rafmacalaba/data-use-mentions-extended huggingface dataset:
14 (growth) is over one that is larger than the elasticity from our benchmark specification. The right hand-side panel in Figure 11 shows the impulse response from a tax shock using the narrative tax shock from Romer and Romer (2010). While Romer and Romer (2010) find that tax news shocks have a strong effect on US growth, we find that the effect on AE growth overall is negative but not statistically significant. That is perhaps due to the shorter sample period used in this paper but also perhaps because when considering AE as a whole a tax hike in the US may also lead to an increase in capital flows to AE therefore counterbalancing the negative growth effect from higher tax in the US. That said, the impulse response from a US tax news shocks show a negative and statistically significant response suggesting that the spillover from US tax news shock to EM is large. The elasticity of the spillover from US to EM is also above one.

Thus far, we have validated only one of our main results that is the spillover from AE/US spillover to EMs are large using a different identification strategy. To also explore whether the other direction of the causality running from EMs to AEs is much smaller we use arguably exogenous variation from natural disaster shocks measured as the damage from large natural disasters (larger than one billion USD) in the relevant group over the total group GDP. Natural disasters have significant consequences. EM-DAT report that the direct economic damage from natural disasters between 1991–2015 is estimated at around $2.5 trillion and led to 1.75 million deaths. In theory, the impact of natural disasters on GDP is unclear (Strömberg, 2007). On the one hand, the loss of productive physical and human capital may reduce GDP. On the other hand, the disaster may provide a positive contribution to measured GDP as reconstruction efforts and humanitarian aid. The net effect of natural disaster is thus an empirical matter.

Impulse responses presented in Figure 12 confirm that a natural disaster shock
**Table 10: Identifying the $3.20 poor based on covariates of poverty**

_<u>(Pooled data across countries with equal weights for each country)</u>_

|**Category**<br>**(1)**|**Per capita**<br>**poor only**<br>**(2)**|**Root N**<br>**poor only**<br>**(3)**|**Diff.**<br>**p-value**<br>**(4)**|**Per capita**<br>**poor**<br>**(5)**|**Root N**<br>**poor**<br>**(6)**|**Diff.**<br>**p-value**<br>**(7)**|**Obs**<br>**(8)**|
|---|---|---|---|---|---|---|---|
|Years of schooling|0.013***|-0.112***|0|-0.324***|-0.449***|0|683,533|
|Asset index|-0.020***|-0.044***|0.0008|-0.667***|-0.691***|0.0008|431,436|
|Asset ownership|-0.010***|-0.026***|0|-0.187***|-0.204
global studies have examined how this distribution of flood exposure differs between rich and poor. Kim (2012) assesses these dynamics at the country‐level, and finds that poor countries tend to be more exposed to natural disasters, including floods, compared to rich countries. More recently, (Winsemius et al. 2015) examined whether poor people _within countries_ are more exposed to flood risk, and found that this was the case for 60% of the 52 countries sampled.

Within Vietnam, studies suggest that floods significantly impact poverty, both quantitatively at the national level using household survey data (Bui et al. 2014) and qualitatively through focus group interviews at the local level in Ho Chi Minh City (World Bank and Australian AID 2014). One study within Vietnam examines the exposure of poor and non‐poor people to floods and found that a disproportionate number of poor people live in highly‐flooded areas of the Mekong Delta (Nguyen 2011).

At a more local scale and especially within cities, land and housing markets often push poorer people to settle in riskier areas. Where markets factor in hazard risks, housing is cheaper where risk is higher (Husby and Hofkes 2015). And, because poorer people have fewer financial resources to spend on housing and a generally lower willingness and ability to pay for safety, they are more likely to live in at‐risk areas (Lall and Deichmann 2012; Fay 2005; Hallegatte et al. 2016).

Empirically, this higher exposure to flood risk for poor urban dwellers is found in about 75% of the countries examined by (Winsemius et al. 2015), and also when using high‐resolution data on household location and flood hazards in Mumbai, India (Patankar 2015). This high exposure of the urban poor to floods has severe implications on the health of children and economic outcomes of adults, as evidenced in HCMC (World Bank and Australian AID 2014).

3
Brazil, they attend schools with lower enrollment in Roraima and Amazonas. This partly may reflect the fact that on average schools in Roraima and Amazonas are bigger than the average school in Brazil. Another interesting finding is that higher proportion of Venezuelans (68%) are attending classes that are below the grades consistent with their age than the Brazilian cohort (53%). This phenomenon may discourage students from attending schools, that may lead to inappropriate behavior like bullying, which will exaggerate the challenges that they already face in integrating. However, the average age of Venezuelans is lower since more Venezuelans are enrolled in grades below 4. About 20% of Venezuelans enrolled in school are enrolled in grade 1. Figure 16 shows that the grade distribution of Venezuelans are skewed to the left compared to the grade distribution of Brazilians.

Overall, descriptive statistics reveal that being demoted to lower class and shortages in having Spanish speaking teachers are major obstacles for Venezuelans to access education and Brazilian government and the international organizations are in the right direction to train teachers and school sta↵s Spanish language (Selee & Bolter, 2020) and o↵er Portuguese language courses to Venezuelan students. At the same time, resources needs to be diverted to increase capacity of schools. Another factor that is not observed in the data is the knowledge of Venezuelans about their rights and low attendance of Venezuelan in school may reflect the lack of knowledge about the education system in Brazil and constraints in accessing equivalence certificates. So, facilitation of credentials certification and provision of information on how to enroll and which documents are needed to enroll may promote more Venezuelans to access education.

# **4.2.2 Labor Market**

As of December 2019, about 29,000 Venezuelans are in RAIS, with about 19,000 employed, compared to 47 million Brazilians.Table 3 shows the summary statistics of the characteristics of Venezuelans and Brazilians, who are employed in the formal sector and represented in the RAIS dataset. The di↵erences in averages are posted
data. We estimate regressions (5) and (6) with dummies equal to 1 if worker _i_ changed job in year _t_ . We carry out similar analysis for the likelihood of job turnover in manufacturing and services. Table 11 shows that women experience higher job turnover in manufacturing but lower job turnover in services and overall. Notably, a relatively low fraction of Indonesians is employed in manufacturing: about 10 percent of the full SAKERNAS sample. However, the availability of preschools in the district is significantly associated with a reduction in women’s probability of changing manufacturing jobs within a year.

18
.081|0.083|0.084|
|higher (2015)|[0.189]|[0.182]|[0.220]|[0.208]|[0.209]|
|Firm age (2018)|12.577|13.509|12.476|12.983|11.185|
||[9.984]|[11.078]|[9.228]|[8.610]|[8.557]|
|Commerce sector|0.482|0.48|0.447|0.497|0.427|
||[0.501]|[0.501]|[0.499]|[0.501]|[0.496]|
|Service sector|0.387|0.416|0.412|0.339|0.416|
||[0.488]|[0.494]|[0.494]|[0.475]|[0.494]|
|Starting capital (USD)|9771.951<br>[20061.252]|7140.508<br>[13389.208]|10526.56<br>[42617.959]|9947.013<br>[22146.332]|9573.12<br>[20069.107]|
|N|168|173|170|177|178|

Notes: This table shows baseline summary statistics using administrative data from SEBRAE-PR, for 2017 and 2018, and RAIS, for 2015, the latest available year at the start of the study. Starting capital comes from the Federal Tax Authority (RFB). Column 1 displays means and standard deviations for the control group. Columns 2 through 5 display means and standard deviations for each treatment group. Material denotes receipt of written or recorded material on business practices,
<!-- Start of picture text --> Stunting gap<br>40%<br>35%<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>1995 2000 2005 2010<br>East Asia and Pacific Europe and Central Asia<br>Latin America & Carribean Middle East and North Africa<br>South Asia Sub‐Saharan Africa<br><!-- End of picture text --> _<mark>Source: Authors’ calculations using DHS and MICS. Results need to be interpreted with caution as the population coverage for some regions and years are below 50%.</mark>_ A comparison of malnutrition across regions suggests that the stunting gap is telling a slightly different story from the stunting headcount for some regions. While the trends of the headcount and gap are the same for each particular region, the ranks of some regions differ depending on the type of measurement being used, i.e. headcount or gap rates. For example, according to the headcount measures in 2005 and 2010, EAP has a greater level of malnutrition than MNA. In contrast, according to the gap measures for the same period, MNA has a greater level of malnutrition than EAP.

Similarly, according to the headcount measures, MNA and LAC have similar headcount rates in 2005, about 24% each, which is significantly higher than that of ECA (17%). However, in terms of the gap measure, MNA has a significantly higher gap rate (13%) than LAC (10%), and LAC is actually closer to ECA (8%). Similarly, for 2010, the headcount measure suggests a sizeable difference between LAC (19%) and ECA (15%). However, the gap measures suggest that both are 19
categorized themselves as having benefited from demobilization allowances. Indeed, many people helped the rebels during the war and they may have wrongly categorized themselves as demobilized ex-combatants. Similarly, some respondents may have claimed to be demobilized in expectation of rewards in exchange for their participation to the survey.

The short-run direct impact of the DDR program is measured by the dummies _Ri_<sup>_FNL_</sup> and _Di_<sup>_FNL_</sup> . In particular, the coefficient β1 measures the impact of having a FNL _returnee_ in the household who did not benefit from reinsertion allowances. The coefficient β2 measures the marginal impact of having benefited from the FNL reinsertion allowance.<sup>19</sup> We therefore compare the _returnee_ households that have received the grant (demobilized) to the _returnee_ households that reported having a member who was part of the FNL but did not receive reinsertion grants (hypothesis 1).

The variables _R_<sup>_CNDD_</sup> _i_<sup>+</sup> and _D_<sup>_CNDD_</sup> _i_<sup>+</sup> capture the long-run evolution of outcomes for CNDD+ households (hypothesis 2). To interpret the coefficients associated with these variables, we need to carefully consider the timing of the demobilization of CNDD+ combatants, which started before the first survey at the end of 2004. Thus, by the time of the QUIBB survey, which is used as our baseline, these ex-combatants had already benefited from reinsertion allowances. Then, they benefited from the in-kind reintegration grants between both surveys. Hence, we are not able to measure the long-term impact of the program on their economic outcomes. What we can assess however, is the evolution of their economic outcomes between the two surveys.

We explain this argument formally. In an ideal scenario in which we would have had data before the peace agreement, let σ denote the short-run impact of the demobilization program, that is, the one-year impact between the reception of
A. Sanghi and D. Johnson (section 4.2.2).

**Chinese companies hire fewer female employees** Chinese companies hire few female employees: women only represent five percent of total employees for Chinese companies on average versus 29 percent for all companies in Kenya. Chinese companies in the services sector have 19 percent female employees compared to 31.8 percent female employees in the services sector overall. In manufacturing, women are only three percent of employees in Chinese companies, but they are 22.7 percent of full-time employees overall (SACE 2014; World Bank Enterprise Survey 2013). 15 percent of the workforce in private companies is female, but only two percent of the workforce is female in state-owned enterprises. Hiring more female employees has a direct impact on poverty because women spend more on health, education, and household durables. When women work, they also tend to increase household savings (Lawson _et al_ 2009).

### **3.2.5 Chinese firms face some competition from the informal sector**

93 percent of Chinese companies in the sample have registered their business. 24 percent of companies reported competition from the informal sector, and informal competition was more likely in services (35 percent) than in manufacturing (13 percent) (SACE 2014).

### **3.2.6 Chinese companies less likely to take credit line**

Chinese firms are less likely to obtain a loan or credit line. Only 26 percent of Chinese companies obtained a loan; 36 percent of all companies in Kenya had a loan. (World Bank Enterprise Survey 2013). Potential borrowers are dissuaded by unfavorable interest rates and complex application procedures. 51 percent of Chinese companies reported not needing a loan because they had sufficient capital (SACE 2014). State owned enterprises had financing from headquarters in China, and private companies accumulated savings and loans from family and friends. Manufacturing and construction companies take as much credit as other companies in Kenya, but only 16
. Following Bruhn and McKenzie (2009), we implemented a stratified randomization protocol by balancing treatment assignment according to the following baseline characteristics of a woman: village of residence, current use of FP, years of schooling, desire for another child, and the number of peers mentioned by her as part of our social networks module. Tables 1 and A.1 show that treatment assignment was balanced across a range of baseline characteristics of the woman including her age, religion, number of children, whether she had at least one son, marital duration, co-residence with the mother-inlaw, number of close peers in the village, contraceptive use, beliefs about her mother-in-law’s opposition to FP use, household asset score,<sup>16</sup> and whether her last visit to an FP clinic was with her husband or mother-in-law.<sup>17</sup> We observe some differences across groups in the mo-

> 15We excluded married women who had not begun childbearing from our study due to the presence of cultural norms that pressurize newly married couples to prove their fertility as soon as possible after marriage before considering FP (Jejeebhoy et al., 2014).

> 16We construct the household asset score using a principal component analysis with the following variables: source of drinking water, type of toilet facility, floor material, roof material, exterior wall material, type of fuel used for cooking, ownership of animals, number of rooms in the household used to sleep.

> 17 Randomization was also balanced across several other variables, such as the Below Poverty Line (BPL) status of and the amount of land owned by the household, whether the woman gave birth last year, her husband’s migration status, the woman’s decision-making ability, the distance from a woman’s home to the ADC and to the closest clinic located in the study area.

6
for applying algorithms to those data at regional and global scales, there has been a recent upsurge of research interest in urban delineation. This has led to the development and application of several new algorithms for the more accurate and consistent delineation of urban areas across countries (see, _inter alia_ , Uchida and Nelson 2009; Dijkstra and Poelman 2014; Zhou, Hubacek, and Roberts 2015; Ch, Martin, and Vargas 2021; Dijkstra et al. 2021).

One shortcoming that most of these algorithms share, however, is that they rely on one or more, essentially arbitrary, thresholds to delineate an urban area. For example, the widely used “degree of urbanization” approach (Dijkstra and Poelman 2014; Dijkstra et al. 2021) relies on two such thresholds to identify urban settlements – namely, an overall population threshold for the settlement and a population density threshold for each 1 km<sup>2</sup> population grid cell that belongs to it.<sup>8, 9</sup> To overcome this shortcoming, we instead apply a modified version of the de Bellefon et al. (2021) “dartboard” algorithm. This algorithm has the advantage that it grounds the identification and delineation of urban areas in a statistical approach which avoids the _a priori_ specification of arbitrary, and globally uniform, population (density) thresholds. In essence, and after performing smoothing, the algorithm identifies urban areas as clusters of statistically significant peaks of population or built-up density. This is done by comparing the actual spatial distribution of population or built-up density with the counterfactual spatial distributions that would result if population / built-up density were instead randomly distributed across a country’s potentially inhabitable (or “livable”) area.

Whereas de Bellefon et al. originally applied their algorithm to France using detailed data on every building footprint in the country as input, we instead apply the algorithm on a much larger spatial scale – i.e., to the entire SSA region – using constrained WorldPop data for
subsidy used it for 4 or fewer months, and 68 percent used it for 6 months or more.

Table 3 examines the correlates of the take-up decision, building on early analysis presented in de Mel et al. (2010) which only had take-up data through to November 2009. We conduct probits of the probability of using the wage subsidy voucher for the wage subsidy only treatment group, and for all treatment groups offered the wage subsidy. The first column examines firm characteristics, the second owner characteristics, and the third both together. We see that take-up rates are lower in Colombo than in the southern cities of Galle and Matara, with Kandy in between. One possible reason is that wage rates are higher in Colombo, so the flat-rate wage subsidy may cover a lower proportion of the worker’s wage there. We find that firm characteristics have very little predictive

> 7 Source: World Development Indicators, World Bank.

13
with demand for inventory and once without. The calibrated demand and supply parameters are used to numerically calculate the effect of each of the different shocks on the observed price in a given year.

**Table 3. Range of elasticities contained in the literature cited in FAPRI database**

|||**Supp**|**ly (1) **|**Dema**|**nd(2) **|**Incom**|**e(3) **|
|---|---|---|---|---|---|---|---|
|**Commodity **|**Region**|**min**|**max**|**min**|**max**|**min**|**max**|
|**Corn**|**Argentina **|0.65|0.75|-0.4|-0.3|0.35|0.45|
||**Brazil**|0.37|0.47|-0.4|-0.1|0.35|0.45|
||**China**|0.08|0.18|-0.14|-0.6|0.75|1|
||**EU**|0.01|0.13|-0.44|-0.24|0.1|0.2|
||**India**|0.16|0.26|-0.28|-0.22|0.75|1|
||**U.S.**|0.45|0.55|-0.24|-0.1|
.18|

The O&M index was constructed completely based on data collected through technical audits and aims to capture the operational sustainability of the schemes. Unlike the community managed schemes, the majority of the KWA schemes carried out yearly water quality testing. 64 percent of KWA schemes reported carrying out yearly water quality testing compared to only 21 percent of Jalanidhi schemes and 13 percent of other community managed schemes. However, Jalanidhi schemes on average have fewer breakdowns and fewer days of water outages compared to either KWA schemes or the other community managed schemes. Also fewer percentage of community managed schemes (both Jalanidhi and other community schemes) reported facing water shortage anytime of the year compared to supply driven KWA schemes.

**Table** **<u>6: Operation and Maintenance Index</u>**

|**Indicators**|**Jalan**<br>**Sche**<br>**Mean**|**idhi**<br>**mes**<br>**SD**|**KWA**<br>**Managed**<br>**Mean**|**Built &**<br>**Schemes**<br>**SD**|**Other Co**<br>**Managed**<br>**Mean**|**mmunity**<br>**Schemes**<br>**SD**|
|---|---|---|---|---|---|---|
|Yearly Analysis of Water Quality|0.21|0.41|0.64|0.49|0.13|0.34|
|Number of Breakdowns Last Year|3.16|2.34|5.17|4.87|5.46|
56.5|56.2|‐0.7|‐0.5|
|Russia|120.0|119.5|189.5|188.1|‐0.5|‐0.7|
|Kazakhstan|96.7|96.2|122.8|122.4|‐0.4|‐0.3|
|Indonesia|36.0|35.8|39.5|39.5|‐0.4|0.0|
|Paraguay|64.3|64.1|92.5|92.3|‐0.4|‐0.1|
|Pakistan|36.2|36.1|42.0|41.9|‐0.4|‐0.2|
|Tunisia|88.1|87.8|104.5|104.1|‐0.3|‐0.3|
|West Bank and Gaza|133.2|132.8|149.1|148.6|‐0.3|‐0.4|
|Peru|69.1|68.9|101.4|101.6|‐0.2|0.2|
|Spain|431.9|431.3|354.2|352.8|‐0.1|‐0.4|
|Iraq|57.1|57.0|58.1|58.0|‐0.1|‐0.2|
|Armenia|60.5|60.6|61.9|61.8|0.1|‐0.1|

Source: Microdata provided by regional focal points, members of the Global Poverty Working Group. Authors’ calculations. Here and thereafter, the POVCAL<sup>11</sup> command was used to get grouped data estimates. Notes: Grouped
# **Table 2: Financial Institution Probability of Default and Exposure to the Sovereign in EMDEs**

|Dependent variable: Median Probability|of Default Fin|ancial Instituti|ons in 60 mo|nths (monthly|data at count|ry level)||
|---|---|---|---|---|---|---|---|
|VARIABLES|(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Claims on Total Gov. and PNFEs (% GDP)|0.161***|0.161***|||0.0718|0.0817|‐0.0233|
||(0.0439)|(0.0440)|||(0.0533)|(0.0511)|(0.164)|
|Sovereign EMBI spread (bps)|||0.125**|0.0989*|0.124**|0.0951|0.0854|
||||(0.0556)|(0.0560)|(0.0590)|(0.0589)|(0.0915)|
|Claims on Tot. Gov.*Sov.EMBI spread|||||||0.0161<br>(0.0284)|
|US VIX Index||0.264***||0.228***||0.231***||
|||(0.0214)||(0.0368)||(0.0383)||
|Constant|0.410***|‐0.224|0.151|‐0.251|‐0.00133|‐0.421|0.225|
to measure governance as divided into two classes: rules-based measures (e.g. does a country have anti-corruption legislation?) and outcome-based measures (e.g. is the anticorruption legislation enforced?). The Worldwide Governance Indicators (WGI) project<sup>3</sup> gathers both rules-based and outcomes-based measures from multiple sources (310 variables from 30 different sources in the latest iteration); clusters them into six dimension of governance (voice and accountability; government effectiveness; control of corruption; regulatory quality; rule of law; and political stability and absence of violence); and then aggregates and normalizes them. Using this wide range of data sources, it is argued, maximizes the use of information and reduces bias.

The work on governance and development is an ongoing process of refinement, new data collection, testing hypotheses and learning. The creators of these various indicators regularly warn against misusing their governance indices; however the measures are nonetheless consistently misused by investors, journalists, and academics. Some of the most common mistakes are to interpret cross-country differences as if there were no measurement error or to judge a country‟s change in its ranking over time as significant when the underlying measures are only relative (Arndt and Oman 2006).

Even when used correctly, the implications for public policy are not straightforward. Improvements in a governance mechanism can be both cause and consequence (i.e. reverse causality), the result of other changes that simultaneously improve country performance, or necessary but not sufficient conditions for improving country performance. Studies of governance that utilize cross-country data, in particular, are subject to an ongoing and vigorous debate and scrutiny over data, methods, and interpretation.

Three lessons can be drawn from the international development literature on governance. First, governance can be measured. While many measures suffer drawbacks, advances in how measurements are made and how they are used (e.g. addressing bias, reporting errors) show that this is still a fruitful endeavor. Second, governance