higher school of economics, moscow 2011 public sector e-innovations the impact of e-government on...
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Higher School of Economics , Moscow 2011
Public Sector E-InnovationsThe impact of e-government on corruption
Dr. Liliana Proskuryakova, National Research University “Higher School of Economics”Dr. Gulnara Abdrakhmanova, National Research University “Higher School of Economics”
apl. Prof. Dr. Hans Pitlik, WIFO - Austrian Institute of Economic Research
Higher School of Economics , Moscow 2011
The focus of the study
photo
• Core missions• “Customer base”• Link with education• Adaptive project portfolio
Aim: assessing innovations in the public sector, which were introduced by selected countries by 2009-2010 in the sphere of e-government, and the interrelation of certain e-government aspects with control of corruption
Hypothesis: supply (e-government infrastructure) and demand sides (use of e-services by citizens and business) of е-government have an impact on good governance and corruption.
Theory:
• E-government can bring the government closer to citizens (Yigitcanlar, Baum, 2006)
• Federal states are more "corrupt“ (Treisman, 2000); different types of decentralization have differential effects on corruption (Fisman, Gatti, 2002)
• The New Public Management approach views citizens as customers (Boston et al., 1996; Kaboolian, 1998; Nagel, 1997)
Higher School of Economics , Moscow 2011
Methodology
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• Core missions• “Customer base”• Link with education• Adaptive project portfolio
• A selection of countries was made for case studies (Canada, Mexico, Russian Federation, the UK)
• Major international ICT and e-government rankings were reviewed and analyzed; selected indicators were identified for further use in statistical and econometric analysis
• A correlation analysis was done for 138 countries and 4 aggregate indicators: the 3 sub-indexes of the WEF Networked Readiness Index and the Transparency International Corruption Perception Index
• A regression analysis was made for Transparency International Corruption Perceptions-index (CPI) for the year 2010 and United Nations' 2010 E-Government Survey- Index of Online Services (OSI) for a sample of 173 countries; In the process of further refinement of methodology UN Telecommunications Infrastructure Index (INFRASTRUCTURE) was matched against the CPI
Higher School of Economics , Moscow 2011
Ranking results E-government Development Index, 2010
Source: United Nations, 2010.
Higher School of Economics , Moscow 2011
Countries ranking by level of ICT development, 2010
Source: ITU, 2010.
Higher School of Economics , Moscow 2011
Distribution of countries by CPI ranking and 3 subindexes of WEF Networked Readiness Index, characterizing ICT infrastructure, individual and business usage of ICT
Sources: data from Dutta, Mia, 2011; CPI 2010
Correlation coefficients between WEF NRI subindexes (2010-2011) and CPI (2010), 138 countries
Higher School of Economics , Moscow 2011
WEF NRI Infrastructure environment
subidex
WEF NRI Individual usage
subidex
WEF NRI Business usage
subidex
Corruption Perceptions Index 2010
0,87 0,87 0,80
The closest connection was established between the level of corruption perception and infrastructure environment and individual ICT usage with the correlation coefficient 0,87
Online Service Quality and Corruption, 2010
Higher School of Economics , Moscow 2011
02
46
81
0
0 UN Online Service Index
Corruption Perception Index (CPI) Fitted values
CPI
Sources: data from United Nations, 2010; CPI 2010
A simple bi-variate OLS regression describes the relationship between CPI-level and Online Service Index (OSI) by (p-values in parentheses)
Higher School of Economics , Moscow 2011
E-government services as determinants of corruption
(1)OLS
(2)OLS
(3)OLS
(4)2SLS
(5)2SLS
(6)2SLS
OSI (t-5) 2.33(0.001)
-0.37(0.472)
-1.16(0.223)
-2.88(0.001)
EGOVRI (t-5) 4.84(0.000)
-2.55(0.257)
GDPpc (t-10) 0.61(0.000)
0.39(0.001)
0.20(0.008)
0.96(0.000)
1.08(0.000)
0.29(0.000)
POLFREE (t-10) 0.11(0.000)
0.09(0.004)
0.05(0.058)
0.18(0.000)
0.19(0.000)
0.07(0.010)
INFRASTRUCTURE (t-5)
7.44(0.000)
9.22(0.000)
Constant -2.55(0.024)
-1.89(0.012)
0.84(0.090)
-4.65(0.000)
-5.04(0.001)
0.53(0.305)
Observations R2 (adj.)
1700.638
1700.676
1700.802
1700.544
1700.489
1700.765
First stage regression: Partial R2 of excluded
instrumentF-stat.
0.29671.86
0.13824.11
0.30660.94
Conclusions (1)
Higher School of Economics , Moscow 2011
• A better ICT infrastructure, greater individual and business ICT usage are associated with better control of corruption, as perceived by citizens
• The major conclusion is somewhat skeptical concerning the supposed anti-corruption effects of e-government. We found no stable relation between the quality of e-government services and the corruption level as measured by the Transparency International CPI
• Our suggestion is that the frequently established positive relation between government adoption of e-services and corruption containment mainly works through the infrastructure channel
• According to the analysis of indicators, it is not the introduction of online services, but rather a better telecommunications and ICT infrastructure, as well as ICT usage that is associated with less corruption in governments as perceived by citizens
Conclusions (2)
Higher School of Economics , Moscow 2011
• Country cases: while some of the least advanced countries are still looking at e-government from a government-centric paradigm, some of the more advanced states have shifted to a citizen-centric perspective, placing greater attention on the framework conditions (e.g. organizational, institutional and social factors) in which e-government is developing, as well as on the outcomes for users• National policies and their implementation: certain successful user-focused e-government programmes were identified • The digital divide remains to be one of the major obstacles to the wide profusion of e-government not only in the developing world, but also in fast growing economies and in the economically advanced countries • International rankings & metrics have their deficiencies: they make very little use of official statistics in certain countries and rely on own sources: expert assessments, databases, questionnaire surveys, own studies of national web-sites
