The distributional effects of NAFTA in Mexico: evidence from a panel of municipalities

This paper studies the regional distribution of the benefits from trade in Mexico after NAFTA. Specifically, we ask whether or not NAFTA has increased the concentration of economic activity in Mexico. Unlike previous work which uses state-level data, we identify the effect of NAFTA on economic activity at the municipal level allowing us to observe detailed growth patterns across space. Further, to explicitly identify the effect of the trade agreement, we compare results for growth in traded and non-traded sectors. Given the spatial nature of these data, we make explicit use of spatial econometrics methods. We find that NAFTA caused the wealthy regions nearest to the border to grow faster than others, increasing regional disparity. Second, we find that larger municipalities experienced greater per-capita economic benefits from NAFTA. This effect is particularly noticeable in the north. Somewhat surprisingly, we find that regions with a less literate workforce and worse infrastructure grew faster than other areas after the trade agreement, decreasing regional disparity. We notice these redistributive effects occur primarily in the non-traded sectors.Regional Disparities, Trade Liberalization, Agglomeration Economies, Economic Growth, Mexico, Transport Cost, Spatial econometrics, Community/Rural/Urban Development, International Development, International Relations/Trade,

On the Finite Sample Properties of Pre-test Estimators of Spatial Models

This paper explores the properties of pre-test strategies in estimating a linear Cliff-Ord -type spatial model when the researcher is unsure about the nature of the spatial dependence. More specifically, the paper explores the finite sample properties of the pre-test estimators introduced in Florax et al. (2003), which are based on Lagrange Multiplier (LM) tests, within the context of a Monte Carlo study. The performance of those estimators is compared with that of the maximum likelihood (ML) estimator of the encompassing model. We find that, even in a very simple setting, the bias of the estimates generated by pre-testing strategies can be very large in some cases and the empirical size of tests can differ substantially from the nominal size. This is in contrast to the ML estimator

Anti-BVDV activity evaluation of naphthoimidazole derivatives compared with parental imidazoquinoline compounds.

Background: Pestivirus genus includes animal pathogens which are involved in economic impact for the livestock industry. Among others, Bovine Viral Diarrhoea Virus (BVDV) establish a persistent infection in cattle causing a long list of symptoms and a high mortality rate. In the last decades, we synthesised and reported a certain number of anti-BVDV compounds. Methods: In them, imidazoquinoline derivatives turned out as the most active. Their mechanism of actions has been deeply investigated, BVDV RNA-dependent RNA polymerase (RpRd) resulted as target and the way of binding was predicted in silico through three main H-bond interaction with the target. The prediction could be confirmed by target or ligand mutation. The first approach has already been performed and published confirming the in silico prediction. Results: Here, we present how the ligand chemical modification affects the anti-BVDV activity. The designed compounds were synthesised and tested against BVDV as in silico assay negative control. Conclusion: The antiviral results confirmed the predicted mechanism of action, as the newly synthesised compounds resulted not active in the in vitro BVDV infection inhibitio

Distributed hydrologic modeling of a sparsely monitored basin in Sardinia, Italy, through hydrometeorological downscaling

The water resources and hydrologic extremes in Mediterranean basins are heavily influenced by climate variability. Modeling these watersheds is difficult due to the complex nature of the hydrologic response as well as the sparseness of hydrometeorological observations. In this work, we present a strategy to calibrate a distributed hydrologic model, known as TIN-based Real-time Integrated Basin Simulator (tRIBS), in the Rio Mannu basin (RMB), a medium-sized watershed (472.5 km2) located in an agricultural area in Sardinia, Italy. In the RMB, precipitation, streamflow and meteorological data were collected within different historical periods and at diverse temporal resolutions. We designed two statistical tools for downscaling precipitation and potential evapotranspiration data to create the hourly, high-resolution forcing for the hydrologic model from daily records. Despite the presence of several sources of uncertainty in the observations and model parameterization, the use of the disaggregated forcing led to good calibration and validation performances for the tRIBS model, when daily discharge observations were available. The methodology proposed here can be also used to disaggregate outputs of climate models and conduct high-resolution hydrologic simulations with the goal of quantifying the impacts of climate change on water resources and the frequency of hydrologic extremes within medium-sized basins

ESG in the financial industry: What matters for rating analysts?

This paper examines ESG rating analysts' views from Sustainalytics in order to highlight the main ESG features discussed across 11 sectors. We perform a topic modeling and a sentiment analysis to identify the content of analysts' opinions on the companies' ESG performance and to uncover the embedded sentiment associated with each ESG feature. The results of the topic modeling consist of 13 topics with a sector driven distribution. The analysis suggests that the best ESG performing financial institutions show to be actively committed to the code of best practice in governance and disclosure transparency. Whereas penalized financial entities seem to manifest less attention to ethical conduct and mis-selling. Furthermore, data privacy and security attract analysts' attention and should be closely monitored by financial entities. Finally, it is important to actively disclose ESG activities as the more information is available the better ESG commitment is reflected in analysts' views

Age-related cognitive decline and the olfactory identification deficit are associated to increased level of depression

Purpose: Previous studies reported a correlation between olfactory function and depression. However, in literature, no data are available for the correlation between depression and all other factors such as age, sex, olfactory, gustatory, and cognitive function in healthy subjects taken together. The aim of this study was to provide a systematic account regarding the association between those variables in a nonclinical population. Methods: Two hundred and seventy-three participants were recruited with an age range of 19–84 years. Olfactory, gustatory, cognitive function, and depression level were evaluated by means of the following tests: the Sniffin’ Sticks test, Taste Strips test, Montreal Cognitive Assessment (MoCA), and Beck Depression Inventory (BDI). Results: In our data, an age-related decrease in olfactory and gustatory function and a decline in cognitive functions such as attention, memory, and language were observed. Instead, no significant differences were observed for the depression level in relation to the different age ranges. However, our results indicated that the depression level could be associated to sex, odor identification impairment, and decreased attention and language. Conclusion: Sex, the odor identification impairment, and an age-related decrease in attention and language are associated with increased level of depression in healthy subjects. Our data can be useful and informative for health care workers, that is, to have adequate preventive strategies to be used whenever these conditions are detected and recognized

Investigating parameter transferability across models and events for a Semiarid Mediterranean Catchment

Physically based distributed hydrologic models (DHMs) simulate watershed processes by applying physical equations with a variety of simplifying assumptions and discretization approaches. These equations depend on parameters that, in most cases, can be measured and, theoretically, transferred across different types of DHMs. The aim of this study is to test the potential of parameter transferability in a real catchment for two contrasting periods among three DHMs of varying complexity. The case study chosen is a small Mediterranean catchment where the TIN-based Real-time Integrated Basin Simulator (tRIBS) model was previously calibrated and tested. The same datasets and parameters are used here to apply two other DHMs-the TOPographic Kinematic Approximation and Integration model (TOPKAPI) and CATchment HYdrology (CATHY) models. Model performance was measured against observed discharge at the basin outlet for a one-year period (1930) corresponding to average wetness conditions for the region, and for a much drier two-year period (1931-1932). The three DHMs performed comparably for the 1930 period but showed more significant differences (the CATHY model in particular for the dry period. In order to improve the performance of CATHY for this latter period, an hypothesis of soil crusting was introduced, assigning a lower saturated hydraulic conductivity to the top soil layer. It is concluded that, while the physical basis for the three models allowed transfer of parameters in a broad sense, transferability can break down when simulation conditions are greatly altered

Stress evaluation in simulated autonomous and manual driving through the analysis of skin potential response and electrocardiogram signals

The evaluation of car drivers\u2019 stress condition is gaining interest as research on Autonomous Driving Systems (ADS) progresses. The analysis of the stress response can be used to assess the acceptability of ADS and to compare the driving styles of different autonomous drive algorithms. In this contribution, we present a system based on the analysis of the Electrodermal Activity Skin Potential Response (SPR) signal, aimed to reveal the driver\u2019s stress induced by different driving situations. We reduce motion artifacts by processing two SPR signals, recorded from the hands of the subjects, and outputting a single clean SPR signal. Statistical features of signal blocks are sent to a Supervised Learning Algorithm, which classifies between stress and normal driving (non-stress) conditions. We present the results obtained from an experiment using a professional driving simulator, where a group of people is asked to undergo manual and autonomous driving on a highway, facing some unexpected events meant to generate stress. The results of our experiment show that the subjects generally appear more stressed during manual driving, indicating that the autonomous drive can possibly be well received by the public. During autonomous driving, however, significant peaks of the SPR signal are evident during unexpected events. By examining the electrocardiogram signal, the average heart rate is generally higher in the manual case compared to the autonomous case. This further supports our previous findings, even if it may be due, in part, to the physical activity involved in manual driving

Supervised learning techniques for stress detection in car drivers

6noIn this paper we propose the application of supervised learning techniques to recognize stress situations in drivers by analyzing their Skin Potential Response (SPR) and the Electrocardiogram (ECG). A sensing device is used to acquire the SPR from both hands of the drivers, and the ECG from their chest. We also consider a motion artifact removal algorithm that allows the generation of a single cleaned SPR signal, starting from the two SPR signals, which could be characterized by artifacts due to vibrations or movements of the hands on the wheel. From both the cleaned SPR and the ECG signals we compute some statistical features that are used as input to six Machine Learning Algorithms for stress or non-stress episodes classification. The SPR and ECG signals are also used as input to Deep Learning Algorithms, thus allowing us to compare the performance of the different classifiers. The experiments have been carried out in a firm specialized in developing professional car driving simulators. In particular, a dynamic driving simulator has been used, with subjects driving along a straight road affected by some unanticipated stress-evoking events, located at different positions. We obtain an accuracy of 88.13% in stress recognition using a Long Short-Term Memory (LSTM) network.openopenZontone P.; Affanni A.; Bernardini R.; Del Linz L.; Piras A.; Rinaldo R.Zontone, P.; Affanni, A.; Bernardini, R.; Del Linz, L.; Piras, A.; Rinaldo, R