For some decades, it has been observed that the possession of natural resources does not necessarily confer economic success. Many countries in Africa and the Middle East are rich in oil and other natural resources, and yet their people continue to experience low per capita income and low quality of life. This puzzling phenomenon is called the “natural resource curse” (Auty 1993
). The term refers to the paradox that countries that heavily depend on natural resources, such as oil, natural gas, and minerals, tend to have less economic growth and worse development outcomes compared to countries with fewer natural resources. Angola, Congo, Nigeria, Venezuela and some Middle Eastern countries are good instances of natural resource-based economies that also suffer low or negative GDP growth and widespread poverty. In contrast, East Asian economies, such as Japan, Korea, Taiwan, Singapore and Hong Kong, have achieved a high-level standard of living despite having no exportable natural resources. Several economic and political explanations have been introduced for this phenomenon; see Badeeb et al. (2017)
for a comprehensive survey.
While most of the research on the resource curse has focused on economic growth, an increasing number of papers have studied the effect of resource dependence on productivity (Papyrakis and Gerlagh 2004
; Gylfason and Zoega 2006
; Farhadi et al. 2015
). Since productivity is a major determinant of economic growth, lower productivity would also mean lower economic growth.1
Indeed, a careful look at productivity growth is important. As pointed out by Easterly and Levine (2000)
, most of the differences in cross-country GDP growth rates are not the result of factor accumulation, but of differences in total factor productivity (TFP) growth (Dasgupta et al. 2005
). Corden and Neary (1982)
, and Corden (1984)
found that in the natural resource-based countries, there is a productivity difference between the resource sector and the non-resource sector due to the Dutch disease mechanism. The Dutch disease channel works as follows: A discovery of natural resources in a country causes overinvestment in the natural resource sector and ignores the sectors that are conducive to long-run growth. This leads to a decrease in TFP, an important factor of the Solow growth model that is vital for continuous growth. The decrease in productivity is reflected in the diminishing growth rate of GDP.
This paper builds its theoretical argument on the strand of literature that studies the likelihood of the resource curse on different macroeconomic factors by focusing on productivity factors. Our work contributes to the literature concerning the natural resource curse in two ways. First, we explore the role of banking development in reducing the resource curse.2
We argue that a well-structured and effective banking sector can weaken the negative link between natural resource dependence and productivity. A key merit of a strong banking sector is its capacity to provide low-cost information about investment opportunities (Saborowski 2009
). This information improves the efficient allocation of resources and allows investors to monitor their investments better.
Second, this study is the first attempt to identify the relationship between natural resource dependence and productivity under a time series framework. The available studies in this field have only been undertaken in a panel of countries (Farhadi et al. 2015
). The results of panel data studies encouraged us to investigate this relationship on a country-specific basis using a time series approach, which is more useful for estimating the relationship (Singh 2008
Our analysis focuses on a resource-based economy, Yemen. This country is among those that are blessed with natural resources; namely, crude oil and natural gas. It is among the 11 oil producing and exporting countries in the Arab region, and is the 32nd biggest oil exporter and 16th biggest seller of liquefied natural gas (World Bank 2002
). It also falls into the group of Arab oil economies that are endowed with limited amounts of oil reserves. The economy of Yemen is highly dependent on this declining resource, which generates more than 70% of government revenue, 80–90% of its exports and accounts for roughly 25% of its GDP. As a result, the Yemeni fiscal position and economic output are highly vulnerable to a shift in international commodity prices and domestic oil outputs.
Economic growth in Yemen was driven by capital accumulation and an expanded labor force (to a lesser extent) but without productivity gain (World Bank 2015
). During 1990–2010, the average annual growth was five percent, but growth per capita was only 1.7 percent due to high rate of population growth. The contribution of capital to GDP per capita was 2.6 percentage points on average, while labor and other human capital contributed an average of only 0.3 percentage points. On average, the contribution of TFP to economic growth per capita was negative, at −1.2 percentage points. This fact suggests that the absence of sustained high growth in Yemen can be attributed to the weak contribution of productivity in economic growth.
The remaining parts of the paper are organized as follows: Stylized facts about the Yemeni economy are presented in Section 2
. The literature review is in Section 3
. In Section 4
, we focus on data and methodology. The empirical results and discussion are presented in Section 5
. Finally, Section 6
provides the conclusion, with implications.
2. Yemeni Economy: Stylized Facts
Yemen is an Arab country in Western Asia occupying the South Western to Southern end of the Arabian Peninsula. The Republic of Yemen was established in May 1990, after the unification of the Yemen Arab Republic (YAR) and the Marxist People’s Democratic Republic of Yemen (PDRY). For the last two decades, the country’s economic performance has been good but unimpressive. On average, the economy grew at five percent annually between 1990 and 2010 (World Bank 2015
). However, due to rapid population growth, GDP per capita rose only 1.3 percent a year, which is not sufficient to reduce poverty. As mentioned earlier, the country’s growth was mainly driven by capital accumulation with very little improvement in productivity.
Investment, which is a main source of productivity, was relatively low, averaging around 18% to GDP over the period 1990–2000. Investment has been mainly concentrated in the private oil related rent-seeking activities. According to the IMF (2001)
, private investment is still ambiguous in spite of many structural reform efforts. Moreover, during 2001–2010, private investment declined to around 10.6% of GDP (see Figure 1
). According to the World Bank (2002)
, the small contribution of private investment in the economy can be attributed to the dominance of the oil sector (which is dominated by public investment) in the economy. Hence, there will be relatively little room for the development of private investment (World Bank 2002
). As a result of the low private investment rate, the gross investment remains relatively low. In addition, the low contribution of public investment is another reason for the low investment rate in Yemen.
Clearly, the deep-rooted obstacles to investment and high real interest rates stifle private investment projects. Nevertheless, this can also be attributed to the ineffective financial sector that lacks the ability to mobilize domestic savings and channel them into productive investments. The World Bank (2013)
argued that the weak performance of the financial sector in Yemen is attributed to the weak legal and judicial environment where the creditors’ rights are not enforced. Intermediation between depositors and private sector credits is less than 10% of GDP. The vast majority of Yemen’s population does not use formal financial services. With reference to bank deposits, only 800,000 people have an account with a formal financial institution. The number of deposits accounts per 1000 people is only about 35 in Yemen, the lowest country globally.
In this paper, we argue that building a sound financial sector in Yemen that channels capital to its most productive uses is beneficial to avoid the potentially negative effect of natural resource dependence on the economy. A sound and efficient financial sector is especially important for sustaining growth in the country because the efficiency of investment (productivity) will overshadow the quantity of investment (capital accumulation) as the driver of growth in the country.
3. Literature Review
Since the late 1980s, a considerable amount of literature that challenges the view of natural resources as a blessing for developing countries has emerged. The literature on this phenomenon has increased significantly (Karl 2005
; Mehlum et al. 2006
; Gylfason 2001
; Gylfason and Zoega 2006
; Sachs and Warner 2001
; Stevens and Dietsche 2008
; Neumayer 2004
; Arezki and Van der Ploeg 2011
). What is more, both economic and social scientists have contributed to showing new aspects of the resource curse. Recently, new reasons and new approaches to this hypothesis have been added. While it is important to study the relationship between resources and overall growth, we still need to identify the channels through which the resource curse works. That is, natural resource dependence can affect growth through its impact on the growth determinants, i.e., physical capital, human capital, social capital and productivity. In this paper, we focus on productivity.3
Papyrakis and Gerlagh (2004)
argued that during a natural resource boom, increased rents in the primary sector cause a reallocation of factors of production from manufacturing towards the booming primary sector. Since the manufacturing sector is often characterized by increasing returns to scale and positive externalities, a decrease in the scale of manufacturing decreases the productivity and profitability of investment. The issue of externalities can find its roots in the work of (Singer (1950)
, p. 476), who stated that manufactures “provide the growing points for increased technical knowledge, urban education, the dynamism, and resilience that goes with urban civilization.” Therefore, trade specialized in natural resources would provide low spillovers compared to the trade specialized in manufacturing. Later, externalities (increasing returns) and natural resources were dealt with by Dutch disease theorists. They built models that considered learning to be a transmission mechanism that was mostly associated with the tradable sector. Van Wijnbergen (1984)
built a two-period model with a tradable and a non-tradable sector. The tradable sector is subject to learning by doing from production. Therefore, the level of production in the first period may affect the outcome of the second period. He showed that with a foreign currency premium that implies a real valuation of the country’s currency, the production of the tradable sector in the first period will be smaller, generating a negative effect in the second period, which damages the welfare of people.
In the same context, Gylfason and Zoega (2006)
, through the endogenous growth model, proved that heavy natural resource dependence leads to distortions in the allocation of installed capital. This is due to a poorly developed financial system and trade restrictions or government subsidies that attract capital to unproductive uses in protected industries or in state-owned enterprises where capital may be less productive than in the private sector.
Recently, Farhadi et al. (2015)
found that natural resource rents have a negative and significant effect on productivity. However, the authors argued that the more market-oriented resource-rich economies may experience significantly higher productivity growth than less market-oriented ones. Additionally, they found that the relationship between natural resource dependence and productivity improves as economic freedom increases.
However, one important question arises here: How could financial (banking) development enhance productivity and thereby mitigate the natural resource effects on productivity? An efficient banking sector contributes to the increase in capital productivity through the two mechanisms of risk reduction and monitoring services. There are several types of risk associated with financial intermediation, such as liquidity risk, default risk, investment risk, and payment risk.
Uncertainty is a problem for economic agents in their daily economic life. It usually arises from the irregularity in business cycles and the possibility of economic shocks and sudden changes in circumstances and conditions. Therefore, the main concern of savers is the speed with which they can liquidate their assets to face the unexpected shocks (the liquidity risk). A well-structured financial sector can reduce the liquidity risk by having good liability and asset management on the one hand, and by diversifying its investments, on the other. Asset management means holding cash and liquidity assets at a level above that required to meet the expected volatility of cash flows. Liability management occurs by determining the desired quantities of assets and then adjusting interest rates to attract the desired levels of deposits to fund the transactions (Buckle and Thompson 1998
). The more financial intermediaries facilitate and ensure the liquidity of savings at any time in the face of uncertain income shocks, the greater the individual’s willingness to save (Caprio and Claessens 1997
). According to Pagano (1993)
, well-structured banks enable individuals to face the liquidity risk and invest most of their funds in more productive and illiquid projects, which would result in higher productivity of investments and higher growth rates. By eliminating self-financed capital investment, banks enable entrepreneurs to face the liquidity shocks without liquidizing their productive assets to compensate (Bencivenga and Smith 1991
5. Empirical Findings and Discussion
As the ARDL approach is applicable to variables with I (0), I (1) or mutually integrated, we check the order of integration of each variable to ensure that no variables are I (2) or beyond. Table 1
summarizes the outcomes of ADF and PP unit root tests on the level and first differences of the variables. The result suggests that all variables are I (1), which supports the use of the ARDL approach to cointegration.
After investigating the time series properties of all the variables, the ARDL approach is used to examine the potential long-term equilibrium. This test is sensitive to the number of lags used. Given the limited number of observations in this study, lags with a maximum of two years have been imposed on the first difference of each variable, and the Schwarz-Bayesian Criterion (SBC) is used to select the optimal lag length for each variable. SBC suggests ARDL (1,1,1,0,0) and (1,1,0,2,0) for our two models, respectively. The result of the ARDL bound test of cointegration is tabulated in Table 2
shows that the F
-statistic is greater than its upper bound critical values at the five-percent level for the case of the M2 model, thereby indicating the existence of cointegration in this model. Moreover, the coefficient of lagged error correction term (ECTt-1
) is significant and negative, which confirms the existence of cointegration. On the other hand, the F
-statistic for the case of the credit to private sector model lies between the upper and lower bound critical values. Hence, it is inconclusive. Therefore, we seek an alternative way by testing the coefficient of lagged error correction term (ECTt-1
), which is considered by Kremers et al. (1992)
as a more efficient way of establishing cointegration. Kremers et al. (1992)
argued that a significant and negative coefficient for ECTt-1
indicates the adjustment of the variables towards equilibrium, and, hence, cointegration. Accordingly, the cointegration is supported by the significant and negative coefficient obtained for ECTt-1
With cointegration among the variables, we can derive the long-run coefficient as the estimated coefficient of the one lagged level independent variable divided by the estimated coefficient of the one lagged level dependent variable and multiply it with a negative sign. Conversely, the short-term coefficient is calculated as the sum of the lagged coefficient of the first differenced variable.
Panel A reports a negative relationship between natural resource dependence and productivity growth in Yemen for both models. This result supports the idea of Gylfason and Zoega (2006)
. Resource dependence reduces the productivity of capital and raises the ensuing rate of depreciation. Hence, a given investment rate is likely to generate a lower rate of growth of output. According to Papyrakis and Gerlagh (2004)
, in resource-dependent economies, during a natural resource boom, increased revenue in the primary sector causes a reallocation of factors of production from manufacturing towards the booming primary sector. As the manufacturing sector is often characterized by increasing returns to scale and positive externalities, a decrease in the scale of manufacturing decreases the productivity and profitability of investment (Sachs and Warner 1995
; Gylfason and Zoega 2006
). In fact, the weak performance of the manufacturing sector during the oil era in Yemen has been empirically proven by Badeeb and Lean (2017)
, and can be considered as an important sign of the negative consequences of natural resource dependence on productivity.9
In order to further illustrate the dominant role of the oil sector on the productivity of the Yemeni economy, one can investigate the ratio of private investment to GDP over the study period, and analyze the situation of private investment and public investment. This investigation can provide another explanation of the negative relationship between natural resource dependence and productivity in Yemen. Figure 2
shows that the ratio of private investment to GDP was decreasing since the level of natural resource dependence reached its peak in 1996, whilst Figure 1
shows the increasing trend of public investment during the same period. This indicates that the greater the level of natural resource dependence, the lower the contribution of private investment to total investment and the higher public investment share. As private investment is more efficient than public investment, high dependence on natural resources replaces a more efficient investment by less efficient investments. Hence, productivity suffers and thereby the economic growth (Banerjee 2011
; Badeeb et al. 2016
We could not find a significant long-run relationship between banking development and productivity, which confirms Rioja and Valev (2004)
argument. Rioja and Valev (2004)
argued that finance does not have an impact on productivity growth in the less developed economies. The impact does not occur until a country has reached a certain income level. Productivity is a long-term investment. Uncertainty and macroeconomic instability prevent the financial sector becoming involved in long-term high-efficiency investments.
The positive sign of the interaction term between banking development and natural resource dependence sheds new light on the role that can be played by banking development in mitigating the resource curse. This result is in line with the emerging consensus in the resource curse literature: the development of financial/banking sector ultimately determines whether the natural resource curse is more or less pronounced (Saborowski 2009
; Van der Ploeg and Poelhekke 2009
). The financial sector can reduce the risk associated with a natural resource dependent environment by having good liability and asset management and diversifying the investments. Furthermore, well-structured banks will enable individuals to invest most of their funds in more productive and illiquid projects, which would result in higher productivity.
In sum, our results reveal that the negative relationship between natural resource dependence and TFP growth is consistent with the resource curse hypothesis, implying that the curse exists. However, the finding suggests that if the level of banking development (represented by credit to the private sector) improves over time, it would slightly mitigate the curse.
The short-run estimation results in error-correction representation are provided in Table 3
Panel B. The coefficient of the estimated error correction model is negative and significant, which confirms the existence of long-run equilibrium among our variables in the two models. In addition, the coefficient suggests that a deviation from the long-run equilibrium following a short-run shock is corrected by about 63% and 43% per year in both models, respectively. Similar to the long-run analysis for the private credit model, a positive relationship exists between the interaction term and TFP. This finding implies the important role of banking development in Yemen in the short run.
On the other hand, we note that the results of the M2 model are not significant. This could be due to a large portion of M2 consisting of currency that is held outside the banks in Yemen. Therefore, an increase in the M2 to GDP ratio may reflect the extensive use of currency rather than an increase in the bank deposits (Abu-Bader and Abu-Qarn 2008
). Hence, its role in mitigating the resource curse is not significant11
Panel C in the same table notes that all models pass all diagnostic tests for serial correlation, autoregressive conditional heteroskedasticity and model specification. The CUSUM and CUSUMSQ in Figure 3
remain within the critical boundaries for the five-percent significance level. These statistics confirm that the long-term coefficients and all short-term coefficients in the error correction model are stable.