rafmacalaba/data-use-mentions-extended huggingface dataset:
**Figure 2-2 Social expenditures as a share of GDP are higher than the sample average at 14.1 percent**

<!-- Start of picture text --> International comparability of government social expenditure<br>25%<br>West Bank and Gaza<br>(2017), 14.1%<br>20%<br>15%<br>10%<br>5%<br>0%<br>Country (CEQ assessment year of survey)<br>Ivory Coast (2015) Iran (2011) Uganda (2016) Indonesia (2012) Guatemala (2014) Albania (2015) Ethiopia Tanzania (2011) Iraq (2018) Togo (2015) Ghana (2012) Armenia (2011) Egypt (2015) Tunisia (2010) Mexico (2014) Colombia (2010) El Salvador (2011) Paraguay (2014) Ecuador (2011) El Salvador (2013) Romania (2016) Mexico (2010) Chile (2013) Honduras (2011) South Africa (2015) Georgia (2013) Croatia (2014) Venezuela (2013) Belarus (2015) Bolivia (2009) Russia (2010) Brazil (2009) Lesotho (2017) Argentina (2012) Botswana (2010)<br>Social expenditure as % of GDP (ranked lowest to highest)<br>West Bank and Gaza (2017)<br><!-- End of picture text --> Source: CEQ Data Center, Government Finance Statistics Manual West Bank and Gaza.

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tive statistics for men and panel B for women. Three main patterns are worth noticing. First, migrants registered in the RAMV census (which made them eligible for PEP) were older, more educated, had been in Colombia longer, and enjoyed better access to public services before migrating, compared to their counterparts who were not registered in the RAMV census and therefore ineligible for PEP. Second, migrant women surveyed in VenRePS were generally younger, had more children, and were more educated than their male counterparts. Third, migrants in the survey had at least the same education as Colombian natives, and those registered in the RAMV census were more educated than Colombian natives. In addition, these migrants were generally younger than natives.

# **V EMPIRICAL STRATEGY**

The fertility implications of regularization cannot be assessed by simply comparing households that were eligible for PEP to households that were not. As illustrated in Table A.1, the two sets of households differ in observable and unobservable characteristics potentially correlated to their fertility outcomes. For instance, migrants who were eligible for PEP were more educated than other migrants and natives. In addition, they might have differed with regard to unobservable traits. For example, migrants who were eligible for PEP could have been better-informed or more ambitious than their ineligible counterparts. Those differences could also explain gaps in fertility rates between the two groups.

To address this challenge, we leverage longitudinal data from VenRePS and estimate the fertility response to being eligible for PEP by comparing changes in fertility rates within the same household before and after the program was implemented. We observe household fertility rates at three points in time: at baseline on the day before the RAMV census (April 5, 2018) and post-treatment in two waves of VenRePS (2020 and 2021). Hence, we stack the data to evaluate the impacts of being eligible for PEP on the probability of having children of T years of age. Specifically, we estimate the following equation: 9
coastal zone, 71% of the urban households have access to electricity. Moreover, 88% of the rural households and 75% of the urban households are using dug wells for accessing drinking water, which, given a total of around 0.5 million functioning tube wells located in the coastal zone (based on DPHE data), equates to around 18 households per well. Moreover, 97% and 98% of the rural and urban households, respectively, have access to some form of sanitation, with 71% and 78% of rural and urban households having improved sanitation facilities.

An overview of the household allocation to assets and the roads per infrastructure type is shown in Figure 3, showing clear size differences in the service areas of different infrastructure assets. One can observe that access to educational facilities is very decentralized (median of 173 households per asset) given the large number of assets, while access to hospitals is very centralized (median 52,787 households per asset) with a relatively small number of main roads used to access these hospitals. Access to health facilities (median 8,619 households per asset) shows a more regional infrastructure services provision with each facility serving a relatively equal sized regional service area. The median number of households per electricity substation is 7,400 households, concentrated in the urban areas.

17
Having set up the analytical framework, this section describes the data inputs that will be used to identify each damage channel and quantify the sectoral and aggregate impacts of climate shocks.

# 5.1 Agricultural yields

**Data on direct losses to crop and livestock yields caused by changes in rainfall levels and seasonal timing are based on loss curves from a comprehensive disaster risk study** (IDB, 2019). Figure 7 shows the direct losses expected from droughts, expressed as a function of their periodicity (i.e. how frequent a drought of a given magnitude is expected, in years), for different climate scenarios (as represented by different RCP pathways). Loss curves were adjusted to be consistent with MGAP’s estimated losses in 2022/23 drought (MEF 2023) a one-in-a-hundred-year event according to Uruguay’s meteorological agency (INUMET, 2023).

**Droughts have historically had a significant direct impact on both livestock and agriculture.** The order of magnitude of damages in the historical/baseline scenario for droughts with return periods of 50 years or less is similar for crops and livestock, i.e. increasing rapidly until around US$600 million. For larger droughts, losses increase in the case of livestock to over US$1.6 billion for return periods of 200 years, while they increase at the margin in the case of crops (Figure 7).

**Climate change is expected to increase the magnitude of damages for crops** . While damages for crops increase monotonically with more extreme (i.e., higher RCP) climate scenarios, livestock damages are expected to change little with respect to historical experience (Figure 7 and Figure 8).  The differences can be explained by the definition of drought used in the IDB (2019) study. As is common in many studies of drought hazard, drought events are characterized using the three-month Reconnaissance Drought Index (RDI),<sup>8</sup> which is shown to replicate better the
takes the value 0). A concise summary of the definitions per outcome variable is presented in Table A1 in the Appendix.

To measure intervention exposure, at the end of the endline survey we asked households what radio stations they listened to most during the intervention period, whether they heard the radio clip (after playing a short audio sequence as part of the automated survey), and whether they received a flyer about solar lighting products during the intervention period. We also documented participation in the call-in contest using call-in data records.

# **_D. Study Participants_**

The baseline survey yielded 5,887 short surveys and 1,198 long surveys, for a total of 7,085 (above the 1,000 households required by the power calculations). Baseline descriptive statistics for the study population are displayed in Table 1 (Column 2).

More than three-quarters of households did not have access to grid electricity, with dry cell batteries (73.2 percent) and candles (36.9 percent) being the most common energy sources in our study population. Awareness and understanding of solar energy is quite high. For instance, the majority of households that are aware of pico-PV technology (81 percent) identify ‘sunny’ as the condition most conducive for solar lighting products (second panel). Despite these high levels of awareness, less than 15 percent of households report to have ever owned a solar lamp and approximately 3 percent report to have ever owned a superior-quality solar lamp (top panel of the table). The average age of the study population is 45, and survey respondent’s gender is approximately equally represented (53 percent men, 47 percent women; third panel of the table).

Of the households eligible for the baseline mobile phone survey, one-third completed the entire survey. Considering that in our study non-response is higher for mobile phone surveys than face-to-face surveys (statistically meaningful non-response rates are observed only for short-survey households), this yielded a disproportional drop in baseline information of about 45
provinces and districts of Vietnam. Then, using the disaggregated data on poverty indexes, we simulate the effect of cash transfers on poverty under different targeting scenarios.

It is found that there is a large variation in poverty between districts and provinces. Mountainous and highland districts have high poverty incidence and severity in 2009. Rural and ethnic minority people account for a large proportion of the poor. There is a strong relationship between inequality and poverty: areas with low inequality are also more likely to have low poverty rates. The spatial distribution of the poor population has changed significantly during the period 1999-2009. In 1999, the poor were highly concentrated in delta regions such as Red River Delta and Mekong River Delta, partly because of the high population density in these areas. Since 1999, the number of the poor people has decreased remarkably in delta regions, but much less markedly in the Northern mountains and central highlands. While the distribution of poor people seems more evenly distributed across the country in 2009, areas with high poverty rates are more concentrated in Mountainous areas than they were a decade ago. Most provinces and districts experienced poverty reduction during 1999-2009, but the extent of poverty decline has been greatest in areas with average poverty levels in 1999. Provinces and districts with high initial poverty achieved smaller reductions in poverty.

We show that poverty rates have become more concentrated in Vietnam between 1999 and 2009. This phenomenon is likely not unique to Vietnam and may be widespread in developing countries. Economic activity and growth are becoming more spatially concentrated, due to agglomeration benefits related to network, technology and human capital externalities. This suggests that spatially targeted re-distribution policy executed alongside the development of high-potential areas may offer one means for a country to maintain growth while also maintaining an equitable distribution of wealth. As poverty becomes more spatially concentrated, the effectiveness of geographic targeting is likely to improve.

We take advantage of the high resolution available in our poverty maps to show that the more finely defined the beneficiary populations, the greater the gains from geographic targeting of
Development Survey (KHDS) was originally designed and implemented by the World Bank and the Muhimbili University College of Health Sciences. It consisted of 915 households from 51 villages that were interviewed up to four times from autumn 1991 to January 1994.<sup>10</sup> The KHDS-2004 survey aimed to reinterview all individuals that were ever interviewed in the baseline survey and were

> 9 Schechter and Yuskavage (2011) empirically document unreciprocated relations in Paraguay. 10 See World Bank (2004).

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## **2.1.2 data**

We draw on geo-referenced conflict-event data taken from the GDELT data set (Leetaru and Schrodt 2013). This established data source has been widely used in recent research (see, e.g., Manacorda and Tesei 2020; Armand et al. 2020; Gallea and Rohner 2021; Naidu et al. 2021) and draws on media and NGO reports from a wide variety of international and domestic sources, covering print, broadcast, and web outlets in over 100 languages.

One advantage of this data source for our purpose is that it is probably the most comprehensive database on conflictrelated events available to date, including over a quarter billion geo-referenced records worldwide for the last three decades. The events are geo-coded with a high resolution. It is particularly critical for us that the events are not aggregated at a higher administrative level, as our identification strategy relies on within-commune variation in the incidence of events between villages assigned or not assigned to the cash transfer program. Because of this comprehensiveness and a high degree of resolution, the data set includes a sizeable number of not only large but also small conflict events, allowing us to have enough statistical variation.<sup>13</sup> The GDELT data contain information about the geo-location of a given conflict event (longitude/latitude) as well as the actors involved and the type of event (based on the CAMEO classification of action categories). In some cases, there is also a link to the original news source.

We first build a village-year variable that captures whether a village is the nearest village to a recorded conflict event, using high-intensity events (based on CAMEO categories 18–20 covering assault, fighting, and mass violence events). We build an alternative variable that measures if a village is within a 10 km radius of such a conflict event. Making use of the event typology, actors involved, and original news source, we are also able to code a battery of different conflict variables
|
|treatment x post x Goal|0.081||||
||(0.087)||||

_Notes._ This table presents the estimates of _β_ 1, _β_ 2, and _γ_ 1 from equation 1 in Panel A and from equation 2 in Panel B. See notes from Table 2 for detail on table structure and control variables in Panel A and notes from Table 3 for detail on table structure and control variables in Panel B. Ever had sex is an indicator that the female (male) has ever had sex, currently has partner is an indicator that the female (male) currently has a partner, and total sex partners ever is the total number of sex partners the female (male) reports. Sexual activity index is the unweighted mean of the indicators after they have been standardized at baseline and endline separately to the mean and standard deviation among females (Panel A) or males (Panel B) in ELA only communities among females who were not assigned to the _Goal_ treatment (or among males whose connected female was in a ELA only community and not assigned to the _Goal_ treatment). Standard errors, clustered at the club level, are presented in parentheses below coefficient estimatesin columns 1–2. .01 ***, .05 **, .10 *. _Source._ Panel A: Female baseline and endline data, balanced panel. Panel B: Male baseline and endline data, balanced panel.

51
that completeness of coverage is of less importance with price indicators compared to quantity indicators, because "prices charged for close substitutes by different firms or in different parts of a country are likely, in many cases, to show similar movements even if their absolute level is a little different". We agree with the statements above. Therefore the calculations in this study are entirely based on the price indicator method. However, in tables 12 and 13 for individual industries in the Statistical Appendix we also present, pr memoria, results using the quantity indicator method.

# Levels of Real Output in 17 Industries

Our detailed analysis covered 17 industries in Brazil, Mexico and the USA. We covered a sample of 171 Brazilian product items and 372 US product items for the Brazil/USA comparison, and 192 Mexiean product items and 342 US product items for the Mexico/USA comparison, This section describes the calculation procedures, and summarizes the estimates of relative output and purchasing power parity (PPP). On the basis of these initial results we try to discern what patterns can be detected.

Table 3.1 shows total gross value of output for these 17 industries as derived from the industrial censuses, expressed in national currencies. For the United States we had to use figures for 1977 from the 1977 Census of Manufactures. For Mexico we added the gross value of output for petroleum refining, which was not presented in the industrial census, presumably - because all this output is produced by one firm PEMEX, the government monopoly. These figures were taken from from the detailed national accounts document Sistema dB Cuentas Nacionales de Mexico (1981). We deducted also indirect taxes and subsidies which were included in the Mexican census figures for malt and malt beverages, tobacco and tobacco products and petroleum refining and products (see also industries A3, A4 and All in the Statistical Appendix). Table 3.1 also shows total gross value of output of manufacturing to demonstrate the size of the sample.
# **IV. Robustness Checks**

We conduct several robustness checks. First, we examine protest spillover at the quarterly frequency. Second, we employ the Standard Poisson estimator. Our results are robust to these checks.

We find that the spillovers are weaker when using quarterly frequency. This is true for both measures of protests. Table 4 shows that past foreign protests, even with large social media penetration, are no longer having spillover effects on domestic protests (columns [1] to [3] of Table 4). The coefficients remain positive but are not statistically significant.

In terms of news-based protests, foreign protests are found to have statistically significant spillover effects on domestic protests at all three thresholds of social media penetration (see columns [4] to [6]). This finding could reflect the stronger tendency for news-based protests to spread than actual protests do. However, the coefficients associated with the effects for the last quarter’s discussions about foreign protests are smaller than those of the last month’s (see Table 2 for a comparison). The effect using quarterly frequency tends to “dilute” the effect captured at the monthly frequency.

**Table 4: Contagion, Distance, and Social Media—Quarterly Frequency**

|Zero-Inflated Poisson|Protest|sper million|persons|N|ews-basedpr|otests|
|---|---|---|---|---|---|---|
||[1]|[2]|[3]|[4]|[5]|[6]|
|Social Media Penetration Threshold|**> 10%**|**> 20%**|**> 30%**|**> 10%**|**> 20%**|**> 30%**|
|Protest pc (t-1)|0.010***<br>(0
underreporting bias. According to information from the Kinshasa port authority (ONATRA) as well, the tonnage shipped from Kinshasa to Brazzaville exceeds that going from Brazzaville to Kinshasa, although according to their statistics by only around 40 percent. Hence, port data in Brazzaville and Kinshasa support our finding of the current dominance of the Matadi-Kinshasa-Brazzaville supply route relative to the Pointe Noire-Brazzaville-Kinshasa route.

_Table 5: Composition of Inbound and Outbound Goods Traffic at the Port of Brazzaville, 2009_

||From Kinshasa to Br|azzaville|From Brazzaville to|Kinshasa|
|---|---|---|---|---|
||_Tons_|_%_|_Tons_|_%_|
|Fuel|131,915|38.4|17,981|61.0|
|Cement|56,675|16.5|530|1.8|
|Flour|30,154|8.8|145|0.5|
|Wood|26,977|7.9|-|-|
|Foodstuffs|26,297|7.7|146|0.5|
|Building materials<br>andgravel|7,860|2.3|2,253|7.6|
|Mineral water|6,282|1.8|472|1.6|
|Vegetable oil|6,114|1.8|-|-|
|Vehicles|4,912|1.4|5,464|18.5|
|Soap|4,340|1.3|-|-|
|Containers|2,991|0.9|76|0.3|
|Empty packaging|899|0.3|181|0.6|
|Sugar|756|0.2|0|0.0|
|Sheet metal|505|0.1|0|0.0|
|Chemicals|487|0
interaction  of  Brazil’s  real  exchange  rate  (RER)<sup>18</sup> and  the  firm’s  export  status.<sup>19</sup> The  time-­‐varying,  firm-­‐level  controls, _! "#_ ,  include  variables  available  in  standard  firm-­‐level data  sets  such  as  log  employment,  and  the  occupational  skill  composition<sup>20</sup> of  the  firm  in addition  to  average  worker  tenure  at  the  firm,  and  controls  for  the  age<sup>21</sup> ,  gender,  and educational  skill  composition<sup>22</sup> of  the  firm.<sup>23</sup> Each  specification  also  includes  firm  fixed effects, ! _!_ ,  accounting  for  time-­‐invariant,  firm  characteristics  and  interactive  region-­‐year fixed  effects, ! _!"_ ,  capturing  the  average  effect  of  policy  changes  that  may  differentially impact  wages  of  firms  in  different  regions  of  Brazil.<sup>24</sup> Here, ! is  an  error  term  that  is _!"_ assumed  to  exhibit  no  serial  correlation,  and  to  be  orthogonal  to  all  regressors.  In  each specification,  the  standard  errors  are  clustered  at  the  industry-­‐year  level  to  account  for  the possibility  of  within-­‐industry,  across-­‐firm  correlation  in  errors  following  Moulton  (1990).

In  interpreting  our  estimates  from  specification  (1)  we  focus  specifically  on  the  magnitude of  the  differential  change  in  average  firm-­‐level  wages  at  exporters  relative  to  non-­‐exporters (!! )  as  well  as  the  overall  wage  impact  of  a  decline  in  protection  for  exporters  (!! +!" )  and non-­‐exporters  (!! ),  separately.  The  responsiveness  of  average  wages  in  firms  serving  only 18  The  real  exchange  rate  series  for  Brazil  is  constructed  in  Muendler  (2003)  and  is  available  at _<u>http:
clans. Based on the information compiled by the Institute for Popular Democracy (IPD) in the Philippines, using raw electoral data from the election commission in the Philippines, about 57 percent (17 out of the sample of 30 municipalities) of municipalities have one or more political clans holding or competing for public office. A scrutiny of the names of mayors who have held office in these municipalities since 1998 also reveals that in one-third of the municipalities one political clan has been entrenched in the position of mayor, alternating between family members when any one candidate faces a term limit.<sup>25</sup> Including fixed effects for the congressional districts to which the municipalities belong may also capture the degree to which electoral competition matters for explaining variation in vote buying. There are four congressional districts in Isabela, of which three are described as being dominated by particular political clans (Carizo, 2010). The fourth district, in contrast to the other three, is described as experiencing intense rivalry among warring clans. There is evidence of greater vote buying in those municipalities that are located in the 4<sup>th</sup> congressional district, which is characterized by more intense political rivalry between clans. However, municipalities in the 4<sup>th</sup> district do not appear to be different in vote buying experience from those in the 1<sup>st</sup> district where a single clan has been known to dominate. The size of the difference in vote buying in municipalities in the 2<sup>nd</sup> and 3<sup>rd</sup> districts (each dominated by a different political clan)

> 25 The Philippines prohibits incumbent mayors in their third term in office from running in elections for a fourth consecutive term. However, candidates can return to contest in a subsequent election, after sitting-out the fourth term. Mayoral elections are scheduled every 3 years.

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