rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 5412

# **Abstract**

Wastewater use in agriculture is a growing practice worldwide. Drivers include increasing water stress, in part due to climate change; increasing urbanization and growing wastewater flows; and more urban households engaged in agricultural activities. The problem with this trend is that in low-income countries, but also in many middle-income countries, it either involves the direct use of untreated wastewater or the indirect use of polluted waters from rivers that receive untreated urban discharges. This poses substantial risks, in particular microbial risks to public health. To address these risks, the World Health Organization in 2006 issued new guidelines for the safe use of wastewater.

This paper aims to highlight the growing importance of improving wastewater use in agriculture across the spectrum from lower to high-income countries. It presents an innovative approach linking key issues related to different aspects of wastewater irrigation to a country’s level of economic development. Based on data presented in the World Bank’s World Development Report, it differentiates between four country income levels to create a typology for analyzing current issues, trends, and priorities for improving agricultural wastewater use with a focus on reducing the risks to public health. It also presents the basic principles of the new 2006 World Health Organization Guidelines, and how to apply them. Beyond regulatory aspects, the paper also discusses other aspects that are important for achieving a more integrated approach to agricultural wastewater use, including institutional/planning, technological, economic/ financial, and social issues. Finally, the paper provides recommendations for moving the wastewater irrigation agenda forward.

This paper—a product of the Energy, Transport and Water Department, Water Anchor—is part of a larger effort in the department to assess the feasibility of interventions for improving wastewater use in agriculture.. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at sscheierling@worldbank.org.

_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 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._ Produced by the Research Support Team
_Railroads_ : At the national level, we employ the density of railroads measured as kilometers of track per 1000 square kilometers in 1900 (Pach ́on & Ram ́ırez, 2006; Thorp, 1998). At the sub-national level, we employ the Interstate Commerce Commission’s data on miles of track per 100 square miles converted to the same units above for consistency in 1899 (ICC, 1899). Individual country level data is not available for Latin America.

_Manufacturing output per capita_ : The value of manufactured products and labor in manufacturing in 1870 taken from NHGIS to compute the per capita and per unit of labor labor manufacturing product.<sup>14</sup> _Population Density in 1900_ : These are collected from census data from the individual countries. Argentina (1895), Brazil (1900), Chile (1907), Colombia (1912), Mexico (1895), Peru (1876), Venezuela (1926), and the US (1900).

_Pre-colonial Population Density_ : This measures the estimated number of indigenous people per square kilometer just before colonization. See Maloney & Valencia (2015) for more detail.

_Slavery_ : As a measure of institutions that is available for the United States sample, we used the 1860 Census as well as the data compiled in Nunn (2008).

_Geographical Controls_ : in addition to the set of sub-national geographical variables collected by Bruhn & Gallego (2011) including temperature, altitude, and annual rainfall, we add a measure of agricultural suitability and river density as developed in Maloney & Valencia (2015). Clearly, populations could also be sustained by marine-based economies where farmland and rivers were of less importance. Hence proximity to the coast for 14https://www.nhgis.org/documentation/gis-data 9
<!-- Start of picture text --> Oil Exporting Countries' Government Revenue (Excluding Grant)<br>1997-2008<br>60<br>0. 50<br>2v 30 —OE ——————<br>= 20 —_—___ ==<br>Ey<br>a 945 —<br>fe)<br>1997-2002 2003 2004 2005 2006 2007 2008<br>—Angola —TM—Cameroun —te=—Chad —— Congo, Rep<br>——EQG —®— Gabon = Nigeria<br><!-- End of picture text -->
# **1 Introduction**

Accurate measures of welfare for spatially disaggregated areas are valuable inputs into the design and evaluation of effective development policies (Atkinson, 2019; Blumenstock, 2016; Ravallion, 2015; Merfeld and Morduch, 2023; McBride et al., 2022). Yet, most estimates of welfare are derived from household surveys that can only produce reliable statistics at higher levels of aggregation, mostly because of the high cost of data collection (Fujii and van der Weide, 2020; Kilic et al., 2017). Small area estimation combines survey data with more comprehensive auxiliary data to obtain estimates for small areas, in this case defined as one administrative level below that which survey estimates are considered to be sufficient reliable to publish. These more granular estimates can improve geographic targeting (Elbers et al., 2007) as well as program and policy evaluation (Ratledge et al., 2021). While some countries can draw on rich administrative data such as income tax records to serve as auxiliary data, developing countries do not typically maintain accurate and up-to-date administrative data sources. It is quite common to predict welfare with census data and a contemporaneous survey – following Elbers et al. (2003) or Molina and Rao (2010). But in poorer countries, census data are usually collected infrequently. As a result, official statistics on welfare in small areas tend to be dated.

Against this backdrop, recent advances in machine learning and the growing availability of non-traditional data sources have led to the proliferation of new options for small area estimation. For example, Blumenstock et al. (2015) use mobile phone records to infer the socioeconomic status of phone owners in Rwanda and Aiken et al. (2023) use mobile phone call data records to predict targeting performance of programs in Togo. However, one drawback of mobile phone data is that – like banking records – the population of mobile-phone owners may be systematically different from the population of those without phones. Satellite-derived geospatial data do not suffer from this selection bias and have become increasingly popular in economics (Donaldson and Storeygard, 2016). Previous research has demonstrated that geospatial data is a promising source of data to estimate economic growth (Henderson et al., 2012), labor force participation (Merfeld et al., 2022), and welfare more generally (Jean et al., 2016; Yeh et al., 2020; Chi et al., 2022; Engstrom et al., 2022; Van der Weide et al., 2024).<sup>1</sup> In this paper, we evaluate four main methods to estimate welfare at low levels of aggregation in developing countries. Importantly, we propose and implement a two-stage weighted residual bootstrap procedure to estimate uncertainty for the set of machine learning methods; estimates of uncertainty are an ongoing, but current unsolved, issue with machine learning predictions (Chi et al., 2022) and our results are encouraging.

> 1Newhouse (2024) provides a recent review of the literature of applications of geospatial small area estimation to wealth and poverty.

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statistics, we re-scale weights so that all countries are equally weighted.

# **A.5 Measures to Minimize Bias and Measurement Error During Survey Design and Implementation**

During the design of the survey questionnaire a number of good practices were considered in order to minimize different types of potential biases. The literature on survey design has identified three types of potential bias and measurement errors. These depend on whether they originate from the non-response, the enumerator or the respondent (Collins, 2003). In this section we describe all the steps taken in the design and implementation of the FAT survey to minimize these errors.

**Non-response bias** . A critical potential bias is associated with non-response in particular questions or non-participation in the survey (Gary, 2007). When this non-response follows a pattern that can be linked to factors correlated to the measured object, this nonresponse is associated with biases. For example, if more technology sophisticated firms refuse to participate because of fear to reveal commercial information, this would result in significant downward bias in estimating the level of technology sophistication. To minimize this risk, we try to maximize participation in the survey and follow three steps. First, we partner with national statistical offices and industry associations to use the most comprehensive and updated sampling frame available, as well as their experience on data collection, which are supported by endorsement letters from local institutions.<sup>32</sup> Having up to date contact details significantly improves response and minimized contact fatigue. Second, we follow a standard protocol in which each firm is contacted several times to schedule an interview. We split the sample in different batches, following the order of randomization within stratum, and provide contact information of subsequent batches only after interviewers have shown evidence that they have exhausted the number of attempts to complete the initial list. Third, we monitor the implementation, validation of skip conditions and outliers (e.g. financial statements’ information) in real time using standard survey software, and request that any missing information are completed through a follow up call, checked by supervisors. This minimizes risks that enumerators skip the order of their randomly assigned list of firms.

**Enumerator bias and error counts** . Minimizing cognitive biases in respondents in face to face and phone interviews starts with making sure that enumerators are able to implement the survey in a clear and consistent manner. To this end, the survey, training, and data collection processes are largely designed to minimize enumerator biases and data collection

> 32These procedures are in line with suggestions of good practice for implementation by (Bloom et al., 2016).

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41 Table 13. Stationary Population for ALl Couwntries or Econamies

|Stationary<br>p|opulation|<br>Year||Stationary<br>p|opulation|Year|
|---|---|---|---|---|---|---|
|<br>Country or ecronWy<br>Millions<br>1|As X of<br>985 pop|when<br>NRR-1|Cowtry or economy|<br>Millions<br>1|As X of<br>985 pop|<br>when<br>NRR=1|
|Africa<br>2573|A60|2045|Mali|39|522|2040|
|Botswana<br>5|441|2025|Mauritania|9|527|2040|
|Burundi<br>24|504|2035|Niger|36|570|2040|
|Comoros<br>2|522|2030|Nigeria|529|530|2035|
|DJibouti<br>2|613|2045|Sao Taom and Principe|<br>0.3|310|2020|
|Ethiopia<br>205|486|2040|Senegal|30|454|2035|
|Kenya<br>124|608|2030|Sierra Leone|18|480|2045|
|Lesotho<br>6|398|2030|Togo|16|527|2035|
|Madagascar<br>52|506|2035|Other West Africa|0.01|186|2010|
|Nalawi<br>42|590|20'0|West Africa total|1038|510|2045|
|Mauritius<br>2|168|2000|||||
|Nazambique<br>74|537|2040|Algeria|81|374|2025|
|Namibia<br>5|478|2035|Egypt, Arab Rep. of|132|273|2020|
|Riunion<br>1|176|2005|Libya|17|463|2025|
|Rwanda<br>40|658|2040|Morocco|59|270|2020|
|Seychelles<br>0.1|186|2010|Sudan|101|459|2035|
|Somalia<br>30|565|2040|Tunisia|18|256|2015|
|South Africa<br>90|283|2020|Other North Africa|1|483-|2035|
|Swaziland<br>3|495|2035|North Africa<br>total|410|328|2035|
|Tanzania<br>123|555|2035|||||
|Uganda<br>82|561|2040|America|1224|183|2030|
|Zaire<br>142|464|2035|Antigua and Barbuda|0.1|<br>170|<br>2030|
|Zafbis<br>37|545|2035|Argentina|52|169|2005|
|Zimbabwe<br>33|394|2025|Bahamas|0.5|209|2005|
|East Africa total<br>1125|487|1045|Barbados|0.3|119|2030|
||||Belize|1|319|2015|
|Angola<br>43|486|2040|Bolivia|24|372|2030|
|Benin<br>22|554|2035|Brazil|306|225|2015|
|Burkina Faso<br>42|532|2040|Chile<br>-|20|162|2000|
|Camroon<br>51|503|2030|Colombia|59|209|<br>2010|
|Cape Verde<br>1|355|2025|Costa Rica|5|216|2005|
|Central African Rep.<br>12|461|2035|Cuba|12|124|<br>2030|
|Chad<br>22|431|2040|Dominica|0.2|197|2005|
|Cwrgo, People's Rep. of the<br>10|541|2030|Dominican Rep.|13|205|<br>2015|
|CMte d'lvoire<br>51|499|2030|Ecuador|26|274|2015|
|Equatorial<br>Guinea<br>2|424|2040|El Salvador|13|276|<br>2015|
|Gabon<br>4|450|2035|Grenedb|0.2|<br>220|<br>2010|
|Gasbia, The<br>3|443|2040|Guadeloupe|0.5|143|2010|
|Ghana<br>58|455|2030|Guatemala|29|369|2025|
|Guinea<br>26|417|2040|Guyana|2|192|2010|
|Guinea-Bissau<br>4|404|2045|Haiti|17|294|2030|
|Liberia<br>11|507|2035|Honduras|16|355|<br>2020|
# **5 Results**

We constructed an anthropometric model in each of the five zones (“ecozones”) of Urban, Plain, Tonlesap, Coastal and Plateau. We combined provinces that are similar in agroclimatic and socio-cultural characteristics because some of the strata had too few observations to carry out meaningful analysis.<sup>9</sup> We ran regressions in each ecozone separately. While individual-level and household-level variables explain only 20 to 30 percent of the variation in the standardized height and weight, we were able to increase the explanatory power of the model to about 40 to 60 percent by including geographic variables and interaction terms. This is consistent with Curtis and Hossain (1998). We checked the robustness of the regression coefficients by randomly dropping some households or clusters as was done in ELL (2002).

The GLS regression results for the Coastal ecozone are presented in Table 1 and Table 2.<sup>10</sup> The point estimate of the variance of the location effect was zero in all strata, even though in some rounds of the simulation ( ̃ _ση,_<sup>(</sup><sup>_k_</sup> (<sup>)</sup> _r_ )<sup>)2was strictly positive because of bootstrapping.The</sup> average proportion of individual effect to the sum of all the disturbance components was found very high with the ratio of individual effect to the overall residuals <u>((ˆ</u> _σσ_ ˆ _<u>δu</u>_<sup>(1)(1)</sup> <u>)</u><sup>)22ranging from</sup> 0.81 to 0.99 and <u>((ˆ</u> _σσ_ ˆ _<u>δu</u>_<sup>(2)(2)</sup> <u>)</u><sup>)22ranging from 0.66 to 0.92.This in turn means that the household effect</sup> is relatively small, though it is in general not negligible. In all the ecozones, the magnitudes of intra-personal correlations as measured by _ρ_ ˆ<sup>(1</sup><sup>_,_2)</sup> were found high, ranging from 0.44 to 0.53. This strongly suggests the importance of the inclusion of intra-personal correlation.

After the predictions for standardized height and weight for each child in the census were made in each round of simulation, they were aggregated to the commune level in Cambodia to arrive at the prevalence of stunting and underweight. Because of missing data in the census and geographic datasets for a small number of communes, we obtained communelevel estimates for a total of 1,594 communes out of the 1,616 communes in Cambodia.

> 9We followed the definition of the ecozones previously used by WFP (2001).

> 10Other regression results are available from the author upon request.

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study sample that were treated were 33%, 66%, or 100%, and Figure 1 plots the intended saturations from the research design against the observed treatment saturations measured through the household surveys. We refer to the within-village controls as S2, and the 15 EAs, where no schoolgirls received transfers could be considered a special case where the share was set to zero. In those EAs, the only individuals treated were _baseline dropouts_ . The sample of untreated schoolgirls in treatment villages allows us to identify any spillover effects of the program. This same universe of would-be-eligible baseline schoolgirls is also identified in the control communities, denoted by C2. A graphic illustration of the research design is presented below: <!-- Start of picture text --> Treatment EAs Control EAs<br>(N=88) (N=88)<br>Conditional T2 Unconditional T1 Only<br>(N=46) (N=27) (N=15)<br>T1 T1 T1 C1<br>Baseline Dropouts CCT CCT CCT Pure<br> (N=804) control<br>T2a S2 T2b S2 S2 C2<br>Baseline Schoolgirls Within- Uncon- Within- Within- Pure<br> (N=2,742) CCT village ditional village village control<br>control CT control control<br>Household transfer<br>randomized at EA level<br>EA treatment saturation<br>randomized<br>Individual transfer randomized<br>at individual level<br><!-- End of picture text --> From December 2007 through January 2008, offers to participate in the program were made. Of the 1,225 girls in the baseline survey who were originally assigned to the treatment, 32 were subsequently deemed ineligible, 24 could not be located, and one refused. Because we continue to code all 57 of these ‘non-compliers’ as treated, we effectively estimate the Intention to Treat Effect of the original treatment assignment. The offer consisted of a household transfer and a transfer directly to the girl, as well as full payment of school fees for girls in secondary school.<sup>12</sup> The household amount was randomly varied

> 12 Students have to pay school fees at the secondary level in Malawi, but not at the primary level.

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provide, highlighting policy questions which have received less attention. Private employment firms often provide a range of services related to job intermediation, including worker profiling, labor market information, CV and interview preparation, job search assistance, and job matching. When outsourcing intermediation to the private sector, both the relative impact of different services, and in particular, the efforts made in providing these services across a heterogeneous pool of job seekers are important to consider when designing incentive schemes.

This paper concentrates on a pilot program in BiH that was narrow in scope and facilitated comparison of employment outcomes across two levels of service provided by a private provider. Further, access to information on counseling sessions and job counselor assessments make it possible to examine both the classification of jobseekers and counselor decisions. Under the pilot, which was rolled out in three calls between August 2020 and April 2021, self-selected participants were randomly allocated to one of two schemes administered by a private sector provider. A base scheme includes many features of interventions highlighted in recent research, including assistance with CVs, providing information about potential occupations (e.g., Abebe et al, 2021; Beam, 2016; Jensen, 2012), and developing job search plans (e.g., Abel, 2019). The second, more intensive scheme, included all features of the base scheme, with additional effort to match jobseekers with potential employers.

Access to job matching relative to only job counseling led to significantly higher rates of employment in the short-run, with smaller effects in the medium-run. Analysis of administrative data reveals that 3 to 6 months after the program, jobseekers assigned to both counseling and job matching services were 5.8 percentage points, or 25.2 percent, more likely to be formally employed than those assigned to only counseling services. In the medium term, 21 to 24 months after the program, this effect is 4.1 percentage points or a 22.6 percent greater likelihood of being formally employed. Moreover, from examining heterogeneity in the impact of assignment to matching, we find that the impact of matching is driven by a larger magnitude effect for those jobseekers who are less likely to find employment without assistance or may be less capable of signaling their ability without a match from the employment services firm, which acts essentially as a referral.<sup>1</sup> Specifically, we find that treatment effects are much larger for younger workers (under the age of 30) and workers with a secondary school degree or less. However, we find no

> 1One of the main methods used by jobseekers to find work across a host of countries continues to entail either references or introductions from family or friends (a social network), or through direct contact with employers (Carranza and McKenzie, 2024). Jobseekers are left at a disadvantage in job search if they have less access to a referral network of skilled peers or lack other means of finding potential employers and signaling their ability.

3
|Thailand|n|0|197|28|1|2|18|3|1|250|
|---|---|---|---|---|---|---|---|---|---|---|
||%|0|78.8|11.2|0.4|0.8|7.2|1.2|0.4|100|
|Turkey|n|0|24|195|2|9|18|1|1|250|
||%|0|9.6|78|0.8|3.6|7.2|0.4|0.4|100|
|Vietnam|n|0|218|31|0|0|3|1|0|253|
||%|0|86.17|12.25|0|0|1.19|0.4|0|100|
|**Total**|**n**|**9**|**1,092**|**827**|**46**|**40**|**323**|**36**|**51**|**2,424**|
||**%**|**0.37**|**45.05**|**34.12**|**1.9**|**1.65**|**13.33**|**1.49**|**2.1**|**100**|

_Source_ : Computation based on the 2019 GIC Survey.

**Table 3. Respondents by income classification of foreign owner country**

|**Country**||**Decline to answer**|**High income**|**Low or middle income**|**Total**|
|---|---|---|---|---|---|
|Brazil|n|0|228|22|250|
||%|0|91.2|8.8|100|
|China|n|6|229|15|250|
||%|2.4|91.6|6|100|
|India|n|2|232|16|250|
||%|0.8|92.8|6.4|100|
|Indonesia|n|0|209|49|258|
||%|0|81.01|18.99|100|
|Malaysia|n|0|217|33|250|
||%|0|86.8|13.2|100|
|Mexico|n|1|222|26|249|
||%|0.4|89.16|10.44|100|
|Nigeria|n|0|83|81|164|
||%|0|50.61|49.39|100|
|Thailand|n|0|229|21|250|
||%|0|91.6|8.4|100|
|Turkey|n|0|222|28|250|
||%|0|88.8|11.2|100|
|Vietnam|n|0|230|23|253|
||%|0|90.91|9.09|100|
|**Total**|**n**|**9**|**2,101**|**314**|**2,424**|
||**%**|**0.37**|**86.67**|**12.95**|**100**|

_Source_ : Computation based on the 2019 GIC Survey.

_Employment:_ About one-quarter of surveyed businesses have more than 250 employees. The remainder are small and medium enterprises (SMEs) with 250 or fewer employees, and roughly half of the SMEs have 100 or fewer employees (Table 4).

**Table 4. Respondents by employment size**

|**Country**|**Don't**<br>**know**|**<100**<br>**employees**|**100-250**|**251-**<br>**1,000**|**1,001-**<br>**10,000**|**>10,000**|**Total**|
|---|---|---|---|---|---|---|---|
|Brazil<br>n|1|133|47|52|16|1|250|
|%|0.4|53.2|18.8|20.8|6.4|0.4|100|

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