rafmacalaba/datause-encoder-data huggingface dataset:
|Country<br>or Region|1990<br>Emissions<br>(btCO2)|2010<br>Forecast<br>(btCO2)|Kyoto<br>Target<br>(% of<br>1990 emissions)|Kyoto<br>Target<br>(% of<br>2010 forecast)|
|---|---|---|---|---|
|USA|4.90|6.31|93%|72%|
|Canada|0.50|0.67|94%|71%|
|Western Europe|3.73|4.43|92%|77%|
|Japan|1.13|1.71|94%|62%|
|Former Soviet Union|3.77|3.20|100%|118%|
|Eastern Europe|1.13|1.12|92%|93%|
|Annex B Total|15.2|17.4|94.5%|82%|

Table 7: Kyoto targets expressed as a fraction of BAU emissions in 2010, as projected in 1997. Only CO2 emissions from fossil-fuel combustion are considered. Source: EIA (1997)

# **6.1 Relative Performances of Quota, LIT and GIT with Regard to Abatement and Price of Carbon**

The condition under which a GIT reduces uncertainty on the abatement effort and the marginal abatement costs relative to a quota is given by Eq.(8). The key parameters in this equation are _ρ_ , the ratio _σY /σE_ , _q_ and _α_ .

Section 5.1 provides a range of plausible values for _ρ_ and _σY /σE_ . In Table 5, _ρ_ ranges between 0 _._ 27 and 0 _._ 44, and the ratio _σY /σE_ between 0 _._ 8 and 22 for developed countries (note that Table 5 shows rounded values). We limit the analysis to developed countries because there is less data on developing countries, and because the ‘tight regime’ assumption is more likely to be valid for industrialized countries, at least for the period 2013-2017.

We first discuss the relative performances of a quota and a linear intensity target. Fig.1 shows the area in the ( _log_ ( _σY /σE_ ) , _ρ_ ) plane where a quota dominates a LIT in terms of uncertainty on abatement effort and marginal abatement costs. The box represents the range of plausible values as extracted from Table 5. In Fig.1, we set _q_ = 0 _._ 75. As shown in Table 7, this value is comparable to the Kyoto targets, under which the whole of Annex B committed to limit emissions to 82% of the BAU level as it was projected in 1997. The value 0 _._ 75 is also in the range of targets that have been proposed in the literature for the second commitment period (see, e.g., Brouns and Ott, 2005), at least for developed countries.

25
corporate bonds, as well as individual and summary transaction data gathered by the NASD.

# **6. REGULATION OF INTERMEDIARIES**

Section 15 of the Exchange Act requires all broker-dealers, unless they transact business in exempt securities, to be registered with the SEC.<sup>133</sup> However, as described below, even broker-dealers who trade only government securities must now register with the SEC. Municipal securities broker-dealers have to register as well. Pursuant to the Exchange Act, the SEC has imposed a comprehensive regulatory scheme on brokerdealers, which includes licensing qualifications, financial responsibility rules, customer protection rules, sales practice rules, and best execution requirements. The following section of this report provides a brief overview of broker-dealer regulation; it does not purport to be a comprehensive description.

## **A.  BROKER-DEALER REGISTRATION**

Section 15(a) of the Exchange Act prohibits any person from engaging in business as a broker-dealer, unless he does business solely intra-state or solely in exempt securities. However, as described above, the Government Securities Act of 1986 imposed a registration requirement on broker-dealers who deal in government securities, provided that they are not banks or other “financial institutions." Broker-dealers who limit their securities activities to government securities register under Section 15C of the Exchange Act, unless they are banks or other financial institutions, in which case they must file a notice with their appropriate federal regulator. General purpose brokerdealers that conduct a government securities business must file a notice with the SEC. Similarly, broker-dealers who deal in municipal securities must register as generalpurpose broker-dealers under Section 15(b) of the Exchange Act, unless they conduct their business solely intra-state, or are banks, in which case they must register under Section 15B as municipal securities dealers. Licensing requirements in all cases include proficiency tests as well as capital adequacy requirements.

## **B.  FINANCIAL RESPONSIBILITY**

1. _Net capital._ Under Exchange Act Rule 15c3-1, broker-dealers are required to maintain minimum net capital adequate to ensure that their assets are sufficiently liquid to meet their obligations to customers, if the broker-dealer were to go out of business.   In the United States, there are relatively fixed standards for determining capital charges, largely based on the type of securities activities that the broker-dealer conducts, and only limited use of value at risk models and internal risk models for setting capital.  The capital rules do not take into account hedged positions on a portfolio basis, which is the way most dealer firms tend to

> 133 Section 15(b) of the Exchange Act.

36
finds the head’s sister and his niece. This could be a two cells or a three cells household, depending on the relationship between these two extra members. If the niece of the household head is also the daughter of his sister, then the two constitute a third cell, while if they are not related, both will be in the head’s cell. To sum up, we are able to identify four kinds of households in the ESPS sample (and of course also in the PSF): (1) single person households, (2) one cell households, (3) more than one cell households with only “nuclear” family members and their offspring (head, spouse(s), children, grand-children), (4) other households. For reasons just explained, some truly one cell households may be misclassified as “other” households, but the opposite cannot happen.

We apply this classification rule to both the PSF and ESPS samples and compare the estimated average household per capita food and total consumption for each category of households and for the entire samples. In order to make meaningful comparisons, we need to account for any sampling frame variation and for the fact that about one year separates the ESPS and PSF surveys. Simple unweighted comparison of surveyed household locations shows that PSF oversampled the Dakar region compared to ESPS (37.5% in PSF versus 11.8% in ESPS). Since households in Dakar are on average wealthier than those in other regions of the country, a simple comparison of average consumption levels is unlikely to reflect solely differences in survey design. Using the survey weights helps to correct imbalances but not completely. The weighted proportion of households in Dakar is still 3% higher in PSF than in ESPS (29.5% for PSF versus 26.6% for ESPS) and very large imbalances appear between the Matam (PSF: 12.8%; ESPS: 3.4%) and Thies (PSF: 6.4%; ESPS: 14.2%) regions. In the face of this, we decided to follow a strategy initiated by DiNardo, Fortin and Lemieux (1996)<sup>2</sup> . Pooling together the PSF and ESPS data, we

> 2 See Nichols (2008).

10
_Services PTAs_ PTAs, followed by South Korea, which has concluded 15 such agreements. The EU is party to 14 agreements, as is Japan; the US, China and Peru each have 13, and Australia and New Zealand have signed 10 services PTAs.

Figure 1: Number of Agreements per Economy in 2016 <!-- Start of picture text --> NEW AGREEMENTS ENTERED<br>2000 - 2016<br>13 - 20<br>11 - 12<br>6 - 10<br>3 - 5<br>2<br>1<br>0 (NO PTA NOTIFIED)<br>IBRD 45683 |<br>MARCH 2021<br><!-- End of picture text -->

Source: Authors’ elaboration using the World Bank DTA 2.0 Database. Note: Zero (‘0’) means that an economy has no agreements notified to the WTO. This could be because there is in fact no services PTA (the vast majority of cases), or because an economy is not a member of the WTO, or even if it is, an agreement may not been notified to the WTO until well after it has taken effect.

As a result of this spread of services PTAs over the past 20 years, the majority of services trade now happens under preferential terms for most countries (Figure 2). The graph depicts the log average value of services exports with partner for which it has a services PTA (red circle), if there is one, and the log average value of services exports with non-PTA partners (black diamond). For most countries PTA trade exceeds services under most favoured nation (MFN) terms. Two notable exceptions are the US and Japan, which trade services extensively with EU member states but as of 2016 neither had a services PTA in place with the EU.

The proliferation of services PTAs has been accelerating since 2000. That said, participation of economies has been highly unequal; for instance, whereas China and Australia 4
65 Nlgwr 1: PFP - Projectlons and Actual* <!-- Start of picture text --> .4 .<br>4~~~~~~~~~~~~~~~~~~~~~1<br>.11 -- -<br>*~., *P 60 6r6o<br>r e ~~~~o $I la<br>VON<br>-Pr.efOUM +taue<br><!-- End of picture text -->

**XP.jebn**

Nlgor 3: PFP - Projections and Actuals <!-- Start of picture text --> 01.4. 1 G O' s o GO M<br>21<br><!-- End of picture text --> <!-- Start of picture text --> I-C G~~~P.eUe + AIulPo.tMe+euI-_ -.....--<br>Niger 5 - PFP Projections and Actuals<br>NIe 5-PPPoetin Pt Jt- _Pw0Vest n cuasNgr6<br>Ai p USwAb<br>&a gxWW t<br>4.~~~~~~~~~~~~~~~~~~~~~-<br>Oes'rm ProiedIons. WPm 07*0. ili IO. 1037V.<br>Adudsm *DRD/C&Aauy idel.<br>Jo ju D* Deeeleen<br><!-- End of picture text --> _Nlger_ 2: PFP - Projections and Aotuial <!-- Start of picture text --> o so<br>Year<br>+A.Is *Aow Tar e4uvAd<br>TOT trms e d Trade<br><!-- End of picture text --> Nlgor 4 - PFP Projections and Actuals <!-- Start of picture text --> .2 * 9 s0o t G9<br><!-- End of picture text --> . Niger 6: PFP - Projections and Actuals <!-- Start of picture text --> .<br>FP-Poeton Vee n cui<br>w P . w<br>Yw<br>+'oj<br><!-- End of picture text -->
# **Table of Contents**

|Ack|nowledgements....................................................................................................................................... 1|
|---|---|
|Intr|oduction................................................................................................................................................... 2|
|1.|Sample and Methodological Notes....................................................................................................... 4|
||DBs by year of establishment............................................................................................................... 6|
||Size of DBs........................................................................................................................................... 6|
||Countercyclical Role of DBs................................................................................................................ 8|
|3.|Ownership and Funding........................................................................................................................ 9|
||Funding............................................................................................................................................... 10|
|4.|Mandates, Business Models and Financial Products.......................................................................... 11|
||Mandates............................................................................................................................................. 11|
||Economic Sectors and Clients Targeted by DBs................................................................................ 12|
||Lending Models: First-Tier and Second-Tier Lending....................................................................... 14|
|5.|Asset Quality and Return.................................................................................................................... 17|
|6.|Corporate governance arrangements.................................................................................................. 19|
|7.|Regulation and Supervision of DBs.................................................................................................... 21|
|8.|Challenges for DBs............................................................................................................................. 22|
|Con|clusions................................................................................................................................................. 23|
|Ref|erences................................................................................................................................................... 26|
|Ann|ex.......................................................................................................................................................... 27|
<!-- Start of picture text --> 0.90<br>075+ @ _—_e—_o—, ae—_e—_ee ONL@<br>—~e—» ° r)<br>0.60<br>x< e<br>zCr) @ — *-° ee _—<br>2z \ =@ NYeoo—_ e@<br>6°ES 0.45 °<br>Te}<br>°<br>ES}<br>0.30<br>0.15<br>0.00<br>2007 2009 2011 2013 2015 2017 2019 2021<br>Year<br>-@- Employer -@- Medium and Large Firms Micro and Small Firms Self-employed<br><!-- End of picture text -->
Bednar-Friedl, B., Biesbroek, R., Schmidt, D. N., Alexander, P., Børsheim, K. Y., Carnicer, J., Georgopoulou, E., Haasnoot, M., Le Cozannet, G., Lionello, P., Lipka, O., Möllmann, C., Muccione, V., Mustonen, T., Piepenburg, D., & Whitmarsh, L. (2022). _Europe: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change_ .

Boasson, E., Wettestad, J., Leiren, M., Szulecki, K., Inderberg, T. H., Bäckstrand, K., Faber, H., Reimer, I., & Banet, C. (2020). _Comparative Renewables Policy: Political, Organizational and European Fields_ .

Buchner, B., Clark, A., Falconer, A., Macquarie, R., Meattle, C., Tolentino, R., & Wetherbee, C. (2019). _Global Landscape of Climate Finance 2019_ .

Choi, H., & Anadón, L. D. (2014). The role of the complementary sector and its relationship with network formation and government policies in emerging sectors: The case of solar photovoltaics between 2001 and 2009. _Technological Forecasting and Social Change_ , _82_ , 80–94.

Cubbin, J., & Stern, J. (2006). The impact of regulatory governance and privatization on electricity industry generation capacity in developing economies. _World Bank Economic Review_ , _20_ (1), 115–141.

Doblinger, C., Surana, K., & Anadón, L. D. (2019). Governments as partners: The role of alliances in U.S. cleantech startup innovation. _Research Policy_ , _48_ (6), 1458–1475. Doumbia, D. (2021). _Power Market Sophistication and Sector Outcomes: A Focus on Social Performance, Electricity Reliability, and Renewable Energy Penetration._ World Bank Policy Research Working Paper No. 9585.

Egli, F., Steffen, B., & Schmidt, T. S. (2019). Bias in energy system models with uniform cost of capital assumption. _Nature Communications 2019 10:1_ , _10_ (1), 1–3.

37
holds in the previous 12 months and identied the age, gender, educational attainment, and the destination country of the remittance sender.

This study uses 7,108 NLSS III households from both the cross-section and panel components that have complete information on the variables used in the analysis. Among the households in the sample, 33.1% (2,212) reported having at least a member abroad in 2010-11. Characteristics of migrants are reported in table (A.1). Migrants tend to be young (mean age of 28), dominated by males (84.4%) and mostly composed of the daughters/sons of the household head. They tend to have achieved some grade level of education, 53.7% have completed between grades 1 and 10, while 20.3% have completed grade 10 (School Leaving Certicate) and intermediate level (high school). India and the Middle East seem to be the favored destinations for most of the migrants, 44.4% and 24.7%, respectively. Although remittances coming from abroad tend to be relatively large, there are very little dierence in total and non-wage household incomes (Figure 2); households with a migrant have slightly higher total and non-wage incomes.

A total of 16,879 working age adults (ages 18-60) are used for the analysis. For the analysis of time allocation in household activities, 4,985 boys and 5,195 girls (both ages 6-17) are added in the sample. Adults from households with and without migrants dier on demographics, household composition, their labor market outcomes, and communities where they reside. Table 1 reports the descriptive statistics of the 16,879 adults used in the analysis. Adults from migrating families, both male and female, are less likely to be employed in wage-employment but are more likely to be involved in self-employment activities compared to the adults from non-migrating families. Consistent with the theory (for the bottom 70% of the households), adults decrease wage-employment with household non-wage income (Figure 3). Migration seems to have a disincentive eect on wage-employment; adults from migrating families are less likely to have wage-work over all the non-wage household income distribution.<sup>5</sup> Adults from migrating families are older by a year and have achieved a year less education than those in households with no migrants. When comparing only female members, females from families with a migrant are more likely to be married and head the household when compared to females from families with no migrants. This is the opposite when comparing male members. Adults from

> 5These results are suggestive, they do not account for the fact that the adult most likely to be the wage earner in the family might be more likely to migrate, leaving adults who are intrinsically less likely to have wage employment behind.

7
Literacy is strongly related to poverty. That is, being able to read is important in determining the likelihood of being in poverty. In Haiti, the P0 is 34 percent for household heads who are literate, and 60 percent for those who are not. Not surprisingly, these head counts are high compared to other countries in Latin America and the Caribbean. A large difference in poverty exists between household heads living in metropolitan and rural areas. P0 for heads who can read is 17 percent in the metropolitan area compared to 47 and 46 percent in rural and other urban areas respectively. Language skills are also strongly related to poverty.

In Haiti, the P0 is lower when the head speaks French, namely 28 and 49 percent for French speakers and non-French speakers respectively.<sup>18</sup> Again the poverty head count is much lower for French-speaking Haitians in urban areas (25 percent) than in rural areas (66 percent).<sup>19</sup> Education levels are very strongly related with poverty. Household heads with completed tertiary education are much less likely to experience poverty than those who have completed secondary or primary only. There appears to be a large difference in P0 between household heads with no education (61 percent) and household heads with completed primary education (43 percent). Household heads who have completed secondary education are much better off (25 percent are poor) than those with only primary education. Of the household heads with completed tertiary education only 5 percent were extremely poor in 2001. These findings indicate that education is key to poverty reduction in Haiti as elsewhere.

Figure 6.1 presents the location differences in P0 for the four education levels. The chart clearly shows that poverty at any given education level is much more widespread in rural areas than in the metropolitan areas. This is also the case for other urban areas that perform worse than rural areas; only household heads with primary education are less poor in urban than rural areas. For all levels P0 is only slightly higher in rural areas if higher at all. Obviously, the presented data do not take into consideration that the quality of education may be lower in rural areas.

Figure 6.1 also shows that there are very large differences in poverty levels by education attained. Most likely the difference has increased over time as in other countries (no household data is available to address this further since the 2001 survey is the very first the country has undertaken). Table 6.2 shows that of household members with an income that places them in the lowest two income quintiles, more than 50 percent have not completed any education level, and only around 30 percent have completed primary education.<sup>20</sup>

> 18 It may be worse noting that very few household heads in the sample speak French.

> 19 Some data limitations have been observed such as: (1) only 39 households speak French at home, (2) 94 household heads have tertiary education completed, and (3) work tenure findings are based on only 1,588 observations.

> 20 Notice that Table 6.2 includes 20,074 individuals.

32
# **1. INTRODUCTION**

Banking in Africa has undergone dramatic changes over the past three decades. While the continent was dominated by government-owned banks until the 1980s and subject to restrictive regulation—including interest rate ceilings and credit quotas—financial liberalization, institutional and regulatory upgrades, and globalization have changed the face of financial systems across the region. Today, most countries have deeper and more stable financial systems, although challenges of concentration and limited competition, high costs, and limited inclusion persist. And while financial service provision used to be limited to a few larger firms and highincome households, the mobile money revolution has improved access to financial services enormously over the past decade and a half. Originally limited to payment services, other financial services are nowadays delivered through digital channels, increasing the outreach of the formal financial system and allowing better access to and use of such services by a large part of the household and enterprise populations.

This paper surveys existing literature and data to take stock of the current state of banking systems across Sub-Saharan Africa and discusses recent developments including innovations that could help Africa leapfrog more traditional banking models.<sup>1</sup> It uses an array of different data sources to document different dimensions of the development of African banking systems,

> 1 In the following Sub-Saharan Africa and Africa are used interchangeably. However, this analysis focuses almost exclusively on Sub-Saharan Africa thus excluding Northern Africa.  While some of the characteristics of Sub-Saharan African financial systems extend to Northern Africa, there is a similar variation across countries in the Northern part of the continent, ranging from a fairly developed system in Morocco to rather shallow financial markets in Tunisia and the Arab Republic of Egypt, to a state-dominated financial system in Algeria. Beck et al. (2011) offer a discussion of both Sub-Saharan and Northern African financial systems.

2
# HOW SMALL SHOULD AN ECONOMY'S FISCAL DEFICIT BE?

# A MONETARY-PROGRAMMING APPROACH

# **Paul Beckerman**

**World Bank.** The writer gratefully acknowledges comments on earlier versions by Zoub.ida Allaoua, Philippe Auffret, Paul Levy, Maria Cristina Almero-Siochi, Antonio Velandia, Fahrettin Yagci, and participants in a workshop session of the World Bank's Quality of Fiscal Adjustment Group (October 21, 1998), including Sara Guershanik Calvo, Hana Polackova, Sergei Shatalov, and Thilakaratna Ranaweera. Particular thanks are due Luis Serven for helpful comments on two versions and Hazel Vargas for virtuoso word processing. Views expressed here are the writer's, and are not necessarily shared by the World Bank or any other institution with which this writer has been associated. _In particular, figures andforecasts presented in the annex discussing forecasts for Ecuador are intended only to illustrate the model described in this paper, and should not be taken to reflect the views of the writer or the WVorld Bank regarding Ecuador's economic performance in 1999 and 2000._