Environmental Variables in National Accounts: A Case Study for Italy

Author(s):  
A. Giannone ◽  
M. Carlucci
Author(s):  
Jorge Salgado ◽  
José Ramírez-Álvarez ◽  
Diego Mancheno

AbstractThe 16 April 2016 earthquake in Ecuador exposed the significant weaknesses concerning the methodological designs to compute—from an economic standpoint—the consequences of a natural hazard-related disaster for productive exchanges and the accumulation of capital in Ecuador. This study addressed one of these challenges with an innovative ex ante model to measure the partial and net short-term effects of a natural hazard-related catastrophe from an interregional perspective, with the 16 April 2016 earthquake serving as a case study. In general, the specified and estimated model follows the approach of the extended Miyazawa model, which endogenizes consumption demand in a standard input–output model with the subnational interrelations and resulting multipliers. Due to the country’s limitations in its regional account records the input–output matrices for each province of Ecuador had to be estimated, which then allowed transactions carried out between any two sectors within or outside a given province to be identified by means of the RAS method. The estimations provide evidence that the net short-term impact on the national accounts was not significant, and under some of the simulated scenarios, based on the official information with respect to earthquake management, the impact may even have had a positive effect on the growth of the national product during 2016.


2020 ◽  
pp. 153465012098345
Author(s):  
Mirela Cengher ◽  
Joy C. Clayborne ◽  
Adrianna E. Crouch ◽  
Julia T. O’Connor

Over 60% of children diagnosed with selective mutism are also diagnosed with Autism Spectrum Disorder. Previous research established that behavioral interventions are effective at increasing speech in children with both diagnoses. However, few studies conducted assessments to determine environmental variables that inhibit speech, and such assessments are necessary for the development of effective and efficient treatments. This case study describes an assessment that evaluated the function(s) of selective mutism. The results confirmed that the participant did not talk to avoid social interaction and that mutism occurred primarily in the presence of multiple, unfamiliar people. Our first treatment focused on increasing tolerance for social interaction, demonstrated by an increase in speech production in the presence of unfamiliar people. Our second treatment focused on increasing qualitative aspects of the participant’s speech (i.e., both responses and initiations). Finally, we taught the participant’s parents to implement the treatment in naturalistic settings, and the participant demonstrated generalization of treatment effects across people and settings. Implications for clinical practice and future research are discussed.


2021 ◽  
pp. 181-196
Author(s):  
Edgar J. González ◽  
Dylan Z. Childs ◽  
Pedro F. Quintana-Ascencio ◽  
Roberto Salguero-Gómez

Integral projection models (IPMs) allow projecting the behaviour of a population over time using information on the vital processes of individuals, their state, and that of the environment they inhabit. As with matrix population models (MPMs), time is treated as a discrete variable, but in IPMs, state and environmental variables are continuous and are related to the vital rates via generalised linear models. Vital rates in turn integrate into the population dynamics in a mechanistic way. This chapter provides a brief description of the logic behind IPMs and their construction, and, because they share many of the analyses developed for MPMs, it only emphasises how perturbation analyses can be performed with respect to different model elements. The chapter exemplifies the construction of a simple and a more complex IPM structure with an animal and a plant case study, respectively. Finally, inverse modelling in IPMs is presented, a method that allows population projection when some vital rates are not observed.


2018 ◽  
Vol 39 (2) ◽  
Author(s):  
Ignacio Ramos Gutiérrez ◽  
Juan Manuel Martínez Labarga ◽  
José Araújo Díaz ◽  
Alejandro G. Fernández de Castro ◽  
Juan Carlos Moreno Saiz

2020 ◽  
Vol 27 (1) ◽  
pp. 43-57
Author(s):  
Martin Grandes ◽  
Ariel Coremberg

Purpose The purpose of this paper is to demonstrate empirically that corruption causes significant and sizeable macroeconomic costs to countries in terms of economic activity and economic growth. The authors modeled corruption building on the endogenous growth literature and finally estimated the baseline (bribes paid to public officials) macroeconomic cost of corruption using Argentina 2004-2015 as a case study. Design/methodology/approach The authors laid the foundations of a new methodology to account corruption losses using data from the national accounts and judiciary investigations within the framework of the Organisation for Economic Cooperation and Development (OECD) non-observed economy (NOE) instead of subjective indicators as in the earlier literature. They also suggested a new method to compute public expenditures overruns, including but not limited to public works. Findings The authors found the costs stand at a minimum accumulated rate of 8 per cent of gross domestic product (GDP) or 0.8 per cent yearly. These findings provided a corruption cost floor and were consistent with earlier research on world corruption losses estimated at 5 per cent by the World Economic Forum and with the losses estimated at between a yearly rate of 1.3 and 4 per cent and 2 per cent of GDP by Brazil and Peru’s corruption, respectively. Research limitations/implications The authors would need to extend the application of their new suggested methodology to further countries. They are working on this. They would need to develop the methodology in full to compute the public works overruns input to future econometric work. Originality/value In this paper, the authors make a threefold contribution to the literature on corruption and growth: first, they laid the foundations toward a new methodology to make an accounting of the corruption costs in terms of GDP consistent with the national accounts and executed budgets; on the one hand, and the OECD NOE framework, on the other. The authors named those corruption costs as percentage of GDP the “corruption wedge.” Second, they developed an example taking corruption events and a component of their total costs, namely, the bribes paid to public officials, taking Argentina 2004-2015 as a case study. Finally, they plugged the estimated wedge back into an endogenous growth model and calibrated the growth–corruption path simulating two economies where the total factor productivity was different, at different levels of the corruption wedge.


2021 ◽  
Vol 221 ◽  
pp. 110874
Author(s):  
Houssain Zitouni ◽  
Alae Azouzoute ◽  
Charaf Hajjaj ◽  
Massaab El Ydrissi ◽  
Mohammed Regragui ◽  
...  

2011 ◽  
Vol 20 (5) ◽  
pp. 929-943 ◽  
Author(s):  
Evangelia G. Drakou ◽  
Athanasios S. Kallimanis ◽  
Antonios D. Mazaris ◽  
Evangelia Apostolopoulou ◽  
John D. Pantis

2015 ◽  
Vol 54 (7) ◽  
pp. 1637-1662 ◽  
Author(s):  
Jason M. Apke ◽  
Daniel Nietfeld ◽  
Mark R. Anderson

AbstractEnhanced temporal and spatial resolution of the Geostationary Operational Environmental Satellite–R Series (GOES-R) will allow for the use of cloud-top-cooling-based convection-initiation (CI) forecasting algorithms. Two such algorithms have been created on the current generation of GOES: the University of Wisconsin cloud-top-cooling algorithm (UWCTC) and the University of Alabama in Huntsville’s satellite convection analysis and tracking algorithm (SATCAST). Preliminary analyses of algorithm products have led to speculation over preconvective environmental influences on algorithm performance. An objective validation approach is developed to separate algorithm products into positive and false indications. Seventeen preconvective environmental variables are examined for the positive and false indications to improve algorithm output. The total dataset consists of two time periods in the late convective season of 2012 and the early convective season of 2013. Data are examined for environmental relationships using principal component analysis (PCA) and quadratic discriminant analysis (QDA). Data fusion by QDA is tested for SATCAST and UWCTC on five separate case-study days to determine whether application of environmental variables improves satellite-based CI forecasting. PCA and significance testing revealed that positive indications favored environments with greater vertically integrated instability (CAPE), less stability (CIN), and more low-level convergence. QDA improved both algorithms on all five case studies using significantly different variables. This study provides an examination of environmental influences on the performance of GOES-R Proving Ground CI forecasting algorithms and shows that integration of QDA in the cloud-top-cooling-based algorithms using environmental variables will ultimately generate a more skillful product.


2017 ◽  
Vol 38 (6) ◽  
pp. e12476
Author(s):  
Fatemeh Pourjomeh ◽  
Mohammad Reza Shokri ◽  
Hamid Rezai ◽  
Hassan Rajabi-Maham ◽  
Elham Maghsoudlou

Sign in / Sign up

Export Citation Format

Share Document