rafmacalaba/datause-encoder-data huggingface dataset:
families that were identified as having arrived on the land prior to the intervention. Their main findings are that entitled families increased their housing investment, reduced household size, and increased children‟s education relative to the control group. The entitled households also had fewer children per household head.

Both the Field (2003a,b) and the Galiani and Schargrodsky (2009) evaluations of land titling programs find substantial benefits of the programs that include increases in mothers‟ working, decreases in fertility, increases in children‟s education, decreasing child labor, and increases in housing investment. Although not that much evidence has been accumulated yet on the effects of land titling programs, they appear to be a promising way of significantly increasing a poor household‟s wealth over the short term and inducing an array of changes in behavior. Requiring that the spouse‟s name appears on the title, as in the Peruvian program, significantly increased the probability that a woman‟s name was included on the title.

# **8  Synthesis and Directions for Future Work**

This paper has studied the effectiveness of a variety of policy interventions and social programs at improving the quantity and quality of women‟s work. Some of these policy interventions were undertaken to increase employment, some to increase female employment, and some for other reasons. All of these programs have been subjected to impact evaluations of different kinds and some also to rigorous cost-benefit analyses. Many were found to be effective in increasing women‟s quantity of work as measured by increased rates of labor market participation and number of hours worked. In some cases, the programs also increased women‟s quality of work, for example, by increasing the capacity for women to work in the formal rather than the informal sector where wages are higher and where women are more likely to have access to health, retirement, and other benefits.

Active Labor Market Programs are often adopted by countries as a way of ameliorating the effects of economic shocks. These programs are not usually targeted at women, although women often seek out the employment and training services offered by these programs. The Latin American programs surveyed in this paper were implemented in Argentina, Mexico, and Peru. The majority were found to increase women‟s employment rates and to increase their exit rate out of unemployment. Oftentimes, the program impacts for women exceeded those for men. Most of the programs did not, however, lead to substantial wage increases, with the exception of 57
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# VI. 4.ONCLUDING REMARKS AND POLICY IMPLICATIONS

6.01 This study of water vending and the willingness of households to pay for water reveals a water supply situation in Onitsha which is really quite extraordinary, and one which puts the policy debates over whether the poor can afford water and whether water should be provided as a subsidized public service in a somewhat different perspective.<sup>At</sup> the time of the fieldwork, the ASWC was not a major actor in the water market, based on market share defined in terms of sales. The private sector vending system was responsible for over 95 percent of the water sales in monetary terms. To argue that the population of Onitsha cannot afford to pay for water is clearly erroneous. Most people in Onitsha are already paying high prices for water from the vending system for service which is inferior to that which could be provided by a well-run piped distribution system.

6.02 At the time of the survey, households in Onitsha were paying water vendors about N 30 million annually (US$7 million). The annual capital and operation and maintenance costs of the New Onitsha Water Scheme are difficult to estimate from the information available. At the time the new system was inaugurated, the World Bank had disbursed approximately US$35 million for construction and engineering services. The Anambra State Water Corporation had probably spent the equivalent of US$5 million. However, the distribution network is not yet complete. Assuming that the total capital costs will eventually reach US$100 per capita for households with private connections when the distribution network is completed and that 80 percent of the population of 700,000 will be connected, then the total capital cost will reach US$56 million. Assuming a capital recovery factor of 0.12 (9 percent interest; 15 years), the annualized capital costs for expanding the system are probably on the order of US$6.7 million. Annual operation and maintenance costs are typically about 50 percent of the annual capital costs for such systems, or US$3.3 million. The total annual cost of the Onitsha water supply system is thus roughly US$10 million, or N 43 million at 1987 exchange rates. Households<sup>in Onitsha are thus</sup> already paying water vendors over twice the operation and<sup>maintenance</sup> costs of the completed piped distribution system, and 70 percent of<sup>the</sup> total annual costs.

6.03 It should be relatively easy for the water authority to capture a large share of the water vendors' market--even if the<sup>prices</sup> charged for water from the piped system are high enough to cover the full costs of supply. However, the results of the household<sup>survey</sup> indicate that people perceive the water available from tanker<sup>trucks and</sup> small retail water vendors to be better in quality than the water available from the old public system. Therefore, in order<sup>to increase</sup> its market share, the ASWC must not only offer a lower-priced<sup>product</sup> than the vendors, but also provide a higher quality product in<sup>terms of</sup> both water quality and reliable service.
# **(ii) Tabulated statistics**

For eight countries, we rely on tabulated or grouped data acquired from these countries’ NSOs to create the 2020 distribution. This information is typically published in the form of quantile growth rates. In several cases, we need to make important assumptions to match this data to our 2019 distributions. For example, the tabulations might use a different income concept, use an equivalence scale, cover different parts of the year or rank households instead of individuals. See Appendix B for further details.

# **(iii) High-frequency phone surveys**

Our next preferred source of data is the HFPSs. At the time of writing, the HFPSs were conducted in 85 countries resulting in 155 harmonized indicators. We use this method for 37 countries covering 13% of the global population. Note that more than half of these countries are in SubSaharan Africa (SSA). For 59% of the population residing in this region, the HFPSs is the only source of information on the income growth of households in 2020. HFPSs are also available for 7 countries in ECA (amounting to 14% of the population in the region), 5 countries in EAP (covering 7% of the regions’ population), and 5 countries in LAC and the Middle East and North Africa (MENA) covering 5% and 4% of the respective regional populations.

While the phone surveys provide information on whether households gained or lost income or consumption since the beginning of the pandemic, they do not contain information on households’ level of income or consumption, nor do they report the size of the change in income or consumption. To utilize the information in the HFPSs, we need to (a) map the changes in income or consumption from the HFPSs to the 2019 welfare distributions discussed above and (b) estimate the size of the change for each household. We need to make several strong assumptions to use the HFPSs, which in many countries is the only data that were (and will ever be) collected in 2020. It is also important to emphasize that for those countries that use the HFPSs, total growth follows the growth in national accounts (adjusted using a pass-through rate as explained in Section 4.2). The phone surveys are only used to allocate this growth to various groups within the country and thus allow for within-country distributional changes.

The method we employ differs a little based on whether the phone survey collected data on income changes (which was asked for 24 countries) or consumption changes (which was asked for 13) countries, since the consumption question only asks whether consumption did not change or fell (but not whether it increased). The method using income changes is described below. Further details, including the method for consumption changes, are provided in the Appendix.

# _(a) Mapping the income changes from the phone surveys to the 2019 welfare distribution_

In the phone survey, households are asked whether they lost, gained, or experienced no change in their income since the start of the pandemic. Employing a multinomial logit regression for each 11
10 R<sup>2</sup> - 0.80 DW - 1.24 Sample: 1988.3 - 1990.12 Now the variance of the transfer is completely insignificant, and the transfer itself is only borderline significant. The DW statistic is in the middle of the inconclusive range (1.l1-1.36) for the relevant number of degrees of freedom; however regressing the error term on its own lag yields a t-statistic on the latter's coefficient of only 1.35, EO we did not apply any further error corrections.

These results are strikingly different from the ones obtained for investment and the CHTES-PAGAfE interest rate differential. The variance plays no role, and the transfer itself only marginally, in explaining the interest rate differential between PAGAFE and tne one month US T-Bill rate; ex:hange rate uncertainty plays no role here since both assets are dollar denominated, and the transfer effect captures the partial reduction in country risk.

# 111.4 SumminC UJD

The transfer effect is significant in explaining the improvement of country risk; however, one should be aware that the PAGAEE rate dropped only 4 percentage points after the Brady deal. Redaction in pure country risk thus explains only 20 percent of the overall drop of 20 percentage points in the differential between CETES and the T-bill rate. According to our econometric results, the entire drop Im the CETES-PAGAFE differential (16 nercentaae points) can be ascribed to the variance effect, and the entire 4 Dercentawe noints drop in the PAGAFE/US Treasury bill differential to the transfer effect. Our econometric results thus show that the variance effect was substantially more important in explaining the interest rate decline after the Brady package than the debt overhang hypothesis. The most important conclusion that follows from our results is that the likely impact of debt restructuring-cum-debt service relief can be much larger than the magnitude of the relief per se coupled with standard growth models would suggest. The secondary effects on private investment through reduced future uncertainty are likely to he more impo-tant than the direct amount of the relief itself. our results point out specifically at the favorable
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# **3. Data**

## _Multidimensional poverty index_

Our analysis uses the Multidimensional Poverty Index (MPI) and its indicators as dependent variables. The MPI, developed by the Oxford Poverty and Human Development Initiative (OPHI), provides a nuanced understanding of global poverty beyond income-based metrics. It incorporates a range of indicators reflecting various deprivations that individuals face across different aspects of life. This multidimensional approach aligns with the idea that poverty is not only about income, but also the inability to access essential services and enjoy a standard quality of life (Alkire et al., 2021). The MPI includes ten indicators grouped under three significant dimensions, as depicted in Table 1.

Table 1: Dimensions, indicators, and weights of the MPI

|**Dimensions**<br>**(Weight)**|**Indicator(Weight)**|**Aperson is deprived of living in a household where...**|
|---|---|---|
|Health (1/3)|Nutrition (1/6)|Any person under 70 years of age for whom nutritional<br>information is available is undernourished.|
||Child mortality (1/6)|A child under 18 has died in the household in the five<br>years preceding the survey.|
|Education (1/3)|Years of schooling<br>(1/6)|No eligible household member has completed six years<br>of schooling.|
||School attendance (1/6)|Any school-aged child is not attending school up to the<br>age at which he/she would complete class 8.|
||Cooking fuel (1/18)|A household cooks using solid fuel, such as dung, crops,<br>shrubs, wood, charcoal, or coal.|
||Sanitation (1/18)|The household has unimproved or no sanitation facility,<br>or it is improved but shared with other households.|
|Living Standards|Drinking water (1/18)|The household’s source of drinking water is not safe, or<br>safe drinking water is a 30-minute or longer walk from<br>home, roundtrip.|
|(1/3)|Electricity (1/18)|The household has no electricity.|
||Housing (1/18)|The household has inadequate housing materials<br>in any of the three components: floor, roof, or walls.|
||Assets (1/18)|The household does not own more than one of<br>these assets: radio, TV, telephone, computer, animal cart,<br>bicycle, motorbike, or refrigerator, and does not own a<br>car or truck.|

Source: Alkire et al. (2021) To identify multidimensional poverty, each household is assessed against the ten indicators. For each indicator, if a household fails to meet the minimum standard, it is considered deprived. Each indicator is weighted equally within its dimension, and the dimensions have specific weights. A household is considered multidimensionally poor if the cumulative weighted score of its deprivations is 1/3 or higher. This threshold indicates that a household is deprived in at least onethird of the weighted indicators. The MPI value is calculated by multiplying the proportion of people identified as multidimensionally poor (incidence) by the average proportion of deprivations experienced by this poor group (intensity) (Alkire et al., 2021). While the MPI captures both the incidence and intensity of poverty, it does not measure the depth of poverty, unlike the adjusted poverty gap.

6
16 the vector of explanatory variables, the dummy indicating that the household has two connections (in-house and private) does not come out significantly. In other words, having the two types of connection does not increase the overall consumption from the piped network, all other things equal. We keep in the model the 3 (over a total of 15) municipality dummies that are significant (Galle Four Gravets, Katana, and Negombo). We also considered some other explanatory variables such as education level or household’s opinions about piped water service (see a discussion of these variables in section 2.1.). None of these variables have a significant influence on water consumption per capita. The survey also includes data on house assets (kitchen assets, toilets) and materials used for building the house. Most of these variables were collinear with income and, for that reason have been taken out of the demand equation.

The above model has been specified under the assumption that households have identical preferences, in particular that price elasticity is the same across households. Such an assumption can be tested by allowing the parameters of the price variable to vary across groups of households. We test whether price elasticity varies across income groups. We allocate households into five groups, based on household monthly wage. Group 1 gathers the “poorest” households, i.e., households who fall in the first quintile of the income distribution, group 5 gathers the “wealthiest” households, i.e., households who are in the fifth quintile of the income distribution. We re-estimate the same model allowing price elasticity to vary across the five income groups. The five coefficients are found to be negative, varying from – 0.55 in the third income group to –0.60 in the second. Estimates are quite imprecise (standard error is about 0.28) and coefficients are not proved statistically different from one income group to the other. We also tested whether income elasticity was different across income groups. In this case also, statistical tests could not reject the null hypothesis that income elasticities are equal.

_c. Estimation of the water demand equation of piped households combining piped water with non-piped water_ We estimate a simultaneous two-equation model, the first one for water consumption from the piped network, and the second equation fitting non-piped water consumption. In both equations the dependent variable is the log of water consumption per capita per month,
# 2.1. Reviewing universal broadband assessments

Previous assessments of universal broadband have focused mainly on the infrastructure costs of delivery. As identified in the introduction, the ‘Connecting Humanity’ assessment by the International Telecommunication Union (ITU) focuses on deploying ~10 Mbps broadband to 90% of the global population over 10 years of age, producing an investment estimate of US$ 428 billion (International Telecommunication Union, 2020). However, there are many caveats and assumptions associated with this estimate. For example, Quality of service is not explicitly considered in this analysis, meaning there is no guarantee that the ~10 Mbps broadband target can be maintained in peak periods. Moreover, the amount estimated by the ITU mainly covers capital and operational expenditure (with a small quantity for contents, skills, and regulation), but does not adopt a full-balance sheet approach to account for user cross-subsidization (which is essential to estimate where short-falls occur, and necessary public subsidies are required). Additionally, questions remain around the economic viability of satellite services being able to support very high adoption (e.g., up to 90%) in certain areas.

A contrasting assessment by the World Bank which does include quality of service suggests the private and public cost of universal broadband could be ~$2 trillion to achieve at least ~10 Mbps universal broadband in low and middle income countries (LMICs), equating to investment of more than 0.67% of annual GDP over the next decade (Oughton et al., 2022b).

Building on the ITU’s global analysis, the United Nation’s states that 1.1 billion new users must be connected in Africa to ~10 Mbps per user by 2030 to achieve SDG 9c, costing approximately $100 billion (Broadband Commission for Sustainable Development, 2019). This analysis anticipates that between 15%-20% of the rural population will be connecting via satellite by 2030, equating to 100 million remote rural users. However, no techno-economic modeling of the satellite sector is carried out. For example, analysis of Space X Starlink and Blue Origin Kuiper indicates that LEO constellations may need to have fewer than 1 user per 10 km<sup>2</sup> to provide ~10 Mbps per user in the busiest hour, with this number needing to be even lower for OneWeb (Osoro and Oughton, 2021). Indeed, the 7