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The Impact of Regulatory Governance and Privatization on Electricity Industry Generation Capacity in Developing Economies

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This article assesses whether a regulatory law and higher quality regulatory governance are associated with superior outcomes in the electricity industry. The analysis, for 28 developing economies over 1980--2001, draws on theoretical and empirical work on the impact of telecommunications regulators in developing economies. Controlling for privatization and competition and allowing for country-specific fixed effects, both regulatory law and higher quality regulatory governance are positively and significantly associated with higher per capita generation capacity. This positive impact increases for more than 10 years, as experience develops and regulatory reputation grows. The results are robust to estimating alternative dynamic specifications (including error correction models), to inclusion of economy governance political risk indicators, and to controlling for possible endogeneity biases. The article concludes with a short discussion of causality in panel data modeling of governance models and the policy implications for regulatory reform. Copyright 2006, Oxford University Press.
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THE IMPACT OF REGULATORY GOVERNANCE AND
PRIVATISATION ON ELECTRICITY INDUSTRY GENERATION
CAPACITY IN DEVELOPING COUNTRIES
By
John Cubbin, City University and Jon Stern, London Business School
ABSTRACT
This paper assesses for 28 developing countries over the period 1980-2001 whether
the existence of a regulatory law and higher quality regulatory governance are
significantly associated with superior electricity industry outcomes. The analysis
draws on theoretical and empirical work on the impact of developing country
telecommunications regulators. The empirical analysis concludes that, controlling for
other relevant variables and allowing for country specific fixed effects, both a
regulatory law and higher quality regulatory governance are positively and
significantly associated with higher per capita generation capacity levels, controlling
for privatisation and competition. In addition, this positive impact continues to
increase for over 10 years as experience develops and regulatory reputation grows.
The results are robust to alternative dynamic specifications, including estimates from
error correction models, to the inclusion of country governance political risk
indicators and to controlling for potential endogeneity biases. The paper concludes
with a short discussion on causality in panel data modelling of governance models
and the policy implications for regulatory reform.
1
1. Introduction
Over the last 10-15 years, a very large amount of attention has been given to the role
of institutions in economic growth. This has, in large part, been driven by economic
policy priorities such as how to develop effectively functioning market economies in
Central and Eastern Europe and the former Soviet Union post-1989; and how to foster
economic growth in lagging world regions such as Sub-Saharan Africa.
In parallel, and partly in response, there have been major explorations of the role of
institutions in the functioning of market economies. In addition, in recent years, there
has also been a substantial empirical literature on the relative roles of institutions,
policy, geography and trade openness on growth performance across countries. This
literature places considerable weight on institutional quality as a major determinant of
variations in long-term growth performance
1
. In particular, Rodrik (2003) argues that
there is a requirement for a “… cumulative process of institution building to ensure
that growth does not run out of steam and that the economy remains resilient to
shocks”
2
.
The arguments above on aggregate growth apply with extra force to utility service
industries. This is because not just are they highly capital intensive, but, in addition,
because most of their assets are very long-lived and (in economic terms) sunk assets.
Hence, an effective institutional framework is essential to sustain growth in output,
efficiency and capacity for commercialised utility service industries such as
electricity, telecommunications, water and similar - particularly if these industries
have significant amounts of private investment (physical and/or financial).
The standard institutional solution to handle these infrastructure industry issues is to
introduce an independent regulatory agency, operating within a clearly defined legal
framework
3
. The regulatory agency is intended to provide the “high quality
institution” which permits and fosters sustained growth in capacity and efficiency in
the utility service industries – particularly the network elements. Hence, whether or
Work on this paper was supported by the research program on Industrial Organization Policy
for Development at the Development Research Group of the World Bank, under the direction
of Ioannis Kessides. We are grateful for helpful comments from seminar participants at the
University of Cambridge and City University as well as from the Editor and three anonymous
referees. The authors alone are, however, solely responsible for the analysis and the views
expressed.
1
See Rodrik , Subramanian and Trebbi (2002) for a recent survey of the literature on studies of
cross-country growth performance.
2
Rodrik (2003), p.25
3
An independent regulatory agency is not the only way of providing the necessary institutional
support either in theory or in practice See Domah, Pollitt and Stern (2002). In addition, an
independent regulator may be combined with a high or a low degree of reliance on contracts
and courts.
There is a major issue of whether or not low income developing countries have the human and
other resources to sustain independent regulatory agencies, particularly regulatory agencies
with a significant degree of discretion. Nevertheless, an independent regulatory agency has
become the standard recommended solution to the private investment problem for utilities in
the same way as an independent central bank has become the standard recommended solution
to handle commitment and time inconsistency problems in monetary policy. See Stern and
Cubbin (2003).
2
not country X has a high or a low quality institution is determined primarily by the
quality of governance of the regulatory agency (conditional on the governance quality
for the country as a whole). As with the aggregate economy, developing countries
with high quality regulatory agencies (as measured by their regulatory governance)
should attract more investment
on a sustained basis into their utility service industries
and at a lower cost of capital, as well as having higher efficiency levels and growth
rates in the regulated utilities
4
.
The perspective outlined above is at the heart of the recent literature on regulatory
governance for utility service industries, particularly the literature that focuses on
developing and transition economies. This perspective is set out in Levy and Spiller
(1994) – which draws explicitly on North (1990) – as well as in a number of
subsequent papers
5
. However, there have also been cases where the apparent outcome
regulatory (and electricity) reforms has disappointed. There have been many case
studies – and these can be very illuminating but do not allow reliable generalisations –
but, until the last 2-3 years, little formal econometric or other statistical testing. Until
recently, however, there has been very little systematic empirical testing of the view –
and policy – that improved regulatory governance increases investment or efficiency
in the electricity industry.
There have been a number of recent systematic empirical studies for
telecommunications. In this paper, we carry out a similar exercise for electricity
supply industries in developing countries. Specifically, we provide an econometric
analysis of the relationship between the quality of regulatory governance and the level
of generation capacity per capita for a sample of 28 Latin American, Caribbean, Asian
and African countries over the period 1980-2001, taking account of privatisation and
competition.
The plan of the paper is as follows. In Section 2, we outline the underlying economic
issues and the main institutional design considerations as well as related recent
research. In Section 3, we outline our modelling approach. Section 4 discusses the
main econometric results from static and dynamic models and Section 5 discusses
issues of endogeneity and causality. Section 6 presents a discussion and concluding
comments
6
.
4
The problems for developing country governments over making credible commitments to
support new investment in the presence of major fixed costs arises in other contexts besides
utility regulation. A good example is export taxes for exportable cash crops. See McMillan
(2001). We are grateful to the editor for this observation.
5
See, inter alia, Smith (1997), Stern and Holder (1999), Noll (2001).
6
See Cubbin & Stern (2004) for a much fuller version of the,paper, particularly of the data and
tables of results.
3
2. Underlying Economic Issues, Institutional Design and Implications
for Empirical Analysis
The main issue on which we focus is the inability of governments to make credible
and binding commitments about utility pricing to sustain private investment while
retaining decision-making powers over these issues.
The discussion of utility service regulation concentrates on commercialised utilities
facing genuine budget constraints, particularly where private investment and/or
private finance is important. The focus of the discussion (and of our empirical work)
is on regulatory governance (e.g. autonomy, accountability, etc) rather than on
regulatory content (e.g. methods of price, investment and related aspects of
regulation)
7
.
The underlying economic issue for utility regulation – as for monetary policy – is that
governments, particularly at certain times, have a strong incentive to behave in a
short-sighted and populist manner that reduces welfare summed over a medium to
long-term period.
2.1 Output Measures for Utility Regulatory Agencies
For utility service industries, there are two main output measures for measures for
utility regulation. These are:
(i) the level and rate of growth of technical efficiency and productivity
(and of quality of service); and
(ii) the level of capacity.
In this paper, we focus on developing countries and, in particular on capacity levels.
Hence, our estimates provide a test of the key policy objective of the World Bank and
many of the countries in the sample. They have consistently cited significantly higher
investment (and private investment) as the single most important reason for the
promotion of independent regulatory agencies in electricity and similar utility service
industries
8
.
In consequence, on this hypothesis, we would predict:
(a) sizeable increases in investment flows (domestic and foreign)
developing country electricity industries following the establishment of
an regulatory agency;
(b) larger increases with higher quality regulatory governance; and
7
We looked, in passing, at methods of price/profits regulation in our empirical work but this
issue was a subsidiary concern for this paper. See Section 4 for the results.
8
The World Bank’s 1994 World Development Report “Infrastructure for Development” is a
good example. See Chapter 3.
4
(c) larger impacts as the regulatory agency gains experience and
reputation.
.
2.2 Previous Literature
Our empirical work adopts and extends the fixed effects panel data modelling that has
been used in the literature on the impact of regulation on telecom outcomes. See, for
instance, Fink, Mattoo and Rathindran (2003), Wallsten (2002) and Gutierrez (2003).
The approach of Gutierrez (2003) is particularly relevant to this paper. He constructs
a regulatory governance index for his sample of 22 Latin American and Caribbean
countries. This 7-element index (derived from the Stern-Holder typology) is
calculated from examination of each country’s telecom laws and changes in the laws.
In our model for electricity outcomes, we adopt a similar approach and make use of a
‘snapshot’ 4-element index as one of our regulatory variables.
Gutierrez (2003) finds statistically and positive direct effects of his regulatory index
both on tele-density and on efficiency. This result occurs both in static and dynamic
models and after testing for the endogeneity of regulation
9
. The estimated effect of a
1-point increase in the index on mainlines per 100 inhabitants varies somewhat
depending on the precise model specification but is, in general, of the order of 20%.
For electricity, there are so far only a very few and very preliminary empirical studies
of the impact of regulation e.g. Zhang, Kirkpatrick and Parker (2002) and a part of
Pargal (2003). Like this paper, they also concentrate on generation capacity but find
only weak effects (if any) of regulation. Their studies also have major problems in
disentangling the effects of regulation per se from those of privatisation and
liberalisation. However, the studies are much more preliminary than those for
telecoms, particularly in regulatory and other data terms. In this paper, we have
access to better data on regulatory governance and its variation across countries.
However, again, data constraints confine us to estimating capacity models for
generation rather than transmission, distribution, sales or commercial losses
10
.
Regulatory issues are, of course, only one aspect of electricity industry reform. For a
comprehensive discussion of electricity reform in developing countries, see Jamasb et
al (2005).
10
Cubbin and Stern (2004) report estimates of the impact of regulation on technical losses.
5
3. Economic Rationale, Model Specification and Modelling Issues
The modelling work reported in this paper is concerned with whether better regulatory
governance in developing countries increases rated generation capacity per capita.
3.1 Economic Rationale
In developing countries, the introduction of explicit regulation is to focus the policy of
the electricity industry on providing sufficient supplies and that, typically, means
increasing investment and capacity.
In some cases, this has been done by harnessing the forces of private ownership and
domestic or foreign private investment. In others, it has to provide a workable
financial framework within which the electricity industry could develop by loosening
the ties with government. This can take place, for example, by a country enacting an
electricity law giving various powers and duties to a Ministry or independent
regulator. Such changes can also increase public investment in infrastructure
industries e.g. by requiring state owned electricity companies to operate in a more
commercial way and hence allowing access to these companies to private debt finance
for investment at a reasonable cost of capital.
Investment is encouraged once effective regulation is available to support a workable
financial framework. If the electricity industry is in private ownership the owners
have the prospect of earning a reasonable return on their investment; if publicly
owned, the industry can become independent of tax revenue or continually increasing
loans. In addition, the existence of an effective regulatory framework can also
encourage the growth of private investment and/or private finance within state
systems, as has been happening in recent years in India and China.
In an unconstrained market economy, per capita generation capacity will adjust to the
level of demand, which will depend upon the level of per capita income, the price of
electricity, and environmental factors such as climate. The price of electricity will be
determined in part by the efficiency of the sector. The latter may depend upon
regulatory factors, but also availability of energy sources such as hydro, gas, oil, and
coal. However, many developing countries with a traditional, vertically integrated
and state-owned electricity sector will be constrained not so much by market demand
but by the availability of continuing subsidy. In constrained developing country
electricity markets with implicit or explicit subsidies, capacity constraints arise
because of
either (a) inadequate government revenues for electricity investment or
subsidy payments;
and/or (b) insufficient revenue flows to support viable private
investment or commercial debt obligations.
Electricity generation models for unconstrained markets typically find per capita GDP
to be the major determinant of electricity demand (and hence of generation capacity).
We therefore include per capita GDP in our model as well as other control variables
that have been found to be statistically significant in previous studies of developing
country infrastructure industry infrastructure industry capital requirements eg the
share of industry in value added, country debt levels and country economy-wide
governance indicators.
6
We include in the model variables for electricity privatisation and competition and
also variables on country governance quality.
An effective regulatory framework can be expected to reduce the constraint on the
operation of the market, increasing supply and moving the outcome closer to the
market equilibrium. The better the governance of the regulator, the greater the
expected increase in capacity and increase in electricity supply.
3.2 Model Specification
The considerations above suggest a model where there is a long-run equilibrium
capacity-output relationship for generation capacity for each country, varying by
country. For developing counties, with supply constrained electricity, improved
regulatory governance is expected to raise equilibrium generation capacity levels.
The adjustment to the new equilibrium is very likely, however, take some time to
achieve.
This suggests a long-run static model of the following form, which is specified below
in panel data format:
Log(ELCAPPC)
it
= (a
0
+ a
i
) + a
1
log(GDPPC)
it
+ a
2
RegVar
it
+ a
3
X
it
+ v
it
(1)
Where Log ELCAPPC is the log of per capita electricity generation capacity
in Gigawatts;
a
0
is a constant term;
a
i
is a time-invariant country specific fixed effect
GDPPC is real per capita national income in $US 1995
11
;
RegVar comprises one or more of the regulatory governance variables
X is a vector of other potentially relevant sectoral and country level
control variables
and
v
it
is an error term
In all cases, the variables are defined for i = 1, …, I countries over t = 1, …., T time
periods.
Note:
1) The X vector of control variables for this equation might well include
domestic fuel/hydro source availability and a variety of other country specific
11
Hence, GDP is on an exchange rate rather than a PPP basis.
7
economic and/or institutional variables. However, we expect that both of
these will largely be captured by the country-specific fixed effects. Similar
arguments apply to institutional/country governance effects since country
rankings on these indicators tend to be relatively stable over 10-20 year
periods.
2) We also explore whether either (a) privatisation and/or (b) competition affect
generation capacity growth. We investigate both direct and indirect effects
(e.g. interactions between these variables and the regulatory variables).
3) On the basis of previous studies of electricity demand, we would expect a
1
to
be close to but probably less than 1.
12
The equation above is a static representation of the model, which provides evidence
on long-run equilibrium effects. We also consider some dynamic error correction
models, which provide evidence on the adjustment time-path and separates short-run
adjustment effects from long-run equilibrium effects.
To ensure that our modelling yields estimates of
supply responses, we confine the
sample of countries to countries with unsatisfied demand for electricity throughout the
1980-2001 period ie developing countries in Africa, Asia and Latin America only.
We exclude both developed countries, and European Transition economies as both
have significant planning margins and/or unutilised capacity for some if not all years
of the period.
3.2 Data
Our sample is of 28 developing countries. We have complete (or near-complete)
generation capacity data on these countries for a 21 year period (1980-2001). This
gives us a longer panel than is usually available for such studies; this greatly reduces
the econometric problems associated with short panels. There are, however, some
missing observations so that it is an unbalanced panel.
Of the 28 countries in the sample, 15 were in Latin America, 6 in the Caribbean, 4
were in Asia and 5 were in Africa. The list of countries includes large countries (e.g.
Brazil and India), small countries (e.g. Jamaica); middle income countries (e.g. Chile
and Mexico) and poor countries (e.g. Ethiopia and Sudan).
The full list of countries and summary regulatory characteristics is listed in Appendix
1. It is worth noting that out of the 26 regulatory reforms listed, only 6 are pre-1995.
The dependent variable in our regressions is per capita generation capacity by
country and year. This is derived from the US Energy Information Agency data on
generation capacity by country (GW) 1980-2001.
12
See Dahl and Roman (2004) Table 5 for a recent survey of electricity demand elasticities.
8
Graphs of the data are reproduced at Appendix 2. Interestingly, they are very
different by country and there are some significant decreases (Nigeria and Nicaragua
1990-95) as well as large jump increases (Paraguay 1985-88). Generation capacity
changes tend to be lumpy so that our dependent variable, does not obviously exhibit
common or stable trends. (Note that the EIA series does not distinguish between
publicly and privately owned generation capacity.)
The key independent variables for this study are the regulatory variables available to
us. There are data for each country on the existence (or absence) of:
(i) an electricity or (energy) regulatory law;
(ii) an autonomous or a Ministry regulator;
(iii) licence fee or government budget regulatory funding; and
(iv) free or mandatory civil service pay scales for regulatory staff.
Each of these is measured by a 0/1 dummy. The dating of the switch from 0 to 1 on
the appropriate variables (subsequently maintained at a constant level) is derived from
the date of enactment of a primary electricity reform or regulatory law (except for
cases where other information was available to provide a known, superior alternative).
Hence, we can investigate the effect of age of the regulatory agency as well as its
existence so that we can estimate alternative measures of the impact of regulation
based on the age of the regulator
13
. Given the time needed to establish a functioning
regulatory entity, the start date for regulation is taken as the year following the
enactment of the law.
The regulatory variables in our index are all measures of formal attributes of
regulation. Unfortunately, no comparable data is currently available on the informal,
practical qualities of electricity regulation (eg transparency and quality of regulatory
processes). The necessary omission of data on these characteristics may lead to
potentially biased estimates and standard errors
14
. In addition, unlike Gutierrez
(2003), we have no time dimension on changes in formal governance attributes
subsequent to the enactment of the primary electricity/energy regulatory law.
The Domah data set is very suitable for a preliminary investigation of the impact of
regulation but is not ideal. In particular, it suffers from an absence of data on the
informal, practical aspects of regulation (e.g. length of tenure of regulatory agency
heads or commissioners, etc.
Although much of the regulatory activity took place in the last half of the data set, the
earlier period is important in effectively establishing benchmark pre-reform levels of
13
These regulatory data were collected in a 2001 study by Preetum Domah. See Domah, Pollitt,
and Stern (2002) for full details. We are very grateful to Preetum Domah for permission to
use the information from his survey in this paper.
14
See Stern & Cubbin (2003) pp. 30-32. where preliminary simulation results based on the
Stern-Holder data set suggest that omitting data on the informal, practical aspects of
regulation can lead to coefficients being under-estimated by around 5-10% and a similar
under-estimate of t-values.
9
generation capacity, and also in reducing some of the biases that can potentially arise
in the use of short panels. However, 20.7% of the total number of country-sample
years were years with an autonomous regulator and 31% with an electricity or energy
regulatory law. By the end of the period, only 2 of the countries had not enacted an
electricity law but there were 9 countries with a Ministry regulator operating under a
law.
15
.
A key feature of the regulatory data is that the correlation between the four regulatory
variables is, not surprisingly, very high. In addition, all of the countries with an
autonomous regulator had an electricity law as did all the countries with licence fee
funding
16
.
The matrix of correlation coefficients between the regulatory variables is as follows:
Correlation Matrix of Regulatory Variables
Electricity
Law
Licence
Fee
Funding
Autonomous
Regulator
Non
Civil
Service
Pay
Scales
Electricity Law 1
Licence Fee Funding 0.849 1
Autonomous Regulator 0.783 0.703 1
Non Civil Service Pay Scales 0.783 0.551 0.443 1
This high level of collinearity between the regulatory variables presents estimation
problems which we discuss in the next section.
For privatisation and competition, we use the Henisz-Zellner-Guilen (HZG) (2004)
electricity data
17
. On privatisation, this data set provides information on the year in
which all countries introduced: (a) minority privatisation of their electricity
industries; (b) majority privatisation of their electricity industries and; (c) total
privatisation of their electricity industries.
The HZG data on competition include a variable for the year in which private firms
were legally allowed to generate electricity for resale. But, this does not necessarily
mean that such electricity sales were important or even present and one-half of the
countries in our sample had this attribute over the whole sample period. More
seriously, this variable provides no information on the market structure of generation
or wholesale electricity purchasing. This variable is, however, unfortunately, the only
consistently available ‘competition’ variable for developing countries over the 1980-
2000 period.
15
For a fuller description of the data and a range of descriptive statistics, see Cubbin and Stern
(2004), Section 4, p.19.
16
Uruguay was a partial exception introducing licence fee funding, three years before its law
came into force.
17
We are grateful to Professor Henisz for permission to use these data.
10
The other main data source was World Bank data including (a) the World Bank
Development Indicators (e.g. for per capita GDP in $US1995, population, etc.) and
the Kaufmann governance indicators.
11
4 Econometric Results
In what follows, we report various results. Section 4.1 covers results from a static,
long-run model and section 4.2 covers dynamic models. We discuss endogeneity and
causality issues in 4.3.
4.1 Econometric Results for Models of Generation Capacity: Static
Model
We started by estimating an OLS equation as a baseline. All equations reported here
are modelled using a fixed effects estimator. Moving from OLS to a fixed effects
model reduced the standard error of the regression by more than one-half. Given the
nature of the underlying model, we would expect a fixed effects model to be more
appropriate than a random effects model. For some of the equations, we tested this
assumption using the Hausman test and the random effects model was consistently
rejected in favour of a fixed effects model.
In Table 1 below, we report some initial results including each of the four regulatory
variables separately. In this table, besides GDP, we also include debt and industry
control variables. The latter had consistently low t-values and are dropped in
subsequent regressions. The results in Table 1 show that the individual regulatory
variables are sizeable and with high t-values, although the coefficient on civil service
pay is the opposite sign to the one predicted.
A sample average country is estimated to increase per capita generation capacity in
the long run by 18% through enacting an electricity law. In this equation, as
elsewhere, the long-run elasticity of per capita electricity generation capapcity to per
capita GDP is estimated as around 0.7 – 0.85. However, the equation clearly fits the
data well and provides powerful initial support for the importance of good regulation
for generation investment.
The problem with the results in Table 1 is that the high level of collinearity between
the regulatory variables implies that the coefficient estimates on the individual effects
are likely to be upward biased when taken in isolation. This conjecture is confirmed
when all four regulatory variables are included in a single regression which results in
the coefficient on the electricity law variable rising to 0.27 and all the other variables
becoming insignificant. Omitting the law variable led to the funding variable
becoming significant but with less than a 1 per cent reduction in the standard error of
the regression – and similarly as further regulatory variables were omitted.
These results provide strong evidence that the high level of multi-collinearity between
the regulatory variables significantly affects the coefficient estimates when included
in combination. The standard statistical solution to this problem is to estimate a
model using principal components to help better identify the effects of the individual
governance elements. We did this and the results showed that only the coefficient
estimate of the first principal component (accounting for 76% of the total index
12
Table 1: Basic Static Generation Capacity Model Results
Dependent Variable = Log(Electricity Generation capacity per capita)
Electricity
Law
Type of
regulator
Funding
Staffing
pay
Explanatory variables
Real GDP per capita (log) 0.722
(8.755)
0.848
(10.667)
0.818
(10.363)
0.747
(8.825)
Electricity Law 0.180
(5.130)
Autonomous regulator 0.100
(2.343)
Licence funding of regulator 0.135
(3.419)
Civil service pay scales non-
mandatory
-0.180
(-4.010)
Debt payments as a proportion of
national income
9.38E-14
(0.021)
1.74E-12
(0.378)
4.99E-12
(1.124)
-1.62E-13
(-0.035)
Industry value added as proportion
of GDP
-0.001
(-0.249)
-0.003
(-0.920)
-0.002
(-0.645)
-0.002
(-0.599)
Estimation method
Fixed
effects
Fixed
effects
Fixed
effects
Fixed
effects
Adjusted R-squared
0.953 0.952 0.953 0.953
S.E. of regression
0.271 0.273 0.271 0.271
F-statistic 385.888 367.622 382.725 375.033
Durbin-Watson
0.165 0.156 0.161 0.162
No of observations
585 577 583 577
Note: t statistics in parentheses
variance) was statistically significant at the 5% level, with a t-value of 3.8. It is also
interesting to note that: (a) the loadings of the individual components in the first
principal component) were broadly similar to one another; and (b) the loading on the
electricity law element was the highest.
The problem with using principal components is that the results do not necessarily
have any economic rationale. Hence, our preferred solution is to assemble the four
regulatory variables into a regulatory index and use that index as an explanatory
variable. This procedure was used in the Gutierrez (2003) telecom regulatory study
and has been used extensively in the literature on the economic impact of independent
central banks. (See Geraats (2002 ) for a recent survey.)
13
The standard procedure, which we have adopted, is to use a simple additive index.
Our index takes the values 0, 1, …, 4 for each country in each year depending on
whether or not the country scores 1 or 0 on each of the four regulatory variables.
However, as pointed out by an anonymous referee, this procedure imposes the
restriction that each of the variables included in the index has the same proportionate
impact on the dependent variable. This is a strong and highly debatable assumption,
but, at least our index is derived from direct observation rather than from
impressionistic indicators
18
. In view of this and other concerns, we report below the
summary results estimating alternative current and lagged versions of various
regulatory variables.
The fixed effects equations in Table 2 were all estimated with per capita GDP and the
HZG privatisation variables as controls. In all the regressions, the coefficient on per
capita GDP was around 0.7 with a t-value of 8 or more. In the table below, we
concentrate on the results concerning the alternative measures of regulatory
governance.
The table shows that, apart from the unlagged index, all the regulatory variables are
positive and significantly different from zero at the 5% level or better. It is noticeable
that the lagged variables (including the 3 year plus dummy) are all larger than the
contemporaneous indicators and have higher t-values. The implication that it takes
time to build up the effect on regulation is supported in the age-quadratic model
where the maximum regulatory impact is estimated to be at around 14 years.
From Table 2, we concentrate in our dynamic modelling on the 3-year lagged index in
Column 2 and the 3-year plus regulator dummy in Column 3. The lagged regulatory
law has the best overall fit but, given its collinearity with the other regulatory
variables, is overall less satisfactory as a descriptor of the regulatory framework.
The lagged index variable implies a maximum impact on per capita generation
capacity of 16% - the same as for the 3-year regulator dummy. Note that the latter,
includes Ministry regulators as well as autonomous ones, although, particularly
towards the end of the period, many Ministry regulators were operating with powers
and duties specified in a regulatory law.
18
Estimation with a Guttman hierarchical index produced very similar results to those using a
simple additive index.
14
Table 2: Static Generation Capacity Model with Alternative Regulatory
Variables
Dependent
Variable =
Log(Electricity
Generation
capacity per
capita)
Regulatory
Index
Lagged
Regulatory
Index
3 Year
Plus
Regulator
Electricity
Law
Lagged
Electricity
Law
Quadratic
in Age of
Regulator
Explanatory
Variables
Regulatory
Index
(t)
0.022
(1.5)
Regulatory
Index
(t-3)
0.041
(2.3)
Independent
or Ministry
Regulator in
place 3
Years or
More
0.164
(2.9)
Electricity
Act
(t)
0.116
(2.6)
Electricity
Act
(t-3)
0.143
(2.8)
Age of
Regulator
0.044
(3.6)
(Age of
Regulator)
2
-0.0018
(-2.8)
Estimation
method
Fixed effects Fixed effects Fixed
effects
Fixed
effects
Fixed
effects
Fixed
effects
Adjusted R-
squared
0.95 0.97 0.95 0.95 0.97 0.96
S.E. of
regression
0.27 0.21 0.27 0.27 0.20 0.21
Durbin-
Watson
0.17 0.17 0.29 0.17 0.30 0.16
No of
observations
557 476 557 557 476 557
Note: t
statistics in
parentheses
One concern about the results in this and the previous table is the low value of the
Durbin-Watson statistic. In the static form of the model, we would not expect this to
lead to biased coefficient estimates but it may lead to over-estimated t-statistics. As a
preliminary test, we estimated the column 3 model incorporating a 1
st
order
autoregressive process. The coefficient on the lagged residuals was 0.79 with a t-
15
value of 42.8. However, the estimated coefficient on the lagged regulatory index was
both positive (0.02) and statistically significant at the 5% level (with a t-statistic of
2.1) and was also positive and statistically significant for per capita GDP. The
estimated Durbin-Watson was 1.74.
The result above suggests that the presence of autocorrelation in the static model does
not significantly affect either the coefficient estimates or their statistical significance.
However, we explore this more fully when we explicitly consider the results from
dynamic models in Section 4.4.
4.2 Econometric Results for Models of Generation Capacity: Static
Model – Privatisation and Competition
Incorporating the Domah data on privatisation and competition into the model did not
produce any significant effects but that data had major weaknesses. Below, we report
results using the better HZG data. In Table 3 below, we report estimates of the
relevant coefficients in fixed effects regressions with the 3-year plus regulatory
dummy as the measure of regulation. In all cases, the estimated overall fit of the
equation and the coefficients on per capita GDP and the 3-year plus regulatory
dummy were within 1% of those reported in Table 2 above. Results using the
regulatory index also produced very similar results.
On ‘competition’, the results above and others show consistent and significant long
run effects on generation capacity levels of around 10-15%. However, the
competition variable as defined provides no information on the amount of private
company electricity generated for sale, let alone whether it was from an independent
power producer selling to a single buyer or more from liberalised wholesale markets.
In consequence, the results are more likely to indicate a degree of country
commitment to electricity reform rather than any economic impact of competition in
generation markets per se
19
. 61% of observations scored 1 on this variable.
On privatisation, the results were varied. Unsurprisingly, there was no evidence that
minority privatisation had any significant effect on generation capacity levels. But,
neither did full privatisation – although that only applied to 2.3% of all observations.
There was some evidence, albeit weak in terms of statistical significance, that
majority privatisation had a long-run positive effect on generation capacity levels of
around 8-10%
We tested for interaction effects between the regulatory variables and both the
privatisation and ‘competition’ variables but none was significant at the 10% level or
better.
19
In many countries, including the UK, the legal right for new entrants to generate for resale was
the first step in electricity reform but achieved little or nothing in itself.
16
Table 3: Static Generation Capacity Model: Alternative Privatization and
CompetitionVariables
Dependent Variable = Log(Electricity Generation capacity per capita)
Explanatory
Variables
All Privatisation and
Competition
Variables
Minority and
Majority
Privatisation
Variables Only
50% or More
Privatisation
Minority
Privatisation
0.05
(0.57)
0.04
(0.49)
Majority
Privatisation
0.09
(1.47)
0.12
(2.13)
100% Privatisation 0.009
(0.07)
Majority or full
privatisation
0.07
(1.24)
Competition
(Legal right to
generate electricity
for resale)
0.14
(3.05)
0.14
(3.03)
0.15
(3.41)
Notes: (i) t statistics in parentheses; (ii) Other independent variables in regression
were per capita GDP and existence of 3-year plus regulator
4.3 Econometric Results for Models of Generation Capacity: Static Model –
Country Governance Effects
On country governance, we firstly included as explanatory variables the Kaufmann
indexes for (i) rule of law and (ii) corruption by country in 1998
20
. The corruption
index was never statistically significant in the fixed effect regressions at the 5% level
or better, either as a separate variable or when interacted with regulatory variables.
Estimated coefficients on the Kaufmann rule of law index were never statistically
significant in its own right but sometimes approached significance when interacted
with the regulatory variables.
The Kaufman rule of law index was, however, highly significant in an OLS equation
– and led to non-significance of the electricity regulatory variable. This last result
(together with the relative constancy of the cross-country rankings of general country
governance indicators over long periods) is a major reason why we believe that the
20
See Mastruzzi, Kraay and Kaufmann (2003).
17
estimated fixed effects may well capture a large part of the country-wide institutional
differences. On this last point, we also found:
(i) No statistically significant correlation between the fixed effects and the
Kaufmann rule of law index;
but
(ii) A sizeable and statistically significant interaction term between the regulatory
index and the Kaufmann rule of law index in a random effects specification (a
coefficient of 0.07 with a t-value of 2.3).
These results provide interesting pointers to the role of governance effects in our
model but are clearly far from conclusive.
As a further test, we included into the static model values of the World Bank
CHECKS index, which is a time-varying index of political risk. The index ““counts
the number of veto players in a political system, adjusting for whether these veto
players are independent of each other, as determined by the level of electoral
competitiveness in a system, their respective party affiliations, and the electoral
rules.”
21
The index yields a minimum score in the absence of an effective legislature.
The index score then increases linearly with the addition of subsequent veto points
22
.
The index is available for all of the countries in our sample for almost all years 1980-
2000.
Including this index in the regressions rather than the single year Kaufmann index is a
much stronger test of whether the estimates of our electricity regulatory governance
effects are biased because of the absence of explicit country governance measures. If,
as appears to be the case, country governance measures vary over time, the potential
impact of this is not captured either by the country specific fixed effects or by
inclusion of the Kaufmann index for 1998.
Including the CHECKS index in our equations confirms the robustness of our
estimates reported in Table 2 but the equation performance is improved by adding the
CHECKS index.
The estimated coefficient on the CHECKS index is correctly signed (positive) and
around 0.015 - 0.02 with t-values of around 2.1 (ie an increase of one point on the
CHECKS index increases expected per capita generation capacity in the long run by
1.5 - 2%). Lagging the CHECKS index variable has a very small impact on the value
on the coefficients for itself or any other variable..
Considering our preferred measures of regulatory governance for electricity, the
coefficient estimate on the (3-year lagged ) electricity act was slightly reduced by
adding the CHECKS index (from 0.20 to 0.16) but its significance level remained
high with an estimated t-value at 4.1. For the 3-year lagged regulatory index, the
21
Beck et al (2001).
22
For further details on the definition of the index, see Beck et al (2001). We are grateful to an
anonymous referee not only for the suggestion that we include the index in our modelling but
also for providing the data for us for the countries and time periods in our sample.
18
coefficient on the regulatory variable was reduced from 0.058 to 0.049 but the t-value
again remained high at 3.5. In addition, the coefficients on privatisation and
"competition in generation” were virtually unchanged from the results reported in
Table 3 above. Hence, we conclude that the impact of the electricity regulatory
governance variables is genuine and not just a proxy for variations in country
governance.
We also tested for interaction effects between the CHECKS index and the electricity
regulatory governance variables. However, the estimated coefficients were both small
and not significantly different from zero (with t-values of around 0.6)
Finally, we estimated an equation for per capita generation capacity including the
CHECKS index and per capita GDP but omitting any electricity regulatory variable.
The resulting coefficient estimate for the CHECKS variable was only slightly
increased (to around 0.025) indicating that the degree of collinearity between our
electricity regulatory variables and the CHECKS index was very small.
These results show that both sectoral regulatory governance and country governance
significantly affect the level of investment in per capita generation capacity but that
the impact of the sectoral variables is rather larger. However, the effects appear to be
empirically separable, at least for electricity. We return to this issue in the
Conclusion.
4.4 Dynamic Models and Autocorrelation
In this section we discuss the results from dynamic models, including error correction
models. Given the nature of the generation investment planning and construction
process, we would expect quite long lags .
Our two main concerns were:
(i) To establish whether or not our results, particularly on the regulatory
variables represented genuine causal processes or were merely spurious
regressions; and
(ii) To consider autocorrelation explicitly within a dynamic modelling
framework, rather than as a statistical autocorrelation “correction”.
To test whether the estimated, long-run static fixed effects levels equations are
genuine rather than spurious regressions, we can check to see whether there appears to
be a plausible adjustment process.
The levels equation can be written as:
Y
it
= φ
i
+ βG
it
+ γR
it
+ υ
it
(2)
which can be estimated as: Y
it
= f
i
+ bG
it
+ c R
it
+ u
it
(3)
19
where Y
it
= log(electricity generation capacity per capita)
G
it
= log(GDP per capita)
R
it
is a regulatory governance variable; and
f
i
is the fixed effect for country i
From (3) we can calculate the implied the steady state, equilibrium, or long term value
of Y
it
, which can be written as:
Y*
it
= φ
i
+ βG
it
+ γR
it
(4)
We now postulate a partial adjustment error correction mechanism under which the
actual value of capacity changes by a constant proportion of last year’s deviation from
the long term value ie
Y
it
= Y
it
-Y
it-1
= - λ (Y
it-1
–Y*
it-1
) (5)
where (Y
it-1
–Y*
it-1
) is last year’s deviation from equilibrium.
Equation (5), can be estimated by taking the residuals u
it
from (3) and estimating
Y
it
= - λ u
it-1
+ e
it
(6)
An alternative procedure is to estimate directly a differenced version of the long-run
relationship, including country-specific fixed effects
Y
it
= - λ (Y
it-1
φ
i
- βG
it-1
- γR
it-1
) + ε
it
(7a)
= λφ - λY
it-1
+λ βG
it-1
+ λγR
i-1t
+ ε
it
(7b)
More specifically, since we are particularly interested in the size and significance of
the regulatory variable, R, we can impose the estimate of β from the long term levels
relationship (3) and estimate
Y
it
= λφλ(Y
it-1
- bG
it-1
) + λγR
it-1
+ ε
it
(8)
Equations (6) and (8) yield alternative estimates of λ, the speed of adjustment, which
can be compared. In addition, we have alternative estimates of γ, the impact of
regulation: firstly, from the levels equation (2); and, secondly, from the associated
differenced equation (8).
The validity of this procedure depends on stationarity of the data generation process.
We tested for stationarity using the Pesharan-Shin W-statistic. Applying this test to
the differenced equation (8), with the regulatory index as our measure of R
it
, the test
clearly rejects the presence of a unit root in the residuals with a t-statistic
of –8.05
23
.
23
Even in the corresponding levels equation, the Pesharan-Shin W-statistic does
not appear to suggest non-stationarity in the residuals, implying that our
generation capacity variable, GDP and our regulatory variables are co-
integrated. Very similar results were obtained on the unit root test with
alternative definitions of the regulatory variable.
20
The key results were:
(i) The estimates of λ, the speed of adjustment, were low at 0.12 but very
similar as between the levels and differenced equations and both with t-
statistics of 8.9.
(ii) The estimates of the impact of regulation in the differenced equation (8)
were positive and significant with t-statistics of 2.0 for the 3-year plus
regulator and 3.2 for the regulatory index.
(iii) The estimated long-run impact on per capita electricity generating capacity
in the differenced equation was 24% for the 3-year plus regulator and
almost 40% for the regulatory index.
(iv) There was no evidence of serial correlation in the differenced equations
(DW of 1.78). The overall fit of the differenced equations was good with
adjusted R
2
of around 0.15 and F statistics of 4.5 or higher
24
.
Further estimated versions of these equations have been produced using the 3-year
lagged regulatory index but also including the HZG privatisation and competition
variables. These indicate a faster overall speed of adjustment with an estimated error
correction term of 0.24-0.27, but this still implies a period of over 5 years before half
of the regulatory effect on capacity is manifest. The full results are set out in Table 4
below.
24
See Cubbin & Stern (2004) for the full results.
21
Table 4: Generation Capacity – Error Correction Models
Dependent Variable:
Log (Per Cap
Generation
capacity)
Levels
1
Log (Per Cap
Generation
capacity)
Differences
2
Log (Per Cap
Generation
capacity)
Differences
3
Explanatory variables
Real GDP per capita (log) 0.751
(10.92)
Index of regulatory governance (0-4)
(t-3)
0.041
(4.454)
Index of regulatory governance (t-4) 0.0126
(1.97)
Lagged Residuals from 1 -0.270
(15.44)
Error Correction Term 0.267
(15.40)
Majority Privatisation 0.125
(3.55)
Majority Privatisation (t-1) 0.054
(2.95)
Legal right of IPP Sales 0.120
(2.50)
Legal right of IPP Sales (t-1) 0.037
(2.82)
Estimation method Fixed Effects Fixed Effects Fixed Effects
Adjusted R-squared 0.974 0.413 0.416
S.E. of regression 0.205 0.076 0.076
F-statistic 581.6 11.34 10.33
Durbin-Watson 0.294 1.80 1.82
No of observations 488 481 481
Note: t –statistics in parentheses
The equations reported in Table 4 demonstrate positive and statistically significant
coefficients in both level and differenced equations not just for the (3-year lagged)
regulatory index but also for majority privatisation and the “competition” variable.
The estimated long run impact on per capita generation capacity from the privatisation
and competition variables as derived from the differenced equation in Column 3 is:
Max Regulatory Majority Privatisation Legal Right for
Index Score IPP Competition
19% 20% 14%
These results are very similar to the corresponding results from the static models
reported in Tables 2 and 3. They provide strong support for the hypothesis that the
impact of regulation and privatisation on generation capacity in developing countries
is positive and sizeable but take some years to build up.
22
5. Endogeneity and Causality in Generation Capacity Models
25
.
5.1 Endogeneity
Much of the literature on regulatory effectiveness expresses concerns over the
endogeneity of:
(a) countries choosing to have an independent/autonomous regulatory
agency; and
(b) the quality of governance of that agency
26
.
The concern is essentially that countries with better (unobservable) governance have
better functioning regulatory agencies eg because they have socio-economic
characteristics that better support the rule of law, contracts and commercialisation.
The problem is that it is very difficult to find good instruments ie instruments that are
both correlated with the suspected endogenous variable and uncorrelated with the
error term so that they can be treated as exogenous. The alternative is to try to model
explicitly the decision to adopt regulatory reform, but this is a difficult task and, as
yet, the results of such modelling have interesting but not very successful
27
.
However, it is possible to use a rank-based instrument firstly to test for the presence
of endogeneity; and, secondly, to derive an IV estimator to control for any
endogeneity
28
.
Adopting this procedure, we find that the coefficient on the residuals of the equation
with the rank-based index in the basic static equation for per capita generation
capacity has a t-value of 1.7, implying that there is marginal evidence of endogeneity
of the Cubbin-Stern regulatory index. However, instrumenting the Cubbin-Stern
index by using its predicted value in place of the actual value produces virtually
identical results – an estimated coefficient of 0.047 with a t-value of 4.3 in the
instrumented case as opposed to an estimate of 0.049 and a t-value of 4.0 in the non-
instrumented case.
Hence, like Edwards and Waverman (2004) and Gutierrez (2003), we find some weak
evidence of endogeneity of regulatory governance quality but very little change in
coefficient estimates from correcting for it.
5.2 Causality
25
We are grateful to Richard Gilbert and Jean-Michel Glachant for helpful discussions on these
issues.
26
See, for instance, Fink et al (2002) and Gutierrez (2004).
27
See, for example, Gual and Trillas (2002).
28
See Edwards and Waverman (2004) who follow Evans and Kessides (1993). For further
discussion of the procedure and its application in this context, see Cubbin and Stern (2004), p
35-36
30
For the reasons stated in Sections 2 and 3, we would not wish to claim that they are applicable
to countries with an excess supply of generation capacity at any time during the period after
1980. This would exclude the Central and East European countries, the CIS and almost all
OECD countries.
23
The question remains as to whether, looking forward, our regulatory governance
coefficient estimates have any causal interpretation. However, the fact that our core
results are maintained even with 3-year lags on the regulatory index and also with
sophisticated dynamic modelling suggests strongly that the results reflect an
underlying causal relationship.
Even if, they are not statistical artefacts arising from failures adequately to address
dynamics or endogeneity, they may still be merely descriptions of a past set of events
that cannot be applied to future electricity regulatory governance changes in sample
countries let alone to the introduction or development of electricity regulation in non-
sample developing countries
30
.
One reason why this issue arises is that the regulatory literature derived from Levy
and Spiller (1994) emphasises country-specific constitutional, legal, economic, and
political differences as being crucial for the success or failure of utility regulation.
Hence, a highly reduced form model that abstracts from all those issues may well fail
to reflect these local issues that seem to be so important in practice.
The answer to both these concerns lies in the importance of the country-specific fixed
effects. With 28 countries each having up to 21 years of data, we can obtain estimates
of the fixed effects which should capture most if not all of the factors identified by
Levy and Spiller and the subsequent literature. Hence, the estimated impact of eg
enacting a regulatory law plus an autonomous regulator in Chile or Sudan (both
countries in our sample) will be very different. That impact is the combination of (a)
the predicted effect of the relevant regulatory variables plus (b) each country’s
predicted fixed effect. The Chilean fixed effect is strongly positive relative to the
sample average whereas that for Sudan is strongly negative.
In other words, the coefficients that we report are ‘highest common factor’ estimates
of the impact of regulatory governance indicators where the fixed effects not just
control for but effectively “wash out” all the Levy and Spiller and similar factors,
including non-time varying cross-country differences in country governance. But,
this means that the regulatory governance effects that we report are not just average
cross-country sample effects but that they refer to a country with average scores on
country-specific fixed effects, including country governance fixed effects. Moreover,
they are the impacts that one might expect, looking forward, for a country:
¾ With an average country specific fixed effect
¾ Implementing an average quality law
¾ Establishing an average quality autonomous regulator, etc.
It is for such a country that one might expect that implementing a best quality
electricity regulator would increase per capita generation capacity in the long run by
around 15-25%. But, in addition, we also find that countries that reduce their
political risk scores increase their expected per capita generation capacity levels over
and above the impact of sectoral regulatory governance impacts (and vice-versa).
24
The policy implication of this is, firstly, that the quality of overall country governance
matters considerably for the impact of regulation on outcomes (eg as in the rule of
law); and secondly, that countries cannot expect to achieve the gains we have
estimated by enacting low quality regulatory laws or introducing autonomous
regulatory agencies with very low staffing levels
31
. However,, the corollary is that the
potential gains from introducing an electricity regulator could be significantly higher
than the average level for countries with good overall governance who deliberately try
to introduce best practice regulatory agencies and practices.
The argument that our results indicate higher potential gains from good regulatory
institutions follows not just from the logic of our fixed effect modelling but is
confirmed by the significance (and orthogonality) of the political risks index in our
model
32
.
31
See Domah, Pollitt and Stern (2002) for a full discussion of regulatory staffing issues and their
implications for the costs and effectiveness of regulation.
32
This view is also supported by the strong impact of the Kaufmann rule of law index variables
in an OLS equation and the highly significant, positive coefficient of an interactive
governance-regulation variable in a random effects equation.
25
6
. Discussion of Results and Concluding Comments
6.1 Discussion of Results
The results of this study seem to provide a broadly consistent picture that the
existence of a regulatory agency with good governance characteristics not only can in
principle improve regulatory outcomes but seems actually to do so in practice. For
electricity supply industries in 28 developing countries in the 1980-2001 period, we
find that an index of regulatory governance is a consistently positive and statistically
determinant of per capita generation. Our results, using fixed effects estimation
methods, are similar to those found in for telecoms in developing countries (e.g.
Gutierrez, 2003).
The main findings are that, for per capita generation capacity in developing countries:
1) The effects of the enactment of (a) a regulatory law, (b) of having an
autonomous regulator and (c) licence fee funding of the regulatory agency
were each positive and statistically significant at the 1% level.
2) Averaging over developing country regulatory agencies, the estimated long-
run impact of our preferred measures of regulation is of the order of 15-25%
ceteris paribus, after controlling for country-specific fixed effects.
3) The effects on per generation capacity are robust to modelling (a) with a
dynamic error correction model and (b) to instrumental variable modelling to
allow for potential endogeneity biases.
On privatisation and competition, there was some evidence of the effects of majority
privatisation and of competition on generation capacity (its legal introduction).
However, the latter is almost certainly more a reflection of a country commitment to
electricity reform than a genuine market competition effect. A positive and well-
determined impact of majority privatisation was found in the dynamic modelling.
On the whole, we were surprised at the strength and robustness of the results. Further,
since the regulatory changes typically took place in the mid-late 1990s and the
regulatory variables are most significant when included with a 3 year lag, we are
clearly not capturing just the Asian IPP or Latin American privatisation booms.
However, the recentness of the regulatory changes may account for the surprisingly
high significance of the impact of passing a regulatory law relative to that of having
an autonomous regulator. It will be very interesting whether the estimated effect of
having an autonomous regulator is higher in 5-10 years time.
In this paper, we have concentrated on the role of regulatory quality for capacity and
investment in the electricity industry. The results we find are very similar in type to
those previously found for telecommunications; and we suspect that similar
approaches could be used to examine the institutional underpinnings for investment in
other infrastructure countries. However, we suspect that the approach adopted here
might be useful for exploring the institutional contribution to capacity expansion for
other industries. This, however, requires long data sets to be available which would,
26
among other things allow robust estimates of country specific fixed effects and,
country governance measures including, if possible, the estimation of sophisticated
dynamic models such as error correction models.
6.2 Concluding Comments
In this paper we have presented evidence which suggests that good regulatory
governance does have a positive and statistically significant effect on some electricity
industry outcomes in developing countries – notably per capita generation capacity
levels - but we have not examined why this is so.
To examine why and how regulation operates to improve outcomes is not a task that
obviously recommends itself to econometric analysis. We suggest that, at least at this
stage, it is better pursued by case studies with econometric work being concentrated
on whether or not the results reported in this paper are confirmed in subsequent
analysis e.g. with superior data, particularly on regulatory practice, privatisation and
competition variables.
Nevertheless, we are confident that the results reported here are consistent with (a) the
literature on the role of institutions in economic growth; and (b) with good country
governance. Indeed, the evidence reported in this paper suggests that good country
governance and specific regulatory effectiveness are mutually re-enforcing. Both the
quality of the electricity regulatory framework and the quality of country governance
(as measured by a political risk indicator) are strongly associated with higher capacity
levels, but, as one might expect, with the sectoral variables having a markedly larger
impact.
The key point is that regulatory agencies with better governance are:
Less likely to make mistakes
More likely to correct mistakes speedily
Less likely to repeat mistakes
More likely to develop procedures and methodologies that involve participants
and develop good practice
More likely to copy and implement best practice from other countries.
All of these reduce uncertainties for commercially operating companies – particularly
private and foreign companies. This is especially important to sustain and encourage
long-lived, sunk investments in highly capital-intensive industries at a reasonable cost
of capital. As such, regulatory agencies, which have and maintain good governance,
provide an effective underpinning for the operation of contracts as well as sound
regulation of monopoly elements.
27
These conclusions are also likely to be relevant for the institutional underpinnings of
other sectors where raising sustained levels of long-lived and/or sunk investments are
important.
28
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31
APPENDIX 1: LIST OF COUNTRIES IN SAMPLE
Country
Year of Regulatory Autonomous
Start Regulator (Y/N)
Argentina 1993 Y
Barbados N/A N
Bolivia 1995 Y
Brazil 1997 Y
Chile Pre-1980 N
Colombia 1993 N
Costa Rica Pre-1980 Y
Dominican Republic 1999 Y
Ecuador 1997 Y
El Salvador 1997 Y
Ethiopia 2000 N
Grenada 1995 N
India 1999 N
Indonesia N/A N
Jamaica 1996 Y
Kenya 2000 Y
Malaysia 1991 N
Mexico 1996 Y
Nicaragua 1996 Y
Nigeria 2001 N
Paraguay N/A N
Peru 1994 Y
Philippines 1988 Y
Sudan 2001 N
Trinidad Pre-1980 Y
Uganda 2000 Y
Uruguay 1998 Y
Venezuela 1999 N
Given the time necessary to establish a functioning regulatory entity, the year of the
start of regulation was typically taken as the year
after enactment of the relavant law.
32
33
Appendix 2 Per Capita Generation Capacity Data by Country 1980-2001
Sources; US Energy Information Agency and World Bank Database
34
35
36
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