rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Consumption of Consumer Electronics:<br>0 Deviation from Model Baseline<br>—— Base<br>3.<br>2-150 —— Scenario A+B<br>5<br>& -200<br>Z|= -250 _<br>ee<br>300, OO<br>-33 5 0<br>2020 2040 2060 2080 2100<br>Year<br><!-- End of picture text -->

<!-- Start of picture text --> Total Electricity Generation:<br>1 x10! Deviation from Model Baseline<br>——<br>8<br>= -0.5<br>Ee<br>-1<br>~— Base<br>Ls —— Scenario A<br>>!) Scenario A+B ]<br>Scenario A+B+C<br>“5 tt<br>2020 2040 2060 2080 2100<br>Year<br><!-- End of picture text --> <!-- Start of picture text --> Total Emissions:<br>Deviation from Model Baseline<br>2000<br>1000<br>a<br>8 0<br>& : .<br>>§ -1000 /<br>~<br>6 — Base ~<br>-3000||~ Scenario A .<br>—— Scenario A+B :<br>-4000<br>2020 2040 2060 2080 2100<br>Year<br><!-- End of picture text -->

<!-- Start of picture text --> Consumption of Silver as End Product:<br>Deviation from Model Baseline<br>0.4<br>_<br>03 —— Base<br>Scenario A<br>7g —— Scenario: A+B<br>B 0.2<br>[o} ‘<br>0.1), LL<br>HS<br>oN<br>0<br>2020 2040 2060 2080 2100<br>Year<br><!-- End of picture text -->
Table 7: LPM and OLS estimates for regressions as correlations for China

||**(1)**<br>**LPM**<br>**R&D**<br>|**(2)**<br>**LPM**<br>**Intg.**<br>|**(3)**<br>**OLS**<br>**R&D**<br>|**(4)**<br>**OLS**<br>**Intg.**<br>|**(5)**<br>**OLS**<br>**R&D**<br>**per empl.**|**(6)**<br>**OLS**<br>**Intg.**<br>**per empl.**|
|---|---|---|---|---|---|---|
|Index * (D/T)|2.946|‐6.216***|‐49.178***|‐2.952|‐5.062|4.587|
||(0.634)|(0.000)|(0.000)|(0.707)|(0.630)|(0.601)|
|Constant|0.444|1.319***|19.302***|16.531***|8.449***|8.549***|
||(0.357)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Observations|38133|38133|25484|31933|24362|30696|
|R2A|0.684|0.508|0.876|0.822|0.828|0.716|
|R2W|0.000|0.000|0.002|0.000|0.000|0.000|
|RMSE|0.264|0.258|0.504|0.684|0.487|0.626|

Note: * p<0.10; ** p<0.05; *** p<0.01, representing p‐values not standard errors. The term (D/T) is computed as the proportion of ICT‐services as input in total input expenditure for each sector. The first to column. Are the transformed variables that vary between 0‐1 and are regressed using LPM. R&D is Research and Development expenditures by firm in USD. Intg. represents the net intangible assets by the firm (such as patent, goodwill, work in progress) in USD. Empl. stands for the number of employees for each firm. R&D Rev Share stands for the R&D expenditures as a share of total revenue for each firm. All dependent variables except R&D Rev Share are transformed into logs. Robust standard errors clustered by sector. Fixed effects by firm, sector and year are applied. A lag of 2 year is also applied.

30
COVID-19 lockdown measures disproportionately impacted the school attendance probabilities of children based on gender. Results of these estimations are reported in Table 3 reports the results of this estimation. First, when we consider the entire sample of children (Table 3, Panel A), we find that neither gender was spared by the socioeconomic impact of COVID-19’s lockdown measures. These measures reduce the school attendance of and this more probabilities boys girls. Although negative impact appears slightly substantial in magnitude for boys than for girls, the resulting gender difference, however, is not statistically significant. This implies that over the whole sample, COVID-19’s lockdown measures have no statistically significant effect on the gender gap in school attendance.

When we partition our sample by gender and by age group, we find that the above conclusion holds for all age groups, including the age groups 5 ́ 11 corresponding to ́ primary school age (Panel B), 12 18 corresponding to secondary school or higher (Panel C), and children aged 15 ́ 18—whose schooling is no longer compulsory in Nigeria— (Panel D). This result suggests that COVID-19’s lockdown measures have no statistically significant effect on gender inequality in school attendance in Nigeria.

We further explore the joint heterogeneity of gender and geopolitical zones. Given the non-representativeness of the COVID-19 NLPS data at the zonal level, we explore the gender effect by interacting COVID-19’s lockdown measures with the child’s gender and her geopolitical zone. We find that COVID-19 shock significantly increases gender inequalities in school attendance in the North-West and South-West zones (Table 4, Panel A). Surprisingly, in South-West Nigeria, we find that COVID-19’s lockdown measures reduce the gender gap in school attendance by roughly 4.32 percentage points. Specifically, the COVID-19’s lockdown measure reduces school attendance for girls living in the South West zone by 3.28 percentage points, while the reduction is 7.61 percentage points for boys living in the same zone. In contrast, in North-West Nigeria— which is one of the regions most affected by child marriage— we find that COVID-19’s lockdown measures increase the gender gap favoring boys in school attendance by 9.15 percentage points. While COVID-19 significantly reduces the school attendance of girls in the North-West zone by 10.89 percentage points, there is no effect on the school attendance of boys in this zone.

26
# 1. Introduction

Even before the start of its civil war in 2014, the Republic of Yemen had one of the lowest living standards in the world. Since 2014, its population has suffered from one of the world’s severest humanitarian crises with a serious further deterioration of living standards, manifested in increased poverty rates, severe malnutrition, food insecurity, and physical insecurity. Basic public health and education services have suffered, infrastructure (including road networks) has been destroyed, and government employees have been left without pay for extended periods. In the background, like many other low-income countries, Yemen is vulnerable to accelerating climate change. Given these circumstances, the country was ill-prepared to deal with the Covid-19 pandemic and international price shocks for food and energy.

Given this, it is more urgent than ever to put an end to the war and identify policies that will help to overcome the humanitarian crisis and put the country on a path toward a better future. With this aim, this paper explores potential scenarios for the economy of the country up to 2030, focusing on alternatives under which the conflict subsides, and the country manages to start addressing the urgent needs of the population and the constraints that stand in the way of resumed sustainable growth and improved living standards.

The analysis is based on simulations with the Sustainable Development Goal Simulation model (SDGSIM), a dynamic computable general equilibrium (CGE) model designed for country-level policy analysis.<sup>1</sup> For this analysis, the model was adapted to the Yemeni context and calibrated to a new dataset, including a social accounting matrix (SAM) for 2020, the most recent year with sufficient information. The analysis, which covers the period 2022-2030, presents a base scenario without significant improvements in the economy or the state of security and six alternative scenarios which, compared to the base, reflect varying degrees of improvement. The scenarios are underpinned by different trajectories along seven dimensions: health and education; infrastructure; social transfers; remittances from abroad; foreign direct investment (FDI); extraction growth for oil and gas production; and foreign grant aid.

The paper is organized as follows: After a brief overview of the model and its database (Section 2a), it presents a short review of literature and earlier applications of CGE modeling to Yemen (Section 2b), and detailed analyses of the base scenario with stagnant performance (Section 3) and the set of alternative scenarios (Section 4). The main findings are summarized in the conclusions (Section 5). The Appendix provides additional information about the model database.

> 1 SDGSIM is documented in detail in Lofgren and Cicowiez (2019). The starting point for SDGSIM was MAMS, a model designed for analysis of strategies related to the MDG agenda. See Lofgren et al. (2013) for documentation that also is relevant to SDGSIM.

1
SENEGAL’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE The hidden cost of SENELEC’s inefficiencies, while moderate in comparison to other countries in the region, are significant and continue to penalize the financial performance of the operator (figure 17). Around threefourth of the hidden costs are the result of underpricing, and transmission and distributional losses, at 21 percent of production, are responsible for the remaining one-fourth. In the case of SENELEC, these hidden costs underestimate the magnitude of the

**Figure 16. The most significant inefficiency is underpricing**

<!-- Start of picture text --> 100.0<br>80.0<br>60.0<br>40.0<br>20.0<br>0.0<br>2003 2004 2005 2006 2007<br>Losses Under pricing Collection inefficiencies<br>Source: Derived from Briceño-Garmendia, Smits, and Foster (2009).<br>Percentage of the revenues<br><!-- End of picture text --> operator’s financial problems, as they do not include the total cost of serving the debt but only the payment of interest and other fees (leaving aside the repayment of the principal).

**Figure 17. Hidden costs of power utilities in select West African countries, as percentage of revenues**

<!-- Start of picture text --> 180<br>160<br>140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>Liberia Niger Mali Ghana Burkina Faso Cote d'Ivoire Senegal<br>Collection Inefficiencies Underpricing Losses<br>Percentage of revenues<br><!-- End of picture text --> _Source_ : Derived from Banerjee and others (2008b) and Briceño-Garmendia, Smits, and Foster (2009).

# Information and communication technologies

## **Achievements**

Telecommunications has been a driving force behind Senegal’s growth over the past decade, which is reflected in the strong contribution of this sector to the country’s GDP—around 65 percent in 2007. The market reforms of the 2000s go a long way toward explaining this progress in the ICT sector. The reforms started with the partial divestiture of SONATEL<sup>11</sup> in 1997, with France Telecom as the strategic partner (later on Orange), followed by the granting of a 20-year mobile license to Milicom Senegal in 1998 and a

> 11 Societé Nationale des Telecommunications du Sénégal.

30
according to the administrative data is higher still. Regressing administrative data on total transfer amounts received by households on the number of children aged 6 to 14 in primary school, in secondary school, and a constant, we find that households received about US$115 in a calendar year if no children attended primary or secondary school, which is roughly equal to the annualized health grant. Beneficiary households report receiving an additional US$55 for every child in primary school.

Results show that the amount earned by children represents a large share of the shortfall in schooling costs. Conditional on engaging in any work for pay, enrolled children in control communities report earning US$22 annually. The same value for children in treatment communities is US$43 annually. Assuming that infra-marginal children in treatment communities exhibit the same annual income, the observed difference in average earnings of US$21 between treatment and control communities corresponds to average earnings of US$75 by marginal children. This income earned by compliers would hence cover roughly half of the upper bound of education costs net of the transfer, approximately equaling US$140.<sup>17</sup> Examining the behavior of siblings of children aged 10 to 14 lends further support to the hypothesis that children work to support their school attendance. We turn first to the eligible older siblings (ages 15 to 17) of the 10-to-14-year olds in our core sample. Panel A of Appendix Table 6 shows that the school participation of these older siblings is not affected, but that these children, too, increase their participation in work for pay outside the household, perhaps helping to offset the education expenditure of their younger siblings. Indeed, Panel A of Table 5 shows larger increases in school and work by 10 to 14 year olds with older siblings than among those without older siblings, a finding comparable to the negative displacement result reported in <mark>Barrera-Osorio et al. (2011).</mark> 17 Note that this shortfall may be an upper bound on the true shortfall if infra-marginal children increased their school expenditure or reduced their participation in work for pay.

18
Table 3: Changes (% from base) in wages for economy-wide labor due to trade and water reforms (CGE model)

||Trade reform|Water reform|
|---|---|---|
|Rural wage|-7.91|-0.56|
|Urban wage|8.23|-0.01|

Table 4: Change (%) in capital returns from perimeter capital input due to trade and water reforms (CGE model)

||Perimete|r 1|Perimet|er 2|Perimet|er 3|Perimet|er 4|
|---|---|---|---|---|---|---|---|---|
|ORMVA|Trade<br>reform|Water<br>reform|Trade<br>reform|Water<br>reform|Trade<br>reform|Water<br>reform|Trade<br>reform|Water<br>reform|
|Doukkala|-6.07|4.64|-32.72|-0.31|||||
|Gharb|-23.19|-0.02|-25.99|3.45|-21.69|-6.38|||
|Hause|-6.36|7.81|-29.19|12.57|-22.93|-10.97|||
|Loukkos|-34.95|8.86|-42.37|12.03|-43.92|14.12|||
|Moulouia|4.06|-0.59|-29.16|4.07|-20.28|8.00|7.00|-1.60|
|Sous-Massa|-7.74|19.96|8.40|0.51|12.01|-1.99|||
|Tadla|-27.78|-3.15|-17.65|-0.44|||||

23
**Table 4. Business entry and the complexity and cost of business regulations**

|VARIABLES|(1)<br>newbiz|(2)<br>newbiz|(3)<br>newbiz|(4)<br>newbiz|(5)<br>newbiz|(6)<br>newbiz|(7)<br>newbiz|(8)<br>newbiz|
|---|---|---|---|---|---|---|---|---|
|GDP per capita (log)<br>FDI<br>Political freedom and civil rights|1.966**<br>(2.443)|1.977**<br>(2.448)<br>-0.00256<br>(-0.763)|1.933***<br>(2.588)<br>-0.00361<br>(-1.167)|2.039***<br>(2.663)<br>-0.00367<br>(-1.176)|1.974**<br>(2.411)<br>-0.00409<br>(-1.295)|1.640*<br>(1.917)<br>-0.00408<br>(-1.291)|1.754*<br>(1.899)<br>-0.00325<br>(-1.003)|0.993<br>(1.114)<br>0.00272<br>(1.044)|
|<br>(FH)|||0.213|0.232|0.294|0.307|0.375|0.401*|
||||(1.172)|(1.257)|(1.430)|(1.491)|(1.560)|(1.965)|
|Trade (X and M)||||0.00605|0.00606|0.00574|0.00829|0.00189|
|||||(1.166)|(1.081)|(1.023)|(1.304)|(0.315)|
|Corruption (CPI)|||||<br>0.0888<br>(0.628)|<br>0.0843<br>(0.596)|<br>-0.0338<br>(-0.189)|<br>-0.00285<br>(-0.0173)|
|Lagged GDP growth||||||0.0359<br>(1.338)|0.0395<br>(1.218)|0.0499*<br>(1.727)|
|Country risk (ICRG)|||||||0.0128<br>(0.322)|0.0154<br>(0.428)|
|Credit to private sector||||||||0.0187***<br>(5.401)|
|DTF (time and motion)|0.0376**|0.0373**|0.0285*|0.0275|0.0256|0.0277|0.0218|<br>0.0312|
||(2.158)|(2.138)|(1.751)|(1.643)|(1.467)|(1.579)|(1.000)|(1.526)|
|Constant|-16.94**|-17.01**|-16.73**|-18.19***|-18.07**|-15.35**|-16.62**|-11.41|
||(-2.400)|(-2.401)|(-2.559)|(-2.684)|(-2.519)|(-2.059)|(-2.014)|(-1.444)|
|Observations|832|829|821|808|782|781|646|548|
|R-squared|0.040|0.041|0.046|0.049|0.051|0.053|0.059|0.147|
|Numberofcod2|123|123|122|121|120|119|94|93|
|t-statistics in parentheses<br>*** p<0.01, ** p<0.05, * p<0.1|||||||||

**Notes:** ***(**)(*) denotes significance at the 1(5)(10) percent level. Standard errors reported in parentheses. This table reports results from OLS panel regressions of new business creation on the distance to the time and motion regulatory frontier. Business creation is defined as number of new businesses per 1,000 people in working-age. Country fixed effects are included in order to capture unobserved between-country differences. The estimations reported include controls for lagged GDP per capita growth, percapita GDP, trade openness, corruption, credit to the private sector, FDI inflow, economic and political risk as well as civil freedoms. All specifications include time dummies to control for any global changes in the macroeconomic environment.

20
days per child, and children exhibited symptoms on average on 18 days. Cold symptoms, coughing, and unusually hot skin were most frequent. Section 3 below gives more detail on how symptoms translate into illness spells and care-seeking recommendations. Appendix B provides a description of how the health calendar information was collected.

**Consultations.** At each surveyor visit, mothers were asked if they discussed their child’s health with anyone in the preceding week, and the surveyors recorded role or occupation of the person seen, facility, waiting time, treatments received and costs incurred.<sup>12</sup>

Table 2 provides an overview of these health care “consultations”, grouped into CSCOM visits; private doctors, hospitals or clinics; pharmacies; and purchases from informal providers. Parents occasionally see several providers in the same day, for example when they get a prescription at a clinic and then purchase medication elsewhere. We group visit records that are linked in this manner and classify them by the “most formal” consultation in the group. The value of care is approximated using the full price of care.<sup>13</sup> Note that the sizable difference in costs and value of care even without a subsidy is due to families seeking outside help to pay for the child’s care, e.g. from relatives or friends.

The table shows that the subsidy program reduced the average cost of a visit to the household by 71%, from CFA 2850 to 933 (476 CFA equaled 1 USD in 2013). Among the formal-care visits in the control and CHW-only groups, 14% and 12% were reported as free, vs. 70% and 77% in the subsidy groups, respectively. Parents paid if they visited other providers than the two partner clinics or did not claim the AfH benefit, or the child received services not covered under AfH.

CSCOMs provide similar value per visit with and without the subsidy (CFA 3652-3807),

> 12In order to facilitate the recording of drugs prescribed, purchased, and taken, mothers were asked to keep the packaging of any medications their child took. The surveyors could search and fill the drug from a database of brand and generic names of about 300 medications commonly sold in Mali.

> 13Value of care is imputed using prices recorded for consultations and medications where the respondent reported having paid the full price themselves. We use these prices combined with provider information, brand name, and point of purchase to construct the median price by source. If the respondent reported another person paying for care or was in the subsidy group, we compare the sum of median prices for the services received with the private expenses the respondent reported and use the higher value. This reflects the cost of care to the family, the NGO, or a third party.

14
**Figure 3.1. Base: GDP at factor cost annual growth 2021-2030 (%)**

<!-- Start of picture text --> 2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>-0.5<br>-1.0<br>-1.5<br>-2.0<br>-2.5<br>2021 2022 2023 2024 2025 2026 2027 2028 2029 2030<br><!-- End of picture text --> Source: Authors' calculations based on IMF (2022).

<!-- Start of picture text --> Figure 3.2. Base: Absorption, GDP, and trade 2022-2030 (bn 2020 YER)<br>2,500<br>2,000<br>1,500<br>1,000<br>500<br>0<br>Absorption Exports Imports GDP at factor cost<br><!-- End of picture text --> Source: Authors' calculations based on simulation results.

14
<!-- Start of picture text --> — manufacturing exports (excluding auto) —*— total exports (excluding auto) —?— auto exports (rhs)<br>20% 2S Nee | 2.0%<br>_ > Ae -<br>0% 0.0%<br>1992, 1993 1994 1995 1996 1997 1998 1999 2.000 2001 2002. 2003 2004 2005 2006 2.007<br><!-- End of picture text -->
Table 3d: Loan intensity for ASI plants

||||L|oan to o|utput le|vels|||Loa|nper em|ployee l|evels||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||1989|1994|2000|2005|2010|Average|1989|1994|2000|2005|2010|Average|
|15|Food products and beverages|0.22|0.27|0.37|0.33|0.33|0.30|14,388|18,084|30,788|32,867|44,129|28,051|
|16|Tobacco products|0.11|0.15|0.13|0.13|0.09|0.12|1,877|1,915|2,294|2,097|1,781|1,993|
|17|Textiles|0.42|0.51|0.56|0.49|0.58|0.51|12,998|16,275|27,563|31,283|44,015|26,427|
|18|Wearing apparel; dressing and dyeing of fur|0.25|0.24|0.28|0.33|0.34|0.29|7,030|6,478|11,590|12,875|15,872|10,769|
|19|Leather tanning; luggage, handbags, footwear|0.27|0.27|0.26|0.21|0.27|0.26|10,106|12,522|14,928|13,157|18,280|13,799|
|20|Wood and wood products; straw and plating articles|0.41|0.47|0.42|0.38|0.35|0.41|16,968|11,335|13,751|18,809|25,461|17,265|
|21|Paper and paper products|0.34|0.52|0.50|0.41|0.50|0.46|24,505|33,025|37,164|39,114|57,419|38,245|
|22|Publishing, printing and media reproduction|0.32|0.27|0.43|0.55|0.54|0.42|8,298|9,758|17,348|28,852|39,149|20,681|
|23|Coke, refined petroleum and nuclear fuel|4.23|0.25|0.13|0.19|0.22|1.00|747,742|46,258|27,341|50,052|44,766|183,232|
|24|Chemicals and chemical products|0.43|0.47|0.46|0.35|0.33|0.41|43,359|46,927|51,959|47,735|53,693|48,735|
|25|Rubber and plastic products|0.33|0.40|0.44|0.31|0.30|0.35|24,406|27,426|37,502|34,552|40,167|32,811|
|26|Other non-metallic mineral products|0.46|0.52|0.63|0.44|0.36|0.48|18,981|19,668|37,941|26,303|27,525|26,083|
|27|Basic metals|0.33|0.45|0.58|0.41|0.50|0.45|24,900|50,509|80,147|76,810|89,505|64,374|
|28|Fabricated metal products, except machinery|0.31|0.44|0.40|0.29|0.37|0.36|14,125|21,504|21,658|24,044|33,272|22,921|
|29|Machinery and equipment, n.e.c.|0.75|0.33|0.31|0.25|0.25|0.38|32,275|15,217|22,525|26,898|32,672|25,918|
|30|Office, accounting and computing machinery|0.43|0.26|0.38|1.07|0.62|0.55|28,652|26,223|57,050|171,016|87,389|74,066|
|31|Electrical machinery and apparatus, n.e.c.|0.37|0.31|0.39|0.24|0.21|0.30|21,484|20,544|34,001|31,008|31,803|27,768|
|32|Radio, television, and comm. equipment|0.75|0.43|0.43|0.38|0.38|0.47|44,716|28,165|49,593|44,201|58,976|45,130|
|33|Medical, precision and optical instruments, watches|0.59|0.71|0.47|0.41|0.28|0.49|7,093|30,032|28,636|30,055|8,979|20,959|
|34|Motor vehicles, trailers and semi-trailers|0.40|0.30|0.53|0.22|0.26|0.34|21,168|18,169|48,417|27,769|34,407|29,986|
|35|Other transport equipment|0.47|0.32|0.35|0.24|0.39|0.35|18,250|14,254|22,970|25,992|51,747|26,643|
|36|Furniture, manufacturing n.e.c.|0.37|0.27|0.35|0.41|0.41|0.36|13,244|15,078|30,301|51,847|47,015|31,497|
||Nationwide|0.45|0.39|0.44|0.35|0.37|0.40|23,180|22,057|32,867|32,944|40,707|30,351|

Notes: See Table 3a.