rafmacalaba/data-use-mentions-extended huggingface dataset:
# **1. Introduction**

High vaccination rates are widely considered an essential component in managing the COVID-19 pandemic. Initially, a major constraint was a lack of supply of vaccines, especially in low- and middleincome countries (Rouw et al., 2021). As vaccines started to become more readily available throughout 2021, it became clear that limited demand also posed a significant challenge in some countries, with many people unwilling to be vaccinated for COVID-19 even when provided the opportunity (Africa CDC, 2021; Solís Arce et al., 2021). Many low- and middle-income countries that received COVID-19 vaccines through the global COVAX facility were unable to fully disperse their first batch of vaccines before they expired. In addition to structural barriers and limitations of the health system, a key reason for the lack of use of COVID-19 vaccines was hesitancy among eligible populations (Mwai, 2021). This raises the question as to what governments could be doing to address people’s hesitancy about receiving a COVID19 vaccine in these settings.

In this paper we report on the findings of research studying drivers of vaccine hesitancy as well as research on ways of increasing people’s willingness to get a COVID-19 vaccine. Our data comes from a combination of a broadly representative phone survey and an online randomized survey experiment in Papua New Guinea (PNG). PNG is an ideal setting to examine this issue as COVID-19 vaccination rates are among the lowest in the world (Howes, 2021); existing evidence suggests there are extremely high levels of vaccine hesitancy (Fox, 2021; Pogo et al., 2021) and this issue is viewed by many commentators as the binding constraint on the country’s pathway out of the pandemic (Howes, 2021). The phone survey used in this paper covered over 2,500 households and was conducted in May and early June 2021. The survey included questions about people’s willingness to get a COVID-19 vaccine and the factors that could be contributing to this, such as beliefs about the
Pirttila and Suoniemi (2014), estimate a model using commodity demands and additional administrative data to explain hours worked for Finland. Their results also reject weak separability, although they find statistically significant relationships between consumption and hours worked for a smaller number of consumption categories than Crawford et al. (2010). Expenditure on housing and on books and magazines are found to be negatively related to hours worked, while office meals and car use are positively related to hours worked.<sup>22</sup> They also examine the use of public services and find that the use of childcare is positively related to hours of work.

While these results suggest an efficiency case for differential taxation, the practical implementation of such an optimal rate structure is challenging. Crawford et al. (2010), for example, emphasize that optimal tax rates will depend not just on the sign of demand responses, but also on their magnitudes as well as on cross-price effects. They conclude that “the limitations and uncertainties of both the theory and empirical work are such that, at least as yet, they provide little firm basis for policy prescription” (p350). They further note that, given the small size of their complementarity estimates, the social gain from implementing differential rates is likely to be small, and these would need to be weighed against the administrative burden (see section 4.4) of implementing a highly differentiated rate structure.

The Mirrlees Review (Mirrlees et al., 2011) to which Crawford et al. (2010) contribute, consequently recommends the adoption of a uniform VAT rate structure for the United Kingdom, with the possible exception of childcare to which a zero rate could be applied.<sup>23</sup> More generally, Sørensen (2007, 2010) argues that a lack of elasticity information, combined with administrative and political economy considerations, suggests that “uniform taxation should be the main guideline for indirect taxation” (Sørensen, 2010; p241). Nevertheless, he also adds
overlap with lockdowns, more high-frequency data sources also point to large contractions in economic activity during lockdowns. Aminjonov et al. (2021) analyze mobility data from Android smartphones to document widespread mobility declines in every

> 8See, e.g., India: https://www.reuters.com/world/india/police-enforce-lockdown-indias-nagpurinfections-surge-2021-03-12/, https://www.aljazeera.com/news/2021/4/21/for-indias-poor-lockdownpolicing-adds-to-pandemic-hardships; and Africa: https://www.dw.com/en/covid-19-security-forces-inafrica-brutalizing-civilians-under-lockdown/a-53192163, https://www.aljazeera.com/news/2020/4/28/unraises-alarm-about-police-brutality-in-covid-19-lockdowns.

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# **5. COVID, Quo Vadis?**

The previous section has posited that the world’s demographic structure is inconsistent with the observed skew in the mortality burden towards HICs. This section will discuss the implications of this finding for the future direction of the pandemic. It will note that while to some degree the contrast between rich and poor countries is simply ‘unreal’, for the most part it will likely be temporary as the full progression of the pandemic is yet ‘unrealized.’

## **Perspective 1: Unreal**

Overshadowing the discussion so far is the specter of poor data quality in dimensions such as accuracy, completeness, consistence, timeliness and validity. The need for adequate measurement and reporting cannot be emphasized enough. Where testing has been limited or untimely, data quality will suffer. Where vital statistics were poorly reported prior the pandemic, the problems will be even worse.

Figure 11. Data Outliers: Countries with Extremely Low Observations of COVID-19 Fatalities by Population Size <!-- Start of picture text --> 60 Congo, Dem. Rep.<br>Niger<br>Thailand<br>Burkina Faso<br>Kenya<br>40<br>Ghana<br>Côte d'Ivoire<br>20<br>Tanzania<br>Uzbekistan<br>Venezuela<br>Sri Lanka Nepal Ethiopia<br>Angola Uganda Vietnam<br>0 Madagascar Mozambique<br>30 60 90<br>Population (millions)<br>Number of COVID−19 deaths<br><!-- End of picture text --> Source: Johns Hopkins University; World Population Prospects. Note: Population is 2020 estimate. Sample restricted to population over 20 million and fatalities under 100.

Some countries with large populations report remarkably few fatalities. Figure 11 shows some outliers of countries with a population of more than 20 million and where fatalities are reported below 100. As of May 23, Mozambique, Uganda and
>0.68|<br>0.00|
|STDEBTK|0.55|<br>0.20|<br>0.29|<br>0.13|0.56|<br>0.10|1.36|0.14|<br>0.71|<br>0.08|
|STDEBTAK**|1.02|<br>0.13|<br>0.39|<br>0.06|0.38|<br>0.03|0.48|<br>0.01|<br>1.33|<br>0.00|
|LTDEBTAK|0.48|<br>0.01|<br>0.24|<br>0.00|<br>0.04|<br>0.00|0.04|<br>0.00|<br>0.14|<br>0.00|

* Note that for 1993-95 we have net loss as LOSS to Ltassets ratio, whereas for 1996 we had gross loss to Itassets ratio.

** Note that the STDEBTA/K ratio consist of total debt arrears for 1996, that is why the number is so large for 1996.

Source: Authors' calculations based on micro data set for 1993 - 1996, NSI, National Statistical Institute, Sofia, Bulgaria The conclusion from this sub-section is that the problem with excessive debt was not just a stock problem but a flow problem as well, despite of all debt write-offs and high inflation, and that the origin of this flow problem is most probably the presence of SBCs in Bulgaria during the observed period. Berglof and Roland (1998) present a sequential model of SBCs of the bank-firm relations in transition economies. The SBCs create an incentive problem because the company will not exert high restructuring efforts if there are refinancing prospects instead of liquidation prospects
2014), the Africa Sentinel Sites soil database (Vagen et al., 2010), the GlobeLand30 land cover database, and the SoilGrids 1km predicted values, are joined through the use of 3D regression kriging founded on random forests modeling (Hengl et al., 2015). A layer of geospatial data is produced for each soil property at anywhere from one to six different soil depths.

Because MAPS georeferenced each agricultural plot, it is possible to join the AFSIS data with the center point of the agricultural plot and extract the point estimates of each soil property of interest, at the soil depths of interest. The following soil properties were extracted from AFSIS and utilized in the soil suitability analysis: cation exchange capacity, electrical conductivity, organic carbon, and pH. Each of these properties were available in depths of 0-5cm, 5-15cm, and 15-30cm. Because MAPS soil samples were taken at depths of 0-20cm and 20-50cm, we use a weighted average of the AFSIS 0-5cm and 5-15cm values for comparison with the MAPS 0-20cm samples. Subsoils, MAPS 20-50cm or AFSIS 15-30cm depths, are not utilized. Table 2 provides summary statistics for key soil properties derived from both the MAPS and AFSIS sources.

_Table 2_ . Comparison of Key Soil Properties Across Data Source

||MAPS|AFSIS|Test<br>of<br>Difference|
|---|---|---|---|
|pH|6.42|5.75|***|
|Cation exchange capacity (CEC, cmol/kg)|13.52|15.15|***|
|Organic carbon (%)|1.47|1.78|***|
|Electrical conductivity (salinity, dS/m)|0.06|0.10|***|
|N|840|840||

Notes: Two-sided p-values reported: *** p<0.01, ** p<0.05, * p
A poor business environment in urban Kinshasa may be at the heart of underemployment. The DRC remains among the most challenging countries in the world to do business. The 2017 Doing Business Report ranks the country 184 of 190 and notes that it performs poorly across all indicators. Similarly, the 2016–2017 Global Competitiveness Index ranked the country 129 of 138. A lack of basic services and core infrastructure—coupled with a weak regulatory environment— coalesces to impede firm entry and the creation of good-quality jobs. This results in a proliferation of informal self-employment jobs with low productivity, and of erratic piecemeal activities with irregular working hours.

# **2.3. Job characteristics**

A large proportion of jobs in Kinshasa are private, informal, and/or self-employment jobs. In 2018, public sector jobs accounted for only 14 percent of jobs in Kinshasa.<sup>4</sup> Around 60 percent of jobs are informal, in that they do not offer a national identification number (IDN) and/or do not keep formal accounting books (Table 3). About half the jobs in Kinshasa are wage jobs, while the other half are self-employment jobs. Not surprisingly, informal jobs are predominantly selfemployment jobs. However, formal jobs are composed of both wage and self-employment jobs. In other words, all self-employment jobs are not necessarily informal, as 32 percent of formal jobs are actually self-employment.

**Table 3: Formality and occupation types in Kinshasa**

||**%**|
|---|---|
|Formal and wage|23.9|
|Formal and self-employment|14.2|
|Informal and wage|9.1|
|Informal and self-employment|43.4|
|Formal total|40.3|
|Informal total|59.7|
|Wage total|45.8|
|Self-employment total|44.9|

_Source_ : Kinshasa Survey 2018.

_Note:_ Occupation types other than wage and self-employment (
# **1.  Introduction**

Empirically estimating government spending multipliers requires the isolation of a source of variation in government spending that is likely to be uncorrelated with contemporaneous macroeconomic shocks. In this paper, I construct an instrument for fluctuations in government spending, drawing on a near-comprehensive dataset of about 60,000 individual loans from official creditors (primarily multilateral development banks and bilateral aid agencies) to developing country governments over the period 1970-2010, as recorded in the Debtor Reporting System database of the World Bank. My identification strategy exploits two key features of this data. First, for many developing country governments, loans from official creditors are a major source of financing for public spending. In my largest sample of 102 countries, disbursements on these loans account for 11 percent of government spending for the median country-year observation, while the 75th and 90th percentiles correspond to 19 and 28 percent, respectively. Second, rather than simply financing the difference between government expenditure and revenue in a given period, these loans typically are tied to specific multi-year spending projects, and accordingly disburse over a period of several years following the original commitment of the loan, with disbursements linked to stages of project implementation.

My core identifying assumption is that the decision to approve a loan in a given year, and to embark on the associated spending plans, is uncorrelated with shocks to growth occurring in subsequent years when the spending plans are implemented and the actual loan disbursements take place. If this identifying assumption holds, and if loan disbursements follow a schedule specified at the time of loan approval, then disbursements occurring in the years following loan approval will also be uncorrelated with contemporaneous macroeconomic shocks. Moreover, the long disbursement profiles observed on these loans imply that disbursements occurring in the years following loan approval are substantial: for the average loan in my dataset, only 22 percent of the original commitment is disbursed in the year that the loan is initially approved, and only a further 18 and 13 percent are disbursed in the first and second years following the approval year.
distinguish between industries that provide capital equipment and those that provide raw materials. In some cases, the finished goods inventories of upstream suppliers represent capital equipment while the raw materials inventory of the customer do not. Finally, we have used the U.S. input-output table rather than one for a developing country; it is likely, however, that at this level of aggregation, the input-output tables are not that different.

# 6. **Conclusion**

This paper has introduced a new cross-country dataset on inventories at the industry level into the literature documenting significant levels of inventory in developing countries. Given the high costs of capital in developing countries, usually in the 15% to 30% rate, the impact on unit costs are enormous. We have explored some broad causes of high raw materials inventory levels across countries in the 1970s and 1980s and can confirm the validity of two causes, infrastructure and poor markets, which have been suggested in case studies. Since high inventories are still a problem today in many developing countries, this paper should be useful in understanding one type of obstacle faced by manufacturing firms in these countries and from a policy standpoint, it indicates the direction to take to address the problem. The policy implications are clear, improvements in infrastructure, roads, ports and telecommunications can have a significant impact in reducing inventory levels, particularly when accompanied with appropriate and effective regulation. Likewise the development and deregulation of associated markets can also have a significant impact on inventory levels and then reducing the costs of doing business.

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allocation mechanism that divides the water among the parties by percentage rather than a fixed amount may reduce possible inter-state tensions when future water flows in the basin are uncertain. Other authors have also pointed to various types of variability management schemes (De Stefano et al. 2012). In situations of drought, for example, authors have discussed the utility of stipulations such as consultations, stricter irrigation procedures, water allocation adjustments, specific reservoir releases, and data sharing (McCaffrey, 2003; Turton, 2003; Drieschova, Giordano, and Fischhendler 2008). For flood issues authors have generally argued that the mere existence of a flood-control treaty may confer the necessary resilience (Bakker 2009) while others have stressed a specific flood-control mechanism such as warning systems, channelization schemes, and levees, among other schemes, as the necessary stipulations needed to confer resilience on river basins (Arnell 2002; Drieschova, Giordano, and Fischhendler 2008).

Perhaps even more important in addressing the uncertainty of climate change is the ability to predict the availability and accessibility of water flow data for river basins in near real time. In fact, the above discussed treaty mechanisms and stipulations are most efficiently negotiated and devised when reliable data are available. In this manner, negotiators and water policy makers are able to make educated decisions pertaining to which stipulations to include or which mechanisms should take priority. They are able to anticipate possible shocks or extreme events and design appropriate institutions. In the next section we describe a number of these existing methodologies, used to anticipate future flows, and then discuss the BWI.

# **EXISTING RUN-OFF ESTIMATION METHODOLOGIES**

Existing methods that assess global climate change in basins typically calculate river flow predictions in basins by calculating a percent change from a long-term normal base time period compared to predictions provided by the Intergovernmental Panel on Climate Change (IPCC) General Circulation Model(s) (e.g. Alcamo et al. 2003; Milly et al. 2005; Palmer et al. 2008). These methods have several shortcomings.
.** Speed of convergence for different incomes between OMS and NMS

||||OLS|||LSDV||
|---|---|---|---|---|---|---|---|
|||EU27|OMS|NMS|EU27|OMS|NMS|
|Mean total household income|Speed of Convergence,β|0.04%|0.00%|0.05%|0.21%|0.21%|0.24%|
||Half-life of convergence|1863|175913|1340|334|338|290|
|Mean agricultural income|Speed of Convergence,β|0.13%|0.13%|0.08%|0.72%|0.76%|0.65%|
||Half-life of convergence|550|525|903|97|91|107|

Notes: (1) Speed of Convergence is calculated using the coefficients of respective variables of interests; (2) Half-life of convergence is calculated as 0.6931/speed of convergence; (3) OLS columns report results using OLS regressions, while LSDV columns results using fixed effect models; (4) OMS are 14 old member states, while NMS are 13 new member states that joined the EU after 2004; (5) Data Source: EU-SILC, Eurostat (2005-2014) 5
“others” for our respondents might be.</mark> <mark>Social desirability bias—the tendency of respondents to answer questions in a way that is “appropriate” or “correct”, rather than a true reflection of their beliefs—can be an issue in measuring sensitive beliefs. Our survey instrument utilizes two approaches to overcome social desirability bias and elicit accurate responses</mark> — <mark>incentivizing survey responses about empirical and normative expectations</mark><sup>3</sup> <mark>, and posing vignettes, in addition to straightforward questions about one’s own beliefs or behavior, where respondents are asked indirectly about their preferences through short stories about hypothetical situations (very similar to their own), and asked what the fictitious character would do in that situation.</mark> <mark>While there is no standard framework to measure cultural schema or mental models, we rely on two types of measurements for this. The first are the set of normative and empirical questions that are reflective of one’s experiences in relation to their environment with regards to utilization and importance of latrines. The second is through analysis of free‐form narrative responses. These implicitly rely on people’s own concepts and associations to interpret the world around them (Crossley, 2002; Bruner, 1990). Narrative responses can evoke the mental models that organize principles for choice and action (Sarbin, 1986).</mark>

# **<mark>Data</mark>**

<mark>Data for this study come from two rounds of survey data—baseline and endline—which were preceded by a qualitative diagnostic round comprised of focus group discussions with two groups of latrine owners—users and non‐users—split by gender. Because the objective of this study was to examine how social norms and cultural schema influence latrine usage among rural latrine owners, we sampled a cluster of villages where latrine penetration was higher than average (based on
0.4 -0.4 -0.2 -0.1 -0.1<br>-2 -1.2 -1.1<br>-3<br>-4<br>-5 -3.7<br>-6 -5.5<br>Total<br>etc Agriculture<br>Construction Water etc<br>Manufacturing Community, Social, Mining and Quarrying Electricity, Gas and Financial, Real Estate, Transport, Storage etc Trade, Hotel and Restaurants<br><!-- End of picture text --> Note: Data is from CEIC. Figures show the difference between the average rate in 2015 and 2016 and 2007 and 2008.

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production (See World Bank 2022 for details).

**The number of firm observations increased over the sample period, possibly confirming new business creation.** Table 3 presents the distribution of firms–private firms and SOE–with revenue and employment data for the period 2011-2019. On average, SOEs represent about 0.24 percent of the sample, with most of the SOEs being directly owned by governments (88 percent) and owned by local or municipal governments (73 percent). Out of a total 613 4-digit NACE industries, SOEs in Romania operate in 192 different 4-digit NACE industries, representing about 31 percent of industries. There is no industry where an SOE is a monopoly. Overall, total sales of SOEs are greater than that of POEs in 24 industries, and in 27 industries, the total employment of SOEs is greater than that of POEs. Table 4 presents the distribution of firms with revenue and employment data for the period 2011-2019 across the sectoral taxonomies developed by Dall'Olio et al. (2022). Approximately 93.5 percent of firm-year observations are in competitive sectors, about 2.1 percent in partially contestable sectors, just 0.5 percent in natural monopoly sectors, and the remainder in sectors that are excluded from the taxonomy. There is a larger proportion of SOEs observations, about 10.3 percent, in natural monopoly sectors relative to competitive sectors, which feature just about 0.15 percent.

Table 3: Distribution of Firm-Year Observations (with Revenue and Employment Data), 2011-2019

||Observ|ations||State|-owned enterpri|ses (SOEs)||
|---|---|---|---|---|---|---|---|
|Year|All firms|SOEs|% Of<br>firms|% Directly<br>owned|% Indirectly<br>owned|% Centrally<br>owned|% Locally<br>
comorbidities and probability factors for severe COVID-19 at the district level. Individual probability factors that are included at this stage are based on existing literature and presently are HIV prevalence (without Viral Load Suppression), anemia prevalence, and obesity prevalence, in addition to respiratory illness. For each of these, prevalence was calculated at the cluster level and the same GAM model was fit to each outcome to generate predicted values at the 1km cell level. Once these gridded surfaces are created at a 1km resolution, estimates are reached at the district level by weighting, using a raster file of population count at the pixel level.

We then calculate the probability of having any of these comorbidities, defined as 1 minus the probability of having none of these comorbidities: <mark>p p =</mark> _<mark>1</mark>_ <mark>− (</mark> _<mark>1</mark>_ <mark>− (</mark> _pri_ ⋅ _pHIV_ ⋅ _pa_ ⋅ _po_ )) _<mark>where pco = proportion with a comorbidity, pri = proportion with respiratory illness, pHIV =</mark>_ _<mark>where pco = proportion with a comorbidity, pri = proportion with respiratory illness, pHIV = proportion with HIV (without viral load suppression), pa = proportion with anaemia and po = proportion with obesity.</mark>_ We apply the same geospatial methods to calculate the proportion of the population that is a smoker based on smoking prevalence within DHS clusters. We additionally calculate the proportion of the population that is over age 50 from WorldPop data.