rafmacalaba/datause-encoder-data huggingface dataset:
traded directly in this framework, and so all labor is domestic, by definition. The nesting structure also reflects the assumption that skilled labor is a proxy for the set of Mode 1 substitutable occupations, while unskilled labor is a proxy for the set of unsubstitutable occupations.

This labor dichotomy serves as an approximation given the level of aggregation in the database being used. Blinder (2007), Jensen and Kletzer (2006), and Bardhan and Kroll (2003) have provided subjective as well as empirically grounded lists of labor groups in the USA at the SOC 2‐digit level that would be substitutable with Mode 1 services. Unfortunately, there are no equivalent studies for India that could provide a basis for greater disaggregation of the higher skilled sections of the Indian labor force. It is reasonable to assume that Mode 1 services are substitutable with just skilled labor, as opposed to unskilled labor or both labor types, since the types of activities that Mode 1 services cover require a high level of skill.

Furthermore, workers are perfectly mobile across sectors, although they cannot move between the skilled and unskilled categories, with the wages of a given labor type equal across industries, although they are not necessarily equal to each other. Since investment in capital – human or otherwise – is not possible in the model, the effects of education on the labor market composition are also are ignored. The numbers of each type of worker are thus fixed. These are tolerable constraints for this analysis, since the length of time that will be analyzed is not long enough to allow for major changes in labor endowment size and composition.

Mode 1 services are traded from one country to another through a one to one “production” relationship with domestically produced “nontradable” services. Improvements in the technology of this transformation production would make it cheaper for nontradable services to be transformed into Mode 1 services. This is akin to a reduction in the technological barriers or transactions costs associated with services trade.

This general equilibrium framework thus allows for analysis of increased outsourcing through increases in the Mode 1 services trade. As services‐trade barriers are reduced, the wages received by different labor groups in India will adjust, even as the factor uses within each industry changes. These changes can then be analyzed to address the research question.

11
has led to a significantly reduced perception of risk on these two counts by the surveyed firms. In the Investment Climate Assessment (ICA) conducted by the World Bank in June 2007, the percentage of manufacturing firms which perceived political instability or uncertainty as a “major-to-severe” constraint dropped to 18 percent in June 2007 from 47 percent in 2003. Similarly, the percentage of manufacturing firms that consider macroeconomic stability to be a major constraint dropped to 28 percent in 2007 from 50 percent in 2003. Table 3 presents the changes in perceptions of manufacturing firms about the investment climate between 2003 and 2007.<sup>35</sup> ICA 2007 also reported that a larger percentage of surveyed manufacturing firms were preparing multiyear business plans in 2007 (58 percent) than five years before (49 percent).

A last remarkable observation is in order from Table 3. A higher percentage of firms considered electricity and transportation to be constraints in 2007 than in 2003. One possibility is that these sectors deteriorated between 2003 and 2007; but that does not appear plausible. The operational performance of Kenya Power and Lighting Company – the power operator improved considerably after its management was contracted out to an international company in 2006. The number of power outages went down by about 40 percent between 2004 and June 2007, and new connections have increased by more than 50 percent.

Table 3 Percent of Manufacturing Firms Perceiving Issue as Being a Major or Severe <u>Constraint to Business</u>

|**Issue**|**2003**|**2007**|
|---|---|---|
|Crime, Theft and Disorder|69|59|
|Tax Rates|69|56|
|Electricity|47|55|
|Corruption|73|54|
|Transportation|36|53|
|Practices of Competitors in Informal Sector|64|50|
|Tax Administration|52|50|
|Customs and Trade Regulations|40|42|
|Telecommunications|45|28|
|Business licensing and Permits|13|28|
|Macroeconomic Instability|50|28|
|Access to Finance|71|26|
|Political Instability|47|18|
|Access to Land|23|16|

> 35 These data are for a panel of 169 manufacturing firms that participated in both surveys.

26
DIALOGIC READING AND STORYBOOK EVALUATION IN KENYA 20 study (Fernald, Prado, Kariger, & Raikes, 2017). To minimize the testing burden, we extensively pretested all items and ordered them by difficulty using item response theory, with a final assessment of 27 Luo items (Cronbach’s alpha: _�_ = 0.78, Cohen’s Kappa for IRR: _�_ = 1), 32 Swahili items ( _�_ = 0.76, _�_ = 0.89), and 34 English items ( _�_ = 0.57, _�_ = 0.95) that demonstrated variability among children in our study area and age range (author manuscript, under review). While this adaptation decreased the comparability of our vocabulary scores to other studies or populations, we were primarily concerned with developing measures that would be appropriate for the multilingual study environment.

In collaboration with local artists, we also developed a locally-appropriate measure of expressive vocabulary after pre-testing suggested that the stimulus words and pictures in existing measures of expressive language were not appropriate to our context (author manuscript, under review). The measure was based on words from the BPVS, ordered by difficulty. This 30-item measure of expressive vocabulary was administered by having the assessor show a picture and ask the respondent to name the object or concept the illustration depicted. Each item was scored as 0 or 1, with credit received for responding in English, Luo, or Swahili ( _�_ =0.66, _�_ =0.95). To assess children’s knowledge of the vocabulary words embedded in the storybooks, we added seven expressive vocabulary items from the project storybooks (crayon, goose, kite, nest, rabbit, toothbrush, and umbrella). For all vocabulary outcomes, age-normalized z-scores were constructed by estimating the relationship between age and the outcome variable nonparametrically (Fan, 1993).

Child characteristics measured at baseline included age (in months), sex, and height (Onis, 2006). Primary caregiver characteristics measured included the caregiver’s relationship to the child, educational attainment (in years of completed schooling), and depressive symptoms in
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34
et al., 2016). For the second phase of the cash transfer program, 558 villages were selected to participate. The geographical targeting process was applied consistently and is not the focus of this paper.

The targeting experiment was implemented in 318 villages selected to benefit from the second phase of the safety nets programs across 18 communes.<sup>9</sup> Once beneficiary villages were randomly identified through public lotteries, a registry survey based on a short questionnaire was collected among all households. The questionnaire contained information to compute PMT and FCS scores for each household. Based on the registry survey, the PMT formula was then applied to determine a quota of beneficiaries per village.<sup>10</sup> The 318 eligible villages were then randomly assigned to have beneficiary households selected through one of three methods: community‐based targeting (CBT), a proxy‐means test (PMT) formula designed to identify the poor, or an alternative formula designed to identify food‐insecure households (FCS). Specifically, within each commune, each of the 318 villages was randomly assigned to one of the three targeting methods (CBT, FCS or PMT). To ensure balance, within each commune, randomization was stratified using a PCA index based on village population, village average PMT score and Gini coefficient.

After beneficiaries were selected by each of the 3 methods, a community validation exercise was organized. While the community validation exercise aimed to correct for any mistargeting, in practice this final step did not change results much. In fact, over 99% of households identified through each of the methods were validated by the community. This outcome does not necessarily mean that community poverty perceptions are the same as those resulting from the different methods. Anecdotal evidence from the field suggests that during the validation process communities tend to refrain from disagreeing with the presented lists of beneficiaries.

We now describe the application of each method in greater detail, before presenting the experimental design and its implementation.

# 3.2 Alternative Methods to Select Beneficiary Households for Safety Nets

Proxy Means Testing (PMT) The PMT method relies on a formula approximating household consumption based on a limited set of household characteristics (Grosh and Baker, 1995). The characteristics include variables such as demographics, asset and or housing information. The information for the formula is both quicker to collect and easier to verify than a consumption aggregate, which requires listing all items purchased or self‐ consumed over a reference period ranging from 7 days for food to 12 months for non‐food expenditures (such as clothes or household durables).

> 9 The 18 communes were chosen by the implementing agency in order to reflect the diversity of project areas. 10 A minimum share of beneficiaries per village was set at 10%, and a maximum share at 70% per village. These were rarely binding in practice, however.

7
The following sections are based on data collection carried out in Masindi, Bushenyi and Tororo districts in 2008/2009 and confirm that current production volumes and yields only make economically viable transport by bicycle and/or motorcycle in most cases. Explicitly, volume is the critical factor in determining mechanization and in the case of Uganda, the current volume marketed is too low. This finding is especially critical to design the infrastructure requirement for farmers to be linked to markets and, in most cases, it can explain, despite a rural road, transport by truck is not much needed. Transport per truck can be only economically viable for high value products, over a relatively long distance (50 km) and with consolidated production (i.e., through an integrated company such as a cotton company which does organize transport or through cooperative mechanisms).

Transport by truck is indeed, by far, the cheapest mode of transport (per tonkilometer): almost ten times less expensive than bicycles and eight times less expensive than motorcycles (see Table 4). However, the story is more complex because per kilometer, transport by truck is more 10 times more expensive than bicycles and motorcycles.<sup>24</sup> It is also worth noting that when operating costs account for the bulk of total transport costs per truck, financing costs, and even more value of time, are the most important cost factor for bicycles and motorcycles, which means that operating costs are minimal.

**Table 4: Transport Costs per Mode of Transport (in US cents per tkm)**

|**Bicycle**|105.9|
|---|---|
|**Motorcycle**|95.9|
|**Truck**|11.2|

Note: mean load is 60 kilograms for bicycles, 110 kilograms for motorcycles and 10 tons for trucks. Value of time is included for bicycles and motorcycles; 1 hour is considered as the average transport time for bicycles (4 km) and 1,5 h for motorcycles (25 km). Source: surveys and DFID (2005) for the value of time.

Obviously, commodities selling prices differ from regions and between them. Therefore, the transport constraint may not be so strong for high value products. In the

> 24 See annex 6 for a detailed statistical description of the fixed and variable costs of different means of transport.

26
and “Assemblage industriel des produits fabriqués localement” are difficult to map to NAT96 sectors). When a certain CII activity was either too general or too specific to map to a NAT96 activity, we did not include it in our database. Coding the Investment Code thus inherently involves a degree of subjectivity, which we have tried to minimize by developing the correspondence between NAT96 and the Investment Code in collaboration with the Tunisian Institut National de la Statistique and a law firm specialized in investment law.

Another challenge we faced is that in some instances instead of being subject to authorization, firms that want to enter a particular have to submit a so-called _cahier de charges_ to the relevant ministry. A cahier de charges is merely a declarative statement on the basis of which entry can only be rejected if it does not meet pre-specified criteria. Sectors subject to a cahier de charges are not considered being subjected to authorization.

Table B1 presents a list of NAT96 sectors subject to authorization and/or FDI restrictions in 2010. The Stata dataset “ICCode9310” contains the regulation data by NAT96 sector for the period 1993-2010. The Excel spreadsheet “ICCode93” provides an overview of the original investment code and the spreadsheet “Decrees” lists all the decrees and how we have coded them.

43
admin population, rather than any national business population and represents populations in countries with sufficient internet penetration.

The data, tools, and further details on the methodology of the Future of Business survey can be found on the Facebook Data For Good website https://dataforgood.facebook.com/dfg/tools/future-of-businesssurvey.

In Table A1 we show all the countries that were included in the Future of Business COVID-19 surveys, and for which waves they were surveyed. Most countries were surveyed in all seven waves; however, sample size constraints meant some countries could only be included for a subset of the waves.

|**Table A1: Econo**|**mies include**|**d in the F**|**uture of**|**Business**|**High Freq**|**uency CO**|**VID-19**|**Surveys**|
|---|---|---|---|---|---|---|---|---|
||||**Wav**|**e of Faceb**|**ook Fob Dat**|**a**|||
|_Country Name_|_Wave 1_|_Wave 2_|_Wave 3_|_Wave 4_|_Wave 5_|_Wave 6_|_Wave 7_|_Total_|
|Argentina|1|1|1|1|1|1|1|7|
|Australia|1|1|1|1|1|1|1|7|
|Bangladesh|1|1|1|1|1|1|1|7|
|Belgium|1|1|1|1|1|1|1|7|
|Brazil|1|1|1|1|1|1|1|7|
|Cambodia|1|1|1|1|1|1|1|7|
|Canada|1|1|1|1|1|1|1|7|
|Colombia|1|1|1|1|1|1|1|7|
|Czech Republic|1|1|1|1|1|1|1|7|
|Denmark|1|1|1|1|1|1|1|7|
|Ecuador|1|1|1|1|1|1|1|7|
|Egypt|1|1|1|1|1|1|1|7|
|France|1|1|1|1|1|1|1|7|
|Germany|1|1|1|1|1|1|1|7|
|Ghana|1|1|1|1|1|1|1|7|
|Greece|1|1|1|1|1|1|1|7|
|Hong Kong|1|0|0|0|0|0|1|2|
|Hungary|1|1|1|1|1|1|1|7|
|India|1|1|1|1|1|1|1|7|
|Indonesia|1|1|1|1|1|1|1|7|
|Iraq|1|1|1|1|1|1|1|7|
|Ireland|1|0|0|0|0|0|1|2|
|Israel|1|1|1|1|1|1|1|7|
|Italy|1|1|1|1|1|1|1|7|
|Japan|1|1|1|1|1|1|1|7|
|Kenya|1|1|1|1|1|1|1|7|
|Malaysia|1|1|1|1|1|1|1|7|
|Mexico|1|1|1|1|1|1|1|7|
|Myanmar|1|1|1|1|1|1|1|7|
|Netherlands|1|1|1|1|1|1|1|7|
|Nigeria|1|1|1|1|1|1|1|7|
|<br>Norway|1|1|1|1|0|0|1|5|
|<br>Pakistan|1|1|1|1|1|1|1|7|
|Peru|1|1|1|1|1|1|1|7|
|Philippines|1|1|1|1|1|1|1|7|
|Poland|1|1|1|1|1|1|1|7|
|Portugal|1|1|1|1|1|1|1|7|
|Romania|1|1|1|1|1|1|1|7|

46
The Colombian government created in 2018 the _Permiso Especial de Permanencia_ (PEP) to facilitate their integration. Prior to the PEP, the government rolled out a voluntary registration process, the _Registro Administrativo de Migrantes Venezolanos_ (RAMV), with the sole purpose of identifying the location and needs of irregular migrants. In July 2018, the government unexpectedly announced that all migrants registered in RAMV, a little more than 440,000, were eligible for applying to the PEP program through a free and online tool. Of those, 64% registered into PEP. PEP granted legal migratory status for two years, access to education and health, eligibility to social programs, job permits, and an identification card to access private services. For a detailed description of the program see Ib ́a ̃nez et al. (2022).

The design and rollout of PEP lend themselves to estimating the causal effects on the resilience of forced migrants to negative shocks. The unexpected and broad nature of the program (available to all RAMV migrants and with no eligibility criteria), allows for ruling out anticipatory effects and strategic behaviors that have affected the evaluations of other similar programs.

# **III Data**

We use two waves of the Venezuelan Refugees Panel Survey (VenRePS), administered in the second semesters of 2020 and 2021. Figure A.1 in the appendix depicts the timeline for the PEP roll-out, the VenRePS waves, and the lockdowns put in place by the Colombian government. The baseline sample covered 2,232 forced migrant households who arrived in Colombia between January 1, 2017 and December 2018, including 1,135 who registered in RAMV and were thus eligible for PEP (from here on RAMV migrants), and 1,097 irregular forced migrants (non-RAMV migrants from here on).

The sample of RAMV migrants was randomly selected from the RAMV census. The sample of non-RAMV migrants was randomly selected by combining databases shared by associations of Venezuelan and referrals from who were sur- refugees migrants being veyed as part of the RAMV sample frame. The sample is representative of the three cities in Colombia that host the largest share of Venezuelan migrants (Barranquilla, Bogot ́a and Medell ́ın) and a fourth “region” that aggregates smaller cities. Ib ́a ̃nez et al. (2022) provides a detailed description on the survey, and shows that the initial conditions before migration to Colombia and the roll-out of PEP for the two sources of the non-RAMV samples (referrals and the list of organizations) are not systematically different.

In the second wave, we re-interviewed 1,432 households (64% of the sample in the first wave). Attrition from the sample was not systematic with respect to baseline conditions, although better-off households (male headed, better educated and with both spouses) and those residing for a longer period in Colombia were less likely to drop out of the sample (see Table A.6 in the Appendix).<sup>3</sup>

> 3Table A.7 also shows that the attrition rate per survey round between PEP eligible applicants and nonapplicants is significant and negative, but small (5.1 p.p.).

2
of program schools represents a significant share of all private schools in these seven districts: using the 2005 National Education Census (NEC), a census of schools in Pakistan, we estimate that the program covers 21% of private schools in these districts (see Table 4).<sup>10</sup> _Program location_ : The program was initially designed to be targeted at districts ranked lowest in terms of adult literacy rates based on 2003/04 Multiple Indicators Cluster Survey data (Government of Punjab 2004). However, in phases 1 and 2, this targeting decision was not applied as PEF was limited in its institutional and logistical capacity at that time to effectively administer the program in poorly-ranked districts that had physical environmental challenges and limited transportation and accommodation options (Malik 2007). In contrast, in phases 3 and 4, the targeting decision was effectively applied. Consequently, as Table 3 shows, 51% and 89% of program schools are located in districts ranked among the bottom-quarter and bottom-half in terms of adult literacy rates, respectively.

_Program school characteristics_ : Table 5 presents the distribution of program schools by selected characteristics measured in September 2008. Aggregating the phases together (Column 5), the mean school size is 351 students. The majority of schools are middle level (59%), coeducational (83%), registered with local government authorities (87%), and rural (55%). Disaggregating by phase (Columns 1–4), the distribution of program schools by these characteristics is roughly comparable except for the level of the school: in contrast to the pattern in the aggregate sample, program schools that entered in phases 1 and 2 were mainly secondary schools (65–73%). School size also appears to be monotonically decreasing with phase; the mean size of program schools which entered in phase 1 is 561 students, whereas that of program schools which entered

> 10 NEC’s coverage of private schools might be incomplete. According to the survey documentation, private schools were identified in the field by interviewers with the assistance of local officials. It is conceivable that private schools that are, for example, unregistered or very small, or operate in obscure locations are more likely to be missed by interviewers. Thus, the estimate of 21% can be considered as an upper-end estimate of FAS program coverage of private schools.

10
||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Age cut=63|Age cut=64|Age cut=66|Age cut=67|
|Treatment|-0.042|0.112|0.295|0.213|
||(0.191)|(0.199)|(0.232)|(0.237)|
|Observations|4436|4098|3225|2988|
|_R_<sup>2</sup>|0.336|0.338|0.330|0.335|

Standard errors in parentheses, * _p <_ 0 _._ 05, ** _p <_ 0 _._ 01, *** _p <_ 0 _._ 001 Table 7: Sensitivity to Bandwidth and Specifications

||h=7|h=8|h=9|h=10|h=11|
|---|---|---|---|---|---|
|Linear|0.447*|0.507**|0.586***|0.489**|0.451**|
||(0.241)|(0.229)|(0.213)|(0.200)|(0.190)|
|N|2696|3100|3645|4223|4728|
|_R_<sup>2</sup>|0.345|0.334|0.329|0.336|0.330|
|Quadratic|0.513|0.434|0.426|0.679**|0.702**|
||(0.347)|(0.326)|(0.306)|(0.290)|(0.278)|
|N|2696|3100|3645|4223|4728|
|_R_<sup>2</sup>|0.346|0.334|0.329|0.336|0.331|
|Cubic|0.585|0.656|0.654|0.397|0.516|
||(0.513)|(0.462)|(0.421)|(0.392)|(0.369)|
|N|2696|3100|3645|4223|4728|
|_R_<sup>2</sup>|0.347|0.335|0.329|0.336|0.331|

* ** *** Standard errors in parentheses, _p <_ 0 _._ 05, _p <_ 0 _._ 01, _p <_ 0 _._ 001

22