rafmacalaba/data-use-mentions-extended huggingface dataset:
Table 1: Summary statistics for employment outcomes

||Mean/Sd|N|
|---|---|---|
|Employed|0.684<br>(0.465)|263743|
|Employed with social<br>security coverage|0.199<br>(0.399)|178424|
|Employed full time|0.580<br>(0.494)|180113|
|Employed with a contract|0.250<br>(0.433)|119315|
|Employed in public sector|0.077<br>(0.266)|180113|
|Monthly wage|1600.044<br>(1643.353)|78219|
|Hours worked per week|31.173<br>(24.138)|263309|
|Wage worker|0.316<br>(0.465)|263743|
|Number of workers in establishment<br>of the main job|460.189<br>(385.523)|70729|
|Working in agriculture sector|0.250<br>(0.433)|179578|

Notes: (i) Variables are constructed based on Egyptian Labor Force Surveys 2006-2019. The sample of interest is males who graduated between 2006-2019, who are at least 15 years old and who have less than 15 years of potential experience. (ii) 31.2% of employed males in the sample did not state whether they are legally employed or working without a contract.

6
Ashwin, Julian, Vijayendra Rao, Monica Biradavolu, Arshia Haque, Afsana Khan, Nandini Krishnan and Peer Nagy, January 2022, “Qualitative Analysis at Scale: An Application to Aspirations in Cox’s Bazaar, Bangladesh,” mimeo, World Bank Balazs, Carolina L and <mark>Rachel Morello-Frosch, 2013, “The Three Rs: How Community-Based Participatory Research Strengthens the Rigor, Relevance, and Reach of Science,”</mark> _<mark>Environmental Science,</mark>_ <mark>Volume 6, Number 1, Pp: 9-16</mark> <mark>Bamberger, M., V. Rao, V. & M. Woolcock, (2010). Using mixed methods in monitoring and evaluation: experiences from international development. In A. Tashakkori and C. Teddlie (eds)</mark> _<mark>S</mark> AGE handbook of mixed methods in social & behavioral researc_ _<mark>h</mark>_ <mark>(pp. 613-642). SAGE Publications, Inc.,</mark> <mark>Ban, Radu, Vijayendra Rao and Saumitra Jha, 2012, “</mark> Who has Voice in a Deliberative Democracy? Evidence from Transcripts of Village Parliaments in India,” _Journal of Development Economics,_ Vol. 99 #10, Pp: 428-438, November Bardhan, Pranab (editor), 1989, _Conversations Between Economists and Anthropologists_ , Oxford University Press, Oxford and Delhi Bardhan, Pranab and Ashok Rudra, 1978, “Interlinkage of Land, Labour and Credit Relations: An Analysis of Village Survey Data in East India,” _Economic and Political Weekly_ , Vol 13, February Bell, Clive, 2020, _Tales of Peasants, Traders, and Officials: Contracting in Rural Andhra Pradesh 198082_ , International Development in Focus. Washington, DC: World Bank.

<mark>Blattman, Christoper, Gustavo Duncan, Benjamin Lessing, and Santiago Tobon, 2021, “Gang
- b) What are the economic activities that women perform and that they recognize as employment and therefore are declared and eventually registered in household surveys?

- c) What are the reasons - according to rural women - for which, certain activities that generate economic returns to the household are not considered to be employment?

- d) To what extent are "domestic production or activities" carried out by women (such as caring for children) that require a lot of time and effort a barrier to participation in economic activities?

Interview guides were piloted and adapted after the pilot experience to enhance the potential depth in data emerging from the interviews. Particular attention was paid to identifying and categorizing tasks performed by women that fall into the ICLS 19 definition of work and in the National Statistical Accounts (NSA) production boundary as shown in Figure 1. The activities inside the NSA production boundary are particularly important as they contribute to the measurements of national income, including gross domestic production. In line with ICLS 13, the 2015 EPHPM aimed to identify the activities in the NSA production boundary and included those as employment. At the same time, in 2018 the Honduran National Institute of Statistics (INE in Spanish) was in the process of adopting the ICLS 19 definition of work. Because of this, understanding which tasks were being performed by women and were likely being undercounted would be useful for the NSO.

|Figure 1. The ICLS 19 definition of work and the SNA 2008|
|---|

|Intended<br>destination<br>of<br>production|_For own f_|_inal use_|||_For use by_|_others_|||
|---|---|---|---|---|---|---|---|---|
|Forms<br>of|Productio|n work|Employment|Unpaid|Other|Vo|lunteer wo|rk|
|work|of services
2. Investment for private sector development are also be justified based on the savings that may be obtained. We observe that tenders with higher participation saw higher rebate rates. Similarly tenders with single bidders saw substantially lower rebate rates.

- 9.10 VALIDATION WITH PERCEPTION INDICATORS

This appendix shows how the impact of e-procurement was perceived by different stakeholders of the procurement process. We reported in the main text the impact of electronic procurement on access, economy and efficiency based on administrative data. These observations are further corroborated by the perception of change in these indicators. The following tables summarize the advantages and disadvantages of the 2 modes as reported by PE officials and bidders.

82
# **III. Poverty Trends**

III.1. Poverty headcount rates at a point in time

- <mark>The 2001 poverty line</mark>

In Rwanda, the 2001 poverty line was defined using data from EICV1, with a reference group including the bottom three deciles of the expenditure distribution. The COLI used to spatially and temporally deflate the consumption aggregate also used the consumption patterns of the bottom 60 percent. This generated a poverty threshold of RWF 64,000 in January 2001 prices, yielding a poverty headcount rate of 58.9 percent.

For two subsequent surveys, EICV2 (2005/06) and EICV3 (2010/11), poverty was estimated using the 2001 poverty line derived from EICV1, after deflating the nominal consumption expenditure in EICV2 and EICV3 to January 2001 prices and comparing them to the poverty threshold of RWF 64,000 per adult equivalent per year. Thus mathematically, poor households in EICV3 for instance were identified using the following inequality: where e����/�� denotes nominal consumption expenditure per adult equivalent of household _i_ in month _m_ ����/�� of EICV3, and π����� is the price index used to deflate nominal consumption expenditure from EICV3 survey months to January 2001 prices (see NISR, 2012). This method yielded a poverty rate of 44.9 percent in 2010/11. Going forward, the government decided to update the poverty line. This was done using EICV4 data.

<mark> The 2014 poverty line</mark> Typically, rapidly changing consumption patterns in a globalized world, combined with improvements in social and economic indicators of well-being warrant a periodic update of the monetary threshold established to evaluate changes in welfare. This is particularly true for developing countries, where the ‘relevance’ of a poverty threshold tends to erode with increasing access to markets, new product varieties and overall economic growth. This was also true in the case of Rwanda, where significant changes in the
by 0.50 percent. The positive coefficient on y�� suggests that the higher the world income, the higher the exports.

The second long-run equilibrium relation describes a strong association between import, REER, and domestic income, and normalization is made on import: im� �0.59reer� �0.55y� �0.05t���� REER possesses a positive coefficient, which is to be expected as devaluation discourages imports. A 1 percent depreciation of the REER is associated with roughly 0.60 percent reduction in the imports of goods and services. A 1 percent increase in domestic income would increase imports by 0.55 percent. The adjustment coefficients show that only import equilibrium corrects to the long-run relation.

Altogether, the estimates show that the sum (in absolute value) of the export and import elasticities with respect to the real effective exchange rate exceeds unity. Therefore, the ML condition is met in the long run, suggesting that a real devaluation of the birr will considerably improve the balance of trade in Ethiopia. This finding is sufficiently robust against a wide array of sensitivity checks, such as using quarterly data for the subperiods 1992-2013 and 2004-13.

**Table 3. Identified Long-Run Structures**

||**Marshall**|**-Lerner co**<br>**Annua**<br>**(1980-**|**ndition**<br>**l data**<br>**2013)**|||**Expo **<br>**Qu**<br><br>**Manufac**|**rts By Se**<br>**arterly da**<br>**(2005-13)**<br>**turing**|**ctor**<br>**ta**<br>**Agric**|**ulture**|
census unit include difficulties in handling the terrain in the country, adverse weather, and security issues (e.g., DHS 2019).

The survey consisted of a household survey, and then separate surveys for eligible men and women in the household. In all households in the survey, all women who were aged 15-49 and who were either usual residents of the household or who spent the night before in the household were eligible to complete the women’s questionnaire; and for every 7
biases could be due to model misspecification, in the sense that the selected covariates are different from those in the true model. Results were very similar to those observed in the previous hybrid simulation with a model selection step. Hence, the results suggest deviations from the assumed model are an issue and the countering of biases is what is driving the seemingly good results for unit-context models in the two-stage sampling scenario, highlighting the importance of proper data transformations and model selection to ensure that model assumptions hold when using Census EB methods.

Given the direction of the bias of unit-context models is not known _a priori_<sup>50</sup> - and that these might present high bias - unit-context models are unlikely to be preferred over traditional FH methods when the census auxiliary data are not aligned to survey microdata, unless the calculation of variances is not possible for various locations as noted before.<sup>51</sup> In this case benchmarking is not a recommended procedure and may not help in reducing the bias. EB estimators are approximately model unbiased and optimal in terms of minimizing the MSE for a given area, thus when adjusted afterwards for benchmarking, that is, so that these match usual estimates at higher aggregation levels, the optimal properties are lost and estimators usually become worse in terms of bias and MSE under the model. When benchmarking adjustments are large, as those likely required by _UC_ variants, it is an indication that the model does not really hold for the data. In the case of _UC_ models we have shown that the model will not hold due to omitted variable bias.

Also, note bias can lead to considerable re-ranking of locations and thus a limit on the acceptable

> 50Notice how under the simulations presented in Figures 1 and 3 the method appears to be upward biased.

> 51This holds for other measures of welfare, and particularly for ELL variants of the unit-context models.

36
and Investment in Latin America,” RES Working Papers 4305, Inter-American Development Bank, Washington D.C.

- Gros, Daniel and Marc Suhrcke (2000). ‘Ten Years After: What is Special About Transition Countries?’ European Bank Working Paper N. 56.

- Hallberg, Kristin. 2000. “A Market-Oriented Strategy for Small and Medium-Scale Enterprises,” Discussion Paper 40, International Finance Corporation.

- Harhoff, Dietmar, and Timm Korting (1998). “Lending relationships in Germany - Empirical evidence from survey data,” _Journal of Banking and Finance_ 22: 1317–1353.

- Hartwell, John M. (1947). “Interest rates by loan size and geographical region,” _Journal of the American Statistical Association_ 42: 242–245.

16
ability to predict which women would become successful supervisors among the shortlisted group of women. Because the factories had historically nearly always promoted men, they had large numbers of skilled and experienced female line operators from which to choose new supervisors. The eligibility criteria and diagnostic tests further ensured that the considered line operators were a largely homogenous sample. While it might be easy for factories to identify operators who would **not** make a good supervisor in their factories, it is much harder to decide the ranking among a sample of women who had **all** been judged to be suitable after an extensive screening. This argument is similar in spirit to the literature on microenterprises, recruitment and venture capitalism, which shows that humans are often inept at predicting which firms, workers or investments will be successful among an already select sample (McKenzie and Sansone, 2017; Hoffman et al., 2018; Nanda, 2016). I conduct extensive checks comparing the two groups using variables from the promotion program baseline, which show that the two groups do not differ on observable criteria.

I use three estimation approaches for the analysis: a machine-learning technique to choose

> 4For funding reasons, the home surveys took place after the conclusion of the promotion program.

3
participation in non-farm activity. However, the land surface does not play a significant role in self-employment activity participation.

The three distance variables, “distance from the village center to the site of the township government”, “distance from the village center to the nearest railway station”, and “distance from the village center to the nearest bus station”, which measure the geographic position of the village, all play significantly roles in non-farm activity participation. In general, the site of the township government is the nearest fair for farm households. The railway station often situates in the hubs of transport/communication, or in urban centers. The distance to railway stations can reflect the cost of a long-distance migration. The results suggest that, households live in villages far from markets and/or hubs of transports are less likely to participate in non-farm activity. The long distance from the market not only increases the transport/commute costs of non-farm activity participation, but also lowers the profitability for the local non-farm business. In particular, if we consider that railway stations often locate in cities where there are more wage-paying job opportunities, the significant negative impacts of the distance between the village center and the nearest railway station on the wage-paying activity participation suggests that the geographic remoteness of a village (which is often aggravated by the insufficiency of the transport infrastructures) is one major barrier for the surplus rural labor to participate in wage-paying activity elsewhere. If we consider that bus stations often locate in local fairs / markets where there are better opportunities for small business, the significant negative impacts of the distance between the village center and the nearest bus station on self-employment activity participation suggests that the segmentation from markets lowers the potential for households to participate in self-employment activity.<sup>29</sup> Improving rural infrastructures and reducing transaction costs are therefore critical for the development of rural non-farm activity.

> 29 The survey data suggests that households living near bus stations are more likely to participate in self-employment activities, such as running stores or restaurants.
the average probability. Location and gender do not appear to determine consistently placing above or below the average probabilities.

|**Tab**|**le 6. Distribution of Educational Opportunities i**|**nCôte d’Ivoire**<br>Estimated<br>probability of|
|---|---|---|
|Type|Description|access(%)|
|1|Rural, female child, head with no primary|39.24|
|2|Rural, male child, head with no primary|52.28|
|3|Urban, female child, head with no primary|57.22|
|4|Urban, male child, head with no primary|68.01|
|5|Rural, female child, head with primary|68.65|
|6|Rural, male child, head with primary|77.23|
|7|Urban, female child, head with primary|75.20|
|8|Urban, male child, head with primary|85.03|
|_Source_|: Authors’ estimates from 2008 household survey.||

<!-- Start of picture text --> Figure 8. Traditional and Op-BIA<br>Net Unitary Benefit by Quintile of Income Net Unitary Benefit by Quintile of Probability<br>1 2 3 4 5 1 2 3 4 5<br>Quintiles of income Quintiles of probability<br>Unitary Private Contribution Gross Unitary Benefit Unitary Private Contribution Gross Unitary Benefit<br>Net benefit Net benefit<br>0 0<br><!-- End of picture text -->

# _Source_ : Authors’ estimates

_Notes_ : Quintiles of income: Q1: CFAF 119,879,000— CFAF 360,300; Q2: CFAF 360,775—CFAF 755,365; Q3: CFAF 756,000—CFAF 1,285,850; Q4: CFAF 1,286,
lack recent census data, we conduct a sensitivity analysis with data from Burkina Faso. The availability of a recent census in Burkina Faso creates an opportunity to assess the estimates produced with geospatial covariates and the unit context model against officially adopted EBP census-based estimates as described below.

Burkina Faso’s National Institute of Statistics and Demography carried out a census in 2018 which was utilized by the Burkina Faso poverty team of the World Bank to generate small area estimates of poverty for Communes using the EBP census methodology (Molina and Rao, 2010) under a two-fold nested error regression model ( <mark>Marhuenda</mark> et al., 2017). Because the census and the survey data are from a similar period, the small area estimates using census auxiliary information are considered the gold standard. Comparing the census-based estimates with estimates produced using geospatial covariates offers an appropriate testing ground for assessing the extent of discrepancies between census-based and geospatial-based estimates. This framework can also be used to compare the estimates produced by different models.

13
1. Bulgaria Poverty and Inequality Trends and EU Comparison**

<!-- Start of picture text --> a. Income-based poverty rate, upper-middle income (UMI) poverty b. Income-based poverty rate, upper-middle income (UMI) poverty<br>line ($6.85 per day, 2017 PPP), Bulgaria, 2006-2020, Percent line ($6.85 per day, 2017 PPP), Selected EU countries, 2020, Percent<br>30<br>12<br>25<br>10<br>20<br>8<br>15<br>6<br>10<br>4<br>5<br>2<br>0<br>0<br>Percent<br>Poverty Headcount (Percent)<br>2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 Luxembourg Slovenia Finland Cyprus Netherlands Belgium Czech Republic Denmark Norway Malta Sweden Austria Estonia Lithuania Slovak Republic Croatia Latvia Hungary Portugal Spain Greece Bulgaria Romania<br><!-- End of picture text --> 2
> The spread of information and communication (ICT) technology unlocks further potential, in particular by making markets more efficient and reducing price dispersion (Aker 2010, Aker and Mbiti 2010). The limited available evidence points to strong, albeit heterogeneous impacts. The rollout of extension programs through ICTs is still in its infancy (Nakasone, Torero and Minten, 2014). Overall though, despite promising examples of positive impacts on rural livelihoods of ICT—the so-called digital dividends—these have not yet scaled up to the extent expected (Deichmann, Goyal, Mishra 2016). Technology is helping address some of the barriers, but as with any successful intervention in agriculture, simultaneous complementary interventions on different fronts will be needed (on which, more below).

A recent update on our understanding of African agricultural and rural areas, drawing on six nationally representative LSMS-ISA household surveys (from Ethiopia, Malawi, Nigeria, Niger, Tanzania, and Uganda)<sup>16</sup> further shows that while fertilizer and agro-chemical application rates remain very low in several countries in the region, they also have become substantial in several others, including in certain areas within countries (Sheahan and Barrett 2016). This has been fueled in part by government subsidy programs and high-level attention afforded to the subject by the 2006 Africa Fertilizer Summit in Abuja. Country-level factors, embodying policy and broader institutional phenomena, appear most important in driving agricultural input adoption.

Careful study of rural factor markets shows substantial activity in labor and land rental markets. Twenty to 40 percent of farmers rent or borrow land and 30 to 50 percent of farmers hire labor across SSA (Dillon and Barrett 2016). At the same time, household factor endowments (labor, land) still affect

> 13 _World Development Indicators,_ consulted November 26, 2016.

14 The Global Rinderpest Eradication Programme report, <u>http://www.fao.org/ag/againfo/resources/documents/AH/GREP_flyer.pdf.</u
# **A4. Supply model**

The supply of physicians and nurses/midwives can be projected to 2030 based on historical data on the increase in physician and nurse/midwives densities (i.e. per 1,000 population) in each country. Yearly health workforce density data since 1990 are available from the WHO Global Health Observatory database. We extract all available data points for physicians and nurses/midwives.

Various econometric approaches can be used to project supply numbers, each with advantages and disadvantages as described in Table A.4. Of the three types of econometric approaches, th growth rate method is the simplest, most straightforward, and requires the least amount of data, but may be less accurate given stronger functional form assumptions. Other methods rely less on the functional form assumption, but require more data points. A moving average or distributed lag model (2) which gives more weight to more recent data requires that data be available for a continuous number of years, and an ARIMA (autoregressive integrated moving average) model (3) requires that a long time series be available (i.e. ideally back to 1980) with very few missing data points. Given the availability of the health worker data, we chose to proceed with the growth rate model, which has previously been employed in health worker projections (Scheffler et al. 2008).

**Table A.4 Econometric modeling approaches for projecting supply**

|**Model**|**Advantages**|**Disadvantages**|
|---|---|---|
|**1. Growth**<br>**rate**| Simple and straightforward<br> Only need 2 data points<br>minimumper country|Potentially less accurate if<br>inappropriate functional form|
|**2. Moving**<br>**average/**<br>**distributed**<br>