rafmacalaba/datause-encoder-data huggingface dataset:
training with 2 to 3 months of on-the-job training in the form of an internship. Some programs additionally provide life skills training.

There has been rapid growth in the number of randomized experiments evaluating these programs. I focus on evaluations of traditional vocational training programs – there have also been several recent evaluations of bundled packages for adolescent girls which incorporate vocational training with other services such as business skills training, empowerment activities, and support in setting up businesses or finding jobs. An example is Adoho et al. (2014) who report early findings from Liberia. They find positive impacts of that program on employment, but estimate that it would take 12 years for participants to recoup the costs of the job skills training provided in that program.

Table 1 summarizes the results from 12 evaluations from 8 countries. The typical evaluation measures impacts 12 to 18 months after the conclusion of the training program, using surveys administered to the treatment and control groups. The use of surveys to measure key employment outcomes raises several concerns. The most major one is that of attrition, with all, but one study, having attrition rates of 18 percent or higher, ranging up to 46 percent in Cho et al. (2013). This attrition is a problem, because we might expect the employment outcomes of individuals who refuse to be surveyed or who cannot be found to differ from those who are interviewed. A typical approach has been to compare attrition rates in the treatment and control groups, and then do a bounding exercise if the attrition rates vary (often the control group is slightly less likely to respond). But it is easy to think of problems which can arise even when the attrition rates are the same for both groups: for example, the attritors in the treatment group may be people who went through the training and did not find it useful and have still not found jobs, while those in the control group could be those who are too busy to answer surveys because they are employed in good jobs. This type of differential response would bias the estimated treatment effect upwards, overstating the impact of training.

A second issue with the use of survey measures of employment is the possibility that those in the treatment groups over-report their employment outcomes to express their appreciation for being given the program, while those in the control group potentially underreport these outcomes because they maintain some hope of still being given the program. Good survey design and survey framing can mitigate these issues. An alternative approach is then to use administrative data on employment from national social security or labor databases. These databases capture formally 5
**Figure 1: World Bank export volume projections for surveyed countries, 2009 (%)**

<!-- Start of picture text --> ‐11.8 Philippines<br>‐10.1 Ukraine<br>‐9.0 Turkey<br>‐6.5 South Africa<br>‐5.5 Egypt<br>‐4.7 Chile<br>‐4.1 Brazil<br>‐3.0 Tunisia<br>‐3.0 Indonesia<br>‐3.0 India<br>1.1 Peru<br>2.0 Kenya<br>3.1 Sierra Leone<br>3.2 Ghana<br><!-- End of picture text --> _Source: World Bank Development Economic Prospects_

**Figure 2: Quarterly exports and imports in surveyed countries, Q3-2008 to Q1-2009**

<!-- Start of picture text --> Exports, quarterly yoy, % Imports, Quarterly yoy, %<br>100.0 150<br>130<br>80.0<br>110<br>60.0 90<br>70<br>40.0<br>50<br>20.0 30<br>0.0 10<br>‐10<br>‐20.0 ‐30<br>‐40.0 ‐50<br>‐60.0<br>Q3/2008 Q4/2008 Q1/2009 Q3/2008 Q4/2008 Q1/2009<br><!-- End of picture text --> _Source: DataStream_

**_B. The crisis has affected trade in different ways_**

While most countries have been severely affected, the impact of the decline has been relatively severe in countries that are integrated and dependent on trade with developed countries, where demand has sharply contracted. In our sample, those countries include Indonesia, the Philippines, South Africa, Tunisia and Turkey. The increased fragmentation of the production that took place in the last two decades has led to the increase in the elasticity of global trade volumes to real world GDP from around 2 in the 1960s to above 3 in recent years. The higher elasticity means that trade would drop faster 10
deployment of political strength by US lobbies during NAFTA talks. Destler (2006) narrates how US Representatives from North Carolina shifted their stance from opposition to the extension of fast-track procedures (a de facto authorization for the Bush administration to pursue NAFTA negotiations), to support to it, once RoO known as “yarn-forward rules” were negotiated for textiles and apparel. These particularly ingenious rules require that the three basic processes of making a piece of apparel (the making of fibber, cloth, then clothing) take place in a NAFTA country. Since textile producers in North Carolina had a comparative advantage in textile production within the NAFTA region, they stood to gain by this policy that forced Mexican producers of apparel to buy textiles from them, instead of from other cheaper sources.

# **3. ROO under NAFTA: description and measurement of their restrictiveness**

Generally, there are two components: regime-wide RoO and product-specific rules of origin (PSRO). Regime-wide RoO are described in appendix A.1. PSRO determine eligibility at the tariff-line level according to two basic criteria.

Firstly, the **_wholly obtained or produced criterion_** is employed when the whole production process and all inputs used in this process should be originated from the same country in order to attribute originating status to the good. Only one country enters into consideration in attributing origin.

Secondly, the **_substantial transformation criterion_** involves two or more countries that take part in the production process. RoO define the methods by which it can be ascertained whether the good has undergone “substantial transformation” in the exported country to be granted “originating” status. It comprises three main components, which can be used in combination with each other or as stand-alone:

**i) A change in tariff classification (CTC)** can be expressed at various levels of aggregation in the Harmonized System (HS) nomenclature<sup>7</sup> . From broader to narrower, origin is granted if the

> 7 The Harmonized Commodity Description and Coding System, commonly known as "HS Nomenclature", is an international multipurpose nomenclature elaborated under the auspices of the World Customs Organization. Although only 116 countries are Contracting Parties to this Convention, more than 190 administrations worldwide apply the US nomenclature, mostly to set up their national customs tariff and for the collection of economic statistical data. The HS

7
# **Table 2: Getting Electricity indicator data (2010/2011)**

||||Private<br>or|Procedures<br>Getting|Time<br>Getting|Cost in<br>USD<br>Getting|Cost as % of<br>GNI per capita<br>Getting|
|---|---|---|---|---|---|---|---|
|Economy|City|Name of the Utility<br>|Public|Electricity|Electricity|Electricity|Electricity|
|||Kabul Electricity Department,<br>Da Afghanistan Breshna||||||
|Afghanistan|Kabul|<br>Sherket(DABS)|Public|4|191|18,040.0|3,711.1|
|Albania|Tirana|CEZ Shperndarje|Private|6|177|24,290.4|615.0|
|Algeria|Algiers|SONELGAZ (via filiale<br>Société de Distribution de<br>l'électricité et gaz d'Alger<br>(SDA))<br>|Public|6|159|78,431.8|1,774.5|
|Angola|Luanda|Empresa de Distribuicao de<br>electricidade(EDEL)|Public|9|69|44,618.7|1,278.5|
|Antigua and<br>Barbuda|St. John's<br>|Antigua Public Utilities<br>Authority|Public|4|42|16,037.5|132.2|
||Buenos Aires|||||||
||(Ciudad|||||||
|Argentina|<br>autonoma de)|EDESUR<br>|Private|6|91|3,567.1|46.9|
|Armenia|Yerevan|Electric Networks of Armenia<br>(ENA)CJSC|Private|6|242|8,645.8|278.9|
|Australia|Sydney|EnergyAustralia|Public|5|75|4,317.5|9.9|
|Austria|Vienna|Wien Energie Stromnetz<br>GmbH|Public|5|23|52,955.2|113.0|
|Azerbaijan|Baku|Bakielektrikshebeke OJSC|Public|9|241|37,732.3|779.6|
|Bahamas||Bahamas Electricity||||||
|,<br>The|Nassau|<br>Corporation|Public|5|67|24,645.8|114.5|
|Bahrain|Manama|Ministry of Electricity and<br>Water (MEW) (Electricity<br>Distribution Directorate, EDD<br>at the MEW)|Public|5|90|13,031.9|67.0|
|Bangladesh|Dhaka|Dhaka Electric Supply<br>CompanyLtd.(DESCO)|Public|8|142|32,898.7|5,576.1|
|Belarus|Minsk|Minskenergo|Public|7|179|76,619.7|1,383.0|
|Belgium|Brussels|Sibelga|Public|6|88|43,803.3|96.7|
|Belize|Belize City|Belize ElectricityLtd(BEL)|Private|5|66|14,941.5|369.4|
|Benin|Cotonou|Société béninoise d'énergie<br>électrique(SBEE)|Public|4|172|115,890.3|15,452.0|
|Bhutan|Thimphu|Bhutan Power Corporation Ltd|Public|6|101|25,473.3|1,261.1|
|Bolivia|La Paz|Electropaz|Private|8|42|21,146.1|1,297.3|
|Bosnia and<br>Herzegovina|Sarajevo|Elektroprivreda BiH|Public|8|125|24,271.6|516.4|
|Botswana|Gaborone|Botswana Power Corporation|Public|5|121|31,005.9|495.3|
|Brazil|São Paulo|AES Eletropaulo|Private|6|57|12,192.8|151.1|
|||Department of Electrical||||||
|Brunei|Bandar Seri|<br>Services (at the Ministry of||||||
|Darussalam|Begawan|<br>Energy)|Public|5|79|12,891.4|49.0|

27
at the grass-roots level, not just at the macro-level (Stedman et al. 2002). Indeed, most peacekeeping operations carried out in the past two decades have tended to penetrate war-torn societies with unprecedented levels of breath and depth, targeting directly the social, economic and political aspects of local people’s lives (Talentino 2007; Pouligny 2006; Sisk and Risley 2005). By providing local-level security and assistance that facilitates economic and social revitalization, peacekeeping operations are presumed to raise the opportunity costs of war and empower those seeking peace.<sup>3</sup> Yet, with very few exceptions (e.g., Caruso et al. 2017; Gordon and Young 2017; Ruggeri et al. 2017; Beber et al. 2016), the question of whether and how peacekeeping operations work at the local level remains elusive, as much of the existing empirical literature has focused at the macrolevel.<sup>4</sup> In this paper, we use a micro-level approach to test some of the hypotheses implicit in the design of multidimensional peacekeeping operations in the context of the United Nations Mission in Liberia (UNMIL).<sup>5</sup> We use matching methods, original survey data, and administrative data from post-war Liberia to measure micro-effects of peacekeeping on security and socioeconomic outcomes. We exploit the fact that when establishing deployment locations in the Liberian countryside, peacekeepers had limited information with which to work. Using coarsened exact matching on administrative data used in United Nations planning, we identify sets of communities that, presumably, were similarly likely to receive a peacekeeping base. Liberia offers a good test of these hypotheses, because extremely low state service capacity allows for the possibility of peacekeepers to have maximal impact.

> The evidence suggests a rather negative picture. We find no support for the claim that deployments increase local security, and find extremely modest effects of deployments on economic 3For example, the presence of a peacekeeping deployment may deter armed groups from engaging in local violence, which in turn may make citizens more comfortable about exercising their political voice and right to participate in the political process than they would otherwise be (Mvukiyehe 2017).

> 4See Fortna and Howard (2008) for a review of this literature.

> 5Our empirical approach follows a handful of studies such as Humphreys and Weinstein (2007) and Gilligan et al. (2011), which examined the disarmament, demobilization and reintegration (DDR) component of the peacekeeping mission in Sierra Leone and Burundi respectively. But our study covers different types of activities and key outcomes.

iii
In addition, bank deposits have been increasing as a result of the flight to banks from the distressed finance companies. Chart T4 shows how liquidity is flowing through the banking system.

<!-- Start of picture text --> CHART T3 CHART T4<br>REAL DOMESTIC CREDIT GROWTH BANKS' EXCESS LIQUID ASSETS (millions of Baht)<br>/~~~~~~~~~~~~~~~~~~~~~~~~~~ 10750200<br>0100 101007 0 11991 01.000 ; ,)<br>Indeed, the permanent closure of 56 finance companies in December 1997<br>eliminated the main source of financing for small businesses and left the consumer credit<br>system in great difficulty. The remaining 35 finance companies and 15 commercial<br>banks were required to make higher provisions, and their percentage of non-performing<br>loans are expected to increase as the economic downturn deepens. 16<br>Chart T5 CHART T6<br>EVOLUTION OF INTERBANK RATE AND INFLATION TREND IN SOME KEY INTEREST RATES<br>0 , .9 i. . ,1 -SJX<br>99.s07 94003 a* 9 O ao 7'0 5 oo 970 en w0 070 w s 01 o 000 000 97 Ir 910 9700 9150: 1700900 11 ,1 9/000 900<br>Prime rat-a FieSd d.e70i W09 (I Ye) e M maI en tI rlte<br>m Inflation (year-on-year) + Intelbank rate (average)<br><!-- End of picture text --> Indeed, the permanent closure of 56 finance companies in December 1997 eliminated the main source of financing for small businesses and left the consumer credit system in great difficulty. The remaining 35 finance companies and 15 commercial banks were required to make higher provisions, and their percentage of non-performing loans are expected to increase as the economic downturn deepens.<sup>16</sup> EVOLUTION OF INTERBANK RATE AND INFLATION

**16**

Based on the latest available data from the Central Bank, the non-performing loans of the banking system represented 8.3% of total loans as of June 1997. By December they were estimated to have reached 18% and are expected to peak 25-30% in the middle of 1998. In May 1998, a further 13 finance companies were closed.

32
but would be a valuable avenue for future research. Here, we view the cooperative as performing the optimization above independently for each species.

_Testing Assumption of Members’ Response to Cooperative Prices_ To test the assumption that cooperative members increase their catch in response to an increase in the cooperative price, we use the daily transaction data with one observation for each recorded sale by a fishing team. In the model, a cooperative member _i_ ’s catch in period _t_ is given by the following: Taking logs gives the following: _log_ ( _qict_ * ) =  _log_ (2)  _log_ ( _Pct_ )  2 _log_ (1  (1  ) _Ht_ )  2 _log_ ( _H t_ )  2 _log_ ( _X t_ )  _log_ (  _i_   ) . (16) The first two terms on the right-hand side pose no complications for estimation, but the remaining terms do. First, _X t_ ,  , and  _i_ are unobservable to us. Second, _Ht_ is implicitly a function of other quantities from the model (see equation 8 above). In the case of either a nonexclusive cooperative or the exclusive cooperative with a large range species, _Ht_ is a function of _Pct_ , _Pmt_ , _X t_ , and parameters  and  . For an exclusive cooperative fishing a small-range species, the same is true, except _Ht_ is not a function of _Pmt_ .

These considerations and equation 16 motivate the following log-linear approximation: _log_ ( _qict_ * ) =  _i_   0 _log_ ( _Pct_ )   1 _log_ ( _Pmt_ )   _ict_ , (17) where<sup></sup> _i_<sup>is a fishing-team fixed effect capturing</sup>  _i_ ,  , and  , and  _ict_ is a residual. The key identification concern is that the residual is clearly correlated with _Pct_ (and, perhaps, with _Pmt_ as well). This is because the residual contains _X t_ , and _X t_ is chosen in part by the cooperative when it sets _Pct_ . In addition, the residual may contain an important factor that is outside the model, timevarying shocks to the cost of fishing.

If one can address this concern, then an estimate of  0 includes two economic items: the direct, positive impact of increasing the cooperative price on an individual’s catch; and the negative, indirect impact coming from the resulting increase in _Ht_ .<sup>20</sup> The term  1 captures the negative impact of increasing _Pmt_ through the resulting increase in _Ht_ . Our primary goal is to 20. We are very appreciative of an anonymous referee who provided this important insight.

2
2 _LF_ ( _g_ , _a_ , _e_ , _t_ ) = _LFPR_ ( _g_ , _a_ , _e_ , _t_ ) * ∑ _Skill_ ( _i_ , _g_ , _a_ , _e_ , _t_ ) _i_ = 1 (10) where LF is the labor force array and LFPR is the labor force participation array.<sup>12</sup> Now, we are ready to turn the LF array into a measure of human capital. Recall that we can predict the individual human capital value for all combinations of gender, age and educational attainment categories HCCat(g,a,e). Then the evolution of human capital is given by In this paper we infer changes in retirement probabilities by age, by looking at changes in participation rates.<sup>13</sup> Hence, we can compare the dynamics of human capital under the status-quo with the dynamics of human capital in the case where participation rates change as a result of changes in pension benefit formulas and eligibility conditions.

Admittedly, there are limitations in the proposed analysis. First, the choice of the measure of human capital only considers the supply side of the labor market, i.e. implicitly assumes that those who enter the labor force will actually get a job and thus become a part of the (productive) human capital stock. Hence, our projections really constitute an analysis of potential human capital. In addition, the individual productivity values that we derive are associated with the interaction of labor supply and demand at a given point in time, and then we assume that this interaction is applicable to the future. Note that the productivity of a given unit of human capital can change as a result of technological progress. Our estimates of human capital should therefore be considered as a lower bound.

_Quantitative example: the case of Morocco_ We illustrate the use of the model in the case of Morocco. Figure 5 illustrates the dynamics of population growth and the skills composition of the labor force as projected

> 12 In practice we had to make certain homogeneity assumption in the case of the labor force participation rate array since the level of the disaggregation of the available data along the educational attainment dimension was insufficient. The ILO publishes age and gender specific labor force participation rate projections for the various countries in the LABORSTA database.

> 13 The model could be easily extended to projecting the age and gender distribution of the population divided into 3 categories: “in school”, “retired”, “potential labor force”. This formulation would allow that the changes in labor force participation induced by changes in the pension system enter the model through the changes in the gender and age specific retirement probabilities. The choice of formulation depends on the application, in particular, whether it is easier to assess the impact of the changes induced by the pension system in terms of retirement probabilities or labor force participation rates.

13
Appendix Table 9.2 shows which mechanisms have sharpened q-values below 0.10, taking as a family the set of 26 outcome-survey round combinations for a given treatment reported in Tables 6, 7 and 9.

|**Appendix T**<br>**Table**|**able 9.2: Sharpenedq-values below 0**<br>**Outcome**|**.10 for mechani**<br>**Treatment**|**sms**<br>**Round**|**p-value**|**q-value**|
|---|---|---|---|---|---|
|6|Percent of Time Sales & Marketing|Insourcing|1|0.0274|0.078|
|7|Innovation Index|Insourcing|2|0.0107|0.069|
|7|Web Effectiveness|Insourcing|2|0.0195|0.072|
|7|Facebook Effectiveness|Insourcing|2|0.0049|0.066|
|7|Global Quality Effectiveness|Insourcing|2|0.0190|0.072|
|7|Global Quantity Effectiveness|Insourcing|2|0.0346|0.079|
|7|Unconditional Overall Social Media|Insourcing|2|0.0103|0.069|
|9|Use Professional Services|Insourcing|1|0.0000|0.001|
|9|Use Professional Services|Insourcing|2|0.0131|0.069|
|6|Percent of Time Sales & Marketing|Outsourcing|1|0.0154|0.032|
|7|Innovation Index|Outsourcing|2|0.0351|0.054|
|7|Web Effectiveness|Outsourcing|2|0.0011|0.006|
|7|Facebook Effectiveness|Outsourcing|2|0.0012|0.006|
|7|Instagram Effectiveness|Outsourcing|2|0.0168|0.032|
|7|Global Quality Effectiveness|Outsourcing|2|0.0082|0.023|
|7|Global Quantity Effectiveness|Outsourcing|2|0.0031|0.012|
|7|Unconditional Overall Social Media|Outsourcing|2|0.0011|0.006|
|7|Conditional Overall Social Media|Outsourcing|2|0.0089|0.023|
|9|Use Professional Services|Outsourcing|1|0.0000|0.001|
|9|Use Professional Services|Outsourcing|2|0.0007|0.006|
|9|Use Professional Services|Consulting|1|0.0002|0.005|
|9<br>Notes: no o<br>Sharpened q|UseProfessionalServices<br>utcome for training had a standardized<br>-values control false discovery rate acr|Consulting<br>q-value below 0.<br>oss 26 outcome-|2<br>10<br>round esti|0.0009<br>mates for a|0.012<br>treatment.|

84
# _Table 1: Baseline data used for simulation modeling, 2022_

|**Province**|**Population**<br>**(3-5y)**<br>**millions**|**Public**<br>**Enrollment**<br>**(3-5y)**|<br>**Private**<br>**Enrollment**<br>**(3-5y)**|**ECE**<br>**Classrooms**|**ECE**<br>**Teachers**|**Classroom**<br>**Construction**<br>**Cost**<br>**Millions,**<br>**PKR**|**New**<br>**Teacher**<br>**Salary**<br>**(annual)**<br>**Millions,**<br>**PKR**|**WASH**<br>**Facility**<br>**Provision**<br>**Millions,**<br>**PKR**|
|---|---|---|---|---|---|---|---|---|
|**Balochistan**|1.36|185,814|66,766|1,430|2,025|3.1|0.3|0.7|
|**Islamabad Capital**<br>**Territory**|0.15|11,241|79,615|174|298|3|0.42|0.8|
|**Khyber Pakhtunkhwa**|3.54|693,349|453,363|2,216|2,216|3|0.46|0.21|
|**Punjab**|9.1|796,756|1,776,028|19,895|18,560|3|0.54|0.6|
|**Sindh**|4.57|822,675|822,675|6,264|16,784|2.5|0.42|1.6|

Notes: Public enrollment statistics are from the Annual School Census of each province. Private enrollment statistics are computed based on the share of the population that reports being enrolled in a private school. The number of ECE teachers and classrooms is estimated based on the figures reported for the primary level in each province's Annual School Census. The cost of classroom construction, teacher salary and the cost of providing a WASH facility are provided by provincial education departments. A WASH facility is comprised of one enclosed flush toilet and one sink connected to piped water.

7