Governance and institutional quality also affect tax revenue collection. In many formerly centrally planned economies, the government and the administration still maintain strong discretionary power over the allocation of resources, implicitly enhancing corruption. Corruption reduces citizens’ trust in government authority. Tanzi and Davoodi (1997) empirically show that high corruption lowers revenues. Bird et al (2004) also find that institutional qualities, such as corruption or political stability, as measured by the governance quality index significantly reduce tax effort. According to the corruption perception index of Transparency International, among the ECA countries, Slovenia and Estonia have the lowest corruption while corruption is most pervasive in Bulgaria and Romania<sup>6</sup> . Torgler and Schneider (2007) show that improving governance and institutional quality increases tax morale and tax compliance. In fact, in many instances, the perception of good governance has a greater impact on tax compliance than reforming tax systems. Several indices of institutional quality (control of corruption, regulatory quality, government effectiveness and political stability) were tested separately for significance. Control of corruption was found to be a significant predictor of tax ratios.

The incentive to evade taxes is greater when the tax burden is higher. Effective tax rates are typically a better measure to reflect a country’s tax policy. However, effective tax rates are not available for most countries in this study, and hence, as done in several other empirical studies, the maximum marginal tax rates have been used to proxy for tax burdens. The maximum marginal PIT used separately as a regressor was not significant, while the maximum marginal CIT and the VAT rate were found to be significant and negative. This implied that there was a greater incentive to evade taxes as the tax rates increased. In addition, the square of the tax rates was also used to capture any potential non-linearity as well as to smooth out the effect of increasing taxes. The square of the tax rates was found to be positive and significant.

Most panel data analysis on tax effort and tax potential done so far have either not used tax policy variables (PIT, CIT and VAT) as a predictor of tax revenue collections, or if they have used it, have found it to be insignificant<sup>7</sup> . This certainly appears counter-intuitive. The Laffer curve effect in tax literature is well known<sup>8</sup> . Tax revenue collections as a proportion of GDP are correlated with tax rates up to a tipping point in the tax rates, after which tax revenue collections show a downward trend as tax rates increase. So, certainly tax rates and the tax structure do have an impact on tax revenue collections.

Hence, it seems surprising that studies so far on tax revenue collections and tax potential have either omitted tax policy variables in their analysis, or if they have included them, found these variables to be insignificant. One possible reason could be the large sample size in these studies and the consequent heterogeneity in the sample that is unable to capture the influence of tax rates<sup>9</sup> .

> 6 Transparency International, Global Corruption Report 2009.

7 Tuan Minh Le et al (2008, 2012) use 5 variables (GDP per capita, population growth rate, trade openness, agriculture and corruption) to predict tax revenue potential. The authors do not include tax policy variables. Gupta (2007) does use tax policy variables (taxes on goods and services, corporate tax and tax on trade) but finds none of these variables significant. Of note is that the sample size falls dramatically to a one-fourth of the original sample when he adds the tax policy variables. Gupta (2007) finally uses just four variables to predict tax potentials in his study-GDP per capita, import share, share of aid and share of debt. Piancastelli (2001) uses three variables –GDP per capita, agriculture and trade- to predict tax revenue potentials. Piancastelli does not explore the impact of tax policy variables on tax revenue collections. A notable exception is Davoodi and Grigorian (2007), where using the instrumental variable approach, the authors find the corporate tax rate significant in explaining the shadow economy. The predicted value of the “shadow” is used as a regressor in the tax potential calculations.

8 Some empirical studies (Trabant and Uhlig 2012), however, find little evidence of the Laffer curve effect.

9 For example, the sample size in Tuan Minh Le et al(2012) is 110 countries covering the period 1994-2009, Gupta (2007) has 105 countries over a 25 year period (with no corrections for possible structural breaks), and the sample in Davoodi and Grigorian (2007) is 141countries over the time period 1990-2007 9