The Impact of Corruption,Rule of Law,Accountability,and Government Expenditure on Government Effectiveness: Evidence From Sri Lanka

Abstract

In most cases, researchers assume that control of corruption, rule of law, accountability, and government expenditure tend to have a positive impact on government effectiveness. Nonetheless, recent theoretical and empirical evidence supports a mixed relationship between these variables. The paper, therefore, seeks to answer the extent to which corruption, the rule of law, accountability, and government expenditure affect government effectiveness. We employed Johansen method of cointegration and vector error correction model to examine the long-run and short-run relationship between the variables under study. By using Sri Lankan data covering the period from 1996 to 2020, we find a significant and positive relationship only between the control of corruption and government effectiveness both in the long run and in the short run. Yet, rule of law has a positive and significant impact on government effectiveness only in the long run. Voice and accountability, and government expenditure affect the government’s effectiveness negatively in the long run and positively in the short run. The article demonstrates that weak anti-corruption mechanisms and weak legal and criminal justice systems seem to have a detrimental impact on government effectiveness in developing countries.

Keywords

Control of corruption rule of law voice and accountability government expenditure government effectiveness cointegration error correction model

Introduction

A substantial amount of research has been conducted to investigate the impact of various independent variables on government effectiveness (GEF). As a result, bureaucratic structure (Court et al., 1999; Rauch and Evans, 2000), cultural and social fragmentation (religion, ethnicity), transparency and openness (Brunetti and Weder, 1999; Islam and Montenegro, 2002), administrative reforms (Brewer, 2004), democracy (Brewer and Choi, 2007), and corruption and political accountability (Adsera et al., 2003; Fisman and Gatti, 2006; Han et al., 2014; Haque, 2001; Jamil et al., 2013; La Porta et al., 1999; Samaratunge et al., 2008) have all played a role in determining GEF. According to a cross-country study conducted by the Asian Development Bank (ADB), governance quality has a considerable impact on economic growth and government performance. Some researchers believe that GEF, political stability, type of political regime, control of corruption (COC), and government expenditure (GEX) have a positive and significant impact on growth and government performance (Aidt et al., 2008; Cooray, 2009; Dzhumashev, 2016; Han et al., 2014; Méndez and Sepúlveda, 2006). Han et al. (2014) conclude that Asia performs significantly better than the rest of the world in terms of the impact of GEF and rule of law (ROL) on economic and government performance. They also suggest that developing countries strive for more effective government, ROL, and strict COC in order to overcome governance crises and increase growth. Similarly, Aidt et al. (2008), Méndez and Sepúlveda (2006), and Fisman and Gatti (2006) demonstrate how corruption has a non-linear effect on government performance depending on the quality of political institutions and types of regime. In this context, the paper seeks to answer the question of how corruption, the ROL, accountability, and GEX affect GEF in Sri Lanka. We are interested in Sri Lanka because of how adversely the country has been affected by the ongoing dire economic crisis and a similar pattern can be observed in many other Asian and African countries. Consequently, Belarus is on the verge of default, as are Lebanon, Suriname, and Zambia, as well as Egypt, Ghana, Tunisia, Pakistan, Chad, and Ethiopia (Ghos, 2022; Jones, 2022). In this regard, the article may provide policy ramifications for other nations that are about to reevaluate governance system flaws and their effects on government performance.

Although there is growing concern about improving governance effectiveness in developing countries, the research based on empirical evidence is limited (Boyne, 2003; Brewer, 2004; Knack and Keefer, 1995; Van de Walle, 2005) in Sri Lanka in particular. Evidence shows that government performance is determined by a rapidly changing socio-economic and political environment that frames public sector reforms, changes, dynamism, and innovation (Brewer et al., 2007: 200; Samaratunge et al., 2008: 200). Brewer shows that contextual factors appear to have a greater influence on GEF (political risk) than different types of governance reforms. Since the 1980s, many developing countries have implemented a variety of measures to address vexing governance issues including corruption, unresponsiveness, the politicization of civil service, ineffectiveness, breakdown of law and order, weak regulatory mechanisms, and lack of transparency and accountability (Haque, 2001; Jamil et al., 2013). In actuality, however, no significant progress has been made in these areas. The review of literature denotes that although promising variables were used in earlier studies to identify the determinants of GEF, the findings were not entirely consistent. As suggested by Aidt et al. (2008), Méndez and Sepúlveda (2006), and Brewer (2004), contextual factors including socio-economic and political variables appear to have a significant impact on government performance.

Despite being in a better position in terms of human development, Sri Lanka is still poorly managed (ADB, 2004). Public policy-making does not reflect the interests of citizens, and public officials are not held accountable for their actions. Considering the governance performance based on some indicators, such as Worldwide Governance Indicators (WWGIs),¹ it is evident that Sri Lanka is far behind on many issues, as mentioned above. Almost all the activities related to the delivery of public services are affected by varying levels of corruption, including obtaining a driver’s license, awarding contracts, procurements, and payments for supplies of goods and services (ADB, 2004; Vinayagathasan and Ramasamy, 2022). The civil war has also considerably afflicted GEF in several ways—since the late 1980s, successive governments have been compelled to invest disproportionately in the defense sector, which negatively impaired public service provision, government revenue, taxation, regulation of public enterprises, foreign direct investment, and subsequently gross domestic product (GDP) growth (Ramasamy, 2020a). Approximately 15% of GEX is made on the defense sector.

In the case of Sri Lanka, public administration has played a crucial role in the socio-economic development of the country since its independence. Despite several reforms, the administration is built upon Weberian bureaucratic rationale and remains highly rigid and hierarchical (Navaratne, 1989, 1991; Somasundaram, 1997; Warnapala, 1974). During the colonial period, it was fairly independent, but with the introduction of the First Republican Constitution in 1972 and then the Second Republican Constitution in 1978, the public administration was largely caught in a political trap, resulting in wicked problems such as corruption, unresponsiveness, poor performance, low-quality services, and becoming loss-making institutions (Liyanage et al., 2019; Navaratna-Bandara, 2013; Ramasamy, 2020a; Warnapala, 1974). A number of reforms were introduced from time to time in line with the instructions of the World Bank, ADB, and the International Monetary Fund, but they were not fully implemented due to a lack of bureaucratic and political commitment and willingness. As a result, corruption, a lack of accountability, and poor institutional performance plague Sri Lanka’s public administration—a type of political regime may also be to blame for this pattern (Root et al., 2001). Accountability is a critical component in developing criteria for measuring the performance of public officials and establishing oversight mechanisms to enhance the quality of public services, institutional performance, and legitimacy of state institutions. Poor accountability weakens and eventually destroys government institutions (Samaratunge and Bennington, 2002; Samaratunge et al., 2008; Nanayakkara, 2015).

In this way, this paper contributes to the literature on governance in particular, because it examines the impact of corruption, the ROL, accountability, and GEX on GEF for the first time. There is some literature that focuses on corruption and growth (Ackerman, 1997; Aidt et al., 2008; Fisman and Gatti, 2006; Mauro, 1995; Méndez and Sepúlveda, 2006; Svensson, 2003, 2005); corruption and government performance (Blackburn and Puccio, 2009; Wedeman, 2002); GEX, governance quality, and growth (Cooray, 2009; Dzhumashev, 2016; Mandl et al., 2008; Ouertani et al., 2018; Schick, 1983); GEF, ROL, and economic growth (Şaşmaz and Sağdiç, 2020); and corruption, ROL, accountability, and GEF (Brewer et al., 2007; Montes and Paschoal, 2016; Ramasamy and Vinayagathasan, 2017; Vinayagathasan and Ramasamy, 2022). Nevertheless, no literature exists that explains the relationship between COC, voice and accountability (VAC), ROL, GEX, and GEF. We provide new evidence of the relationship between these variables in both the short run and long run in this way. Furthermore, the existing literature, as mentioned above, yields inconsistent results regarding the relationship between the variables chosen for this study. As there is a gap in the existing literature on the impact of corruption, ROL, accountability, and GEX on GEF, we intend to explore this gap in the Sri Lankan context. Thus, this paper seeks to answer the following questions: how do corruption, the ROL, accountability, and GEX affect GEF—there is currently no evidence to support this relationship. The rest of the paper is organized as follows: the second section contains the theoretical insights; the third section contains data, variables, and methodology; and the fourth section contains results and discussions, followed by the conclusion and policy implications.

Theoretical standpoints and review of literature

The interest in measuring government performance has grown dramatically in recent years, with variables such as public sector productivity, a strong legal system, accountability, and the efficiency of government spending remaining critical. Acemoglu and Robinson (2012) and Rodrik (2008) argue that government performance is inextricably linked with good governance. They further unveil that, while the intrinsic value of governance quality as a development goal is now universally accepted, its instrumental value as a means to improve government performance or outcomes is still poorly understood, despite the proliferation of literature. This demonstrates that the nature of policies pursued, as well as quality of government and institutional performance, influences development outcomes or government performance. Thus, there is a close relationship between government performance and governance quality in the long run. For instance, destructive policies, high inflation, high black-market premiums, and adverse budget deficits (BDs) all continue to harm economic growth and government performance in developing countries (Acemoglu and Robinson, 2012; Fisman and Gatti, 2006). An influential study by Norris (2012) shows a positive relationship between government performance and governance quality—it is argued that both democratic attributes (voice, accountability, transparency, responsiveness) and state capacity (ROL, COC, effective policy implementation) are vitally significant to maintaining government performance and governance quality. Some authors have explored the relationship between governance quality and human development. Gerring et al. (2011), for example, explain the link between governance quality and economic growth by taking socio-economic development into account. Kumar (2013) notes how discriminatory and lower quality of governance lead to poor development outcomes and government performance. In a similar vein, Blaydes and Kayser (2011) associate quality of governance with distribution, standards of living, and performance.

According to some authors, there is a strong and positive relationship between governance quality, economic growth, and GEF (Gerring et al., 2005; Persson and Tabellini, 2006). Citizens’ allegiance to the current system of government and its performance are affected by governance quality. The rule-based governance system appears to be a much more relevant system of governance, with impartiality and the absence of corruption proving to be important attributes to ensure governance quality, and improve institutional performance, and citizens’ adherence to the system of governance (Park, 2016). GEF is also measured based on the degree of openness in disclosing government information, financial soundness, cost-effective service provision, value for money, quality services, and social, economic, and procedural justice.

With the frequent and widely adopted governance reforms over the last quarter century, GEF has become a central line of research for academics interested in the public sector, specifically, scholars in public administration and management (Farazmand, 2017; Haque, 2001; Ingraham and Moynihan, 2000; Lee and Whitford, 2009: 249). The widely held definition of governance is provided by Kaufmann and Kraay (2002: 5) and (Kaufmann et al., 2004, 2008), that is, “governance as the traditions and institutions by which authority in a country is exercised.” They have included three dimensions: (1) the process by which governments are selected, monitored, and replaced; (2) the capacity of the government to formulate and effectively implement sound policies; and (3) the respect of citizens and the state for the institutions that govern their economic and social interactions (Kaufmann et al., 2010: 103). The second and third dimensions are extremely relevant to this article (see Table 1 for more details on operational definitions).

Table 1.

Data description.

Variables	Source	Definition	Expected sign^a and empirical support
Government effectiveness (GEF)	WWGI of World Bank (2021)	Reflects perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government’s commitment to such policies. The index has ranked between 0 (lowest GEF) and 100 (highest GEF)	This is the dependent variable, so no prior expectation
Control of corruption (COC)	WWGI of World Bank (2021)	Reflects perceptions of the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as ‘capture’ of the state by elites and private interests. The index has rank between 0 (highest corrupt) and 100 (lowest corrupt)	Positive (Aidt et al., 2008; Cooray, 2009; Han et al., 2014) Non-linear effect (Aidt et al., 2008; Fisman and Gatti, 2006)
Rule of law (ROL)	WWGI of World Bank (2021)	Reflects perceptions of the extent to which agents have confidence in and abide by the rules of society, and in particular the quality of contract enforcement, property rights, the police and the courts, as well as the likelihood of crime and violence. The index has rank between 0 (lowest level of ROL) and 100 (highest level of ROL)	Positive (e.g. Han et al., 2014)
Voice and accountability (VAC)	WWGI of World Bank (2021)	Reflects perceptions of the extent to which a country’s citizens are able to participate in selecting their government, as well as freedom of expression, freedom of association, and a free media. The index has rank between 0 (lowest level of VAC) and 100 (highest level of VAC)	Positive (e.g. Norris, 2012)
Government expenditure (GEX)	CBSL (2021)	Reflects the central government expenditure which includes recurrent, capital and net lending. It is measured as percentage of Gross Domestic Product	Positive (e.g. Cooray, 2009)

WWGI: Worldwide Governance Indicator^b; CBSL: Central Bank of Sri Lanka^c.

The expected signs have been developed in line with theoretical prepositions where low level of corruption, strict rule of law principles, and robust accountability mechanisms are more likely to improve government effectiveness. This has been explicated in several theories including theories of governance (Fukuyama, 2013), theories of corruption (Klitgaard, 1998; Mungiu-Pippidi and Johnston, 2017; Rothstein, 2011), and theories of quality of government (Bågenholm et al., 2021; La Porta et al., 1999; Rothstein and Teorell, 2008).

The WWGIs data can be accessed through https://info.worldbank.org/governance/wgi/.

The CBSL data can be accessed through https://www.cbsl.gov.lk/sites/default/files/cbslweb_documents/publications/annual_report/2021/en/15_Appendix.pdf.

When measuring GEF in Sri Lanka, corruption receives a lot of attention. Jamalmanesh et al. (2014: 553) argue that COC has a significant impact on the ROL and GEF—they show that citizens will feel cheated if they believe corruption is widespread, their tax money is not spent wisely, their government lacks accountability, and the ROL fails to protect them. It implies that increasing GEF reduces the informal economy as well as improving bureaucratic quality and the ROL are more likely to increase GDP growth and decrease corruption.

There is an established view that efficient government institutions help foster economic growth and GEF (Fisman and Gatti, 2006; Mauro, 1995; North, 1990; Shleifer and Vishny, 1993). Corruption continues to be antithetical to the quality of government (Rothstein, 2011; Rothstein and Teorell, 2008), acting as an illegal tax that distorts decision-making and GEF. A growing body of literature supports the claim that corruption, rent-seeking, and a weak legal system shrink the range of opportunities available to developing countries as investments become less productive, cost of capital increases, and private investment, Foreign Direct Investment (FDI), and foreign aid all decline (Davis, 2003; Mauro, 1995). Mauro (1995) argues that corruption reduces government efficiency because officials and politicians steal public money for personal gain, rendering the government inefficient in the provision of public services and policy-making. Some scholars find a positive correlation between weak ROL, corruption, and GEF (Kaufmann and Kraay, 2002). In contrast, the ROL is criticized for its inability to influence government performance (Messick, 1999).

Gupta et al. (1998: 29) show that a one standard deviation rise in the growth rate of corruption reduces poor income growth by 7.8% per year. Some argue that in developing countries, excessive political intervention, unmanageability, aloofness, unaccountability, cronyism, and nepotism are profoundly entrenched (Haque, 2001; Narayan et al., 2000; Ramasamy, 2020b; The World Bank, 2004). As argued by Haque (2001: 1423), poor governance not only creates negative perceptions regarding the performance and integrity of government institutions, but it is also likely to adversely affect people’s trust in governance, and its legitimacy—and people tend to consider their government as unaccountable and unresponsive (Jamil et al., 2013: 1424). Simply put, this is a failure of state-centered governance.

Some literature illustrates that in poor economies an increase in government spending has a negative impact on growth (Baldacci et al., 2004; Dzhumashev, 2016; Gupta et al., 2001, 2005; Mauro, 1995; Park et al., 2005; Tanzi and Davoodi, 1998). According to these studies, the negative association between government spending and growth is caused by corruption, which leads to larger economic inefficiencies and government ineffectiveness as the size of the public sector increases. The existing literature demonstrates that bureaucratic corruption distorts the effectiveness of public spending mainly by altering the structure of the public budget to create and extract rents (Blackburn et al., 2006; Delavallade, 2006; Del Monte and Papagni, 2001; Dzhumashev, 2016; Gupta et al., 2001; Keefer and Knack, 2002; Mauro, 1995). These studies also explain that an increase in public spending encourages more rent-seeking and corruption because low average wage rates reduce the costs of private rent-seeking and corruption. Consequently, corruption and rent-seeking entail social losses not only through rent dissipation but also by creating more distortions in the public sector activities. As a result, a rise in public spending dampens the potential for economic growth.

This explains why an increase in GEX weakens growth and effectiveness of the government in low-income economies. However, in the literature, there is no formal explanation of how corruption relates to governance quality through the size of public expenditure. To establish this link, this article develops an empirical model of how corruption, government spending, and other governance indicators affect the effectiveness of the government. Using cross-section of 71 economies, Cooray (2009) examines the effects of government size and quality of government on economic growth, where size of the government is measured by GEX and quality of government is measured by governance indicators. This study concludes that both increased public spending and good governance can improve growth outcomes. The author also contends that countries with good- or high-quality governance make better use of public funds and/or that increased public expenditure leads to better governance. Using cross-sectional data from 71 countries, developed, developing, and transition countries from 1996 to 2003 period, it was discovered that there is a positive relationship between GEX, governance quality, and development outcomes (Cooray, 2009). However, less attention has been paid to governance quality in measuring GEF in developing countries in the existing literature.

Data, variables, and methodology

Data and sources

This study uses annual time series data of Sri Lanka over the period 1996–2020. Sri Lanka can be considered as a pertinent case to study GEF due to current economic and political crisis. The developmental phase of Sri Lanka can be divided into two periods, first that of the war followed by peace building since 2009 (Athukorala and Jayasuriya, 2013; Venugopal, 2018; Yan, 2020). During the first term of the Mahinda Rajapaksa government, from 2005 to 2010, Sri Lanka’s economy grew steadily reaching a GDP of $42.1 billion in 2009, nearly doubling the 2005 GDP (Venugopal, 2015; Yan, 2020: 824–826). During its second term, after the end of the civil war, the Mahinda Rajapaksa government managed to bring about Sri Lanka’s economic performance for the first 3 years (2010–2012). Yet, the growth in the first quarter of 2013 in Sri Lanka decreased to 6.4%, country showed a sharp decline in both exports, especially industrial, and imports (Wickramasinghe, 2014: 202). Evidence shows that a strong correlation appears among the fluctuations in the growth rate, the election cycle, and the term of government (Venugopal, 2015; Yan, 2020). To take account of these paradoxes, the article analyzes the core factors of GEF in Sri Lanka from a governance perspective.

The data description is given in Table 1. The graphs below illustrate the development of selected variables over the time period considered for the study.

Figure 1 depicts the trends of selected governance indicators over the study period, and it clearly shows the fluctuation with moderate performance. As shown in Figure 2, the Gross Domestic Product Growth Rate (GDPGR) fluctuates over the time, indicating the poor performance in some periods, whereas GEX decreases steadily.

Figure 1.

Trend of governance indicators.

Figure 2.

Trend of economic indicators.

Variables

Based on the reviewed literature, we tried to use GEX as a proxy for size of the economy,² education index³ (EDUI) as a proxy for educational status of the country, BD, COC, VAC as a proxy for transparency and openness, ROL, regulatory quality (REQ), and political stability and the absence of violence (PSAV) as explanatory variables—these play a significant role in the process of improving GEF. However, the results of correlation test (see Table 2 in Appendix 1) reveal that most of the explanatory variables are highly or moderately correlated with one another. Therefore, we dropped some of the insignificant variables such as PSAV, BD, and EDUI, which have highest p-value (0.599, 0.791 and 0.595, respectively) or less t-value (see Table 3 in Appendix 1) and correlated with many explanatory variables, from the model. Moreover, there are extensive definitional debates on ROL versus REQ. There exist theoretical problems in defining ROL and REQ. Also, we found weak correlation between these two concepts (see Table 2 in Appendix 1) and coefficient of REQ is insignificant (see Table 3 in Appendix 1) for the case of Sri Lanka. Nevertheless, the Nobel Prize Winner Paul Krugman pointed out that the ROL isn’t everything, but, in the long run, it is almost everything. Thus, we also omitted REQ from our model. Hence, we used GEF as dependent variable and COC, ROL, VAC, and GEX as explanatory variables for this study. A summary of descriptive statistics of selected variables is given in Table 4 in Appendix 1.

Methodology

Following the empirical literature related to this study, we developed the long-run relationship between the variables with some modification of including regime changes and exogenous shocks as dummy variables in the model, which is constructed as below

{GEF}_{t} = α_{0} + \sum_{i = 1}^{4} α_{i} X_{i t} + β W_{t} + \sum_{j = 1}^{4} λ_{j} R C_{j} + u_{t}

(1)

where t is the time period, $X_{i}$ is the $4 \times 1$ vector $[{COC}_{t}, {ROL}_{t}, {VAC}_{t}, {GEX}_{t}]$ of explanatory variables, W is the internal war (there was an internal conflict between government and Liberation Tigers of Tamil Eelam: LTTE for long period) dummy variable which takes the value 1 $(W = 1)$ if the study period was politically stable (i.e. 2002–2005 (peace time) and 2009–2020 (end of the war period)), otherwise the value of W is zero $(W = 0)$ (i.e. 1996–2001 and 2006–2008). $R C_{j}$ is the presidential era dummy variables. This study dataset covers four presidential era;⁴ therefore, $j = 1, 2, 3, 4$ . Hence, $R C_{1} = 1$ if Year ≤ 2004 and otherwise $R C_{1} = 0$ . Next, $R C_{2} = 1$ if 2005 ≤ year ≤ 2014 and otherwise $R C_{2} = 0$ . Next, $R C_{3} = 1$ if 2015 ≤ Year ≤ 2019 and otherwise $R C_{3} = 0$ . Next, $R C_{4} = 1$ if $Year \geq 2020$ otherwise $R C_{4} = 0$ . Here, we assume RC4 as reference category for RC dummy as it includes only one year (i.e. 2020) period in power and war period is considered as an omitted group for the internal war dummy. u is the white noise error term and variables are explained as earlier.

Johansen cointegration technique was adapted to test the existence of cointegrating relationship between the variables. If it is detected, we would employ an error correction model (ECM) to investigate the short-run dynamic relationship and the long-run adjustment from the short-run disequilibrium due to exogenous shocks. The ECM takes the form as

Δ Z_{t} = α_{0} + Π E C T_{t - 1} + \sum_{i = 1}^{p - 1} Φ_{i}^{*} Δ Z_{t - i} + β W_{t} + \sum_{j = 1}^{4} λ_{j} R C_{j} + ε_{t}

(2)

where Π and $Φ^{*}$ are functions of the Φ’s, if $Π = 0$ , then there is no cointegration, if Π has full rank, K, then the Z’s cannot be I(1) but are stationary and $Π = α β^{'}$ , where α is the $(5 \times 1)$ vector of the speed of adjustment coefficient, $β'$ is the $(1 \times 5)$ vector of cointegrating coefficients, $Z_{t} = [{GEF}_{t}, {COC}_{t}, {ROL}_{t}, {VAC}_{t}, {GEX}_{t}]$ ’ vector of endogenous variables, $Z_{t - i}$ is the lagged value of the variable $Z_{t}$ , W and $R C$ are explained under equation (1), $E C T_{t - 1}$ is the error correction term, and $ε_{t}$ is the white noise error term.

However, before estimating the model by the Johansen technique, first, we employed augmented Dickey–Fuller (ADF) and Phillips Perron (PP) unit root test approaches to ensure the stationarity property of each series. In the second step of the estimation procedure, we will adopt either one or more of the following criteria: Akaike information criterion (AIC), Schwartz information criterion (SC), Likelihood ratio statistics (LR), final prediction error (FPE), and Hannan–Quinn information criterion (HQIC) in order to identify the optimum lag length that can be included in the model.

Finally, we used the Granger causality test to identify the causality relationship between the variables under consideration. The model is given below

Δ {GEF}_{t} = a_{1 t} + \sum_{i = 1}^{p} γ_{i} Δ {GEF}_{t - i} + \sum_{i = 1}^{p} δ_{i} Δ {COC}_{t - i} + u_{1 t}

(3)

Δ {COC}_{t} = a_{2 t} + \sum_{i = 1}^{p} θ_{i} Δ {GEF}_{t - i} + \sum_{i = 1}^{p} ρ_{i} Δ {COC}_{t - i} + u_{2 t}

(4)

Using either F-test or chi-square test, we examine the following hypothesis:

For equation (3), $H_{0} :$ corruption does not Granger causes GEF, and for equation (4), $H_{0}$ : GEF does not Granger causes corruption.

We will reject $H_{0}$ , when the test statistic is greater than the critical value and conclude that corruption Granger causes GEF in equation (3); that is, $\sum_{i = 1}^{p} δ_{i}$ is jointly significant in equation (3), and GEF Granger causes corruption in equation (4), that is, $\sum_{i = 1}^{p} θ_{i}$ is jointly significant in equation (4). If both parameters are not significant, then no causality exists between these two variables. Likewise, we can perform the test for all other variables included in the model.

Results and discussion

Results of unit root test

Before performing the cointegration and ECM, we employed the ADF and PP unit root test approaches to determine the order of integration of each series, which are included in this analysis. According to Table 5, both the ADF and PP unit root test methods confirmed that all five variables are non-stationary at the 5% level of significance in level form. However, all became stationary at their first difference in both methods, suggesting that all variables considered in this study are integrated into order one.

Table 5.

Results of ADF and PP unit root test.

Form of variables	Variables	ADF with intercept	PP with intercept
Level	GEF	0.0813*	0.0979*
	COC	0.1553	01904
	ROL	0.4296	0.3667
	VAC	0.3261	0.5477
	GEX	0.3943	0.4025
First difference	GEF	0.0003***	0.0000***
	COC	0.0016***	0.0000***
	ROL	0.0026***	0.0097***
	VAC	0.0431**	0.0421**
	GEX	0.0002**	0.0010**

ADF: augmented Dickey–Fuller; PP: Phillips Perron; GEF: government effectiveness; COC: control of corruption; ROL: rule of law; VAC: voice and accountability; GEX: government expenditure.

The table provides probability values. We have included intercept only in the model.

, **, and *** represent the variables are stationary at 10%, 5%, and 1% level of significance, respectively.

As the entire series is integrated in the same order [I(1)], we employed the Johansen method of cointegration to identify the number of cointegrating equations and the long-run relationships. We consequently adopted one lag as an optimum lag length that can be included in the model using the AIC.

Johansen cointegration test results

The trace and maximum Eigenvalue statistics of the Johansen cointegration technique detected two and one cointegrating relationship, respectively, in the system of equation at 5% significance level since we reject the null hypothesis at rank 0 and 1 under trace statistics, and 0 under Eigenvalue statistics (see Table 6). Yet, we failed to reject the null hypothesis at rank 1. This indicates a possible long-run relationship between the variables under consideration in this study.

Table 6.

Results of Johansen and Juselius cointegration rank test.

Null hypothesis	Alternative hypothesis	p-value (trace statistic)	p-value (eigenvalue statistics)
$H_{0} : r = 0$	H₁ : r ≥ 1	0.0000***	0.0000***
$H_{0} : r = 1$	$H_{1} : r \geq 2$	0.0054***	0.0591*
$H_{0} : r = 2$	$H_{1} : r \geq 3$	0.1321	0.0712*
$H_{0} : r = 3$	$H_{1} : r \geq 4$	0.0411**	0.1537
$H_{0} : r = 4$	$H_{1} : r = 5$	0.1250	0.1250

The table provides probability values.

, **, and *** indicate the rejection of the null hypothesis at 10%, 5%, and 1% levels of significance, respectively.

Table 7 explains a positive and statistically significant relationship between the COC and GEF in the long run. That is, if the government can effectively control corruption through robust anti-corruption measures (increase in corruption percentile rank), it tends to raise GEF in the long run while other variables remain constant. This has been confirmed by Montes and Paschoal (2016), Jamalmanesh et al. (2014), Aidt et al. (2008), Méndez and Sepúlveda (2006), and Kaufmann et al. (2000). Furthermore, some studies (Baldacci et al., 2004; Dzhumashev, 2016; Gupta et al., 2001, 2005; Mauro, 1995; Park et al., 2005; Tanzi and Davoodi, 1998) argue that corruption leads to larger economic inefficiencies and government ineffectiveness as the size of the public sector increases. The Corruption Perception Index for Sri Lanka (Transparency International) shows a steady increase in corruption with fluctuations in certain periods from 1998 to 2020. It could be argued that corruption has slowed down business, trade, investment for economic growth, and foreign direct investment due to government inefficiency and endemic political and administrative malpractices which in turn subverted the quality of public services, raised inflation, income inequality, and poverty in Sri Lanka, all of which might affect GEF. Mauro (1995) claims that resources that should be spent on public service and human well-being and economic development are embezzled by public officials and politicians before they reach their ultimate objectives. Corruption also significantly reduces public spending allocations and weakens government policies and programs related to socio-economic development. Montes and Paschoal (2016), based on a sample of 130 countries, found that corruption has detrimental impact on GEF and perceived reduction in corruption lead an increase in GEF. Thus, it is clear that widespread corruption resulted in huge public debt and higher rates of inflation subverting GEF in Sri Lanka. Holmberg et al. (2009: 145) provide statistical evidence that when corruption decreases, it may have a positive impact on subjective health, life expectancy, infant mortality, GDP per capita, GDP growth, life satisfaction, and poverty. In developing countries, state capture leads to government ineffectiveness where government institutions serve the private interest rather than public. In such instances, businesses tend to have undue influence over government decisions through bribes and illegal payments and informally allow large economic interests to distort the legal framework, policymaking process, investment for human development, and eventually, influence the management of the economy as a whole (Ackerman, 2015: 39; Aidt et al. 2008; Fisman and Gatti, 2006; Méndez and Sepúlveda, 2006; Narayan et al., 2000: 73). Yet, to the best of our knowledge, there is no much evidence exploring the relationship between COC and GEF, namely, in the developing country context.

Table 7.

Results of the long-run relationship using the Johansen method.

	Coefficient	t-value
COC	0.251812***	15.847
ROL	1.249479***	42.791
VAC	−0.798358***	−50.818
GEX	−0.785363***	16.320
cons	−51.03627***	3.0025

COC: control of corruption; ROL: rule of law; VAC: voice and accountability; GEX: government expenditure.

t-values are given in the table.

***indicate the rejection of the null hypothesis at 10%, 5%, and 1% levels of significance, respectively.

This study also finds a significant and a positive relationship between the ROL and GEF, indicating that a strong legal system (law enforcement) tends to increase GEF. The evidence demonstrates a fluctuating trend in the ROL over the study period. As shown in Figure 1, after 2008, there was a significant downfall in ROL as the Sri Lanka’s legal system was subjected to political manipulation and control. This was evidenced in 2013, when the Chief Justice of the Supreme Court Shirani Bandaranayake was impeached by Parliament and removed from office by the Mahinda Rajapaksa government. This was a reaction to a number of rulings against the government by the Supreme Court over certain legislations which had severe implications on public finance, democracy, and good governance. She was replaced by Mohan Peiris, who was considered to be a close ally of then President Rajapaksa. Figure 1 further reveals an improvement in ROL after 2015 along with increasing trend of GEF, but after 2018, it again slows down significantly owing to constitutional coup, Easter Sunday bombings, anti-minority violence, emergence of authoritarian government in 2019 led by a strong man, and unfair enforcement of Covid regulations. During this period, the GEF also had fallen down remarkably. At the same time, there was a remarkable increase in ROL and GEF during 2002 and 2003 (see Figure 1) which is due to the Ceasefire Agreement signed by the Ranil Wickramasinghe’s government and the LTTE—which created a conducive climate for peace, public trust in government, investment, human rights, democracy, trade, business, absence of violence, and so on. Overall, as per the results of the study, the relationship between ROL and GEF is also very strong, both in the long run and in the short run. It is possible to argue that corruption has a corrosive effect on GEF in Sri Lanka, and that it is more strongly correlated with the latter than accountability. This corroborates with the existing evidence of Persson et al. (2013), Rothstein (2011), Uslaner (2008), Kaufmann et al. (2008), and Kaufmann and Kraay (2002)—all argue that strong legal system is more likely to have a positive impact on GEF.

Furthermore, the theoretical evidence of the article shows a positive correlation between accountability and GEF, but we find a negative and strongly significant relationship in the long run for Sri Lanka. This is another puzzle that we find in this article. This association can occur due to certain reasons. As such, Sri Lanka has had experienced three decades of intensive civil war which largely suppressed citizens voice, media freedom, civil liberty, freedom of association, and political rights. Successive governments effectively used the Prevention of Terrorism Act to stifle the above. Furthermore, during and after the end of the war, state institutions and civil society were securitized and militarized along with a powerful executive presidential system, which in turn led to the politicization of civil service, or excessive political control over the bureaucracy, resulting in a lack of institutional arrangements for checks and balances, weak parliament oversight, endemic corruption, patronage, and clientelistic politics. All of which apparently afflicted the established norms of accountability and procedural justice. On the contrary, the above factors closed all the avenues to hold politicians and public officials accountable through social accountability mechanisms. The political change that took place in 2019 provided space for military and the repressive authoritarian regime which affected the civic space to raise voice, accountability, and transparency. In other words, over-centralization of power in the hands of executive President implied politically flawed governance that is more prone to corruption, lack of accountability, and transparency. In a similar vein, based on a cross-country study, Montes and Paschoal (2016) argue that an increase of ROL tends to improve GEF, which can be used as a strategy for this purpose.

Based on a cross-country study, Brewer et al. (2007: 240–241) claim that accountability is associated with GEF, implying that GEF is enhanced when corruption is low and societies have VAC. They found that both accountability and corruption are significantly correlated with GEF, indicating that countries with higher scores on the accountability and COC index have higher GEF. This ascertains what we know from prior research on government performance: more open and transparent societies are more likely to be more effective in delivering public services and directing public funds to the desired program. This shows the causal relationship and makes causal assertions more credible. As a result, our correlations suggest that increased accountability and COC leads to better GEF in Sri Lanka.

This study also demonstrates a negative and statistically significant association between GEX and GEF over time, which means GEX is far less likely to contribute for GEF or performance (see Figure 2). This pattern can be explained in the following manner. Sri Lanka has been fallen in a huge debt crisis for several reasons, resulting in massive public debt and debt service payments. Most importantly, the post-independence period appears to have seen an increase in budget and balance of payment deficit, import dependency, a sharp decline in domestic product, currency depreciation, poor fiscal management, monetary policies, and corruption—all of these factors may have prevented effective GEX and subsequently led to poor performance and development outcomes (Kelegama, 2005; Venugopal, 2015). Corruption affects public expenditure in different ways. For instance, the successive governments have been spending huge amount on defense (15% from the GDP in 2022) even after the end of the civil war, and it appears to be an area for large-scale corruption when purchasing military equipment, which is not disclosed in the media and not easy to measure due to limited access to information. This finding is consistent with some of the most influential studies on military expenditure and corruption (d’Agostino et al., 2012; Gupta et al., 2001; Transparency International, 2002). “It is often argued that the limited competition in the defense sector leads to a relatively high level of informal contracts and to rent-seeking activities, providing fertile ground for the growth of corrupt practices” (Transparency International, 2002). It was noted that previous loans taken by the Government were mainly invested in non-tradable sectors, which do not generate adequate foreign exchange revenues to cover even a portion of the debt services. Millions were spent on ineffective mega development projects such as Mattala airport, Hambanthota harbor, lotus tower in Colombo, and International cricket stadium in Hambanthota (Athukorala and Jayasuriya, 2013; Kelegama, 2004; Venugopal, 2015, 2018; Wickramasinghe, 2014). In sum, the successive government failed to strike a balance between income and expenditure, as well as import and export, through effective economic management, tax policies, fiscal policies, and anti-corruption initiatives thereby creating a favorable environment for FDI, tourism, and so on. Expenditure on the above nature leads to inefficiency as a result of misguided development projects and policies.

Dzhumashev (2016) demonstrates that government spending fosters corruption and rent-seeking, which in turn distorts the structure and size of government spending. Therefore, in low-income economies, increases in government spending tend to generate larger social losses due to higher levels of rent dissipation and a concomitant rise in corruption and government inefficiency. Consequently, in such economies, an increase in government spending is more likely to result in a decline in economic growth.

Error correction model test results

Table 8 presents the results of VECM. As found in the existing literature (e.g. Jamalmanesh et al., 2014; Kaufmann and Kraay, 2002; Mauro, 1995; North, 1990; Shleifer and Vishny, 1993), panel 1 depicts a positive and statistically significant correlation between COC and GEF in the short run. Whereas, in contrast to the theory, the ROL affects the government’s effectiveness negatively in the short run. Similar finding is observed in Şaşmaz and Sağdiç (2020: 214). Although it is argued that law abidance improves institutional quality and enhanced legality decreases informal transactions in economy, they claim that it has no statistically significant impact. Moreover, Messick (1999) illustrates that ROL is criticized for its inability to influence government performance.

Table 8.

Results of Vector Error Correction Model.

Panel 1: Short-run relationship
Independent variables (lagged value)	Dependent variables (current value)
Independent variables (lagged value)	${GEF}_{t}$	${COC}_{t}$	${ROL}_{t}$	${VAC}_{t}$	${GEX}_{t}$
$Δ {GEF}_{t - 1}$	0.7122 [1.365]	1.0863*** [3.258]	0.4807 [1.564]	−0.3258 [–1.003]	0.3674** [2.134]
$Δ {COC}_{t - 1}$	0.2418** [2.023]	−0.2203 [–1.246]	−0.1911 [–1.172]	−0.0346 [–0.201]	−0.0235 [–0.257]
$Δ {ROL}_{t - 1}$	−1.0247 [–1.434]	−0.4861 [–1.064]	−0.3892 [–0.924]	0.5391 [1.211]	−0.4014* [1.702]
$Δ {VAC}_{t - 1}$	0.6418* [1.728]	0.1821 [0.545]	−0.0303 [–0.098]	−0.3587 [–1.101]	0.0793 [0.459]
$Δ {GEX}_{t - 1}$	1.2610* [1.832]	0.8659* [1.647]	0.5159 [1.064]	−0.1598 [–0.312]	0.0543 [0.199]
RC1	2.1194* [1.749]	11.404*** [2.938]	4.1848 [1.169]	−1.8269 [–0.483]	2.6129 [1.303]
RC2	−2.2057 [–0.577]	−5.7752** [–2.368]	−0.5861 [–0.260]	−0.2371 [–0.099]	−2.0040 [–1.589]
RC3	−4.7981 [–1.062]	−1.0635 [–0.368]	3.0377 [1.142]	4.9707* [1.767]	1.6232 [1.089]
W	0.0407 [0.017]	1.2922 [0.869]	0.6852 [0.501]	−0.5903 [–0.407]	−1.0369 [–1.351]
C	1.7006 [0.636]	−1.7826 [–0.687]	−2.0815 [–0.871]	0.1717 [0.128]	−0.4032 [–1.223]
Panel 2: Long-run adjustment coefficient
${ECT}_{t - 1}$	−0.8025*** [–2.988]	−0.2812*** [–4.392]	−0.3878 [–0.809]	0.4069 [0.804]	−0.5814** [–2.167]

Test statistics values are given in square brackets.

, **, and *** show that variables are statistically significant at 10%, 5%, and 1% levels of significance, respectively.

Furthermore, in the short run, this study finds a positive and statistically weak significant (10% level of significance) correlation between VAC and GEF. It indicates that greater transparency in the legal system tends to increase GEF only in the short run, but not in the long run. Citizens’ active participation in governance, freedom of expression, freedom of association, and free media, on the other hand, are more likely to improve the GEF in the short run but not in the long run. The findings of Samaratunge et al. (2008) also conclude that poor accountability weakens and impoverishes government institutions.

Moreover, in the short run, we detected a positive and weakly significant (10% level of significance) link between GEX and GEF. This indicates that the effectiveness of government spending determines the quality of government. Figure 2 depicts the decreasing pattern of GEX while the rate of economic growth fluctuates over the time period under consideration. In the case of Sri Lanka, successive governments have spent more on recurrent expenditure rather than on investment expenditure, resulting in poor economic well-being and government performance. This finding coincides with Cooray (2009) who explains that the size of the government, as measured by GEX, and the quality of government, as measured by governance indicators, are important for economic growth. The author suggests that effective spending by government will enhance the effectiveness of the government.

The study demonstrates the fact that regime change has no statistically significant impact on GEF except for the Chandrika Kumaratunga regime which has a positive and weakly significant impact on it. Based on the results, one could possibly argue that there is no strong causal relationship between regime change and GEF. That is, Chandrika Kumaratunga’s government (1994–2005) was more effective (2.1194 times) compared to Gotabaya Rajapaksa government (2020). Whereas, the Mahinda Rajapaksa regime as well as the National Unity government of Maithripala Sirisena and Ranil Wickremesinghe (2015–2019) were more ineffective (i.e. 2.2057 and 4.7981 times, respectively) than the Gotabaya Rajapaksa regime. Even though Gotabaya Rajapaksa’s government performed poorly in 2021 and 2022, this was not properly reflected in the study due to unavailability of data for this period. This could explain why the Gotabaya regime was more effective than the regimes of Mahinda Rajapaksa and Maithripala Sirisena. Partisan politics, policy inconsistency, political instability and frequent elections, the discontinuation of development initiatives of successive governments, narrow ethno-religious party politics, a lack of institutional capacity, and political manipulation may all contribute to poor GEF (Venugopal, 2015, 2018). However, there is no evidence in the existing literature to explain the relationship between regime changes and GEF, indicating the need for further research on this theme.

The GEF was relatively high during the peace talk period (2002–2006) and post war period (2009 to till date) when compared to the war period, though this is not statistically significant. There could be several reasons why GEF was slightly higher during the peace talks including a shift in government focus from war to economic well-being. So that then governments created a favorable environment for business and services, investments, tourism, local production, migration, popular support, and legitimacy. There was a substantial increase in foreign aids and financial support from the International Monetary Fund (IMF), World Bank (WB), and ADB for economic revival, economic rebuilding initiatives, new institutional set up for the North and East, and Tsunami recovery support from international communities—all of which could have marginally increased economic performance and GEF (see Figure 1). Yet, this period was to materialize in such a way that it would boost government performance and the economy (DeVotta, 2005: 179–180; Venugopal, 2009).

Panel 2 of Table 8 illustrates the speed of adjustment coefficients, which explain how GEF adjusts from a short-run deviation due to external shocks to a long-run steady-state line. A negative and statistically significant error correction coefficient $(- 0.8025)$ of $GEF$ (first elements of $E C T_{t - 1}$ ) reveals that GEF moves downward toward the long-run steady-state line with a speed of 80.25% in each period, one period after the exogenous shocks.

Results of Granger causality test

The Granger causality test identified unidirectional causality between corruption control and GEF; GEX and GEF; GEF and the ROL; GEF and accountability; and, in the long run, ROL and accountability (see Table 9). This reveals that the level of corruption and GEX affects GEF; at the same time, GEF influences the ROL and accountability; and the ROL leads to accountability. These results demonstrate that if the government is competent enough to control corruption, it is more likely to increase GEF. Also, if the government spends its revenue efficiently, it becomes more effective. If the government pursues a strong legal system, the public can demand accountability through established procedures, legal and institutional means. There is a strong bilateral causality between the COC and the ROL. That is, when the government pursues a strong legal system, it aids in the COC, and in turn, when the government becomes effective in controlling corruption, it enables the government to effectively implement the ROL.

Table 9.

Results of VECM Granger causality/block exogeneity Wald test.

Null hypothesis	Probability value	Decision
DCOC does not Granger Causes DGEF	0.039**	Reject $H_{0}$
DROL does not Granger Causes DGEF	0.299	Accept $H_{0}$
DVAC does not Granger Causes DGEF	0.370	Accept $H_{0}$
DGEX does not Granger Causes DGEF	0.042**	Reject $H_{0}$
DGEF does not Granger Causes DCOC	0.449	Accept $H_{0}$
DROL does not Granger Causes DCOC	0.004***	Reject $H_{0}$
DVAC does not Granger Causes DCOC	0.161	Accept $H_{0}$
DGEX does not Granger Causes DCOC	0.280	Accept $H_{0}$
DGEF does not Granger Causes DROL	0.006***	Reject $H_{0}$
DCOC does not Granger Causes DROL	0.025**	Reject $H_{0}$
DVAC does not Granger Causes DROL	0.760	Accept $H_{0}$
DGEX does not Granger Causes DROL	0.550	Accept $H_{0}$
DGEF does not Granger Causes DVAC	0.045**	Reject $H_{0}$
DCOC does not Granger Causes DVAC	0.380	Accept $H_{0}$
DROL does not Granger Causes DVAC	0.075*	Reject $H_{0}$ (10%)
DGEX does not Granger Causes DVAC	0.755	Accept $H_{0}$
DGEF does not Granger Causes DGEX	0.130	Accept $H_{0}$
DCOC does not Granger Causes DGX	0.460	Accept $H_{0}$
DROL does not Granger Causes DGEX	0.630	Accept $H_{0}$
DVAC does not Granger Causes DGEX	0.820	Accept $H_{0}$

Probability values are given in table.

, **, and *** show that variables are significant at 10%, 5%, and 1% levels of significance, respectively.

Conclusion

In this article, using time series data for Sri Lanka between 1996 and 2020, we examined the relationship between corruption, the ROL, VAC, GEX, and GEF. Since the explanatory variables of this study tend to have different effects on GEF in the long run and in the short run, but the impact is weak in the short run, one could not make a concrete inference on what constitutes or determines GEF in Sri Lanka. That is, both in the short and long terms, corruption has a favorable and highly substantial impact on the effectiveness of the government. Whereas ROL has a long-term positive and strong impact that is extremely significant on GEF, but it has a short-term negative impact that is not significant. In addition, while there is a positive and weakly significant relationship between VAC and GEF; GEX and GEF in the short run, there is a negative and strongly significant relationship between these variables over the long run. This article finds empirical evidence for this trend in other studies as well—scholars have not yet developed precise and conclusive criteria for defining and assessing GEF (Lee and Whitford, 2009; Rainey, 2003; Selden and Sowa, 2004).

The key findings of this empirical analysis can be summarized using three points. First, corruption is intrinsically connected with the effectiveness of government and the ROL. It has been observed that robust COC is more likely to improve GEF in developing countries. Second, the traditional argument that strengthening the ROL is a good anti-corruption strategy holds true for developing countries. As such, it does show a significant relationship in Sri Lanka over time. Third, efficient public expenditure will improve the quality of government in less developed countries, thereby promoting economic outcomes. More importantly, the paper raises a research conundrum. That is, despite theories and existing literature demonstrating a positive relationship between the ROL and GEF as well as the ROL and COC (and vice versa), we were unable to detect a strong and significant relationship in Sri Lanka, both in the long run and in the short run. This indicates further investigation in other contexts.

In a similar vein, no evidence was found to show a relationship between ROL and GEF in the short run in this study. Another puzzle is that the relationship between variables differs in the short run and in the long run (GEF and GEX and VAC). That is, VAC and GEXs have a negative long-term impact on government quality while having a positive short-term impact. So far, no scholarly work has attempted to explain this pattern in other contexts. Thus, we leave it for further research. In addition, Chandrika Kumaratunga’s government was more effective compared to Gotabaya Rajapaksa regime, whereas Mahinda Rajapaksa’s and Maithripala Sirisena’s regimes were more ineffective than the Gotabaya Rajapaksa government. The study found that the government was more effective during peacetime than during war time.

This article is preliminary with several limitations; however, we anticipate that the results will foster discussion and further research on GEF in South Asia, where more empirical and scholarly works are desired. Although the WWGIs dataset presents a general picture of each indicator, interpreting our results poses some limitations; the correlation is very high among all indexes. Yet, it produces results that differ from those found in country-specific and cross-country studies, raising the question of measurement validity of the dataset—and what precisely these indicators measure. Several scholars have raised this issue (Andrews, 2010; Arndt, 2008; Brewer et al., 2007; Pollitt, 2011; Sanchez and Ballesteros, 2013; Svensson, 2005; Van de Walle, 2006). They argue that, while these indicators draw a broader picture of a country’s quality of governance or performance—and are produced by powerful, expertly staffed organizations—they have limitations such as biases in measurement, lack of transparency, data limitation, and the problem of data robustness, measurement errors, reliability, and measurement validity. Some debate the extent to which these variables are explanatory and provide conceptual meaning for data. All of these factors may have some limitations on our findings, as we could not find a strong and positive correlation with the exception of corruption control and GEF.

Footnotes

Appendix 1

Table 4.

Descriptive statistics of selected variables.

	GEF	COC	ROL	VAC	GEX
Mean	49.03394	48.51084	55.66102	39.44787	19.23889
Median	49.18033	50.97087	56.21535	43.34975	20.04670
Maximum	56.25000	54.30107	62.37624	46.26866	24.97618
Minimum	41.87192	41.14833	46.00939	28.07882	14.66776
Standard deviation	3.624007	4.309279	3.726018	6.565642	3.096528
Skewness	0.079347	−0.364982	−0.528828	−0.533901	0.099748
Kurtosis	2.559286	1.627981	3.216168	1.633075	1.735805
Jarque-Bera	0.228555	2.515920	1.213922	3.134048	1.706238
Probability	0.892010	0.284233	0.545005	0.208665	0.426084
Sum	1225.848	1212.771	1391.526	986.1968	480.9721
Sum square deviation	315.2022	445.6772	333.1970	1034.584	230.1237
Observations	25	25	25	25	25

GEF: government effectiveness; COC: control of corruption; ROL: rule of law; VAC: voice and accountability; GEX: government expenditure.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Ramasamy Ramesh

Thanabalasingam Vinayagathasan

Notes

Author biographies

Ramesh Ramasamy, PhD, teaches Political Science at the University of Peradeniya, Sri Lanka. His research interests have been on the quality of government, corruption, institutional trust, public service delivery and citizenship rights. His recent publications include; Key challenges of studying public administration in developing countries: An exploration from Bangladesh and Sri Lanka (Public Education Affairs, 2022); Institutional Trust Paradox: Understanding the Dynamics of Trust among Different Ethnic Groups in Sri Lanka (Contemporary South Asia, 2022); and Corruption -Poverty Nexus: Evidence from Panel ARDL Approach for SAARC Countries (Asian Journal of Comparative Politics, 2022). Ramesh Ramasamy can be contacted at: ramnaresh45@yahoo.com

Thanabalasingam Vinayagathasan holds a doctorate in Public Economics and Masters of Public Policy from National Graduate Institute for Policy Studies (GRIPS), Tokyo, Japan, and a MA in Economics and BA (Hons) in Economics from the University of Peradeniya, Sri Lanka, where he has been working as a Senior Lecturer. His researches, including empirical analyses relating to economic growth, monetary policy, inflation, poverty, FDI, exchange rate, finance, corruption, good governance, and COVID-19 pandemic issues, have been published in international journals and discussion paper series. He was awarded with MEXT scholarship and GRIPS fellowship. He had worked in international non-governmental organizations such as World Vision and Save the Children.

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