Social capital

By a comparative static analysis, the authors were able to derive a variety of useful insights. Most importantly, they showed the following factors to discourage participation in education and ceteris paribus lower the share of high-skilled labor: (i) higher subjective discount rate, (ii) lower elasticity of intertemporal substitution, (iii) an increase in the price of the natural resource (see the dashed arrows in Figure 16), and (iv) worse institutional quality. Please note that a lower share of high-skilled labor is always coupled with lower rates of economic and manufacturing growth, enrolment, and human capital accumulation. The first factor is related to the crowding-out through the false sense of security provided by the resource revenues. In this context, the myopic behavior translates to an excessive discounting of the future and crowds-out education. The second factor resembles the volatility curse explanation as highly volatile resource revenues introduce uncertainty about the expected net returns on investments, so that households attach more importance to current consumption. Put differently, volatility causes the elasticity of intertemporal substitution to decrease and crowds-out education. The third factor is the human capital equivalent of a basic Dutch disease model: A higher commodity price translates to a higher value of the marginal product in extraction and diminishes the wage premium of high-skilled labor, or in other words, it crowds-out education by increasing the opportunity cost. Deindustrialization in this framework is driven both by the resource movement effect and the decline of the positive spillovers in manufacturing due to the slower accumulation of human capital. The fourth factor concerns the quality of governance in terms of industrial and trade policies (not shown in Figure 16).

Poor governance diminishes the profitability of manufacturing, cuts the premium on high-skilled labor, and ultimately crowds-out education. To relate this mechanism to natural resources, one would need to show that abundance tends to worsen the quality of governance. Chapter 1 will intend to confirm this hypothesis in a political economy framework. However, even at this point, theoretical and empirical evidence seems convincing enough to recognize the crowding-out of human capital⁹⁸ as an indirect but significant transmission channel of the resource curse. Furthermore, due to the expected secondary interactions between the components, human capital accumulation is likely to be inseparable from a broader context of socioeconomic progress. Hard to be captured by formal theoretical and statistical models, unfortunately this embeddedness is even more pronounced in the nature of the next component…

of self-enforced forms of cooperation as opposed to third-party enforcement¹⁰¹. Furthermore, the accumulation of social capital could be interpreted as the elimination of social tension or as an improvement in cohesion, both of which fostering inclusion and trust among the agents. However, independently from the exact definition, “one would expect communities blessed with high[er] stocks of social capital to be safer, cleaner, wealthier, more literate, better governed, and generally happier” (Woolcock, 1998, p. 155). Therefore, the possibilities of theoretical conceptualization into formal models are not much restricted. Social assets might be introduced in the form of new production factors or components of produced capital, as technological improvements to resemble an increased efficiency due to the lower transaction costs, as well as variables governing the quality in an institutional framework.

Moreover, there are no obvious mechanisms nor widely accepted explanations of how social capital is accumulated.

Characteristics of the process are rooted in culture, tradition, religion, art, and other premodern sources, while also being described under fundamentally different assumptions. Most importantly, libertarian think-tanks argue that social capital is spontaneously generated through repetitive free-market interactions¹⁰², whereas left-wing advocates emphasize the role of the state as it “can embody or promote meaningful social solidarity” (Fukuyama, 1995, p. 103).

Furthermore, just like its source, the product of social capital is also inherently embedded into the environment and does not manifest itself as private property. That is, the returns are mostly realized in form of public goods.

Alike other capital components, social assets may also differ in their quality in terms of varying or even negative returns. Portes (1998) identified four of these adverse effects: (i) the exclusion of outsiders, (ii) excessive claims on group members, (iii) restrictions on individual freedom, and (iv) downward leveling norms. The first one implicitly follows from the enhanced efficiency of cooperation among the insiders that necessarily restrict outsiders. In this case benefits are realized as club goods through formal or informal networks¹⁰³. This mechanism was observed among the traditional immigrant communities in the United States based in either familiar, ethnic, or religious connections. The second effect arises as a free-riding problem when less diligent members of a group successfully enforce different claims and get privileged access to the common resources, diminishing the overall efficiency of the cooperation. The third effect is related to the first dimension of social capital and tends to be stronger in smaller communities with dense multiplex networks. Social binding on a small scale translates to closer and emotionally driven relations referred to as bonding. Local norms and traditions embodied in these intense connections induce peer pressure in the form of excessive expectations and obligations on individuals, restricting their personal freedom and autonomy. Costs of meeting these expectations are often so high that they force younger members of the community to leave as net benefits from cooperation turn negative. Finally, downward leveling norms affect groups defined by an experience of common adversity and opposition towards the mainstream society. In this case, individual success stories undermine group cohesion and result in norms that operate to keep the members in place while forcing more ambitious individuals to escape.

Notwithstanding its intuitively significant role¹⁰⁴ in socioeconomic development, quantifying and measuring social capital turned out to be more than challenging. Problems stem from the relative ambiguity of the definition

101 This aspect gained significant academic interest from game theory.

102 An evolutionary interpretation is that the efficiency improvements achieved through repetitive interactions would sort out other structures of less efficient norms and behavioral patterns. Hence, social capital builds up as a set of best practices related to different economic and social interactions. This explanation only requires the acceptance of efficiency as a shared value, a condition that directly follows from the rationality assumption.

103 In Spain and other Hispanic countries, the word “enchufe” (plug; contact) or “enchufado/a” (plugged in; connected) refers to favors received or other advantages derived from informal personal connections. Similar expressions in Hungarian are “csókos” (literally: the one with a kiss) or “liebling” (from the German word meaning favorite or darling). Enchufe is closely related but still different than corruption as the benefits are not coupled with direct compensation but work on a looser basis of reciprocity. However, it still leads to the exclusion of outsiders and ultimately to an inefficient allocation of the resources.

104 Let us imagine the macroeconomy as an engine where the design represents the technology, different mechanical parts are components of capital other than social, and labor is the fuel. In this metaphor, social capital would be lubricating the system as motor oil to make it run smoothly and efficiently. The improved performance of the engine resembles economic growth, while the longer expected lifespan represents the positive spillovers on the accumulation of produced capital.

which allows for a wide range of possible interpretations and drives abstract mathematical models that are hard to test empirically. “Putnam’s instrument” for example, measuring the density of voluntary organizations, is a potential proxy to estimate social capital if it is understood in the sense of self-enforced cooperation. However, this concept fails to capture the share of the stock that is embodied in non-institutionalized interactions. A similar problem arises if social capital is estimated by the sum of all group memberships in a society. Moreover, group-based measures largely ignore the quality of the stock that ultimately determines the growth effects. A possible solution is to take qualitative measurements from large-sample surveys as it is attempted by the United Nation’s World Social Capital Monitor. This instrument is based on eight questions to be evaluated on a scale of ten and aims to address different manifestations of social capital such as trust, helpfulness, hospitality, and others¹⁰⁵. However, this estimation only captures the perceived level of the stock while the currently available dataset is limited to 76 countries¹⁰⁶.

Taken these methodological issues, together with those that concern natural wealth, establishing a reasonable channel for the crowding-out of social capital is definitely a hard case. Nevertheless, as Auty (1998b) argues, both natural and social capital must be incorporated into the concept of sustainable development. In an attempt to achieve that, I will go back to square one and try to capture the common fundamentals beyond the different definitions of social capital. As it only exists in interactions, the concept necessarily requires some form of cooperation, which also implies the presence of common goals or shared values. From this very basic argument it follows that homogenous groups accumulate social capital faster simply because they are more likely to have common interests. Therefore, on the macro scale, less polarized societies are expected to build up higher stocks.

Dimensions of polarization might cover ethnic, religious, political, but most importantly economic aspects. Section 1.1.3 already showed how the first three factors interact with natural wealth to increase the risk of internal conflict in fractionalized, or in other words, more polarized countries. Since point-source resources undermine social cohesion, conflicts are up to be interpreted as the erosion of social capital that later necessarily leads to economic decline (see Figure 2). Thus, natural wealth does crowd-out social capital by increasing the tension between interest groups and deteriorating the means of cooperation. In the dimension of quality, polarization necessarily causes exclusion so that regardless of the internal cooperation in separated fractions, the overall efficiency is likely to decrease, causing social capital to yield low or negative returns as described by Portes (1998).

However, in accordance with the goal of this thesis, as well as with Knack & Keefer (1997), I consider income inequality to be a more direct and relevant proxy for polarization¹⁰⁷. Opposed to Kuznets’ law, income inequality measured by the Gini-coefficient was shown to be monotonously and negatively related to economic growth, while the famous inverted U-shape was only verified in terms of the initial incomes (Gylfason & Zoega, 2002, 2003). Here again, a plausible interpretation is that high inequality means lower levels of social capital so that the adverse effects simply follow from the Solow model. In terms of the resource curse hypothesis, if natural wealth tends to increase inequality, it also crowds-out social capital and ultimately causes growth failures. Carmignani (2013) developed a theoretical framework for this transmission channel based on the uneven geographical distribution of the reserves.

Under such condition, search and extraction resembles a stochastic Poisson process which implies that both the expected value and the variance of resource rents are going to be higher in more abundant countries¹⁰⁸. That is, natural wealth contributes directly to economic growth, but it also necessarily increases inequality. More recently, Behzadan et al. (2017) adapted this concept into a dynamic Dutch disease model and demonstrated that in case of otherwise identical countries, higher inequality induces lower output and less learning-by-doing in manufacturing.

105 Available at https://trustyourplace.com

106 As of 2019.

107 For more about how the concept of social capital is related to economic equality, work and social satisfaction, transparency, as well as to innovation, please see the case-study about the European Union by O’Connell (2003).

108 More specifically, the model predicts a positive linear relation between resource abundance (see Section 3.1 for disambiguation) and the variation of the corresponding rents (as realized by individual actors). Therefore, income inequality grows with natural wealth, which is in my interpretation, equivalent with the crowding-out of social capital.

Thus, the more unequal is the distribution of the resource rents, the stronger is the disease. Both studies, as well as Leamer et al. (1999) and Gylfason & Zoega (2002), reported corresponding empirical evidence¹⁰⁹, even if resource rich countries were also shown to underreport data on inequality (Parcero & Papyrakis, 2016).This bias might partially explain the failure of my attempt to replicate these results.

I examined the nexus of resource rents and the Gini coefficient in a 2016 cross-country dataset, and although I did identify a positive relation, the OLS regression turned out to be statistically insignificant¹¹⁰. As this model was limited in observations, I replaced the variables with their averages over the period from 2000 to 2017, which increased the sample size to 157 countries. However, the regression applied to the extended dataset yielded similar results; a weak and statistically insignificant, but at least positive relation¹¹¹. Based on that, I cannot confirm nor reject the crowding-out hypothesis, but considering the previously cited theoretical and empirical achievements, I still find the concept more than plausible.

Figure 17: Crowding-out in two stages¹¹²

As noted before, similarly to the human component, social capital is also deeply embedded and likely to simultaneously induce secondary spillovers while also suffer from them. Hence, recontextualizing the crowding-out mechanism as a two-step process might dissolve some theoretical confusion and provide new insights for empirical research (see Figure 17). The idea that social capital plays an important role in the creation of human capital dates back to Coleman (1988) and was at least implicitly postulated by several others since then. However, it is also reasonable to assume a two-way interaction as better educated and healthier actors seem more likely to organize themselves, set long-term goals, find synergies, build strategies, and cooperate more efficiently in general. That is, the accumulation of human and social capital is interconnected by mutually positive spillovers. In accordance, Gylfason & Zoega (2003, p. 557) reject the necessity of the “perceived but poorly-documented trade-off between efficiency and equality” arguing that equality drives investments into education, which yields high-enough social return to counterbalance the efficiency losses from redistributive policies aimed on eliminating income inequality.

They developed an intergenerational model of capital accumulation which shows how inequality tends to diminish education both in quality and quantity¹¹³, while also linking this effect to the aforementioned positive relation between equality and growth. Furthermore, by assuming complementarity between physical and human capital,

109 The case study about Russia in Section 5.2 will present further evidence on the regional level.

110 OLS estimation by the author with n = 75 and R² = 0,011. Coefficients are insignificant on all reasonable levels, t-statistic for β1 is 0,905. Data sources: https://data.worldbank.org/indicator/SI.POV.GINI and https://data.worldbank.org/indicator/NY.GDP.TOTL.RT.ZS.

111 OLS estimation by the author with n = 157 and R² = 0,002. Coefficients are insignificant on all reasonable levels, t-statistic for β1 is -0,661. Data sources: https://data.worldbank.org/indicator/SI.POV.GINI and https://data.worldbank.org/indicator/NY.GDP.TOTL.RT.ZS.

112 Author’s compilation. Dashed arrows represent direct negative effects in the first stage while continuous arrows resemble the positive spillovers causing the indirect crowding-out in the second stage. Here, the direct positive impact of natural capital is not shown but included in the model as in Figure 12.

* Includes financial capital. For the discussion, please see Section 2.2.4.

** Includes foreign capital. For the discussion, please see Section 2.2.5.

113 By attracting labor away from high-tech industries that require educational investment. For the details, please see the original paper.

Natural capital Human capital Social capital*

Physical capital**

Economic growth

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.