rafmacalaba/data-use-mentions-extended huggingface dataset:
the sample, 41.5 percent classified as rural. This addresses a common criticism of other widely used surveys in the region which disproportionately represent urban populations.

# **2.4 Timing of the Survey**

The survey took place at a specific time of the COVID-19 crisis, and it is important to contextualize this period and to verify whether our data was not overly impacted by events that took place around the time of the survey. As mentioned above, the survey was implemented during the months of June and July 2021 close to the timing of Wave 6 of the Arab Barometer. As shown in Figure 1 using Arab Barometer data, trust in the government benefitted from an initial increase in third quarter of 2020 following the decrease in 2018 mentioned in the introduction. By 2021, when the study’s survey took place, trust was back to levels more in line with the historical average. The increase 15Regions covered are Oriental, Fes-Meknes, Rabat-Sale-Kenitra, Beni Mellal-Khenifra, Grand Casablanca-Settat, Marrakech-Safi, Draa-Tafilalet, and Souss-Massa.

> 16To stratify the sample and determine the weights for each group, the sampling methodology used the HCP’s latest available population census dating from 2014.

8
indicators measured at the regional level, as well as the _Global Competitiveness_ indicators at the country level. We find that the three sets of indicators offer independent sources of variations in explaining firm performance. Among them, the _Enterprise Survey_ indicators explain the largest share of variations in firm performance. Our investigation also suggests that the de jure _Doing Business_ indicators are more effective in improving firm performance in countries with better contract enforcement.

By comparing the three sets of estimates, we are able to modify our priors on the effects of the business environment. In particular, we increase our confidence on the four aspects of the business environment with which the multiple sources of data yield more consistent findings: access to finance, electricity, internet, and human capital. For these indicators, results from at least two data sets are robust and the third set does not contradict the other two. We are less sure about the negative effect of corruption. We also find substantial heterogeneity of the effects of the business environment by firm ownership and size. In terms of productivity, foreign-owned firms benefit more from the maintenance of physical safety and from ease in obtaining construction permits, and gain competitive advantages with respect to domestic firms when domestic infrastructure or access to finance is worse. Foreign-owned firms are less burdened in terms of firm growth by difficulty in land access. For relatively small firms, particularly important BE elements are containing corruption, availability of both basic and modern infrastructure, a better human capital environment, and land access. The benefit of access to finance shows in firm growth for smaller firms, but in productivity for large firms. Similar to foreign firms, relatively large firms benefit more from physical safety.

Our research has several policy implictions. First, because using multiple sources of data allows us to alter our priors about the importance of various elements of the business environment, we believe it is important to collect new data, and to exploit under-utilized data to enrich our understanding of the effects of the business environment. Second, we believe it is crucial
portfolio. Closing the infrastructure gap, while decarbonizing the electricity and power sectors and increasing their climate resilience, will require significant investments. In the aftermath of the COVID-19 crisis, fiscally constrained governments will surely aim to mobilize private financing of infrastructure through PPP arrangements. Improving the design and management of infrastructure PPPs to mitigate potential fiscal risks while ensuring financially responsible and timely implementation of infrastructure projects to address the infrastructure deficit is therefore an important agenda in the developing world.

The analysis identifies several overarching lessons from the global PPP data. Measures that reduce the financing risk of a project, such as providing support through capital grants, revenue subsidies, or in-kind transfers, can reduce the rates of early termination. Risk depends partly on the sector and size of the PPP project. PPPs in the power and seaport sectors are less likely to experience early termination than PPPs in the other infrastructure sectors analyzed. Early terminations are also more common for larger projects. Delegating PPP contracting and monitoring to state and local governments should be considered when it is institutionally and economically possible, because it reduces the probability of early termination. PPPs in countries with stronger constraints on executive power have lower probability of early termination. Macrofinancial shocks are an important cause of early termination of PPPs, highlighting the importance of macroeconomic management in enabling sustainable funding and financing of PPP projects.

Government capacity to prepare, procure, and manage PPP projects must be built to ensure that the expected efficiency gains are achieved and the fiscal risks from contingent liabilities are contained and properly managed. Good practices on preparation, procurement, and management of PPPs can help governments improve their ability to take advantage of PPPs at more acceptable levels of risks (World Bank 2018). The fiscal implications of PPPs, including their budgetary, accounting, and reporting treatment, need to be determined, and the Ministry of Finance or central budget authority needs to have the authority to approve or reject a PPP based on the long-term financial implications of the project (World Bank 2018).

Given the complexity, magnitude,
variables should be well correlated with poverty and have the following three characteristics (Coady et al., 2004):

- The set of variables should be small enough that it is feasible and cost‐effective to collect the required data from a significant share of the population.

- Each selected variable must be easy to measure and observe.

- Each selected variable should be difficult for the household to manipulate.

A broad range of candidate variables is typically drawn from a detailed survey data set—for example, a household budget survey or a multi‐topic survey—that includes detailed information on consumption, employment, education, health, housing, and family structure for a population representative sample. In this paper, we use HIES 2016, the most recent detailed household survey of this type available.

We first reduce the set of candidate variables through analysis of variance (ANOVA), in which we try to model the determinants of the log of per capita household consumption. We use ANOVA to discard variables with low partial sum of squares (SS), in other words, variables that are poor at explaining variation in consumption. This yields three plausible models of the determinants of the log of per capita household consumption. The first is a typical PMT model that includes characteristics of the household head but does not include any variables with an SS below 2. The second is restricted to variables with SS above 3.5, and excludes characteristics of the household head. The third model is much more selective and drops any variables with an SS below 10.<sup>6</sup> This exercise was initially conducted using the HIES 2012/13 and feedback was obtained from various Sri Lankan government agencies during 2015‐17 before finalizing the model specifications.

An OLS regression of the log of per capita household consumption on the three model specifications is then conducted to simplify interpretation of the magnitude and significance of coefficients for predicting poverty. We then measure the extent to which the model correctly identifies the poor. Following Narayan and
investigates the impacts of forced displacement in the Republic of Yemen’s conflict using rare data collected during active conflict. The setting is one in which a large, forced displacement crisis began immediately after the conflict escalated in March 2015, where displacement might have been forced by either violence or the humanitarian situation caused by the conflict (e.g., TFPM 2016; OCHA 2018; etc.). Our data are from the World Food Programme, which has been implementing a monthly monitoring survey collecting food security and displacement information that includes both cross2
0.0117|0.0158|-0.0142|0.00958|0.0114|
|||(0.0262)|(0.0291)|(0.0412)|(0.0438)|(0.0453)|
|**preneur**<br>**Model**|Ever been a business||0.427***|0.344**|0.421***|0.392**|
|**Entre**<br>**Role**|owner||(0.128)|(0.133)|(0.102)|(0.177)|
|**e skills**|Digitspan score|||-0.00905<br>(0.0483)|-0.0397<br>(0.0684)|-0.105<br>(0.0873)|
|<br>**itiv**|Raven test score|||-0.0614**|-0.00855|0.0186|
|stics<br>**ogn**||||(0.0246)|(0.0268)|(0.0725)|
|teri<br>**n-c**|Personal intitative|||-0.397|-0.774***|-0.556|
|arac<br>**no**||||(0.267)|(0.190)|(0.494)|
|Ch<br>**and**|Error competence|||0.173|0.666**|1.090***|
|iner<br>**ive**||||(0.243)|(0.324)|
home, and require extensive face-to-face interactions.

The measures of income safety by occupation are strongly correlated with the relative task content of those occupations, as shown by the literature (Autor, Levy and Murnane, 2003; Autor and Handel, 2013). To calculate the task content of a given occupation, we use the US Department of Labor O*NET abilities survey, which provides information on various characteristics of occupations. We follow the same “basic” definitions of manual and communication skills used in the literature. We thus define manual tasks as those that require “movement and strength,” and we define communication tasks as those that require oral and written expression and comprehension. We then determine the extent to which various jobs entail such tasks by merging these measures with the EU-LFS data and constructing concordances between SOC and ISCO categories of occupations.

12
**Table 5 Definition and source of variables for exploratory regression analyses.**

|**Variable name**|**Definition**|**Source**|
|---|---|---|
|**Per capita calorie**supply<br>(kcal/capita/day)|Estimates of per capita food supplies available for human consumption during the reference period in terms<br>of quantity, caloric value, protein and fat content (2020). Calorie supplies are reported in kilocalories (1<br>calorie = 4.19 kJ. Availability does not imply actual consumption due to intervening factors such as food<br>waste, and likely exceeds values from dietary intake surveys|FAOSTAT|
|**MDD-W**|% of women who consumed at least five out of ten predefined food groups, 2021|Gallup World Poll (2021) /<br>Food systems dashboard;|
|**MDD**(6-23 months)|Percentage of children 6–23 months of age who consumed foods and beverages from**at least five** **out of**<br>**eight**defined food groups during the previous day. Various years, 2014-2019 (1 observation from 2011, 1<br>from 2008 and 1 from 2006)|UNICEF|
|**NCD-Protect**score among adults ≥<br>15|NCD-Protect scores are on a scale of 0 to 9; higher scores indicate greater protection against<br>noncommunicable diseases. Data are for individuals ≥15 years of age from the 2021 Gallup World Poll for<br>2022|World Gallup Poll<br>(GDQP) / Food systems<br>dashboard|
|**NCD-Risk**score among adults ≥ 15|NCD-Risk scores are on a scale of 0 to 9; higher scores indicate greater risk for noncommunicable diseases.<br>Data are for individuals ≥15
we unfortunately have no further information. We instead assume that building quality is homogenous across building type and construct eight different damage curves with different quality rating scenarios (ranging from 0 to 7). Initial analysis showed some effect based on the quality assumption, but lacking any meaningful information we decided to use a mean quality assumption of 4 across the countries.

To model estimated damage due to a particular earthquake event the data from the ShakeMaps and GESI are used. Then, one identifies the value of peak ground acceleration that each nightlight cell experiences by matching each earthquake point with its nearest nightlight cell. If the cell is further away than 1.5 kilometers or if it experiences shaking (PGA) of less than 0.05g the value is set to 0. In order to derive a cell _i_ specific earthquake damage index, _ED_ , the following equation is applied: where DR is the damage ratio according to the peak ground acceleration, pga , and the urban‐ rural qualification k of cell i , defined for a set of 8 different building quality q categories.

Once the damage index has been constructed, the different data sets are combined and a final data set with the nightlights, population and earthquake data are made, ref Figure 2. With this combined data set, we do a case study of the 2016 Aceh Earthquake and we run the fixed effect regression: 15
In light of the difficulty of gathering treatment cost data for each country separately, the cost of treatment for a disease in each separate country was estimated using an indirect method based on such costs for the USA. The cost of treating a disease over the lifetime of the disease was first computed for the USA using data on all the health expenditures for treating cancer for 2009 (AMERICAN CANCER SOCIETY, 2009). These numbers were then made country specific by assuming that such costs were proportional to the per capita income in each country using Purchasing Power Parity (PPP) per capita GDP for each country. This is a conservative estimate since not all medical costs go down proportionately with the decrease in per capita income.<sup>18</sup> As a first step, the _direct_ costs of the health externalities of DDT were estimated by computing the extra incidence of _each_ disease from DDT exposure in a country given the respective RR in each of the three population groups: IRS spray operators, IRS exposed households and non-IRS exposed households. The additional incidence of a disease in a country was then multiplied by the cost of treating the disease per patient over the lifetime of the patient in that disease category in each country. The costs from all the diseases were then aggregated to estimate the total _direct_ cost of health externalities arising from the DDT exposure in a given country.

As an illustrative example, the increased _direct_ costs from DDT exposure across all countries in Sub-Saharan Africa are shown in Figure 2.

# [ **Insert Figure 2]**

Under the _widespread DDT use_ scenario, estimates indicate an increased _direct_ cost of more than $3.03 billion, while a _restricted DDT use_ scenario will result in a cost increase of nearly $0.60 billion. However, it should be noted that this is an underestimate as it does not include the effect of DDT in all disease categories because of the non-availability of data for some of the diseases and the costs should be used as a lower bound.

> *
# Assumptions and Limitations

## Assumptions

Several simplifying assumptions underpin the final Cost Time model. Some, e.g. the absence of snowfall and landslides, are unavoidable over‐simplifications given currently available data. These can be modeled in a separate simulation environment but not on a nationally representative average. Accounting for other factors, such as disabilities or encumbered travel speeds, is possible but would introduce greater errors elsewhere in the model. We may address such factors in separate future analysis, as with monsoon and walking travel.

### **Data accuracy**

- All roads are completely accounted for

- All road surface and condition data are accurate and up‐to‐date

- All service facilities are completely accounted for

- All service facilities in a given category are the same in terms of services and care

- WorldPop population density data for 2015 are accurate

### **Travel modalities**

- Walking individuals travel unencumbered, i.e. without carrying significant loads

- Walking individuals have no disabilities or injuries that affect travel speed

- Vehicles can only move on roads. Some very small roads are only accessible to motorcycles.

- There are no traffic jams.

- Planes and boats are never used for travel

- Individuals _immediately_ take the fastest possible means of land transportation in a given cell. (E.g. not only do people always drive on roads, they spend no time waiting for transport to arrive.)

- Landslides, floods, snowfall, road maintenance and other movement‐blocking events never occur.

### **Roadways**

- Every travel route above 2400m is an unpaved walking path, _unless_ the data set explicitly says otherwise (e.g. in Mustang).

- Every DRCN marked Hill or Mountain in provinces 6 and 7 is an unpaved walking path, _unless_ the data set explicitly says otherwise.

- Every SRN marked Hill or Mountain in provinces 6 and 7 is an unpaved road, _unless_ the data set explicitly says otherwise.

## Limitations

We must
A problem arises as weight and height depend on both age and gender (and other factors such as genetic variation) but it is possible to use physical measurements by comparing indicators with the distribution of the same indicator for a “healthy” reference group. In this way we use z-scores (standard deviation scores) which is the most common way of expressing anthropometric indices.<sup>16</sup> Table 6 presents some summary statistics.

The value of the mean of the haz z-score is –2.21, reflecting long-term cumulative inadequacies of health and/or nutrition.<sup>17</sup> There seems to be no wasting problem, the mean of whz is 0.23. Using the summary measure of nutritional status (waz) there is mild underweight, probably caused by long-term nutritional problems.

For the education outcome, we generated an index of school performance for children between ages 7 and 18, that is, children in primary and secondary school. The index is the ratio of years of education completed by an individual and the years of education this individual should have for his age.<sup>18</sup> The mean of this index for rural areas is 0.49 including children not attending school (approximately 45% of the sample).

We now describe the exercises performed using the same matching methods as before. We want to assess the effects of participation in cotton production in other dimensions than monetary income. We estimate the effects on child nutrition and education of a switching from subsistence to cotton. Table 6 reports the results.

We estimate differences in outcomes for the sample of all infants and young children (0 to 59 months old), for the subsample of males, and for the subsample of females. Interestingly, we find statistically significant effects only in terms of stunting, or long-run nutritional gains. On

> 16A z-score is defined as the difference between the value for an individual and the median value of the reference population for the same age or
in this paper suggests that more attention should be placed on the skills and training of the health workforce to ensure that patients receive quality care, regardless of the type of provider they see. While post-secondary education is associated with better diagnostic ability, more investigation and data are needed to understand how medical training and on-the-job training build on basic provider skills and competencies.

The analysis of a caseload indicator arises from a recognition of a global human resources shortage and an uneven distribution of health workers, both at the global and local level. Specifically, the World Health Organization (WHO) estimates that the Africa region bears more than 24% of the global burden of disease but has access to only 3% of the health workers.<sup>36</sup> Contrary to expectations, this analysis indicates relatively low caseload overall in the period before COVID-19, with 43% of health facilities seeing fewer than 5 patients per provider per day. This is in line with recent analyses in Latin America which look at efficient allocation of care and found substantial variation. In Ecuador, a World Bank report estimated that approximately one-third of primary care facilities have low case counts (fewer than 500 outpatient visits per month).<sup>37</sup> Similarly, in Peru, analysis found that around 10% of health facilities have fewer than 30 total patient visits per day.<sup>38</sup> Although it is difficult to determine the optimal theoretical caseload, in both of these cases other similarly equipped facilities are able to see three to four times the volume of patients.

Low caseload might raise some concerns about effective allocation of human resources in health care delivery, but it also points to the success of the global effort to bring health facility and health care providers closer to people, especially in rural and remote areas.<sup>39</sup> While this expanded geographical access to care is important, especially from an equity perspective, low caseload in primary care facilities across countries provides further evidence
data for about 90% of the potential universe throughout the period we consider. No comparison over time is possible for social protection spending, since we have only one data point for each country. When contrasting the HCI or its components with spending values, we use the most recent data point available from each country. Health data are most dense. Specifically, 98% of countries have data from 2016, meaning that the vast majority of countries have data from the most recent possible year. For education, however, the most recent observation of spending data ranges in years from 2000 to 2016 (with 52% from 2016 and 86% from 2010 and more recent). Social protection spending data are from 2009-2016. In short, only in very selected cases do measures of the outcomes and the spending refer to the same year. The government domestic expenditure on health, education, and social protection in the most recent year is available for 124 countries and is used in our estimates of “social sector spending”.

# B. Data density

High-quality data are the prerequisite to understand the relationship between social sector spending and human capital outcomes. In this section we discuss how spending data availability has evolved over time and across regions. For this purpose, we divide the time period under consideration into four sub-periods (2000-2003, 2004-2007, 2008-2011, 2012-2016), with the different length of the last one explaining the difference in the number of potential observations.

Data density for domestic current health spending has been consistently high, with a coverage of at least 96% of all potential observations throughout the entire time period (Table 2). Remarkably, low-income countries (LICs) have seen a significant increase from 88% in 2000-2003 to 93% in 2011-2016. The picture is completely different when we look at the density of spending data for education, with coverage peaking at 67% in the time period between 2008 and 2011 (Table 2). Perhaps even more worryingly, we do not observe a stable upward trend in data availability