rafmacalaba/datause-encoder-data huggingface dataset:
(http://unstats.un.org), while GDP growth rates are from the International Monetary Fund (www.imf.org).

# **Sensitivity Analysis**

We used the Latin Hypercube Sampling (LHS) method to assess uncertainty surrounding the effect of each scenario in each region (McKay, Beckman et al. 1979), which has been used extensively by epidemiologists to evaluate a number of different models (Blower and Dowlatabadi 1994; Tanaka, Small et al. 2000; Currie, Williams et al. 2003). Because each input parameter is treated as a separate random variable, LHS is an efficient sampling design for dealing with large numbers of input parameters and is significantly more efficient than simple random and fractional-stratified sampling designs (see Blower and Dowlatabadi 1994).<sup>10</sup> We generated 100 samples of the model parameters for each region (as computed in the program _Palisade @Risk_ ), assuming that model parameters act independently and take values that are triangularly distributed between lower and upper limits and the point estimate (Table 3). This analysis of unpredictability allows for three sources of uncertainty: (1) in regional trajectories of the TB epidemic before implementation of the Global Plan; (2) in the epidemiological response to a combination of interventions, given our imperfect understanding of TB’s natural history; and (3) in whether the interventions will be carried out precisely as specified in the Global Plan scenarios described below. To account for variation of the third kind, we allowed for errors of ± 20 percent in the annual case detection rate and ± 10 percent in treatment success. Thus, for example, casedetection ranges between 80 percent and 120 percent of the anticipated value in each year of implementation of the Global Plan. The elasticity of the VSLY with respect to GDP also was included.

> 10 In a standard Monte Carlo simulation, each input parameter is randomly selected from within a probability distribution function (pdf) for each simulation. In LHS, each parameter distribution is stratified into equiprobable intervals and each interval is sampled exactly once (without replacement). An input vector is then generated composed of the random samples of each of the input parameters for each simulation and each value of every parameter is only used once, which increases efficiency.

15
WPS3789

# **SRI LANKA’S**

# **MIGRANT LABOR REMITTANCES:**

**Enhancing the Quality and Outreach of the Rural Remittance Infrastructure**

Esperanza Lasagabaster Samuel Munzele Maimbo Sriyani Hulugalle World Bank Policy Research Working Paper 3789, December 2005 _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ Esperanza Lasagabaster (elasagabaster@worldbank.org) is Senior Financial Economist, Samuel Munzele Maimbo (smaimbo@worldbank.org) is Senior Financial Sector Specialist and Sriyani Hulugalle (shulugalle@worldbank.org) is a Senior Economist in the Finance and Private Sector Unit, South Asia Region of the World Bank. The authors gratefully acknowledge comments from Anjali Kumar, Mona Sur, Dina Umali-Deininger, Simon C. Bell, Alawode Abayomi, Haans Boon, Peter Harold, Ismail Radwan, Ole Andreason, and Ranee Jayamaha for their valuable comments. The authors wish to record the excellent administrative support provided by K. Nalin Perera, Maria Marjorie Espiritu, Sakm Abdul Hye, and Malathi Ratnayake. The paper draws on a larger Economic and Sector Work study on remittances, which was funded by the South Asia Region Vice Presidency and South Asia Finance and Private Sector Development Unit of the World Bank.
Table 6A: Effects of Peers on Financial Investment on Childrens Education

||**Rural (R)**|**Urban (U)**|**R–U**|**Rural (R)**|**Urban (U)**|**R–U**|
|---|---|---|---|---|---|---|
|Father Edu|0.048***|0.109***|-0.061***|0.049***|0.112***|-0.063***|
||(0.006)|(0.010)|(0.011)|(0.006)|(0.010)|(0.012)|
|Peer Edu|0.026|0.141***|-0.115**|0.026|0.135***|-0.110**|
||(0.023)|(0.046)|(0.050)|(0.022)|(0.045)|(0.050)|
|Number of School Age Children||||-0.022<br>(0.019)|-0.081***<br>(0.026)||
|Constant|0.059|-0.819*||-0.043|-0.812*||
||(0.206)|(0.448)||(0.203)|(0.444)||
|Includes Ability|No|No|No|Yes|Yes|Yes|
|Observations|9463|8311|17774|9463|8311|17774|

Notes: Standard errors are clustered at the community level (*** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1). The dependent variable is educational expenditure adjusting for inflation. Inflation rates are calculated using price of rice separately for rural and urban areas. A peer is someone who was born in the respondents district within _±_ 3 years of respondents birth. Rural and Urban are defined based on location of birth. If the respondent was born in a big city or town, it was considered as Urban, and if the respondent was born in a village, it was considered Rural. Cognitive ability index is expressed in percentile; it is constructed taking the first principal component of Raven Test and two memory memory tests net of age and age square. Ability controls include cognitive ability index and its square.

Table 6B: Peer Effect and Intergenerational Educational Mobility

||**Rural (R)**|**Urban (U)**|**R–U**|**Rural (R)**|**Urban (U)**|**R–U**|
|---|---|---|---|---|---|---|
|Father Edu|0.325***|0.528***|-0.203**|0.307***|0.471***|-0.164|
||(0.064)|(0.089)|(0.101)|(0.063)|(0.087)|(0.100)|
|Father Edu Sq|0.013***|0.012***|0.001|0.013***|0.012***|0.001|
||(0.002)|(0.002)|(0.003)|(0.002)|(0.002)|(0.003)|
|Father Edu_×_Peer|-0.020***|-0.037***|0.017|-0.022***|-0.034***|0.012|
||(0.008)|(0.010)|(0.011)|(0.007)|(0.009)|(0.011)|
|Peer Edu|0.840***|0.753***|0.088|0.579***|0.650***|-0.071|
||(0.170)|(0.233)|(0.285)|(0.150)|(0.217)|(0.262)|
|Peer Edu Sq|0.003|0.014|-0.01|0.016*|0.014|0.002|
||(0.010)|(0.013)|(0.016)|(0.009)|(0.012)|(0.015)|
|Constant|-0.223|-0.684|0.461|-0.253|-0.76|0.298|
||(0.755)|(1.140)|(1.372)|(0.632)|(1.071)|(1.241)|
|Includes Ability|No|No|No|Yes|Yes|Yes|
|Observations|9907|5087|14994|9907|5087|14994|

Notes: Standard errors are clustered at the community level (*** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1). A peer is someone who was born in the respondents district within _±_ 3 years of respondents birth. Rural and Urban are defined based on location of birth. If the respondent was born in a big city or town, it was considered as Urban, and if the respondent was born in a village, it was considered Rural. Cognitive ability index is expressed in percentile; it is constructed taking the first principal component of Raven Test and two memory memory tests net of age and age square. Ability controls include cognitive ability index and its square.

38
||(23.66)|(48.72)|(41.87)|(45.07)|
|---|---|---|---|---|
|Others|-42.68**|-88.13**|-30.49|-26.83|
||(21.08)|(43.05)|(65.66)|(79.01)|
|Firm age (years)|0.2|0.71|0.46|0.42|
||(0.46)|(0.95)|(0.67)|(0.70)|
|Industry (base = mining)|||||
|Food & beverages|33.51|-100.96*|70.56|1.46|
||(30.03)|(55.52)|(49.40)|(26.47)|
|Textiles and leather|18.65**|-67.9|22.38|-22.09|
||(8.78)|(61.76)|(18.45)|(17.09)|
|Pulp & paper|50.24*|-44.35|69.28*|20.58|
||(27.29)|(31.55)|(36.85)|(49.40)|
|Chemicals|72.18***|-27.88|160.67***|100.26***|
||(24.02)|(60.28)|(36.49)|(31.95)|
|Medical|279.24|79.76|686.83*|573.04*|
||(194.64)|(111.22)|(366.71)|(346.37)|
|Fiber, rubber & plastic|29.2|-59.85*|40.74|-22.71|
||(28.77)|(32.48)|(74.32)|(17.33)|
|Smelting|18.62|-30.11*|46.8|-20.37|
||(23.73)|(17.80)|(34.14)|(29.70)|
|Machinery manufacture|7.39|-60.81|-24.94|-61.55|
||(12.60)|(50.83)|(19.93)|(41.75)|
|Utility|21.58**|-4.32|0.34|-49.59|
||(9.66)|(68.11)|(47.24)|(71.06)|
|Transportation|147.44*|93.82***|442.96***|380.16***|
||(86.75)|(11.30)|(95.04)|(57.75)|
|Others|46.08***|-11.19|36.24|-16.45|
||(17.25)|(47.13)|(71.49)|(39.33)|
|Time trend|3.5|5.87|2.79|0.95|
||(8.26)|(7.28)|(18.68)|(17.70)|
|Constant|-6.66|134.60***|65.27|126.36|
||(70.41)|(46.86)|(122.78)|(99.61)|
|No. of obs(clusters)|967(3)|914(4)|659(4)|664(4)|
|within_R_<sup>_2_</sup>|0.53|0.01|0.14|0.96|
|between_R_<sup>_2_</sup>|0.12|0.77|0.98|0.95|
|overall_R_<sup>_2_</sup>|0.24|0.57|0.96|0.96|
|Breusch and Pagan Lagrang|ian Multiplier te|st for rando|m effects||
|Test statistics: χ<sup>2</sup>(1)|9.05***|8.91***|8.61**|8.52***|
# **B Comparison VG and non-VG households**

Table 16: Comparison endline characteristics VG versus non-VG households

||.<br>**All**|**N**|**Non-VG**|**N**|**VG**|**N**|
|---|---|---|---|---|---|---|
|Age HH head|47.0|1319|47.3|260|46.9|1059|
||(10.2)||(0.72)||(0.38)||
|HH size|5.23<br>|1319|5.19<br>|260|5.24<br>|1059|
||(2.04)||(0.12)||(0.084)||
|Head able to write|0.73<br>|1319|0.78<br>|260|0.72<sup>_∗_</sup><br>|1059|
||(0.44)||(0.025)||(0.015)||
|Head able to read|0.70|1319|0.75|260|0.69<sup>_∗_</sup>|1059|
||(0.46)||(0.029)||(0.015)||
|Years of education HH head|5.91|1319|6.30|260|5.82|1059|
||(4.78)||(0.38)||(0.18)||
|Primary economic activity is agriculture|0.59<br>|1319|0.56<br>|260|0.59<br>|1059|
||(0.49)||(0.037)||(0.031)||
|HH owns agricultural land|0.37|1319|0.37|260|0.36|1059|
||(0.48)||(0.042)||(0.022)||
|HH assets: Bicycle|0.25|1319|0.31|260|0.23<sup>_∗_</sup>|1059|
||(0.43)||(0.044)||(0.019)||
|HH assets: Four wheeler|0.034|1319|0.038|260|0.033|1059|
||(0.18)||(0.0086)||(0.0064)||
|HH assets: Chairs|0.55|1319|0.58|260|0.54|1059|
||(0.50)||(0.037)||(0.026)||
|HH assets: Tables|0.40|1319|0.39|260|0.40|1059|
||(0.49)||(0.028)||(0.025)||
|HH assets: Chairs/tables|0.60|1319|0.63|260|0.59|1059|
||(0.49)||(0.041)||(0.026)||
|HH assets: Beds|0.47|1319|0.52|260|0.46|1059|
||(0.50)||(0.039)||(0.020)||
|HH assets: Cupboard|0.41|1319|0.42|260|0.41|1059|
||(0.49)||(0.036)||(0.016)||
|HH assets: Refrigerator|0.12|1319|0.16|260|0.11<sup>_∗_</sup>|1059|
||(0.33)||(0.022)||(0.012)||
|HH owns TV|0.64|1319|0.68|260|0.63|1059|
||(0.48)||(0.034)||(0.023)||
|HH assets: Radio|0.051|1319|0.065|260|0.047|1059|
||(0.22)||(0.017)||(0.0063)||
|HH assets: Phone|0.91|1319|0.91|260|0.91|1059|
||(0.28)||(0.020)||(0.011)||
|HH assets: Sewing Machine|0.16|1319|0.20|260|0.16|1059|
||(0.37)||(0.030)||(0.013)||
|HH assets: Fans|0.79|1319|0.79|260|0.79|1059|
||(0.41)||(0.025)||(0.016)||
|HH assets: Electric appliances|0.79|1319|0.79|260|0.79|1059|
||(0.41)||(0.025)||(0.016)||
|HH assets: Jewellery|0.48|1319|0.52|260|0.47|1059|
||(0.50)||(0.034)||(0.033)||
|HH owns livestock|0.24|1319|0.23|260|0.24|1059|
||(0.42)||(0.034)||(0.018)||
|HH assets: Agricultural equipment (e.g. tractor|etc.)<br>0.039|1319|0.065|260|0.033<sup>_∗∗_</sup>|1059|
||(0.19)||(0.019)||(0.0091)||

Note: Standard deviations in parenthesis in first column (All);43 Standard Errors in parenthesis in other columns, clustered at the gram panchayat;
|Headcount poverty rate|**17.1***|**17.3***|**19.4**|**16.4**|**17.1***|**16.5**|**17.3***|**17.6***|**18.2***|17.8|
|---|---|---|---|---|---|---|---|---|---|---|
||(1.1)|(1.3)|(1.5)|(1.3)|(1.2)|(1.3)|(1.3)|(1.3)|(1.1)|(1.1)|
|Near-poverty rate|**10.5***|**10.9***|**10.7***|**11.3**|**10.8***|**10.3***|**10.8***|**10.8***|**10.5***|10.2|
||(0.7)|(0.8)|(0.8)|(0.8)|(0.8)|(0.8)|(0.8)|(0.8)|(0.7)|(0.7)|
|Extreme poverty rate|**9.8***|**9.4***|**10.9**|**8.4**|**9.3***|**9.0**|**9.5***|**9.6***|**10.4***|9.8|
||(0.8)|(1.0)|(1.1)|(0.9)|(0.9)|(1.0)|(1.0)|(1.0)|(0.9)|(0.8)|
|Poverty gap|**4.6***|**4.5***|**5.2**|**3.9**|**4.4***|**4.2**|**4.5***|**4.5***|**4.9***|4.6|
||(0.4)|(0.4)|(0.5)|(0.4)|(0.4)|(0.4)|(0.4)|(0.4)|(0.4)|(0.3)|
|USAID Poverty gap|**26.9***|**25.8***|**27.0***|**24.0**|**25.6***|**25.5***|**25.8***|**25.7***|**26.8***|25.9|
||(1.1)|(1.1)|(1.4)|(1.1)|(1.1)|(1.2)|(1.1)|(1.1)|(1.1)|(1.1)|
|N|4,644|4,644|4,644|4,644|4,644|4,644|4,644|4,644|4,644|4,644|
|**_Panel E: Viet Nam 2014-2016_**|||||||||||
|Headcount poverty rate|14.7|13.1|6.0|8.3|**10.3**|12.3|13.1|11.2|**9.0**|9.6|
||(0.5)|(0.6)|(0.5)|(0.6)|(0.6)|(0.6)|(0.6)|(0.6)|(0.5)|(0.4)|
|Near-poverty rate|9.4|8.6|4.9|6.1|**7.2***|8.0|8.6|7.8|**6.9***|6.9|
||(0.4)|(0.4)|(0.3)|(0.4)|(0.3)|(0.4)|(0.4)|(0.4)|(0.3)|(0.3)|
|Extreme poverty rate|1.9|1.9|0.6|**1.2***|**1.5**|2.0|2.0|1.9|1.9|1.2|
||(0.2)|(0.3)|(0.1)|(0.2)|(0.2)|(0.3)|(0.3)|(0.3)|(0.4)|(0.2)|
|Poverty gap|3.9|3.6|1.5|**2.3**|**2.8**|3.4|3.6|3.1|**2.7**|2.5|
||(0.2)|(0.2)|(0.2)|(0.2)|(0.2)|(0.2)|(0.2)|(0.3)|(0.3)|(0.2)|
|USAID Poverty gap|**26.5***|**27.2**|**24.5**|**27.4**|**26.9***|27.9|**27.4**|28.0|30.0|26.1|
||(0.7)|(1.0)|(1.3)|(1.3)|(1.0)|(1.0)|(1.0)|(1.4)|(1.9)|(0.9)|
|N|9,347|9,347|9,347|9,347|9,347|9,347|9,347|9,347|9,347||
|**_Control variables_**|||||||||||
|Food expenditures|||Y||||||||
|Non-food expenditures||||Y|||||||
|Furnishings and household expenses|||||Y||||||
|<br>Health expenditures||||||Y|||||
|Education expenditures|||||||Y||||
|Utilities expenditures||||||||Y|Y||
|Household assets & house characteristics||Y|Y|Y|Y|Y|Y|Y|||
|Demographics & employment|Y|Y|Y|Y|Y|Y|Y|Y|Y||

**Note:** Empirical distribution of the error terms model with bootstrapped SEs is used. The standard errors are calculated using 100 bootstrap replications and are adjusted for complex survey design. All estimates are obtained with population weights. The normal linear regression model with the theoretical distribution of the error terms employs cluster random effects. Imputed poverty rates for 2019/20 use the estimated parameters based on the 2016/17 data. 100 simulations are implemented. ’Near poor’ status is defined as living on an income between 100 and 125% of the poverty line. All indicators are expressed in %. True rate is the estimate directly obtained from the survey data. Estimates shown in boldface or with a “*” respectively fall within the 95% confidence interval or one standard error of the true rate.

53
a good condition. For tenants, we simply ask how they would rate their relationship with the property owner from 1 (very poor) to 10 (excellent).

_Perception of service quality_ : Tenant / Property owner agrees or strongly agrees that they are satisfied with utility services, the utility services improve people’s lives and provides clear communication, the government is trying to improve their lives, and (reverse coded) the government is not interested in helping the community.

_Perception of service fairness_ : Tenant / Property owner agrees or strongly agrees that the utility enforcement mechanisms are fair and bills are accurate and fair.

_Activism_ : Whether the compound has a committee, tenants have reached out to community leaders, participated in community meetings, or are members of community committees.

_Psychological well-being_ : We include a set of standardized measures to capture different dimensions of psychological well-being among tenants, including Cohen’s four-item stress scale (Cohen et al., 1983), depression (Center for Epidemiologic Studies Depression (CES-D) seven-point scale (Radloff, 1977)), optimism (Rosenberg optimism questionnaire (Rosenberg, 1965)), and the World Value Survey (WVS) measures of happiness, trust and life satisfaction. The psychological well-being index in turn is a standardized weighted average of these sub-indices.

In addition to the indices, we measure rent and rental income, migration and general socioeconomic measures of property owners and tenants to explore possible effects on rent and associated gentrification. We report the results from the indices in the main paper and include the results of all sub-indicators in the appendix Tables A1 and A2.

# **8 Results**

## **8.1 Baseline Balance**

We present descriptive statistics and baseline comparisons between treatment and control groups for our primary outcome measures using the administrative payment data on 6 August 2018 and 28 October 2018 to coincide with the download dates for the data sets used for the randomized assignment of the engagement and enforcement interventions respectively. Appendix Table A3 presents comparisons for each group and we find balance on most key measures covered.<sup>21</sup> Only 2 out of 45 comparisons are statistically significantly different from zero at conventional levels.

> 21Regressions used to estimate the treatment effects reported below are replicated including variables that are not balanced as covariates and neither the sign nor significance of any of the estimates change.

16
# ECONOMIC CRISIS, STRUCTURAL ADJUSTMENT, AND HEALTH IN AFRICA Francois Diop, Kenneth Hill, and Ismail Sirageldin

The relation between economic change and health have long been of interest to social and medical scientists. Empirical study has generally focused on mortality -- or its complement, survival, expressed as life expectancy at birth -- because of the serious problems of definitions in collecting sound morbidity data. Although a long-run general relation between economic and health conditions is evident in both longitudinal and cross-sectional analysis, debate has centered on the relative importance to mortality reduction of income gains and of improvements in public health or medical technology. In a series of cross-sectional compiarisons, Preston (1975, 1985) showed that the cross-national relation between life expectancy at birth and per capita income changed substantially between the 1930s and the 1960s, and again between that period and the 1970s. Most of the gains in life expectancy for a typical country appeared to result from the upward shift in the relation (that is a higher life expectancy for a given per capita income) rather than from a higher income. The persistently strong relation between per capita income and life expectancy, and the change in the relation over time, led Preston to conclude that 'income has been a trivial factor in recent mortal4ty trends [but] is still a critical determinant of mortality levels...' The short-run effects of economic fluctuations on mortality have also attracted considerable interest. Detecting such effects requires long time series of observations, but sufficently accurate time series are available for few developing countries. However, the development of reasonably accurate time series of numbers, vital events and simple economic indicators covering several centuries up to the nineteenth century, for a number
One plausible attribution to the different implications of hosting refugees on household welfare (the status of poverty) depending on the type of welfare (poverty) metric comes from the nature of the metrics themselves in that wealth (wealth poverty status) is less sensitive than consumption expenditure (consumption poverty status) to short-term shocks.

Displacement of individual hosts from salaried employment—in temporary labor activities, on the extensive margin—and a spike in prices of agricultural inputs (seed and fertilizer) but not changes in self-employment in non-farm businesses, societal cooperation within the customary labor-sharing arrangements, and prices of food items are among the key mechanisms driving the adverse consumption effects of hosting refugees.

The findings highlight the need for robust development interventions that can offset the welfare loss of hosting refugees, as reflected in lower consumption expenditure per capita, and thereby ease refugee integration. Cash transfer programs are available and have been shown to be successful in improving household welfare in many parts of the developing world (Bastagli et al., 2019). Thus, inclusion of the degree of participation in temporary (casual) labor as one of the targeting parameters of such programs in major refugee-hosting rural areas is a potential avenue of the interventions. Investing in skills and entrepreneurship training to capacitate rural hosts to engage more in self-employment in non-farm businesses or take up salaried permanent employment, as opposed to salaried temporary employment, is another potential avenue to keep them from the stiffer labor market competition that is induced by the inflow of refugees, which is peculiarly concentrated in salaried temporary labor activities. The provision of subsidized agricultural inputs (seed and fertilizer) to major refugee-hosting farm households could also be another potential avenue of the interventions.

# **References**

<mark>Abadie, A., Athey, S., Imbens, G. W., & Wooldridge, J. (2017).</mark> _<mark>When should you adjust standard errors for clustering?</mark>_ <mark>National Bureau of Economic Research Working Paper S</mark> eries No. <mark>24003</mark> _<mark>.</mark>_ <mark>Al-Hawarin, I., Assaad, R., & Elsayed, A. (2018).</mark> _<mark>Migration Shocks and Housing: Evidence from the Syrian Refugee Crisis in Jordan</mark>_ <mark>. Economic Research Forum Working Paper S</mark> eries <mark>No. 1213</mark> _<mark>.</mark>_ <mark>Alix-Garcia, J., & Bartlett, A. (2015). Occupations under fire: the labour market in a complex emergency.</mark> _<mark>Oxford Economic Papers</mark>_ <mark>,</mark> _<mark>67</mark>_ <mark>(3), 687-714.</mark> 33
PROVISION OF WATER TO THE POOR IN AFRICA

## **Figure 3.2    Delegated management models**

<!-- Start of picture text --> Direct contract<br>Contract with institution support<br>Water<br>Utility Utility<br>Water Payment Supervision of the service Payment<br>Standpipe operator* Water committee Support institution**<br>Water Payment Stipend Payment<br>Consumer Standpipe operator*<br>Water Payment<br>Consumer<br>Private : Benin, Burkina Faso, Private : Mozambique,<br>Niger, Rwanda, Kenya, Senegal, Ethiopia, Malawi and Tanzania<br>Mozambique and Malawi<br>CBO: Lesotho, Madagascar, CBO: Zambia and Senegal<br><!-- End of picture text --> Private : Benin, Burkina Faso, Niger, Rwanda, Kenya, Senegal, Mozambique and Malawi CBO: Lesotho, Madagascar, Malawi and Tanzania

- Standpipe operator can be a private individual or a community based organization

** Support Institution: Local leaders, local authority administrators or NGOs

Another important disincentive to payment of standpipe fees is that standpipes receive less attention from the utility. In Maputo, standpipes account for less than 1 percent of total water consumption from the utility and less than 0.5 percent of total revenues for the utility (Boyer 2007). For certain cities with available data, we find similar low percentages for the utility’s market share. In Dakar and Addis Ababa, standpipes account for 6.4 percent and 8 percent of total revenues respectively (Boyer 2007; AICD WSS database). The fact that standpipes comprise a small percentage of the utility business, together with the highly polititical reaction to shutting down the operation of standpipes when bills are unpaid, creates incentives for utilities to focus their attention on increasing their revenue base through better maintenance and improved billing collection from household connections. In some other cities, utilities informally admit to rationing water to standpipes in order to minimize losses.

# Community-based management

_Local leaders:_ There are wide variations on how communities and their representatives are involved in standpipe management and oversight. In some cases, utilities have assumed that local leaders will represent the broader public interest of their communities, and have handed over responsibility for operations and maintenance to these leaders (in parts of Maputo, Blantyre, Addis Ababa, and Dar es 19