rafmacalaba/data-use-mentions-extended huggingface dataset:
self-assessment tools. These different methods could offer their own strengths and weaknesses.

Except for the IIAG that focuses on Africa, all the remaining tools are global. The ODIN covers the most countries, to be followed by the SPI (and the SCI), the GDB (and the ODB), and the IIAG (54 countries during 2010-2019). Regarding measuring recent country progress over time, the ODIN and SPI are the two available choices, with the SPI showing somewhat less volatility.

Since strong statistical capacity is crucial for monitoring SDG progress and other key development outcomes, we examine their relationships with all the tools. For the overall SDG index, the SPI shows the strongest correlation. The SPI also exhibits stronger correlations with other popular development indexes such as the Economics Complexity Index, the OECD Better 12
2024b).

In this paper, we present a harmonized panel of LSMS-ISA surveys from seven Sub-Saharan African countries, namely Ethiopia, Malawi, Mali, Niger, Nigeria, Tanzania and Uganda. This harmonized panel dataset (hereafter HP) covers over 200,000 agricultural plot observations, over 400,000 individuals and 58,000 households, over the time period of 2008 to 2021. The data are nationally representative of these countries, which comprise 39% of the population and close to a third of the poor in Sub-Saharan Africa (Azevedo, 2011; World Bank, 2022). The data are also representative of the household and smallholder agriculture sector of the study countries. The longitudinal nature of the data allows tracking households, farms, and individuals across time. The HP provides longitudinal records for close to 58,000 agricultural and non-agricultural households and just over 400,000 individuals, allowing researchers to analyze patterns, dynamics, and trajectories over time.

The HP datasets are compiled from the public data releases of the LSMS-ISA surveys, which can be downloaded from the World Bank’s Microdata Library (see Table 1). The HP consists of four datasets, one containing records of households, one of individuals, one of agricultural plots, and one of crops on each plot. The preparation of the HP consisted of constructing, cleaning, and harmonizing close to 150 agricultural, household, and individual indicators with the objective of creating a data asset that is ready for analysis. The datasets are fully mergeable with publicly available raw LSMS-ISA datasets such that users can add additional variables from the LSMS-ISA surveys, and tailor the dataset to according to their research needs. Household, community, and farm locations are georeferenced, so that the datasets can be enriched with geospatial information. The HP datasets allow for highly disaggregated analyses at the 2
# **4.4 Implications for the Cost of Delivering Training**

Consider a government or NGO deciding on how to best price training in order to train a target number of firms at the lowest possible cost. For example, if JBDC wants to train 1,000 firms like those in the BDM sample, what price should it charge to minimize its costs of doing so? The answer depends not just on the cost of training, but also on the costs of recruitment and training demonstrations, and on the impact of price on having people show up and attend training. It costs US$25,117 to recruit 831 firms to attend demonstration sessions. This includes the costs of telemarketers to call firms, advertising costs, venue hire, JBDC staff travel costs, and paying the trainers to conduct the demonstration sessions.<sup>24</sup> Taken together, this works out to be US$30 per firm that takes part in the training demonstration, or approximately 20 percent of the cost of training.

Table 10 then combines this cost data with the impact of prices on demand (measured here as attending at least one training session) in order to determine the cost of training 1,000 firms at different prices. For example, when training is free, only 1,316 firms need to get recruited to get 1,000 firms to attend training, but the full US$150 cost of training each one needs to be paid, for a total cost of US$189,474 for firms like those in the BDM sample. Increasing the price to J$3,000 (US$22.5) increases recruitment costs, since 1,587 firms need to be recruited to have 1,000 firms attend training, but these firms then cover 15% of the cost of their training, for a total cost of US$175,119. However, when training is priced at full cost, it is very expensive to recruit firms, and so even
definition on SOEs to allow cross-country comparison, it is relevant to have a unified conceptual framework to classify economic sectors based on the rationale for government presence in the provision of goods and services to inform assessments of SOEs, guide reform efforts in individual countries, and facilitate cross-country analysis of SOE presence in the economy.<sup>12</sup> Although several examples of sectoral analysis of SOEs can be found in the literature and efforts are being made by specific countries to provide a framework for analyzing the rationale behind SOE presence in various sectors of the economy,<sup>13</sup> a globally accepted approach guided by the economic literature has been missing. As a result, most SOE analysis remains either restricted to specific sectors (i.e., a number of studies cover universally accepted sectors in which SOEs are present across countries such as electricity and water

> 11 Sanchez-Navarro, Goodwin, & Kikeri (2021) present a full description of the different alternatives for reform building on this taxonomy is presented in the CPSD SOE Knowledge note indicating the different menu of options of reform. For instance, it proposes management arrangements and concessions can be more suitable for natural monopoly sectors, whereas divestiture may be more suitable for large SOEs in competitive sectors that could be served by (or in competition with) the private sector.

> 12 A caveat of previous academic literature and policy-related workstreams is the lack of a uniform definition of SOEs across countries and between economic activities, which hinders cross-country comparisons and analyses of SOE performance. To resolve this lack of harmonization, Dall’Olio et. al. (2022) building on the International Monetary Fund’s Government Finance Statistics Manual (2014) proposes a corporation as an SOE based on the government’s share of control for a corporation as well as the corporation’s role in the market. That is, an entity is considered a StateOwned Enterprise if it satisfies the following conditions:

> i. It is controlled by government units or by other public corporations
# **Identification strategy, sample selection and data**

There are few surveys in Egypt including information on both health and labor market variables. Several rounds of the Demographic and Health Survey (DHS) focus mainly on health and only little attention is paid to labor. EHHUES 2002 is relatively rich in both dimensions, as it includes information on occupation, hours worked, cash and in kind payments, frequency of payments, although its primary focus is on health conditions, health care utilization and expenditure. Unfortunately this survey is cross-sectional only, further complicating the challenge of identifying the causal impact of chronic conditions. It has long been recognized that especially education and health are likely to be correlated with unobservable individual characteristics. These same individual characteristics are also determinants of individual productivity and individual propensity to supply work. Therefore, the omission of unobservables would make both education and health endogenous. A strategy to deal with unobservables is that of removing them by exploiting the within-individual variation, i.e. by exploiting the variations over-time in labor market outcomes and health of each individual. As unobservables can often be assumed constant, within-individual estimators, such as first-difference, are able to remove them from the model and still to identify the structural parameters. In our context, within-individual estimators would net out both idiosyncratic and common components of ability. This option is ruled out by the cross-sectional nature of our data. Another possible identification strategy is that of Instrumental Variables (IV). Here we make use of them only to solve additional problem of measurement error – as we shall discuss below –, but IV methods are often used to disentangle the impact of health and education from that of unobservables. The EHHUES2002 does not include variables suitable to be used as instruments, and external data, for instance, on reforms are difficult to find. Although the levels of enforcement of any reform in different governorates are likely to differ, increasing the attractiveness of IV, reliable and complete data on the effective enforcement at different times and places are difficult to obtain
exports to the region. In contrast, if the ...rms of non-participating countries are also entitled to access the entire region by conforming to the least costly standard, then they too reap bene...ts. In this case, it is shown that ...rms from the set of countries ("developing") where it is more costly to meet a standard may bene...t more, because there is a greater reduction in their compliance costs thanks to the decline in the stringency of the standard.

In order to test the empirical validity of these propositions, we construct the ...rst dataset that directly identi...es policy initiatives of di¤erent types on standards for manufacturing industries in 42 countries over the period of 1986-2001.<sup>8</sup> These include all OECD countries and 14 developing countries that are the largest exporters of manufactured goods outside the OECD and account for over 80% of non-OECD manufactured exports. The

> 8 In contrast, the existing Perinorm dataset describes standards and technical regulations, but does not allow an unambiguous measurement of harmonization or mutual recognition.

5
on Efficiency, Implementation and Equity Concerns," funded<sup>by the research committee of the</sup> World Bank will address this issue.

This literature review then serves to provide the necessary foundation<sup>of existing nornative and</sup> positive studies relevant to pricing irrigation water<sup>as related to this project and will be included</sup> as a chapter in the aforementioned project. In addition, it is hoped<sup>that the included literature and</sup> discussion will provide a useful reference and foundation<sup>of relevant pricing issues for irrigation</sup> practitioners.

Tsur and Dinar (1995, 1997) analyzed different pricing<sup>practices vis-a-vis their efficiency</sup> performance, cost of implementation, and equity effects. Along<sup>these lines, this literature survey</sup> seeks to review and synthesize the most relevant and current research<sup>available pertaining to the</sup> many aspects of irrigation water pricing. The body of literature<sup>examining these movements is</sup> vast and diverse. Most works are normative in nature, dealing<sup>with how water should be priced,</sup> with some description of actual practicalities and applications.<sup>A few are purely description</sup> (e.g., Dinar and Subramanian, 1997). There are many compilations,<sup>which include the literature</sup> of a particular aspect of water pricing and irrigation:

- _Conflict and Cooperation on Trans-Boundary Water Resources,_<sup>edited by Richard Just</sup> and Sinaia Netanyahu.

- _Markets for Water: Potential and Performance,_ edited by K.<sup>William Easter, Mark W.</sup> Rosegrant, and Ariel Dinar.

- _Decentralization and Coordination of Water Resource Management,_<sup>edited by Douglas</sup> D. Parker and Yacov Tsur.
<mark>Other inputs, recorded at the plot level, include land input (cultivated plot and farm area in hectares); labor inputs including labor days spent on the plot by family members, total labor days spent on the plot including by hired labor and free labor from non-family members, hired labor value; use of organic and inorganic fertilizer inputs, inorganic fertilizer quantity in nitrogen equivalent, inorganic fertilizer value. We also include information on the number of seasonal crops grown on the plot, which was the “main crop” (i.e. the crop type with the highest value of production on the plot) and the share of output attribute to the “main crop” type. Agricultural asset indices are computed using a principal component analysis (PCA), quantifying asset ownership in single dimensions drawn from inventories of household agricultural assets (either from agriculture or household questionnaire). A regression method is used to predict factor scores</mark> (Rencher, 2002).

<mark>Variables on the production environment and practices include crop shocks; irrigation status of the plot; use of erosion protection techniques; use of a tractor on the plot and plot ownership modalities, including plot ownership and possession of a formal title for the plot; livestock ownership; perennial crop production; number of plots under management (cultivated or not); and number of plots left fallow by the household in the current agricultural season; beginning and end dates of the current agricultural season.</mark> <mark>We draw on the household questionnaire to create variables describing plot manager characteristics (the plot manager is the household member mainly responsible for the cultivation of the plot), which include plot manager age, education, and gender.</mark> <mark>All variables described above are based on farmer self-reports. In addition, we include key geospatial variables, which are provided as part of the publicly available data files. These include agro-ecological zones, distance of the household from the nearest market and population
method to estimate vulnerability from cross-sectional or short panel data, thus resolving the problem of missing lengthy panel data (see, e.g., Chaudhuri, Jalan, and Suryahadi, 2002; for applications). The two key features of the model are (i) the error term in the consumption regression or the unexplained variance in the consumption of otherwise identical households, is decomposed into household-specific and community (woreda)-specific shocks to household consumption; and (ii) the variance of these two types of shocks is then modeled as a function of observable household and community (woreda) characteristics.<sup>8</sup> community (woreda) characteristics.<sup>8</sup> Specifically, let denote households at level one and denote communities (woredas) at level two, with households being nested within communities (woredas). Consumption, ii= 1, ... , NN jj= 1, ... , JJ _c_ , of household _i_ in community _j_ is specified as _c_ , of household _i_ l i i i 0ii 1ii cc = ββ + ββ XX + ee Substituting (2) & (3) into (1) yields the regression equation l i 00 01 10 11 i i i (4a) ii ii 0ii 1ii Thus, there are 3 error terms to be estimated cc = γγ + γγ ZZ + �γγ + γγ ZZ �XX + uu + uu , XX , and + ee i whereby i captures the idiosyncratic shocks and i capture the community covariate shocks. Specifically, 0ii 1ii captures the uu uu ee ee <u>direct effect of covariate shocks affecting the intercept of each community and thus all households</u> 0ii 1ii 0ii uu aallaa uu XX uu in the same community in the same manner, and i captures the indirect effect of covariate shocks. Equation (4a) may be rewritten as equation (4b) below whereby the term 1ii i , the indirect uu XX effect of covariate shocks, introduces
growth poles have led to a huge reduction in global poverty but not in the poorest region of the world**

||China|India|SSA|
|---|---|---|---|
|1980 - Incomeper capita,PPP(constant 2005 International dollars)|$524|$895|$1789|
|2005 - Incomeper capita, ,PPP(constant 2005 International dollars)|$4100|$2300|$1759|
|1980: % ofpopulation below$1.25/day|85%|65%|53%|
|2005: % ofpopulation below$1.25/day|16%|41%|51%|
|1980: Population belowpovertyline|835,062,177|462,737,493|212,499,751|
|2005: Population belowpovertyline|207,552,224|455,784,361|384,212,419|
|_Source: World Development Indicators, 2011_.||||

# **Section 1.2: Structural transformation and catch-up**

The path to prosperity in the developed countries was marked by a process of structural transformation reflected in the structure of employment and value-added in primary, secondary and tertiary industries. Long term economic trends from the pre-industrial stage of development in a large number of developed countries confirm that at the end of each episode of catch-up, the fast grower‘s economy had a structure which was closer to that of a developed as opposed to a low-income country (Syrquin 1988, page 206; Chenery 1979, page xvi; Abramowitz 1983, page 6
people’s reporting or people’s behaviors?_ Experience with measuring microenterprise profits suggests that respondents may report more accurately after one or two rounds of surveys, perhaps as they better understand the concepts being asked and can recall them better when asked for a second or third time (Samphantharak and Townsend, 2009; Fafchamps et al, 2010a). This suggests a benefit to researchers of conducting more than one pre-treatment survey. However, in some circumstances there will be a concern that asking the same question multiple times will

> 10 Researchers interested in time since treatment effects can then combine multiple measures around 1 year, around 2 years, etc. See De Mel et al. (2008) for an example, in which quarterly waves are combined to look at how treatment effects vary over the first year since treatment, second year since treatment, etc.

22
of non-agriculture workers|-0.25|-0.06|.05*|0.15|-.27***|0.08|
|Agriculture income|-5.27***|-2.21***|-6.74***|-3.12***|-7.12***|-3.28***|
|Non-agriculture income|-14.51***|-7.01***|-7.96***|-3.28***|-13.85***|-7.04***|
|Capital income|1.25***|1.24***|-1.81*|-0.15|2.18***|1.51***|
|Remittances|-5.49***|-2.81***|-2.48***|-.79***|-6.4***|-3.46***|
|Total effect|-33.13|-14.13|-24.34|-9.01|-32.32|-14.03|

_*p<0.05, **p<0.01, ***p<0.00._ Source: World Bank Staff Calculation using 1995 and 2010 NLSS 33
# **8  Conclusions and Discussion**

This study suggests that, where human capital is scarce, social capital might have important implications for child development. According to my findings an 11 months old child whose mother has no formal education but is a member of at least one community organization is 1.1 centimeters taller than a child whose mother has the same educational level but does not participate in any community group. This effect is quite sizable considering that it is equivalent to the effect of maternal education, which has been proved to be one of the main child development determinates.

Looking at maternal membership when the child is 5 years old, there is no evidence of a statistically significant association with child nutritional status. There are different alternative explanations which could justify this result. For example, it might be the case that maternal membership is more effective when the child is younger because some of the community organizations organize initiatives directly intended to help mothers with new born children. Alternatively, this result might be consistent with the theory that early investments in children are more effective than investments in later stages.

Looking at the pathways through which social participation affects child HAZ, descriptive statistics suggest that social participation facilitates access to resources by expanding mothers’ network and because being a member is a pre-requisite to access additional resources. However, the support received does not seem to be a pathway through which the association between maternal membership and child nutritional status flows. Alternative pathways involving no tangible elements such as the acquisition of useful information or a change in attitudes and investment behavior might play a crucial role in explaining the association between maternal social capital and child development, as suggested by previous literature. However, identify causality is difficult and data limitations do not allow for further exploration of this hypothesis.

This study represents a step forward in the comprehension of the role of social capital in child development. In fact, it is the first comprehensive study of the impact of social capital on child nutritional status using data from a developing country. Furthermore,
as several of the low-income countries in debt distress have found it difficult to contain inflation to single digits.

The preceding narrative about current global fragilities places in context the topic I wish to address: Economic and financial crises in their various “guises or varieties.” Historically,

> 6 World Bank (2020c). The country risk ratings and other details are available at https://www.worldbank.org/en/topic/debt/brief/covid-19-debt-service-suspension-initiative.

> 7 Based on Fitch, Moody’s, and Standard and Poor’s sovereign ratings.

> 8 See Farah-Yacoub et al. (2021, forthcoming) for an encompassing database on sovereign defaults over 1800-2020.

6
by households appear to be regressive since non-lifeline consumers will in principle receive high-income transfers due to their relatively high consumption. However, we show that the extension of the subsidy from the initial three months to 12 months, for [only] lifeline customers, made the subsidy progressive than regressive.

Using the actual consumption of both consumer groups, we compute the [expected] subsidy (implicit transfer) to the median lifeline and non-lifeline customers (households) during the period as shown in Figure **??** . First, we compute the monthly subsidy received by the median household in each consumer group between April and December 2020 by multiplying the subsidy rate by their respective average monthly consumption. Averaging this over the period, our estimates suggest that on average, the median lifeline and non-lifeline household received GHS 12 (USD 2.14) and GHS 29 (USD 5.18) per month these over the 12 months that lifeline consumers respectively.<sup>26</sup> By aggregating period received the subsidies, the median lifeline customer received a total of GHS 139 (USD 25). On the other hand, non-lifeline consumers received only GHS 86 (USD 15) over their three-month subsidy period. Thus, cumulatively, the total subsidies to lifeline customers were about 67% higher than those to non-lifeline customers. Admittedly, these estimates are only indicative, and may not reflect the true amount received by households largely because the subsidies were administered at the meter level, and given the prevalence of shared metering particularly among low-income households, the share of the transfer received by households may in practice be much lower (Berkouwer et al., 2022).

# **6 Discussion and Conclusion**

This paper examines the effect of the COVID pandemic on electricity consumption patterns among households and firms in Sub-Saharan Africa using administrative data on billing records from Ghana and Rwanda. We also explore the effects of SAH orders (lockdown) and pandemic relief measures such as electricity subsidies on household consumption.

Six main results emerge