rafmacalaba/datause-encoder-data huggingface dataset:
Figure 10: Exchange rate, cedi to US dollar, 2000 to early 2015 <!-- Start of picture text --> 40000.0<br>35000.0<br>30000.0<br>25000.0<br>20000.0<br>15000.0<br>10000.0<br>5000.0<br>0.0<br><!-- End of picture text --> Source: Data from Bank of Ghana.

# **9 Declining real GDP growth**

While the discovery of oil initially boosted economic growth, real GDP growth in Ghana— notwithstanding the onset of oil production—has declined significantly since 2011. Data from the Ghana Statistical Service (Government of Ghana 2011) show that real GDP growth increased from 8.4 per cent (without oil) in 2008 to 15 per cent in 2011 (when it ranked among the highest in the world). Since 2011, however, real GDP growth (including oil) has slowed down—to 7.9 per cent in 2012, and then to around 4.2 per cent in 2014 (Figure 11). The 2015 budget projected further declining growth—to 3.5 per cent, at a revised oil price of US$52.8 per barrel.<sup>6</sup> Non-oil GDP growth has similarly declined from 9.4 per cent in 2011. The actual growth rate in 2015, at just under four per cent, was only marginally higher than in the year 2000, and less than half the rate achieved in 2008 without oil.

The decline in real GDP growth can be attributed to a number of factors including (i) the increase in current expenditure at the expense of capital expenditure, (ii) the greater fragility of both the private sector (partly due to a reliance on public contracts that could not be honoured given the need for adjustment post-elections) and the financial sector (given the accumulation of arrears and

> 6 The initial budget projection was based on a price of US$99.3 per barrel.

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of the transition, a period that usually coincided with the launch of privatization. Yet, they have been among the fastest growing economies since then -- in the last ten to fifteen years. In contrast, China did not lead its transition with large scale privatization and it avoided the transition recession observed in Central and East Europe (CEE) and the Commonwealth of Independent States (CIS). However, it is relatively soon to draw strong conclusions from the Chinese experience with privatization, and there is a paucity of econometrically convincing studies at this stage. The evidence assembled in this study suggests that privatization and performance are related but that the relationship is more complicated than has been assumed.

First, privatization to foreign owners is found to result in considerably improved performance of firms virtually everywhere in the transition economies – an effect that is best characterized as a fairly rapid shift in performance rather than a gradual improvement over an extended period of time. Second, the performance effect of privatization to domestic owners has on average been less impressive and it has varied across regions. The effect has been smaller, often delayed, but positive in CEE; it has been nil or even negative in Russia and the rest of the CIS. This divergence of findings between the CEE and CIS coincides with differences in policies and institutional development in the two regions, with the former increasingly adopting European Union (EU) rules and joining the EU, and the latter proceeding slower in introducing a market friendly legal and institutional system. Third, in China the results to date are less clear cut and relatively more estimates suggest that privatization to domestic owners improves the level of performance, perhaps because of the benefits of the gradual reform process.

In-depth firm-level studies further suggest that concentrated (especially foreign) private ownership has a stronger positive effect on performance than dispersed ownership in CEE and CIS, but foreign joint ventures rather than wholly owned foreign firms have a positive effect on the level of total factor productivity in China. Worker ownership in CEE and CIS (collective ownership in China) does not seem to have a negative effect. Data from CEE and CIS suggest that new firms are equally or more efficient than firms privatized to domestic owners, and foreign start-ups appear to be more efficient than domestic ones. Interestingly, contrary to assumptions of many theoretical models, as well as evidence from some developing countries (e.g., Mexico), privatization in the post28
# **1 Introduction**

Since the Covid-19 pandemic started in early 2020 there has been an increasing interest in understanding firms’ resilience to economic shocks. Since firms are increasingly operating within global value chains (GVCs), it is of particular interest to scholars and policymakers to understand the vulnerabilities that come along with this interconnectedness in order to better prepare for future shocks. Covid-19 disrupted many GVCs and led to supply shortages in different sectors all around the world (Cerdeiro and Komaromi, 2020). Additionally, the pandemic revealed structural impediments in the trade sector that could be addressed with the right policy changes. However, determining whether participation in GVCs is beneficial or detrimental for firms during a global crisis situation remains a difficult endeavor, as illustrated by the fact that previous studies reveal heterogeneous effects between different firms (Cirera et al., 2021), sectors (Espitia et al., 2022), countries (Bruhn et al., 2021) and outcome measures (Constantinescu et al., 2022).

In this paper, we analyze how the pandemic affected firm-level imports and exports in Indonesia as well as quantify the role of non-tariff measures on imports, using detailed monthly firm-level trade data from February 2019 to June 2021. We measure the effect of the pandemic on a variety of outcomes, including firm-level export and import values, volumes, number of transactions, and number of products. To measure the effects of NTMs on firm-level trade, we compare various firmlevel import and export outcomes before and after Covid-19 for firms exposed to NTMs compared to firms that were not, including firm-, HS-8 product-, and partner country fixed effects. We find evidence that certain types of NTMs, such as the port of entry restrictions on imports, negatively affected firm-level exports during the pandemic.

In the second step, we compare the impact of the pandemic on firms that participated in GVCs with those that did not. GVC firms are defined as firms that both imported and exported in the Covid-19 pre-period (February 2019-November 2019). All other firms are categorized as nonGVC firms. Since in GVCs, tariffs and non-tariff barriers are effectively a tax on exports (because imports are essential for exports), to further understand the effect of import policies, we differentiate between GVC firms that faced non-tariff measures (NTMs) versus those that did not. To assess the level of NTMs faced by a firm, we rely on data from the World Bank Jakarta NTM database. This data improves on other NTM data by UNCTAD-ERIA, as it is at a higher frequency (monthly), more updated (up to December 2021), and has a time dimension (panel). This makes it appropriate for the purposes of this analysis because we can capture changes in NTMs real-time during the pandemic as opposed to annual data.

Indonesia makes an interesting case for multiple reasons. High levels of raw material exports have led to high forward participation in GVCs, but this has been on a downward trend in recent years (Figure 1). The backward participation is still in its infancy, with low level of foreign value 2
the change in producer surplus can be generalized as follows: The change in producer surplus can be evaluated either via equation (12), using retail margins from existing literature and our estimate of real-sales response, or via equation (13).

# **5.4 Calibration**

Given the formulas afforded by our theoretical framework, we now calibrate the right-hand-side terms using estimates independently obtained from separate regressions reported above or from existing literature. We then compare the combined calibrated pass-through elasticity with the reduced-form pass-through elasticity estimated for the left-hand side. We also calculate local incidence. These results are reported in Table 8.

As the first step, we restate two empirical estimates for _εQD,S ,b_ , the MPCF elasticity, in equation (8). We take these estimates, discussed earlier in Section 4.2, to serve as the lowerand upper-bound anchors determining reasonable bounds of price elasticities to consider. For the lower-bound anchor, we net out the price effect from the MPCF of SNAP consumers in elasticity form as reported in Table 7, column (2), row 4: 0.411 - 0.0724 = 0.339. We take this to be the lower bound because, in our definition, _εQD,S ,b_ is a partial derivative assuming no price change, whereas the estimate in Table 7 is the response in equilibrium with the price change. The real-sales response should be higher without the price change.<sup>42</sup> For the upper-bound anchor, we take the MPCF estimated by HS—whose magnitude is higher with a very small standard error—and multiply it by 0.9, the mean of SNAP benefits to food spending in the CEX, to obtain an elasticity: 0.58 * 0.9 = 0.522. Since this MPCF was estimated using variation in the timing of program exit across individuals, price responses are not a factor in this estimate. While this estimate is not necessarily biased upward, we take this to be an upper bound to be conservative in our calibration of the price response. These numbers are shown in the first and second rows of the first column of Table 8.

The second column of Table 8 restates the benefit-pass-through elasticity estimated in Table 6, column (3), row 1: 0.0724. Our aim is to check whether this number falls in line with the theory-based calibrations.

The third column of Table 8 reports the shift magnitude,<sup>_∂_</sup> _∂b_<sup>_<u>p</u>_</sup> _pb_<sup>=</sup> _εQ−D,SεD_ _<u>,b pQpQ</u>_<sup>_D,S_.As discussed</sup>

> 42Another possible reason making this a lower bound is imperfect designation of SNAP versus non-SNAP consumers in the consumer scanner data.

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storms would tend to cancel each other out when estimation does not differentiate by severity of the disaster.

Finally, regarding agricultural growth, moderate storms have a negative and significant effect in year 1 of the event. However, in the following year, the effect is positive, significant, and of about the same size, cancelling the previous one.

# **V. Concluding Remarks**

This study has analyzed the path of macroeconomic adjustment and recovery in the aftermath of four types of natural disasters, namely, droughts, floods, earthquakes, and storms. Specifically, we have measured and examined the mean response of GDP per capita growth and its major components, agricultural and non-agricultural per capita value-added growth. Applying a VARX methodology on a panel of 87 countries and 48 years (1960-2007), we find heterogeneous effects on a variety of dimensions. First, the effects of natural disasters are stronger, for better or worse, on developing than on rich countries. Second, while the impact of some natural disasters can be beneficial when they are of moderate intensity, severe disasters do never have positive effects. Third, not all natural disasters are alike in terms of the growth response they induce, and, perhaps surprisingly, some can entail benefits regarding economic growth. Even within commonly used categories of natural disasters (e.g., climatic), different types of disasters can and do have different effects (e.g., droughts vs. floods).

Let’s focus the conclusion on the results for developing countries. Droughts have an overall negative effect on GDP growth. As expected, the effect is stronger for agricultural growth, but it is also negative for non-agricultural activities. For agricultural growth, the negative effect of droughts is immediate, while for non-agricultural growth, the negative impact is felt also with some delay. The cumulative negative response to droughts is 1.7 percentage points (pp) for GDP growth and 1.6 pp for agricultural growth.

32
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Policy Research Working Paper 8243

# **Abstract**

This paper evaluates the heterogeneous impact of spillovers from multinational corporations (MNCs) to domestic enterprises in the developing world. It empirically investigates two transmission channels of knowledge spillovers. First, direct contractual linkages between indigenous firms and MNCs. Second, indirect demonstration effects accrued by domestic firms by imitating foreign technologies either through observation or by hiring workers trained by MNCs. The paper focuses on the impact of spillovers on high-growth firms, which are enterprises with high job creation rates and, therefore, assumed to have high absorptive capacities. The paper also evaluates spillovers stemming from MNCs with different motivations to invest in developing countries. Employing a survey of around 71,000 firms across 50 sectors in 122 developing countries, the paper shows that high-growth firms internalize spillovers through both avenues and that contractual linkages are the most powerful transmission channel. FDI embedded in global value chains generates larger spillovers to high-growth domestic firms than investment that seeks to serve the host economy. There is no evidence that natural resource-seeking FDI generates spillovers. The results have important implications for policy design, as public funding in developing countries is often directed to support programs that seek to connect domestic suppliers with MNCs.

This paper is a product of the Trade and Competitiveness Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at danielreyes@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
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inequality (usually measured by the Gini coefficient) the evidence remains very mixed: many studies find no evidence of openness on inequality, or that openness increases inequality at all levels of development.<sup>2</sup> More intriguing to many is the lack of robustness towards expectations from the standard Heckscher-Ohlin (HO) trade model: conflicting evidence that greater openness reduces (increases) inequality in developing (developed) countries and very qualified support for the hypothesis that endowments matter along the expected lines (see below), not to mention little support for robust results between trade liberalization and inequality.<sup>3</sup> One might also add that the lack of correlation between factor endowments and inequality should come as a surprise to scholars working on the institutional foundations of development who generally find strong evidence that endowments matter in the evolution of a country’s inequality (Hoff (2004)).

Perhaps this should not come as a surprise and not concern us too much if, via other channels such as growth, increased openness reduces poverty. After all, HO theory should only be expected to inform us about the relation between endowments and factor rewards in response to a reforminduced change in relative factor demands rather than between endowments and overall income inequality which is determined by many other factors. And, as pointed out by Baldwin (2004, p. 517) in his review of the trade liberalization and growth literature, since trade liberalization is rarely applied in isolation, it makes little sense to try and isolate its effects from those of associated policies.

> 2 Barro (2000), Lundberg and Squire (2003) and Milanovic (2005) find that openness increases inequality whereas Edwards (1997), Ravallion (2001) or Dollar and Kraay (2002) find no significant relationship.

> 3 See Anderson (2005) for a survey of the conflicting evidence on openness and inequality, and Winters et al. (2004) for a survey of the evidence on trade liberalization and poverty. Spilimbergo et al. (1999), Milanovic (2005) and Bensidoun et al. (2005) are the studies most closely related to ours.

3
investors to currency and inflation risks. Therefore, investors would be more willing to go long in hard currencies than in Chilean pesos (i.e., holding hard currencies allows investors to hedge part of the inflation risk) only if these risks commove strongly negatively in the long run, which does not seem to be the case of Chile.<sup>19</sup> Not being exposed to currency or inflation risk, indexed peso bonds are relatively less risky than peso and hard-currency bonds, especially at longer maturities, which could account for the willingness of Chilean investors to buy more long-term indexed peso instruments. Therefore, for some types of instruments, asset managers might perceive a tradeoff between maturity, on the one hand, and currency and inflation risks, on the other hand. When they can reduce those risks, they seem more willing to invest more long term. This confirms the importance of comparing pension and mutual fund portfolios using instruments with different currency denominations.<sup>20</sup> Figure 9 compares the holdings of Chilean mutual and pension funds in hard currencies and indexed pesos to those of US multi-sector mutual funds. The figure shows that, even when investing in these two types of instruments, Chilean investors still tend to real returns. Thus, the reduction in real returns resulting from inflation can potentially affect their ability to capture new affiliates or generate outflows. Although there is not much evidence that the number of affiliates changes with returns, the ranking of PFAs by returns (typically used in publicities) seems to be positively correlated with the number of affiliates across PFAs (Cerda, 2005).

> 19 While the correlation between monthly inflation and depreciations of the Chilean peso against the US dollar between 1990 and 2008 is about 0.17, the correlations between annual and bi-annual inflation and depreciations are 0.35 and 0.49, respectively.

> 20 As mentioned above, portfolio allocation not only depends on the risk of different assets, but on their risk-return profile. The discussion of this section abstracts from return differentials and focuses only on risk differentials at different maturities. Establishing the optimal portfolio allocation from the historical returns of various assets requires data on the yield of a meaningful number of comparable maturity bins for instruments in pesos, hard currencies, and indexed pesos. This is beyond the scope of this paper. However, a simple calculation based on Central Bank of Chile data on the yields of four overlapping maturity bins for each currency class indicates that a mean-variance investor with a one-month horizon and subject to a noshort-sale constraint would invest only in indexed peso instruments. This evidence is available upon request.

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