rafmacalaba/datause-encoder-data huggingface dataset:
PROVISION OF WATER TO THE POOR IN AFRICA

# **Table 1.3   Coverage of water supply in the largest cities**

23 cities included in AICD study

||||||Oth|er|Sll|Rti f|
|---|---|---|---|---|---|---|---|---|
|Country|Largest city|Household<br>connection<br>(%)|Standpipes/<br>kiosks<br>(%)|Water<br>tankers<br>(%)|Household<br>resellers<br>(yes/no/%)|Water<br>vendors<br>(yes/no)|ma<br>piped<br>networks<br>(yes/no)|ao o<br>stdp-kiosk/<br>unconnect.<br>(%)|
|Benin|Cotonou|31|N/V|N/A|yes|no|yes|N/V|
|Burkina|||||||||
|Faso|Ouagadougou|34|61|N/A|no|5|no|92|
|Ethiopia|Addis Ababa|39|40|N/A|yes|yes|no|66|
|Mozambique|Maputo|26|26|N/A|26|yes|12|35|
|Niger|Niamey|31|21|N/A|no|10|no|30|
|Nigeria|Kaduna|48|2|N/V|yes|yes|no|3|
|Rwanda|Kigali|35|51|3.21|10|no|no|79|
|Senegal|Dakar|77|19|N/A|yes|no|no|81|
|South Africa|Johannesburg|88|12|0.24|no|no|no|98|
|DRC|Kinshasa|36|N/V|N/A|yes|no|yes|N/V|
|Ghana|Accra|56|N/V|N/V|yes|yes|no|N/V|
|Kenya|Nairobi|51|41|N/V|no|8|9|84|
|Lesotho|Maseru|33|16|1.00|31|5|no|24|
|Malawi|Blantyre|47|N/V|N/A|yes|no|no|N/V|
|Namibia|Windhoek|73|20|N/A|no|no|no|74|
|Sudan|Great Khartoum|27|0.11|0.43|yes|60|no|0.1|
|Zambia|Lusaka|27|58|N/A|yes|yes|no|79|
|Cape Verde|Praia|34|60|6.30|no|no|no|90|
|Chad|N'Djamena|22|N/V|N/V|yes|yes|yes|N/V|
|Cote d'Ivoire|Abidjan|65|N/V|N/A|yes|no|yes|N/V|
|Madagascar|Antananarivo|42|34|N/A|yes|8|yes|58|
|Tanzania|Dar es Salaam|29|4|2.00|35|2|yes|6|
|Uganda|Kampala|30|5|N/V|yes|yes|yes|7|
||Average|43|28|2.20|N/V|N/V|N/V|53|
||Median|35|21|2|N/V|N/V|N/V|66|
||Minimum|22|0.11|0|10|2|6|0.1|
||Maximum|88|61.0|6|35|60|12|98.1|
||Number of countries with<br>relevant presence|all|all|11/23 (48)|17/23 (74)|14/23 (61)|9/23 (39)||

Source: AICD WSS data, other.

Note: For the unconnected market, the data obtained from independent studies have been highlighted. The remaining data come from utility and government sources.

These factors point to a trend of utility overestimates of coverage from public standpipes. But in some countries, that overestimation is mitigated by substantial mobile resale of standpipe water. In cities where standpipe coverage is very low, vendors sell water door-to-door or from existing household connections. In these cases, while people may occasionally obtain their water from the standpipe, they also obtain it from vendors who buy it from the standpipe. Since utilities and standpipe operators do not keep track of 7
Table 1: Application characteristics, by district

||Gatsibo|Kayonza|Kirehe|Ngoma|Nyagatare|Rwamagana|All|
|---|---|---|---|---|---|---|---|
|Applications|390|310|462|381|327|315|2,185|
|Qualifed|333|258|458|365|272|277|1,963|
|Has TTC score|317|233|405|338|260|163|1,716|
|Mean TTC score|0.53|0.54|0.50|0.53|0.54|0.55|0.53|
|SD TTC score|0.14|0.15|0.19|0.15|0.14|0.12|0.15|
|Qualifed female|0.53|0.47|0.45|0.50|0.44|0.45|0.48|
|Qualifed age|27.32|27.78|27.23|27.23|26.98|27.50|27.33|

applied to adjacent pairs of districts. In Appendix C, we use this geographical feature of applications to test for cross-district labor-supply effects and fail to reject the null that these are zero.

# **3.2 Teacher attributes**

During February and March 2016, we visited schools soon after they were enrolled in the study to collect baseline data using surveys and ‘lab-in-the-field’ instruments. School surveys were administered to head teachers or their deputies, and included a variety of data on management practices—not documented here—as well as administrative records of teacher attributes, including age, gender, and qualifications. The data cover all teachers in the school, regardless of their eligibility for the intervention. Teacher surveys were administered to all teachers responsible for at least one upper-primary, core-curricular subject and included questions about demographics, training, qualifications and experience, earnings, and other characteristics.

The ‘lab-in-the-field’ instruments were administered to the same set of teachers, and were intended to measure the two characteristics introduced in the theory: intrinsic motivation and ability. In the model, more intrinsically motivated teachers derive a higher benefit (or lower cost) from their efforts to promote learning. To capture this idea of other-regarding preferences towards students, taking inspiration from the work of Ashraf et al. (2014), we used a framed version of the _Dictator Game_ (Eckel and Grossman, 1996).<sup>20</sup> Teachers were given 2,000 Rwandan francs (RWF) and asked how much of this money they wished to allocate to the provision of school

> 20Previous work shows the reliability of the DG as a measure of other-regarding preferences related to intrinsic motivation (Banuri and Keefer, 2016; Brock et al., 2016; Deserranno, 2019).

15
I implement this as follows. I remove from the LEAPS sample households where all the children are boys (106 households) or households where all the children are girls (75 households). These households are not informative for the comparison performed here. I then enter dummy variables for the interaction between households and years. Standard errors are clustered by individual to allow for the correlation of observations from the same individuals over time. The coefficients of interest are the interactions between gender of the child, whether or not the family is in the treatment district and whether or not the data are from post-program rounds ���, ����� ���. In Table 3, I report the results from this within family difference-indifferences estimator. As suggested above, the point estimates are positive and increase over time from practically (and statistically) no difference in the first post-program year to a one percentage point difference and then a statistically significant three percentage point difference over the course of the LEAPS survey.

The evidence in Table 3 suggests that by round 4 of the LEAPS survey, female children in the treatment district are roughly 3 percentage points more likely to be attending public schools than their male siblings relative to those in the comparison districts. Models that control for characteristics of the individual are similar and insensitive to choice of specification used.<sup>11</sup>

# **VI. Evidence of program impact on household time allocation**

Having provided several pieces of evidence which suggest that the stipend program influenced girls’ enrollment, I turn to the main question posed at the outset of this paper: Do households reallocate time use when conditional cash transfers are provided to them? To answer it, I employ a standard difference-in-difference approach, briefly explained below.

Consider the stylized example depicted in Figure 6. Data are available on an outcome (� for households from two groups – those eligible for treatment (����) and those ineligible for

> 11 A series of indicators for age or a cubic specification in age provide the same results.

12
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- <mark>Gibson, Rosalind S (2005).</mark> _<mark>Principles of Nutritional Assessment</mark>_ <mark>. Oxford university press, USA.</mark>

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EAST AFRICA’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE The capacity constraints faced at the ports of Mombasa and Dar es Salaam, coupled with extremely lengthy import and export procedures, adds considerable time to the clearing of goods from ports (figure 2.9). The long detention of goods in port has become a

**Figure 2.9. Average wait times in East Africa compared with ports worldwide**

<!-- Start of picture text --> 30<br>25<br>20<br>15<br>10<br>5<br>0<br>Los Angeles Europe Hong Kong Mombasa Dar es<br>Salaam<br>Average wait times (days)<br><!-- End of picture text --> Source: JICA 2010.

major obstacle to distribution. Detailed studies on importing goods along the northern corridor indicate that waiting times at ports account for a significant portion of the total time associated with transporting goods along the corridor. This problem is particularly acute for railways. At the port of Mombasa, for example, cargo is often detained because of the serious shortage of railway capacity, exacerbated by poor port infrastructure, such as inadequate berths and yard and delays in customs processes (JICA 2010).

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_Figure 22: Structure of household expenditure by decile_ <!-- Start of picture text --> 100% taxes, insurance, debt servicing<br>90% alimony, help to relatives<br>80% construction and purchase of real estate<br>deposits and savings<br>70%<br>other goods and services<br>60%<br>healthcare<br>50%<br>sports, leasure, hotels, restaurants<br>40%<br>transport and communcations<br>30% purchase of a car<br>20% durables<br>10% utilities<br>0% clothes, footwear<br>1 2 3 4 5 6 7 8 9 10 food products<br><!-- End of picture text --> Source: Authors’ estimates based on HSS data.

As food is a main line of households’ expenditures and their share in total expenditures depends on the well‐ being of the household, food consumption was most affected by the crisis, with households either cutting their expenditures on food items, or switching to cheaper items. In relative terms, the downward adjustments in food expenditures were the largest for the bottom decile, and become progressively smaller (although still negative) as we move up the income distribution (Figure 23).

In addition to cutting food expenditures, the recession years are also characterized by reductions in expenditures on utilities across the whole distribution, but with higher income households adjusting their utilities expenditures to a greater extent, as many households in the bottom decile are either already consuming the minimum necessary energy amounts for basic needs, or lack the ability to make energy efficiency increasing alterations.

The population vulnerable to the risk of absolute poverty reduced mainly food expenditures, not adjusting expenditures on utilities, public transportation, communication and other basic services in contrast to other population groups. Households from the richest decile, for the most part, did not have to reduce their expenditure on food. They adjusted their consumption largely through reducing in 2015‐2016 expenditures on durable goods and services (communication, culture, leisure), which are more salient in the consumption of households in the top decile relative to lower income households.

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McKinsey & Company (2009). Pathways to a Low Carbon Economy: Version 2 of the Global Greenhouse Abatement Cost Curve.

<u>http://www.mckinsey.com/client_service/sustainability/latest_thinking/greenhouse_gas_abat ement_cost_curves</u> National Inventory Report, Third national Communication of Georgia to the UNFCCC, Government of Georgia, United Nations Development Program (UNDP), Global Environment facility (GEF) (2015).

<mark>National Statistical Service of the Republic of Armenia (NSSRA), (2014). Statistical Yearbook 2014.</mark> <u><mark>http://armstat.am/en/?nid=586&year=2014</mark></u> <u>National Statistics office of Georgia (2014), Energy Balance of Georgia 2013, Statistical publication, Tbilisi, Georgia. National Statistics office of Georgia (2014), Statistical Yearbook of Georgia 2014, Tbilisi, Georgia.</u> <mark>Public Services Regulatory Commission of the Republic of Armenia (2014). Various Notifications related to electricity tariff.</mark> <u><mark>http://psrc.am/am/sectors/electric/tariffs</mark></u>

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- United Nations Framework Convention on Climate Change (UNFCCC) (2013). Report of the Conference of the Parties on its nineteenth session, held in Warsaw from 11 to 23 November

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degrees of ownership which has implication on how decisions are made and bargaining power (Doss et al., 2020; Doss et al., 2011).<sup>15</sup> One could argue that these variables can already be categorized into bundles of rights depending on the question of interest. Under the Evidence and Data for Gender Equality (EDGE) initiative, for example, the United Nations has emphasized the concept of _SDG ownership_ : having either documented ownership, right to bequest, or right to sell.<sup>16</sup> Under Schlager and Ostrom’s (1992) classification, on the other hand, rights are designated into three categories: management, alienation, and exclusion. Management rights are related to patterns of use and ability to improve on the land. For example, decision-making and improvement rights over land in the LSMS+ survey modules can be classified into the management category. Exclusion rights are those related to determining who has access or the ability to transfer land, while alienation rights relate to whether the land can be sold or rented. Economic ownership measure, as captured in the LSMS+ survey modules, does not fit into this standard classification. However, economic ownership is still of interest because it provides a more tangible idea of who would receive the valuation of the land when a land transfer occurs. Documented ownership, on the other hand, might increase the land’s ability to be transferred or used as collateral as well as an official recognition that would improve its tenure security (see, for example, Sitko et al., 2014; Jacoby and Minten, 2007). Our methodology will help us analyze whether we empirically see these theoretical bundles and categorization in the data. We can contrast how these bundles compare with what the owners view as enough rights to report themselves as landowners.

# **2.2 What Do the Data Tell Us about Ownership?**

Table 2 presents the share of individuals owning non-residential (primarily agricultural) land across the three countries. About a third or more of rural men and women own non-residential land, with a slightly higher share of men than women owning land. While urban respondents are less likely to own non-residential land, the shares are still substantial in urban Malawi and Tanzania — spanning about 19 percent of urban women and men in Tanzania, and 14 and 10 percent of women and men, respectively, in Malawi. Reported ownership masks exclusive and joint ownership which varies by country context. For example, Ethiopia’s reported owners are more likely to be joint owners while Tanzania and Malawi have a more equal distribution among the two types of owners.

> 15 A number of papers have discussed the effects of joint ownership and joint titling of land on welfare measures (Wiig, 2013; Newman et al., 2015; Kabumbuli, 2016; Agarwal, 2003).

> 16 This is related to the Sustainable Development Goals (SDGs); guidelines can be accessed at https://unstats.un.org/edge/publications/docs/Guidelines_final.pdf

8
is 0.15, i.e. a 30.7% reduction in sales (the observed mean change) would imply an average decline of permanent workers of -4.6%. However, the dummies for types of labor confirm that production and nonproduction workers saw a much larger decline in employment.

The sector dummies are jointly significant at the 10% level and show that the downward adjustment was the strongest in the manufacturing, garments and trade industries. The size dummies are both individually and jointly insignificant, showing that firms of all size were affected equally by the economic crisis (ceteris paribus).

The model in the previous section shows that firms will be more likely to smooth the use of inputs if they expect the demand shock to be temporary. Therefore we next include an interaction term between the demand shock and a dummy variable whether the firm expects the demand shock to be temporary in the regression model (5). The firms have been asked about their prediction for the workforce in the next 12

**Table 5. OLS Regression of percentage change in permanent workers on demand shock**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||||Credit co<br>|nstrained<br>|
|_Demand shock_|||No|Yes|
|Change in sales (%)|**0.15**(3.76)|**0.14**(3.26)|**0.13**(1.94)|**0.13**(2.42)|
|Change in sales x temporary(%)||0.02 (0.42)|**-0.10**(1.74)|0.10 (1.40)|
|_Type of labor (omitted: managers)_|||||
|Professionals|1.38 (1.03)|1.40 (1.04)|-0.77 (0.37)|**2.83**(1.63)|
|Skilledproduction workers|-2.00 (0.84)|-2.00 (0.84)|-3.56 (1.65)|-1.05 (0.29)|
|Unskilledproduction workers|-3.97 (1.45)|-3.97 (1.46)|-1.98 (0.92)|-5.30 (1.22)|
|Non-production/service workers|**-2.89**(2.45)|**-2.89**(2.44)|**-4.55**(2.78)|-1.87 (1.13)|
|_Sector (omitted: (Other) manufacturing) _|||||
|Garments|0.68 (0.19)|0.61 (0.17)|3.17 (0.58)|2.25 (0.45)|
|Trade|-1.48 (0.38)|-1.44 (0.38)|-2.76 (0.66)|2.35 (0.41)|
|Tourism|5.41 (1.53)|5.39 (1.53)|3.23 (1.04)|8.28 (1.53)|
|Other|4.54 (1.39)|4.51 (1.38)|2.95 (1.04)|6.83 (1.57)|
|_Size_ _(omitted: Small)_|||||
|Medium|-2.62 (1.39)|-2.72 (1.44)|-0.31 (0.11)|-3.19 (1.21)|
|Large|-3.77 (1.22)|-3.82 (1.22)|-0.29 (0.09)|-3.87 (0.84)|
|_Phnom Penh (dummy) _|-1.93 (0.54)|-1.79 (0.52)|-0.46 (0.10)|-2.02 (0.40)|
|_Constant_|5.26 (1.01)|5.25 (1.02)|1.86 (0.32)|3.77 (0.48)|
|Number of observations|802|802|322|480|
|R<sup>~~2~~</sup>|0.09|0.09|0.09|0.13|

Note: standard errors are clustered at the firm level and t-values are reported in parentheses. Coefficients which are significant at 10% are reported in bold.

20
Figure 5: Additional Hurricane Damage from Climate Change in 2100 using the Minimum Pressure Model <!-- Start of picture text --> 45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>-5<br>Gulf Southeas Mid New<br>Florida<br>States t Atlantic England<br>CNRM -1 7 8 0 0<br>ECHAM 41 10 7 0 0<br>GFDL 22 30 5 0 0<br>MIROC 14 20 2 1 0<br>Billion USD/yr<br><!-- End of picture text --> <!-- Start of picture text --> 45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>-5<br>Gulf Southeas Mid New<br>Florida<br>States t Atlantic England<br>CNRM -1 7 8 0 0<br>ECHAM 41 10 7 0 0<br>GFDL 22 30 5 0 0<br>MIROC 14 20 2 1 0<br>Billion USD/yr<br><!-- End of picture text --> <!-- Start of picture text --> Gulf Southeas Mid New<br>Florida<br>States t Atlantic England<br>CNRM -1 7 8 0 0<br>ECHAM 41 10 7 0 0<br>GFDL 22 30 5 0 0<br>MIROC 14 20 2 1 0<br><!-- End of picture text --> Note: Predicted expected damages from climate change measured in billions USD/yr.

Figure 6 displays the same regional results using wind speed instead of minimum pressure to predict damages. The results in Figure 6 are based on the identical set of storms as in Figure 5. The different prediction of damages is due solely to the damage function. The overall damages are lower with the wind speed model. The regional distribution of damages is also slightly different. The impacts in the Gulf Coast would be relatively lower and the impacts in the Southeast relatively higher with the wind speed model. The relative damages in the rest of the regions remain similar.

18