirical studies have shown that switching costs reduce market competition leading to higher prices, lower product and service quality, and lower customer welfare. Given their negative consequences, national regulatory authorities have designed policies aimed at reducing switching costs and fostering competition. One of the most important of these, in the mobile communications industry, is mobile number portability (MNP). The aim of this paper is to investigate the effect of MNP on switching costs in mobile phone services. First, a hierarchical Bayes model is proposed to measure switching costs at the customer level and to investigate the impact of MNP on them. Second, this study examines the drivers of MNP adoption by customers using a binary logit specification. The results reveal that this regulatory policy has significantly reduced the cost of switching and that MNP adoption partially depends on customer-related variables.     

Inventory service-level optimization within distribution network design problem

We propose a sequential heuristic approach to optimize inventory service levels in a two-stage supply chain. The proposed approach deals with service level and inventory decisions, simultaneously with network design decisions, and incorporating unfulfilled demand costs in a previous inventory-location model. A two-step formulation is considered, where the first step optimizes service level and the second step addresses location and inventory decisions. Each algorithm iteration solves an inventory-location model for a fixed service level, and then the service level is updated in order to reach an equilibrium condition between operating system and unfulfilled demand costs. The algorithm converged in three iterations for a set of sample instances, obtaining the same outcome in comparison with a more intuitive, exact, but more time-consuming search procedure.     

Market efficiency and learning in an artificial stock market: A perspective from Neo-Austrian economics

An agent-based artificial financial market (AFM) is used to study market efficiency and learning in the context of the Neo-Austrian economic paradigm. Efficiency is defined in terms of the “excess” profits associated with different trading strategies, where excess is defined relative to a dynamic buy and hold benchmark in order to make a clean separation between trading gains and market gains. We define an Inefficiency matrix that takes into account the difference in excess profits of one trading strategy versus another (signal) relative to the standard error of those profits (noise) and use this statistical measure to gauge the degree of market efficiency. A one-parameter family of trading strategies is considered, the value of the parameter measuring the relative informational advantage of one strategy versus another. Efficiency is then investigated in terms of the composition of the market defined in terms of the relative proportions of traders using a particular strategy and the parameter values associated with the strategies. We show that markets are more efficient when informational advantages are small (small signal) and when there are many coexisting signals. Learning is introduced by considering “copycat” traders that learn the relative values of the different strategies in the market and copy the most successful one. We show how such learning leads to a more informationally efficient market but can also lead to a less efficient market as measured in terms of excess profits. It is also shown how the presence of exogeneous information shocks that change trader expectations increases efficiency and complicates the inference problem of copycats.     

Rural entrepreneurship in place: an integrated framework

ABSTRACT

Agglomeration-oriented theories have grown significantly in the past decade in the explanation and promotion of entrepreneurship. Theoretical frameworks and normative models such as entrepreneurial ecosystems are insufficient to observe, explain, and inform policies at the communal level in rural contexts. In this paper, we propose a socio-spatial lens as a more fruitful way of understanding the holistic picture of rural entrepreneurship. By means of abductive research, we explore the distinct elements of entrepreneurial places in rural contexts and derive an integrated meso-level framework, comprising place-sensitive determinations and dimensions, to observe and further analyse the enabling conditions of such places. The findings obtained and the framework developed will be of great use for the evaluation and decision-making, regarding entrepreneurship in rural communities.     

The Validity of Gibrat’s Law: Evidence from Ecuadorian Service SMEs

The objective of this research is to analyze the relationship between size and growth through the validation of Gibrat's law. A sample of 17,082 Ecuadorian companies in the service sector is used for the period 2010–2015. The estimation method used is that of quantile regression for panel data; the results suggest that small companies grow faster than their larger counterparts. The results show that the growth, measured through sales and employment, of SMEs in the service sector in Ecuador is not independent of firm size and therefore does not correspond to a random process.