rafmacalaba/data-use-mentions-extended huggingface dataset:
Table 1: Statistics of poverty estimates in the focus countries.

|**Country**|**Burkina**<br>**Faso**|**Chad**|**Guinea**|**Mali**|**Niger**|
|---|---|---|---|---|---|
|Year of most<br>recent<br>census|2018|2009|2014|2009|2012|
|**Regions**||||||
|Number of<br>regions in<br>sample|13|22|8|9|8|
|Number of<br>regions in<br>census|13|23|8|9|8|
|Median cve<br>of sample<br>estimates of<br>headcount<br>poverty rates<br>for regions|0.114|0.123|0.069|0.085|0.079|
|**Target area**||||||
|Name of<br>target area|Commune|Department|Subprefecture|Commune|Commune|
|Number of<br>target areas<br>(population)|351|112|343|704|266|
|Number of<br>target areas<br>(sample)|234|99|251|244|228|
|Median cve<br>of sample<br>estimates of<br>headcount<br>poverty rates<br>for target<br>areas|0.435|0.271|0.370|0.415|0.425|

Notes: Sample estimates are obtained from the 2018 round of the EHCVM in each country. Median cve of the sample estimates refers to the median estimated coefficient of variation across target areas. Departments in Chad are
improved status of women. Methods employed to test these hypotheses include instrumental variables, careful construction of control groups (e.g., through propensity score matching), differencein-difference, and fixed effects estimation based on panel data. Because these are recent studies, most results have not yet appeared in the peer-reviewed literature. They include Gonzalez-Eiras and Rossi (2007) on health outcomes in Argentina; IEG (2008) on health knowledge and behaviors in a crosssection of countries; ADB (2010) on a range of health, education, time allocation, and empowerment outcomes in Bhutan; Khandekar et al. 2009a and b on education in Bangladesh and Vietnam; Jensen and Oster (2009) on the impact of cable television (made possible by electrification) on women‘s status in rural India; and perhaps most often cited, Dinkelman (forthcoming) on employment in South Africa and Grogan and Sadanand (2009) on fertility in Guatemala. With the exception of Gonzalez-Eiras and Rossi (2007), most of these studies focus on rural electrification, which is more likely than urban electricity projects to present sufficient variation in space and time to allow identification of impacts. In the following sections, we summarize the evidence from these studies of gender-differentiated impacts of electricity, beginning with the impacts of electricity in the home and ending with impacts of electricity in the community and of women‘s involvement on the supply side of energy, including motive energy. The specific methodologies underlying these findings are reported in footnotes.

# **_4.2.2 Time allocation and employment_**

**A few studies convincingly identify gender-differentiated, causal impacts of rural electrification. Dinkelman finds that electrification of rural communities in South Africa results in a 9 percentage point increase in female employment, but no comparable increase in male employment (Dinkelman forthcoming).  Grogan and Sadanand (2009) obtain a similar estimate of a 9 percentage-point increase in female employment, versus no impact on male employment, from rural electrification in Guatemala.  Both studies attribute this largely to
# **2a. Survey Design**

This research is based on a cross-sectional study conducted across the state of Jharkhand over an eight-week period in January to February 2015. Study participants were selected by using a multistage cluster sampling method. To determine an appropriate statewide sample size, we assumed a conservative prevalence of key outcomes such as “NEET” (not in training, education, or employment) and symptoms of depression of 15%. A minimum sample size of 3,057 individuals was needed for an assumed precision level of 5%, 80% power with Type 1 error of 5%, a design effect of 2, and a 10% non-response rate. To allow for subgroup estimates and over-sampling of urban areas for urban estimates, a sample size of 3,900 was targeted, and 3,942 were surveyed. Ultimately, 150 enumeration areas (EA) were randomly selected proportionate to size from a Census 2011 data set, out of which 105 were in villages (rural) and 45 were in towns (urban). Among the 150 selected EAs, one was inaccessible due to Maoist insurgence and was replaced using the same method.

Within each EA, 26 households were selected using a geographic house listing method. In case of large EAs having more than 300 households, EAs were divided into segments of 150 or more households, where one segment was randomly selected for house listing. In the rest of the EAs, the entire households in the EAs were listed. To ensure adequate representation across age groups, listed households were stratified according to the presence of at least one adolescent girl or young woman within the specified age groups (11-14, 15-17, 18-21, and 22-24). Interviewers used a randomization mechanism to pick a direction and the number of dwellings to pass in order to reach the first sampling unit. Interviewers then selected every other dwelling in that direction applying a systematic random sampling method until all 26 surveys within the EAs were completed. A total of 4,559 dwellings were approached;
<!-- Start of picture text --> (c) Distribution of Extended Agricultural (d) Distribution of Restricted Agricultural<br>Spending (%) Spending (%)<br>100%<br>100%<br>90%<br>90%<br>80% 80%<br>70% 70%<br>60% 60%<br>50% 50%<br>40% 40%<br>30% 30%<br>20% 20%<br>10% 10%<br>0% 0%<br>Current Investment Current Investment<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2000 2001 2002 2003 2004 2005 2006 2007 2008<br><!-- End of picture text --> Note: In Figure 3b, 3c and 3d, ―restricted‖ spending refers to narrowly defined agricultural spending (including spending in core public goods, such as research, extension, irrigation), and ―extended‖ refers to rural development spending broadly defined (including expenditures in productive infrastructure, such as roads, rural electrification, etc.). The detailed definition is provided in Annex 2. ― _Prefectura_ ‖ refers to the departmental government, currently called ̳Gobernaciones‘.

Source: World Bank APER (2010).

**Figure 4. Per Capita Agricultural Spending by Department (constant 2005 prices)**

<!-- Start of picture text --> Chuquisaca La Paz Cochabamba Oruro Potosi Tarija Santa Cruz Beni Pando<br>2003 2007<br>0<br><!-- End of picture text --> Source: Authors from World Bank (2010) APER database.

The increasing trend of per capita agricultural public spending, however, is not uniform across departments (Figure 4). This is partly due to transfer mechanisms currently in Bolivia. Central government transfers constitute almost the sole source of income for departmental governments and the largest
# **2. Access to community-level disaster preparedness mechanisms**

Most households live in communities where communal-level preparedness is low. By the end of 2021, only 9 percent of households had access to an emergency plan, and less than 25 percent of households had committees or community-trained members for disaster response (Figure 6a). The most common, but still scarce community coping mechanisms, were the evacuation routes (61 percent) and emergency shelters<sup>10</sup> (47 percent). Between the end of 2021 and early 2023 there was an increase of at least 2 percentage points in the proportion of households that reported having access to all community coping mechanisms except for shelters, with a 2-percentage-point drop (Figure 6). Nevertheless, rural households are less prepared than urban households for some mechanisms. Even though the difference is often less than 5 percentage points, in 2023, this gap is closed, and rural households show a better level of preparation than in previous years.

**Figure 6: Community disaster mechanisms by household’s area of residence (%) – (2021-2023)** a) By sex of head of household (2021)                                    b) By sex of head of household (2022)

<!-- Start of picture text --> 61.259.660.6 66.665 66<br>47.74546.7 44.2<br>43.1 43.5<br>26.7<br>21.326.523.3 17.62219.3 26.3 26.4 2223.8 22.6<br>9.6 8.5 9.1 9.8 8.8 9.5<br>Evacuation Shelter Trained Committee Emergency Evacuation Shelter Trained Committee Emergency<br>routes community plan routes community plan<br>Urban Rural Total Urban Rural Total<br>c) By sex of head of household (2023)<br>63.3<
_**|0.237|0.35|
|**_ES9_**|11.33|11.73||**_IGME9 _**|0.322|0.35|
|**_ES16_**|14.25|14.18||**_IGME16_**|0.521|0.35|

Notes: (1) The data used are CFPS 2016 with children aged 18-30. (2) IGMEK is Intergenerational Marginal effect when father has K years of schooling, (3) ESK is expected schooling when father has K years of schooling. K=0,6,9,16. (4) Robust standard errors in parentheses; standard errors clustered at province-level in square brackets; *** p<0.01, ** p<0.05, * p<0.1.
one other survey that addressed the same issue. In a 1996 survey of 900 lower-income urban households, Caskey (1997) asked households without deposit accounts, why they do not have an account. He provided respondents with a list of possible reasons from which they could select one or more. They could also provide a reason that was not on the list. As shown in Table 6, 53 percent of the respondents cited "don't need account because we have no savings" as a reason, making this the most frequent reason cited; another 45 percent cited bank fees or minimum balance requirements.

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use to balance the data and summarizes our results. The final section shows how we disaggregate two services sectors.

# **Construction of the input-output table**

We follow Light (2010) and the structure of the supply-use table in the construction of the initial estimate of the input-output table. Figure 1 shows the structure of a typical inputoutput table. There are three major matrices in the table: Value-added, intermediate use, and final demand.

The supply-use table and the official GDP data are reported with details for 16 sectors. The intermediate use matrix thus has 16 rows and 16 columns. Correspondingly, the final demand matrix has 16 rows, and the value-added matrix has 16 columns.

Figure 1. The structure of an input-output table <!-- Start of picture text --> Intermediate use Final demand<br>Value-added<br><!-- End of picture text --> The value-added data in the supply-use table is taken directly from official GDP data for 2006. We estimate the matrix with value-added data in our input-output table using the same type of data, but update to 2007.

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3 describes the data used, the variables generated, and the empirical strategy this analysis employs. Section 4 presents the main results. Section 5 discusses conclusions, policy implications, and offers recommendations.

# 2. Background and related literature

This paper contributes to a large body of literature exploring the effect of respondent recall on measurement accuracy in survey data. Systematic reviews of this literature are provided in Biemer et al. (2011), Bound et al. (2001), Groves and Lyberg (2010), and Meyer et al. (2015), among others. The role of recall error in survey data has been explored in a range of topics, including consumption expenditure and food intake (e.g. Beegle et al., 2012b; Troubat and Grünberger, 2017; Backiny-Yetna et al., 2017; Brzozowski et al., 2017; Engle-Stone et al., 2017; D’Alessio, 2017; Schündeln, 2018; Zezza et al., 2017), household enterprises (De Mel et al., 2009; Liedholm, 1991) and income measurement (Moore et al., 2000).

One strand of the literature focuses on the cognitive processes underlying survey response and has built an understanding of how these can lead to measurement error. Sudman and Bradburn (1973) distinguish between _recall decay_ and _telescoping_ . Telescoping refers to inaccurately remembering _timing_ of events, leading to an event of interest incorrectly being moved into the reference period, which, in turn, may lead

> at the end of the main agricultural season, complemented by an Economy module implemented in four visits during the agricultural year. Similarly, the Uganda Annual Agricultural Survey 2019 (AAS), is planned with a post-planting and a post-harvest visit in both agricultural seasons, for a total of four visits.  In contrast, many LSMS-ISA surveys are now implementing two visits, one at the end of the main planting season and one at the end of the main harvest. Finally, some specialized farm surveys visit households/farms
general paucity of data on earnings or wages in Azerbaijan and other countries, especially among young workers, means that SWT data provide a rare opportunity to assess the emergence of gender wage differentials.

## **2.2. Variables**

> 2 The survey also includes a questionnaire administered to the employers of young people in the sample. The Employer and Managers Module of the survey includes detailed information about the characteristics of enterprises including their recruitment, hiring, and training practices.

> 3 For further information on the SWT survey, see the following website: <u>http://www.ilo.org/employment/areas/WCMS_159352/lang--en/index.htm.</u>

4
choice of welfare indicator which is used to distribute resources _across_ districts. Under this assumption, which seems quite reasonable, the actual estimates of changes in poverty under different targeting regimes will be biased (up, if there is positive intra-district targeting, down if the converse is true), but the preferred rank-order of the indicators used to assign resources _across_ districts should be unaffected.

The simulations assume that benefits from program investments in a district accrue entirely to the residents of that district. This might not hold, say, if beneficiaries of a food distribution program implemented in one district are residents of a different district. But intra-district spillovers are unlikely to be systematic--that is, they should not consistently favor residents of one kind of district over residents of another. Intra-district spill-overs should therefore not affect the rank order of indicators either.

Some additional assumptions have to be made about the impact of transfers on various poverty measures. Poverty in Peru has generally been defined as an individual's inability to meet a specified level of expenditures-the poverty line-when individual expenditures are approximated by total household expenditures divided by the number of eligible household members.<sup>6</sup> We must therefore translate expenditures by social programs into household expenditures--by first translating program expenditures into changes in household income, and then estimating the proportion of additional disposable income that is spent. As a matter of convenience, I have assumed that all program expenditures translate into additional household income and that all of this additional income is spent7 Finally, the simulations assume that the cost of administering programs is constant across regions, and ignore the effects of transfers on behavior such as migration towards districts which 6 The 1995 and 1996 INEI household surveys, which used income to measure poverty, are exceptions. 7Two points are worth noting here. First, many social programs in Peru are involved with the construction of small-scale infrastructure. The wages paid to laborers in these projects are only a fraction of the total cost, and other benefits--say, of having
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Aguayo-Tellez, Ernesto, Jim Airola and <u>Chinhui Juhn</u> (2010), “Did Trade Liberalization Help Women? The Case of Mexico in the 1990s,” NBER Working Paper No. 16195. Alesina, Alberto and Romain Wacziarg (1998), “Openness, Country Size and Government,” _Journal of Public Economics_ , 69(3): 305-321.

Altonji, J. G. and R. M. Blank (1999), "Race and Gender in the Labor Market," Handbook of Labor Economics, Volume 3, Eds. O. Ashenfelter and D. Card. Amsterdam: Elsevier Science.

Amin, M. and Soh, Y. C. (2020). Does Greater Regulatory Burden Lead to More Corruption? Evidence Using Firm-Level Survey Data for Developing Countries. Forthcoming _World Bank Economic Review_ . <u><mark>https://doi.org/10.1093/wber/lhaa007</mark></u> Amin, Mohammad and Asif Islam (2015), “Does Mandating Nondiscrimination in Hiring Practices Influence Women's Employment? Evidence Using Firm-level Data,”
a 24.4% yield decline relative to the historical average (Appendix C.D).<sup>31</sup> Figure IIb shows that rice imports were negligible before the import ban, as Sri Lanka had achieved self-sufficiency in rice production, but, starting in late 2021, rice imports surged up to January 2022, remaining elevated throughout the rest of the year. Even this surge was not enough to fully make up for lost production, though, with the availability of rice for domestic consumption falling by 22% between 2021 and 2022.<sup>32</sup> Figure III presents rice yields at the DS level for the last Maha season before the ban (September

> internationally traded commodities such as fertilizer.

> 29Both outcome variables (extensive and intensive margins) are deseasonalized by subtracting their HS8 productmonth-specific averages in 2017-2019, before the fertilizer ban.

> 30The Maha season is the main rice cultivation season in Sri Lanka, running from September to March of the following year (the second season, Yala, runs from May to August).

> 31Ozdogan et al. (2024) confirms that the notable decline in remote sensing rice yield estimates over the 2021-2022 period cannot be explained by variation in environmental variables and is directly correlated with the reduction in fertilizer availability ensuing from the import ban.

32We define the amount of rice available for domestic consumption in a given year as the sum of production and net imports. This amount fell from 5.2 billion kg in 2021 to 4 billion kg in 2022. Since we lack trade data for November and December 2022, we instead compute net imports for 2022 based on the period from November 2021 to October 2022. Correspondingly, net imports for 2021 are computed based on the period from November 2020 to October 2021. If there had been no change in net imports, rice availability in 2022 would have been 3.40 billion kg. Therefore, the import surge offset one third of the production losses.
, ceramic, painted or ... Scallops<br>Other knitted fabrics, cotton fabrics Evaporated milk without sugar or sweetener<br>Preforms bottles Other preparations used for animal feed<br>Other garments, knitted cotton Other cotton sweaters<br>Bar of iron or non-alloy steel with indentations or ...Zinc, not alloyed, with a lower zinc content of ... Bananas like "cavendish valery" freshOther molluscs and aquatic invertebrates, ...<br><!-- End of picture text --> _Source: SUNAT - MEF, Own Elaboration_ Finally, the analysis turns to the diversification of the destination export markets for which a HerfindahlHirschman Index can be calculated for total, traditional and non-traditional exports (See Figure 12). While the total index seems to be dominated by the evolution and value of the traditional grouping, it is significant to note that destination concentration tended to be higher during the bulk of the period; The index follows two phases, one of declining diversification until about 2004, and then a sharp reversal ending in 2009. However, when looking at non-traditional exports, a greater diversification trend is evident since 2004, which possibly can be related to the gradual maturation of market-friendly reforms and greater private sector expectations in a context of both a growing international and domestic, which promoted the rapid expansion of new products and destinations. By contrast, the index of traditional exports exhibits less dramatic swings between 1993 and 2009, as the only significant novelty has been the addition of only one new significant trading destination to the traditional list – i.e, the People‟s Republic of China, for which exports from Peru increased from 5.5% in 1993 to 19.1% in 2010.

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# _(B) Uzbekistan_

<!-- Start of picture text --> Self-Reported Vaccination Intentions in Uzbekistan<br>Apr-22<br>Mar-22<br>Feb-22<br>Jan-22<br>Dec-21<br>Nov-21<br>Oct-21<br>Sep-21<br>Aug-21<br>Jul-21<br>Jun-21<br>May-21<br>Apr-21<br>0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%<br>Definite yes Lean yes Lean no Definite no<br><!-- End of picture text -->

# _(C) Tajikistan_

<!-- Start of picture text --> Self-Reported Vaccination Intentions in Tajikistan<br>Mar-22<br>Feb-22<br>Jan-22<br>Dec-21<br>Nov-21<br>Oct-21<br>Sep-21<br>Aug-21<br>Jul-21<br>0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%<br>Definite or Already Lean yes Lean no Definite no<br><!-- End of picture text --> _Source: World Bank “Listening to Central Asia” Question: “If any vaccine to prevent COVID-19 were available today, would you...” Options included: a) Definitely get the vaccine, b) Probably get the vaccine, c) Probably NOT get the vaccine, d) Definitely NOT get the vaccine, e) Already got the vaccine, f) Do not know._ As each of the three surveys uses a panel design, individual responses can be tracked over time to identify transitions in responses. Identifying these changes is particularly useful to describe changes in views among previously vaccine hesitant respondents. Table 1 panel (A) shows the panel transition matrix of reasons for vaccination refusal from April 2021 to May 2022 in Kazakhstan, panel (B) shows the same for Uzbekistan
in the form of social assistance transfers that include provision of food (inkind or vouchers) and school feeding can be leveraged to direct food system change towards desirable outcomes and to manage trade-offs, including among specific target groups. Schools can serve as a good platform to ensure access to safe and nutritious school meals which along with education and social and behavior change communication can support the establishment of healthy habits from early in life.

# **4.4 Demand-side considerations**

The diet in Bangladesh is increasingly reflecting an urban lifestyle of convenient foods and an over consumption of rice and foods rich in salt, sugar, and oil <mark>(Cabinet Division of the People’s Republic of Bangladesh and WFP 2019)</mark> . The Fill the Nutrient Gap analysis in Bangladesh <mark>(Cabinet Division of the People’s Republic of Bangladesh and WFP 2019)</mark> showed low consumption of fruit, vegetables, and animal source foods due to additional time and labor required to source and prepare the foods, higher consumer trust in packaged foods and concerns related to food safety of fresh fruits and vegetables. Nutritious foods, especially green leafy and other

> 13 The discussion is further elaborated by two of the authors of this document in Mehra et al. 2021. “Greening Food Demand in Less-Advanced Economies” in _The Economics of Sustainable Food,_ edited by Batini, N, 140-154. Washington, DC: Island Press.

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