rafmacalaba/datause-encoder-data huggingface dataset:
34 Table 6: A Gravity Flow Model Projections of the Influence of NAFrA on US Non-Oil Imports

||||Predicted Imlort|s Under NAFTA|
|---|---|---|---|---|
|Exporter||1989 Actual U.S.<br>Imports_(S Mill.)_|Value<br>_(S_Mill.)|Percentage<br>Change(%)|
|NAFTA Members||103,623|113,696|9.7|
|Canada||80.632|88,130|9.3|
|Mexico||22,991|25,566|11.2|
|East Asia and Pacific 1/||80.400|79,722|-0.8|
|Hong Kong||10.237|10.062|-1.7|
|Indonesia||2,431|2.414|-0.7|
|Rep. of Korea||20.557|20,416|-0.7|
|Malaysia||4.688|4.649|-0.8|
|Taiwan, China||25.626|25,433|-0.8|
|Philippines|.|3.307|3.288|-0.6|
|<br>Papua New Guinea||32|32|-0.3|
|Singapore||8,987|8.927|-0.7|
|Thailand||4,536|4.500|-0.8|
|South Asia||4.735|4.611|-2.6|
|Bangladesh||475|471|-0.7|
|Myanmar||18|18|-3.1|
|<br>India||3,186|3.074|-3.5|
|Sri Ianka||491|487|-0.8|
|Pakistan||565|561|_-0.6_|
|Others|||||
|EFrA||13,360|13,146|-1.6|
|European Comnuuniy||84,762|83,562|-1.4|
|<br>Japan||96.949|96,377|-0.6|
|<br>Odher Industrial 2/||9,623|9,527|-1.0|
|North Africa & Middle East||711|695|-2.3|
|South & Central America 3/||21.027|20,821|-1.0|

1/ China was exduded from the East Asian totals due to the centrally planned nature of this country's trade regime. 21 Austalia, New Zealand. Israel and Turkey.

3/ Excludes Mexico.

Note: The 1989 US inports were obtained from the UN COMTRADE data base.

The gravity flow model suggests that East Asia's aggregate trade diversion losses will be in the order of $689 million annually --close to the partial equilibrium model's projection of $384 million.' For E st Asia as a whole, this translates into a loss of about four-fifths of X7 There is one imporan¶ difference between the two models. China was excluded from the gravity flow projections. Due to the centrally planned nature of their trade regimes, data for all the (present and fonner) socialist countries were excluded from tqe gravity model estimation as the incorporation of these countries in the analysis would have severely biased our rcsults (see Appendix II).
|_married_|Is the<br>individual<br>married?|||
|---|---|---|---|
|_female_|Is the<br>individual a<br>female?|||
|_formal_education_|Any formal<br>education?|||
|_primary_education_|Completed<br>primary<br>education?||“Primary education” consists of:<br>-<br>6 years in NGA<br>-<br>6 years in MALI<br>-<br>7 years in TZA<br>-<br>8 years in ETH<br>-<br>6 years in NIGER<br>-<br>8 years in MALAWI<br>-<br>7 years in UGANDA<br>NGA: not asked for old members<br>who are not in school (w1, w2)|
|_height_|Individual<br>height||Absent in MLI and NER|
|_weight_|Individual<br>weight||Absent in MLI and NER|
|_wasting_|Child with<br>wasting||Absent in MLI and NER, TZA wave<br>4, wave5|
|_haz06_|Height-for-<br>age Z-score|These estimates are calculated<br>using the 2006 WHO child<br>growth standards|Absent in MLI and NER, TZA wave<br>4, wave5|
|_waz06_|Weight-for-<br>age Z-score|<br>These estimates are calculated<br>using the 2006 WHO child<br>growth standards|Absent in MLI and NER, TZA wave<br>4, wave5|
|_whz06_|Weight-for-<br>length/height<br>Z-score|These estimates are calculated<br>using the 2006 WHO child<br>growth standards|Absent in MLI and NER, TZA wave<br>4, wave5|
|_bmiz06_|BMI-for-age<br>Z-score|<br>These estimates are calculated<br>using the 2006 WHO child<br>growth standards|Absent in MLI and NER, TZA wave<br>4, wave5|
|_farm_work_|Individual has<br>worked in<br>own farm in<br>(past) 7 days|in last 7 days, or typical 7<br>days|In MLI, NER and NGA (w1, w2),<br>this information is only available for<br>individuals’ “main job”|
|_SOB_work_|<br>Individual has<br>worked in<br>own business<br>in (past) 7<br>days|in last 7 days, or typical 7<br>days|In MLI, NER and NGA (w1, w2),<br>this information is only available for<br>individuals’ “main job”|
|_wage_work_|Individual has<br>worked for<br>own wage in<br>(past) 7 days|in last 7 days, or typical 7<br>days|In MLI, NER and NGA (w1, w2),<br>this information is only available for<br>individuals’ “main job”|
|_farm_hrs_|<br>Number of<br>hours spent in<br>own farm ag<br>work in (past)<br>7 days||In MLI, NER and NGA (w1, w2),<br>this information is only available for<br>individuals’ “main job”|

39
Appendix Table 2: Differences between randomized sample and a sample of completed training

||||**Pooled S**|**ample**||||**Couples'**|**Training**|||**I**|**ndividual**|**Training**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Rand<br>Sam|omized<br>ple<br>|Treated|Sample<br>|Difference<br>in Means|Rand<br>Sam|omized<br>ple<br>|Treated|Sample<br>|Difference<br>in Means|Rand<br>Sa|omized<br>mple<br>|Treated|Sample<br>|<br>Difference<br>in Means|
||Obs.|Mean<br>(A)|Obs.|Mean<br>(B)|(B-A)|Obs.|Mean<br>(A)|Obs.|Mean<br>(B)|(B-A)|Obs.|Mean<br>(A)|Obs.|Mean<br>(B)|(B-A)|
|Age of||||||||||||||||
|household<br>head|1218|45.23|629|44.86|-0.37|617|45.36|362|44.78|-0.59|601|45.09|267|44.96|-0.13|
|Household<br>size|1491|5.28|712|5.60|0.32*|741|5.31|403|5.71|0.39*|750|5.24|309|5.45|0.21|
|Number of<br>adults|1218|2.89|629|2.93|0.04|617|2.84|362|2.96|0.12|601|2.94|267|2.88|-0.06|
|Number<br>of<br>children|1218|2.94|629|2.98|0.03|617|2.99|362|3.03|0.04|601|2.90|267|2.90|0.00|
|Dependency<br>ratio|1216|1.17|628|1.15|-0.02|616|1.20|361|1.15|-0.06|600|1.13|267|1.15|0.02|
|Married<br>planter|1491|0.72|712|0.78|0.07***|741|0.73|403|0.79|0.06**|750|0.70|309|0.78|0.07**|
|Polygamous<br>relationship|1491|0.10|712|0.13|0.03|741|0.10|403|0.13|0.03|750|0.09|309|0.12|0.03|
|Matrilineal<br>ethnicity|1214|0.26|628|0.28|0.02|617|0.29|362|0.33|0.04|597|0.23|266|0.21|-0.02|
|Planter has<br>IGA|1215|0.69|628|0.70|0.01|617|0.71|362|0.72|0.01|598|0.66|266|0.67|0.01|
|Asset index|1491|11.34|712|10.82|-0.52***|741|11.20|403|10.60|-0.60***|750|11.48|309|11.11|-0.36|
|Credit<br>constrained|1218|0.13|629|0.14|0.01|617|0.13|362|0.16|0.03|601|0.12|267|0.10|-0.02|
|Share of plots<br>owned|1207|0.93|621|0.93|-0.00|613|0.93|358|0.92|-0.01|594|0.93|263|0.93|0.00|
|Average||||||||||||||||
|distance to<br>plots<br>(minutes)|1205|70.54|621|71.33|0.78|612|72.30|358|78.06|5.76|593|68.73|263|62.16|-6.57*|
|Number of<br>plots|1207|2.33|621|2.24|-0.09|613|2.39|358|2.31|-0.08|594|2.28|263|2.14|-0.14|
|Average plot<br>size|1198|3.51|618|3.82|0.31**|608|3.49|356|3.75|0.26|590|3.53|262|3.90|0.38|

3
income countries, combined with the sustained demand for health and public services by the aging OECD population, is likely to translate into a pronounced shift of production towards services at a global level. The same pattern is observed in Africa. The growing share of services is also associated with a falling contribution of other sectors. In SSA, this is mainly oil sector and manufacturing (see Figure 5).

As a region SSA is expected to trade a lot more with other low- and middle-income countries and also to some extent with its regional partners at the expense of trade with high-income countries. Trade shares of SSA countries within Africa will increase. This is only due to the increase of their significance in the 3 largest economies/regions: South Africa, Nigeria and the rest of Sub-Saharan Africa. For the remaining smaller economies the importance of trade with other countries within the region is expected to decline as they diversify their economies and reduce the initially relatively higher intra-regional trade shares. All countries in SSA are expected to trade a lot more with other lower and medium income countries (LMYs) over time. The share of exports of SSA to LMYs is expected to increase by 17 percentage points from the level of 41 percent in 2007. The share of imports of SSA from LMYs is also expected to expand by 13 percentage points from the level of 46% in 2007.

**Figure 6 Income Distribution in SSA in the Baseline.**

<!-- Start of picture text --> PPP$2.00<br>PPP$1.25<br>$0 $.24 $1.8 $13.26 $98<br>Log of Daily Per Capita Income/Consumption, PPP$ (2005)<br>2010 2015 2025<br>.5<br>.4<br>.3<br>.2<br>Density Function<br>.1<br>0<br><!-- End of picture text --> Figure 6 shows the evolution and positive shift towards the right of Sub-Saharan Africa’s income distribution under the baseline scenario. From 2010-2015, the region’s per capita income grows annually at 3.0 percent, albeit with mixed outcomes within the region likely due to the continuing effects of the slow recovery in high-income countries from the economic recession of 2008-09. Positive growth was largely influenced by some populous countries such as Nigeria (5.28%), Cote d’Ivoire (6.59%), Ethiopia (6.59%), Ghana (3.07%), South Africa (3.03%), and Uganda (2.21%). Even so, income remains stagnant in several countries, with Namibia (-3.47%) and Cameroon (-1.39%) suffering more drastically. As the global economy expands more steadily during 2015-2025, the region’s per capita income grows at the long-term rate of 2.75% a year and it is more evenly distributed across countries. Countries that experience negative growth in the first phase resume their growth trajectory at 2.4% a year.

8
Credit Research Initiative of the National University of Singapore. 2018. “Probability of Default (PD)” White Paper.

Cruces, J., and C. Trebesch. 2013. “Sovereign Defaults: The Price of Haircuts”. American Economic Journal: Macroeconomics 5 (3): 85–117.

De Bruyckere, V., M. Gerhardt, G. Schepens, and R. Vander Vennet. 2013. “Bank/sovereign risk spillovers in the European debt crisis”. _Journal of Banking & Finance_ 37: 4793‐4809.

Dell’Ariccia, G., C. Ferreira, N. Jenkinson, L. Laeven, A. Martin, C. Minoiu, and A. Popov. 2018. “Managing the Sovereign‐Bank Nexus.”. Departmental Paper 18/16. Monetary and Capital Markets Department. International Monetary Fund. Washington DC.

Duan, J. C. 2010. “Clustered Defaults”. Risk Management Institute Working Paper. National University of Singapore. March.

FSB (Financial Stability Board). 2010. “Reducing the Moral Hazard Posed by Systemically Important Financial Institutions”. FSB Recommendations and Timelines. Basel, Switzerland. <u>http://www.fsb.org/wp‐content/uploads/r_101111a.pdf?page_moved=1.</u> FSB (Financial Stability Board). 2011. _Key Attributes of Effective Resolution Regimes for Financial Institutions_ . Financial Stability Board. Basel, Switzerland. <u>http://www.fsb.org/wp‐ content/uploads/r_141015.pdf.</u> FSB (Financial Stability Board). 2018. 4th Annual Report, 1 April 2016 ‐ 31 March 2017. Basel, Switzerland. http://www.fsb.org/wp‐content/uploads/P160118.pdf.

Gennaioli, N., A. Martin, and S. Rossi. 2014. “Banks, Government Bonds, and Default: What do the Data Say?” IMF Working Paper WP/14/120. International Monetary Fund. Washington DC.

IIF (Institute of International Finance). 2018. “EM Government Debt‐Pitfalls Ahead.” Global Debt Monitor Snapshot. Institute of International Finance. Washington DC. June.

IMF (International Monetary Fund). 2012. _A Survey of Experiences with Emerging Market Sovereign Debt Restructurings_ . Monetary and Capital Markets Department. International Monetary Fund. Washington DC.

IMF (International Monetary Fund) 2014. _From Banking to Sovereign Stress: Implications for Public Debt_ . International Monetary Fund. Washington DC.

Laeven, L. and F. Valencia. 2018. “Systemic Banking Crises Revisited”. IMF Working Paper WP/18/206. International Monetary Fund. Washington DC.

Lanotte, M., G. Manzelli, A.M. Rinaldi, M. Taboga, and P. Tommasino. 2016. “Easier said than done? Reforming the prudential treatment of banks’ sovereign exposures”. Questioni di Economia e Finanza Ocassional Papers No.326. Banca D’Italia. Rome, Italy.

41
a different perspective, in the mid-nineteenth century, Karl Marx saw capitalism as exacerbating inequality, making capital owners richer and workers poorer over time. He thought that this polarization of income could lead to a revolution, where a communist system eventually would replace capitalism (Marx 1867). The complex relationship between income distribution and growth has continued to receive attention from many other economists, including the seminal works of Simon Kuznets (1955) and Nicholas Kaldor (1957). Furthermore, the study of inequality and growth has been facilitated by developments in data collection on poverty, wealth, and labor market conditions. For instance, Charles Booth (1891), in _Life and Labour of the People in London_ , published maps describing wealth and poverty levels street by street in the city of London. About the same time in the United States, Carroll Wright, the first US Commissioner of Labor, was a pioneer in the collection of labor market statistics. He initiated the collection of data on wages and labor conditions of women and also published studies describing how the adoption of new machinery affected wages and employment. These advances in data collection continued over the twentieth century and made it possible to conduct a systematic analysis on the links between growth and inclusiveness.

Multiple channels link growth to inclusion and inclusion to growth, making it difficult to determine causation. Moreover, many factors affect growth and inclusion simultaneously. Compounding these issues, data on poverty and inequality have been difficult to compile, are collected and measured infrequently, and are often unreliable. Estimates are sensitive to assumptions on factors such as capital gains and untaxed income (Cerra et al. 2021, Chapter 1) and alternative measures may show different trends (Blotevogel et al. 2020). Empirical studies, especially those exploring the link between growth and inequality, sometimes find inconsistent results, no doubt due to these multiple channels, endogenous relationships, and poor data quality. As a starting point, the next section presents key stylized facts and trends of inequality, poverty, and economic growth across different world regions and over time. Sections 3 and 4 then discuss the channels linking the variables on this nexus, drawing on the theoretical and empirical literature. Section 5 concludes with the key takeaways and policy implications.

# **2. Trends in Inequality, Poverty, and Growth**

Market-based income inequality has risen steadily in advanced economies and some large emerging market economies. Figure 1 shows the evolution across country groups of income inequality, measured by the Gini coefficients for market-based income (before taxes and 2
<!-- Start of picture text --> 4.00%<br>Non Weak Moderate Strong<br>convergence convergence convergence convergence<br>3.00% *<br>2.00% x x *<br>*<br>*<br>1.00%<br>“oe it a<br>a se all Roy Xd e ., > PS Rg ? e oe Ss ” a e<br>RY£1 aeis is? ev » &S ssxc |. & & aros ss Loex ye”RX g feyS .ae oe<br>o» S? ys x g S i) SF ~~<br>FP<br>9 s<br>-1.00% &<br>-2.00%<br>m 2000-2017 m 1990-2017 * : Commodity exporter<br><!-- End of picture text -->
being accessible places developed through inclusive processes. While much has been written on the topic by urban designers and architects, most of this literature consists of theoretical design principles or case studies situating public spaces in the historical context of specific cities (see, for example, Madanipour, 2013). It is generally acknowledged that public spaces are promising sites to promote social interaction and cohesion (Buijs at al., 2010), but to our knowledge, there are no rigorous assessments of whether urban design can foster intergroup exposure by increasing the willingness of citizens from diverse backgrounds to use a public space.

This study makes two contributions to the relatively scant literature on social cohesion, intergroup exposure, and public spaces. First, it sheds some light on the link between urban design and social cohesion, pointing to potential interactions and trade-offs between the two, in the context of a socially diverse megacity. Second, it takes advantage of innovative VR technology to generate empirical evidence on an understudied area, namely the interplay between intergroup exposure and urban design of public spaces. The findings not only have implications for policy makers on building cohesion in socially stratified societies, but also illustrate the value of technology in identifying and addressing behavioral barriers to social interaction.

# **3. Study Design, Data, and Methodology**

This study was conducted in Karachi between April and May 2019. Over 2,000 youth between the ages of 18 and 30 years old<sup>4</sup> were recruited from 21 randomly selected locations across the city to participate in the intervention. The locations for the experiment were selected from 50 localities included in the list of Urban Census Circle, with an average of 96 participants per location. To recruit the participants, data collectors identified target individuals in public places (e.g., colleges, universities, shopping malls, cinemas, parks or theatres), as well as used door-to-door recruiting and provided transportation to a nearby field office for participants to take the experiment.

The sampling method was Probability Proportional to Size (PPS), ensuring that areas with larger population were more heavily represented in the sample. To the extent possible, the sample sought diversity in terms of age, gender and ethnicity. Due to sensitivities about ethnic affiliation, the survey did not capture ethnicity directly, but instead proxied this variable through native language, with the caveat

> 4 This age group was chosen given the demographic structure of the population of Karachi. The choice of intervention was also motivated by the likelihood that VR would be appealing to this age group.

6
Table 3. Debt in major country groups during global recessions

||Govern|ment de|bt (perc|ent of|GDP)|Priv|ate debt|(perce|nt of GD|P)|Tot|al debt|(percen|t of GD|P)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||t-1|t|t+3|t+5|t+10|t-1|t|t+3|t+5|t+10|t-1|t|t+3|t+5|t+10|
|World||||||||||||||||
|1975|31.8|35.2|38.2|39.0|53.7|84.8|85.8|88.3|87.3|100.5|116.6|121.0|126.5|126.2|154.3|
|1982|41.3|45.3|53.7|60.0|61.2|88.3|90.7|100.5|112.4|122.2|129.6|136.0|154.3|172.3|183.4|
|1991|55.6|57.4|66.5|66.0|65.4|117.8|120.3|124.2|120.1|126.5|173.4|177.7|190.7|186.1|191.9|
|2009|63.9|74.5|79.3|78.5|83.7|137.2|143.3|135.7|139.8|149.1|201.2|217.9|215.0|218.3|232.8|
|2020|83.7|98.7||||149.1|164.6||||232.8|263.3||||
|Advance|d econo|mies||||||||||||||
|1975|34.7|37.4|39.7|40.9|54.7|97.9|98.0|101.4|102.4|115.1|132.6|135.4|141.2|143.2|169.7|
|1982|42.8|46.4|54.7|58.7|61.9|103.9|106.1|115.1|125.3|136.1|146.7|152.5|169.7|184.0|198.0|
|1991|56.4|58.4|69.1|71.9|70.1|134.6|135.4|140.4|138.1|146.5|191.0|193.8|209.5|210.0|216.7|
|2009|77.7|91.0|105.6|103.7|103.9|168.1|171.8|164.5|162.8|164.8|245.8|262.8|270.2|266.6|268.7|
|2020|103.9|122.8||||164.8|179.2||||268.7|302.0||||
|EMDEs||||||||||||||||
|1975|20.8|25.8|31.4|31.2|49.3|29.4|28.6|33.2|33.1|37.8|50.2|54.4|64.6|64.3|87.1|
|1982|35.9|41.0|49.3|67.0|57.2|36.0|34.9|37.8|40.0|47.7|71.9|75.9|87.1|107.0|104.9|
|1991|51.6|52.0|52.5|42.1|47.8|39.5|41.2|49.0|47.8|51.5|91.1|93.3|101.6|89.9|99.3|
|2009|33.5|38.6|36.6|39.8|54.0|69.0|81.0|88.5|104.3|125.5|102.5|119.6|125.1|144.1|179.6|
|2020|54.0|63.1||||125.5|142.1||||179.6|205.2||||
|EMDEs|excludin|g China||||||||||||||
|1975|20.8|25.8|31.4|31.2|49.3|29.4|28.6|30.2|30.2|33.4|50.2|54.4|61.7|61.5|82.7|
|1982|35.9|41.0|49.3|67.0|57.2|33.6|32.0|33.4|34.5|42.6|69.4|73.0|82.7|101.6|99.8|
|1991|51.6|52.0|52.5|45.4|53.2|34.5|35.7|45.2|42.7|39.9|86.1|87.7|97.7|88.0|93.1|
|2009|35.4|40.0|37.5|39.8|51.9|56.2|59.2|58.8|64.8|68.2|91.5|99.2|96.3|104.6|120.1|
|2020|51.9|60.6||||68.2|75.6||||120.1|136.2||||

Source: Kose et al. (2021).

Note: Year “t” denotes the year of global recessions: 1975, 1982, 1991, 2009, and 2020. Data are available for up to 192 countries, including 39 advanced economies, 153 EMDEs.

34
###

MICs. This analysis is based on this paper’s accompanying dataset on _Financing for refugee education in low- and middle-income countries_ (REFD, 2024), and supplemented with additional sources where relevant.

# Key fact 1: Refugees are overwhelmingly hosted in LICs and MICs

and/or persecution has increased threefold, from 33.7 million in 2012 to 107.8 million people by the end of 2022<sup>5</sup> (UNHCR, 2023). Of those displaced, 40.1 million are classed as refugees, asylum seekers, or other persons in need of international protection.

Figure A shows the number of refugees<sup>6</sup> in the 20 largest host countries as of end-2022. The bars show each countries’ total refugee population, while the line captures the cumulative total of refugees hosted in these countries as a proportion of the global refugee population. Figure A shows that a relatively small number of countries host the majority of the world’s refugees, and that low- and middle-income countries host a disproportionately large share of this population (70 percent of refugees globally), with only 4 of the top 20 host countries high income countries (HICs) in 2022.<sup>7</sup> _Figure A: Distribution of refugees globally, 2022_ <!-- Start of picture text --> 4,500,000 80%<br> 4,000,000 70%<br> 3,500,000<br>60%<br> 3,000,000<br>50%<br> 2,500,000<br>40%<br> 2,000,000<br>30%<br> 1,500,000<br>20%<br> 1,000,000<br>10%<br> 500,000<br> - 0%<br><!-- End of picture text -->

## _Source: UNHCR (2023)_

Of the 107.8 million recorded as forcibly displaced as of end-2022, an estimated 43.3 million (40 percent) were children below the age of 18. Figure B shows the proportion of school-age

> 5 In 2023, an additional 5.9 million refugees were registered with the United Nations Relief and Works Agency for Palestine Refugees (UNRWA, 2023).

> 6 As outlined previously, ‘Refugee’ is defined by this report and its accompanying dataset as those categorized by the UNHCR as either ‘refugees’, ‘asylum seekers’, or ‘other people in need of international protection’ (UNHCR, 2023). 7 Income classifications made in accordance with World Bank country-level classifications.

7
<!-- Start of picture text --> No. of IDA-only Active Projects per FY<br>3<br>~<br>o<br>o<br>=<br>3<br>oO 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br><!-- End of picture text --> <!-- Start of picture text --> ~ Net Commitment of IDA-only Active Projects per FY ($ m)<br>=F<br>8<br>oOo<br>i—JSoo<br>wv<br>o<br>oO<br>o<br>So<br>oa<br>oO<br>N<br>o<br>So<br>:2<br>=<br>oO 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br><!-- End of picture text --> <!-- Start of picture text --> o Disbursements of IDA-only Active Projects per FY ($ m<br>Ss<br>a<br>oOo<br>oao<br>=<br>8ry-<br>o<br>So<br>a<br>o~<br>o<br>o<br>a<br>°<br>=<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br><!-- End of picture text --> <!-- Start of picture text --> IDA-only Project Portfolio<br>by Number of Active Projects per FY<br>S<br>Oo<br>o<br>Ee<br>2<br>@o<br>a<br>oO<br>wT<br>8<br>oO<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br>HN AlwaysFCS [EM Partial FCS<br>f Never FCS<br><!-- End of picture text --> <!-- Start of picture text --> IDA-only Project Portfolio<br>by Net Commitments of Active Projects per FY ($ m)<br>8<br>Sooo<br>2<br>Ooa<br>wToO<br>oO<br>N<br>:oOIMI<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br>HN AlwaysFCS [I Partial FCS<br>f Never FCS<br><!-- End of picture text --> <!-- Start of picture text --> Total IDA-only Project Portfolio<br>by Disbursements of Active Projects per FY ($ m)<br>8<br>Q o<br>ge2Oo<br>a<br>wvoO<br>&<br>o<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br>HN AlwaysFCS [EM Partial FCS<br>MM Never FCS<br><!-- End of picture text -->