Composite Governance Index for the Pacific Island. Countries. Ronald Duncan, Teuea Toatu, and Azmat Gani. University of the South Pacific, Suva, Fiji Islands.
A Conceptual Framework for the Development of a Composite Governance Index for the Pacific Island Countries
Ronald Duncan, Teuea Toatu, and Azmat Gani University of the South Pacific, Suva, Fiji Islands
Pacific Institute of Advanced Studies in Development and Governance Governance Programme Working Paper August, 2004
Professor Ron Duncan is Executive Director, Pacific Institute of Advanced Studies, Dr Teuea Toatu is Fellow in Economics, Pacific Institute of Advanced Studies in Development and Governance, and Dr Azmat is Lecturer, Economics Department. This research has been undertaken with the benefit of AusAID’s financial support of the Good Governance Programme at the University of the South Pacific.
A Conceptual Framework for the Development of a Composite Governance Index for the Pacific Island Countries
Ronald Duncan, Teuea Toatu, and Azmat Gani
Abstract While poor governance has been recognised by donors and multilateral agencies as a major contributing factor to the poor economic performance of most of the Pacific Island countries (PICs), there have hitherto been limited attempts to assess the quality of governance in the individual PICs. The exclusion of the PICs from the institutional and governance indices produced by the various risk-rating agencies has been largely responsible for this shortfall. This paper begins to address this shortcoming by suggesting a conceptual framework to be used for the development of a composite governance index which is as comparable as possible to the indices compiled for other countries around the world. Although the indexation method applied in this study is a simple one, its main advantage is that it is more objective than others as it is based mostly on data rather than on perceptions, thereby minimising subjectivity and personal bias in its compilation.
April 2003
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A CONCEPTUAL FRAMEWORK FOR THE DEVELOPMENT OF A COMPOSITE GOVERNANCE INDEX FOR THE PACIFIC ISLAND COUNTRIES
Introduction
Research on the role of governance in economic development has been constrained by difficulties in finding appropriate and objective measures of the quality of governance, and the institutional environment it reflects. While a plethora of qualitative measures/indicators has been developed by various risk-rating agencies and multilateral organisations, the usefulness of these indices for empirical research has been found wanting. There have been two main criticisms of these indices from an empirical perspective. Firstly, the indices are based on perceptions, and thus are subject to a high degree of subjectivity and bias.1 Secondly, they cover only a limited number of countries (due to the high costs of the surveys and expert-opinion polls involved). This means that, in the absence of alternative measures, a study of the role of institutions or governance in countries other than those covered by these indices cannot be easily undertaken. This study seeks to fill this void by proposing alternative measures of governance that are mostly based on data and are therefore more objective, and can be compiled without the need for expensive in-country surveys. The primary objective is to develop a conceptual framework that may be used for the construction of governance indices, especially for those countries not covered by the institutional indices referred to above. The Pacific Island countries (PICs) are one of those excluded countries, and thus such a framework will enable for the first time an 1
The two methods used by risk-rating agencies to derive these ‘perception’ indices are expert opinion polls and cross-country surveys of firms’ managers and general citizens. 3
analysis of the quality of governance in the PICs, and its implications for the growth performance of these countries. The study could also help in facilitating the setting up of a statistical basis on which the necessary data can be collected at regular intervals for each PICs. The governance indicators should provide useful benchmarks against which the governments of these countries can judge their performance against other countries and against their own performance over time. The rest of the paper proceeds as follows. The next section, Section II, briefly examines some of the approaches to indexation and evaluates their applicability in the context of the current effort to develop governance indices for the PICs. Section III discusses in detail the indexation approach that the study employs, the components (sub-indices) of the composite governance index to be developed, and the variables used as performance indicators of good governance. It also highlights the limitations of these indices and the method used in constructing them. Section IV contains the concluding remarks.
II.
APPROACHES TO CONSTRUCTING COMPOSITE MEASURES
There are four indexation approaches that this study considered in determining the construction of its composite index of governance. These are: the unobserved component approach as used by Kaufmann, Kraay and Lobaton (2002); the geometric mean index approach as applied by Hufter and Shah (1999); the arithmetic mean index approach as used by Manning, Mukherjee, and Gokcekus (2000); and, finally, the principal component analysis approach as applied by Toatu (2002). Aggregation of variables by way of indexation has four main advantages. First, they are based on a methodology that provides a consistent framework for placing data from various sources into common units. Second, the aggregate 4
indicators span a much larger sample of countries, permitting comparisons across a much larger set of countries than is possible using any single indicator. Third, the aggregate indicators are more precise measures than any individual indicator (as much as a consumer price index (CPI) based on a basket of several goods is a more realistic indicator of the level of inflation than a CPI based on one commodity only). Also, aggregation can iron out or offset the statistical biases or measurement errors in the variables chosen. Finally, aggregation makes possible analysis of the changes or movement in the composite index by reference to changes in the values of the constituent variables, thereby facilitating comparison performance across countries.
The Unobserved Components Model
This approach was adopted by Kaufmann, Kraay and Zoido-Lobaton (KKL) to construct an aggregate governance indicator based on a wide range of indicators from different sources. KKL developed an unobserved components approach with reference to what they saw as six fundamental aspects of governance: voice and accountability; political stability; government effectiveness; regulatory quality; rule of law; and control of corruption (see Box 1.1). They grouped 194 indicators covering the period 2000-2001 into six clusters corresponding to these six concepts of governance, and computed aggregate indicators spanning up to 175 countries. These indicators were drawn from 17 different sources of governance data constructed by 15 different organisations, and thus differed along several dimensions. Despite this heterogeneity, KKL took the view that within each cluster, each of these concepts is an imperfect indicator of the corresponding broader concept of governance.
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BOX 1.1: Kaufmann, Kraay and Lobaton (KKL) Approach
Governance Clusters (1)
Voice and Accountability • Measures the extent to which citizens are able to participate in the selection of a government • Independence of the media
(2)
Political Stability • Measures the likelihood that the government in power will be destabilised or overthrown through unconstitutional means.
(3)
Government Effectiveness • Measures the quality of public service provision, the quality of the bureaucracy, the competence of civil servants, the independence of the civil service from political pressure, and the credibility of the government’s commitment to policies. • Focuses on the “input” required for the government to be able to produce and implement good policies and deliver public goods.
(4)
Regulatory Quality • Focuses on measures of the incidence of market-unfriendly policies such as price control or inadequate bank supervision, as well as the burden imposed by excessive regulation.
(5)
Rule of Law • Measures the extent to which agents have confidence in and abide by the rules of society. • Effectiveness and predictability of the judiciary • Enforceability of contracts
(6)
Control of Corruption • Petty/grand corruption • State capture
Source: Kaufmann, Kraay and Lobaton (2002)
The unobserved components (UC) model expresses the unobserved data as a linear function of the observed governance plus a disturbance term capturing perception errors and/or sampling variation in each indicator. Expressed algebraically,
Y j ,k = α k + β k ( g j ) + ε j ,k
(1)
where g j denotes an unobserved index of one of the indicators of governance, e.g. rule of law, bureaucratic efficiency and effectiveness, and corruption, in country j. Y j ,k is the observed score of country j on indicator k; α k and β k are unknown
parameters which map unobserved governance g j into the observed data Y j ,k . The disturbance term ε j , k captures two sources of uncertainty in the relationship between 6
true governance and the observed indicators. First, the particular aspect of governance covered by indicator k is imperfectly measured in each country, reflecting either perception errors on the part of the experts (in the case of a poll of experts) or sampling variation (in the case of surveys). Second, the relationship between the particular concept measured by indicator k and the corresponding broader aspect of governance may be imperfect. The model is based on three key assumptions: (1) that the measurement errors in individual indicators of governance are uncorrelated across indicators; (2) that the relationship between unobserved governance and observed indicators is linear; and (3) that the distribution of unobserved governance across countries is normal. From Equation (1), KKL computed the mean of the unconditional distribution of governance, given the observed data for each country, and used it as an estimate of the level of governance in that country. In like manner, they computed the variance as an estimate of the precision of the aggregate governance measure for each country. If the parameters α k , β k and σ ε (k ) 2 were known, an obvious way to estimate g j would be to re-scale the observed scores by subtracting α k and dividing by β k , and then construct a weighted average of these re-scaled scores:
Yˆ j ,k =
Y j ,k − α k
βk
= g j + ε j ,k
(2)
However, these parameters are unknown for every indicator and, therefore, in order to compute the mean and the variance of g j , these unknown parameters need to be estimated first. KKL got around this difficulty by exploiting the assumption of normality of g j and ε j , k to write down the likelihood function of the observed data,
7
and maximise this function with respect to α k , β k and σ ε (k ) 2 to obtain estimates of the unknown parameters. Despite the overly simplifying assumptions underpinning the model, the derived governance indicator did not produce the desired effects. In particular, although it was possible to identify statistically significant differences between countries at opposite ends of the distribution of governance, it was much more difficult to discriminate among the majority of countries with any degree of confidence.
The Geometric Mean Approach This approach was adopted by Hufter and Shah (hereafter, HS) in their 1999 paper describing the construction of a composite index for ‘governance’. In developing this index, HS focused on four key observable aspects of governance as follows: citizen voice and exit; government orientation; social development; and economic management (see Box 1.2 below).
BOX 1.2: Hufter and Shah Governance Index Index Name
Component Indices
CP
PF
Political Freedom
PS
Political Stability
GO
Citizen Participation Index
Government Orientation Index
JE
Judicial Efficiency
RT
Bureaucratic Efficiency
CO
Lack of Corruption
SD
Social Development Index
HD
Human Development
GI
Egalitarian Income Distribution
EM
Economic Management Index
OO
Outward Orientation
CB
Central Bank Independence
DB
Inverted Debt to GDP Ratio
Source: Hufter and Shah (1999)
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The procedure for the construction of a composite index based on this approach is straightforward. HS first constructed the indices for each of these four components of governance, using data from the relevant indices developed by riskrating agencies referred to already. Then, they constructed the overall composite index of governance quality based on these four sub-indices. In algebraic terms, the index of the governance quality (GQI) is:
GQI = CP α1 * GO α 2 * SD α 3 * EM 1−α1 −α 2 −α 3
(3)
Where: CP = PF θ * PS 1−θ GO = RT K1 * CO K 2 * JE 1− K1 − K 2 SD = HD γ * GI 1−γ EM = OO M 1 * CB M 2 * DB 1− M 2 − M 2
The exponents α , θ , K, γ , and M are weights indicating relative importance of the components to the overall governance assessment. The assignment of appropriate weights for each category is a subjective and sensitive issue. HS circumvented this dilemma by allocating equal weights to the constituent variables. Equal weighting means that potential biases or errors do not unduly influence the composite index. Like the unobserved-components model, the efficacy of the HS model depends critically on the quality of data used in the analysis. For example, does the better availability of data from developed countries mean that these countries, as a group, are rated higher or lower than the least developed countries?
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The Arithmetic Mean Approach This method was used by Manning, Mukherjee and Gokcekus (2000) – hereafter called MMG - to assess the impact of institutional change on public sector performance. MMG operated on the premise that public officials’ performance – and hence the performance of their organisations - depends on the institutional environment in which they operate. According to these authors, the essential elements of an effective institutional environment in the public sector are rule credibility, policy credibility, and resource adequacy and predictability. It is this institutional environment that drives the performance of the public officials in so far as it shapes their expectations of future constraints and incentives. The MMG approach involves a public service-wide survey of the opinions of officials. Indicators were compiled with rule credibility, policy credibility, and resource adequacy and predictability measured in terms of these indicators on scales that ranged from 0 (worst) to 10 (best). The indicators for the three components of the institutional environment – rule credibility, policy credibility and resource adequacy and predictability - were derived by taking the simple arithmetic average of the scores on the constituent elements of that component. The overall institutional environment indicator was then derived as a simple arithmetic average of the three components of the institutional environment.
The Principal Component Analysis (PCA) Approach
An approach that can be usefully applied to the construction of a composite index is the Principal Components Analysis (PCA) methodology. Toatu (2002) used this technique for the construction of his composite corruption index for the PICs,
10
using as proxies selected public finance variables believed to be closely linked to corrupt or rent-seeking practices within the public sector. In brief, the PCA methodology is a statistical technique that seeks to linearly transform an original set of variables into a substantially smaller set of uncorrelated variables that represents most of the information in the original set of variables. Thus, if the variables in the variable set are correlated, and especially if they are highly correlated, then one can linearly transform the p-correlated variables into a relatively small set of k-uncorrelated variables. The goal is to derive the k set of variables that will maximise the variance accounted for in the original p variables. The k-derived variables are called the “principal components”. For example, if three principal components account for most of the variance in an original set of 20 job satisfaction measures, then we have in effect reduced the dimensionality of the data set from 20 correlated dimensions to three uncorrelated dimensions and thus considerably simplified the structure of the job satisfaction variable domain. The number of principal components depends on the degree of correlation amongst the variables in the variable set. If there are no exact linear dependencies among the p variables, then there are as many principal components as there are variables. On the other hand, if there are exact linear dependencies (i.e., any one variable in the variable set can be written as an exact linear combination of one or more of the remaining variables), then the variables are redundant since one or more variables can be dropped from the variable set without any loss of information. One can perfectly predict the values of the excluded variables from the remaining variables. When there are exact linear dependencies, the dimensionality of the
11
variable space is accordingly reduced. The number of principal components is equal to the dimensionality of the variable set.2
Which approach to use?
Although the approaches outlined above differ in their ways of aggregating the variables for the purpose of constructing an overall index, they all have the same objective of reducing the dimensionality of the data set characterised by a large number of correlated variables. They do so by focusing on key observable aspects of the institution and/or governance dimensions that are thought to capture most of the information contained in the set. In constructing their aggregate indices, KKL, HS and MMG rely on the qualitative institutional indicators produced by risk-rating agencies such as Transparency International, Freedom House, and Business Environment Risk Intelligence. However, as already explained, these indicators are qualitative and they do not cover most of the Pacific islands. As such, they are of little use for analysis of the quality of governance in the PICs. The approach adopted in this paper is a combined version of the four approaches above. It has one distinguishing feature, however, in that it is based on measurable data, instead of perceptions (as the qualitative indices produced by the risk-rating agencies do). This is done to minimise the degree of subjectivity and bias. Also, it employs an arithmetic mean indexation approach (instead of the geometric mean indexation approach) to avoid the need to weight the variables, which could only introduce bias. What follows is a detailed description of this approach and the constituent indices comprising the composite governance index for the PICs.
2
For a detailed exposition of the principal-components methodology, see Dunteman, G. 1989, Principal Components Analysis, Sage Publications, London. 12
III.
THE COMPOSITE GOVERNANCE INDEX FOR THE PICs
The construction of composite measures of the quality of governance is not an easy task, given the multi-faceted nature of governance. While no single index can conceptually capture all aspects of governance, a focus on the key observable aspects of governance can be helpful in deriving an aggregate index based on these constituent variables. However, one must be guided in this process by one’s concept of good governance. According to the World Development Report 2002, governance is seen as “the art of providing effective institutions”. Based on this definition, a country is well governed if it operates within the framework of strong institutions. Strong institutions are defined as those institutions that promote respect for the rule of law, ensure political freedom and civil liberty, protect human and property rights and the sanctity of contracts, foster the effective operation of the public service, and facilitate the development and effective functioning of market institutions. The quality of governance, therefore, is a function of the quality of this enabling institutional environment and the extent to which the rules and constraints imposed by these institutions are respected by the authorities and citizens of the state. The construction of a composite governance index entails the following tasks: (1) determine the key dimensions of the enabling institutional environment; and (2) ascertain measures or indicators of the effectiveness of those institutions. Following the KKL and HS frameworks, the components of the composite governance index considered in this study are the rule of law, government effectiveness, social development and regulatory quality.3 These indices are chosen to provide an indication of a government’s ability to: (1) ensure political transparency, and an effective system of law and voice for all its citizens; (2) provide efficient and effective 3
Refer to Appendix 1 for a detailed description of the performance indicators for these indices, as well as the
scaling and conversion method used for constructing them. 13
public services; (3) promote the health and well-being of its citizens; and (4) create a favourable climate for stable economic growth. These factors are among those cited by the World Bank (1992) as representing the most important goals that ought to be pursued by governments. (Doing it this way implies that we know what the important dimensions of governance are, and then we find data to measure them by. Alternatively, we could let the data tell us what the important dimensions of governance are through principal components analysis. We may justify taking the first approach by arguing that there is now fairly widespread agreement about what the important dimensions of governance are.) The various proxies suggested for the measurement of the sub-sets of governance indicators and the reasons behind their inclusion are presented below.
(1) Rule of Law Index This index is composed of four sub-indices – political freedom, political stability, judicial effectiveness and media independence. The index measures (1) the extent to which agents have confidence in and abide by national laws, (2) the effectiveness and predictability of the judiciary, (3) the enforceability of contracts, and (4) the level of political freedom and civil liberty. The ‘political freedom’ sub-index assesses the ability of citizens to influence the quality of governance they receive. That is, if citizens enjoy political freedom and civil liberty they can use that freedom to check on the way the government runs the affairs of the nations through the policies and programs that it undertakes. The study relies on Freedom House’s published ratings for data on this index. The ‘political stability’ sub-index seeks to measure the extent to which the governing regime is prone to wrenching changes and disruptions. The rationale is that if a country is highly politically unstable, mechanisms for protecting institutions are 14
more fragile. The indicators or proxies for this index are the number of regime turnovers relative to the number of years since independence, number of parties constituting the government, the share of independent candidates in Parliament, and the share of military expenditure in the total budget. It has been noted that it is more difficult to undertake and sustain economic reform programs when coalition governments are in power than single party governments. This is understandable because of the power that coalition members hold over the major party. The same kind of argument underlies the recognition of the number of independents in parliament. The share of the military budget could be an indication of the power that the military holds over the government and its capacity for independence. The ‘judicial effectiveness’ sub-index measures the effectiveness of the judiciary in terms of its predictability, reliability and independence. The rationale is that the entire legal system and all other institutions of the state will be a farce if the judiciary lacks credibility and effectiveness. The measures of judicial effectiveness used in the study are the backlog of court cases, the deterrent effect of court judgements, the share of the judiciary’s budget in government’s total budget, law and order expenditure as a percentage of total expenditure, and court officials’ competence (as measured by their qualifications and years in service). The logic for the use of the shares of the judiciary’s budgetary allocation and law and order expenditure is that if these resources are lacking the likelihood that there will be a backlog of unresolved court cases and that courts decisions will be suspect is high. The ‘media independence’ sub-index reinforces the political freedom index above. The rationale is that without an independent media, government activities are obscured from public scrutiny, thereby preventing informed appraisal of the government by the public. Thus, media independence has the potential to contribute to better governance of the state through its scrutinising and reporting roles. The 15
indicators used for measuring the independence of the media are the proportion of shares or capital in the media owned by the government, and the extent to which the media is allowed to report freely.
(2) Government Effectiveness Index This index measures the effectiveness and efficiency of the government in providing public goods and services, the quality of the bureaucracy, the independence of the civil service from political pressure, and the effectiveness of government’s economic policies. The index comprises three sub-indices: bureaucratic efficiency, economic management, and the lack of corruption. The argument is that if the public service is oriented towards serving the people, the government will adopt effective economic policies, and bureaucratic inefficiencies and corruption would be minimal Bureaucratic efficiency is measured using the following indicators: ratio of tax revenue collected to total tax revenue budgeted, budget overruns, number of years for which public accounts are behind, expenditure on maintenance as a percentage of total expenditure, telephone mainlines per employee, and competence of civil servants (as measured by the number of qualified personnel relative to the total workforce). The justification for these indicators is as follows. Revenue arrears, budget overruns, prolonged telephone installations and public account backlogs are clearly reflective of bureaucratic inefficiency, which may be explained by the lack of competent civil servants. Inadequate budgetary allocation for maintenance and repair could severely affect the efficiency of the bureaucracy, and hence the effective provision of public goods and services. The ‘economic management’ index seeks to measure the effectiveness of the government in terms of its economic policies. In this study, the quality of a government’s economic management is assessed through the following three 16
indicators: the effectiveness of fiscal policy, the effectiveness of trade policy (outward orientation), and the effectiveness of monetary policy. These policy indicators encapsulate measures of government’s commitment to undertake growth-enhancing reforms. The performance indicator for the effectiveness of fiscal policy is the debt-toGDP ratio. The rationale is that a government that does not conduct prudent fiscal policy is likely to operate beyond its means, resulting in the unsustainable accumulation of government debt. For instance, a government that has a poorly developed revenue collecting system is more likely to incur debt (in search of revenues to finance its operations) than a government that has an effective revenue collecting system. The performance measures for the effectiveness of trade policy are the volatility of tariff rates, share of import duties in total tax revenue, and barriers to foreign direct investment (as measured by the average number of pending and unapproved applications). These are crude measures of the degree of openness of an economy. The high volatility of tariff rates implies that import restrictions have been widely used to protect particular investments. A high share of import duties in government’s tax revenue indicates a high degree of protectionism against imports – an unwise economic strategy as what a country gains from trade is its ability to import things it wants (Duncan, et al 1999). Likewise, the high numbers of unapproved and pending investment applications imply a protectionist approach against foreign investors. The indicator used to measure the effectiveness of monetary policy is the central bank’s independence from the government. Unless the central bank operates independently of government, the effectiveness of monetary policy will be questionable, as its direction and focus will likely be dictated by the government’s 17
fiscal policy. The indicators used for measuring the independence of a central bank include the variability of the capital adequacy ratio, the limits on lending to the government, the terms of office for the chief executive, and the formal structure of policy formulation, as measured by the composition of the Policy Committee. The bank’s independence is more likely to be compromised the higher the variability of the capital adequacy ratio, the higher the limit on government borrowings, and the shorter the terms of office for the chief executive. Finally, the ‘corruption’ index measures the extent to which the allocation of public resources by the government is prone to corrupt and/or rent-seeking motives. Indicators for corruption that are used in the study are the ratio of government consumption to total expenditure, the ratio of subsidy to total expenditure, the ratio of government expenditure on economic services to total expenditure, and the ratio of expenditure on oversight agencies to total expenditure. As argued by Toatu (2002), the level and size of these public finance variables have important linkages to corrupt and rent-seeking motives. The ‘government consumption expenditure’ variable is regarded as an outcome of corrupt or rent-seeking practices to the extent that the components of this expenditure item are highly susceptible to manipulation or appropriation by the policy-makers in pursuit of their private interests. Such components include overseas travel expenses, ministerial allowances, land rents, and other recurrent expenditure items, most of which are straight consumption expenditures with substantial pay-offs to the “beneficiaries”. The presumption, therefore, is that a high level of expenditure on this variable implies a high level of corruption and/or rent-seeking activities amongst government officials or politicians, especially where this expenditure is dominated by these rent-prone components. The ‘expenditure on subsidy’ variable is regarded as the possible outcome of corrupt and rent-seeking practices in so far as this expenditure item is vulnerable to 18
abuse by politicians and senior government officials for private gain. For instance, a subsidy may be granted to a company or industry not on grounds of social need but because of the pecuniary gains that the policy maker expects to derive from such resource transfer. This action may be motivated by the fact that the senior official or minister responsible owns shares in the company concerned, or perhaps the subsidised industry would benefit the politician’s constituency and hence such a subsidy could work to consolidate his or her power base in the electorate. In these circumstances, the redistributive role of the government suffers because program benefits or subsidies do not go to the most needy and most essential but to the best-connected. This has the potential to lower overall domestic investment. The study by Ades and Di Tella (1997) has shown a statistically significant and positive relationship between the levels of corruption and subsidy. The ‘expenditure on economic services’ variable is used as a proxy for the degree of government intervention in what would otherwise be private sector activities. The proposition is that such intervention is largely motivated by the rentseeking opportunities that such activities present to the decision-makers. This is particularly true in the case of most of the Pacific countries where the government is the sole supplier of goods and services. When governments engage in the provision of goods and services such as retailing, transportation and communications, agriculture, mining and commerce, which are normally within the province of the private sector, politicians and/or senior government officials often a have vested interest or direct involvement in the design and distribution of these activities. The latter includes decisions regarding sub-contracting and award of tenders on projects relating to these activities from which they obtain “commissions”. It also includes indirect benefits such as opportunities to employ one’s relatives in the government-run companies.
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A crucial pre-requisite for the containment of corruption in the public service is a transparent and accountable government. The variable used in the study as a measure of transparency and accountability is the share of oversight agencies (such as the audit office and the office of ombudsman) in the total budget. A higher share implies government’s strong commitment in support of the work of these agencies, and hence of the need to foster integrity and fiscal discipline in the public service. Therefore, the higher the expenditure on oversight agencies the less the problems of poor governance.
(3) Social Development Index This index gauges the quality of governance with reference to the quality of life that citizens enjoy. Two aspects of social development are considered: human development and equality in the distribution of income. For the human development sub-index, data from the United Nations’ human development index (HDI) will be used, focusing mainly on data relating to access to water and sanitation, adult and youth illiteracy rates, and infant mortality rates. Use is made also of the relevant public finance variables that provide a good indication of the government’s commitment to promoting social development. The most notable of these variables is the ratio of expenditure on social services (health and education) to total expenditure. A higher ratio implies government’s strong commitment to promoting social development and the well-being of its people. For the ‘egalitarian income distribution’ sub-index, the indicators used are the proportion of public investment made in rural areas and the distribution of ownership of key public assets (such as radios, TV stations, and telecommunications). We are not concerned about inequality of incomes per se but rather the inequality of incomes
20
that is caused by urban bias in public expenditure and the monopolies over fixed resources granted by government to individuals.
(4) Regulatory Quality Index This index focuses on measures of the incidence of market-unfriendly policies such as price controls or inadequate bank supervision, as well as the burden imposed by excessive regulation. It comprises two sub-indices: financial institutions and market competition. The proposition is that if the nation’s regulatory framework is strong, financial institutions and market competition are likely to be strong also. The measures of the effectiveness of financial institutions used in the study are the level of financial deepening, as measured by the ratio of M2 to GDP and the contract-intensive-money (CIM) ratio, and the number of domestic companies listed in the stock exchange. The latter is a good indicator of the confidence of investors in the effective operation of the financial/capital market. The usefulness of the M2/GDP ratio as an indicator of financial deepening is well documented in the literature (see, for example, Knapman and Zhukov, 2001). The CIM index was the invention of Clague, Keefer, Knack, and Olsen (1999). The authors defined the CIM index as the ratio of non-currency money to total money supply (M2). The authors viewed the financial portfolio holdings of nationals as being a good indicator of the effectiveness of institutions in protecting property and contractual rights. The rationale is that if institutions cannot provide assurance for the security of property rights (e.g., in terms of adequate third party enforcement), nationals would be less likely to allow other parties to hold their money in exchange for some compensation, and hence CIM would be correspondingly lower. That is, they would prefer to hold their financial assets in the form of cash (rather than depositing them, for example, with the banks). The higher the CIM, the greater the 21
ability of firms to raise capital, the higher the rate of investment, and hence the faster the rate of economic growth. The measures for market competition used are the waiting time for the delivery of services (such as the installation of new telephone lines) and noncongestion of public facilities and services. The argument is that when there is strong competition in the market for the production of goods and services, delays and inefficiencies in the production of these goods and services are minimal. The opposite holds if the production of these goods and services are monopolised by a single producer, as is the case in the telecommunications industry in the PICs.
Limitations of GGI As with the other indices, the framework suggested in this paper for the development of the PICs’ good governance index (GGI), and its constituents, has inherent shortcomings. A caveat is, therefore, in order at this juncture. First and foremost, it must be emphasised that these indices provide only suggestive measures of the quality of governance. Definitive conclusions about the validity of the results based on these indices depend to a great extent on the efficacy of the proxies used for the construction of these indices. The important point to note is that indicators are not ends in themselves; rather they are the means for decisionmakers to raise questions and highlight issues for further decision and investigation in light of local, country-specific knowledge. Being imprecise measures of the variables being measured, they do not tell the full story. Thus, any inferences that may be drawn from the results based on the application of these indices should bear in mind these limitations. Secondly, the shortcomings related to the reliability of the data must be taken into account when drawing inferences from the results derived from the use of these 22
indices. Of all the South Pacific countries, only Fiji has a fully developed statistical bureau and hence is the only one with reasonably reliable data. The lack of reliable data may render suspect the reliability of these indices and of the governance assessment based on these indices. Finally, the ratios from which these indices are derived may not be directly comparable. For instance, two countries may have the same ‘expenditure on social services to total expenditure’ ratios (one of the indicators for the Human Development Index), yet they differ with respect to the composition of this ratio. One country may focus on preventive health care, while the other one focuses on curative health care. Which country should be ranked higher on a good governance scale? This is a dilemma that may pose problems in the application of these indices.
IV.
Concluding Remarks
While poor governance has been recognised by donors and multilateral agencies as a major contributing factor to the poor economic performance of most of the PICs, there have hitherto been limited attempts to assess the quality of governance in these countries. The exclusion of the PICs from the institutional and governance indices produced by the various risk-rating agencies has been largely responsible for this shortfall. This study begins to address this shortcoming by suggesting a conceptual framework to be used for the development of a composite governance index for the PICs. Although the indexation method applied for the construction of this index is a simple one, it has the advantage of being more objective as it is based on data rather than on perceptions, thereby minimising subjectivity and personal bias in the compilation of this index.
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Appendix 1 The Components of a Composite Governance Index for the Pacific Island Countries INDICES
INDICATORS/MEASURES
Rule of Law Index • Political Freedom
(1)
Freedom House Index
•
(1)
Regime turnover relative to number of years since independence Share of independent party in parliamentary seats Number of parties constituting the government Military expenditure as % of total Govt expenditure
Political Stability
(2) (3) (4) •
Judicial Effectiveness
(1) Backlog of court cases (2) ‘Deterrent’ effects of court’s judgment (3) Judiciary’s budget as % of total government budget (4) Expenditure on law and order as % of total government expenditure (5) Competence of court officials -qualifications and number of years in service
•
Media Independence
(1) Proportion of Government ownership or control of media (2) Extent to which media is allowed to report freely
Government Effectiveness Index •
Bureaucratic Efficiency
(1) Tax revenue collected as % of total tax revenue estimated in Budget (2) Budget overruns (Actual expenditure – Budgeted expenditure)/Total expenditure (3) Backlog of public accounts – number of years public accounts are behind (4) Competence of civil servants – number of qualified personnel relative to total workforce (5) Telephone mainlines per employee (6) Expenditure on repair and maintenance as % of total expenditure
•
Economic Management
(1) Volatility of tariff rates (1) Debt to GDP ratio (2) Central bank independence, as measured by • Variability of capital adequacy ratio • Variability of government’s borrowing limit • Terms of office of Bank’s CEO (4) Investment barriers as measured by the number of ‘pending’ and unapproved foreign investment applications (5) Import duties as % of tax revenue (6) Commitment to undertake reforms (number of reforms implemented/total reforms required by National Development Plan or Strategy.
•
Lack of Corruption
(1) Inverted ratio of Government consumption to total expenditure (2) Inverted ratio of subsidy to total expenditure (3) Inverted ratio of economic services expenditure to total expenditure (4) Transparency and accountability, as measured by share of oversight agencies in total budget
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Social Development Index •
Human Development
(1) Access to safe water and sanitation (2) Infant mortality rate (3) Expenditure on social services (Health and Education) as % of total government expenditure (4) Adult and youth illiteracy rates (5) Life expectancy
•
Egalitarian Income Distribution
(1) Proportion of projects implemented in rural areas (2) Distribution of ownership of key public infrastructures such as TV, radio, utilities etc (3) Ownership of resources (eg, hectares of land per head) (4) Labour force as % of total population
Regulatory Quality Index •
Strong Financial Institutions
(1) M2/GDP (2) Contract-intensive-money (CIM) ratio (3) Listed domestic companies on stock exchange as % of total registered companies
•
Strong Competition
(1) Shorter waiting time for services to be delivered (2) Uncongested use of public services/facilities (3) % of budget allocated to privatisation program
Description of the Scaling and Conversion Method The composite good governance index (GGI) is made up of the following sub-indices: the rule of law index, government effectiveness index, social development index, and regulatory quality index. These sub-indices are broken into further sub-indices, as in the table above.
The scores on the performance indicators for the indices are calculated on a scale ranging from 0 to 10, with 0 being the worst and 10 the best. This is done by converting first the ratios or values obtained on the variables constituting the index to a maximum point of 10. For example, suppose that the ‘share of expenditure on social services in total expenditure’ (one of the indicators for the human development index)
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for Fiji was 25%. In terms of a categorical scaling point of 10, this is equivalent to a score of 2.5 (i.e., 0.25 x 10).
In the case of ratios where a high value implies bad outcomes, such as the public finance ratios in the corruption index (e.g., the government consumption/total expenditure ratio), rescaling is done as follows: (1 – ratio) x 10.
The GGI is derived as the simple arithmetic mean of the scores of the constituent subindices comprising it.
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References
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Duncan, R., and S. Pollard, 2002. “A Framework for Establishing Priorities in a Country Poverty Reduction Strategy”, ERD Working Paper Series No. 15, Asian Development Bank, Manila.
Duncan, R., Sandy Cuthbertson, and M. Bosworth, 1999. Pursuing Economic Reform in the Pacific, Pacific Studies Series, Asian Development Bank, Manila.
Clague, C, Philip Keefer, Stephen Knack and Mancur Olson, 1999. “ContractIntensive Money: Contract Enforcement, Property Rights, and Economic Performance”, Journal of Economic Growth, 4: 185-211.
Hufter, J., and Anwar Shah, 1999. “Applying a Simple Measure of Good Governance to the Debate on Fiscal Decentralization”, Working Paper, The World Bank.
Kaufmann, D, Aart Kraay, and Pablo Zoido-Lobaton, 1999. “Aggregating Governance Indicators”. Manuscript, The World Bank.
Kaufmann, D, Aart Kraay, and Pablo Zoido-Lobaton, 1999a. “Governance Matters”, Policy Research Working Paper 2196, The World Bank.
Kaufmann, D, Aart Kraay, and Pablo Zoido-Lobaton, 2002. “Governance Matters II”, Policy Research Working Paper 2772, The World Bank.
Kaufmann, D, and Aart Kraay, 2002. “Growth without Governance”, Working Paper, The World Bank.
Knapman, B., and E. Zhukov (ed). Financial Sector Development in Pacific Island Economies. ADB, Manila.
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Manning, N., R. Mukherjee, and O. Gokcekus, 2000. “Public Officials and their Institutional Environment”, Manuscript, World Bank, Washington, D.C. Toatu, Teuea, 2001. “Unravelling the Pacific Paradox”, Paper Presented at the Third Global Development Network Economic Research Medal Award Competition, Sponsored by the World Bank, Rio de Janeiro, Brazil, 9-12, December 2001.
Toatu, Teuea, 2002. “Analysing the Growth Performance of the Pacific Island Countries – The Institutional Approach”, Unpublished PhD Thesis, National Center for Development Studies, The Australian National University, Canberra.
Toatu, Teuea, 2003. “The Institutional Framework of Economic Development”, Working Paper, Asia Pacific School of Economics and Management (APSEM), The Australian National University, Canberra.
World Bank, 1992. Governance and Development, Washington, D.C.
World Bank, 2001. World Development Report 2002, Washington, D.C.
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