rafmacalaba/datause-encoder-data huggingface dataset:
distribution of consumption is closer to the 2018/19 NLSS distribution – we only apply this transformation to the 2018/19 GHS to exploit information for an overlapping year.

We find that assuming a lognormal distribution is a reasonable approximation for our data, in line with other literature on survey-to-survey imputations (Tamakatsu et al., 2021). Specifically, we compare the distribution of the imputed consumption vector resulting from our main model to the one obtained after applying zero-skewedness transformations and compare whether different moments of the distribution estimated with the latter are closer to the _true_ distribution from the 2018/19 NLSS. We find that the poverty headcount rate at the US$1.90 poverty line is 42.4 percent when using a zero-skewedness transformation and 42.6 percent when using a Box-Cox transformation. Both estimates confirm the robustness of the imputation exercise for 2018/19. However, the difference with the 2018/19 NLSS official poverty headcount rate (39.1 percent) is slightly higher than what we obtain with our preferred specification (41.9 percent), suggesting that assuming a lognormal distribution in the imputations is a good fit for our data. Looking at different moments of the distribution confirms this finding.

# 7. Discussion

The main strength of this analysis is that two very different approaches yield very similar estimates of a poverty trend for Nigeria in the period between 2009 and 2019. In this section, we discuss the implications of the main findings and discuss possible caveats of this analysis.

# _<u>7.1. Implications for Nigeria’s poverty trend</u>_

Figure _6_ combines the main results of the backcasting exercise and of the survey-to-survey imputations using all waves of GHS data. As noted in Section 5.2, the survey-to-survey imputations seem to overestimate poverty by around 2 percentage points when compared to the backcasted estimates and to the 2018/19 NLSS official poverty rate. Notwithstanding this difference in levels, the two methodologies yield a very similar poverty trend showing a decline in poverty in the first half of the decade and a stagnation and even reversal in poverty reduction during the economic recession following the 2016 oil price crisis.

To illustrate this point further, Figure _6_ also compares the main results of this analysis with an alternative poverty trend which interpolates between the 2003/4 NLSS and 2018/19 NLSS household consumption vector, that is, excluding 2009/10 HNLSS, using growth rates in total real GDP per capita and the World 21
entered alone. The conditional model comes out again positive, whereas the limited model once more obtains a negative coefficient result.

Table 4: Baseline results following equation (2)

||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|---|---|---|---|---|---|---|
||**Digital+**|**Digital**|**Publishing**|**Telecom**|**IT & Info**|**Business**|
|MODEL**CB**OP|-0.372|-0.488|-0.599|-0.276|0.073|-0.386|
||(0.172)|(0.319)|(0.223)|(0.323)|(0.875)|(0.236)|
|MODEL**CB**CT|0.455*|0.670|0.349|0.482**|0.077|0.073|
||(0.087)|(0.124)|(0.452)|(0.037)|(0.840)|(0.751)|
|MODEL**CB**LT|-0.692**|-0.942*|-1.908***|-0.643|-0.653|-0.198|
||(0.014)|(0.053)|(0.004)|(0.135)|(0.252)|(0.705)|
|Obs|4096|4096|4096|4096|4096|4096|
|P-R2|0.983|0.962|0.958|0.961|0.928|0.968|
|MODEL**DR**OP|-0.578**|-1.232***|-1.536***|0.041|-1.062**|-0.559|
||(0.046)|(0.004)|(0.002)|(0.899)|(0.026)|(0.195)|
|MODEL**DR**CT|0.246|1.019**|0.166|0.374*|1.237***|0.023|
||(0.297)|(0.012)|(0.662)|(0.052)|(0.002)|(0.939)|
|MODEL**DR**LT|-0.660*|-1.206**|-0.740|-0.326|-1.157**|-0.078|
||(0.075)|(0.032)|(0.218)|(0.488)|(0.016)|(0.883)|
|Obs|4096|4096|4096|4096|4096|4096|
|P-R2|0.983|0.962|0.959|0.961|0.928|0.967|
|MODEL**CB**OP|-0.118|0.927*|0.216|-0.302|1.778***|0.004|
||(0.652)|(0.065)|(0.644)|(0.440)|(0.000)|(0.992)|
|MODEL**CB**CT|0.485**|-0.023|0.465|0.448|-0.684**|0.128|
||(0.040)|(0.956)|(0.276)|(0.147)|(0.043)|(0.648)|
|MODEL**CB**LT|-0.493|-0.277|-1.752***|-0.756|0.049|-0.261|
||(0.186)|(0.638)|(0.006)|(0.221)|(0.935)|(0.683)|
|MODEL**DR**OP|-0.639**|-2.007***|-1.831***|0.148|-2.144***|-0.659|
||(0.035)|(0.000)|(0.000)|(0.723)|(0.000)|(0.202)|
|MODEL**DR**CT|-0.022|1.078***|-0.086|0.083|1.413***|-0.079|
||(0.931)|(0.010)|(0.841)|(0.775)|(0.000)|(0.804)|
|MODEL**DR**LT|-0.325|-1.144*|-0.242|0.119|-1.196***|0.012|
||(0.468)|(0.065)|(0.691)|(0.842)|(0.004)|(0.984)|
|Obs|4096|4096|4096|4096|4096|4096|
|P-R2|0.983|0.964|0.959|0.961|0.931|0.968|
|Control GRAV|Yes|Yes|Yes|Yes|Yes|Yes|
|Control C|Yes|Yes|Yes|Yes|Yes|Yes|
|FE exp+ imp|Yes|Yes|Yes|Yes|Yes|Yes|

Note: * p<0.10; ** p<0.05; *** p<0.01. The dependent variable is bilateral digital services exports (DSX) using the underlying gross trade data from the TiVA database. Sector groupings in each column can be found in Annex Table A2. Robust standard errors are clustered at the country-pair level. Fixed effects for exporter and importer 28
Table 4: Policy levers that affect the can-do relationship

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Do|Can-do Gap|Do|Do|
|self-satisfaction|-0.0375|0.110|-0.0527|0.248|
||(0.0834)|(0.168)|(0.0853)|(0.310)|
|feeling valued by the facility|-0.378<sup>_∗∗_</sup>|0.167|-0.334<sup>_∗∗_</sup>|-1.326<sup>_∗∗_</sup>|
||(0.154)|(0.251)|(0.138)|(0.514)|
|job satisfaction|0.0119|0.234|-0.0394|-0.0112|
||(0.183)|(0.231)|(0.173)|(0.535)|
|positive facility characteristics|0.0782<br>(0.0979)|-0.167<br>(0.141)|0.0968<br>(0.0872)|-0.0261<br>(0.257)|
|positive worker behavior|0.395<sup>_∗∗∗_</sup>|-0.264|0.368<sup>_∗∗_</sup>|0.874|
||(0.146)|(0.298)|(0.149)|(0.548)|
|Can|||0.209<sup>_∗∗_</sup><br>(0.0864)|-0.422<br>(0.570)|
|Can_×_self-satisfaction||||-0.698<br>(0.747)|
|Can_×_feeling valued by the facility||||2.235<sup>_∗∗_</sup><br>(1.016)|
|Can_×_job satisfaction||||-0.195<br>(1.443)|
|Can_×_positive facility characteristics||||0.279<br>(0.645)|
|Can_×_positive worker behavior||||-0.867<br>(1.172)|
|Constant|0.297<sup>_∗∗∗_</sup>|-0.00902|0.237<sup>_∗∗∗_</sup>|0.469<sup>_∗∗_</sup>|
||(0.0844)|(0.115)|(0.0878)|(0.226)|
|Observations|616|616|616|616|
|Adjusted_R_<br>2|0.043|0.022|0.074|0.092|

Clustered standard errors in parentheses _∗ p <_ 0 _._ 1,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01 24
**Figure 8: Estimated value of assets after the crisis compared with regional averages prior**

<!-- Start of picture text --> to the crisis<br>1,600,000<br>1,432,052<br>1,400,000<br>1,200,000<br>1,000,000<br>800,000<br>604,593<br>543,558 559,919<br>600,000 371,258<br>340,343<br>400,000<br>173,583 141,616 172,730 130,744<br>200,000<br>0<br>Estimated value of assets (FCFA)<br><!-- End of picture text --> Source: Listening to Displaced People Survey 2014/ EMOP 2011.

The final element that corroborates the shift towards a greater sense of poverty amongst the displaced is the discontinuation of schooling. 19% of those residing in refugee camps, 7% of the returnees and 5% of the IDPs took their children out of school (Figure 9).

**Figure 9: Did your children continue their education during displacement (%)**

<!-- Start of picture text --> 68<br>61<br>40<br>IDPs<br>28 Refugees<br>22<br>19 Returnees<br>10<br>5 7<br>Yes, the majority Yes, the minority No<br><!-- End of picture text --> Source: Listening to Displaced People Survey, 2014.

School attendance improved and between August 2014 to February 2015 most children attended school, though school attendance varies over time (particularly in Kidal) and remains relatively low in the refugee camp in Niger (around 75% --Figure 10).

16
Terminals Ltd. (SLMTL) to run the Port of Vieux Fort. While the SLMTL was expected to have private management, it is still 100 percent owned by the SLASPA.

**Table 3.1 List of Select OECS Port Authorities**

|**Country**|**Port**|**Port Authority/Regulator/Operator**|
|---|---|---|
|Antigua|St. John’s|Antigua and Barbuda Port Authority|
|Dominica|Woodbridge Bay/Roseau|Dominica Air and Sea Ports Authority|
|Grenada|St. George’s|Grenada Ports Authority|
|S Ki d Ni|Basseterre|St. Christopher Air and Sea Ports Authority|
|t. tts an evs|Long Point|Nevis Air and Sea Ports Authority|
|St. Lucia|Castries<br>Vieux Fort|St. Lucia Air and Sea Ports Authority<br>St. Lucia Marine Terminals Ltd.|
|St. Vincent and the Grenadines|Campden Park/Kingstown|St. Vincent and the Grenadines Port Authority|

_Source:_ Authors’ compilation. _Note:_ OECS = Organization of Eastern Caribbean States.

**Because of a lack of independent regulatory bodies, adjustments to tariffs and fees are generally made on an ad hoc basis.** With the exception of St. Kitts and Nevis and St. Vincent, all ports base fixed criteria for adjusting handling charges on periodic reviews, often separated by more than three years. Adjustments to handling charges in St. Kitts and Nevis are done freely, based on market conditions. In St. Vincent handling charges are adjusted upon petition from the regulatory board. In general, the process of tariff adjustments is not very clear. Authorities at St. Vincent report making adjustments by petition and, at St. Kitts, on a market basis; Dominica uses the free along side (FAS) system. With the exception of Antigua, all criteria for adjusting port fees are set by the port authority (table 3.2). In many cases, fees are contained in the law providing the port its authority, and changes by the port authority must be approved by parliament or another higher government level before being applied.

**Table 3.2 Select Regulatory and Institutional Frameworks at a Glance**

||**Antigua &**<br>**Barbuda**|**Dominica**|**Grenada**|**St. Kitts &**<br>**Nevis**|**St. Lucia**|**St. Vincent**<br>**& the**<br>**Grenadines**|
|---|---|---|---|---|---|---|
|Corporatization of<br>serviceprovision||No|||Partial|–|
|Sector reformyear|No reform|2007|||No reform||
|Legislationyear|1973|2006|1978|1993|1983|1975|
|Decentralization||||No|||
|Foreign owner<br>restrictions||||No||Yes|
|Port terminal<br>concession||||No|||
|Independent<br>regulatoryagency||||No|||
|Criteria for tariff<br>adjustment|At petition to the<br>regulatoryboard|Other|Freely bas<br>con|ed on market<br>ditions|Other|At petition to the<br>regulatoryboard|
|Criteria for handling<br>charges adjustment|Fixed periodic rev|iew of more th|an 3 years|Freely based<br>on market<br>conditions|Fixed periodic<br>review of more<br>than 3years|At petition to the<br>regulatory board|
|Criteria for port fees<br>adjustment|Other|||Set by port|authority||

_Source:_ Authors’ compilation.

22
<!-- Start of picture text --> 1i<br>0.9 i —— share to CCT<br>i sreresee=" Observed<br>o) H<br>ie) |<br>207 i<br>206Ssa iii<br>S05 i<br>£04 j<br>£03 i<br>© 02 i<br>0.1 i<br>0 i<br>10 12 14 16 18 20 22<br>Ely|s=1] - Ely|s=0]<br><!-- End of picture text -->
1.1 introduction

3 is the problem of firm heterogeneity. The two biases are linked when zero flows are caused by the fixed cost of investing abroad, and only the more productive I build on the insight of the new trade literature that firms are heterogenous _within_ industries in terms of productivity, size, use of inputs, and wages.<sup>4</sup> This translates into distinct decisions by firms (that co-exist within a narrowly-defined industry) on whether or not to export, or undertake FDI, or just serve the domestic market (Greenaway & Kneller 2007; World Trade Organization 2008; Helpman 2011). When profits are a function of varying productivity and differing fixed costs, Helpman et al. (2004) show that there is a natural sorting of firms, with the most productive self-selecting to undertake FDI. The next tier serves the foreign market through export, and the least productive serve the domestic market only (Appendix B).<sup>5</sup> The earlier generation of “new” trade models that integrated economies of scale and monopolistic competition was a breakthrough in understanding a new source of comparative advantage (Helpman 2011). However, they addressed neither heterogeneity nor country selection. In particular, the assumption of symmetry in firm size and productivity leading to a prediction that all firms export to all countries is not supported by evidence at the firm level.<sup>6</sup>

- 4 The seminal paper of this new literature is Melitz (2003). He explains why only a fraction of firms export and why exporters are larger and more productive than non-exporters. Note, though, in the Melitz model, all firms pay the same wage. This model has generated a vast empirical literature on firm-level approach to international trade. It has also been applied to study the quantitative effects of simulated trade policy reforms (for a recent work, see Balistrery, Hillberry and Rutherford, 2011).

- 5 This is supported by data. Helpman et al. (2004) show for US manufacturing firms across 52 sectors and 38 countries that multinational enterprises had 15 percent more labor productivity than exporters in 1994; exporters were 39 percent more productive than non-exporters. Girma et al. (2004), Girma & Pisu (2005), and Arnold & Hussinger (2005) all find significant productivity differences between firms that invest abroad and those that do not. Chen & Moore (2010) find in the case of French multinational firms that those with low productivity are less likely to invest in host countries with a small market size, high production costs, or low trade costs.

- 6 The new trade models of the 1980s successfully explained the phenomenon of intra-industry trade as caused by product differentiation on the part of firms operating under economies of scale. However, firms were assumed to be symmetric. All of them traded, but only the volume depended on trade costs. In other words, trade costs affected only the intensive margin (the volume of export per firm), not the decision of whether to export in the first place.
<!-- Start of picture text --> High income 71,815<br>_— |“<br>Low income 5,517<br>0 25,000 50,000 75,000<br>Number of Papers using Data<br><!-- End of picture text -->

<!-- Start of picture text --> ed<br>Suope ScenaAS Ls<br>ee (iii)i<br>a Lda<br>‘ate Ares & careoean |TM<br>Sub-Saharan Africa | 143.1<br>sauA -<br>0 100 200 300 400 500<br>Number of Papers using Data per capita<br><!-- End of picture text -->
# **Continuity of Supply**

The continuity of electricity supply is influenced by the performance of all stages of electricity supply: generation, transmission and distribution.

## **Box 1  Key Technical Performance Indicators**

## **Unit Capability Factor (UCF)**

Unit capability factor is the percentage of maximum energy generation that a plant is capable of supplying to the electrical grid, limited only by factors within control of plant management. A high unit capability factor indicates effective plan programs and practices to minimize unplanned energy losses and to optimize planned outages, maximizing available electrical generation.

## **Unplanned Capability Loss Factor (UCLF)**

Unplanned capability loss factor is the percentage of maximum energy generation that a plant is not capable of supplying to the electrical grid because of unplanned energy losses (such as unplanned shutdowns, outage extensions or load reductions). Energy losses are considered unplanned if they are not scheduled at least four weeks in advance. A low value for this indicator indicates that important plan equipment is reliably operated and well maintained.

## **Unplanned automatic grid separations per 7000 operating hours (UAGS)**

This indicator expresses how often a generator is separated from the external grid, in both an unplanned and automatic (manual actions are excluded manner); it is given as a rate per 7000 operating hours, thereby taking into account the wide variety of operating regimes. _Source_ : UNIPEDE (2001).

# **_Generation Plant Performance_**

Traditionally, the primary measures of plant reliability have been the Equivalent Availability Factor (EAF) and the Equivalent Forced outage Rate (EFOR) in North America (NERC), and the Unit Capability Factor (UCF) and the Unplanned Capability Loss Factor (UCLF) in Europe (UNIPEDE). In analyzing Eskom’s generation plant performance we will adopt the UNIPEDE technical performance indicators (box 1).

One key step towards meeting the objective of providing reliable electricity is to maintain and improve the availability of generating units. In the late 1980s, Eskom’s generation plant performance had deteriorated, there was a high incidence of costly plant damage, and excessive unit trips, to the extent that by 1989 its Unplanned Capability Loss Factor (UCLF) reached the unacceptably high rate of 14 percent (figure 7).

Similarly, during the same year, Eskom’s Unit Capability Factor (UCF) stood only just above 78 percent, and by 1990 it declined further to around 75 percent (figure 14).<sup>32</sup> These low plant availability rates resulted from many base load units requiring enhanced maintenance due to breakdowns. During 1990, Eskom’s generating units

> energy.  It is a continuous-process industry, so power interruptions can lead to the loss of production in progress.

> 32 The unexpected bad weather during 1998 was also a major contributor towards the deterioration in these measures.

19
<!-- Start of picture text --> « Shares of expenditure to total expenditure « Government (%GDP) «<br>120 Wi Capita! 9) Goods andsenices -1 = 90<br>Hh other Qj interest Budget deficit<br>100 -2 —— Govemment debt [RHS} 85<br>80 =<br>TUTTE& E 4ee :75<br>60 5 70<br>40 -6 65<br>7 60<br>20<br>0 | a i 9 50<br>2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018<br><!-- End of picture text -->

—T7 —_— ~~-~~ i
# 5 Regional infrastructure funding

The preceding sections identified key physical gaps in East Africa’s regional infrastructure backbone. Filling those gaps and meeting the region’s connectivity targets would entail annual spending of almost $2.9 billion dollars over a period of 10 years (table 5.1). That level of spending would buy a basic regional package that included the infrastructure required for full regional power trade, a complete regional road network, and fiber optic links connecting all countries to submarine cables. The level of spending required to meet the same targets in the EAC countries alone would be much smaller—around $513 million annually.

To put this in perspective, the total amount of infrastructure spending needed in the broader East Africa region to fulfill both regional and national infrastructure demands is $4.3 billion a year. The regional portion therefore represents 12 percent of the region’s overall requirement. However, in the East Africa area (excluding Sudan),<sup>12</sup> the total amount needed for infrastructure is $6.1 billion per year, of which the regional component accounts for only 28 percent.

Spending requirements vary greatly across countries and sectors. The power sector has the largest requirements in terms of investments, operations, and maintenance—$2.5 billion annually for East Africa and a far less daunting $365 million for EAC. Transport spending needs are significantly smaller, amounting to $304 million for East Africa and $141 million for EAC. ICT investments pale in comparison to the other sectors, totaling $10 million per year for East Africa and $7 million for EAC.

The countries that would bear the heaviest spending burden in absolute terms are Ethiopia and Sudan. To meet regional targets, both would need to spend $1.2 billion annually on regional infrastructure, mainly in the power sector. Within EAC, Tanzania has the largest spending burden ($212 million), chiefly for power-related interconnectors.

**Table 5.1 . Annual regional spending requirements by sector over 10 years**

||Transp|ort|Pow|er|ICT||Tota|l||
|---|---|---|---|---|---|---|---|---|---|
|US$|||||||||Total<br>|
|millions|Investment|O&M|Investment|O&M|Investment|O&M|Investment|O&M|needs|
|Burundi|1|3|1|0|1||3|3|7|
|Kenya|17|27|3||2|0|22|27|49|
|Rwanda|1|2|59||1|0|60|2|62|
|Tanzania|29|32|44|104|3|0|76|136|212|
|Uganda|14|16|145|9|0|0|159|25|183|
|**EAC**|**61**|**80**|**252**|**113**|**7**|**0**|**320**|**193**|**513**|
|Ethiopia|23|34|1,008|98|1|0|1,032|132|1,164|
|Sudan|65|42|1,032|52|2|0|1,099|94|1,192|
|**East**<br>**Africa**|**149**|**155**|**2,292**|**263**|**10**|**0**|**2,451**|**418**|**2,870**|

Source: AICD calculations.

12 Sudan was not included in this calculation due to data limitations.
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20