rafmacalaba/data-use-mentions-extended huggingface dataset:
countries as well as emigrants returning and visiting family from high malaria settings could both impact malaria incidence. There are detailed case data available from Richard Toll district where each case was investigated and travel information was included on the infected individual based on survey data. Out of 161 cases, there are 9 cases where the traveler was from outside of Senegal, or only 5.59% of cases. The small impact of international migration likely arises from the fact that while some of Senegal's neighbors such as Mali have a higher malaria incidence (89 cases per 1000), the only international border near the pre-elimination districts studied here is Mauritania, which has a lower incidence rate than northern Senegal of only 0.4 cases per 1000. Nevertheless, future work could try to incorporate the impact of international travelers.

# 7 Conclusion

The paper quanties the negative externality of population movement on disease incidence and reversing gains in elimination in Senegal, and it proposes a cost-eective targeting strategy. This type of study is made possible by new big data collected by telecommunications companies making the measurement of short term movement possible and the initiative taken by the Ministry of Health to collect surveillance data on a monthly level for each health post. These data made it possible to study how short term movement into low-malaria areas increases incidence. The study nds that for each imported case of malaria per 1000, there are around 1.7 cases of malaria per 1000 reported at health post areas in the pre-elimination districts. Using these ndings, I calculate that implementing a policy in certain destination areas and targeting travelers from the most cost-eective districts during particular months results in the largest drop in cases (over 50% as many cases treated or averted as compared to the next best strategy). To our knowledge, this is the rst study to evaluate the cost eectiveness of dierent types of targeting schemes for malaria directed at travelers.

While the work here focuses on malaria, it is possible to implement this type of model
Using the threshold PPMT method presents a trade-off; while almost all of those picked by the method at a very high confidence level would accurately be referred for the government support, it may exclude a significant number of cross-eligible households. The share of cross-eligible households referred by the method, however, would increase by decreasing the confidence level and/or extending the government program to less poorer households (increasing eligibility thresholds).

Moreover, as discussed, the PPMT can also be exploited by applying the more flexible ranking method, which would overcome such a trade-off. The high and significant rank correlations (0.7) between the PPMTs and full PMT (Table A1) suggest that the two formulas order the households similarly and it is appropriate to employ the “ranking” approach. We set the “pre-set” targeting rate same as the MoLSA simulated program size. For instance, for the program targeting households below the poverty line i.e., bottom 18 percent nationally, we set 18 percent as the “pre-set” targeting rate for the ranking method, i.e., the bottom 18 percent of the households in the PPMT score would be regarded as eligible.<sup>8</sup> Under such scenario, both the PPMT-SEVAT and the PPMT-Legacy VA models would refer about 36 percent of the extremely vulnerable households to the government support. Part II of Table 5 shows that a high referral success (e.g., 77 percent in the PPMT-SEVAT model) and a low failure to refer (30 percent in the PPMT-SEVAT model) is achieved with the ranking method. That is, simply ranking referrals based on their score but not placing a maximum referral threshold means many more households will be referred and with relatively low referral failure.

Given the significant difference between the scope and hence the survey methodologies between the SWIFT on which the PPMT is designed and the CLCI’s verification field-test survey to which the PPMT is applied, the ranking method is more appropriate; a small reduction in successful referral rates
sup> Such analysis is particularly crucial in conflict areas, which may be most susceptible to shocks, but which usually have little quantitative data. Our findings inform current policy discussions within Afghanistan and, more generally, within the international development community. They

> 47 See Arimond and Ruel (2004) for evidence from 11 Demographic and Health Surveys worldwide.

> 48 World Food Program and UNICEF (2008); See also Grantham-McGregor _et al._ (2001) for a review of studies on iron deficiency and childhood cognitive development.

> 49 The Islamic Republic of  Afghanistan (2010) has published initial findings in the _National Risk and Vulnerability Assessment 2007/08: a profile of Afghanistan_ . The report is being used to better inform policy makers on many dimensions of wellbeing, including  progress towards the Millennium Development Goals (MDG). The reduction of hunger – part of MDG1 – is a priority for the Government of Afghanistan and is a target under the Economic and Social Development Pillar of the Afghanistan National Development Strategy. (IMF, 2008)

23
in two provinces; v) the marginal propensity to create intransitive relations among provinces, which is proportional to the number of stars detected in the network: i.e., province _i_ is linked to provinces _j_ and _k_ , which are not connected to each other; and vi) the number of links in the network (which is used in this model as a sort of intercept). Each variable is a proxy for a potential driver of migration. Variables i) and ii) are used to measure the economic and physical distance between two provinces, as in the gravity approach; variable iii) controls for potential risk factors pushing migrants to leave one province; variable iv) identifies provinces that are highly attractive for migrants; and variable v) identifies the presence of provinces that act as a bridge between unconnected provinces, having the advantage of a brokerage position (Burt, 1992) and potentially playing the role of hub in the network: i.e. provinces

> 10 Data on conflict events are from the ACLED data set (Raleigh et al. (2010).

10
from landlord behavior, then legislation binding landlords to long term contracts would do little to encourage investment.

Finally, it is worth asking about the economic signi fi cance of underinvestment on tenanted land. Of course, the full extent of the dynamic inefficiency cannot be assessed without accounting for all the different types of land-speci fi c investment. This paper focuses on just a single investment, albeit an important noncontractible one, the use of farmyard manure. Nevertheless, it is still instructive to ask, based on our estimates, how yields would be affected by giving land ownership rights to tenants. Fortunately, we have access to results from a large number of agricultural experiments in India compiled by 24
true for many SDG indicators. In such cases, missing data can lead to severe misinterpretations of progress over time.

We turn next to examining the data set perhaps most relevant for monitoring progress on the SDGs, the UN’s official SDG database.

# _Overview of the UN SDG Database_

The United Nations (2018b) curates a rich database that tracks the SDG indicators for all countries with data coming from various sources and dating back to as early as 1983. Our preliminary assessment indicates that this database consists of data coming from around 200 data sources, where each source is defined as one that contributes 40 or more country-year-indicator observations. Clearly, identifying these different sources and harmonizing any potential consistencies would be an important task that demands time and resources, but that can help improve the quality of this database.

Such a task is beyond the scope of this paper. Thus for now, we assume that all the data are comparable, and we focus on the easier task of identifying missing data. For this purpose, to obtain an overview of this database, we consider only the most recent 5-year period, 2012-16, where the data are most complete.<sup>19</sup> We simply check on the data by summarizing, for each SDG in Table 4, the number of countries (Table 4, column 1) and the number of indicators (Table 4, column 1) covered in the database. We also show the total number of country-indicator-year data points available (column 3), and the percentage of non-missing data points (column 4) for each SDG. For comparison, we show the full numbers of countries, indicators, and all country-indicator-year data

> 19 Data are not incomplete for 2017 and 2018 in this database, we return to more discussion in the next example.

20
expenditure survey are spending on insurance and other financial services including bank charges.

A 1998 survey for Ghana interviewed about 6000 households included as the last section a “credit, assets and savings” module (Section 12). Here credit respondents were asked (i) in respect of each loan obtained or repaid in the past 12 months, the amount, the source, the purpose, what guarantees were required by the lender and what repayments have been made and (ii) whether and why any loan application was refused. Any savings accounts (including informal _susu_ accounts) were also measured together with flows in and out. Household spending on “financial services not elsewhere specified” was also requested.. Questions on credit were also asked in respect of household-controlled enterprises. Here were required the main sources of finance and of credit, whether a credit application was refused and gross borrowings and repayments in a twelve month period.

> 13 I will not attempt to benchmark these against the leading comparators in the advanced economies such as the US’s Survey of Consumer Finances, the UK’s Household Resources Survey or the Italian Survey of Household Income and Wealth.  The enormous economies of scale in statistics collection militates against such comparisons especially in respect of small economies.

> 14 Interestingly this module was placed no. 6 of 13.  Most interview designers in contrast prefer to leave the sensitive financial modules to the end of the interview to avoid offputting effects that might damage response quality for the other themes.

11
(in 1994 Pesos)||||||
|0 - less than primary|7,530 7,674|<br>1.9%|_9,514_|8,308|<br>-12.7%|
|1 - primary complete|12,705 11,316|-10.9%|15,888|13,222|-16.8%|
|2 - secondary complete|15,343 14,097|<br>-8.1%|16,995|15,978|<br>-6.0%|
|3- preparatory complete|18,643 25,709|<br>37.9%|19,483|27,898|<br>43.2%|
|4- university and above|33,899 55,428|<br>63.5%|38,741|62,769|<br>62.0%|
|_weighted average_|_13,057 16,65_|_8 27.6%_|_14,26_|_8 16,93_|_1_<br>_18.7%_|

Source: Own calculations based on ENIGH84 and ENIGH94. All figures are weighted averages using expansion factors to achieve representation at the national level.

One explanation for a deteriorating income distribution that is closely connected to the advance in education attainment has been made by Ram (1 990). Education is generally considered to have an equalizing effect on incomes, which would indicate a positive relation between measures of income inequality and of education inequality. However, the dispersion of schooling attainment and the mean level of schooling need not be positively related in a monotonic manner. Upon exploring this relationship by way of a cross-country analysis, Ram finds a curvilinear (Kuznets-type inverted-U) relation between the mean level of schooling and schooling inequality in the labor force.<sup>4</sup> That is, as
Tomé and<br>Principe|Enquête sur le conditions de vie|2000|
|Senegal|Enquête auprès des ménages|2001|
|Serbia|Household Budget Survey|2003-04, 06-<br>08|
||LivingStandard Survey|2003|
|Seychelles|Household Budget Survey|2006|
|Sierra Leone|Integrated Household Survey|2003|
|Slovenia|Household Budget Survey|2000,04-05|
|Solomon Islands|Income and Expenditure Survey|2005|

19
transfers of food decrease prices due to increases in supply, whereas equivalently valued cash transfers have a negligible impact on prices. Filmer et al. (2018) analyze a randomized evaluation of a Philippine cash transfer program and show prices of perishable protein-rich foods rose as a result of the transfers, leading to a negative impact on the nutrition of non-beneficiary children. Banerjee et al. (2021) study an experiment in Indonesia that randomized rice vouchers versus rice deliveries across districts. They find that the former intervention led to higher transfers due to lower leakages; while they do not explicitly calculate price incidence and their household-survey-based price difference is imprecise, our back-of-the-envelope calculation based on their numbers suggest a price incidence of 0.16%, comparable in order of magnitude to our finding for the US context. A literature on the price effect of minimum wages also exists.<sup>9</sup> Village-level transfers directly

> 7Other work includes Beatty and Tuttle (2015), who report an MPCF of 0.484 estimated using the Consumer Expenditure Survey data and the matching-on-observables method, comparing food expenditure patterns of SNAP-recipient and non-SNAP recipient households before and after the 2009 ARRA.

> 8We lay out the main differences between our papers in Appendix Section A.

> 9Aaronson et al. (2008) provide evidence for complete labor-cost pass-through in restaurants. Leung (2021) suggests that in addition to the cost channel, changes in product demand could further increase prices under variable markups while Renkin et al. (2020) highlights the effects on the costs of both manufacturers

4
self-assessment of performance ranking in class but also with their performance in the test that was conducted under the survey. However, while more homework time is associated with student selfassessment of performance ranking in class, it is actually negatively correlated with the test score produced by the survey. While we believe student self-assessment of their performance standing in class is largely accurate—as it is a common practice that schools regularly rank students by their examination scores—these results may shed some light on the likelihood that different sets of tests tend to generate different rankings.

Figure 3.19 Factors associated with the student’s hours of sleeping and homework per day (marginal change in probability)

- a) Determinants of sleeping hours per day

<!-- Start of picture text --> Mother's education post-secondary<br>Mother's education sr. sec<br>Mom's education jr sec<br>Family economic condition-well off (student response)<br>Family economic condition-middle (student response)<br>Student performance ranking - high (self assessment)<br>student performance ranking - midde (self assessment)<br>Student standardized test score<br>School is in rural area<br>school is in city<br>-0.2 -0.15 -0.1 -0.05 0 0.05 0.1 0.15<br>b) Determinants of homework hour per day<br>Mother's education post-secondary<br>Mother's education sr. sec<br>Mom's education jr sec<br>Family economic condition-well off (student response)<br>Family economic condition-middle (student response)<br>Student performance ranking - high (self assessment)<br>Student performance ranking - midde (self assessment)<br>Student standardized test score<br>School is in rural area<br>school is in city<br>-0.4 -0.3 -
Energy demand is insensitive to lower income dummies for rural households, after which energy demand responds positively and significantly with higher income (Table 5). More precisely, energy demand for end-use energy in rural India does not respond to income until after the 5th income decile or the 50<sup>th</sup> percentile. Energy consumption up to this income level can be considered the bare minimum necessary for basic needs such as cooking, heating or lighting. The poverty line can be calculated based on the average consumption of end-use energy for households that at the 5<sup>th</sup> income decile 5. This figure which can be characterized as the energy poverty line for rural India is 3.4 kgOE per person per month (Table 6). The situation is somewhat different for urban areas as the demand for energy increases significantly after the 2<sup>nd</sup> income in decile. This means that for urban areas the energy poverty line based on the average energy at the 2<sup>nd</sup> income decile is 2.4 kgOE per person per month.<sup>13</sup> That the urban energy poverty line is slightly lower than the rural one is probably because of greater access to modern energy and a wider range of available energy services.

The energy poverty headcount is based on the number of people above and below the energy poverty line. Likewise the incidence of more general income poverty is based on the number of people below the expenditure poverty line as defined by the National Sample Survey Organization in India. The goal is to make a comparison between energy poverty and the more general income poverty index.<sup>14</sup> One interesting finding is that for the end-use measure, energy poverty in rural areas at about 57 percent is more than double the rate for urban areas at 28 percent. Energy poverty is much worse than expenditure poverty in rural areas, while the values of the two measures are much
12 between collection and its ultimate use in the household. Overall, intervention water points had only slightly lower levels of fecal indicator bacteria than control water points; log10MPN/100mL was 0.17 lower in intervention water points (95% CI -0.32– -0.02), with a mean of 1.71 in the control group. This is consistent with a meta-analysis of five WASH trials that found only a 6% reduction in prevalence of enteropathogens in environmental samples [32].

In addition, two-thirds of our respondents reported spending at least one day per week working on an agricultural plot (modal response = 4 days). Of those who did, 95% report open defecation while in the field, and 91% report drinking from surface water or unprotected springs. This highlights the challenge of delivering safe and comprehensive WASH services in some agricultural settings.

Our finding of no effect on diarrhea contrasts with a meta-regression (conducted as part of a systematic review) which found use of an off-site improved water source reduces the relative risk of diarrhea by 19% compared to unimproved water [14]. The same analysis found that basic sanitation without sewer connection lowered the relative risk of diarrhea by 21% relative to unimproved or limited sanitation, which also contrasts with our results, while hygiene interventions reduced the relative risk of diarrhea by 30%. However, another review found that effective handwashing promotion typically requires daily to fortnightly contact between the promoter and participant [31]. It is possible that our intervention achieved that frequency of contact during the most intensive 90-180 days of implementation, but also likely that effects would have faded out by our measurements over three years later.

We found no effect of the intervention on handwashing with soap or ash during structured observations of study participants by our research team. However, participants in the intervention group were 9 pp more likely to report washing with soap or ash the previous day. This underscores the limitations of self-reported data, particularly when the socially desirable outcome is likely
> The patterns found will hopefully inspire future country-level research using cause-of-death data to get a fuller understanding of relevant drivers of age-gender mortality patterns. It is worthwhile to explore structural socioeconomic conditions that vary with country income and sex, which would also interact with both infection exposure and fatality rates. This would include, for example, the structure of jobs and employment patterns (well documented to vary by country income and sex), especially the extent of remote work opportunities which may reduce exposure to infections during a pandemic. Another example is the extent of residency in long-term care facilities. Older persons, especially older women, have higher likelihoods of residing in nursing homes in wealthier countries. When these facilities are of poor quality and safety they result in greater exposure (Webster 2021). A third area could be the profile of co-morbidities pre-existing in the population. For example, women are relatively more obese than men in low- and middle-income economies but not in highincome countries (Ameye and Swinnen 2019).

<mark>Lastly, the analysis in this paper is also a reminder of the value of demographic data and the value of efforts by country governments and international organizations to promote and standardize vital statistics data.</mark> 6
above all to cooking but also includes, for example, child caring, preparing the beds before going to sleep, and repairing clothes. The Pico‐PV lamp replaces above all wick lamps and is used by female adults that had not been using any particular lighting device before.<sup>14</sup> Male adults also use the lamp mostly for housework, although these are more diverse activities than for women. For male adults, the Pico‐PV lamp replaces wick lamps, ready‐made torches, and hand‐crafted LEDs and is also used by men who had not used any artificial lighting device before for housework activities.

**Table 7. Activity Using Pico‐PV Lamp, Adults and Children in Treatment Households (%)**

|||**First Activit**|**y**|**Second Activ**|**ity**|**Third Activ**|**ity**|
|---|---|---|---|---|---|---|---|
|Female adult >17 years old|N=149|Housework|87|Study|5|Eat|4|
|Male adult >17 years old|N=60|Housework|71|Recreation|10|Study|10|
|Children 6 to 17 years old|N=56|Study|75|Housework|16|Recreation|4|

_Note:_ Information on activities stem from an open question among treatment households at follow‐up, asking for the main activities the different lamp users are exercising while using the lamp.

_Source:_ Household data set 2011/2012.

Table 8 shows that housework is done primarily during daytime, also in the treatment group, and the total _time dedicated to domestic_ work per day does not change significantly. The _total time household members are awake_ per day does not change significantly,
observations with zero quantities. Furthermore, we exclude products with detailed product descriptions corresponding to more than one HS10 code (i.e., which appear to have changed their HS10 code over time or are entered as different HS10 codes when coming from different source countries). We also exclude HS10 codes with more than one product description. Additionally, we eliminate all goods belonging to the “Not Elsewhere Specified or Indicated” (NESOI) categories as they may possibly contain non-homogeneous goods across time. With these filters in place, we retain over 95 percent of the observations in the full sample. Data on GDP per capita were obtained from the World Development Indicators (WDI) and the Global Development Finance (GDF) databases of the World Bank. They are adjusted for Purchasing Power Parity (PPP) and are recorded in constant 2005 U.S. dollars.

# III. Results

We begin by characterizing the relative unit values of imports. As in Schott (2004), unit values were calculated simply as the quotient of general imports values and quantities. Within any 10-digit good for any given year, we then have a distribution of unit values of imports from the different source countries. For each good i and exporting country c, in time period t, we generate a measure of relative quality R as: where uitc denotes the unit value of the good and u<sup>90</sup> it<sup>denotes the value at the 90th percentile</sup> of the unit value distribution across countries for that HS 10 good. Ritc denotes the relative quality of the country’s export of that good, i.e., quality relative to other countries exporting the same good. We obtain the time average for a given country-good pair as: