A structural analysis on Gravity of Trade: on removing distance from the model

The Gravity Model is the workhorse for empirical studies in International Economies for its empirical power and it is commonly used in explaining the trade flow between countries; it relies on a function that relates the trade with the masses of the two countries and the distance (as a proxy of the trasport costs) between them. However, the notion that using of distance functions in conventional interaction models effectively captures spatial dependence in international flows has long been challenged. It has been recently fully recognized that a spatial interaction effect exists essentially due to the spatial spillover and the third country effect. This motivates the introduction of the spatial autoregressive components in the so-called spatial gravity model of trade. A so-called weight matrix is used in order to define the set of the spatial neighbors and it is traditionally based on the inverse of the distance. Two issues follow from this standard procedure: the first regards the biasness of the distance if it is used as a proxy of the transport costs in a panel data, the second is related to the collinearity emerging if we use distance twice. So, several attempt were made in the recent literature having the scope of remove the distance. We propose a theoretically consistent procedure based on Anderson, Van Wincoop derivation model, and some ad-hoc tests, relating to this attempt. The empirical results based on a 22-years panel of OECD countries are conforting, and they allow us to estimate the model without the distance, if properly replaced by a set of fixed effects. This article, in addition, fits in the dispute about how to estimate the multilateral resistance terms.Comment: 17 page

A structural analysis on Gravity of Trade regarding the possibility to remove distance from the model

The Gravity Model is the workhorse for empirical studies in International Economies and it is commonly used in explaining the trade flow between countries. Recently, several studies have showed the importance of taking into account the spatial effect. The standard procedure until now was to account the transport cost using geographical distance as a proxy, and the spatial effect trough a weighted matrix constructed on inverse distance. Two issues follow from this standard procedure: the first regards the biasness of the distance if used as a proxy of the transport costs, the second is related to the collinearity emerging if we use distance twice. So, several attempt were made in the recent literature having the scope of remove the distance. We propose a theoretically consistent procedure based on Anderson, Van Wincoop derivation model, and some ad-hoc tests, relating to this attempt. The empirical results based on a 22-years panel of OECD countries are conforting, and they allow us to estimate the model without the distance, if properly replaced by a set of fixed effects

The migration network effect on international trade

This paper studies the relationship between migration and trade, with the aim of measuring both direct and indirect network effects. We analyze trade of diferentiated and homogeneous goods using an econometric approach inspired by spatial econometrics, proposing a new way to define country neighbors based on the most intense links in the migration network. We find that migration significantly affects trade across categories both in direct and in indirect way. The indirect impact highlights a stronger competitive effect of third country migrants for homogeneous goods. We also confirm that the effect of migration channels is higher on differentiated goods

Measuring players' importance in basketball using the generalized Shapley value

Measuring players’ importance in team sports to help coaches and staff with the aim of winning the game is gaining relevance, mainly because of the advent of new data and advanced technologies. In this paper we evaluate each player’s importance - for the first time in basketball - as his/her average marginal contribution to the utility of an ordered subset of players, through a generalized version of the Shapley value, where the value assumed by the generalized characteristic function of the generalized coalitional game is expressed in terms of the probability a certain lineup has to win the game. In turn, such probability is estimated by applying a logistic regression model in which the response is represented by the game outcome and the Dean’s factors are used as explanatory features. Then, we estimate the generalized Shapley values of the players, with associated bootstrap confidence intervals. A novelty, allowed by explicitly considering single lineups, is represented by the possibility of forming best lineups based on players’ estimated generalized Shapley values conditional on specific constraints, such as an injury or an “a-priori” coach’s decision. A comparison of our proposed approach with industry-standard counterparts shows a strong linear relation. We show the application of our proposed method to seventeen full NBA seasons (from 2004/2005 to 2020/21). We eventually estimate generalized Shapley values for Utah Jazz players and we show how our method is allowed to be used to form best lineups

The relationship between players’ average marginal contributions and salaries: an application to nba basketball using the generalized shapley value

Measuring players’ importance in basketball is allowed by many proposed advanced measures based on play-by-play data, such as the adjusted plus-minus, the wins above replacement, and the generalized Shapley value. In this paper we focus on the latter one in order to study whether, for a player, obtaining a large salary can be explained by its average marginal contribution to the team performance. In order to explore this issue, a linear regression model strategy where the logarithm of salary (Y ) depends on the generalized Shapley value (X ) is proposed and applied to players of selected National Basketball Association (NBA) teams over selected seasons. A leave-one-out cross validation shows that the accuracy in predicting whether free-agent players will obtain a more profitable contract solely basing on their generalized Shapley value is generally fairly good

The relation between global migration and trade networks

In this paper we develop a methodology to analyze and compare multiple global networks, focusing our analysis on the relation between human migration and trade. First, we identify the subset of products for which the presence of a community of migrants significantly increases trade intensity, where to assure comparability across networks we apply a hypergeometric filter that lets us identify those links which intensity is significantly higher than expected. Next, proposing a new way to define country neighbors based on the most intense links in the trade network, we use spatial econometrics techniques to measure the effect of migration on international trade, while controlling for network interdependences. Overall, we find that migration significantly boosts trade across countries and we are able to identify product categories for which this effect is particularly strong

A Spatial-Filtering Zero-Inflated Approach to the Estimation of the Gravity Model of Trade

Nonlinear estimation of the gravity model with Poisson-type regression methods has become popular for modelling international trade flows, because it permits a better accounting for zero flows and extreme values in the distribution tail. Nevertheless, as trade flows are not independent from each other due to spatial and network autocorrelation, these methods may lead to biased parameter estimates. To overcome this problem, eigenvector spatial filtering (ESF) variants of the Poisson/negative binomial specifications have been proposed in the literature on gravity modelling of trade. However, no specific treatment has been developed for cases in which many zero flows are present. This paper contributes to the literature in two ways. First, by employing a stepwise selection criterion for spatial filters that is based on robust (sandwich) p-values and does not require likelihood-based indicators. In this respect, we develop an ad hoc backward stepwise function in R. Second, using this function, we select a reduced set of spatial filters that properly accounts for importer-side and exporter-side specific spatial effects, as well as network effects, both at the count and the logit processes of zero-inflated methods. Applying this estimation strategy to a cross-section of bilateral trade flows between a set of 64 countries for the year 2000, we find that our specification outperforms the benchmark models in terms of model fitting, both considering the AIC and in predicting zero (and small) flows