rafmacalaba/data-use-mentions-extended huggingface dataset:
the terms and refer to the set of fixed effects by exporter and importer, respectively. Finally, o is the residual term. Sectoral fixed effects are not applied in the model, since the analysis oo oo δδ γγ considers the sum of trade following the different digital services groupings, as outlined in Annex εε Table A2. Both equations show that the model is estimated using data for a single year, which therefore presents a cross-country section. Panel regressions are also performed for robustness check later on. The study takes the year 2015 given that this year is the latest year available for the dependent variable.

Note that the model is estimated with PPML with fixed effects as recommended by Santos Silva and Tenreyro (2006). By doing so, Fally (2015) shows that the estimated fixed effects correspond exactly to the terms required by the structural model. Furthermore, regressions are estimated with robust standard error clustered by country-pair. The dependent variable, D o , denoting digital services trade, is also bilateral and includes all directions of trade. That means that, in accordance with the recent literature, it also includes DD internal trade, which is equal to the domestic production that is both produced and consumed in a given country. The reason for doing so is that otherwise the estimated fixed effects would not relate to the output and expenditure terms set out by the gravity theory of trade. Yet, not all datasets provide this domestic trade. The best available source is the gross trade data from the OECD TiVA data. The advantage of this source is that it contains harmonized trade and production data, so selftrade is simply calculated as production less total world exports. Using the most recent version of the TiVA dataset, the analysis covers services trade and production data for 64 exporters and importers for the year 2015, the latest year available.<sup>21</sup> Finally, both equation (1) and (2) also include the term C ∗intlo . This vector refers to several control variables included in
driving the differences between the internet and mobile phone surveys.

We investigate the potential of non-response driving the results in two ways. First, we compare the internet surveys to pre-conflict and other post-conflict data sources to ensure that the surveys are detecting at least some of the large changes in well-being that have been independently reported by other data sources. The internet surveys corroborate a number of these independently reported patterns. Food insecurity drastically rose following the start of the conflict from approximately 10 percent of the population to between 40 and 60 percent of the population during the time period under analysis (e.g., IPC 2015; Favari et al. 2022), and 58.5 percent of internet respondents report having had trouble purchasing food in the market in the month before the survey<sup>19</sup> ; underemployment drastically escalated following the start of the conflict with the majority of households critically relying on humanitarian assistance as the primary source to afford necessary purchases (e.g., IPC 2017; IPC 2018; WFP 2019; Favari et al. 2022), and only 18 percent of internet respondents reported working three or more weeks in the month before the survey; and violence has drastically escalated since the start of the conflict and affected more regions of the country by the time period under analysis here than in the first months of the escalation (e.g., Tandon and Vishwanath 2020; OCHA 2021), and 77 percent of respondents in the internet survey either witnessed violent incidents themselves or know someone who has witnessed violence. Combined, these results are consistent with sample selection not precluding the internet surveys from detecting many of the large changes that have occurred since the start of the Second, in the basic empirical strategy that we present in the next section, we also investigate whether differences in unobservable sample selection between internet and mobile phone surveys make it too difficult to compare the two modalities. Specifically, we compare the difference between internet and mobile phone surveys in sensitive questions to the difference between non-sensitive
, the calculations remain the same regardless of whether the split-off household is found and interviewed or if it becomes part of the attrition calculations, although in the case of attrition, it is necessary to make assumptions about the number of households formed by the lost members.

The generalized formula for the probability of selection at this step is: where _z_ is the number of new households entering the survey from a given parent household (as multiple split-off members can form either a single new or multiple new households) and _q_ is the number of households in the EA in round 1.

It may seem somewhat counterintuitive that the sum of the conditional probabilities of selection into tracking for the parent and split-off households is not equal to one, since they stemmed from the same initial household. Since these probabilities total less than one, when the inverse is taken, the weight will increase by some number greater than one. When this step is incorporated into the final weight calculations, it will increase the total sum of the weights in such a way as to mirror the natural population growth of new households created between survey rounds.

# _4) Fair share correction_

Since the follow-up rules for the panel survey include interviewing split-off members in their new households along with all the current members of that household, the weights must allow for the incorporation of those who now live with original sample members. For example, if a young man living with his parents in round 1 has formed a new household in round 2 and resides with his wife and newborn child, the wife and infant will be incorporated into the survey and thus require a probability of selection. Such corrections, known as fair share corrections, are routinely used to distribute weight to new sample 5
- fear that a creditor might seize the savings of a delinquent debtor;

- fear that a former spouse might seize the savings of an individual behind on his childsupport payments;

- fear that welfare eligibility would be threatened by a substantial account balance or by a history of deposits from under-the-table earnings;

- a desire to hide earnings from the tax authorities; or

- fear that a bank might report suspected illegal immigrants to the Immigration and Naturalization Service or that the INS could use bank records to discover their presence.

- Interestingly, the least important reasons people give for being unbanked are the hours and locations of bank branches.

The OCC survey was typical of other surveys in the way that it investigated why people did not to have bank accounts. In the OCC survey, if a person did not have an account, the surveyor read a list of possible reasons to explain the lack of an account and asked the respondent to choose the main reasons that applied to him or her. For our purposes, however, there are two problems with the data. First, the most common reasons found in other surveys – the person did not want an account because he or she had no savings – was not included as a possible choice. Second, the public data set excluded the responses of people who selected such reasons as: the bank would not let them open an account, their bank account could be frozen by a creditor, or they thought that they would need a Social Security card to open an account.

Despite these shortcomings, the OCC data are still broadly consistent with the findings of other surveys. As shown in Table 8, the two most common reasons that people cited from this list of possible reasons are the lack of money necessary to open an account and bank fees. But 58 percent of the unbanked indicated that none of the listed reasons were important to them. This is consistent with the finding from other surveys where the most common reason people give for not having an account is that they do not have
# **IV. Methodological design**

This section describes the sources of information and the working sample, the characteristics of the study’s population sample, the design of the impact evaluation of the program, as well as the methods and variables used in the analysis of intergenerational occupational mobility and the determinants of occupational attainment.

# A. _Sources of information and working samples_

The central source of information is the database from the Evaluation of Rural Households Survey (ENCEL in Spanish) 1997-2017 of PROSPERA.<sup>16</sup> In addition to the information from ENCEL, the National Income and Expenditure Household Survey (ENIGH in Spanish) 2016, representing the makeup of the population at both the national and federal state levels, was used as a source of information to validate the socioeconomic strata, as well as compare the characteristics of our study group with the same age group from the population of the country.

For the analysis on intergenerational mobility and determinants of occupational attainment we used the baseline from the ENCEL, provided by the Socioeconomic Characteristics of the Households Survey (ENCASEH in Spanish) collected in 1997, as well as the 2017 round of ENCEL. In the construction of the comparison groups we used information of the whole panel of ENCEL, as well as historic administrative data of PROSPERA that, among other variables, details the period and amount of cash transfers received between 1997 and 2017 for each household of the selected youths. Finally, for the estimation of the weights of the propensity score method we used the ENCASEH 1997.<sup>17</sup> Between 1997 and 2017, ten rounds of the ENCEL have been collected: including seven rounds between 1997 and 2000 and also in 2003, 2007 and 2017. The rounds between 1997 and 2000, captured information from the entirety of the households residing in an experimental sample of 506 rural localities (320 treatment and 186 control) of high or very high marginality, located in seven states of the country: Guerrero, Hidalgo, Michoacán, Puebla
on its value. The data, however, indicate immigrants are more likely to move on to another country _k_ from _j_ when compared to locals, which implies this second component is likely to be negative. We assume that the transit cost is a function of birth country _i_ and destination country _k_ .<sup>6</sup> The common cost component is given by

> 6Unfortunately we cannot use destination specific parameters in these expressions, since we use only the transit migration data to the US. However, we can use bilateral and origin specific parameters.

12
linked to indicators of financial development.<sup>9</sup> We use the average from 1981 to 2006 (rather than from the actual year of the WBES survey) because data on trust are available for distinct years for different countries, and relying on the survey year would lead to too many missing values for trust for our countries. Averaging also has the benefit of smoothing the measurement errors of trust.

Zak and Knack also note that their framework can help account for other findings from the cross-country growth literature. For example, the negative link between ethnic fractionalization and growth found in Easterly and Levine (1997) and between income inequality and growth in Easterly (1999) are both substantially weakened when levels of interpersonal trust are included in regressions. Although the channels through which social ties and the institutional environment affect trust are more clearly laid out than for the polarization measures, we include measures of ethnic and linguistic fractionalization among the set of potential instruments. We expect them to be negatively linked to financial development indicators.

# **_Legal Origin_**

LaPorta, Lopez-de-Silanes, Shleifer, and Vishny (LLSV) (1997, 1998) first documented that the legal protection of outside investors was stronger in common law countries (English legal origin) than in civil law countries rooted in Roman legal traditions, particularly those of French legal origin. Their interpretation was that English common law traditions evolved to protect private property and support private market outcomes, whereas French civil law tended to unify the legal system and cement state control of the judicial system often to the detriment of private market development. Because legal traditions were introduced in most countries by outside colonizers, LLSV argued that legal origin could be treated as exogenous in regressions explaining financial development and growth. Using legal origin as an instrument for investor protection, they then demonstrated a strong link between those protections and measures of financial development. We therefore include dummy variables for English and French legal origins in the set of potential instruments. We expect English (French) legal origin to
*374**|5|855|3,690|900|**5,450**|
|Tracheal, bronchus, and lung cancer|3|460|1,970|801|**3,234**|21|4,783|26,023|6,958|**37,785**|
|Tuberculosis|37|141|43|3|**223**|231|1,301|775|44|**2,350**|
|Stroke|156|2,224|5,712|1,958|**10,049**|441|6,391|21,814|8,495|**37,141**|

_Source:_ GBD Results Tool (database), Global Burden of Disease Study 2016, Global Health Data Exchange, Institute for Health Metrics and Evaluation, Seattle, http://ghdx.healthdata.org/gbd-results-tool.

**Table 3 – Tobacco-Related Events, by Gender, 2016**

||**All**|**Women**|**Men**|
|---|---|---|---|
|Bladder cancer|17,898|4,051|13,847|
|Cervical cancer|15,718|15,718|0|
|Chronic obstructive pulmonary disea|334,223|83,351|250,873|
|Colon and rectum cancer|73,256|39,421|33,835|
|Esophageal cancer|7,943|1,510|6,433|
|Ischemic heart disease|1,200,833|651,505|549,328|
|Larynx cancer|9,221|748|8,473|
|Leukemia|10,843|5,276|5,567|
|Lip and oral cavity cancer
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de Gregorio, Jose. 1992?. Growth in Latin America
those found in the empirical macroeconomic literature (Bernanke and Gurkaynak, 2001; Gollin, 2002). They are also highly significant and robust to the various experiments we perform.

We also find little evidence of heterogeneity across countries in the output elasticities of the inputs of the aggregate production function. Specifically, the output elasticity of infrastructure does not seem to vary with countries‘ level of per capita income, their infrastructure endowment, or the size of their population.

The rest of the paper is organized as follows: Section 2 describes the dataset. Section 3 lays out the methodological approach. Section 4 describes the empirical results. Finally, section 5 concludes.

# **2. Data**

Our goal is to estimate the contribution of infrastructure capital to output in a large panel data set using an infrastructure-augmented aggregate production function framework, in which aggregate output is produced using non-infrastructure physical capital, human capital, and infrastructure. The data set is a balanced panel comprising annual information on output, physical capital, human capital, and infrastructure capital for 88 industrial and developing countries over the period 1960-2000, thus, totaling 3,520 observations. The Appendix lists the sample countries used in the analysis.<sup>8</sup> Real output is measured by real GDP in 2000 PPP US dollars from the Penn World Tables 6.2 (Heston, Summers, and Aten, 2006). The data on physical capital was constructed using the perpetual inventory method. To implement it, the initial level of the capital stock was estimated using data on the capital stock and real output from PWT 5.6 for those countries for which such data is available. We extrapolate the data for countries without capital stock information in PWT 5.6 by running a cross-sectional regression of the initial capital-output ratio on (log) real GDP per worker.<sup>9</sup> As a robustness check, we also constructed an alternative ̳back-cast‘ projection of capital per worker. Specifically, to construct an initial capital
cover more policy areas than earlier PTAs: agreements signed before 1991 included on average 9 provisions whereas agreements signed between 2005 and 2015 included on average 15 provisions; more than 50 percent of agreements include deeper provisions such as anti-dumping and countervailing measures, rules on competition, movement of capital and intellectual property rights. Evidence presented by Hollweg and Rocha (2017) also suggests that GVC-related trade – proxied by trade in parts and components - is higher on average for countries that have signed deeper agreements (Figure 16), where depth of an agreement is the number of legally enforceable provisions.

> 25 The World Bank data set on PTAs content; see Annex 3 for further details.

13
|(0.46)|(1.04)|
|Observations|2292|2292|2292|2283|2292|2292|
|No. of households|451|451|||451|451|
|R-squared|0.16|0.15|||0.03|0.02|

_Notes:_ All regressions include household level fixed effects. Col. 1, 2,5, 6 use a fixed effect linear probability model while col. 3 and 4 report results from fixed-effect tobit models. Plot and soil characteristics are included throughout. Robust t statistics in parentheses. *, **, and *** denote significance at 10%, 5%, and 1%.

27
, iron supplementation, and antimalarial drugs. We also find a statistically significant relationship between wealth and receiving advice on nutrition during the pregnancy, having the due date estimated, and measurement of uterine height.

Table 5, columns 3 and 4, explores within‐facility wealth gradients using the direct observation data. A one standard deviation increase in wealth is associated with a 0.6 percentage point increase in completeness of care (p‐value < 0.05). The coefficient does not change when we add controls for women’s characteristics. When we focus on the individual elements of the content of care in the direct clinical observations, as seen in table C.6, in appendix C, we find a statistically significant relationship for a much lower share of the items and at a lower level than when we consider the items reported in the household survey. These items include hemoglobin testing, prescription or provision of iron, as well as 13
For this, a detailed inventory of durable goods is needed, including information on their value (either original purchase date and value, or current replacement value). POF, however, collects information only on quantity, mode of acquisition, year, and state (new or used) in its durable goods inventory, but not on values. Therefore, an estimate of flow of services cannot be reliably obtained for durable goods owned by the household.<sup>12</sup> For the calculation of poverty rates, however, the omission of the flow of services from a number of durable items from the consumption aggregate may be of relatively little concern (World Bank, 2007).

- _Other miscellaneous items._ In addition, following the guidelines in Deaton and Zaidi (2002), we excluded loan and interest payments, other capital account transactions, remittances and other transfers (for instance, child support), and property-related taxes that do not bear any relation to services provided. A list of the excluded items is provided in Appendix B.

- 3) _Housing._ The consumption aggregate also includes the value of rent for households that rent their dwelling. For households that own their house or do not pay rent for any other reason, we include implicit rent in the consumption aggregate. There are different ways to arrive at a value for housing services consumed by nonrenters, which have different advantages and disadvantages (see Deaton and Zaidi, 2002). In POF, households that own their dwelling (even if still paying the mortgage) or that

> 11 In a variation of the main specification, we exclude all toys and recreational goods and services; domestic electrical appliances; domestic nonelectrical appliances; and furniture, regardless of the average amount spent on these. Results are presented in section 4.

> 12 Oliveira et al. (2016) calculate and include in their aggregate the value of services provided by the following durable goods: Goods in modules “Machines, equipment and household utilities acquisition” and “
.jrc.ec.europa.eu/atlas2016Overview.php.</mark>

- <mark>Picard, Richard R., and R. Dennis Cook. 1984. "Cross‐validation of Regression Models."</mark> _<mark>Journal of the American Statistical Association</mark>_ <mark>79 (387): 575–83.</mark>

- <mark>Potere, David, Annemarie Schneider, Shlomo Angel, and Daniel L. Civco. 2009. "Mapping Urban Areas on a Global Scale: Which of the Eight Maps Now Available Is More Accurate?"</mark> _<mark>International Journal of Remote Sensing</mark>_ <mark>30 (24): 6531–58.</mark>

- <mark>Rozenfeld, H. D., D. Rybski, X. Gabaix, and H. A. Makse. 2011. “The Area and Population of Cities: New Insights from a Different Perspective on Cities.”</mark> _<mark>American Economic Review</mark>_ <mark>101 (5): 2205–25.</mark>

- <mark>Salesses, Philip, Katja Schechtner, and César A. Hidalgo. 2013. "The Collaborative Image of the City: Mapping the Inequality of Urban Perception."</mark> _<mark>PLOS One</mark>_ <mark>8 (7): e68400.</mark>

- <mark>Schneider, Annemarie. 2012. "Monitoring Land Cover Change in Urban and Peri‐urban Areas Using Dense Time Stacks of Landsat Satellite Data and a Data Mining Approach."</mark> _<mark>Remote Sensing of Environm</mark>_ <mark>ent 124: 689–704.</mark>

- <mark>Schneider, Annemarie, Mark A. Friedl, and David Potere. 2009
Figure 7: Cost of Girl Inactivity or Joblessness, relative to Boy’s Inactivity or Joblessness Rates, as % of annual GDP (Target 2) <!-- Start of picture text --> 6.0%<br>5.0%<br>4.0%<br>3.0%<br>2.0%<br>1.0%<br>0.0%<br>-1.0%<br>Inactivity Jobless-lower bound Jobless-upper bound<br><!-- End of picture text --> Note: Jobless-lower bound reduces the inactivity rate by gross upper secondary enrolment rates thereby dropping students . Jobless-upper bound reduces the inactivity rate by gross upper secondary and by partial net lower secondary thereby dropping students.

Source: Authors’ computations based on data from ILO KILM and WDI Figure 8: Adolescent Fertility Rate per 1000 Adolescent Girl, 2007 <!-- Start of picture text --> 160 148<br>130 133<br>140 124<br>120 102 103<br>100<br>80 67 70 72 75<br>60<br>35<br>40 24<br>20 8 8 10<br>0<br>Adolescent fertility rate per 1000 women (15-19)<br><!-- End of picture text --> Source: World Bank HNPStats 29