Abstract The purpose of this study is two folds: firstly, to analyze the direction of causalitybetween corruption and political instability directly and secondly, to determine the causalitybetween corruption and political instability indirectly through judicial inefficiency in Pak-istan. The causality between corruption, political instability and judicial inefficiency is testedby applying Toda–Yamamoto Granger causality test. The results show that there is a lackof direct causal relationship between corruption and political instability. However, politicalinstability and corruption cause each other indirectly through judicial inefficiency. The studyhighlights the critical role of judicial inefficiency leading to an important policy implication.
Corruption generally defined as the misuse of public power for private interest is a generaland comprehensive concept (Aidt 2003; p. 632). The encyclopedic and operational definitionused by the World Bank (1997) and Transparency International (1999) is that corruption is themistreatment of public power for personal advantages or profits. The rate of corruption andits impact on economies is different across developing and developed countries because ofthe different state capabilities and political and social perspectives (Khan 2004; Ullah 2006).There are many forms of corruption like stealing of state assets, bribery, fraud, backing,embezzlement, extortion and insider trading and nepotism (Langseth 2002). The corruptionhas been viewed either as a structural dilemma of economics or at the same time as a culturalor personal ethical problem (Andving 2000). Corruption impinges on the proper functioning
A. Ismail · K. Rashid (B)Department of Management Sciences, COMSATS Institute of Information Technology,Abbottabad, Pakistane-mail: [email protected]
of any country and its institutions and hampers the ability of the state to provide public goodslike education, security, health etc.
1.1 Corruption in Pakistan
Corruption takes place in societies where there is a lack of transparency, accountabilityin government functions and widespread misuse of authority. In such communities, publicand private sector institutions are weak and underdeveloped (Khan 2004; Ullah et al. 2010).Corruption is alleged to have worsened significantly over the last 20 years in Pakistan (Trans-parency International 2011). Pakistan has been included in a number of research studies oncorruption, both on national and international levels. Since 1995, annual international studiesconducted by Transparency International have measured the corruption using the CorruptionPerception Index (CPI). Pakistan has been included in almost all the surveys conducted bythe Transparency International.
In recent years, the presence of corruption has attracted the attention of economists andpublic opinion all over the world. Awareness of the dramatic effects of corruption on acountry’s development leads to an investigation of why corruption exists and what makes itsincidence so different between countries.
Similarly, the widely accepted definition of political instability was given by Alesina andcoworkers in 1996. Alesina et al. (1996) defined political instability as the propensity of thechange in the actions of the decision making body (head of a state) either with legal or illegalways. In other words, political instability is the instability with-in governments, change inauthority and security that the society acquires out of these alterations in a country. As shownby the previous studies that simply criminalizing corruption and punishing offenders doesnot work effectively without some extensive consideration of the additional imperfections(social, political, economic and cultural factors) that are critical in causing corruption.
Seldadyo and Haan (2006) identified four comprehensive categories of different factorsaffecting corruption, that is (1) socio-economic, demographic and economic institutions, (2)political factors, (3) judicial and bureaucratic factors and (4) religious and geo-cultural factors.
Economic factors consist of broad range of economic variables as validated by severalstudies. For example, the economic variables that can influence the extent of corruption areincome (Treisman 2000; Herzfeld and Weiss 2003; Braun and Di Tella 2004; Chang andGolden 2004; Damania et al. 2004; Brown et al. 2005; Lederman et al. 2005), income dis-tribution (Paldam 2002), government expenditure (Bonaglia et al. 2001; Fisman and Gatti2002; Ali and Isse 2003), black market premium (Van-Rijckeghem and Weder 1997; Brunettiand Weder 2003) and inflation (Braun and Di Tella 2004). Among economic institution vari-ables, the most significant variables are foreign aid (Ali and Isse 2003; Tavares 2003), importshares (Treisman 2000; Frechette 2001; Fisman and Gatti 2002; Herzfeld and Weiss 2003),trade openness (Knack and Azfar 2003; Persson et al. 2003; Gurgur and Shah 2005) andeconomic freedom (Paldam 2001; Graeff and Mehlkop 2003; Gurgur and Shah 2005; Kuni-cova and Ackerman 2005). Similarly, the most influential demographic variables comprisesof schooling (Rauch and Evan 2000; Frechette 2001; Ali and Isse 2003; Alt and Lassen 2003;Persson et al. 2003) and population (Fisman and Gatti 2002; Knack and Azfar 2003; Tavares2003; Damania et al. 2004). Similarly, the dominant political factors of corruption consist ofdemocracy and civil liberty (Goldsmith 1999; Wei 2000; Herzfeld and Weiss 2003; Knackand Azfar 2003; Damania et al. 2004; Kunicova and Ackerman 2005), press freedom andmedia (Brunetti and Weder 2003; Lederman et al. 2005; Suphachalasai 2005) and politicalinstability (Leite and Weidemann 1999; Park 2003; Kaufmann et al. 2006a, 2006b; Serra2006; Lawal 2007).
123
Time series analysis
The judicial system and the excellence of bureaucracy are central factors influencingcorruption. Significant bureaucratic and regulatory factors include government wage (Rauchand Evan 2000; Alt and Lassen 2003; Herzfeld and Weiss 2003), quality of bureaucracy(Brunetti and Weder 2003; Gurgur and Shah 2005), merit system (Rauch and Evan 2000)and rule of law (Broadman and Recanatini 2000; Park 2003; Ali and Isse 2003; Herzfeld andWeiss 2003; Damania et al. 2004). Finally, geography, religion and culture may also matterfor explicating corruption. Important factors include population with particular religiousaffiliation (La Porta et al. 1998; Paldam 2001; Herzfeld and Weiss 2003; Persson et al.2003; Chang and Golden 2004), natural resources (Leite and Weidemann 1999), ethnicheterogeneity (Treisman 2000; Bonaglia et al. 2001; Herzfeld and Weiss 2003; Lederman etal. 2005; Suphachalasai 2005) and latitude (La Porta et al. 1998).
Among different factors causing corruption (as mentioned above), present study exclu-sively focuses on the association between political instability and corruption. This study hastested the model for Pakistan proposed by Damania et al. (2004) in which political instabilityand corruption are not causing each other directly but indirectly through judicial inefficiency.
The objectives of the study are as follows.
• To determine the direction of causality between corruption and political instability; and• To determine whether corruption and political instability cause each other directly or
there are some other intervening variables which can affect the relationship betweenthem specifically the role judicial inefficiency.
1.2 Gap in the literature
The current paper fills the gap in the literature by using sophisticated econometric techniquesand recent data set to provide a conclusive evidence of the relationship between corruptionand political instability. The results of the study are interpreted in the context of Pakistanienvironment. Moreover, the direction of causality between the variables is also analyzed.These results suggest that corruption and political instability are cointegrated. Moreover, thereexists no direct causality between corruption and political instability in Pakistan. However,political instability and corruption affect each other indirectly through poor judicial efficiency.
Following up the introduction, the rest of the paper is organized in the following fivesections. Section 2 discusses the literature review. Section 3 presents the data source andmethodological framework. The empirical findings are discussed in Sect. 4. Finally, Sect. 5provides the conclusion, policy implications and future research recommendations.
2 Literature review
Corruption is a source of concentration of wealth in few hands thus enhancing the gap betweenrich and poor. It also provides the wealthy class to protect their interests and activities. That,in turn, can cause social unrests like crimes, terrorism and political instability. Researchershave found that corruption and political instability are positively linked and corruption issignificant in predicting political instability. The significant studies on the said relationshipinclude McMullan (1961), Huntington (1968), Mauro (1995), Bardhan (1997), Tanzi (1998),Leite and Weidemann (1999), Ackerman (1999), Kaufmann et al. (1999a), Mulloy (1999),Mo (2001), Adsera et al. (2003), Le (2003), Park (2003), Pellegrini and Gerlagh (2008),Lederman et al. (2005), Andvig (2006),Kaufmann et al. (2006a), Serra (2006), Lawal (2007).
Corruption has the ability of stimulating political instability, brain drain, intimidatinglaw and order and causing inefficiency in economic activities (Lawal 2007). Corruption
123
A. Ismail, K. Rashid
conduces to depress growth (Mauro 1995; Mo 2001) and higher political instability (Le et al.2004). Quantitative studies have also pointed out that corruption is positively associated withpolitical instability and the correlation has been strong at the regional level with the regionsbeing depicted as corrupt are the most pretentious by political instability and contrariwise(Le 2003). Corruption challenges the popular authenticity of political institutions leading topolitical instability and the violence (Mulloy 1999 cited in Pellegrini and Gerlagh 2004).
Corruption stimulates income inequality (Gupta et al. 2002) and may possibly increasepolitical instability through income gap between rich and poor (Mo 2001). High incomeinequality leads to stronger motivations for the ‘have nots’ to indulge in illegitimate or aggres-sive reprisal against ‘the haves’, especially if that wealth is considered to have been achievedillegally. Furthermore, corruption assails the fundamentals of democratic structure or whatFriedman terms the social fabric, inevitably contributing to political instability. Corruptionis found to have considerable positive impact on political instability and trade volume. Con-sequently, corruption has adverse consequences on growth through political instability, lowinvestment on human and physical capital (Mo 2001; Pellegrini and Gerlagh 2004; Hodge etal. 2009). Corruption moreover causes political instability, deteriorates administrative capa-bility destabilizing the democracy. Mo (2001) explained in the analysis that corrupt countriesare exposed to higher political instability. Similarly, Aidt et al. (2008) explained in their studythat the negative consequences of corruption on growth are heightened in the economies withpoor governance quality.
Lederman et al. (2005); Park (2003), and Leite and Weidemann (1999) found that corrup-tion is intensified in precarious polities. Quantitative analyses have signified that corruptionis positively interconnected with political instability (Mauro 1995). Through growing griev-ances among the people, corruption generates political instability in the course of popularsupport for political alteration (McMullan 1961). Serra (2006) concluded that corruptionis higher in politically rickety countries. Campos and Giovannoni (2007) found that polit-ical instability does not have much impact on lobbying but has positive consequences oncorruption.
There are several opinions regarding the impact of an increased or decreased politicalinstability on corruption. Some authors regard the political instability as a significant predictorof corruption. The major studies are as follows: Terrones (1990), Barro (1991), Murphy etal. (1991), Shleifer and Vishny (1993), Alesina et al. (1996), Caselli et al. (1996), Easterlyand Levine (1997), Leite and Weidemann (1999), Olson (2000), Persson et al. (2003), Jain(2001), Easterly (2003), Fredriksson and Svensson (2003), Park (2003), Lambsdorff (2006),Serra (2006), Aidt et al. (2008), Shera (2011).
The term corruption embraces a wide range of interrelated but diverse, modes in whichpublic officials can inappropriately achieve private gains, such as misuse or embezzlement ofpublic funds, welcoming bribes for favoritism or licenses, or contracting in nepotism. Politicalinstability can have distinct effects on different types of corrupt activities. Campante et al.(2008) pointed out that the countries such as Brazil (in the early 1990s) and Pakistan havestruggled with a combination of low stability and high corruption. Easterly (2003) inferredthat due to political instability, the successive governments of Pakistan are involved in heavylooting.
Respective preceding studies have referred to a prospective association between corruptionand instability. Shleifer and Vishny in 1993 explained in the analysis that weak decentralizedgovernments would have high levels of corruption which provided an indication about therelationship between political instability and corruption. Moreover, Campos and Giovannoni(2007) found that political instability do not have much impact on lobbying but have positiveconsequences on corruption.
123
Time series analysis
Leite and Weidemann (1999), Treisman (2000) and Persson and Tabellini (2003) found thatamong other socio-political factors causing corruption, political instability is more prominentfactor. Corruption is rife in fragile states and it exploits and intensifies state weakness. If nottackled down early, corruption can counteract citizens’ conviction in the state and as a resultdeteriorate state legitimacy.
Serra (2006) identified lower economic prosperity; democracy and political instability areamongst the imperative determinants of corrupt activities (cited in Jain 2001; Lambsdorff2006). The dominant factors that also have been recommended to affect corruption are theelectoral system (Persson et al. 2003; Kunicova and Ackerman 2005), governmental admin-istration (Brown et al. 2005; Chang and Golden 2004) and political instability (Park 2003).
Vigorous democracies are usually allied with more political stability. Numerous authorshave pointed out that political instability, which is frequently significant not only in authori-tarian regimes but also in democracies, may be coupled with higher corruption levels. Thisis the inspection of Treisman (2000) and Persson et al. (2003) who argued that bureaucratsare more tempted to indulge in corruption and rent-seeking activities in politically unstableenvironment. Moreover, Treisman (2000) suggested that a higher level of political instabilityis connected with more corruption.
Adsera et al. (2003) found a significant manifestation of positive relationship betweenpolitical instability and corruption. Similarly, Terrones (1990) and Murphy et al. (1991)emphasized that governments menaced by political instability may be persuaded to exercisecorruption to indemnify the allegiance of the organizations that might assist it to stay in powerlike the police, the army, the administration etc.
Corruption is generally widespread wherever there are other types of institutional inade-quacies, such as political instability, weak governmental and judicial structures and bureau-cratic red-tape. Shera (2011) indicated that institutional inadequacies are intimately connectedwith each other and in fact they nurture each other. For example, red-tape enhances corrup-tion and corrupt bureaucrats possibly will increase the extensiveness of red-tape so they cancontract extra kickbacks. Moreover, Shera (2011) also pointed out that corruption is, in lieu,higher where political instability is a leading dilemma.
Fredriksson and Svensson (2003) have dissertated that the ultimate impact of politicalinstability on corruption depends generally on the overall intensity of corruption. The empir-ical evidence persuaded the conception that political instability and corruption are positivelycolligated. Political instability strengthens the tendencies of corrupt activities. Damania et al.(2004) foresighted that in politically unsound polities the institutions necessary to monitorand enforce complaisance is weak. In such countries, corruption therefore is more perme-ate. Political instability lessens judicial effectiveness, which in turn successively encouragescorruption. Thus, the connection between political instability and corruption is not directbut the link arises indirectly via its upshot on the extent of judicial competence. Corruptionseems rampant in those countries which are caught up in the trap of high political instabilitycoupled with low growth (Bruno 2002). Mo (2001) pointed out that the most imperativechannel through which corruption distress economic growth is through political instability,which describes approximately 53 % of the entire effect. Corruption also trims down thelevel of human capital and the percentage of private investment.
Ayee (2002) portrayed that corruption obliterates the legitimacy of government in theopinion of those who can execute something about the situation. As a result, it contributesto instability. In Ghana and other West African states, corruption and misuse of public fundshave frequently been eluded among the rationales for armed forces takeovers.
The occurrence of corruption in any state is not a rapid onslaught but instigates as gradualdisputes against institutional standards and the rule of law. If it relics uncurbed it becomes
123
A. Ismail, K. Rashid
widespread and as a result private interest (individual and group) vie with national interest.When private interest becomes dominant, the state is then destabilized and is not capable toexecute its central responsibilities. The state will then provide signals of frailty, with viciousconflicts as one of the potential indications.
Pellegrini and Gerlagh (2008) have alluded that corruption is a predominant phenomenaldistressing all societies to different extent and at disparate times. The extent of corruptionvaries in different countries. In some societies, no business deal is agreed without corruption,whereas in other countries, it is considered as an exemption and rarely permitted. Analy-sis showed that corruption is more persistent in developing countries. Further, Pellegriniand Gerlagh (2008) studied the relationship between corruption and political instability andconcluded that high political instability be liable to elevate corruption.
Regardless of the theoretical perceptivities, various authors have not found evidence of asignificant relationship between these variables. Damania et al. (2004) in their analysis wereunable to capture the direct connection between political instability and corruption. Howeverthe relation is indirect. Similarly, Seldadyo and Haan (2005, 2006) did not find politicalinstability to be an influential determinant of corruption.
3 Methodology
In this study, the empirical work is based on the secondary data for the time period between1984 and 2011. The data on corruption and political instability index is collected from thepublication of Political Risk Services “International Country Risk Guide” (ICRG), while thedata on the judicial inefficiency (rule of law index) is taken from Kaufmann et al. (2010)“Rule of Law index”. A time series data analysis for Pakistan is conducted with the help ofvarious statistical techniques. Long run relationship between corruption, political instabilityand judiciary is analyzed by applying cointegration analysis developed by Johansen in 1991and the direction of causality is determined by applying Toda–Yamamoto Granger Causalitytest.
3.1 Econometric methodology
The core purpose of this study is to examine the causality between corruption and politicalinstability. The study employs the Granger causality test based on Augmented level VARwith integrated and cointegrated processes developed by Toda and Yamamoto (1995). Gen-erally, causality between two economic variables has been tested using Granger and Simscausality test (see Granger 1969; Sims 1972). This Granger test is implemented by runningthe following regression:
A significant test statistic indicates that the x variable has a predictive value for forecastingmovements in y in addition to the information contained in the past values of y.
Although, the traditional pair-wise Granger causality test is more informative than simplecorrelation coefficients, but there are few short-comings of the test. Firstly, Granger causality
123
Time series analysis
test addresses causality by only relying on temporal precedence and foretelling content as theessential criterion for one variable to Granger cause other variable. Another shortcoming ofthe test is that the critical values of the test are only valid for stationary variables that are notbound mutually in the long run by a cointegrating association (Granger 1988). Recent studieshave revealed that the conventional F-test for determining joint significance of regression-derived parameters, used as a test of causality, is not valid if the variables are nonstationaryand the test statistic does not have a standard distribution (Gujarati 1995). This makes thecausality test results somewhat weak and restricted on the nonexistence of cointegrationamong the related variables.
The present study applies the more robust test in order to determine the causality and thedirection of causality among the variables developed by Toda and Yamamoto (1995) extendedby Rambaldi and Doran (1996). Rambaldi and Doran (1996) explained that the advantageof using the Toda–Yamamoto procedure is that in order to test Granger causality in theVAR structure, it is not necessary to bind the variables for the integration and cointegrationproperties.
3.2 Toda–Yamamoto methodology
Toda and Yamamoto (1995) procedure uses a Modified Wald (MWALD) test for restrictionson the parameters of the VAR (k) model. This test has an asymptotic chi-squared distributionwith (k) degrees of freedom in the limit when a VAR (k+dmax) is estimated (where k is thelag order of VAR and d-max is the maximal order of integration for the series in the system).The underline objective of the Toda–Yamamoto causality test is to overcome the problemof invalid asymptotic critical values when causality tests are performed in the presence ofnonstationary series or even cointegrated.
Two steps are involved to implement the Toda–Yamamoto based Granger causality test.The first step involves determination of the lag length (k) and the maximum order of inte-gration (d-max) of the variables in the system. Given VAR (k) selected, and the order ofintegration (d-max) is determined, a level VAR can then be estimated with a total of k+d-max lags. The second step is to apply standard Wald test to the first k VAR coefficient matrixto make Granger causal inference.
In order to test for Toda and Yamamoto (1995) based Granger causality between cor-ruption, political instability and judicial inefficiency, the study estimates the following VAR(k+d-max) model. The Matrix notation of the VAR model is as follows:
[C O R Rt
P It
]=
[α1
α2
]+
k+d∑
i=1
[β1i δ1i
β2i δ2i
] [C O R Rt−i
P It−i
]+
[ε1t
ε2t
](2)
The study is also aimed to investigate causality between corruption, political instability andjudicial inefficiency so the resulting Matrix notation of the VAR model is as follows:
⎡
⎣C O R Rt
P It
JU DI N Ft
⎤
⎦ =⎡
⎣α1
α2
α3
⎤
⎦ +k+d∑
i=1
⎡
⎣β1i δ1i θ1i
β2i δ2i θ2i
β3i δ3i θ3i
⎤
⎦
⎡
⎣C O R Rt−i
P It−i
JU DI N Ft−i
⎤
⎦ +⎡
⎣ε1t
ε2t
ε3t
⎤
⎦ (3)
Where CORR = corruption, PI = political instability and JUDINF = judicial inefficiency.α1, α2 and α3 are the intercepts. k is the optimal lag order, d is the maximal order of integrationof the variables in the system. β1i , β2i , β3i , δ1i , δ2iδ3i , η1i , η2i and η3i are the coefficients ofCORRt , PIt and JUDINFt respectively. ε1t , ε2t andε3t are errors terms that are assumed tobe white noise and t is the time subscript.
123
A. Ismail, K. Rashid
3.2.1 The models
The study estimates the following bivariate VAR (k+d-max) models.
C O R Rt = α1 +k+d∑
i=1
β1i C O R Rt−1 +k+d∑
i=1
δ1i P It−1 + ε1t (4)
P It = α2 +k+d∑
i=1
β2i P It−1 +k+d∑
i=1
δ2i C O R Rt−1 + ε1t (5)
and multivariate VAR (k+d-max) models;
C O R Rt = α1 +k+d∑
i=1
β1i C O R Rt−1 +k+d∑
i=1
δ1i P It−1 +k+d∑
i=1
θ1i JU DI N Ft−1+ε1t (6)
P It = α2 +k+d∑
i=1
β2i C O R Rt−1 +k+d∑
i=1
δ2i P It−1 +k+d∑
i=1
θ2i JU DI N Ft−1+ε2t (7)
JU DI N Ft = α3 +k+d∑
i=1
β3i C O R Rt−1 +k+d∑
i=1
δ3i P It−1 +k+d∑
i=1
θ3i JU DI N Ft−1+ε3t (8)
Each variable is regressed on each other variable lagged from one (1) to the k+d-max lags andthe restriction that the lagged variables of interest are equal to zero is tested. The optimal laglength (k) of the VAR is determined by the Akaike information criteria (AIC) and SchwarzInformation Criterion (SIC). From Eq. (3), the null hypothesis can be drawn as “politicalinstability does not Granger-cause corruption” if δ1i = 0 against the alternate hypothesis“political instability does Granger-cause corruption” if δ1i �= 0. Similarly, from Eq. (4), thenull hypothesis can be drawn as “corruption does not Granger-cause political instability” ifβ2i = 0 against the alternate hypothesis “corruption does Granger-cause political instability”if β2i �= 0. Similar hypotheses are drawn from remaining multivariate equations.
4 Data analysis and results
This section consists of the determination of the long run relationship between corruption,political instability and judicial inefficiency. This relationship is determined by using coin-tegration analysis technique.
4.1 Application of unit root test
In order to check the order of integration of the variables, the Augmented Dickey-Fuller(ADF), unit root test is applied with the following null and alternative hypotheses. Thenull hypothesis suggests that corruption, political instability and judicial inefficiency are notstationary. On the other hand, the alternative hypothesis suggests that these variables arestationary. The results are presented in Table 1.
The results indicate that all the variables are not stationary in their respective levels butare stationary at their first difference. Hence, the null hypothesis of nonstationary for all thevariables is rejected at first difference at the particular significance level explained by thecritical values inside the parenthesis. Consequently, all the variables are integrated of orderone (Table 1). As the required condition for continuing the process (that is all variables are
123
Time series analysis
Table 1 ADF unit root test on corruption, political instability and judicial inefficiency
Variables ADF test statistic
Levels 1st Difference
Intercept Intercept and trend Intercept Intercept and trend(Critical value) (Critical value) (Critical value) (Critical value)
integrated to order one) is met. Next section aims to determine the optimum lag length usingthe Schwartz Bayesian criteria (SBC) and Akaike information criteria (AIC).
4.2 The selection of lag length
Next, AIC and SBC information criteria is employed to determine and select the optimumlag length of the VAR (k) model.
Table 2 presents the results of the choice criteria. In this particular case, both statisticspropose the lag length of 2 as optimal. Next step is to test the cointegration between corruptionand political instability. This would help us to identify the equilibrium relationship betweenthese two variables. Generally, in order to check the cointegration between two variables,two-step residual based test of Engle and Granger (1987) is applicable. But there are fewshortcomings of the test. Firstly, when estimating the long run relationship between thevariables; one has to put one variable as a dependent and the other one as an independentvariable. The test does not suggest anything about which of the variables can be used as anindependent variable and which one as a dependent variable and why. Secondly, in two stepresidual based test, any error introduced in the first step is carried out in the second step.Hence, it may lead to a wrong inference about the relationship. So in order to avoid thesedifficulties, the more appropriate method is employed to determine the long run relationship.
4.3 Johansen cointegration analyses
This study starts with the null hypothesis of no cointegration (r = 0) among the variables.The study utilizes both the Trace and maximum Eigen value tests. The criterion for therejection of null hypothesis is that trace value and max Eigen value should be greater than
Table 2 Choice criteria forchoosing the order of the VARmodel
Lag AIC SC
2 −1.747544 −1.312968
1 −1.289842 −1.029096
0 1.39902 1.485935
123
A. Ismail, K. Rashid
Table 3 Cointegration test results
H0 H1 Max- Eigenstatistics λmax
Critical values Trace statisticsλtrace
Critical values
5 % Prob. 5 % Prob.
Cointegration test results for corruption and political instability
r = 0** r > 0 13.33068 13.26 0.0698 15.78106 15.49 0.0453
r ≤ 1 r > 1 2.450376 3.84 0.1175 2.450376 3.84 0.1175
Cointegration test results for corruption, political instability and judicial inefficiency
r = 0** r > 0 53.36786 21.13 0.000 63.37700 29.79 0.0000
r ≤ 1 r > 1 10.00492 14.26 0.2116 10.00913 15.49 0.2800
r ≤ 2 r > 2 0.004216 3.84 0.9469 0.004216 3.84 0.9469
** Denotes rejection of the hypothesis at 5 % significance levelr Shows the cointegration relationship among the variables
95 percent critical values (Johansen 1991). If both the statistics are less than the said criticalvalue then the null hypothesis cannot be rejected.
As presented in Table 3 above, it is found that there exists one cointegration relationshipbetween corruption and political instability. Similarly, the result of one cointegration rela-tionship is found between corruption, political instability and judicial inefficiency. However,the cointegration results do not indicate the direction of the long-run relationship between thevariables, therefore the next step is to perform Granger-causality test in order to determinethe direction of causality between variables (Kalyoncu and Yucel 2006) after the existenceof cointegration.
4.4 Granger causality based on Toda–Yamamoto methodology
The empirical results of Granger causality test based on Toda and Yamamoto (1995) method-ology are reported in Table 4. The optimum lag length of VAR is k = 2 based on SIC and AICcriterion. However, all the variables are stationary at the first difference. Therefore d-max = 1.Accordingly, the study estimates a system of VAR at levels with a total of k+d-max = 2 + 1 = 3lags in the model.
The results for Toda–Yamamoto Granger causality test on corruption, political instabilityand judicial inefficiency are reported in Table 4. It is shown that there is a lack of causal rela-tionship between corruption and political instability. However, it is shown from the results thatbi-directional causality is running between corruption and judicial inefficiency. The results areconsistent with the mechanism that fragile (strong) legal and regulatory framework enhances(reduces) the opportunities of corruption and similarly, higher (lower) corruption is expectedto reduce (enhance) judicial efficiency (Damania et al. 2004). Similarly, bi-directional causal-ity is running between political instability and judicial inefficiency. The results support anargument that greater levels of judicial efficiency (inefficiency) are expected in politically sta-ble (unstable) regimes. Similarly, a strong (weak) legal and regulatory framework increasespolitical stability (instability).
It is obvious from the results that judicial inefficiency is critical in determining the levelsof corruption and political instability. Thus, the relationship between corruption and polit-ical instability is not direct but through judicial inefficiency, corruption is causing politicalinstability and political instability is simultaneously causing corruption. Political instability
123
Time series analysis
Table 4 Toda–Yamamoto Granger causality test
Granger causality test between corruption, political instability and judicial inefficiency
Null and alternative hypotheses χ2-Statistics Null hypothesis testing
Value d.f Prob.
1 H0: PI does not Granger cause CORR 0.32337 1 0.5696 Accepted
H1: PI Granger causes CORR
2 H0: CORR does not Granger cause PI 0.3549 1 0.5514 Accepted
H1: CORR Granger causes PI
3 H0: PI does not Granger cause JUDINF 5.6325 1 0.0264 Rejected
H1: PI Granger causes JUDINF
4 H0: JUDINF does not Granger cause PI 9.8461 1 0.0017 Rejected
H1: JUDINF Granger causes PI
5 H0: CORR does not Granger cause JUDINF 4.41616 1 0.0189 Rejected
H1: CORR Granger causes JUDINF
6 H0: JUDINF does not Granger cause CORR 4.8473 1 0.0277 Rejected
H1: JUDINF Granger causes CORR
CORR corruption, PI political instability, JUDINF judicial inefficiency and d.f degrees of freedom
is producing an environment in which corruption turns out to be more invasive and tendsto persist. Similarly, corruption is harder to eliminate in politically unstable regimes andbecome self-sustaining. The findings of the study are consistent with the one predicted byDamania et al. (2004). Higher level of political instability is coupled with a greater judicialinefficiency (a lower level of the rule of law). It is found that ineffective judicial systems arepositively associated with corruption. The results suggest that political instability encour-ages institutional structures that are more favorable for corruption. Hence, cetris peribus,corruption levels will be high in politically unsound systems (Damania et al. 2004).
5 Conclusion
This paper empirically investigates the relationship between corruption, political instabilityand judicial inefficiency in Pakistan by using Cointegration and Causality Techniques for thetime period from 1984 to 2011. The cointegration analysis, which examines the existence oflong term relationship between the variables, reveals that there exists a long run relationshipbetween corruption, political instability and judicial inefficiency. Furthermore, the resultsreveal that there is a lack of causal relationship between corruption and political instabilitydirectly. However, judicial inefficiency is a central factor causing both corruption and politicalinstability. The results prove that bi-directional causality exists between political instabilityand judicial inefficiency and similarly between corruption and judicial inefficiency. Thus,political instability is causing corruption indirectly through its effects on institutional qualityand levels of judicial efficiency. It has no direct impact on corruption and similarly, corruptionis causing political instability via judicial inefficiency. Political instability and corruption hasno direct effect on each other, once judicial inefficiency is controlled for. The analysis predictsthat weak institutional structures will be more pervasive in unstable political systems, which,in turn, creates productive environment for corruption. Similarly, a high level of corruptioninduces judicial inefficiency, which results in an increase in political instability.
123
A. Ismail, K. Rashid
5.1 Policy implications
Results of the study prove that corruption and political instability are strongly related to eachother indirectly via judicial inefficiency. Since, these variables are obstacles for the smoothfunctioning of the society. So, keen attention should be given to weaken the relationshipbetween corruption and political instability by improving the quality of legal and regula-tory frameworks. For this purpose, there should be an independent judicial process, whichwill strengthen the democracy, restore trust between federation and provinces, efficient anticorruption agencies and facilitate the quick privilege of justice. By lowering the extent ofcorruption helps a country to sweep over other institutional weaknesses, just as tumblingother institutional weaknesses aid to incapacitate corruption. Moreover, increased politicalstability leads to greater institutional quality and thus institutions necessary to monitor andenforce complaisance will be stronger. Finally, there is an urgent need for a better networkingand coordination among various sections of the society such as lawyers, journalists, humanrights activists and students.
5.2 Future research recommendations
Future research can be conducted to investigate the other important aspects of corruption inPakistan. It is suggested that researchers can analyze the relationship of corruption with othersocio-economic and political variables. Furthermore, these researchers can also determinethe relationship between corruption and political instability for the cross country analysis.This will give them a different nature of the relationship between these variables and analternate policy implication.
References
Ackerman, R.S.: Corruption and Government: Causes, Consequences and Reform. Cambridge UniversityPress, Cambridge (1999)
Adsera, A., Boix, C., Payne, M.: Are you being served? Political accountability and quality of government.J. Law Econ. Organ. 19(2), 445–490 (2003)
Aidt, T.S.: Economic analysis of corruption: a survey. Econ. J. 113, 632–652 (2003)Aidt, T., Dutta, J., Sena, V.: Governance regimes, corruption and growth: theory and evidence. J. Comp. Econ.
36(2), 195–220 (2008)Alesina, A., Ozler, S., Roubini, N., Swagel, P.: Political instability and economic growth. J. Econ. Growth
1(2), 189–211 (1996)Ali, M.A., Isse, H.S.: Determinants of economic corruption: a cross-country comparison. Cato J. 22(3), 449–
466 (2003)Alt, J.E., Lassen, D.D.: The political economy of corruption in American states. J. Theor. Politics 15(3),
341–365 (2003)Andving, J.C.: Research on Corruption: a policy oriented survey. Commissioned by NORAD, Final Report,
December, Oslo (2000)Andvig, J.C.: Corruption and fast change. World Dev. 34(2), 328–340 (2006a)Ayee, J.: Political and social consequence of corruption, in corruption and development in Africa. In: Proceed-
ings of a seminar organized by the Ghana Academy of Arts and Sciences with Friedrich Ebert Foundationfrom 17–19, p. 36 (2002)
Bardhan, P.: Corruption and development: a review of issues. J. Econ. Lit. 35, 1320–1346 (1997)Barro, R.J.: Economic growth in a cross section of countries. Q. J. Econ. 106(2), 407–443 (1991)Bonaglia, F., Braga de Macedo, J., Bussolo, M.: How globalization improves governance. Discussion Paper No.
2992. Centre for Economic Policy Research, Organization for Economic Co-operation and Development,Paris, France (2001)
Braun, M., Di Tella, R.: Inflation variability and corruption. Econ. Politics 16, 77–100 (2004)Broadman, H.G., Recanatini, F.: Seeds of corruption: do market institutions matter?. The world bank policy
research working paper No. 2368 (2000)
123
Time series analysis
Brown, D.S., Touchton, M., Whitford, A.B.: Political polarization as a constraint on government: evidencefrom corruption on SSRN, http://ssrn.com/abstract=782845 (2005)
Brunetti, A., Weder, B.: A free press is bad news for corruption. J. Public Econ. 87, 1801–1824 (2003)Bruno, V.: Corruption in a model of growth: political reputation, competition and shocks. Public Choice
110(1/2), 23–40 (2002)Campante, F.R., Chor, D., Do, D.: Instability and the incentives for corruption. SMU economics and statistics,
working papers series (2008)Campos, N., Giovannoni, F.: Lobbying corruption and political influence. Public Choice 131(1), 1–21 (2007)Caselli, F., Esquivel, G., Lefort, F.: Reopening the convergence debate: a new look at cross-country growth
empirics. J. Econ. Growth 1(3), 363–389 (1996)Chang, E.C.C., Golden, M.A.: Electoral systems, district magnitude and corruption. Paper presented at the
2003 annual meeting of the American Political Science Association, August 28–31, 2003 (2004)Damania, R., Fredricksson, P.G., Mani, M.: The persistence of corruption and regulatory compliance failures:
theory and evidence. Public Choice 121, 363–390 (2004)Easterly, W., Levine, R.: Africa’s growth tragedy: policies and ethnic divisions. Q. J. Econ. 5(1), 331–398
(1997)Easterly, W.: Political economy of growth without development: a case study of Pakistan. In: Rodrik, D. (ed.)
Search of Prosperity: Analytical Narratives of Growth. Princeton University Press, Princeton (2003)Engle, R.F., Granger, C.W.J.: Cointegration and error correction representation, estimation and testing. Econo-
metrica 55, 251–276 (1987)Fisman, R.J., Gatti, R.: Decentralization and corruption: evidence across countries. J. Public Econ. 83, 325–345
(2002)Frechette, G.R.: A panel data analysis of the time-varying determinants of corruption. Paper presented at the
EPCS (2001)Fredriksson, P.G., Svensson, J.: Political instability, corruption and policy formation: the case of environmental
policy. J. Public Econ. 87, 1383–1405 (2003)Goldsmith, A.A.: Slapping the grasping hand: correlates of political corruption in emerging market. Am. J.
Econ. Sociol. 58(4), 865–883 (1999)Graeff, P., Mehlkop, G.: The impacts of economic freedom on corruption: different patterns for rich and poor
countries. Eur J Political Econ. 19, 605–620 (2003)Granger, C.W.J.: Investigating casual relationship by econometric models and cross spectral methods. Econo-
metrica 37, 424–458 (1969)Granger, C.W.J.: Some recent developments in the concept of causality. J. Econom. 39, 199–211 (1988)Gujarati, D.: Basic Econometrics, 3rd edn. McGraw-Hill, New York (1995)Gupta, S., Davoodi, H., Alonso-Terme, R.: Does corruption affect income inequality and poverty? Econ. Gov.
3(1), 23–45 (2002)Gurgur, T., Shah, A.: Localization and corruption: Panaceaor Pandora’s Box. World Bank policy research
working paper 3486 (2005)Herzfeld, T., Weiss, C.: Corruption and legal (in)effectiveness: an empirical investigation. Eur. J. Political
Econ. 19, 621–632 (2003)Hodge, A., Sriram S., S. Prasada, D.S. R., Duhs, A.: Exploring the links between corruption and growth.
Discussion paper No. 392, School of Economics, The University of Queensland, Australia (2009)Huntington, S.P.: Modernization and Corruption. Political Order in Changing Societies. Yale University Press,
New Haven (1968)Jain, A.K.: Corruption: a review. J. Econ. Surv. 15, 71–121 (2001)Johansen, S.: Estimation and hypothesis testing of cointegration vectors in Gaussian vector autoregressive
models. Econometrics 59, 1551–1580 (1991)Kalyoncu, H., Yucel, F.: An analytical approach on defense expenditure and economic growth: the case of
Turkey and Greece. J. Econ. Stud. 33(5), 336–343 (2006)Kaufmann, D., Kraay, A., Zoido-Lobaton, P.: Governance matters. World bank policy research working paper
no. 2196. World Bank, Washington, DC (1999)Kaufmann, D. Kraay, A., Mastruzzi, M.: Governance matters V: governance indicators for 1996–2005. World
bank policy research working paper no. 4012, Washington, DC (2006)Kaufmann, D., Kraay, A., Mastruzzi, M.: The worldwide governance indicators project: answering the critics.
World bank, Washington, DC (2006)Kaufmann, D., Kraay, A., Mastruzzi, M.: The worldwide governance indicators : a summary of methodology.
Data and analytical issues. World bank policy research, working paper No 5430 (2010)Khan, M.: State Failure in Developing Countries and Institutional Reform Strategies, pp. 165–195. Oxford
University Press and World Bank, Oxford (2004)Knack, S., Azfar, O.: Trade intensity, country size and corruption. Econ. Gov. 4, 1–18 (2003)
Kunicova, J., Rose Ackerman, S.: Electoral rules and constitutional structures as constraints on corruption.British J. Political Sci. 4, 573–606 (2005)
Lambsdorff, J.G.: Causes and consequences of corruption: what do we know from a cross-section of countries?In: Rose-Ackerman, S. (ed.) International Handbook on the Economics of Corruption, pp. 3–51. EdwardElgar, Cheltenham (2006)
Langseth, P.: Global dynamics of corruption, the role of United Nations, in strengthen judicial integrity andcapacity in Nigeria. State integrity meeting in Lagos, May 2002
La Porta, R., Lopez-de-Silanes, F., Shleifer, A., Vishny, R.W.: The quality of government. J. Law Econ. Organ.15, 79–222 (1998)
Lawal, G.: Corruption and development in Africa: challenges for political and economic change. Humanit.Soc. Sci. J. 2(1), 1–7 (2007)
Le, B.P.: Buying peace or fueling war: the role of corruption in armed conflict. J. Int. Dev. 15, 413–426 (2003)Lederman, D., Norman, V.L., Rodrigo, R.S.: Accountability and corruption: political institutions matter. Econ.
Politics 17, 1–35 (2005)Leite, C.A., Weidemann, J.: Does mother nature corrupt? Natural resources. Corruption and economic growth,
IMF working paper, 85 (1999)Le, Q., Mehlkop, G., Graeff, P.: The Mechanism of Corruption and Political Instability. Mimeo, Seattle (2004)Mauro, P.: Corruption and growth. Q. J. Econ. 60(3), 681–712 (1995)McMullan, M.: A theory of corruption. Sociol. Rev. 9(2), 181–201 (1961)Mo, P.H.: Corruption and Economic Growth. J. Comp. Econ. 29(1), 66–79 (2001)Mulloy, P.: Statement on corruption delivered by assistant secretary of commerce, Patrick Mulloy, to the OSCE
review conference, Istanbul. http://www.useu.be/ISSUES/osce1110.html (1999)Murphy, K., Shleifer, A., Vishny, R.: The allocation of talent: implications for growth. Q. J. Econ. 106(2),
503–530 (1991)Olson, M.: Power and Prosperity: Outgrowing Communist and Capitalist Dictatorships. Basic Books, New
York (2000)Paldam, M.: Corruption and religion: adding to the economic model. Kyklos 54, 383–414 (2001)Paldam, M.: The cross-country pattern of corruption: economics, culture and the seesaw dynamics. Eur. J.
Political Econ. 18, 215–240 (2002)Park, H.: Determinants of corruption: a cross-national analysis. The Multinatl. Bus. Rev. 11(2), 29–48 (2003)Pellegrini, L., Gerlagh, R.: Corruption’s effect on growth and its transmission channels. Kyklos 57(3), 429–456
(2004)Pellegrini, L., Gerlagh, R.: Causes of corruption: a survey of cross-country analyses and extended results.
Econ. Gov. 9(3), 45–63 (2008)Persson, T., Tabellini, G., Trebbi, F.: Electoral rules and corruption. J. Eur. Econ. Assoc. 1(4), 958–989 (2003)Persson, T., Tabellini, G.: The Economic Effects of Constitutions. MIT Press, Cambridge (2003)Political Risk Services: International country risk guide dataset. http://www.icrgonline.com (2004)Rambaldi, A.N., Doran, H.E.: Testing for Granger non-causality in cointegrated systems made easy. Working
papers in econometrics and applied statistics 88, Department of Econometrics, The University of NewEngland (1996)
Rauch, J., Evan, P.: Bureaucratic structure and bureaucratic performance in less developed countries. J. PublicEcon. 75, 49–71 (2000)
Seldadyo, H., de Haan, J.: The determinants of corruption: a reinvestigation. Paper presented for the epcs 2005conference. European Public Choice Society, Durham (2005)
Seldadyo, H., de Haan, J.: The determinants of corruption: a literature survey and new evidence. Paper presentedfor the epcs 2006 conference. European Public Choice Society, Turkey (2006)
Serra, D.: Empirical determinants of corruption: a sensitivity analysis. Public Choice 126(1), 225–256 (2006)Shera, A.: Corruption and the impact on the economic growth. J. Inf. Technol. Econ. Dev. 2(1), 39–53 (2011)Shleifer, A., Vishny, R.: Corruption. Q. J. Econ. 108, 599–617 (1993)Sims, C.: Money, income and causality. Am. Econ. Rev. 62(4), 540–552 (1972)Suphachalasai, S.: Bureaucratic corruption and mass media. Paper presented at the 2005 EPCS (2005)Tanzi, V.: Corruption around the World: causes, consequences, scope, and cures. IMF Staff Pap. 45(4), 559–594
(1998)Tavares, J.: Does foreign aid corrupt? Econ. Lett. 79, 99–106 (2003)Terrones, M.: Influence activities and economic growth, unpublished (1990)Toda, H.Y., Yamamoto, T.: Statistical inference in vector autoregressions with possibly integrated processes.
J. Econ. 66(1–2), 225–250 (1995)Transparency International: Corruption perceptions index. http://www.transparency.de/documents/cpi/index.
html (1999)Transparency International: Transparency international corruption perceptions index. Berlin, Germany (2011)
Treisman, D.: The causes of corruption: a cross-national study. J. Public Econ. 76(3), 399–457 (2000)Ullah, M.A.: Corruption, income inequality and economic growth: M. Phil. Thesis. Quaid-i-Azam University,
Islamabad (2006)Ullah, M.A., Arthanari, T.S., Urquhart, C.: Using thematic analysis approach to investigate perceptions of
corruption and its consequences. Paper presented at The Australian Sociology Association Conference,Sydney, Australia (2010)
Van-Rijckeghem, C., Weder, B.S.: Corruption and the rate of temptation: do low wages in the civil servicecause corruption?. IMF working paper WP/97/73 (1997)
Wei, S.: Local corruption and global capital flows. Brookings Pap. Econ. Activity 2, 303–352 (2000)World Bank: Helping countries combat corruption. Washington, DC. The World Bank. Poverty reduction and
