rafmacalaba/data-use-mentions-extended huggingface dataset:
experienced would increase children’s odds of developing to potential—but COVID-19 setbacks have increased the number and severity of challenges families face.

Figure 1. The likelihood of being developmentally on track declines with the number of risk factors, children aged 3 and 4 in Sindh <!-- Start of picture text --> Percent of children 3-4-years old Percent of children developmentally on track<br>25.00 100<br>90<br>83.02<br>20.00 80<br>69.71<br>70<br>57.77<br>15.00 44.76 60<br>43.23<br>37.43<br>50<br>10.00 40<br>26.46<br>30<br>5.00 20<br>10<br>0.00 0<br>0-1 2 3 4 5 6 7+<br>Number of risk factors<br>(stunting, less than primary maternal education, no ece, poorest wealth quintile, rural area,<br>disability, stimulation (engaged in <4 stimulating activities with a caregiver, harsh<br>punishment)<br>Percent of children 3-4-years old<br>Percent of children developmentally on track<br><!-- End of picture text --> Source: Authors’ calculation using data from the Multiple Indicator Cluster Survey 2018–19.

Risk factors included being in the lowest income quintile, stunted, living in a rural area, having a functional disability, mother with less than a primary school education, low early stimulation at home (less than four activities in the previous 24 hours), exposed to harsh parenting, and not enrolled in early childhood education.

COVID-19 has introduced loss and hardships for families worldwide, including through reduced household resources and increased caregiver distress, which pose direct threats to young children’s wellbeing. In Pakistan, the pandemic has
# **5. Trade policy uncertainty, trade, and GVCs**

We next use the World Trade Uncertainty (WTU) Index instead of the Economic Policy Uncertainty Index as a measure of policy uncertainty. The behavior of the WTU Index differs from the broader indicator of economic policy uncertainty, as shown in Figure 3. The different pattern captures the large increase in trade policy uncertainty in the post-2017 period, although the limited (in some cases no) variability of the WTU Index in previous years may also reflect the lack of attention to trade policy uncertainty in media reports in the pre-2017 period as discussed in Section 3.

**Figure 3.** **_The Economic Policy Uncertainty (EPU) Index versus the Trade Policy Uncertainty Index._**

<!-- Start of picture text --> 1100<br>1000<br>900<br>800<br>700<br>600<br>500<br>400<br>300<br>200<br>100<br>0<br>World Economic Policy Uncertainty (EPU) Index<br>World Trade Policy Uncertainty Index<br>Economic Policy Uncertainty Index, Trade Policy Uncertainty Index,<br>by country selected countries<br>800 40<br>600 30<br>400 20<br>200 10<br>0 0<br>AUS BRA CAN CHL CHN AUS BRA CAN CHL CHN<br>COL DEU ESP FRA GBR COL DEU ESP FRA GBR<br>GRC IND IRL ITA JPN GRC IND IRL ITA JPN<br>KOR MEX NLD RUS SGP KOR MEX NLD RUS SGP<br>SWE USA SWE USA<br>1997q1=100<br>1997q1 1998q4 2000q3 2002q2 2004q1 2005q4 2007q3 2009q2 2011q1 2012q4 2014q3 2016q2 2018q1<br>1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
# **Alternative Distance Measures**

Alternative distance measures allow to test whether the results remain robust when the formal firm exhibits different characteristics. The first alternative measure includes the distance of informal businesses to larger formal firms that participated in the World Bank Enterprise Survey (ES). Given that larger firms tend to be more technologically advanced, the use of this alternative distance informs us whether the distance effect is driven by the larger firms in the sample. Meanwhile, the second measure includes all formal firms that were enumerated (i.e., their GPS locations are recorded) regardless of whether they surveyed. Hence, it captures the fact that an informal business might be closer to another formal firm than suggested by the baseline measure. Lastly, the results using the distance to formal firms (micro and large) that have a website are presented to confirm whether the findings are driven by technologically more advanced formal firms.

As shown in Table 8, the alternative distance measures generate results that are in line with the baseline. Throughout the different specifications of the model, the direction and magnitude of the estimates are robust, with statistical significance dropping for the adoption of personal computers when using the model of all enumerated formal firms or the specification with formal firms with websites.<sup>18</sup>

**Table 8: Alternative Distance Measures**

||(1)<br>PC|(2)<br>Tablet|(3)<br>Phone|(4)<br>MM|(5)<br>Website|
|---|---|---|---|---|---|
|Distance of Informal to Closest Formal Firm (Baseline)|-0.596*|-0.741***|-0.180***|-0.202***|-0.197|
||(0.349)|(0.
Model with fixed effects by the municipality and by year for 2005, 2010, and 2014, also using the FAIS formulated as the instrumental variable. The nonmonetary dimensions are expressed in 0–100, understanding 100 as 100% of coverage. Weighed results show the same model adding importance by the size of the municipal population. ∞ Fiscal revenue items in the logarithm of the average monthly per capita value three previous years. ‡ Occupied per inhabitant, employees of the economic units or indirect workers, reported in the economic census of the circa year. ‡ ‡ Productivity by the total of occupied and active of the economic units of each municipality. Standard error in parentheses. *** p < .01 ** p < .05 * p < .1 41
simulation exercise. Using the Ethiopia Rural Household Survey, Porter (2012) found that more extreme shocks impact consumption to a far greater extent than lesser shocks. Rainfall in the bottom quintile of the 30 year village distribution caused up to 20% drop in household consumption, whereas for the next quintile of the rainfall distribution (i.e. less than average rainfall, but to a lesser extent), the impact was around 2% (and nonsignificant). Therefore in the simulations we also allow crop-loss to have a non-linear impact on consumption, increasing ten-fold for shocks that are more than two standard deviations above the long-run mean.

The baseline consumption from the “fixed effects” or household and community characteristics is given by _Xhjktβ_<sup>ˆ</sup> . Together, with the estimates of shock-induced consumption losses this allows us to calculate consumption for different states of the world for each household.

# **3.3 Estimating vulnerability**

We create various vulnerability measures using the estimated CDF of _ch,t_ +1 for each household, noting that these capture vulnerability to the shocks that are captured in our data.<sup>12</sup> We first define all those with a predicted consumption lower than the national poverty line as poor. The national absolute poverty numbers that are used by the Government of Ethiopia (Woldehanna and Porter, 2013) are based on the Foster-Greer-Thorbecke (FGT) family of poverty indices (Foster et al., 1984). A household is defined as poor if their total expenditure on all items is less than the national absolute poverty line of 3,781 birr in December 2010 prices. This is the amount of money needed to purchase food of 2,200 kilocalories for every adult-equivalent in the household, and other (extremely basic, necessary) items for everyone in the household.

We next define as “vulnerable”, anyone with a probability of being poor higher than 0.5 across the 6,000 states of the world simulated (Pritchett et
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such arms deals we could have expected corruption and lack of democratic accountability to accentuate the discovery effect on arms deals. This is not what the results suggest. Instead we find a positive and significant effect only in the most democratic and least corrupt countries (these results are similar using the World Governance Indicators’ measure of corruption). This may be because a minimum threshold of state capacity is required to generate revenues from the oil find. These interactions are not statistically significant however. Hence I do not want to stress these results, especially as there might not be much relevant difference in institutional quality across countries in the sample. As for the role of military heads of state, the results are different whether I use SIPRI or COMTRADE data. The latter suggests that discoveries
schools (and the outside sector). Figure 6 provides evidence that this key assumption is well-supported in our PISA data. Here, we plot empirical cumulative distribution functions (CDFs) of the PISA-based people management score by sector and find that the private sector CDF (dashed blue plot) clearly first order stochastically dominates the public sector CDF (solid red plot).<sup>19</sup>

**Timing.** The timing of the game is as follows.

> 19We show empirical CDFs by country in Latin America in Figure B.4 in Appendix B. The private sector CDF first order stochastically dominates the public sector CDF in Colombia, Peru, Costa Rica and Mexico.

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# **2.3 Factset data on firm linkages and carbon emissions**

We use data on firm relationships from FactSet, a private company specializing in market intelligence and firm networks. FactSet collates data from various sources, including annual reports, investor presentations, press releases, and company websites. Among FactSet’s offerings, the FactSet Fundamentals provides consolidated financial statements and firm information for publicly traded companies, while FactSet Relationships covers supply chain relationships, including suppliers, customers, and partnerships, as well as firm-to-affiliate relations.

The FactSet Revere Supply Chain Relationships database is regarded as one of the most comprehensive sources for global firm supply chain information (Huang, Lin, Liu, and Tang, 2023). This dataset integrates information from official firm filings (10-K, 10-Q, and 8-K reports submitted to the SEC), investor presentations, press releases, and company websites (FactSet, 2021).<sup>1</sup> FactSet analysts normalize relationship types by cross-referencing a firm’s records with its partners’ (“reverse” links), providing a comprehensive coverage of supply chain linkages (Huang, Lin, Liu, and Tang, 2023).

As of March 2024, FactSet actively monitored over 54,000 “source” companies, resulting in a dataset of more than 2 million relationship links involving approximately 360,000 entities. Each link includes information on start and end dates, with historical data available from 2003 and global coverage starting in 2014, expanding to Latin America in 2016.<sup>2</sup> The database specifies the nature of each relationship. For our study, we focus on buyer and supplier relationships. At any given time, we observe the parent and ultimate parent firm of a company, and any subsidiaries, if applicable.<sup>3</sup> The dataset also includes firm name, industry affiliation, headquarters country, and entity type, which we use in our analysis. Annual sales data, converted to constant 2020 U.
country pairs that have shallow agreements (see columns 1 and 2). Specifically, the positive impact of a one‐day reduction in trading times is 1.1 percent higher (6.3 percent) for country‐pairs that are part of the European Union, the deepest agreement in the BRI sample of countries, compared to countries that are part of very shallow agreements (agreements including only one provision).

In terms of market access, the positive impact of reducing trading times is dampened for country‐ pair‐products with lower levels of tariff liberalization (see columns 3 and 4). Whereas a one‐day reduction in trading times without reductions in tariff rates will increase average exports of

> 13 The IV regressions are excluded from this exercise given the complexity to instrument and interpret the interaction between endogenous variables.

18
-1|-1|-1|
|Dominican Republic|-1|-1|-1|-1|0|-1|
|El Salvador|-1|-1|-1|-1|-1|-1|

Source: Own estimations based on SEDLAC (CEDLAS and the World Bank).

*For Mex: Projections for 2015, 2017, and 2019. For Guatemala: Projections from 2015 onwards

The middle class has slightly increased in the region and remains the largest socioeconomic group in the LAC region since 2010. This group is measured as the share of the total population between $13 and $70 a day or $14 and $81 a day expressed in 2011 PPP or 2017 PPP, respectively. Between 2010 and 2020, changes in the middle class at the LAC level are relatively small as increases in the three subregions (i.e., Andean, Central America, and Southern Cone) and Mexico are offset by Brazil. In 2017, the middle class in the region grew by less than 1 pp (0.9 pp), or 4.8 million more middle-class people, with the 2017 PPPs. The largest changes in millions of middle-class people in 2017 are observed in the Southern Cone (2.2 million more people) and Brazil (2 million fewer people). The change in the Southern Cone is mainly driven by Argentina.

# Regional and country-level profiles

This section focuses on assessing regional profiles of the poor at the upper-middle-income line ($6.85 in 2017 PPP), the vulnerable, and middle-class populations. Even though a significant shift in poverty impacts poverty profiles, these movements are not big enough for inducing changes in policy choices (see Table 5). For instance, the poor are still more concentrated in urban areas, belong to the cohort between 15 and 64 years old, are slightly more educated, work in services, and are less self-employed. Similarly, vulnerability decreases markedly with the new thresholds
, walls, floor materials, access to piped water and electricity, number of bedrooms, presence of kitchen and type of toilet, type of cooking fuel used. We create asset index based on ownership of residential plot, non-agricultural buildings, farm machinery and implements, livestock and a long list of durables, such as radio, sewing machine, television set, microwave oven, etc.

We expect CCTs to affect empowerment through several channels. Empowerment is a complex construct, with a wide range of measures used to capture empowerment empirically, from socio-economic proxies to more direct measures, such as relative autonomy index (RAI) or self-efficacy index. In this study, we rely on a wide range of measures of empowerment used in previous literature. In the discussion of the theory of change below we link channels to specific measures of empowerment. However, we use these connections as an organizational device, rather than assert dominance of specific channels behind changes in empowerment. The measures we use represent the same latent construct and are 10
_Vertical Unbundling_|0.40|0.80|0.80|0.80|0.00|0.80|55%|
|_Horizontal Unbundling_|0.22|0.22|0.44|0.22|0.00|0.44|24%|

Source: Rethinking power sector reforms, 2015 The commercial and operational efficiency of utilities may be a reflection of the corporate governance and management practices followed at the utility. To understand this relationship better, the corporate governance data for utilities in the rethinking power sector reform project were collected. The data reflect the practices followed in 2015. Given the structure of the power sector in 2015, all distribution companies follow exactly the same governance and management practices given their common ownership and nesting in a holding company. Thus, for this section we do not look at Cairo North and Alexandria separately.

Table 6: Utility governance of Alexandria and Cairo North with their comparators, 2015<sup>16</sup>

||Egypt|India,|Odisha|Indi|a,AP|Ind|ia,Raj|Pa|kistan|Morocco|International|
|---|---|---|---|---|---|---|---|---|---|---|---|
||EEHC(Discos)|WESCO|CESU|APSPDCL|APEPDCL|JVVNL|JDDVNL|LESCO|KE|ONEE|benchmark|
|**Overall Utility Governance**|**55%**|**68%**|**26%**|**52%**|**52%**|**63%**|**63%**|**52%**|**90%**|**53%**|63%|
|**
Equation (2) also includes fixed effects by country ( δ� ), sector ( θ� ) and time ( γ� ), respectively. Note that despite the fact that our dependent variable is given at the firm‐level, we cannot include firm‐ level fixed effects because of the repeated cross‐sectional nature of the Enterprise Survey data set and so following developments of the same firm over time is not possible. Finally, the ε���� is the error term, which for the LPM regressions are clustered by sector country. For our Probit regression, we are unable to cluster, but the data are grouped by sector.

Our second approach is using country‐specific firm‐level data. We have firm‐level data sets from Malaysia, Vietnam and China. Obviously, the three data sets differ in variable coverage which means that the innovation indicators are not consistent across each other despite all three data sets report companies’ balance sheets information. Data are available for the manufacturing sector only.

In the regression specification presented below in equation (3), the innovation variables are again summarized in a vector called INNO in which the dependent innovation variables are dummies as well. Hence, INNO���� ∈�0,1� in equation (3). The empirical setup is largely similar compared to equation (2) with only some minor differences. One is that our data usage indicator as defined in equation (1) needs to be adjusted as we do not observe software use and labor in any of the countries. Data on these two variables are hard to find for any of the East Asian countries. Second is that the regression equation is specified for one country only so that policy changes over time are the focus (as opposed to policy differences across countries in the cross‐section analysis). In order to analyze this latter aspect in more detail, our DL measure will now be lagged with 2 years when possible and 1 year otherwise.

In all,
housing supply is inelastic in the short run. The effect is negative and statistically insignificant for below-median rents, but positive and statistically significant for high-rent housing. They interpret this as a sign of residential segregation, with natives moving out of lower-priced neighborhoods where refugees settled.

The only other country affected by the Syrian crisis where studies are available is Jordan. Fallah et al. (2018) look at Syrian refugees in Jordan and find that locals have not experienced negative labor market outcomes if one considers labor market participation, employment, employment by type or wages. They find no difference between the labor market outcomes of locals living in areas with a high share of refugees and those who do not. Fakih and Ibrahim (2016) look at Syrian refugees in three governorates of Jordan and use a longitudinal vector autoregressive model (VAR) to assess the impact of refugees flows on local employment. Similarly to Fallah et al. (2018) they find no correlation between refugee flows and local

> 31 Based on UNHCR data as of January 2019 (https://data2.unhcr.org/en/situations/syria).

58
## **IV.B INEGI’s 2015 Intercensus**

A second part of the analysis that will be detailed in the next section uses the 2015 intercensal survey, which is collected by INEGI.<sup>12</sup> This survey is gathered at the midpoint between censuses and contains information on individual’s income, education, dwelling’s characteristics, durable asset ownership, and consumption vulnerability measures (among other things). The survey includes information from individuals in more than 6 million households from all Mexican municipalities. The access to this data with restricted geographical identifiers was obtained through INEGI’s microdata lab.<sup>13</sup> Our analysis focuses on individuals aged 0 to 21 in 19972000 as those individuals had the highest potential exposure to PROSPERA. In total, we use 1,875,039 individuals in all the localities (urban and rural) from 24 Mexican states in our analysis.<sup>14</sup> Importantly, the use of this ulterior data source allows to look directly into measures of food consumption insecurity and resilience, while also allowing for a sharper definition of treatment status as we will explain later. At the same time with this much larger sample, we are also able to validate the results obtained from the previous data sources, in a fully representative sample of the Mexican population.<sup>15</sup>

# **V Empirical Strategy**

## **V.A Analysis Using the PROSPERA ENCEL Panel**

**Long-Term Effects.** For identification of the long-term effects of PROSPERA, we first rely on the 1997-2000 RCT experiment, which gives the cleanest source of variation available. However, since the control localities became treated about two years after the start of Progresa (i.e. by the end of 1999), this strategy should be interpreted as an _early_ versus _late treatment_ or as a treatment intensity deriving from the extra months of transfers the original treated received. _Table 5_
Table 5: Pre-Shipment Inspections (PSI)

||(1)<br>TFPQ|(2)<br>Markup|(3)<br>TFPR|
|---|---|---|---|
|Pre-shipment inspections (output)|0.056|0.330**|0.386**|
||(0.060)|(0.155)|(0.184)|
|Pre-shipment inspections (input)|-0.052|-0.906**|-0.958*|
||(0.206)|(0.398)|(0.534)|
|Observations|59,791|59,791|59,791|
|R-squared|0.981|0.694|0.955|
|Plant FE|yes|yes|yes|
|2-dig ind-year FE|yes|yes|yes|
|Region-year FE|yes|yes|yes|
|5-dig HH-year FE|yes|yes|yes|
|Other NTMs|yes|yes|yes|

**Note:** This table shows the effect of pre-shipment inspections (PSI), applied either to output or inputs, on plant’s TFPQ (col. 1), markup (col. 2) and TFPR (col. 3) in the years between 2008 and 2015. _Output PSI_ is the share of HS-10 digits products subject to at least one PSI within the corresponding five digit industry. For each five digit industry, _Inputs PSI_ is the weighted sum of output PSI in all five digit supplying industries. The coefficients are obtained by the estimation of Equation 1. All specifications include controls for other NTMs and import tariffs applied on plant’s outputs and inputs, plant fixed effects, 2-digit industry-year fixed effects and macro region-year fixed effect. The Herfindahl-Hirschman Index is computed for each 5-digit industry in the base year and interacted with year fixed