rafmacalaba/data-use-mentions-extended huggingface dataset:
the national median._ _Source: Own estimates based on HBS (2021)._

**The overlap between energy and monetary poverty varies considerably between the different**

**measures.** By construction, all monetary poor are energy poor in the low-income-high-costs (LIHC) measure only if they have energy spending above the national median<sup>37</sup> (see Figure 10). An additional share of vulnerable households falls below the threshold (AROP poverty line) when subtracting their household energy expenditure from their available household income. Still, some have energy spending shares below the national median, so these households remain unaffected by energy poverty. The P10 measure highlights the most significant intersection between households facing energy and monetary challenges. Specifically, it reveals that 20% of all Bulgarian households experience both energy and monetary poverty. Additionally, a striking 80% of those classified as income poor also fall into the category of energy poverty. The overlap is significantly lower among other measures. For example, only 14.3% of income poor households are also energy poor under the LIHC measure. The overlap between the different measures and at-risk of poverty rates only varies slightly when using income or expenditure as the denominator (compare Figures 11 and 12)<sup>38</sup> . The limited overlap in the LIHC, the 2M, and M/2 measures means that policies targeting the energy poor

> 37 Households are considered poor when their income falls below the AROP poverty line. The at-risk-of-poverty threshold is set at 60 % of the national median equivalized disposable income after social transfers. Household members are made equivalent by weighting each according to their age, using the so-called modified OECD equivalence scale. Therefore, when subtracting energy expenditures from their income, the remaining income will always fall below the AROP poverty line for these households that have energy spending above the median (condition 2).

> 38 Note that the M/2 measures are the same in both figures, as they, by
_-_ _<u>Figure 7. Evolution in the Share of HH living in Sub standard Housing</u>_ <!-- Start of picture text --> Upon arrival Currently<br>LEBANON KURDISTAN JORDAN<br>Notes : Authors’ own tabulation using data from SRHCS.<br>.<br>Figure 8. Rental Markets for Syrian Refugees outside Camps (2015)<br>Pays rent on land or dwelling Difficulty paying rent Written rental contract Rent/HH income<br>LE B ANON KUR DISTAN JOR DAN<br>Notes : Authors’ own tabulation using data from SRHCS. To minimize the influence of<br>outliers and measurement error, ratio between rent and household income does not take into<br>account households with ratio values greater than 2.<br>- -<br>Figure 9. Labor Market Outcomes, Working Age Syrian Refugees (20 60 years old)<br>Kurdistan Lebanon Jordan, camp Jordan, out of camp<br>LAB OR FOR C E JOB LE SSNE SS E M PLOYM E NT WAGE C ONTR AC T<br>PAR TIC IP AT IO N R ATE E M PLOYM E NT WOR KE R<br>R ATE (C ONDITION A L (C ONDITION A L (C ONDITION A L<br>ON ON ON WAGE<br>PAR TIC IP AT IO N) E M PLOYM E NT) E M PLOYM E NT)<br>Notes : Authors’ own tabulation using data from SRHCS.<br>98.3% 98.7%<br>75.3% 78.3% 78.3%<br>56.5%<br>SHARE<br>99.5% 96.8% 97.3%<br>90.6%<br>76.3%<br>71.4% 70.8% 69.7%<br>63.
sup> is positive and it captures the gains from return migration. By contrast, a migrant decides to stay abroad if the value of the latent variable _R_<sup>_∗_</sup> is negative. For identification of equation (4), we use the number of active armed conflicts ( _Conflictc,t_ ) in the countries of destination of Egyptian migrants at the time of migration (for current migrants) and at the time of return migration (for return migrants). We rely on this instrument for the identification of the return migration equation since most Egyptians migrate to other Arab countries and since, in recent years, many of these countries experienced a series of conflicts. To name just a few, these conflicts include

> return migrants. For instance, the share of individuals with secondary education or above secondary education is 15 percentage points higher among current migrants relative to return migrants.

> 17 Information on diplomatic representation from the Correlates of War Diplomatic exchange data set is available every five years between 1950 and 2005. When matching diplomatic representation with the year of migration from the ELMPS 2012, we use the closest year prior to the year of migration. For example, if an individual migrated in 1973, we use diplomatic representation in the year 1970.

> 18 See the Data Appendix B.1 for a description of the Correlates of War Diplomatic Exchange Data set.

14
absence of a protective benefit against harassment is 70 percent of willingness to pay when there is a protective benefit. What explains the fixed-amenity value of the reserved space even when it is not effectively reserved?

First, we show that the “high-level” users, who we showed in Sections 5.4 and 6.4) report the highest level of harassment and have the highest willingness to pay to ride in the reserved space, also ascribe the largest fixed-amenity value to the reserved space. Figure A10 examines how demand for the reserved space by these groups varies by the _de facto_ effectiveness of the reservation rule. High-level users of the space have not only higher demand overall, but also demonstrate a higher demand for the reserved space even when there is effectively no difference between the reserved and public spaces in terms of presence of men and, therefore, protection from harassment.

Second, we show that attitudes and perceived social norms around women’s choice of space are correlated with women’s demand for the reserved space. We showed in Section 4.3 that men and women commuters tend to associate women riding in the public space as more open to advances. Table 4 uses these same platform survey and IAT data to relate women’s demand for the reserved space with their perceived risk of harassment and their first- and second-order beliefs about women’s choice of space. Specifically, we regress a dummy for usually riding in the reserved space (which corresponds to being a high-level user in our experimental sample) on the following measures of the respondent’s first- and second-order beliefs: the respondent’s IAT D-score associating the choice of the public space with sexual openness (column 1); the explicit measure of the respondent’s _first-order_ belief that women who choose the public space are provoking harassment (column 2); and her _second order_ belief about how other women (column 3) and men (column 4) commuters would answer this question.

We observe a small, imprecisely estimated positive relationship between
<mark>extreme poverty. As part of this effort, the World Bank established “The Commission on Global Poverty,” chaired by Sir Anthony Atkinson, to come up with different recommendations on how to improve on the measurement and monitoring of global extreme poverty. This report highlighted current shortcomings in the global poverty measurement infrastructure in detail and offered several suggestions on how to improve the monitoring of global extreme poverty.</mark>

**Table 1. International poverty rate using $1.90 poverty line (in USD 2011 PPP)** **<u>and number of extreme poor by region, 2013</u>**

|**Region**|**International**<br>**poverty rate**<br>**(%)**|**Number**<br>**extreme poor**<br>**(millions)**|
|---|---|---|
|East Asia and Pacific|3.54|71.02|
|Europe and Central Asia|2.15|10.30|
|Latin America and the Caribbean|5.40|33.59|
|Middle East and North Africa|n.a.|n.a.|
|South Asia|15.09|256.24|
|Sub-Saharan Africa|40.99|388.72|
|World Average|12.55|766.01|

Source: Estimates obtained from PovcalNet on September 15, 2017.

<mark>An important recommendation provided by the Commission Report is to calculate and include the ‘total error’ of the poverty estimates for each country.</mark><sup>4</sup> <mark>Specifically, Recommendation 5 suggests that poverty estimates should be based on a total error approach, evaluating the possible sources, and magnitude, of error, particularly non-sampling error and the error introduced by the
, architectural and engineering designs, new financial products and mineral exploration. Next (at 1.6 percent of GDP) are economic competencies, which include brand equity development through advertizing and market research, employee training and organizational improvement.<sup>7</sup> Lastly, business computer software and database investments total about 1 percent of GDP. However, spending on

> 7 This is an underestimate because of missing data on labor training expenditures in service sector firms.

4
# **_Table A4.6a Relationship between Task Team Leader (TTL) turnover and IEG outcome rating, ordered probit_**

||**Depen**<br>|**dent variable: I**<br>|**EG 1-6 outcom**<br>|**e rating**<br>|
|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|
|Dummy for preparation TTL change|-0.056|||-0.020|
|in the 1st or 2nd ISR|(0.046)|||(0.047)|
|TTL turnover during first half (Number of||-0.412***||-0.305*|
|TTLs/Number of ISRs)||(0.158)||(0.170)|
|TTL turnover during second half (Number of|||-0.614***|-0.623***|
|TTLs/Number of ISRs)|||(0.182)|(0.176)|
|Observations|2,763|2,762|2,757|2,752|
|Pseudo R-squared|0.081|0.082|0.082|0.083|

Note: Robust standard errors clustered at the country level are reported in parentheses (***p<0.01,

**p<0.05, *p<0.10). All regressions are estimated using Ordered Probit and controlling for region, sector, IBRD/IDA, original commitment amount of project, original length of project, CPIA score of project country at approval, change in the CPIA score
section 3 are largely consistent with the predictions of the theoretical framework. A variety of robustness tests in this section address potential concerns regarding sample selection, omitted variables and endogeneity bias. The final section summarizes the findings and discusses their implications.

# **2. Data and Empirical Strategy**

_Measuring trust in country systems_ _R_ We operationalize _i_ , the share of recipient-managed aid, using data from the OECD _A i_ DAC‟s Paris Declaration Monitoring Survey (PDMS). The PDMS was designed to assist in measuring progress toward the Paris Declaration‟s objectives between 2005 and 2010. Among the 12 PD indicators established with goals set for 2010, 9 of them are measured using data from the PDMS and 3 from other sources. Most of these indicators are beyond the scope of the present study. We measure trust in country systems with PD Indicator 5a: use of country public financial management (PFM) systems as a percentage of aid for the government sector.<sup>3</sup> This indicator is constructed as a simple average of three sub-indicators from the survey: use of national (i) budget execution procedures, (ii) financial reporting procedures, and (iii) auditing procedures, each as a percentage of aid for the government sector. Detailed criteria for these three dimensions of use-of-PFM-systems are provided in Appendix 1. Correlations among these sub-indicators average .73 (ranging from .54 to .77). Findings presented below change very little if any one of its three components is analyzed instead of Indicator 5a. Mean use of PFM systems

> 3 Aid for the government sector accounts for about 83% of total aid disbursements reported in the PDMS, and excludes aid disbursed to NGOs, parastatals or private companies unless it is provided “in the context of an agreement with officials authorized to act on behalf of central government” (OECD, 2008a).  Empirical results reported below are unaffected if government sector aid is replaced with total aid in the denominator.

7
obtained from data generated by different survey methods. At a practical level, they also underline the importance of consistency and best practice in survey design.

# **6. Conclusion**

This paper investigates whether survey design matters for estimating returns to education using a field experiment, and finds that it does. Using a randomized intervention that implemented two variations of survey design, namely use of a commonly used short versus a detailed labor questionnaire, and self‐response compared to response by proxy, we find that estimated returns to education differ dependent on the survey instrument, but not on the type of respondent, for both men and women. The short questionnaire leads to biased estimates of returns to education relative to the detailed questionnaire. The biases are substantial and significant, resulting in higher estimates in the short questionnaire, ranging from 6 and 8 percentage points higher for tertiary educated men and women respectively, to 14 percentage points higher for primary educated women. These results are robust when accounting for non‐linearity of education and taking both endogeneity of education and selection into wage work into account, making use of commonly applied estimation and identification methods. The results are consistent with suggestive evidence from qualitative research, including respondent debriefing studies in the US which show that screening questions can have important effects on the labor statistics they generate.

These observed differences are of a similar magnitude as the estimation bias related to endogeneity which is the subject of considerable attention in the literature; they are also of a similar magnitude as the differences in estimated returns between gender, levels of education, across sectors (public, formal private and informal private sectors) observed in the region, and therefore deserve attention.<sup>36</sup> While this paper does not aspire to obtaining more accurate estimates of returns to education for Tanzania ‐ the data was not collected with this aim in mind and using standard questionnaires, and the sample is also small and not nationally representative ‐ it is useful to consider the existing results for Tanzania, in particular
# **Firm Resources, Strategies, and Survival and Growth during COVID-19:**

# **Evidence from Two-Wave Global Surveys**

Sheng Fang Chorching Goh Shaomin Li L. Colin Xu Keywords: Covid-19, Survival, Organizational Resource, Strategic Change, Channel Stability, Firm Performance Views expressed in this article are the authors' own and do not reflect the positions or policies of the World Bank and its member countries.

Authors information: Sheng Fang, Beijing Normal University at Zhuhai, Zhuhai, China 519087, Email: shengfang@bnu.edu.cn; Chorching Goh, The World Bank, Washington, DC 20433, Email: cgoh@worldbank.org; Shaomin Li, Old Dominion University, Norfolk, VA 23529, Email: sli@odu.edu; Lixin Colin Xu, The World Bank, Washington, DC 20433, Email: lxu1@worldbank.org.
Participants also reported that many clients who sought sex work attempted to negotiate on the price, arguing that they could not afford the prices that they were previously paying prior to COVID-19. Some participants shared that they were initially reluctant to negotiate however ultimately opted to accept a lower price rather than forgo the work.

_“Clients bargained on the price that we gave them because they complained on being broke. So instead of paying 10,000 [TZS] for instance, they’d pay 5,000 [TZS]. Therefore, the price decreased compared to how it was before the pandemic.”_ _“I had to change how I charged my clients. This was because most of them complained on being broke because of the pandemic, so I always had to bargain on my charges. I had to agree with them because if I did not, they left for other women and my biggest fear by then was to go back home from work without having earned anything. Before the pandemic, I used to charge them 30,000 TZS but during the pandemic it dropped up to between 10,000 to 5000 TZS.”_ While negotiating was still common for some, most participants explained how prices had begun to return to pre-pandemic levels. Survey data indicate that there was a decrease in the average price per client from 6.6 USD in 2018 to 6.2 USD in 2021, though the difference was nonsignificant.

Participants also reported a significantly lower average number of clients per week in 2021 (6.2) than in 2018 (8.6). The lowest levels were experienced in 2020, with participants reporting an average of 4.8 per week. However, most interviewees explained that they started to observe higher demand for work when restrictions on gatherings and workplace closures were lifted.

_“In the first wave of the pandemic, there were less clients but then as time went on, the demand started getting back to normal, but they have not yet reached the normal
# TABLE OF CONTENTS

|Acknowledgemen|ts|i|
|---|---|---|
|Chapter<br>I|The Purpose<br>of the Present<br>Study and Previous<br>Research<br>on Purchasing<br>Power and Productivity|1 -<br>10|
|Chapter II|Reconciliation<br>of Industrial Census Data with the<br>National Accounts|11-<br>28|
|Chapter III|Binary Comparisons of Real Output and Purchasing<br>Power, Brazil/USA. Mexico/USA in 1975|29-<br>55|
|Chapter IV|Binary<br>Comparisons<br>of Real Outputand Purchasing<br>Power, Mexico/Brazil<br>in 1975|56-<br>68|
|Chapter V|Methodology of Matching Procedures: The Problem<br>and a Proposed Short-cut|69 -<br>80|
|Chapter VI|The Comparative Merits of Census Unit Values and<br>Specification<br>Pricing|81-<br>83|
|Chapter VII|Labour Productivity|84 -<br>91|
|Chapter VIII|<br>Summary and Conclusions|92- 100|
|References||101- 108|
|Statistical A|ppendix<br>Notes<br>Tables (in total about 200 pages for 17 industries<br>bound separately; available on special request; the<br>results for grain mill products, tables A1.1 to A1.<br>are appended here by way of example)|109- 119<br> <br>13,|
types of costs such as system design rigidity (that is, not being able to accommodate certain atypical cases) and IT system breakdowns. Once again, given the overall positive effects documented by past studies, we propose the following hypothesis: H3: E-procurement improves efficiency of public tendering administration.

# 4. DATA AND INDICATORS

# 4.1 DATA SOURCES

37. The sample for this study comprises tenders in the respective district-level PEs of BWDB, LGED and RHD, that is for a total of 185 PE clusters. To ensure homogeneity, our sample only includes public works projects, which represent the bulk of procurement spending in these organizations. In BWDB, most works projects consist of the construction of embankments, canals, riverbank protection work, dredging and flood control work. In LGED, most works projects comprise of construction and maintenance of small roads, schools, buildings etc. In RHD, most works projects consist of the construction of big district and subdistrict roads and highways.

38. Administrative data on e-procurement tenders come from the e-procurement database, exported by the government for the research team. It captures the full population of e-procurement tenders for FY 201112 to FY2017-18, totaling 191,785 tenders. A survey firm collected data from procurement records for a representative sample of paper-based tenders processed between 2011-12 to 2016-17. The sampling frame consisted of the full population of about 31,500 manual tenders. A stratified sampling method was used where the strata were defined by district and method of procurement (open tendering method and limited tendering method), and financial year. Strata with few tenders were oversampled, with the sample size of the stratum being equal to the population of that stratum if the population was not greater than 3 tenders. The physical files for about 15% of the tenders that were sampled could not be located (e.g. because flooding destroyed the records); hence, they had to be replaced from the same stratum. Some of the replacements had to be made from different strata, especially in the cases
according to observable characteristics that are correlated with income. In response 5 we distribute equally the fixed budget to all households below PMT threshold, up to the point where the number of beneficiaries corresponds to the number of pre-shock poor; while in response 6 we do the same, but up to the point where the number of beneficiaries corresponds to the number of post-shock poor (so that there will be more beneficiaries, but the transfer will be lower).<sup>13</sup> Once again payments are in addition to any pre-shock transfer that households may have received but exclude households that may have benefitted from other COVID-19 support programs.

Note that, while the proxy-means indicator may be relatively efficient in identifying pre-shock poor, it may not necessarily be a good indicator of _new_ poor because asset holdings may not change immediately nor predict vulnerability to the shock, and PMT scores are usually not recalculated in the aftermath of a shock. Hence, accurate identification of post-shock beneficiaries using a pre-shock PMT index may vary substantially according to the nature of the shock, depending on whether the shock affected distributional rankings.

At times, however, policy makers may be able to make use of an indicator identifying which households may have suffered a shock. For instance, following a drought one may not be able to perfectly identify the value of the loss, but a good approximation could be obtained by estimating households’ land holdings. Similarly, information of an informal workers’ sector of activity could provide some indication of the extent of the income loss she may have suffered during a crisis.

To reflect the availability of this “imperfect” information, we consider two additional responses (7 and 8). In response 7 we distribute the fixed budget among households that suffered the shock with certainty. We call this a “perfect loss” indicator. In response 8, we introduce an exclusion error of 25% to mimic the case of imperfect information.

The final two responses (9 and 10) combine the PMT indicators with the “perfect
liable duty.

> 52 Ianchovichina (2004) discusses in detail the implementation of the empirical model in GEMPACK (Harrison and Pearson, 1996) and the changes to the GTAP data necessary to incorporate duty drawbacks. 53 The growth projections are based on World Bank projections for aggregate real GDP and factor input growth in each region in the model and a residually determined total factor productivity growth rate to ensure consistency between the two. These projection rates are shown in Table 4 of Ianchovichina and Martin

33
|0.000|
|||(0.003)||(0.005)|
|Father Edu Sq_×_Ability Index||0.002||-0.001|
|||(0.002)||(0.003)|
|Father Edu Sq_×_Ability Index Sq||-0.000||0.000|
|||(0.000)||(0.000)|
|Constant|5.432***|5.510***|5.673***|5.889***|
||(0.201)|(0.282)|(0.237)|(0.490)|
|Observations|10239|10239|5238|5238|

Notes: Standard errors are clustered at the community level (*** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1). Rural and Urban are defined based on location of birth. If the respondent was born in a big city or town, it was considered as Urban, and if the respondent was born in a village, it was considered Rural. Cognitive ability index is expressed in percentile; it is constructed taking the first principal component of Raven Test and two memory memory tests net of age and age square.

1