rafmacalaba/datause-encoder-data huggingface dataset:
Figure 6: Cumulative Real Growth in Credit to the Private Sector by Provider (2000Q12005Q3) <!-- Start of picture text --> 240%<br>250%<br>200%<br>140%<br>150%<br>94%<br>100%<br>68%<br>52%<br>50%<br>18%<br>-31% -12% -17%<br>0%<br>Com Banks Sofoles Non-bank Infonavit Domestic Dev Banks Dev Banks Cross- Foreign<br>-50% FI bonds 2nd tier 1st tier border bonds<br>bank credit<br>Real growth (%)<br><!-- End of picture text --> Source: BOM and SHCP.

Note: FIRA and Financiera Rural lending, certain public sector retirement programs financing mortgages (e.g. FOVISSSTE) and mortgage/asset-backed securities are excluded. Only performing loans are included in the analysis. Domestic bond issuance only includes outstanding bonds issued by non-financial private sector firms. Non-bank FIs (Financial Institutions) includes SAPS, OACs and credit unions. Information for development banks is separated into first-tier and second-tier (i.e. via commercial banks and Sofoles) lending. Some numbers might differ from other Figures because they are deflated using the retail price index as opposed to the GDP deflator.

The share of debt financing to the private sector that is provided (directly or indirectly) by the public sector has risen. While development banks have generally moved from first- to second-tier lending, their funding (direct and indirect) of private sector finance has risen in the last five years in terms of GDP (see figures 7 and 8)<sup>11</sup> . In addition, development banks have began to provide a larger volume of partial credit guarantees to banks and Sofoles, which is used to promote lending to underserved sectors (e.g. small and medium-sized enterprises or SMEs, low-income housing and agriculture). Although the volume of these guarantees remains relatively low (around 0.5 percent of GDP as of 2005Q3), their effect is amplified by the fact that they are used to leverage a greater amount of lending and that they concentrate on specific sectors of the economy.

> 11 Development banks have also made a few private equity investments, but these remain small in size (around US$250 million in 2005).

10
Systems and Innovations Research.

Ponssard, J-P. Walker, N. (2008), “EU Emissions Trading and the cement sector: A spatial competition analysis,” forthcoming in Climate Policy.

Quirion, Philippe and Damien Demailly (2006), "Leakage from climate policies and border tax <u>adjustment: Lessons from a geographic model of the cement industry," Working Papers</u> halshs-00009337_v1, HAL.

Reinaud, Julia (2005), “Industrial Competitiveness under the European Union Emissions Trading Scheme,” International Energy Agency, IEA information Paper.

Rimmer, Maureen T. and Alan A. Powell (1992), “An Implicitly Directly Additive Demand System: Estimates for Australia,” Impact Project Preliminary Working Paper, No. OP-73, October.

Stern, Nicholas Herbert, 2007, The _Economics of Global Climate Change: The Stern Review_ . Cambridge UK: Cambridge University Press.

Stern, Nicholas Herbert, 2009, “Transatlantic Perspective on Climate Change and Trade Policy.” Keynote address, Peterson Institute for International Economics, March 4.

Strand, Jon (2009), “Revenue Management Effects Related to Financial Flows Generated by Climate Policy,” WB Policy Research Paper 5053. The World Bank: Washington DC.

Swiler, Laura P. and Gregory D. Wyss (2004). “A User’s Guide to Sandia’s Latin Hypercube Sampling Software: LHS UNIX Library/Standalone Version” _Technical Report_ SAND20042439, Sandia National Laboratories, Albuquerque, NM.

United Nations (2007), World Population Prospects: The 2006 Revision Population Database, United Nations Population Division, New York, NY, http://esa.un.org/unpp/.

UNDP, 2007/2008, Human Development Report, “Fighting climate change: Human solidarity in a divided world,” New York.

van der Mensbrugghe, Dominique (2006), “Linkage Technical Reference Document,” _processed_ , The World Bank.

van der Mensbrugghe, Dominique (2008), “The Environmental Impact and Sustainability Applied General Equilibrium (ENVISAGE) Model,” _processed_ , the World Bank.

World Trade Organization (2009). “Trade and Climate Change: A Report by the United Nations Environment Program and the World Trade Organization,” Geneva.

World Bank (2009), World Development Report 2010: “Development and Climate Change,” The World Bank: Washington DC.

23
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Our findings shine new light on the rapid growth and diverse drivers behind crypto-assets activity and offer support to the notions that crypto-assets are perceived as a risk asset, a potential macro hedge, and a potential tool to support cross-border payments. However, our results should be interpreted with caution. The crypto-assets industry represents an emerging heterogeneous asset class and while our sample spans over 130 countries, it covers just 27 months which include the COVID-19 pandemic and an environment of extraordinary fiscal, monetary, and financial sector conditions which may change when policy support is withdrawn. Moreover, our crypto-assets volume data are based on estimates and rely on important limiting assumptions. Further, our data exclude significant volumes of “off-chain” transactions. Finally, several other factors also inhibit us from drawing strong conclusions: the industry’s track record is relatively short, the technologies are still rapidly evolving, and there are significant uncertainties around future global and national adoption and regulation.

# **References**

Athey, S., Parashkevov, I., Sarukkai, V., and Xia, J. (2016). Bitcoin Pricing, Adoption, and Usage: Theory and Evidence.

Auer, R., and Claessens, S. (2020). Cryptocurrency Market Reactions to Regulatory News. Federal Reserve Bank of Dallas. Globalization Institute Working Papers 381.

Auer, R., and Tercero-Lucas, D. (2021). Distrust or Speculation? The Socioeconomic Drivers of US Cryptocurrency Investments. BIS Working Paper 951.

Basel Committee on Banking Supervision (2021). Consultative Document: Prudential Treatment of Cryptoasset Exposures.

Baur D., Lee A. and Hong, K. (2018). Bitcoin: Medium of Exchange or Speculative Asset?, J Int Financial Mark Inst Money 54:177–189.

Blau, B., T. Griffith, R. Whitby (2021). Inflation and Bitcoin: A Descriptive Time-Series Analysis. Economic Letters 203.

Buterin, V. (2013). Ethereum Whitepaper.

Conlon, T., S. Corbet, and R. McGee (2021). Inflation and cryptocurrencies revisited: A Timescale Analysis, Economics Letters 206.

Committee on Payments and Market Infrastructures (CPMI) and the International Organization of Securities Commissions (IOSCO) (2021). Application of the Principles for Financial Market Infrastructures to stablecoin arrangements.

Chainalysis (2021). 2021 Geography of Cryptocurrency Report.

37
be such an issue in their studies given that the tariffs were reduced uniformly or proportionally across sectors. This is not true at least for Colombia; the liberalization was not uniform. Edwards (2001) notes that the trade liberalization reform of Colombia (“La Apertura”) was “announced during the presidential campaign [of Cesar Gaviria] as a ‘gradual’ and ‘selective’ process.” As can be observed from Figures 2 through 4, there is quite some variation in the tariff reductions across sectors.<sup>24</sup> Moreover, the Spearman’s rank correlations of tariffs in Table 1 indicate that the correlations reduced over time, implying a selective process as opposed to a uniform one in liberalization. Otherwise, the ranking of sectors in terms of their protection rate would not change.

The average advalorem tariff rates in my sample of 4-digit ISIC industries declined from 43% in 1983 to about 14% in 1992 and stayed around that rate in the following years (Table 2). The dispersion of tariffs across sectors also declined (Table 2). If we just look at the standard deviations, the decline appears to be markedly higher. However, we need to take into account the differences in the magnitude of tariffs across periods and the coefficient of variation<sup>25</sup> is a better measure for that matter. The decline in the dispersion is notably lower when we use the coefficient of variation. However, this outcome does not indicate that political economy is no more a factor in tariffs after reform. The decrease in the dispersion is predicted by my models given the fact that the liberalization occurs through the elimination of some extra channels other than the political economy channel.

In my theoretical models and the main estimations, I use tariff rates as a measure of protection for import-competing sectors. I also have access to effective rate of protection (ERP) data which I use to augment my results with tariffs. The effective rates take into account the tariffs on inputs, and they are based on the value added. They are considerably higher than the nominal rates but show a similar pattern with the tariffs as can be observed from Figure 1.

Before I discuss my empirical methodology and the results, in the next section I briefly review the existing empirical literature to the extent that it relates to this paper.

# 4 Empirical Literature Overview

Tybout (1991) briefly reviews the literature that contains implications for the linkages between trade and productivity, and he indicates that the net effect of a liberalization episode is ambiguous.

> 24Note that the reductions are computed as percentages to account for the variation in the initial tariffs.

> 25Coefficient of variation is the mean divided by the standard deviation of a given group.

14
30 efficiency, not equity, and that he “deliberately excluded from the list anything which was primarily redistributive [because he] felt that the Washington of the 1980s (...) was essentially contemptuous of equity concerns”

**Figure 13.  International Education Expenditure vs. PISA Scores (2006)**

<!-- Start of picture text --> 550 Hong Kong FinlandKorea<br>Netherlands<br>Macao, China AustraliaNew ZealandBelgium Japan Switzerland<br>500 Czech RepublicIrelandFranceGermanyUK EstoniaNorwayAustriaPolandSloveniaDenmarkSwedenIceland<br>Slovak Republic Lithuania Spain LatviaLuxembourgHungary<br>Azerbaijan United States<br>Greece Croatia Portugal Italy<br>450<br>Israel<br>Uruguay Turkey<br>Romania Chile Thailand Mexico Bulgaria<br>400<br>Indonesia<br>Argentina Jordan<br>Colombia<br>Brazil<br>Tunisia<br>350<br>Kyrgyz Republic<br>300<br>0 5 10 15 20 25 30<br>Expenditure per student, primary (% of GDP per capita)<br>Mathematics score in PISA 2006<br><!-- End of picture text --> _Note: Public expenditure per student is the public current spending on education divided by the total number of students in the primary level. Source: Program for International Student Assessment (2006) and World Development Indicators (2010)._ When it comes to economic development, equity considerations are not simply a helpful corollary, but an essential ingredient, especially in unequal Latin America. For one thing, equity is in itself as important a developmental objective as growth—not least considering that excessive inequality of outcomes and high inequality of opportunity constitute an insult to our basic sense of justice. To the extent that reforms’ uneven outcomes reflect unequal opportunities—across racial or ethnic groups and between those born poor as those born rich— their sense of unfairness also undermines the legitimacy of those reforms. Moreover, by failing to more explicitly deal with equity, the Washington Consensus neglected what is in effect a key impediment to growth. High income inequality not only hinders the impact of growth on poverty reduction but can itself contribute to low growth which, in turn, makes it difficult to reduce inequality. Institutions and policies in contexts with high poverty rates and heavy concentrations of income may themselves be a source of growth-impairing inefficiency (Aghion et al., 1999; World Bank, 2006; Levy and Walton, 2009; Demirguc-Kunt and Levine, 2009). Under those conditions, the interplay among political parties, business organizations and powerful large labor unions can result in rent-seeking and monopoly behavior that undermines the growth gains the reforms were meant to capture. Similarly, imperfect credit markets combined with unequal
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-18-

# TWO 2 KEY MACROECONOMIC INDICATORS QP_ mutae er. at GNP)

<!-- Start of picture text --> lIt EaL Rev. Eat.<br>190 1861 1962 1963 1#64 1965 1986 1987 1967.<br>Total CAuinmpm 84.1 62.0 61.8 6. 83 60 77.7 77.9 76.5<br>Privae 71 73 711 73. 74. 72. U 668 67.0<br>Ogyerinmeut 12.3 10.7 10U 10.2 6.8 84 s 9.0 9.5<br>Fixd bvgtl eu 19.5 1 1. 1. 1. 20. .6 2.7 24.9<br>PE. 10*9 11.7 11.5 106 9 11.7 14.0 14.0 13.6<br>P divate 6 7.2 7.3 8.2 62 . 9. 9.7 11.3<br>SteakChequ 1.9 26 1.5 1.1 L4 09 1.4 0.1 0.1<br>P~I1. 0.5 1.5 05 -4.4 0.0 -0 0.1 -0.5 -0.1<br>Private 1.4 1.1 1.0 1. 1.4 1.0 13 0.6 0.2<br>Cat Ahvoo&u - -3 -2.2 -5 -2. -1.9 -2 -1.7 -1.5<br>I.ats at£ & NIS 150 16.5 17.9 19. .1 2L5 27 21.0 aA<br>Eptb a u= & M 7. 10.8 14 7 19 2 18.0 19.3<br>Mtemo tm<br>(13 %)<br>GNPpowth -LI 4.1 4.5 3.3 59 L5 o 6.8<br>~aisrate.<br>CII (yea e g) 110.9 368 2L1 31.4 44 45. 34 38<br><!-- End of picture text --> <!-- Start of picture text --> sE oSPOeS, SS<br><!-- End of picture text --> Figure 3 <!-- Start of picture text --> PUBLIC SAVINGS RATE: 1967-87<br>Sher* of Pub l00860* hnW<br>so -<br>70<br>20 -<br>40<br>1967 1970 1973 1976 1979 *912 t985<br>Co *t_ *<br><!-- End of picture text -->
- **_There is a significant difference in sector distributions between the private and government led investments, with the former overwhelmingly concentrated in manufacturing and service industries and the latter in construction and mining._**

Figure 2 below shows the respective sector distribution of private and government led projects. As seen, 36% of private projects are in manufacturing and 22% in the service sector. In contrast, government projects are 35% in construction and 25% in mining. It is notable how little SOEs are engaged in manufacturing, i.e., a mere 6%; and how small the private involvement is in the construction sector, a mere 5%. In the mining sector, however, private participation seems to catch up with their SOE peers, reaching 16% of the total by end-2011.

**Figure 2: Sector spread of Chinese OFDI in Africa, comparing government and private led projects**

<!-- Start of picture text --> Government led Investment Projects Private led Investment Projects<br>15%<br>6%<br>21% Manufacturing Manufacturing<br>6% 36%<br>Mining Mining<br>25%<br>4% Contracting Contracting<br>Trade Trade<br>9% 22%<br>Agriculture Agriculture<br>Other 5% 16% Other<br>35%<br><!-- End of picture text --> _Source: Shen and Wang._

- **_The biggest share of OFDI to Africa comes from firms based in coastal China, where the private sector leads the domestic manufacturing and export industries._**

Figure 3 shows the top ten provinces from which Chinese OFDI to Africa originates, along with investment by the central SOEs grouped as a whole. Central SOEs excluded, in all provinces (except Beijing, a city with provincial status), investment from private firms exceeded that of SOEs. Zhejiang, a coastal province, leads China’s OFDI in Africa with 191 projects, followed by two other coastal provinces, Shandong and Jiangsu, with 113 and 103 projects respectively. This mirrors well the role of the private sector in the domestic economy in those provinces, especially regarding the manufacturing and export-oriented industries. For instance, Zhejing province, with 97% of its industrial and commercial output being contributed by the private sector, witnessed 88% of its OFDI to Africa being privately initiated. Shandong and Jiangsu, also known for their vibrant private sector economies, found nearly 70% and 74% of all their OFDI to Africa from private firms, respectively.

7
The dataset covers the period 1970-2009 and contains economic, political and institutional characteristics of 151 countries, thereby extending the analysis in Barro (2003) by roughly 15 years and 80 countries. We estimate different specifications of growth regressions controlling for fixed country and period effects, trying to explain a large share of within-country variations in growth rates.

The findings suggest that education significantly increases GDP per capita growth rates, an effect that is larger in countries with a relatively low initial GDP per capita. Furthermore, investment, openness to trade as well as natural resources are found to be robust determinants of income growth, all related positively to growth. Government consumption seems to decrease GDP growth rates in the long run. Relating institutional characteristics of countries to the estimated countryspecific fixed effects shows that there exists a strong positive correlation between them. Although no causal interpretations can be made in this context, the finding supports the supposition that fixed effects capture the persistent part of the institutional environment and an improvement of the quality of institutions is favorable for a country and translates into higher GDP per capita growth.

Based on the performed growth regressions, we predict Ghana’s future growth potential for different scenarios. In a baseline scenario, where investment, natural resources, government expenditures and other covariates are assumed to remain at the level of 2005-09, we find that average growth rates are slightly above 4% in the upcoming two decades. In a more favorable scenario, where it is assumed that the covariates behave as they did since the 1980s, predicted growth rates increase by about 1%-point as compared to the baseline scenario. Finally, when taking into account the expected oil production in Ghana, GDP growth is likely to reach 6.2-6.5% in the next 15-20 years.

The paper is organized as follows. Before the results are discussed in Section 5, the model specification is described in Section 2 and the econometric methodology and the data are described in Sections 3 and 4. Section 6 applies the results to predict GDP per capita growth rates for Ghana until 2030 and Section 7 summarizes and concludes.

# **2 Model Specification**

The specifications of the growth regressions are based on the findings in Barro (2003), and augmented by variables that are of particular interest for Ghana.

The dependent variable is the arithmetic average of the real GDP per capita growth rate of a country over each 5-year period during 1970-2009. The basic specification includes country fixed 3
see Korboe (1992) for a definition and discussion of the importance of this structural arrangement in Ghanaian housing consumption decisions.

This sort of housing deprivation and cost is unusual because, as shown in Table 1, the per capita income in Accra is considerably higher, ranging from two to almost three times the level of incomes in the other cities. Such higher incomes should translate into consumption of higher levels of housing services, but in fact, the opposite is happening. Indeed, for the observed result to occur—i.e., higher levels of income and lower levels of housing services—requires that Ghanaian families must be unable to use their higher incomes to access better housing as would be expected. It suggests, in other words, that the policy impediments in Accra must be much more severe than they are in the other cities.

**Table 1. Income, Housing Costs and Conditions in Select African Cities**

|||**Income per capita**<br>**per month (USD**<br>**PPP)**|**Expenditure on**<br>**housing and**<br>**services USDPPP**|**House condition**<br>**rating (1-5)**|**Sanitation rating**<br>**(1-5)**|**Water rating (1-5)**|**Floor area per**<br>**person (m2)**|**Exp on house as %**<br>**of income**|**Exp. on housing +**<br>**services as % of**<br>**income**|**Exp. on sanitation**<br>**as % of income**|**Exp. on water exp.**<br>**as % of income**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|**Accra**|_Mean_<br>_Median_|*******240<br>186|49.1<br>33.8|*****2.5<br>3.0|2.7<br>2.0|2.8<br>2.0|*******4.6<br>3.0|*****5.9<br>0.2|******22.1<br>19.7|*******5.0<br>3.9|7.4<br>5.8|
|**Addis Ababa,**<br>**Nairobi**|_Mean_|83|13.6|2.7|3.2|2.8|6.1|9.5|21.9|0.1|8.4|
|**,**<br>**Dar es Salaam**|_Median_<br>*******<br>******<br>*****|46<br>Differenc<br>Differen<br>Differen|7.2<br>e in mean<br>ce Statistic<br>ce statistic|3.0<br>s statistic<br>ally signi<br>ally signi|3.0<br>ally signi<br>ficant at<br>ficant at 1|2.0<br>ficant at<br>5 percen<br>0 perce|5.0<br>1 percen<br>t level<br>nt level|0.6<br>t level|14.3|0.0|5.1|

Note: Ratings of 1-5 for housing, water, and sanitation are defined in the Annex.

One way to think about the range of policy factors that could affect supply has been developed by Green, Malpezzi, and Mayo (2005) based on the work of Mayer and Somerville (2000). They provide a clever summary equation, based on the mono-centric city model, that allows broad inferences to be drawn about how elastic a specific city’s 8