rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 6: Impact of U.S. interest rate shocks on EMDE fiscal variables** A.  Impact of 25 basis point shock on primary balance over 8 quarters

B. Impact of 25 basis point shock on gross government debt over 8 quarters <!-- Start of picture text --> Percent of GDP<br>3.5 Expenditure<br>3.0 Revenue<br>2.5<br>2.0 Primary balance<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>1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8<br>Real Inflation Reaction<br><!-- End of picture text --> <!-- Start of picture text --> Percent of GDP<br>3.0<br>Mean 90% confidence interval<br>2.0<br>1.0<br>0.0<br>-1.0<br>-2.0<br>-3.0<br>-4.0<br>-5.0<br>1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8<br>Real Inflation Reaction<br><!-- End of picture text --> C. Impact of 25 basis point shock on the share of government debt held by external creditors over 8 quarters D. Impact of 25 basis point shock on the share of foreign-currency government debt over 8 quarters <!-- Start of picture text --> Percent<br>5 Mean 90% confidence interval<br>4<br>3<br>2<br>1<br>0<br>-1<br>-2<br>1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8<br>Real Inflation Reaction<br><!-- End of picture text --> <!-- Start of picture text --> Percent<br>3 Mean 90% confidence interval<br>2<br>1<br>0<br>-1<br>-2<br>-3<br>-4<br>1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8<br>Real Inflation Reaction<br><!-- End of picture text --> E. Impact of 25 basis point shock on the share of short-term government debt over 8 quarters <!-- Start of picture text --> Percent<br>2.0 Mean 90% confidence interval<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>1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8<br>Real Inflation Reaction<br><!-- End of picture text -->

# _Source:_ World Bank

_Notes:_ Panel local projection model with fixed effects and robust standard errors. See table A3 for details. Models estimated over periods as long as 1997Q2-2019Q4; they exclude observations during global financial crisis (2008Q4-2009Q4) and the COVID-19 pandemic.

A. “Expenditure” and “Revenue” reflect the mean response of each variable to the underlying shock. These responses come from separate models and may not add up to the change in the primary balance. B. Reported as cumulative multiplier ( ). tt+h tt−1 yy −yy 27
||Full s|ample|>=50 p|ercentile|<50 pe|rcentile|75 pe|rcentile|25 pe|rcentile|
|---|---|---|---|---|---|---|---|---|---|---|
|Overall PCI|0.006***||0.003***||0.002||0.002***||-0.002||
||(0.002)||(0.001)||(0.002)||(0.001)||(0.003)||
|Entry||0.018*||0.004||0.019*||-0.001||0.026**|
|||(0.010)||(0.004)||(0.010)||(0.003)||(0.012)|
|Land||0.012*||0.012***||-0.001||0.007**||-0.002|
|||(0.007)||(0.004)||(0.007)||(0.003)||(0.010)|
|Transparency||-0.000||-0.002||0.007||-0.001||0.011|
|||(0.011)||(0.005)||(0.014)||(0.004)||(0.014)|
|Time||0.008||0.009*||0.003||0.005||0.006|
|||(0.010)||(0.005)||(0.011)||(0.004)||(0.011)|
|Informal||0.018**||0.008*||0.013||0.010***||0.006|
|||(0.009)||(0.004)||(0.008)||(0.003)||(0.009)|
|Proactive||-0.001||-0.000||-0.005||0.004||-0.016*|
|||(0.008)||(0.004)||(0.007)||(0.003)||(0.009)|
|Labor||-0.005||-0.003||-0.010*||-0.003||-0.014**|
|||(0.006)||(0.003)||(0.006)||(0.003)||(0.007)|
|Legal||0.009||-0.001||0.006||-0.007**||-0.003|
|||(0.007)||(0.002)||(0.009)||(0.003)||(0.010)|
|Share of large<br>firms in|0.002**|0.002**|0.000|0.001*|0.003***|0.002**|0.000|0.001*|-0.002|0.002*|
|province|||||||||||
||(0.001)|(0.001)|(0.000)|(0.000)|(0.001)|(0.001)|(0.000)|(0.000)|(0.003)|(0.001)|
|Capital<br>Intensity ln(K/L)|-<br>0.162***|-0.161***|-<br>0.073***|-0.073***|-<br>0.144***|-0.142***|-0.042*|-0.042*|0.003**|-0.122***|
||(0.037)|(0.036)|(0.024)|(0.024)|(0.020)|(0.019)|(0.022)|(0.022)|(0.001)|(0.016)|
|Firm size (ln<br>employment)|0.011|0.013|-<br>0.110***|-0.108***|0.018|0.020|-<br>0.116***|-0.114***|-<br>0.124***|-0.045|
||(0.057)|(0.057)|<br>(0.038)|(0.038)|(0.042)|(0.042)|<br>(0.035)|(0.035)|<br>(0.016)|(0.041)|
|Constant|3.792***|3.731***|5.144***|5.122***|2.726***|2.622***|5.709***|5.723***|-0.048|1.775***|
||(0.359)|(0.355)|(0.243)|(0.245)|(0.269)|(0.249)|(0.315)|(0.317)|(0.040)|(0.173)|
|Observations|662,732|662,732|325,625|325,625|337,107|337,107|158,791|158,791|162,304|162,304|
|R-squared<br>Adjusted R-|0.057|0.057|0.078|0.079|0.059|0.060|0.089|0.089|0.044|0.045|
|<br>squared<br>Number of|0.0563|0.0567|0.0771|0.0777|0.0586|0.0590|0.0868|0.0876|0.0420|0.0428|
|firmid|335,829|335,829|179,696|179,696|223,609|223,609|94,194|94,194|128,587|128,587|
|N-cluster|63|63|63|63|63|63|63|63|63|63|

Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

# 5 Robustness Checks and IV Regressions

## _5.1 Robustness checks_

We perform robustness checks on the results in a balance panel and subsample of firms that are located in SEZs. First, the balance panel allows us to control for firms that may relocate to the province in response to improvements in the business environment. In these cases, a positive coefficient for the overall PCI might capture a selection of better firms into the province rather than an improvement of productivity 15
22

**_Question 7.02_** _: In your industry, how commonly would you estimate that firms make undocumented extra payments or bribes connected with getting connected to public utilities? 1= common, 7 = it never occurs._

The average rating by income groups is:

- High-income countries: 6.30

- Upper-middle-income countries: 5.33

- Lower-middle-income countries: 4.59

- Low-income countries: 3.33

**_Question 7.04_** _: In your industry, how commonly would you estimate that firms make undocumented extra payments or bribes connected with public contracts? 1= common, 7 = it never occurs._

The average rating by income groups is:

- High-income countries: 5.42

- Upper-middle-income countries: 3.93

- Lower-middle-income countries: 3.58

- Low-income countries: 2.83

These responses suggest that the poorer a country is, the bigger is the perceived corruption problem in infrastructure. The presentation of the data, however, poses a few problems. One is that for confidentiality reasons the data cannot be presented at the sector level—because in many countries infrastructure service providers are monopolies, and reporting what the sector thinks about corruption would boil down to reporting what the firm thinks. But the main problem with this data is that it is based (as most international databases are) on executive surveys. Though useful, this approach tells us little about what the government thinks about corruption. And, more important in this context, it tells us nothing about how poor people see the problem. Unfortunately, the few surveys polling large numbers of people around the world (such as Gallup International) do not cover infrastructure services.<sup>29</sup>

# **The effects of corruption in infrastructure**

While there are many publications on how corruption affects growth, foreign direct investment, trade, and public sector finances, there is little evidence on how it affects infrastructure performance. Most of the existing evidence is for utilities. In recent years I have come across just six publications analytically assessing the impact of corruption on infrastructure services: Davis (2003) on South Asia; Lovei (1998), Lovei and McKenchie (2000), and Clarke and Wu (2004) on Eastern Europe; Rossi and del Bo (2004) on Latin America; and Estache and Kouassi (2002) on Africa. The first four papers are direct assessments and the last two indirect (through regressions) of the efficiency performance of the operators. There are also a number of surveys collected by countries and donor agencies, but these are not widely available. Other publications of relevance

> 29 A notable exception is Latinbarometro for Latin America, which conducts a regular “confidence vote” survey on privatized utilities that includes questions about corruption. The detailed data from these surveys are not available to the research community, however.
- 22 -

autoregressive models are estimated (Table 6).<sup>5</sup> The results are broadly consistent with Table 4. Among transport variables, the global market connectivity, namely, transport costs to the port, turned out most important, followed by local connectivity measured by road density. Therefore, the transport connectivity from cities to the port should be prioritized when public infrastructure investment is considered.

**Table** **<u>6. Spatial autoregressive estimation with multiple transport variables</u>**

||Spatial autoreg.|Spatial IV|
|---|---|---|
|ln_RDDN_|0.469 ***|1.044 ***|
||(0.154)|(0.312)|
|ln_TCITY_|-0.149 *|-0.280 **|
||(0.090)|(0.135)|
|ln_TPORT_|-2.435 **|-2.930 **|
||(1.036)|(1.181)|
|ln_NUM_|0.112|0.104|
||(0.075)|(0.071)|
|ln_POP_|0.669 ***|0.875 ***|
||(0.212)|(0.229)|
|ln_WAGE_|0.135|0.151|
||(0.155)|(0.165)|
|ln_EDU1_|2.765 **|2.835 **|
||(1.183)|(1.351)|
|ln_EDU2_|-2.946|-6.665 **|
||(3.581)|(3.039)|
|ln_RENT_|0.135|0.078|
||(0.122)|(0.115)|
|ln_AGV_|-0.109 ***|-0.082 **|
||(0.037)|(0.034)|
|ln_TAX_|-0.035|0.085|
||(0.340)|(0.347)|
|ln_POWER_|0.789|2.001 *|
||(0.729)|(1.056)|
|constant|-7.646|-7.498|
||(6.496)|(6.577)|
|Obs|118|118|
|λ|0.475 ***|0.629 ***|
||(0.084)|(0.112)|
|ρ|-0.939|-0.880|
||(1.239)|(1.681)|

5 The rationale for the first additional instrument is the same as the rail line from Dar es Salaam to Kigoma. The Central Railway aimed at not only Kigoma but also Mwanza. The rationale for the second instrument is based on the historical fact that Britain and Germany were aggressively racing to develop railroads along the de facto border line in the 1890s, to claim colonial rights. Therefore, the areas closer to the border line would be more likely to receive “exogenous” benefits from railways and other transport modes developed subsequently.
<!-- Start of picture text --> Population Growth controlling by income group<br>-LANDSLIDE-<br>os<br>045<br>04 44 4<br>035<br>§ 03 419 (S77<br>284 @ Lower income<br>g 0.25 @ Midde income<br>Ss o2 437 451 DHigher income<br>2 0.15<br>o1<br>0.05<br>ii)<br>No Hazard Hazard<br><!-- End of picture text --> <!-- Start of picture text --> Population growth controllingfor income group<br>-EARTHQUAKE-<br>04<br>035 605<br>236<br>o3 at<br>@ 025<br>ro @ Lower income<br>§ o2 653 _— @ Midde Income<br>2 015 D Higher income<br>=<br>01<br>0.05<br>0<br>No Hazard Hazard<br><!-- End of picture text -->
Table 5 (continue). Sectoral output change in regions under ETS and ETS-cum-RES cases (%).

||Co|ns|Tra|ns|Wsa|le|Est|a|Ot|s|
|---|---|---|---|---|---|---|---|---|---|---|
||S1|S2|S1|S2|S1|S2|S1|S2|S1|S2|
|Total|-0.16|-0.16|-1.43|-1.41|-0.75|-0.74|-0.99|-0.96|-0.50|-0.50|
|BJ|-0.06|-0.07|-0.32|-0.35|0.06|-0.01|-0.04|-0.07|0.10|0.04|
|TJ|-0.39|-0.40|-1.17|-1.10|-0.44|-0.40|-0.93|-0.86|-0.13|-0.12|
|HB|-0.23|-0.24|-3.77|-3.83|-3.02|-3.06|-3.91|-3.96|-2.72|-2.79|
|SX|-2.28|-2.23|-17.39|-16.82|-10.88|-10.52|-16.91|-16.47|-3.08|-2.90|
|IM|0.04|0.04|-1.31|-1.18|0.08|0.08|-1.05|-0.96|0.15|0.18|
|LN|-0.10|-0.10|-2.21|-2.24|-1.75|-1.80|-2.47|-2.47|-1.54|-1.58|
|JL|-0.19|-0.19|-0.93|-1.02|-0.50|-0.62|-1.22|-1.32|-0.79|-0.91|
|HLJ|-0.16|-0.16|-2.66|-2.67|-1.76|-1.79|-2.00|-2.02|-1.38|-1.45|
|SH|-0.06|-0.05|-0.52|-0.46|-0.23|-0.19|-0.27|-0.24|0.01|0.03|
|JS|-0.01|-0.01|-0.40|-0.35|-0.14|-0.11|-0.43|-0.37|0.07|0.10|
|ZJ|-0.04|-0.04|-0.69|-0.76|-0.27|-0.32|-0.35|-0.44|-0.32|-0.39|
|AH|-0.20|-0.19|-1.56|-1.47|-0.92|-0.86|-0.91|-0.84|-0.80|-0.75|
|FJ|0.01|0.01|-0.16|-0.12|0.13|0.16|-0.41|-0.34|0.13|0.16|
|JX|-0.08|-0.08|-2.59|-2.61|-1.03|-1.12|-0.96|-1.01|-0.59|-0.64|
|SD|-0.05|-0.05|-0.31|-0.24|1.06|1.07|-0.85|-0.82|0.15|0.14|
|HN|0.01|0.01|-1.05|-1.01|-1.16|-1.11|-1.08|-1.01|-0.70|-0.68|
|HB|-0.44|-0.43|-3.19|-3.09|-2.21|-2.14|-3.74|-3.63|-1.97|-1.91|
|HN|-0.66|-0.64|-1.56|-1.50|-1.33|-1.28|-1.48|-1.40|-0.99|-0.94|
|GD|0.07|0.05|0.49|0.51|0.58|0.60|0.49|0.50|0.46|0.47|
|GX|-0.24|-0.24|-0.67|-0.65|-0.78|-0.75|-0.70|-0.66|-0.30|-0.30|
|HAN|-0.02|-0.02|-1.21|-0.99|0.41|0.56|-0.98|-0.81|0.20|0.28|
|CQ|-0.27|-0.27|-1.77|-1.69|-1.99|-1.92|-1.68|-1.59|-0.60|-0.56|
|SC|-0.08|-0.07|-3.33|-3.21|-2.63|-2.52|-2.53|-2.42|-1.31|-1.25|
|GZ|0.01|-0.08|-3.32|-3.98|-2.09|-2.69|-3.78|-4.15|-0.83|-1.20|
|YN|-0.06|-0.06|-1.62|-1.51|-1.33|-1.22|-1.73|-1.59|-0.07|-0.02|
|SAX|0.38|0.39|-0.69|-0.68|-0.30|-0.31|-0.18|-0.17|-0.54|-0.55|
|GS|-0.61|-0.61|1.92|1.93|2.04|2.11|0.03|0.07|0.25|0.26|
|QH|-0.09|-0.09|-0.65|-0.65|-0.37|-0.37|-1.20|-1.19|-0.11|-0.12|
|NX|-0.02|-0.03|-1.60|-1.72|-1.18|-1.29|-1.80|-1.78|-0.05|-0.18|
|XJ|-0.13|-0.13|-2.83|-2.80|-1.51|-1.53|-2.53|-2.48|-1.99|-1.99|

Note:

1. S1 and S2 denote ETS and ETS-cum-RES cases, respectively.

2. The value 0 in the table indicates zero output of that sector in the base case.

The results show that the outputs in transport, wholesale, real estate, and other services sectors under ETS-cum-RES case are more than the ETS case. This is because the RES policy would create more demand for goods and services to be produced from 26
- **3 Prevalence of stunting and access to adequate food security, child care, and health and environment**

In line with the common practice in the literature, the report adopts height‐for‐age as the measure of chronic malnutrition. Given that stunting, height‐for‐age more than two standard deviations below the median height‐for‐age for the particular age and gender, captures chronic malnutrition, it is chosen over wasting, weight‐for‐height more than two standard deviations below median weight‐for‐height, since the latter most frequently indicates a recent episode of severe weight loss associated with starvation and/or severe disease resulting in acute malnutrition.

# **3.1 Prevalence of stunting**

Although stunting remains high in most of the study countries, it has decreased significantly over time for both under two‐year olds as well as for under five‐year olds. Figure 2 depicts stunting in the under two‐year group in the eight countries and Figure 3 depicts stunting in the under five‐year group. It is important to bear in mind that for Bolivia, Cambodia, Ethiopia, Nepal, Peru, and Zimbabwe the stunting rates are based on stunting rates reported by the StatCompiler of DHS for the indicated years using all the children with height information. For Bangladesh the information is derived from the HK and IFPRI data sets and for Indonesia from the RKD data set. In both age groups the seven countries for which there are multiple years of data show no increases in stunting. In Ethiopia and Nepal the reductions have been the greatest. From 2001 to 2011, Nepal experienced a 17 percentage point reduction in the prevalence of stunting in Nepal in the under 5‐ year cohort and 15 point reduction in the under 2‐year cohort. In Ethiopia from 2000 to 2011, stunting reduced by 13 percentage points in both age cohorts. Ethiopia and Nepal were also the two countries with the highest baseline stunting rates. In addition, Peru achieved large reductions in stunting, by nearly halving the stunting prevalence in the under 5‐year old cohort so that in 2012 the prevalence of stunting was 18%. In the other countries the reductions have been in the right direction but modest reductions of five percentage points or less.

19
Table 6. Effect of a 1 Percentage Point Increase in the Income Share Held by the 3<sup>rd</sup> Quintile on Share of Population With Secondary Education

||Level of Economic Development:<br>1970 GDP per capita|Level of Economic Development:<br>2010 GDP per capita|
|---|---|---|
||(1)|(2)|
|Brunei|4.77|7.16|
|Cambodia|-1.29|2.25|
|Indonesia|-2.02|3.11|
|Laos|-2.15|2.58|
|Malaysia|-0.30|4.89|
|Philippines|-0.38|2.83|
|Singapore|1.60|7.10|
|Thailand|-0.36|4.26|
|Vietnam|-2.18|2.65|
|ASEAN Average|-0.26|4.10|
|ASEAN Median|-0.38|2.83|

Note: The table reports effects on the share of population with secondary education (in percent) of a 1 percentage point increase in the income share held by the 3<sup>rd</sup> quintile. For column (1) the predicted effects are generated by combining estimates reported in Brueckner and Lederman (2015) with the values of countries' GDP per capita in 1970. For column (2) the predicted effects are generated by combining estimates reported in Brueckner and Lederman (2015) with the values of countries' GDP per capita in 2010. Specifically, the estimated relationship for country _c_ and period _t_ is (Share of Population Secondary Schooling)ct = -9.6*Q3ct +1.5*Q3ct*ln(initial GDP per capita)c.

20
Figure 3. Sukuk Market by Placement <!-- Start of picture text --> 236<br>250<br>20<br>200 35<br>150<br>100 75<br>181<br>13<br>50<br>52<br>0 10<br>International Domestic<br>Malaysia GCC Rest of the World<br>$ billion<br><!-- End of picture text --> _Source:_ Islamic Finance Information Service database (http://www.islamicfinanceservice.com) as of April 15, 2014. _Note:_ GCC = Gulf Cooperation Council.

Governments account for a larger share of sukuk issuances, but corporate sukuk represent 40 percent of the overall market and have taken the lead in the long-term segment.<sup>13</sup> Governments are particularly dominant in the short-term market (figure 4). Short-term sukuk are viewed as essential for deepening and development of the Islamic interbank market and is issued in many countries by central banks to support monetary policy implementation. The Central Bank of Bahrain is a prominent example, through a regular issuance program for short-term sukuk in Bahraini dinars. In the long-term sukuk market, corporate issuances picked up and have taken the lead since 2012. In GCC countries, particularly, corporate issuers are far more active than sovereigns, partly because of the strong fiscal position of some countries in the region, and thus governments do not necessarily need funding from the capital markets.

> 13Although a closer look suggests that many corporate issuers are state-controlled entities and therefore could be categorized as quasi-sovereign.

5
financial instruments (issuance of equity, securities, etc.) to contribute to the funding of resolution tools (e.g. bail‐in) are lacking.

**_Access to data and information sharing._** Sound and adequate data systems as well as compliance and accounting standards are a prerequisite for implementing effective resolution regimes. However, these are often weak in many EMDEs. Moreover, there is a need to strengthen the exchange of information between all domestic safety‐net players regarding resolution. Data reliability issues hamper efforts to undertake recovery and resolvability assessments of banks.

# 5. Bibliography

Acharya, V., I. Drechsler, and P. Schnabl. 2012. “A tale of two overhangs: the nexus of financial sector and sovereign credit risks”. _Financial Stability Review_ 16. Banque de France. April.

Acharya, V., I. Drechsler, and P. Schnabl. 2014. “A Pyrrhic Victory? Bank Bailouts and Sovereign Credit Risk”. _The Journal of Finance_ Vol LXIX (6). December.

Altavilla, C., M. Pagano, and S. Simonelli. 2016. “Bank exposures and sovereign stress transmission”. Working Paper Series No. 1969. European Central Bank. Frankfurt, Germany.

Balteanu, I, and A. Erce. 2014. “Banking Crises and Sovereign Defaults in Emerging Markets: Exploring the Links” Documentos de Trabajo No. 1414. Banco de España. Madrid, Spain.

Bottero, M., S. Lenzu, F. Mezzanotti. 2016. “Sovereign Debt Exposure and the Bank Lending Channel: Impact of Credit Supply and the Real Economy”. Temi di Discussione (Working Papers) No. 1032. Banca D’Italia. Rome, Italy.

Breckenfelder, J., and B. Schwaab. 2018. “Bank to sovereign risk spillovers across borders: Evidence from the ECB’s Comprehensive Assessment”. European Central Bank, Financial Research. Frankfurt, Germany.

BCBS (Basel Committee on Banking Supervision). 2017. “The Regulatory Treatment of Sovereign Exposures”. Discussion Paper. Bank for International Settlements. Basel, Switzerland. December.

Brunnermeier, M., L. Garicano, P. Lane, M. Pagano, R. Reis, T. Santos, D. Thesmar, S. Van Nieuwerburgh, and D. Vayanos. 2016. “The Sovereign‐Bank Diabolic Loop and ESBies”. American Economic Review 106 (5): 508‐12.

CGFS (Committee on the Global Financial System). 2011. “The Impact of Sovereign Credit Risk on Bank Funding Conditions”. CGFS Papers 43. Bank for International Settlements. Basel, Switzerland. July.

Correa, R., and H. Sapriza. 2014. “Sovereign Debt Crises”. International Finance Discussion Papers No. 1104. Board of Governors of the Federal Reserve System. Washington DC. May.

40
NIGER’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Improvements in NIGELEC’s performance resulted in lower hidden costs over time. The burden of hidden costs, as a percentage of revenues, dropped from 21 percent in 2007 to 10 percent in 2009 (figure 15a). Due to the increase in operating expenses, driven largely by the rising prices of oil, in 2008 NIGELEC was unable to recover costs and suffered losses of around 4 percent of its revenues. Relative to hidden costs in power utilities in West Africa, NIGELEC’s are modest (figure 15b).

**Figure 14. High tariffs in Niger relative to other countries with thermal-based generation**

<!-- Start of picture text --> 50<br>40<br>30<br>20<br>10<br>0<br>U.S. cents per kWh<br>Liberia Chad Burkina Faso Cape Verde Senegal Mali Uganda Niger Kenya Congo, Rep CAR Rwanda Bénin Cote d'Ivoire Namibia Madagascar Cameroon Ghana Botswana Mozambique Lesotho Tanzania South Africa Ethiopia Zimbabwe Nigeria Malawi DRC Zambia<br><!-- End of picture text --> _Source:_ Briceño-Garmendia and Shkaratan (2010); AICD estimates.

_Note:_ Average effective tariff estimates are based on prices paid by domestic and nondomestic customers, weighted by their contributions to total consumption.

DRC = Democratic Republic of Congo; CAR = Central African Republic.

# **Figure 15 Hidden costs for NIGELEC have decreased over time**

Percentage of revenues <!-- Start of picture text --> a. NIGELEC’s hidden costs<br>25<br>20<br>15<br>10<br>5<br>0<br>Collection Inefficiencies Underpricing Losses<br>Percentage of revenues<br><!-- End of picture text --> b. Hidden costs in West African utilities <!-- Start of picture text --> 180<br>160<br>140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>Collection Inefficiencies Underpricing Losses<br>Percentage of revenues<br><!-- End of picture text --> _Source_ : Derived from Briceño-Garmendia, Smits, and Foster (2009).

27