2.2 Crowding-out
2.2.4 Financial capital
their model features secondary spillovers eroding the physical stock through the human component (Gylfason &
Zoega, 2002). In this context, the resource curse is a malicious cycle where inequality crowds-out education and leads to further specialization in extraction, which consequently slows down the accumulation of both human and physical capital by driving an even less equal distribution of the income114. Now, accepting inequality as the proxy for social capital yields us the conceptual model of crowding-out in two stages as shown in Figure 17.
The first stage refers to the direct crowding-out of social and human capital, respectively by increasing income inequality and diminishing the returns on education (see Figure 16). Additionally, the Corden-Neary model describes the direct crowding-out of physical capital in terms of the deindustrialization driven by the resource movement and spending effects. However, due to the secondary spillovers in the interaction of human and social capital, an indirect form of crowding-out arises in the second stage. Primary effects cause secondary damages as losses in social capital impede the accumulation of human capital and vice versa, creating the malicious cycle. Negative growth effects are transmitted by mechanisms described earlier in Section 2.2.2 (see Figure 16), as well as through the secondary spillovers affecting the accumulation of physical capital (shown in the lower section of Figure 17)115. The two-stages model incorporates the idea that components of produced capital do not emerge in isolation and counts for the secondary interactions between them. In this framework, the adverse growth effects of the crowding-out of social capital arise as contributions to the indirect crowding-out of human and physical capital. Then, the subsequent growth failures are well-understood in terms of the Solow model. Moreover, the two-step process also explains the empirical experience of crowding-out being more pronounced if measured against the overall stock of real capital.
That is because aggregated indicators such as the adjusted net savings reflect all the spillovers, while separate measurements on each of the components necessarily fail to capture secondary interactions.
a sound project can obtain finance, and [as] the confidence with which investors anticipate an adequate return.”
Furthermore, “a developed financial sector can gauge, subdivide, and spread difficult risks […] at low cost” (Rajan &
Zingales, 2003, p. 9). Hence, through its fundamental impact on the efficiency of resource allocation, financial capital plays an essential role in economic growth119. Especially nowadays, as since the 1990’s financial development sped up again in a process called financialization. It refers to the growing importance of financial markets due to an orientation towards the concept of shareholder value120. According to G. F. Davis & Kim (2015), this shift leads to substantial changes in corporate strategy, encourages outsourcing, and drives higher compensation on the top. They also argue that the process has a potential to induce further changes in the broader society, including the growth of inequality in the first place. Therefore, in terms of the resource curse theory, financialization is likely to interact with natural wealth to amplify the crowding-out of social capital. However, this might be just a short chapter or even a dead end in financial development.
Agnello et al. (2012) argue that income inequality follows from the unequal access to productive opportunities, while appropriate financial reforms can improve this situation by increasing the efficiency of risk allocation, equalizing access to credit, and reducing the variation of expected marginal returns. By the panel analysis of 62 countries from 1973 to 2005 they found evidence that, among other factors, eliminating high reserve requirements and restricting directed credits drive a more equal distribution of the income. Corresponding results were reported by Hamori & Hashiguchi (2012), who examined the same nexus over a longer period of 40 years and on a larger sample of 126 countries. They used private credit-to-GDP ratios and measures of the money supply as proxies for financial deepening and found both variables to be significantly and positively related to equality. Both papers suggest that developed financial markets contribute to the stock of social capital121 but the excess financialization of the economy leads to a diminishment in its quality. Additionally, like many others, these studies accept the measures of money supply, private lending, and liquidity in general as good estimations to describe financial development.
Intuitively, one might expect the markets of resource rich economies to be flushed with liquidity originating from the windfall revenues. However, as it now seems usual in case of natural wealth, common wisdom fails again.
Bhattacharyya & Hodler (2014) examined how resource rents affected the aforementioned private credit-to-GDP ratio from 1970 to 2005 on a sample of 133 countries. They found a negative log-log relation between the changes in resource rents and private credit122, indicating the crowding-out of liquidity. Furthermore, this result was shown to be robust under different proxies, samples, and model configurations. Instead of resource intensity, Gylfason (2006) selected the share of natural capital as a measure of abundance to demonstrate the same nexus. He found a highly significant, exponential, and negative relation with the M2 monetary aggregate, another common measure associated with the broad supply of money. These studies suggest that the natural wealth causes a relative shortage of liquidity, thereby eroding the efficiency of resource allocation and restricting capital formulation in general.
Moreover, as financial underdevelopment means worse capabilities in accommodating uncertainty and spreading risk, crowding-out is likely to amplify the negative effects of commodity price volatility. Low levels of trust combined with weak contract enforcement and myopic behavior create the perfect circumstances for the volatility curse to reach its full strength. A sound revenue management, which would be essential to counterbalance boom-and-burst cycles, seems nearly impossible under such conditions.
119 This intuitive hypothesis has decisive empirical support. Please see Aziakpono (2011) for a detailed survey.
120 On contemporary markets, “almost any kind of cash-flow could be securitized and turned into a financial instrument.” (G. F. Davis & Kim, 2015, p. 217)
121 As the term is used in this thesis: Generally measured by income equality and particularly including financial capital as one of its institutionalized subcomponents.
122 They also found this effect to be significantly stronger in countries that does not qualify as democracies and argue that the quality of political institutions largely affects the outcome. This institutional condition will be discussed in Chapter 4, with a particular focus on the interaction of natural wealth and democracy in Section 4.2.2
Figure 18: Log-linear estimation on the crowding-out of liquidity123
Figure 19: Log-log estimation on the crowding-out of liquidity124
123 OLS estimation by the author with n = 154 and R2 = 0,133. Coefficients are significant at the 1% level, t-statistic for β1 is -4,83. Data sources: https://data.worldbank.org/indicator/FM.LBL.BMNY.GD.ZS and https://data.worldbank.org/indicator/NY.GDP.TOTL.RT.ZS.
124 OLS estimation by the author with n = 150 and R2 = 0,262. Coefficients are significant at the 1% level, t-statistic for β1 is -7,25. Data sources: See above. This sample is smaller due to the logarithmic transformation as four micro-states; Antigua and Barbuda, Grenada, St. Kitts and Nevis, as well as the West Bank and Gaza had not reported any incomes from extraction.
China
Congo DR Iraq
Kuwait
Liberia Suriname
Chad
Timor-Leste
y = -2.5369x - 0.4544 -2.0
-1.5 -1.0 -0.5 0.0 0.5 1.0
0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 55%
Natural logarithm of broad money (fraction of GDP)
Resource rents (% of GDP)
Argentina
China
Congo DR Japan
Kuwait
Maldives
Chad Vietnam y = -0.1256x - 1.1408
-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0
-13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0
Natural logarithm of broad money (fraction of GDP)
Natural logarithm of resource rents (fraction of GDP)
Considering its widespread consequences, I took another look on the relation of natural wealth and financial development. For consistency, I kept the usual proxy for intensity, the share of resource rents in the GDP, to estimate liquidity as measured by the supply of broad money125. On a sample covering 154 countries in 2016, I was able to confirm the crowding-out using either linear126, log-linear (see Figure 18), or log-log (see Figure 19) regressions. In accordance with the earlier investigations, I found the log-log model to have the highest explanatory power, suggesting that the crowing-out of financial capital takes place with constant elasticity. Again, this happens as higher risks and less options for institutionalized interactions between creditors and debtors decrease liquidity and erode the efficiency of resource allocation. In other words, extractive economies fail to “grease the wheel of production and exchange” (Gylfason, 2006, p. 222), which ultimately causes them to necessarily underperform their development potential. Moreover, as to be explained in the next section, exchange is also affected on the international level.