rafmacalaba/datause-encoder-data huggingface dataset:
**<u>Table 1. Labor value added in exports (LVAX), 2011, $ million, ratio and share of gross exports</u>**

|||||LVAXbr|eakdown||LVAX|Share<br>||
|---|---|---|---|---|---|---|---|---|---|
||||||R|atio|<br>h|of|I|
|Rank|Country|LVAX|Dir|ect|direct|/indirect|sare<br>in<br>|LVAX<br>in|ncome<br>group|
||||_skilled_|_unskiled_|_skilled_|_unskilled_|exports|LVAD||
|1|United States|1,000,205|109,347|380,793|0.68|1.09|0.58|0.10|HOECD|
|2|China|795,419|69,004|177,323|0.39|0.48|0.42|0.21|LM|
|3|Germany|667,647|163,843|228,359|1.25|1.59|0.43|0.35|HOECD|
|4|Japan|451,779|65,360|114,491|0.49|0.83|0.49|0.15|HOECD|
|5|United<br>Kingdom|276,966|55,780|113,022|1.47|1.61|0.43|0.21|HOECD|
|6|<br>France|270,351|58,713|70,809|0.91|0.93|0.38|0.20|HOECD|
|7|Italy|210,013|47,246|53,385|0.83|1.02|0.36|0.23|HOECD|
|8|Canada|168,277|32,726|53,155|0.92|1.13|0.38|0.17|HOECD|
|9|Korea, Rep.|155,684|43,822|30,716|0.88|0.98|0.27|0.32|HOECD|
|10|Spain|149,242|36,254|48,885|1.21|1.43|0.39|0.19|HOECD|
|11|Switzerland|144,794|38,428|45,765|1.23|1.56|0.44|0.33|HOECD|
|12|Netherlands|120,826|25,528|41,351|1.18|1.28|0.31|0.29|HOECD|
|13|Taiwan, China|117,504|19,743|39,104|0.61|1.50|0.32|0.45|H|
|14|Brazil|109,097|13,178|32,262|0.50|0.87|0.41|0.08|UM|
|15|Australia|105,658|14,094|30,419|0.57|0.84|0.44|0.14|HOECD|
|...<br>106|Madagascar|921|187|303|1.10|1.17|0.34|0.18|L|
|107|Senegal|855|194|308|1.28|1.54|0.24|0.18|LM|
|108|Malawi|758|103|320|0.95|1.41|0.41|0.27|L|
|109|Burkina Faso|754|38|440|0.80|1.93|0.24|0.20|L|
|110|Togo|716|72|434|0.90|3.31|0.40|0.41|L|
|111|Zimbabwe|674|139|244|0.99|1.62|0.26|0.17|L|
|112|Lao PDR|616|60|267|0.80|1.25|0.21|0.20|L|
|113|Armenia|599|150|223|2.13|1.42|0.32|0.15|LM|
|114|Kyrgyzstan|516|72|222|1.34|1.31|0.19|0.24|L|
|115|Benin|503|65|313|3.38|2.98|0.46|0.16|L|
|116|Mongolia|500|91|217|1.04|2.08|0.15|0.17|LM|
|117|Nepal|416|101|151|1.24|1.81|0.31|0.05|L|
|118|Brunei|403|123|165|2.32|2.62|0.05|0.10|H|
|119|Rwanda|390|96|164|1.41|2.69|0.39|0.11|L|
|120|Guinea|215|16|126|0.50|2.91|0.13|0.16|L|

Note: Income group is defined according to the World Bank country group 2011 classification. H-OECD = high income OECD country; H = other high income country; UM = upper middle income country; LM = lower middle income country; L = low income country.

In Table 2 we investigate to what extent LVAX mirrors the stages of economic development. To do so we regress direct (columns 1) and total LVAX share in exports (columns 2) on the log of each country’s GDP per capita measured at PPP current dollars. We find no systematic relationship between level of development and labor intensity of exports. However, once we include country and year fixed effects, a positive and significant correlation is unveiled for both direct and total LVAX share in exports (columns 3 and 4). As countries develop economically, their exports become more labor intensive. As there is no pretense of identifying any causality here, the relationship could also be expressed the other way around: 21
### 5.2 Joint effects of conflict and transportation costs

As an alternative, to examine interactions, we create a binary variable for high conflict areas and introduce an interactive term to the model. Specifically, Equation 5.1 is modified as follows: 0 1 2 3 ′ (5.2) ii ii ii ii ii ii ii Here, the binary variable WW = ββ + ββ FF + ββ takes the value of one if conflict near the household and markets are both ddCC + ββ FF ∗ddCC + XX γγ+ εε greater than the median, and zero otherwise. The other variables are defined as in Equation 5.1 above. By ii ddCC including an interaction term, we are able to estimate how transport cost in areas with high level of conflict.

We instrument for cost to market using our Natural-Historical Path variable, and the instrument for conflict is the level of social fractionalization, and distance to east border. We include both a linear and squared term of fractionalization to account for the diminishing marginal effects of fractionalization on a fixed portion of land.

As a robustness check, we calculate a set of Conley Bounds for the coefficient of interest. To illustrate this, we can rewrite equation (5.1) as follows: 0 1 ′ 0N + ∑ + ∑ �2 + MM HH KK KK ∑ ii ii MM ii HH ii ii ii KK={HH,MM} 1KK ii KK={HH,MM}(5.3) 2KK ii WW = ββ + ββ FF + ββ CC + ββ CC + XX γγ+ λλ PP λλ FF λλ �FF DD KK KK={HH,MM} KK ii ii λλ DD +uu The traditional IV strategy assumes that the parameters 0 , and in Equation 4 are all equal to DD zero. The Conley bound specification, shown in Conley et al (2012), allows these parameters to be close 1KK 2KK KK λλ , λλ , λλ λλ to zero, but not actually equal to zero. In other words, we allow the IVs to be only plausibly exogenous. By allowing the values of these parameters to vary, we can test how sensitive the estimates are to different degrees of exogeneity, thereby testing the validity of our instruments as well as our estimates.

# **6. Empirical Results**

## **6.1 Main findings**

The first indicator of household welfare we analyze is the “wealth index”, which is readily available in the DHS data, and is an estimate of a household’s long term standard of living. The second indicator, a multi-dimensional poverty measure, is generated specifically for this study following Alkire those which involved the extraction of minerals or hydrocarbons from the ground. Mining facilities that were in the USGS data set but not included in this analysis include facilities like cement plants, or steel mills, which are likely concentrated in large cities or manufacturing areas. We also excluded plants that were labeled as being closed.

19
# **Graph (19): Scaled Residuals of Parsimonious Open Economy VECM:**

<!-- Start of picture text --> 0.5 r Drm2<br>0.0<br>-0.5<br>1980 1985 1990 1995 2000 2005 2010 2015<br>r Drgdp<br>0.25<br>0.00<br>-0.25<br>1980 1985 1990 1995 2000 2005 2010 2015<br>5 r DDISC<br>0<br>-5<br>1980 1985 1990 1995 2000 2005 2010 2015<br><!-- End of picture text --> 81
exemption threshold from LE 5,000 in annual income to LE 6,500 in annual income, while also increasing the number of tax brackets with the aim of encouraging individuals to formalize by somewhat decreasing the tax burden. However, tax revenues from the personal income tax remain low, averaging 1.4 percent of GDP from FY11 to FY16. The corporate income tax rate was decreased from 25 percent to 22.5 percent also in 2015 to help with higher tax collection but the base is limited, and taxes collected have been declining since FY10, reaching 3.8 percent of GDP in FY16 from 5.0 percent of GDP in FY10. Egypt recently implemented a property tax. However, very few tax revenues have been collected under the property tax due to very low compliance.

As part of the most recent wave of economic reforms, the personal income tax threshold was again increased in July 2017 to LE 7,200 and all brackets apart from the highest one now have tax breaks. This is not expected to continue in the medium term, as the government seeks to increase its revenues, and is only a short-term measure to alleviate the burden of the reforms aimed at macrostabilization. The medium-term goal of the government is also to encourage the formalization of small and medium enterprises to also widen the tax base; a law on the taxation of small and medium enterprises is on the government’s agenda but has not yet been released.

This study covers the majority of indirect taxes and the personal income tax. The team was able to obtain information on Egypt’s personal income tax. The property tax is not included in the study as it was not in effect in 2015. We now turn to the description of the CEQ methodology.

There is one final point worth noting. The results obtained from the application of the CEQ methodology for Egypt in terms of inequality and poverty reduction follow closely the overall structure of expenditures and revenues of the government. In the Egyptian context, this means that the positive effects on inequality and poverty reduction of the fiscal policies are partly explained by the large deficit ran by the government. In fact, the overall budget deficit was 11.4 percent of GDP in FY15 (Figure 6). This unsustainable imbalance should be kept in mind as a frame to interpret the long-term ability of Egypt’s fiscal policies in addressing inequalities.

**Figure 6. Total Revenues, Expenditures, and Overall Budget Deficit (percent of GDP)**

<!-- Start of picture text --> 35% 13.5%<br>12.90%<br>30% 13.0%<br>12.5%<br>12.5%<br>25%<br>12.0%<br>12.0%<br>20% 11.4%<br>11.5%<br>15% 10.9%<br>11.0%<br>10%<br>10.5%<br>5.0%<br>4.2%<br>5% 10.0%<br>0% 9.5%<br>FY13 FY14 FY15 FY16 FY17 Jul/Dec‐FY17 Jul/Dec‐FY18<br>Revenues (LHS) Expenditures (LHS) Overall Deficit (RHS) Jul/Dec Deficit (RHS)<br><!-- End of picture text --> _Source:_ Based on data from Ministry of Finance.

_Note:_ LHS = left-hand side; RHS = right-hand side 16
Table 8a: Change in the volume of exports in 2030, percent change from baseline

||World|BRI<br>Area|Cambodia|China|Indonesia|Lao<br>PDR|Malaysia|Philippines|Thailand|Vietnam|
|---|---|---|---|---|---|---|---|---|---|---|
|Agriculture|2.8|2.0|-13.5|7.2|4.7|-4.9|-12.6|0.5|-5.5|1.6|
|Minerals n.e.s.|-0.3|-0.7|-2.4|-0.5|-1.3|4.6|-3.8|-1.4|-8.4|-1.7|
|Coal|2.4|2.6||-9.4|1.5|132.9|9.9|-6.6|32.7|-12.1|
|Oil|0.0|-0.5||-2.8|-0.7|8.1|-31.6|1.0|-41.2|-6.0|
|Gas|0.3|0.4||3.5|1.5|-0.8|-16.3||-16.6|9.0|
|Textiles|1.7|1.8|3.1|2.6|2.1|17.5|-2.2|-0.6|-8.6|0.4|
|Wearingapparel|1.2|1.4|1.8|0.8|0.9|-5.0|-11.7|-1.6|-8.6|0.4|
|Leathergoods|1.5|1.7|13.3|1.4|-0.6|22.6|-1.6|-1.0|13.8|3.5|
|Processed foods|2.2|2.3|-4.1|1.8|3.4|-2.5|-1.8|2.5|1.0|3.0|
|Woodproducts|2.5|2.3|-12.2|11.7|-0.6|-4.2|-15.3|-3.8|-11.7|-3.7|
|Paper products,<br>publishing|2.1|1.9|24.4|4.6|1.3|-7.8|-7.4|4.4|3.3|-0.2|
|Petroleum, coal<br>products|2.0|4.6||1.7|-2.9|11.0|3.1|-0.9|26.2|-1.8|
|Chemical, rubber,<br>plastic|1.4|2.8|-6.6|2.9|-0.9|17.5|-1.8|-0.2|-0.6|3.1|
|Energyintensive man.|0.8|1.0|18.8|1.0|5.0|9.1|1.1|-1.1|0.8|-1.0|
|Metalproducts|2.3|3.7|29.6|4.3|3.7|47.7|5.2|5.1|12.1|8.5|
|Electronics|3.7|7.4|26.1|2.4|-4.5|6.7|39.9|8.9|76.9|3.3|
|Machinery and<br>equipment|2.7|4.4|31.5|4.5|4.4|36.3|8.7|0.0|30.5|2.2|
|Transport equipment|1.9|3.8|40.5|2.5|3.1|31.9|8.4|-2.4|19.4|2.5|
|Manufactures,n.e.s.|2.2|3.1|21.0|1.6|0.7|8.6|-22.1|-1.9|6.5|4.6|
|Electricity|0.3|0.4||0.6||9.0|-1.3||-3.3|-0.8|
|Construction|2.8|3.4|9.7|6.0|1.3|20.4|-6.2|1.3|4.8|6.0|
|Trade services|2.0|2.3|-2.3|3.6|1.6|6.2|-3.1|1.2|-13.8|2.7|
|Other transport|1.8|3.2|30.8|4.3|1.1|7.9|-6.8|1.1|0.0|11.3|
|Water transport|0.5|2.7|33.7|5.7|-0.3|10.1|8.6|-0.9|2.1|13.5|
|Air transport|1.7|5.7|32.0|6.0|1.7|14.2|10.2|1.7|13.3|12.4|
|Hospitalityservices|0.3|-2.9|-15.6|-0.7|-0.7|7.5|-22.8|-1.6|-12.0|-4.1|
|Other business services|0.1|-1.7|-12.3|-0.8|-1.0|-7.0|-21.7|-2.3|-23.4|-4.8|
|Other services|0.0|-0.9|-4.9|-0.3|-0.2|-1.9|-6.8|-0.8|-9.4|-1.6|
|**Agriculture**|2.8|2.0|-13.5|7.2|4.7|-4.9|-12.6|0.5|-5.5|1.6|
|**Manufacturing**|2.1|3.9|5.4|2.7|1.6|4.5|17.7|3.5|19.1|2.6|
|**Services**|0.8|0.5|8.9|1.9|0.3|4.7|-7.5|-0.7|-6.1|1.1|
|**Other**|0.1|-0.2|-2.4|-3.0|1.0|4.2|-21.1|-1.6|-16.4|-6.8|
|**Total**|1.7|2.8|5.6|2.7|1.4|1.5|12.4|2.5|14.9|2.0|

- 30 -
**_<u>________________________________________________________</u>_**

Hellqvist, Marcus. 2003. April. _<u>Trade Facilitation from a Developing Country Perspective</u>_ <u>. Swedish</u> National Board of Trade.

Hellqvist, Marcus. 2003. March. _<u>Trade Facilitation: Impact and Potential Gains.</u>_ Swedish National Board of Trade.

Hertel, T., T. Walmsley, and K. Ikatura. 2001. _Dynamic Effects of the ‘New Age’ Free Trade Agreement between Japan and Singapore_ . Journal of Economic Integration 24: 1019–49.

Hummels, D. 2001. _Time as a Trade Barrier_ . Unpublished. Department of Economics, Purdue University, West Lafayette, Indiana IMD (Institute for Management Development). Various years. _World Competitiveness Yearbook_ . Lausanne.

Kaufmann, Kraay, and Zoido-Lobaton. 2002. _Governance Matters II: Updated Indicators for 2000–01_ . World Bank Working Paper 2772, The World Bank, Washington, D.C.

Lakshmanan, T.R, U. Subramanian, W. Anderson, F. Leautier. _<u>Integration of Transport and Trade Facilitation: Selected Regional Case Studies.</u>_ <u>2001. The World Bank, Washington, D.C.</u> Leonard, J. 2001. _Impact of the September 11, 2001, Terrorist Attacks on North American Trade Flows_ . E – Alert, Manufacturers Alliance, Arlington,Virginia.

Limão, Nuno and Anthony J. Venables. 1999. Infrastructure, Geographical Disadvantage, and Transport Costs. World Bank Policy Research Working Paper No. 2257, The World Bank, Washington, D.C.

Messerlin, Patrick A., and Jamel Zarrouk. 2000. _Trade Facilitation: Technical Regulations and Customs Procedures._ The World Economy 23 (4): 577–593.

Molnar, E. and L. Ojala. 2003. _Transport and Trade Facilitation Issues in the CIS-7, Kazakhstan and Turkmenistan._ Paper prepared for the Lucerne Conference of the CIS-7 Initiative (20-22 January), The World Bank, Washington, D.C.

Moenius, Johannes. 2000. _Three Essays on Trade Barriers and Trade Volumes._ Ph.D. dissertation. University of California, San Diego, California Mueller-Jentsch, Daniel. 2002. _Transport Policies for the Euro-Mediterranean Free-Trade Area. An Agenda for Multi-modal Transport Reform in the Southern Mediterranean._ World Bank Technical Paper 527, The World Bank,Washington, D.C.

Murphy, Paul R. and James M. Daley. 1999. Spring. _Revisiting Logistical Friendliness Perspectives of International Freight Forwarders_ . Journal of Transportation Management _._ Ng, Francis and Alexander Yeats. 1998. November. _Good Governance and Trade Policy : Are They the Keys to Africa’s Global Integration and Growth?_ World Bank Working Paper., The World Bank, Washington, D.C.

OECD (Organisation for Economic Development and Co-operation). 2002a. _The Impact of the Terrorist Attacks of 11 September 2001 on International Trading and Transport Activities_ . Unclassified Document TD/TC/WP (2002)9/Final. Paris.

67 ____________________________________________________________________________________________________________
**Table 8: School characteristics**

|||Dev7 cou|ntries|Vietn|am|
|---|---|---|---|---|---|
|Variable|Description|MS|Valid N|MS|Valid N|
|PRIVATESCL|Private school<br>dummy variable|0.1714<br>(0.3768)|41182|0.0832<br>(0.2762)|4882|
|SC02Q02|Funding for school|25.7233|34621|16.6104|4848|
||from student fees|(36.0117)||(26.3564)||
|VILLAGE|School located<br>in a village|0.1403<br>(0.3473)|41347|0.4584<br>(0.4983)|4882|
|TOWN|School located<br>in a town|0.4508<br>(0.4976)|41347|0.3101<br>(0.4626)|4882|
|CITY|School located<br>in a city|0.4089<br>(0.4916)|41347|0.2315<br>(0.4218)|4882|
|CLSIZE|Average class size|35.013<br>(9.764)|40771|42.5043<br>(8.7236)|4882|
|SCHSIZE|Number of enrolled<br>students at school|1057.0332<br>(924.2422)|35062|1302.9009<br>(648.6821)|4882|
|PCGIRLS|Proportion of<br>girls at school|0.4900<br>(0.2597)|36342|0.5282<br>(0.0801)|4882|

Notes: The variables relate to the questionnaires administered to schools. For a more detailed description of variables, please see Tables A2, A3, A4 in the Appendix.The variable means of Dev7 and Vietnam are statistically different at the 95% significance level. Figures in parenthesis represent standard deviations.

The average class size in Vietnam is higher, with 43 students compared to 35 students in Dev7 countries, and the schools in Vietnam are bigger, with average enrollment of 1,303 students compared to 1,057 in Dev7. There is also a slightly higher percentage of girls in Vietnamese schools.

# **2.9 School Resources**

The comparison of Vietnam and Dev7 regarding school resources may be showing that Vietnam makes a deeper effective investment in education (Table 9). Schools in Vietnam have a lower number of computers per student (0.22) compared to a Dev7 (0.39). However, the ratio of computers connected to the Internet is slightly higher in Vietnam (78% compared to 76%). Indices on quality of school educational resources (SCMATEDU) show Vietnam with -0.4941 value and Dev7 with -0.8145 value, and similar higher Vietnam level exists for quality of physical infrastructure at the school (SCMATBUI). There is also a higher proportion of schools that offer additional math classes. These differences indicate that Vietnam has made it a priority to invest in Basic Education that compensates to some extent for its income disadvantage compared to the Dev7. With regard to extra-curricular activities; there is a mixed picture. Not all extra-curricular activities are shown in Table 9, but some indicate lower prevalence in Vietnam compared to Dev7 - for instance school band and math club (not shown, with similar pattern are chess club, IT club, art club). Some activities have higher prevalence in Vietnam - school play/musical, mathematics competition, and sports 16
<!-- Start of picture text --> Number of episodes a Intra wars<br>3 = External wars<br>2<br>1<br>0<br>1960-1979 1980-1999 2000-2018<br><!-- End of picture text --> <!-- Start of picture text --> Percent of total<br>100 m=SSA mSAR =MNA ®LAC SECA ®EAP<br>80<br>60<br>40<br>20<br>9<br>Intra wars External wars<br><!-- End of picture text --> <!-- Start of picture text --> Number of episodes<br>0.06 m Advanced economies<br>= EMDEs<br>0.04 mesLIC:<br>0.02<br>0.00<br>1960-1979 1980-1999 2000-2018<br><!-- End of picture text --> <!-- Start of picture text --> Number of episodes = 1960-1979<br>15 = 1980-1999<br>= 2000-2018<br>1.0<br>0.5<br>0.0<br>EAP ECA LAC MNA SAR SSA<br><!-- End of picture text -->
<!-- Start of picture text --> New Displacements, DRC<br>2000000<br>1500000<br>1000000<br>500000<br>Year<br>2012 2016 2020<br>Newly Displaced Persons Due to Conflict<br><!-- End of picture text --> Figure 2: Temporal Trends in IDP Flows, Democratic Republic of Congo 2018). Many are displaced multiple times in short bursts as the security situation in their communities fluctuates (Zeender & Rothing 2010). When fleeing violence, IDPs in DRC oftentimes attempt to stay close to their home communities so they can monitor their properties with the intention of returning once the security situation improves (White 2014).

In areas near violence, host communities are frequently and repetitively asked to host IDPs: by one count, host families often host IDPs around three to four times, for around three months on average (Simpson 2010). Alternatively, many IDPs flee to urban areas, increasing slum areas in cities like Goma and Bukavu (Zeender & Rothing 2010). Urban IDPs are less reliant directly on host families and many have found work in urban areas (Jacobs, Lubala Kubiha & Katembera 2020).

In summary, the dynamics of displacement in eastern DRC are fluid, with vulnerable popula8
Table B.2: The ratio of the effect on the poverty rate and the poverty gap of moving from 10 to a 20 percent increase in prices. All considered <u>products, no wage impacts</u>

|||Povert|yrate||Povert|y gap||
|---|---|---|---|---|---|---|---|
|||10%|20%|Ratio|10%|20%|Ratio|
||Rural|0.5|1.0|2.1|0.3|0.6|2.1|
|Bolivia|Urban|0.6|1.0|1.7|0.2|0.4|2.1|
||Total|0.5|1.0|1.9|0.2|0.5|2.1|
||Rural|0.3|0.6|2.5|0.6|1.3|2.3|
|Cambodia|Urban|0.5|1.0|2.1|0.2|0.4|2.2|
||Total|0.3|0.7|2.4|0.5|1.1|2.3|
||Rural|1.9|2.3|1.2|0.7|1.5|2.2|
|Madagascar|Urban|1.8|3.6|2.0|1.0|2.1|2.1|
||Total|1.8|3.1|1.7|0.9|1.9|2.1|
||Rural|0.6|1.3|2.3|0.2|0.5|2.1|
|Malawi|Urban|0.4|1.0|2.5|0.1|0.2|2.3|
||Total|0.5|1.3|2.4|0.2|0.5|2.1|
||Rural|1.5|2.3|1.6|0.8|1.8|2.1|
|Nicaragua|Urban|2.7|4.3|1.6|0.9|1.9|2.1|
||Total|2.1|3.4|1.6|0.9|1.8|2.1|
||Rural|-0.1|0.2|-3.1|0.1|0.3|3.9|
|Pakistan|Urban|0.8|1.8|2.1|0.2|0.4|2.1|
||Total|0.3|0.8|3.1|0.1|0.3|2.8|
||Rural|-0.1|-0.3|5.5|-0.1|-0.2|1.8|
|Peru|Urban|-0.1|0.1|-1.0|-0.1|-0.1|1.9|
||Total|-0.1|-0.2|3.3|-0.1|-0.2|1.9|
|Vi|Rural|-1.4|-2.3|1.7|-0.4|-0.7|1.8|
|etnam<br>2004|Urban|0.2|0.2|1.2|0.0|0.0|4.1|
||Total|-1.0|-1.7|1.7|-0.3|-0.5|1.7|
|Vit|Rural|-0.9|-1.7|1.8|-0.4|-0.6|1.7|
|enam<br>1998|Urban|0.3|0.8|2.8|0.1|0.3|2.2|
||Total|-0.6|-1.0|1.7|-0.2|-0.3|1.6|
||Rural|1.1|2.1|1.9|0.8|1.8|2.1|
|Zambia|Urban|0.6|1.0|1.8|0.5|1.0|2.0|
||Total|0.8|1.6|1.9|0.7|1.4|2.1|
||Rural|0.5|0.7|1.7|0.4|0.8|2.3|
|Average|Urban|0.9|1.6|1.5|0.4|0.8|2.3|
||Total|0.6|1.1|2.2|0.4|0.8|2.1|

Average excludes Vietnam 1998 The ratios presented in Table 2 are, in most cases, relatively close to two for significantly big changes, suggesting that it may not be unreasonable to roughly double the effects of a 10 percent increase in prices in order to obtain a rough estimate of the effects of a 20 percent change in prices. However, there are a few notable exceptions, particularly the impact on rural poverty in Pakistan, where a small reduction in poverty from a 10 percent increase in prices changes into a much larger increase for a 20 percent increase in prices. Another standout result occurs in rural Peru, where the reduction in rural poverty is 5.5 times as great for an increase of 20 percent in prices.

47
**<mark>Table 7. P</mark> ercentage of Sub‐sample of Private Connections in Each Tariff Block** **<u>of the Water IBTs (Addis Ababa, 2016)</u>**

||**Wate**|**r**||
|---|---|---|---|
||||**Average price**|
|**Block No.**|**No. Observations**|**Percentage**|<br>**paid by households in**<br>**this block**|
|**1 [0 – 7 m3]**|182|50%|0.08|
|**2 [>7 – 20 m3]**|151|41%|0.18|
|**3 [>20 – 40 m3]**|28|8%|0.23|
|**4 [>40 – 100 m3]**|5|1%|0.29|
|**5 [>100 – 300 m3]**|0|||
|**6 [>300 – 500 m3]**|0|||
|**7 [>500 m3]**|0|||
|**Total**|366|100%||

# **_Estimates of the magnitude of electricity and water subsidies received by households with both private electricity and private water connections_**

The average monthly electricity use for the 366 households with private electricity and water connections was 276 kWh. The average monthly cost for the utility to provide such service was $25, yet customers paid a monthly electricity bill of about $6.50; hence their average monthly subsidy was $18.40, which amounted to some 4.5% of household income. The average monthly water use for the 366 households with private electricity and water connections was 10 m<sup>3</sup> . Since the average cost of providing this service was $8.50 and since costumers paid a monthly water bill of only $1.30, it follows that their average monthly water subsidy was $7.20 (1.8% of household income). Thus the magnitude of the electricity subsidy was more than twice that of the water subsidy. The combined 19