Commodity Dependence and Human Development

Work-in-progress, please do not quote!

Janvier D. Nkurunziza and Sofia Cazzaniga

Special Unit on Commodities United Nations Conference on Trade and Development

This version: 29 July 2015

Abstract: This paper explores the relationship between commodity dependence and human development measured as the human development index (HDI). Commodity dependence negatively affects human development through several channels, including the negative secular terms of trade affecting commodity-dependent developing countries (CDDCs), slow economic growth, high macroeconomic instability, and political instability. The paper finds that although the effect of commodity dependence on human development is negative, on average, this relationship is complex. It could differ depending on the level of dependence as well as the type of commodity a country depends on. This negative effect is strongest in countries where commodities account for more than 60 per cent of total merchandise exports.

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1. Introduction In 2012-2013 the number of countries deriving at least 60 per cent of their

merchandise export earnings from commodities stood at 94 out of 135 countries

studies.1 Commodity dependence, measured by the ratio of commodities exports to

total merchandise exports, is even stronger in Africa. Ninety-four per cent of African

countries were considered as commodity-dependent developing countries (CDDCs) in

2012-2013. This is the group of countries where primary commodities represent at

least 60 per cent of total merchandise exports.

High commodity dependence might not appear as an issue during periods of high

commodities prices. Indeed, commodity exporters derive important rents from their

commodity sectors during periods of price booms such as the one experienced over

the last decade. For most CDDCs, high prices usually imply higher export revenues

and faster economic growth. However, due to their failure to smooth the spending of

commodity windfalls over periods of price booms and busts and use these resources to

invest in economic structural transformation,2 most CDDCs experience periods of

economic growth during booms and long depressions afterwards (Bevan et al., 1993).

For example, in Africa, the commodities price boom of the 1970s was followed by a

period of low rates of economic growth in the 1980s and 1990s before the beginning

of a new period of high growth rates corresponding with the new boom starting in the

early 2000s.

Over the long term, high dependence on primary commodities is associated with poor

development outcomes, including unfavourable terms of trade, slow growth,

macroeconomic instability, as well as political instability. As price takers, CDDCs

have also been exposed to the negative consequences of commodity price volatility

that has characterized commodities markets in recent history. In this regard, a number

of empirical studies find that natural-resource dependent economies have generally

fared worse than other economies in terms of economic performance (van der Ploeg,

2011; Hausmann et al., 2007; Rodriguez and Sachs, 1999). The main channel though

1 Unless otherwise specified, data used is from UNCTADStat, UNCTAD's database. 2 For the definition of structural transformation and its implications, see for example AfDB et al. (2013)

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which commodity dependence negatively affects economic performance is the so-

called "resource curse" discussed in the next section. This concept refers to the

inability of resource-rich countries to transform their natural resource wealth into

socio-economic benefits.

The main objective of this paper is to empirically explore the effect of commodity

dependence on human development using a disaggregated dataset covering the period

from 1995 to 2012. The choice of the sample period is motivated not only by data

availability but also the need to explore the extent to which the most recent

commodity boom might have altered the traditional negative relationship between

commodity dependence and an indicator of development such as the human

development index, contributing to the literature on commodity dependence,

development and structural transformation. A negative association between

commodity dependence and human development despite the recent commodity boom,

combined with a decline in manufacturing prices due to the delocalization of

manufacturing in low-cost countries such as China, would suggest that these major

events have not altered the long-term view that commodity dependence is indeed a

curse.

The paper proceeds as follows. The next section briefly discusses the three main

channels through which commodity dependence may affect development. The third

section uses recent disaggregated data to revisit the negative relation between

commodity dependence and human development. Section 4 is an empirical

econometric analysis of the relationship between human development and commodity

dependence in a multivariate framework. Section 5 concludes and suggests some

policy suggestions.

2. Commodity dependence and development: channels

Commodity dependence affects economic performance through the so-called

"Resource Curse" (Humphreys et al., 2007) via three different channels. The first is

the commodities' negative terms of trade channel. This argument is developed in the

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Prebisch-Singer hypothesis, which posits that primary commodity exporters are

penalized by the secular negative terms of trade (Prebisch, 1950; Singer, 1950).3

The last decade experienced two opposed developments that could have altered the

trend in developing countries' terms of trade. On the one hand, since the late 1990s,

there has been a generalized global decline in manufacturing export prices particularly

as a result of cheap exports from China and their effect on the prices of competitors

(Kaplinsky, 2006). On the other hand, primary commodities export prices increased

across all commodity families since the early 2000s in the context of the recent

commodity boom (UNCTAD, 2015). Has the combination of low manufacturing

prices and high commodities prices altered the long-term trajectory of terms of trade

and hence the commodity dependence argument in favour of CDDCs?

A recent econometric study covering 25 commodities, half of which spanning the

period from 1650 to 20054 found that over the long run, a large number of primary

commodities are characterized by deteriorating terms of trade even when part of the

recent commodity boom period is included in the analysis.5 More specifically, twelve

out of 25 commodities analyzed display a significant negative trend whereas the

remaining commodities are zero-trending. There is no evidence of positive trending

terms of trade for any commodity, supporting the Prebisch-Singer hypothesis (Harvey

et al, 2010). A more recent study by Ertern and Ocampo (2013, p.14) proposes a

refinement to the interpretation of the Prebisch-Singer hypothesis. It finds that over

the 20th century, non-oil primary commodity prices display a "sequential decline in

mean prices through super-cycles" suggesting that the price trends do not necessarily

follow a stochastic trend as often hypothesized.

The second channel relates to the Dutch Disease phenomenon. It refers to the

appreciation of CDDC's currencies due to large inflows of foreign currency, following

the discovery of natural resources such as oil, or an important increase in commodities

3 This hypothesis has been tested econometrically and found to hold despite the occurrence of relatively short periods of high commodity prices (Lutz, 1999). 4 Three more commodities price series begin in the 18th century, eight in the 19th century and two in 1900 (Harvey et al, 2010). 5 Generally, although the results from some studies are mixed, many of them are based on questionable assumptions about the order of integration of the price variable and they fail to account for structural breaks in their modeling strategies, leading to erroneous conclusions.

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export prices during commodity booms. The appreciation of the domestic currency

makes traditional exports less competitive on international markets while making

imports cheaper. This hampers economic diversification and could lead to de-

industrialization (Agénor and Montiel, 1996). Moreover, given that commodities price

booms are usually followed by periods of busts characterized by low prices, exchange

rates fluctuate with commodities price fluctuations. And exchange rate fluctuations

are detrimental for local commodity producers who are generally paid in local

currency. Furthermore, global commodities price fluctuations increase country-

specific macroeconomic volatility (Andrews and Rees, 2009; Ferreira, 2012). In

addition, commodities price fluctuations spread economic shocks among commodities

exporting countries leading to economic slowdown in these countries.

The third channel is political instability that often results from fights for the control of

rents associated with commodities windfalls in natural resource-rich countries (Caselli

and Tesei, 2011). Economic analyses of political instability have shown a close

relationship between natural resource dependence, poor governance and conflict. For

example, the risk of civil war increases with the ratio of primary commodities' exports

to GDP. The relationship is nonlinear, with the risk of civil war peaking at 0.33 when

primary commodities represent 25 per cent of GDP (Bannon and Collier, 2003, p.3).

Controlling the rents associated with primary commodities not only helps to explain

why wars start, particularly in societies where there are political grievances, but also

contributes to prolonging the conflict. In turn, civil wars affect the economy through

the destruction of productive resources, disruptions to economic activity, diversion of

resources from productive to un-productive sectors, dissaving, and portfolio

substitution, as agents tend to move their financial assets outside the country

concerned (Collier, 1999). These effects outlast the conflict so political instability

affects the economy for several years after the end of the conflict.

It would be misleading to conclude from this discussion that commodity dependence

is systematically associated with poor human development. There are countries that

have not become victims of the resource course despite having high ratios of primary

commodities to total merchandise exports. In these countries, the dependence on

primary commodities has not compromised their economic performance. In fact, some

of these countries, including Australia, Canada and Norway are among the world's

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most developed nations. This suggests that many other factors, including the level of

institutional development, interact with commodity dependence to produce the

negative relationship between commodity dependence and development in CDDCs. In

most developing countries, commodity dependence goes hand in hand with under-

development as the rest of this paper will show.

3. Descriptive analysis In this paper, country i's commodity dependence at time t is defined by the ratio of its

commodities (primary commodities, precious stones and non monetary gold, that is:

SITC 0 +1+2+3+4+68+667+971) exports to total merchandise exports. It is given as:

𝐶𝐷!" =!"##"$%&' !"#$%&'!"

!"#$% !"#$!!"#$%& !"#$%&'!" (1)

𝑤𝑖𝑡ℎ 𝐶𝐷!" ≤ 1. High values imply that a country is highly dependent on primary

commodities exports, thus is more vulnerable to exogenous shocks both to

commodities prices and to supply of and demand for such commodities.

Note that the values assumed by 𝐶𝐷!" depend not only on the volume of exports but

also prices. Therefore, with the same volume of commodities, the index of commodity

dependence may increase or decline depending on the change in commodities prices

relative to the change in the prices of other merchandise exports. Commodity

dependence can also change due to the composition of exports. Even at the same price

and with the same volume of commodity exports, a country's commodity dependence

could change if the composition of its total merchandise exports (the denominator in

Equation 1) changes. For example, structural transformation in a commodity-

dependent country would normally increase non-commodity merchandise exports,

reducing dependence even as the country exports the same value of commodities. In

fact, this is what structural transformation is implicitly expected to achieve.

3.1. Commodity exports and human development A number of studies have investigated the deleterious effect of commodity

dependence or resource intensity on per-capita income and aggregate long run

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economic growth (van der Ploeg, 2011; Hausmann et al., 2007; Rodriguez and Sachs,

1999). However, the study by Carmignani and Avom (2010) appears to be among the

first to investigate the effect of commodity dependence on development by shedding

light on the interaction between commodity dependence and non-monetary aspects of

development. The study finds that "non-monetary" indicators of development (e.g.

health and education) are negatively correlated with commodity dependence through

macroeconomic volatility and distributional inequalities.

This section builds on Carmignani and Avom (2010) and uses the most recent

disaggregated data to determine the effect of commodity dependence on human

development, measured by the Human Development Index (HDI). HDI combines

three different aspects of development: health, education (non-monetary aspects) and

living standards (monetary aspect) as measured by Gross National Income (GNI) per

capita.6

Using yearly data over the period from 1995 to 2012 covering a global sample of

countries, a measure of commodity dependence is constructed as in equation (1). In

order to show the differentiated effect of commodity dependence in developing

countries relative to developed countries, a dummy variable capturing the two groups

is introduced in the equation relating HDI to commodity dependence.

𝐻𝐷𝐼!" = 𝛼 + 𝛽𝐶𝐷!" + 𝛿 𝐶𝐷!" ⊗ 𝐷!" +∈!" (2)

where DDE is a dummy variable that takes a value of 1 if a country is developed and

zero otherwise.7 Note that in estimating equation (2) data is clustered on the basis of

the countries' main export category, as it is expected that different HDI observations

within each group may not be independent. Five merchandise export groups are

considered: agricultural products (food and non-food items); fuels; fabrics and

6 See UNDP's Human Development Reports for more details on HDI and its components. 7 Developed countries in the sample are Australia, Austria, Belgium, Bulgaria, Canada, Czech Republic, Denmark, Estonia, Finland, France, Germany, Hungary, Iceland, Ireland, Israel, Italy, Japan, Latvia, Lithuania, Luxembourg, Malta, Netherlands, New Zealand, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, Switzerland, United Kingdom, and United States. Note that for robustness checks we performed the same analysis eliminating Eastern Europe countries (former eastern bloc countries), but the results do not change significantly.

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apparels; precious stones, metals and ores; and other manufactures. In equation (2),

the coefficient β captures the effect of commodity dependence on HDI in developing

countries, while (β+δ) captured the effect on developed countries.

Table 1: Commodity dependence and HDI: naïve pooled regression Dependent variable is HDI

Parameters Coefficients β -0.23**

(0.10) δ 0.51***

(0.07) α 0.74***

(0.07) (β+δ) 0.28**

(0.14)

R-Squared 0.40

Observations 2743

Figure 1 is a scatter plot of HDI and the ratio of commodity dependence with the

green points representing countries that are major oil exporters. The fitted regression

line is based on the results in Table 1.

Figure 1: Scatter plot of HDI and commodity dependence

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As expected, the fitted line in Figure 1 shows a negative relationship between HDI

and CD. Decomposing this effect by developing and developed countries, the results

in Table 1 confirm the negative and statistically significant relationship between HDI

and commodity dependence in developing countries. However, for developed

countries, the coefficient is positive and significant, suggesting that commodity

dependence is associated with high HDI. We hypothesize that this result is due to an

omitted variable bias. Estimating the same regression excluding major oil exporters

from the sample, the results change, confirming even further the negative relationship

between commodity dependence and HDI (Table 2).

Table 2: Commodity dependence and HDI in non-oil exporting countries Dependent variable is HDI

Parameters Coefficients β -0.30**

(0.09) δ 0.55***

(0.08) α 0.75***

(0.07) (β+δ) 0.25

(0.15)

R-Squared 0.50

Observations 2272

Note: Standard errors adjusted for 4 clusters Figure 2 is the scatter plot corresponding with the sample of non-oil exporting

countries and a fitted regression line based on the results of Table 2.

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Figure 2: HDI and commodity dependence in non-oil exporting countries

The effect of commodity dependence on HDI is still negative and even stronger

(looking at the slope of the fitted line) than in the previous case, denoting a more

detrimental effect of commodity dependence on human development in this group of

countries. Interestingly, once oil-exporting countries are removed from the sample,

the effect of commodity dependence in developed countries becomes statistically non-

significant. These results suggest that oil exporting countries share the atypical pattern

of combining high levels of commodity dependence with high HDIs. This may result

from the fact that oil is a particular commodity which, unlike other typical primary

commodities, tends to sustainably generate high export revenues which in turn

increase GNI per capita, a component of HDI.

Whether or not high HDI in this group of oil-dependent countries also reflects high

education and health standards depends on the way in which oil export revenues are

spent by governments to promote welfare and development. A number of these

countries have relatively high HDIs which are driven by GNI per capita, with

relatively high GNI per capita coexisting with relatively low education and/or health

standards. These countries display HDIs that are higher than their non-income HDIs.

This group includes Chad, Angola, Equatorial Guinea, and Gabon (UNDP, 2011).

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The preceding discussion suggests that the extent of the effect of commodity

dependence on human development might differ according to the type of commodities

exported by a specific country. In order to explore this hypothesis, we re-estimate

equation (2), focusing on the group of developing countries, by dropping countries

categorized in each of the five export groups previously described.8 This is a test of

the sensitivity of HDI to specific commodity groups.

Table 3 below reports the β-coefficients which represent the sensitivity of HDI to CD

in developing countries. The entries read as "countries major exporters of …." Values

in the diagonal report β-coefficients of regressions when observations (country-years)

referring to one group are dropped. For example, β in position (2,2) with the first

number referring to row and the second to column, that is fuel, is derived after

dropping all country-years referring to fuel exporters. The value of the coefficient is -

0.30, the same as the one computed earlier. Outside the diagonal, the coefficients

show the effect of dropping two groups of commodities. For example, coefficient (4,

3) is the β-coefficient when country-years representing mining and apparel are

dropped. The coefficient is -0.28, which is slightly stronger than the average full-

sample coefficient of -0.23.

Table 3: HDI Sensitivity to dependence on different commodity export groups

Food Fuel Apparel Mining Food -0.17 -0.27** -0.24** -0.11 Fuel -0.30** -0.37** -0.32** Apparel -0.29*** -0.28** Mining -0.22

Figure 3 highlights with different colors country-years for each of the five export

groups. It visually helps to interpret the results of table 3 above.

8 If a country’s main export in a specific year t falls in the mining group, for example, it is categorized as a mainly mining exporter in that year. Hence countries’ export groups might change over time. ** and *** represent statistical significance at 95 per cent and 99 per cent, respectively.

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Figure 3: HDI and commodity export groups

Figure 3 could be easily divided into four quadrants with a vertical line at CD of 0.5

and a horizontal line at HDI of 0.6 (as the minimum value is about 0.2). Countries in

the lower-right quadrant are characterized by high commodity dependence and low

HDI. This group of countries appears to be dominated by agricultural exporters and,

to some extent, mining exporters. Indeed, among the countries in this group are very

poor countries such as Benin, Burundi, Ethiopia, Gambia, Guatemala, Malawi, Mali,

Paraguay, and Uganda. The group of agricultural exporters is so important that

excluding them from the sample used to estimate the effect of CD on HDI changes the

β-coefficient from a statistically significant coefficient of -0.23 to a statistically non-

significant coefficient of -0.17. In contrast, the upper-left quadrant is dominated by

manufactures exporters with low commodity dependence and high HDI.

Countries in the lower-left quadrant are not commodity dependent but display low

levels of human development. These are exporters of low-skilled manufactures,

namely countries whose main exports are in the textile, apparel and garment group.

This category includes countries such as Cambodia, Bangladesh, Pakistan, Sri Lanka,

Lesotho, Madagascar, Mauritius, Haiti, Macao, and Tunisia. Textile and clothing

industries can theoretically offer an opportunity for poor countries to add value to

primary commodities in order to help escape the natural resource curse. However, as

Keane and te Velde (2008) explain, adequate policies, appropriate institutions and a

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properly functioning private sector are important pre-requisites for a successful

management of the benefits accruing from the clothing and textile industry. Without

these pre-requisites, developing countries lack the capacity of moving up from simple

textile manufacturing to other associated high-value activities like design, marketing

or branding.

Therefore, even though the textile industry can potentially be the "first step up the

value-added manufacturing ladder" (Keane and te Velde, 2008), it is not always the

case that local government and private sector can or have interest in harnessing this

potential. Among the countries that have successfully taken advantage of the textile

industry are Costa Rica, Mauritius, and the Asian Tigers, a group of countries

characterized by high HDIs. From a statistical point of view, dropping these countries

from the sample does not substantially affect the general result, as the β-coefficient

slightly increases from -0.23 to -0.29.

The empirical results discussed in this section are not particular to the dependent

variable used. Substituting HDI with an income variable, namely GNI per capita and

executing the estimations presented above leads to comparable results.9

3.2. Dynamics of the effect of commodity dependence on human development So far, the analysis of the effect of commodity dependence on human development

has focused on the average effect over the sample period (1995-2012). However, there

is no theoretical justification for a constant β-coefficient. Indeed, the relationship

between commodity dependence could depend on commodity cycles, strengthening or

weakening, depending on the extent to which an increase or reduction in commodity

dependence is transmitted to human development through a change in income,

education or health standards. For example, during commodity booms, commodity

dependence increases but at the same time high prices generate additional resources

that can be used to improve human development through investments in education

and health services.

9 These empirical results could be provided upon request.

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We investigate the differential effect of CD on human development over time by

performing cross-sectional regressions of HDI on commodity dependence for each

year in the sample period. Figure 4 plots the empirical β-coefficients for the 18 years

of the sample.

Figure 4: Changes in β-coefficients over the sample period

Figure 4 shows that the negative effect of commodity dependence on human

development declines (in absolute value) over time, from about 0.26 in the mid-1990s

to 0.22 in the late 2000s. The interpretation is that a change in commodity dependence

in the mid-1990s was associated with a stronger effect on human development relative

to the late 2000s. Even though these results are just descriptive, they suggest some

marginal reduction in the effect of commodity dependence on human development

over time.

3.3 Commodity dependence and volatility

What is the relationship between commodity dependence and economic vulnerability?

Section 2 has argued that commodity exporting countries are exposed to the vagaries

of terms of trade shocks which are transmitted to the economy and create

macroeconomic instability. Indeed, limited diversification both in production and

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export markets exposes CDDCs to supply and demand shocks associated with the

commodity economy. Over the last few years, supply shocks have included weather

conditions for agricultural commodities, domestic policies in major exporting

countries such as export bans, and low investments in new sources of supply

particularly when prices are low. Demand shocks have included the health of the

global economy, economic growth in major importing economies such as China,

interest rates in major economies such as the United States and the Euro area, and

economic restructuring in large commodity consumers, particularly China (UNCTAD,

2015). Supply and demand shocks create economic uncertainty which is detrimental

to economic growth and human development as Figure 5 illustrates.

Figure 5: Macroeconomic instability and human development

Figure 5 is a scatter plot of each country’s average commodity dependence and the

log standard deviation of the annual growth rate of per-capita GDP over the sample

period.10 As expected, commodity dependence seems to be positively correlated with

macroeconomic instability.

10 Growth of GDP per capita is from World Development Indicators of the World Bank.

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4. Econometric analysis In the previous section the effect of commodity dependence on human development is

explored in a bivariate model which does not control for other potential determinants

of human development. This section explores the same relationship but within a

multivariate model. We first discuss the variables other than commodity dependence

that might explain human development. Secondly, we estimate multivariate models of

HDI in which commodity dependence is one among other explanatory variables.

4.1. Discussion of the variables used in the model

Our choice of covariates, in agreement with the literature (e.g. Carmignani and Avom,

2010; Gupta et al. 2002; Carmignani, 2008) consists in the following variables:

(i) Trade: as explained by Carmignani and Avom (2010), openness to trade

captures the positive effect of globalization on social outcomes,

particularly human capital formation. This implies a positive relationship

between trade and human development. The variable is measured as the

total of imports and exports over GDP (Source: WDI);

(ii) (ii) Government expenditure: this variable is measured as the sum of

government spending on goods and services over GDP. It is expected to

capture spending on social sectors with high expenses expected to increase

human development. Hence, a positive relationship with human

development is expected (Source: WDI);

(iii) Institutional quality: inefficiencies in government spending and corruption

may reduce or even cancel the positive effect of government spending on

human development. In order to control for this effect, we include a

variable on institutional quality. It is an index constructed by considering

the first principal component of six individual indicators, namely voice and

accountability, political stability and absence of violence/terrorism,

government effectiveness, regulatory quality, rule of law, and control of

corruption (Source: World Governance Indicators, WGI11).

(iv) Latitude: is a variable measuring the distance separating a country and the

Equator. This variable has been used in the literature to capture the effect 11 www.govindicators.org

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of being in the tropical zone, which is associated with challenges such as

malaria prevalence (Source: La Porta et al., 1999). Hence, the longer the

distance, the higher the human development, implying a positive sign

between this variable and human development

(v) Dummy variables: we include dummy variables for undeveloped countries

(UNDE), major apparel exporters (APPAREL), major fuel exporters

(FUEL), major mining (MINING) and major agricultural exporters

(AGRICULTURAL).

The effect of commodity dependence on human development is expected to be non-

linear. At low levels of dependence, the effect should be limited. For example, in

diversified economies, commodity dependence is low and; these economies tend to be

developed hence with high HDIs. In order to take into account these hypotheses, we

first estimate a quantile regression which allows the effect of commodity dependence

to vary over the distribution of the dependent variable, namely human development.

4.2. Quantile regression

The classical least-square regression assumes that the conditional mean function of

the dependent variable y can be modeled as a linear combination of the covariates, i.e.

[ ] ∑=

+=k

iiik xxxyE

11,..., βα . In other words, the conditional mean summarizes all the

relevant information on the relationship between y and xi, i=1, …,k. However, it might

be the case that the estimated βs are not good estimates as the magnitude of the effect

of covariates might be different at different levels of the dependent variable. In this

case, it is relevant to investigate the impact of the covariates on the whole distribution

of the dependent variable: they may increase its dispersion (increase

heteroskedasticity), induce multimodality, or compress or stretch a tail of its

distribution. Conditional quantile regression models are used in this case: the p-th

quantile of a random variable y is assumed to be a linear function of the independent

variables, i.e. [ ] ∑=

+=k

ii

pi

pky xxxpQ

1

)()(1 ,..., βα . The factor loadings βi

(p) represent the

effect of the xi on the p-th quantile of y; they reveal more information than the OLS

loadings, especially if the distribution of the dependent variable y is not symmetric.

Figure 1 shows the results of the quantile regression.

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Figure 6: Empirical results of the quantile regression

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Commodity dependence has a negative effect on human development and the effect is

strongest at relatively low levels of HDI. The elasticity of commodity dependence

relative to human development is about -0.20 for HDI below 0.6, implying that for

every percentage increase in commodity dependence, human development declines by

0.20 percentage points. As Figure 6 shows, beyond an HDI of 0.6-0.7, the effect of

commodity dependence weakens dramatically, reaching -0.04 when HDI is close to

1.0. This result establishes the fact that the analysis of the effect of commodity

dependence on human development must differentiate between high human

development countries where this effect is not strong and low human development

countries where the effect is strong.

Going one step further, we identified the level of commodity dependence of the

countries with an index of human development below 0.6. The finding is that on

average commodity exports represent 63 per cent of these countries total merchandise

exports. As a result, with some simplification, we draw from this finding that the ratio

of commodity dependence where the phenomenon starts to strongly affect human

development appears to be around 0.6. Hence, we conclude that the category of

commodity-dependent developing countries includes countries where commodity

exports represent at least 60 per cent of their total merchandise exports.

What about the effect of other variables used as controls? Trade, government

expenses and institutional quality seem to have a positive effect on human

development. However, the coefficients are so small that this effect is weak. For

example, the effect of trade is strongest when HDI is 0.5 but the coefficient is equal to

0.0006. For institutional quality, the effect is almost zero from an HDI value of 0.1.

Nevertheless, the concavity of the trade and government expenditure effects suggests

that as the population of a country enjoys a high level of human development, a

marginal increase in government spending or trade does not have much effect on HDI.

Up to the median level of HDI, increases in government expenditure and trade have a

positive albeit weak effect on human development.

The variable measuring the geographical position of a country which captures climatic

and geographic conditions appears to be a strong determinant of human development.

All the factor loadings β(p) are positive, implying that the longer the distance from the

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Equator, the higher the HDI. The steep decline of β(p)s along the distribution of HDIs

implies that the effect of the geographical variable quickly weakens as HDI increases.

In other words, geographical conditions weakly impact human development when the

latter is already high.

With respect to the effects of the dummy variables, we focus on two. First, exporting

apparels is negatively associated with human development and this effect is stronger

in low HDI countries, weakening from -0.14 in the first decile to -0.09 at the median.

This is in line with the argument of Keane and te Velde (2008) discussed earlier.

Secondly, fuel exporters are associated with high HDI but this effect is complex. This

effect declines from the first to the second deciles before increasing to reach its

maximum at the 4th decile. Thereafter, it declines steadily, from 0.15 at the 4th decile

to 0.06 in the 9th decile. The implication is that the benefits to human development

from oil exports are more important in low HDI countries which are generally more

dependent on this export than higher-HDI countries which are more economically

diversified.

It should be acknowledged that some regressors are potentially endogenous; the

quantile regression does not control for endogeneity. For example, commodity

dependence might be endogenous to HDI as arguably, countries with low human

development might lack the technology, expertise and institutions that would help

them to diversify their economies and escape the commodity dependence syndrome.

Indeed, despite the negative effect of commodity dependence on human development,

many CDDCs remain commodity-dependent even after enjoying important

commodity windfalls during commodity booms such as the one experienced in the

2000s. We attempt to control for endogeneity using a dynamic panel data model.

First, the index of human development is expected to change slowly given the nature

of the variables composing it. This suggests an inclusion of the lagged value of HDI

in the model.12Secondly, as suggested by the quantile regression results, the effect of

12 A country's current HDI is reasonably similar to its previous HDI. So we might suspect that HDI is non-stationary. Yet HDI is bounded between zero and one, thus from a theoretical point of view we expect it to be stationary. Moreover the hypothesis of stationarity is supported by unit root panel data tests. We perform two types of tests: the first test has a null hypothesis of a common unit root across all

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commodity dependence on human development is non-linear. One could think of the

relationship as looking like an inverted U-shape: at low levels of commodity

dependence (that is the case of an advanced economy with developed manufacturing

and/or services sector such as Norway and Canada), an increase in commodity

dependence might have a positive effect on human development as it might increase

export revenues (see Carmignani and Chowdhury, 2007).

On the other hand, at high levels of commodity dependence a further increase in CD

reduces HDI as a result of factors associated with the natural resource curse. This

could be the case with many oil exporters in Africa which are highly commodity-

dependent. For example, Angola's ratio of commodity dependence is almost one and

the country is among the countries with a low human development index. Other oil

exporters such as Chad, Republic of Congo and Equatorial Guinea share this pattern

(United Nations, 2015). Therefore, we account for the non-linearity of commodity

dependence by entering in the HDI equation the variable and its squared term.

Table 4: Results of dynamic system GMM models

Model 1 Model 2 Model 3 Model 4

Lagged HDI

Commodity dependence

Square commodity dependence

Commodity boom dummy

Trade openness

Government consumption

Institutional quality

Fuel dummy

Agriculture dummy

Mining dummy

Apparel dummy

Upper middle income economies

Lower middle-income economies

Low-income economies

0.980**

-0.059**

0.045**

0.002**

0.987**

-0.049**

0.038**

0.001**

-0.001

0.000

0.002 **

0.979**

-0.042**

0.024**

0.002**

-0.000

0.000

0.001**

0.014**

0.004

-0.000

0.006

0.961**

-0.043**

0.032**

0.002**

-0.001

-0.000

0.001**

0.003

-0.008*

-0.014**

panels; the second test is based on the Augmented Dickey Fuller and Phillips-Perron Fisher Chi-square statistics (we consider all the versions with and without time trend and drift).

22

Constant 0.031** 0.027**

0.024**

0.046**

Number of observations 2033 2028 2028 2028

Arell.-Bond test for AR(1) (pval) 0.000 0.000 0.000 0.000

Arell.-Bond test for AR(2) (pval) 0.367 0.364 0.302 0.332

Hansen test of overid. Restrictions 0.826 0.792 0.726 0.745 ** and * represent statistical significance at 5 per cent and 10 per cent, respectively

In all models in Table 4, the results of the tests for first and second-order

autocorrelation are as expected. The test for identifying restrictions also confirms that

we cannot reject the null hypothesis that the set of instruments used to account for

endogeneity are appropriate.

The positive sign and strong coefficient of the lagged value of HDI suggests that the

current value of the variable is a good predictor of its future value, confirming that the

process of human development improves slowly. The signs and statistical significance

of the other key variables are qualitatively similar to the previous ones based on a

quantile regression. Taken together, the statistical significance and negative sign of

commodity dependence and the positive sign of its squared term illustrate the non-

linearity of the relationship between commodity dependence and human development.

This confirms the U-shaped relationship. However, as these are average regression

coefficients giving the average trend over the distribution of the variable, they do not

provide an accurate description of the relationship.

The convex shape of the relationship between commodity dependence and human

development does not necessarily mean that it is perfectly U-shaped. It might have an

asymptotic trend towards infinity, in this case towards CD=1; there might even be

inflection points or shape changes that cannot be detected by regression coefficients.

In order to investigate more closely this relationship, we fit a non-parametric curve

(see Figure 7) which best fits the data.

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Figure 7: Commodity dependence and human development

Although there is a generally downward-trending relationship between commodity

dependence and human development, the relationship is complex. The fitted curve

seems to have inflection points, first at low levels of commodity dependence where

the relationship changes from positive to negative. There is also a dramatic increase in

the negative slope of the curve in the last two deciles of the commodity dependence

variable, suggesting a very strong negative effect of commodity dependence on

human development when commodity dependence is very high. Moreover, before

CD~0.65, the curve appear convex, then turns concave thereafter. Where a country

falls on this curve could help to determine the policies needed in order to reduce the

negative effect of commodity dependence and the country's human development.

4. Conclusion and policy suggestions The analysis in this paper has focused on the relationship between commodity

dependence and human development, using UNDP’s HDI which combines three

aspects of human development, namely income per capita, education and health

standards. The findings confirm that generally, there is a negative relationship

between commodity dependence and human development, but this relationship is not

linear. At very low levels of commodity dependence, it seems to be positively

24

associated with human development. This is due to an income effect where a marginal

increase in commodity dependence implies higher export revenues that are used to

raise the level of human development as would be the case in countries such as

Norway and Canada.

In contrast, the analysis has shown that in commodity-dependent developing countries,

where commodity exports represent more than 60 per cent of total merchandise

exports, commodity dependence is associated with low human development. These

countries might benefit from higher export revenues during commodity booms, as

experienced in Africa during the 2000s, and post high levels of economic growth, but

they may lack the human capital and institutions needed to transform these

commodity windfalls into human development improvements. In fact, regression

results have shown that improvement in human development is very slow, suggesting

that deliberate policies to improve it could be needed to speed up progress. The

analysis has also shown that not all commodities affect human development the same

way. Countries relying on agricultural exports seem to suffer more from their

dependence on commodities relative to fuel exporters, for example, which seem to

benefit.

One policy implication of this analysis is that CDDCs have every reason to diversify

away from commodities in order to improve their human development. It is surprising

that despite the rhetoric about the need for economic diversification and structural

transformation over the past decades (e.g. AfDB, 2013), countries have not, generally,

reduced their commodity dependence. If anything, commodity dependence has

increased over time, suggesting that this issue probably needs to be revisited in a

different light in order to come up with new more successful approaches.

For example, it is important to understand the major risks facing a country’s

commodity exports before crafting a diversification strategy. Recent research shows

that agricultural commodities exports are more prone to idiosyncratic risk-based

factors whereas minerals are generally more sensitive to global risk (Nkurunziza and

Tsowou, 2015). This implies that to reduce commodity dependence and its negative

effect on human development, exporters of agricultural commodities could diversify

towards other unrelated commodities within the same sector. In contrast, minerals

25

exporters would benefit by diversifying vertically given that adding new mineral

products to their export basket would expose them to the same market risk.

It is also important to note that successful diversification requires a long term vision

and the setting up of an appropriate policy environment which fosters its

implementation. For example, the analysis in this paper has shown that diversifying

away from raw cotton exports into manufacturing of textiles and apparels would not

take a country very far in terms of setting off an export diversification process which

improves human development; these manufactured products are also associated with

low human development. Therefore, addressing commodity dependence in a way that

improves human development requires leadership and human capital capable of

developing the right strategy, and put in place a policy environment and a set of

institutions that help the process of diversification. Technological and financial

resource mobilization strategies must be an integral part of the process.

26

References AfDB, OECD, UNDP and UNECA (2013). African Economic Outlook. Structural Transformation and Natural Resources. OECD Publishing. Agénor, P-R and Montiel, P (1996). Development Macroeconomics. Princeton: Princeton University Press. Andrews R., Rees D., 2009, Macroeconomic volatility and terms of trade shocks, Research Discussion Paper, Reserve Bank of Australia. Bannon, I and Collier, P, eds. (2003). Natural resources and violent conflict: options and actions. Washington, D.C., The World Bank. Bevan, D., Collier, P. and Gunning, JW (1993). Controlled Open Economies: A Neoclassical Approach to Structuralism. Journal of Development Economics. 41 (1), 209-211 Carmignani, F., 2008, The impact of fiscal policy on private consumption and social outcomes in Europe and the CIS. Journal of Macroeconomics, 30, 575-598. Carmignani F., Avom D., 2010, The social development effects of primary commodity export dependence, Ecological Economics, 70, 317-330. Caselli, F and Tesei, A (2011). Resource Windfalls, Political Regimes and Political Stability. Working Paper 17601. National Bureau of Economic Research, Cambridge, MA. Chaban M., 2009, Commodity Currencies and Equity Flows, Journal of International Money and Finance, 28, 836-852. Cleveland, W. S. 1993. Visualizing Data, Hobart Press, NJ. Cleveland, W, S, 1994, The Elements of Graphing Data, Hobart Press, NJ. Collier, P (1999). "On the economic consequences of civil war" Oxford Economic Papers, 51, 168-183. Erten, B. and Ocampo, J. A (2013). "Super Cycles of Commodity Prices Since the Mid-Nineteenth Century" World Development, 44, 14-30. Ferreira Filipe S., 2012, Equity Order Flow and Exchange Rate Dynamics, Journal of Empirical Finance, 19, 359-381. Gupta, S., Verhoven, M., Tiongosn, E., 2002, The effectiveness of government spending on education and health care in developing and transition economies. European Journal of Political Economy, 18, 717-737.

27

Harvey, D., Kellard, N., Madsen, J and Wohar, M (2010). "The Prebisch-Singer hypothesis: four centuries of evidence" Review of Economics and Statistics, 92 (2), 367-377. Hausmann, R, Hwang, J and Rodrik, D (2007). "What you export matters" Journal of Economic Growth, 12, 1-25 Humphreys, M, Sachs, J D, and Stiglitz, J E (2007). Escaping the Resource Curse. New York: Columbia University Press. Kaplinsky, R (2006). "Revisiting the revisited terms of trade: will China make a difference?" World Development, 34 (6), 981-995 Keane J., Willem te Velde D., 2008, The role of textile and clothing industries in growth and development strategies, Overseas Development Institute. La Porta, R., Lopez-de-Silanes, F., Shleifer, A., Vishny, R., 1999, The quality of the government, Journal of Law, Economics and Organizations, 15, 222-279. Lutz, M (1999). "A general test of the Prebisch-Singer hypothesis" Review of Development Economics, 3 (1), 44-57. Nkurunziza, J. D and Tsowou, K (2015). Volatility in global commodities markets and implications for diversification policies. Mimeo. UNCTAD. Prebisch, R (1950). "The Economic Development of Latin America and Its Principal Problems" Economic Bulletin for Latin America, 7, 1-12 Rodriguez, F and Sachs, J (1999). "Why do resource-abundant economies grow more slowly?" Journal of Economic Growth, 4 (3), 277-303 Singer, H., (1950). "The Distribution of Gains between Investing and Borrowing Countries" American Economic Review, Papers and Proceedings, 40, 473-485 UNCTAD (2015). Recent developments and new challenges in commodity markets and policy options for commodity-based inclusive growth and sustainable development. Paper presented at the Multi-year Expert Meeting on Commodities and Development, UNCTAD. Geneva. UNDP (2011). Human Development Report 2011--Sustainability and Equity: A Better Future for All. New York. United Nations (2015). State of Commodity Dependence 2014. UNCTAD, New York and Geneva. Van der Ploeg, F (2011). "Natural resources: curse or blessing?" Journal of Economic Literature, 49 (2), 366–420