# **4.6 Protected areas**

The strong reliance on the agriculture, forestry and fishing for economic growth gives context to the negative coefficient estimates of forests and agriculture. Growth in these renewable resources is in the interest of the economic growth. The story is less clear-cut for protected areas, which account for wildlife reserves and ecosystem services, such as billions of dollars saved in drinking water treatment costs due to forests and wetlands, or revenues from international tourism (Dudley et al., 2010; World Bank, 2018). Biodiversity and the loss thereof is a core issue that has been gaining importance. The loss of pollinators is associated with a loss of global crop yield between US$235 and US$577 billion (IPBES, 2019). van Toor et al. (2020) estimate that investments worth EUR 510 billion by Dutch financial institutions are highly dependent on ecosystem services.

Table 3 presents evidence that higher borrowing costs may be related to expansion in protected areas. Earlier, we explained this finding through the opportunity cost motive, which underlies the valuation of protected areas (World Bank, 2018). This explanation finds further support in Table 5, which shows that borrowing costs mainly increase in B-rated countries and have no noteworthy effect in A-rated countries. At the same time, Figure 7c shows that areas under protection and conservation were predominantly designated in A-rated countries. Thus, even though A-rated countries experienced a much larger growth in protected areas, both in terms of area and number, it did not affect their borrowing costs. The opposite holds for B-rated issuers, which lends credibility to the opportunity costs of foregone agricultural rents. This suggests that protected areas are costly, non-productive investments that are unlikely undertaken because they are economically worthwhile. Thus, the luxury investment explanation seems more plausible.

# **4.7 Latent factor analysis**

The question of how many factors to choose for _퐹푡_ can be answered in various ways. Unfortunately, the limited size of the cross-section _푁_ = 37 renders popular approaches infeasible. Information criteria in the spirit of Bai et al. (2002) proved inappropriate as they tended to select as many factors as there are observations, a problem described in Elliott et al. (2016). The eigenvalue-ratio test and growth-ratio test (Ahn et al., 2013) indicate _퐾_ = 1, as shown in Figure 8. However, Figure 8 also reveals that a second factor may be relevant as well. We turn to economic arguments to decide between _퐾_ = 1 and _퐾_ = 2.

We follow Stock et al. (2005) and correlate the first and second factors of a specification assuming _퐾_ = 2 with observed macro-financial variables in Figure 9. Figure 9a shows that the first latent factor is most highly correlated with the J.P. Morgan Global Bond Index for the European Monetary Union (GBI-EMU) with correlation coefficient _푟_ = 98 _._ 2%, the MSCI World Index with _푟_ = 89 _._ 2%, and the J.P. Morgan Emerging Market Bond Index family (EMBI, EMBI-div, EMBI+) with _푟_ ≈ 87 _._ 3%. The relevance of the GBI-EMU is sensible since our sample includes 10 Eurozone countries and eight additional European Economic Area members. Figure 9b shows that the second factor is most correlated with the J.P. Morgan Global Bond Index for the Emerging Markets (GBI-EM), which tracks local currency government bonds in emerging markets. Since the composition of the B-rated countries contains several emerging market economies, it makes sense to include this factor as well. This gives us confidence that _퐾_ = 2 factors are necessary to isolate the relevant global bond dynamics.

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