rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Appendix 1: Carbon Footprint Calculations for WBG Air Travel for Fiscal Year 2012 (FY12)**<sup>**32**</sup>

We have had available about 90 percent of the total data for the air travel activity for the entire WBG, both for headquarters and for field offices, for the period March 2011 – February 2012. This data set contains a total of 322,331 trips covering about 642 million miles, of which roughly 57% were trips ticketed by Amex at headquarters.<sup>33</sup> The actual, total traveled distance by the entire WBG in FY-12 (July 2011-June 2012) was 677 million miles, or about 3.2 percent lower than the 699 million miles flown in CY09. Here, all calculations are made on the (slightly more limited) data set available to us. When making the carbon footprint calculations for FY-12, in Section 5.4, our calculated data are scaled up with an expansion multiplier of 1.055 (the relative increase necessary to represent the entire FY-12 flight activity).

The breakdown of FY-12 travel by class can be found in Table A1.1 below. For FY-12 there is very little known first class travel, in contrast to CY-09 where such travel comprised almost 8 percent of all HQ-booked trips by WBG staff. This is due to a changed and standardized policy whereby first-class travel is authorized only in extremely few cases. “Economy” and “economy plus” trips are combined under the single category “Coach”. We have no information about the “unknown” class category, and simply assume that its class-specific distribution is the same as for the “unknown” category.

In 2012 only 0.1% of miles traveled by WBG staff were, officially, on first class (assuming again that first class does not appear more frequently in the “unknown” category).<sup>34</sup> This almost disappearance of first class as a separate flight class
be mitigation strategies available (e.g., the possibility of switching to a nearby safer well). All these conditions were present in the empirical context of this study.

The first condition is satisfied by the large majority of respondents (76%) who did not know whether their well water was safe or unsafe to drink (Table 1). In addition, very few wells had visible signs of safety status such as paint, so even households with strong priors about the safety of their well water may have valued the possibility to demonstrate water quality to others by displaying test results.<sup>14</sup> That the tests could provide new information also required trust, as there is growing evidence that lack of trust in health-related information may hinder the adoption of behavior that could reduce health risks (Cohen et al. 2015, Bennett et al. 2017, Alsan and Wanamaker 2017, Martinez-Bravo and Stegmann 2019). Although the data do not include measures of trust, water tests were not a novelty because many wells had been tested in the past in Sonargaon. In addition, earlier work carried out in the neighboring Araihazar sub-district found switching rates from unsafe to safe wells after testing of between one-third and three-quarters (see Section 1 for references), consistent with a high degree of trust in the results.

The second condition (relevance of the information) is also satisfied because virtually all respondents knew in a general sense about the presence and health risks of arsenic in tube well water. Data on risk perceptions collected in neighboring Araihazar in 2008 indicated that a majority of respondents were aware not only of the serious nature of arsenic risk, but also that the risk becomes more severe with prolonged exposure, see Tarozzi (2016, Figure 4) for details.

Finally, the third condition (perceived availability of alternative sources of drinking water) was also likely satisfied given that well-sharing was already practiced in the area (see Section 3). To some extent households were therefore already aware of the possibility of
an employer of unskilled workers. This sector spends nearly 60% of total costs on nonqualified workers’ salaries (Annex Table 1). Fourth, this assessment relies on the estimates of investment needs for infrastructure in the MENA region and its three subregions, presented in Table 5. Thus, we link the demand for infrastructure with the supply side effects of infrastructure provision on employment and bring realism into the projections.

Table 8 illustrates our methodology for the oil importing countries. The detailed results for the two other subregions are provided in Appendix Tables 2 and 3. The direct job per billion $ in the sector presented in column (1) is obtained by combining the shares of qualified and nonqualified labor inputs in Appendix Table 1 and the wages in Table 7.<sup>12</sup> The share of each sector in the infrastructure needs of the region in column (2) is taken from Table 5. From (1) and (2) we derive the number of direct jobs created in each sector by US$ 1 billion of infrastructure investment (3). We use the type 2 multipliers of the Egyptian IO table (4) to estimate the total job creation in each subsector (5).

**Table 8 Estimated, potential job creation in OIC**

||Direct job /<br>billion $ in<br>the sector (1)|Share of investment<br>needs (2)|Direct job /<br>billion $ in<br>infrastructure<br>(3)|Type II<br>multiplier (4)|Total<br>jobs (5)|
|---|---|---|---|---|---|
|Paved Roads|95000|0.24|22353|1.09|24359|
|Roads|45000|0.10|4538|1.09|4945|
|Rails|68333|0.02
**Behavioral Framework** We assume that a firm’s current profit depends on its own state variables, including firm productivity ( _φ_ ), exchange rates ( _E_ ), and capital stock ( _K_ ). Profits are also influenced by other common factors, such as factor prices. At the start of each period, an incumbent exporting firm observes the exchange rate of each source country, its productivity and capital stock, and makes two decisions simultaneously: (1) how much to use in variable inputs, including imported materials, _P_<sup>_W_</sup> _M_<sup>_W_</sup> , which is part of imports, _IMP_ , and (2) the optimal level of investment, which includes imported capital goods, _I_<sup>_W_</sup> . We abstract from modeling firm entry and exit decisions in export markets, as the customs transaction data cannot reliably distinguish true exits from missing values. Following OP, we assume that productivity ( _φ_ ) evolves according to a Markov process, leading to a Markov perfect Nash equilibrium in firms’ decision regarding capital goods imports. With this framework, we can use observed capital goods imports to control for the unobserved productivity, accounting for exchange rates, capital stock, and other common state variables. The imported capital stock of each firm, _Kit_<sup>_W_, is constructed using the perpetual inventory method, with a depreciation rate</sup> of _γ_ :

where (11) describes the law of motion for firm-level imported capital stock, which is determined by adding current-period imported capital good investment to the existing imported capital stock, after accounting for depreciation. In other words, at the start of each period, after observing productivity, exchange rates, and current imported capital stock, firms decide on their investment in imported capital goods. This investment augments the existing capital stock, net of depreciation, to establish the capital stock level for the following period.

**Estimation** The regression specification in
<sup>th</sup> or the 75<sup>th</sup> and 25<sup>th</sup> . Only Macedonia shows a spread in performance that is similarly large as in Argentina, while depending on the measure, Colombia and Turkey show a variation that is slightly larger than in the HPC group, sometimes surpassed by England, but substantially lower than in Argentina and Macedonia. This pattern is also evident in the graphical depiction of Figure 1. The descriptive pattern shows that the Argentine education system has a shortcoming with respect to ensuring educational equity and providing support for the relatively weak students.

Another feature emerging from the descriptive data is that the performance of the 75<sup>th</sup> and 90<sup>th</sup> percentile students in Argentina is actually similar to (or even slightly better than in) Colombia. The lower mean performance and larger variation in Argentina arise from the

> 7 The fact that England is the highest performing among the considered countries may be related to the fact that it is the only country where most students start school at age five, so that England tested fifth-grade rather than fourth-grade students.

8
74|3.05|n|
|**Sierra Leone**|2003|2.72|71.3|1.94|0.03|0.03|0.00|1.28|16.10|n|
|**Suriname**|1999|2.02|15.9|0.32|0.01|0.00|11.25|0.29|0.61|n|
|**Syrian Arab**|2004|70.02|92.2|64.56|0.97|0.86|20.20|1.39|0.02|y|
|**Tajikistan**|2004|8.77|84.1|7.38|0.13|0.25|0.61|1.51|1.70|n|
|**Togo**|2006|4.96|12.2|0.61|0.01|0.23|0.00|0.22|6.42|n|
|**Turkey**|2005|781.24|98|765.62|14.55|0.89|16.49|1.86|0.04|y|
|**Uganda **|2009|39.81|12.1|4.82|0.09|0.75|12.52|0.22|3.10|n|
|**Yemen**|2005|46.13|99.1|45.71|0.82|1.28|0.00|1.78|0.87|y|

Source: World Bank Databank

# **_Qard-al-Hasan_ as a tool for financial inclusion**

Qard-al-Hasan (QH henceforth) is defined in _Shariah_ as an interest free loan.
decreasing score variability. Torgler, Schaffner, and Macintyre (2010), e.g., have used an index that covers the following questions to be able to measure tax morale:

1.  Given present tax burdens, one can hardly blame tax evaders.

2. Given the easy availability of opportunities to evade taxes, one can hardly blame tax evaders.

3. If in doubt about whether or not to report a certain source of income, I would not report it.

4. Since the government gets enough taxes, it does not matter that some people evade taxes.

5. Taxes are so heavy that tax evasion is an economic necessity for many to survive.

6. If I receive $2000 in cash for services rendered, I would not report it.

7. Cheating on taxes is justifiable in light of the unfairness of the tax system.

8. Taxes are something which is taken away from me.

9. Since everybody evades taxes, one can hardly be blamed for doing it.

10. There is nothing bad about under-reporting taxable income on one‘s tax return (1=strongly disagree; 2=disagree; 3=neutral; 4=agree, 5=strongly agree).

In general, single-item measures in cross-cultural comparisons should be treated with some caution. For example, in countries where tax revenues are collected to finance a ―dictator‘s war machine‖, tax evasion might be justifiable, and there could even be a ―moral duty‖ not to pay taxes. Similarly, in authoritarian political systems people will search for ―voice‖ or ―exit‖ mechanisms via tax resistance to express their preferences (Torgler, 2001). Moreover, there is still the potential problem in the survey studies that some individuals may excuse their non-cooperative behavior in the past by declaring relatively high tax morale values.

# _3. Correlation between Tax Compliance/Tax Evasion and Tax Morale_

A number of previous studies have investigated the simple correlation between tax morale and the size of the shadow
Policy Research Working Paper 10374

# **Abstract**

A 2021–22 survey of trade promotion organizations conducted by the World Bank to understand how the COVID-19 pandemic affected their functioning suggests four main findings. First, trade promotion organizations in high-income countries are larger than those in low-income countries, suggesting a stronger capacity to adapt during the pandemic. Second, trade promotion organizations in high-income countries saw a significant jump in their median budget in 2021 indicating a strong response effort. In low-income countries, the budgets of trade promotion organizations declined. Third, most trade promotion organizations in high-income countries put in place a COVID-19 recovery plan, while none of the trade promotion organizations in low-income countries had one in place at the time of the survey. Fourth, trade promotion organizations used several virtual tools during the pandemic, such as business-to-business matching events and training of small firms on e-commerce, and are expecting to increase the use of all virtual tools post COVID-19. Trade promotion organizations’ use of virtual tools in low-income countries remained limited..

This paper is a product of the Development Research Group, Development Economics and the Finance, Competitiveness and Innovation Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at agrover1@ifc.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or
that agricultural land does not become a non-renewable resource due to land degradation is of paramount importance to the economy of a nation and the world.

This wealth category is divided into cropland, which represents the value of primary crops of cereals, fruits, vegetables for human consumption, and pastureland, which is mainly concerned with indigenous livestock production for meat, milk and other products (World Bank, 2018). The value of land is also partly derived from "water supply, soil fertility and pollination services" (Lange et al., 2018). FAOSTAT data shows that agricultural land area has been steadily growing for B-rated countries (annual growth rate of 0.14% or total growth rate of 1.32% between 2009–2018, excluding Italy and Spain) and shrinking for A-rated countries (annual growth rate of -0.15% or total growth rate of -1.29%). Given that agriculture, forestry and fishing constitute a larger share of GDP value-added in B-rated countries, this discrepancy is not entirely unexpected.

At the same time, both cropland and pastureland deteriorate the environment. The IPBES (2019) estimates that “[a]pproximately 25 per cent of the globe’s greenhouse gas emissions come from land clearing, crop production and fertilization, with animal-based food contributing 75 per cent of that.” Livestock has a disproportionately strong social and environmental impact compared to its economic relevance, as it is the main factor for deforestation and accounts for 18 percent of greenhouse gas emissions (Steinfeld et al., 2006). The FAO cites soil loss and degradation as threats to agriculture itself (FAO, 2018). IPBES (2019) estimates that 23 percent of the world’s 20
Sixth, we define four composite locations (henceforth, locations) based on population and income. Population (number of children below age 5) captures potential market size for child care services while (median family) income captures the ability to pay for them. We assign households to locations based on their observed place of residence and 2000 census county data on population and family income (see Appendix C.2.2 for further details). Locations 1 through 4 are defined as follows: low population (“small”), low income; low population, high income; high population (“large”), low income; and high population, high income, respectively. While admittedly coarse, these locations clearly vary in _ex ante_ household characteristics. For instance, low-income locations have a higher share of single-mother households and a lower share of college-educated parents than high-income locations (Table C.2.) Marital status does not vary much by location size conditional on location income yet parental education rises substantially with location size. In our empirical implementation, locations differ not only as described but also in location-specific parameters such as parental wage offer distributions and center costs, as explained below.

This setting gives rise to four heterogeneous, distinct child care markets. For example, the large, high-income location includes a large amount and share of college-educated parents and supports greater quality and variety in center-based care than other locations. What this setting does not feature, however, is heterogeneity _within_ locations, such as differences among a city’s high- and low-income neighborhoods. Since our dataset is representative of the US population, it does not allow us to characterize child care markets at the neighborhood level. Nonetheless, we argue that neighborhood-level differences are less important in child care markets than in K-12 school markets, where catchment areas impose a strong geographic dimension to the choice set. Child care services for 2-year-olds are less subject to such constraints, as parents use child care providers located not only in their neighborhood but also close to their workplace
Transportation analysis is among the most promising applications for CDR models. Social and economic characteristics are only implicitly recorded in the CDR data, and thus strong assumptions and complex modeling are required to infer them. Spatial behavior, on the other hand, is explicitly recorded by the locations of cellular antennae. For this reason, purely spatial analysis tends to be simpler and more robust than other forms of CDR analysis.<sup>21</sup> Because of the relative simplicity of the analysis, some MNOs and third-party consultants are beginning to offer standard transportation data products to local and national governments.

## **_Disaster Preparedness and Response_**

Large population movements often occur in the wake of natural disasters, and these movements can render pre-disaster surveys and censuses obsolete. In order to provide humanitarian assistance and restore basic services in disaster-affected areas, governments and organizations require revised population data that can be collected swiftly and at a modest cost. This was the case in Haiti after the 2010 earthquake, which prompted several researchers to examine the potential of CDR data to quickly produce high-frequency tracking information on short-term population displacement. This method was found to correspond closely with other estimates of population mobility. Moreover, it was found that that detailed historical data on predisaster population mobility could be used to predict residents’ responses to the earthquake, allowing agencies to better prepare for future disasters.<sup>22</sup> Similar techniques have since been applied successfully during and after other natural disasters.<sup>23</sup>

# **Conclusion**

The dramatic expansion of mobile phone use in developing countries in recent years has yielded a rich and largely untapped source of information about the characteristics of communities and regions. CDR-based research methods have the potential to provide detailed and reliable estimates of poverty rates in real time and at a far lower cost than traditional surveys. These methods have especially promising applications in developing countries such as Guatemala, which suffer from high rates of poverty and inequality, and where limited fiscal and budgetary
general household surveys for 21 low- and middle-income countries (LMICs) that have internationally comparable disability questions, this paper disaggregates human development indicators across disability status to assess the situation of persons with disabilities and disability related inequalities and facilitate future monitoring. We aim to contribute to SDG target 17.18 “to increase the availability of data disaggregated by disability”.

Disability status is measured through self-reports of functional difficulties (e.g. difficulty seeing, hearing) as per the Washington Group Short Set (WGSS) or questions deemed similar. The WGSS is a widely accepted and internationally tested tool (Groce and Mont 2017) that can be used for disaggregation based on disability (Loeb 2015, Washington Group on Disability Statistics 2016). Statistics are comparable across disability status within a country and to a large 2
This procedure is useful for our purposes and is common in the literature, for example Guasch, Laffont and Straub (2006) successfully adopt it.

# **3 Data and Literature Review**

In the previous section we had to introduce Leibniz’s fictitious “the best of all possible worlds” scenario to imagine that projects are evaluated independently and variables are available. Actually this procedure is rather standard in several MDBs and at the same World Bank. This institution presents a whole department dedicated to evaluating projects and other Bank products: the Independent Evaluation Group (IEG, formerly known as OED, Operations Evaluation Department). Its evaluation goals intend to provide an objective assessment of the results of the World Bank’s work, through independent and rigorous analysis<sup>2</sup> . Though these data may be criticized and discussed, we feel that they embody important pieces of information, which may lead to useful policy recommendations.

In this part, we would also like to stress that a few papers have already used IEG data to research on policy-relevant issues, though with different research questions and methodologies<sup>3</sup> . For example Isham, Kaufmann and Pritchett (1997) and Isham and Kaufmann (1999) link a specific indicator of project performance<sup>4</sup> with country-level variables, respectively civil liberties and sound macroeconomic policies. Other papers connect project performance to country-wide variables, including institutional quality (Dollar and Levine 2005), conflict (Chauvet et al 2010) and volatility (Guillamont and Laajaj 2006). Another line of research, attempts to explain project-level data with project-level observations: Deininger, Squire, and Basu (1998) analyze the synergies between project success and knowledge/advisory products produced by the World Bank<sup>5</sup> ; Pohl and Mihaljek (1998) analyze the degree of project uncertainty between calculated ex-ante and realized ex-post project performances (ERR); while both Dollar and Svensson (2000), Kilby (2000
Variables**

Nineteen outcome variables (Table 2) related to service utilization, quality of care and nutrition status were selected from the DHS survey. Demand-side explanatory variables (or potential determinants) selected from the DHS were socio-economic and demographic characteristics and location of service seekers (Table 3). Supply-side explanatory variables chosen from the BFHS and the DHS include – type of provider, quality of care, availability of services (infrastructure, drugs, equipment and skilled staff) and readiness for care provision (Table 4). For the BFHS data, where possible, similar variables were combined and aggregated to create indices (e.g., medical equipment index and drug availability index) to reduce the variance of similar variables based on the categorization or criteria of indexing provided by the BFHS survey (more details can be found in BFHS reports). For utilization of reproductive and maternal care and quality of ANC, the unit of analysis was an individual woman. The unit of analysis for child related indicators was a child under-5. All study variables were selected based on the contextual literature and compatibility for a comparative analysis across time points.<sup>16,19–22</sup>

**Table 2:  Outcome variables**

|**Domain**|**Indicator**|
|---|---|
|Utilization|**Antenatal care (ANC)**<br>•<br>4+ ANC visits<br>•<br>ANC from medically trained provider<br>•<br>ANC in public sector|
||**Delivery**<br>•<br>Skilled birth attendance (i.e., delivery assisted by skilled staff such as doctor, nurse or<br>midwife)<br>•<br>Delivery in public sector|
||**Postnatal care (PNC)**<br>•<br>Availed atleast onePNC (for mother) within 42days|

4