1

Growth Failure of Resource-rich Economies:

Role of Government Activities 1

Minoo Farhadi, Md. Rabiul Islam and Solmaz Moslehi

Monash University

22 October 2013

Abstract

Considering various channels through which government activities can affect economic performance is

essential in resource-rich economies. Governments typically receive revenues from natural resources in these

economies and are often characterized by large interference in private sector, weak law enforcement, high

inefficiency and low level of political freedom. To find the relationship between resource rent, government

quality and economic growth, this study provides an empirical investigation by applying system GMM

technique in a panel of 99 countries for the period 1970-2010. This paper considers five dimensions of

government quality including government intervention in market, government efficiency, provision of public

good, size of public sector and political rights to examine, in a unified specification, how the impact of natural

resources on productivity growth depends on the quality of government in the economy. Interaction effects

between resource rents and five different channels suggest that negative effect of resource rents on growth is

lower in countries with better quality of government.

Key words: O13, O40, O57

JEL classifications: natural resources, government quality, productivity growth

1 We would like to thank Pedro Gomis Porqueras , Paul Raschky and Sisira Jayasuriya for their comments

2

1. Introduction

The idea that the abundance of natural resource in many countries is more detrimental rather than

beneficial has attracted some attention in the recent literature. Since the pioneering contribution of

Sachs and Warner (1995), many studies attempt to shed light on the widely accepted paradox, called

“natural resource curse”, that highlights the fact that on average resource-rich countries experience

less economic growth and worse development outcomes compared to non-resource-rich countries2.

There are different explanations on how natural resource abundance leads to reduction in growth rate

through various transmission mechanisms 3. Among them, the low quality of institution is identified

as one of the critical aspects that explain the growth failure of resource-rich economies4.

The purpose of this paper is to explore the role that institutions, specifically those in the

public sector or government, play in the growth process of the countries with natural resources. In

other words, it is of interest in this paper to study how different types of government activity affect

growth of these countries. Moreover, how much of resource-curse depends on quality or

performance of a government. And finally, this paper examines whether a good government reduces

the negative effect of natural resource on economic growth.

To address the above questions, first it is crucial to specify what defines a “good-

government”. So far, there is no standard definition of “good government” and this concept has been

interpreted in various terms5. A “good-government” can work to improve economic growth, decrease

inequality, secure individuals’ rights, maximize social welfare in the society, promote individuals’

happiness and etc. This paper considers a narrow interpretation of a “good-government” by looking

at the role of government in promoting economic growth.

Next, it is crucial to specify how to quantify government activities. Empirical studies that look

at the impact of government on economic growth mostly just consider government spending (or

consumption) and/or taxation as a proxy for government activities6. Although government size is a

good proxy of government activities in developed countries, it cannot inform us about how well a

government is functioning in the developing countries. This is especially so in the resource-rich

2 See Frankel (2010) for comprehensive survey of literature review on natural resource curse.

3 See Van der Ploeg (2011) who discusses different channels that we might observe “resource curse".

4 See Mehlum et al. (2006) and Boschini et al (2007).

5 Among various interpretations, the World Bank (1994) defines a good-government as the “manner in which power is

exercised in the management of a country’s economic and social resources”, while based on United Nations

Development Programme (1996) the primary duty of a good government in a society should be reduction in poverty by

providing effective and accountable public services, and promotes the rule of law. 6 See Bergh and Henrekson (2011) and Romp and De Haan (2007) who review the extensive literature on government

size and economic growth. Also another strand of literature goes one step beyond and disaggregates government

spending into different components (productive vs. non-productive expenditure), e.g. Bleaney et al.(2001).

3

economies because public sectors in these economies receive revenues from natural resources and

hence are financially capable of intervening in different markets through regulations. A high degree

of government intervention can affect economic activities in several ways such as influencing

relative prices, regulating factors and goods’ markets, producing different commercial goods and

services through state-owned enterprises. Moreover, in a resource-rich economy a government

dampens economic performance by being inefficient, ineffective and authoritarian.

Thus, to comprehensively study the role of government activity in promoting growth in

economies with natural resource, it is essential to take into account not only the government size but

also other aspects of government activities. In order to do, we follow the classification introduced in

the seminal work of La porta, Lopez-de-Silanes, Shleifer and Vishny (1999) where five dimensions

of good government, including interference with the private sector, efficiency, output of public

goods, size of public sector and political freedom are introduced.

This paper makes the following contributions to the empirical literature. First, this paper takes

the aforementioned classification of the quality of government introduced by La porta et al. (1999)

and tests the relationship between different aspects of government activities and economic growth in

a unified specification. Some of these aspects previously have been neglected in the growth literature

on the curse of natural resource. Among five dimensions, government efficiency (Brueckner , 2010;

Kolstad and Søreide, 2009) and political freedom (Collier and Hoeffler, 2009; and Al-Ubaydli, 2012)

receive more attention in the natural resource literature. Furthermore, studies that investigate the

relationship between interference of government in the private sector and growth in resource-rich

economies only look at the role of government to promote growth by protecting property rights and

enforcing contracts (see among others, Mehlum et al., (2006). To the best of our knowledge, the

links between growth and other types of government interventions in market activities (e.g.

government regulation of business, credit, money and labour market) have not received much

attention in the literature. Little research, if any, have considered the link between government size

and growth in economies with natural resources (see Atkinson and Hamilton, 2003). Few, if any,

studies have paid attention to the link between growth and provision of infrastructure in these

economies

Second, this paper takes into account the interaction effect between different aspects of quality

of government and natural resource rents. More specifically, we test the hypothesis that resource

rent, alone, cannot be a proper determinant of economic growth. Other aspects of government

activities may temper how the rents from natural resources affect growth. In order to test our main

hypothesis we consider the interaction effect between resource rents and five aspects of quality of

government. This interaction term has been taken into account in other studies that look at institution

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and growth, among others see Boschini and et al. (2007). However, few studies look at different

aspects of government activities (e.g. Bulte and Damania, 2008; Collier and Hoeffler, 2009;

Brückner, 2010; and Bjorvatn et al, 2012 only consider one or two aspect(s) with interaction terms).

Third, this paper applies system GMM technique to estimate empirical models for a panel of 99

countries over the period 1970-20107. The interaction terms of natural resource rents with different

aspects of government quality have positive sign, which highlight that the negative effect of natural

resource on growth is less in countries with well-functioned and more efficient governments. These

results might be driven by a high quality of government in developed countries in the sample, some

of which are resource-rich (e.g. Australia and Norway)8. Hence, we restrict the sample to the panel of

63 developing countries over the period 1970-2010 and find empirical support for our results in the

smaller sample. Moreover, to assess the impact of different types of natural resource rents we

disaggregate the natural resource rents data into three groups: oil, oil and gas, and the combination of

oil, gas and mineral. The aim of using different subgroups is to further examine the individual impact

of each type of natural resource rent on the economic growth across sample countries.9

The rest of the paper is organized as follows. Section 2 presents various channels whereby

government activities affect economic performance and also the literature review associated with

that. Section 3 and 4 discuss the empirical framework, methodology and data respectively. Section 5

tests the link between alternative channels of government activities and TFP growth by using panel

regressions. Different aspects of government activities and different types of natural resources and

their interactions are considered for a sample of total countries (99 countries) and a sample of

developing countries (63 countries) in the period 1970-2010. Section 6 reports the robustness of

results with respect to inclusion of control variables, allowing for external instruments and

alternative measure of resource rents and TFP growth. Section 7 concludes.

2. Different Dimensions of Quality of Government and Economic Growth

La Porta et al. (1999) introduces five different aspects for quality of government: interference in

private sector, government efficiency, provision of public goods, government size and political

freedom. This section reviews the literature associated with each aspect and discusses how

7 Indeed, to examine the growth impact of various government activities in natural resource countries using a more

sophisticated econometric analysis that account for the presence of endogenous variables seems inevitable. 8 Because developed countries, mostly have a lower level of government intervention in market, higher level of law

enforcement, more efficient government and higher level of political right than developing countries. 9 According to Brunnschweiler (2008) various measures of resource rents can deliver different estimation results.

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government performance can affect economic growth through each channel in economies with

natural resources.

2.1. Interference in Private Sector

Governments may intervene in market activities to correct market failures and to achieve social

efficiency and equity. A good government provides a productive environment for the operation of

market economy by introducing a secure property rights, enforcement of contracts, market oriented

policies, less restricted regulations and stable monetary regimes. But the over-regulations of

government (excessive application of rules and regulations) distort incentives, market activities and

competitions, which can have significant counterproductive outcomes. In resource-rich economies,

governments typically receive revenues from natural resources that allow them to implements

unproductive policies and excessive regulations. The scope of government interventions in economic

activities in these countries is substantial and includes various dimensions. In what follows we

briefly discuss the role of property rights and government regulations in business, credit and money

market, and state-owned enterprises, respectively.

Secure property rights and a good legal structure are recognized as important preconditions for

a successful economy. They provide incentives for individuals to undertake entrepreneurial activities

and enforce contract to reduce transaction costs. Furthermore, secure property rights are likely to

decrease distortion in the allocations of resources such that more resources are devoted to long-term

investments and savings rather than immediate consumption. Resource-abundance countries are

often characterized by a weak level of property rights and legal structure10

. For example, the average

of this index for resource-rich economies in our sample is 4.7 in 2000s which is less than the average

of all countries, 5.8, indicating that governments in these economies fail to provide secure property

rights and strong rule of law. We classify countries with an average ratio of total rents (oil, gas and

mineral) to GDP between 1970-2010 above 10 precent to be in the resource-rich group, as in

Mehlum, Moene and Torvik (2006). This is close to the mean of natural resource rents (8.5) for the

whole sample period between 1970 and 2010.

Government over-regulation increases transaction costs and distorts competition, which

decreases productivity and efficiency. The windfall income from natural resources allows

governments in resource-rich economies to intervene in various markets and private sectors’

activities. To protect certain producers and industries, a government in a resource-rich economy

10

Legal system and property right, as a proxy for institutions has received some attentions in the natural resource curse

literature. As an example, Mehlum, Moene and Torvik (2006) by using institution quality index, which includes rule of

law, show that improvement in rule of law reduces the negative effect of natural resources on growth.

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imposes restrictions on markets, such as raising barriers to entry into some industries. It is more

costly (in terms of time and resources) for local and private businesses in resource-rich than

resource-poor economies to start and run operations since credits and licenses are more difficult to

obtained. On average, the procedures (number), time (days) and cost (% of income per capita)

involved in starting a new business in resource-rich economies, are 8.83, 35.7 and 51.5, respectively.

While the average number of procedures, number of days and cost for the sample of all countries, are

7.7, 24.73 and 26.8 respectively.11

Credit markets are highly regulated in resource-rich economies and governments are the main

owner of banks in the majority of these economies12

. Government over-regulations of credit markets

affect individuals’ borrowing and lending decisions and consequently limit entrepreneurial activities.

It is worth noting that monetary policies may also affect credit markets as monetary disturbances

and unexpected inflation change the value of money and thereby threaten entrepreneurial activities.

A high dependency on natural resources causes that monetary policies cannot be independent of

fiscal policies and therefore a monetary authority does not have the appropriate instruments to

control liquidity growth and inflation in a resource-rich country13

.

So far, we discussed how government regulations indirectly affect growth and production.

Government can also influence economic growth directly through the activities of state-owned

enterprises (SOEs). When there is a market failure SOEs have a legitimate role to play.

Nevertheless, on average, the performance and efficiency of state-owned firms are lower than their

private counterparts14

. A prevalent idea in resource-rich countries is that the natural resources are

national properties and thus SOEs are able to manage them in a more efficient way. However, some

empirical studies find that the production efficiency and profitability of national oil companies is

significantly lower than the private ones (Al-Obaidan and Scully, 1992; Victor, 2007 and Eller et al.

2011)15

. One of the reasons, as discussed by Wolf (2009), can be that SOEs in resource-rich

11

We calculate these averages from World Bank Doing Business dataset (2012). 12

Beck (2011) discusses the structure of financial system in economies that rely on natural resource exports and compare

them with other economies. 13

In resource-rich economies, monetary policy is subordinated to fiscal financing requirements through two separate

channels; first, a government uses pure seigniorage that is a central bank finances government deficit by increasing

inflation. Second, a government in a resource-rich economy finances government expenditure by the revenue from

natural resources, which is in terms of foreign-exchange and hence increases high-power money. See Da Costa and

Olivo (2008) who study how fiscal dominance weakens a monetary policy formulation and implementation in oil

exporting countries. 14

Shirley and Walsh (2000) investigate the relationship between ownership and economic performance by reviewing all

empirical studies during 1971-2000 across different nations. They found that out of 52 empirical studies, only 5 of them

show the preference of public ownership to the private one. 15

As an example, Eller and et al (2011) compare the efficiency of public and private oil companies and find that private

firms are more likely to cluster near the frontier while public ones are tend to be clustered far from the frontier.

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economies are more likely to follow their political goals rather than solve issues related to market

failures.

The Economic Freedom Index, EFI, by Fraser Institute is recently used in the empirical

studies to quantify the degree of government intervenes in different types of market activities; see

survey by Berggren (2003). This index includes a number of separable dimensions.16

Despite the

attention devoted to examining the relationship between total EFI and growth (e.g. De Haan and

Sturm, 2000; Heckelman, 2000; Carlson and Lundström, 2002; Dawson, 2003; Justesen, 2008)17

, this

relationship in resource-rich economies has not received much attention in the literature. To fill this

gap, this paper looks at the impact of total Economic Freedom Index and its components on growth

in resource-rich economies.

2.2. Government Accountability and Efficiency

The association between resource rents and corruption, as a proxy for government efficiency, and

through corruption on economic growth has received a great deal of attention in the literature (Leite

and Weidmann, 1999; Sala-i-Martin and Subramanian, 2003; Isham et. al, 2004; and Kolstad and

Søreide, 2009)18

. As shown in Méon and Sekkat (2005), among others, the magnitude of the impact

of corruption on growth depends on the quality of government institutions. Good political institutions

promote an accountability of politicians (Robinson et al, 2006), while weak governance rises the

harmful impact of corruption on economic growth. Governments are less dependent on tax revenues

in resource-rich economies and therefore citizens have less incentive to monitor and control

government activities as they pay less tax. This consequently leads to less responsibility,

accountability and efficiency of governments. Devarjan et al (2010) find a negative relationship

between oil-dependency and the responsibility of government and show that a broad taxation can

improve the accountability of government while natural resource dependency lowers it. The lack of

state-citizen relationship decreases society’s expectation from a government in a resource-rich

economy and makes room for a government to abuse the public gain for its private goals (Ross,

1999).

16

Legal system and property rights, sound of money, freedom to trade internationally and regulation and government size

are five components of EFI; see subsection 4.2 for details. Following Gwartney, Lawson and Hall (2012, p.11) the

central components of economic freedom are “personal choice, voluntary exchange coordinated by markets, freedom to

enter and compete in markets, and protection of persons and property”. 17

Among them Carlsson and Lundström (2002 and Justesen (2008) further investigate the growth effects of various

components of the EFI. 18

Note that the final impact of corruption on rate of growth is still uncertain and ambiguous. Johnson et al (2011) explain

some reasons on why it is difficult to find empirical support for this relationship.

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This weak accountability of government to citizen increases corruption in different forms like rent-

seeking activities19

.

2.3. Provision of the Public Goods

A government can enhance economic growth by providing public goods such as infrastructure,

which raises the social welfare by increasing in productivity and efficiency of market activities.

Infrastructure can be divided into two categories: social (soft) infrastructure and economic (hard)

infrastructure20

. Although considerable attention has been devoted to the growth impact of

infrastructure spending in developed economies, empirical studies of countries with natural resources

have mainly investigated the effect of “social infrastructures” (e.g. human capital) on economic

growth; not much attention has been devoted to investigate the role of “economic infrastructure”.

Comparing the average of infrastructure investment in rich and poor resource countries highlights

two important facts. First, there is a substantial gap between the amount being spent on infrastructure

in non-resource and resource-rich developing countries (see IMF report, 2012). Second, on average

the quality and efficiency of infrastructure is lower in resource-rich economies. Based on the Public

Investment Management Index (PIMI), natural resource countries have a significantly lower level of

PIMI scores compared to other countries since huge windfall revenues encourage investments in

non-productive rather than productive activities such an infrastructure and education (see Dabla-

Norris et al., 2011).

2.4. Government Size

Although many theoretical and empirical studies have examined the growth impact of government

size, but existing empirical literature cannot provide clear-cut evidence on the net effect of

government expenditure on the economic growth. While some scholars find a positive relation (e.g.

Easterly and Rebelo, 1993) others obtain a negative or not significant link (e.g. Landau, 1986; Evans

and Karras, 1994). In the case of natural resource economy, however, this nexus relation despite its

essential role has not received much attention, see Atkinson and Hamilton (2003).

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In these types of activities individuals and groups tend to have more lobbies to benefit from resource rents. The

important point here is not the amount of rents they have used but the time, skill, talent and energy which they spend on

unproductive activities in order to obtain more shares of rents (Baland and Francois, 2000; Torvik, 2002).

20 Provision of electricity, water supply, telecommunications, roads, highways, railroads and airports can be called

“Economic infrastructure” while provision of health, education and promoting cultural standards of the population are

titled as “Social infrastructure” (Fourie, 2006).

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It is worth noting that not only the contribution of public expenditure on economic growth is

important, but also the efficiency associated with these spending should be taken into account21

.

Arezki et al. (2011) argue that in resource-rich economies the problem is not low public investment

rather it is inefficiencies in government investments. The low efficiency of public investment may be

due to various shocks related to natural resource revenues, such as changes in price of recourses.

They provide some evidence that following commodity price booms in resource-rich economies

governments usually undertake undesirable and unproductive investment projects and hence prolong

the duration of existing inefficient policies. This implies the procyclical nature of fiscal policies in

these economy which hinders the economic growth by distorting the allocation of resources in non-

effective way (among other, see Landau, 1985, and Peden and Bradley, 1989).

2.5. Political Freedom

A number of scholars observe a negative impact of natural resource revenues on the level of

democracy and examine the hypothesis that democracy is more absent in countries with natural

resource revenues (Mahdavy, 1970; Ross, 2001). According to Ross (2001), there are three

mechanisms through which having oil reserves can lead to authoritarianism. First is a reinter effect in

which government tries to hinder democracy by imposing less tax and spending more in order to

reduce citizen demand for government accountability. Second is a repression effect where the

governments are more likely to pay attention to their internal security powers to be able to resist

external democratic forces. The last one is called a modernization effect where the democratic

changes, usually associated with economic growth, were absent from growth led by oil exports.

3. Empirical Framework

The proposed effects of resource rents and government performance on productivity growth are

tested using the following unbalanced panel regression model:

ittitititititiit XQGRRQGRRTFP 1,1,1,31,21,10ln (1)

where, ΔlnTFP is total factor productivity (TFP) growth measured by the first difference of the

logarithm of TFP; RR is the ratio of resource rent to GDP, where three alternative resource rent

measures are used, e.g. oil rent, oil and gas rent, and the sum of oil, gas and mineral rents as total

21

According to Grossman et al (1999), the inefficient provision of public goods and services negatively affect growth of

economies.

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rent; QG indicates alternative proxies for government quality prescribed by La Porta et al. (1999),

viz., economic freedom (EF) for interventionism, corruption (COR) for public sector efficiency, per

capita stock of paved road (PRD) for public good provision, ratio of general government final

consumption expenditure to GDP (GCN) for government size, and political right (PR) for political

freedom; X stands for the vector of control variables including distance to technology frontier,

investment ratio, openness, urbanization and landlockedness; π is unobserved country specific fixed

effects, is time dummy, and εit is the random error term. The subscript ‘i’ denotes a particular

country, whereas, subscript ‘ t ’ indicates a particular time period. We first estimate our baseline

model and then include control variables to check the robustness of our estimated results. Since the

effects of resource rent and government performance on productivity growth are not likely to be

instantaneous, we use lagged value of explanatory variables to estimate current TFP growth. The

nature of the panel is unbalanced because data are not available for all the sample countries over the

entire sample periods.

The regression model in equation 1 is estimated using data over the period 1970-2010 for a

panel of 99 countries: 36 high income countries and 63 low and middle income developing countries.

The countries included in the sample are listed in the appendix Table A1. In order to filter out the

business cycle effects, TFP growth is measured in 5-year differences and the explanatory variables

are measured in 5-year lags22

.

4. Methodology and Data

4.1. Methodology

The empirical estimates of equation (1) are based on the system GMM estimator that has been

widely used to deal with unobserved heterogeneity and endogeneity biases in the estimation. In the

presence of heteroskedasticity the system GMM estimator is more efficient than the simple IV

estimator (Baum et al., 2003). Furthermore, Bond et al. (2001) show that the system GMM estimator

is the preferred approach for the estimation of empirical growth models due to its superior ability in

exploiting stationarity restrictions. The standard set up of a dynamic panel model takes the following

form:

ittitititiit Xyyy 1,1,1, )1( (2)

22 The sample includes as many countries as possible that have resource rent data spanning at least 20 years and for

which at least ten annual observations are available.

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where, y is the logarithm of total factor productivity (TFP), yit – yi,t-1 is TFP growth, X is a set of

potentially endogenous explanatory variables which may include resource rent, government quality

and control variables, πi are time invariant country specific effects, t are time dummies, and εit is

white noise. This model can be re-parameterized as follows:

ittititiit Xyy 1,1, (3)

There is a clear simultaneity problem in model (3) as the lagged dependent variable (yi,t-1) is

correlated with the time-invariant country specific fixed effects (πi) and therefore, within group or

fixed effects estimators become inconsistent even if εit is not serially correlated. In order to eliminate

country level fixed effects, we can take the first difference of equation (3) to obtain:

itttitiit Xyy 1,1, (4)

where, Δ is the first difference operator. In order to deal with the endogeneity of the regressors and

the correlation between transformed lagged dependent variable (Δ yi,t-1) and transformed error term

(Δ εit) in equation (4), one should apply instrumental variable estimator.

The relationship between resource rent, government quality and growth are more likely to be

affected by endogeneity and reverse causality and therefore, difference generalized method of

moments (GMM) estimator could be used to mitigate those problems, where the endogenous

explanatory variables are instrumented with their suitable lags so that the instruments are not

correlated with the error term (Arellano and Bond, 1991). However, Arellano and Bover (1995) and

Blundell and Bond (1998) argue that the lagged level of the endogenous variables may be poor

instruments for the first differenced variables. They propose the popular system GMM estimator that

combines the regressions in differences and in levels in a system of equations using the lagged

differences instruments for the level series, and the lagged levels of instruments for the differenced

series.

In Monte Carlo simulations, Blundell and Bond (1998) observe that the system GMM

estimator produces efficiency gain when the number of time series observation is relatively small.

Furthermore, Beck et al. (2000) argue that a system GMM estimator is efficient in exploiting time

series variations of data, by accounting for unobserved country specific effects, and allowing for the

inclusion of the lagged dependent variables as regressors, thereby better controlling for the

endogeneity of the all explanatory variables. However, too many instruments relative to the number

of country-level observations may lead to the overfitting of the endogenous variables and weaken the

test for overidentifying restrictions (Roodman, 2009). In order to mitigate instrument proliferation,

we have used one or two lags of the explanatory variables as instruments and collapsed the

instruments through additions into smaller sets. We also report the number of instruments used in

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our system GMM estimation. Furthermore, we have used Windmeijer (2005) finite-

sample correction for the variance of linear efficient two step system GMM estimator.

Arellano and Bover (1995) and Blundell and Bond (1998) suggest several specification tests

that must be satisfied while using system GMM estimators. The validity of the instruments can be

examined with the Hansen test of the over-identifying restrictions, and direct tests of serial

correlation in the residuals or error terms. The key identifying assumption in the Hansen test is that

the instruments used in the model are not correlated with the residuals. The AR(1) test checks the

first-order serial correlation between error and the level equation. The AR(2) test examines the

second-order serial correlation between error and the first-differenced equation. The null hypotheses

in the serial correlation tests are that the level regression shows no first order serial correlation, as

well as that the first-differenced regression exhibits no second-order serial correlation.

4.2. Data

The data used to estimate equation (1) are constructed as follows. TFP is recovered from the

aggregate production function, Y = AKαL

1-α, where, Y is the real GDP, K is the real physical capital

stock, and L is the total labour force. Therefore, TFP is measured by A = TFP = y/kα, where y is the

output-worker ratio (Y/L) and k is the capital-worker ratio (K/L). Capital’s income share α is set to

0.30 following Gollin (2002). K is constructed using the perpetual inventory method, Kit = Iit+ (1-δ)

Kit-1 where, I is the real investment, is the depreciation rate which is assumed to be 5% following

Bosworth and Collins (2003). The initial capital stock is estimated using the Solow model steady-

state value of I0/ (δ+g), where I0 is the initial real investment, and g is the growth rate in real

investment over the period 1970-2010. These data are obtained from the Penn World Table PWT 7.1.

The ratios of oil, natural gas and mineral rents to GDP are used as alternative proxies for

natural resource rents. Oil rents are the difference between the value of crude oil production at world

prices and total costs of production. Natural gas rents are the difference between the value of natural

gas production at world prices and total costs of production. Mineral rents are the difference between

the value of production for a stock of minerals at world prices and their total costs of production.

Minerals included in the calculation are tin, gold, lead, zinc, iron, copper, nickel, silver, bauxite, and

phosphate. The data are taken from the World Development Indicators and the estimates are based

on sources and methods described in World Bank (2011).

The economic Freedom Index from the Fraser Institute is used to measure government

intervention. This is an 11-point ordinal scale, ranging from 0 to 10, where a higher value indicates

greater economic freedom. This freedom index consists of five broad areas: size of government,

13

property rights and legal structure, sound money, international trade and trade policies and regulation

of business, labour and credit markets (Gwartney et al., 2012). The international Country Risk

Guide’s (ICRG) corruption index is used to measure public sector efficiency. The index ranges from

0 to 6 where higher value indicates less corruption. Public good provision is proxied by per capita

stock of paved road or bitumen-surfaced roads. Government size is measured by the ratio of general

government final consumption expenditure to GDP. Finally, Freedom House’s political rights index

is used as a proxy for political freedom. The index ranges from 1 to 7 where a higher value indicates

less political freedom. The complete definition of variables used in this study is reported in Appendix

Table A2.

Table 1: Descriptive Statistics (1970-2010)

TFPG RRO RROG RROGM EF COR PRD GCN PLR

Total sample (92 countries)

Mean 0.49 6.88 7.94 8.72 5.90 3.29 3.80 15.64 3.67

Std. Dev. 1.76 14.40 15.66 15.63 1.19 1.41 5.07 6.23 2.22

Minimum -14.42 0.00 0.00 0.00 2.30 0.00 0.01 3.14 1.00

Maximum 8.17 80.24 85.50 85.50 9.08 6.00 27.23 43.48 7.00

Observations 760 741 740 740 791 792 792 727 790

Developing Countries (63)

Mean 0.34 6.65 7.53 8.71 5.46 2.70 1.31 14.03 4.38

Std. Dev. 1.95 13.03 13.85 13.83 1.00 1.05 1.89 5.83 1.94

Minimum -14.42 0.00 0.00 0.00 2.30 0.00 0.05 3.14 1.00

Maximum 8.17 72.49 72.49 72.49 7.87 6.00 19.14 43.48 7.00

Observations 488 462 461 461 503 504 504 459 502 Notes: TFPG = total factor productivity (TFP) growth, RRO = the ratio of oil rent to GDP, RROG = the ratio of oil & gas rents to

GDP, RROGM = the ratio of oil, gas & mineral rents to GDP, EF = Fraser Institute’s economic freedom of the world summary index

(higher value means greater economic freedom), COR = International Country Risk Guide’s (ICRG) corruption index (higher value

indicates less corruption), PRD = per capita stock of paved road, GCN = ratio of general government final consumption expenditure to

GDP, and PLR = Freedom House’s political rights index (higher value implies less political freedom). Productivity growth,

government expenditure and rent variables are expressed in percentage. All the data are measured in five-year intervals. The growth

rates are annualized.

Table 1 presents the descriptive statistics for the variables used in this study. The upper panel

shows the summary statistics for the entire sample whereas the lower panel provides statistics for

only developing countries. As would be expected, the mean values of resource rent in developing

countries are similar to the world average; however, the mean values of alternative measures of

government quality are significantly lower than the world average. For example, the average stock of

per capita road for total sample is 3.80 kilometres, whereas for developing countries it is only around

1.31 kilometres per thousand populations.

14

Table 2: Correlation Matrix for Total Sample (1970-2010)

TFPG RRO RROG RROGM EF COR PRD GCN PLR

TFPG 1.00

RRO -0.10**

1.00

RROG -0.07**

0.97**

1.00

RROGM -0.09**

0.96**

0.98**

1.00

EF 0.18**

-0.11**

-0.08**

-0.11**

1.00

COR 0.10**

-0.23**

-0.24**

-0.26**

0.38**

1.00

PRD 0.07 -0.14**

-0.14**

-0.16**

0.41**

0.57**

1.00

GCN -0.07 0.06 0.05 0.05 0.06 0.34**

0.31**

1.00

PLR -0.11**

0.33**

0.33**

0.33**

-0.38**

-0.48**

-0.52**

-0.15**

1.00 Notes: ** indicates 5% level of significance. See also notes to Table 1.

Table 2 shows the pair-wise correlation matrix for the variables reported in this study. This

correlation matrix examines the pattern of relationships between the variables under consideration.

Productivity growth is found to be negatively and significantly (at 5% level) correlated with

alternative measures of resource rent, e.g. the share of oil or oil & gas or oil, gas & mineral rents in

GDP. These findings are consistent with the natural resource curse hypothesis implying that

countries with abundant natural resources experience lower rates of economic growth than countries

with few natural resources. Higher economic and political freedom and lower corruption are

correlated with higher economic growth. Infrastructure and government consumption expenditure do

not show any significant correlation with growth. Correlation analysis demonstrates that there is no

evidence of a high correlations (0.80 or above) between resource rent and government performance

variables and thus the empirical estimates are less likely to be affected by multicollinearity problem.

5. Empirical Results

The aforementioned facts, discussed in details in Section 2, highlight that economies with natural

resources are often characterized by a large intervention in market activities through regulations,

weak law enforcement, inefficient public sector and a low level of political right. This means that to

study the impact of natural resources on economic growth we should take into account not only the

effect of the overall size of government budget relative to GDP, but also other aspects of government

activities. This section reports the results for five different channels through which governments

affect economic growth. The main hypothesis emphasizes on the growth impact of the interaction

term between natural resource rents and five different aspects of quality of government. We use three

different measures of natural resource rents: oil, gas and mineral rents. This shows us whether

different types of natural resource rents have different impacts on economic growth. We also

consider two samples: a panel of 99 countries includes all countries that has available data; a panel of

63 countries that just includes low and middle income countries. This lets us to study whether the

15

sign of interaction term between natural resource rents and quality of government changes in a

smaller sample.

5.1. Interference in Private Sector (Channel-1)

Table 3 reports the estimation results of equation (1) for total and developing sample countries using

three alternative resource rent measures, e.g. oil rent, oil & gas rents, and oil, gas and mineral rents

as total rent. Specifically, it examines the effects of natural resources and government intervention on

productivity growth. The coefficients of resource rent appear to be significantly negative in the

regressions for both samples. The negative effects of resource rent on TFP growth is consistent with

the ‘resource curse’ hypothesis implying that countries with abundant oil, mineral or other natural

resources tend to grow more slowly than resource poor countries (Sachs and Warner, 1995, 2001;

Auty, 2001; Kaldor et al., 2007; Frankel, 2012).

A higher score of the economic freedom index represents a lower government intervention.

Economic freedom is found to have consistent positive though weakly significant effects on growth,

indicating that the lower the government intervention the higher is the productivity growth. The

results are consistent with the findings of Cole (2003), Powell (2002) and Gwartney (2009) who

argue that higher economic freedom provides better protection of private property and lower

government interference. Therefore countries with greater economic freedom have higher economic

growth compared to their counterparts with less economic freedom.

The interaction between resource rent and economic freedom are found to be very important

for growth for all the countries as well as developing countries. This interaction effect estimates how

the effect of natural resources on productivity growth depends on the government intervention in the

economy. The estimated coefficients of the interaction between resource rent and economic freedom

are positive and statistically significant; suggesting that resource-rich economies with less

government interference may experience significantly higher productivity growth23

.

The primary advantage of the economic freedom index is that it provides a concise and summary

measure of government restrictions on free-market activity; however, the index is not without critics

(Garrett and Rhine, 2011).

23 It should be noted that although there is too much government interference in resource-abundance economies, but on

average governments in these countries adopt market-oriented reforms less than other countries. Amin and Djankov

(2009) find robust empirical evidence for the negative relationship between natural resource abundance and market-

oriented reforms. Less implementation of market-oriented reforms in resource-rich economies is mainly due to the fact

that governments have windfall income from natural resources. Moreover, as discussed by Acemoglu and Robinson

(2006) the implementation of market-oriented reform depends on the possibility of political replacement of incumbent

government. In most of resource-rich economies the probability that the incumbent government is replaced is low.

16

One criticism is that the index is less precise in measuring economic freedom than many of

the component variables used to create the index, thus generating bias in empirical models (Hanson,

2003). Economic freedom is a multi-dimensional phenomenon and therefore, different areas of

economic freedom may have different impacts on economic growth (Carlsson and Lundström, 2002

and Justesen, 2008).

Table 3: Economic Freedom, Resource Rent and TFP Growth

(Channel-1: Government Intervention)

Total Sample (99 countries) Developing Sample (63 countries)

Oil Rent Oil &

Gas Rent

Oil, Gas &

Mineral Rents

Oil Rent Oil &

Gas Rent

Oil, Gas &

Mineral Rents

RRi, t-1 -1.64* -1.31

* -1.95

** -0.76

* -0.60 -1.26

**

(-1.88) (-1.74) (-2.42) (-1.78) (-1.46) (-2.22)

EFi, t-1 0.04 0.05* 0.03 0.04

* 0.05

** 0.03

(1.53) (1.67) (1.12) (1.86) (2.15) (1.47)

EFi, t-1×RRi, t-1 0.33**

0.27**

0.36***

0.14* 0.12 0.24

**

(2.38) (2.37) (2.96) (1.76) (1.61) (2.38)

Observations 721 720 720 457 456 456

Hansen-test (p-val) 0.83 0.91 0.72 0.84 0.87 0.84

AR(2)-test (p-val) 0.43 0.44 0.34 0.48 0.52 0.64

Notes: Dependent variable is total factor productivity (TFP) growth in every five year interval from 1970-2010. ‘RRi,t-1’ indicates 5-

year lagged ratio of resource rent to GDP. ‘EFi,t-1’ indicates 5-year lagged economic freedom of the world summary index that ranges

from 0-10 where 0 corresponds to ‘less economic freedom’ and 10 to ‘more economic freedom’. All explanatory variables are treated

as potentially endogenous in system GMM estimator. The number of instruments used in the system GMM estimation is 16. The

instrument count is based on the number of ‘collapsed’ instruments, using the xrabond2 specification from Roodman (2009). All

regressions employ orthogonal deviations. Coefficients are based on the two-step system GMM estimation, using the Windmeijer

correction. The Hansen test checks the validity of the instruments where the null hypothesis is that the instruments are not correlated

with the residuals. The null hypothesis of the AR(2) test is that the error terms in the 1st-differenced regression exhibit no 2nd order

serial correlation. All regressions satisfy the F-test for the joint significance of the coefficients and the AR(1) test for 1st order serial

correlation but the results are not reported here to conserve space. Log level of initial total factor productivity, a constant term,

country, and time dummies are included but not reported for brevity. 2nd lag of the explanatory variables are taken as instruments for

the differenced equation whereas 1st differences of the explanatory variables are taken as instruments for the level equation. The

numbers in parentheses are t-statistics and are based on robust standard errors. *, ** and *** denote 10%, 5% and 1% significance

levels, respectively. See also notes to Table 3.

According to Gwartney et al. (2012) economic freedom consists of five broad areas: size of

government, property rights and legal structure, sound money, international trade and trade policies

and regulation of business, labour and credit markets. A single freedom measure may not reflect the

complex economic environment and a highly aggregated index makes it difficult to draw policy

conclusions (Carlsson and Lundström, 2002). Therefore, to mitigate empirical bias and to formulate

appropriate policy, one should estimate regression models using freedom index for each area in

addition to the overall index. A disadvantage with decomposing a summary index is the potential

problem with multicollinearity between the variables constructing the index (Carlsson And

Lundström, 2002). Table A3 to A7 report estimated effects of resource rent and individual

component of economic freedom index on economic growth. Estimated results demonstrate that

natural resources could increase productivity growth in countries with greater freedom in legal

17

system & property rights, international trade and minimal regulation. These results are in the line of

the findings of Justesen (2008) who observes that less regulation of business, labour and credit

markets promotes growth and therefore, less regulation may reduce the negative impact of natural

resource on growth. However, the size of government and access to sound money do not have any

significant effects on growth. In other words, not all components of economic freedom affect growth

equally (Compton et al., 2011). On average, higher economic freedom improves economic growth in

resource-rich countries.

5.2. Government Efficiency (Channel 2)

Resource-rich countries have a tendency to be corrupt because resource windfall encourages their

governments to engage in non-productive activities such as rent seeking (Collier and Hoeffler, 2009;

Bhattacharyya and Holder, 2010; Bruckner, 2010; Arezki and Bruckner, 2011; Haber and Menaldo,

2011). Turning to the impact of public sector efficiency in the context of resource abundance, the

positive and significant coefficients of the interaction between resource rent and corruption in Table

4 indicates that on average, resource-rich economies with relatively low levels of corruption have

experienced higher productivity growth. This result is consistent with the findings of Mehlum et al.

(2006) who argue that the negative impact of natural resource abundance can be reduced or avoided

in the presence of good rule of law institutions that mitigate corruption.

Table 4: Corruption, Resource Rent and TFP Growth

(Channel-2: Public Sector Efficiency)

Total Sample (99 countries) Developing Sample (63 countries)

Oil Rent Oil &

Gas Rent

Oil, Gas &

Mineral Rents

Oil Rent Oil &

Gas Rent

Oil, Gas &

Mineral Rents

RRi, t-1 -1.30**

-1.88**

-1.80**

-0.52* -0.54

* -0.84

*

(-2.29) (-2.23) (-2.46) (-1.93) (-1.79) (-1.98)

CORi, t-1 -0.05 -0.08 -0.08 0.01 0.01 0.02

(-1.16) (-1.27) (-1.31) (0.15) (0.08) (0.75)

CORi, t-1×RRi, t-1 0.32**

0.33**

0.30**

0.42**

0.43* 0.67

**

(2.05) (2.06) (2.11) (2.13) (1.98) (2.26)

Observations 722 721 721 458 457 457

Hansen-test (p-val) 0.19 0.27 0.43 0.75 0.78 0.23

AR(2)-test (p-val) 0.66 0.80 0.96 0.31 0.33 0.21 Notes: ‘CORi,t-1’ indicates 5-year lagged International Country Risk Guide’s (ICRG) corruption index that ranges from 0 to

6 where 0 corresponds to ‘more corruption’ and 6 to ‘less corruption’. See also notes to Table 3.

5.3. Provision of the Public Goods (Channel-3)

Cross-country empirical estimates indicate that the contribution of infrastructure services to

economic growth is indeed substantial and in general exceed the cost of providing these services

18

(Canning and pedroni., 1994; Esfahani and Ramirez, 2003). We explore whether the provision of

hard infrastructure helps alleviate the resource curse by interacting public infrastructure and resource

rents. The positive and significant coefficients of the interaction between public infrastructure and

resource rents in Table 5 indicates that higher per capita stock of paved road makes a greater

contribution to productivity growth in natural resource countries.

Table 5: Infrastructure, Resource Rent and TFP Growth

(Channel-3: Public Good Provision)

Total Sample (99 countries) Developing Sample (63 countries)

Oil Rent Oil &

Gas Rent

Oil, Gas &

Mineral Rents

Oil Rent Oil &

Gas Rent

Oil, Gas &

Mineral Rents

RRi, t-1 -0.29**

-0.21**

-0.21* -0.32

** -0.28

** -0.26

**

(-2.40) (-2.01) (-1.98) (-2.51) (-2.36) (-2.53)

lnPRDi, t-1 -0.02 -0.02 -0.02 0.01 0.01 0.01

(-1.04) (-1.19) (-1.37) (0.14) (0.07) (0.02)

lnPRDi, t-1×RRi, t-1 0.19**

0.19***

0.20***

0.29* 0.26

** 0.26

**

(2.27) (2.67) (2.79) (2.00) (2.18) (2.00)

Observations 722 721 721 458 457 457

Hansen-test (p-val) 0.40 0.52 0.37 0.84 0.88 0.59

AR(2)-test (p-val) 0.95 0.94 0.91 0.39 0.43 0.48 Notes: ‘PRDi,t-1’ indicates 5-year lagged per capita stock of paved road or bitumen-surfaced roads that exclude stone,

gravel, water-bound gravel, oil-bound gravel and earth roads. ‘ln’ indicates natural logarithm. See also notes to Table 3.

5.4. Government Size (Channel-4)

Government size may have an adverse impact on economic growth and therefore, countries with big

government can use economic openness and sound economic policies to mitigate negative effects of

big government (Bergh and Karlsson, 2010). Romero-Avila and Strauch (2008) find a negative

relationship between growth and both public consumption and total government revenue.

Government consumption has a significant and negative effect on growth productivity in both

samples when total rents from oil, gas and mineral are considered. Turning to the impact of

government consumption in the context of resource abundance, the positive and significant

coefficients of the interaction between resource rent and government consumption in Table 6

indicate that resource-rich economies spending less for government consumption enjoy higher

productivity growth than their counterparts with higher consumption in government sector. This

result is consistent with Atkinson and Hamilton (2003) who argue that resource abundant countries

with higher government consumption, on average, experience lower economic growth.

19

Table 6: Government Consumption, Resource Rent and TFP Growth

(Channel-4: Government Size)

Total Sample (99 countries) Developing Sample (63 countries)

Oil Rent Oil &

Gas Rent

Oil, Gas &

Mineral Rents

Oil Rent Oil &

Gas Rent

Oil, Gas &

Mineral Rents

RRi, t-1 -2.86**

-2.61**

-2.85**

-3.14**

-3.29***

-3.23**

(-2.32) (-2.31) (-2.43) (-2.53) (-2.66) (-2.19)

lnGCNi, t-1 -0.19 -0.18 -0.21* -0.14 -0.14 -0.18

*

(-1.65) (-1.50) (-1.69) (-1.44) (-1.36) (-1.70)

lnGCNi, t-1×RRi, t-1 1.10**

1.00**

1.08**

1.16**

1.23**

1.20**

(2.54) (2.46) (2.58) (2.36) (2.51) (2.06)

Observations 709 708 708 450 449 449

Hansen-test (p-val) 0.43 0.51 0.34 0.29 0.21 0.15

AR(2)-test (p-val) 0.52 0.57 0.53 0.81 0.87 0.84 Notes: ‘GCNi,t-1’ indicates 5-year lagged ratio of general government final consumption expenditure to GDP. See also notes

to Table 3.

5.5. Political Freedom (Channel-5)

Weak political institutions help divert production factors away from constructive uses to destructive

rent-seeking activities in the natural resource sector that reduce welfare in the economy (Trovik

2002; Olsson, 2007). The negative coefficient on political rights implies that an increase in political

freedom (lower value of political right index) has a positive and significant effect on productivity

growth in both samples and for different measures of resource rents.

Table 7: Political Rights, Resource Rent and TFP Growth

(Channel-5: Political Freedom)

Total Sample (99 countries) Developing Sample (63 countries)

Oil Rent Oil &

Gas Rent

Oil, Gas &

Mineral Rents

Oil Rent Oil &

Gas Rent

Oil, Gas &

Mineral Rents

RRi, t-1 -2.36***

-1.82**

-1.66* -0.81

** -0.78

* -1.19

*

(-3.51) (-2.23) (-1.88) (-2.53) (-1.97) (-1.93)

PLRi, t-1 -0.05***

-0.06***

-0.06***

-0.02* -0.02

** -0.08

**

(-2.63) (-2.82) (-2.91) (-1.91) (-2.11) (-2.42)

PLRi, t-1×RRi, t-1 0.53***

0.45***

0.42**

0.15***

0.15**

0.27**

(3.41) (2.80) (2.50) (2.83) (2.28) (2.09)

Observations 721 720 720 457 456 456

Hansen-test (p-val) 0.25 0.41 0.39 0.50 0.59 0.76

AR(2)-test (p-val) 0.20 0.32 0.50 0.72 0.75 0.47 Notes: ‘PLRi,t-1’ indicates 5-year lagged political rights index that ranges from 1 to 7 where 1 corresponds to ‘more political

freedom’ and 7 to ‘less political freedom’. See also notes to Table 3.

The positive and significant coefficients of the interaction between resource rent and political rights

in Table 7 indicate that resource-rich economies having greater political rights experience higher

productivity growth. This result is consistent with the findings of Al-Ubaydli (2012) who argues that

20

natural resources have an adverse impact on economic performance of authoritarian regimes but this

is not the case for democratic countries.

5.6. Marginal Effects of Resources on Growth

So far, the effects of natural resource and government intervention on productivity growth for five

different channels have been examined, but the estimated magnitudes reported in above tables are

not comparable. In order to examine the impact of total resource rent (oil, gas and mineral rents) on

different levels of government institutions, we have calculated the marginal effects of a standard

deviation change of total resource rent at different institutional levels of the government. The

estimated coefficients from Tables 3-7 for both samples of total 99 countries and 63 developing

countries is used to calculate the marginal effects following the below specification:

1,1,31 )ˆˆ( titiit stdRRQGTFPGrowth

(5)

where, QG is the different level of government institutional quality, and stdRR is a standard

deviation change in the total resource rent.

Table 8: Marginal Effects of Resources on Growth

(Total resources for different levels of government quality) Government

Institution

Channel-1 (Economic

Freedom)

Channel-2

(Corruption) Channel-3

(Public Good) Channel-4

(Govt. Size) Channel-5

(Political Freedom)

Total-99 Dev-63 Total-99 Dev-63 Total-99 Dev-63 Total-99 Dev-63 Total-99 Dev-63

Worst -0.175 -0.098 -0.281 -0.116 -0.169 -0.141 0.191 0.179 0.200 0.097

Average 0.027 0.007 -0.127 0.134 -0.019 -0.046 0.006 -0.021 -0.018 -0.001

Av.+ 1 std. 0.094 0.040 -0.061 0.231 0.025 -0.009 0.073 0.044 0.127 0.071

Best 0.206 0.087 0.001 0.440 0.070 0.070 -0.252 -0.257 -0.194 -0.127 Notes: Av. + 1 std. indicate average plus one standard deviation government institutions. As per definition of the institutional

variables, the highest (lowest) value is considered as the best (worst) government institution for Channel 1, 2 & 3, whereas, the lowest

(highest) value is considered as the best (worst) government institution for Channel 4 & 5. Since the measurement scale of the

government institutions in channel 4 & 5 is reverse (the lowest score indicates the highest quality of institutions), the positive

(negative) coefficients in the last four columns indicate negative (positive) resource effects on growth.

Table 8 reports the growth impact from a one standard deviation increase in total resource rent,

evaluated at four different levels of the quality of government institutions ranging from the worst to

the best quality. For example, using the fourth column’s (oil, gas and mineral rents for total sample)

coefficients in Table 3 and the mean level of economic freedom (5.904) gives: (-1.95+0.36 × 5.904)

× 0.156 = 0.027 as shown in the 3rd

row (average) of the 1st column of channel-1 in Table 8. The

interpretation is that, other things remain unchanged; a country with average level of economic

freedom would increase its annual growth rate by 0.027 per cent if it were to increase its oil, gas and

mineral rents by one standard deviation. Similarly, reading the Table 8 top-down, the calculations in

the 1st column of channel 2 for total sample suggest that a one standard deviation increase in total

21

resource rent is consistently negative but less so as public efficiency improves (i.e. corruption

decreases) and becomes positive at the highest level of efficiency (i.e. lowest level of corruption).

For other 3 channels of government institutions (Channels 3-5) the pattern remains very similar in

both total and developing countries; the effects of total resource rent becomes more positive as

governmental institutional quality improves. These results are consistent with the findings of

Boschini et al. (2007) who argue that the effects of natural resources on economic development

improve with institutional quality.

6. Robustness Tests

A series of sensitivity checks are undertaken in this section to ensure the robustness of the results

reported in the previous section we include additional control variables, allow for external

instruments, and use an alternative measure of resource rent and TFP. The estimated results are not

reported here to conserve space but they are provided in the Appendix. Since our results are not

sensitive to the way resource rent is measured, it is computed as the ratio of total resource rent (oil,

gas and mineral rents) to GDP. Unless otherwise stated, the estimates are based on a system GMM

estimator and the variables are measured in 5-year intervals.

6.1. Inclusion of Control Variables

As discussed above, productivity growth is likely to be influenced by factors other than resource rent

and government quality. Therefore, as a robustness check this study will consider several control

variables, such as share of investment in GDP, openness, urbanization and landlockedness. The ratio

of investment to GDP is included in the regression as it is found by Levine and Renelt (1992) to be

the most robust determinant of growth among a large set of growth variables. The investment ratio

can influence growth through the production function or through productivity externalities. Trade

openness has often been stressed as being an important factor for development and growth, where

trade openness is defined as import penetration and foreign direct invest among other factors

(Radelet et al., 2001). Urbanization promotes agglomeration externalities such as localized

information and knowledge spillovers, thereby promoting economic growth (Gallup et al., 1999;

Allen, 2003). Landlocked countries have lack of access to the sea and hence they are relatively

poorer than the coastal countries (Gallup et al., 1999).

[Table A8 Here]

22

The estimated results reported in Table A8 are fairly consistent with the results reported in Table 3-7.

On average, resource-rich economies can experience higher economic growth only with higher

government quality. Considering the control variables the coefficients of investment and

urbanization are found to be positive and significant. Again, the positive but statistically insignificant

coefficient of openness is not particular to resource rich economies. Almost all empirical attempts to

find a significant, positive relationship between economy-wide productivity and trade openness have

failed (see for an overview, Madsen, 2009). The coefficient of landlockedness has expected negative

sign, but its impact on growth appears to be insignificant. The results are consistent in both the

samples of total and developing countries. The coefficients of control variables are not reported to

conserve space; however, the results will be available on request.

6.2. Allowing for External Instruments

The relationship between resource rent, government quality, and productivity growth is likely to be

affected by endogeneity and reverse causality. Higher expected growth may facilitate more

investment in government sector. Although system GMM estimator may capture unobserved

heterogeneity and possible endogeneity in the model, there could still be endogeneity as well as

omitted variable bias and thus a robustness check is warranted.

System GMM estimator is primarily designed for internal instruments (lagged differences

and lagged levels of the explanatory variables), but it does allow external instruments to deal with

the endogeneity problem (Roodman, 2009). La Porta et al. (1999) provide a number of exogenous

determinants of government performance, such as, legal origin, latitude, ethno-linguistic

fractionalization, and religion which are used as external instrument in this study. Table A9 reports

the results after considering both internal and external instruments for natural resource and

government performance. The results are qualitatively similar to those obtained in Table 3-7. Overall

the empirical findings support that natural resources improve productivity growth in countries with

better quality of government.

[Table A9 Here]

6.4. Allowing for Alternative Measures of Resource Rent

As discussed by Brunnschweiler (2008), different measures of resource rents can deliver

different estimation results. Hence, it is important to check the robustness of results with alternative

measures of resource rents. Resource rent has thus far been measured as the ratio of oil, oil and gas,

23

or oil, gas and mineral rents to GDP. We also run the regressions using per capita resource rent as

explanatory variables. Total per capita rent has negative and significant effect on growth, except in

one channel in the total sample, and interaction terms between different aspects of quality of

government and resource rents are positive and statistically significant. The regression results

reported in Table A10 are largely in line with those obtained in Table 3-7 implying that better

government quality enhances productivity growth in resource-rich economies.

[Table A10 Here]

6.5. Alternative Measure of TFP

To check the robustness of the results to measurement of TFP, output per worker is used as proxy for

TFP since Y/ (KαL

1-α) and labour productivity is growing at the same rate along the balanced growth

path. The principal results, which are reported in Table A11, are similar to the results reported in

table 3-7. The coefficients of the interaction between resource rent and government performance are

consistently significantly positive suggesting that on average resource-rich economies with relatively

better government performance have experienced higher economic growth.

[Table A11 Here]

7. Conclusion

This paper contributes to the natural resource-curse literature by paying careful attention to different

aspects of government activity and testing the relationship between each of them and economic

growth in a unified specification for the panel of 99 countries over the period 1970-2010. Previous

literature that studies the link between government and economic growth, mainly look at the effects

of government expenditure or taxation on growth. Some other studies, particularly within the natural

resource-curse literature, also look at the other aspects such as efficiency of government (e.g.

corruption) and political freedom (e.g. democracy). There is a lack of research on the effect of other

government activities (such as government intervention in the market, government interference with

private sector and public good provision) on economic growth. This paper fills this gap in the

literature.

The main findings of this study are in line with the emerging consensus in the resource curse

and economic growth literature: the quality of the institution determines how natural resources affect

economic growth. We find that different measures of natural resource rents such as, oil, oil and gas,

and oil, gas and mineral, have negative and significant effect on productivity growth in both samples

of total 99 countries and 63 developing countries. The interaction effects between natural resource

24

rents and different aspects of government quality are positive, which implies that a good government

reduces the negative effect of natural resource rent on growth. Hence, resource-rich countries that

have experienced a ‘curse’ appear to be those countries that may have low quality of government

such as large intervention in market activities, an inefficient public sector, a high level of

government size, a low level of public goods provision or a low level of political freedom. To be

more precise about the intervention in market activities, the results of this paper further suggest that

resource-rich economies can enjoy higher productivity growth by having lower interference in

private sector through regulations, better law enforcement and greater freedom in legal system and

international trade. Finally, the marginal effects of a standard deviation change at different levels of

government quality are calculated for five different channels of quality of government. Results show

that the impact of natural resources on economic growth improves as quality of government

improves.

The policy advice of the findings of paper is clear. Economic and/or political reforms that

improve any aspects of quality of government in a resource-rich economy reduce the likelihood that

the resource curse occurs. Of course, this is not an easy task as empirical studies suggest that there is

a negative relationship between natural resource abundance and the implementation of market-

oriented and political reforms; see Amin and Djankov (2009) and Acemoglu and Robinson (2006).

This consideration is left for future research.

25

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29

APPENDIX

Table A1: Sample Countries

Total Sample (99 Countries)

Developing Sample (63 Countries)

Albania Gabon Papua New Guinea Albania Kenya

Algeria Germany Peru Algeria Libya

Angola Ghana Philippines Angola Malaysia

Argentina Greece Poland Argentina Mexico

Australia Guatemala Portugal Bangladesh Morocco

Austria Honduras Qatar Benin Mozambique

Bahrain Hong Kong Romania Bolivia Namibia

Bangladesh Hungary Russia Botswana Nicaragua

Barbados India Saudi Arabia Brazil Nigeria

Belgium Indonesia Senegal Bulgaria Pakistan

Benin Iran Singapore Cameroon Papua New Guinea

Bolivia Ireland South Africa Chile Peru

Botswana Israel Spain Colombia Philippines

Brazil Italy Sri Lanka Congo Romania

Bulgaria Jamaica Sudan Congo, D. R. Russia

Cameroon Japan Sweden Costa Rica Senegal

Canada Jordan Switzerland Cote d`Ivoire South Africa

Chile Kenya Syria Cuba Sri Lanka

Colombia Korea, South Tanzania Dominican, R. Sudan

Congo Kuwait Thailand Ecuador Syria

Congo, D. R. Libya Togo Egypt Tanzania

Costa Rica Malaysia Trinidad and Tobago El Salvador Thailand

Cote d`Ivoire Mexico Tunisia Ethiopia Togo

Cuba Morocco Turkey Gabon Tunisia

Cyprus Mozambique United Arab Emirates Ghana Turkey

Denmark Namibia United Kingdom Guatemala Uruguay

Dominican, R. Netherlands United States of America Honduras Venezuela

Ecuador New Zealand Uruguay India Vietnam

Egypt Nicaragua Venezuela Indonesia Yemen

El Salvador Nigeria Vietnam Iran Zambia

Ethiopia Norway Yemen Jamaica Zimbabwe

Finland Oman Zambia Jordan

France Pakistan Zimbabwe Notes: Total sample includes high, medium and low income countries; whereas, developing sample includes medium and low income

countries. According to the ‘World Bank country classification’ based on 2012 GNI per capita, world economies are divided into four

categories, such as (i) low income, US$1,025 or less; (ii) lower middle income, US$1,026 - US$4,035; (iii) upper middle income,

US$4,036 - US$12,475; and (iv) high income, US$12,476 or more.

30

Table A2: Data Appendix

ΔlnTFP : ‘TFP growth rate’ over the sample period 1970-2010 has been calculated using data from the 7.1 version of

the Penn World Table (see http://pwt.econ.upenn.edu/php_site/pwt_index.php).

RR: ‘the ratio of resource rent to GDP’, where three alternative resource rent measures are used, e.g. oil rent (RRO), oil

and gas rent (RROG), and the sum of oil, gas and mineral rents as total rent (RROGM). The data are collected from 2012

World Development Indicators online database (see

http://databank.worldbank.org/ddp/home.do?Step=12&id=4&CNO=2).

QG: Five alternative channels of ‘quality of government’ proposed by La Porta et al. (1999) are used: (a) Channel-1 :

interventionism is proxied by Fraser Institute’s economic freedom of the world summary index (EF) that ranges from 0-

10 where 0 corresponds to ‘less economic freedom’ and 10 to ‘more economic freedom’, compiled from Fraser Institute

database (see http://www.freetheworld.com/). EF has five different components: (i) Government size index (GS) that

ranges from 0-10 where 0 corresponds to ‘large general government consumption’, ‘large transfer sector’, ‘many

government enterprises’, and ‘high marginal tax rates and low income thresholds’, and 10 to ‘small general government

consumption’, ‘small transfer sector’, ‘few government enterprises’, and ‘low marginal tax rates and high income

thresholds’, (ii) Legal structure and security of property rights index (LEG) that ranges from 0-10 where 0 corresponds to

‘no judicial independence’, ‘no trusted legal framework exists’, ‘no protection of intellectual property’, ‘military

interference in rule of law’, and ‘no integrity of the legal system’ and 10 corresponds to ‘high judicial independence’,

‘trusted legal framework exists’, ‘protection of intellectual property’, ‘no military interference in rule of law’, and

‘integrity of the legal system’, (iii) Access to sound money index (SM) that ranges from 0-10 where 0 corresponds to

‘high annual money growth’, ‘high variation in the annual rate of inflation’, ‘high inflation rate’, and ‘restricted foreign

currency bank accounts’ and 10 corresponds to ‘low annual money growth’, ‘low or no variation in the annual rate of

inflation’, ‘low inflation rate’, and ‘foreign currency bank accounts are permissible without restrictions’, (iv) Freedom to

trade internationally index (INT) that ranges from 0-10 where 0 corresponds to ‘increasing tax rate on international

trade’, ‘slow import or export process’, ‘small trade sectors relative to the population and geographic size’, ‘exchange

rate controls are present and a black-market exists’, and ‘restrictions on the freedom of citizens to engage in capital

market exchange with foreigners’ and 10 corresponds to ‘no specific taxes on international trade’, ‘swift import or export

process’, ‘large trade sectors relative to the population and geographic size’, ‘no black-market exchange rate’, and ‘no

restrictions on the freedom of citizens to engage in capital market exchange with foreigners’, and (v) Regulation of

credit, labour and business index (REG) that ranges from 0-10 where 0 corresponds to ‘low percentage of deposits held

in privately owned banks’, ‘high foreign bank license denial rate’, ‘private sector’s share of credit is close to the base-

year-minimum’, ‘deposit and lending rates is fixed by the government and real rates is persistently negative’, ‘high

impact of minimum wage’, ‘widespread use of price controls throughout various sectors of the economy’, and ‘starting a

new business is generally complicated’ and 10 corresponds to ‘high percentage of deposits held in privately owned

banks’, ‘low foreign bank license denial rate’, ‘private sector’s share of credit is close to the base-year-maximum’,

‘interest rates is determined primarily by market forces and the real rates is positive’, ‘low impact of minimum wage’,

‘no price controls or marketing boards’, and ‘starting a new business is generally easy’; (b) Channel-2: public sector

efficiency is proxied by International Country Risk Guide’s (ICRG) corruption index (COR) that ranges from 0 to 6

where 0 corresponds to ‘more corruption’ and 6 to ‘less corruption’, compiled from the political risk service (PRS) group

(see http://www.prsgroup.com/icrg.aspx). Although it considers financial corruption in the form of demands for special

payments and bribes connected with import and export licenses, exchange controls, tax assessments, police protection, or

loans, this corruption index is more concerned with actual or potential corruption in the form of excessive patronage,

nepotism, job reservations, 'favor-for-favors', secret party funding, and suspiciously close ties between politics and

business, (c) Channel-3: public good provision is proxied by per capita stock of paved road (PRD) or bitumen-surfaced

roads that exclude stone, gravel, water-bound gravel, oil-bound gravel and earth roads, collected from infrastructure

database of Harvard University (see http://www.hsph.harvard.edu/david-canning/data-sets/); (d) Channel-4: government

size is proxied by the ratio of general government final consumption expenditure to GDP (GCN), collected from 2012

World Development Indicators online database (see

http://databank.worldbank.org/ddp/home.do?Step=12&id=4&CNO=2); (d) Channel-5: political freedom is proxied by

Freedom House’s political rights index (PLR) that ranges from 1 to 7 where 1 corresponds to ‘more political freedom’

and 7 to ‘less political freedom, extracted from Freedom House database (see http://www.freedomhouse.org/).

31

Table A3: Government Size, Resource Rent and TFP Growth

(Channel-1: Government Intervention)

Total Sample (99 countries) Developing Sample (63 countries)

Oil Rent Oil &

Gas Rent

Oil, Gas &

Mineral Rents

Oil Rent Oil &

Gas Rent

Oil, Gas &

Mineral Rents

RRi, t-1 -0.78 -0.86* -1.13

** -0.23 -0.22 -0.48

(-1.45) (-1.81) (-2.02) (-0.44) (-0.43) (-0.70)

GSi, t-1 -0.01 -0.01 -0.01 0.01 0.01 -0.01

(-0.69) (-0.11) (-0.51) (0.24) (0.38) (-0.09)

GSi, t-1×RRi, t-1 0.18* 0.17

* 0.22

** 0.02 0.02 0.07

(1.80) (1.90) (2.09) (0.23) (0.26) (0.60)

Observations 719 718 718 456 455 455

Hansen-test (p-val) 0.77 0.42 0.59 0.92 0.88 0.79

AR(2)-test (p-val) 0.63 0.52 0.50 0.46 0.53 0.53 Notes: ‘GSi,t-1’ indicates 5-year lagged government size index that ranges from 0-10 where 0 and 10 indicate highest and lowest

government expenditure, respectively. See also notes to Table 3.

Table A4: Legal System & Property Rights, Resource Rent and TFP Growth

(Channel-1: Government Intervention)

Total Sample (99 countries) Developing Sample (63 countries)

Oil Rent Oil &

Gas Rent

Oil, Gas &

Mineral Rents

Oil Rent Oil &

Gas Rent

Oil, Gas &

Mineral Rents

RRi, t-1 -1.26* -1.29

* -1.32 -0.92

** -0.89

** -1.25

***

(-1.72) (-1.74) (-1.36) (-2.09) (-2.12) (-2.95)

LEGi, t-1 0.04* 0.05

* 0.05

** -0.01 -0.01 -0.02

(1.89) (1.71) (2.07) (-0.35) (-0.01) (-0.97)

LEGi, t-1×RRi, t-1 0.28**

0.28**

0.30* 0.21

* 0.19

* 0.31

***

(1.99) (2.01) (1.70) (1.85) (1.89) (3.10)

Observations 721 720 720 457 456 456

Hansen-test (p-val) 0.31 0.37 0.33 0.77 0.51 0.73

AR(2)-test (p-val) 0.25 0.23 0.22 0.53 0.58 0.70 Notes: ‘LEGi,t-1’ indicates 5-year lagged legal structure and security of property rights index that ranges from 0-10 where 0 and 10

correspond to lowest and highest legal and security system, respectively. See also notes to Table 3.

Table A5: Access to Sound Money, Resource Rent and TFP Growth

(Channel-1: Government Intervention)

Total Sample (99 countries) Developing Sample (63 countries)

Oil Rent Oil &

Gas Rent

Oil, Gas &

Mineral Rents

Oil Rent Oil &

Gas Rent

Oil, Gas &

Mineral Rents

RRi, t-1 -0.60 -0.51 -1.03 -0.15 -0.05 -0.64

(-0.67) (-0.65) (-1.13) (-0.33) (-0.13) (-1.10)

SMi, t-1 0.01 0.01 -0.01 0.02 0.02 0.01

(0.79) (1.21) (-0.21) (1.46) (1.61) (0.51)

SMi, t-1×RRi, t-1 0.15 0.12 0.19 0.01 0.01 0.09

(1.31) (1.22) (1.65) (0.13) (0.06) (1.00)

Observations 722 721 721 458 457 457

Hansen-test (p-val) 0.40 0.51 0.30 0.61 0.63 0.43

AR(2)-test (p-val) 0.86 0.90 0.75 0.38 0.42 0.44 Notes: ‘SMi,t-1’ indicates 5-year lagged access to sound money index that ranges from 0-10 where 0 and 10 correspond to highest and

lowest financial access, respectively. See also notes to Table 3.

32

Table A6: Freedom to International Trade, Resource Rent and TFP Growth

(Channel-1: Government Intervention)

Total Sample (99 countries) Developing Sample (63 countries)

Oil Rent Oil &

Gas Rent

Oil, Gas &

Mineral Rents

Oil Rent Oil &

Gas Rent

Oil, Gas &

Mineral Rents

RRi, t-1 -1.12* -0.97

* -1.08

* -1.18

* -1.28

* -1.09

*

(-1.76) (-1.82) (-1.71) (-1.84) (-1.99) (-1.81)

INTi, t-1 0.02 0.02 0.02 -0.01 0.01 0.01

(1.38) (1.63) (1.45) (-0.07) (0.58) (0.26)

INTi, t-1×RRi, t-1 0.18* 0.15

* 0.18

* 0.19

* 0.21

* 0.18

*

(1.73) (1.87) (1.86) (1.83) (1.99) (1.83)

Observations 716 715 715 453 452 452

Hansen-test (p-val) 0.78 0.72 0.73 0.84 0.64 0.70

AR(2)-test (p-val) 0.94 0.87 0.81 0.37 0.40 0.44 Notes: ‘INTi,t-1’ indicates 5-year lagged freedom to trade internationally index that ranges from 0-10 where 0 and 10 correspond to

lowest and highest openness, respectively. See also notes to Table 3.

Table A7: Regulation, Resource Rent and TFP Growth

(Channel-1: Government Intervention)

Total Sample (99 countries) Developing Sample (63 countries)

Oil Rent Oil &

Gas Rent

Oil, Gas &

Mineral Rents

Oil Rent Oil &

Gas Rent

Oil, Gas &

Mineral Rents

RRi, t-1 -0.87* -0.79 -1.14

** -0.34 -0.33 -0.32

*

(-1.67) (-1.61) (-2.01) (-1.46) (-1.51) (-1.68)

REGi, t-1 0.04 0.04 0.04 0.03***

0.02***

0.03***

(1.20) (1.07) (1.03) (4.43) (3.56) (3.77)

REGi, t-1× RRi, t-1 0.17* 0.17

* 0.23

** 0.10

** 0.11

** 0.10

**

(1.85) (1.93) (2.23) (2.24) (2.56) (2.48)

Observations 720 719 719 457 456 456

Hansen-test (p-val) 0.41 0.47 0.55 0.54 0.43 0.26

AR(2)-test (p-val) 0.61 0.60 0.54 0.65 0.74 0.39 Notes: ‘REGi,t-1’ indicates 5-year lagged regulation of credit, labor and business index that ranges from 0-10 where 0 and 10

correspond to highest and lowest regulation, respectively. See also notes to Table

33

Table A8: Government Performance, Resource Rent and TFP Growth (Using Control Variables)

Total Sample (99 countries) Developing Sample (63 countries)

Ch1 Ch2 Ch3 Ch4 Ch5 Ch1 Ch2 Ch3 Ch4 Ch5

Total Rent i,t-1

(Rent/GDP)

-1.40* -0.15* -0.23** -3.15*** -1.24** -1.61** -0.85* -0.37*** -3.40** -1.17**

(-1.90) (-1.80) (-2.27) (-3.79) (-2.45) (-2.18) (-1.83) (-2.97) (-2.18) (-2.12)

Ch1 i,t-1 (EF

Summary Index)

0.08*** 0.02

(2.83) (0.68)

Ch2 i,t-1 (Corruption

Index)

0.01 0.05*

(0.62) (1.68)

Ch3 i,t-1 (ln (Paved

Road/Population))

-0.01 -0.03

(-0.74) (-1.01)

Ch4 i,t-1 (ln (Govt.

Consum. /GDP) )

-0.23* -0.20*

(-1.83) (-1.74)

Ch5 i,t-1 (Political

Rights Index)

-0.03* -0.06**

(-1.78) (-2.19)

Ch1 i,t-1

×Total Rent i,t-1

0.27** 0.28**

(2.41) (2.27)

Ch2 i,t-1

×Total Rent i,t-1

0.07** 0.68**

(2.20) (2.13)

Ch3 i,t-1

×Total Rent i,t-1

0.20*** 0.42***

(2.92) (3.16)

Ch4 i,t-1

× Total Rent i,t-1

1.15*** 1.25**

(3.73) (2.02)

Ch5 i,t-1

×Total Rent i,t-1

0.29*** 0.25**

(2.91) (2.37)

Observations 706 707 707 703 706 446 451 451 447 450

Hansen-test (p-val) 0.75 0.89 0.18 0.14 0.18 0.54 0.30 0.79 0.27 0.66

AR(2)-test (p-val) 0.44 0.38 0.90 0.51 0.53 0.71 0.25 0.46 0.90 0.68 Notes: ‘Ch’ indicates channel. Control variables (5-year lagged investment ratio, openness, urbanization ratio and landlockedness dummy) are included

but not reported to conserve space. The number of instruments used in the system GMM estimation is 21. Estimated results are robust while using ‘oil’

and ‘oil & gas’ rents as alternative proxies for ‘resource rent’. See also notes to Table .

Table A9: Government Performance, Resource Rent and TFP Growth (Using External Instruments)

Total Sample (99 countries) Developing Sample (63 countries)

Ch1 Ch2 Ch3 Ch4 Ch5 Ch1 Ch2 Ch3 Ch4 Ch5

Total Rent i,t-1

(Rent/GDP)

-1.83* -0.21** -0.37*** -2.17*** -0.95** -0.91** -0.67** -0.27*** -3.50** -1.03*

(-1.85) (-2.11) (-4.12) (-3.94) (-2.13) (-2.36) (-2.07) (-3.39) (-2.62) (-1.84)

Ch1 i,t-1 (EF

Summary Index)

0.04** 0.02

(2.16) (0.87)

Ch2 i,t-1 (Corruption

Index)

-0.01 0.02

(-0.88) (0.71)

Ch3 i,t-1 (ln (Paved

Road/Population))

0.01 -0.02

(1.05) (-0.92)

Ch4 i,t-1 (ln (Govt.

Consum. /GDP) )

-0.07* -0.08

(-1.67) (-0.96)

Ch5 i,t-1 (Political

Rights Index)

-0.01 -0.05**

(-1.09) (-2.50)

Ch1 i,t-1

×Total Rent i,t-1

0.31** 0.16**

(2.18) (2.29)

Ch2 i,t-1

×Total Rent i,t-1

0.07** 0.54**

(2.10) (2.45)

Ch3 i,t-1

×Total Rent i,t-1

0.23*** 0.31**

(3.80) (2.60)

Ch4 i,t-1

× Total Rent i,t-1

0.75*** 1.24**

(3.97) (2.33)

Ch5 i,t-1

×Total Rent i,t-1

0.16** 0.24**

(2.04) (2.29)

Observations 720 721 721 708 720 456 457 457 449 456

Hansen-test (p-val) 0.37 0.21 0.33 0.12 0.37 0.13 0.40 0.75 0.26 0.71

AR(2)-test (p-val) 0.41 0.43 0.90 0.75 0.55 0.55 26 0.46 0.74 0.61 Notes: ‘Ch’ indicates channel. In addition to ‘internal instruments’ (lagged value of explanatory variables), the following ‘external instruments’ are used in system GMM estimations: legal origin, latitude, ethno-linguistic fractionalization and religion. The number of instruments used in the system

GMM estimation is 23. Estimated results are robust while using ‘oil’ and ‘oil & gas’ rents as alternative proxies for ‘resource rent’. See also notes to

Table 3.

34

Table A10: Government Performance, Resource Rent and TFP Growth (Using per capita resource rents as

explanatory variables)

Total Sample (99 countries) Developing Sample (63 countries)

Ch1 Ch2 Ch3 Ch4 Ch5 Ch1 Ch2 Ch3 Ch4 Ch5

Total Rent Per

Capita i,t-1

-1.32* -0.29 -0.60* -1.23** -0.38* -1.90* -2.23* -0.61*** -3.39* -1.99*

(-1.80) (-1.47) (-1.84) (-2.03) (-1.90) (-1.89) (-1.66) (-3.04) (-1.89) (-1.85)

Ch1 i,t-1 (EF

Summary Index)

0.03** 0.05***

(2.39) (2.77)

Ch2 i,t-1 (Corruption

Index)

-0.01 -0.01

(-0.23) (-0.12)

Ch3 i,t-1 (ln (Paved

Road/Population))

0.10 -0.03

(1.41) (-0.76)

Ch4 i,t-1 (ln (Govt.

Consum. /GDP) )

-0.13 -0.20

(-1.10) (-1.64)

Ch5 i,t-1 (Political

Rights Index)

-0.05** -0.07**

(-2.11) (-2.18)

Ch1 i,t-1 × Total Rent

Per Capita i,t-1

0.20* 0.32**

(1.89) (2.19)

Ch2 i,t-1× Total Rent

Per Capita i,t-1

0.15** 1.51*

(2.15) (1.93)

Ch3 i,t-1 × Total Rent

Per Capita i,t-1

0.16* 0.22**

(1.78) (2.19)

Ch4 i,t-1 × Total Rent

Per Capita i,t-1

1.39*

0.41** (1.72)

Ch5 i,t-1 × Total Rent

Per Capita i,t-1

(2.12) 0.09** 0.34*

(2.37) (1.95)

Observations 720 721 721 708 720 456 457 457 449 456

Hansen-test (p-val) 0.18 0.32 0.20 0.55 0.15 0.17 0.82 0.32 025 0.45

AR(2)-test (p-val) 0.49 0.82 0.27 0.70 0.87 0.51 0.19 0.45 0.86 0.92 Notes: The number of instruments used in the system GMM estimation is 16. Estimated results are robust while using per capita ‘oil’ and ‘oil & gas’

rents as alternative proxies for ‘per capita resource rent’. See also notes to Table .

Table A11: Government Performance, Resource Rent and Growth (Using output per labor growth as dependent

variable)

Total Sample (99 countries) Developing Sample (63 countries)

Ch1 Ch2 Ch3 Ch4 Ch5 Ch1 Ch2 Ch3 Ch4 Ch5

Total Rent i,t-1

(Rent/GDP)

-1.88** -0.28 -0.18 -3.47** -2.22*** -1.38* -0.49 -0.24* -4.48*** -0.93*

(-1.99) (-1.12) (-0.69) (-2.58) (-2.81) (-1.85) (-1.14) (-1.86) (-2.85) (-1.96)

Ch1 i,t-1 (EF

Summary Index)

0.06** 0.05**

(2.13) (2.21)

Ch2 i,t-1 (Corruption

Index)

-0.05 0.07

(-1.02) (1.46)

Ch3 i,t-1 (ln (Paved

Road/Population))

0.03 0.01

(1.37) (0.24)

Ch4 i,t-1 (ln (Govt.

Consum. /GDP) )

-0.34*** -0.29**

(-2.63) (-2.50)

Ch5 i,t-1 (Political

Rights Index)

-0.08*** -0.06**

(-3.39) (-2.03)

Ch1 i,t-1

×Total Rent i,t-1

0.38** 0.26*

(2.60) (1.97)

Ch2 i,t-1

×Total Rent i,t-1

0.15* 0.50*

(1.67) (1.70)

Ch3 i,t-1

×Total Rent i,t-1

0.18** 0.23*

(2.12) (1.82)

Ch4 i,t-1

× Total Rent i,t-1

1.35*** 1.69***

(2.86) (2.71)

Ch5 i,t-1

×Total Rent i,t-1

0.52*** 0.22**

(3.28) (2.05)

Observations 724 725 725 712 724 460 461 461 453 460

Hansen-test (p-val) 0.80 0.64 0.45 0.47 0.58 0.69 0.39 0.78 012 0.37

AR(2)-test (p-val) 0.33 0.28 0.84 0.40 0.61 0.55 0.23 0.55 0.85 0.43 Notes: Dependent variable is the growth rate of output per labor. The number of instruments used in the system GMM estimation is 16. Estimated results are robust while using ‘oil’ and ‘oil & gas’ rents as alternative proxies for ‘resource rent’. See also notes to Table 3.