Appendix 6: Data

Sample (1975 - 2000)

Variables Obs Mean Std. Dev.

Power Generation (kWh per capita in log) 1363 4.60 1.53 Phone Lines (per 1000 people in log) 1326 2.46 1.56

Literacy Rate in % 1542 59.79 23.00

Immunization DPT in % 1284 62.72 26.37

Immunization MSL in % 1257 63.38 25.10

Polity Score 1704 -2.75 6.16

Resource Rent in % GDP 1565 11.04 16.11

Sub-Sample: Rulers with Finite Term

Variables Obs Mean Std. Dev.

Power Generation (kWh per capita in log) 843 4.65 1.35 Phone Lines (per 1000 people in log) 829 2.59 1.49

Literacy Rate in % 985 64.57 22.25

Immunization DPT in % 885 65.68 23.42

Immunization MSL in % 869 66.53 22.55

Polity Score 1105 -0.88 6.31

Resource Rent in % GDP 1041 7.56 10.53

Sub-Sample: Rulers without Finite Term

Variables Obs Mean Std. Dev.

Power Generation (kWh per capita in log) 520 4.51 1.78 Phone Lines (per 1000 people in log) 497 2.26 1.65

Literacy Rate in % 557 51.34 21.85

Immunization DPT in % 399 56.15 30.98

Immunization MSL in % 388 56.32 28.82

Polity Score 599 -6.19 4.02

Resource Rent in % GDP 524 17.96 21.99

Table 5.4: Descriptive Statistics

The definitions and sources of the used control variables are the following:

• The variable “Open economy” captures the fraction of years of open trade since 1950.

An economy is classified as “open” (as defined by Sachs and Warner [1997]) if (1) average tariffs ≤ 40%, (2) non-tariff barriers cover less than 40% of total trade, (3) black market premiums are less than 20%, (4) the country is not a socialist country, and (5) the government does not monopolize any major exports.

• The variable “Area” captures the log of the total land area computed with the unit of square kilometers. It is taken from the World Bank [2006c].

• The variable “Population” measures the log of the total population as provided in World Bank [2006c].

120 5.6. APPENDIX

• The variable “Governance” is an Index of Government anti-diversion policies. The index includes the risk of expropriation, contract enforcement, government corruption, law & order and bureaucratic quality. This variable is taken from Hall and Jones [1999].

• The variable “British Colony” is a dummy variable, capturing whether a country is a former British colony. It is taken from Fearon and Laitin [2003].

• The variable “French Colony” is a dummy variable, capturing whether a country is a former French colony. It is taken from Fearon and Laitin [2003].

• The variable “Urban Population” measures the percentage of urban population in the total population as reported by World Bank [2006c].

CHAPTER 6

The Missing Input: Accounting with and for Natural Capital

6.1 Introduction

The so-called natural resource curse has been shown most famously by Sachs and Warner [1997]. Ever since then the paradox that on average resource wealth hurts countries and in particular results in poor economic performance has received a lot of attention. First perceived as a puzzle, it has today become common knowledge not only among academic scholars, but also of institutions such as the International Monetary Fund or the World Bank that have started to adjust their policies accordingly.

So far the literature on the phenomenon experiences several serious shortcomings though.

For one the measures of resource wealth employed are far from perfect. They suffer from endogeneity problems that bias regression results and lead subsequent interpretations in the wrong direction. Also, most measures arguably assess resource dependence rather than re-source abundance. In the present chapter we address these issues and look at both, the effect of resource dependence (properly instrumented) as well as of resource abundance (using newly available data that offers us a more satisfactory proxy). In this novel setting, we are able to change the perspective on the resource curse.

There is a large body of literature that finds resources to be a curse rather than a blessing for many countries. According to this literature, countries rich in natural resources tend to fare worse in many respects compared to their resource poor counterparts. They often have low or even negative growth rates (i.e. also low income levels) and all kinds of important developmental indicators, such as life expectancy, child mortality or access to water seem to be negatively affected by the presence of resources (see for example Karl [1997], Sachs and

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122 6.1. INTRODUCTION

Warner [1997], Auty [2001b] , Bulte et al. [2005]).

All of the above-mentioned works use a similar measure of resource wealth, usually pri-mary good exports as a fraction of total exports or GDP. This variable - being a measure of resource dependence rather than abundance - suffers from endogeneity problems. For exam-ple in an income regression it is unclear whether resource dependence induces low income, or whether a country is resource dependent because it has low income and the manufacturing sector is underdeveloped. In the present chapter we remedy this problem by properly instru-menting for resource dependence.

Most recently Collier and Goderis (2007) introduced a new dimension into the resource curse literature, explicitly distinguishing between short run and long run effects of commodity price booms, and finding a positive short term, but a negative long term effect of resources.

They, like us, acknowledge potential endogeneity problems with the resource measure. Within their panel cointegration methodology, they claim however that endogeneity is not substantial, while our chapter takes the view that potential endogeneity might be a core issue and proposes to address it explicitly by an IV approach.

While speaking about a resource“curse” people loosely think about the effect of resources on a country’s development and performance. We argue that one has to be careful to distin-guish the terms resource abundance and resource dependence. If one is interested in the effect of resources on a country’s development, that is if one wants to find out whether there is a

“curse of natural resources”, one should look at the effect of resource abundance. The relevant measure for resource abundance is a measure of resource stocks. Most of the resource curse literature has - by employing the above-mentioned measure - looked at the effects of resource dependence. In the present chapter we look at the effect of resource stocks, thus properly accounting for resource wealth of a country.

We complement prior work that has acknowledged the difference and tried to distinguish between the effects of resource abundance and resource dependence (Ding and Field [2004], Brunnschweiler and Bulte [2008a]). Ding and Field include a resource abundance as well as a resource dependence variable in their growth regressions. In addition they also instrument for the resource dependence measure. As an instrument they choose rule of law, a variable we cannot find convincing in because it is not clear that this variable fulfills the requirement of exogeneity.

Brunnschweiler and Bulte also look at the effect of abundance as well as dependence on economic growth. They concentrate on the effect of mineral (subsoil) resources whereas we consider a wider range of resources and allow for the fact that different kinds may have different effects. Also, due to limited data availability, Brunnschweiler and Bulte have used

CHAPTER 6. THE MISSING INPUT: ACCOUNTING WITH AND FOR NATURAL

CAPITAL 123

samples of very few observations for their regressions. In the present chapter we improve upon this point.

In addition to employing more recent and improved data than the above cited works, we are among the first ones to consider the effect of natural resource abundance on per capita income levels (arguably a superior measure of welfare as compared to economic growth).

To our knowledge this issue has only been looked at by Brunnschweiler and Bulte [2008b].

They find, albeit in a different context, results comparable to ours. Furthermore we take the analysis to a different level and investigate the relationship between resource abundance and the accumulation of important input factors such as human and physical capital as well as productivity.

Moreover, the present chapter contributes to the literature on Development Accounting.

This technique is concerned with investigating the roots of income differences across coun-tries. Output is split up into its components, recognizing that it essentially stems from different inputs such as human capital and physical capital. The part of output that cannot be explained by these input factors is assumed to be due to productivity, the famousA in a production func-tion. As a consequence cross-country differences in output can be traced back to differences in the amount of inputs. Very often it turns out that the difference in inputs cannot explain the large differences in output. Thus, the residual A is assigned an important role in explain-ing cross-country income differences. The - in this way backed out - productivity levels vary greatly across countries. Traditionally the inputs considered are physical and human capital (labor). One important input factor - natural resources (especially important in poorer coun-tries) - has not been taken into account so far (Caselli and Feyrer [2007]). The simple reason for this shortcoming is that, until very recently, the data was not available. We make use of the newly offered data and investigate how estimated productivity levels change.