Analysing spatial spillovers in corruption: A dynamic spatial panel data approach

Donfouet, Hermann Pythagore Pierre; Jeanty, P. Wilner; Malin, Eric

doi:10.1111/pirs.12231

Cited by 16 publications

(41 citation statements)

References 66 publications

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“…Given that economic development is negatively correlated with crime ( Donfouet, Jeanty, & Malin, 2018 ), one of the SDGs is specifically concerned with fighting crime. Namely, as part of Goal 16, the U.N. has set out a range of targets aimed at reducing criminal activity around the world, such as significantly reducing all forms of violence, ending trafficking, promoting the rule of law and combating organised crime ( U.N., 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Leveraging machine learning in the global fight against money laundering and terrorism financing: An affordances perspective

Canhoto

2021

Journal of Business Research

112

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Financial services organisations facilitate the movement of money worldwide, and keep records of their clients’ identity and financial behaviour. As such, they have been enlisted by governments worldwide to assist with the detection and prevention of money laundering, which is a key tool in the fight to reduce crime and create sustainable economic development, corresponding to Goal 16 of the United Nations Sustainable Development Goals. In this paper, we investigate how the technical and contextual affordances of machine learning algorithms may enable these organisations to accomplish that task. We find that, due to the unavailability of high-quality, large training datasets regarding money laundering methods, there is limited scope for using supervised machine learning. Conversely, it is possible to use reinforced machine learning and, to an extent, unsupervised learning, although only to model unusual financial behaviour, not actual money laundering.

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Section: Introductionmentioning

confidence: 99%

Leveraging machine learning in the global fight against money laundering and terrorism financing: An affordances perspective

Canhoto

2021

Journal of Business Research

112

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“…For example, Güçlü [33] incorporates spatial links into the Kuznets curve by analyzing the relationship between economic growth and environmental degradation for Turkish NUTS-3 regions in the years 2008-2013. The spatial environmental Kuznets curve was also used in the works by the following: Tan [34], Donfouet et al [35], McPherson and Nieswiadomy [36], Burnett and Bergstrom [37], and Tevie et al [38]. These researchers used simple spatial models, such as the spatial autoregressive model (SAR) and the spatial error model (SEM).…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of Spatial Effects in the Relationship between CO2 Emissions and Renewable Energy Consumption in the Context of Economic Growth

Jankiewicz

Szulc

2021

Energies

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The paper presents a spatial approach to the analysis of the relationship between air pollution, economic growth, and renewable energy consumption. The economic growth of every country is based on the energy consumption that leads to an increase in national productivity. Using renewable energy is very important for the environmental protection and security of the earth’s resources. Promoting environmentally friendly operations increases awareness of sustainable development, which is currently a major concern of state governments. In this study, we explored the influence of economic growth and the share of renewable energy out of total energy consumption on CO2 emissions. The study was based on the classical environmental Kuznets curve (EKC) and enriched with the spatial dependencies. In particular, we determined the spatial spillovers in the form of the indirect effects of changes in renewable energy consumption of a specific country on the CO2 emissions of neighboring countries. A neighborhood in this study was defined by ecological development similarity. The neighborhood matrix was constructed based on the values of the ecological footprint measure. We used the spatio-temporal Durbin model, with which the indirect effects were determined in relation to the spatially lagged renewable energy consumption. The results of our study also show the strength of the effects caused by imitating actions from the states with high levels of environmental protection. The study was conducted using data for 75 selected countries from the period of 2013–2019. Cumulative spatial and spatio-temporal effects allowed us to determine (1) the countries with the greatest impact on others and (2) the countries that follow the leading ones.

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“…As an extension of a general nesting spatial model for cross‐section data to a space‐time model for panel data, the SPDM has received increasing interest in recent literature due to the increased availability of panel datasets where spatial units are followed over time and the more modeling possibilities that panel datasets could offer for researchers (Anselin, Le Gallo, & Jayet, ; Elhorst, , ). In fact, the SPDM has been widely applied in recent literature on regional science (Arbia & Fingleton, ; Donfouet, Jeanty, & Malin, ; Elhorst & Fréret, ; Ramos, Suriñach, & Artís, ). The general expression of the SPDM is given as follows (Elhorst, ):

Y_{t} = ρ bold-italicW Y_{t} + α ι_{N} + X_{t} bold-italicβ + bold-italicW X_{t} bold-italicθ + bold-italicμ + ξ_{t} ι_{N} + u_{t},

u_{t} = λ bold-italicW u_{t} + ε_{t},

where Y is an N × 1 vector of the dependent variable for every unit i ( i = 1, 2, …, N ) in one cross‐section, t is an index for the time dimension, X is an N × K matrix of independent variables.…”

Section: Model Specifications Data and Variablesmentioning

confidence: 99%

A spatial panel data analysis of China's urban land expansion, 2004–2014

Wang

2019

Papers in Regional Science

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Drawing upon a panel dataset on China's state-owned construction land supply at the prefecture level, this paper adopts the spatial panel data model to investigate the existence of spatial dependence in the mechanisms behind China's urban land expansion from 2004 to 2014. The empirical results show that China's urban land expansion has been mainly influenced by a quadruple process of urbanization, industrialization, globalization and decentralization. The strong existence of spatial dependence in China's urban land expansion can be reflected by the significant coefficient estimates of the spatial lags of both the dependent variable and independent variables. KEYWORDS China, spatial dependence, spatial panel data model, urban land expansion, urbanization

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Analysing spatial spillovers in corruption: A dynamic spatial panel data approach

Cited by 16 publications

References 66 publications

Leveraging machine learning in the global fight against money laundering and terrorism financing: An affordances perspective

Leveraging machine learning in the global fight against money laundering and terrorism financing: An affordances perspective

Analysis of Spatial Effects in the Relationship between CO2 Emissions and Renewable Energy Consumption in the Context of Economic Growth

A spatial panel data analysis of China's urban land expansion, 2004–2014

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