rafmacalaba/data-use-mentions-extended huggingface dataset:
# **4.2 Data Collection Wave 2: Survey**

The objective of the second wave of data collection was to explore the mechanisms underlying the results from the first wave survey as well as the findings from the ward level administrative data on presumptive taxpayers. The second wave survey questionnaire was designed to elicit recall of the tax officials visits and identify any spillover in neighboring businesses of treated wards, as well as to capture persistent changes in beliefs and attitudes that could have been attributed to the intervention.

First, we explore whether there is any persistent effect of the treatment on tax morale, which was identified in the first wave of the survey among the original sample of businesses. We indeed find that the treated disagree more strongly with the idea of refusing to pay taxes until better services are provided in the second round, confirming the persistent treatment effect from the visits. We also find that the aggregate measure of an individual’s tax morale, the own index, is positive and statistically significant. The remaining three measures of tax morale are statistically insignificant. Hence, we are able to replicate the results from the first wave along the exact same dimensions.

As we explained above, the effect of the treatment on tax morale could have one of two explanations. The first explanation is that the treatment effect was comparatively unimportant, driven by a temporary concern or fear due to the presence of the tax officials when the survey was undertaken. The second explanation is that the treatment effect reflected a change in the way the TRA is perceived by businesses because this interaction was different from a typical one, which is solely focused on audit activity and tends to be -at a minimum -burdensome to the business. As we were able to replicate the results after more than a year and a half without the presence of a tax official, we believe this provides strong evidence that the treatment effectively changed tax morale along certain (albeit limited) dimensions, and that the observed effect was not merely driven by a temporary concern
WBES with other measures of the political, institutional and business environment at the country level. We describe below how we measure our key variables.

**Infrastructure.** Two related measures capture the extent of modern communication infrastructure: telephone density (from WDI indicators), and the local (i.e., city-industry-size) share of firms using websites for their businesses (based on WBES).  Since telephone density and local web intensity are closely correlated, and they both measure the use of modern telecommunication, we rely on factor analysis to obtain their principal factor, and call it Telecom.<sup>10</sup>

**Geography** has been suggested as an important reason for Africa’s lack of development (Collier and Gunning 1999; Sachs and Warner 1997).  In particular, Africa has more landlocked countries, which heightens the need for coordination with neighboring countries. Given the significantly higher trade costs associated with borders, being landlocked necessarily impedes international trade.  Another somewhat subtle aspect of geography for Africa is its small population in general.  The low fertility of land and other aspects of geography imply that African countries tend to have sparsely populated land.  To capture the geographic elements above, we thus rely on (1) a dummy variable for whether a country is landlocked and (2) domestic market size, as proxied by country population.  We also explore in robustness tests the importance of natural resource endowments, such as the share of land in tropical areas.

**Political and institutional factors** .  This category includes three political and institutional factors that might influence firm performance.  The first is ethnic fractionalization, a key feature that affects many formal and informal institutions in Africa (Easterly and Levine

> 10 One omission in our measure of infrastructure is the lack of road network data.  While the WDI has data on paved roads, the data are very unreliable.  For instance, India is said to have longer road network than China, a possibility that is instantly dismissed for anyone
. “A World that Counts: Mobilizing the Data Revolution for Sustainable Development,” Independent Expert Advisory Group on Data Revolution for Sustainable Development (November 2014).

United Nations, Department of Economic and Social Affairs. “United Nations E-Government Survey E- Government for the Future We Want,” New York (2014).

Vickery, Graham. “Review of Recent Studies on PSI Reuse and Related Market Developments,” Information Economics, Paris.

World Bank. “Country Policy and Institutional Assessment, Africa: Assessing Africa’s Policies and Institutions,” Office of the Chief Economist, Report 88991 (2014).

———. “Open Data for Sustainable Development,” Transport and ICT Global Practice (2015).

———. _World Development Report 2004: Making Services Work for Poor People_ (WDR 2004), Washington, DC (2003).

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SOEs.<sup>10</sup> Many of these SOES are chronic loss-makers, resulting in a severe misallocation of credit. In addition, using a smaller sample than we do, Ayyagari, Demirguc-Kunt, and Maksimovic (2010) find that Chinese firms, state-owned or non-state, that report government help as instrumental in obtaining a bank loan do not show subsequent improvements in growth, reinvestment or productivity, unlike other recipients of bank loans.<sup>11</sup> By relying on institutions to identify firms facing financial constraints, our study is closest to Hoshi, Kashyap, and Scharfstein (1991) who classify Japanese firms by whether they belong to a business group (keiretsu) and find that group members have lower investment-cash flow sensitivities than other firms.

Another reason the FHP methodology works well in the Chinese context is that, due to China’s underdeveloped capital market (Gordon and Li, 2003), external finance for Chinese firms is largely limited to bank credit, making it easier to understand the trade-offs they make between internal funds and limited sources of external funds. In studies using U.S. data, sample firms tended to be large publicly traded firms. The debate there hinged on whether the lack of dividend payments (the FHP approach) was a more reliable a priori indicator of financial constraints than indicators based on qualitative information in their annual 10-K reports

> 10 See Brandt and Li 2003; Boyreau-Debray, 2003; Cull and Xu, 2003; Gordon and Li, 2003; Allen, Qian, and Qian, 2005; Boyreau-Debray and Wei, 2005; Bai et al. 2006; Li et al. 2008; Cull, Xu, and Zhu, 2009; and Gordon and Li, 2011.

> 11 At first blush, findings from Ayyagari, Demirguc-Kunt, and Maksimovic (ADKM) (2010) on the ineffectiveness of informal finance in spurring firm growth and productivity would appear to contradict those from Allen, Qian, and Qian (AQQ) (2005). However,
In recent years, a number of different studies have picked up on the HMS methodology, either expanding or applying it to a new focus.<sup>6</sup> Kleimann (2014) used the HMS methodology partly as a basis to study the coverage and depth of the ASEAN agreement as well as of agreements concluded by individual ASEAN members with third countries. In addition, Kleimann (2014) also attempts to analyze the depth of some WTO+ provisions, i.e. to what extent the PTAs create rules that go beyond the WTO legal status quo.<sup>7</sup> Villalta Puig and Dalke (2016) apply the HMS methodology to analyze the legal enforceability of sanitary and phytosanitary measures (SPS) and technical barriers to trade (TBT) provisions in Canada’s PTAs. Kohl, Brakman and Garretsen (2016) focus on 13 WTO+ and 4 WTO‐X provisions identified by HMS and expand the coverage of preferential trade agreements to 296.<sup>8</sup> Despite some minor differences in the definition of legal enforceability, the coding strategies in HMS and Kohl at al. (2016) are compatible and provide quite similar results when comparing them across US and EU PTAs.

Nevertheless, the HMS approach to the collection and analysis of the content of PTAs is not the only one. The Design of Trade Agreements Database (DESTA) developed by Dür et al. (2014) looks in more detail at the content and depth of specific provisions in PTAs for a very large sample of trade agreements. While coverage is limited to 10 policy areas, the coding covers a number of aspects for each area ranging from the simple inclusion of a chapter in the PTA to details on national treatment or harmonization of policies.<sup>9</sup> The new database presented in this paper covers all PTAs notified to the WTO and in force as of December 2015.<sup>10</sup> This amounts to a total of
assess how urbanization is linked to poverty and welfare measures. The sample for the HIES 2016/17 is explicitly designed to produce reliable poverty estimates at the three levels, and is implicitly designed to produce an urban sample large enough to understand Bangladesh’s urbanization patterns.

# **3.2. Data used for the sampling design**

The data used for the design of the sample for the HIES 2016/17 come from the latest round of the HIES collected in 2010 and include 12,240 households. The HIES 2010 sample was designed to provide reliable poverty and welfare statistics only at the division level for both urban and rural areas.

Designing a household survey sample often entails using data from a previous household survey to estimate the key parameters needed to determine the sample size and how to allocate it across strata. In the case of Bangladesh, the latest round of the HIES was collected six years ago. Given the rapid growth observed in the country over the past decade, however, it is not clear whether these data provide a good representation of the actual living standards in the country or not. Still, previous household survey rounds usually provide the best source of information to design a sample.

The main goal of Bangladesh’s HIES 2016/17 survey was to estimate reliable poverty rates at more disaggregated levels than the HIES 2010 survey.<sup>7</sup> Standard errors of mean poverty rates estimated at the district-level poverty using the HIES 2016/17 might end up being a little bit higher than what is predicted under the proposed sampling design because reliable districtlevel poverty rates cannot always be constructed based on the HIES 2010. That is, some of the parameters estimated from the HIES 2010 to design the sample for the HIES 2016/17 might not provide accurate information of living standards in 2016/17. To address this concern, we analyze the robustness of the proposed sampling design for the HIES 2016/17 in several ways. The results of this robustness analysis are presented in Section
THR also limits the ability to target the transfer to the intended beneficiary, as households may allocate the food rations to any household member. Jacoby (2002) introduced the notion of the intrahousehold flypaper effect to assess how much of the food benefit actually ‘sticks” to the intended recipients. Evidence from Bangladesh and the Philippines suggest that the transfers are quite sticky (H. G. Jacoby, 2002; Ahmed, 2004); if they are not, then the nutritional impact of school feeding hinges on the degree to which rations provided to the child’s household are consumed by younger children.

The lack of targeting within schools also compromises efficiency. While school feeding programs are often placed purposively in particularly vulnerable regions or schools, they are rarely targeted to vulnerable children within schools. This means that well-nourished children who would attend school in the absence of the school feeding program still receive food. The cost of school feeding programs (ranging from $10-$60 per student per year, with the lower costs generally pertaining to snacks) might therefore be better spent on other potentially more sustainable and higher-impact interventions such as deworming, uniform and book purchasing, or teacher training (Tan, et al., 1999; Miguel & Kremer, 2004; Evans, Kremer & Ngatia, 2008).

This discussion suggests many outstanding evaluation questions related to school feeding. Many of the studies reviewed above have methodological flaws that limit causal inference and external validity. We address some of these shortcomings here through the use of a household-based sample that includes non-enrolled children; a longitudinal quasi-experimental design comparing multiple modalities, and the use of propensity-score weighting techniques to construct plausible counterfactuals.

7
**Table 6: Use of fixed GDP weights in calculating relative ratings**

||Pre-Crisis<br>(1)|Post-Crisis<br>(2)|
|---|---|---|
|L.Net Inflows|0.094**|0.105***|
||(0.039)|(0.038)|
|Relative rating|0.169*<br>(0.087)|0.178**<br>(0.080)|
|Growth diff.|0.198**<br>(0.099)|0.263***<br>(0.087)|
|Int. rate diff.|0.03|0.451***|
||(0.117)|(0.118)|
|VIX|-0.118***|0.055|
||(0.041)|(0.040)|
|Country F.E.|Y|Y|
|Obs.|780|859|
|Countries|26|26|
|Adj. R-squared|0.295|0.238|

Notes: All regressions have net capital inflows as the dependent variable and are estimated using a dynamic panel data model with a lagged dependent variable. The sample includes unbalanced quarterly panel data for 26 emerging and frontier market economies for the 1998-2017 period. The pre-crisis period includes Q1 1998-Q2 2008 while the post-crisis period includes Q2 2009-Q4 2017. Heteroskedasticity-consistent robust standard errors are presented in parenthesis. * indicates significance at 10 percent, ** at 5 percent, and *** at 1 percent.

**Table 7: Robustness of baseline results to the use of gross capital inflows**

||**Abs**<br>|**olute rating**<br>|**Rela**<br>
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the “Quran” were also positively correlated with work stoppage, suggesting that increased religiosity may have played a role as a coping mechanism for those that stopped work in the early stages of the crisis.

# _4.2 Predicting Variation over Time during the Pandemic_

Table 4 reports the results of demeaned OLS regressions to predict income loss and working status by real-time indicators using all waves of HFPS data. Demeaning the data by region effectively controls for time-invariant characteristics of the region. The second-to-bottom row of column 1 shows that the predictive power for total income loss since the previous survey is 0.22. The explained variation by real-time indicators for the three subcategories of income loss is slightly larger than that for the total income loss, as shown in columns 2-4. The R<sup>2</sup> for farm income loss, nonfarm income loss, and wage income loss is 0.25, and 0.26, and 0.23 respectively. The last column presents the predicting power for whether the respondent was working when the HFPS survey was administered. The mean R<sup>2</sup> is 0.15. The results in Table 4 imply that the prevalence of income loss and working status during the survey period is meaningfully associated with real-time indicators during the pandemic. However, this association is weaker than the cross-sectional variation using wave 1, because it does not include any of the correlation between real-time indicators and time-invariant characteristics of the region such as the region’s level of development prior to the crisis.

The signs of the statistically significant variables generally make intuitive sense when predicting variation over time. Increased Google searches for “Milk” and “Bible” are a reaction to negative shocks and are therefore positively correlated with income loss and negatively correlated with current work. Google searches for “Gym” are positively associated with total income declines, suggesting that exercise may be a source of stress relief when income declines. Searches for “Jobs” are positively correlated with farm income declines and wage
# **_The quota system introduced in the National Elections Act_**

The 2008 National Elections Act mandated that 25 percent of the seats in both State and National Parliamentary Assemblies should be held by women. This act follows article 32.2 of the Constitution which reads: “The State shall promote women rights through positive discrimination actions” (UNDP 2018, 11). One of the rationales for setting various quotas for Parliaments is that the inclusion of a diversity of groups, for example, gender, ethnic, religious, will not only benefit the members of those groups but will also benefit society as a whole. In Sudan, this quota system led to changes in the share of women in Parliament: while in 1980, only 5 percent of the seats in Parliament were held by women, by 2010, that figure rose to 28 percent (UNDP 2018). At present, 133 of the 426 seats in Sudan’s National Assembly, or 31 percent, are held by women (International IDEA, International Parliamentary Union, and Stockholm University 2020).

However, there is an ongoing discussion about the quota system and whether it really paved the way for increased female participation in decision-making positions. According to the 2018 Arab Barometer, while 79 percent of respondents are supportive of quotas to increase women’s political representation, 83 percent believe that men make better political leaders than women. The Afrobarometer (2018) provides additional telling insights related to the rights to be elected: 61 percent of female respondents agree that women should have the same chance as men to be elected to political office, compared to 36 percent of men. Despite the comparatively high percentage of women in Sudan’s National Assembly (as a comparator, only 23 percent of the seats in the US House of Representatives are held by women), there has been little discussion on reforming the aspects of the 1991 MPSL that subordinate women to men. Tonnessen and El Nagar (2013) posit several reasons for this. Namely, the 2008 National
remembering and understanding? Answering these questions require a larger employer survey and possibly a competence assessment of engineering graduates.<sup>14</sup>

**_Summary Analysis of Skill Gaps_** : The previous sections show that employers are likely to perceive _Soft Skills_ more important than _Professional Skills_ . However, engineering graduates with limited and weak _Professional Skills_ are undesirable for employers.  The survey results, for instance, show a clear signal to the Problem Solving that is under _Professional Skills_ .  As shown earlier, Problem Solving has the largest gap in _Professional Skills_ and the second least satisfying skill of all skills.

Wide gaps can be observed among almost all skills. This is more obvious for higher order skills, such as Problem Solving that falls in _Professional Skills_ . Further, the mean scores of skill gaps in _Professional Skills_ are higher than those in _Soft Skills_ , which are 0.91 and 0.88 points, respectively. Therefore, the importance of _Professional Skills_ should not be disparaged.

# **6. Summary Findings and Policy Implications**

Educating engineers with a comprehensive and deep set of skills that are in demand would be of tremendous importance for the employability of individual engineers and for the country‘s development. Large economic sectors, such as IT, infrastructure, power and water, rely critically upon engineering skills and technologies. This employer survey provides important new insight on which specific skills are important for employers and where the graduates currently fall short. In what follows, we present the main findings and the policy implications that we draw from each finding. However, it is important to keep three caveats in mind: (i) the quality improvements in education lie squarely within the scope of pedagogy, education policy and education management, which is outside of the scope of this paper; (ii) the engineers evaluated by employers should be seen as the end product of the entire education system, not just engineering education. The engineering colleges receive graduates from the secondary education system with a set of skills
respectively, 48%, 31% and 6% of the total government revenue. It means this scenario will turn like the cash transfer scenario favoring high-income households (high-income households pay a higher tax).

> 11 The annual average exchange rate between the United States dollar (US$) and Egyptian pound (EGP) increased from 16.8 in 2019 to 30 in 2023. Out of 31 national or sub-national economies that have introduced carbon tax to date, only 10 have a carbon tax rate higher than $36/tCO2 by the end of 2022 (World Bank, 2023).

> 12 In this static model, the baseline scenario represents the economic situation presented by the Social Accounting Matrix (SAM), the main economic database used for the model. In the baseline, all variables and parameters, such as sectoral value added, outputs, labor, capital, intermediate consumption, household income and savings, government revenue, taxes, etc. are those presented in the SAM. The existing policies, such as fuel subsidies, are also presented in the SAM.