Citizenship and power in an agent-based model of tax compliance with public expenditure

In this paper we present a model of tax compliance with heterogeneous agents who maximize their individual utility based on income and the conjectured level of per capita public expenditure. We formally include psychological drivers in this model. These drivers affect individual behavior, such as risk aversion, together with appreciation of public expenditure, expectations about peers’ compliance and a natural inclination to comply, all of which we summarize in a quality termed “citizenship”. The enforcement system, based on random inspections, is standard and only partially known to agents.The agent-based model is simulated under a variety of settings, representing different “societies”. We use the artificial data produced by the model to estimate the effects of taxpayers’ traits on personal tax behavior and to build a compliance societal slippery slope. At the individual level, we find a positive dependence of compliance on all variables, with the significant exception of the tax rate, which has a negative impact. As far as societies are concerned, we show how aggregate tax compliance depends on composite indices of citizenship and power, and we find that the former is more important than the latter.     

Behavioral dynamics of tax evasion – A survey

Since the 1950s (Schmölders, 1959) it is well known that behavioral aspects have an influence on tax evasion or tax compliance. In particular, interactions among the various entities involved in the taxation process (e.g. taxpayers, law makers, tax practitioners, tax authorities, etc.), and the dynamics that these interactions may generate, seem to play an important role for the actual level of tax compliance. However, the mainstream neoclassical approach to tax evasion (Allingham & Sandmo, 1972) cannot account for such interactions and dynamics. Therefore, during the last two decades new approaches (e.g. lab experiments, agent-based modeling, etc.) have been developed with a view to model how behavioral dynamics may foster or prevent tax evasion. In addition, empirical evidence has been generated that supports a role for such interaction dynamics. In this contribution we survey the main developments in this research area and provide some suggestions for further research.     

A Multivariate Intervention Model for the Dutch Mint Circulation: Estimation and Monte Carlo Simulation

For each of the five Dutch coinage denominations, a transfer-function model is estimated. The output variables are monthly observations of coins in circulation. Two input variables represent transaction flows; all other inputs are step functions, representing the occurrence of interventions. Using the method of cross-correlating the residuals of the individual equations, a multivariate transfer-function model is constructed and estimated. Next Monte Carlo simulation is applied to derive expectations and variances of the yearly addition to the stock of coins until 1996. Our results enlighten on some aspects of a problem situation faced by the Dutch State Mint.     

Growth curve analysis of complete and incomplete longitudinal data

Growth curve analysis is reviewed for both complete and incomplete data structures. Specific attention is paid to the incomplete data model of Kleinbaum and its applicability to the mixed longitudinal study. Design considerations and efficiency for various incomplete longitudinal studies are also discussed.     

Public Life in Gulfton: Multiple Publics and Models of Community Organization

Summary

Based on a case study of six community organizations in the Gulfton neighborhood in Houston, Texas, this paper proposes that community organization models need to consider that highly diverse and often contentious community efforts within a single community represent well the context of life in contemporary heterogeneous urban neighborhoods. Despite reservations, we find this diversity of organizational efforts and even the tensions among them generally positive, as they often reflect the most vibrant forms of public life in our otherwise privatizing world. Rethinking the diversity of community organizations as multiple publics in a privatizing context provides new openings for the importance and value of community organization within schools of social work and the larger society.     

喷涂工艺薄膜厚度模型的构建与应用

为研究12寸晶圆切割过程中喷涂工艺设备参数对保护性薄膜厚度的影响,提高设备调整效率和工艺质量,采用均匀设计的实验方法,以薄膜在测试量块上的厚度为目标,对影响厚度的主要因素:涂料压力、微调阀和雾化压力进行研究。对实验数据采用SPSS进行非线性逐步回归得到了薄膜厚度的数学模型,揭示了各因素的交互关系,为测机调整提供数据依据,节省大量反复测机时间,在保证工艺质量的同时提高了喷涂设备的生产效率。该模型已成功运用于某芯片封装测试厂的喷涂设备,并取得了显著效果。     

Fitting Second-Order Finite Mixture Models to Data with Many Censored Values Using Maximum Likelihood Estimation

Finite mixture models, that is, weighted averages of parametric distributions, provide a powerful way to extend parametric families of distributions to fit data sets not adequately fit by a single parametric distribution. First-order finite mixture models have been widely used in the physical, chemical, biological, and social sciences for over 100 years. Using maximum likelihood estimation, we demonstrate how a first-order finite mixture model can represent the large variability in data collected by the U.S. Environmental Protection Agency for the concentration of Radon 222 in drinking water supplied from ground water, even when 28% of the data fall at or below the minimum reporting level. Extending the use of maximum likelihood, we also illustrate how a second-order finite mixture model can separate and represent both the variability and the uncertainty in the data set.     

A recursive algorithm on estimating the parameters in multilevel models subject to the measurement errors on the covariates

Multilevel models are popular models for analysing data inheriting a hierarchical structure. They are used in diverse fields including social, medical, economical and biological sciences. These models encounter some problems in estimating the parameters, if there are measurement errors in either explanatory or response variables. A common approach to tackle this obstacle is to consider the pseudo variables and follow some simulation methods to estimate the parameters. We propose a new algorithm constituting the iterative and simulation extrapolation steps in turn. To evaluate the proposed algorithm, various simulation studies are also conducted. Moreover, we investigate the implementation of our method on a real data set concerning the cost and expenditure of the households in Tehran city in the year 2007.     

Rank estimation of regression coefficients using iterated reweighted least squares

This paper is concerned with the rank estimator for the parameter vector β in a linear model which is obtained by the minimization of a rank dispersion function. The rank estimator has many advantages over the regular least squares estimator, but the inaccessibility of software to carry out its computation has limited its use. An iterated reweighted least squares algorithm is presented for the computation of the rank estimator. The method is simple in concept and can be carried out readily with a wide variety of statistical software. Details of the method are discussed along with some results on its asymptotic distribution and numerical stability. Some examples are presented to show advantages of the rank method.     

Testing for serial correlation in the presence of dynamic heteroscedasticity

Standard serial correlation tests are derived assuming that the disturbances are homoscedastic, but this study shows that asympotic critical values are not accurate when this assumption is violated. Asymptotic critical values for the ARCH(2)-corrected LM, BP and BL tests are valid only when the underlying ARCH process is strictly stationary, whereas Wooldridge's robust LM test has good properties overall. These tests exhibit similar bahaviour even when the underlying process is GARCH (1,1). When the regressors include lagged dependent variables, the rejection frequencies under both the null and alternative hypotheses depend on the coefficientsof the lagged dependent variables and the other model parameters. They appear to be robust across various disturbance distributions under the null hypothesis.