rafmacalaba/datause-encoder-data huggingface dataset:
LOWER-MIDDLE-INCOME ECONOMIES

# 1. Estiaates and Projections

|vow|<br>_Pepaltation_|~~~~~S<br> ratio|.<br>-<br>Pwrlod|rt<br>rate|Co|cm|TFN|Nlot|'_<br>Flm|1s<br>t|x 009|
|---|---|---|---|---|---|---|---|---|---|---|---|
|1965<br>1990<br>1995<br>2000<br>2005<br>2010<br>2015<br>2020<br>2025<br>2050<br>2075<br>2100<br>215<br>2150|694678<br>moo5<br>**6t5oJ**<br>941339<br>1024526<br>1107554<br>1191494<br>1273192<br>1356994<br>1693991<br>1664105<br>1973496<br>20i5499<br>20Z5627|75.4<br>71.6<br>68.0<br>63.1<br>58.5<br>54.5<br>52.3<br>51.7<br> <br>51.9<br>56.0<br> <br>63.4<br>69.3<br> <br>_2.0_<br>73 4|1965-89<br>1990-94<br>1995-99<br>2000-04<br>2005-09<br>2010-14<br>2014-19<br>2020-24<br>2025-49<br>2050-74<br>2075-99<br>2100-24<br>12-49|2.19<br>2.04<br>1.85<br>1.69<br>1.56<br>1.46<br>1.36<br>1.2*<br>0.89<br>0.43<br>0.19<br>0.03<br>0.04|30.8<br>28.5<br>25.9<br>23.6<br>22.1<br>21.0<br>19.3<br>18.6<br>15.8<br>13.3<br>:Z, 4<br>12.0<br>118|8.3<br>7.5<br>6.8<br>6.3<br>6.2<br>6.2<br>6.1<br>6.1<br>7.0<br>9.1<br>10.5<br>11.1<br>11.4|3.91<br>3.51<br>3.17<br>2.87<br>2.67<br>2.55<br>2.46<br>2.39<br>2.20<br>2.07<br>2.06<br>2.06<br>2.06|62.0<br>63.5<br>65.0<br>66.5<br>67.6<br>68.8<br>70.0<br>71.2<br>74.3<br>77.7<br>79.7<br>81.3<br>62.0|66. 7<br>68.6<br>70.5<br>72.3<br>73.5<br>74.6<br>75.9<br>77.2<br>60.3<br>83.9<br>86.1<br>86.0<br>8.6|62<br>35<br>47<br>40<br>35<br>30<br>26<br>21<br>12<br>6<br>4<br>2<br>2|4<br>73<br>S3<br>53<br>47<br>40<br>34<br>28<br>16<br>7<br>5<br>4<br>3|

## 2. Age structure (percent): 1990 and 2025

<!-- Start of picture text --> 75+<br>70-74<br>65-89<br>60-84<br>55-59<br>50-54<br>45-49<br>40-44<br>n 22WiM 35-39<br>30-34<br>25-29<br>20-24<br>oxooo _ ~~~~10-14' 3<br>5-9<br>0-4<br>2 0 2 * s 8 10 l¶ 8 6 4 2 0 2 4 6 8 10<br><!-- End of picture text --> 10 D 6 4 2 0 2 * s 8 10

## 3. Distribution of po-alation (percent): 1990 and 2025

<!-- Start of picture text --> tsicH (is 4%)WIH )lM<br>iM04 Cs "ll I (S<br>LO10~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~O C14<br>LOW-MID (14 7U1<br><!-- End of picture text --> <!-- Start of picture text --> 7 - \<br>FPMOCS ar)<br><!-- End of picture text --> <!-- Start of picture text --> 1%<br><!-- End of picture text --> 19
# _1._ **_Introduction_**

Market risk perceptions have long been recognized to be a function of firm-specific variables and, in particular, firm value as embodied by its constituent contracts.' In this paper, we analyze the credit spread determinants and dynamics of the Ras Laffan Liquified Natural Gas Cornpany (Ras Gas for short) in terms of the project's contractual structure. We pursue two objectives with this study. On the one hand, we attempt to provide some empirical evidence on credit spread determinants from the perspective of the firm as a nexus of contracts. Prior studies on financial and organizational design based on large samples have focused on one contractual relationship at a time and are unable to identify the precise risk distribution and its evolution over a longer period. On the other, we wish to draw attention to the field of project finance that offers many exciting and unique opportunities to investigate issues of fundamental importance in finance. Indeed, no other practical case corresponds more closely to the standard setting of corporate finance models in terms of time structure with corresponding resolution of uncertainty, small number of investors and classes of financial claims, typical actions taken, a single indivisible investment, etc.

The view of the firm as a nexus of contracts, first formulated in the seminal papers by Alchian and Demsetz (1972) and Jensen and Meckling (1976), underlies much of modern corporate finance. In particular, it serves as the foundation of many theories of capital structure design and corporate governance, i.e., the allocation of return and control rights (Zingales, 2000). According to this view, the firm is defined in terms of the individual contracts that govern its existence such as labor and other input contracts, financial contracts including covenants and
**Figure 19: Flooding - Macroeconomic impacts under the RCP 4.5 scenario**

<!-- Start of picture text --> Real GDP Capital Stock<br>% % % %<br>0.2 0.2 0.5 0.5<br>0.0 0.0 0.0 0.0<br>-0.5 -0.5<br>-0.2 -0.2<br>-1.0 -1.0<br>-0.4 -0.4<br>-1.5 -1.5<br>-0.6 -0.6<br>-2.0 -2.0<br>-0.8 -0.8<br>-2.5 -2.5<br>-1.0 -1.0 -3.0 -3.0<br>-1.2 -1.2 -3.5 -3.5<br>2020 2040 2060 2080 2100 2020 2040 2060 2080 2100<br>Mean Median Mean Median<br><!-- End of picture text --> Note: Results are presented as percent deviations from baseline. Confidence intervals of 68 percent (dark blue) and 95 percent (light blue) are shown.

# 6.3 Compound climate and macroeconomic shocks<sup>12</sup>

**Climate shocks do not occur in a vacuum; economies are continuously subject to a number of exogenous macroeconomic shocks of varying frequencies and intensities** . Exogenous macroeconomic shocks are events that originate outside an economy but can have significant effects on the evolution of relevant aggregate variables. Many of such shocks, for good reasons, are a staple in macroeconomic projection sensitivities, risk analyses and stress tests. Four main types of exogenous macroeconomic shock are considered for this analysis:

_Shocks to the US Interest Rate_ : Uruguay, like many other economies, may be affected by changes in the US interest rate. A higher US interest rate can lead to higher borrowing costs for the Uruguayan government. This can result in increased interest payments, potentially straining the country's fiscal position and limiting the availability of funds for other government expenditures.

_Export Prices_ : Since Uruguay heavily relies on exporting agricultural commodities, a decline in global commodity prices can adversely affect its export revenue. This can lead to lower foreign exchange earnings, reduced export-led growth, and potential trade imbalances.

_Import Prices_ : Changes in import prices can affect inflationary pressures within Uruguay and affect external accounts. If import prices rise due to exogenous shocks, it can lead to higher input costs for domestic producers, potentially resulting in increased consumer prices. This can impact domestic consumption and the overall cost of living. External accounts can deteriorate and put pressure on the exchange rate.

> 12 We have presented the effects of stochastic drought shocks and the effects of stochastic flood shocks. Results for the combined effect of drought and flood shocks are omitted here for ease of exposition, but are listed as ‘combined climate shocks’ in Annex A.

26
assistance were related to property expropriation without fair compensation.<sup>8</sup> Frequent violations of property rights are no doubt largely attributable to the ambiguity of the legal status of the land. For instance, users of Allocated land, a form of land that was allocated to a user during the central planning regime, neither have formal ownership nor have the rights to sell or use their land as collateral.<sup>9</sup> Table 1 describes the differences in the legal status between entitled land and Allocated land. Compared to fully titled land, Allocated land is exposed to a higher risk of expropriation by the government. Indeed, Allocated land is designed as state-owned land for temporary, free-of-charge usage by firms via an administrative order, and the government retains the legal right to reclaim the Allocated land without compensation to the current users.<sup>10</sup> Although many local governments, such as the Shenzhen government, stipulated a 30-year tenure for all Allocated land when the city was established,<sup>11</sup> the land tenure for these lands had either expired or was approaching their expiration dates by 2009. Cases of local governments expropriating Allocated land are common. For instance, the Shenzhen government reclaimed 12 plots of Allocated land without compensation to current land users in one campaign aimed at improving land-use efficiency during 2012.<sup>12</sup> The serious concern before the 2009 _Urban Renewal Program_ in Shenzhen is that there were no formal procedures to negotiate and to extend the tenure of Allocated land. In other words, the negotiation costs incurred by users of Allocated land to sell their land to more efficient users of their land were prohibitive, inducing the pressure for reforming the land rights system (Demsetz 1967).

> 8 See http://www.ce.cn/cysc/fdc/fc/201401/22/t20140122_2176546.shtml.

> 9 The Allocated land is a legacy of the centrally planned economic system that dominated China prior to the 1990s. In the centrally planned economy – in which all market-based land transactions were forbidden – the direct distribution from the government to SOEs was the only method of conveying land to its users. The legal status of Allocated land and corresponding weak property rights protections remained largely unchanged during the progressive land system reforms from the 1990s to the 2000s.

> 10 See article 47 of the “Interim Regulations of the People's Republic of China Concerning the Assignment and Transfer of the Right to the Use of the State-owned Land in Urban Areas”.

> 11 See “Provisional Regulations of Land Management for the Shenzhen Special Economic Zone” from the Shenzhen government in 1981.

> 12 See _Huaxia News_ , March 16, 2013, http://sh.house.sina.com.cn/news/2013-03-16/08552392170.shtml.

12
# **3. Definitions of Baseline and Scenarios**

We use historical data and projections from the U.S. Energy Information Administration (EIA) on the average price of crude oil. According to the EIA reference case projection made in 2010, the oil price would reach $107 per barrel (in 2009US$) in 2020. We also assumed the EIA projection of the oil price in our baseline case. We then considered three alternative scenarios to represent an increase in oil prices by 25%, 50% and 100% from the corresponding baseline values starting from 2012. Besides EIA, other organizations, such Internal Energy Agency (IEA) also project energy prices. However, the projection between EIA and IEA do not vary much. Moreover, the reference case oil price, which is exogenous to our model, is not very important in our study because we are measuring the impacts of deviations of oil prices from the reference case.

Table 1 presents percentage changes in oil prices from the 2009 level under the baseline and various oil price increase scenarios. For example, a 50% increase in oil price from the baseline in 2020 refers to a 147% increase compared to the 2009 level.

**Table 1: Percentage change in oil price from the current (i.e., 2009) price level**

|Year|Baseline||Scenario||
|---|---|---|---|---|
|||+25%|+50%|+100%|
|2009|0||||
|2010|19||||
|2015|54|92|131|208|
|2020|65|106|147|230|

_Note: the scenarios are implemented starting from 2012._

# **4. Simulation Results**

In this section we present key results obtained from the simulations of increased oil prices. The results include impacts on GDP, sectoral outputs and international trade.

# **4.1 Impact on Gross Domestic Product (GDP)**

Figure 1 presents the impacts on real global GDP of 25%, 50%, 100% and 150% increases in the world oil price. The change in global GDP is inversely related to the changes in oil price. In 5
peats after a very long period (around 2<sup>126</sup> ). Stata provides the option to set an initial “seed” that is useful for reproducing results of anything that requires random numbers, such as simulations or bootstrap-based computations. If the seed is reset too frequently, however, the KISS generator is reduced to a less sophisticated generator, with the result that such draws appear less “random.” (The most recent version of Stata’s documentation discusses this case explicitly as well, admonishing the user to reset the seed sparingly.) Marsaglia (1968) pointed out a weakness in congruential generators of the form:

# new _I_ = _K ×_ old _I_ modulo _M_

He showed that though these numbers appear at first glance to be almost random, that _n_ -tuples of them fall in relatively few planes in _n_ dimensions; the KISS algorithm, used by the Stata `runiform()` function, is not a generator of this kind. Though it includes a linear congruential generator (LCG), it also includes three others, each with a different period. The resulting random numbers withstand a variety of statistical tests. As a simple illustration, the first five thousand draws from Stata’s `uniform()` command are plotted against the second five thousand draws in the left panel of Figure 5. No pattern is discernible. The left panel was generated after setting the initial “seed” only once. However, if after each pair of draws, the seed is incremented and reset—so that the first pair come from seed 1, the second from seed 2, and so on—the striped pattern in the right panel appears.

This is by no means a bug, however. As explained on pp. 223-224 of the Stata Data Management manual (StataCorp 2009), setting the seed in Stata’s RNG always re-initializes three of the four recursions to the same starting values, only using the user-provided seed to change the starting point for the LCG. Thus, the differences from one seed to another arise from the LCG alone, and the problems that Marsaglia identified return. It does not matter that the seeds were small integers; large seeds incremented methodically would produce the same pattern.<sup>3</sup> The seed resetting facility is meant to allow replication of program results, however, not access to independent streams of random numbers. Though some software packages

> 3 Though undesirable in some applications, this pattern is quite unlike the recent problem with `rnormal()` .

6
**There is also scope for calibrating coverage ratios and fees** .  Some schemes should consider reducing slightly their coverage ratios to levels closer to international standards. Most schemes should consider linking both coverage ratios and fees more closely to risk. Hybrid payment rules could be tested in some countries, depending on the effectiveness of loan collection procedures, but most MENA PCGs should probably avoid excessively demanding payment rules, as this could reduce significantly the attractiveness of the scheme.

**In some MENA countries guarantee schemes could play a more proactive role in capacity building** .  The low share of SME lending in some MENA countries may be due not only to weak financial infrastructure but also to weak lending technologies.  In these cases, the PCG can play a fundamental role in jumpstarting SME lending while also improving risk management practices of domestic banks.

**Finally, guarantees schemes should institutionalize a comprehensive review process.** In order to ensure cost-effectiveness, MENA guarantee scheme should conduct systematic assessments of outreach, additionality, and customer satisfaction (bankers and borrowers). This comprehensive review should be conducted in a regular basis using appropriate analytical tools, including an SME survey and a banking survey. One of the best practices to consider is  he _Comprehensive Review_ conducted in Canada every five years by the Small Business Financing Program (Table 19).

**Table 19: Building** **<u>blocks for a comprehensive review</u>**

|**Measurement of**<br>**Additionality**|Measure of the financial and economic impact of the guarantee fund, using<br>survey and econometric techniques.|
|---|---|
|**Cost-benefit analysis**|Comparison of all costs and benefits of the guarantee scheme, in order to<br>measure its net social impact.|
|**Identification of SME**<br>**finance gap**|Identification of the characteristics of firms that are credit constrained based<br>on the business survey and bankers’ feedbacks, in order to readjust eligibility<br>criteria|
|**Assessment of operational**<br>**parameters**|Assessment of the key operational parameters on the guarantee scheme, based<br>on discussions with stakeholders and using comparisons with international best<br>practices.|
|**Assessment of internal**<br>**processes**|Review of internal processes and systems of the guarantee funds, including<br>quality management, operational costs, credit risk management, recovery<br>process etc.|
|**Financial projections**|Simulation of the need for funding over the medium term, based on<br>assumptions on outreach and default rate|
|**Awareness and customer**<br>**satisfaction**|Measurement of the awareness and satisfaction of bankers and firms though<br>surveys.|

34
# **Figure 1: Account Penetration**

# **_Panel A: Regional Comparison_**

<!-- Start of picture text --> 100% 89%<br>80%<br>55%<br>60%<br>45%<br>39%<br>33%<br>40%<br>23%<br>15%<br>20%<br>0%<br>High income East Asia & Europe & Latin South Asia Africa Middle East<br>Pacific Central Asia America &<br>Caribbean<br>Percent of Adults<br><!-- End of picture text -->

**_Panel B: Sub-Regional Comparison_**

<!-- Start of picture text --> 60%<br>51%<br>40%<br>28%<br>23%<br>20%<br>20%<br>11%<br>0%<br>Central Africa North Africa Western Africa Eastern Africa Southern Africa<br>Percent of Adults<br><!-- End of picture text --> _Source_ : Demirguc-Kunt and Klapper, 2012.

In Africa men are more likely than women to have an account at a financial institution though the gender gap is relatively small compared to other regions. Adults in the highest withineconomy income quintile are almost four times as likely to have a formal account as those in the lowest income quintile, on average. Similarly, adults with a tertiary education and those ages 2564 are particularly likely to report having an account at a formal financial institution (Figure 2).

5
Table7.2 Elrnenls in early **U.S. laws to** regulate unfairly traded **imports,** 1890-1922

|Act and date|Major elements|
|---|---|
|Sherman Act, 1890|oConspiracy or combination|
||Si Restraint, monopolization, or attempt to monopolize interstate<br>or foreign commerce|
||oCriminal statute, strictly construed|
||* Fine, imprisonment; triple damages|
|Section 73, Wilson 'ariff Act, 1894|a Conspiracy or combination engaged in importing<br>a Intent to restrain trade, increase the pricc in the United States<br>eCriminal statute, strictly construed|
||* Fine, imprisonment; triple damages|
|Antidumping Act, 1916|oImporting below actual market value<br>* Intent to injure a U.S. industry|
||* Criminal statute, strictly construed|
||* Fine, imprisonment; triple damages|
|Antidumping Act, 1921|a Importing below fair value|
||oInjury to a U.S. industry|
||a Administrative determination by the secretary of the treasury|
||* Special duty, equal to the difference between the fair value and<br>the import price|
|Section 316, Fordney-McCumber Tariff Act, 1922|* Unfair method of competition and unfair acts in importation<br>* Effect or tendency is to destroy or substantially injure<br>* Tariff commission, with court review of questions of law only|
||* Additional duty to offset such act or methid|

Sources: Dale (1980, chapter 1); Viner (1923, chapter 13); US Tariff Commission (1919, appendix).

- 44 -
# **7.  Implementation Issues, Costs and Local Employment Benefits**

The results in Section 4 indicate that coordinated upgrading of the Sub-Saharan road network is likely to generate a very large expansion of interregional trade. Piecemeal, local improvements do not have the same potential, because the cost calculations of interregional shippers will be dominated by conditions in the lowestquality network links. The success of upgrading would depend on smooth traffic flow over the whole network, with minimal delays at national borders and open roads within countries. We recognize that implementation of such a program would be difficult, but our results suggest that the potential payoff is sufficiently high to warrant a serious discussion.

In this section, we offer some preliminary thoughts about appropriate institutional arrangements and administrative measures, along with the associated costs. We present estimates of direct and indirect benefits for the rural poor generated by upgrading and maintaining the road system. We also provide rough estimates of the costs associated with administration of a trans-African network. We recognize the uncertainty in these estimates, but for the present purposes we are interested in orders of magnitude, not precise figures. We have already estimated maintenance costs from our econometric analysis of projects in the ROCKS database.

# **7.1  Overall Organization and Financing** .

Coordinated upgrading and maintenance of the network could be accomplished by an all-Africa convention on overland trade, implemented in collaboration with multilateral and bilateral donors, that would cover 5 years of upgrading and 10 years of 37
**willing** to **take** action against perceived or real corruption<sup>and general</sup> inefficiencies of the public bureaucracy and public sec to:. A_cot is may also be taken against eco nomic ills resuli n;t fir-.<sup>fis..a.</sup> inefficient public production. Yet these actions art , t.a:c8e at the general structure or specific econTiiL: austerity r:iasures , he ".a. th inefficiencies of a particular £irm, unless the rovis serviCesSirm directly to a particular constituency. n r.this case, prrotest. a-.d a. tior to improve the performance of the public enterprise, HirseChml-an's **v;ci-e'** require organization of the affected group .622 **Thb** dlv, th* citizen/taxpayer may be a beneficiary of public sector inefficiency. The **lack of a** control mechanism for **thM** managers<sup>or employees may result in</sup> a slack work environment at guaranteed wages. Thls the citizen'taxpa,er is not likely to exert pressure on public<sup>f.rms to minirmize.-osts</sup> as does the shareholder of a private firm.

35. Public enterprises, in contrast to private firms, often do not face the threat of insolvency, the ultimate disciplinary threat fac.ing private firms in a competitive market. A public firm may be prote.ted by the government from bankruotcy "y ic.ds ! to **'.ohr** c:t is , tax exsr;.'i_`;ns or soft credit terms. As Ai>.usseJ a'ove, expeocati.ns - -. n transfer I-ayments to, covor :c, . "cc: **t** 'th ' n,rrL-o 'e" ccnstrai-.t an: re ves s- - **.** .e :c .'.. '.1.-. . . . **_m_** _c4:_ :n zat cin.ct>,

# Empirical Evidence

36. :) Despite * .,, te ,.; '. **b"t** = .- .. - inefficien-cy of pub.i f i.ms _n. .

**_62/_** A. Hirs chman, Exit, Vo **4,I '_.v iv:-** ' 1970, p.3C.

63/ Private firms, however, vay alsc e:: .v Tover.c.-ent prote-t -on **nro** insolvency. Large private firms which emp';y a large number _of_ **worker** may be saved from hankruptcy by the government in order to prevent **r%ass** unemploymernt and social. dislocatiln. Chrys'r Corp)raton ;r **nt P,.'.** ' S one well-known exam.ple.

**_25_**