Deterrent Impact of Penal Policies on Telecom-Fraud in China: A Spatial and Empirical Analysis

Straipsnyje nagrinėjamos branduolinio ginklo nenaudojimo normos ir branduolinio atgrasymo strategijos (ne)suderinamumo galimybės. Siekiant išgryninti teorinius koncepcijų sąveikos variantus, pirmojoje straipsnio daly­je atliekama teorinė konstruktyvistinės normų bei realistinei srovei priskiria­mos atgrasymo teorijos analizė. Išskiriant galimo koncepcijų suderinamumo ir ryškiausios priešpriešos taškus, konstruojamas tam tikras teorinis rėmas empirinei JAV atvejo analizei, kuri pateikiama antrojoje straipsnio dalyje. Tyrimas, grindžiamas kokybine turinio analize, atskleidė, kad nagrinėjamu 2009–2015 metų laikotarpiu JAV branduolinio ginklo nenaudojimo norma dėl dinamikos įgijo tradicijos formą. Ši normos forma palieka nedidelę bran­duolinio ginklo panaudojimo galimybę, kuri yra būtina atgrasymui. Taip bent teoriškai išsprendžiamas koncepcijų suderinamumo klausimas. Tačiau dėl tokio nenatūralaus nenaudojimo normos ir atgrasymo derinimo labiausiai nukenčia atgrasymo patikimumo elementas, o tai JAV branduolinę laikyseną paverčia neaiškia ir ateityje gali kelti tam tikrą grėsmę.

How (Not) to Lie with Spatial Statistics

Micro, Small, and Medium Enterprises (MSMEs) play an important role in the Indonesian economy, including in Lampung Province. Although the government has issued various policies to support the development of MSMEs, the effectiveness of these policies still needs to be evaluated in depth. This research aims to evaluate the effectiveness of government policies in supporting MSMEs in Lampung through empirical data analysis. The research uses an empirical data analysis approach to evaluate the effectiveness of MSME support policies in Lampung. The analytical method in this research uses literature study through collecting and analyzing secondary data in the form of journal articles, government documents, and other scientific articles about entrepreneurship and MSMEs sourced from libraries. It is hoped that the results of this research will provide a comprehensive evaluation of the effectiveness of government policies in supporting MSMEs in Lampung, as well as identifying supporting and inhibiting factors for policy success. This research found that government policy in supporting MSMEs in Lampung had a positive impact in encouraging the growth of the MSME sector in Lampung. However, there are still challenges that still need improvement, namely lack of socialization, and limited access for MSMEs in remote areas. Keywords: empirical analysis, government policy, MSMEs

New spatial econometric techniques and applications in regional science

Although deterrence theory was a central focus in the study of International Relations during the Cold War, attention has shifted away from deterrence since the end of that conflict. Nonetheless, deterrence is a general phenomenon that is not limited to any particular time or space. Moving beyond a simple focus on the US-Soviet relationship, scholars have recently begun further explorations of deterrence, through development of theory, analysis of policy alternatives, and empirical analysis. This article seeks to evaluate where deterrence theory stands today through: (1) a consideration of distinctions between different strands of theory; (2) a discussion of the assumption of rationality in deterrence theory; (3) an examination of three important distinctions in deterrence; (4) an evaluation of the difficult task of testing deterrence theory, and (5) an overview of recent theoretical developments. The primary conclusion is that perfect deterrence theory provides a logically consistent alternative to classical deterrence theory and therefore provides the most appropriate basis for further theoretical development, empirical testing, and application to policy.

The Brazilian National Hansen's Disease Control Program recently identified clusters with high disease transmission. Herein, we present different spatial analytical approaches to define highly vulnerable areas in one of these clusters. The study area included 373 municipalities in the four Brazilian states Maranhão, Pará, Tocantins and Piauí. Spatial analysis was based on municipalities as the observation unit, considering the following disease indicators: (i) rate of new cases/100,000 population, (ii) rate of cases <15 years/100,000 population, (iii) new cases with grade-2 disability/100,000 population and (iv) proportion of new cases with grade-2 disabilities. We performed descriptive spatial analysis, local empirical Bayesian analysis and spatial scan statistic. A total of 254 (68.0%) municipalities were classified as hyperendemic (mean annual detection rates >40 cases/100,000 inhabitants). There was a concentration of municipalities with higher detection rates in Pará and in the center of Maranhão. Spatial scan statistic identified 23 likely clusters of new leprosy case detection rates, most of them localized in these two states. These clusters included only 32% of the total population, but 55.4% of new leprosy cases. We also identified 16 significant clusters for the detection rate <15 years and 11 likely clusters of new cases with grade-2. Several clusters of new cases with grade-2/population overlap with those of new cases detection and detection of children <15 years of age. The proportion of new cases with grade-2 did not reveal any significant clusters. Several municipality clusters for high leprosy transmission and late diagnosis were identified in an endemic area using different statistical approaches. Spatial scan statistic is adequate to validate and confirm high-risk leprosy areas for transmission and late diagnosis, identified using descriptive spatial analysis and using local empirical Bayesian method. National and State leprosy control programs urgently need to intensify control actions in these highly vulnerable municipalities.

This study compares the effectiveness of COVID-19 control policies on the virus’s spread and on the change of the infection dynamics in China, Germany, Austria, and the USA relying on a regression discontinuity in time and ‘earlyR’ epidemic models. The effectiveness of policies is measured by real-time reproduction number and cases counts. Comparison between the two lockdowns within each country showed the importance of people's risk perception for the effectiveness of the measures. Results suggest that restrictions applied for a long period or reintroduced later may cause at-tenuated effect on the circulation of the virus and the number of casualties.

Sub-Saharan Africa countries face the challenge of reducing rural poverty and reversing the declining trends of agricultural productivity and the high levels of soil nutrient depletion. Despite of numerous efforts and investments, high levels of poverty and resource degradation persist in African agriculture. The Millennium Development Goals Report (MDGR) states that the majority of people living below the poverty line of $1.25 a day belong to Sub-Saharan Africa (SSA) and South Asia. About two thirds of the global rural population lives in mixed crop-livestock systems (CLS), typical of SSA, where interactions between crops and livestock activities are important for the subsistence of smallholders. CLS are characterized by high degree of biophysical and economic heterogeneity, complex and diversified production system that frequently involves a combination of several subsistence and cash crops and livestock. Increasing crop productivity is clearly a key element to improve living standards and to take these people out of poverty. However, agricultural productivity in most of SSA has been stagnant or increased slowly. In addition, the likely negative impacts of climate change on agriculture have accentuated the vulnerability of smallholders. The international research community has once more the eyes on SSA with the recently proposed post-2015 MDGs, the Sustainable Development Goals that emphasize the need to achieve sustainable development globally by 2030 by promoting economic development, environmental sustainability, good governance and social inclusion. Governments and scientists are making considerable efforts to develop strategies that include structural transformations of the different sectors of the economy in search of the recipe to achieve the SDGs. Most of these strategies are based on policy and technology interventions that seek to achieve the “win-win” outcomes and move from the usual “tradeoffs” between poverty-productivity-sustainability to synergies. A key message of this thesis is that achieving the goal of sustainable development in semi-subsistence African agriculture will require better understanding of the poverty-productivity-sustainability puzzle: why high poverty and resource degradation levels persist in African agriculture. I hypothesize that the answer to this puzzle lies, at least in part, in understanding and appropriately analyzing key features of semi-subsistence crop-livestock systems (CLS) typical of Sub-Saharan Africa. The complexity and diversity of CLS often constrain the ability of policy or technology interventions to achieve a “win-win” outcome of simultaneously reducing poverty while increasing productivity sustainably (i.e., avoiding soil nutrient losses). This thesis focuses on the Machakos Region in Kenya. Machakos has been the center of many studies looking at soil fertility issues and its implications for poverty and food security, including the well-known study by Tiffen et al. (1994). Recently, the Government of Kenya developed the Kenya Vision 2030, a long-term development strategy designed to guide the country to meet the 2015 MDGs and beyond. The agricultural sector is recognized as one of the economic actors that can lead to reduce poverty if appropriate policies are in place. For the Vision 2030, the key is to improve smallholder productivity and promote non-farm opportunities. The Vision 2030 was used to assess if the implementation of some of the proposed plans and policies can lead to a sustainable agriculture for smallholders in the Machakos region. This thesis describes and uses the Tradeoff Analysis Model (TOA), an integrated modeling approach designed to deal with the complexities associated to production systems such as the CLS and at the same time, quantify economic and sustainability indicators for policy tradeoff analysis (e.g., poverty indexes and measures of sustainability). The TOA was linked to Representative Agricultural Pathways and Scenarios to represent different future socio-economic scenarios (based on the Vision 2030) to assess the impacts of policy interventions aimed to move agricultural systems towards meeting sustainable development goals. One important finding is that the complex behavior of CLS has important implications for the effectiveness of policy interventions. The Machakos analysis provides important findings regarding the implementation and effectiveness of policy interventions addressing poverty and sustainability in Africa and other parts of the developing world. The analysis shows that policy interventions tend to result in much larger benefits for better-endowed farms, implying that farm heterogeneity results in differential policy impacts and that resilience of agricultural systems is likely to be highly variable and strongly associated with heterogeneity in bio-physical and economic conditions. The results shows that a combination of these interventions and strategies, based on the GoK Vision 2030 and the Machakos County plans, could solve the poverty-productivity-sustainability puzzle in this region. The pathway from tradeoffs to synergies (win-win) seems to be feasible if these interventions and strategies are well implemented, however the analysis also shows that some villages may respond better to these strategies than others. The analysis suggests that these interventions may actually benefit most the areas with better initial endowments of soils and climate. The analysis also suggested that prices (e.g., maize price) play a key role in the assessment of policy interventions. There is an increasing recognition that analysis of economic and environmental outcomes of agricultural production systems requires a bottom-up linkage from the farm to market, as well as top-down linkage from market to farm. Hence, a two-way linkage between the TOA model and a partial equilibrium market model (ME) was developed. The TOA model links site-specific bio-physical process models and economic decision models, and aggregate economic and environmental outcomes to a regional scale, but treats prices as exogenous. The resulting TOA-ME allows the effects of site-specific interactions at the farm scale to be aggregated and used to determine market equilibrium. This in turn, can be linked back to the underlying spatial distribution of economic and environmental outcomes at market equilibrium quantities and prices. The results suggest that market equilibrium is likely to be important in the analysis of agricultural systems in developing countries where product and input markets are not well integrated, and therefore, local supply determines local prices (e.g., high transport costs may cause farm-gate prices be set locally) or where market supply schedules are driven not only by prices but also by changes in farm characteristics in response to policy changes, environmental conditions or socio-economic conditions. The results suggest that the market equilibrium price associated to a policy intervention could be substantially different than the prices observed without the market equilibrium analysis, and consequently could play an important role in evaluating the impacts of policy or technology interventions. As mentioned above, climate change poses a long-term threat for rural households in vulnerable regions like Sub-Saharan Africa. Policy and technology interventions can have different impacts under climate change conditions. In this thesis the likely economic and environmental impacts of climate change and adaptations on the agricultural production systems of Machakos are analyzed. Climate change impact assessment studies have moved towards the use of more integrated approaches and the use of scenarios to deal with the uncertainty of future condition. However, several studies fall short of adequately incorporating adaptation in the analysis, they also fall short of adequately assessing distributional economic and environmental impacts. Similarly, climate change is likely to change patterns of supply and demand of commodities with a consequent change in prices that could play an important role in designing policies at regional, national and international levels. Therefore, a market equilibrium model should also be incorporated in the analysis to assess how markets react to changing prices due to shifts in supply and demand of commodities. The TOA-ME was used to incorporate the elements mentioned above to assess the impacts of climate change. Using data from 5 Global Circulation Models (GCMs) with three emission scenarios (SRES, 2000) to estimate the climate change projections, these projections were used to perturb weather data used by a crop simulation model to estimate the productivity effects of climate change. Land use change and impacts on poverty and nutrient depletion at the market equilibrium were then assessed using the TOA-ME model. The simulation was carried out for three scenarios, which are a combination of socio-economic and climate change scenarios: a baseline scenario that represents current socio-economic conditions and climate conditions, a climate change and current socio-economic scenarios (i.e., future climate change with no policy or technology intervention), and a climate change and future socio economic conditions which are a consequence of rural development policies. Our findings show that in this particular case, the changes on precipitation, temperature and solar radiation do not show a significant difference among the selected emission scenarios. However, the variability is significant across GCMs. The effects of climate change on crop productivity are negative on average. These results show that policy and technology interventions are needed to reduce this region’s vulnerability. Furthermore, the socio-economic scenarios based on policy and technology interventions presented in the case study would be effective to offset the negative effect of climate change on the sustainability (economical and environmental) of the system across a range of possible climate outcomes represented by different GCMs. Finally, the results show that ignoring market equilibrium analysis can lead to biased results and incorrect information for policy making, in particular for the scenario based on policy and technology interventions. One of the major conclusions of the thesis are that policy interventions aimed to deal with poverty and sustainability can have unintended consequences if they are not accompanied by a set of policy strategies and investments. For example, increasing the maize price can result in substitution from subsistence crops to maize, without much increase in nutrient inputs, thus increasing soil nutrient losses. The analysis shows that improving soil nutrient balances by increasing fertilizer and manure use is critically important, but is not enough to move the system to a sustainable path. There is no one factor that can reverse the negative nutrient balances and move the system towards sustainability. Rather, a broad-based strategy is required that stimulates rural development, increases farm size to a sustainable level, and also reduces distortions and inefficiencies in input and output markets that tend to discourage the use of sustainable practices. The Machakos case shows that a combination of these interventions and strategies, based on the GoK Vision 2030 and the Machakos County plans, could solve the poverty-productivity-sustainability puzzle in this region.

A LTHOUGH hostage seizures are a small percentage of terrorist incidents, they represent some of the most spectacular and influential events.2 The takeover of the American embassy in Tehran on November 14, 1979, the seizure of eleven dPEC oil ministers on December 21, 1975, and the capture and killing of nine Israeli athletes on September 5, 1972, are incidents not easily forgotten. From 1968 through 1982, of the approximately 8,000 reported terrorist events, 540 (7 percent) were transna-

BackgroundBayesian models have been applied throughout the Covid-19 pandemic especially to model time series of case counts or deaths. Fewer examples exist of spatio-temporal modeling, even though the spatial spread of disease is a crucial factor in public health monitoring. The predictive capabilities of infectious disease models is also important.MethodsIn this study, the ability of Bayesian hierarchical models to recover different parts of the variation in disease counts is the focus. It is clear that different measures provide different views of behavior when models are fitted prospectively. Over a series of time horizons one step predictions have been generated and compared for different models (for case counts and death counts). These Bayesian SIR models were fitted using MCMC at 28 time horizons to mimic prospective prediction. A range of goodness of prediction measures were analyzed across the different time horizons.ResultsA particularly important result is that the peak intensity of case load is often under-estimated, while random spikes in case load can be mimicked using time dependent random effects. It is also clear that during the early wave of the pandemic simpler model forms are favored, but subsequently lagged spatial dependence models for cases are favored, even if the sophisticated models perform better overall.DiscussionThe models fitted mimic the situation where at a given time the history of the process is known but the future must be predicted based on the current evolution which has been observed. Using an overall ‘best’ model for prediction based on retrospective fitting of the complete pandemic waves is an assumption. However it is also clear that this case count model is well favored over other forms. During the first wave a simpler time series model predicts case counts better for counties than a spatially dependent one. The picture is more varied for morality.ConclusionsFrom a predictive point of view it is clear that spatio-temporal models applied to county level Covid-19 data within the US vary in how well they fit over time and also how well they predict future events. At different times, SIR case count models and also mortality models with cumulative counts perform better in terms of prediction. A fundamental result is that predictive capability of models varies over time and using the same model could lead to poor predictive performance.In addition it is clear that models addressing the spatial context for case counts (i.e. with lagged neighborhood terms) and cumulative case counts for mortality data are clearly better at modeling spatio-temporal data which is commonly available for the Covid-19 pandemic in different areas of the globe.

Modeling spatial and spatio-temporal continuous processes is an important and challenging problem in spatial statistics. Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA describes in detail the stochastic partial differential equations (SPDE) approach for modeling continuous spatial processes with a Matern covariance, which has been implemented using the integrated nested Laplace approximation (INLA) in the R-INLA package. Key concepts about modeling spatial processes and the SPDE approach are explained with examples using simulated data and real applications. This book has been authored by leading experts in spatial statistics, including the main developers of the INLA and SPDE methodologies and the R-INLA package. It also includes a wide range of applications: * Spatial and spatio-temporal models for continuous outcomes * Analysis of spatial and spatio-temporal point patterns * Coregionalization spatial and spatio-temporal models * Measurement error spatial models * Modeling preferential sampling * Spatial and spatio-temporal models with physical barriers * Survival analysis with spatial effects * Dynamic space-time regression * Spatial and spatio-temporal models for extremes * Hurdle models with spatial effects * Penalized Complexity priors for spatial models All the examples in the book are fully reproducible. Further information about this book, as well as the R code and datasets used, is available from the book website at http://www.r-inla.org/spde-book. The tools described in this book will be useful to researchers in many fields such as biostatistics, spatial statistics, environmental sciences, epidemiology, ecology and others. Graduate and Ph.D. students will also find this book and associated files a valuable resource to learn INLA and the SPDE approach for spatial modeling.

Financial development and income inequality in China – A spatial data analysis

Background/Aim: Dengue Fever is a vector-borne disease that predominantly effects countries in tropical latitudes. In recent years, the incidence rate in many countries has been increasing, identifying the need for accurate predictive models to forecast future increases or changes in geographical spread, to allow adequate control plans to be implemented. We develop and assess several models of Dengue Fever incidence at the ‘City’ administrative level in China using autocorrelation, population, weather, climate, vegetation, and land use variables. Subsequently, Dengue Fever case counts are forecast under two climate change scenarios for the years 2050 and 2100.Methods: A list of Dengue Fever cases was obtained from the China Center for Disease Control (CDC) which contained details of individual Dengue cases from 2009 through to 2015. Weather data was obtained from the China Bureau of Meteorology, population data from the China Bureau of Statistics, vegetation and land use data from the National Aeronautics and Space Administration (NASA). The datasets were combined, cleaned and processed to a weekly time scale. Initial descriptive analysis included incidence rates, correlation, and spatial and temporal autocorrelation analysis. Several models were then built and assessed for their suitability. These included the Zero-Inflated Negative Binomial Model (ZINB), the Distributed Lag Non-Linear Model (DLNM), the Generalised Additive Mixed Model with random effects (GAMM), the Cubist Model, and the Random Forest Model. Climate forecast data was obtained from the Intergovernmental Panel on Climate Change (IPCC). The Random Forest model was selected as the most suitable for future predictions and a loop was created that forecast cases week-by-week under the Representative Concentration Pathways (RCP) 4.5 and 8.5 for the medium- and long-term. Case counts were converted to incidence rates and mapped. The contribution of additional variables was assessed by examining the effects of changes to these variables on case counts.Results: Models were successfully fitted for all five methods. While many studies identified through a literature review used some form of Negative Binomial or Poisson model, the ZINB was not suitable for this data because of both the variation in cases across different City categories, and distribution of case counts. Additionally, the non-linear nature of the relationship between cases and weather variables also presented a problem when modelling. The DLNM also did not yield satisfactory results, with the model overpredicting almost all case counts. The GAMM fit the observed data very well, however, it was reliant on the random effects specified for ‘City’ to produce such a good fit and difficulty extracting the random effects meant that this model could not be used for predictions.The Cubist model fit the data exceptionally well, but the rule-based system prevented prediction using this dataset. Finally, a Random Forest Model was investigated. This model was successful in fitting the observed cases without using the City, Latitude or Longitude as covariates. It also allowed non-linear effects to be considered and achieved consistent results across numerous random seeds.The case count projections produced from this model were converted to incidence rates and plotted. Predominantly a greater number of cases were predicted to occur in the long-term and in the higher concentration pathway when compared to the medium-term and lower concentration pathway across all epidemic scenarios. Across China the average yearly observed incidence rate is approximately 0.62 cases per 100,000 population. For the mid-term projections at an epidemic scale, the rates increase to 1.80 and 1.93 cases per 100,000 population for RCP 4.5 and RCP 8.5 respectively. In the long-term the overall incidence rate increased further and the difference between pathways becomes more pronounced at 2.77 and 3.76 cases per 100,000 population respectively.Overall, any epidemic is likely to be experienced most strongly in the south-eastern regions on China, with high rates projected along the southern coastal regions of Guangdong, Hainan, and Guangxi Provinces, similar to that currently experienced. Guangzhou City has the largest total number of cases predicted for epidemic scenarios in each pathway, however Sanya City in the nearby Hainan Province has the largest incidence rate per capita under epidemic conditions for each pathway. This City was also forecast to experience the greatest difference between the forecast incidence rates and current incidence rates under each scenario. Forecasts project the epidemic reaching further inland.Conclusion: The Random Forest algorithm is a suitable method to model case counts of Dengue Fever in China. Under climate change scenarios it is likely that China will see an increase in the number of Dengue Fever cases observed nationally. Furthermore, there are several areas that do not currently experience many cases. These will be at an elevated risk due to the absence of underlying immunity. Surveillance and vector control measures should be implemented in these areas.

Spatial petrophysical property modeling is a crucial step in reservoir characterization as it directly affects heterogeneity and flow modeling. So it is essential to look for an optimal algorithm that leads to capture the most realistic spatial modeling. Many conventional kriging algorithms have been adopted for spatial permeability modeling such as simple, ordinary, and universal kriging. All these approaches are linear unbiased estimators as covariance structure is estimated first, and then used for interpolation leading to ignore the effect of uncertainty in the covariance structure on subsequent predictions. To overcome the restrictions of unbiased prediction in conventional approaches, Bayesian Kriging has been recently suggested to take into account the uncertainty about Variogram parameters on subsequent predictions. Bayesian Kriging incorporates a prior knowledge about observations such as expert grasp and outcome from neighboring data to be considered as a qualified guess in spatial estimation procedure. Commonly, the prior distribution is classified in term of Variogram parameters such as coefficients, data variance, range, and nugget to be adopted as a qualified guess in the spatial estimation. The qualified guess allows uncertainty estimation reduction to achieve more realistic spatial modeling and improved reservoir characterization. The observation uncertainty is represented as a posterior distribution and predictive parameter distribution avoiding unrealistic small regions within the observations to attain optimal unbiased linear interpolation through Bayesian kriging algorithm. Due to the some similarity between Bayesian Kriging and Universal Kriging, which incorporates 2D trend in the spatial modeling, the two algorithms were considered for comparative spatial modeling of formation permeability in a real heterogeneous sandstone reservoir. The spatial modeling was also done through simple and ordinary kriging for extensive comparison. A statistical sampling approach was considered to rank and select the three quantiles P10, P50, and P90 of the created equiprobable reservoir stochastic images. The entire work was done through R, the most open-source statistical computing language.

For studying both individual-level and small-scale contextual influences on the effects of family policies on fertility, Multilevel Event History methods are the state-of-the-art. But in many countries, these methods cannot be applied because the available individual-level data are inadequate. This paper uses an alternative methodological framework that can be of help in these cases. It utilizes small-scale macro data, which is analyzed with Exploratory Data, Cluster, and Spatial Panel Model Analysis techniques. In a case study on the western German city of Bremen, the potential of this approach, as well as its limitations, are investigated. The study analyzes the impact of the parental leave reform of 1986 and the child benefit reform of 1996 on fertility levels in different city quarters (Stadtteile) of Bremen. The results indicate that both family policy reforms had, at least in the short-term, a significant impact on fertility levels. These positive effects were stronger in economically disadvantaged quarters. The findings also suggest that the reforms affected the timing more than the quantum of fertility. With regard to the methodological framework, we can conclude that the Spatial Analysis with small-scale macro data is a useful alternative when there is no individual-level data available for carrying out a Multilevel Event History Analysis.