rafmacalaba/datause-encoder-data huggingface dataset:
The Central region, which is the least poor region, is estimated to have the second highest increase in the poverty headcount as a result of increases in staple food prices. The poverty headcount ratio in the Central region is estimated to increase by 3.2 percentage points, with approximately equal increases in rural and urban areas. Most of the higher poverty rates in the Central region are attributable to maize, which increased in price by 159% in the Central region between October 2007 and October 2008.

The poverty headcount measure tells us how many poor there are, but it tells us nothing about _how poor_ they are. The poverty gap measure provides information about the depth of poverty, detecting increases or decreases in the consumption levels of poor households even if they do not reach the poverty line. Table 4 presents simulation results for changes in the poverty gap measure arising from food price increases. It shows a very large increase in the national poverty gap of 2.2 percentage points, which is a 25% increase over the 8.7% recorded by the 2005–06 UNHS. Unlike the poverty headcount ratio, the poverty gap increase is larger in rural areas (2.3 percentage points) than in urban areas (1.6 percentage points). Maize and cassava price increases are the main contributors to the increased poverty gap in both rural and urban areas.

Regional disaggregation of the poverty gap reveals that the sharpest increase is in the Northern region, whose increase of nearly 6 percentage points far exceeds that of the Central (1.9), Eastern (1.4), or Western (0.4) regions. Most of the poverty gap increase in the Northern region comes from cassava price increases. The increase in the poverty gap is largest in rural Northern region, but also very high in urban areas of the Northern region.

Closely related to the poverty gap measure is the concept of the poverty deficit. The poverty deficit is the amount of money required to lift all of the poor exactly to the poverty line. One can think of it as the cost of a perfectly targeted transfer that would eliminate poverty as defined by the consumption poverty line used in Uganda. It is calculated as the poverty gap index multiplied by the poverty line and the country’s population. From the results of the 2005–06 UNHS, the aggregate poverty deficit may be calculated as approximately 49 billion Ugandan shillings per month (8.7% x 21,000 Ugandan shillings x 27 million Ugandans), which is equal to approximately 3.7 percent of Uganda’s GDP. Based on the estimates in Table 4, that amount would increase by about 25% to 62 billion Uganda shillings per month, or 4.7 percent of GDP.

14
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# **PROCUREMENT IN INFRASTRUCTURE: WHAT DOES THEORY TELL US?**<sup>**¶**</sup>

Antonio Estache Université Libre de Bruxelles Atsushi Iimi World Bank (FEU) Christian Ruzzier Université de Toulouse Key words: Public procurement; infrastructure development; contract theory; principal-agent problem; incentives; hold-up problem; auction theory; renegotiation. JEL classification: C78, D44, D86, H44.

> ¶ We are most grateful to Daniel Benitez, Paul Noumba Um, Juan Ortner, Tomás Serebrisky and Tina Søreide for their insightful comments on an earlier version of this paper.
For store size, we use (log of) floor area of the shop as reported in the survey ( _Size_ ).<sup>18</sup> Since direct measures of entry barriers are not available, we follow Sevensson and Fisman (2007) in using the percentage of stores’ senior management’s time (averaged at the city level) that is spent in dealing with business regulations as reported in the survey ( _Regulation_ ). Lastly, for retailer density, we use total employment in retail and distribution sector as a proportion of total adult population at the city level ( _Retailer density_ ). Data for retailer density is from Census of India (1991). We are not aware of any data on the number of retail shops in India.

Column 3 of Table 5 reports regression results with the additional controls mentioned above. We find that retailer density has a positive effect on competition significant at less 5% level (p-value of .042). The estimated effect is quite large in magnitude. An increase in retailer density from its 25<sup>th</sup> to the 75<sup>th</sup> percentile value increases the probability of a store facing significant competition by 10.4 percentage points. As expected, more burdensome regulation has a negative effect on the level of competition, significant at close to 10% level (p-value of .104). Lastly, size has a positive effect on competition significant at less than 5% level. Below we show that the significant size-competition relationship is probably a result of different products sold by stores of varying size. Lastly, there is not much change in the estimated coefficients of the remaining variables from above.

In column 4 of Table 5 we control for some additional store characteristics. The motivation here is that if store characteristics vary systematically with non-workers and they have a direct effect on competition also then our results above could still suffer from

> 18 Our results do not change much if we use labor employment or annual store sales as measures of storesize.

16
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Bank’s commodity database (“Pinksheet”), reported in nominal U.S. dollar terms. Prices were deflated by the U.S. Consumer Price Index. The choice of price series was based on a number of criteria in order to ensure that the respective commodities have been important throughout the sample period, they are representing all major commodity groups, and, to the extent possible, price signals have been generated by market-based mechanisms. The prices were grouped into the six categories: energy, base metals, precious metals (Figure 1) and fertilizers, annual agriculture, perennial agriculture (Figure 2). Details on data description and sources are given in Appendix A.

# **5. Heterogeneity (“chaos”)**

Decomposition results are summarized in Table 3. The first column reports the share of the price variation accounted by permanent shocks, followed by the contribution of the three transitory components (next three columns).<sup>**9**</sup> The subsequent two columns give the number of the medium-term and business cycles. The last two columns show results from a trend regression of the permanent shock component—the parameter estimate of the time trend and the Root Mean Square Error (RMSE).

On average across commodities, permanent shocks accounted for 46 percent of price variability while of the remainder, medium-term and business cycles accounted for 33 percent and 17 percent, respectively. These results confirm earlier findings from the macroeconomic literature relevant to EMDEs (Aguiar and Gopinath 2007) as well as the supercycle literature (Erten and Ocampo 2013) which emphasized the larger role of the permanent shock component. The predominance of the medium-term cycle in the transitory component is also in line with Aldasoro _et al_ . (2020) and Cao and L’Huillier (2018) who established a greater role of medium-term cycles than business cycles in output fluctuations and domestic financial cycles. Lastly, only four percent of commodity price variability is attributed to shocks that are unwound in less than two years, a result which is consistent across all groups (except the fertilizer group) and most commodities. The lesser importance of the business cycles and short term fluctuations are also consistent with Cashing, McDermott, and Scott (2002) who analyzed the nature of cycles for 36 real commodity prices during 1957-1999 and concluded that (p. 292) “... cycles in economic activity alone do not drive the evolution of commodity prices, and that other factors, particularly supply conditions in individual commodity markets, are likely to be a key determinant of cycles in commodity prices.” The above averages, however, mask heterogeneity. Shocks associated with medium-term frequency accounted for 60 percent and 27 percent of price variability in energy and metals, respectively but only 14 percent in agriculture (Figure 3). These results are consistent with Fernández, Schmitt-Grohé, and Uribe (2020) who find that the

> **9** The results pertaining to commodity groups have been derived on an individual commodity basis and aggregated to group level by using the weights reported in the first column of Table 3.

_— 9 —_
Tariff Index Theory J.E. Anderson 31

# ARpendix 2. **CobbtDouglas** Simulations

This appendix presents the results of the simulation of the Cobb-Douglas model. The two tariff case is chosen with a tariff iniially equal to .5 for good 2 with 10% of expenditure on it and .2 for good 1 with 5% of expenditure on it. The initial tradeweighted mean tariff is equal to 40%. In the Cobb-Douglas model, so long as the mean and variance of tariffs move in the same direction, the TRI must also move in that direction. The interest is in the cases where this does not hold. The simulations show four cases of tariff changes for which the mean and variance (and coefficient of variation) move in opposite directions. The first two rows show the relative tariff changes (from the initial base of `z = .2 and r2 = .5). The next four rows of the table illustrate that any possible combination of TRI change with mean and offsetting variance or coefficient of variation change may be found. For tariff changes of the type used here, the mean or variance (or coefficient of variation) index would be wrong half the dme.

|cases|I|II|III|IV|
|---|---|---|---|---|
|relative tariff 1<br>|1.458333333<br>|<br>1.125<br>|0.91666667<br>|1.18333333<br>|
|relative tariff 2|0.8|0.96666667|0.96666667|0.93333333|
|relative mean tariff<br>|0.958333333<br>|1.04166667<br>|0.83333333<br>|1.01666667<br>|
|relative variance<br>|1.007631258<br>|0.11111111<br> <br>|1.36111111<br>|0.00444444<br>|
|relcoeffvar|1.047452219<br>|<br>0.32<br>|1.4<br>|0.06557377<br>|
|TRI|0.99192921|0.99836569|1.04294631|1.00648972|
Realistic values for S for a given shipment are in months. If S = 1 month, then γ = 33-67. For two months, γ = 17-35. (these values are consistent with typical probabilities of stock-outs).

For distributions such as the ones listed previously and consistent with observations of shipments to/from LLDCs, it means that **T(** γ **) is a multiple of the observed average lead time by a factor of three or more** (Appendix 5). The hedging inventory level is computed below for the example of a median time of 13 days, including a baseline of 3 days, and various levels of standard deviation. This data is typical of LLDCs in Western or Southern Africa.

**Table 9. Inventory Levels and Transport Time Standard Deviation**

|Stock Out<br>|Standard<br>|Dev<br>|iation (<br>|days)<br>|
|---|---|---|---|---|
|Level|2.5|5|7.5|10|
|||T(γ)|days||
|10%|25|30|35|38|
|5%|28|35|41|46|
|2.5%|31|41|49|55|
|1%|35|48|59|68|

T( γ ) are at least equal to twice the lead time. This implies that the economic impact is twice that of inventory costs linked to the lead time. Another illustration of this multiplier is from the road/rail modal choice for exports from Central Asia (Appendix 4). Supposing that the modal choice is due to the low reliability, hence high T( γ ) of rail _visà-vis_ that of road transport: _Road_ Pr _emium_ = _RoadRate_ − _RailRate_ , = _valueofinventory_ × ( _T_ ( γ ) _forRail_ − _TmeanforRoad_ ) or _T_ ( γ ) = _Road_ Pr _emium_ / _valueoftime_ + _TTmeanforRoad_ ) 50
The reallocation of tourism expenditure corresponds to a relocation of demands for output between sectors. To maintain a balanced SAM a final correction was needed to ensure that the value of output equals total demand in each sector. Capital earnings are increased in sectors where demand is increased, and decreased in sectors where demand is decreased.

The SAM includes 12 household accounts. 6 accounts represent rural households and 6 accounts represent urban households. 2 accounts in each group represent households below the food poverty line and between the food and basic needs poverty lines. The other accounts are grouped according to the education of the head of the household.17 The SAM contains nine types of labor: Adults are grouped both according to gender and to one of four levels of education. All child labor (age 10 to 14) is the 9th and final category. Capital and agricultural land and a factor called a subsistence factor are the three remaining primary factors of production.

The subsistence factor is a composite of land, labor and capital used in the production for home (own) consumption by households.18 The subsistence factor is used in the agricultural and food-producing sectors. In each sector the SAM shows the value of output allocated for home consumption and of output allocated to the market, both of which are coming from the same activity.

# **Trade Data by Regional Partner and Sector**

To obtain the shares of imports and exports from the different regions of our model, we used trade data for 2007 obtained from WITS access to the COMTRADE database. The regions of our model are Tanzania, the European Union, the East African Customs Union plus SADC and the Rest of the World. For the European Union, we took the 27 member countries as of 2007. In appendix A, we calculate and report data for the East African Customs Union and

> 17 Average per capita income in the SAM is calculated as USD 2.2 per day. This calculation is based on income data in the SAM, the number of heads in each of the household accounts as reported by Thurlow and Wobst (2003), and a purchasing power parity conversion between Tanzanian Schillings and USD of 274.16 (see Heston et al. (2006)). The market exchange rate for 2001 reported by the Bank of Tanzania is 876.4 Tanzanian Schillings per USD. 18 Data do not allow a breakdown of the subsistence factor on its shares of the other primary factors.

32
# **1. Introduction**

Political disagreements between the Ethiopian federal government and the Tigray regional state ensued into full-scale war on November 4, 2020, with the armed forces of the Tigray region on one side and the federal army and its allied forces from Amhara region and neighboring Eritrea on the other. The conflict played out in most parts of Tigray while also spilling over into parts of Amhara and Afar regions. As a direct consequence of the war, a deep humanitarian crisis continues to unfold, with widespread loss of life, displacement of people, property damages, and disruptions to economic livelihoods documented in the press.<sup>1</sup> In November 2021, the United Nations estimated that about 7 million people in Tigray, Amhara and Afar regions have been directly affected, with 2.4 million internally displaced persons in dire need of food assistance.<sup>2</sup> As the conflict continues, these figures are rising. A more recent assessment by the World Food Programme shows that across the three conflict-affected regions (Tigray, Amhara and Afar) more than 9 million people need humanitarian food assistance and 83 percent of people in Tigray are food insecure (WFP, 2022).<sup>3</sup> Despite these high-level assessments and anecdotal accounts, to date there has not been any micro-level assessment of the welfare impacts of the armed conflict on affected households. Using a unique High-Frequency Phone Survey (HFPS) dataset, we provide the first empirical evidence of the (near real-time) impacts of the conflict on food security, access to food markets and disruptions to livelihood activities. We also evaluate the distributional patterns of these effects across economic sectors, geography and household characteristics.

Much of the earliest work on the adverse impacts of violent conflict is cross-country in nature, examining macro-level relationships between conflict and economic growth and development outcomes. Most of these cross-country studies find significant negative impacts of conflicts on macro-level economic outcomes (Barro, 1991; Gupta et al., 2004), some of which are transitory and are followed by quick recovery (Miguel and Roland, 2006; Justino and Verwimp,

> 1 <u>https://www.wfp.org/news/severe-hunger-tightens-grip-northernethiopia#:~:text=The%20Tigray%20Emergency%20Food%20Security,extreme%20coping%20strategies%20to%20 survive.</u>

> 2 <u>https://news.un.org/en/story/2021/10/1102182</u>

> 3 The war has led to significant internal and cross-country displacements. Immediately after the outbreak of the war, around 63,000 refugees fled to Sudan (UNOCHA, 2021).

2
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## Administradora de Fondos de Pensiones PROVIDA S.A. Chile. Memoria Anual 1992.

Aldrich, Jonathan. 1982. "The Earnings Replacement Rate of Old Age Benefits in Twelve Countries, 1969-1980." _Social Security Bulletin 45(11):_ 3-1 1.

Asher, Mukul G. 1985. (Australian National University). Forced Saving to Finance Merit Goods: An Economic Analysis of the Central Provident Fund Scheme of Singapore. Canberra, Australia: Centre for Research on Federal Financial Relations; Occasional Paper no. 36.

-. 1992a. "Income Security for Old Age: The Case of Malaysia". National University of Singapore, Department of Economics and Statistics.

-. 1992b. "The Singapore Central Provident Fund". National University of Singapore,

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-. 1992 (September). Income Security for the Old Age: The Case of Singapore. Singapore: National University of Singapore.

ASTEK. 1986. Indonesia. Regulations on Employee's Social Insurance. Jakarta. Indonesia: Public Relations Bureau. Perum ASTEK.

- -. 1992. _Astek Annual Report._ Jakarta, Indonesia.

Atkinson, A.B. 1991b. "A National Minimum? A History of Ambiguity in the Determination of Benefit Scales in Britain." In T. and D. Wilson, eds., _The State and Social Welfare._ London: Heineman.

Banco de Prevision Social. 1989. Uruguay. Boletin Estadistico Nro. 32. Uruguay: Asesoria Economica y Actuarial. Banco de Prevision Social.

Barreto de Oliveira, Francisco E. 1993 May. Seguridade social na America Latina: a experiencia de Argentina, Brasil, Chile e Venezuela. Inter-American Development Bank (IDB): Rio de Janeiro. Brazil.

Bateman, Hazel and John Piggott. 1993. "Australia's Mandated Private Retirement Income Scheme: An Economic Perspective." SERG Research Paper Series; 10.

Bayo, Francisco, Actuary. 1991. Informe de la Valoracion Actuarial. Tegucigalpa: Unpublished.

Belize Social Security Board Annual Report 1989.

126
# **Central Bank Governance and Reserve Portfolios Investment Policies:**

# **An Empirical Analysis**<sup>**1**</sup>

Daniela Klingebiel, Carmen Mileva Herrero Montes, Marco Ruiz, and James Seward<sup>2</sup> The World Bank, 1225 Connecticut Ave NW, Washington, DC 20433, USA JEL Classification: E5, G11, D81 Keywords: central banks, risk tolerance, reserve management, governance arrangements for central bank reserve management

1. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations or those of the Executive Directors of the World Bank or the governments they represent. 2. We thank Mikaela Ballon Carneiro for her excellent support in the literature review and data analysis sections. An initial draft of this paper benefitted from written comments by Eric Bouye, Pierre Cardon, Stijn Claessens, Erik Feyen, Bjoern Geir, Renee Licorish, Mike McMorrow, Steen Byskov, and Shinya Tamada. An earlier version of the paper also benefitted from comments and suggestions from colleagues of the World Bank Treasury’s Asset Management and Advisory Department.
|**Country**|**Reference**<br>**year**|**Region**|**Reference utility**|**FY-end**|**Utility service**<br>**coverage**|**Financial**<br>**statements**<br>**publicly**<br>**available**|**Audited**<br>**statements**<br>**available**|**Comments**|
|---|---|---|---|---|---|---|---|---|
|**Seychelles**|2014|Island|PUC (Public Utilities<br>Corporation)|31-Dec|Electricity &<br>water &<br>sewerage|Yes|Yes||
|**Sierra Leone**|2012|Western|NPA<br>(National Power<br>Authority)|31-Dec|Electricity<br>only|No|No||
|**South Africa**|2014|Southern|Eskom|31-Mar|Electricity<br>only|Yes|Yes|Eskom used as the reference utility which<br>represents more than 60% of the retail<br>market in South Africa. Sufficient data not<br>available for municipalities.|
|**Sudan**|2014|Eastern|Sudan Electricity<br>Distribution<br>Company|31-Dec|Electricity<br>only|No|No||
|**Swaziland**|2014|Southern|Swaziland Electricity<br>Company|31-Mar|Electricity<br>only|Yes|Yes||
|**Tanzania**|2014|Eastern|TANESCO (Tanzania<br>Electricity Supply<br>Company)|30-Jun|Electricity<br>only|Yes|Yes|TANESCO changed its fiscal year so in the case<br>of Tanzania an 18-month period is covered in<br>the analysis reflecting the latest financial<br>statements available covering the period Jan<br>2014–Jun 2015.|
|**Togo**|2013|Western|CEET (Compagnie<br>Energie Electrique du<br>Togo)|31-Dec|Electricity<br>only|No|No|Majority of grid power is purchased from CEB,<br>a regional utility supplying Benin and Togo.|
|**Uganda**|2014|Eastern|Umeme|31-Dec|Electricity<br>only|Yes|Yes|Umeme accounts for 94% of end-user sales in<br>the reference year. Balance of sales are done<br>by UETCL (exports) and small rural<br>concessions.|
|**Zambia**|2014|Southern|ZESCO|31-Dec|Electricity<br>only|No|Yes|ZESCO FY-end changed from Mar 31 to Dec 31<br>in FY14.|
|**Zimbabwe**|2012|Southern|Zimbabwe<br>Transmission and<br>Distribution<br>Company|31-Dec|Electricity<br>only|No|Yes||

_Source_ : World Bank staff’s review of utility data and publications. _Note_ : FY = financial year; MYTO = Multi-Year Tariff Order (not a utility); UETCL = Uganda Electricity Transmission Company Limited.

84