Page 5 - Working Paper (The Myths and Realities of Tax Performance Under Semi-Autonomous Revenue Authorities)
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DDTC Working Paper 0213

                      3.1.  Data                                    second  phase, we use a  panel  regression  using
                                                                    pooled  OLS,  fixed  effect  and  random  effect.  All
                      In  this  study,  we  will  use  panel  data  (cross-  three approaches are used to find the relationship
                   countries  and  over  time).  The  panel  data  covers   between  tax  institutions and all  ex-ante  related
                   49  countries,  over  the  period  of  2000-2011  (12   factors.
                   years).  We  choose  the  analysis  period  based  on
                   several  considerations. At the end of the 1980s    For multivariate model where there are at least
                   to  the 1990s,  many countries  were  changing the   2 variables, we initially conduct diagnostic test to
                   institutional model of the tax authority into SARA.   describe  data  normality, check  multicollinearity,
                   As assumed in the post-transition from a model of   heteroscedasticity, and autocorrleation  test in
                   non-SARA, SARA in some countries becomes stable   order to select best linear unbiased estimators. We
                   so that  more reliable  for comparison.  Therefore,   use Breusch-Pagan and White-Hetero tests to detect
                   it would be valid if the period of analysis begins   heteroscedasticity problems. Furthermore, we also
                   in  2000,  49 countries that  are chosen represent   did error plotting to identify autocorrelation. After
                   countries  that  apply  SARA  (28  countries)  and   conducting diagnostic test, we come up with model
                   non-SARA (21 countries), regional coverage (Asia,   specification below:
                   Pacific, Africa, America, and Europe), income level
                   (low-middle  income  groups,  medium-high,  and   Tax ratio it
                   income  high)  classification  according  to  United
                   Nations or the World Bank classifications.       = α + β instmodel + β independent variables + ε it
                      The  data  used for  this analysis  is extrated
                   from  various  reliable  sources.  For  economic    We choose  tax  ratio as dependent variable;
                   and  social  indicators, data  was  extracted from   whereas, institutional  model and  other control
                   Government  Finance  Statistics  (IMF)  and  the   variables as independent variable. We, then, apply
                   World  Development  Indicators  (World  Bank).   multivariate analysis to find robust results, which
                   The  institutional  variables  extracted  from  OECD   are: pooled ordinary least square (pooled OLS) and
                   (2013),  ADB  (2012),  Mann  (2004),  and  Taliercio   linear panels consist of fixed and random effect.
                   (2004). For the data covering taxation conditions
                   were taken from the World Bank  Enterprise          Please note, regression with panel data contains
                   Survey,  while  the  governance  indicators  were   possibility  that  the  unobservable  variable  (u_it)
                   taken from the Worldwide Governance Indicators   will be correlated with the explanatory variables
                   (World Bank). In addition, there are several other   (independent).  If  there  is  a  conviction  that
                   data taken from a variety of sources, e.g. tax morale   unobservable variables are not correlated with the
                   and shadow economy.                              explanatory variables,  it  is better to use random
                                                                    effect (RE). Whereas, if there is a possibility that
                      3.2.  Estimation Strategy                     there is correlation among the two variables, it
                                                                    would be better to use fixed effect (FE).
                      The analysis will be divided into 3 stages. The
                   first phase provides some descriptive statistics of   FE estimator is unbiased under the assumption
                   the characteristics of taxation in the non-SARA and   of  strict  exogeneity  for  explanatory  variable.  FE
                   SARA countries, for example: tax compliance, tax   allows to have correlation between unobservable
                   bribery indications, to the structure of tax revenues.   variable  and independent  variable  in each time
                   We will testing the so-called myths concerning that   period. However, the FE approach has implications
                   tax conditions under SARA were much better.      for the possibility of a  variable  that  is not
                                                                    constant over  time, such  as gender  or  distance.
                      The second stage, or the core of the research,
                                                                    Generally,  choices  over  FE  or  RE  will  be  decided
                   will  try to measure the magnitude of the impact
                                                                    through several  methods,  such as hausman test.
                   of institutional tax authorities to tax revenues by
                                                                    Nevertheles, we will not do hausman test for this
                   multivariate model. It is true that, in the literature,
                                                                    analysis. 16
                   SARA  is  not  necessarily  statistically  linked  to
                   tax revenues or the ratio of tax revenue to Gross   Lastly, we will also show factors that influence
                   Domestic Product (GDP), but more on the efficiency   the decision to adopt the SARA system using binary
                   and effectiveness of the agency in collecting taxes.   choice model. In this model, the dependent variable
                   From  this  perspective,  aspects  of  efficiency  and   is in the form of binary variables, which in this case
                   effectiveness will indirectly increase tax revenue.   is the institutional model of tax administration, i.e.
                   At  this  stage,  the tax  revenue is assumed to be   SARA (1) and non-SARA (0). We utilize a panel logit
                   influenced  not  only  by  factors  of  institutional
                   models, but also from an economic level, economic
                                                                    16 For more detail explanations, please refer to Jeffry M. Wooldridge,
                   structure, demographic and social factors.  In the   Econometric Analysis of Cross Section and Panel Data. (Massachusetts:
                                                                    The MIT Press, 2002).
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