rafmacalaba/datause-encoder-data huggingface dataset:
indicator of individual economic well-being in the economic literature focusing on developing countries (Steckel 1995; Jensen 2005; Sahn and Younger 2009; Molini et al. 2010). Using BMI, van de Walle (2013) identifies relative disadvantage for ever-widowed women relative to others in Mali. DHS-reported BMI is based on physical measurements, and arguably more objective than self-reported measures of health status. Sahn and Younger (2009) argue that the advantages of using BMI (as opposed to, say, household consumption) include that it is measured at the individual, not the household level; it reflects command over food but also sanitation conditions; it accounts for caloric consumption relative to needs; and errors in its measurement are likely to be random (also see Steckel 1995).

From a health standpoint, BMI can also be too high. Obesity is an increasing concern in developing countries, as it has been in the developed world for some time. A systematic review of studies on the prevalence of obesity among adults finds that about 20 to 35 percent of Nigerians are overweight, while 8 to 22 percent are obese (Chukwuonye et al. 2013).<sup>12</sup> Importantly, while socio-economic status is negatively associated with obesity in developed countries, this association is usually found to be positive in developing countries (McLaren, 2007). For example, Wittemberg (2013) finds that the relationship between BMI and socioeconomic status is non-decreasing over the entire range of income/wealth in South Africa and this relationship holds for other countries in SSA. In this paper our focus is on the lower end of the BMI distribution and potential undernutrition as opposed to obesity.

A positive relationship between marriage and BMI has been documented for many countries, both developed and developing.<sup>13</sup> In Ethiopia, single women are more likely to be undernourished relative to currently married women (Girma and Genebo 2002). Given high marriage rates, almost all single women are adolescents or young, when nutritional needs increase substantially due to the spurt of growth. Early childbearing may further deteriorate a young woman’s nutritional status.

**_Covariates and other variables_** : All regressions, described below, control for a large set of individual and household level characteristics, a dummy for the 2013 survey round, and state

> 12 24% of women15-49 are overweight (BMI of 25 or higher) and 7% are obese (30 or higher) based on the pooled 2008 and 2013 DHSs. 11% are underweight (BMI lower than 18.5). These means mask significant geographic variation: overweight rates are highest in the South (around 31%); underweight is highest in the North East and North West at 17% and 15% of all women, respectively.

> 13 See Jeffery and Rick (2002) for the US, Tavani et al. (1994) for Italy; Nagata et al. (2009) for Guatemala;  Girma and Genebo (2002) for Ethiopia.

11
-

- OECD (2004): ITCS International Trade by Commodity Database, Database Edition – –

- Harmonized System 1996 [HS Rev.2] EU-15 Extra-EU vol. 2004 rel. 01.

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- ences A Missed Opportunity, _Journal of World Trade_ , 29(6), pp. 79–96.

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- Santos-Paulino, A. and A. P. Thirlwall (2004): The Impact of Trade Liberalisation on Exports, Imports and the Balance of Payments of Developing Countries, _The Economic Journal_ , 114, pp. F50–F72.

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- UNCTAD (2001 _a_ ): Duty and Quota Free Market Access for LDCs: An Analysis of Quad Initiatives, Technical Report UNCTAD/DITC/TAB/Misc.7, United Nations Conference on Trade and Development, Geneva.

- UNCTAD (2001 _b_ ): Improving Market Access for Least Developed Countries, Technical Report UNCTAD/DITC/TNCD/4, United Nations Conference on Trade and Development, Geneva.

- UNCTAD (2003): Trade preferences for LDCs: An early assessment of benefits and possible improvements, Technical Report UNCTAD/ITCD/TSB/2003/8, United Nations Conference on Trade and Development, Geneva.

23
39 Table 11 (continued)

|Country or ecomyW|1985|1990|1995|2000|2005|2010|2015|2020|2025|
|---|---|---|---|---|---|---|---|---|---|
|Portugal|10,157|10,271|10,408|<br>10,548|10,677|10,775|10,846|10,901|10,948|
|Romania|22,740|23,235|23,760|24,334|24,909|25,361|25,706|26,003|26,312|
|Spain|38,523|39,373|40,255|41,061|41,701|42,135|42,438|42,738|43,023|
|Sweden|8,350|8,367|8,363|8,343|8,306|8,267|8,227|8,171|8,085|
|Switzerland|6,458|<br>6,494|<br>6,496|<br>6,478|<br>6,435|<br>6,372|<br>6,294|<br>6,202|<br>6,090|
|United Kingdom|56,618|<br>57,205|<br>57,641|<br>57,913|<br>58,010|<br>58,077|<br>58,173|<br>58,250|<br>58,218|
|Yugoslavia|23,123|<br>23,816|<br>24,461|25,066|25,561|25,941|<br>26,229|<br>26,463|<br>26,667|
|Other Europe (a)|253|<br>264|<br>275|<br>287|<br>297|<br>307|<br>317|<br>328|<br>335|
|U.S.S.R.|278,618|290,977|302,059|312,232|321,408|329,778|338,008|345,262|352,319|
|Oceania|24,458|26,244|27,896|29,381|30,763|32,060|33,300|34,420|35,392|
|Australio|15,758|<br>16,813|<br>17,677|<br>18,336|<br>18,894|<br>19,379|<br>19,813|<br>20,170|<br>20,444|
|FiJi|700|<br>750|<br>805|<br>860|<br>912|<br>964|<br>1,019|<br>1,073|<br>1,123|
|French Polynesia|175|204|235|<br>266|<br>294|<br>318|<br>339|<br>360|<br>380|
|Guam|123|<br>138|<br>152|<br>166|<br>179|<br>191|<br>203|<br>213|<br>223|
|Kiribati|64|<br>69|75|<br>81|88|<br>95|<br>101|<br>108|<br>114|
|New Catedonia|150|<br>161|<br>172|<br>t85|<br>198|<br>210|<br>221|<br>231|<br>241|
|New Zealand|3,254|<br>3,354|<br>3,450|<br>3,548|<br>3,639|<br>3,715|<br>3,790|<br>3,855|<br>3,907|
|Pacific<br>Islands|48|164|182|202|224|246|265|283|301|
|Papua New Guinea|3,329|3,740|4,186|4,652|5,113|5,579|6,037|6,468|6,856|
|SolomonIslands|273|325|383|448|519|595|674|753|830|
|Tonga|97|104|113|122|132|142|153|164|174|
|Vawatu|131|151|174|198|225|254|285|317|<br>347|
|WesternSamoa|163|171|183|199|216|233|251|268|286|
|other MicronesIa (a)|23|25|28|30|33|35|38|40|42|
|Other Polynesia (a)|70|<br>75|<br>81|88|97|<br>104|<br>111|<br>117|<br>124|
|World total|4,843,331|5,281,816<br>|<br>5,743,944<br>|<br>6,204,958<br>|<br>6,645,894|7,064,02|2<br>7,472,393<br>|<br>7,876,265<br>|<br>8,262,434|
|Growth rate (percent) (b)|1.73|<br>1.68|<br>1.54|<br>1.37|<br>1.22|<br>1.12|<br>1.05|<br>0.96||

(a) For a listing of countries in the "otheru categories, see Table 12. (b) Average amuial growth rate over each five-year period. The projected average amuol growth rate for 1985-2000 is 1.64 percent. The projected rate for 2000-2025 is 1.15 percent.
# **Table Va: Welfare Outcomes**

||(1)|(2)<br>Change i|(3)<br>n Month|(4)<br>s of Rese|(5)<br>rve Food|(6)<br>|(7)|(8)<br>Chan|(9)<br>ge in Lea|(10)<br>n Season|(11)<br>Meals|(12)|(13)|(14)<br>Change|(15)<br>in Consu|(16)<br>mption A|(17)<br>ssets (ln|(18)<br>)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Treated|0.79|0.66|0.70|0.61|1.86**|1.67*|-0.24*|-0.22*|-0.19|-0.18|-0.26|-0.23|-1.50*|-1.40*|-1.75*|-1.65*|-3.44**|-3.32**|
||(0.69)|(0.77)|(0.94)|(0.98)|(0.73)|(0.84)|(0.12)|(0.12)|(0.14)|(0.14)|(0.18)|(0.18)|(0.81)|(0.81)|(0.98)|(0.97)|(1.37)|(1.37)|
|Treated*Very far|||0.70|0.71|||||-0.06|-0.06|||||-2.16***|-2.19**|*||
||||(0.53)|(0.54)|||||(0.21)|(0.20)|||||(0.55)|(0.55)|||
|Treated*Female|||-0.22|-0.42|||||-0.15|-0.12|||||3.06**|3.22**|||
|Head|||(1.45)|(1.32)|||||(0.24)|(0.24)|||||(1.42)|(1.42)|||
|Treated*Maize kg|||||-0.00|-0.00|||||0.00|0.00|||||0.01**|0.01**|
|(BL)|||||(0.00)|(0.00)|||||(0.00)|(0.00)|||||(0.00)|(0.00)|
|Maize kg (BL)|-0.00***|-0.00***|-0.00***|-0.00***|<br>-0.00|-0.00|-0.00|-0.00|-0.00|-0.00|-0.00|-0.00|-0.00|-0.00|-0.00|-0.00|-0.01**|-0.01**|
||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|
|Fertilizer Subsidy||-1.04||-1.06||-0.96||0.20||0.19||0.20||0.67||0.84||0.50|
|||(1.23)||(1.21)||(1.19)||(0.13)||(0.13)||(0.13)||(0.79)||(0.80)||(0.76)|
|Female HH Head|-0.91|-0.86|-0.82|-0.68|-0.93|-0.89|-0.16|-0.16|-0.09|-0.11|-0.15|-0.16|0.01|-0.02|-1.39|-1.50|0.10|0.07|
||(0.64)|(0.61)|(0.97)|(0.88)|(0.64)|(0.61)|(0.13)|(0.13)|(0.20)|(0.20)|(0.13)|(0.13)|(0.75)|(0.74)|(1.15)|(1.14)|(0.70)|(0.70)|
|Age of HH Head|0.12|0.10|0.12|0.10|0.12|0.11|0.04|0.04*|0.04|0.04*|0.04|0.04*|0.07|0.08|0.05|0.06|0.07|0.08|
||(0.10)|(0.10)|(0.10)|(0.10)|(0.10)|(0.10)|(0.02)|(0.02)|(0.02)|(0.02)|(0.02)|(0.02)|(0.18)|(0.17)|(0.17)|(0.17)|(0.18)|(0.17)|
|Age of HH Head|-0.00|-0.00|-0.00|-0.00|-0.00|-0.00|-0.00|-0.00|-0.00|-0.00|-0.00|-0.00|-0.00|-0.00|-0.00|-0.00|-0.00|-0.00|
|squared|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|
|Primary School|0.43|0.53|0.41|0.52|0.31|0.41|0.29**|0.28*|0.30**|0.28**|0.30**|0.28*|0.60|0.55|0.63|0.56|0.82|0.77|
||(0.69)|(0.63)|(0.72)|(0.66)|(0.71)|(0.65)|(0.14)|(0.14)|(0.14)|(0.14)|(0.15)|(0.14)|(0.96)|(0.94)|(0.94)|(0.92)|(0.96)|(0.94)|
|HH Size|0.25|0.25|0.25|0.25|0.22|0.22|-0.01|-0.01|-0.01|-0.01|-0.01|-0.01|-0.32|-0.32|-0.29|-0.28|-0.27|-0.27|
||(0.19)|(0.18)|(0.20)|(0.19)|(0.18)|(0.17)|(0.03)|(0.03)|(0.03)|(0.03)|(0.03)|(0.03)|(0.23)|(0.22)|(0.23)|(0.22)|(0.22)|(0.22)|
|Constant|-0.53|0.62|-0.53|0.61|-0.95|0.15|-0.36|-0.58|-0.40|-0.61|-0.35|-0.57|5.90|5.16|6.37*|5.46|6.55*|5.98*|
||(1.87)|(1.86)|(1.90)|(1.94)|(1.91)|(1.85)|(0.44)|(0.49)|(0.44)|(0.49)|(0.43)|(0.48)|(3.85)|(3.69)|(3.80)|(3.60)|(3.80)|(3.58)|
|Observations|436|436|436|436|436|436|443|443|443|443|443|443|477|477|477|477|477|477|
|R-squared|0.088|0.096|0.089|0.099|0.097|0.105|0.061|0.071|0.063|0.072|0.061|0.071|0.059|0.062|0.092|0.097|0.086|0.088|

_Standard Errors in Parenthesis (* p<0.10, ** p<0.05, *** p<0.01)_ 37
**Table 1.3. Shorrocks Mobility Index and Short-Term and Long-Term Inequality (balanced sample for the whole period 1994-** **<u>2015)</u>**

|||**Gini Ind**<br>|**ex**<br>||**Variance of Lo**<br>|**g Income**<br>|**No. of**|**No. of**|
|---|---|---|---|---|---|---|---|---|
|**Period**|_M_<sup>_s_</sup>|**Short-term**<br>**inequality**|**Long-term**<br>**inequality**|_M_<sup>_s_</sup>|**Short-term**<br>**inequality**|**Long-term**<br>**inequality**|<br>**observations**|<br>**individuals**|
|1994-1998|0.21|0.42|0.33|0.42|0.72|0.41|3 336|834|
|1998-2004|0.21|0.38|0.30|0.45|0.55|0.30|5 004|834|
|2004-2009|0.16|0.31|0.26|0.36|0.35|0.22|5 004|834|
|2009-2015|0.16|0.27|0.22|0.31|0.24|0.17|5 838|834|
|1994-2004|0.27|0.39|0.28|0.52|0.61|0.29|7 506|834|
|2004-2015|0.23|0.29|0.22|0.43|0.29|0.17|10 008|834|
|1994-2015|0.34|0.33|0.22|0.58|0.42|0.17|16 680|834|

**Note** : Estimation results are obtained based on total household income per capita. All numbers are deflated with December to December regional CPIs and weighted with population weights

**<u>Table 1.4. Medium-Term and Long-Term Income Mobility, RLMS 1994-2015 (percentage)</u>**

||<br>**Fitz**<br>|<br>**gerald approach**<br>|<br>**Wooldridge**<br>|**approach**<br>|
|---|---|---|---|---|
||**Unconditional**|**Conditional**|**Unconditional**|**Conditional**|
|**Panel A: 1994-2004**|||||
|Upward mobility|29.1|39.4|29.7|47.6|
|Immobility|49.8|49.8|47.3|47.3|
|Downwardmobility|21.0|36.1|23.0|30.8|
|**Panel B: 2004-2015**|||||
|Upward mobility|27.5|39.1|30.6|47.3|
|Immobility|47.4|47.4|47.7|47.7|
|Downwardmobility|25.1|38.7|21.7|29.6|
|**Panel C: 1994-2015**|||||
|Upward mobility|34.4|45.7|28.4|36.3|
|Immobility|44.7|44.7|57.5|57.5|
|Downwardmobility|20.9|37.1|14.1|17.2|

**Note** : Estimation results are obtained based on total household income per capita. The terciles are defined using the cross-sectional sample for each year. All numbers are deflated with December to December regional CPIs and weighted with longitudinal weights, where the second survey round in each period is used as the base year. To obtain longitudinal weights we combine cross-sectional weights with the inverse dropout probabilities, which are estimated using methods suggested by Wooldridge (2002) and by Fitzgerald _et al._ (1998). Estimation sample size is 1890 and 4727 panel individuals from the 5th and 13th rounds of the RLMS, 1713 and 3555 panel individuals from the 13th and 24th rounds of the RLMS and 663 and 2371 panel individuals from the 5th and 24th round of the RLMS respectively.

52
38. Stiglitz J (1976): “The E¢ ciency Wage Hypothesis, Surplus Labor and the Distribution of Income in L.D.C.’s”, Oxford Economic Papers, 28(2), July 1976, pp. 185-207.

39. Temple, Jonathan R. W. and Wößmann, Ludger (2006), “Dualism and cross-country growth regressions”, Journal of Economic Growth, September 2006, 11(3), 187-228.

40. Yang, Dennis, 1997. "China’s land arrangements and rural labor mobility," China Economic Review, vol. 8(2), pages 101-115 41. Vollrath, Dietrich, 2009. "How important are dual economy e¤ects for aggregate productivity?" Journal of Development Economics, vol. 88(2), pages 325-334.

42. Wolshon, Brian, 2004, "Geometric Design of streets and Highways", in Myer Kutz ed. Handbook of Transportation Engineering, McGraw-Hill publishers: New York.

43. World Bank, 2008, Land Reforms in Sri Lanka: A Poverty and Social Impact Analysis, South Asia Region.

44. World Bank, 2010, Sri Lanka: Connecting People to Prosperity, South Asia Region 32
Tatyana Bogomolova, Gregorio Impavido, Montserrat Pallares-Miralles

**Figure 43: Projected number of beneficiaries (thous.  persons)**

<!-- Start of picture text --> 300.00<br>250.00<br>200.00<br>150.00<br>100.00<br>50.00<br>0.00<br>2003 2006 2009 2012 2015 2018 2021 2024 2027 2030 2033 2036 2039 2042 2045 2048 2051 2054 2057 2060 2063 2066 2069 2072 2075<br>Year<br>government teachers<br>People (000'<br><!-- End of picture text -->

**Source** : WB calculations based on PROST

All assumptions used in the simulations were discussed and validated by MPS officials.

# **Modeling benefits**

Benefits are recalculated each year by using indexation rules and adding entry pensions of new retirees. As mentioned before, in the baseline scenario it is assumed that everybody chooses a CPG and a reduced pension.

According to the current rules, pensions are indexed to wages with the indexation mechanism working as follows: wage indexation is applied to full pensions and the commuted part is not indexed, meaning that the reduced pension increases at a higher rate than the wage growth.

This mechanism was approximated by modeling a case where everybody received full pension indexed to wages and another case where everybody received 1/3 of full pension not indexed at all and then subtracting appropriate second case outputs (pension payments and IPD) from the first case. This approach can take care of pensions for those who retired after the base year. Adjustments to wage indexation were used in the first years of the simulation period to take into account higher than wages growth rate for pensions of those who retired before the base year. Restoration of pensions to full pension after 15 years was modeled in a separate case and then integrated into the outputs.

Pensions for new old age retirees in the baseline scenario are modeled by applying the benefit formula currently set by law to individuals’ wages and years of service at the time of retirement. CPG payments are also calculated by applying the existing formula which applies the same commutation factor of 15 to all retirees regardless of their age. Assumptions regarding years of service at retirement (Figure 44) are based on actual data

- 88 -
Figure 8: Explaining change in sales: Country-level versus firm level attributes <!-- Start of picture text --> (a) Shapley decomposition of change in sales (b) Elasticity of change in sales to severity<br>80 0<br>72.1 72.5<br>60<br>-.1<br>40<br>-0.161<br>-.2<br>20 17.9 14.4<br>0 3.7 6.4 0.1 4.9 8.0 -.3<br>Wave 1 Wave 2<br>-0.342<br>Firmcharacteristics Country fixedeffects Severity of the crisis(weighted Googlemobility index) -.4<br>Change in saleswave 1 Unexplained variance Wave 1 Wave 2<br>R2 explained by group<br>Shapley Value (%) - Change in sales<br>Change in sales elasticity<br>1 point increase in severity of the crisis<br><!-- End of picture text --> _Third_ , part of this heterogeneity in recovery is related to firm size and sector. The gaps observed in improvements across firm sizes and sectors, documented by Apedo-Amah et al. (2020) are still relevant in the recovery process. Micro and small firms have seen their sales improve but still are 36% and 32% below 2019 levels (Figure 9, panel a). More importantly the gap with medium and large firms increased, especially in relation to large firms. Thus, the gap in sales recovery between smaller and larger firms has widened, and micro and small firms are recovering at a slower pace.<sup>15</sup> Likewise, the gap in recovery for the hospitality sector is still large and the drop in sales is far below its pre-COVID-19 levels in sales, -45%.<sup>16</sup> Other services, while improving is also -31% sales below 2019 levels (Figure 9, panel b). The shock continues to hit these two services sectors with high intensity, while retail and knowledge services are recovering to a level comparable to that in manufacturing.<sup>17</sup>

> 15The elasticity of mobility disruptions to sales by firm size group does not vary significantly across firm size group, thereby suggesting that disruptions in mobility at the country level play little role in explaining the heterogeneity of the recovery across size groups (see Appendix Figure C4).

> 16In Appendix Figure C5, we show that the hospitality sector also exhibits a higher probability of falling into arrears and that the gap against other sectors has not reduced significantly.

> 17The recovery in the hospitality sector is very much linked with the severity of mobility disruptions, as illustrated in the elasticity of changes in sales. While other sectors can adjust by selling online or have addressed supply and labor bottlenecks, hospitality depends almost entirely on face-to-face interactions with consumers. See Appendix Figure C6.

16
follow from this adjustment are our estimated JA trade costs going forward.<sup>38</sup> For these calculations we also assign zero values to the small number of sea-shipped products with fitted values of � _τJA,t_<sup>_k_below zero.39</sup> Table 4 reports summary statistics for the distribution of estimates of _<u>τ</u>_<sup>_~~k~~_</sup> _JA_<sup>for</sup> the year 2016. The first three rows contain estimates predicted from columns 3-5 in Table 3. Our preferred estimates come from Column 5, which include flexibly defined distance terms and U.S. trade policy controls. In these estimates - labelled “All Controls” in Table 4 - the simple average tariff-equivalent estimate of the JA is 30.6 percent, while the trade weighted average is 53.6 percent. 87 percent of final products have a positive JA tariff-equivalent trade cost.

The second row of Table 4 shows 2016 estimates of _<u>τ</u>_<sup>_~~k~~_</sup> _JA_<sup>’s using the same meth-</sup> ods, but calculated from Column 4 of Table 3 estimates, the regression that excludes trade policy controls. In these estimates, the simple average value of _<u>τ</u>_<sup>_~~k~~_</sup> _JA,_ 2016<sup>is 35.5</sup> percent. The third row uses estimates from column 3, where the effects of distance are assumed common across products. This specification produces lower values of _<u>τ</u>_<sup>_~~k~~_</sup> _JA,_ 2016<sup>,withasimpleaverageof6.4percent.Theestimatedeffectsofseaship-</sup> ping are smaller when the effects of distance are constrained to be the same across products, and containerization is estimated to do more to offset other sources of JA costs. We prefer the estimates in row 1 of Table 4 because they contain the largest set of control variables.

We undertake a number of exercises to check the robustness of our results.

> 38The application of _V shrJA,tk_<sup>to the predicted trade costs is an effort to be conservative in our</sup> estimates. We focus our efforts on quantifying the implicit distortion that causes PR importers to substitute towards rest of the world (ROW) products and away from U.S. products. It is likely that the JA also causes U.S. products to be shipped to PR by air rather than by waterborne transport. Arguably this distortion is evident in the positive JA tariff equivalents that we zero out because shipments - in fact - travel by air rather than by sea. While this is plausible, assigning positive JA tariff equivalents to shipments that travel by air risks overstating the economic burden of the JA. We choose to be more conservative and treat goods travelling to PR by air as entirely unaffected by the JA.

> 39These are containerized products with extremely low weight-to-value ratios.

32
Financial Regulation and Government Revenue

- Kenya remunerated its 91-days Treasury Bills between 8 and 20% in the last auctions, held between June and July 2014, with an average of 9.5%.<sup>8</sup>

- Uganda, analogously, priced its 91-days Treasury bills at 9.5% in the auction of 25 June 2014.<sup>9</sup>

Therefore, considering Ethiopia’s first bond price as a lower bound and the Kenya/Uganda rates as a plausible counterfactual, we can infer that a likely rate for its alternative bond, priced competitively on financial markets, would be 9.5%, with the following range [5%,14%] as observed by auctions for Kenya and Uganda. This provides a simple reference against which to measure the revenue gains: Here, _RI_ = 9 _._ 5% (or 6% and 12% in the lower and upper bound cases, respectively) would be the price charged on international markets, _RG_ = 3% the current NBE bill remuneration, and _G_ the volume of NBE bills. Overall, this gain totals 908.49 million birr per year (43 million USD), between 279.5 and 1537.4 depending on the possible interest rate range, which accounts for 11% of the deficit or 1.5% of tax revenue.

# **4.2 Diverting Private Lending from the Commercial Bank of Ethiopia**

The CBE is the largest Ethiopian commercial bank and one of the largest in Africa. It has more than 800 branches, seven million account holders, and 18,000 employees: its assets at the end of the 2011 fiscal year amounted to 226.338 million birr, equivalent to 11.372 million USD. It is owned by the Ministry of the Economy and is a grand contributor to government public finances, its mission being to “realize stakeholders’ needs through enhanced financial intermediation globally and supporting national development priorities”.<sup>10</sup> In fact, after the NBE and the Development Bank of Ethiopia, it is the largest bank holding government bonds, with 3% of the total; as a reference, all other banks combined hold less than 1% (Ministry of Finance and Economic Development 2012).

From the CBE balance sheet for July 2012, we can see that the CBE holds no NBE bills, but

> 8Refer to the Central Bank of Kenya Treasury Bills Average Rates at https://www.centralbank.go.ke/index.php/treasury-bill/91-days.

> 9Refer to the Results of the Treasury Bills Auctions by the Bank of Uganda https://www.bou.or.ug/bou/collateral/tbill ~~s f~~ orms/2014/Jun/resultstbil ~~l 9~~ 22.html.

> 10Refer to the CBE web site for more information, http://www.combanketh.et/AboutUs/CompanyProfile.aspx

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