rafmacalaba/datause-encoder-data huggingface dataset:
- SMSEE, 2017. Report on Brick Kiln Stack Emission Monitoring in Kathmandu Valley. SMS Environment and Engineering Pvt. Ltd. (SMSEE), Kathmandu.

- Stockwell, C.E., Christian, T.J., Goetz, J.D., Jayarathne, T., Bhave, P.V., Praveen, P.S., Adhikari, S., Maharjan, R., DeCarlo, P.F., Stone, E.A., Saikawa, E., Blake, D.R., Simpson, I.J., Yokelson, R.J., Panday, A.K., 2016. Nepal Ambient Monitoring and Source Testing Experiment (NAMaSTE): emissions of trace gases and light-absorbing carbon from wood and dung cooking fires, garbage and crop residue burning, brick kilns, and other sources. Atmos. Chem. Phys. 16, 11043-11081.

- Subedi, S.K., 2020. Study of Characteristics of Bricks Produced in Kathmandu, Nepal. American Journal of Civil Engineering 8, 64-76.

- Tandon, S., Gupta, S., Singh, S., Kumar, A., 2017. Respiratory Abnormalities among Occupationally Exposed, Non-Smoking Brick Kiln Workers from Punjab, India. Int. J. Occup. Environ. Med. 8, 166-173.

- TERI, 2017. Resource Efficient Bricks (REB): Benefits of Producing Clay-fired REBs. The Energy and Resources Institute (TERI), New Delhi.

- Thygerson, S.M., Sanjel, S., Johnson, S., 2016. Occupational and environmental health hazards in the brick manufacturing industry in Kathmandu valley, Nepal. Occupational Medicine & Health Affairs 4, 248.

- Tibrewal, K., Venkataraman, C., Phuleria, H., Joshi, V., Maithel, S., Damle, A., Gupta, A., Lokhande, P., Rabha, S., Saikia, B.K., Roy, S., Habib, G., Rathi, S., Goel, A., Ahlawat, S., Mandal, T.K., Azharuddin Hashmi, M., Qureshi, A., Dhandapani, A., Iqbal, J., Devaliya, S., Raman, R.S., Lian, Y., Pandithurai, G., Kuppili, S.K., Shiva Nagendra, M., Mukherjee, S., Chatterjee, A., Najar, T.A., Jehangir, A., Singh, J., Sinha, B., 2023. Reconciliation of energy use disparities in brick production in India. Nature Sustainability 6, 1248-1257.

- Timilsina, G., Malla, S., 2023. Brick Production in Nepal: A Rapid Survey. Unpublished Document. World Bank, Washington, DC.

- US EPA, 1995. Compilation of Air Pollutant Emission Factors, Fifth Edition. US Environmental Protection Agency, Washington, DC.

- Valdes, H., Vilches, J., Felmer, G., Hurtado, M., Figueroa, J., 2020. Artisan Brick Kilns: State-ofthe-Art and Future Trends. Sustainability 12, 7724.

- Vasić, M.V., Goel, G., Vasić, M., Radojević, Z., 2021. Recycling of waste coal dust for the energyefficient fabrication of bricks: A laboratory to industrial-scale study. Environ. Technol. Innov. 21, 101350.

- WECS, 2022. Energy Sector Synopsis Report 2021/2022. Report No. WECS 03/077/078. Water and Energy Commission Secretariat (WECS), Government of Nepal (GON), Kathmandu.

- WECS, 2023. Energy Sector Synopsis Report 2023. WECS, GON, Kathmandu.

41
trade data, road transport quality indicators, actual road distances, and estimates of economic scale for trading partners.

## **Table 2:  Road Transport Quality Indices for Sub-Saharan Countries**

|Country|Road Transport<br>Quality Index|Country|Road<br>Transport<br>QualityIndex|
|---|---|---|---|
|South Africa|100.0|Cameroon|18.4|
|Botswana|87.5|Mauritania|16.6|
|Zimbabwe|50.0|Mali|16.5|
|The Gambia|41.6|Kenya|16.3|
|Sudan|40.4|Angola|15.8|
|Togo|37.0|Ethiopia|15.1|
|Senegal|36.0|Cote d'Ivoire|14.4|
|Nigeria|32.3|Congo|13.6|
|Swaziland|27.4|Guinea-Bissau|13.2|
|Ghana|27.0|Somalia|12.4|
|Namibia|25.9|Rwanda|12.4|
|Lesotho|25.7|Niger|11.0|
|Zambia|25.1|Burundi|10.9|
|Benin|25.1|Uganda|10.7|
|Eritrea|25.0|SierraLeone|9.6|
|Guinea|23.1|Liberia|7.1|
|Mozambique|23.1|EquatorialGuinea|6.5|
|BurkinaFaso|21.2|Tanzania|6.2|
|Malawi|20.4|Central African Republic|4.4|
|Gabon|19.2|Dem. Rep.ofCongo|3.8|
|Djibouti|18.5|Chad|1.8|

# **4.1  Gravity Model Estimation: Data**

We estimate a standard gravity model for inter-country trade in Sub-Saharan Africa using the IMF Direction of Trade (IMFDOT) data.<sup>15</sup> For paired trading partners, we calculate average trade flows for the period 2000-03.<sup>16</sup> Our economic scale variables for each trading pair are average exporter and importer GDP’s at purchasing power parity for

> 15 The IMF direction of trade database includes total merchandise trade exports and total imports by partner, reported yearly, quarterly and monthly. This database provides more complete information on African trade flows than the UN COMTRADE data.

> 16  We estimate average flows to compensate for the frequency of missing values in the annual data series. We use the period 2000-03 to match the trade data with available data for the right hand variables in the model.

11
**<mark>Table C.2b: Statistics on goodness-of-fit for estimated models for relations with customers</mark>**

|Model|Npar|LL|p-value<br>ofL<sup>�</sup>|BIC|CAIC|AWE|Entropy<br>R<sup>�</sup>|Classification<br>AWE|ICL-BIC|
|---|---|---|---|---|---|---|---|---|---|
|3 classes: no correlations|74|-17791.0|1.00|36182.5|36256.5|37004.9|0.795|38274.2|37451.8|
|4 classes: no correlations|99|-17232.9|1.00|35269.0|35368.0|36369.2|**0.881**|**37397.2**|**36297.0**|
|5 classes: no correlations|124|-17023.1|1.00|35052.3|35176.3|36430.3|**0.847**|37934.9|36556.8|
|6 classes: no correlations|149|-16863.9|1.00|34936.8|35085.8|36592.7|0.837|38431.0|36775.1|
|3 classes: 1-2 corr|90|-17537.6|1.00|35805.4|35895.4|**36805.6**|0.826|38019.6|37019.4|
|4 classes: 1-2 corr|115|-17115.7|1.00|35164.4|35279.4|**36442.4**|0.847|**37801.5**|36523.4|
|5 classes: 1-2 corr|140|-16923.7|1.00|34983.2|35123.2|36539.1|0.838|38151.5|36595.7|
|6 classes: 1-2 corr|165|-16765.3|1.00|34869.3|35034.3|36703.0|**0.850**|38375.4|36541.7|
|3 classes: 4-5 corr|90|-16969.3|0.946|34668.7|34758.7|35668.9|0.719|**36705.8**|**35705.6**|
|4 classes: 4-5 corr|115|-16801.2|1.00|34535.5|34650.5|35813.5|0.635|38458.7|37180.6|
|5 classes: 4-5 corr|140|-16678.4|1.00|34492.6|**34632.6**|36048.5|0.642|39227.5|37671.6|
|6 classes: 4-5 corr|165|-16568.8|1.00|34476.3|34641.3|36310.0|0.659|39426.6|37592.8|
|3 classes: 1-2, 4-5 corr|106|-16878.0|1.00|34616.1|34722.1|**35794.1**|0.596|38353.2|37175.2|
|4 classes: 1-2, 4-5 corr|131|-16723.8|1.00|34510.4|34641.4|35966.2|0.653|38335.1|36879.3|
|5 classes: 1-2, 4-5 corr|156|-16608.2|1.00|34482.0|34638.0|36215.7|0.631|38954.5|37220.8|
|6 classes: 1-2, 4-5 corr|181|**-16502.6**|1.00|34473.7|34654.7|36485.3|0.651|39712.8|37701.3|
|3 classes: 1-2, 4-5, 4-6, 5-6 corr|138|-16697.7|1.00|34515.1|34653.1|36048.8|0.632|38488.3|36954.6|
|4 classes: 1-2, 4-5, 4-6, 5-6 corr|163|-16561.5|1.00|**34445.6**|**34608.6**|36257.0|0.754|38054.9|**36243.4**|
|5 classes: 1-2, 4-5, 4-6, 5-6 corr|188|**-16450.4**|1.00|**34426.0**|**34614.0**|36515.4|0.673|39157.9|38891.2|
|6 classes: 1-2, 4-5, 4-6, 5-6 corr|213|**-16366.0**|1.00|**34460.1**|34673.1|36827.3|0.665|40385.1|38018.0|

Note: p-values of L<sup>�</sup> were obtained using the bootstrap chi<sup>2</sup> procedures of Latent GOLD (Vermunt and Magidson 2016, p. 52) Appendixes, page 17
Lamont, Michele, 2000, _The Dignity of Working Men: Morality and the Boundaries of Race, Class and Immigration_ , Russel Sage Foundation and Harvard University Press, New York and Cambridge MA.

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<mark>Parthasarathy, Ramya, Vijayendra Rao and Nethra Palaniswamy, 2019, “Deliberative Democracy in an Unequal World: A Text-As-Data Study of South India’s Village Assemblies.”</mark> _American Political Science Revie_ _<mark>w</mark>_ <mark>113, no. 3: 623–40.</mark> Rao, Vijayendra, Kripa Ananthpur and Kabir Malik, 2017, “The Anatomy of Failure: An Ethnography of a Randomized Trial to Deepen Democracy in Rural India,” _World Development_ , Volume 99, Pp:: 481497 <mark>Rao, Vijayendra, 1997a, “W</mark> ife Beating in Rural South India: A Qualitative and Econometric Analysis,” _Social Science and Medicine_ <mark>, Vol. 44, # 8, Pp. 1169-1180, April</mark> <mark>Rao, Vijayendra, 1997b, Can Economics Mediate the Relationship Between Anthropology and Demography,</mark> _Population and Development Review_ , _2_ _<mark>3</mark>_ <mark>(4), 833–838</mark> <mark>Rao, Vijayendra, 2019, “Process-Policy & Outcome-Policy: Rethinking How to Address Poverty & Inequality,”</mark> _<mark>D</mark> aedalu_ _<mark>s</mark>_ <mark>; 148 (3): Pp 181–190.</mark> 22
4 something themselves.<sup>1</sup> Wang and Kurzman (2007) estimate that the costs of vote-buying and all other campaign expenditures associated with the elections of a single county executive in Taiwan, China amounted to at least eight million US dollars. Assuming the costs of this single election were 1 percent of total campaign costs incurred by the Kuomintang across all county and national legislative elections, total campaign costs would have amounted to 3.5 percent of government spending in 1993.<sup>2</sup> Wurfel (1963) estimated campaign costs in the Philippines elections in the 1950s and in 1961 (Ferdinand Marcos came to power in 1965) at approximately 13 percent of the national budget. A large share of the expenditures went to vote-buying and, as Wurfel (1964) argues, much came from government financing: the incumbent Nacionalistas relied on government financing and the opposition Liberals on private wealth in the 1957 elections. In contrast, OpenSecrets.blog, an activist organization that tracks campaign costs in the United States, estimates that the total costs of the 2008 US elections were a tiny fraction of government spending – $5.8 billion, half of which they attribute to the presidential race, when general government final consumption expenditure was 17 percent of total national income of approximately $14.2 trillion. Keefer (2002) reports estimates by highly placed insiders who claimed that presidential campaigns in the Dominican Republic cost at least $20 million, or $2.50 per Dominican, compared to approximately $1.00 per American represented by the $193 million campaign of George W. Bush in 2000. Adjusted for differences in purchasing power parity-adjusted per capita income, which was more than seven times greater in the United States, the differences were on the order of 18 to one.

> 1 Direct (and often illegal) transfers to voters are not the only form of vote-buying.  Government spending can also finance vote-buying through the expansion of pre-existing transfer programs or the acceleration of infrastructure projects.  It can also finance vote-buying indirectly, as when government-funded infrastructure projects are used to raise money from contractors to finance vote-buying, as in Samuels 2002.

> 2 Total costs were likely more, taking into account campaigns for the national legislature that occurred in December 1992. The costs of campaigning in the single county were 248 million Taiwanese dollars.  Final government consumption expenditures in 1993 were 971,912 million Taiwanese dollars, according to the National Statistics website, http://ebas1.ebas.gov.tw/pxweb/Dialog/statfile1L.asp.
# Bibliography and references

This country report draws upon a wide range of papers, databases, models, and maps that were created as part of the Africa Infrastructure Country Diagnostic. All of these can be downloaded from the project Website: www.infrastructureafrica.org. For papers go to the document page (www.infrastructureafrica.org/aicd/documents), for databases to the data page (www.infrastructureafrica.org/aicd/tools/data), for models go to the models page (www.infrastructureafrica.org/aicd/tools/models), and for maps to the map page (www.infrastructureafrica.org/aicd/tools/maps ). The references for the papers that were used to compile this country report are provided in the table below.

## General

AICD (Africa Infrastructure Country Diagnostic). 2010a. ―ECOWAS’s Infrastructure: A Regional Perspective.‖

- ———. 2010b. ECCAS’s Infrastructure: A Regional Perspective. AICD REC Report, Africa Region, World Bank, Washington, DC.www.infrastructureafrica.org.

- Banerjee, Sudeshna, Quentin Wodon, Amadou Diallo, Taras Pushak, Helal Uddin, Clarence Tsimpo, and Vivien Foster. 2009. ―Access, Affordability, and Alternatives: Modern Infrastructure Services in Africa.‖ AICD Background Paper 2, Africa Region, World Bank, Washington, DC.

- Foster, Vivien, and Cecilia Briceño-Garmendia, eds. 2009. _Africa’s Infrastructure: A Time for Transformation_ , ed. Paris and Washington, DC: Agence Française de Développement and World Bank.www.infrastructureafrica.org.

World Bank. 2009a. Enterprise Survey. Washington, DC: World Bank.

- ———. 2011a. _Doing Business 2011: Making a Difference for Entrepreneurs_ . Washington, DC: World Bank.

## Financing

- Briceño-Garmendia, Cecilia, Karlis Smits, and Vivien Foster. 2009. ―Financing Public Infrastructure in Sub-Saharan Africa: Patterns and Emerging Issues.‖ AICD Background Paper 15, Africa Region, World Bank, Washington, DC.

## Growth

Calderón, César. 2009. ―Infrastructure and Growth in Africa.‖ Policy Research Working Paper 4914, World Bank, Washington, DC.

- Escribano, Alvaro, J. Luis Guasch, and Jorge Pena. 2010. ―Assessing the Impact of Infrastructure Quality on Firm Productivity in Africa.‖ Policy Research Working Paper 5191, World Bank, Washington, DC.
In the results section the paper presents a high-resolution map of cholera risk followed by the results of scenario analysis exploring the difference in level of association between the covariates and risk reduction. It also presents the results of combined scenarios which mirror a series of alternatives proposed under a World Bank investment which was being restructured at the time of the analysis. It ends with discussion and limitations.

This paper contributed to informing the targeting of existing investments, and the methods employed here can continue to be used to inform future investments by the Government of the Republic of Zambia once these projects are closed. The granularity of the analysis which has brought together multiple data sources enhances our understanding of the pattern of underlying risk across the city, the role of different putative risk factors, and the potential impact of different infrastructure provision or other mitigation strategies. High quality, spatially referenced, data have been generated on the location of the cholera cases themselves, the configuration and quality of existing infrastructure, population access to safe water and sanitation, and other relevant environmental variables. The modeling techniques used here contribute to a growing field of geospatial modeling work in disease modeling that aims to identify and estimate relative contributions of factors that contribute to outbreaks (Kulldorff and Nagarwalla 1995; Besag and Newell 1991), including cholera. For example in Bangladesh, Ali et al defined areas of high cholera risk based on environmental risk factors such as proximity to surface water, high population density and low educational status (Ali et al. 2002; Bi et al. 2016); in Ghana Anamzui-Ya measured proximity to refuse dumps and water reservoirs within communities of Kumasi and used a spatial conditional autoregressive (CAR) model to determine the spatial dependency of cholera prevalence on both digitized imagery and RapidEye image. They found an inverse spatial relationship between cholera prevalence and proximity to both refuse dumps and classified reservoirs (Anamzui-Ya 2012). In Zimbabwe (Luque Fernandez et al. 2012) identified a spatial pattern in the distribution of cholera cases in an epidemic with Harare, characterized by a lower cholera risk in the suburbs with highest elevation. A parallel study to this one was also conducted in Harare, Zimbabwe taking into account a similar suite of geospatial covariates (Joseph et al. 2023). There have also been other efforts to map cholera risk in Zambia using spatial methods. For example Tambatamba et al analyzed the distribution of cholera cases, the mode of cholera transmission, and the risk factors affecting cholera infection in a peri-urban area of Lusaka by using a Geographic Information System (GIS) and a matched case-control method (Tambatamba et al. 2008). Other authors looked explicitly at precipitation patterns, and the association between drainage network availability and cholera cases with a regression analysis (Sasaki et al. 2009).
# PREFACE

Populator projections for all countries and econories are prepared annually by the PopIation **and Human** Resources Department of the World Bank. They are published first in summary form in th& Bank's _World Developmwnt Report_ and later in greater detail as working papers and, in alternate year, as a book. This set of projections, the thirteenth in the series, was prepared for _World Developmcn Report 1990._ No other publication is planned this year.

Separte working papers cover each of the Dank's four regions. Africa (Sub-Saharan), **Asia** (including Oceania); Europe, the Middle East and North Africa (including the U.S.S.R.); and Latin America ad the Caribbean (and Northern America). Each of the papt ;ncludes tables on regional and global aggregates, and together they cover all countries of the world.

hno preaentStion of the data has undergone a major revision in this edition. Included as a new ftature are selective demographic indicators for the recent past, as well as information on contracpdve use ar1 on population policy where this is available.

We intend these papers to provide a convenient and up-to-date reference on the recent demographic past and likely future of each country. We welcome comment on their utility, and how this might be enhanced.

Anthony R. Measham Chief Population, Health, and Nutrition Division v
is, potentially encouraging or deterring women’s participation in management (Blum, Fields, and Goodman 1994). On the other hand, especially in developing economies where there is a lack of basic public services, several economy-wide characteristics, including culture and social attitudes towards women and gender discrimination in access to finance, may limit women’s participation in the labor market and hence in top management positions (Roger Clark, Thomas W. Ramsbey, and Emily S. Adler 1991; Tanja van der Lippe and Liset van Dijk 2002; Alexander Muravyev, Oleksandr Talavera and Dorothea Schäfer 2009; World Bank 2011, Mary Hallward-Driemeier , Tazeen Hasan and Anca Bogdana Rusu 2013; Mohammad Amin and Asif Islam 2015). Simply put, we can classify the types of obstacles faced into two broad categories - micro-level firm characteristics or organization structures; and macro-level or economy-wide institutional and public good access constraints that have a gender bias.

In developing economies, one of the most important factors for women’s career paths is their level of education (World Bank 2011). It is well known that compared to men, women lack access to high levels education in low income economies (Duflo 2005, Andrew Morrison Dhushyanth Raju and Nishtha Sinha 2007, World Bank 2011). The repercussion is that women are at a disadvantage right at the beginning of their careers. Being already hindered at the beginning of the career ladder, the climb up is all the more difficult regardless of potential opportunities. It may be that only after the education obstacle has been overcome that several other firm-specific and economy-wide factors come into play. Thus, ceteris paribus, one can expect greater access to education having a strong positive effect on the likelihood of female top managers in developing economies. However, the connection between access to education and women’s career achievements is not so obvious. For example, schools in developing economies tend to be underfunded, and given the public resources available, it is not completely surprising that they may be of poor quality (Jere Berhman 2009). Thus, if schooling quality is poor, increasing access to education may not prove helpful to women in their careers. Furthermore, other informal 6
# **Figure 10. Importance of China in Foreign Equity Portfolios**

This figure shows the evolution of foreign equity position in China. Panel A shows the evolution of China’s weight in foreign equity positions relative to all emerging economies (CPIS Weight) and China’s weight in the MSCI Emerging Markets Index (MSCI Weight). The panel also plots (in red) two counterfactual scenarios for China's weight in the MSCI in 2018-20: with an inclussion factor (IF) for the A shares equal to 100 percent and 0 percent. Panel B shows the evolution of foreign equity positions in China and other emerging economies (in grey) in billions of 2011 U.S. dollars (USD). The definition of emerging economies follows the MSCI classification of emerging countries in 2020. Sources: Coordinated Portfolio Investment Survey (CPIS) and MSCI.

## **A. CPIS Weight versus MSCI Weight**

<!-- Start of picture text --> 50%<br>IF = 100%<br>45%<br>40%<br>35%<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020<br>CPIS Weight MSCI Weight<br>China's Weight<br><!-- End of picture text --> <!-- Start of picture text --> IF = 100%<br>IF = 0%<br><!-- End of picture text -->

## **B. Foreign Equity Positions in China and other Emerging Markets**

<!-- Start of picture text --> 1,400<br>1,200<br>1,000<br>800<br>600<br>400<br>200<br>0<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020<br>China Other Emerging Economies<br>Billions of USD<br><!-- End of picture text -->