rafmacalaba/datause-encoder-data huggingface dataset:
agreement is ratified, its content is not expected to change unless there is an enlargement in the future. The variable takes a value of zero before an agreement is signed and then turns on to the above sum after the signature and remains the same until the end of the sample period. Therefore, the identification of the impact of depth comes from time variation within each pair of countries.

Another way to measure depth is to separate the disciplines between those that fall under the current mandate of the WTO (such as _tariffs_ , _customs_ and _anti-dumping_ ) and those that go beyond this mandate and are not subject to any kind of WTO agreements (examples are agreements on _investment_ , _competition policy_ , _labor market laws_ , or _environmental regulations_ ). Following (Horn, Mavroidis, & Sapir, 2010) we call the former measure of depth WTO-plus and the latter WTO-X and define them as: i = ∑14 i and i = ∑38 i , where i pp xx pp are 14 WTO+ disciplines and WWTTWWT WWDD ii pp=1 TTD DD ii i are 38 WTO-X disciplines included in an WWTTWWTTWWTTWWTT ii xx=1 TTD DD ii TTD DD ii xx agreement between countries and in year ii . TTD DD DD jj TT In the second stage of our analysis, we use dummy variables to identify the commitment towards individual disciplines in PTAs. While including these dummies sequentially, we also make sure that we control for the rest of the PTA content by including a variable of the “remaining” depth as the sum of disciplines except the one singled out in the specification. We sequentially tested all core disciplines mapped in the database with clear economic content, to eventually restrict ourselves only to those having a significant impact on FDI. As shown in Figure 2, the selected disciplines are also the most frequent WTO-X areas included in PTAs.

# b. Investments

Reliable investment data are not as readily available as trade data. When available the data usually come from surveys conducted by Central Banks or Statistical Offices and are used to comply with reporting obligations such as balance of payments or international investment position statistics. The data reporting often lacks uniformity 9
|||**Modified**|**Local (l)**|
|---|---|---|---|
|**NAICS**|**Industry Name**|**Herfindahl**|**Regional (r)**|
|484220|Specialized Freight (except Used Goods) Trucking, Local|0.001201|l|
|484110|General Freight Trucking, Local|0.00134|l|
|541219|Other Accounting Services|0.001874|l|
|493110|General Warehousing and Storage|0.002287|l|
|561110|Office Administrative Services|0.003353|l|
||Other Electronic and Precision Equipment Repair and|||
|811219|Maintenance|0.003672|l|
||Commercial and Industrial Machinery and Equipment|||
|811310|(except Automotive and Electronic) Repair|0.004667|l|
|561410|Document Preparation Services|0.004911|l|
|493130|Farm Product Warehousing and Storage|0.005773|l|
|493190|Other Warehousing and Storage|0.005910|l|
|561499|All Other Business Support Services|0.006142|l|
|561910|Packaging and Labeling Services|0.006186|l|
|541614|Process, Physical Distribution, and Logistics Consulting|0.006207|l|
|493120|Refrigerated Warehousing and Storage|0.006388|l|
|541612|Human Resources Consulting Services|0.006618|r|
|541511|Custom Computer Programming Services|0.006821|r|
|811213|Communication Equipment Repair and Maintenance|0.007847|r|
|541613|Marketing Consulting Services|0.007908|r|
|488510|Freight Transportation Arrangement|0.008280|r|
|541512|Computer Systems Design Services|0.008515|r|
|562112|Hazardous Waste Collection|0.008640|r|
|488991|Packing and Crating|0.009334|r|
|488210|Support Activities for Rail Transportation|0.009852|r|
||Administrative Management and General Management|||
|541611|Consulting Services|0.010226|r|
|541519|Other Computer Related Services|0.010330|r|
|811212|Computer and Office Machine Repair and Maintenance|0.011539|r|
|541513|Computer Facilities Management Services|0.019112|r|
|562211|Hazardous Waste Treatment and Disposal|0.023096|r|
|541214|Payroll Services|0.037047|r|

Table 6.2b: Supporting industries classified as local or regional 21
seeking care if they reported being sick. This helps to differentiate preventative care from required care. Individuals who received any treatment when sick reported to have used one of the following providers: doctors, healers, home treatment (self-prescription), private physicians, and hospitals. Given the limited sample size, we were unable to gather detailed information for those who were hospitalized or used a healer (among individuals registered for poverty family benefits only 64 were hospitalized and 24 received heath treatment from a healer).

Data from the 2004 ILCS allows simulating whether or not households are entitled to receive family poverty benefits according to a means testing algorithm that defines eligibility (see appendix).<sup>2</sup> Based on this algorithm, we constructed a dummy variable that takes the value of one if a user is eligible for family poverty benefits and the values zero if otherwise. Our sample is delimited to those users identified as being eligible. To identify a control group we look at whether “eligible” households are a) registered to receive BBP benefits and b) whether they actually get the benefits. Users with access to BBP were identified using two primary questions: the first identifies whether a user lives in a household registered to get family poverty benefits, and the second asks whether the individuals in the household actually receive the benefits. In relation to expenditures on health, the survey contains detailed questions on the cost of treatment received from different providers. Since the survey was conducted over a 12-month period, the comparability of the quintiles should not be affected by seasonal fluctuations.

Sample size and simple descriptive statistics are presented in Table 1. Our analysis is conducted on those individuals eligible for family poverty benefits who are registered for the BBP (3,316 individuals). About 77 percent of them (2,422 individuals) actually receive BBP benefits while the remaining 13 percent (714 individuals), being registered, do not yet receive benefits. Given that rural areas are more vulnerable to poverty, it is not surprising that eligible

> 2 2004 ILCS data allow to replicate the main variables needed to estimate household eligibility scores for the poverty family benefits system in Armenia. However, data do not contain information to estimate some of parameters included in the eligibility formula (they are Pr and Pf; see Appendix for more details). As such, while our eligibility dummy is a good proxy for actual eligibility, it may contain some errors of inclusion and exclusion that are beyond our control.

7
There are myriad definitions of ownership, control, and use of assets; they represent the wide variety of rights over assets. Schlager and Ostrom (1992) characterize different bundles of rights along a continuum from access or use rights to control rights to ownership rights. According to the Collective Action and Property Rights (CAPRi) program, use rights might include the right to access the resource (e.g. walk across a field), withdraw from a resource (e.g. pick wild plants), or exploit a resource for economic benefit (e.g. fish commercially). Control or decision-making rights include the rights of management (e.g. plant a crop), exclusion (i.e. prevent others from accessing a resource), or alienation (e.g. rent out, sell, or give away the rights) (CAPRi, 2010). Transfer rights, including bequeathing, as well as the ability to distribute benefits from the assets, may be considered control or decision-making rights.

A major challenge in measuring asset ownership is that the approaches necessarily vary substantially by asset type and by context. Understanding the definition of ownership in each setting requires an understanding of what rights are generally associated with ownership. This may include the full bundle of use and control rights or it may be defined as the right of alienation. The System of National Accounts uses two definitions of ownership, distinguishing between the legal owner and the economic owner.

“The legal owner of entities such as goods and services, natural resources, financial assets and liabilities is the institutional unit entitled in law and sustainable under the law to claim the benefits associated with the entities. By contrast, the economic owner of entities such as goods and services, natural resources, financial assets and liabilities is the institutional unit entitled to claim the benefits associated with the use of the entity in question in the course of an economic activity by virtue of accepting the associated risks” (UN Statistics, 2009. p. 195).

In practice, the approaches to collecting ownership data in household surveys do not map cleanly onto the concepts of the various rights. Household surveys tend to gather information on reported (or perception-based) ownership, documented ownership, or occasionally both. For reported ownership, the respondent(s) is/are asked who owns an asset, while for documented ownership, the respondent(s) is/are generally asked whose name(s) is/are listed as owner(s) on an ownership document such as a title, will, or receipt.<sup>6</sup> While documented ownership is often considered the most secure, it is only relevant for certain assets, and only in places where the enforcement of the associated rights is effective.

Ownership, whether reported or documented, does not overlap consistently with the other property rights articulated above. When there are benefits, such as tax reductions in India for property registered to women, this does not necessarily translate into increased control

> 6 Only rarely do enumerators ask to see copies of the ownership documents.

5
<!-- Start of picture text --> Impact o AFTA on ASEAN New Member Countries<br>ASEAN Member Country Overall Effect Major Specific Effects<br>(from reductions in imports from<br>partner countries). However,<br>gains are small because<br>Myanmar's tariff rates already<br>low. Most gains are from<br>improved terms-of-trade from<br>tariff cuts in partner countries.<br>Vietnam Somewhat beneficial Benefit is small because<br>Vietnam's share of trade with<br>ASEAN is initially small. Also,<br>gains from improved access to<br>partner markets limited because<br>exports to ASEAN are via<br>Singapore where protection is<br>nearly zero. AFTA benefits<br>agricultural sector through better<br>._______________ access to ASEAN markets.<br>Source: Fukase and Martin (2001)<br><!-- End of picture text --> Overall, the simulation results for the new member countries in Fukase and Martin (2001) are consistent with those for the ASEAN5 countries in DeRosa (1995) and in Lewis and Robinson (1996). All new member countries are positively affected by AFTA, although the effects are relatively small. Also in line with DeRosa's findings, Fukase and Martin report that benefits increase if AFTA commitments are extended on a non-discriminatory basis (MFN liberalization), which implies that "AFTA may be used as a useful stepping ston.e to further liberalization because it exposes domestic industry to greater competition, and creates a situation where there are substantial benefits from reducing the trade diversion associated with discriminatory liberalization."'<sup>81</sup> The simulation shows that Vietnam especially stands to benefit in this way implying "that the sectoral protection currently given to capital-intensive and 'strategic' industries is costing Vietnam's economy as a whole, imposing substantial implicit taxes on the rest of the economy."'<sup>8 2</sup> The above quantitative analyses address the _static_ benefits and costs of membership in AFTA. **[t** is equally, if not more, important to examine the _dynamic_ effects of an RTA. This would involve the assessment of any factor that would impact the economic growth rate of a member country over the medium- to long-termn. While current literature does not support a theory that RTAs _always_ lead to dynamic gains for developing countries,1<sup>83</sup> the possibility that dynamic effects of regional integration might contribute to growth through AFTA is examined by Fukase and Martin (2001). The authors determine the degree of convergence (in this case, narrowing of the difference between national per capita income over time)1<sup>84</sup> as the means to assess this growth. As far as developed countries are concerned, there exists some strong evidence that an RTA increases trade between members leading to the convergence of per capita incomes across countries. The EC, the European Free Trade Association (EFTA), and the U.S. and Canada are cited as exarmples of regional groupings that experienced this after signing RTAs.'<sup>85</sup> However, there is little work in this area related to developing countries.

55
# Table 7

# _Delivery – 5 largest regression coefficients_

|Delivery variable|Program impact<br>on student<br>learning|
|---|---|
|Training takes place in university or training center|0.385**<br>(0.142)|
|Follow-up visits to review material|0.256<br>(0.156)|
|Most common profile of the direct trainers is researchers|-0.196<br>(0.336)|
|Most common profile of the direct trainers is local government officials|-0.170<br>(0.257)|
|Proportion of training spent practicing with other teachers|0.169<br>(0.134)|
|Observations|26|
|Standard errors inparentheses. ***p<0.01, **p<0.05, *p<0.1.||

33
migration for women, but increase migration of children for work, especially for boys. To carry out their analysis, they use the migration-related questions on where the individual lived before moving to the current area of residence, when he/she moved, and the stated motive for doing so. Although this allows one to retrieve the district of origin and destination as well as the time of migration of individuals interviewed, they notice that the lack of information at origin before moving is a limitation for their study.

Di Falco et al. (2022) use LSMS-ISA data to construct a multi-country panel data set covering Ethiopia, Malawi, Niger, Nigeria, and Uganda that is merged with high-resolution gridded precipitation historical records from the Climate Research Unit to analyze the effects of cumulative drought shocks on the decision to migrate in rural households. While confirming the existence of an immediate, although small, impact on migration decisions in the aftermath (i.e., the subsequent year) of a severe and extreme drought shock, they interestingly show that this impact is long-lasting, increasing migration for at least five years after the shock occurs, and not even fading or diminishing over time. Furthermore, they find that the effect of multiple recently experienced droughts (past five years) accumulates over time, which results in a much higher number of migrants than one would expect based on the immediate effect of the shock only. The authors emphasize that this has relevant implications for the study of climate-induced migration and make a plea for advancing the research on the cumulative impacts of climate change on determining migratory flows in the long-run while at the same time improving the availability of detailed data on migration.

The fact that out of the vast and growing literature reviewed before, only five studies employ LSMS-ISA data (and even with conflicting findings), is a clear indicator of the currently limited capacity of the LSMS-ISA data sets to provide a basis for meaningful analysis on climate change and migration. Figure 1 below provides an idea of the type of migration tracking that is possible using the LSMS-ISA data.

Let us focus, as a benchmark (see below for a comparative analysis of all LSMS-ISA surveys), on the Tanzanian National Panel survey, which was used by three out of the five studies above. All the available migration information in the questionnaires is essentially limited to a few questions in two sections in the Household Questionnaire, Modules B and G.<sup>14</sup> Module G, named “Children Living Elsewhere – Migration” contains some information on households responding affirmatively to the question: “ _Do you have any children 15 and older who live elsewhere (outside this household)_ ?” such as information on the

> 14 Cf. https://microdata.worldbank.org/index.php/catalog/76/related-materials. We use questionnaires from the second round

> (2008-2009) as the benchmark here.

27
# **3 Methodology**

Let’s consider, for a given household, the adult equivalent (AE) structure as follows: In this equation, _i_ stands for each additional adult in the household, while _j_ represents each child. The parameters  _i_ and  _j_ are then the relative costs respectively for an adult _i_ and for a child _j_ of the household, with  equal one for the first adult. These relative costs generally depend on a set of social and demographic characteristics such as age and gender. The variables<sup>_n_</sup> _a_<sup>and</sup><sup>_n_</sup> _c_<sup>represent respectively the number of adults and the</sup> number of children in the household. The parameter  is the size-elasticity which captures the economies of scale in the household. The per capita approach which is often used for poverty assessment studies in developing countries corresponds to the case where  _i_ ,  _j_ and  are all equal to one. But in assuming that economies of scale exist in the household, many equivalence scales can be derived from this general formula. Two equivalence methods are considered here, namely the FAO/WHO approach and the square root approach. In the first case,  _i_ and  _j_ will be provided by the required calories intake for each adult and each child regarding their age and sex. In the second case,  _i_ and  _j_ are set to one, while  is equal to 0.5. Equation (1) becomes: 0.5 _N eq_   _na_  _nc_  (2) As suggested by Deaton (2003), Deaton and Zaidi (2002) and recently by Ravallion (2015), a straightforward way to adjust consumption by the number of adult equivalents is to select a reference household as "pivot", such that poverty in households with the same demographics remains unaffected by changes in the parameters. This adjustment may be done through two equivalent procedures. The first one is to reflect information on the "pivot" in the equivalence scale such that the expenditure per AE is unchanged for the "pivot" households. The second procedure is to keep the usual expenditure per AE and to use information on the "pivot" rather to alter the poverty line of each household. In that case, the poverty line will be changed for "pivot" households in the same proportion as 8
To apply our occupation-level measures to other countries, we aggregate our SOC8-digit measures to the SOC2-digit level using U.S. employment weights from the Current Population Survey (CPS).

The second step is to assess the actual availability of internet services by occupation and country. For this step, we combine information on the share of internet users by country and income level from the Gallup World Poll 2019 with data on average wages by occupation (at the two-digit disaggregation) from ILOSTAT. Gallup survey data provide the share of internet access at home among the top 60 percent and the bottom 40 percent of the income distribution in each country. This distinction enables us to account for the fact that internet use is positively correlated with income levels. The ILOSTAT data allow us to rank occupations by their average wages and assign them to either the top 60 percent or the bottom 40 percent of the income distribution in their corresponding country, which in turn allows us to concord them with the Gallup data to construct country-specific measures of internet penetration by occupation.

Once we have both the share of DN2020 jobs that require internet and the internet penetration across occupations, we calculate the share of jobs that can be done from home by summing the share of jobs that can be done from home and do not require internet with the share of jobs that can be done at home and need internet multiplied by the relevant internet access rates at home from Gallup.

Most of our global analysis relies on the ILOSTAT database (See ILO 2020 for details) that provides information on wages and employment numbers per occupation for over 180 countries. We restrict the country coverage to 107 countries for which 2-digit occupations are available. We also use individual data from the most recent (2018) European Labor Force Survey (EU LFS) as well as the labor force surveys from several large countries – Brazil (2017), India (2012), Mexico (2018), and Turkey (2018). These data sets are the most recent surveys and include education level, formality status, age, wages, and occupational category of a large and representative sample of the working population and enable us to validate the conclusions derived from the analysis based on ILOSTAT data.

8
intensive sectors, the welfare changes are all _quantitatively_ small, generally measuring much less than 1 percent from the base value of welfare. We examine the issue more carefully below along with the fiscal and economic results.

**_The macroeconomic and fiscal results_** of the various tax policies are shown in Table 6.  Except for the sales tax on energy intensive sectors, the negative impact on GDP or consumption is generally less than 1 percent for both elasticity cases.  Following the welfare results, the GDP and consumption effects of a carbon tax remain generally the same in both elasticity cases.  Likewise, the impacts for a sales tax on energy are much less in the rigid case when compared to the corresponding numbers in the reference case. Although the percent changes are generally small, the annual losses in output could amount to about $330 million for the carbon tax case.

**<u>Table 6: Fiscal and Macroeconomic results by Tax Policy</u>**

||**tax on**<br>**carbon**<br>**emission**|**sales tax**<br>**on**<br>**energy**|**sales tax**<br>**on**<br>**energy**<br>**intensive**<br>**sectors**|**tax on**<br>**carbon**<br>**emission**|**sales**<br>**tax on**<br>**energy**|**sales tax**<br>**on**<br>**energy**<br>**intensive**<br>**sectors**|
|---|---|---|---|---|---|---|
||**I. Referenc**|**e Case**||**II. Rigid Cas**|**e**||
|Carbon tax per metric ton of|||||||
|CO2emissions|0.10|||0.17|||
|Rand|96.25|||165.22|||
|USD|12.72|||21.84|||
|Additional tax rate||0.33|0.44||0.44|0.48|
|Ratio of additional tax revenue<br>from meeting CO2 Emission<br>target to total tax revenue|0.09|0.30|0.75|0.16|0.50|0.91|
|GDP (percent change)|-0.20|-0.41|-1.47|-0.20|-0.02|-1.76|
|Consumption (percent change)|-0.32|-0.66|-2.34|-0.31|-0.03|-2.80|
|Factor Adjustment (percent)|||||||
|Capital|0.67|1.32|3.04|0.90|2.10|3.70|
|Energy|4.81|10.54|7.31|4.80|9.86|7.20|
|Low-skilled labor|0.50|0.89|3.30|0.78|1.53|3.90|
|Medium-skilled labor|0.48|1.07|3.06|0.79|1.85|3.77|
|High-skilled labor|0.37|0.84|2.46|0.60|1.44|3.01|

_Source:_ CGE model simulations. _Notes_ : Factor adjustment is percent of total employed workers who must find another job as a result of the policy shock. Energy = coal, electricity & gas, and petroleum; energy intensive sectors = basic iron and steel, transportation, basic non-ferrous metals, and metal products excluding machinery.

The degree of substitution possibility has the expected effects on the tax rates, being higher for the lower elasticity case. In order to attain a 15 percent reduction in 17