rafmacalaba/data-use-mentions-extended huggingface dataset:
of projects is a likely source of variation in impacts. CDD projects present specific challenges when it comes to ensuring that processes laid out during the design phase are followed through – particularly when it comes to facilitation and the participatory process. This is particularly true for large-scale, national CDD platforms where there might important trade-offs between getting to scale and maintaining the initial intensity of the intervention as designed. CDD programs cover large areas with investments that are spread out and which employ a very large numbers of facilitators, hence relying to a significant extent on the training of community groups and volunteers. There is a level of design complexity required for programs that have the explicit goals of including marginalized and vulnerable groups in decision-making for example, or those that focus on building climate resilience by integrating available national/sub-national data on climate vulnerability and community knowledge of climate risks (which likely reside in very different epistemological spaces). To understand variation in CDD impacts (again within specific sub-types of projects aiming at the same type of result) it will be critical to understand the extent to which design choices have been translated into practice as intended yet still locally accepted. Expanding/standardizing the use of process evaluations in CDD programs is an important step in assessing fidelity to design and ensuring that implementing agencies can take timely corrective action. For example, a process evaluation of the Philippines _NCDDP_ showed significant discrepancies between the participatory planning cycle as designed and as understood and implemented in practice by community facilitators. Assessing the effectiveness of quality assurance mechanisms put in place at the project level (supervision, community-based monitoring, training and coaching of facilitators, technical oversight of infrastructure investments) through qualitative analysis and process monitoring, and creating feedback loops with implementation, must be an important part of a future research agenda. This is needed so we can gain a better understanding of the causal transmission

> 42 See Bold et al (2018) for a formal assessment of the different outcomes attained in an identical education program (itself previously determined to “work” in India as a
# **Appendix C Aggregating Triweekly Interviews**

# **Table C1 Interview Schedule**

|Day interview was<br>conducted on|Number of<br>interviews<br>conducted|Percent of<br>total|
|---|---|---|
|Sunday|151|1.76|
|Monday|9|0.11|
|Tuesday|2,659|31.05|
|Wednesday|177|2.07|
|Thursday|2,722|31.79|
|Friday|131|1.53|
|Saturday|2,714|31.69|

# **Table C2: Assessing Imputation Bias**

||Usual Hours (a)<br>Conditional on<br>working that day of|Actual Hours (b)<br>Conditional on working<br>(Average hours worked|mean bias (a-b)|N|
|---|---|---|---|---|
|day|the week|reported during HF|||
|interview<br>refers to|(reported at 3 month<br>follow up)|frequency survey)|||
|Sunday|9.07|8.43|0.63|15|
|Monday|9.45|8.11|1.34|126|
|Tuesday|9.55|7.32|2.23|58|
|Wednesday|9.54|7.48|2.06|41|
|Thursday|8.92|8.02|0.90|26|
|Friday|8.96|8.10|0.86|25|
|Saturday|7.67|5.67|2.00|3|
|Average|9.36|7.85|1.51|294|
# 6. Conclusions

Household budget surveys (HBSs) are crucial to establish poverty monitoring systems and to design evidence-based policies. This paper summarized microdata challenges the MENA region faces related to measuring well-being and discussed ways to overcome these challenges.

Despite improvements in public access to microdata, data availability is poor in the MENA region, comparable to the much poorer Sub-Saharan Africa (SSA), and lags the rest of the world. Lack of data timeliness further compounds the availability challenge: seven of the 13 developing countries in MENA had fewer than three surveys between 2004 and 2018. In many countries, the most recent surveys were conducted between 2006 and 2014, making their data outdated. HBSs also become outdated for other reasons: some countries take an exceedingly long time between data collection, producing and publishing a poverty estimate, and sharing the data with organizations such as the World Bank for use in international poverty comparisons.

As a result of these issues, measuring monetary poverty in MENA is complicated, at the country and at the regional level. Inability to measure poverty and other social-economic characteristics on a regular basis negatively affects national decision makers and hinders the ability to track progress against the Sustainable Development Goals (SDGs). Outdated surveys result in outdated consumption distributions, leading to inaccurate poverty monitoring and the exclusion of countries from global and regional poverty counts.

The priority for filling data gaps to measure well-being in MENA should be (i) improving access to existing HBSs and, (ii) a more regular collection of HBS data. International organizations, including the World Bank, can play a supportive role by providing technical assistance (TA) and sharing expertise in data collection, poverty measurement methods, microdata anonymization, and data sharing practices. International organizations can also provide funding or co-finance microdata collection in countries with limited resources.

If data collection is not possible, one can turn to data imputation techniques to obtain poverty estimates. This approach requires at least one up-to-date consumption survey and a non-consumption survey that collects similar individual and household socio-economic characteristics.
|||2012/2013|5,015|1,889|
|||2014/2015|3,352|2,127|
|||2019/2020|1,184|312|
|||2020/2021|4,709|1,564|
|Uganda|Uganda National Panel Survey (UNPS)|2009/2010|2,975|1,883|
|||2010/2011|2,716|1,886|
|||2011/2012|2,850|2,020|
|||2013/2014|3,119|2,190|
|||2015/2016|3,305|1,868|
|||2019/2020|3,098|1,845|
|Total|6 countries|27 waves|105,945|54,237|

_Note_ : The table summarizes the household data details for each country, per LSMS Basic Information Documents.

32
<br>(0.035)|Handling & inspection - Developed||-0.032<br>(0.031)|
|Observations|864,039<br>864,039|Observations|864,039|864,039|

_Note: Robots reflects log robot stock. We approximate robot applications at the country-industry-level by interacting the (country-level) share of a given robot application with the (country-industry-level) of robot stock. Quality refers log quality, estimated via Khandelwal et al (2013). Country income status is defined by the World Bank (2000). Controls include (log) employment and (log) real value added per worker at the country-industry-year level. The regressions also include exporter*10-digit product and exporter*year fixed effects. IV results are presented in Table A8 and A9. Robust standard errors clustered at the exporter-industry level are reported in parentheses. *** p<0.01, ** p<0.05, * p<0.1._ 34
_Notes:_ All regressions control for pre-treatment outcomes, demographic controls (including dummies for age, gender, academic year, and ethnicity), and municipality fixed effects. Robust standard errors are clustered at the school level in parentheses. All figures are rounded to three decimal places. _Source:_ Own survey data, school year 2015/2016, own calculations.

Column 2 of Table 3 shows the average effect of the respective experimental condition on grit, which is our main outcome of interest. The finding is surprising and goes in the opposite direction of the hypothesized effect: it seems as if the intervention actually reduced students’ self-reported grit, particularly in case of student self-learning (by about 5% SD).

Grit is measured using the Short Grit Scale (Duckworth and Quinn, 2009), which is a standard instrument commonly used in the literature. The eight items in the scale set out to capture the two facets of grit: perseverance of effort and consistency of interest.

When looking at these two facets of grit separately (Table 3 Columns 3 and 4), we see an interesting pattern: while the intervention increased the perseverance-of-effort facet of grit (in both treatment arms, with again slightly stronger effects in the teacher-delivery treatment, about +6% SD _versus_ +5% SD), it reduced the consistency-of-interest facet (again in both treatment arms, by about -10% SD or more). Compared to students in the control group, treated students report being more industrious and hard-working but also report losing interest more quickly. Both facets combined then yield an insignificant (teacher-delivery) or even negative impact of the intervention (student self-learning treatment) on grit.

The divergence between the two facets of grit is puzzling. There could be several reasons behind this finding. First, some aspects of the Short Grit Scale may be less suited to the age group target of the intervention (that is, the Short Grit Scale being used in a younger cohort than it was originally developed for). For example
23 The third RDM step in Figure 2.1, Scenario Discovery helps address these questions (Bryant _et al._ 2010; Hallegatte _et al._ 2012). As described in more detail in Appendix B, we apply scenario discovery algorithms to the database of 1,000 cases to identify the combinations of uncertain conditions that most reliably distinguish those 36 cases in which it satisfies objectives from the 974 cases where it does not.

Scenario Discovery reveals that four conditions together describe a scenario in which the Baseline Strategy satisfies objectives:

1. The increase in rainfall intensity is less than 6%,

2. The Saigon River rises by less than 45 cm, 3. Ho Chi Minh City’s population is les than 18 million, and 4. Ho Chi Minh City’s poverty rate is less then 23%.<sup>18</sup> Assumptions about population distribution and about the vulnerability of the population have much less effect on whether or not the Baseline Strategy satisfies decision makers’ risk management objectives.

Scenario discovery also reveals that future population and poverty rates are statistically less important predictors of strategy performance than are rainfall intensity and Saigon River rise. Moreover, as long as rainfall intensity and Saigon River rise remain below 6% and 45cm, respectively, the Baseline Strategy satisfies objectives in almost the full range of population (up to 19.1 million) and poverty rate (up to 25%) that we consider in this study. Therefore, we focus our attention on rainfall intensity and Saigon River levels in assessing robustness.

Figure 5.4 plots the range of rainfall intensity increase (6%, on the horizontal axis) and Saigon River levels (45 cm, on the vertical axis) under which the Baseline Strategy meets its objectives. We use this visualization to compare these conditions to the best available scientific evidence about future rainfall intensity and Saigon River levels. The IPCC Special Report on Extreme Events (IPCC 2012) suggests that future rainfall intensity could increase by as much as 35
time, most time use studies focus only on primary activities, ignoring the fact that individuals often perform two or more tasks at the same time. This implies that the time allocated to activities such as childcare and housework which are also performed as secondary activities is underestimated and might not present an accurate description of time use (Floro and Miles, 2003; Suh, 2016).

This paper uses data for 19 countries, including 14 stand-alone time use surveys and 5 household surveys with time use modules collected between 2006 and 2014 (Appendix Table A1). Our sample includes 7 countries from the Latin America and the Caribbean region (Argentina, Colombia, Ecuador, Guatemala, Mexico, Peru and Uruguay), 3 countries from the Europe and Central Asia region (Albania, Moldova and Turkey), 2 countries from the East Asia and Pacific region (Mongolia and Timor-Leste), 2 countries from the Middle East and North Africa region (Iraq and West Bank and Gaza), 2 countries from South Asia (Bangladesh and Pakistan), two from Sub-Saharan Africa (Ghana and South Africa), and 1 from North America (USA).

Instruments and modes of data collection vary widely across countries. Data collection for 10 of the 14 stand-alone time use surveys is based on diaries, whereas the remaining 4 employ stylized questions. All the time use modules embedded in household surveys employ stylized questions. Depending on the country, the reference population ranges from 7 years and above (Guatemala) 7
in Section 4.2 and Appendix 2) provides information on household expenditures on various types of school fees faced by migrants and local residents in 15 cities across China. Figure 1 graphs the distribution of mean (total) school fees paid by migrant households, which ranges from 1,100 yuan in Bengbu (a city with a dominant food industry in the Northern Anhui province) to more than 4,500 yuan in Shenzhen (the fastest growing migrant receiving city in the South).<sup>7</sup> School fees as a share of migrant households’ consumption range from 4 percent in Hangzhou to 25 percent in Shenzhen, and represent, on average, 10 percent of migrant household consumption. For local households (i.e., urban residents with a _hukou_ ), school fees represent on average 11 percent of their consumption. School fees can be further broken down into different components, with tuition fees representing 40 percent and 31 percent of total school fees for migrants and urban residents respectively.

Figure 2 plots the shares of migrant households that bring school-age children with them against the median school fees at the city level. These two variables show a negative relationship, with a correlation coefficient of -0.28.<sup>8</sup> Tuition fees in rural areas were formally abolished by the central government in 2006, thus the urban school fees paid by migrant households represented additional education expenses to their budget (which they would not have to pay in rural areas). The central government announced the abolition of these fees in urban areas as well after 2008 (State Council, 2008), but in practice, migrant households still often have to pay various “hidden” school fees (Li, 2013; Lu and Zhou, 2013). Using the RUMiC survey, we estimate that migrant households received a school fee reduction of approximately 20 percent, but not 100 percent, in 2008 (Appendix 3, Table 3.4).<sup>9</sup
figure reveals that UP women devote a higher proportion of their time to “other paid jobs” than non-UP women and substantially less of their working time to own livestock rearing, and other household businesses (23%) than non-UP primary women (58%). The relative time allocation for UP men resembles that of UP women; however, UP primary men are more than twice as likely to be engaged in income-generating activities compared to UP women (69% vs. 31%), and conditional on working, primary men work almost five times more hours than primary women, accounting for 14 vs. 3 full-time-day equivalents (not shown).<sup>10</sup>

> 9 For non-UP households these data were only collected at follow-up and reported by the primary woman. We use data on control UP households (excluding treated UP households) and non-UP at follow-up to describe the labor market characteristics and analyze differences across socioeconomic conditions in absence of the intervention (UP control vs. nonUP) as well as across gender (UP men vs. UP women in the control group). Across each productive activity inside and outside the household, we asked for the number days worked, hours per day worked, and corresponding earnings. 10 A full-time-day equivalent is defined as 8 hours of work

8
– <u>Figure 7 Demand for redistribution and Gini index, 2009</u> Source: own elaboration based on ISSP Social Inequality data set; Gini indices from Bussolo et al. (2018) and Milanovic (2018) Note: Net demand for redistribution is equal to the percentage of people strongly agreeing with the statement “it is the responsibility of the government to reduce income differences between people with high incomes and those with low incomes” minus the percentage strongly disagreeing with that statement. National weights used. Gini index estimated on per capita household income.

While demand for redistribution seems to be uncorrelated to objective inequality, when comparing it to perceptions of equality the situation is completely different. As shown in Figure 8, the correlation between demand for redistribution and perceptions of equality is striking. The more individuals perceive their society to be equal, the less they express agreement with redistribution being a government responsibility. This evidence suggests that demand for redistribution is tightly linked to how individuals _perceive_ their society to be, rather than what their society actually _is_ , at least when using a common, crosscountry consistent measure, i.e., the Gini index.

20
Next we decompose job creation into entry and expansion and job destruction into exit and contraction: where I is an indicator variable equal to one if the expression in the brackets holds and zero otherwise, and _git_  2 denotes an entrant.

where I is an indicator variable equal to one if the expression in the brackets holds and zero otherwise, and _git_  2 denotes an exit.

We commence with an analysis of firm dynamics in Cote d‟Ivoire over the sample period (Figure 4). Entry rates average 12.4% over the period, which is somewhat higher than the 8% average entry rates calculated by Klapper et al. (2010) for a broad sample of low- and middleincome countries based on data from the World Bank Entrepreneurship Survey. However, the high degree of volatility associated with Cote d‟Ivoire‟s entry rates suggests a challenging business environment. Similarly, we find an average turnover rate of 24% with a high degree of volatility. These findings are consistent with Klapper et al. (2006) and Bartelsman et al. (2009) who find a (contemporaneous) relationship between entry rates and the business environment. We also examine one-year survival rates of firms in Cote d‟Ivoire (Panel B) and discover that on average, 83% of firms survive until age 2.

Next, in Figure 5, we summarize employment dynamics at new and exited firms and jobs created and lost at incumbent firms. Panel A shows that on average, the job destruction rate at exiting firms is 7.3% versus 8.0% due to firings at incumbent firms. In comparison, the job creation rate at new firms is 8.0% versus 6.7% due to hiring at incumbent firms. Panel B shows the annual average job creation and destruction rates decomposed by source. Contrary to the findings in Panel A, hiring and firings at incumbent firms has a much stronger impact on net employment.

14
confirm that worse performing public provincial banks are more likely to be privatized, all other things being equal, than better performing banks. The coefficient on net worth over liabilities is negative, indicating that banks with higher net worth were less likely to be privatized. The coefficient on the percent of loans that are not overdue is also negative. This indicates bank's with better loan portfolios were less likely to have been privatized. The spline, which allows the coefficient to take different values after definitions concerning loan classification and reserve requirements changed, is statistically significant with a positive sign.<sup>23</sup> This might indicate that quality of the portfolio became more important after reserve requirements were tightened.<sup>24</sup> Stricter reserve requirements after 1994 might have increased the costs that public provincial banks with poor loan portfolios imposed upon the provinces, increasing the likelihood of privatization. Banks with a high percentage of loans to the public sector were more likely to be privatized than those with lower percentages of loans to the public sector. This variable is weakly negatively correlated with the other performance variables - banks with a high percentage of loans to the public sector tend to have lower net worth (correlation of -0.08 in June 1992) and less "normal" loans (-0.27 in June 1992). One possible reason for this might be that banks with a large public sector loan portfolio made more politically motivated loans. The change in net worth over liabilities is included to test whether privatization is more, or less, likely when the performance of the bank is declining. Although the negative coefficient indicates that privatization is less likely whether Provincia de Buenos Aires is included or not. Table 6 shows results with the Banco de Provincia de Buenos Aires omitted.

22 The independent variables in this model change, at most, every six months. Government deficit data is only available on a yearly basis, bank quality variables are available for June and December of each year.

23 See
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**Blau, Francine D., Marianne A. Ferber, and Anne E. Winkler.** “The economics of women, men and work.” _Prentice-Hall Englewood Cliffs, NJ_ (2013).

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**CEDLAS, and the World Bank.** “Socio-economic database for Latin America and the Caribbean _.” Centro de Estudios Distributivos, Laborales y Sociales,_ Facultad de Ciencias Economicas, Universidad Nacional de La Plata, La Plata, Argentina; _the World Bank,_ Washington DC (2019).

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33
remains significant debate about potential backfire effects of making misinformation claims more salient and memorable (APA, 2023; Ecker et al., 2022; Nan et al., 2022). In addition, effectiveness of accuracy primes may be limited by challenges in reaching all platforms where people are exposed to misinformation at scale (Roozenbeek & van der Linden, 2022; Traberg et al., 2022; van Bavel et al., 2021).

While there is a growing body of evidence supporting the effectiveness of inoculation against misinformation, this research remains largely limited to Western, educated, industrialized, rich, and democratic (WEIRD) societies (Henrich et al., 2010; Nan et al., 2022) with a few notable exceptions (e.g., Arechar et al., 2023; Athey et al., 2022; Bilo-Thomas et al., 2021; Harjani et al., 2023). However, many threats from misinformation are found in other regions, including the Middle East. For example, a survey of Jordanian Arab residents found that over 60% believed that COVID-19 is a man-made virus, and about 25% believed that the COVID-19 vaccine is a way to implant microchips into people and that vaccines cause infertility, exacerbating vaccine hesitancy (Sallam et al., 2020, 2021). Such misinformation has been circulated extensively through social media (Al-Jalabneh & Safori, 2020; Alkhwaldeh & Emam, 2020; Habes et al., 2023), yet solutions for this cultural context are underexplored.

To address this knowledge gap, we test the effectiveness of a chatbot-based inoculation game with comprehensive and brief versions (i.e., active inoculation), infographics (i.e., passive inoculation), and a placebo compared to no inoculation through a randomized experiment delivered by WhatsApp in Jordan ( _N_ = 2,851). Specifically, participants learn common misinformation techniques through chatbot-based games or infographics. Then, we measure whether participants can accurately discern headlines using misinformation tactics and their 4