E-government as an anti-corruption tool

Transcription

1 E-government as an anti-corruption tool Thomas Barnebeck Andersen Department of Economics, University of Copenhagen March 2008 Abstract This paper estimates the impact of changes in e-government on changes in corruption over the period for a sample of 126 non-oecd countries. Removing country xed e ects and invoking external instruments, the paper demonstrates a positive and economically interesting e ect. Keywords: Corruption, ICT, E-Government JEL Classi cations: D73, H11, O1, O57 I would like to thank Phil Abbott, Carl-Johan Dalgaard, Henrik Hansen, Theo Ib Larsen, John Rand, Martin Rama, Pablo Selaya, Clay G. Wescott, and Darell M. West for comments on earlier versions of this project. All remaining errors and omissions are mine. Address for correspondence: Thomas Barnebeck Andersen, Department of Economics, University of Copenhagen, Studiestræde 6, DK-1455, Copenhagen K, Denmark; Thomas.Barnebeck.Andersen@econ.ku.dk 1

2 E-government o ers a partial solution to the multifaceted problem of corruption. It reduces discretion, thereby curbing some opportunities for arbitrary action. It increases chances of exposure by maintaining detailed data on transactions, making it possible to track and link the corrupt with their wrongful acts. By making rules simpler and more transparent, e-government emboldens citizens and businesses to question unreasonable procedures and their arbitrary application. (Global Corruption Report 2003, p. 30). 1 Introduction Corruption is commonly considered to be one of the most signi cant obstacles to economic development. 1 In fact, commentators and NGOs have emphasized that in order to meet the Millennium Development Goals corruption must be reduced. 2 In this regard, the result presented here, namely that an increase in the use of e- government 3 is likely to reduce corruption, is constructive. The mechanisms through which e-government works are straightforward: E-government reduces contact between corrupt o cials and citizens and increases transparency and accountability. As the opening quotation demonstrates, the potential of e-government in the ght against corruption has not slipped the attention of practitioners. The Asian Development Bank (ADB) have provided a long list of examples of e-government initiatives worldwide along with interesting anecdotal evidence intended to document achievements (Wescott, 2003). In Pakistan, the entire tax department is undergoing restructuring and information and communication technology (ICT) systems are being introduced with the stated purpose of reducing contact between tax collectors and tax payers. In the Philippines, the Department of Budget and Management has 1 A classic reference is Mauro (1995), who nds that corruption hampers economic growth. 2 In a press release (dated September 14, 2005), Transparency Internation claims that Millennium Development Goals are unreachable without commitment to ghting corruption. 3 One de nition of e-government (or digital government) is public sector use of the Internet and other digital devices to deliver services, information, and democracy itself. (West, 2005, p. 1.) Another de nition is that e-government is "the process of connecting citizens digitally to their government in order that they might access information and services o ered by government agencies." (Lau et al., 2007, p. 2.) 2

3 established an on-line e-procurement system that allows public bidding for suppliers. This system has increased transparency in transactions. In the Indian state of Andhra Pradesh, where 40% of its 76 million people cannot read, 214 deed registration o ces have been fully computerized. This has made the process of deed registration easy and transparent. The process started in April 1998 and by February 2000 about 700,000 documents had been registered. Before the introduction of online registration, opaqueness of procedures forced citizens to employ middlemen who used corrupt practices to obtain services. In several Asian countries, governments are introducing smart cards that help citizens access health-care services without having to provide corruption-prone cash payments for these services. An impressive and well-known example of the potential of e-government in empowering citizens to challenge corrupt and arbitrary bureaucratic action is the Bhoomi (meaning land) system from Karnataka, India, where the introduction of an electronic land record system serving roughly 7 million farmers has saved clients some 1.32 million work days in waiting time and Rs. 806 million in bribes (World Bank, 2004). 4 The main function of the Bhoomi system is to maintain records of rights, tenancy and cultivation, which are crucial for transferring or inheriting land and obtaining loans. Under the old system, some 9,000 village accountants, each serving three or four villages, maintained land records. Farmers had to seek out a village accountant in order to obtain a copy of the record or make changes. Accountants were not easily accessible and farmers faced long delays; two out of three paid bribes, and over two-thirds paid more than Rs. 100, compared to the o cial service fee of Rs. 2. Under the electronic Bhoomi system, farmers can enter a Bhoomi kiosk and get these records or le for changes in 5-30 minutes. Moreover, all requests are served on a rst-come, rst-served basis. 5 4 See Chawla and Bhatnager (2004) for a case study of the Bhoomi system. 5 Other examples of e-government include Christal in Argentina, a website aiming at disseminating online information concerning the use of public funds; the Central Vigilance Commission website in India, where the public among other things can report information about wrongdoings of public servants; an on-line Customs Bureau system in the Philippines, which has lessened the cost of trade for businesses, reduced opportunities for fraud 3

4 Summing up, the anecdotal evidence suggests that e-government eliminates many opportunities for corruption. To the best of my knowledge, this proposition has not been subjected to systematic empirical scrutiny. 6 The present paper attempts to correct this shortcoming. The discussion proceeds as follows: Section 2 provides details on speci cation and identi cation issues; Section 3 discusses the data; Section 4 provides econometric results; and Section 5 concludes. 2 Empirical Framework As a consequence of governance indicators being somewhat persistent, empirical papers studying the determinants of corruption typically rely on the variation in corruption levels across countries. In the present case, however, the inevitable choice of focus is on changes in corruption. E-government is a recent phenomenon, and, as such, cannot have a ected corruption levels prior to its inception, adoption and widespread use. As a result, we must ask whether changes in e-government, EGOV i = EGOV i;final EGOV i;initial ; can explain changes in corruption, CCI i = CCI i;final CCI i;initial ; over a time-period during which e-government has been in operation, t 2 finitial; finalg : More speci cally, let the level of corruption in country i be given by CCI i;t = t + i + EGOV i;t + u i;t (t = nal, initial): (1) The term i = Z 0 i captures any time-invariant variables whether observed or unobserved, whereas t is a period speci c intercept. The rst di erence of equation and boosted revenue collection of the Customs Bureau; and several computerized interstate check posts in Gujarat, India, which has signi cantly reduced corruption at check posts. See < for more information on these and other initiatives. 6 The lack of hard evidence linking e-government and corruption is recognized by a leading e- government proponent (United Nations Development Programme, APDIP e-note 8, 2006). 4

5 (1) gives CCI i = + EGOV i + u i ; (2) where = final inital and u i = u i;final u i;initial. OLS on (2) provides a consistent estimate of if Cov (EGOV; u) = 0: To see what this strict exogeneity assumption entails, note rst that the constant term in (2) ensures that E (u) = 0; which in turn reduces Cov (EGOV; u) = 0 to the orthogonality condition E (EGOV 0 u) = 0: Consistency of OLS therefore requires that E(EGOV 0 final u final) + E(EGOV 0 initial u initial) E(EGOV 0 initial u final ) E(EGOV 0 final u initial) = 0: If (1) is correctly speci ed, orthogonality between EGOV final and u final and between EGOV initial and u initial is ensured. Nothing, however, ensures orthogonality between EGOV initial and u final or EGOV final and u initial. It may be reasonable to assume that the latter two orthogonality conditions are satis ed, but it does not follow from a correctly speci ed conditional expectation, i.e. from E (u i;t j EGOV i;t ; t ; i ) = 0 for all i (see Wooldridge, 2002). That being said, equation (2) is robust to many potential endogeneity problems. Importantly, rst differencing removes any country xed e ects, i = Z 0 i ; thus removing any potential omitted time-invariant variables. Nevertheless, measurement error in the e-government variable and the possibility of omitted time-varying factors call for an instrumental variables procedure. I will make use of two demographic instruments. These are the (log of the) size of the population in the initial period, P OP i;initial ; and the share of the population residing in urban areas in the initial period, URBAN i;initial : The latter instrument can be justi ed by invoking the so-called urban density theory (Forman et al., 2005), which states that ICT adoption costs decrease with a higher urban density. There are three main reasons for this hypothesis: (i) a higher availability of complementary ICT technology such as broadband services in urban areas; (ii) a higher availability of ICT know-how; and (iii) knowledge spillovers and learning e ects. The former instrument exploits the idea that a government Web presence is (by and large) a xed cost technology, meaning that costs per user are decreasing in the number of 5

6 users (i.e. larger populations). Validity of the exclusion restriction requires that the initial size of the population and the initial urban density has no impact on changes in corruption (over the period under study) once all time-invariant factors are removed. Since there are two excluded instruments and only one endogeneous variable, validity of the exclusion restriction can be tested using a test for overidentifying restrictions (OID). That is, we can test whether instruments are uncorrelated with the error term. Since timeinvariant factors are eliminated from the model, only time-variant factors can lead to omitted variables endogeneity. Consequently, if the model passes the OID test, some con dence in the orthogonality between error term and instruments is in order. As a nal speci cation issue, one may argue that the log of real GDP per capita, log(ry CAP i;t ); belongs in equation (1). As it stands, it is relegated to the error term. A rst di erence transformation then implies that log(ry CAP i ) must be included in (2), where log(ry CAP i ) = log(ry CAP i;final ) log(ry CAP i;initial ) is the (continuously compounded) growth rate in real GDP per capita over the period. This leads us to CCI i = + EGOV i + log(ry CAP i ) + e i : (3) Equation (3) will be estimated below, but it is worth stressing that adding regressors comes at a cost. To the extent that real GDP per capita is measured with error, we introduce an additional source of bias. In fact, measurement error endogeneity will typically bias all estimates. Moreover, a di erence transformation may exacerbate the measurement error problem (Deaton, 1997). The importance of this trade-o is sometimes underappreciated (Swann, 2006). Obviously, one could try to come up with a new external instrument, but this is di cult and introduces new complications. The upshot of all this is that the parsimonious approach pursued via estimation of (2) and then conducting OID tests has a lot to recommend it (see Hall and Jones, 1999). 6

7 3 Data In order to measure corruption, I rely on the well-known Control of Corruption Index (CCI) compiled by Kaufmann et al. (2007). The CCI indicator, which ranges from 2:5 (worst) to 2:5 (best), attempts to measure the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as capture by elites and private interests (Kaufmann et al., 2007, p. 4). The indicator for each country is based on a large number of individual data sources, which are then aggregated into a single measure by an unobserved components model. This means that the aggregate measure is a weighted average of the underlying individual data sources, with weights re ecting the precision of each of these underlying data sources. By virtue of being a solution to a statistical signal extraction problem, the aggregate CCI indicator is presumably more informative than any individual data source. This makes the CCI the most comprehensive measure of corruption around. CCI is calculated as the di erence between CCI in 2006 and 1996, i.e. CCI i = CCI 2006;i CCI 1996;i : As noted by Kaufmann et al. (2007), despite being somewhat persistent, governance indicators do change over relatively short periods such as a decade. This is illustrated in Figure 1, which plots the 1996 CCI score on the horizontal axis, the 2006 CCI score on the vertical axis and a 45-degree line. Countries above the 45-degree line corresponds to improvements in corruption, while countries below the line saw deteriorations in corruption. 7

8 Figure 1: Variation in corruption levels over the period. SGP CCI in ZAR HKG BHS CHL MLT EST SVN QAT BWA URY ISRCYP OMN KWT BHR ZAF HUN LVA JOR SVK MUS MYS CZECRI SAU TUN BRN LTU POL NAM TUR BGRHRV SLV GHA MAR COL PAN IND SUR ROM TTO THA DZA BIH CUB MDG GEO MKD YUG MEX JAM LKA TZA BRA SEN PER BFAARGEGY ETH BOL IRN ARM MOZ DOM GUY MLI LBNCHN MNG GTM MDA ALB GMB ECU UKR SYR VNM LBR GAB HNDRUS BLR IDN UGA MWI PHL YEM ZMB NPL NIC TJK CMRLBY KEN PAK UZB AZE GNB TGO KAZ NER GIN LAO COG KHM AGOKGZ PRY SDN VEN PNG SLE CIV TKM NGA BGD IRQ ZWE GNQ HTI MMR SOM PRK CCI in 1996 Notes: Scatter plot of Control of Corruption in 1996 (horizontal axis) versus Control of Corruption in 2006 (vertical axis). The full line is the 45-degree line. The sample used is the largest estimation sample used in the empirical analysis below (number of observations is 126). The country label is the World Development Indicator country code. 8

9 The e-government variable, EGOV; used in this paper ranges from 0 (low) to 100 (high). The variable was compiled by a research team headed by Darrell M. West of Brown University during June and July The methodological framework, upon which EGOV is based, is outlined in West (2005). The team made an assessment of 1; 782 national government websites for 198 nations around the world. A range of sites within each country were analyzed to get a full sense of what is available in particular nations. Websites were evaluated for the presence of various features dealing with information availability, service delivery, and public access. Interestingly, a common service featuring on government websites in the 57 countries having services that were fully executable online in 2006 was the possibility of reporting fraud and corruption (West, 2006). Moreover, in 91% of all countries government websites o ered visitors contact material so that visitors could a person in a government department other than merely the webmaster. This arguably reduces the distance between o cials and citizens. There is no data on e-government dating back to Nevertheless, the technology is of recent vintage. E-government relying on the World Wide Web (the Web) cannot be older than the Web itself, which is dated back to 1991 (West, 2005). However, the process of commercializing the Web took o with the release of the Netscape browser in December Since EGOV only measures e-government relying on the Web, e-government in 1996 is coded as zero. Consequently, EGOV is calculated as EGOV i = EGOV 2006;i EGOV 1996;i = EGOV 2006;i : The EGOV variable only measures a subset of e-government, namely Internetbased e-government. The use of smart cards in health-care, for example, is not directly captured by EGOV. However, it seems plausible that Internet-based e- government and other types of e-government are highly (positively) correlated. At any rate, the Internet remains the most popular e-government delivery system (West, 2005). 8 7 The report is available online at: < 8 In the United States, 81% of all federal e-government initiatives are delivered via the Web (West, 2005). In Britain, Directgov - an o cial Webpage launced in aims to contain the whole of 9

10 The two demographic instruments, i.e. population size and urban population as a share of total population, and real GDP per capita are all taken from the World Development Indicators Finally, I have dropped all OECD countries since the typical OECD country has seen a much larger increase in e-government and has much less corruption as compared to the typical non-oecd country. This step is innocuous if we just remember that we are estimating the impact of e-government on corruption conditional on being an non-oecd country Econometric Results The rst column in Table 1 provides OLS results. Inspection reveals that EGOV is signi cant at the ve percent level, and that the rst di erence speci cation explains four percent of the variation in CCI: Using 2SLS estimation, reported in column (2), the slope estimate increases with more than a factor two, while the standard error goes up with slightly less than a factor two. Overall, this increases the statistical signi cance of the EGOV slope estimate. Columns (3) and (4) show that nothing really changes by including log(ry CAP ). 11 the British state in one place: < 9 Real GDP per capita is not available for 2006; 2005 values are used instead. 10 For completeness, while OLS results are a ected by the inclusion/exclusion of OECD countries, 2SLS estimates of both equations (2) and (3) are roughly una ected. 2SLS estimation of equation (2) using only the selected sample of non-oecd countries is only consistent for the entire population if E (u j P OP initial ; URBAN initial ; OECD) = 0, where OECD is a dummy (see Theorem 17.1 in Wooldridge, 2002). 11 In columns (3) and (4), 13 observations are lost due to missing real per capita GDP values. 10

11 Table 1: Results from OLS and 2SLS OLS 2SLS OLS 2SLS Dependent variable: CCI (1) (2) (3) (4) EGOV 0.021** 0.051*** 0.017* 0.042*** (0.009) (0.017) (0.009) (0.016) log(rycap) 0.494** (0.227) (0.244) CONSTANT ** *** ** *** (0.262) (0.462) (0.253) (0.429) Observations First-stage F statistic H0: Instruments = 0 Hausman test H0: DEGOV is exogenous (p-value) Hansen J statistic, OID H0: Valid exclusion restriction (p-value) R-squared Notes: Dependent variable is the change in Control of Corruption over the period. Robust standard errors are reported in parentheses. Asterisks ***,**,* indicate signi cance at the 1, 5, and 10 percent level, respectively. The instruments used in column (2) and (4) are the log of population and the share of the population residing in urban areas. 11

12 Turning to the strength and validity of identi cation, note rst that the association between EGOV and the two instruments is much stronger than what is required by the widely used rule-of-thumb value, suggested by Staiger and Stock (1997). 12 Moreover, with a p-value of 0:112 in column (2) and a p-value of 0:181 in column (4), we cannot reject instrument orthogonality at conventional levels. Consequently, we can have some con dence in 2SLS estimates; and, as a result, in the Hausman test, which deems EGOV endogenous in column (2), but exogenous when log(y CAP ) is included, cf. column (4). Finally, one may visually inspect the robustness of the exogenous variation used for identi cation via the partial regression leverage plot (see Krasker et al. 1983), cf. Figure 2. Speci cally, panels A and B show the exogenous variation used for identi cation in column (2), whereas panels C and D show the exogenous variation used for identi cation in column (4). In full accord with the hypothesis underlying the identi cation strategy, countries with larger populations and larger relative urban populations have seen larger increases in e-government. Moreover, no individual country (or cluster of countries) seems to be driving 2SLS projections. Turning next to the di erence between OLS and 2SLS, note that b 2SLS > b OLS is to be expected as a consequence of attenuation bias, resulting from classical errorsin-variables. Using a standard result in the econometrics of measurement error, we can pin down the standard deviation of true e-government, EGOV 2006 ; relative to the standard deviation of measured e-government, EGOV2006. Using either the estimates in columns (1) and (2) or the estimates in columns (3) and (4) in the following formula: plim b OLS b 2SLS! 1=2 = EGOV 2006 EGOV2006 ; we get EGOV 2006 ' 0:64 EGOV2006 : That is, the standard deviation of the true e- government variable is roughly 64% of the measured e-government variable. To be 12 If the F -value associated with the null hypothesis of zero explanatory power of the two instruments is above ten, inference based on 2SLS will not su er from size distortions (Staiger and Stock, 1997). 12

13 sure, sampling error is also at work. Figure 2: Exogenous variation in e-government. e( DEGOV X ) SGP AZE NPL GNQ KAZ UKR SVN EST MKD LAO SVK LKA MYS TUR NGA GEO YUGSYR GNB QAT BIH TJK KEN SDN PRK EGY BGD PAK HTI LBY HKGROM PHL MLT MNG LVA GTMHUN GHA CZE CHL TTO ZWEBLRUZB PER ZAF POL HRV MMRVNM BOL AGO GUY CYP JAM LTU SLE OMN PANSOM BRN BHR MUS GMB PNG ALB NIC LBN SEN YEM UGA MOZ ECU JOR ETH ZAR COL IRN NAM LBRCOG TKM PRY MDA MWI SLV ZMBTUN THA ISR CIV IRQ SAU KGZ ARM KWT NER BGR MAR DZA KHM MLIMDG SUR BWA GAB HND ARG BFA DOM TZA BHS URY CUB CRI CMR VEN TGO GIN MEX IDN RUS BRA IND CHN e( DEGOV X ) TTO AZE HKG UKR QAT EST MKD KAZ TUR MLT GNQ SVN SVK MYS LBY CHL GEO PRK NGA SYR YUG CZE RUS BRA NPL EGY ROMPHL LVA HUN PER POL BLR LBN BHR SDN BIH PAK MNG LAOLKAGNBTJK KEN BGD HTI GHA GTM IND CHN HRV CYP ZAF BOLLTU OMN JOR MEX ISR COL KWT UZB ZWE JAM AGO BRN PAN ECU SAU IRN NIC TUN IRQ BGR GUY VNM MMR SEN PRY COG SLV ARM ARG MOZ SOM SLE YEM ZAR MUS ALB GMBTKM MDA ZMB CIV LBR MAR SUR DZA GAB BHS URY THA UGA KGZ ETH BWA CUB VEN PNG NAM DOM MWI HND NER MLI MDG IDNCRI KHM CMR BFA TZA GIN TGO SGP e( LOGPOP X ) coef = , (robust) se = , t = e( URBAN X ) coef = , (robust) se = , t = 5.92 Panel A Panel B e( DEGOV X ) SGP NPL GNB AZE MKD SYRMYS KEN UKR TUR NGA SVNLAO SVK HTI ZWE LKAKAZ PAK SDN EGYBGD EST YUG MLT TJK GEO GTMHKG ROM PHL BRA CZE GHACHLPERZAF MNG BIH HUN GUY JAM BOL UZB ZARPOL RUS TTO PNG HRV LVA SLE PRY PAN LBN SEN YEM UGA AGO ECU VNM COL MEX GMB COGNICSLV MWI JOR ZMB ISR BLR CIV ETH THAIRN BHR LTUNER MOZ SAU MUS NAM ALB TUN KWT KGZ MDA MAR MDG BGR DZA GAB HNDMLI ARG IDN SUR LBR BWA ARM BFA KHM URYDOM TZA TGO CRI CMR VEN GIN IND CHN e( DEGOV X ) TTO MLT HKG MKD AZE MYS TUR UKR GNB SVN SYR EST CHL SVK KAZ CZE BRA NPL NGA PER HTI ZWE EGY PAK GEO YUG ROMPHL LAO KEN SDN GTM MNG ZAFHUN BOL LVA POL JOR COL RUS LBN BHR ISR KWT MEX LKA GHAJAMHRV ECU SAU TJK BGD BIH IND PRYPAN BLR GUY ZARUZB NIC SLV COG LTUIRN SLE GMB SENAGO CHN CIV TUN GAB ARG YEM PNG VNM ALB MUS THA ZMB MDA MARSUR BGR URY DZA VEN UGA MWIETHNAM MOZ KGZ ARM NER HND MDG LBR DOM BWA MLI IDN CRI CMR KHM BFA TZA TGO GIN SGP e( LOGPOP X ) coef = , (robust) se = , t = e( URBAN X ) coef = , (robust) se = , t = 5.76 Panel C Panel D Notes: In Panel A, e(degov j X) is the residuals from an OLS regression of EGOV on a constant and log(pop). e(logpop j X) is the residuals from a regression of log(pop) on a constant and URBAN. The plot is then constructed as a scatter plot of the two vectors of residuals. The full line is the associated regression line. The partial regression plots in Panels B-C are constructed in parallel ways. 13

14 Figure 3 depicts the 2SLS estimated impact of EGOV on DCCI reported in column (2) in Table 1. The gure shows that the point estimate is not likely to be driven by outliers. Figure 3: Estimated impact of e-government on corruption. e( DCCI X ) TTO LVA BHS QAT LBR TJK EST MLT TZA GEO ZAR YUG SLV MKD BGR OMN ETH SLE HRV JORBHR SAU BWA GAB GHA GTM URY ZMB BOL TUN LTU IRN COL HND TKM CMR PAN KEN THA PAK INDSYRSUR GNB TGO LAO LKA SOM VNMSDN SEN UZB JAM AZE ROM ECU ARM BLR UKR HKG BFA KHM MUS BIH AGO NGA KAZ TUR LBY MEX RUSKWT SVK ZAF DZA IRQ CHL SGP UGA SVN BRN MYSHUN MWI GUY MLI MOZ KGZ IDN COG BRA DOM PHL YEM MAR POL CZE PER VEN CUB ARG NPL GNQ HTI MDA CRI LBN MMR NAM PRY EGYCHN NIC NER MDG ISR ALB PNG BGD CYP MNG GMB ZWE GIN CIV PRK e( DEGOV_hat X ) coef = , (robust) se = , t = 3.3 Notes: DEGOV_hat contains the predicted values from a regression of EGOV on a constant and the two instruments. e(degov_hat j X) contains the residuals of an OLS regression of DE- GOV_hat on a constant. e(dcci j X) contains the residuals from an OLS regression of the CCI on a constant. The plot is then constructed as a scatter plot of the two vectors of residuals. The full line is the associated regression line. In sum, we may with some con dence treat b 2SLS = 0:051 as an unbiased estimate of the impact of e-government on corruption. 13 From equation (1) this implies 13 I rely on column (2), as opposed to column (4), since the sample is larger. In column (4), I loose observations due to missing real per capita GDP values. 14

15 that CCI ' 0:051 EGOV: To get a sense of what this means, one may begin by observing that moving from the 10th percentile to the 90th percentile in the e- government distribution (in the sample used in Table 1) implies moving from 20:7 to 32:3, i.e. EGOV = 11:6: Using the estimates in column (2), this di erence predicts an increase in the CCI 2006 score of CCI ' 0:6; corresponding to a move from the 10th percentile to the 52th percentile. However, actually moving from the 10th percentile to the 90th percentile in the corruption distribution means going from 1:09 to 0:56, i.e. CCI = 1:63: Hence the economic impact of e-government is considerable, although not implausible. 5 Concluding Remarks This paper documents a positive impact of increases in the use of e-government on improvements in levels of corruption over the period In addition, the economic signi cance of this relationship is economically interesting. Viewed against the backdrop of the anecdotal evidence discussed in the Introduction, the empirical analysis provided in this paper lends some support to the view that e-government is a potentially useful tool in the global e ort to reduce corruption. 15

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17 [11] Wescott, C., E-government to combat corruption in the Asia Paci c Region. Available online at: < [12] West, D., Digital Government. Princeton University Press [13] West, D., Global E-Government, Available online at: < [14] Wooldridge, J., Econometric Analysis of Cross Section and Panel Data. MIT Press [15] World Bank, Making Services Work for the Poor. World Development Report 17