rafmacalaba/datause-encoder-data huggingface dataset:
estimates the effect of the two treatment types (10- and 20-sessions of the CBT workshop) controlling only for school fixed effects. Columns (2) and (6) show the results for the baseline specification discussed above, with full set of controls. In Columns (3) and (7) we present the results combining the two treatment types. Since only those students who appeared in the school records in April 2017 were invited to the survey measuring socio-emotional skills, there might have been self-selection. For this reason, in columns (4) and (8) we present the results accounting for the possible self-selection into the survey for applying two-stage Heckman procedures, with teacher fixed effects as exclusion restriction. The underlying rationale is that specific teachers might have been more active than others in keeping children in school until the survey was taken.

**Table 3: ITT on Socio-emotional skills**

||(1)<br>St|(2)<br>d Bartlett sc|(3)<br>ore - PLAN|(4)<br>EA|(5)<br>St|(6)<br>d Bartlett s|(7)<br>core - Ba|(8)<br>rOn|
|---|---|---|---|---|---|---|---|---|
|10 Sessions|0.178***<br>(0.067)|0.165**<br>(0.068)|||0.051<br>(0.059)|0.047<br>(0.060)|||
|20 Sessions|0.166**<br>(0.074)|0.175**<br>(0.074)|||-0.035<br>(0.065)|-0.025<br>(0.066)|||
|Combined 10 and 20<br>sessions|||0.169***<br>(0.060)|0.168***<br>(0.061)|||0.017<br>(0.053)|0.027<br>(0.054)|
|Inverse of Mills Ratio||||-0.057<br>(0.181)||||0.009<br>(0.157)|
|Observations|1279|1241|1241|1203|1466|1422|1422|1382|
|Mean Dep. Control<br>Group|-0.000|-0.001|-0.001|-0.001|0.000|-0.000|-0.000|-0.000|
|SD<br>Dep.<br>Control<br>Group|1.000|1.004|1.004|1.005|1.000|0.998|0.998|0.999|

Notes: columns 1 to 4 show the intention to treat (ITT) effects of PODER on the Bartlett score of an Exploratory Factor Analysis (EFA) capturing socio-emotional skills (SES) based on the PLANEA instrument. Columns 5 to 8 show the same effect but using the BarOn instrument. Columns 3 and 7 combines, in a single group, students in treatment 1 and 2. Columns 4 and 8 controls for potential self-selection into the test. All specifications include school fixed effects.

The effect on the PLANEA score is large and statistically significant, about 0.17 and 0.18 standard deviations (sd), for the 10 and the 20 sessions respectively. Both effects, separately or combined are statistically significant at conventional statistical level and are robust to potential selection into the exam. When we look at the impact on the BarOn inventory, we find an effect that is small for both treatment groups and statistically not significant. When the two treatment effects are combined, the resulting effect is about 0.03 sd after accounting for possible selection into the exam (column 8 in Table 3).

Table 4 reports the intention to treat effects of PODER on math test scores, enrollment in 11<sup>th</sup> grade, re-taking COMIPEMS 2017 or a measure of school engagement combining both 11<sup>th</sup> grade enrollment or re-taking COMIPEMS. Overall, we find a negative but small and statistically insignificant effect on math test scores and the probability of enrolling in 11<sup>th</sup> grade. The effect is 17
**Table 12: Determinants of energy intensity (2002-2004, CRS, Pooled effects)**

|**Dependent**<br>**variable=ln(energy/output)**|**Four indust**|**ries**|**Pulp and**<br>**indus**|**Paper**<br>**try**|**Cement ind**|**ustry**|**Iron and**<br>**indus**|**Steel**<br>**try**|**Aluminum i**|**ndustry**|
|---|---|---|---|---|---|---|---|---|---|---|
||Coef.|P-value|Coef.|P-value|Coef.|P-<br>Value|Coef.|P-<br>Value|Coef.|P-<br>Value|
|**Constant**|-0.8562|0|-0.8998|0.003|-0.2463|0.025|-1.7287|0.002|-1.063|0.071|
|**Ln(price of energy/price of**<br>**output)**|-0.7619|0|-0.4585|0.018|-0.464|0|-0.6985|0.019|0.1842|0.81|
|**Ln (technology development)**|-0.0571|0|-0.0142|0.313|-0.017|0|-0.0577|0.023|-0.102|0.055|
|**Ln(Price of energy/price of**<br>**output)*Ln(technology**<br>**development)**|-0.0402|0|0.0032|0.862|-0.0188|0|-0.0467|0.082|-0.103|0.225|
|**Foreign capital intensity**|-0.037|0.913|-0.61|0.301|0.0602|0.875|4.5695|0.192|-21.3|0.257|
|**Foreign capital**<br>**intensity*Ln(technology**<br>**development)**|-0.0142|0.429|0.0255|0.64|-0.017|0.285|-0.4026|0.195|1.7709|0.244|
|**Collectives**|-0.4721|0|-0.0512|0.855|-0.3727|0|-0.9786|0|-0.104|0.755|
|**Foreign**|-0.3521|0.125|0.3376|0.355|-0.5466|0.077|-0.9174|0.061|-0.132|0.889|
|**Hong Kong SAR, China; Macao;**<br>**Taiwan, China**|-0.3336|0.004|-0.152|0.453|-0.1994|0.02|-0.0609|0.854|-0.727|0.009|
|**Shareholding**|-0.1274|0.065|0.1649|0.294|-0.2243|0|-0.2541|0.142|-0.579|0|
|**Private**|-0.1111|0.402|0.0801|0.806|-0.1352|0.076|-1.1279|0.054|-1.114|0.002|
|**Other**|(omitted)||(omitted)||(omitted)||(omitted)||(omitted)||
|**North**|-0.258|0.043|-0.867|0.004|-0.1765|0.044|0.9218|0.057|0.1285|0.743|
|**Northeast**|(omitted)||(omitted)||(omitted)||0.833|0.102|(omitted)||
|**East**|-0.1097|0.338|-0.6622|0.008|-0.2021|0.01|0.9543|0.036|0.0509|0.883|
|**South**|0.0662|0.575|-0.2483|0.32|-0.0736|0.357|0.88|0.069|0.1164|0.733|
|**Southwest**|-1.0961|0|(omitted)||-0.0865|0.699|(omitted)||-0.306|0.408|
|**Year 2003**|-0.1107|0.114|-0.0416|0.754|-0.0434|0.376|-0.2629|0.119|-0.109|0.456|
|**Year 2004**|-0.0996|0.161|-0.0392|0.769|0.0272|0.59|-0.3607|0.034|-0.119|0.447|
|**R**<sup>**2**</sup>**(obs.)**|0.5126 (758|)|0.2586 (14|4)|0.4802 (335|)|0.5059 (20|8)|0.6798 (71)||

36
|Regional Development Project<br>Rosanna Nitti|
|---|
|Regional Development Project<br>Rosanna Nitti|
|Programmatic Development PolicyOperation 1<br>Afsaneh Sedghi|
|Competitiveness DPL<br>John Gabriel Goddard|
|CENDRAWASIH UNIVERSITY: THE RESPEK PROGRAM IN PAPUA AND Sonya Woo|
|CENDRAWASIH UNIVERSITY: THE RESPEK PROGRAM IN PAPUA AND Sonya Woo|
|CENDRAWASIH UNIVERSITY: THE RESPEK PROGRAM IN PAPUA AND Sonya Woo|
|Tajikistan PDPG6<br>Marina Bakanova|
|NIGER Skills Development for Growth Project<br>Pamela Mulet|
|NIGER Skills Development for Growth Project<br>Pamela Mulet|
|NIGER Skills Development for Growth Project<br>Pamela Mulet|
|NIGER Skills Development for Growth Project<br>Pamela Mulet|
|NIGER Skills Development for Growth Project<br>Pamela Mulet|
|NIGER Skills Development for Growth Project<br>Pamela Mulet|
|NIGER Skills Development for Growth Project<br>Pamela Mulet|
|NIGER Skills Development for Growth Project<br>Pamela Mulet|
|NIGER Skills Development for Growth Project<br>Pamela Mulet|
|NIGER Skills Development for Growth Project<br>Pamela Mulet|
|NIGER Skills Development for Growth Project<br>Pamela Mulet|
|NIGER Skills Development for Growth Project<br>Pamela Mulet|
|NIGER Skills Development for Growth Project<br>Pamela Mulet|
|NIGER Skills Development for Growth Project<br>Pamela Mulet|
|International Road Assessment Program(BloombergRS10)<br>Marc Sanford Shotten|
|International Road Assessment Program(BloombergRS10)<br>Marc Sanford Shotten|
|AFR3 Implementation of a business incubator in Mozambique<br>Sofya Muradyan|
|AFR3 Implementation of a business incubator in Mozambique<br>Sofya Muradyan|
|AFR3 Implementation of a business incubator in Mozambique<br>Sofya Muradyan|
|JSDF-INDONESIA: EMPOWERING WOMEN OVERSEAS MIGRANT WO Sri Kuntari|
|JSDF-INDONESIA: EMPOWERING WOMEN OVERSEAS MIGRANT WO Sri Kuntari|
|JSDF-INDONESIA: EMPOWERING WOMEN OVERSEAS MIGRANT WO Sri Kuntari|
|JSDF-INDONESIA: EMPOWERING WOMEN OVERSEAS MIGRANT WO Sri Kuntari|
|JSDF-INDONESIA: EMPOWERING WOMEN OVERSEAS MIGRANT WO Sri Kuntari|
|Time Release Studies on Measuringand BenchmarkingCustoms andZeina A. Samara|
|Time Release Studies on Measuringand BenchmarkingCustoms andZeina A. Samara|
|Tunisia Governance and OpportunityDPL<br>Antonio Nucifora|
|Tunisia Governance and OpportunityDPL<br>Antonio Nucifora|
|Tunisia Governance and OpportunityDPL<br>Antonio Nucifora|
|PMR TURKEY<br>Ayse Yasemin Orucu|
|PMR TURKEY<br>Ayse Yasemin Orucu|
|JSDF-INDONESIA: EDUCATION FOR VERY POOR CHILDREN<br>Sentot Surya Satria|
|JSDF-INDONESIA: EDUCATION FOR VERY POOR CHILDREN<br>Sentot Surya Satria|
|JSDF-INDONESIA: EDUCATION FOR VERY POOR CHILDREN<br>Sentot Surya Satria|
|GFDRR MAINSTREAMING DISASTER REDUCTION IN Burkina Faso<br>Hocine Chalal|
|GFDRR MAINSTREAMING DISASTER REDUCTION IN Burkina Faso<br>Hocine Chalal|
|GFDRR MAINSTREAMING DISASTER REDUCTION IN Burkina Faso<br>Hocine Chalal|
|JSDF-INDONESIA: IMPROVING FOOD SECURITY BY EMPOWERING PONatasha Hayward|
|JSDF-INDONESIA: IMPROVING FOOD SECURITY BY EMPOWERING PONatasha Hayward|
<!-- Start of picture text --> @ 0.80<br>£<br>= 0.70<br>i=<br>‘oa<br>> 0.60<br>é Assets_1<br>gE 9.50 —— Assets 2<br>=<br>i)=< 0.40 Assets3<br>2 0.30 —— Assets_4<br>o<br>=<br>2wi 0.20 —_—_— Assets_5<br>Ww<br>2<br>0.10<br>6 7 8 9 10 11 12<br>Month<br><!-- End of picture text --> <!-- Start of picture text --> 0.80<br>Z 0.75<br>s<br>= 0.70<br>£<br>a oe<br>5 —— Assets_1<br>a 0.60<br>a —— Assets_2<br>— 0.55<br>==< 0.50 Assets3<br>5 — Assets_4<br>a 0.45<br>= — Assets_5<br>S 0.40<br>0.35<br>aL<br>0.30<br>None Primary Middle Secondary Post-Secondary<br>Mother’s Education<br><!-- End of picture text -->
In no case was there a significant fall in spending on durable goods between 2008 and 2009, and in some cases there were increases, such as for purchases of automobiles/vans/pickups. This is confirmed by the data in the bottom panel of Table 7, which reports on the stock of cars, vans, and motorbikes owned by households. The number of such assets held per household generally rose in 2009 relative to 2008. As a general rule, any drop in spending occurred between 2007 and 2008, not later.

The consumption of non-durables was also higher, or at least not lower, in 2009 than in 2008, and this includes items such as clothing, meat, fish, and prepared food. There is one major exception, and this is the consumption of home-produced “grains and cereal productions”, in practice mainly rice. Although _purchases_ of cereals rose substantially in 2009, this was offset by a clear drop in own-consumption, although the mean value of the fall in cereals consumption between 2008 and 2009 – at about 14.4 baht per person per month – was small compared to the overall rise in the value of consumption per capita over the same period, which we estimate at 363 baht per person per month.<sup>2</sup> Rice production per capita hardly changed between 2007 (405 kilos of milled rice per capita) and 2008 (404 kg.) and actually rose in 2009 (412 kg.), although it should be noted that the bulk of the harvest arrives in November and is consumed the following year. We surmise that consumption smoothing, to the extent that it occurred, did not occur at the expense of investment in durable goods or schooling, but more probably through lower saving.

**Table 7.  Measuring the Impact of the 2008-09 Recession on Real Expenditure, by Main Expenditure Heading**

||Real<br>expend/|Chan|ge in real exp<br>compared to|enditure/c<br>2007:Q1|apita|
|---|---|---|---|---|---|
||cap|2007<br>|2008<br>_baht/month_|2009|2010|
|Electricity|570.6|58.6|175.9<br>_0.00_|164.4<br>_0.13_|284.2<br>_0.00_|
|Clothes, skirts and trousers|292.1|10.4|-24.5<br>_0.00_|15.5<br>_0.00_|3.9<br>_0.16_|
|Soap, toothbrush, shampoo, powder, etc.|247.7|2.8|-2.9<br>_0.04_|18.7<br>_0.00_|20.5<br>_0.47_|
|Purchase automobile, van, pickup|1046.3|88.4|-261.1<br>_0.00_|104.4<br>_0.00_|-221.5<br>_0.00_|
|Purchase motorcycle, bicycle, other vehicle|229.0|-26.8|-68.1<br>_0.00_|-37.7<br>_0.01_|-65.2<br>_0.01_|
|Travel: visiting hometown/relatives, religious activity, other|158.6|10.8|17.1<br>_0.25_|35.3<br>_0.02_|32.7<br>_0.74_|
|Telephone rate and service|508.9|0.8|-55.0<br>_0.00_|-17.3<br>_0.00_|-64.6<br>_0.00_|

> 2 From Table 2, we calculate this as 98 – (-265) = 363.  For cereals, the number is -6.9 –(7.5) = -14.4.

Losers and Winners in Recession: Thailand 2008-09 23
**Table A3.CU:  Father's Education and Educational Expenditure on Children**

|||**URBAN**<br>**CFPS**<br>|**CHINA**<br>**2010**<br>||
|---|---|---|---|---|
||**(6-22) Yea**|**rs Children**|**(8-23) Yea**|**rs Children**|
|Father's Sch.|236.9|162.8|225.4|138.3|
||(35.0)***|(34.1)***|(32.9)***|(30.5)***|
||[38.7]***|[36.0]***|[39.1]***|[33.9]***|
||{63.9}***|{47.5}***|{72.6}***|{56.4}**|
|Father's Sch. * Daughter|36.8|18.6|27.5|13.5|
|Dummy|(48.2)|(46.9)|(49.2)|(47.4)|
||[39.2]|[39.6]|[38.6]|[38.9]|
||{31.9}|{31.5}|{24.4}|{27.9}|
|Daughter Dummy|-71.2|181.1|22.4|267.5|
||(400.2)|(386.8)|(413.7)|(397.5)|
||[327.1]|[348.5]|[334.6]|[364.5]|
||{275.1}|{305.8}|{252.7}|{318.8}|
|Intercept|827.4|4137.0|1034.3|4592.9|
||(278.8)***|(337.8)***|(264.5)***|(330.1)***|
||[300.0]***|[397.1]***|[295.5]***|[401.7]***|
||{316.8}**|{436.8}***|{292.6}***|{488.9}***|
|No. of Children||-2222.0||-2401.2|
|||(132.7)***||(154.4)***|
|||[211.3]***||[233.4]***|
|||{366.3}***||{412.4}***|
|No. Observations|2784|2784|2435|2435|

Notes: (1) The data used are CFPS 2010. (2) Robust standard errors in parentheses; standard errors clustered at county-level in square brackets; standard errors clustered at province-level in braces; *** p<0.01, ** p<0.05, * p<0.1.
barely functioning and instead the local population prefers to use the informal justice system” (Devlin et al., 2009). Given this background, the Taliban have been able to maintain pressure on Kunduz despite the success of Operation Enduring Freedom in removing them from power in Kabul.

The Taliban renewed their offensive against Kunduz on 24 April 2015 by striking at four districts outside the city. By the end of that week they controlled several major suburbs. In response, the Government of Afghanistan dispatched ANA forces, supported by U.S. fighter jets. Still, throughout the summer the Taliban continued to make gains around the city. On the morning of 28 September, Taliban troops routed the government troops that were holding the city. The following day, the ANA launched a counterattack with support from US special forces and airstrikes. Fierce fighting continued to October 13, with claims and counter-claims about who controlled the city. Nonetheless, on 13 October the Taliban withdrew, citing “the prospect of additional casualties and ammunition expenditure” (Nordland, October 13, 2015).

We exploit the CDR data to demonstrate how subscribers from private firms, along with general mobile phone users, responded to the unexpected Taliban seizure of Kunduz in lateSeptember and October 2015. In Panel A of Figure 3, we plot normalized call volumes for all towers in a 70 km radius of the Kunduz city center over a 24-week period centered on the takeover of the city on September 28 (marked by the black dashed line). We divide calling towers into two categories based on if the tower is located within a 10 km radius of the city center and thus covers urban areas (marked in green), or if the tower is located in a 10-70 km radius and thus covers rural areas and neighboring small cities (marked in orange).<sup>21</sup> We also divide callers based on if they are corporate lines subscribers (dashed line for “private”), or if they are part of the entire population of subscribers (solid line for “all”). These two categorizations result in four combinations, and we normalize each over the 24-weeks by subtracting the mean and dividing by the standard deviation for comparability.

> 21Appendix Figure A1 shows a map with the locations of towers in each radius.

17
items and CPIs for nonfood items (such as Cambodia and the Lao People’s Democratic Republic), and, in some cases, the ratio between regional and national poverty lines (for example, Central African Republic, Chad, Niger, and Uganda). A common choice would be S 2,3�, where denotes the number of Primary Sampling Units, which would be read as follows: a Paasche pp SS �SS, FF, DD index calculated at the PSU-level, covering food items only ( _J=F_ ), and based on consumption SS patterns of households in the bottom second and third deciles of the per capita expenditure distribution.

distribution. Consistently with the notation introduced for the SPI, the national level poverty line becomes to emphasize that it depends on both coverage and reference group _R_ . Accordingly, zz equation (4) can be rewritten as: zz(JJ, RR) JJ h <mark>(8)</mark> S xx ss ≤zz(JJ, RR) In equation 8, households are identified as poor based on a single poverty line. The same equation SS (SS, JJ, RR) indirectly identifies a set of implicit multiple poverty lines. Indeed, it is convenient to distinguish implicit or indirectly estimated variables from directly estimated variables by denoting the former with a tilde: Equations 8 and 9 are interchangeable. One may use the real consumption aggregate compared with a single poverty line or the nominal consumption aggregate compared with a set of indirect poverty lines. The number of indirect poverty lines depends on the aggregation level S, while the consistency of with respect to the definition of the consumption aggregate ss zz̃ (SS, JJ, RR) depends on the coverage of the index. ss zz̃ (SS, JJ, RR) There is also a second empirical strategy based on the _direct_ estimation of a set of multiple poverty lines. Let ) denote the poverty line directly estimated for stratum , where is the number of strata considered; ss ss<sup>′</sup> is the implicit coverage of the line; and is the reference group zz (SS<sup>′</sup> , JJ<sup>′</sup> , RR ss SS<sup>′</sup> utilized in stratum _s_ . A household h belonging to stratum _s_ is poor if an only if the following is ss<sup>′</sup> JJ<sup>′</sup> RR true: h ) <mark>(10)</mark> ss ss<sup>′</sup> A key point is that a set of multiple lines xx ≤zz (SS<sup>′</sup> , JJ<sup>′</sup> , RR ) implicitly defines a set of spatial price deflators, as follows: ss ss<sup>′</sup> zz (SS<sup>′</sup> , JJ<sup>′</sup> , RR ) S ) = =<sup>zzss(SS′, JJ′, RRss′)</sup> <mark>(11)</mark> )] ) ss ss<sup>′</sup> zz (SS<sup>′</sup> , JJ<sup>′</sup> , RR ss ss<sup>′</sup> �(SSSS<sup>′</sup> , JJ<sup>′</sup> , RR ss ss<sup>′</sup> EE[zz (SS<sup>′</sup> , JJ<sup>′</sup> , RR zz̃(SS<sup>′</sup> , JJ<sup>′</sup> , RR<sup>′</sup> 8
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# **Abbreviations and Acronyms**

3ie International Initiative for Impact Evaluations BACO best available charitable option CBA cost-benefit analysis CBCSE Center for Benefit-Cost Studies of Education CEA cost-effectiveness analysis CUA cost utility analysis DHS Demographic and Health Surveys DIME Development Impact Evaluation ERR economic rate of return GEA general efficiency analysis IE impact evaluation IEG Independent Evaluation Group IER Impact Evaluation Repository LSMS Living Standards Measurement Survey SIEF Strategic Impact Evaluation Fund VFM Value for Money DFID U.K. Department for International Development USAID U.S. Agency for International Development MCC Millennium Challenge Corporation NGO nongovernmental organization SROI social return of investment _All currency amounts are in U.S. dollars unless otherwise indicated._ v
Table 3 summarizes Table 2 in a cross-tabulation of dominant aggregate sectors by country. Of the 39 areas in the top 3 deciles, methane emissions are dominated by agriculture in 23, fuel in 15 and waste in 1. Agricultural emissions have important roles in 4 countries (China, Brazil, India and Pakistan), while fuel-based emissions dominate in the remaining 7 (United States, Indonesia, Islamic Republic of Iran, Nigeria, Qatar, Russia, Saudi Arabia).

**Table 1: Global methane emissions, 2018**<sup>**6**</sup>

|**EDGAR Sector**|**Emissions**<br>**(mt) 2018**|**Cum.**<br>**%**|
|---|---|---|
|Enteric Fermentation|108.4|28.9|
|Waste Water Handling|44.4|40.7|
|Fuel Exploitation - Gas|43.6|52.3|
|Agricultural Soils|37.9|62.4|
|Fuel Exploitation - Coal|36.7|72.2|
|Solid Waste Landfills|34.9|81.5|
|Fuel Exploitation - Oil|32.2|90.1|
|Manure Management|12.3|93.4|
|Energy For Buildings|12.3|96.7|
|Oil Refining and Transformation Industry|6.3|98.3|
|Agricultural Waste Burning|2.0|98.9|
|Solid Waste Incineration|1.5|99.3|
|Road Transportation No Resuspension|1.0|99.5|
|Combustion For Manufacturing|0.7|99.7|
|Power Industry|0.5|99.8|
|Chemical Processes|0.3|99.9|
|Iron And Steel Production|0.2|100.0|
|Fossil Fuel Fires|0.2|100.0|
|Railways Pipelines Off-Road Transport|0.0|100.0|
|Total|375.2||
|CO2 Conversion Factor|25.0||
|C02 Equivalent|9,381.2||
|**Source: EDGAR (2022)**|||

> 6 For detailed sector definitions and descriptions, see: EDGAR Sector-Specific Gridmaps, 1970 – 2021 https://edgar.jrc.ec.europa.eu/dataset_ghg70#sources IPCC 2006 Reporting Guidance and Tables

> https://www.ipcc-nggip.iges.or.jp/public/2006gl/pdf/1_Volume1/V1_8_Ch8_Reporting_Guidance.pdf

5
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Furthermore, production factors capital and labor are assumed to be fully mobile between sectors, whereas land and natural resources factors are sluggish to move.

In addition, government savings are made exogenous; therefore, any reduction in the subsidy, increases government income and is entirely spent by the Nigerian government, in the same proportions as in the baseline data. This is a stylized assumption that helps us interpret the results, but it also reflects that fact that the additional income obtained from reducing subsidies is likely to be spent on current consumption. The closure is shown in Appendix 2.

# **2.3 Data**

Two datasets are employed to study the effects of the subsidy removal: the GTAP database (Narayanan et al., 2012) and a 2006 Nigerian SAM (Nwafor et al., 2010).

The 2006 SAM distinguishes 62 sectors and commodities, of which more than half are agricultural sectors/commodities. The SAM has a separate sector for petroleum products, a sector for petroleum and natural gas, and an aggregated sector for other mining. In light of the data provided in the SAM, we aggregate the 134 regions in the GTAP 8.1 database into 13 regions (Table 1).

**Table 1: Regional Aggregation of GTAP 8 Database**

|No.|Code|Description|
|---|---|---|
|1|Japan|Japan|
|2|China|China|
|3|USA|United States of America|
|4|EU_25|European Union 25|
|5|MENA|Middle East and North Africa|
|6|Ghana|Ghana|
|7|CotedIvoire|Côte d'Ivoire|
|8|Nigeria|Nigeria|
|9|Cameroon|Cameroon|
|10|SouthAfrica|South Africa|
|11|RWestAfrica|Rest of Western Africa|
|12|RestofSSA|Rest of Sub-Saharan Africa|
|13|RestofWorld|Rest ofWorld|

Our regional aggregation emphasizes countries neighboring Nigeria and includes countries that are involved in the Nigerian petroleum trade, such as the United States and European Union and major trading partners: China, Japan and South Africa, and aggregated regions MENA, SubSaharan Africa, the rest of West Africa and the rest of the world.

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