rafmacalaba/data-use-mentions-extended huggingface dataset:
ETB 3.1 billion. _Simulation 3_ considers a scenario where PSNP transfers that reach non-poor children (estimated as ETB 2.5 billion) are redirected and equally distributed to monetarily poor children based on their status at the final income. This policy – at zero fiscal cost – amounts to a reduction in monetary poverty by 6.9% and inequality by 2.5%, with almost no difference between boys and girls. The poverty reduction effect of redistribution transfers from non-poor to poor children is larger for urban than for rural children, though more equalizing for rural children. The effects of retargeting PSNP transfers are substantially larger than those that result from doubling them. The joint welfare effects of doubling and retargeting PSNP transfers ( _Simulation 4_ ) are sizeable, as monetary child poverty would fall by 8.8%. That translates to a slight reduction in the child poverty headcount rate from 25.8% to 23.5%. With this joint policy change, girls and children in urban areas would benefit slightly better than boys and those in rural areas. The policy change is also associated with the largest drops in inequality for all children (3.1 %) and other child groups, compared with other simulations.

Two assumptions are made a priori about the simulations. First, the extra transfers can induce a reduction in the labor supply by beneficiaries. Such changes in behavior are ruled out by our simulations. Second, the simulations do not take into account the additional administrative costs related to enrolling currently unenrolled students and increasing the amount and redistribution of PSNP transfers. However, these limitations are unlikely to change the main takeaways from the simulation exercises.

# **6. Conclusion**

The study investigates the fiscal space for children in Ethiopia using the Commitment to Equity for Children (CEQ4C) methodology. The analysis is based on 13,820 children (0-17 years old) from the 2018/19 Ethiopia Socioeconomic Survey. Individual and household level information collected from the survey is combined with budget figures and
low average income per capita. Many rich municipalities gained a lot, and many poor ones lost a lot.

A few factors limited FUNDEF crowd-out of local money. First, all fund receipts had to go to education (by law). Beneficiaries could potentially use fund receipts for other purposes by reducing _voluntary_ (above 25% of revenue) education spending. However, 37% of municipalities had _zero_ voluntary education spending in 1997 (the minimum was binding), and it was close to binding for many more. If these municipalities cut taxes in response to new money, they would have to reduce non-education spending, which might not be possible. Second, many

> 21The National Institute of Educational Studies and Research (Instituto Nacional de Estudos e Pesquisas Educacionais An ́ısio Teixeira) (INEP), an agency under the Ministry of Education, conducts the survey. State and municipal departments of education collect the data, with public and private school cooperation. The transfer of public funds depends on schools’ cooperation with the survey, making the public school data especially reliable.

> 22In 1998, six states got federal top-off: Bahia, Cear ́a, Maranh ̃ao, Par ́a, Pernambuco, and Piau ́ı. The per-child minimum changed annually. It began to vary with child age (in 2000) and school type (rural/ urban) (in 2005).

> 23In Portuguese, Fundo de Manuten ̧c ̃ao e Desenvolvimento da Educa ̧c ̃ao B ́asica.

10
for intermediate goods, but this kind of disaggregated data is not necessary for this paper and the assumptions necessary for that are not part of the data used in this paper.

# **Results 4**

Before focusing on the trade effect of trade agreements in South Asia, it is a good idea to compare with the literature. The literature usually uses manufacturing data due to data availability and for comparability. Table (1) shows the results of the world average RTA effect using manufacturing trade data. Column 1 shows that without including domestic trade flows the effect is 0.02. When domestic trade flows are taken into account in the estimation the RTA coefficient increases. Column 2 shows that including domestic trade flows without controlling for border effects increases the RTA coefficient but it suffers from an upward bias as explained by Bergstrand et al. (2015). Column 3 uses domestic trade flows and control for border effects and shows a benchmark result of 0.10. This RTAs effect is below those seen in the literature when using small samples. For instance, Bergstrand et al. (2015) reports a mean coefficient of around 0.50 when covering 56 countries and Baier et al. (2019) reports a mean coefficient of 0.49 with a sample of 53 countries. But 0.10 is in line with studies covering most countries in the world economy. Recent examples of these studies are Felbermayr et al. (2022) use a sample of 186 countries and report a RTA coefficient of 0.12, and Larch et al. (2019) that reports a coefficient of 0.11. These results in table (1), and the small part of the literature covering as many countries as possible, show that the inclusion of more countries (that tend to be developing economies) uncovers a lower average RTA effect. These benchmark results will be key in understanding the effects of the heterogeneous trade agreements.

With these benchmark results at hand, I now focus on the RTA effects specific to
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- Wesolowski, Amy, Taimur Qureshi, et al. (2015).
Note: The vertical axis measures the WFH index adjusted by internet access at home, in standard deviations from the mean of the (A) PIAAC, (B) STEP and (C) LMPS samples. A higher value indicates that jobs are more amenable to WFH.

The sectors that emerge as more amenable to WFH tend to be the same across most countries in the PIAAC and LMPS data sets. These sectors include ICT, professional services, the public sector, and finance (Figure 7). In contrast, jobs in hotels and restaurants, agriculture, construction, and commerce are the least amenable to WFH.

# **Figure 7. WFH index by sector of economic activity, PIAAC sample**

<!-- Start of picture text -->
1.20<br>1.00<br>0.80<br>0.60<br>0.40<br>0.20<br>0.00<br>-0.20<br>-0.40<br>-0.60<br>-0.80<br>-1.00<br>A=Agriculture, forestry and fishing I=Hotels and Restaurants T=Activities of households as employers F=Construction G=Commerce S=Other service activities H=Transportation and storage E=Water N=Administrative activities C=Manufacturing B=Mining and quarrying Q=Human health and social work activities R=Arts, entertainment and recreation P=Education D=Utilities O=Public administration L=Real estate activities K=Financial and insurance activities M=Professional activities J=Information and communication<br><!-- End of picture text -->

Note: The vertical axis measures the WFH index adjusted by internet access at home, in standard deviations from the mean of the PIAAC sample. A higher value indicates that jobs are more amenable to WFH.

Finally, we regress the WFH index for each data set on
In recent years a number of methods have been developed to assess the level of applied policies in services. Two approaches related to the creation of an index measuring the restrictiveness of service trade policies are worth noting: the first one, the Services Trade Restrictions Index (STRI) was developed by the World Bank; global patterns of policy restrictiveness revealed by that exercise were described in Borchert et al. (2014) and the Database is documented in Borchert et al. (2012). The data were released on the World Bank’s Open Data portal.<sup>18</sup> The second method, named Services Trade Restrictiveness Index (also STRI by its acronym) was developed by the OECD, and results were first published in 2014 (Grosso et al. 2014). Although both approaches use similar sets of underlying data, the methodology used to construct the respective indices differs in a number of ways.<sup>19</sup> Yet both STRI approaches measure the restrictiveness of an economy’s policy regime for the sectors and modes covered. The resultant restrictiveness indices depict overall patterns in policy, across economies, modes, and sectors.<sup>20</sup> The method used in the current paper is an updated version of the STRI developed by the World Bank a few years back. In updating the World Bank STRI, the main principles were maintained but a number of improvements were introduced, in particular the use of an algorithm and an automatized aggregation method to compute the STRI. Introducing these technical improvements removes the subjective bias that may have existed in the previous World Bank STRI. In particular, the aggregation methodology represents an innovation for practical and conceptual reasons: it entails the practical advantage of reproducibility as well as the conceptual advantage of capturing the combined restrictiveness of multiple measures in a well-defined and theory-consistent way. An additional improvement relates to the consideration of regulatory and competition policy measures. This improved methodology will be described below.

# **Basic principles**

The
# **Section 3c. Summary Statistics- Updated Well-Being Measures**

Table 3 presents estimates of these updated measures of well-being.<sup>22</sup> All of the indicators captured in the MPM aside from the educational attainment of adults have a higher share of the population deprived than is estimated to be monetarily poor. Approximately 42 percent of the population lives in a household with poor school attendance; 61 percent of the population does not have access to improved drinking water; and above 80 percent of the population has poor access to electricity and poor access to improved sanitation. Combined, the population in PNG experiences extensive deprivations across all dimensions of well-being.

At odds with the level of economic activity in the country, PNG ranks near the bottom in each indicator captured and in the overall MPM. Aside from the educational attainment of adults, for no measure reported in Table 3 does PNG rank above the 13’th percentile of countries reported in the World Bank’s PIP. And in one of those indicators, access to improved drinking water, PNG ranks last out of 112 countries for which the indicator is estimated; and in two other indicators- access to improved sanitation, and access to electricity- PNG ranks between the second and sixth percentiles of countries.

Given the widespread deprivation in PNG across all indicators, there is a significant share of the population for which these indicators overlap. Table 3 illustrates that 74.5 percent of the population are deprived in indicators equivalent to at least one dimension of the MPM. Very few countries in the world either have as high a share of the population that is multidimensionally poor, or are as consistently one of the most deprived countries in the world across all indicators. PNG has the 118th highest share of the population that is multidimensionally deprived out of the 122 countries for which the measure was able to be constructed; and PNG respectively ranks 120th across the 122 countries measured in the median rank across all indicators and the MPM itself.

Figure 3 reports the share
<br>August|140 000<br>Households|
|Famille « Istimarat » (excluding<br>Families with limited income)<br>|200 TND<br>(70 dollars)|May 2020|301149<br>Households|

Source: Data accessed from Ministry of Social Affairs Simulating the impacts of the pandemic on welfare in the presence of all the above mitigation measures announced,<sup>14</sup> we observe that compensatory transfer measures initiated by the government will have a positive impact on poverty. Specifically, the increase in poverty will be 6.5 percentage points if mitigation measures are in place vis-à-vis in their absence when the increase in poverty will be 7.3 percentage points<sup>15</sup> (see Table 4).

**Table 4: Impact of COVID19 with mitigation measures**

|Before||After mitigation|Difference|Difference|
|---|---|---|---|---|
|COVID|After COVID|measure|(without|(with|

> 14 We provide a description of the government measures announced on March 21, 2020. The simulations are based on the announced measures as there is no access to information on actual spending under these measures announced.

> 15The data used to estimate these welfare impacts in the presence of mitigation effects do not allow us to focus on a region, hence we do not provide estimates of these impacts across regions. Moreover, the activities targeted by government measures are more concentrated in coastal regions. The South Region, where some of the riots are concentrated, is characterized by the oil extraction industry and some chemical industries, which do not employ a large workforce.

17
causal impacts of the program on a specific child, because childbearing is endogenous to treatment. However, as the causal effects of cash transfers targeted to females of childbearing age (rather than to pregnant women or mothers) is an important policy question, we attempt to disentangle the selection-driven components of fertility and parentage from the direct treatment effects on the actual sample of children born. The technical details of the assumptions required and the sequence of adjustments – to move from the overall reduced-form difference between children born to mothers in the treatment and control groups towards a more standard causal effect on the children actually born – are outlined in Appendix C.

To investigate how differential exposure to CCTs and UCTs drives treatment effects, we consider the sample of children born during three epochs. The first epoch captures those _directly exposed_ to the program, meaning those born during the program.<sup>26</sup> This cohort is exposed for a maximum of two years, with some combination of in utero and child exposure depending on the exact birth date of the child. The second epoch covers those born within nine months of the end of the program, who were _exposed in utero_ for a maximum of nine months. Finally, the third epoch covers those born more than nine months after the end of the program, who were _not exposed_ to cash transfers either as children or in utero and could only benefit from the program due to improved outcomes of their mothers **.** We concentrate our analysis on height-for-age z- scores (HAZ), which is an objectively measured indicator of stunting that affects almost 50% of children under the age of five in Malawi, and is a strong predictor of productivity as an adult in low income settings (LaFave and Thomas 2016).<sup>27</sup>

> 26 The percentage of baseline schoolgirls who reported having been ever pregnant was less than 2% at baseline. Hence, children directly exposed to the program in this stratum are almost exclusively born during the intervention.
# _2. Lending performance_

According to WEDP MIS reports, 2,538 new clients who have received WEDP loan between July 2017 to July 2018, a 48% increase from the number of new clients the year before. So far, a total of 9,338 loan data has been uploaded in MIS, which can track WEDP loan by city and by disbursing MFI respectively.

Table 4: Number and Size of Loans by City (as of June 2018)

|**City**|**Number of Loans**|**Ave. Loa**|**n Size**|
|---|---|---|---|
|Addis Ababa|5085|ETB|274,057|
|Bahir Dar|1064|ETB|389,659|
|Hawassa|312|ETB|256,170|
|Adama|1269|ETB|192,032|
|Mekelle|1180|ETB|172,883|
|Dire Dawa|232|ETB|118,095|
|Assela|13|ETB|253,077|
|**Total**|**9155**|**ETB**|**236,568**|

Table 5: Number and Size of Loans by MFIs (as of July 2018)

|**MFI**|**Number of Loan**|**Ave. Loan Size**|
|---|---|---|
|WASASA|358|ETB 356,022|
|SFPI|308|ETB 418,498|
|OCSSCO|1,380|ETB 283,019|
|ADCSI|2,957|ETB 224,826|
|ACSI|1,079|ETB 628,908|
|DECSI|1,195|ETB 290,835|
|HARBU|249|ETB 291,332|
|OMO|204|ETB 413,501|
|
unemployment was perceived as too narrow, since farmers producing for family use and other individuals performing own-use production work, by virtue of being considered as employed, were ineligible for classification under unemployment (ILO 2013a).<sup>6</sup> By narrowing employment to work performed for pay or profit, the revised ICLS standards are expected to render employment and unemployment statistics in developing countries more responsive to cyclical fluctuations, economic crises and structural change, which may be a desired feature to guide economic and social policy reforms. A related practical concern with the 13<sup>th</sup> ICLS standards was that many countries, de facto, did not fully adhere to the standards but excluded some groups of workers (e.g. some own-use production workers, certain volunteer workers, etc.) from the employment measure, which impaired cross-country comparability of labor statistics.

The 19<sup>th</sup> ICLS also addressed the long-standing critique that own-use services (domestic housework and childcare, as examples), which are disproportionately performed by women and children, were treated

> 6 Because the new ICLS standards limit employment to work performed for pay or profit, own-use production workers now qualify for unemployment, conditional on whether they are available and actively searching for employment. However, it remains to be seen whether many farmers producing mainly or only for own use will meet the ‘actively searching for employment’ criterion. Other measures of labor underutilization, particularly time-related underemployment, may be more relevant for this population group (see ILO 1998, 2013c for more details).

6
-|---|
|||_Scenar_|_io 1_|_Scenar_|_io 2_|_Scenar_|_io 3_|_Scena_|_rio 2_|_Scena_|_rio 3_|
|||Gasoline|Diesel|Gasoline|Diesel|Gasoline|Diesel|Gasoline|Diesel|Gasoline|Diesel|
|Phase 3|Oct-24|176|138|247|228|217|188|40%|66%|23%|36%|
|Phase 4|Apr-25|176|138|346|377|268|256|40%|66%|23%|36%|
|Phase 5|Oct-25|176|138|484|625|330|348|40%|66%|23%|36%|
|Phase 6|Apr-26|176|138|||406|474|||23%|36%|
|Phase 7|Oct-26|176|138|||500|646|||23%|36%|

Source: authors’ estimations. Note: these estimations are based on data available as of May 2024 and are subject to changes in volatile variables. For example, changes in the foreign exchange rate and global oil prices will undoubtedly have an impact on these estimations.

These subsidy removals would generate significant additional fiscal savings. If prices remain unchanged, total subsidy spending is projected to reach 3, 2.7 and 2.4 percent of GDP in 2024, 2025 and 2026, respectively. The decline in oil prices and the smooth nominal depreciation expected in the medium term would reduce subsidy spending as a share of GDP. In the second scenario, subsidy spending is expected to reach 3, 1.3 and 0.3 percent of GDP in 2024, 2025 and 2026, saving 0.05, 1.3 and 2.2 percent of GDP, respectively. In the third scenario, subsidy spending is expected to reach 3, 2 and 0.9 percent of GDP in 2024
# **3.0 - VULNERABILITIES AND FAILURES**

The following results consider all reviewed data including outage information, facility-specific curtailments and case studies. Table 2 presents the major outages in the United States (2000-2016) summarized by cause. Half of all events are attributed to natural hazard events. Notably, where outages were due only to damage to operability disruptions, more than half of all classified events were due to the transmission and distribution infrastructure.

<u>Mukherjee, Nateghi, and Hastak (2018)</u> examined the cause, location, duration and impact (MW lost, customers affected) of every major electrical outage in the United States between 2000-2016. Over 1,500 events were included in the dataset, and each outage event was categorized by the major cause. Fifty percent of all major outage events in the examined period were due to natural hazard events, Table 2. System Operability Disruptions account for approximately 8% of outages, of which more than 50% were transmission/ distribution interruptions issues (where classified). Fuel Supply Emergency caused outages 3% of the time, with data on the fuel type for each outage: coal (35%), hydroelectric facilities (12%), natural gas (14%) and petroleum (2%) and unknown accounting for the rest.

McCall et al. (2016) examined the impact of water-related issues on generation facility operation. It focused on all water-related curtailments in the United States between 2000-2015. A total of 37 events were recorded for nuclear, coal and hydroelectric facility operations.

The majority of curtailments occur due to water intake and/or discharge issues related to water temperature (>90%) with many of these (79%) occurring during heatwave/drought conditions. Specific design and operation parameters associated with environmental regulations and permitting were the major cause of curtailment (water temperatures

**Table 2 - Major Outages in the Continental United States** (2000-2016), summarized from Mukherjee et al. 2018
*st**</sup>**-5**<sup>**th**</sup>**largest generators**<br>**(as % of total capacity).**<br>**? HHI of generation firms (index)**|||
|_? Access to networks_|**? Are there provisions for generators to**<br>**have access to transmission network? (no, yes**<br>**– rTPA, nTPA)**||Steiner (2001)|
|_? Retail market opening_<br>_? Interconnections_|**? Degree of market opening (% of total**<br>**consumption liberalised)**<br>**? Threshold level for retail customer choice**<br>**(MW, MWh)**<br>? Retail competition (no., % share of<br>independent suppliers in different market<br>segments)<br>? Degree of switching from incumbent<br>suppliers (no. and % of eligible customers)<br>? Interconnections with other systems<br>(capacity, % share of total market)||Steiner (2001)|
|_? Independent Power_<br>_Producers (IPPs)_|**? Significance of IPPs (no., share, and type -**<br>**long-term contract, merchant)**|||

21