On The Effect of Digital Transformation on Corruption

FACULTY OF ECONOMICS AND BUSINESS

On The Effect of Digital Transformation on Corruption An inter-country analysis

Robert Haafst

Research Proposal

FACULTY OF ECONOMICS AND BUSINESS CAMPUS CAROLUS ANTWERP KORTE NIEUWSTRAAT 33 2000 ANTWERP PHONE. + 32 3 201 18 40 [email protected]

Academic Year 2016-2017

Abstract Often, digitalization, digitization, and technological diffusion are considered to stimulate major leaps forward in economic prosperity. On the other hand, corruption is often seen as slowing down and setting back economic growth. Although the link between technological availability and corruption is studied in detail, the actual adoption of available technology not much. Therefore, the proposed research paper includes a longitudinal intra-country analysis, which shows the effects of digital transformation on corruption. The working hypothesis is that countries with a high level of digital transformation have a low level of corruption, mainly because technology diffusion and digital transformation increases public awareness and information levels, and as a result it increases detection risks. As a result, this paper argues to include two distinct composite variables, one capturing digital transformation from the citizen side, and one measuring it from the institutional side. To test robustness and sensitivity, all variables are analysed against three different corruption measurement models.

JEL Codes:

D73; G18; H8; H11; K4; O5

Keywords:

corruption; democracy; digitalization; e-government

Contents

1

Introduction ............................................................................................................. 3

2

Background & Context ........................................................................................... 4

3

2.1

Digitalization .................................................................................................... 5

2.2

Place in literature ............................................................................................ 6

2.3

Hypotheses ..................................................................................................... 6

Methodology ............................................................................................................ 9 3.1

Dependent Variables .................................................................................... 10

3.2

Independent Variables .................................................................................. 10

3.3

Moderating variables ..................................................................................... 11

3.4

Control variables ........................................................................................... 12

3.5

Conceptual model ......................................................................................... 14

4

Limitations & Restrictions .................................................................................... 15

5

Literature ................................................................................................................ 16

Appendix 1 .......................................................................................................................... i Appendix 2 ......................................................................................................................... ii

1

Introduction

“…while having a meeting with one of the representatives of the company, he suddenly mentions that soon there will be a multimillion tender from the government. Bids have to be send by post only, and for the sum of $ 100.000, he would make sure that other bids would magically disappear, and that we would get a contract of at least $ 1 million… (Haafst, 2016)” This is one of the numerous examples one could think of, showing the ease of corruption when there is no digital trail. As of today, The World Economic Forum estimates that the total cost of corruption is around USD 2.6 trillion, and The World Bank estimated that approximately USD 1 trillion is paid in bribes every single year. Compared to the total world economy of USD 73.7 trillion (World Fact Book, 2015 est.), total corruption cost would be roughly 3.5% of the world economy. Although these numbers are widely quoted by numerous scholars, no foundation or methodology can be found to verify these numbers, hence they may be subject to discussion. Due to the high claimed cost of corruption, several organizations such as Transparency International are trying to combat corruption. On the other hand, digitalization has a positive effect on the economy. The European Commission estimates that digitalization could contribute up to USD 3.7 trillion to the world economy (The European Commission, 2016). Surprisingly, those two factors have not been widely researched together. Obviously, on their own, the amount of empirical research conducted on corruption or digitalization is immense. As will become clear in the next chapter, most studies used digitalization as a dependent variable, whereas the proposed research indicate digitalization measures as the independent variables. Now that internet, mobile, and other technological diffusion is of high density, one might argue that this diffusion also generates a higher level of digitalization of (government) processes. Additionally, the paper investigates whether there is a correlation or causal effect to be seen when linked with corruption. Hence, the proposed paper emphasizes purely on the digitalization aspect, and the effect it has on corruption at state level, making it a sui generis paper, evolving around the research question: “Does digitalization in a country significantly affect the level of corruption at state-level?” The proposed paper studies a longitudinal relationship. Moreover, the study is done inter-country, covering 171 countries. The analysis comprises factor analysis, but also multi-level analysis, which in turn allows for identification of both individual and contextual determinants of corruption. Moreover, the marginal effect of digitalization on corruption is analysed. It is expected that digitalization affects corruption significantly, and that a higher level of digitalization would result in a less corrupt government. Not only contributes this paper theoretically, also practical use may be found. Assuming none of the hypotheses are rejected, then governments could use the model to devise a key on lowering corruption. Moreover, corruption combating organizations could use the model as a theoretical foundation to their plea for lower corruption. Finally, grant giving organizations could use to model to devise budgets or grants on digitalization or corruption. In the methodology section, every variable is explained in detail. Prior to the methodology section, the literature is reviewed, and right after the methodology, several indications are given about the limitations and restrictions of the proposed study.

3

2

Background & Context

Corruption is a complicated topic, not so much because of lack of research, but rather because there is no clear, worldwide accepted definition of corruption. The most used definitions in scholarly papers are for example: “The behaviour that deviates from the formal duties of a public role because of privateregarding (personal, close family, private clique) pecuniary or status gains; or violates rules against the exercise of certain private-regarding behaviour” (Klitgaard, 1991:23), or “Misuse of entrusted power for private gain” as noted by UNDP (2008). However, in practical sense, corruption may not be that clear-cut. What may be considered an act of corruption in Europe, might be seen as a kind act of friendship in Africa (Bailard, 2009). Next to that, one must account for stigma effects, and that many cases of corruption are unaccounted for. Subsequently, corruption is fairly difficult to measure or quantify, making figures and statements mere estimates and perceived values, as discussed by Neiva de Figueiredo (2013). Furthermore, corruption is defined as either collusive or extortive, where collusive is the most difficult to prove, as both parties gain from corruption, while with extortive corruption, there is a clear dominant and submissive role (Brunetti & Weder, 2003). Basically, three conditions must be met in order for corruption to flourish (Aidt, 2011; Tanzi, 1998). The first condition is that public servants must possess a certain authority to design, bend, alter, or administer regulations in a discretionary manner. Secondly, when someone has discretionary power, he or she must be able to funnel money or gifts into his/her own account, through the regulations set by the institution. Lastly, the institutions must be weakly regulated or controlled, and the incentive to work without corruption must be low. In other words, as soon as an institution is weak, and a servant has an incentive to extract or create gifts by use of their discretionary powers, corruption can grow (Tanzi, 1998). Blended in the abovementioned conditions, four groups of causes have been defined, which are used in the methodology section as well (Gunardi, 2008). First, there are the economic and demographic factors. If salaries are low, people are more prone to corruption in order to make ends meet. Second is the group of political institutions, within which one could think of polarisation (left vs right wing), or civic involvement in politics. If civic involvement is low, the prone to corruption is higher (Treisman, 2000). The third group, named the judicial and bureaucratic factors, includes the rule of law, and effectiveness of the government. If the rule of law is low in a country, and the efficacy of the government is low as well, then most likely corruption is higher than countries who have a high rule of law (Aidt, 2011; Treisman, 2007). The last group comprises the geographical and cultural factors. Variables included in this group are for example the primary religion of a country, or even the latitude/longitude of the country, (i.e. where the country is geographically located). For example, countries with a dominant Protestantism belief system are less prone to corruption then countries with other belief systems (Aidt, 2011; Gunardi, 2008). Nowadays, it is commonly accepted that corruption hinders democracy, and amongst other things, stalls economic growth as also noted by Lambsdorff (1999). However, this was not always seen as straight forward. Back in the 1960s, corruption could be seen as positive for underdeveloped countries, as it could boast innovation and efficiency (Leff, 1964; Leys, 1965). Nonetheless, various studies (such as Méon & Sekkat, 2005) proved the opposite, and rather claim that corruption creates inefficiency, and lowers the efficacy of governmental institutions. Moreover, corruption contributes to fiscal deficits, nonalignment of markets, and political instability (Szeftel, 2000; Tanzi, 1998). Most existing literature focusses on the relationship between independent variables and a dependent variable being either (perceived level of) corruption or digitalization. For example, Bojanic & Madsen 4

(2014) and Brunetti & Weder (2003) both studied the effect of free (digital) media on corruption, while Andersen, Bentzen, Dalgaard, & Selaya (2011), Bologna (2014), Goel, Nelson, & Naretta (2012), and Ionescu et.al. (2016) tried to link the effect of internet diffusion and usage with corruption. Furthermore, Bailard (2009) explored the correlation of mobile diffusion and corruption. These studies all have a link with digitalization, and relate one particular aspect of digitalization with corruption. One study (Shelley, 1998) turned it around, and argued that a higher degree of digitalization could actually cause a higher degree of corruption. Because of the secrecy around corruption, it is almost impossible to measure objective actual corruption data, and therefore it is deemed unavoidable to base the analysis on subjective corruption. Subjectivity in its turn, can be split into perceived and experienced corruption, both of which have their own distinct drawbacks. The major flaw with perceived values is that according to Lambsdorff (2005) there might be a significant gap between facts and perception. On the other hand, experience values might be problematic, mainly due to the reporting bias (Lambsdorff, 2005). Treisman (2007) takes it one step further, and argues that perceived and experienced values might measure factors other than corruption, in this case possibly the degree of public identification.

2.1

Digitalization

Digitalization is a broad concept which results in various rather confusing and overlapping concepts. Oxford Dictionaries (2016) defines digit(al)ization as: “The conversion of text, pictures, or sound into a digital form that can be processed by a computer”, while the academic concept is much broader. Khan (2016) describes three different concepts which are closely linked: digitization, digitalization, and digital transformation. The definition of Oxford Dictionaries comes close to the meaning of digitization, i.e. the conversion. Then, after conversion, tier two is digitalization, which might be seen as the usage. Finally, tier three is referred to as the effect of the usage. Gutiérrez and Gamboa (2010) argued for an additional division in tier 2. According to their study, there is a significant difference between access to digital work, and its actual usage. In addition to corruption, digitalization in its own domain has been studied in depth as well. Most notable studies are those where digitalization is explained through socio-economic variables, as shown for example by Gutiérrez & Gamboa (2010). Moreover, numerous studies link media (Bojanic & Madsen, 2014; Coleman, 2010; Howard & Hussain, 2011; Olson & Pollard, 2004), globalization (Clegg, Clarke, & Ibarra, 2001), or the crisis of 2008 (Preston & Rogers, 2012) with digitalization. The majority of studies (e.g. Lera-lopez (2010) & Vicente Cuervo and López Menéndez (2006)), who try to explain digital transformation use the variable ‘secure servers’ as made by The World Bank in cooperation with Netcraft. Suggested by Dr. N. Manley (IT Business Analyst), and supported by the conclusions of the articles, I think that those studies are in fact not measuring the internet technology of a country, but rather if the country is a so called ‘internet exchange’. Several countries in the world are strategically chosen as a datacentre. One could see these countries as cross-roads for all data in the world. Therefore, as can be shown with comparing secure server data from the World Bank with a map of internet exchanges, one sees a near perfect overlap (The World Bank, 2016a). Therefore, most likely those studies are falling short in construct validity. Moreover, many studies describe the link between the internet and corruption. Shelley (1998) states that corruption and crime actually increased due to internet, mainly because of the anonymity of the Internet. Ionescu, Mirea and Blajan (2011) extend the argument, and note that the majority of fraud and corruption is practised through a person inside the organization or government. Hence, when governmental services are digitalized, the corruption simply shifts from an offline to an online practice. 5

Almost simultaneously, Andersen, Bentzen, Dalgaard, and Selaya (2010) describe quite the opposite. According to their critical study, Internet is a powerful anti-corruption technology, and also an important driver of economic growth. Carr & Jenson (2014) disagreed with both studies, and argued that it is not internet or digitalization that influences corruption, but rather the bureaucratic procedures that complement digitalization. Bologna (2014) adds to that statement that there is a difference between low and high-income countries. He suggested that there is a strong positive correlation between income and [digital] literacy, and a negative correlation between [digital] literacy and corruption, which seems to be supported by the ‘Digital Divide’ critique as mentioned by Gutiérrez & Gamboa (2010). Finally, Bailard (2009) supports that idea by means of mobile phone usage. He states: “Decentralization of information leads to diminishing corruption opportunities”.

2.2

Place in literature

Combining scholars from the sociology and economy field, the proposed paper investigates the tier three level of digitalization, thus digital transformation, and corruption. By using data which contains both the perceived and experienced values from The World Bank in conjunction with data derived from Transparency International, a more reliable conclusion can be made (Bojanic & Madsen, 2014). However, as Neiva de Figueiredo (2013) correctly noted, one must proceed with caution, as it remains subjective data. Unlike numerous studies (e.g. Bojanic & Madsen (2014) and Erumban & de Jong (2006)), not one or several ‘simple’ indicators are used, but rather the devised model includes compounded variables. Within the model, three compounded explanatory variables are used, argued mostly by Choi (2014) and Gaskins (2013), as these compound variables would explain the actual usage of digitalization from three sides. First, there is the need for a digital infrastructure, through which all digital processes flows. In the proposed model this is the first independent variable. Then, flows from the citizens to the government and back to themselves, is recorded under the second independent variable. Finally, the level of digitalization of the government is captured in the third variable. The complete schematics is discussed in further detail in section 3.2. Moreover, using all three compound variables simultaneously might have an interesting and unknown effect, as it has not discussed in literature before.

2.3

Hypotheses

In order to answer the research question, four main hypotheses are tested. If all hypotheses are combined, one could construct the following figure, where IV stands for independent variable, M1 and M2 are the moderating variables, and DV is the dependent variable. Figure 1: Hypotheses Model

M1

H3

IV1-3

DV H4

H1 & H2

M2

6

Hypotheses 1 and 2 are focussing on the main relationship between the independent and dependent variable, while hypotheses 3 and 4 are coping the interaction effect of the moderating variables. Further details on variables and their theoretical foundation are discussed in the methodology section. First, the main relation is to be tested. In this case, the correlation between digitalization and corruption. It is to be expected that a country with a high degree of digitalization, has a low level of corruption, as studies show that specific parts of digitalization also correlate negatively with corruption. Such as internet dispersion (Andersen et al., 2011; Bojanic & Madsen, 2014; Goel et al., 2012), mobile phone dispersion (Bailard, 2009), or digital media access (Brunetti & Weder, 2003). Besides, if the correlation is indeed significant, a marginal effect can be calculated, which allows for a statement such as an increase of the digitalization score by one unit, yield a change of corruption of X units. Although only the correlation can be proved, the proposed paper includes a Granger Causality test (Granger, 1969; Huysmans, Martens, Baesens, Vanthienen, & Van Gestel, 2006), in order to provide an important indication on possible causality. As described by Goldin (2015), correlation does not imply causality, and vice versa. As Huysmans, Martens, Baesens, Vanthienen, & Van Gestel (2006) rightfully argue, most studies show a mere correlation of one variable with another, and wrongfully derive a causational relationship, resulting in a false-cause fallacy. Therefore, to test the combined effect of the independent variables, hypothesis 1 is tested using a fixed effects analysis, including a multivariate FTest, and devised as: Hypothesis 1: There is a significant negative correlation between the degree of digitalization in a country and the level of corruption. Brought forward in the methodology section, it has been chosen to use three compound variables as independent variables: e-Government, e-Citizenship, and the Network Readiness Index (NRI). By doing so, the complete spectrum of digitalization is covered, as e-Government covers digitalization from the government side, e-Citizenship cover the citizen perspective, and finally the NRI lays down how the technical part is. It is expected that each of the independent variables has an individual negative correlation with corruption. Each single effect is tested by means of fixed factor analysis, together with multi-level analysis. Then, multivariate J and F-Tests are performed. Subsequently, the hypotheses are: Hypothesis 2a: e-Government is individually negative correlated with the corruption index. Hypothesis 2b: e-Citizenship is individually negative correlated with the corruption index. Hypothesis 2c: Network Readiness Index is individually negative correlated with the corruption index. In the proposed model, two moderating variables are included, based upon suggestions of Bohara, Mitchell, & Mittendorff (2004), Bojanic & Madsen (2014), Bologna (2014), and Rami, Jetter, & Montoya (2015), who discussed that democracy in itself has as a significant effect on both corruption and digitalization. On top of that, Treisman (2000) discussed that the effect of digitalization on corruption diminishes when there is low to none democratic surrounding. Besides that, it has been argued by Bohara et al. (2004) that the effect is only significant if a country has a durable polity, and Rami et al. (2015) calculated that the cut-off point is 25 years. Therefore, by combining those factors, the following hypothesis is tested using multiple regression effect analysis, together with a T-Test, and constructed as: Hypothesis 3: The effect of digitalization on reducing corruption is only significantly positive when long-term democracy is present.

7

Lastly, Agostino, Dunne, & Pieroni (2016) and Dzhumashev (2014) argued that there is a moderating effect stemming from the income of countries. Though there are scholars who proved otherwise (Gaskins, 2013; Litina & Palivos, 2016) in certain conditions. However, Gaskins (2013) noted that some none-effects are probably caused by the division used: either low or high income. Therefore, the figures from The World Bank are combined with a more extensive division by OECD. As a result, the proposed paper acknowledges four groups: Low, lower-middle, upper-middle, and high-income. Supported by Kalenborn & Lessmann (2013), it is expected that all income groups are significant correlated with corruption, but that the high-income group is more negatively correlated as compared to the low-income group. By means of multiple regression effect analysis together with a T-Test, the following hypothesis is tested: Hypothesis 4: There is a significant difference in the effect of digitalization on corruption between income groups of countries. In conclusion, various tests and methodologies are used, which are discussed in detail in the methodology section. To sum up, fixed effects, multivariate, multilevel, and multiple regression effects analysis is used. Then, the (multivariate) J-, F-, and T-Tests are performed. Additionally, and not tested through hypotheses, a marginal effects analysis, a granger causality test, and robustness analysis are conducted.

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3

Methodology

The basic model consists of various independent, control, and moderating variables. All these variables come from various databases and pools, for which a summary can be found in table 1. The major condition for the combined dataset is that only countries which are present in every variable, should be included. Moreover, only the years which are present in every dataset will be included in the final dataset. This new compiled dataset, exists in the end of 171 countries, and for the years 2001-2015. Table 1: variable summary Type

SPSS Code

Name

Dataset

Source

DV1

CorrWBCC

Control of Corruption Index

WGI 19962015

IV1

NRI

Network Readiness Index

2001-2015

Worldwide Governance Indicators, The World Bank http://www.govindicators.org Global Information Technology Report http://reports.weforum.org/global-informationtechnology-report-2015/

IV2

eCIT

e-Citizenship, consists of: Mobile Telephone Subscriptions Fixed Broadband Subscriptions % of individuals using the internet e-Government, consists of: e-Government Development Index

IV2a IV2b IV2c IV3

eGOV IV3b

IV3b

ICT Dataset 2000-2015 ICT Dataset 2000-2015 ICT Dataset 2000-2015

International Telecommunication Union http://www.itu.int (per 100 inhibitants) International Telecommunication Union http://www.itu.int International Telecommunication Union http://www.itu.int

Compiled 2002-2015

Waseda e-Gov Dev: 2003-2015 https://www.waseda.jp/top/en-news/28775

e-Government Participation Index

Compiled 2000-2015

Measures of Democracy Level of income [High vs Low] GDP/Capita

1810-2014

M1

mDEMOC

M2

mGINC

CV1

cGDP

CV2

cVA

Voice and Accountability

WGI 19962015

CV3

cLAW

Rule of Law

1984-2015

CV4

cREG

Regulatory Quality

1984-2015

CV5

cRELI

Religion

1981-2014

1960-2015 1950-2015

9

UN Development Index 2002-2014 http://www.unos.ch Brown university e-GOV 2002-2008 http://www.browstats.com.edu UN 2003-4-5 then biannual-2015 http://www.unos.ch OECD Digitalization Government Index 2000-2014 http://www.oecd.org/digitalization https://services.fsd.uta.fi/catalogue/FSD1289 ?lang=en&study_language=en The World Bank / OECD Data.worldbank.org Penn World Tables http://www.rug.nl/ggdc/productivity/pwt/ Worldwide Governance Indicators, The World Bank http://www.govindicators.org Worldwide Governance Indicators, The World Bank http://www.govindicators.org Worldwide Governance Indicators, The World Bank http://www.govindicators.org World Value Survey http://www.worldvaluessurvey.org/ CIA World Factbook http://www.cia.gov/worldfactbook Religious Intelligence http://www.religiousintelligence.co.uk

To test for robustness, the following dependent variables are used: DV2 CorrCPI Corruption CPI 1995Transparency International Perception Index 2015 http://www.transparency.org/cpi2015 DV3 CorrWBES What Business WBES Enterprise Surveys, The World Bank Experience Survey Biannual http://www.enterprisesurveys.org – Corruption Where: DV=Dependent Variable, IV=Independent Variable, M=Moderating Variable, CV=Control Variable

3.1

Dependent Variables

Currently, there are four main indices that measure corruption which are all used extensively in research. First, there is the Corruption Perception index (CPI), as drawn up by Transparency International. As the name implies, the measure focus’ is on perception. Secondly, there is the What Business Experience Survey – Corruption (WBES), as compiled by The World Bank. This measure is using experiences of Western-European Managers while doing business abroad. Thirdly, there is the World Bank Control of Corruption Index (WBCC), compiled as well by The World Bank. This measure includes both experiences and perceptions, making it the most comprehensive variable. The last measure is drawn up by the Political Risk Service (PRS), and is named the International Country Risk Guide Index (ICRG). This measure is using less sources then the other indices, and incorporates mainly perceptions (Goel et al., 2012; Kalenborn & Lessmann, 2013; Treisman, 2000, 2007, 2014). By suggestion of the conclusions of Treisman (2007), it has been chosen to use the WBCC measure as the main dependent variable for the model, consequently named CorrWBCC in the model. The main point for including this variable, is that it is the most comprehensive variable (Kalenborn & Lessmann, 2013). Besides that, it includes both perceptions and experiences (Treisman, 2014). The variable is measured on a scale from -2.5 to 2.5, which is then inversed, in order that a high value would correspond with a high level of corruption. Moreover, the variable is transposed to a 1 to 10 scale, to allow easy comparison with the other variables. Although the variable CorrWBCC is the main dependent variable for the model, it has been chosen to use two other measures as a robustness test. First, against the Corruption Perception Index, as drawn up by Transparency International. In the forthcoming dataset, the variable is indicated as CorrCPI. And secondly, the WBES (What Business Experience Enterprise Survey – Corruption) variable, which is compiled by the World Bank. This variable will be named as CorrWBES. Apart from the [perception – experience] difference, these three indices are different in other aspects as well. In the CorrCPI only countries are included when they appear on at least three sources out of twelve. Next to that, where CPI and WBCC are measured yearly, the WBES is measured biannually. It has been chosen to not include the ICRG measure in the analysis, although amongst others, Agostino, Dunne, & Pieroni (2016) and Goel et al. (2012) did use the ICRG measure. However, as the ICRG is already incorporated in CorrWBCC and in CorrCPI, it has been thought unnecessary to measure the 4th index, as it would not add any significant value, because it correlates 0.97 and 0.98 respectively for CorrWBCC and CorrCPI, as also discussed by Kalenborn & Lessmann (2013).

3.2

Independent Variables

Digitalization can be measured through numerous variables, all somewhat related to each other. It has been suggested that in order to capture the complete domain of digitalization, three distinct parts are needed. These three flows are schematically drawn in figure 2. First, there is the need for infrastructure, 10

through which all digital processes flows. In the proposed model as well as in the schematic overview, this is the first independent variable (𝐼𝑉1 ). Then, flows from the citizens to the government and back to themselves, is recorded under the 2nd independent variable, which is (𝐼𝑉2 ). Finally, to capture the level of digitalization of the government, which encompasses flows from the government to the citizens, as well as back to itself, independent variable 3 is introduced (𝐼𝑉3 ). Figure 2: Digitalization Flows Government

𝐼𝑉2

𝐼𝑉2 Infrastructure 𝐼𝑉1 𝐼𝑉3

𝐼𝑉3

Citizens

Before any digitalization takes place, one needs obviously the infrastructure. Therefore, the first independent variable is designed to capture this infrastructure. Gutiérrez & Gamboa (2010) and Leralopez (2010) discussed that not only usage, but the technology behind it is important as well. Mainly because if the technology is limited, or insecure, the usage would be less or different (Gaskins, 2013). Therefore, inspired by Choi (2014) and Lonescu et.al. (2016), the Networked Readiness Index (NRI) is taken up into the model as 𝐼𝑉1 , and SPSS coded as NRI. Then, this infrastructure is used both by citizens and governments. Bailard (2009), Gutiérrez & Gamboa (2010), and Lera-lopez (2010) measured Internet usage and mobile phone diffusion separately. However, it has been thought that a compounded vector variable would better fit the model, as also indicated by Mungiu-pippidi & Dada (2016). This compounded variable (𝐼𝑉2 ), named eCIT in SPSS, is the simple mean of standardized values of the following indicators: mobile phone subscriptions, fixed broadband connections, and internet users. Moreover, the variable is transformed to a value ranging from 1 to 10, where 10 implies the highest form of e-Citizenship. Next to a measurement for e- citizenship, there is also an e-government variable. Where Lonescu et.al. (2016) use the UN’s e-Government Participation Index, Choi (2014) and Gaskins (2013) use the UN’s e-Government Development Index to basically describe the same concept. This paper combines the two into a new independent variable ( 𝐼𝑉3 ) : eGOV, by taking the simple mean of both indices. Furthermore, the variable is transformed, to establish a range from 1 to 10, with 10 being the highest form of e-Government. In the literature, various other variables are suggested, though they are not included in the model of this analysis. For example, Bojanic & Madsen (2014) and Brunetti & Weder (2003) discussed the importance of press freedom and internet freedom. These are not included in the proposed models, due to the fact that they have a limited timeframe (2011-2015), and a limited availability of countries. Moreover, Bojanic & Madsen (2014), Goel et al. (2012), and Mo (2001) showed the importance of democracy as an independent variable. However, supported by Gutiérrez & Gamboa (2010), the proposed paper shows that democracy is more moderating digitalization.

3.3

Moderating variables

In the proposed model, two moderating variables are included. First, democracy is included, for which it is argued that it affects both the slope and the intercept of the independent variables. Secondly, 11

countries are classified whether they are a high or a low income country. It is suggested that the intercept differs between those two groups. As noted in the previous paragraph, democracy is of high importance. The working hypothesis is that democracy is moderating the effect of digitalization. However, democracy can be measured in many ways. Goel et al. (2012) measured democracy using the democracy index from Political Risk Services, which ranges from -4 to 4. Bojanic & Madsen (2014) and Dzhumashev (2014) preferred to use the subjective index as made by Freedom House, which ranges from 0 to -6. This paper uses the broader, and much more extensive database of Vanhanen’s index of democracy, as this could be seen as more reliable (Gutiérrez & Gamboa, 2010). In this paper’s model, this variable is named mDEMOC. However, one might argue that a ‘new’ democracy, coming from e.g. an authoritarian nation, does not change instantly all its values to a western-like democracy. Therefore, a dummy (DDEMOC) will be incorporated capturing the long-term democracy of a country. As a result, countries which have been perceived as a democracy for less than 25 years, will have no moderating effect, while countries which are a long-term democracy do (Bohara et al., 2004; Kalenborn & Lessmann, 2013; Rami et al., 2015). In order to incorporate both the index and the dummy, the following equation is derived: 𝛿𝐷𝑈𝑀𝑀𝑌(𝛽𝑥 𝐼𝑁𝐷𝐸𝑋) 𝑜𝑟 𝛿𝐷𝐷𝐸𝑀𝑂𝐶(𝛽7 𝑚𝐷𝐸𝑀𝑂𝐶) Supported by Agostino, Dunne, & Pieroni (2016) and Dzhumashev (2014), who argued that there is a robust moderating effect stemming from the income of countries, it has been decided to incorporate the income of countries. Rather than using real income, it has been advised by Dzhumashev (2014) and Rami et al. (2015) to use income groups, and subsequently thus dummy variables. However, where Dzhumashev (2014) argued for a classification of low-income vs. high-income, it has been thought to reclassify according the OECD who uses a four-way classification system, which changes every year, as per suggestion of Gaskins (2013) and Kalenborn & Lessmann (2013). Based upon GNI/capita (Gross National Income), a country is either classified as a low, lower-middle, upper-middle, or high income country (The World Bank, 2016b). The yearly changing classification can be found in appendix 2, table 6. The dummy variable is named mGINC in the dataset, and assumes values of 0 for the low income class, 1 for lower-middle, 2 for upper-middle, and the value 3 for the high income class. As noted earlier on, it is expected that the high-income group is more negatively correlated with corruption as compared to the low income group. Furthermore, it has been argued by Bohara, Mitchell, & Mittendorff (2004) and Goel et al. (2012) that governmental expenditure has some effect on the relationship between e-government and corruption. They used various classes of government expenditure. One can argue however, that if the income of a country is high, the (general) expenditures of a country would be high as well, when compared to lowincome countries. Therefore, this variable is not included in the analysis.

3.4

Control variables

As there are numerous other variables correlating either positively or negatively with corruption, it is necessary to control for some of them. As discussed in the previous chapter, there are four causal groups identified. The groups, control variables selected, and their expected sign are shown in table 2.

12

Table 2: Control Variables Group

Variable

economic & demographic political institutions judicial & bureaucratic factors

GDP per Capita Voice & Accountability Rule of Law Regulatory Quality Dominant Religion

geographic & cultural factors

Variable Nr & SPSS Code 𝐶𝑉1 cGDP 𝐶𝑉2 cVA 𝐶𝑉3 cLAW 𝐶𝑉4 cREG 𝐶𝑉5 cRELI

Expected correlation -

In the proposed model, at least one variable per group should be controlled for, according to Aidt (2011) and Gunardi (2008). In his seminal work, Gunardi (2008) analysed 68 indicators of corruption, of which only 12 passed robustness and sensitivity tests. A table of those 12 variables can be found in appendix II, table B. From that table, four variables are selected, and on top of that, one other variable is selected based upon other literature. From the economic and demographic group, it has been decided to include GDP per Capita, in the model marked as cGDP. Almost all literature on corruption controls for GDP. However, there are inconsistencies regarding the source. Huysmans, Martens, Baesens, Vanthienen, & Van Gestel (2006) use GDP figures derived from the World Bank, while Andersen et al. (2011) derive GDP from the World Development Indicators. Hessami (2014) thought to retrieve GDP figures from the OECD, and Gupta, De Mello, & Sharan (2001) base their analysis on the IMF. Although these sources measure the same, sometimes they do differ slightly. Based upon Agostino et al. (2016) and Feenstra, Inklaar, & Timmer (2015), the proposed models include real GDP, taken from the Penn World Tables. The variable is nationalized, standardized, and converted into an international format, in order to make exact comparisons possible, which would not be directly possible if the variable was drawn from OECD, World Bank, or IMF. Then, from the political institutions group, the choice has been made to select the composite variable capturing voice and accountability, which is named cVA in the models. This variable is taken from the World Governance Indicator database, as compiled by the World Bank. The variable itself is compounded from 65 different variables, of which 40 are representative variables, and 25 are nonrepresentative variables, drawn from 31 different databases, which can be seen in appendix 3, table A4. Both Kaufmann, Kraay, & Mastruzzi (2009) and Mattozzi & Merlo (2007) stress the importance of a measure of accountability, because it creates a perception of more transparency, hence the possibility of lower corruption. Moreover, Erumban & de Jong (2006) found that countries with a high power distance, and thus a low public voice, have lower diffusion levels of digitalization. Therefore, as presented by Gunardi (2008), the variable must be included as a confounding variable. The third group, comprising judicial and bureaucratic factors, present two variables. First of all, the ‘Rule of Law’ as noted by Gunardi (2008), and supported by Bohara et al. (2004) and Goel et al. (2012), is included in the models. This variable performed well under the strictest robust and sensitivity tests measured by Gunardi (2008). Named in the models as cLAW, it measures the strength and impartiality of the legal system, and is taken from the World Governance Database. Unsurprisingly, this variable correlates negatively with corruption (Kaufmann et al., 2009), and positively with digitalization (Wright, 2005). The second variable from this group, is the measurement of regulatory quality, in the model named as cREG, and originates from the World Governance Database as well. This variable measures the quality of bureaucracy and institutional strength of a country, with values ranging from -4 to 4, where 4 equals the best regulatory quality (Brunetti & Weder, 2003). Gunardi (2008) showed that this variable is highly correlated with some, but not all, corruption indices, because it did not pass all robustness tests. On top 13

of that, Goel et al. (2012) calculated that it has in fact an effect both on corruption and technology diffusion. Geographic and cultural factors form the last group, from which it has been chosen to control for the effects of religion. Both Gunardi (2008) and Treisman (2000) showed that countries with reformed Christianity - i.e. Protestants and Anglicans - as the dominant religion, have lower levels of corruption, compared to countries where reformed Christianity is not dominant. A possible reason might be that the Protestant work ethos is much higher in comparison with other religions, whilst another reason might be that the division between state and religion is more pronounced in Protestantism than in other religions (Treisman, 2000). On the other hand, Golan & Stettner (2008), found out that religion also has a significant effect on internet diffusion, both positively correlated (Protestant), and negatively correlated (Islam). In the model this variable is named cRELI, and stems from various sources, as there was no single source comprising the complete envisaged time-span. Therefore, a selection of 4 sources are taken, to compile consistent data. First, the Longitudinal database of the World Value Survey (WVS) is used. The WVS surveys around the world, hence, the dataset comprises data points spanning multiple years. The second source is the CIA World Factbook as used by most scholars such as Connolly-Ahern & Golan (2007) and Golan & Stettner (2008). An additional reasoning of not solely taking the CIA World Fact Book, is that the CIA World Factbook is not always properly updated, and does not lend itself to a proper longitudinal analysis (Collin & Martin, 2012:41). Then the third and fourth source are two religious organizations, each claiming to have the most proper statistics, which are Religious Intelligence, and, The ARDA. The four sources are combined, in order to calculate the weighted dominant religion in a country. Not every religion is taken into account. Only the 9 major religions around the world are taken up into the models.

3.5

Conceptual model

The basic model can also be shown as an equation. In that case, the model is as per following equation: 𝐶𝑜𝑟𝑟𝑢𝑝𝑡𝑖𝑜𝑛𝑖𝑡 = 𝛽0 + 𝛽𝑥 𝐼𝑛𝑑𝑒𝑝𝑒𝑛𝑑𝑒𝑛𝑡𝑖𝑡 + 𝛽𝑥 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑖𝑡 + 𝛿𝑀1,2𝑖𝑡 + 𝛿𝑀1𝑖𝑡 (𝛽𝑥 𝐼𝑛𝑑𝑒𝑝𝑒𝑛𝑑𝑒𝑛𝑡𝑖𝑡 ) + 𝛼𝑖 + 𝑢𝑖𝑡 𝑊ℎ𝑒𝑟𝑒: 𝑖 = 𝑐𝑜𝑢𝑛𝑡𝑟𝑦 (𝑁 = 171); 𝑡 = 𝑦𝑒𝑎𝑟 (𝑇 = 15, [2001; 2015]); 𝐼𝑛𝑑𝑒𝑝𝑒𝑛𝑑𝑒𝑛𝑡 = 𝑎𝑙𝑙 𝑖𝑛𝑑𝑒𝑝𝑒𝑛𝑑𝑒𝑛𝑡 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠; 𝐶𝑜𝑛𝑡𝑟𝑜𝑙 = 𝑎𝑙𝑙 𝑐𝑜𝑛𝑡𝑟𝑜𝑙 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠; 𝛽0 = 𝑡ℎ𝑒 𝑖𝑛𝑡𝑒𝑟𝑐𝑒𝑝𝑡, 𝛽𝑥 = 𝑃𝑎𝑟𝑎𝑚𝑒𝑡𝑒𝑟 𝑠𝑙𝑜𝑝𝑒𝑠, 𝑤ℎ𝑒𝑟𝑒 𝑥~[1 − 8]; 𝛿 = 𝑃𝑎𝑟𝑎𝑚𝑒𝑡𝑒𝑟 𝑠𝑙𝑜𝑝𝑒 𝑓𝑜𝑟 𝑚𝑜𝑑𝑒𝑟𝑎𝑡𝑖𝑛𝑔 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠; 𝑀 = 𝑀𝑜𝑑𝑒𝑟𝑎𝑡𝑖𝑛𝑔 𝑉𝑎𝑟𝑖𝑎𝑏𝑙𝑒; 𝛼𝑖 = 𝑡ℎ𝑒 𝑢𝑛𝑜𝑏𝑠𝑒𝑟𝑣𝑒𝑑, 𝑡𝑖𝑚𝑒 𝑖𝑛𝑣𝑎𝑟𝑖𝑎𝑛𝑡, 𝑖𝑛𝑑𝑖𝑣𝑖𝑑𝑢𝑎𝑙 𝑠𝑝𝑒𝑐𝑖𝑓𝑖𝑐 𝑒𝑓𝑓𝑒𝑐𝑡; 𝑢𝑖𝑡 = 𝑡ℎ𝑒 𝑖𝑑𝑖𝑜𝑠𝑦𝑛𝑐𝑟𝑎𝑡𝑖𝑐 𝑒𝑟𝑟𝑜𝑟 𝑡𝑒𝑟𝑚 As can be derived from the model, the variables are shortened as if it were one independent and one control variable, therefore, the beta is shortened as well to 𝛽𝑥 . It is done in order to not lose oversight of an equation spanning seven lines. Naturally, 𝛽1 , 𝛽2 , and 𝛽3 are the slopes for the independent variables, and 𝛽4 through 𝛽8 are the slopes for the control variables. Moreover, as becomes evident from the equation, it is expected that both moderating variables have an effect on the intercept, and on top of that, democracy has an additional effect on the slopes of the independent variables.

14

4

Limitations & Restrictions

No research is flawless, in the light of limitations and restrictions. Therefore, in this section the limitations, restrictions, and threats to validity and reliability are discussed consecutively. The first major limitation concerns the time series included. The study consists of 15 years, which, together with the 4001 omitted values, could make the within-variation a little low, resulting in a limited fixed effects estimator. In a few years, when more data points can be included, stronger conclusions could be drawn. Secondly, the study measures a time period, within which a lot has happened in the world. These exogenous events may have an indirect effect on the model. A simple example can make this abundantly clear: At the end of the 2000s we have seen an economic crisis unlike anything we have seen before. Governments clearly could not and cannot cope with the changed economic environment, resulting in mass layoffs in recent years. When in that period the perceived value of corruption is measured, it might be that instead of corruption levels of a government, the dissatisfaction with the government is being tested. If more data points become available, the significance of these exogenous events might be lowered. Thirdly, there might be an issue with QMC (Quasi Multi Collinearity), as some variables possibly test the same concept. Although the LSE (Least Squares Estimators) are still unbiased, the standard errors might increase, and tests may be invalid or unreliable. During the analysis, extra caution must be given to the VIF value. On the other hand, almost any variable might show some QMC. Fourthly, all measures of corruption have flaws. It is foreseen that the perceived measurement shows a stigma bias, and that there ought to be a significant gap between facts and perception. On the other hand, experience values might be problematic, mainly due to the reporting bias. However, for as long as there is no objective measure of corruption, it is deemed fit to proceed as is. On top of that, by using three distinct models, the variance with facts might be lowered. Lastly, due to time constraints, and because of a too wide scope of research, there are various restrictions on this particular research, which might be most interesting to follow up on in further studies. A list of those hypotheses are found in Appendix 1. These may present another view on the topic, using another line of approach.

15

5

Literature

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European Journal of Political Economy, 28(1): 64–75. Golan, G., & Stettner, U. 2008. From Theology to Technology: A Cross-National Analysis of the Determinants of Internet Diffusion. Journal of Website Promotion, 2(3): 63–75. Goldin, R. 2015. Causation vs Correlation. Causation vs Correlation. http://www.stats.org/causationvs-correlation/. Granger, C. W. J. 1969. Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3): 424. Gunardi, H. S. 2008. Corruption and Governance around the World: An Empirical Investigation. Rijksuniversiteit Groningen. http://www.rug.nl/research/portal/publications/pub(f2c01b20-8c9f4494-b9f0-b3031814eaea).html. Gupta, S., De Mello, L., & Sharan, R. 2001. Corruption and military spending. European Journal of Political Economy, 17(4): 749–777. Gutiérrez, L. H., & Gamboa, L. F. 2010. Determinants of ICT Usage among Low-Income Groups in Colombia, Mexico, and Peru. The Information Society, 26(5): 346–363. Hessami, Z. 2014. Political corruption, public procurement, and budget composition: Theory and evidence from OECD countries. European Journal of Political Economy, 34: 372–389. Howard, P. N., & Hussain, M. M. 2011. The Role of Digital Media. Journal of Democracy, 22(3): 35– 48. Huysmans, J., Martens, D., Baesens, B., Vanthienen, J., & Van Gestel, T. 2006. Country corruption analysis with self organizing maps and support vector machines. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3917 LNCS: 103–114. Ionescu, L., & others. 2016. E-Government and Social Media as Effective Tools in Controlling Corruption in Public Administration. Economics, Management, and Financial Markets, 11(1): 66–72. Kalenborn, C., & Lessmann, C. 2013. The impact of democracy and press freedom on corruption: Conditionality matters. Journal of Policy Modeling, 35(6): 857–886. Kaufmann, D., Kraay, A., & Mastruzzi, M. 2009. Governance Matters VIII Aggregate and Individual Governance Indicators 1996–2008. Policy Research Working Paper. no. WPS4978. Khan, S. 2016. Leadership in the digital age – A study on the effects of digitalisation on top management leadership. Stockholm University, 54. Klitgaard, R. 1991. Controlling Corruption. Berkely: University of California press. Lambsdorff, J. G. 1999. Corruption in empirical research: A review, 6: 17. Durban: Transparency International, processed. Leff, N. H. 1964. Economic development through bureaucratic corruption. The American Behavioral Scientist, 8(3): 8–14. Lera-lopez, M. B. F. 2010. Differences in digitalization levels : a multivariate analysis studying the global digital divide, 39–73. Leys, C. 1965. What is The Problem About Corruption? The Journal of Modern African Studies, 3(2): 215. Litina, A., & Palivos, T. 2016. Journal of Economic Behavior & Organization Corruption , tax evasion 17

and social values. Journal of Economic Behavior and Organization, 124: 164–177. Mattozzi, A., & Merlo, A. 2007. The Transparency of Politics and the Quality of Politicians. The American Economic Review, 97(2): 311–315. Méon, P. G., & Sekkat, K. 2005. Does corruption grease or sand the wheels of growth? Public Choice, 122(1–2): 69–97. Mo, P. H. 2001. Corruption and Economic Growth 1, 79: 66–79. Mungiu-pippidi, A., & Dada, R. 2016. Measuring Control of Corruption by a New Index of Public Integrity, 415–438. Neiva de Figueiredo, J. 2013. Are corruption levels accurately identified? The case of U.S. states. Journal of Policy Modeling, 35(1): 134–149. Olson, S. R., & Pollard, T. 2004. The Muse Pixeliope Digitalization and Media Literacy Education. American Behavioral Scientist, 48(2): 248–255. Preston, P., & Rogers, J. 2012. Crisis, digitalisation and the future of the internet. (L. Van Audenhove, Ed.)Info, 14(6): 73–83. Rami, S., Jetter, M., & Montoya, A. 2015. The Effect of Democracy on Corruption : Income is Key, 74: 286–304. Shelley, L. I. 1998. Crime and Corruption in the Digital Age. Journal of International Affairs, 51(2): 16. Szeftel, M. 2000. Clientelism, corruption & catastrophe. Review of African Political Economy, 27(85): 427–441. Tanzi, V. 1998. Corruption Around the World: Causes, Consequences, Scope, and Cures. Staff Papers - International Monetary Fund, 45(4): 559. The

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Servers.

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19

Appendix 1

Table 3: Possible hypotheses further research Digital diffusion needs to exceed a threshold before having a significant effect on corruption e-citizenship needs to exceed a threshold-level before having a positive effect on reducing corruption e-government needs to exceed a threshold-level before having a positive effect on reducing corruption There exists a significant inverse causal relationship between digitalization and corruption.

I

Appendix 2

Table 4: CPI Sources

Corruption Perceptions Index: Full Source Description 12 data sources were used to construct the Corruption Perceptions Index: 1. African Development Bank Governance Ratings 2. Bertelsmann Foundation Sustainable Governance Indicators 3. Bertelsmann Foundation Transformation Index 4. Economist Intelligence Unit Country Risk Ratings 5. Freedom House Nations in Transit 6. Global Insight Country Risk Ratings 7. IMD World Competitiveness Yearbook 8. Political and Economic Risk Consultancy Asian Intelligence 9. Political Risk Services International Country Risk Guide 10. World Bank - Country Policy and Institutional Assessment 11. World Economic Forum Executive Opinion Survey (EOS) 12. World Justice Project Rule of Law Index

II

Table 5: Table of robust variables

Table 6: Sources Voice & Accountability Representative Sources EIU

Economist Intelligence Unit Riskwire & Democracy Index

FRH

Freedom House

GCS

World Economic Forum Global Competitiveness Report

GWP

Gallup World Poll

IPD

Institutional Profiles Database

PRS

Political Risk Services International Country Risk Guide

RSF

Reporters Without Borders Press Freedom Index Non-representative Sources

AFR

Afrobarometer III

BTI

Bertelsmann Transformation Index

CCR

Freedom House Countries at the Crossroads

GII

Global Integrity Index

IFD

IFAD Rural Sector Performance Assessments

IRP

IREEP African Electoral Index

LBO

Latinobarometro

MSI

International Research and Exchanges Board Media Sustainability Index

OBI

International Budget Project Open Budget Index

VAB

Vanderbilt University Americas Barometer

WCY

Institute for Management and Development World Competitiveness Yearbook

WJP

World Justice Project Rule of Law Index

Table 7: Income Classification 2000 Low income (L) Lower middle income (LM) Upper middle income (UM) High income (H)

2001

2002

2003

2004

10,065

2005

2006

2007

2008

2009

12,195

2010

2011

2012

2013

2014

2015

12,475

IV

FACULTY OF ECONOMICS AND BUSINESS CAMPUS CAROLUS ANTWERP KORTE NIEUWSTRAAT 33 2000 ANTWERP PHONE. + 32 3 201 18 40 [email protected]