rafmacalaba/data-use-mentions-extended huggingface dataset:
is a dire need to make a dedicated financial investment in health security. Policy makers in Côte d'Ivoire could, therefore, allocate pandemic funds in national budgets and explore opportunities to collaborate with philanthropic and multilateral organizations to increase domestic resource matching. As a result of their collaboration, for every dollar spent by policy makers in Côte d'Ivoire on pandemic preparedness and response, the matching donor contributes U$2 (a 2:1 matching ratio). Policy makers could use these funds to invest more in the surveillance system, allowing outbreaks to be contained before they spread. Furthermore, modern, and strong laboratory

> 7 <u>https://data.who.int/dashboards/covid19/vaccines?m49=384&n=c</u>

> 8 The data are from the 2021 Global Health Security index (https://www.ghsindex.org/country/cote-divoire/). The data suggest that Côte d’Ivoire has low score in preventing (12.4), detecting (29.6), responding (35.3), health (17.1), norms (47.7), and risk (45.2).

15
growth in total wages|13.1%|32.3%|
||**Panel **|**C: Worker panel - full RAIS**|
|Male|72%|45%<br>240 669 880|
|Age|33.9|10.4<br>241 057 542|
|Higher education|10%|30%<br>241 057 542|
|Previous formal sector attachment|37%|37%<br>241 057 542|
|Average months worked per year|6.9|3.8<br>177 895 587|
|Average monthly real wages|2.3|2.1<br>98 146 366|

_Note:_ The worker-level statistics in panel A are based on the sample of workers in firms with a non-missing GFC firm shock measure in 2008 used in the main regressions. The firm-level statistics in panel B are based on all firms with non-missing GFC shock measure in 2008 used in the main regressions. The worker-level statistics in panel C are based on all workers in the RAIS database.

40
existence of pre-trends.

We define treatment status at the community-cohort level. Children from a given cohort are considered treated if a pre-primary existed in their community when they were aged 6. We measure impacts of access to pre-primary education on two outcomes: enrollment and adequate progression rates. We restrict our sample to include cohorts born between 1988 and 1996 to observe outcomes during the whole relevant age range (7-12) for each included cohort. This restriction ensures that differences in estimated effects by age are not driven by heterogeneity in cohort composition, but rather by average dynamic effects of pre-primary access.

For both measures, the denominator corresponds to population counts by communitycohort-age. To construct these counts, we use data on population by community and age from the 1994 and 2002 Population Census and assume that cohort sizes remain constant over time. For example, the number of students aged 6 in 2000 in a given community is assumed to be equal to the number of students aged 8 in 2002 in that community. We compute counts for cohorts 1988-1989 using the 1994 Population Census. For the 19901996 cohorts we use the 2002 Census. We favor the use of the 2002 Census because it is closer to the period of analysis (1995-2008). However, the assumption of constant cohort sizes by community over time becomes problematic when focusing on children aged 13 and older in the 2002 Population Census: in these rural communities students might migrate to attend secondary education. This is why we compute cohort sizes for the 1988 and 1989 cohorts from the 1994 Population Census.<sup>6</sup> The numerator of the enrollment rate is the count of primary school students by community-cohort-age. The numerator for the progression rate corresponds to the number of students progressing adequately given their age. For example, for those aged 8, this includes all students in second or higher grades. The unit of observation of the resulting data set is at the community-cohort-age level. This structure of the data makes it possible to estimate impacts at ages 7 to 12, though we mainly
was leased out, the survey asks its owner about the characteristics of the tenant, including his principal assets, and about the duration of the tenancy. For reasons that will become apparent shortly, we focus only on those landlords who report at least two leased plots, which gives us a sample of 345 tenanted plots owned by 127 landlords in 52 villages. The median elapsed tenure in this sample is 4 years and the mean is 6 years.

Column (1) of Table 2 reports a regression of log duration on an array of tenant, landlord, and plot characteristics, and on geographical dummies for each of the 18 tehsils (administrative units below a district). Less than a third of the total variance in log(duration) is explained by these covariates. Tenants with larger land holdings appear to be less mobile, staying with a given landlord longer. But tenants’ ownership of a tractor or plow has no effect on duration, nor do any of the landlord asset variables.

The regression in the second column of Table 2 includes landlord fi xed effects. All the landlord characteristics and tehsil dummies consequently drop out. Remarkably, this regression explains 80 percent of the variance in log duration. This means that a landlordspeci fi c unobservable, which is uncorrelated with landlord assets, accounts for at least half of the variation in tenancy durations in our sample.<sup>32</sup> Evidently, a large portion of the variation in μoc consists of heterogeneity in the behavior of landlords toward all their tenants taken as a group. One interpretation of this heterogeneity is that there are those who can acquire reputations as ‘good’ landlords who do not opportunistically evict their tenants and others who cannot. If so, then not only can we use duration data to test proposition 2, but we can interpret any tenure security effect as arising largely from landlord commitment problems.

# 4.3 Main results: The tenure security effect

Among the 563 tenanted plots in our sample, the median number of years that
“Quality of Institutions and Private Participation in Transport Infrastructure Investment: Evidence from Developing Countries.” _Transportation Research A_ 70: 50–58.

- Polackova, Hana. 1998. _Contingent Government Liabilities: A Hidden Risk for Stability._ Policy Research Working Paper 1989, Washington, DC: World Bank.

- Pratap, Kumar V., and Rajesh Chakrabarti. 2017. _Public-Private Partnerships in Infrastructure: Managing the Challenges._ Singapore: Springer.

- Reyes-Tagle, Gerardo, and Karl Garbacik. 2016. _Policymakers' Decisions on Public-Private Partnership Use: The Role of Institutions and Fiscal Constraints._ IDB Technical Note No. 1169, Washington, DC: Inter-American Development Bank.

- Royston, Patrick, and Mahesh K. B. Parmar. 2002. “Flexible Parametric Proportional‐Hazards and Proportional‐Odds Models for Censored Survival Data, with Application to Prognostic Modelling and Estimation of Treatment Effects.” _Statistics in Medicine_ 21 (15): 21752197.

33
Table 4: Treatment Effects of Additional Information in Audit Study

||(1)<br>Any re|(2)<br>sponse|(3)<br>Interview|(4)<br> request|
|---|---|---|---|---|
|Certifcate (_β_1)|0.015|0.016|0.009|0.010|
||(0.009)|(0.009)|(0.004)|(0.006)|
|Certifcate _×_ HighIntensity (_β_2)|-0.027|-0.028|-0.014|-0.017|
||(0.013)|(0.014)|(0.009)|(0.010)|
|Mean outcome|0.130|0.130|0.088|0.088|
|# applications|3992|3992|3992|3992|
|# vacancies|998|998|998|998|
|# resumes|717|717|717|717|
|Vacancy fxed efects||_×_||_×_|
|Email address fxed efects||_×_||_×_|
|Resume covariates||_×_||_×_|

Note: Coefficients are from regressing each outcome on a vector of treatment assignments and, in columns 2 and 4, vacancy fixed effects, email address fixed effects, a vector of prespecified workseeker covariates (measured skills, education, age, gender, past employment, and the scan quality of documents they include in their application). The vacancy-level treatment HighIntensity _v_ is included in columns 1 and 3 but omitted in columns 2 and 4 because it is collinear with the vacancy fixed effects. Heteroskedasticity-robust standard errors shown in parentheses clustered by resume and vacancy. The mean outcomes are for applications sent without
|PPR<br>Title|Working Paper Series<br>Author|Date|Contact|
|---|---|---|---|
|WPSII1 Recent World Bank Activities in Energy|Industry<br>and Energy<br>Department|||
|WPS112AVisual Overview of the World Oil<br>Markets|Kay McKeough<br>Jose Escay<br>Sompheap Sem|||
|WPS113 Public Sector Pay and Employment<br>Reform|Barbara Nunberg|October 1988|J. Cheeseman<br>61703|
|WPS114 Africa Region Population<br>Projections<br>1988-89 edition|My T. Vu<br>Eduard Bos<br>Rodolfo A. Bulatao|October'<br>1988|S. Ainsworth<br>31091|
|WPS115 Asia Region Population<br>Projections<br>1988-89 edition|My T. Vu<br>Eduard Bos<br>Rodolfo A. Bulatao|October 1988|S. Ainsworth<br>31091|
|WPS116 Latin America and the Caribbean<br>Region Population<br>Projections<br>1988-89 edition|My T. Vu<br>Eduard Bos<br>Rodolfo A. Bulatao|October 1988|S. Ains.orth<br>31091|
|WPS117 Europe, Middle East, and North Africa<br>(EMN) Region Population Projections||||
|1988-89 edition|My T. Vu<br>Eduard Bos<br>Rodolfo A. Bulatao|October 1988|S. Ainsworth<br>31091|
|WPS118 Contract-Plans<br>and Public<br>Enterprise<br>Performance|John Nellis|October 1988|R. Malcolm<br>61707|
|WPS119 Recent Developments in Commodity<br>Modeling: A World Bank Focus|Walter C. Labys|October 1988|A. Daruwala<br>33716|
|WPS120 Public Policy and Private Investment<br>in Turkey|
# **1 Context**

Electricity sectors in Sub-Saharan Africa (SSA) lag behind all other regions of the world. Installed capacity of power generation for the entire continent is less than that of Spain, over 600 million people lack access to electricity, and power consumption of 375 kilowatt-hours (kWh) per capita per year is low by any standard. The results are frequent load-shedding, expensive short-term rental of emergency power generation, and, in nearly all cases, high reliance on costly diesel for off-grid captive power generation even in large cities.

Progress toward universal access is slow. According to the SE4ALL Global Tracking Framework (SE4ALL 2015a), access to electricity in the region in 2012 was only 35 percent, having increased from 26 percent in 2000. With per capita gross domestic product (GDP) of US$3,274 in the same year measured in current dollars at purchasing power parity (World Bank 2016a), this low access rate in part reflects the inability of many households to pay for electricity. That said, by contrast, when South Asia was at the same level of this measure of GDP per capita in 2005–2006, its rate of access was already double that in SSA. The rural/urban population divide in the two regions are comparable, with the rural population making up 68 percent of the total in South Asia and 64 percent in SSA in 2012 (although population density is likely to be lower in SSA). The largest difference was in the rural access rates: a mere 15 percent in SSA in 2012, against more than triple that in South Asia in 2005–2006.

The low access rates in many countries in SSA mean that electricity customers today are relatively welloff, and yet in 2013 less than 20 percent of utilities reported a net profit. As noted in Dobozi (2016), the main cause of the slow progress in access expansion in SSA is the poor financial viability of electricity utilities. The sector needs substantial capital infusion. The annual cost of addressing
940|1,965|1,904|13,338|6,866|<br>5,996|8,024|
|15|0.85|0.85|0.85|1,881|1,940|1,965|1,904|17,929|7,377|<br>6,416|8,664|
|16|0.74|0.77|0.68|1,881|1,940|1,965|1,904|7,683|5,891|<br>4,379|8,823|
|17|0.43|0.44|0.39|1,881|1,940|1,965|1,904|1,302|5,646|<br>3,685|7,404|
|18|0.00|0.00|0.00|1,881|1,940|1,965|1,904|12,551|12,551|<br>8,481|14,282|

_Source_ : Authors’ estimations based on data from the 2016/17 GLSS and the Ghana Ministry of Education.

_Note_ : NPV = net present value. Discount rate for the calculations is 8 percent. Calculations assume a working life from age 15 to 60, except when the individual is in school; they also assume that students have no labor earnings. The probability of advancing an additional year of schooling is estimated for all individuals ages 15–45. We use advancement probabilities and private costs for each gender in the gender-specific results, and the “all” columns for the results that combine both genders.

22
# **GUIDELINES TO USE DASHBOARD**

1. Select the relevant dataset

- a. FINDEX

2. Select the indicator that is going to be analyzed 3. Select the controllist number with the relevant combination of control groups

## FINDEX:

- a. Controllist 31: age, education, gender

- b. Controllist 32: age, education, gender, income

4. Select appropriate filter criteria to generate the country distribution a. Selected World Development Indicators

- b. Selected FINDEX variables

5. Adjust the Lower bound and Upper Bound cursors to narrow the values of the filter criterias.

27
The facility survey collected information on staffing, expenditures, medical equipment, drug availability from the facility administrator, and provider knowledge about the appropriate clinical procedures for quality prenatal care. As part of survey, enumerators also conducted exit interviews with approximately 10 women who visited the facility to collect information on the actual clinical services (quality) provided during their prenatal care visit. The sample of facilities was well balanced at baseline. There were no differences in means for the 17 baseline characteristics presented in Table 3.

The household survey consists of a random sample of 13 households living in each facility’s catchment area, for a total sample of 2,158 households. To build the sample, we first sampled 13 census zones (each containing approximately 15 to 20 households) from each facility’s official list of zones in their catchment area. We then physically listed all households in the sampled zones and randomly selected one household with at least one child under 5 years old from each zone. The surveys were conducted by trained enumerators hired by external firms specialized in data collection. Interviewers did not inform respondents that the survey was associated with the P4P program, but rather that the survey was being conducted to assess health conditions.

Response rates were high as only 2 percent of sampled households refused to participate in the interview. In the follow-up survey, 88 percent of the baseline households were reinterviewed. The rate of attrition from the baseline sample was not statistically different between the treatment and comparison groups (12 percent each). Households that could not be found or interviewed were replaced with randomly selected households from the same zones. In addition, the household data were well balanced between treatment and comparison groups at baseline. Only 2 of the 30 characteristics reported in Table 4 were significantly different at the 5% level between treatment and control groups at baseline.

# **b. Provider Resources**

Health clinics in rural Rwanda are staffed with about 6 nurses and 4 medical technicians or midwives (Table 3 Panel A). There are
environmental and health risks of nuclear energy. This tendency was reinforced by fading memories of the Three Mile Island and Chernobyl accidents. Moreover, dramatic increases in fossil fuel prices prior to the current global economic contraction and the imperative to cut greenhouse gas emissions came to the fore of public concerns and debates, likely contributed to more positive attitudes toward nuclear power . Between 2005 and 2008, the percentage of Europeans favoring nuclear power increased from 37 to 44 percent, while the share of those opposed to it declined from 55 to 45 percent (EU, 2008). Recent international polls sponsored by the nuclear industry showed substantial support for nuclear power. A survey of more than 10,000 people in 20 countries found that more than two-thirds of the respondents believed that their countries should begin using or increase the use of nuclear energy (NEI, 2009). In the United States, those in favor moved from 49 percent in 1983, when the question was first asked, to 74 percent in 2010 (figure 2). Moreover, by 2010, those who ―strongly favor‖ nuclear energy outnumbered those who are ―strongly opposed‖ by more than three to one (NEI, 2010).

**Figure 2 Changing public attitudes toward nuclear power in the United States, 1983–2010**

<!-- Start of picture text --> 80<br>74%<br>Favor<br>60<br>40<br>Oppose<br>20<br>23%<br>0<br>1983 1985 1987 1989 1991 1993 1995 1998 2000 2002 2004 2006 2008 Mar-10<br><!-- End of picture text --> _Source_ (NEI, 2010).

The extraordinary events in Fukushima quickly and fundamentally altered the nature of the nuclear debate.<sup>2</sup> Once again, the future of nuclear power is clouded in uncertainty. These

> 2 The severity of the nuclear event at Fukushima Daiichi has been rated 7 on
# 2.4 Malaria Data

Low-incidence areas close to elimination can experience the largest externality from population mobility for three key reasons: (1) without these travelers the disease could be reduced to zero and require lower government expenditures; (2) in high malaria areas, people have usually built up an immunity to the disease; therefore, a traveler entering a high malaria area is less likely to lead to a new infection even if he or she infects additional mosquitoes 8
Croke et al. (2016) studies]” (p.177-8). This suggests that just focusing on the weight outcome, deworming is highly cost effective relative to another widely implemented intervention. As noted by Galloway et al. (2009), school feeding is implemented in over 72 countries by the World Food Programme alone. To the extent that school feeding programs aim to produce child weight gain, deworming is likely to be a highly cost effective option for policymakers who already support school feeding.<sup>28</sup> Of course, a complete cost-effectiveness analysis of mass deworming treatment would also need to consider effects on later educational and labor market outcomes, in addition to child weight gains, and as noted above these are often substantial (Ahuja et al., 2015; Baird et al., 2016; Bleakley, 2007; Croke, 2014; Ozier, 2015).

# **6 Conclusion**

We began with the question of whether the expected benefits of mass drug administration according to WHO guidelines for deworming exceed the cost.

To summarize, the null hypothesis of a common effect of zero weight gain from multiple-dose mass drug administration is rejected at the 10% level using the TMSDG data. Employing either of two classification systems used in previous Cochrane Reviews would lead to rejection at p<0.01. Any one of five other updates to the data leads to rejection of the null hypothesis at the 5% level. Combining all updates leads to rejection with p<0.001.

> 28 Note that this comparison is imperfect for a number of reasons, including that the cost figures for deworming are for India whereas those for school feeding are based in three African countries. Moreover, both school feeding and deworming may affect other outcomes. However, it is unlikely that appropriate adjustments for these factors would overturn the conclusion that deworming is highly cost effective in increasing weight relative to school feeding programs, given that the cost per kg of weight gain is an order of magnitude higher
_ANDR EE_<sup>_ ́_</sup> _DECEMBER - 2021_ _8_ direction so that old price data quickly loses its relevance.<sup>11</sup> Kalman filtering is a popular method that is known to produce results that are optimal in common settings. These methods are discussed at length by Durbin and Koopman (2013). The state of the methodologies at present, however, is such that the current application brings together too many issues to derive a state space specification that generalizes across all data situations. Essentially, a good approach may be specified for narrow studies of prices within any given country individually, but likely not across all prices and all countries.<sup>12</sup>

# _B. The predictive imputation framework_

The literature on missing data often distinguishes between different mechanisms of missing data: MCAR (missing completely at random), MAR (missing at random), and MNAR (missing not at random). This taxonomy, introduced by Rubin (1976) and well-explained by van Buuren (2012), becomes important when missing data occurs at the covariate side, and different corrections are needed for unbiased inference depending on the type of missing data problem. In the current application, it is required that the data is at least MAR and possibly MNAR and that the value of one food item can be accurately predicted by making reference to prices of other food items and the location in space and time.<sup>13</sup> As such, the

> 11Closely related interpolation methods are plagued by similar issues. For example, linear interpolation takes information from the future to the past. The linear interpolation estimate for _c_ 5 can only be made after _c_ 6 has already been observed. Alternatively, moving average imputation based on past values results in biases when used for real-time monitoring. When prices are rising, a moving average imputation of _a_ 6 would be based on an average of lower prices and a real-time monitoring system using moving average imputation would regularly suggest
|(12.78)|(10.25)|(8.17)|(15.75)|(11.16)|(14.06)|
|Observations|769241|66040|58198|14983|80725|94219|

Note: Table 1 provides a detailed descriptive analysis of various firm characteristics along with key outcome measures for the cleaned panel as well as in 2012, 2014, and 2016. The table is segmented in 2013 by firm Offshore status, comparing offshore and onshore firms in the year before the tax reform. This table documents the evolution of firm attributes and performance indicators (Social Security Records Outcomes, Tax Record Outcomes, and Customs Records Outcomes) over time.

We also document differences between onshore and offshore firms. Table 1 shows that in 2013, the year before the reform, 43% of offshore firms are foreign-owned vs 1% of onshore, and offshores are younger - less than 7 years on average versus almost 10 years for onshores. In terms of sectors, offshores are much more likely to be in manufacturing (41% versus 29% for onshore) and services (45% versus 22%), while onshore firms are more likely to be in retail and wholesale.

12