rafmacalaba/datause-encoder-data huggingface dataset:
individual drivers of development, these global scenarios assume independence across countries, which (again) prioritizes the broadest possible exploration of possible futures, but leads to the inclusion of unlikely scenarios (e.g., where agriculture productivity grows very quickly in some countries but declines in others, which would assume some technological disconnection across countries).

# **3 Results**

## **3.1 Future poverty without climate change**

We first explore the number of people in extreme poverty in 2030 in the baseline scenarios (i.e., without climate change). Figure 1 shows the distribution of extreme poverty incidence in 2030 across the 1,000 baseline scenarios per country. These distributions are to be used carefully because each scenario represents a possible future, but they are not equally likely. However, we do not attribute any “probability” to these scenarios so that the figures in this section should be considered as histograms and not as probability distributions.

On average across the scenarios, there are 313.5 million people in extreme poverty in 2030. While this number is substantially lower than the poverty headcount in 2015, which amounts to 736 million people (World Bank, 2018), it is still far from the zero extreme poverty target. In most scenarios, the SubSaharan Africa region contributes the largest number of poor people (224.4 million people on average), followed by South Asia (30.6 million people on average), East Asia and Pacific (11.8 million people on average), and Latin America and Caribbean (1.9 million people on average).

<!-- Start of picture text --> 1000<br>800<br>600<br>400<br>313.5<br>224.4<br>200<br>0 11.8 1.9 30.6<br>EAP LAC SAS SSA Global<br>Region<br>(million, 2030)<br>People in extreme poverty<br><!-- End of picture text --> Figure 1: People in extreme poverty, without climate change in 2030 Figure 1 also shows that the total number of people in extreme poverty exceeds 590 million in more than 250 scenarios. There are therefore many futures among our scenarios in which extreme poverty remains close to its current levels. These scenarios tend to be associated with the SSP4, which assumes high population growth and slow progress in education and productivity.

On the other hand, there are also 250 scenarios with fewer than 170 million poor people. In the bestcase scenario there are only 9.3 million people in extreme poverty globally. This shows that approaching the eradication of extreme poverty by 2030 requires all our assumptions to be among the most optimistic, confirming that this objective is very ambitious at this point. Introducing the impacts of COVID-19 would make this objective even more difficult to achieve.

## **3.2 Climate change impacts on extreme poverty**

We now turn our attention to the poverty headcount under scenarios where all climate impact channels are simultaneously included. Figure 2a shows the distribution of the number of additional people falling 4
ZIMBABWE‘S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Ampah, Mavis, Daniel Camos, Cecilia Briceño-Garmendia, Michael Minges, Maria Shkratan, and Mark Williams. 2009. ―Information and Communications Technology in Sub-Saharan Africa: A Sector Review.‖ AICD Background Paper 10, Africa Region, World Bank, Washington, DC.

- Econet Wireless Zimbabwe. ―Limited Final Results For the Financial Year Ended 28 February 2010.‖ www.b2i.us/profiles/investor/fullpage.asp?f=1&BzID=1685&to=cp&Nav=0&LangID=1&s=0&I D=9346.

- Mayer, Rebecca, Ken Figueredo, Mike Jensen, Tim Kelly, Richard Green, and Alvaro Federico Barra. 2009. ―Connecting the Continent: Costing the Needs for Spending on ICT Infrastructure in Africa.‖ AICD Background Paper 3, Africa Region, World Bank, Washington, DC.

- Ndlela, Dumisani. 2010. ―Zim Newspaper Readership Slumps, TV Viewership Grows.‖ BizCommunity.com, July 21. www.bizcommunity.com/Article/238/23/50215.html.

Orascom Telecom. ―Annual Report 2009.‖ www.otelecom.com/Investor_Relations/AnnualReports.aspx.

- POTRAZ. 2009. ―Zimbabwe Country Report.‖ Presented at the 12th CRASA Annual General Meeting, April 2. www.crasa.org/countryreport09.htm.

Smarts, Nchidzi. 2010. ―BTC Signs Bandwidth Deal with Zim‘s Powertel.‖ The Botswana Gazette,

- February 3. www.gazettebw.com/index.php?option=com_content&view=article&id=5321:btcsigns-bandwidth-deal-with-zims-powertel&catid=13:business&Itemid=2.

- Technology Zimbabwe. 2010. ―TelOne has 15 km of Fibre to Mutare and is  ̳Progressing Well‘.‖ June 24. www.techzim.co.zw/2010/06/telone-has-15km-of-fibre-to-mutare-and-is-―progressing-well‖/.

# Irrigation

Svendsen, Mark, Mandy Ewing, and Siwa Msangi. 2008. ―Watermarks: Indicators of Irrigation Sector Performance in Africa.‖ AICD Background Paper 4, Africa Region, World Bank, Washington, DC.

- You, L., C. Ringler, G. Nelson, U. Wood-Sichra, R. Robertson, S. Wood, G. Zhe, T. Zhu, and Y. Sun. 2009. ―Torrents and Trickles: Irrigation Spending Needs in Africa.‖ AICD Background Paper 9, Africa Region, World Bank, Washington, DC.

# Power

Briceño-Garmendia and Shkaratan. 2011. ―Power Tariffs: Caught Between Cost Recovery and Affordability‖ Policy Research Working Paper forthcoming, World Bank, Washington DC

- Eberhard, Anton, Vivien Foster, Cecilia Briceño-Garmendia, Fatimata Ouedraogo, Daniel Camos, and Maria Shkaratan. 2008. ―Underpowered: The State of the Power Sector in Sub-Saharan Africa.‖ AICD Background Paper 6, Africa Region, World Bank, Washington, DC.

- Foster, Vivien, and Jevgenijs Steinbuks. 2009. ―Paying the Price for Unreliable Power Supplies: In-House Generation of Electricity by Firms in Africa.‖ Policy Research Working Paper 4913, World Bank, Washington, DC.

52
# **9 Bibliography**

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Altonji, J. and R. Blank, “Race and Gender in the Labor Market,” in O. Ashenfelter and D. Card (eds) _Handbook of Labor Economics_ , Vol. 3, Elsevier Science, B.V., Amsterdam, 3143–259, 1999.

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26
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(safeguards), and this increases the chance that trade remedies imposed during the crisis will eventually be removed without resort to WTO dispute settlement.<sup>20</sup> Third, public resources are best utilised to combat the free-rider and problems of coordinating and organising exporters in different countries that are jointly affected by a trading partner’s new trade barrier that violates the WTO principle of national treatment. Because antidumping and countervailing measures are exporter-specific, to the extent that trade remedies need additional publicly-funded information, the analysis should target new trade barriers implemented as global safeguards, and not antidumping or CVDs.

# **_Other tariff and non-tariff barriers imposed at the border?_**

WTO Members do not limit their policy interventions to trade remedies such as antidumping, countervailing duties, or safeguards. Thus focusing exclusively on these policies is likely to miss part of the story of WTO violations affecting developing country exports during the crisis. Consider table 2, which provides a summary of the Global Trade Alert’s reporting of various types of trade-related policies during the global economic crisis. While the table indicates that trade remedies result in an important category of new trade-related measures, policymakers have also resorted to _other_ types of border measures (such as increased tariffs, new quotas, or import bans) to restrict market access to foreigners during the crisis.

20 This is not to indicate that WTO Members all follow the sunset provisions on antidumping – there is some evidence to the contrary. See, for example Moore (2006) as well as Cadot, de Melo and Tumurchudur (2007). Hence whether sunset provisions are followed in the future will require additional monitoring.

25
**<u>Appendix table 2: Determinants of visible land-related investments: Tobit estimates</u>**

||Trees Planted (Tobit)|
|---|---|
||Coefficients<br>Marginal effects|
|Rights without approval|0.689***<br>0.159***|
||(7.84)<br>(8.00)|
|Rights with outside approval|0.586***<br>0.135***|
||(5.43)<br>(5.49)|
|Knowledge of land law at the hh level (index)|0.631**<br>0.146**|
||(2.09)<br>(2.09)|
|Freehold and_mailo_tenure|1.266***<br>0.310***|
||(3.89)<br>(3.69)|
|Freehold and_mailo_occupant/tenant|-1.172***<br>-0.254***|
||(3.41)<br>(3.64)|
|No. of years possessed|0.040***<br>0.009***|
||(4.84)<br>(4.86)|
|Distance from house|-0.040***<br>-0.009***|
||(9.73)<br>(10.63)|
|Parcel area in acres|0.118**<br>0.027**<br> <br>|
||(2.55)<br>(2.56)|
|Parcel area squared|-0.002*<br>-0.001*<br> <br>|
||(1.74)<br>(1.74)|
|Good land quality|-0.104<br>-0.024|
||(0.55)<br>(0.56)|
|Poor land quality|-0.689***<br>-0.151***|
||(2.61)<br>(2.75)|
|Flat land|-0.945***<br>-0.221***|
||(5.08)<br>(5.03)|
|Steeply sloped land|0.841**<br>0.210**|
||(2.53)<br>(2.35)|
|Irrigated land|0.595<br>0.146|
||(0.71)<br>(0.67)|
|Swamp/Wetland|-0.156<br>-0.036|
||(0.17)<br>(0.18)|
|Household head's age|0.039<br>0.009|
||(1.15)<br>(1.15)|
|Head's age squared|-0.001*<br>-0.000*|
||(1.77)<br>(1.77)|
|Head's education in years|0.137***<br>0.032***<br> <br>|
||(5.75)<br>(5.81)|
|Female headed household|0.605**<br>0.146**|
||(2.38)<br>(2.28)|
|Household size|0.061**<br>0.014**|
||(2.22)<br>(2.22)|
|Value of all assets three years ago (Log)|-0.181***<br>-0.042***|
||(2.70)<br>(2.71)|
|Distance to the nearest district town in miles|-0.029<br>-0.007|
||(1.32)<br>(1.32)|
|Public electricity accessible to households|<br> <br>0.212<br>0.050|
||(0.95)<br>(0.94)|
|Constant|-3.776***|
||(4.57)|
|Observations|2185|
|LR chi2(17)|452.75***|

Absolute value of z statistics in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1% Marginal effects evaluated at the mean for continuous variables, or as a discrete change from 0 to 1 for dummies.

29
Chapin, F. S., III, E. S. Zavaleta, V. T. Eviner, R. L. Naylor, P. M. Vitousek, H. L. Reynolds, D. U. Hooper, S. Lavorel, O. E. Sala, S. E. Hobbie, M. C. Mack, and S. Diaz. 2000. Consequences of changing biodiversity. Nature 405:234–242.

Coady, D., M. El-Said, R. Gillingham, K. Kpodar, P. Medas, and D. Newhouse, 2006: The magnitude and distribution of fuel subsidies: evidence from Bolivia, Ghana, Jordan, Mali, and Sri Lanka, IMF Working Paper WP/06/247, Washington, DC.

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- Freund, C. L., and C. I. Wallich, 1995: Raising household energy prices in Poland. Who gains? Who loses? Policy Research Working Paper 1495, The World Bank, Washington, DC.

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33
# **Figure 4 Effect of Closed-Ended Matching Grant**

<!-- Start of picture text --> Spending<br>on other<br>public<br>goods<br>A<br>C<br>G<br>E 33% subsidy<br>O F B D<br>Spending on assisted public<br>goods<br><!-- End of picture text --> _Source_ : Shah (1994) Why are conditional closed-ended matching grants common in industrial countries when they seem ill designed to solve problems and inefficiencies in the provision of public goods? The answer seems to be that correcting for inefficiencies is not the sole or perhaps even the primary objective. Instead, grants are employed to help local governments financially while promoting spending on activities given priority by the grantor. The conditional (selective) aspects of or conditions on the spending are expected to ensure that the funds are directed toward an activity the grantor views as desirable. This, however, may be false comfort in view of the potential for fungibility of funds. The local matching or cost-sharing component affords the grantor a degree of control, requires a degree of financial accountability by the recipient, and makes the cost known to the granting government.

Conditional closed-ended matching grants have advantages and disadvantages from the grantor’s perspective. While such grants may result in a significant transfer of resources, they may distort output and cause inefficiencies, since the aid is often available only for a few activities, causing overspending on these functions while other functions are underfinanced. If capital outlays are subsidized while operating costs are not, grants may induce spending on capital-intensive alternatives.

Conditional open-ended matching grants are the most suitable vehicles to induce lowerlevel governments to increase spending on the assisted function (table 1). If the objective is simply to enhance the welfare of local residents, general-purpose nonmatching transfers are preferable, as they preserve local autonomy.
# **Does the Environment Matter for Poverty Reduction?**

**The Role of Soil Fertility and Vegetation Vigor in Poverty Reduction**

_Martin Heger_<sup>_a_</sup> _, Gregor Zens_<sup>_b_</sup> _, and Mook Bangalor_<sup>_c_</sup> _aWorld Bank, ENR GP_ _bVienna University of Economics and Business_ _cLondon School of Economics and Political Science_

**JEL:** O11, O13, Q15, I32

**Key Words:** land use; environment; poverty; soil quality; global panel

This paper is a product of the Environment and Natural Resources (ENR) Global Practice (GP) of the World bank. It was prepared for the for a larger World Bank program linking human dimensions to environmental factors. 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 authors may be contacted at <u>mheger1@worldbank.org. The invaluable contributions of the following individuals in creating the panel</u> data set are greatly appreciated: Jia Jun Lee, Therese Norman, Hrishi Patel, Aminul Islam, Joseph Muehlhausen, and Alice Lin.
# **_Sukuk in the International Markets_**

Sukuk are developing as a global asset class, supporting development with the participation of a wide range of issuers and investors irrespective of religious orientation. On the demand side, participation of non-Islamic investors that treat both conventional instruments and sukuk as part of their investable pool of products is growing. For example, European and U.S. institutional investors, especially insurance and hedge funds, are known to be active investors in international sukuk placements. In Malaysia, the largest sukuk market to date, conventional investors actively invest in either or both sukuk and conventional securities depending on risk and return offered.

On the supply side, other than entities from countries with significant Muslim population, nonIslamic issuers are increasingly tapping the sukuk markets. These include government entities, corporations, and multilateral financial institutions (see table 3). Most of the international issuance has so far been by government or quasi-government entities because of the size and investor confidence, particularly given the issues associated with the structure and asset ownership. Although the first several issuers were entities from countries with majority Muslim population, such as those in the Middle East (e.g., Bahrain, Kuwait, Saudi Arabia) and Southeast Asia (e.g., Brunei, Indonesia, and Malaysia), issuers from non-Islamic countries or jurisdictions have also come into the picture (e.g., Germany, Hong Kong (China), Luxembourg, South Africa, and the United Kingdom), and several others are in the pipeline (e.g., the Republic of Korea).

Table 3. International Sukuk Offerings by Country of Origin a. Sovereign, <u>Quasi-sovereign, or Subsovereign</u><sup>a</sup>

|Countryof Obligor<sup>b</sup>|Location of Issue/Offer/Listing|
|---|---|
|Bahrain|London (United Kingdom); Frankfurt, Berlin<br>(Germany)|
|Saxony-Anhalt (Germany)|Luxembourg|
|Hong Kong (China)|Dubai; Hong Kong (China); Malaysia|
|Indonesia|Singapore|
|Kazakhstan|Malaysia|
|Luxembourg|Luxembourg|
|Malaysia|Hong Kong (China); Malaysia|
|Qatar|London|
|Saudi Arabia|Dublin (Ireland); London; Frankfurt|
|South Africa|Luxembourg|
|Turkey|Dublin|
|United Arab Emirates|London; United States|
|United Kingdom|London|
|b. Corporation<br>Countryof Obligor<sup>b</sup>|Location of Issue/Offer/Listing|
|Germany|Luxembourg|
|Hong Kong (China)|Bermuda|
|Indonesia|Singapore|
|Malaysia|United States; London; Dublin; Hong Kong<br>(China); Singapore; Frankfurt|
|Qatar|Dublin; London; Luxembourg|
|Saudi Arabia|Dublin; Malaysia|
|Singapore|Malaysia|
|Turkey|Dublin; London; Frankfurt|
|United Arab Emirates|London; Frankfurt|
|United States|London; Malaysia; Frankfurt; Berlin|

_Source:_ Bloomberg, various sources.

17
22

# TABLE 3

# SOCIAL WELFARE GAINS OR LOSSES FROM PRIVATE MONOPOLY OPERATION OF NATURAL MONOPOLIES WITH EXPECT OF PYltAMIDING VERTICA DISTORTIONS

(4) (5) (6)

|(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|
|PRICE<br>|PERCENT<br>CHANGE<br>IN<br>|SOCIAL WELFARE<br>CHANGE AS %<br>OF INDUSTRY<br>|SOCIAL W<br>AS % OF I<br>IF COSTS|ELFARE CH<br>NDUSTRY<br>DECLINE|ANGE<br>OUTPUT<br>BY:|
|ELASTICITY|PRICE|OUTPUT||||
||||0.1|0.2|0.3|
|0.111111<br>|450<br>|-157.50<br>|-148.96<br>|-140.36<br>|-131.67<br>|
|0.176471<br>|283.333<br>|-99.17<br>|-90.61<br>|-81.94<br>|-73.15<br>|
|0.25<br>|200<br>|-70.00<br>|-61.42<br>|-52.67<br>|-43.77<br>|
|0.333333<br>|150<br>|-52.50<br>|-43.89<br>|-35.07<br>|-26.03<br>|
|0.428571<br>|116.667<br>|-40.83<br>|-32.19<br>|-23.28<br>|-14.08<br>|
|0.538462<br>|92.8571<br>|-32.50<br>|-23.82<br>|-14.80<br>|-5.42<br>|
|0.666667<br>|75<br>|-26.25<br>|-17.53<br>|-8.38<br>|1.20<br> <br>|
|0.818182<br>|61.1111<br>|-21.39<br>|-12.62<br>|-3.33<br>|<br>6.50<br> <br>|
|1<br>|50<br>|-17.50<br>|-8.68<br>|0.80<br>|<br>10.93<br>|
|1.222222<br>|40.9091<br>|-14.32<br>|-5.42<br>|4.27<br>|14.76<br> <br>|
|1.5<br>|33.3333<br>|-11.67<br>|-2.68<br>|7.28<br>|<br>18.22<br> <br>|
|1.857143<br>|26.9231<br>|-9.42<br>|-0.32<br>|9.99<br>|<br>21.51<br> <br>|
|2.333333<br>|21.4286<br>|-7.50<br>|1.76<br>|12.53<br>|<br>24.83<br>|
|3<br>|16.6667<br>|-5.83<br>|3.64<br>|15.07<br>|28.44<br>|
|4<br>|12.5<br>|-4.38<br>|5.43<br>|17.83<br>|32.83<br> <br>|
|5.666667<br>|8.82353<br>|-3.09<br>|7.25<br>|21.28<br>|<br>38.99<br> <br>|
|9|5.55556|-1.94|9.48|26.76|<br>49.88|

Note: This table embodies the same assumptions **as** the previotus table except that it assumes that the pyramiding vertical distortion effects of intermediate **good** price distortions increase the welfare losses of monopoly pricing by 40 percent.
**Table 7 Interaction** Effects **of** Curricular Content **and Educational Expansion on Economic Growth, 1960-1985 (Less-developed country sample)**

(Unstandardized regression coefficients are reported; dependent variable is per capita RGDP 1985)

||Equation:|(A)|(B)|(C)|
|---|---|---|---|---|
||Average Yearly<br>Instructional Time (Total)||.00022||
|Average|Mathematics|||-.00148|
|Annual|Sciences|||.00280**|
|Instructional|Languages|||.00030|
|Time in|Arts/Music|||.00191*|
|Each|All Social Studies|||-.00075|
|Subject|Moral/Religion|||.00098|
|Area|Physical Ed.|||-.00198|
|(circa 1960):|Pre-Vocational Ed.|||-.00133***|
|Educational<br>|Primary<br>Education|.0043**|.0025|.0030|
|Enrollment<br>Rates:|Secondary<br>Education|.0034*|_.0054**_|.0122**|
|Control|Lagged<br>Dependent Var.|.66**|.684*|.67**|
|Variables:|Mining/Oil<br>Exporters|.10*|.09*|.08|
||Export Commod.<br>Concentration|-.004**|-.004**|-.003**|
||Total Fertility<br>Rate (1965)|.044|.048|.039|
||Adjusted R<sup>2</sup>|.839|.840|.867|
||Constant|.79|.71|.73|
||Number of Cases|43|43|43|

> * Unstandardized regression coefficient is at least 1.5 times its standard error

> ** Unstandardized regression coefficient is at least 2.0 times its standard error