rafmacalaba/datause-encoder-data huggingface dataset:
education distribution of the sentinel survey mirrors that of all women in the DHS. Note that for age/education cells with less than 20 observations we do not reweigh the data (i.e., the weight of unity is assigned). The first/second set of weights will be used in the probit models from which we will then make DHS based predictions for ANC attendees and for all women (aged 15-49).<sup>18</sup> The age/gender distributions for various groups of women, and the resulting sets of weights, are presented in Tables 3a-3b.<sup>19</sup> The results indicate that for any given age group, the sample of women from the sentinel survey has a higher share of those with secondary+ education. The constructed weights correct for this problem.

The next step of the analysis involves constructing a set of variables at the commune/district level (vector **X** _r_ in our model) that are likely to influence an individual’s chance of HIV infection. To do that we draw variables from a rich set of data sources including the 1998 Malawi National Census, 2001 Health Facilities data, 2001 GIS data, and the 2000 DHS. Table A1 in the Appendix presents the list of district/subdistrict level variables used in the analysis, their sources, units of variables’ measurement, initial level of aggregation, the level of aggregation used in the analysis, and the rationale for these variables. To use these commune/district level means in our analysis we merge both the sentinel survey and the DHS data with these regional variables on the basis of the identification numbers for respective geographic units (EA/TA/district).

Obviously, not all of the variables listed in Table A1 can enter the regression due to a high degree of collinearity among many variables. Hence, the subsequent step of the analysis involves selecting a sub-set from all possible regional variables to enter the final regression model. A separate model is estimated for each of the three major regions in the country (North, Central, and South) to allow parameter estimates to vary across regions. For each region we select a specification of the model that achieves a high degree of

> 18 Note that although by re-weighing the sentinel data we can correct for the differences in age/gender distributions across the two data sources (sentinel survey and the DHS 2000), there are likely to be the differences in other unobserved characteristics that we are not able to control for. Due to this reason the sample of ANC attendees in the DHS, which is initially (before re-weighing) closer to the sentinel survey sample in terms of its age/education structure, is likely to give more accurate predictions (since it is likely to have fewer differences in unobservable characteristics as well).

> 19 Further discussion in this paper focuses on the results for all women aged 15-49 for the purpose of comparison with the same age group in the sentinel data and the DHS.

10
NIGER’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 10. Benchmarking water and sanitation indicators**

||**Unit**|**Low-**<br>**income**<br>**countries**<br>**Niger**|**Middle-**<br>**income**<br>**countries**|
|---|---|---|---|
|||Mid-2000s<br>1998<br>2006|Mid-2000s|
|Access to piped water|% pop|10.5<br>**4.4**<br>**5.8**|52.1|
|Access to standposts|% pop|16.2<br>12.5<br>16.0|<br>18.9|
|Access to wells/boreholes|% pop|38.3<br>73.0<br>76.4|<br>6.0|
|Access to surface water|% pop|37.4<br>9.8<br>1.8|<br>13.0|
|Access to flush toilets|% pop|4.9<br>0.8<br>1.0|<br>40.8|
|Access to improved latrines|% pop|9.9<br>8.1<br>12.4|<br>1.4|
|Access to traditional latrines|% pop|50.1<br>8.1<br>5.1|<br>30.4|
|Open defecation|% pop|40.3<br>82.9<br>81.5|14.3|
|||2001<br>2005<br>2007||
|Domestic water consumption|liter/capita/day|50.7<br>37<br>41<br>44|154|
|Revenue collection|% sales|92.7<br>80<br>88<br>90|100.0|
|Distribution losses|% production|34.3<br>22<br>19<br>17|26.8|
|Cost recovery [1]|% total costs|56.0<br>41<br>57<br>67|80.6|
|Operating cost recovery|% operating costs|65<br>161<br>113<br>130|145|
|Labor costs|connections per<br>employee|159<br>116<br>171<br>182|369|
|Total hidden costs as % of revenue|%|163<br>88<br>75<br>59|140|
|||Niger<br>Scarce water|Other<br>|
|||resources<br>2005<br>2006|developing<br>regions|
|Residential tariff|US cents per cubic meter|34<br>34<br>60||
|Nonresidential tariff<br>_Source:_Demographic and Health Sur|US cents per cubic meter<br>veys and AICD water and san|76<br>80<br>120<br>itation utilities database(www.infrastructureafrica.org/aicd/to|3–60<br>ols/data<br>).|

_Source:_ Demographic and Health Surveys and AICD water and sanitation utilities database (www.infrastructureafrica.org/aicd/tools/data). _Note:_ Access figures from the Demographic and Health Surveys for 1998 and 2006. [1] Cost recovery is estimated based on the assumption of a capital cost of 40 cents/m3. — = Not available.

In 1999 the government introduced an ambitious urban water sector reform aimed at improving the operational and financial performance of the sector by reducing operating costs, introducing private sector commercial management, achieving financial autonomy, and expanding access. The new institutional framework involved four main actors: the government, which was responsible for defining sector policy, managing water resources, and developing tariff policy; a multisector regulatory agency (Autorité de Regulation Multi-Sectorielle, ARM), responsible for developing a regulatory framework and monitoring urban water services and contractual arrangements among stakeholders; a public asset-holding company (Société de Patrimoine des Eaux du Niger, SPEN), responsible for investments; and a private operator selected by competitive bidding (Société d’Exploitation des Eaux du Niger, SEEN), in charge of delivering services. In March 2001 SPEN and the government granted SEEN a 10-year performance concession to operate the water supply systems of 51 urban centers—representing 72 percent of the population.

23
attainment of basic needs for survival.<sup>23</sup> So one can set _p_ ∂ _X_ 2 _n_ / ∂ _Y n_ sufficiently close to zero at _y_ min such that ∂ _v_ / ∂ _y n_ > 0 . At higher incomes, the social aspect of the positional good starts to influence budget allocations; in particular, I assume that the neighbor’s marginal propensity to max spend on the positional good rises to near unity at _y_ (the boundary condition can be relaxed without changing the result). Then ∂ _v_ / ∂ _y n_ < 0 at _y_ = _y_ max . By continuity there will be a unique switch point in the income space, below which ∂ _v_ / ∂ _y n_ > 0 and above which ∂ _v_ / ∂ _y n_ < 0 .

We will see later that some new micro evidence on RD in a poor country points to the existence of just such an interaction effect between “own income” and the marginal welfare impacts of having better off friends and neighbors.

# **4. Pitfalls in testing for relative deprivation**

As was noted in Section 2, the literature has reported evidence of RD effects in subjective welfare data. However, there are reasons to suspect that past tests are biased toward finding signs of RD when it is not in fact present.

One source of bias is that the mean income of neighbors—strictly it is typically the mean income of survey respondents in a geographic area that includes the respondent—is picking up a spatial autocorrelation in income measurement errors. Such spurious social effects would appear to be a common problem in past tests for relative deprivation. Ravallion and Lokshin (2007) attempt to solve the problem by using a better measure of own-economic welfare than income, where “better” is judged from the point of view of predicting subjectively assessed welfare. The obvious place to look is the respondent’s own perception of economic welfare, which will automatically reflect geographic factors with direct bearing on economic wellbeing.

A less familiar source of spurious RD effects is that different people are likely to attach different meanings to the categories used in satisfaction or happiness questions.<sup>24</sup> People answer such questions relative to their personal frame-of-reference (FOR), which depends on their own knowledge and experience. And this is likely to be correlated with characteristics of where they

> 23 One does not need a kink in the demand functions, which can still be continuous with continuous first derivatives.  All one requires is that the income gradient is sufficiently low at low incomes.

> 24 This is an instance of what is called differential item functioning (DIF) in the literature on educational testing where DIF exists if students with equal latent ability have different probabilities of giving a correct answer; for an overview of the history and methods of addressing DIF see Angoff (1993).

13
**Water Quality, Brawn, and Education: The Rural Drinking Water Program in China**<sup>**1**</sup>

Lixin Colin Xu World Bank Jing Zhang Renmin University of China

**Keywords** : Water treatment, education, brawn, gender, fiscal programs. **JEL codes** : I0, I1, I20, J16, O10.

1 We thank Hanan Jacoby, Gary Libecap, Mingxing Liu, Liping Lu, Jintao Xu, Yang Yao, Junjian Yi and other participants of seminars at the Walter H. Shorenstein Asia-Pacific Research Center at Stanford University, the Bren School of UC Santa Barbara, Peking University, Shanghai Jiaotong University, Renmin University of China and Central University of Finance and Economics for their useful comments. We are especially grateful for substantial and constructive comments by Sebastian Galiani, Corresponding authors: Jing Zhang, China Financial Policy Research Center, School of Finance, Renmin University of China, 59 Zhongguancun Street, Beijing 100872, China, zhangjingecon@gmail.com; L. Colin Xu, mail stop MC 3-307, World Bank, 1818 H Street, N.W. Washington, DC 20433, lixin.colin.xu@gmail.com.
19 pandemic (since March 2020).<sup>19</sup> In the follow-up phone surveys (starting from May 2020 to May 2021), the above labor market participation questions were asked referencing the time as since last call, which is approximately one month.<sup>20</sup> As far as these changes in reference period trigger similar implications across all regions, they will be captured by round dummies in our estimations.

We define three indicator variables for each of the major employment activities, taking a value of 1 if any member of the household participated in farming, non-farm own business, and wage employment, respectively, and 0 otherwise. We also generate an indicator variable for participation in any of these livelihood activities.<sup>21</sup> Figure 5 (in Section 3) provides temporally and spatially disaggregated trends in households’ participation in these activities.

# **3. Descriptive Trends in Welfare Outcomes**

## **3.1 Food Insecurity**

We start by describing trends in household level aggregate food security outcomes. Figure 2 reports trends in overall prevalence of food insecurity across survey rounds and the four major highland regions in Ethiopia, Amhara, Oromia, SNNP and Tigray. The two measures of food insecurity, prevalence of moderate or severe food insecurity and probability of food insecurity, were constructed using the eight FIES questions as described in section 2.3. Panel (a) shows that prevalence of moderate or severe food insecurity (experienced during the previous month) more than doubled in Tigray after the outbreak of the civil war, jumping from about 30 percent during the immediate pre-war round to about 67 percent in May 2021. In comparison, food insecurity slightly declined in Amhara (from 37 percent to 29 percent) but slightly increased in Oromia and SNNP (from 45 percent to 49 percent, and from 45 percent to 48 percent, respectively).<sup>22</sup>

> 19 For wage-related activities this continued to be one week (since last week).

> 20 The specific labor market participation questions are: (i) since last call, did you or any member of your household work on a family farm growing crops or raising livestock? (ii) since last call, did you or any member of your household operate a non-farm family business?, and (iii) in the last week, were you able to do your wage/salary job as usual either from place of work or work from home?.

> 21 It is thus important to note that our estimations on labor market participation aim to quantify the implications of the war at the extensive margins of the outcomes.

> 22 Note that the war expanded to Amhara and Afar regions after late June 2021. This period is not covered by this study.

15
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|**Overv**|**Tab**<br>**iew of Average R**<br>Percentage o|**le 4:**<br>**esidentialW**<br>f utilities whos<br>to be|**ater Tariffs**<br>e average tariffs appear<br>|
|---|---|---|---|
|By Income|Too low to<br>cover basic<br>O&M|Enough to<br>cover most<br>O&M|Enough for O&M and<br>partialcapital|
|HIC|8|42|50|
|UMIC|39|22|39|
|LMIC|37|41|22|
|LIC|89|9|3|
|By Region||||
|OECD|6|43|51|
|LAC|13|39|48|
|MENA|58|25|17|
|EAP|53|32|16|
|ECA|100|0|0|
|SAS|100|0|0|

Source: Adapted from Komives et alia (2005:21) This evidence suggests strongly that, no matter how desirable the objective of full cost recovery may be for water, its practicability is questionable. Adding sanitation to the package of services provided, as many utilities do, although coverage is typically lower in developing countries, makes cost recovery even less feasible or even desirable. The externalities associated with sanitation services imply that some subsidization funded out of general tax revenues is efficient. Clearly the presence of significant subsidies on current operations substantially reduces the prospects for attracting long term private finance for the necessary investments in the expansion of water and sanitation services.

While political will and populist politics have often been cited as reasons for the persistent poor performance in cost recovery, problems of affordability in low income countries may also account for the inertia in reducing subsidies for residential water. The impact will vary from country to country, depending on water and sanitation coverage among the poorest households, which in regions like Africa is quite low, the costs consumers bear to cope with poor service, and the scope, often unexplored, to restructure tariffs to cross subsidize poor consumers. Recent estimates for Delhi indicate that substantial increases in water tariffs may still be affordable for poor households, especially if the coping costs, sometimes two or three times what is paid to the utility, can be reduced.(World Bank: 2006). South Africa has achieved cost recovery, yet subsidizes basic consumption levels for rich and poor alike, with stiff tariff increases<sup>12</sup> . Overall, Komives et alia (2005:171) estimate, based on a review of a number of studies, that subsidies for electricity and water supply and sanitation are equivalent to roughly three to four per cent of household expenditure or income of poor households that receive them. This relatively modest number nonetheless indicates that there is substance to the concern 12 Although as mentioned above, this has not lead to a high volume of PPI transactions, water charges do offer a revenue stream that can be capitalized to mobilize finance in capital markets.

11
# Table **I**

# Sample Statistics for Pooled Data

**(in** percent)

> **(69** countries, **1971-90]**

|Variable|Observations|Mean|Std.Dev.|Maximum|Minimum|
|---|---|---|---|---|---|
|_Curfe_|**1013**|**77.09**|12.02|**100.00**|**29.16**|
|_Capfe_|**1008**|**22.37**|11.28|70.84|**1.11**|
|_Deflfe_|**931**|**13.52**|**_9.51_**|_53.04_|**0.16**|
|_Hhfe_|**995**|**5.69**|**3.62**|**32.77**|0.54|
|_Edufe_|**998**|14.04|**_5.95_**|34.71|1.02|
|_TaCTe_|**957**|**8.69**|_5.54_|**48.27**|**0.08**|
|_Other/Te_|**887**|**58.39**|**12.19**|**89.73**|**26.13**|

_Notes: CurlTe_ **=** Ratio of current to total expenditure; _Cap/Fe_ **=** Ratio of capital to total expenditure; _Deffe_ **=** Ratio of defense to total expenditure; _HIhfFe_ **=** Ratio of health to total expenditure; _EdulTe_ **=** Ratio of education to total expenditure; _TaclTe_ **=** Ratio of transport and communication to total expenditure; _Other/Te_ **=** Ratio of other to total expenditure.

**25**
Policy Research Working Paper 8311

# **Abstract**

This study uses text mining techniques on almost 900 presidential “state-of-the-union”—type speeches from 10 Latin American countries from 1819 to 2016. The paper documents a sharp increase in recent decades in references to poverty and inequality. The study’s long-term view shows that the way in which poverty and inequality are discussed has been changing. Using a Latent Dirichlet Allocation algorithm, the paper shows that in recent years poverty has been increasingly discussed as a broader multidimensional challenge that requires a variety of social programs.

Inequality has been increasingly framed as an issue of equal opportunities, whereas previously there was a greater focus on social justice. The paper assesses whether the prevalence of poverty and inequality in presidential speeches correlates with measures such as social public spending, as well as the poverty and inequality levels of the country. It finds that during the 2000s, the countries that discussed poverty and inequality at greater length were also the ones that increased social spending and reduced poverty and inequality the most.

This paper is a product of the Poverty and Equity Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at ocalvogonzalez@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
Table 5. OLS regressions with household fixed effects: Probability of being in the bottom quintile of individual wealth, but in top quintile of household wealth

|||Cambodi|a||Malawi|||Ethiopia|||Tanzani|a|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Full<br>sample|Men|Women|Full<br>sample|Men|Women|Full<br>sample|Men|Women|Full<br>sample|Men|Women|
|Female|-0.021**|||-0.024*|||-0.002|||0.02|||
|HH head|-0.023**|-0.070***|-0.078**|-0.023*|-0.096|0.015|-0.016***|-0.047***|-0.058***|0.017|-0.012|0.024|
|Age: <=30|0.024|-0.012|0.075***|0.035**|-0.105|0.120***|0.033***|0.023|0.084***|0.024*|-0.022|0.036|
|Age: 50+|-0.023*|-0.005|-0.037*|-0.026|-0.102|0.036|-0.026***|-0.033|-0.03|-0.042**|-0.075*|-0.058*|
|Years of schooling|-0.002|0.001|-0.010**|-0.004**|-0.007|-0.008|-0.003***|-0.003|-0.007***|-0.009***|-0.004|-0.013***|
|Marital status:<br>married|-0.059***|-0.022|-0.074***|-0.152***|-0.212***|-0.097***|-0.097***|-0.060***|-0.096***|-0.106***|-0.078*|-0.121***|
|Marital status:<br>separated|0.045|0.073|0.069|-0.029|-0.092|-0.022|-0.005|-0.019|0.029|-0.063***|-0.077|-0.063*|
|Marital status:<br>widowed|0.04|-0.031|0.067*|-0.084*|-0.119|-0.102*|-0.033*|0.052|0.005|-0.065**|-0.172*|-0.031|
|Last 7 days: work<br>for wage/salary|-0.003|-0.003|-0.003|-0.036**|-0.096*|-0.044|-0.008|-0.029|0.007|-0.012|-0.014|0.043|
|Last 7 days:<br>worked/ran NFE|-0.035**|-0.085**|-0.001|-0.032**|-0.059|-0.089*|-0.014|-0.029|-0.019|-0.025|-0.051|-0.034|
|Last 7 days: work<br>in agriculture|-0.008|0.016|-0.009|-0.024**|-0.013|-0.047*|-0.006|-0.016|0.002|-0.043***|-0.105***|-0.003|
|Observations|3,938|1,845|2,093|4,912|2,243|2,669|15,388|7,235|8,153|2,983|1,407|1,576|
|R-squared|0.061|0.079|0.103|0.13|0.263|0.148|0.082|0.109|0.111|0.106|0.121|0.167|
|Number of HH|1,512|1,363|1,479|2,445|1,786|2,200|6,770|5,514|6,294|1,184|987|1,098|

<u>Notes:</u> (1) All estimates weighted with the household sampling weights, and standard errors clustered by enumeration area. ***p<0.01 **p<0.05 *p<0.10 (2) Estimates based on specification (C), i.e. imputing missing data on valuation, and using self-reports among joint owners. Results were similar for the other specifications (A), (B) and (D) as described in Table 4.

23
Still, even in places where NERICAs have been introduced, overwhelming evidence from the field of substantial yield benefits are slim. This point is emphasized by Orr et al. (2008), who discuss the gap between the potential of NERICA varieties and evidence of its success. For example, the studies by Diagne cited earlier show mixed outcomes. On one hand, an impact evaluation suggests that NERICA varieties generated significantly higher yields in Benin, but similarly structured evaluations provide no strong evidence of broad yield improvements in Côte d’Ivoire, or Guinea. In Uganda, where rice is a relatively new crop, Kijima et al. (2008, 2010) report favorable yield outcomes for some NERICA varieties. Moreover, consistent with evidence on yields, studies suggest consumption benefits in Benin (Adekambi et al., 2007) and Uganda (Bergman-Lodin 2005; Kijima et al. 2006). At the same time, a recent study by Kijima et al. (2010), suggests that heterogeneous outcomes should be expected. Based on a panel of rice farmers in Uganda, their study suggests that differences related to climate and alternative income sources, as well as differences among farmers in their capacity to replicate seeds of NERICA varieties, can lead to different adoption outcomes.

While there is an urgent need to increase the number of evaluations of the impact of NERICA varieties on yield, there are suggestions that evaluations should be broadened beyond measures of land productivity. Wopereis et al. (2008) emphasize this point in their discussion of the role farmer preferences should play in the evaluation of new varieties. For example, Diagne suggests the reason farmers might prefer NERICA varieties to traditional varieties with similar yields has to do with the shorter growing season for NERICAs. This characteristic reduces the risks associated with terminal droughts, saves on labor and sometimes allows for a second rice crop. Because evaluations tend to focus on yield (land productivity) rather than labor or total factor productivity, the benefits of NERICAs go unmeasured under normal weather conditions. There is support for the later view in a study by Dalton (2004), which suggests that the shorter growing cycle, rather than potential yield benefits are NERICA’s most attractive feature.

# **Lessons from Asia**

In this section, we turn to the prospects of expanding low-land rice in Africa, based on lessons from Asia’s experiences. This is not to diminish the ample opportunities for learning from Africa’s own experiences. As will be discussed later, there are several examples of significant productivity gains in Sub-Saharan Africa and yields in Egypt are among the highest in the world. Still, Asia’s successful Green Revolution is well established and well studied and this is helpful when considering Africa’s future. It is also worth pointing out that the lessons from Asia mostly concern breakthroughs in irrigated and semi-irrigated lowland rice, so the lessons drawn here pertain to the particular places with conditions that are conducive to these rice production systems. Consequently, the degree to which Asia’s technologies are directly transferable to Africa will be 11