rafmacalaba/datause-encoder-data huggingface dataset:
the burden of disease, behind the top-ranked factor of childhood underweight (which accounts for about 8.8% of the burden of disease).<sup>17</sup> Against this background of strong indications of a significant socio-economic problem, but contradictory and very limited availability of HAP data, the subsequent sections sketch out the survey and HAP monitoring conducted in Madagascar and the econometric findings on the effectiveness of various mitigation measures.

# **3. Household Air Pollution Factors and Sampling in Madagascar**

Previous studies on HAP have identified several potential determinants: fuel type, stove type, cooking locations, structural characteristics of houses/kitchen and household ventilation practices, such as opening of windows and doors.<sup>18</sup> The level of emissions from fuel use depends on the efficiency of combustion and heat transfer, and hence on stove type. Given the level of emissions from fuel and stove use, the extent and duration of pollutant concentration in a space depends on the location of a kitchen (inside the house/within-dwelling; in a space attached to the house/“attached” kitchen; in a space enclosed by walls, a roof at a little distance from the house/“detached” kitchen and in the open air); the extent of ventilation: the porous nature of materials used to construct the roof and walls of the kitchen, size and placement of doors and windows.<sup>19</sup> All of these factors may be important in Malagasy households, which exhibit significant diversity in cooking fuels, stove types, cooking locations, and quality of ventilation.

In order to ensure cost-effective coverage of the key determinants of HAP concentrations, a stratified sampling technique was followed in this study to select households for indoor air monitoring in rural Madagascar. Stratification was based on region, fuel and stove type. Households were selected from the towns of Ambositra<sup>20</sup> , located in the central highland,

> 17 Ibid.

> 18 Dasgupta et al, 2009, Dasgupta et al, 2006a, Dasgupta et al, 2006b, World Bank, 2002, Brauer and Saxena, 2002, Moschandreas et al, 2002, Freeman and Sanez de Tajeda, 2002

> 19Ambient air quality is also an important determinant of HAP. A quicker dispersion of pollutants is expected during strong windy conditions. Suspension time of fine particulates in air is reduced when it rains, and also in summer when ground temperature is generally higher.

> 20Ambositra is located at an altitude of 1,295m from sea-level; experiences a lower rainfall and cooler temperature but has a distinct wet (November-March) and dry (April-October) seasons. It has a population of 44,726 (August 2008 estimate). Most of the houses are made of brick and wood and the kitchens are typically located inside the house. Cooking with charcoal and traditional charcoal stoves is a common practice in Ambositra.

5
the end of the consultation. For each method discussed during the counseling session and not chosen by the client, the _app_ records the reason why she did not want to adopt it. The result is a rich database containing adoption decisions, along with relevant client characteristics and preferences.<sup>12</sup>

## **3.2 Outcomes**

The primary outcomes considered in this study are the shares of clients who adopted (i) a LARC, (ii) a SARC, or (iii) neither. We denote the last group as having _adopted no modern method_ although it includes clients who adopted LAM, condoms, or other traditional methods. We mainly focus our attention on the adoption of LARCs due to low demand for SARCs among our study population (Table A4 provides a detailed breakdown of the method mix). The primary outcomes are constructed using data collected on the tablets, which are cross-checked with hospital administrative records that are further verified by a third-party independent auditor. A non-negligible number of clients do not adopt a method during their initial visit but return to HGOPY to adopt a method later – e.g., after taking some time to think about their decision; having discussed it with their partners, spouses, or parents; needing to collect the money necessary to adopt the method; etc. For these clients, the data across consultations are linked so that the most up to date outcome is considered.<sup>13</sup> Similarly, some clients may return to the hospital to switch or discontinue their adopted method, which are reflected in the primary outcomes if they happen within the study period.<sup>14</sup> Please see Appendix D for ethical considerations, which include IRB approvals, study registration, and protocols.

# **4 Results**

## **4.1 Estimation strategy**

We begin by presenting the impact of offering discounts for LARCs using: where _Yi_ denotes the outcome for client _i_ . _Li_ denotes binary LARC price indicators for whether clients were offered LARCs at _Low_ (CFA 150 or 0) or _Mid_ prices (CFA 2,000 or 1,000), respectively. _Xi_ denotes covariate adjustments, which, by design, only includes an indicator variable for SARCs

> 12We supplement these data with follow-up surveys of HGOPY clients, who were enrolled into the adaptive experiment that followed the pilot, within one to two weeks of their initial FP counseling session. We use these data to assess if service quality and client satisfaction differ by counseling approach.

> 13Survey evidence from the sample of clients enrolled in the ongoing adaptive experiment indicates that every client who adopted a method later, adopted it at HGOPY.

> 14Approximately 90% of clients, who have returned to HGOPY to adopt (or remove or switch) their chosen method, have done so within 100 days of their initial visit. Therefore, our data include clients whose initial visit was between 9 June 2020 and the 9 March 2021, who may have returned to HGOPY for a follow-up visit until 17 June 2021.

9
ones. The importance of country policies and institutions in accounting for the cross-country variation in project performance will be understated to the extent that the Bank uses its influence to select a small set of projects that are more likely to succeed in poor countries with weak institutional capacity, while financing “typical” projects in richer and/or better governed countries.

# _Project Outcome Data_

Our primary project-level outcome variable is a subjective assessment of the extent to which a project met its stated “development objective”. These project outcome assessments are available since 1972, when the Independent Evaluation Group (IEG) (previously known as the Operations Evaluation Department (OED)), was established within the World Bank. For projects evaluated prior to 1995, our main project outcome measure is a binary rating of satisfactory/unsatisfactory (IEGSAT). For projects evaluated since 1995, a six-point rating scale is used ( _IEGRATE_ ). For the post-1995 period we use _IEGRATE_ , and in addition we extend the binary _IEGSAT_ over the entire sample period by recoding the top three categories of _IEGRATE_ as “satisfactory”.

These project outcome ratings come from three sources. As noted above, all completed projects since 1972 have a satisfactory/unsatisfactory rating drawn from the ICR. These can be thought of as an initial "self-evaluation" by World Bank staff and management of the project. These ratings are produced by the task manager, and are subject to review by World Bank management of the country/region where the project took place. After 1995, all such ICR-based evaluations were also subject to an additional layer of validation by IEG, based on available project documentation (these desk reviews were variously known as “Evaluation Summaries” or “Evaluation Memoranda”, which we refer to as EVM). Finally, over the entire period since 1972, a sample of about 25 percent of projects completed each year are selected by IEG for a more detailed ex-post evaluation, known as “Project Performance Audit Reports” (PPAR). These typically occur several years after project completion – the mean lag in our core sample between project completion and these detailed IEG evaluations is 3.8 years, as opposed to 1.5 years for the first two types. In order to control for any effect these variations in 7
**Figure 2. The tasks content of jobs across countries, US-based indexes.**

<!-- Start of picture text --> Non-Routine Analytical, US Non-Routine Interpersonal, US Routine Cognitive, US<br>AM GEGEGEAMGEGE AM GEGEGEAMGEGE COCOCOCOCOCOCO<br>PHPHPHPHPHPH<br>VN GHGHVNGH GHBOVNVNLKVNPHLKPHBOBOBOVNBOBOPHBOLKLKPHBOPHBOPHBOLKPHBOLKPHBOPHLKLKLKPHBOPHBOLKPHLKPHBOPHLKLKLKLKRSLKLKRSCOCORSCORSCOCORSCORSRSCORSRS VN GHGHVNGHVN VNVN GHBOVNVNLKVNPHLKPHBOBOBOVNBOPHBOLKBOLKPHBOPHBOPHBOLKPHBOLKBOPHLKLKLKBOBOLKLKBOLKLKLKLKRSLKLKRSCOCORSCORSCOCORSCORSRSCORSRS VN GHGHVNGHVNVNVNGHBOVNVNLKVNPHLKBOPHBOBOBOVNBOPHLKBOLKPHBOPHBOPHBOLKBOPHLKBOPHPHLKLKBOLKPHPHBOLKPHLKPHBOGEPHGELKGELKAMGELKGELKRSLKLKRSRSRSRSRSRSRSRS<br>VN VN AM<br>VN<br>750 800 850 900 950 750 800 850 900 950 750 800 850 900 950<br>GDP level, log points GDP level, log points GDP level, log points<br>Routine Manual, US Non-Routine Manual, US<br>VNVNVN<br>VNVN VNVNVN<br>VN VNVN<br>GHGHVN GH GHBOLKVNLKPHBOPHBOBOBOVNBOPHBOLKLKPHBOPHBOPHBOLKBOPHLKBOPHPHLKLKLKBOPHPHBOLKPHLKBOPHPHLKLKLKLKRSLKLKRSRSRSRSRSRSRSRS VN GHGHVN GH GHBOLKVNLKPHBOPH BO BOVNBOPHLKBOLKPHBOPHBOPHBOLKBOPHLKBOPHPHLKLKLKBOPHPHBOLKLKBOLKLKLKLKRSLKLKRSRSRSRSRSRSRSRS<br>COCOCOCOCOCOCO PHPHPH<br>AM GEGEGEAMGEGE AM GEGEGEAMGEGE COCOCOCOCOCOCO<br>750 800 850 900 950 750 800 850 900 950<br>GDP level, log points GDP level, log points<br>.2<br>.2 .2<br>0 .1<br>0<br>0<br>-.2<br>-.2<br>Skill index Skill index Skill index -.1<br>-.4 -.4<br>-.2<br>-.6 -.6 -.3<br>.6 .8<br>.6<br>.4<br>.4<br>.2<br>.2<br>Skill index Skill index<br>0<br>0<br>-.2 -.2<br><!-- End of picture text --> Note: Each point shows the skill content of jobs using O*NET-based indexes per country and year. The horizontal axis measures GDP per capita in PPP from WDI.

When looking at the total changes in the task content of jobs in all 11 countries, there are certain differences across countries and some key stylized facts emerge. In most countries, US- and STEPbased indexes show an increase in the non-routine cognitive content of jobs and a decline in the routine intensity (Figure 3). However, while the non-routine manual content of jobs increased in seven countries according to the STEP-based indexes, only three countries experienced such increase according to the O*NET-based index. Moreover, in the countries where this index declined according to both measures (as in the Philippines, Serbia and Ukraine), the decline was smaller in magnitude according to the STEP-based index than according to the O*NET-based one.

# **Figure 3. Changes in the skill content of jobs**

12
ZAMBIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# Roads

## **Achievements**

Zambia has made major progress with its main trunk road network. Despite relatively low road densities, analysis suggests Zambia’s primary and secondary networks provide basic regional and national connectivity, linking the provincial capitals to Lusaka, and Lusaka to the main international border crossings. More than 80 percent of Zambia’s paved road networks are in good or fair condition, on par with its middle-income neighbors and well ahead of the typical performance of resource-rich countries in Africa (table 2). The establishment of a second-generation road fund in the country resulted in a stable allocation of resources to the sector. Zambia is one of the few countries in the region with a road sector budget in excess of what is needed to maintain the main road network, and adequate to address the rehabilitation backlog (figure 4). During the early 2000s, Zambia spent 3 percent of GDP on the roads sector on average—a relatively high allocation.

**Table 2. Zambia’s road indicators benchmarked against Africa’s low- and middle-income countries**

||Unit|Resource rich|**Zambia**|Middle-income<br>countries|
|---|---|---|---|---|
|Paved road density|km/1,000 km<sup>2</sup><br>of arable land|97.6|**56.3**|146.8|
|Unpaved road density|km/1,000 km<sup>2</sup><br>of arable land|128.2|**95.0**|257.8|
|GIS rural accessibility|% of rural pop within 2 km from all-season<br>road|19.7|**16.8**|22.9|
|Overengineering of network|% of main road network paved despite low<br>traffic volumes|15.0|**65.0**|20.0|
|Paved road traffic|Average annual<br>daily traffic|1,408.2|**736.6**|2,558.3|
|Unpaved road traffic|Average annual<br>daily traffic|54.2|**45.2**|14.9|
|Paved network condition|% in good or fair condition|67.9|**83.0**|82.0|
|Unpaved network condition|% in good or fair condition|61.4|**25.0**|57.6|
|Perceived transport quality|% firms identifying as major business<br>constraint|27.4|**10.6**|4.8|

_Source:_ Gwilliam and others 2008, derived from AICD national database downloadable from http://www.infrastructureafrica/aicd/tools/data _Note:_ GIS = geographic information system.

8
6

**300**

**1**

**400**

**GNP Index (1978.100) GNP and Price Percentage Change 20**

; 2CO~~~~10 ......... **rz. ... ...... | 1978 1980 1982 1984 1986 1988 1990 1992 Year** r **GNP Index GNP Growth Rate Inflatlnn Rate**

Figure 1: Economic Growth and Inflation in China
The contribution of global value chains to propagating the negative effects of the global crisis remains unsettled.<sup>6</sup> The U.S. trade data highlight that arm’s-length trade accounted disproportionately for the overall post-crisis trade slowdown. This reflected a higher pre-crisis average and a weaker post-crisis rebound in arm’s-length trade growth compared with intra-firm trade. During the crisis itself, the U.S. data suggest a broad-based trade collapse in which intra-firm and arm’slength trade contracted to similar degrees. By 2014, intra-firm trade growth had returned close to its pre-crisis average (4.3 percent of exports and 5.0 percent for imports). In contrast, arm’s-length trade growth remained significantly below its high pre-crisis average: its growth slowed to a post-crisis annual average of 4.7 percent compared to 11.3 percent during 2002-08 (Figure 1).

# **4 Factors contributing to the sharp post-crisis slowdown in arm’s-length trade**

On average, arm’s-length U.S. trade growth exceeded U.S. intra-firm trade growth by 1.6 percentage point pre-crisis (2002-08), but fell short of U.S. intra-firm trade growth by 1.7 percentage point post-crisis (2010-14). This sharp slowdown in arm’s-length trade reflected in part compositional effects in response to global macro-economic trends. In addition, several other factors may have disadvantaged firms trading at arm’s-length, raised the cost of arm’slength transactions, and hence discouraged arm’s-length trade.

**Compositional effects.** First, a greater share of arm’s-length exports than intra-firm exports is shipped to EMDEs, especially BRICS economies. Just as the rapid pre-crisis growth in EMDEs lifted arm’s-length export growth, their sharp post-crisis growth slowdown dampened it (Figure 6; Didier et al. 2016). Second, arm’s-length exports and imports include a greater share of sectors that grew rapidly pre-crisis but have struggled post-crisis (textiles and apparel and machinery) or sectors that benefited from the pre-crisis commodity price boom (mining, metals, and energy; Figure 5). The collapse in metals and energy prices from their peak in the first quarter of 2011 has weighed on trade (World Bank 2015a and 2015b; Baffes et al. 2015). These compositional differences are the main reason behind the steeperthan-average slowdown in arm’s-length trade growth. Had the composition of arm’s-length

> 6Global production chains may have facilitated the transmission of output contractions across the global economy through intra-firm contagion (Bems, Johnson, and Yi 2009). Conversely, they may have strengthened the resilience of trade by facilitating better access to finance or due to the stability of long-established contractual relationships in supply chains (Altomonte and Ottaviano 2009; Bernard et al. 2009).

9
Figure 5: Employment distribution by state and foreign ownership: Manufacturing <!-- Start of picture text --> All Private Domestic, private<br>All Private Domestic, private<br>All Private Domestic, private<br>All Private Domestic, private<br>1 − 4 5 − 9 10 − 19 20 − 99 100+ 1 − 4 5 − 9 10 − 19 20 − 99 100+ 1 − 4 5 − 9 10 − 19 20 − 99 100+<br>1 − 4 5 − 9 10 − 19 20 − 99 100+ 1 − 4 5 − 9 10 − 19 20 − 99 100+ 1 − 4 5 − 9 10 − 19 20 − 99 100+<br>1 − 4 5 − 9 10 − 19 20 − 99 100+ 1 − 4 5 − 9 10 − 19 20 − 99 100+ 1 − 4 5 − 9 10 − 19 20 − 99 100+<br>1 − 4 5 − 9 10 − 19 20 − 99 100+ 1 − 4 5 − 9 10 − 19 20 − 99 100+ 1 − 4 5 − 9 10 − 19 20 − 99 100+<br>Cameroon (2008)<br>Ghana (2013)<br>Rwanda (2013)<br>50 50<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br>50 50 50<br>40 40 40<br>30 30 30<br>20 20 20<br>10 10 10<br>0 0 0<br>50 50 50<br>40 40 40<br>30 30 30<br>20 20 20<br>10 10 10<br>0 0 0<br>50 50 50<br>40 40 40<br>30 30 30<br>20 20 20<br>10 10 10<br>0 0 0<br>50<br>40<br>30<br>20<br>10<br>0<br>Burkina Faso (2015)<br><!-- End of picture text --> _Source:_ Establishment censuses obtained from the statistical agencies of the select countries; see section 3. _Note:_ The figure is constructed based on data for establishments in the manufacturing sector. Establishments with missing employment data are excluded.

15
|P094755|2011|2013|3/23/2013|5 Closed|Yemen,Republic of|
|---|---|---|---|---|---|
|P094755|2011|2013|6/26/2013|6 Closed|Yemen,Republic of|
|P094755|2011|2014|1/18/2014|7 Closed|Yemen,Republic of|
|P094755|2011|2015|8/23/2014|8 Closed|Yemen,Republic of|
|P094755|2011|2015|2/5/2015|9 Closed|Yemen,Republic of|
|P094755|2011|2016|7/21/2015|10 Closed|Yemen,Republic of|
|P094755|2011|2016|1/28/2016|11 Closed|Yemen,Republic of|
|P094755|2011|2017|7/8/2016|12 Closed|Yemen,Republic of|
|P094755|2011|2017|1/15/2017|13 Closed|Yemen,Republic of|
|P094755|2011|2018|7/17/2017|14 Closed|Yemen,Republic of|
|P094758|2005|2008|8/29/2007|1 Closed|Djibouti|
|P094758|2005|2009|8/15/2008|2 Closed|Djibouti|
|P094784|2008|2008|1/29/2008|1 Closed|Ecuador|
|P094784|2008|2009|9/26/2008|2 Closed|Ecuador|
|P094784|2008|2009|12/12/2008|3 Closed|Ecuador|
|P094784|2008|2009|5/29/2009|4 Closed|Ecuador|
|P094784|2008|2010|12/19/2009|5 Closed|Ecuador|
|P094784|2008|2010|5/17/2010|6 Closed|Ecuador|
|P094784|2008|2011|2/23/2011|7 Closed|Ecuador|
|P094784|2008|2012|8/13/2011|8 Closed|Ecuador|
|P094784|2008|2012|5/2/2012|9 Closed|Ecuador|
|P094784|2008|2013|11/21/2012|10 Closed|Ecuador|
|P094784|2008|2014|7/9/2013|11 Closed|Ecuador|
|P094784|2008|2014|8/17/2013|12 Closed|Ecuador|
|P094784|2008|2014|3/7/2014|13 Closed|Ecuador|
|P094784|2008|2015|7/21/2014|14 Closed|Ecuador|
|P094795|2007|2012|3/25/2012|1 Active|China|
|P094795|2007|2012|4/22/2012|12 Active|China|
|P094795|2007|2013|5/15/2013|13 Active|China|
|P094795|2007|2014|11/10/2013|14 Active|China|
|P094795|2007|2021|7/20/2020|15 Active|China|
|P094818|2005|2008|8/29/2007|1 Closed|Djibouti|
|P094818|2005|2009|7/24/2008|2 Closed|Djibouti|
|P094833|2005|2008||0 Closed|Ukraine|
|P094833|2005|2009||0 Closed|Ukraine|
|P094833|2005|2010||0 Closed|Ukraine|
|P094833|2005|2011||0 Closed|Ukraine|
|P094833|2005|2012||0 Closed|Ukraine|
|P094869|2007|2007||0 Closed|Dominica|
|P094869|2007|2008|7/4/2007|1 Closed|Dominica|
|P094869|2007|2008|1/19/2008|2 Closed|Dominica|
|P094869|2007|2008|6/27/2008|3 Closed|Dominica|
|P094869|2007|2009|12/18/2008|4 Closed|Dominica|
|P094869|2007|2009|6/28/2009|5 Closed|Dominica|
|P094869|2007|2010|11/6/2009|6 Closed|Dominica|
|P094869|2007|2010|6/24/2010|7 Closed|Dominica|
|P094869|2007|2011|6/24/2010|7 Closed|Dominica|
_Appendix 2: Obstacles to SADC trade_

**Figure 6: Sub-indicators of Enabling Trade Index against log GDP per capita**

<!-- Start of picture text --> Transport and communications infrastructure Market access<br>Madagascar<br>Lesotho<br>Malawi Mauritius<br>Moz am Zambiabique Namibia Botswana<br>Ta n za nia<br>S outh Africa Mauritius Zimbabwe South Africa<br>Botswana<br>Namibia<br>Zimbabwe MalMo azaM wadm Tanzania iZambiabiqueLesothoagascar<br>4 6 8 10 12 4 6 8 10 12<br>ln(GDP per Capita), 2005 PPP ln(GDP per Capita), 2005 PPP<br>Border administration Business environment<br>Mauritius MauritiusBotswana<br>South Africa Botswana Malawi Tanzania Zambia Namibia South Africa<br>Namibia M adagas c a r<br>Mal Moza a Mw adagam T i Z anzania biquLesothoambia se car Moz ambique L e so tho<br>Zimbabwe<br>Zimbabwe<br>4 6 8 10 12 4 6 8 10 12<br>ln(GDP per Capita), 2005 PPP ln(GDP per Capita), 2005 PPP<br>6<br>6<br>5 5<br>4 4<br>3 3<br>2 2<br>7 7<br>6 6<br>5<br>5<br>4<br>4<br>3<br>3<br>2<br><!-- End of picture text --> Source: World Economic Forum

**Figure 7: Trading on Time and Logistics Performance Indices against log GDP per capita**

<!-- Start of picture text --> Doing Business Trading on Time Index Logistics Performance Index<br>Mauritius<br>Madag Tanza sc ni a r Seychelles<br>MozambiqueLesotho South Africa South Africa<br>SwazilandNamibia Botswana<br>Congo Mala w iZambia Co n go M adagascar Tanzania Mauritius<br>Zimbabwe Angola M oz am Zambiabique Angola Botswana<br>Namibia<br>4 6 8 10 12 4 6 8 10 12<br>ln(GDP per Capita), 2005 PPP ln(GDP per Capita), 2005 PPP<br>7<br>6<br>5<br>4<br>3<br>2<br>5<br>4<br>3<br>2<br>1<br><!-- End of picture text --> Note: The World Bank Doing Business (Trading on Time) indices are constructed as follows: The underlying variables (export and import costs, time and documents) are first standardized (mean 1 and variance 1). The simple average of these is then converted to a 1 to 7 scale (1 corresponds to worst outcome, 7 to best outcome). The underlying trading times, documents and costs are the most recent values available from the World Bank Doing Business Indicators (accessed 1 June 2010) and the Logistic Performance Index (2009).

24