rafmacalaba/datause-encoder-data huggingface dataset:
P a g e <u>|</u> **34**

capacity to pay. We suggest that each city establish and constantly monitor the distribution of households’ income in order to ensure its housing policy is responsive to changes in demand.

With the data available we have estimated an income distribution profile for Mumbai and Ahmedabad<sup>9</sup> (Figure 4a and b). Incomes are shown at INR 2,500 intervals from 0 to INR 100,000 per month. Each income profile includes 2 graphs, an histogram showing the number of households in each income interval (on the primary vertical axis on the left) and a cumulative curve showing the percentage of total households below a specific income (expressed on the secondary vertical axis on the right).

The data shown on Figure 4 shows how useful accurate information on city-wide income distribution can be for setting housing policy. The profiles of household income in Mumbai and Ahmedabad are markedly different. In Mumbai is a more prosperous city (median income is around INR 20,000 per month and average income is 40,000 as compared to a median of 15,000 and a mean of 28,000 in Ahmedabad) but Mumbai has a far more substantial slum population.

The shape of the distribution curve may change with time. Some households will shift to the right when their incomes increase because of higher productivity, on the other hand, an influx of migrants from rural areas may increase the number of households in the left part of the graphs. These changes will put pressure on the housing stock and will test the elasticity of housing supply as households’ income changes. This phenomenon can be observed in Mumbai where higher income groups represent a more substantial share of the population.

Once income distributions are available, identifying households that would be eligible for assistance in any likely supply conditions, i.e. those facing a binding demand side constraint is the first step. These households will need direct assistance. We can define this group as those currently living in slums who could not possibly afford the _construction cost_ (at current market prices) of a minimum one story simple dwelling of say 130 square feet<sup>10</sup> with access to water and toilet. The problem of the land cost affordability must be treated separately after addressing the supply side and regulatory constraints. Very roughly, we calculate

> 9 Annex 2 provides details of methodology and sources for developing this income distribution data. 10 This cost represents the price of building a dwelling of decent quality in wood and brick. The cost of a high rise apartment meeting formal standards would be much higher per square foot. In any case, such a small home would not meet formal standards, but it would provide much improved accommodation for poor slum dwellers.

34
# **Figure 7: Changes in renewable natural capital components**

# **(a) Growth in planted forest area**

Planted forests refers to “Forest predominantly composed of trees established through planting and/or deliberate seeding” (FAO, 2020). These values therefore do not include naturally regenerating forests. Percentages on the y-axis refer to average annual growth rates between 2009 and 2020, obtained from FAOSTAT. The size of the bubbles reflects the GDP value-added of agriculture, forestry and fishing.

<!-- Start of picture text --> 6% GDP Value Added (Agriculture, Forestry, Fishing) ROU<br>1% 5% 10% 15%<br>5%<br>BRA<br>MEX<br>4%<br>3%<br>CAN MYS<br>2% B-rated COL<br>AUS FRA CHN A-rated (1.3%) THA PER IDN TUR<br>1% SWE USA FIN (0.6%) PHL<br>GBR IRL IND<br>0% NLDDNK DEU AUT KOR JPN SVK POL ESP ITA ZAF RUSBGR HUN PRT<br>1% NOR CHE<br>0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0<br>Credit quality step (0=AAA, 2=BBB+, 4=BB)<br>Percentage change<br><!-- End of picture text -->

# **(b) Growth in area under organic agriculture**

Organic agriculture refers to “area certified organic and/or in conversion to organic” (Willer et al., 2020). Percentages on the y-axis refer to average annual growth rates between 2009 and 2020, obtained from FAOSTAT. The size of the bubbles reflects the GDP value-added of agriculture, forestry and fishing.

<!-- Start of picture text --> 40% GDP Value Added (Agriculture, Forestry, Fishing) MYS RUS<br>1% 5% 10% 15% 159.4% growth<br>9.4% of GDP<br>BGR<br>30%<br>B-rated ZAF IDN<br>20% (18.8%, ex MYS: 11.7%) PHL<br>AUS<br>THA<br>10% FRA A-rated IRL PER IND<br>0% SWE NLDCANCHEDEU USA DNKFIN AUT KOR CHNJPN(5.0%) SVKESPPOL ITA MEX HUNROU PRT BRA TUR<br>NOR<br>GBR<br>COL<br>10%<br>0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0<br>Credit quality step (0=AAA, 2=BBB+, 4=BB)<br>Percentage change<br><!-- End of picture text -->

# **(c) Growth in protected areas**

The left panel shows the total area expansions under national and sub-national protection (as share of total country area). The right panel shows the number of new protected areas designated per country. Areas are included if they have an IUCN designation (I-VI) between 2009 and 2020. Marine protected areas are excluded.

<!-- Start of picture text --> Average protected area expansions Number of protected area designations<br>0.175 5,000 Bond groups<br>0.150 A-rated<br>4,000 B-rated<br>0.125<br>3,000<br>0.100<br>0.075 2,000<br>0.050<br>1,000<br>0.025<br>0.000 0<br>USA FIN<br>CHE<br>CAN<br>AUS AUSSWE<br>DEU<br>USA<br>SWEAUT<br>DEUPER<br>NLD<br>KORCOLFRANORESP GBRFRABRANORKORCOL<br>ZAFTURRUSCHNMYSIRLINDPOLROUBGRIDNITAJPNDNKHUNPHLSVKPRTCHETHABRAFINGBRMEXCAN IRLMYSINDRUSCHNROUIDNPHLITATHADNKPOLPRTJPNBGRHUNSVKNLDAUTPERMEXESP<br><!-- End of picture text --> 22
Headey, B., R. Veenhoven, and A. Wearing. 1991. “Top-Down versus Bottom-Up Theories of Subjective Well-Being.” _Social Indicators Research_ 24: 81–100.

- Helliwell, J. F., R. Layard, and J. Sachs (eds.). 2015. _World Happiness Report 2015_ . New York: United Nations.

- Ianchovichina, E. I., J. L. Loening, and C. A. Wood. 2014. “How Vulnerable Are Arab Countries to Global Food Price Shocks?” _The Journal of Development Studies_ 50 (9): 1302–19.

- Ianchovichina, E., L. Mottaghi, and S. Devarajan. 2015. _Inequality, Uprisings, and Conflict in the Arab World_ . Washington, DC: World Bank.

- Inglehart, R., R. Foa, C. Peterson, and C. Welzel. 2008. “Development, Freedom, and Rising Happiness: A Global Perspective (1981-2007).” _Perspectives on Psychological Science_ 3: 264–85.

- Iqbal, F., and Y. Kiendrebeogo. 2015. “The Reduction of Child Mortality in the Middle East and North Africa: A Success Story.” Policy Research Working Paper No. 7127, World Bank, Washington, DC.

- Kaufmann, D., A. Kraay, and M. Mastruzzi. 2011. “The Worldwide Governance Indicators: Methodology and Analytical Issues.” _Hague Journal on the Rule of Law_ 3 (02): 220–46.

- Klandermans, B. 1997. _The Social Psychology of Political Protest_ . Oxford, England: Blackwell.

Korotayev, A., and J. V. Zinkina. 2011. “Egyptian Revolution: A Demographic Structural Analysis.” _Entelequia: revista interdisciplinar_ (13): 139–69.

Lagi, M., K. Z. Bertrand, and Y. Bar-Yam. 2011. _The Food Crisis and Political Instability in North Africa and the Middle East_ . New England Complex System Institute.

Layard, R. 2006. _Happiness: Lessons from a New Science_ . Penguin.

Lewbel, A. 2012. “Using Heteroscedasticity to Identify and Estimate Mismeasured and Endogenous Regressor Models.” _Journal of Business & Economic Statistics_ 30.

- Malik, A., and B. Awadallah. 2013. “The Economics of the Arab Spring.” _World Development_ 45: 296–313.

Maslow, A. H. 1943. “A Theory of Human Motivation.” _Psychological Review_ 50: 370–96.

- OECD (Organisation for Economic Co-operation and Development). 2009. “Ownership Structures in MENA Countries: List Companies, State-Owned, Family Enterprises and

27
<!-- Start of picture text -->
**<br>xaaa Zax *<br>PT or<br>VW §<br>t —o/ *<br>[,— + + + “s<br>Saw<br><!-- End of picture text -->

<!-- Start of picture text -->
**<br>xaaa Zax *<br>PT or<br>VW §<br>t —o/ *<br>[,— + + + “s<br>Saw<br><!-- End of picture text -->

<!-- Start of picture text -->
**<br>xaaa Zax *<br>PT or<br>VW §<br>t —o/ *<br>[,— + + + “s<br>Saw<br><!-- End of picture text -->

<!-- Start of picture text --> Ke)<br>8<br>‘Ss<br>s4A<br>o<br>go<br>3<br>oN<br>q<br>x<br>a<br><!-- End of picture text --> <!-- Start of picture text --> 5 4 3 2 1 +0 414 #2<br>Timeline<br>Source: Authors' calculations based on Penn World Tables 9.0<br><!-- End of picture text --> <!-- Start of picture text --> 3<br><!-- End of picture text --> <!-- Start of picture text --> 4<br><!-- End of picture text --> <!-- Start of picture text --> Public Debt<br><!-- End of picture text --> <!-- Start of picture text --> AX é cia ee<br>fu fe<br>rR<br>. KH<br>\_/<br><!-- End of picture text --> <!-- Start of picture text --> a<br>aw<br>On<br>a° AX é cia ee<br>© fu fe<br>Ze rR<br>°<br>=ao$= . KH<br>o \_/<br><!-- End of picture text --> <!-- Start of picture text -->
-5 -4 -3 -2 -1 0 1 2 3 4 5<br>Timeline<br>Source: Authors' calculations based on the IMF Historical Public Debt Database<br><!-- End of picture text -->

<!-- Start of picture text --> fo)<br>N<br>So<br>=<br>2<br>Bo<br>©<br>Zo<br>Ey<br><x<br>fo)<br>©<br><!-- End of picture text --> <!-- Start of picture text --> Annual inflation<br><!-- End of picture text --> <!-- Start of picture text --> rate<br><!-- End of picture text --> <!-- Start of picture text --> 5 4 3 2 +1 #0 14 #2 3 4 =«65«65«65«65<br>Timeline<br><!-- End of picture text --> <!-- Start of picture text --> 5 4 3 2 +1 #0 14 #2 3 4 =«65«65«65«65<br>Timeline<br>Source: Authors' calculations based on the IMF International Financial Statistics<br>** Statistically significant, 90% confidence<br>* Source: Penn World Tables 9.0<br><!-- End of picture text --> <!-- Start of picture text --> 5 4 3 2 +1 #0 14 #2 3 4 =«65«65«65«65<br>Timeline<br>Source: Authors' calculations based on the IMF International Financial Statistics<br>** Statistically significant, 90% confidence<br>* Source: Penn World Tables 9.0<br><!-- End of picture text --> <!-- Start of picture text --> 5 4 3 2 +1 #0 14 #2 3 4 =«65«65«65«65<br>Timeline<br>Authors' calculations based on the IMF International Financial Statistics Database<br>Statistically significant, 90% confidence<br><!-- End of picture text --> <!-- Start of picture text --> Def. 1: All conflicts<br><!-- End of picture text --> <!-- Start of picture text --> 2: High intensity<br><!-- End of picture text --> <!-- Start of picture text --> Def.reaches3: T=01000 when<br><!-- End of picture text --> <!-- Start of picture text --> conflictdeaths exceeds<br><!-- End of picture text --> <!-- Start of picture text --> 1000 battle-related<br><!-- End of picture text -->
policy was tested during the period of capital surge in 2003-2008. During the surge episode, unprecedented large inflows of capital were associated with an acceleration in money supply. The statistics in Table 1 below confirm the policy approach to surges. The Reserve Bank of India, confronted with large capital inflows, accumulated reserves, and initially sterilized capital inflows by way of open market operations. However it did not sterilize the entire impact of its reserve accumulation, because of the finite stock of government securities it held, which it could use for this purpose. Thus it signed a Memorandum of Understanding with the Government of India in 2004 for issuance of Treasury Bills and dated Government Securities under the Market Stabilization Scheme (MSS), and used it to absorb liquidity. Due to the partial sterilization of reserve accumulation, both narrow and broad money supply increased during the surge; the extent of which increased in the later years of the surge (also see Figure 16).

While policy interest rates in India have not ordinarily reacted to the surges and stops of capital flows, some monetary accommodation seems to have occurred through changes in money supply during 2003-2008. Besides the open market operations and market stabilization scheme, the cash reserve ratio was used to neutralize the expansionary impact of foreign exchange purchases on domestic monetary and liquidity conditions.<sup>26</sup> Policy interest rates increased during the surge, due to a spike in inflation during this period.<sup>27</sup>

**Table 1: Money Supply and Policy Rates during Capital Flow Surge**

**(** Annual averages over the period indicated)

||Reserves<br>change<br>($ bn)|Exchange<br>rate (%<br>change)+|Reserve<br>Money<br>growth<br>(%<br>change)|Reserve<br>Money in<br>circulation<br>with<br>public (%<br>change)|M3<br>growth<br>(%<br>change)|Policy Rate:<br>Repo/Reverse<br>Repo/MSF<br>Rate|CRR|
|---|---|---|---|---|---|---|---|
|Pre surge 2000-01-2002-03|12.7|3.96|9.6|12.8|15.2|8.08*|6.7|
|Surge first three years:2003-<br>04, 2004-05, 2005-06|25|-3.3|15.8|14.9|16.6|6.3|4.8|
|Surge last two years: 2006-<br>07,2007-08|79|-3.5|27.4|17.4|21.5|7.53|6.1|

Note:* Average over 2001-02 and 2002-03. + a positive change is depreciation. Source: RBI Handbook of Statistics on the Indian Economy

> 26 The increase in CRR was reflected in a much larger increase in reserve money that the RBI issued, but a smaller increase in reserve money in circulation that was held by people.

> 27 The episode reversed the steady decline in CRR and policy rates that started in the 1990s as a part of the broader financial liberalization.

23
# **Figure 5: Labor force participation , with interaction for ability in English. Males.**

<!-- Start of picture text --> 0<br>-0.02<br>-0.04<br>-0.06<br>-0.08<br>-0.1<br>-0.12<br>-0.14<br>Figure 6: Labor force participation, with<br>interaction for ability in English. Females.<br>0.1<br>0.05<br>0<br>-0.05<br>-0.1<br>-0.15<br>-0.2<br>-0.25<br>Whites out englishWhites out not englishBlacks out englishBlacks inBlacks out not englishAsians out englishAsians inAsians out not englishHispanics out englishHispanics inHispanics not english<br>Whites out englishWhites out not englishBlacks out englishBlacks inBlacks out not englishAsians out englishAsians inAsians out not englishHispanics out englishHispanics inHispanics not english<br><!-- End of picture text --> Data from the 2000 Census IPUMS data (5% sample), population aged 25-65. The figures display the marginal coefficients and their 95% confidence intervals on interaction terms for racial groups, foreign born status (“in” for natives, “out” for foreign born) and ability in English in probit regressions with labor force participation as the dependent variable. “White natives” is the omitted dummy. The regression results are further detailed in table A2, columns 3 (males) and 4 (females).

23
# **Structural Transformation in Africa: A Historical View**

Maria Enache, Ejaz Ghani, and Stephen O’Connell

## **Keywords: Africa, Growth, Jobs, Structural Transformation.**

**Contact information.** Enache: World Bank, mariavenache@gmail.com. Ghani: World Bank; eghani@worldbank.org. O’Connell: City University of New York; soconnell@gradcenter.cuny.edu.
but not on consecutive days.

3. Allowed to exceed limit 3 days a year.

|**Table S1.2a Effect of Delhi Metro on Air Qu**<br>**Polynomial**<br>|**ality: Fourth**<br>|**Order Time**<br> <br>|
|---|---|---|
|**Metro Extension**|**NO2**|**CO**<br>**PM2.5**<sup>**1**</sup>|
||(Percentage<br>pollutant)|change in level of|
|**ITO**|||
|**Yellow Line First Extension (Jul 03, 2005)**|-6.5|-69.6***|
|**Std. Error**|(16.5)|(10.6)|
|**Observations**|1457|1497|
|**Blue Line Introduction (Dec 31, 2005)**|-30.5**||
|**Std. Error**|(9.4)||
|**Observations**|1639||
|**Blue Line Second Extension (Nov 11, 2006)**|-10.6**|-13.1<br>-22.2|
|**Std. Error**|(9.9)|(8.3)<br>(19.6)|
|**Observations**|1605|1605<br>1268|
|**Siri Fort**|||
|**Blue Line Second Extension (Nov 11, 2006)**|-26.4|-3.0|
|**Std. Error**|(23.9)|(9.9)|
|**Observations**|1601|1532|
|<br>Source: Authors' analysis based on data describ|<br>ed in the text.||
|1. Estimates for NO2 and CO pertain to a nine w|eek window,|and for PM 2.5 to a|

58
In(y) = — .975 + 0.393 In(GDPpc) — 0.185 In(u5m) — 0.003ruralshare + 0.016lifeexp + (0.354 + 0.060)In ~~—)~~ wo -feef In(y) 1.837 4+ 0.450 — 0.307 In(u5m) + 0.017lifeexp — 0.008ruralshare + 0.350ln ~~(—)~~ «o- (uf
Table A1: Digital <u>products,</u> list of NAICS

|NAICS6|NAICS6 Description|
|---|---|
|334413|Semiconductor and Related Device Manufacturing|
|323120|Support Activities for Printing|
|334419|Other Electronic Component Manufacturing|
|334519|Other Measuring and Controlling Device Manufacturing|
|334417|Electronic Connector Manufacturing|
|332913|Plumbing Fixture Fitting and Trim Manufacturing|
|335999|All Other Miscellaneous Electrical Equipment and Component Manufacturing|
|333992|Welding and Soldering Equipment Manufacturing|
|333999|All Other Miscellaneous General Purpose Machinery Manufacturing|
|336320|Motor Vehicle Electrical and Electronic Equipment Manufacturing|
|333318|Other Commercial and Service Industry Machinery Manufacturing|
|333997|Scale and Balance Manufacturing|
|334118|Computer Terminal and Other Computer Peripheral Equipment Manufacturing|
|334210|Telephone Apparatus Manufacturing|
|334111|Electronic Computer Manufacturing|
|334112|Computer Storage Device Manufacturing|
|334418|Printed Circuit Assembly (Electronic Assembly) Manufacturing|
|333242|Semiconductor Machinery Manufacturing|
|335311|Power, Distribution, and Specialty Transformer Manufacturing|
|334416|Capacitor, Resistor, Coil, Transformer, and Other Inductor Manufacturing|
|334510|Electromedical and Electrotherapeutic Apparatus Manufacturing|
|334290|Other Communications Equipment Manufacturing|
|334220|Radio and Television Broadcasting and Wireless Communications Equipment Manufacturing|
|334310|Audio and Video Equipment Manufacturing|
|334614|Software and Other Prerecorded Compact Disc, Tape, and Record Reproducing|
|334613|Blank Magnetic and Optical Recording Media Manufacturing|
|334511|Search, Detection, Navigation, Guidance, Aeronautical, Nautical, Instrument Manufacturing|
|334513|Instruments Manuf. for Measuring, Displaying, and Controlling Industrial Process Variables|
|334412|Bare Printed Circuit Board Manufacturing|
|334515|Instrument Manufacturing for Measuring and Testing Electricity and Electrical Signals|
|335929|Other Communication and Energy Wire Manufacturing|
|335921|Fiber Optic Cable Manufacturing|
|336411|Aircraft Manufacturing|
|333316|Photographic and Photocopying Equipment Manufacturing|
|333314|Optical Instrument and Lens Manufacturing|
|334516|Analytical Laboratory Instrument Manufacturing|
|334517|Irradiation Apparatus Manufacturing|
|334514|Totalizing Fluid Meter and Counting Device Manufacturing|
|334512|Automatic Environmental Control Manuf. for Residential, Commercial, and Appliance Use|
|336414|Guided Missile and Space Vehicle Manufacturing|

40
should be identified ex-ante but which are more common as part of contract restructuring, at least in the transport sector. In Argentina, this subsidy took the form of a forgiveness of accumulated payments due to the government for the right to operate the concession. In general, these grants or subsidies have no provision for repayment. A common approach to commit to subsidies ex ante in some OECD countries is the provision of subsidies through shadow tolls. Under a shadow toll, the government contributes a specific payment per vehicle to the concessionaire. Because they are paid over time, they may be less of a burden on the public budget. The drawback of shadow tolls, though, is that they may not provide investors with much protection from revenue risks. In addition, the payment of shadow tolls over time creates a credit risk for concessionaires. These inefficiencies can be reduced in a number of ways, such as a declining payment schedule as volumes increase or a maximum traffic level beyond which shadow tolls are not paid. Output-based aid (OBA) is another example of subsidy driven PPP. In this case, it is a mechanism for providing explicit performance-based subsidies to support the delivery of basic services where policy concerns—such as limited affordability for some consumers, a desire to capture positive externalities, or the infeasibility of imposing direct user fees—justify public funding to complement or replace user fees.

# **5.2 Institutional roles of government in PPPs**

While these financing dimensions of the additional role of government in PPP are quite essential, they are relatively simple in relation to regulation and monitoring responsibilities, the second main role of the public sector in PPP. Normally the government that perceives the need for an infrastructure project and determines whether it is suitable for PPPs. In some countries, special units are prepared to package and preparare the PPPs on behalf of the government.<sup>18</sup> The specifics, of course, will depend partly on the political and economic situation facing the country, as well as the characteristics of the project itself. It might be necessary to enact specific legislation, or even to change the constitution, to enable the financing to proceed. (Many national constitutions prohibit private ownership or control of essential public facilities.) In addition, since PPPs are critically dependent on

> 18 Dutz et al (2006)

20
<!-- Start of picture text --> 250 800<br>702<br>208 700<br>200<br>600<br>471<br>500<br>150<br>400<br>99<br>100 300<br>62<br>50 22 24 33 200 122<br>100 30 7 26<br>outages per year (thousands) 0 0<br>Average number of people affected by Average duration of outages (hours)<br>Chittagong Dhaka Khulna Rajshahi Rangpur Sylhet Chittagong Dhaka Khulna Rajshahi Rangpur Sylhet<br><!-- End of picture text -->

# 3.2. Outage duration in the US and Europe

The adverse impact of power outages on people and businesses is ultimately determined not only by their frequency, but also their duration. The over 2,000 power supply disruptions recorded by the US Department of Energy (2018) range in duration from a few seconds to several weeks. Long lasting outages can be particularly costly, as they require firms to adjust their production process, operate costly backup generation capacity, or invest in alternative energy sources. Firms in different sectors are likely to be affected to different extents, not least due to varying degrees of energy intensity of production (Allcott, Collard-Wexler and O’Connell, 2016; Ramachandran, Shah and Moss, 2018).

While Section 3.1.1 puts in evidence the important role of natural shocks in causing power outages in Europe and the US, the importance of natural shocks becomes even more evident when considering outage durations (Figure 3.9). In the US, power outages caused by natural shocks last on average 59 hours (or 2.4 days); this makes outages due to natural shocks more than twice as long as outages due to nonnatural causes, and 6.5 times as long as outages due to load shedding. This means that 69% (or 5,636 hours, or 234 days) of the average annual outage duration between 2000 and 2017 in the US is caused by natural shocks while non-natural causes, including vandalism, are responsible for about 26% (or 2,143 hours, or 89 days) of the annual duration.

In Europe, the gap between climate-induced outages and other outages is even wider: on average, climate-induced outages last 9 hours, which is 4.5 times as long as outages caused by non-natural external causes and 9 times as long as load-shedding events. Annually, natural shocks are responsible for 54% of the average annual outage duration in the considered European countries.

17