Economic integration and regional convergence: effects of NAFTA on local convergence in Mexico, 1980–2008

Continental integration processes can alter traditional development axes. Paelinck and Polèse´s work in 1999 explains that in the case of Mexico there is a tension between the U.S. border and the rest of the country: Mexico´s integration in the NAFTA should have reduced the U.S. border attraction, extending the growth to territories located between this border and Mexico City. To test this hypothesis, we propose a spatial conditional β-convergence model that uses as regressors both the distances to the U.S. border and to Mexico City, together with other control variables. This model is applied to the period from 1980 to 2008 using GVA at the municipality level. Working with municipal-level data allows to observe convergence patterns across space and identify the effects of location. The time-span studied distinguishes between before and after NAFTA. Estimates based on Mundlak´s approach were obtained for time-invariant regressors. Results show that during the pre-NAFTA period there was a general process of convergence, but it is mainly explained by the faster growth of municipalities located near to the U.S. border. However, post-NAFTA, convergence in municipalities disappeared and the effect of distance to the U.S. border reversed its sign, as predicted in Paelinck and Polèse´s model.     

Decomposing income inequality in a backward pre‐industrial economy: Old Castile (Spain) in the middle of the eighteenth century

Research on economic inequality in early modern Europe is complicated by the lack of appropriate data for reconstructing income or wealth distributions. This article presents a study of income inequality in mid‐eighteenth‐century Old Castile (Spain) using the Ensenada Cadastre, a census conducted between 1749 and 1759. The article describes the information provided by this census and then discusses its advantages and disadvantages for reconstructing income profiles and calculating income inequality. This is followed by analysis of a dataset derived from the Cadastre that consists of more than 4,000 observations from Palencia (a province in northern Spain) and contains information on sources of household income, each household head's main occupation, residence location, and other household characteristics. Demographic data from this census is used to weight observations in the sample and thereby minimize selection bias. Findings show that inequality in eighteenth‐century Spain was probably substantial despite its relative backwardness; that the relationship between inequality and per capita income was not clear‐cut and was probably influenced by measurement of the higher incomes; and that although income inequality was largely driven by uneven land distribution, labour income also contributed to overall inequality—especially in urban centres.     

Estimating the effect of technological factors from samples affected by collinearity: a data-weighted entropy approach

Measuring the effect of technological activities on productivity growth is an issue that attracted much attention in recent works on empirical econometric studies. Specifically, in the field of regional economics, several attempts have been made in order to quantify the contribution of R&D to labor productivity growth at a regional scale, considering both the internal R&D and the effects obtained by geographical spillovers. The results obtained, however, are characterized by a huge variability and in many cases there is no empirical evidence of positive contributions of R&D activities to productivity growth. Our argument is that this can be a consequence of dealing with samples’ affect by a high level of collinearity. This paper proposes the use of the data-weighted prior (DWP) estimator suggested by Golan (J Econom 101:165–193, 2001). The main advantage of this estimator is that it discriminates between relevant and irrelevant regressors better than other estimators when dealing with highly collinear samples. We evaluate the performance of the DWP estimator by Monte Carlo simulations and illustrate how it works by means of a real-world example.     

LIFE‐CYCLE LABOR SEARCH WITH STOCHASTIC MATCH QUALITY

In the United States, unemployment, job finding, and separation rates decline as worker age increases. To explain these facts, we build a search and matching model of the labor market that incorporates a life‐cycle structure and features random match quality as well as human capital accumulation. The calibrated model successfully reproduces the empirical patterns of unemployment and job transition rates over the life cycle and generates plausible wage implications. We then explore the efficiency implications of the model and find that the differences between the market and planner allocations are more important for older workers.     

Neural networks for estimating Macro Asset Pricing model in football clubs

The recent crisis caused by COVID-19 directly affected consumption habits and the stability sof financial markets. In particular, the football industry has been hit hard by this pandemic and therefore has more volatile stock prices. Given this new scenario, further research is needed to accurately estimate the value of the shares of football clubs. In this paper, we estimate an asset pricing model in football clubs with different compositions of risk nature using non-linear techniques of artificial neural networks. Usually, asset pricing models have been estimated with linear methods such as ordinary least squares. Our results show a precision higher than 90% for all the estimated models, which far exceeds those shown by linear methods in the previous literature. We find that the residual represents about 40% of the variance of the price-dividend ratio. Long-term risks follow in importance, and above all, the habit component and its behaviour in the face of changes. The importance of the residual component exists due to a low correlation between the asset price and consumer behaviour, but to a much lesser extent than that shown in previous studies. The estimation carried out with artificial neural networks, both the Deep Learning methods and especially the Quantum Neural Network, opens up new possibilities to estimate more efficiently the pricing of financial assets in the football industry.     

Factors that determine the degree of fulfilment of expectations for entrepreneurs from the business incubator programmes

International Entrepreneurship and Management Journal - The uncertain economic situation that was experienced because of the global financial crisis in 2008 and the exponential growth in the use of...