rafmacalaba/datause-encoder-data huggingface dataset:
# 1. Introduction

## Importance and causes of healthcare associated infection

Healthcare associated infections (HAI) remain a global health challenge. An HAI is a condition resulting from an infection acquired while a patient is present in a health care setting (Horan et al, 2008).<sup>1</sup> HAIs impact patients and their families by causing illness, prolonged hospital stay, potential disability, excess costs and sometimes death. HAIs help drive the challenges associated with antimicrobial resistance (AMR) and as a result are more costly to treat and impact the patient’s health more (Serra-Burriel et al, 2020). Rates of HAI are elevated in SubSaharan Africa due to poor infection control practices, inappropriate use of limited resources, under-staffing of healthcare facilities and overcrowding of hospitals (Irek et al, 2018; Ogunsola et al; 2020). Pooled estimates from Abubakar et al (2022) estimate 12.76% (95% confidence interval 10.30 – 15.23) of inpatients are infected with an HAI. However, singlehospital studies from SSA show significantly higher rates, especially for surgical site infections.

HAIs have multiple causes. Khan et al (2017) identify three major reservoirs and transmission routes: (1) endogenous native microflora of patient leading to cross-infection from one site to another; (2) direct patient-to-patient or staff-to-patient pathogen transmission (hands, saliva, fecal matter); and (3) other environmental sources (water, food, equipment, surfaces, and waste). Watson et al (2019) map the transmission pathways for different types of HAIs stemming from inadequate water, sanitation, hygiene, and HCWM services, shown in Figure 1. Health care waste, especially contaminated surgical waste, often acts as a reservoir for pathogenic virulent microorganisms, and it is suggested that 20%–25% of the waste produced by health care outlets is of a toxic nature and considered to have high potential to cause HAIs (Khan et al, 2017). HAIs can also be due to implants and prostheses, most commonly including central line-associated bloodstream infections (CLABSIs), catheter-associated UTIs, and ventilator-associated pneumonia (VAP) (United States CDC, 2023).

### **Figure 1. Conceptual framework visualizing the relationship between water, sanitation, and hygiene (WASH) and HAIs.**

2
observations; it would also result in a higher proportion of events censored by the survey..

Note that because of restriction of the sample to births in the sixty months preceding the survey, the longest possible interval between birth and onset of next pregnancy is sixty months. In Egypt, as in other moderate and high fertility settings, the majority of parity progressions will occur long before sixty months (Hoberaft and McDonald, 1984). Furthermore, the causal factors of principal concern in this analysis, namely death and weaning, should have little influence on the risk of pregnancy after sixty months.

We impose several additional restrictions on the data, to control potentially confounding factors. Non-Muslim women are excluded, in an effort to eliminate unmeasured heterogeneity in post-partum practices (abstinence, child feeding). This restriction eliminates less than ten per cent of births occurring during the reference period. Multiple births are excluded because information on the breastfeeding of such births is known to be unreliable in many fertility surveys. This restriction affects about two per cent of births Finally, we control sexual exposure by excluding births to women with marital disruption prior to any one of the three events and women who report that their husband was abroad at the time of interview. Unmarried women and women separated from their spouses have very low risk of becoming pregnant. Few children are lost because of these restrictions. The sample for analysis consists of 7375 children.

To mitigate spurious relationships among child death, weaning and next pregnancy, a standard set of variables are included in all estimations as 15
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Table 11: Employment growth, 2016-2020.

|||All sample||Com|petitive se|ctors|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|BOS|0.0508***|||0.0401*|||
||(0.0191)|||(0.0226)|||
|BOS * 2020|0.536|||0.686|||
||(0.423)|||(0.672)|||
|Public||0.0121|||0.00327||
|||(0.0160)|||(0.0181)||
|Mixed||0.0422|||0.0151||
|||(0.0313)|||(0.0274)||
|Participated||0.113**|||0.173*||
|||(0.0535)|||(0.0976)||
|Public * 2020||2.160|||2.217||
|||(1.653)|||(2.231)||
|Mixed * 2020||0.0304|||0.0816||
|||(0.0698)|||(0.0997)||
|Participated * 2020||-0.0896|||-0.0983||
|||(0.0557)|||(0.102)||
|Ownership 10-_<_25%|||0.0357|||0.0794|
||||(0.0325)|||(0.0515)|
|Ownership 25-_<_50%|||0.219*|||0.346|
||||(0.129)|||(0.294)|
|Ownership 50-_<_100%|||0.0423|||0.0268|
||||(0.0277)|||(0.0256)|
|Ownership 100%|||0.0147|||0.00636|
||||(0.0157)|||(0.0179)|
|Ownership 10-_<_25% * 2020|||0.0167|||-0.00300|
||||(0.0426)|||(0.0623)|
|Ownership 25-_<_50% * 2020|||-0.205|||-0.262|
||||(0.132)|||(0.299)|
|Ownership 50-_<_100% * 2020|||0.0137|||0.0676|
||||(0.0572)|||(0.0836)|
|Ownership 100% * 2020|||2.056<br>(1.574)|||2.104<br>(2.118)|
|Firm size|Yes|Yes|Yes|Yes|Yes|Yes|
|Firm age|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector 2d|Yes|Yes|Yes|Yes|Yes|Yes|
|Year|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|10762276|10762276|10762052|9827417|9827417|9827251|
|R-squared|0.00618|0.00620|0.00620|0.00643|0.00645|0.00645|

_Note:_ The table presents the results of firm-level regressions of yearly employment growth rates on ownership status. All specifications include fixed effects to control for initial firm size, initial firm age, 2-digit industry and year. Data from RAIS, from 2016 to 2020. Robust standard errors in parentheses. Significance levels: * 10%, ** 5%, *** 1%.

18
half by 2030.<sup>21</sup> Such efforts are predicated on an ability to reliably assess and monitor progress since tracking poverty trends can help us understand which policies work and which do not work, and how efficient they are. Producing reliable poverty estimates by conducting household expenditure (consumption) or income surveys, however, requires significant financial and technical resources. As a result, consumption surveys are typically conducted every few years by statistical agencies, and poverty estimates are not available in the intervening years during which surveys have not been implemented. Another challenge to tracking poverty trends is that questionnaire design may change over time, thus making consumption data and poverty estimates not comparable between different rounds.

A notable example of incomparable consumption data over time is perhaps India’s NSSs. In the late 1990s, the National Sample Survey Office (NSSO) revised the questionnaire of the NSS in 1999/2000 (55<sup>th</sup> round) to make household survey consumption data more consistent with those from national accounts. Major changes to the questionnaire were implemented such as changing the recall period for household durables and education expenses from a 30-day interval to a 365day interval, and using both the traditional 30-day recall period as well as a new 7-day recall period for food items. The Government of India subsequently estimated that the poverty rate fell by 10 percentage points between 1993/1994 and 1999/2000. However, researchers provided different estimates ranging from only somewhat lower than the official estimates to a mere three percentage point decline in poverty during the decade of the 1990s (see, e.g., Deaton and Kozel (2005)).<sup>22</sup> Where consumption data are either incomparable across two survey rounds or missing in one survey round but not the other, but other characteristics (��) that can help predict consumption data

> 21 For details see https://sustainabledevelopment.un.org/sdg1.

> 22 See also Dang and Lanjouw (forthcoming) for a discussion of more recent, albeit less severe, comparability issues with the NSSs in the late 2000s.

21
8

# **Box 2: The Electronics industry responds to civil society demands for better work practices**

“In January 2004, the release of a report by the Catholic Agency for Overseas Development “Clean Up Your Computer: Working Conditions in the Electronics Sector” shook and shocked the industry... based on interviews with workers in Mexico, Thailand and China in factories outsourced by IBM, HP and Dell. It revealed unsafe and hazardous working environments and many other worrying labour conditions. The campaign led many brand name firms, with HP as one of the leaders, and contract manufacturers in North America to come together and create the Electronics Industry Citizenship Coalition in 2004. There are forty members in the Coalition today that includes all the major contract manufacturers.

In 2004, the coalition created an industry wide standard, called the Electronic Industry Code of Conduct (EICC), based on the HP Supply Chain Social and Environmental Responsibility Code of Conduct, to replace company-specific codes. Its aim was to ease the ability of subcontracted firms and suppliers to comply with a vast amount of diverse customer requirements and standards by harmonising them into one approach for the industry. The key tool of the EICC is a self assessment questionnaire (SAQ), which was based on questionnaires created by brand name firms like HP and others. The answers to the SAQ can be posted on an on-line database, E-TASC, where all members can access and assess for themselves the compliance of suppliers. These tools come at a high cost. The subscription fee per supplier site is 500 USD and an additional 15,000 USD for use of the whole SAQ tool. The EICC also reviews and certifies third party auditors.

Source: Reichert, 2010.

# <u>Governments as standard-setters</u>

Governments increasingly set standards governing the traded goods sectors. Unlike corporate sector standards where suppliers can perform at differential levels (and where suppliers may be rewarded or punished for over- or underperformance) these legislated standards are mandatory, transparent and provide little leeway to producers. Government standards can also vary in their sectoral purview. For example the USA mandates Hazard Analysis and Critical Control Point (HACCP) certification (which requires processes to reduce the risk of contamination in food production) for imports of juice and meat, but for other foodstuffs conformance is voluntary. With growing international cooperation, particularly in Europe, many legislated standards are no longer set by individual governments, but by groups of governments, as in the case of standards set by the European Union Commission. For example, the EU has adopted a suite of standards governing the “farm-totable” chain, targeting a series of linked product and process standards governing food safety, animal health, animal welfare and plant health. These cover both domestic firms selling into the EU and exporters to the EU.
some goods that are no longer relevant from a technological point of view. For example, the original definition includes items such as “audio tapes, cassette, full length”. We therefore restricted the focus to the subset of digital NAICS industries that used digital inputs in a meaningful way. Specifically, we use a table from the BEA Input-Output accounts data from 2012, which contains 405 x 405 industries (identifiable to 6-digit NAICS), to restrict further the original set of digital industries, defined as those that have at least 10% of digital inputs over total inputs. Table A1 in the appendix reports the set of digital goods we identified according to these criteria. We then used a concordance to match these industries to the HS classification used in trade statistics.

## **2.3 Other variables**

We further complement our data set with information on gravity-type variables, notably distance and language, which come from CEPII, as well as GDP and population, which we take from the Penn World Tables.

# **3 Key features of Africa’s imports of digital goods**

In this section, we document key stylized facts on Africa’s imports of digital goods over the period of analysis. In 2019, Africa imported $59.4 billion worth of digital goods. Figure 1 shows that digital goods constitute a smaller share of total imports of AfCFTA members (about 13 percent) compared to the rest of the world (around 21 percent). The figure also shows that between 2012 and 2019, the relative importance of digital goods in total imports remained relatively stable in AfCFTA, while it increased moderately in the rest of the world, from around 19 percent to 21 percent.

Table A.2 reports the main source markets for the imports of digital goods from AfCFTA members in 2019. We observe that the great majority of digital goods in Africa are sourced from higher-income countries and large emerging economies. Specifically, 35 percent were sourced from China, 27 percent from the European Union, 7 percent from the United States, and 7 percent from India. South Africa is, by far, the main source of digital goods among other AfCFTA members, but accounted only for about 3% of total imports of digital goods. Taken together, other members of AfCFTA accounted for only around 1 percent of total digital goods imports, including Egypt (0.3 percent), Morocco (0.2 percent), Botswana (0.2 percent), Mauritius (0.2 Second, it identified specific goods and services categories within BEA’s supply-use framework relevant to measuring the digital economy. Third, BEA used the supply-use framework to identify the industries responsible for producing these goods and services.

6
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|||-Tble|SiChs9|IA%v*U4|bIlLtYr_And_|Us*0o Setvio|e||||
|---|---|---|---|---|---|---|---|---|---|---|
||||U4|oftSexvLo4|Sy ftildraz||||||
||1980|_<br>l41k|||41k2|19_|_|1987||1498<br>198|
|No of VLsits<br>FlrstVtdt-6Ho<br>X1<br>N0|511|68|65||75|75|69|79|||
|FLratVLOit- 6-24 Mo.(1)|24|24|23|21||19|17|13|12||
|Infants<br>wJ30<br>(X)|1I*|24*|20*|-<br>45*|74|72<br>-|93|89|81||
|<br>Measles<br>(X)|33|43|22|47|6°|69|1IJ|102|94||
|<br>OPT<br>(X)|4|AS|30|54|58|64|77|83|77||
|<br>Polio<br>(1)|is|4S|*S|51|S7|62|i6|82|77||
|_No._of Ihm4 Visits|||||||||||
|I of C4Udgen WeLghed||||2.2|3133|30|36.2'|38.8|37.9|39.1|
|^ Doesont *ount<br>so"<br>hfanl|v@cinsted|atdeivery|atthe_Ua_|_Xvprsity_<br>_or_|liternity|Bospitals.|||||
||||minstrgy|<br>of Bealth|Stsflna|Levels|||||
||_' Qk_|.4.221|_-19S2_|_4198_|_;984_|_42S5_|_1I9_|19B7|1||
|Po.of toftions|||||||||||
|Doctors|273|_sl0_|393|414|588|317|365|397|167||
|Public<br>_8_<br>_lth_<br>Nurses|lBt|133|179|178|194|169|17J|176|15S||
|Other Restered<br>Nug|2,124|1 869|1.918|3,957|1.99|1,815|1,640|1.548|1411||
|<br>Enrolled<br>AssistantIutgeo|<br>1,178|788|998|986|96|830|746|846|108||
|<br>CoevmwL4 Realth<br>AldW4|1,190|1,174|1,183|1,173|1,$8|555|386|SO|539||
|UutritIJIiets|<br>16|<br>10|11|12|1t|10|1*|<br>23|24*||
|* InDCu4ing Ass istants|||||||||||
|§||||Ih~~~~~~~<br>of|~~~~~~1abr<br>Clnc||||||
||,.o|42_1|_1.21_|.<br>1983|198|1985|1986|1987|||
|Clinc 2w.||361|364|172|$75|369|360|363|||
|<br>I||193|198|203|256|201|191|191|||
|II<br>.6|||86|88||88|t|89|||
|III||76|76|7S|78|77|77|78|||
|IV||2|2|2||2|2|3|||
|V||2|2|1||1|1|2<br>2|||

Sourrce Ministry of beLth Iniormation System.
|||PRE Working Paper S|eries|Contact|
|---|---|---|---|---|
||_ltALe_|ALdhDr|_DIQ_|fgr paper|
|WPS443|The Inflation-Stabilization Cycles<br>in Argentina and Brazil|Miguel A. Kiguel<br>Nissan Liviatan|||
|WPS444|The Political Economy of Inflation<br>and Stabilization in Middle-income<br>Countries|Stephan Haggard<br>Robert Kaufman|June 1990|A. Oropesa<br>39176|
|WPS445|Pricing, Cost Recovery, and<br>Production Efficiency in Transport<br>A Critique|Rachel E. Kranton|June 1990|W. Wright<br>33744|
|WPS446|MEXAGMKTS: A Model of Crop<br>and Livestock Markets in Mexico|Gerald T. O'Mara<br>Merlinda Ingco|July 1990|C. Spooner<br>30464|
|WPS447|Analyzing the Effects of U.S.<br>Agricultural Policy on Mexicar<br>Agricultural Markets Using the<br>MEXAGMKTS Model|Gerald T. O'Mara|July 1990|C. Spooner<br>30464|
|WPS448|A Model of U.S. Corn, Sorghum,<br>and Soybean Markets and the<br>Role of Government Programs<br>(USAGMKTS)|Richard E. Just|July 1990|C. Spooner<br>30464|
|WPS449|Analysis of the Effects of U.S.<br>Macroeconomic Policy on U.S.<br>Agriculture Using the USAGMKTS<br>Model|Richard E. Just|July 1990|C. Spooner<br>30464|
|WPS450|Portfolio Effects of Debt-Equiy<br>Swaps and Debt Exchanges<br>with Some Applications to<br>Latin America|Daniel Oks|June 1990|S. King-Watson<br>31047|
|WPS451|Productivity, Imperfect Competition<br>and Trade Liberalization in<br>the C6te d'lvoire|Ann E. Harrison|July 1990|S. Falion<br>38009|
|WPS452|Modeling Investment Behavior in<br>Developing Countries: An<br>Application to Egypt|Nemat Shafik|June 1990|J. Israel<br>31285|
|WPS453|Do Steel Prices Move Together?<br>A Cointegration Test|Ying Qian|June 1990|S. Lipscomb<br>33718|
|WPS454|Asset and Liability Management<br>in the Developing Countries: Modern<br>Financial Techniques -- A Primer|Toshiya Masuoka<br>|June 1990<br>33767|S. Bertelsmeier|
||**Table A5: I**|**nteraction ter**|**m results for**|**Starting a Bus**|**iness (DTF)**|||
|---|---|---|---|---|---|---|---|
|Dependent variable:<br>_Female Workers_|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|
|Exports (%, cell<br>average)*Starting a|-0.004***<br>(0.002)|-0.004**<br>(0.002)|-0.004**<br>(0.002)|-0.005**<br>(0.002)|-0.004**<br>(0.002)|-0.003**<br>(0.002)|-0.005***<br>(0.002)|
|<br>Business (DTF)||||||||
|Exports (%, cell average)|0.458***|0.444***|0.426***|0.488***|0.457***|0.398***|-0.573|
||(0.131)|(0.132)|(0.146)|(0.152)|(0.162)|(0.126)|(0.509)|
|Starting a Business (DTF)|0.092**|0.089**|0.104***|0.086**|0.064|0.120***|0.147**|
||(0.038)|(0.036)|(0.037)|(0.036)|(0.043)|(0.045)|(0.061)|
|Exports (%, cell|||||||0.116**|
|average)*GDP per capita<br>(logs)|||||||(0.055)|
|No. of Workers|||||||-0.004|
|(logs)*Starting a Business<br>(DTF)|||||||(0.014)|
|No. of Workers (logs)||0.504**|0.668***|0.712**|0.886***|0.898***|1.166|
|||(0.243)|(0.256)|(0.279)|(0.292)|(0.292)|(1.038)|
|GDP per capita (logs)||13.923***|14.248***|15.284***|14.997**|5.817|3.360|
|||(5.053)|(5.257)|(5.321)|(5.827)|(5.669)|(5.718)|
|Herfindahl-Hirschman|||-6.833**|-5.371|-8.963**|-5.828|-5.922|
|Index|||(3.410)|(3.421)|(4.054)|(3.648)|(3.654)|
|Age of Firm (logs)|||<br>-0.526|<br>-0.897***|<br>-0.681*|<br>-0.832**|<br>-0.846**|
||||(0.354)|(0.339)|(0.362)|(0.359)|(0.362)|
|Foreign Ownership|||-0.957|-0.221|-0.038|0.049|-0.004|
|(proportion)||||||||
||||(0.846)|(0.881)|(0.972)|(0.963)|(0.964)|
|Female Ownership Y:1||||6.229***|6.017***|5.975***|5.978***|
|N:0||||||||
|||||(0.614)|(0.651)|(0.652)|(0.652)|
|Skills Obstacle (0-4)||||0.610***|0.692**|0.651**|0.649**|
|||||(0.227)|(0.269)|(0.269)|(0.268)|
|How Much Of An||||-0.042|0.020|-0.006|0.000|
|Obstacle: Transport?||||||||
|||||(0.193)|(0.209)|(0.209)|(0.209)|
|Firm Purchased Fixed||||-0.992*|-1.110**|-1.063*|-1.029*|
|<br>Assets Y:1 N:0||||||||
|||||(0.527)|(0.560)|(0.557)|(0.557)|
|Power Outages (days)|||||<br>-0.172**|<br>-0.152**|<br>-0.156**|
||||||(0.070)|(0.068)|(0.068)|
|Courts Obstacle (cell|||||-0.030|-0.562|-0.438|
|average)||||||||
||||||(0.759)|(0.742)|(0.739)|

59