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The Zambian Resource Curse and its influence on Genuine Savings as an indicator for “weak” sustainable development

We are in part to blame, but this is the curse of being born with a copper spoon in our mouths

Kenneth Kaunda

Former President of Zambia

Abstract

The empirical evidence that economies predominantly reliant on their natural resources are characterized by slower economic growth—the so-called Resource Curse (RC)—is in many ways confirmed by the case of Zambia. Haber and Menaldo (Am Polit Sci Rev 105(1):1–26, 2011) identify Zambia’s extreme dependence on copper exports as one of the worldwide most striking examples for a country suffering from this “curse.” In topical literature, the RC is traced back to the generation of natural resource rents regardless of economic performance, which among other problems leads to suboptimal reinvestment. The World Banks indicator for the “weak” sustainable development of a country—the so-called Genuine Savings (GS)—considers exactly this reinvestment of rents from the depletion of natural capital rents into physical or human capital. Although it has been shown empirically that countries dependent on primary exports on average feature negative GS rates and that the determinants of the RC influence both present economic growth and future sustainability as measured by GS, no case studies have been conducted to confirm this. Against this background, we qualitatively survey the relationship between the most discussed determinants causing the RC in Zambia and the country’s GS rate. We show that all theoretical relationships between the GS rates of a country and RC determinants such as consumption behavior, volatile world market prices, the so-called Dutch disease as well as political and institutional structures apply to Zambia between 1964 and 2010: an extreme dependency on copper exports and insufficient reinvestments of income from the depletion of Zambia’s natural capital constitutes one of the main reasons for slow growth and negative GS until the copper price booms in the second half of the 2000s.

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Notes

  1. Other case studies have been conducted by McPherson (1993), Shafer (1994), Fox and Greenberg (2001), and the World Bank (2011b).

  2. The Dutch disease model is the original and most prominent theory behind the RC presented by Corden and Neary (1982) and Corden (1984), which we present and analyze in detail in Sect. 4.2.2.

  3. Zambia borders on Angola, Botswana, DR Congo, Malawi, Mozambique, Tanzania and Zimbabwe.

  4. The Human Development Index is an indicator provided by UNDP that includes life expectancy, education, and different income indices used to rank the human development of countries: In 2012 Zambia ranked 143 out of 187 countries.

  5. In 2010, of a total of US$ 7.2 billion in commodity exports, the top ten primary exports account for US$ 6.4 billion; refined copper alone amounting to US$ 4.6 billion. Source: United Nations Comtrade International Merchandise Trade Statistics: http://comtrade.un.org/pb/CountryPagesNew.aspx?y=2010.

  6. Sachs and Warner (1995/1997) show a consistent decrease of more than 1 % of GDP growth with a 10 % increase in the average share of primary exports in GNI, regardless of which additional explanatory variables are used in the model. This correlation is not as high in the study by Boos and Holm-Müller (2013), who show only a 0.3 % decrease for a larger sample and longer time series. However, they demonstrate the additional relationship between primary exports and GS rates. With samples featuring between 84 and 103 countries in models with up to eleven explanatory variables, Boos and Holm-Müller (2013) estimate an average decrease in GS rates by 1.4 % if primary exports increase by 10 %.

  7. The rather famous name of this model (described in detail in Corden 1984)—Dutch disease—originates from the decline in the Dutch manufacturing sector after the discovery of new sources of natural gas in the North Sea (The Economist 1977).

  8. The GINI index measures the extent to which the distribution of income among households within an economy deviates from a perfectly equal distribution; a GINI index of 0 represents perfect equality, while an index of 100 implies perfect inequality.

  9. The global average lies at a total of US$ 115,484 (physical: US$ 20,329; natural: US$ 7119; intangible: US$ 88,361) per capita (World Bank 2011a).

  10. NCT comprise all exchanges with foreign countries of goods and services as well as income and financial items without a quid pro quo (World Bank 2014a).

  11. To date, this covers coal, crude oil, and natural gas.

  12. This covers bauxite, copper, iron, lead, nickel, phosphate, tin, zinc, gold and silver.

  13. Roundwood harvest minus natural growth; this is set to zero if growth exceeds depletion (World Bank 2014a).

  14. The total average for the period between 1964 and 2012 was around 11 % of GNI, far below the world average of 23 %.

  15. The total average was approximately 80 %, below the world average of 85 % and only slightly above the 78 % average of the high-income OECD countries. Compared to other African countries such as neighboring Mozambique with an average of 110 %, this is rather low.

  16. We do not discuss the contents of GS in general, for a critique of the calculation method see Boos (2015). However, it is disputable whether investment in K H should only be defined by education expenditures and if this only counts for a working life or not (Dasgupta 2004; O'Sullivan and Sheffrin 2007). We argue with Baird et al. (2011) that health and income are strongly related and in a situation like Zambia’s, health expenditures also comprise investment in K H. We also show in Figs. 6 and 7 not only the adjusted NNS (ANNS) with education, but also including health expenditures, which results in an average difference of 6.7 % of GNI (available since 1995).

  17. Dividing 100 % of expenditures by the average 40 years of a Zambian’s working life (the Zambian retirement age of 55 minus the UNESCO definition of the beginning of a working life at 15) ascribes 2.5 % of education expenditures to every working year.

  18. The difference between education expenditures and investment in K H is explained by the exclusion of investment in K P, such as school buildings, since it is already included in NNS.

  19. Social costs from CO2 emissions are assumed to be US$ 20 (in 1990 US$) per emitted metric ton (Frankhauser 1994).

  20. This originated from an article by “The Economist” analyzing the decline in the Dutch manufacturing sector after the discovery of natural gas sources due to a crowding out of investments (The Economist 1977).

  21. See for example Gylfason et al. (1999), Gylfason (2000, 2001a, b, 2006), Auty (2001, 2007), Mehlum et al. (2006), Robinson et al. (2006), Van der Ploeg and Poelhekke (2009), Humphreys et al. (2007), Lederman and Maloney (2007) or Frankel (2012) for a comprehensive overview of the discussion.

  22. The theoretical framework by Boos and Holm-Müller (2012) divides exogenous explanations for the effects of the RC on GS into short- and medium-term volatility of international commodity markets as well as long-run terms of trade (ToT) effects, both of which we examine in the section on volatile world market prices (Sect. 4.2.1), and the Dutch disease with all its effects in Sect. 4.2.2.

  23. We follow the same framework (Boos and Holm-Müller 2012) in dividing the endogenous explanations into the political system (Sect. 4.3.1) and the quality of institutions (Sect. 4.3.2), but one could argue that the distinction between politics and institutions is overly meticulous since the political system is a part of the institutions. However, we explicitly use this division since indeed both affect each other but simultaneously cause different rather independent effects from each other. Institutions can be instable and corrupt in democracies and vice versa. Even if both can only develop in the same direction, they have different effects on GS since the transmission channel through which the RC functions is different in the case of the quality of the political system itself or its bureaucratic apparatus.

  24. We use a simple Pearson correlation coefficient: This is defined as the covariance of two variables divided by the product of their standard deviations, showing a value in the range of −1 for a complete negative correlation to 1 for total positive correlation (Griffiths et al. 2008). We only use these correlation coefficients through the rest of our analysis to emphasize our qualitative arguments without applying them within the discussion due to the possibility of error between only two variables. However, we have attempted different models with multiple variables without significant results to benefit our analysis.

  25. The Zambian copper mines were nationalized into the Zambia Consolidated Copper Mines (ZCCM).

  26. Net barter terms of trade index: percentage ratio of the export unit value indexes to the import unit value indexes measured relative to the base year 2000 and available since 1980 (World Bank 2014a).

  27. Terms of trade adjustment: capacity to import less exports of goods and services in constant ZMK (World Bank 2014a).

  28. The ZMK consists of 100 ngwee. Due to the extremely high inflation in the last two decades the government revalued the currency in 2013 and dropped three zeros.

  29. As the theory would indicate (Sachs and Warner 1995/1997; Boos and Holm-Müller 2012), the correlation coefficients between ToT and GDP (independently of which measure is used) are all positive (by more than 0.5), while ToT is negatively correlated to GS rates (by more than −0.3).

  30. The World Bank’s “real effective exchange rate is the nominal effective exchange rate (a measure of the value of a currency against a weighted average of several foreign currencies) divided by a price deflator or index of costs” (World Bank 2014a).

  31. Official data by the “Central Statistical Office Zambia” show US$ 5.4 billion (46.5 %) in 2007 and US$ 6.9 billion (46.5 %) in 2008: http://www.zamstats.gov.zm/real.php. Arnold and Mattoo (2007) even cite a 64 % service sector share of Zambian GDP.

  32. PIV shows the quality of “democratic and autocratic authority in governing institutions […] from fully institutionalized autocracies through mixed, or incoherent, authority regimes […] to fully institutionalized democracies. The ‘Polity Score’ captures this […] spectrum […] from −10 (hereditary monarchy) to +10 (consolidated democracy)”: http://www.systemicpeace.org/polity/polity4.htm.

  33. Freedom House rates political rights and civil liberties individually from 1 (highest) to 7 (lowest level of freedom) and values the status of free (1.0–2.5), partly free (3.0–5.5), or not free (5.5–7.0) from 1972 on: http://www.freedomhouse.org/report/freedom-world-2012/methodology.

  34. “Political rights […] are based on an evaluation of three subcategories: electoral process, political pluralism and participation, and functioning of government” (Puddington 2013, p. 32).

  35. “Civil liberties […] are based on an evaluation of four subcategories: freedom of expression and belief, associational and organizational rights, rule of law, and personal autonomy and individual rights” (Puddington 2013, p. 32).

  36. Structural Adjustment Programs in general are not without their critics and especially in Zambia’s case—where all five formal programs with the IMF between 1976 and 1991 collapsed (McPherson 2004c; White and Edstrand 1994)—the nature of the country’s economic structure and the weakness of world commodity markets made short-term gains impossible (Loxley 1990; Callaghy 1990). The abandonment of the program in favor of a “growth from own resources” program also failed (Kayizzi-Mugerwa 1990). The economic performance after this last Structural Adjustment Program at the beginning of the 1990s was especially alarming. Following World Bank/IMF standards much progress in liberalization was accomplished, but the manufacturing output decreased by 10 % and GDP per capita shrank by a yearly average of 2.8 % from the end of the 1980s to the mid-1990s (Saasa 1996).

  37. For example, the so-called Keinbaum Report was neglected, which provided a detailed analysis of privatization options and recommended the rapid unbundling and selling of the mines, while a World Bank report by twelve mining specialists, which called for an immediate revival of the mines, was never released (McPherson 2004b).

  38. In September 2011, Michael Sata was elected as the fifth Zambian president. Since none of the decisive indicators changed and Sata’s reforms did not properly start by the end of our analysis in 2012, we chose to leave out this last year in the political discussion. Additionally, none of Sata’s plans to develop the economy were ever quite realized since he died in office in October 2014.

  39. The “International Country Risk Guide” by “The Political Risk Group” (PRS) monitors 22 variables based on expert assessments in more than 140 countries. The index of corruption is part of its “Political Risk Rating” and uses a scale from 0 (highest possible corruption) to 10 (lowest corruption) (Dietz et al. 2007; The Political Risk Group 2013).

  40. “Freedom from Corruption” is part of the “Index of Economic Freedom” by The Heritage Foundation (2015), which monitors 10 variables from property rights to entrepreneurship from 0 (highest possible corruption) to 10 (lowest corruption).

  41. The correlation coefficient between international copper prices and the “Corruption Perceptions Index” by “Transparency International” was at 0.78 from the end of the 1990s to 2012: http://archive.transparency.org/policy_research/surveys_indices/cpi.

  42. The “Corruption Perception Index” (CPI) by “Transparency International” (TI) captures the “perceptions of corruption of those in a position to offer assessments of public sector corruption” drawn “from independent institutions specializing in governance and business climate analysis” from 0 to 10 (Transparency International 2012).

  43. Most industrialized countries have positive GS rates (in 2012, only 6 % of the high and upper middle income countries had negative GS rates) while developing countries more frequently suffer from low or negative GS, in which resource-dependent play a particular role. In 2012, of a total of 45 countries with more than 10 % natural resources depletion (12.9 % in Zambia) only ten countries had positive GS rates higher than 10 % of GNI (6.8 % in Zambia) (World Bank 2014a).

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Boos, A., Holm-Müller, K. The Zambian Resource Curse and its influence on Genuine Savings as an indicator for “weak” sustainable development. Environ Dev Sustain 18, 881–919 (2016). https://doi.org/10.1007/s10668-015-9667-5

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Keywords

  • Zambia
  • Resource Curse
  • Genuine Savings
  • Weak sustainability
  • Natural capital
  • Sustainable development