rafmacalaba/data-use-mentions-extended huggingface dataset:
of papers have shown that state-owned banks could be useful in smoothing the lending cycle (Micco and Panizza, 2006, , Brei and Schclarek, 2013, Cull and Martinez-Peria, 2013, Coleman and Feler, 2015, Bertay et al., 2015, Duprey 2015, Allen et al., 2017, Ture, 2021, and Panizza, 2022). Additional research that used more recent data and moved beyond pure cross-sectional regressions did not find a significant correlation between government ownership of banks and economic growth or financial depth (Levy Yeyati et al., 2007, and Panizza, 2022). Argentieri (2022) studies the effect of bank privatization in Brazil, and finds that privatized banks became more profitable, that they also contracted their credit supply, and that this credit contraction had a negative effect on economic activity (see also Sanches et al., 2018).

One problem with the existing literature is that country or industry-level data do not allow controlling for unobservable factors that are jointly correlated with state ownership and the outcomes of interest. Given that the cross-country and over time distribution of government-owned banks is not random, the correlation between the observed level of state-ownership and each of financial and economic growth could either be driven by political failures (in this case, a larger share of government-owned banks causes worse economic outcomes) or by the presence of market failures (in this case, economic outcomes cause the share of stateowned banks) or by the joint presence of these different types of failures (see Rodrik, 2012, for a formal model). Models that include country-fixed effects can partly address this problem, but only if the source of endogeneity only varied across countries and is time-invariant within countries.

The literature that uses bank-level data can partly address endogeneity concerns by looking at what different types of banks do while controlling for country-year fixed effects. However, this strand of literature can tell us what banks do (for instance if they lend more or less during recessions), but does not tell us anything about the impact
scores. Readiness measures a country’s ability to leverage investments and convert them into adaptation actions, looking at three components: economic readiness, governance readiness, and social readiness. For the calculation, each sub‐indicator of vulnerability and resilience is scaled to give a score between 0 and 1, with all components weighted equally. Results from ND‐GAIN can be viewed online.<sup>15</sup>

**OECD –** **<u>Guidelines for Resilience Systems Analysis.</u>** The OECD has completed a “how‐to‐guide” for a Resilience Analysis Tool, which has been piloted in 3 countries, and allows users to design roadmaps for boosting resilience in a system, community or state. Indicators are based on the status of assets identified for resilience, with type/status of assets context‐specific (OECD 2014). The relevant indicators depend on the resilience strategy followed in a given country. The process is based on a consultation process in the country (initial pilot took 5 weeks – 2 weeks preparation and 3 weeks in‐country; consultations to follow will likely take less time). The methodology was first developed through a pilot in the Democratic Republic of Congo, and has since also been piloted in Lebanon and Somalia. The results from the three pilot countries can be viewed online.<sup>16</sup>

**GIZ – Two indicator‐based approaches.** Germany’s Development Agency, the GIZ, has developed a Vulnerability Assessment Sourcebook, which provides guidelines for developing vulnerability indexes and for using this index to measure changes over time (GIZ 2014). The GIZ is also currently developing a methodology on Climate Resilience Indicators, which aims to assess resilience at country‐level based on globally available data complemented by country‐specific data. An example of a Climate Resilience

> 13 http://www.wcdrr.org/uploads/Sendai_Framework_for_Disaster_Risk_Reduction_2015‐2030.pdf

> 14 http:
with NFHS as benchmark. A candidate reason for this discrepancy is the difference in instrument design (these indicators are not in the set of targeting variables). In the NFHS, access to water and toilet within the household premises are collected through a detailed list of options, eliciting specific types of water sources and toilet waste disposal technologies available to the household. The CPHS in contrast, collects this information through binary yes or no questions without distinguishing between sources or disposal methods.

Comparison of CPHS and NFHS in 2019 (restricted to 14 states where asset ownership and public service access data is presently available) serves as a validation, as the reweighting for this year does not include asset ownership or access to services as target variables (these indicators are not available in the PLFS-2019). The results in Panel (b) of Figure 3 confirms the bias correction that is achieved for these non-target variables.

The largest gap in asset ownership between the CPHS and NFHS 2019 is for households owning television sets (10 percentage point) and air conditioning units (6 percentage points). The reweighting procedure does however reduce the bias by a significant margin: without reweighting, households owning TV sets would be 24 percentage points higher in the CPHS.

_Education levels:_ Undereducated people are severely under-represented in the CPHS with only 2 percent of the 2018 adult population (ages 15 to 49 years) having not received a formal education. By comparison, the periodic labor force survey (PLFS) from the same year estimates that the share of adults without formal education is 17 percent. By 2019, adults without formal education are virtually eliminated from the CPHS sample, while the PLFS-2019 continues to estimate this share of the population at approximately 17 percent. Somanchi (2021) similarly observes that female illiteracy is estimated with a significant bias in the CPHS (in selected states the mean values from the CPHS-2019 are as much as 45 percentage points lower than what is observed in the NFHS-5).

Figure 4 compares adult education levels (ages 15 to 49)
for a perception of performance related to a local comparison group, we took a variable asking for the perceived performance compared to colleagues in 1989.

**Employment histories were constructed by taking advantage of the information collected on job market performance for each year between 1989 and 2006.** The second section contains an extensive set of questions about life events and labor market history since 1989 to 2006, year by year. Unemployment may have been underreported, since working at least one part of the year is enough to consider the year as employed. If more than one position was held during one year, they are all reported in different cells. This information is used to construct the labor history of the interviewed person, including time worked in the public or private sector and number of different jobs held in this time.  We restricted the information to the years when the interviewed person was between 18 and 65 years old.<sup>5</sup>

**We also took advantage of the information on labor histories to construct a measure of public employment for every year between 1989 and 2006.** Using the information on enterprise ownership, we were able to obtain the share of employment, excluding public administration, corresponding to the public sector. As can be expected, this share was considerably high in 1989 (with the exception of Turkey), and has fallen dramatically for most countries by 2006.

**The data at the national level was obtained from the World Development Indicators database (World Bank 2013)** . We used GDP per capita in real terms, Gini coefficients, and the ratio of the share of income the top 20% and the bottom 20%. In many cases the data for the years 1989 and 2006 was not available, so we replaced for the closest year available, with up to three years of difference.

# **4. Perceptions of Economic Mobility and Their Correlates**

**a) What are the perceptions of economic mobility observed in ECA?*
(Atuhumuza, et al., 2019). A similar multi-country mystery shopper study in Ghana, Mexico, and Peru found that actors posed as potential customers rarely received the cheapest product offer or the correct costs. The reason may be that financial provider staff are often paid more when customers accept more expensive products; that creates an incentive for staff to withhold information or even to actively mislead (Giné and Mazer, 2022).

Developing and enforcing clearer regulatory guidelines about disclosure and pricing transparency could help build trust in the financial system—another factor, as discussed, that makes unbanked adults wary of opening an account. When presented in a clear and transparent way, account holders can understand product information and use it to make product decisions that fit their needs, as shown in lab experiments in Mexico and Peru (Giné, et al., 2017).

Financial literacy training is another way to improve financial knowledge and encourage more responsible financial behaviors (Kaiser, et al., 2022). A meta-analysis of 76 randomized experiments finds that traditional classroom-based financial education, has, on average, improved financial knowledge and financial behavior (Kaiser, et al., 2022). In addition, financial training structured as learning-by-doing during financial account onboarding can lead to regular use of accounts and help users become savvier financial customers (Breza, et al., 2020; Lee et al., 2021). The evidence also points to the importance of real-time information to increase trust and usage—for example, through functionality that allows users to check balances using a card, phone, or the internet (Bachas, et al., 2021).

Beyond the direct steps that financial institutions can take to improve transparency and embed clarity in product design, regulators also have an opportunity to expand and innovate, to identify bad actors and enforce the rules. As noted throughout this paper, digital financial services have already transformed how people move and manage their money in every region of the world. Although millions of people have been able
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# **1. Introduction**

Can Twitter data be used to monitor attitudes towards COVID-19 vaccination? How do estimates of vaccination hesitancy rates derived from Twitter data compare to estimates obtained from more conventional survey data? Who are the most effective messengers on Twitter and what are the most effective messages used on either side of the discourse (supporters versus opponents of COVID vaccination)?

We investigate these questions in an application to the Middle East for which we have both conventional survey data on COVID vaccination attitudes and a database of tweets that feature the most prominent COVID hashtag in Arabic spanning the period from mid-December 2020 until mid-April 2021. To identify a given Twitter account’s attitude towards COVID vaccination (for all accounts observed in our database), we adopt the following approach. First, we identify the clusters of user accounts observed in our data. (Clusters are groups of users who are more prone to interacting with each other on Twitter. The resulting clusters, while purely identified by exploiting data on retweet behavior, tend be divided across geographic and ideological lines.) Second, we identify the 50-100 most influential accounts from each cluster. These influencers represent the central nodes in the network of Twitter accounts. Third, we manually inspect the Twitter feeds of these influencers for explicit references towards COVID vaccination that allows us to label them as either pro-vaccination or anti-vaccination (or neutral with respect to COVID vaccination). Fourth, and finally, we iteratively impute the vaccination attitude of other users in our database (i.e. who participated in the discourse on COVID vaccination on Twitter) based on their retweeting of users whom vaccination attitude has been established. We hereby rely on the assumption that a retweet without the addition of any comments is interpreted as an endorsement.

We find that COVID vaccination hesitancy on Twitter in the Middle East is estimated at just below 20 percent, which is of a comparable magnitude as hesitancy rates estimated from survey data, provided that the appropriate filters are applied. At first glance, Twitter-based estimates of hesitancy
FIGURE 3: DATA COLLECTED FROM PROCURING ENTITIES AND E-PROCUREMENT SYSTEM19 <!-- Start of picture text --> 2011-12 2012-13 2013-14 2014-15 2015-16 2016-17 2017-18<br>Number of Manual<br>3953<br>Tenders in Dataset 3737<br>1952<br>424<br>248<br>5<br>Number of e- 87,912<br>procurement<br>Tenders in Dataset<br>45,488<br>32,024<br>17,810<br>8,045<br>15 491<br>Number of firms 56058<br>registered in the e-<br>GP system 40472<br>25996<br>17271<br>9698<br>285 1078<br><!-- End of picture text --> Source : Data collected from e-procurement system and procuring entities 39. The survey teams for manual tenders also conducted in-person interviews with about 600 procuring entity officials from February to June 2018. This additional survey served to capture district officials’ perceptions of the e-procurement system and to obtain measures of the costs associated with administering the procurement process manually vis-à-vis electronically. Each procuring entity office at the district level is headed by an executive engineer (XEN). In addition to the executive engineer, the procuring entity offices also have Sub-divisional Engineers (SDE), Assistant Engineers (AE) and Sub-Assistant Engineers (SAE), who work in conjunction with the XEN to dispose of the duties of the procuring entity office.

40. Furthermore, a survey of about 600 firms participating in public works contracts of the 3 agencies was done from June to August 2019, to capture firm transaction costs. To sample firms for the survey, the population of firms were stratified into 4 strata: (i) eGP winners (firms with revenue from eGP tenders at least 3 times more as compared to manual) – 175 such firms were selected; (ii) Manual winners (firms with revenue from
# **1. Introduction**

Sovereign spreads rose rapidly in emerging markets and developing economies (EMDEs) at the onset of the Covid19 pandemic, a period during which governments faced rising borrowing costs and fiscal expansion needs, albeit at varying degree across countries. We empirically investigate whether the existence of a fiscal rule was associated with lower or higher sovereign spreads in EMDEs during the recent Covid-19 pandemic shock.<sup>1</sup> In theory, the relation between the existence of a fiscal rule and sovereign spreads during economic downturns is ambiguous. On the one hand, fiscal rules could signal fiscal responsibility and serve as a commitment device to reassure financial markets’ confidence, lowering sovereign spreads. On the other hand, fiscal rules might exacerbate the negative shock by constraining the government’s flexibility to conduct countercyclical fiscal policy, rising even more sovereign spreads.<sup>2</sup> We first document trends in fiscal rule adoption prior to and throughout the recent pandemic using the IMF Fiscal Rule Dataset (IMF, 2022). We then empirically investigate whether fiscal rules are associated with lower spreads and whether this relationship held through the pandemic. Next, we distinguish between rules that were continually enforced throughout the pandemic, rules that were temporarily abandoned due to escape clause usage, and rules that were suspended due to discretionary fiscal policy. In each of the empirical analyses described above, we use daily data of sovereign spreads from countries included in the J.P. Morgan Emerging Markets Bond Index Global (EMBI Global). Our baseline sample includes 58 EMDEs and spans the period between January 2019 through the first five months of 2022.<sup>3</sup> Our tests include controls such as global, regional, and corporate factors, country-specific proxies of economic activity, and various policy-related variables.

We document three novel findings regarding the relationship between fiscal rules and sovereign spreads. First, we show that countries with fiscal rules in place faced lower sovereign spreads relative to countries without rules before and
## **5.4 Impacts in the follow-up survey round**

The follow-up survey round was collected in 2022, roughly 1.5 years after all HSP direct rental assistance had ended. At that point the only statistically significant and robust estimated effect of the program is related to child socio-emotional well-being: the standardized Strengths and Difficulties Questionnaire (SDQ) scale decreased by 0.56 standard deviation units in treated households (q-value=0.01). This is a large magnitude and indicates that there was a meaningful decline in child socio-emotional well-being (as reported by parents), an unintended adverse consequence. There are multiple potential explanations for the worsening of child socio-emotional well-being, including short-run (midline) food insecurity, greater COVID-19 exposure, household composition impacts, and changes to respondent time use, though the persistence of the negative effect on child well-being after those effects have dissipated is perhaps surprising. A further channel relates to the nature of social interactions with neighbors, discussed in the next section. Besides that effect, across the wide range of household outcome measures gathered (including in all pre-specified primary outcomes) there were no other statistically significant results that survived a multiple hypothesis testing adjustment.

# **6 Impacts on Neighbor Attitudes**

The 2022 survey of a representative sample of Jordanian neighbors captures the spillover impact of the refugee-targeted assistance program on Jordanians attitudes towards refugees at least one year after program implementation ended (detailed in Figure 2).

We focus first on the pre-specified primary outcomes (Table 7, Panel A). Perhaps unexpectedly, neighbors who live near treatment households experience a significant decline in the social attitude and perceptions index (effect -0.33 standard deviation units, q-value _<_ 0.08).

A closer examination reveals that this negative effect is primarily due to diminished social ties between Syrian refugees and their Jordanian neighbors (Table 7, Panel B): There is a notable de28
whether child labor statistics depend on to whom the survey questions are asked (Dillon et al. 2012, Dammert and Galdo 2013, Janzen 2018). While the ILO guidelines for survey design and measurement of children’s work suggest that the child should answer the labor module him or herself (ILO 2008), substantial variation across household surveys exist. For instance, and although different surveys might entail different definitions of child labor, the Statistical Information and Monitoring Programme on Child Labour (SIMPOC) stand-alone child labor surveys collect labor information from both children and proxies, while the <mark>Multiple Indicator Cluster Surveys (MICS)</mark> gather labor information from the most knowledgeable adult member of the household, and UNICEF MICS surveys direct questions to the mother or primary caretaker. Furthermore, policymakers and researchers substantially rely on country-level labor force surveys (LFS), which provide in most cases either self or proxy reporting measures of child labor but not both measures due to the costs and logistics of fieldwork. A comprehensive review of LFS shows, for instance, that in half of the national-level LFS up to 50% percent of the responses are provided by proxy informants (ILO 2018). In Africa, the setting of our survey design intervention, the rate of proxy respondents ranges from 34% in Nigeria to 99% in Mali for the 15 to 24 age group (Desiere and Costa 2019).<sup>1</sup> The effect of the type of respondent on child labor statistics is not clear a priori if both respondents recall different types of activities with different errors. Child-reported information may be more accurate than proxy responses if a child knows best how she allocates her time, while at the same time, recall of past activities may be cognitively burdensome if the child may not fully understand what “work” entails or how to use recall-count strategies to track her activities and hours. On the other hand, a proxy respondent may be familiar with the children’s activities depending on the frequency
the chemical products industry was very different from that of the entire manufacturing sector. There was a substantial drop during the 1991—1995 period because of a massive expansion of production capacity in the 1980s, which was not met with an increase in demand.<sup>17</sup> The net entry effect was also small, mostly due to the relatively lower entry and exit rates for the industry.

Productivity growth was much higher in the electronic and electrical equipment industry for most of the period examined. While the within effect was the most important factor in this industry’s productivity growth, the net-entry effect also made a sizable contribution. Moreover, the between effect played an important role in the 1991—1995 and 2001—2006 periods, accounting for about 20 percent of productivity growth. The productivity growth rate was also high in transportation. Similar to the other industries, the within effect explained most of the productivity growth, with a relatively small role on the part of net entry and between-plant reallocations.

# **3.5 Alternative Decomposition: Dynamic Olley** – **Pakes Method and Results**

Melitz and Polanec (2015) extend the Olley and Pakes (1996) productivity decomposition methods to include the contributions of entering and exiting firms to aggregate productivity growth. Using Slovenian manufacturing data, Melitz and Polanec (2015) showed that other decomposition methods, such as FHK, may overstate the contribution of entry and exit due to differences in the reference (or benchmark) productivity. Although we do not find substantial net-entry effects from the FHK decomposition method, we apply the Dynamic Olley Pakes decomposition method (DOPD) as an alternative method. Using a similar notation to the FHK in equation (2), the

> 17 The production capacity of petroleum and chemical products doubled in the 1980s, from 505,000 tons in the late 1970s to 1.16 million tons (in terms of ethylene production) in 1989.

17