rafmacalaba/data-use-mentions-extended huggingface dataset:
members in panel surveys. See Rendtel and Harms (2009) for a discussion of several different methods of weight correction.

Therefore, for any given household with new members, there are two ways that a household can become part of the survey: either by being selected initially for round 1 (and during the subsequent rounds of subselection), or by receiving a member that came from a household that was selected for round 1 (and during the subsequent rounds of sub-selection). In order to properly account for the household’s current probability of selection, it would be necessary to calculate the exact probability of selection for each new member brought into the household, and adjust the weights accordingly. This adjustment is necessary since households receiving members have higher probabilities of selection (and therefore necessarily lower weights) because the household could have been selected into the survey in multiple ways. The precise calculation, however, would be impossible in all but the rarest of circumstances.<sup>8</sup> Since it is not possible to compute round 1 probabilities of selection for every member of each sample household in a subsequent round, simplifying assumptions are necessary to continue with the weight calculations. The first simplifying assumption is that the new members to the survey arrived together from one other household. This would be the situation in most common cases, for example, a man and woman marrying and setting up a new household, or an older relative moving in with adult children. In certain cases, however, arriving members come from more than one household. Therefore, assuming only two source households slightly underestimates the probability of selection (and therefore over-estimates the weights) for those cases where members actually came from more than two households. However, as long as the incidence of these cases is believed to be relatively rare, any resulting bias should be negligible.

The second necessary simplifying assumption is that the arriving members have the same probability of selection, on average, as those original members living in the household to which they come
Policy Research Working Paper 9807

# **Abstract**

Using official COVID-19 death counts for 64 countries and excess death estimates for 41 countries, this paper finds a higher share of pandemic-related deaths in 2020 were at younger ages in middle-income countries compared to high-income countries. People under age 65 constituted on average (1) 11 percent of both official deaths and excess deaths in high-income countries, (2) 40 percent of official deaths and 37 percent of excess deaths in upper-middle-income countries, and (3) 54 percent of official deaths in lower-middle-income countries. These contrasting profiles are due only in part to differences in population age structure. Both COVID-19 and excess death age-mortality curves are flatter in countries with lower incomes. This is a result of some combination of variation in age patterns of infection rates and infection fatality rates. In countries with very low death rates, excess mortality is substantially negative at older ages, suggesting that pandemic-related precautions have lowered non-COVID-19 deaths. Additionally, the United States has a younger distribution of deaths than countries with similar levels of income.

This paper is a product of the Human Development Global Practice and the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http:// www.worldbank.org/prwp. The authors may be contacted at gdemombynes@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations
blood tests. We therefore use only the screening tasks to construct three measures of performance: whether the worker performed any of the screening actions (60 percent of participants), whether she performed all five (22 percent), and the total number of actions she performed (two of five, on average).

The PFP trial started in July 2014, when the two financing interventions were rolled out. The evaluation endline survey that contained our experiment was conducted between August and October 2017. The impact evaluation found that districts with PFP or DFF performed better than those in the control group and the impacts of PFP and DFF were comparable, with few exceptions (Kandpal et al., 2019). Both PFP and DFF had a practically and statistically significant impact on the quantity of key maternal and child health services. For example, both significantly increased fully immunized child coverage and modern contraceptive prevalence. However, directly observed clinical quality of care, which may be most directly related to provider effort, showed limited gains, especially in the DFF arm.

For context, 75 percent of all health workers in our sample reported working seven days a week, for an average of six hours a day. The median monthly gross salary is 43,000 Nigerian Naira or about USD 113. Wages are stagnant and often paid with a delay: only a third of health workers reported receiving a salary increase in the last two years, and a quarter said they had not received their full pay for the previous month. Indeed, 63 percent reported not having received their entire salary for the past year.

# **4.2 Outcomes, moderators, and possible confounders**

Our measure of overall performance is the proportion of clinical actions that participants correctly identified as medically appropriate for each partograph case. Since actions can be clinically indicated or not, we score as correct those actions that are indicated and named by respondents as well as actions that are unnecessary and not named.<sup>5</sup> In other words
percent. Those with an average bound tariff between 30 and 50 percent must bind at 27 percent or less. Those with average bound tariffs between 20 and 30 percent are to bind at an average of 18 percent or less. Those with an average bound tariff below 20 percent must bind at an average rate equal to that arising from a 5 percent cut in 95 percent of tariff lines.

RAMs receive a grace period of 3 years and an extended implementation period of 3 years. In contrast with the case of agriculture, they do not receive smaller cuts in tariffs. However, very recent acceded members benefit from tariff reduction exemption. The NAMA proposal includes provision for sectoral initiatives, for which participation is not mandatory, but agreement is to be reached when 90 percent of world trade is included. In most cases, it is proposed to move to zero tariffs on these products.

# **Specifying Cuts in Tariffs**

To provide a preliminary assessment of the implications of the modalities for the applied protection, we use the MAcMapHS6 version 2.1 database (Boumellassa, Laborde and Mitaritonna, 2009) for 2004 together with a set of bound tariff rates for which _ad valorem_ equivalents have been calculated on the same basis. We first cut the bound tariff rates using the approaches considered in the modalities, and then assess their implications for applied rates. Where the draft agreements involve a range, we generally use the mid-point. The specific choices of parameters used are set out in Table 2. In this analysis, we use the conventional assumption that applied rates are not reduced unless the new bound rate falls below the baseline applied rate<sup>5</sup> , assumed to be the applied rate in the tariff baseline which is which is for 2004 - MAcMapHS6 v2.1 dataset – with several key updates. We take into account for some internationally-binding commitments to reform that will affect the tariffs that would have applied in 2025 in the absence of an agreement. The adjustments to the baseline tariff include WTO commitments
This gender gap in hours of work holds across all seasons: 11hr in the main rainy season, 13hr in the short rainy season, and 7hr in the harvesting season. Moreover, and similar to the extensive margin results, we observe that children reported on average 2.2 (or 9.5%) higher worked hours than the mean hours obtained from the proxy. These self/proxy reporting differences also show variability across seasons: 2.8hr (18%) in the main rainy season, -0.05hr (-0.2%) in the short rainy season, and 4hr (12%) in the harvesting season. These results are statistically significant at 10% for all but the short rainy season. While the unconditional hours spent on-farm activities reported by proxies are lower than the self-reported for both boys and girls, however, we do not observe statistically significant child/proxy discrepancies for girls relative to boys either in the pooled data or in each season separately. Indeed, the coefficient of inter-variability of the self-reported measures of worked hours is twice the size of the coefficient of inter-variability computed for farm labor participation for boys and girls, suggesting the noisier nature of reporting worked hours.

# **4.2 Regression Results**

Tables 3 and 4 depict the OLS results for the 30-day and 7-day recall periods at the extensive (columns 1-3) and intensive (columns 4-6) margins following equations 1 and 2.<sup>14</sup> We test directly whether differences in the reported participation in farm activities are affected by the gender of the child and whether gender survey treatment effects vary across seasons. Results show statistically significant gender gaps for self/proxy reporting in child labor statistics. Column 2 in Table 3 shows that girls have 6.4 percentage points higher discrepancy in the participation of farm activities report compared to boys, or a 13.5% higher rate of participation than the mean participation obtained from the proxy.

18
- **9 Macro-validation and limitations of the analysis**

- **9.1 Macro-validation**

**This section presents an overview of how well each instrument is modeled by comparing survey estimations with official fiscal administrative data.** We call this process “macro-validation” because it allows us to validate our methodology by comparing the results in the survey at the national level with the results in the national accounts (Table 9-1). Note that East Jerusalem is excluded for all the instruments shown in the table as well as for consumption.

- **Total consumption:** As outlined earlier, Disposable Income in this study is equivalent to the household consumption aggregate in the PECS 2016/17. The survey has good coverage of households’ consumption as total consumption in the PECS represented about 81 percent of the official aggregate in national accounts.<sup>77</sup> We expected a slight gap between the survey and macro estimations of household consumption due to methodological differences between the survey and national accounts and both non-response rates of wealthier households and underestimation of income levels of wealthier households (see the section on limitations).

- **Taxes:** All direct taxes simulated in the PECS have good coverage of their respective administrative absolute values; that is, the amounts allocated in the survey are close in size to the amounts reported in the executed budget. The PIT covers 85 percent of the official administrative value of tax collections; pension contributions covered 108 percent and the property tax covered 84 percent. Indirect taxes—customs duties, VAT and fuel excise—covered 66, 95 and 73 percent, respectively, of total administrative value of tax collections. The Property Tax, and the VAT were scaled to match the effective rate relative to consumption (0.03 and 6.6 percent, respectively).

- **Transfers:** The total of CTP and in-kind transfers in the survey covered 65 percent of the administrative data, where income from pensions covered 107 percent (similar
are considered.

As a first step, we conducted a comprehensive literature search to identify primary research articles that included all three components described in our conceptual framework, namely: a diet metric (or a specific dietary factor), a nutrition or health outcome, and economic cost. These criteria yielded a body of literature that was limited in scope, especially in terms of the life stage groups represented.

For this reason, we conducted a second comprehensive literature search that expanded the inclusion criteria to include systematic review papers that assessed the evidence for the contribution of a diet exposure measure to a nutrition or health outcome described in our conceptual framework and reported an estimate of effect size based on a meta-analysis of observational study data. This was expected to help the mapping of evidence from the first literature search in two ways. First, the biggest gap from the first search was the lack of evidence on the contribution of diet to nutrition and health outcomes for the various life stage groups (beyond adulthood). Second, while there are a reasonable number of studies which provide estimates of the cost of undernutrition and overweight/obesity in LMICs (Horton et al. 2024), there are very few studies that incorporate the contribution of the diet to these outcomes. Since conducting a full literature review on the diet and outcome components would yield a very large body of evidence on these relationships without necessarily providing the desired information on the effects, we chose to limit the second search to studies that consolidated this body of evidence in a systematic way, including calculation of effect sizes. See **Table 1** for a PICO summary of the elements that guided Search 2.

## **Eligibility criteria**

Peer-reviewed journal articles were included if they were: published during 2000-2023 (arbitrary cutoff used to focus on evidence produced with a recent timeframe since dietary patterns are changing rapidly in LMICs), written in English, and involved human participants of any age living in a low- or middle-income country (using the World <u>
in Section 5.2, this term can be thought of as the vertical upward shift in the aggregate demand curve caused by a marginal change in benefits affecting SNAP consumers’ demand. For the demand elasticity for food ( _εD_ ), we obtain from the data directly an estimate of -0.709, which falls within the typical range reported in the literature as summarized by Andreyeva et al. (2010).<sup>43</sup> We obtain from USDA data the proportion of sales accounted for by SNAP recipients (<sup>_<u>pQ</u>_</sup> _pQ_<sup>_D,S_):</sup> 0.168. Combining these numbers together with the aforementioned estimates for _εQD,S ,b_ , we obtain a range of [0.0802, 0.124].

In order to calibrate the benefit pass-through elasticity ( _ερ_ ), we consider the last two remaining terms: the unit-subsidy pass-through elasticity and the slope effect from equation (8). For the unit-subsidy pass-through elasticity, we calibrate the term at 0.5 based on Besanko et al. (2005).<sup>44</sup> For the slope effect, which reflects incremental pricing power that producers can exercise based on the benefit-induced change in the slope of aggregate demand, we assume this is close to zero. This assumption follows as a corollary to our previous assumption in Section 5.4 that the inverse aggregate-market-demand curve _p_ ( _Q, b_ ) shifts up by a constant amount in response to a benefit increase.<sup>45</sup> Multiplying the shift-magnitude range by 0.5, we arrive at a range of [0.0401, 0.062]. Comparing the range [0.0401, 0.062] with our reduced-form estimate 0.0724 and its standard error 0.0269 in Table 6, we see that the range falls well within the confidence interval of our reduced-form estimate
activity in the week prior to the interview. Therefore we can identify whether children attend school and/or work. For those who report working, the survey asks the number of hours of work.

Of the original 208 experimental villages, eight are excluded from our analysis. Two villages could not be resurveyed due to concerns for enumerator safety; two villages were incorporated in PAL prior to the pretreatment survey; two villages were deemed ineligible for the experiment because they were receiving the conditional cash transfer program, _Oportunidades_ , contrary to PAL rules; and two villages are geographically contiguous and cannot be regarded as separate villages.<sup>10</sup> Observable characteristics of excluded villages are balanced across treatment arms (results available upon request). Of the remaining 200 villages, three received the wrong treatment (one in-kind village did not receive the program, one cash village received both in-kind and cash transfers, and one control village received in-kind transfers). We include these villages and interpret results as intent-to-treat estimates.

For all children born between 1998 and 2004 - those who were 6 years old or younger at the time of the first PAL survey - we merge individual and household level information both from the baseline and the follow-up evaluation surveys with grades 3 through 6 ENLACE test score data between 2007 and 2013.<sup>11</sup> The ENLACE identifies students through the government-issued identifier, the _Clave Única de Registro Poblacional_ (CURP), which is formed by an algorithm combining the first name, surname, date of birth, sex, state of birth, and two randomly generated digits. The PAL household surveys do not contain the CURP, but do contain all of its constituent demographics from which we generated a quasi-CURP (lacking the random digits). We thus form an unbalanced panel of seven cohorts spanning seven academic years.

Each year, 20 percent of exam takers are randomly selected to complete the _ENLACE de Contexto,_ a multiple choice survey which elicits information about child labor, parental and student sociodemographic characteristics
# **2. Data and Context**

2.1 Household survey data Our main data source is the multi-topic Household Income and Expenditure Survey (HIES) collected by the Liberia Institute of Statistics and Geo-Information Services (LISGIS). We use the latest two rounds, 2014 and 2016. Both rounds are representative at the national level and, for rural areas – i.e., localities with population less than 2,000 -- representative at the county level (Liberia is divided into 15 counties). Both surveys also use the same Enumeration Areas (EAs), but fresh households were selected from each EA in each round.

The 2014 round of the HIES was planned for the full calendar year, but only administered from January through July due to the outbreak of EVD in the country. When fieldwork was halted in August 2014, approximately half of the data had been collected. Fortunately, the HIES sample covered during the first two quarters of 2014 was designed to be nationally representative. The HIES 2016 started in January of that year --- after which time only a few cases of Ebola were reported --- and continued through December.

Both the 2014 and 2016 HIES allow us to construct a household consumption aggregate; we use per capita expenditures to proxy household welfare. Consumption expenditures comprise food and non-food components and are reported in Liberian dollars (see LISGIS 2017 for details). To sum up, our analysis of household welfare impacts will effectively use an EA-level panel between 2014 and 2016, focusing, for purposes of comparability, on households surveyed in the first semester (January-July) of each round.

HIES 2016 provides extensive and detailed information on farm labor and agricultural production based on recall of the last completed farming season. The agriculture module also includes questions about the perceived impact of the Ebola crisis 5
Community Health Workers as Key Providers of Easy-to-Use Contraceptive Injectables: Experimental Evidence from Rural Burundi<sup>1</sup> Michele Andreottola<sup>3</sup> Olivier Basenya<sup>4</sup> Victor Orozco-Olvera<sup>3</sup> Arndt Reichert<sup>5</sup> Paula Spinola<sup>2</sup><sup>_,_6</sup>

# **Key words:** Family planning, fertility preferences, contraceptive injections, community health

1 Computational replicability was verified by the World Bank Development Impact (DIME) Analytics team. We thank the World Bank’s Strategic Impact Evaluation Fund for providing financial support for this research. We are grateful to the Burundi’s Ministry of Health, particularly Cyrille Sindahabaye, Stanislas Nyabenda, Olivier Gahungere, and Anaclet Nahayo, for promptly sharing relevant information on the evaluated intervention. We appreciate the detailed feedback provided by Ann-Kristin Reitmann, Christoph Strupat, and Jan Priebe. We also thank the participants of the 2024 German Health Economics Association conference and the University of Hannover internal seminars. This study was registered with the stated objective of assessing the privacy benefits of Sayana Press at the American Economic Association’s registry for randomized controlled trials (https://www.socialscienceregistry.org/trials/3947). This research received ethical clearance from Solutions IRB under the protocol 0373).

- 2Corresponding author: paulade@iadb.org

- 3DIME, The World Bank.

> 4Burundi Country Office, The World Bank.

- 5Institute of Health Economics, University of Hannover, Germany.

> 6Inter-American Development Bank - IDB.
relative political stability as a peaceful democratic transition took place from Jean-Bertrand Aristide to René Préval.

Although schooling levels in Haiti today remain low, school attainment improved substantially during the last half-century. Among those born 1950-1954, only 35 percent completed even one year of education and just 10 percent finished primary school. Completed schooling levels are much higher among those in later cohorts. The proportion of people who attended at least some school increased to 61 percent for those born 1965-1969, and 88 percent for those born 1980-1984.

A useful way to visualize differences in school attainment by group and changes by cohorts is through survival plots, which indicate the percentage of people who completed _at least_ each level of education. Figure 2 presents these survival estimates using the DHS data from 2005 for our three cohorts. The figure shows a large improvement between our first two cohorts, chiefly in primary education (up to six years of education.). The fraction who completed at least first grade increased by 26 percentage points between the 1950-1954 and 1965-1969 cohorts, but primary school dropout rates remained high, as can be seen by the steep decline of the survival curve over the first few years of schooling. Among those in the second cohort (born 1965-69), only one-third finished primary education. In contrast, in the most recent cohort (born 1980-84), two-thirds completed primary education. Despite the poor state of Haiti’s education system, primary enrollment has taken great strides since the 1950s.

Two relatively sharp drop-offs—following fifth grade and sixth grade—are visible in the profiles for the most recent cohort. The drop-off following sixth grade is common to many countries, corresponding to the transition from primary to secondary. However, the sharp decline after _fifth_ grade is unusual. In the Haitian school system, at the end of the sixth grade, students have the option of taking a national exam. Passing this national exam permits the student to continue to secondary education. Two possibilities exist to explain the drop-off after fifth grade in the data. If