rafmacalaba/datause-encoder-data huggingface dataset:
|Affordable Mortgage Finance DPL|Sahar Ahmed Nasr|
|---|---|
|Affordable Mortgage Finance DPL|Sahar Ahmed Nasr|
|Affordable Mortgage Finance DPL|Sahar Ahmed Nasr|
|Affordable Mortgage Finance DPL|Sahar Ahmed Nasr|
|TA for Jakarta Flood Mitigation|Arlan Rahman|
|SYC PAR IDF|Rafael Munoz Moreno|
|SYC PAR IDF|Rafael Munoz Moreno|
|SYC PAR IDF|Rafael Munoz Moreno|
|NanGuangRailwayProject|Gerald Paul Ollivier|
|NanGuangRailwayProject|Gerald Paul Ollivier|
|NanGuangRailwayProject|Gerald Paul Ollivier|
|NanGuangRailwayProject|Gerald Paul Ollivier|
|NanGuangRailwayProject|Gerald Paul Ollivier|
|NanGuangRailwayProject|Gerald Paul Ollivier|
|NanGuangRailwayProject|Gerald Paul Ollivier|
|NanGuangRailwayProject|Gerald Paul Ollivier|
|NanGuangRailwayProject|Gerald Paul Ollivier|
|Cote d'Ivoire: Economic Governance & RecoveryGrant(EGRG)II|Edward PhilipEnglish|
|Cote d'Ivoire: Economic Governance & RecoveryGrant(EGRG)II|Edward PhilipEnglish|
|Mauritius Third Development PolicyLoan|Sawkut Rojid|
|Mauritius Third Development PolicyLoan|Sawkut Rojid|
|Mauritius Third Development PolicyLoan|Sawkut Rojid|
|Afghanistan - StrengtheningHealth Activities for the Rural Poor(SH|AInaam Ul Haq|
|Afghanistan - StrengtheningHealth Activities for the Rural Poor(SH|AInaam Ul Haq|
|Afghanistan - StrengtheningHealth Activities for the Rural Poor(SH|AInaam Ul Haq|
|Afghanistan - StrengtheningHealth Activities for the Rural Poor(SH|AInaam Ul Haq|
|Afghanistan - StrengtheningHealth Activities for the Rural Poor(SH|AInaam Ul Haq|
|Afghanistan - StrengtheningHealth Activities for the Rural Poor(SH|AInaam Ul Haq|
|Afghanistan - StrengtheningHealth Activities for the Rural Poor(SH|AInaam Ul Haq|
|Afghanistan - StrengtheningHealth Activities for the Rural Poor(SH|AInaam Ul Haq|
|Afghanistan - StrengtheningHealth Activities for the Rural Poor(SH|AInaam Ul Haq|
|Regional Trade Facilitation Project II|Arnaud D. Dornel|
|Regional Trade Facilitation Project II|Arnaud D. Dornel|
|Regional Trade Facilitation Project II|Arnaud D. Dornel|
|Regional Trade Facilitation Project II|Arnaud D. Dornel|
|Regional Trade Facilitation Project II|Arnaud D. Dornel|
|Regional Trade Facilitation Project II|Arnaud D. Dornel|
|Regional Trade Facilitation Project II|Arnaud D. Dornel|
|Regional Trade Facilitation Project II|Arnaud D. Dornel|
|Regional Trade Facilitation Project II|Arnaud D. Dornel|
|Regional Trade Facilitation Project II|Arnaud D. Dornel|
|RESTORING EQUITABLE GROWTH AND EMPLOYMENT PROGRAMM|AMark Roland Thomas|
|RESTORING EQUITABLE GROWTH AND EMPLOYMENT PROGRAMM|AMark Roland Thomas|
|RESTORING EQUITABLE GROWTH AND EMPLOYMENT PROGRAMM|AMark Roland Thomas|
|Creation of Permanent Institutional Framework for RegulatoryImp|aRinku Chandra|
|Creation of Permanent Institutional Framework for RegulatoryImp|aRinku Chandra|
|Creation of Permanent Institutional Framework for RegulatoryImp|aRinku Chandra|
**Figure 10:** S **hares of Intra‐regional trade by World Regions and GVC categories, 2014**

<!-- Start of picture text --> Apparel Motor Vehicles<br>100% 100%<br>80% 80%<br>60% 60%<br>40% 40%<br>20% 20%<br>0% 0%<br>ECS EAS SSA NAC LCN MEA SAS ECS NAC SSA EAS LCN MEA SAS<br>Intra‐regional Exports Intra‐regional Imports Intra‐regional Exports Intra‐regional Imports<br>Electronics Footwear<br>100% 100%<br>80% 80%<br>60% 60%<br>40% 40%<br>20% 20%<br>0% 0%<br>ECS EAS SSA NAC LCN MEA SAS SSA ECS LCN EAS NAC MEA SAS<br>Intra‐regional Exports Intra‐regional Imports Intra‐regional Exports Intra‐regional Imports<br>Textiles<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>ECS SSA NAC EAS LCN MEA SAS<br>Intra‐regional Exports Intra‐regional Imports<br><!-- End of picture text --> <u>Notes: Values in percentage shares from original variables expressed in thousand current US$ and calculated on the total number</u> of 256 countries/geographies with available data on GVC Exports in at least some years of the period 1988‐2014 (HS 1988/92 nomenclature, six‐digits, mirror data). All trade data is drawn from Comtrade on WITS, and data on countries' region and income group in each year comes from WB classifications.

<u>Source: Trade: World Bank MC‐GVC database (http://wits.worldbank.org/WITS/WITS/AdvanceQuery/GVC/GVCQueryDefination.aspx?Page=GVCIndicator).</u> 26
Table 2:

|||_Outward g_|_reenfeld F_|_DI, by sec_|_tor_||
|---|---|---|---|---|---|---|
|||_Volu_|_me_||_Number of_<br>_projects_|_Average_<br>_size_|
||(1)|(2)|(3)|(4)|(5)|(6)|
|ED×CREDIT/GDP×GFC0810<br>(ED×CREDIT/GDP×GFC0810 )×BCRIS|-1.380<sup>∗∗∗</sup><br>(0.362)<br>-1.251<sup>∗∗</sup><br>(0.598)|-1.394<sup>∗∗∗</sup><br>(0.375)|1.458<br>(1.007)|-1.455<sup>∗∗∗</sup><br>(0.387)|-0.218<sup>∗</sup><br>(0.127)|-1.125<sup>∗∗∗</sup><br>(0.419)|
|(...)×systematic BCRIS||-1.493<sup>∗</sup><br>(0.776)|||||
|(...)×borderline BCRIS<br>||-0.267<br>(1.096)|||||
|(...)×alternatively defned BCRIS<br>|||-3.617<sup>∗∗∗</sup><br>(1.054)||||
|ln(sector-specifc value addedt)<br>||||0.575<br>(0.413)|||
|ln(sector-specifc value addedt−1)||||0.060<br>(0.189)|||
|Observations<br>|5504<br>|5504<br>|5504<br>|2886<br>|5504<br>|2652<br>|
|∗∗∗p<0.01 ∗∗p<0.05 ∗p<0.10. Cluster-robust st<br>country fxed effects, and time-varying sector fx<br>on external fnance. GFC0810: global fnancial c|andard errors<br>ed effects are<br>risis 2008-20|in parenthese<br>included in<br>10. BCRIS: b|s. Time-invar<br>all regression<br>anking crisis|iant country<br>s. ED: secto<br>.|-sector fxed effec<br>r-specifc measure|ts, time-varying<br>of dependence|

systemic and borderline crises. financial crisis when it was systemic. In column (3), we use the alternative measure of significant financial distress in the banking system, based on the joint observation of negative output growth and deceleration of private credit growth. The coefficients on the interaction terms suggest that the volume of relative outward FDI in financially vulnerable sectors declined in a statistically significant way only in countries exhibiting signs of a banking crisis. Overall, the distinction between countries according to the degree of financial distress in their banking system during the 2008-2010 period provides strong support for a credit channel effect of the global financial crisis on FDI.<sup>34</sup> In column (4), we include the contemporaneous and lag of value added to control for the effects of the global crisis working through manufacturing activity.<sup>35</sup> The

> 34We also investigated whether the impact of the global financial crisis on the relative volume of outward FDI in financially vulnerable sectors in more financially developed countries was greater in more “financially integrated” economies, as reflected by the ratio of the sum of foreign debt liabilities and assets to GDP in 2007 or the outstanding amount of international loans from non-resident banks to GDP ratio (Data from Lane and Milesi-Ferretti (2007) or Beck et al. (2009)). We failed to find any statistically significant additional effect. A banking crisis appears thus to give a superior signal about the degree of financial distress in the banking sector of a given country during the period 2008-2010.

> 35Data come from UNIDO Industrial Statistics Database (INDSTAT4), http://www.unido.org/en/resources/statistics/statistical-

18
7 Finally, basic services affect school attendance, as well as educational performance, which in turn may be linked with health outcomes. The literature quoted in Brenneman and Kerf (2002) shows that electricity allows for more studying, transport promotes easier establishment of schools and higher attendance because of reduced transit time and safer passage to schools, and access to piped water frees girls to go to school and contributes both to higher achievement and attendance by promoting better health.

# **3. Modeling Child Health Outcomes Using Cross-Country Quintile Data**

In order to assess the determinants of child health, we use a database constructed by Gwatkin et al. (2000) from Demographic and Health Survey (DHS) conducted in 43 developing countries<sup>9</sup> . The data are broken down by asset quintiles based on a principal component analysis of various asset variables available in the surveys. The use of asset quintiles follows from the fact that DHS data do not include income or consumption.<sup>10</sup> The advantage of using these data is that we are able to go beyond traditional crosscountry regressions by exploiting some of the variability in outcomes and explanatory variables observed within country. In addition, using quintile level data substantially increases the sample size for the regressions.

Our measures of child health outcomes are the rate of infant mortality, the rate of child mortality, and the rate of malnutrition. These three outcomes are assumed to be determined by three broad categories of variables: the prevalence of diseases in the community, the general health condition of a child, and whether the child is medically treated when ill. Our objective is to estimate a reduced form model of the determinants of these various aspects of a child’s health as a function of key policy variables.

Since disease prevalence is affected by the availability and quality of infrastructure such as water and sanitation, electricity, and the quality of the dwelling (particularly whether the floor is dirt or a finished surface), we include such variables as explanatory factors. Specifically, the household infrastructure variables are the shares of household within each asset quintile with a sand or pounded earth floor, with access to

> 9 The list of countries is given in appendix. For information on DHS see <u>http://www.measuredhs.com/</u> 10  Filmer and Pritchett (1998) show convincingly that that an asset index, calculated as it is here, using principal component techniques, is an excellent proxy for household income.
Tyler (2000) separate the direct returns to measured skill from the indirect returns of more schooling and suggest that perhaps one-third to one-half of the full return to higher achievement comes from further schooling. Note also that the effect of quality improvements on school attainment incorporates concerns about drop out rates. Specifically, higher student achievement keeps students in school longer, which will lead among other things to higher graduation rates at all levels of schooling.<sup>16</sup> Knighton and Bussière (2006) find that higher scores at age 15 lead to significantly higher rates of post-secondary schooling of Canadian 19-year-olds. This finding is particularly interesting for the international comparisons that we consider below, because the analysis follows up on precisely the international testing that is used in our analysis of economic growth.<sup>17</sup>

# **_2.3 Impacts of Educational Quality on Individual Incomes—Developing Countries_**

Questions remain about whether the clear impacts of quality in the U.S. generalize to other countries, particularly developing countries. The literature on returns to cognitive skills in developing countries is restricted to a relatively limited number of countries: Ghana, Kenya, Morocco, Pakistan, South Africa, and Tanzania. Moreover, a number of studies actually employ the same basic data, albeit with different analytical approaches, but come up with somewhat different results. Table 2.1 provides a simple summary of the quantitative estimates available for developing countries.

(1996), in considering the factors that influence school attainment, find that individual achievement scores are highly correlated with continued school attendance. Neal and Johnson (1996) in part use the impact of achievement differences of blacks and whites on school attainment to explain racial differences in incomes. Their point estimates of the impact of cognitive skills (AFQT) on earnings and school attendance appear to be roughly comparable to that found in Murnane, Willett, Duhaldeborde, and Tyler (2000). Behrman, Kletzer, McPherson, and Schapiro (1998) find strong achievement effects on both continuation into college and quality of college; moreover, the effects are larger when proper account is taken of the various determinants of achievement. Hanushek and Pace (1995) find that college completion is significantly related to higher test scores at the end of high school.

16 This work has not, however, investigated how achievement affects the ultimate outcomes of additional schooling. For example, if over time lower-achieving students tend increasingly to attend further schooling, these schools may be forced to offer more remedial courses, and the variation of what students know and can do at the end of school may expand commensurately.

17 The OECD tested random samples of 15-year-old students across participating countries under the PISA program in 2000. Students taking these tests in Canada were then followed and surveyed in 2002 and 2004. See Section 4, below.

11
of the poverty implications. Section 2 describes the methodology while sections 3 and 4 present the baseline and the counterfactual simulation analysis. The results are summarized in section 5, and conclusions are offered in section 6.

# **II. Methodology**

## **_The GLOBE-DYN model_**

The magnitude of AMR’s future global impact depends on many variables with uncertain evolution and with repercussions that may be felt throughout the global economy. To be able to consider the combined consequences of alternative expert estimates and meta-analysis of influencing factors, we use a recursive dynamic global CGE model, GLOBE_DYN, calibrated to GTAP data, v8.1.

Regions are mapped into four income regions based on the current World Bank country classification: low income, lower middle income, higher middle income, and high income.<sup>6</sup> As microeconomic assessments of the effects of AMR become more available, further disaggregation and differentiation of countries are certainly possible. The database is aggregated into six sectors (crops, livestock, mining, processed food, manufacturing, and services). The model is a member of a family of CGE models that models trade relationships using principles described in the 1-2-3 model (de Melo and Robinson, 1989; Devarajan, _et al_ ., 1990) or the standard multi-sectoral version for developing countries (Dervis, de Melo, and Robinson, 1982; and Lofgren _et al_ ., 2002).

A baseline scenario is defined to track the economic trends of “a world without AMR,” which essentially resembles the long-term growth projections of the World Bank (2016). These projections are similar to the long-term economic projections by research institutions in Europe, such as Fouré et al. (2012 and 2013) and Gros and Alcidi (2013). It also tracks the future labor supply, which is taken as the working-age population (15-64 years of age), drawing from the demographic projections and medium fertility scenario of the United Nations (2013).

The baseline in the recursive-dynamic model is generated by solving the model on an annual basis with exogenous updates between each solution for the following indicators (imposed as levels or GDP shares): GDP, investment, the real level of government expenditure, the fiscal balance (also called government savings), the trade balance (a deficit in most developing countries), and the total supplies of non-capital factors (skilled and unskilled labor, land, and natural resources). In each period, the fiscal balance is cleared via adjustments in household income taxes (treated as lump-sum tax). The exogenous trade balances are achieved via adjustments in real exchange rates. Markets for outputs and factors are cleared via price (or wage) adjustments The capital stock is updated endogenously: for any period after the base year, it is

> 6 Unlike Taylor et al. (2014), we avoided a geographical classification since the different regions would have included countries at different income levels. For example, if Sub-Saharan Africa were a region, then it would have brought together a relatively high-income country like South Africa and a low-income country like Niger.

5
MALI’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE strategy of the Government points toward a separation of water and electricity using a stepped approach and an asset holder/ affermage model. However, many aspects of the restructuring have not been clarified. For instance, mechanisms for private sector involvement have not been established (including the position of the current private shareholder in EDM) and the government has not been explicit on whether operations and commercial services will fall under one or more companies.

# **Box 1. Hidden costs in utilities**

A monetary value can be attributed to observable operational inefficiencies: mispricing, unaccounted-for losses, and undercollection of bills, to mention three of the most conspicuous operational inefficiencies, by using the opportunity costs of operational inefficiencies: tariffs for uncollected bills and production costs for mispricing and unaccounted for losses. These costs are considered hidden as they are not explicitly captured by the financial flows of the operator. Hidden costs are calculated by comparing a specific inefficiency against the value of that operational parameter in a well-functioning utility (or the respective engineering norm) and multiplying the difference by the opportunity costs of the operational inefficiency.

**Figure 11. Hidden costs for Mali’s water utility are among the highest in the region**

<!-- Start of picture text --> 140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>Mali Liberia Niger Cote Ghana Benin Senegal Burkina<br>d'Ivoire Faso<br>Collection Inefficiencies Underpricing Losses<br>Percentage of revenues<br><!-- End of picture text --> Source: Derived from Briceño-Garmendia, Smits, and Foster 2009.

Another challenge in the sector is linked to demography. Rapid urbanization has put pressure on water supply infrastructure. Water production capacity is deficient in Bamako, Kayes, Gao, Koutiala, and Kidal. This situation is due to growth in the number of consumers: more than 110 percent between 2000 and 2004. In the five cities mentioned, the number of urban connections grew from 55,665 in 2000 to 115,799 in 2009. Keeping pace with urbanization would require 10,120 new connections per year. Yet between 2000 and 2009 only 6,790 new connections were installed. To take just one example, it is urgent to mobilize the financing needed to complete the Kabala Water project to ensure adequate water supply for Bamako and cure the recurrent water shortages in many neighborhoods of the capital city. During a meeting organized for that purpose on April 30, 2010, donors confirmed their willingness to support the government’s efforts with a likely funding envelope of $220 million.

23
-12 -

The needed Institutional changes go beyond the financial sector, and, as argued above, are critical In the area of marketing. Reforms In marketing boards and procurement agencles are urgently needed to promote competition, since many of these organizations exhibit monopoly power and overly tax the farmer. Their noncompetitive behavior reduces farmers' supply response and Inhlbits Improvement In the rural sector. Experience In developing countries has shown that attempts to Interfere directly with supply through ratloning, licensing, targeting and other direct controls have been open to abuse. At best, the attempts breed inefficiency. At worst, they Increase monopoly power, Increase Inequality, and encourage corruption. The key characteristics to strive for are accountability, non-interference, and responsiveness to market forces which will generate competitive behavior.

Property Rights, Land Tenure, and Privatization Structural adjustment policies often Involve reassessing property and land rights as well as titling the land. A major Issue here Is whether and how the absence or ambiguous definition of land rights harm agricultural productivity. One usual line of reasoning Is that when land or property rights are not well defined, farmers are reluctant to sink fixed (irreversible) Investment or to make longterm Investments In their land, even though doing so Is socially efficient. Another Is that farmers access to credit Is hampered, since their land Is not accepted as collateral.

Evidence of the reality of these links Is ample. For example Feder's t1990] case study on Thailand shows that the lack of land title prevents small farmers from access to credit and Induces underinvestment of capital. The policy implicatlon Is that property rights should _be_ fuly exclusive, transferable, alienable, and enforceable to facilitate the development of an active land or rights
_Table 3: Average value of the variables included in the estimation of the production function_

|**Variable**|**2000**|**2006**|**2009**|**2012**|
|---|---|---|---|---|
|**_Student's characteristics_**|<br>|<br>|<br>||
|Gender (female)|0.56|0.53|0.54|0.52|
|Attedingat least 10thgrade(not over age)|0.72|0.72|0.64|0.64|
|**_Socioeconomic background_**|<br>|<br>|<br>||
|Wealth index|‐0.90|‐1.27|‐0.95|‐0.90|
|Mother's highest level of education|||||
|_Did not complete primary school_|_3%_|_12%_|_11%_|_8%_|
|_Complete primary school_|_37%_|_25%_|_22%_|_19%_|
|_Complete uppersecondary school_|_17%_|_15%_|_12%_|_20%_|
|_Complete lower secondary school_|_24%_|_18%_|_18%_|_22%_|
|_Complete tertiary_|_19%_|_30%_|_37%_|_31%_|
|Father's highest level of education|||||
|_Did not complete primary school_|_3%_|_12%_|_12%_|_9%_|
|_Complete primary school_|_38%_|_24%_|_24%_|_22%_|
|_Complete uppersecondary school_|_19%_|_16%_|_12%_|_21%_|
|_Complete lower secondary school_|_20%_|_21%_|_20%_|_22%_|
|_Complete tertiary_|_20%_|_26%_|_31%_|_26%_|
|Books at home|||||
|_0‐10_|_31%_|_28%_|_28%_|_34%_|
|_11‐100_|_31%_|_54%_|_53%_|_52%_|
|_more than 100_|_38%_|_19%_|_18%_|_14%_|
|**_School characteristics_**|<br>|<br>|<br>||
|Public school|0.58|0.61|0.62|0.65|
|Rural area|0.24|0.26|0.29|0.25|
|School size|||||
|_Up to 200 students_|_18%_|_21%_|_14%_|_21%_|
|_200‐500 students_|_40%_|_39%_|_41%_|_38%_|
|_500‐1000 students_|_25%_|_25%_|_31%_|_33%_|
|_> 1000 students_|_18%_|_15%_|_14%_|_9%_|
|Student‐teacher ratio (full time + 0.5 part‐time)|8.44|11.36|12.00|9.12|
|Proportion of teachers with tertiary education|0.06|0.16|0.11|0.16|
|Computer with web connection available per student|0.01|0.02|0.03|0.06|
|Shortage/Inadequacy of instructional material (e.g.<br>textbooks)|0.27|0.28|0.39|0.40|
|**_Institutional setting_**|<br>|<br>|<br>||
|Autonomy ‐ firing teachers|0.35|0.37|0.39|0.41|
|Autonomy ‐ formulating the school budget|0.33|0.33|0.37|0.35|
|Autonomy ‐ deciding on budget allocations|0.49|0.62|0.72|0.53|
|Autonomy ‐ establishing student disciplinary policies|0.92|0.69|0.72|0.80|

14