metadata: authors: 0: author: Scognamillo,Antonio
1: author: Mele,Gianluca
2: author: Sensini,Luca
count: World
docna: 0: docna: Nonrenewable resources, income inequality
            and per capita GDP : an empirical analysis
docty: Policy Research Working Paper
subtopic: Economic Theory & Research,Industrial Economics,Educational Sciences,Poverty Reduction Strategies,Coastal and Marine Resources,Economic Growth,Energy and Natural Resources,Global Environment
teratopic: Macroeconomics and Economic Growth,Environment,Rural Development,Industry
abstracts: cdata!: This analysis examines the relationship
            between nonrenewable resource dependence, economic growth
            and income inequality. It uses a two-equation system in
            which the Gini index and GDP per capita are the dependent
            variables and the stock of nonrenewable resources as a share
            of national wealth -- i.e. resource dependence -- is the
            independent variable. Using a dataset that includes
            information on 43 countries from 1980 to 2012, this paper
            estimates several model specifications in order to check the
            robustness of the results under different assumptions and to
            account for income-group-related heterogeneity among
            countries. The baseline model provides strong evidence that
            natural resource dependence is negatively correlated with
            both per capita GDP and the Gini index; in other words,
            resource dependence is associated with lower income levels,
            but also with a more equal distribution of income.
            Interestingly, however, after controlling for country income
            group, the sign and magnitude of these relationships appear
            to become dependent on national-level structural
            characteristics. Among higher-income countries, greater
            nonrenewable natural resource dependence is associated with
            lower income inequality, while there is no statistically
            significant correlation with GDP per capita. Among the
            lower-income group, greater dependence on nonrenewable
            natural resources is associated with both higher levels of
            income inequality and lower per capita GDP. Further analysis
            focusing on a subsample of non-renewable resource rich
            countries confirms these findings.
repnme: repnme: Nonrenewable resources, income inequality
            and per capita GDP : an empirical analysis
display_title: Nonrenewable resources, income
            inequality and per capita GDP : an empirical analysis
listing_relative_url: /research/2016/09/26805462/nonrenewable-resources-income-inequality-capita-gdp-empirical-analysis
topicv3: Economic Growth m1326241 471,Natural Resources m1326446 1979,Education m1327048 470,Environment, Natural Resources and Blue
            Economy m1327391 566,Income Inequality m1503891 2668
publishtoextweb_dt: 2016-09-22T00:00:00Z
historic_topic: Macroeconomics and Economic Growth,Environment,Rural Development,Industry
no_of_pages: 21
author_keys: 0: author_key: 1141463
model_extractions: WPS7831 Policy Research Working Paper 7831 Nonrenewable resources, Income Inequality and per Capita GDP An Empirical Analysis _Antonio Scognamillo Gianluca Mele Luca Sensini_ Macroeconomics and Fiscal Management Global Practice Group September 2016
Policy Research Working Paper 7831

# **Abstract**

This analysis examines the relationship between nonrenewable resource dependence, economic growth and income inequality. It uses a two-equation system in which the Gini index and GDP per capita are the dependent variables and the stock of nonrenewable resources as a share of national wealth—i.e. resource dependence—is the independent variable. Using a dataset that includes information on 43 countries from 1980 to 2012, this paper estimates several model specifications in order to check the robustness of the results under different assumptions and to account for income-group-related heterogeneity among countries. The baseline model provides strong evidence that natural resource dependence is negatively correlated with both per capita GDP and the Gini index; in other words, resource dependence is associated with lower income levels, but also with a more equal distribution of income. Interestingly, however, after controlling for country income group, the sign and magnitude of these relationships appear to become dependent on national-level structural characteristics. Among higher-income countries, greater nonrenewable natural resource dependence is associated with lower income inequality, while there is no statistically significant correlation with GDP per capita. Among the lower-income group, greater dependence on nonrenewable natural resources is associated with both higher levels of income inequality and lower per capita GDP. Further analysis focusing on a subsample of nonrenewable resource rich countries confirms these findings.

This paper is a product of the Macroeconomics and Fiscal Management Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at gmele@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Nonrenewable resources, Income Inequality and per Capita GDP: An Empirical Analysis**

_Antonio Scognamillo_<sup>_1_</sup> _; Gianluca Mele_<sup>_2_</sup> _; Luca Sensini_<sup>_3_</sup> Keywords: Natural Capital, Economic Growth, Income Inequality JEL codes: C01, C13, C3, E01, D31, D63, O11, O43, Q32

> 1 Food and Agriculture Organization of the United Nations (FAO), antonio.scognamillo@fao.org

> 2 The World Bank; <u>gmele@worldbank.org</u>

> 3 University of Salerno; lsensini@unisa.it
Nonrenewable resources, Income Inequality and per Capita GDP: An Empirical Analysis

# 1. Introduction and Literature Review

A large body of literature has examined the relationship between natural resources, economic growth, and income inequality, revealing a set of complex and often ambiguous associations. The relationship between natural resources and economic growth is especially controversial. On the one hand, general economic theory suggests that a booming natural resource sector will boost economic growth, and that it will facilitate— though not necessarily cause—improvements in poverty and shared-prosperity indicators. Ideally, rising natural resource output would increase public revenues, enabling greater public investment in physical and human capital, while the private returns to resource production would encourage greater private investment (both domestic and external), as well as higher rates of household savings. On the other hand, the substantial literature on the so-called “Dutch disease” finds that a resources boom can divert resources away from the non-resource tradable sectors (especially manufacturing), distort the growth of nontradables (especially services), and put upward pressure on the exchange rate. These effects erode the competitiveness of exports and give imports an advantage over domestic production, undermining long-term growth (Sachs and Warner 1999).

The World Bank (2012) analyzed the impact of natural capital<sup>4</sup> on income by examining the adjusted net savings of resource-rich countries. It found that the sustainable management of natural resources has a positive and significant correlation with economic resilience and welfare gains. The World Bank analysis underscored that the elements of natural capital cannot be managed in isolation, but require an integrated approach to “regulating” and “provisioning” natural resource exploitation in order to preserve the underlying stock of natural capital.

Other studies have focused on the depletion of natural resources. Dasgupta (2010) concludes that GDP per capita and the Human Development Index (HDI) should not be regarded as comprehensive indicators of economic and social welfare, as they do not reflect the depletion of the natural capital stock, and that more sophisticated forms of wealth accounting that include produced, human and natural capital are more appropriate measures of inclusive and sustainable development. The World Bank has long been a major advocate for integrating the sustainable management of natural capital into growth strategies. Cleveland et al. (1996) analyzed the relationship between natural capital, ecosystem services and economic growth, highlighting that economic growth models tend not to account for either the degradation of critical, non-replaceable ecosystem services or the depletion of natural capital. The study concludes that these elements should be taken into account in future analyses of the link between natural capital and growth.

In recent years increased attention to the issue of income inequality has added a new dimension to the debate over the role of the depletion of natural capital in economic growth and development. Barbier (2014) argues that understanding the depletion of natural capital is crucial to assessing both the extent to which natural capital will

> 4 Natural capital includes both nonrenewable resources such as oil and minerals and renewable resources such as arable land, forests and fisheries, as well as environmental quality.

2
Inequality, Economic Growth and Nonrenewable Resources contribute to future economic growth and the pace at which inequality has increased over time. According to Barbier (2015), environmental degradation and the widening gap between the world’s rich and poor are symptomatic of gradually intensifying structural imbalances in how natural capital is used to create economic wealth and how that wealth is distributed. On the one hand, natural capital is undervalued and thus overexploited, while on the other hand the human capital stock is insufficient to meet demand, which drives rising income inequality. This structural imbalance between natural and human capital creates obstacles to innovation, growth and prosperity.

Gill et al. (2014) focus on the use of natural resources in Eurasia, demonstrating that the region has benefited from its natural wealth through rising income levels and improvements in living standards. Inequality, however, has also increased over time and across countries, yet it remains lower than in most parts of the developing world, especially Latin America. Alessandrini and Buccellato (2009) find a significant positive relationship between income inequality and economic dependence on natural resources. This link is especially strong for mining commodities and exports that are not used in domestic value chains. These conditions are associated with a dramatic increase in household-level inequality, since only a few households typically control resource revenues.

Fum and Hodler (2010) focus on the political-economy aspects of resource management and income inequality. They present empirical evidence that ethnic polarization increases the probability that a large stock of natural resources will correlate with inequality. Bourguignon and Morrisson (1990) find that mineral resource endowments, rather than other variables such as GDP, are an important determinant of the income distribution. Taking a microeconomic perspective, Loayza, Teran and Rigolini (2013) find strong evidence of spatially dependent inequality outcomes. Households located in physical proximity to mining districts in Peru tend to experience the largest gains in living standards, consumptions and literacy, while consumption inequality increases in all districts of a resource-producing province.

Although many empirical studies have investigated the link between natural capital, growth and inequality, very limited research is available on how all three factors interact. Moreover, much of the existing literature on the subject fails to adequately account for the role of wealth stocks. A thorough review of the literature would seem to indicate that the quantitative relationship between the exploitation of natural resources, economic growth and income inequality has been studied only by Gylfason and Zoega (2002) and Alessandrini and Buccellato (2009).

The following analysis tests the existence of a relationship between the dependence on nonrenewable natural resources (hereafter dependence) and per capita income and income inequality. The analysis is conducted in the spirit of Sachs and Warner (1995; 2001) and uses panel data for 43 countries for which data is available from 1980 to 2012 to examine, under different specifications, a system of two equations in which the dependent variables are per capita GDP and the Gini index and the independent variable is the dependence on nonrenewable natural resources. While Gylfason and Zoega (2002) and Alessandrini and Buccellato (2009) use a recursive model and focus on crosssectional data, this analysis attempts to account for the endogeneity problem generated 3
Nonrenewable resources, Income Inequality and per Capita GDP: An Empirical Analysis by feedback effects across variables and control for the presence of unobserved country heterogeneity.

The empirical results from the baseline model provide evidence of a negative association between resource dependence and GDP per capita, which is consistent with the existing literature on the Dutch disease effect. However, the empirical evidence shows that Dutch disease is not consistent or inevitable, as it is influenced by the institutional heterogeneity across countries (Bunte 2011). For example, a given country’s institutional framework and the relative effectiveness of its policy interventions can either mitigate or magnify the effect on income distribution. The intensity of Dutch disease effects depends in large part on whether and how natural resource revenues are used to promote social equity through investment in human capital, public goods and services, and targeted poverty-reduction programs.

Robust public investment and redistributive fiscal policies could more than compensate for the increase in income inequality due to the inter-sectorial wage differential caused from the reallocation of the workers, as the resource sector develops. In order to explore the implications of the national policy framework, an analytical specification was devised to account for the heterogeneity of country income groups. This specification uses a dummy variable to assess the correlation between resource dependence, economic growth and income inequality among both lower-income countries and higher-income countries. The findings show that among higher-income countries a greater degree of resource dependence is associated with an _improvement_ in the income distribution and has no statistically significant correlation with economic growth. Conversely, greater resource dependence among lower-income countries is associated with widening income inequality and lower economic growth.

These findings imply that the relationship between natural resource dependence and economic growth and income inequality hinges on a given country’s income level. Replicating the analysis on a subsample of resource-rich countries confirms the robustness of these findings. The findings are also consistent with previous studies that have argued that the “resource curse” is not caused by natural resource endowments per se, but rather by country-level characteristics such as the quality of natural resource management or the nature of the fiscal policies financed by resource revenues (see, e.g. Gylfason, 2001; Robinson, Torvik, and Verdier 2006).

Following this introductory section, the paper is structured as follows: Section 2 describes the study’s methodology and clarifies the empirical strategy for obtaining consistent parameter estimates under the specific assumptions imposed on the model; Section 3 discusses the dataset constructed for this analysis and provides some descriptive statistics; Section 4 presents the results of the analysis; and Section 5 draws policy implications and conclusions.

4
GINI i¢ = Bo + BiNKDi¢ + BoXie + Qi + Ur it GDP; = B3 + BaNKDit + BsYit +0; + U2 it ai ul and u2 GINI 54 = Bo + BiNKDi~ + BoGDPi¢ + B3Xit + a + Ur it GDPit = By + BSNKDit + BeGINIi¢ + B7Yie + ai + U2 it
Nonrenewable resources, Income Inequality and per Capita GDP: An Empirical Analysis where a consistent estimate of the model relies on a set of instrumental variables (IV) that are correlated with the endogenous variable but are correctly excluded (as exogenous) from the main equation.

Again assuming that the error terms of the two equations are not correlated, an instrumental variable fixed-effect estimator (IVFE) consistently estimates each equation separately while accounting for both the endogeneity issues and the unobservable heterogeneity across countries.<sup>6</sup> Finally, allowing for a correlation between the equation’s error terms using a Three Stage Least Square estimator (3SLS),<sup>7</sup> which estimates the coefficient of the two equations simultaneously, further increases the efficiency of the estimates.

The same estimation procedure is applied to the subsequent model specifications, and for the sake of brevity each equation is not formalized. The second model specification accounts for heterogeneity related to country income group by adding a dummy variable to each equation that identifies lower-income countries. The interaction of this variable with the nonrenewable resource dependence index distinguishes the results for higherincome and lower-income countries. Finally, a third model specification is restricted to a subsample of non-renewable resources rich countries.

# 3. DATA

The dataset comprises 43 countries from five continents and covers the period from 1980 to 2012. It includes all countries for which complete data on nonrenewable resources are available and nonzero. Using the World Bank classification system, the information contained in the dataset can be divided into four groups, as shown in Table 1: 13 highincome countries, 14 upper-middle-income countries, 12 lower-middle-income countries and 4 low-income countries. Figure 1 illustrates the geographic coverage of the dataset. The countries included on our dataset are: Algeria, Argentina, Australia, Bolivia, Botswana, Brazil, Bulgaria, Cameroon, Canada, Chile, China, Colombia, Democratic Republic of Congo, Ecuador, Egypt, France, Gabon, Ghana, Greece, India, Indonesia, Ireland, Italy, Japan, Republic of Korea, Malaysia, Mexico, Mongolia, Morocco, Mozambique, New Zealand, Norway, Peru, Philippines, South Africa, Spain, Thailand, Tunisia, Turkey, United Kingdom, United States, Venezuela and Zimbabwe.

Table 1: Countries in Dataset, Classified by Income Groups

|**Income group Frequency: **|**Absolute**|**Relative**|**Cumulative**|
|---|---|---|---|
|High income: OECD|13|30%|30%|
|Upper middle income|14|33%|63%|
|Lower middle income|12|28%|91%|
|Low income|4|9%|100%|
|Total|43|100%||

> 6 The exogeneity of the instruments was successfully verified by a Sargan/Hansen-J test.

> 7 The simultaneous equation models include country dummies in order to control for unobserved country heterogeneity.

6
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Nonrenewable resources, Income Inequality and per Capita GDP: An Empirical Analysis analysis focuses on resource dependence rather than resource abundance, as the former is more immediately relevant to economic growth and income inequality.

Table 2: Average Nonrenewable Resource Dependence and Abundance by Income Group (1980-2012)

|**D**|**ependence**|**Log_Abundance**|
|---|---|---|
|High income|2%|7.8|
|Upper middle income|15%|8.6|
|Lower middle income|8%|7.3|
|Low income|11%|6.6|

The data on income inequality is sourced from the World Bank’s “All-the-Ginis Database,” which was last updated in 2014.<sup>8</sup> The database collects Gini indexes from multiple sources into long time series. The data has been standardized for this analysis via the so-called “choice-by-precedence approach,” which reflects each dataset’s reliability, degree of variable standardization, and consistency of geographical coverage. GDP and population figures have been collected from the United Nation’s UNCTAD-STAT database. Table 2 presents the descriptive statistics for per capita GDP and the Gini index by country income group. The Gini index peaks among the upper-middle-income group, falls among the lower-middle-income and low-income groups and is lowest among the high-income group. These data are consistent with the relationship between inequality and GDP described by Kuznets (1955).

Table 3: GDP per capita and Gini index for Countries in Dataset, classified by Income Group (1980-2012)

|**GDP per capita**<br>**(US$ thous)**|**Gini index**|
|---|---|
|High income<br>34.5|33.9|
|Upper middle income<br>8.4|49.1|
|Lower middle income<br>2.8|40.5|
|Lowincome<br>1.4|42.7|

The data for control variables was collected from different sources. Data on the structural and cyclical characteristics of national economies comes from the World Bank’s World Development Indicator Database<sup>9</sup> and UNCTAD-STAT;<sup>10</sup> the figures on education are sourced from Barro & Lee (2013);<sup>11</sup> those on the real effective exchange rate (REER) are taken from Darvas (2012);<sup>12</sup> and metal- and oil-price figures are sourced from the IMF’s Primary Commodity Prices database.<sup>13</sup> Referring to the original datasets will provide further details on the methodology and sources used. Table 4 shows income-level-related heterogeneity across countries for the specified variables.

> 8 Available at <u>http://econ.worldbank.org/projects/inequality</u>

> 9 Available at <u>http://data.worldbank.org</u>

> 10 Available at <u>http://unctadstat.unctad.org</u>

> 11 Available at <u>http://www.barrolee.com</u>

> 12 Available at <u>http://bruegel.org</u>

> 13 Available at <u>http://www.imf.org</u>

8
Inequality, Economic Growth and Nonrenewable Resources Table 4. Indicators of Heterogeneity across Countries in the Dataset, for Control Variables

||Panel A:<br>Education<br>ratio<sup>14</sup>|Variable inclu<br>CPI|ded in the GINI equation<br>Services value<br>added (% of<br>GDP at market<br>prices)|<br>Manufacturing<br>value added<br>(% GDP at m.<br>prices)|Agriculture<br>value added<br>(% of GDP at<br>m. prices)|
|---|---|---|---|---|---|
|High income|4.6|80.9|66.3|17.6|3.9|
|Upper middle income|0.9|65.3|52.3|17.1|7.9|
|Lower middle income|0.7|75.7|48.5|18.6|18.1|
|Low income|0.4|63.7|45.4|14.6|29.4|
|Average|1.9|73.2|54.8|17.4|11.5|
||Panel B:<br>(log)Labor<br>Force|Variables inclu<br>(log)Gross<br>fixed<br>capital<br>formation|ded in the GDP equatio<br>(log)Export<br>(log)RE|n<br>ER<br>World oil<br>price index|<br>World<br>metal price<br>index|
|High income|9.5|11.7|11.8<br>4.5|<br>73.0|91.9|
|Upper middle income|9.0|9.7|9.9<br>4.6|<br>73.0|91.9|
|Lower middle income|9.8|9.4|9.4<br>4.7|<br>73.0|91.9|
|Low income|9.0|6.7|7.4<br>4.8|<br>73.0|91.9|
|Average|9.4|9.9|10.1<br>4.6|73.0|91.9|

Finally, to introduce the empirical analysis Table 5 summarizes the pairwise correlations between the most relevant variables. As shown in the table, resource dependence is positively associated with the Gini index and negatively associated with GDP per capita at a significance level greater than 1 percent. In other words, the simple correlations show that the more dependent a country is on nonrenewable resources, the higher its Gini coefficient and the lower its GDP per capita.

Table 5: Correlation Matrix for the Main Variables

||Resource<br>Dependence|Gini index|Log GDP|
|---|---|---|---|
|Resource Dependence|1|||
|Gini index|0.242***|1||
|GDPpercapita|-0.142***|-0.350***|1|

Note: * ** and *** denote significance at the 10% 5% and 1% levels, respectively.

14 Education ratio is the ratio between the share of the population with secondary and tertiary education and the share of population with primary or no education.

9
Nonrenewable resources, Income Inequality and per Capita GDP: An Empirical Analysis

# 4. EMPIRICAL ANALYSIS

This section summarizes the results of the analytical methodology described above. The specifications and the estimation techniques have been systematically adapted according to the different assumptions characterizing each model. The baseline model estimates the average empirical associations for all the countries included in the dataset, and is then adjusted to assess heterogeneity between country income groups. Finally, the model was tested on a sub-sample of resource-rich countries<sup>15</sup> , with a view to test the robustness of the empirical association.

## 4.1 The Baseline Model: Pooling All Countries

Table 6 presents the results from the recursive baseline model. The two equations have been estimated separately using a fixed-effect estimator and simultaneously using SURE plus country-level dummy variables. We find that, on average, nonrenewable resource dependence is negatively associated with both income inequality and GDP per capita. In the simultaneous estimation both coefficients increased because of the correlation between the error terms of the two equations.

Table 6: Estimated Results from the Recursive Model Specification (baseline model)

||**Fixed**<br>|**Effect**<br>|**SU**<br>|**RE**<br>|
|---|---|---|---|---|
||**Gini index**|**GDPc **|**Gini index**|**GDPc**|
|Non Renewable resource dependence|-0.059**|-0.087**|-0.149***|-0.132**|
|High-low education ratio|-0.639***|-|-0.203|-|
|High-low education ratio square|0.021***|-|0.010**|-|
|Consumer price index (2005)|-0.001|-|0.002|-|
|Services value added share|0.078*|-|0.071|-|
|Manufacturing value added share|-0.047|-|0.069|-|
|Agriculture value added share|-0.091*|-|-0.112**|-|
|Log labor force|-|-7.916***|-|-17.991***|
|Log gross fixed capital formation|-|3.802***|-|7.472***|
|Log exports of goods and services|-|2.422**|-|3.422***|
|Log REER|-|5.082***|-|1.353|
|World oil price index|-|0.067***|-|0.067***|
|WorldMetalpriceindex|-|0.013|-|0.006|
|Observations|1015|1034|873|873|
|R2|0.058|0.383|0.910|0.858|

Note: * ** and *** denote significance at the 10% 5% and 1% levels, respectively.

Table 7 shows the results of the non-recursive baseline model specification estimation— that is, the results obtained once the endogeneity related to the feedback effect of including the Gini in the GDP equation, and vice versa, has been accounted for. The estimation results confirm the previous findings from the recursive model, though the

> 15 Based on an IMF definition of ‘resource‐rich’ countries.

10
Inequality, Economic Growth and Nonrenewable Resources magnitude of the coefficient generally increases after the feedback effect has been taken into account.

Table 7: Estimated Results from the Non-Recursive Model Specification (baseline model)

||**IV Fixe**<br>|**d Effect**<br>|**3S**<br>|**LS**<br>|
|---|---|---|---|---|
||**Gini index**|**GDPc**|**Gini index**|**GDPc**|
|Non Renewable resource dependence|-0.149***|-0.443***|-0.151***|-0.467***|
|High-low education ratio|-0.082|-|-1.110***|-|
|High-low education ratio square|0.008|-|0.025***|-|
|Consumer price index (2005)|0.005|-|0.027***|-|
|Services value added share|0.072|-|0.035|-|
|Manufacturing value added share|0.055|-|0.145**|-|
|Agriculture value added share|-0.124**|-|-0.105**|-|
|GDP|-0.040|-|-0.005|-|
|Log labor force|-|-11.564**|-|-9.069**|
|Log gross fixed capital formation|-|9.842***|-|7.658***|
|Log exports of goods and services|-|1.344|-|3.123**|
|Log REER|-|0.764|-|1.458|
|World oil price index|-|0.078***|-|0.055***|
|World metal price index|-|-0.015|-|0.003|
|Gini index|-|-1.877***|-|-2.073***|
|Observations|873|873|873|873|
|R2|0.113|0.245|0.901|0.799|

Note: * ** and *** denote significance at the 10% 5% and 1% levels, respectively.

Overall, these results indicate that a positive variation in the dependencies is empirically associated with a negative variation in both GDP per capita and the Gini index. In other words, resource dependence is associated with lower levels of GDP per capita, but also with lower income inequality. These results are consistent with the literature on Dutch disease, which explains them as an effect of the diversion of labor and capital from the industrial sector to the natural resource sector, combined with the negative effect of real exchange-rate appreciation on the tradable sector. However, the model’s findings suggest that, on average, the inter-sectoral shift in labor patterns—which affects the distribution of income through rising wage differentials between sectors—is offset by the investment of additional fiscal revenue in pro-poor policy interventions. The literature highlights that the sign of the relationship between resource dependence and inequality is likely to depend on each country’s institutional framework and the quality of its policy interventions. These factors determine whether—and how—natural resource revenues support rising productivity and consumption among lower-income households. The model’s findings suggest that, on the whole, policies designed to promote shared prosperity more than offset the increase in inequality caused by the inter-sectoral wage differential.

However, pooling such a heterogeneous mix of countries may undermine the reliability of the model’s conclusions. The next section explores the issue of country-level heterogeneity by adding dummy variables to the baseline specification. The objective is to disentangle the influence of specific countries or groups of countries by disaggregating 11
Nonrenewable resources, Income Inequality and per Capita GDP: An Empirical Analysis the dataset based on a likely explanatory variable, which in this case is country income level.

# 4.2 Controlling for Heterogeneity between Income Groups

The model specification presented below attempts to differentiate the impact of resource dependence on higher- and lower-income countries. The model has been adjusted to include a dummy variable equal to 1 for low- and lower-middle-income countries and to 0 for high- and upper-middle-income countries. Within this framework, the coefficient associated with the interaction variable will measure the association between resource dependence, GDP per capita and the Gini index among lower-income countries, while the coefficient associated with the non-interaction variables permits to isolate the average relationship for higher-income countries. Table 8 summarizes the results obtained from the recursive model.

Table 8: Estimated Results from the Recursive Model Specification (income-group heterogeneity)

||**Fixed**<br>|**Effect**<br>|**SU**<br>|**RE**<br>|
|---|---|---|---|---|
||**Gini index**|**GDPc**|**Gini index**|**GDPc**|
|Nonrenewable resource dependence|-0.082***|-0.006|-0.190***|0.124|
|Lower income|dropped|dropped|4.386*|47.628***|
|Lower income* Nonrenewable resource dependence|0.068|-0.370***|0.115**|-0.720***|
|High-low education ratio|-0.622***|-|-0.179|-|
|High-low education ratio square|0.021***|-|0.010**|-|
|Consumer price index (2005)|-0.002|-|0.000|-|
|Services value added share|0.081*|-|0.071|-|
|Manufacturing value added share|-0.043|-|0.068|-|
|Agriculture value added share|-0.087|-|-0.110**|-|
|Log labor force|-|-6.538**|-|-17.058***|
|Log gross fixed capital formation|-|3.433***|-|7.237***|
|Log exports of goods and services|-|2.348**|-|3.519***|
|Log REER|-|5.852***|-|2.742|
|World oil price index|-|0.068***|-|0.066***|
|Worldmetalpriceindex|-|0.016|-|0.010|
|Observations|1015|1034|873|873|
|R2|0.060|0.392|0.911|0.863|

Note: * ** and *** denote significance at the 10% 5% and 1% levels, respectively.

The results show that higher levels of resource dependence are associated with lower levels of income inequality among countries in the higher-income group, with no negative effect on GDP per capita. Conversely, higher levels of resource dependence are associated with greater income inequality and lower GDP per capita in lower-income countries. Again, the estimated results of the non-recursive model, summarized in Table 9, confirm those of the recursive model.

12
Inequality, Economic Growth and Nonrenewable Resources Table 9: Estimated Results from the Non-Recursive Model Specification (income-group heterogeneity)

||**IV Fixe**<br>|**d Effect**<br>|**3S**<br>|**LS**<br>|
|---|---|---|---|---|
||**Gini index**|**GDPc**|**Gini index**|**GDPc**|
|Nonrenewable resource dependence|-0.185***|-0.008|-0.173***|-0.007|
|Lower income|dropped|dropped|5.637**|49.933***|
|Lower income* Nonrenewable resource<br>dependence|0.100**|-0.638***|0.070|-0.640***|
|High-low education ratio|-0.000|-|-0.440*|-|
|High-low education ratio square|0.006|-|0.014**|-|
|Consumer price index (2005)|0.004|-|0.012**|-|
|Services value added share|0.072|-|0.063|-|
|Manufacturing value added share|0.047|-|0.086|-|
|Agriculture value added share|-0.123**|-|-0.123**|-|
|GDPc|-0.046|-|-0.032|-|
|Log Labor Force|-|-15.487***|-|-15.901***|
|Log gross fixed capital formation|-|7.880***|-|7.164***|
|Log exports of goods and services|-|3.043**|-|3.765***|
|Log REER|-|2.477|-|2.754|
|World oil price index|-|0.069***|-|0.064***|
|World metal price index|-|0.004|-|0.009|
|Gini index|-|-0.616|-|-0.616|
|Observations|873|873|873|873|
|R2|0.119|0.445|0.911|0.863|

Note: * ** and *** denote significance at the 10% 5% and 1% levels, respectively.

Accounting for the heterogeneity between country income groups yields a very different picture than that shown in the baseline models. From a methodological perspective, this heterogeneity affirms the importance of considering structural differences between countries at different stages of development. From a macroeconomic perspective, it lends credibility to the idea that the “resource curse” is a function of country-level structural characteristics, such as the management of natural resources rents, the sophistication of public financial management systems and the impartiality of policy processes.

# 4.3 Analyzing a Subsample of Resource-Rich Countries

The model presented below focuses on a subset of countries defined as nonrenewable resource rich according to the IMF’s 2013 Resource Governance Index Report.<sup>16</sup> The full sample included countries in which nonrenewable natural resources played a relatively modest economic role. Excluding these countries can shed light on the possibility that the empirical associations between resource dependence and the Gini index and GDP per capita are affected by the relative economic importance of natural resources. Focusing on a subsample of resource-rich countries also tests the robustness of the estimated relationships by reducing the original sample by one-third<sup>17</sup> . Table 10 summarizes the subsample’s composition by income group of the resulting restricted panel.

> 16 Available at <u>http://www.resourcegovernance.org/resource-governance-index/report</u>

> 17 Based on the IMF classification of resource-rich countries (see note 16), thirteen countries are dropped from our original dataset: Argentina, Bulgaria, France, Greece, Ireland, Italy, Japan, Republic of Korea, New Zealand, Spain, Thailand, Tunisia, and Turkey. The resulting sub-group thus comprises the remaining

> 30 countries.

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Nonrenewable resources, Income Inequality and per Capita GDP: An Empirical Analysis Table 10: Countries included in Resource-rich Sub-group, classified by Income Groups

||**Absolute**|**Relative**|
|---|---|---|
|High income|5|16.7%|
|Upper middle income|11|36.7%|
|Lower middle income|10|33.3%|
|Low income|4|13.3%|
|Total|30|100%|

According to the IMF report, “nations [rich in nonrenewable natural resources] produce 85% of the world’s petroleum, 90% of its diamonds and 80% of its copper, generating trillions of dollars in annual profits.” Table 11 summarizes the empirical results of the recursive model estimated for the subsample of resource-rich countries. These figures confirm the results of the full-sample model. The magnitude of the coefficient, as expected, slightly increases relative to the full-sample model, but the sign of the associations and their economic implications remain unchanged.

Table 11. Estimated Results from the Recursive Model Specification (resource-rich subsample)

||**Fixed**<br>|**Effect**<br>|**SU**<br>|**RE**<br>|
|---|---|---|---|---|
||**Gini index**|**GDP**|**Gini index**|**GDP**|
|Nonrenewable resource dependence|-0.067**|0.008|-0.177***|0.122|
|Lower Income<br>|dropped|dropped|6.090**|57.336***|
|Lower income * Nonrenewable resource<br>Dependence|0.078|-0.362***|0.129**|-0.756***|
|High-Low education ratio|-0.418***|-|-0.159|-|
|High-Low education ratio square|0.015***|-|0.008*|-|
|Consumer Price Index (2005)|-0.010*|-|-0.003|-|
|Services value added share|0.130***|-|0.117**|-|
|Manufacturing value added share|-0.006|-|0.064|-|
|Agriculture value added share|-0.175***|-|-0.154**|-|
|Log Labour Force|-|-8.651***|-|-26.131***|
|Log Gross fixed capital formation|-|0.975|-|5.280***|
|Log Exports of goods and services|-|2.663**|-|6.042***|
|Log REER|-|5.057***|-|3.325|
|World Oil price index|-|0.081***|-|0.091***|
|WorldMetalpriceindex|-|0.018|-|-0.001|
|Observations|667|707|580|580|
|R2|0.096|0.311|0.911|0.864|

Note: * ** and *** denote significance at the 10% 5% and 1% levels, respectively.

These findings are further confirmed and indeed reinforced in the non-recursive model results summarized in Table 12. In this case, resource dependence among higher-income countries is _positively_ associated with GDP per capita, suggesting that in wealthier countries natural resources are a blessing rather than a curse. In fact, greater resource dependence in higher-income countries is associated with lower levels of income inequality and greater GDP per capita. However, the opposite is true for lower-income 14
Inequality, Economic Growth and Nonrenewable Resources countries, in which an increase in resource dependence is associated with higher rates of income inequality and lower GDP per capita.

Table 12: Estimated Results from the Non-Recursive Model Specification (resource-rich subsample)

||**IV Fixe**<br>|**d Effect**<br>|**3S**<br>|**LS**<br>|
|---|---|---|---|---|
||**Gini index**|**GDPc**|**Gini index**|**GDP**|
|Nonrenewable resource dependence|-0.175***|0.385**|-0.194***|0.355**|
|Lower income|dropped|dropped|-12.765***|51.841***|
|Lower income* nonrenewable resource|0.121**|-0.960***|0.167***|-0.925***|
|dependence|||||
|High-low education ratio|0.337|-|1.754***|-|
|High-low education ratio square|-0.001|-|-0.030***|-|
|Consumer price index (2005)|-0.001|-|-0.009|-|
|Services value added share|0.114**|-|0.132***|-|
|Manufacturing value added share|0.002|-|-0.078|-|
|Agriculture value added share|-0.157**|-|-0.093|-|
|GDPc|-0.085|-|-0.195***|-|
|Log labor force|-|-30.421***|-|-27.100***|
|Log gross fixed capital formation|-|0.548|-|4.939*|
|Log exports of goods and services|-|9.189***|-|5.571**|
|Log REER|-|4.338*|-|3.613|
|World oil price index|-|0.084***|-|0.088***|
|World metal price index|-|0.027|-|0.011|
|Gini index|-|1.337**|-|1.096*|
|Observations|580|580|580|580|
|R2|0.162|0.229|0.903|0.835|

Note: * ** and *** denote significance at the 10% 5% and 1% levels, respectively.

The subsample analysis appears to confirm the robustness of the full-sample results. Restricting the analysis to resource-rich countries also supports the conclusion that the impact of resource dependence on income inequality and economic growth is determined by country-level characteristics that affect the management of natural resources. The overall conclusions of the analysis are presented in the following section.

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Nonrenewable resources, Income Inequality and per Capita GDP: An Empirical Analysis

# 5. CONCLUSIONS

This study was designed to investigate the empirical relationships between dependence on nonrenewable natural resources and income inequality and economic growth. The baseline model was recursive, with a fixed-effect estimator. It regarded GDP per capita and the Gini index as dependent variables. A non-recursive model was then used to address the endogeneity problem arising from the feedback effects across equations in a panel environment, which included a set of instrumental variables and an instrumental variable fixed-effect estimator. Finally, two previous models were estimated jointly by accounting for the potential correlation among the error terms of the equations via the use of a seemingly unrelated regression estimator (SURE) for the first model, and a threestage least square estimator (3SLS) for the second.

The results of the baseline model showed that an increase in resource dependence is associated with lower GDP per capita but a more equal income distribution. A subsequent specification tried to address the problem of heterogeneity between countries at different income levels, since the descriptive statistics clearly pointed to substantial variations between country income groups. Controlling for the heterogeneity between higher-and lower-income countries reveals dramatically different effects for each of these two groups. Among higher-income countries resource dependence is associated with lower levels of income inequality, and there is no statistically significant correlation between resource dependence and GDP per capita. However, among lower-income countries greater resource dependence is associated with greater income inequality and lower per capita GDP.

A third specification restricted the analysis to a subsample of resource-rich countries, which included about two-thirds of the countries included in the original full sample. The objective of this third specification was to increase the robustness of the findings by accounting for the unequal distribution of natural resources across countries. Its results largely corroborate those of the other models. Moreover, the non-recursive model estimation reveals a positive association between resource dependence and GDP per capita among resource-rich higher-income countries, further supporting the idea that whether natural resources are a blessing or a curse depends on each country’s specific political-economic and public administrative characteristics.

Taken together, these findings point to a number of important policy implications. While resource dependence initially appears to correlate with lower per capita income, this association is nullified or even reversed among high-income countries. One prospective explanation for this finding is that wealthier countries are systematically more likely to manage resource revenues effectively, and that they are more capable of mitigating their exposure to the inherent multidimensional volatility of the resource sector. By contrast, poorer countries are more likely to experience boom-and-bust economic cycles driven by unmediated external shocks and pro-cyclical expenditure policies, and due to weaker public financial and administrative systems, they may be less able to use resource revenues to promote broad-based improvements in productivity. These findings appear to confirm the results of previous analyses, and they further underscore the importance 16
Inequality, Economic Growth and Nonrenewable Resources of both reinforcing macroeconomic resilience and investing resource revenues in physical and human capital in order to facilitate the growth of the non-resource economy.

Fiscal stabilization and public expenditure policies involve complex issues that extend well beyond the scope of this paper. However, the heterogeneous results obtained by distinguishing between higher- and lower-income countries strongly suggest that the policy framework plays a decisive role in determining whether natural resources are a blessing or a curse.

The fact that, within our group of “lower income” countries, the positive relationship between dependence and income polarization is so strong (contrary to what this analysis finds in the higher income group) speaks volumes about the governance problems of most natural resource rich developing countries. These findings reaffirm the critical importance of effectively managing resource revenues in order to build a more prosperous and equitable economy.

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Nonrenewable resources, Income Inequality and per Capita GDP: An Empirical Analysis

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Inequality, Economic Growth and Nonrenewable Resources

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