rafmacalaba/datause-encoder-data huggingface dataset:
COST RECOVERY, EQUITY, AND EFFICIENCY IN WATER TARIFFS IN AFRICA

**Figure 4. Cost of water and GNI per capita**

<!-- Start of picture text --> 3.5<br>y = 0.4273x - 0.6733<br>R 2 = 0.1682<br>3<br>2.5<br>2<br>1.5<br>1<br>0.5<br>0<br>0 0.5 1 1.5 2 2.5 3 3.5 4<br>log (GDP Per Capita/100)<br>Effective Tariff at 10m3 Monthly Consumption<br><!-- End of picture text --> _Source:_ AICD WSS Survey Database, 2007; World Development Indicators (WDI), 2007.

# 3 Do tariffs recover costs?

## Cost-recovery policies in African utilities

New demographic trends have made the challenge of meeting service-delivery objectives acutely felt in Africa; consequently, the drive toward cost recovery is even more urgent. Population numbers in the service areas of utilities and operators are continuously increasing, often in periurban areas because of rising trends in urbanization. To service previously unserved areas and to maintain existing connections on a sustainable basis requires investment in new networks and the maintenance of existing systems at least partially funded by the internally generated revenues of service providers. From the supply side, service providers need to recover costs by imposing and collecting consumer tariffs in order to be financially sustainable. Enforcing cost-recovery tariffs enables utilities to leverage resources from other sources—donors and, particularly, the private sector. From the demand side, the ability of consumers to pay tariffs influences the capacity of utilities to be financially capable of expanding access and ensuring service quality.

Recovering the cost of providing service, at least for O&M, is a stated objective of water utilities around the world. The majority of African countries also report that their water tariffs are set with the goal of cost recovery. Only Chad reports that tariffs are set without a specified cost-recovery mandate. Benin also states that there is no specified tariff policy, although in practice the utilities aim to recover O&M costs, plus some investment costs. Including Benin, 19 countries are expected to fully recover O&M costs as well as some amount of investment costs through tariffs. Two countries (Sudan and Ghana) indicated that there was no requirement to recover investment costs, only O&M. In the case of urban wastewater, no country requires covering any part of the investment cost. For the 10 utilities for 9
National average absence rates in these countries have been somewhat lower than in the case of the primary schools shown above, at 7 to 8 percent in Lao PDR and 16 percent in Cambodia (Benveniste, Marshall, and Santibañez, 2007; World Bank, 2008). In Mongolia, a mixed sample of schools covering different ages, from Grades 1 through 10, recorded average absence rates of 16 percent in rural areas but only 5 percent in urban areas (World Bank, 2006).

There are reasons why absence might be lower at the lower‐secondary level than in primary: secondary schools are less likely to be in remote locations and the parents of secondary‐school students are likely to be better‐educated and wealthier, both of which may increase accountability. But because few studies report separately the absence rates at both the primary and lower‐secondary level in the same country, we do not know whether absence is really less prevalent at higher levels of schooling, or whether Lao PDR and urban Mongolia simply have lower absence rates than other countries. One clue may be provided by a study of Bangladesh (Chaudhury, Hammer, Kremer, Muralidharan, and Rogers, 2004), which finds a secondary‐school absence rate 2 percentage points higher than the primary rate. This suggests that there may at least be no general tendency toward lower secondary‐school absence rates.

# **_Changes over time_**

To assess how teacher absence is likely to respond to particular policies and initiatives, it would be useful to know how the problem changes over time. Rigorous evidence on this question is just now beginning to appear. The only countries for which we know of nationally representative surveys directly observing teacher absence more than a year apart are Uganda and Indonesia. In the case of Uganda, in 2006 survey teams returned to the schools that had been surveyed in 2002‐3. Although no report on the findings is yet available, preliminary estimates suggest that the nationwide average absence rate fell by several percentage points.<sup>6</sup> In the Indonesian case, too, preliminary analysis shows a reduction of similar magnitude between 2002/3 and 2008, with an even larger average drop in panel schools that were visited in both rounds.<sup>7</sup>

> 6 This statement is based on personal communication from the author, James Habyarimana.

> 7 Based on calculations by the team, which includes one of us (Rogers).  For these findings, see “Can Teacher Effort Be Improved?  Evidence from Indonesia (preliminary analysis)”, presented by Menno Pradhan at the 2008 World Bank Human Development Forum (Washington, DC, November 3),.
**Table 5: Definition of dummy variables**

|Variable|Description<br>Number of observations|
|---|---|
|_PC_<br>_D_|Equal to 1 if the firm is private and operates under a price cap<br>scheme<br>117|
|_H_<br>_D_|Equal to 1 if the firm is private and operates under an hybrid<br>scheme<br>65|
|_RoR_<br>_D_|Equal to 1 if the firm is private and operates under a rate of<br>return scheme<br>23|
||Public firms (default category)<br>265|

# _Results_

OLS estimates of the labor requirement function are reported in column (1) of Table 6. To avoid possible omitted variable biases, we include GNP per capita and country fixed effects as controls. As usual for translog function approximations, outputs have been mean corrected.

In the two models presented in Table 6, the first-order output coefficients are significant at the 1% level and have the expected signs regarding economic behaviour— an increase in output is associated with an increase in the use of labor. The coefficient on kilometres of distribution network is not significant. The coefficient of the time trend is negative and significant at the 1% level; the average rate of labor productivity growth is in the range of 5.5% to 6%.

The coefficient on RoR is positive but not significant, indicating that private firms operating under RoR are not significantly different from public firms in terms of their efficiency levels. The PC and HR dummies are negative and significant at the 1% level. They are also significantly different from each other, suggesting that private firms operating under PC are the most efficient in this sample. These coefficients are also significant in economic terms: private firms operating under PC and hybrid schemes use, respectively, about 55% and 25% less labor to produce a given bundle of output, given the capital input, than public firms and private firms under RoR.

18
Does Automation in Rich Countries Hurt Developing Ones? Evidence from the U.S. and Mexico<sup>_∗_</sup> Erhan Artuc<sup>_†_</sup> Luc Christiaensen<sup>_‡_</sup> Hernan Winkler<sup>_§_</sup>

> _∗_ We are grateful to Paulo Bastos, Alvaro Gonzalez, Leonardo Iacovone, Daniel Lederman, Andrew Mason, Bob Rijkers, David Robalino, Carlos Rodriguez-Castelan and Joana Silva for helpful comments and discussions. We are thankful to Isidro Soloaga for sharing the file to construct Mexican functional territories, and to Lourdes Rodriguez Chamussy for sharing the correspondence tables for the Mexican occupational classifications. We remain responsible for any errors.

> _†_ The World Bank, eartuc@worldbank.org

> _‡_ The World Bank, lchristiaensen@worldbank.org

> _§_ The World Bank, hwinkler@worldbank.org
||(0.390)|(0.085)|
|---|---|---|
|Constant|5.103***|6.894***|
||(0.730)|(0.276)|
|Location (within country) Fixed Effects|YES|YES|
|Number of observations|276|430|
|Adjusted R2|0.299|0.402|

note: *** p<0.01, ** p<0.05, * p<0.1

# **Figure 1: Country Dominance**

<!-- Start of picture text --> Robustness Check: Country Exclusion<br>0.000 Country Excluded<br>AGO ARG BFA CMR CPV ZAR GHA GTM KEN MMR PER RWA<br>‐0.010<br>‐0.020<br>‐0.030<br>Coef.<br>‐0.040<br>‐95% CI<br>‐0.050 +95% CI<br>‐0.060<br>‐0.070<br>‐0.080<br>‐0.090<br>Coefficient of Total Water Shortage Duration<br><!-- End of picture text --> 48
Table 3 shows that Isaac’s inclusion of the phylogenetic isolation factor changes the weight ratios substantially, particularly for species in the lowest threat category (Least Concern). We explore the implications for hypothetical areas A and B in Table 4. A is populated by only 2 species, both rated as Critically Endangered. B is populated by 20,000 species, but all are rated as of Least Concern. Our extinction risk indicator for each area is the sum of normalized extinction probabilities for resident species. Assignment of weights for Mooers' IUCN-derived 50-year extinction probabilities yields a total risk indicator of 2 for A -- twice the total for B, because each Critically Endangered species is weight-equivalent to 20,000 Least Concern species. In contrast, assignment of the Isaac weights yields an overall risk rating for B (1,250) that is 625 times greater than the rating for A (2), because each Critically Endangered Species is weight-equivalent to 16 species of Least Concern. The other two cases are intermediate, but far closer to the 50-year IUCN case.

**Table 4:  Implications of alternative weighting schemes**

||||T|otal|Scores<br>||
|---|---|---|---|---|---|---|
|||||E<br>Pro<br>Fu|IUCN<br>xtincti<br>babilit<br>ture Y|<br>on<br>ies:<br>ears|
|Area|Species<br>Count|Status<br>(Uniform<br>Within Areas)|Isaac|50|100|500|
|A|2|CR|2|2|2|2|
|B|20,000|LC|1,250|1|2|10|

# **6.3  Biome Vulnerability**

Measures based on animal species alone provide an incomplete accounting of biodiversity.

A more complete measure would incorporate plants and insects, using indices similar to those we have developed for animals. Although no such indices exist at the requisite geographic scale, WWF has provided a first approximation by segmenting the world into 825 terrestrial ecoregions 22
The risk of sexual abuse is also high in the poor neighborhoods according to data. Data indicate that 6 percent of the youth had their first sexual relationship with a family member. One poor explanation is because of the precarious housing and high occupancy per room.

<!-- Start of picture text --> Figure 5: Age of Youth and Adults When Their First Child Was Born<br>(percent)<br>50<br>45 Youth Adults<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Age 10-12 Age 13-15 Age 16-18 Age 19-21 Age 22-24<br><!-- End of picture text --> <!-- Start of picture text --> 50<br>45 Youth Adults<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Age 10-12 Age 13-15 Age 16-18 Age 19-21 Age 22-24<br>Source: Data collected by authors in poor urban neighborhoods in the Northeast Brazil.<br><!-- End of picture text -->

# **Drug Use**

In many Brazilian shantytowns, drugs are fairly easily accessible. The risk of falling into drug use is therefore large. Data reveal that 34 percent of the youth population are taking or have taken soft or hard drugs. Moreover, 14 percent answered that they take drugs on a daily basis.

18
loans made a greater contribution to the total credit portfolio.<sup>37</sup> With the aging of the portfolio, the amount of NPLs has grown, which has had an adverse impact on its share in the total portfolio. However, when analyzing the level of NPLs, one should bear in mind the level of allocated provisions. During the five years from 2003 to 2007, the annual change of the amount of provisions has always been positive, while the amount of NPLs has experienced a slowdown (Chart 19). As a result, in this period banks have built up an excess of provisions over NPLs by more than 3.7 billion denar. This excess provided banks with an adequate buffer, which was used during the following three years (2008–2010). At the end of 2010, the provisions-to-NPLs ratio is still adequate and enables full coverage of the NPLs with the allocated provisions (100.7 percent).

**Chart 19. Annual change of nonperforming loans (NPLs) and provisions in the pre-crisis, crisis, and post-crisis periods**

<!-- Start of picture text --> 5,000 140.0%<br>120.0%<br>4,000<br>100.0%<br>3,000<br>80.0%<br>2,000<br>60.0%<br>1,000<br>40.0%<br>- 20.0%<br>(1,000) 0.0%<br>2003 2004 2005 2006 2007 2008 2009 2010<br>NPLs (annual change - rhs) Provisions (annual change - rhs) Provisions/NPLs (lhs)<br>In millions of denars<br><!-- End of picture text --> Source: NBRM What were the lessons learned during the implementation of the macroprudential measures? First of all, maybe the most valuable experience was related to the role of the NBRM. This central bank established monetary policy and carries out supervisory responsibilities. In addition, beginning from 2006, the NBRM has intensified its duties related to the monitoring of financial stability.<sup>38</sup> As a result of the fact that all these tasks are under one roof, the NBRM has at its disposal adequate monetary and supervisory data to detect the main vulnerabilities of the banking and the financial system and to act accordingly. The crisis has proven that the efficiency of the undertaken measures is largely dependent on the right data and the timely reaction.

Regardless of these sound preconditions, it was rather challenging to choose the most appropriate measures. First of all, the NBRM, as all other central banks in this period, had little

> 37 As a reminder, the analysis in the second section of this paper regarding the credit quality of the loan to households was performed with the exclusion of new credit exposures.

> 38 The NBRM has issued its first Financial Stability Report, which reflects the trends and movements in 2006. With the adoption of the new Law on the NBRM (December 2010), financial stability is identified as one of the main tasks of the NBRM.

27
ically, after the apprenticeship positions offered by firms were validated by program staff, a comprehensive baseline survey was implemented in each firm with confirmed positions. Separately, baseline data were collected among youth deemed eligible after they successfully passed the motivation interview. Baseline data collection among firms and youth took place in each locality before the randomization was performed. The baseline and enrollment phase took place between July 2014 and October 2014. The selection of firms and youth took place shortly after, and placements were mostly completed by January 2015.

The follow-up survey for the impact evaluation took place between March 2016 and June 2016. It was collected on average 20 months after the start of the program.<sup>16</sup> Since most apprenticeships last 24 months, results based on the follow-up survey should be interpreted as providing short-term impacts while apprentices are still in the program.<sup>17</sup> Substantial efforts were made to minimize attrition during the follow-up survey.<sup>18</sup> As a result, 1,661 youth were surveyed, implying an attrition rate of 9.3% (or 171 youth, 84 in Treatment and 87 in Control). Similarly 674 firms were surveyed, leading to 7.8% attrition (or 57 firms, 26 in Treatment and 31 in Control).<sup>19</sup> Tables A3 and A4 present baseline characteristics and balance checks for youth, respectively firms. Both tables have the same structure, the left panel is devoted to the analysis of baseline data (including on the last row the share of youth or firms with available data). The right panel presents baseline characteristics of follow-up respondents and related balance checks.

Table A3 shows that youth interested in formal apprenticeships are 20.7 years old on average, and mostly men (87 percent). They have some (but limited) education, as 63 percent have completed primary school and 17 percent lower secondary school. 45 percent of applicants aspire to a wage job, and 54 percent to become self-employed. There are few significant differences between the treatment and control group of youth, who are largely

> 16Figure A2 documents precisely the timing of surveys as a function of the randomization date.

> 17 754 of the 914 youths in the treatment group (or 82%) were in trades where the apprenticeship contract lasted 24 months.

> 18An unfortunate IT issue with the online server used for electronic data collection led to the loss of baseline data for 26% of youths (475 youth, 250 in Treatment and 225 in Control) and 5% of firms (37 firms, 18 in Treatment and 19 in Control). The problem was concentrated in two localities. The loss of some baseline data limited availability of contact information to track youth (and firms) at follow-up. This contributed to a lower response rate among youths in localities where IT issues occurred.

> 19It is worth to note that part of this attrition is due to firm closure. We designed a specific data collection instrument for registered employers whose firm had closed by the time of the follow-up survey. 12 cases were identified. "True" attrition is limited to 6.2% (45 firms, 23 in Treatment and 22 in Control).

13
educated farmer parents, even though nonfarm households have significantly higher income. Evidence from three econometric approaches shows that the effects of having better educated parents, especially with nonfarm occupations, cannot be explained solely by genetic correlations across generations; the results suggest a causal effect on children’s schooling.

Estimates of cross-partial effects without imposing arbitrary functional forms show that there is little evidence of complementarity between parental education and non-farm occupation in children’s educational mobility which contradicts widely held perceptions. In fact, there is some evidence that nonfarm occupation may be a substitute for parents’ education for sons educational attainment during the reform period. A comparison of parents and children’s generations shows that for girls, the role of family background in schooling attainment remains largely unchanged across generations, but for boys, family background has become more important over the reform period.

# **References**

Aaronson, D and Bhashkar Mazumder (2008). Intergenerational Economic Mobility in the United States, 1940 to 2000, Journal of Human Resources, University of Wisconsin Press, vol. 43(1).

Abadie, A and Guido W. Imbens, 2002. Simple and Bias-Corrected Matching Estimators for Average Treatment Effects, NBER Technical Working Papers 0283, National Bureau of Economic Research, Inc.

Ai, C. and E. Norton, “Interaction Terms in Logit and Probit Models, Economics Letters, 80, 2003, pp. 123-129.

Asadullah, M Niaz, 2012. Intergenerational Wealth Mobility in Rural Bangladesh, Journal of Development Studies, Taylor & Francis Journals, vol. 48(9), pages 1193-1208, September.

38
can be used to identify (observed) achievement vectors as poor or non-poor, i.e., determine whether u(z) > u(ˆxi).<sup>68</sup> Ideally, the proposed method selects the parameter values (w, k, z) that best approximate the inter-personal well-being comparisons following from the information on preference u. Formally, for the AF identification method, which relies on dichotomous status vectors si, these parameter values solve the problem where Bias quantifies the deviations from<sup>69</sup> In practice, some parameters value can be constrained by the data, which is typically the case for some cutoffs zj. An equivalent problem can be defined for PX identification when replacing status si by deprivation di.<sup>70</sup> It could be interesting to develop such method so as to derive the weights of a global multidimensional poverty indicator from subjective well-being data and then compare the bias associated to the derived weights with the bias associated to equal-weights.

The method outlined above readily extends to more general well-being indicators, which compare any two achievement vectors.<sup>71</sup> One natural way of selecting the parameters defining the individual well-being indicator uˆ is to select them in a way that maximizes the correlation between uˆ(ˆxi) and u(ˆxi), where u stands for the information on preference u (see footnote 68).

thresholds z<sup>e</sup> j<sup>.</sup> 68 For instance, preference u can be assumed to be the preference that is most in line with the subjective well-being data, as is done in Decancq et al. (2019), or with the preference elicitation survey data, as is done in Decancq and Nys (2021).

69 Examples of how to quantify “Bias” are proposed at the end of Section 3.2.

> 70Observe that both AF and PX methods are linear identification methods. This linearity constrains the substitutability-complementarity across different dimensions, i.e., the shape of their identification contour, as shown with an illustrative example in Maniquet (2023). Some authors proposed approaches for going beyond linearity. This is for instance the case of Jones (2022) when identifying the poor based on status si or the case of Maasoumi and Racine (2016) when relying on achievements vectors xi (although the latter authors do not aim at aggregating dimensions as a function of preferences). Jones (2022) suggests a relatively simple identification method based on “limit vectors”. One open question is how to efficiently elicit these limit vectors in practice when the number of variables is as large as a dozen. Such elicitation may require an algorithm more parsimonious than that proposed by Decancq and Nys (2021).

71Poverty identification methods merely compare any achievement vector to a fixed reference threshold u.

35
Rodrik, D. (2000): “Institutions for High-quality Growth: What They Are and How to Acquire Them”, _Studies in Comparative International Development_ , vol.35, no.3, pp.3-31.

Rodrik, D. (2002): “Institutions Rule: the Primacy of Institutions over Geography and Integration in Economic development”, NBER Working Paper 9305. Sachs, J. (2001): “Tropical Underdevelopment”, NBER Working Paper 8119. Sachs, J. and A. Warner (1995): “Economic Reform and the Process of Global Integration”, _Brookings Papers on Economic Activity_ , 1, pp.1-118. Seguino, S. (2000): “Gender Inequality and Economic Growth: A Cross-Country Analysis”, _World Development_ , 28 (7), pp. 1211-1230.

Standing, G. (1999): “Global feminization through flexible labor: a theme revisited”, _World Development_ , 27(3), pp. 583-602.

Tzannatos, A. (1995): “Growth, Adjustment and the Labor Market: Effects on Women Workers". World Bank, Education and Social Policy Department.

Tzannatos, A. (1999): “Women and Labor Market Changes in the Global Economy: Growth Helps: Inequalities Hurt and Public Policy Matters”, _World Development_ , 27(3), pp.551-69.

Visser, Jelle (1999): “Global Trends in Unionisation: A Report to the ILO Task Force on Industrial Relations”, mimeo. Cesar Department of Sociology, University of Amsterdam.

Wood, A. (1991): “North-South trade and female labour in manufacturing: An asymmetry”, _Journal of Development Studies_ , vol. 27, no.2.

United Nations (1999): _1999 World Survey on the Role of Women in development: Globalization, Gender, and Work_ . Report of the Secretary-General.

World Bank (2001): _Engendering Development_ . _Through Gender Equality in Rights, Resources, and Voice_ . World Bank Policy Research Report. New York: Oxford University Press.

34