rafmacalaba/data-use-mentions-extended huggingface dataset:
Self-assessment rental values are often used for the construction of important economic indicators, such as CPIs (as measures of inflation), national accounts, and income and consumption aggregates for the analysis of household living standards. Further research should concentrate on how household budget surveys, which are the typical source of information for these matters, might be improved to capture rental market values more accurately. Possible ways forward might include the oversampling of atypical housing types (including the oversampling of tenants), expert assessments of the dwellings of the sampled households, and linking sampled populations to administrative data containing dwelling prices.
## **Figure 4: One-step ahead forecasting scheme**

We use the following setup to make one-head ahead forecasts, in order to reflect the information available for decision makers at each point in time. The black lines and dots represent the observed data based on which the model is estimated. The red squares represent the one-step ahead forecasts. The estimation period is at least 15 time observations long which is about half of the total available data.

<!-- Start of picture text --> t t+1<br>... ...<br>... ...<br>... ...<br>T-1 T<br>... ...<br>Oct 2009 Apr 2013 Feb 2019<br>Minimum estimation period Expanding forecasting window<br>... ... ...<br><!-- End of picture text -->

# **5 Forecasting**

Using the selected variables and estimated model parameters we now demonstrate the model’s prediction performance. At each point in time we calculate the one-step ahead prediction and compare it to the famine risk distribution of the next FEWS NET release. This imitates the situation of policy makers, who decide based on the available information. Let I _푇_ denote all information since the beginning of recorded famine history in October 2009 until the hypothetical decision date _푇_ . Then the one-step ahead prediction is defined as In order to avoid incorporating future information in the one-step ahead predictions, which would not have been available, we estimate the model only using the information until _푇_ and then make a forecast for _푋푇_ +1| _푇_ . We then re-estimate the model with information until _푇_ + 1 and make a forecast for _푋푇_ +2| _푇_ +1. The minimal observation period is at least _푇_ = 15 such that there is sufficient information to fit the model. We note, however, that the set of selected variables in Tables
toilet facilities, and electricity. Coverage bias was not much of a concern in Nigeria, but this can be largely attributed to relatively higher mobile phone penetration in the country compared to Ethiopia, Malawi, and Uganda.

However, a more serious problem and common in all four countries is non-response bias due to unsuccessful contact with the respondent. The successfully contacted sample was biased towards wealthier households with higher living standards. This bias can be largely attributed to the difficulties contacting respondents either as a result of poor network reliability, respondent’s phone being turned off or unpowered, or the respondent not picking up the phone. This shows that even though higher phone penetration can lead to lower coverage bias, there is still substantial potential for bias due to unsuccessful contact with respondents in these countries. On the other hand, bias due to non-interview (refusals and breakoffs) did not introduce much additional bias in any of the countries.

The overall bias found is substantial and widespread across different characteristics. The bias nearly always tends to favor wealthier households and thus poorer households are underrepresented. This direction in the bias, left unaddressed, would result in biased estimates from the interviewed sample that do not fully reflect the situation of poorer households in the county. This is a population of critical interest to policy makers since poorer households are likely most vulnerable to the negative impacts of the COVID19 crisis. Counteracting this bias is therefore essential to ensure that the results more closely reflect the reality of the poor and provide accurate information to policy makers.

The study has shown that these phone survey biases can be substantially reduced by applying survey weight adjustments using information from the representative F2F survey from which the sample is drawn. This was demonstrated using a wide array of demographic and socioeconomic variables that are often included in standard household consumption and well-being measurement surveys. While these bias adjustments did not fully eradicate the bias across all dimensions, they were highly effective at reducing bias. This highlights one advantage to drawing phone survey samples from existing face-to-face, representative surveys rather than
we introduce here the CPD approach based on the CPI price data. Before getting there, however, we briefly touch upon another approach in spatial adjustment, which calculates a spatial price index based on multiple poverty lines.

## _Multiple poverty line approach_

An alternative and less common approach to measuring poverty is to construct regional poverty lines that reflect regional differences in the costs and needs to achieve the threshold utility. Poverty is measured by comparing each household’s nominal (that is, not spatially deflated) consumption aggregate to the region’s poverty line.<sup>15</sup> An advantage of this approach is that regional price levels of non-food items can be flexibly calculated. Since non-food allowance is typically determined based on the expenditure shares of households around food poverty lines (Ravallion and Bidani 1994), price data for non-food items is not required. When analyzing inequality or comparing household welfare across regions, one can construct a spatial price index by taking ratios of regional poverty lines. Due to the complexity of the methodology, however, regional poverty lines are used in only a few countries. The analysis of this approach is outside the scope of this paper.

## _CPD approach_

A challenge in spatial comparison of prices is that some items are not sold in some regions, and therefore, those prices are missing when calculating a price index. The CPD approach was originally developed to handle such missing values in price observations across regions (Rao 2004, 2005; Summers 1973).

15 Examples include Ravallion and Lokshin (2006) for the Russian Federation; Jolliffe, Datt, and Sharma (2004) for the Arab Republic of Egypt, and Marivoet and de Herdt (2015) for the Democratic Republic of Congo.

11
Estimating the Economic and Distributional Impacts of the RCEP simulates the main policy changes expected under the agreement. This analysis builds on an earlier study by Ferrantino et. al. (2019),<sup>7</sup> and the full details of the CGE model are presented in van der Mensbrugghe (2019).

The model was calibrated with the latest available information, using the GTAP database v.10 (Aguiar et. al., 2019). The aggregation of regions and sectors for the model is presented in tables AI.3 and AI.4 in Appendix I. We included most RCEP members and their relevant partners outside the region, i.e., Europe, the United States and India. Tariffs are from the MacMap<sup>8</sup> database and are reduced over time in the baseline considering all agreements in force. It includes agreements in force in the region, such as CPTPP, China-Korea FTA, and ASEAN. Finally, tariffs were also adjusted in order to include the tariff increases implemented between China and the US starting in 2018 (Li, 2018). The information was taken from Li (2018). The model was calibrated with ad valorem equivalents of nontariff measures from Cadot, Gourdon, and van Tongeren (2018) for goods and Hoekman and Shepherd (2019) for services. However, we also run the model with AVEs of non-tariff measures for goods from Kee and Nicita (2018) in a sensitivity analysis.

The baseline, or Business-as-Usual (BaU) scenario, runs from 2014 through 2035. The baseline targets real GDP growth and calibrates labor productivity, which is then fixed in the simulation scenarios. GDP projections are taken from the World Economic Outlook database, November 2019 (IMF 2019). Thus, the BaU does not consider the contraction in GDP due to the COVID-19 pandemic. While for most countries the GDP per capita growth rates are quite stable in the long run, some quickly aging economies or those with slowing population growth rates
_Plasmodium falciparum_ malaria for 2007. These estimates are then compared with those derived under existing surveillance-based approaches to arrive at the final data used in the malaria mapping (Hay et al., 2009).

<u>(http://www.map.ox.ac.uk/media/maps/pdf/mean/World_mean.pdf, accessed 2011)</u> Malaria maps generally separate the malaria endemicity into three broad categories by _Plasmodium falciparum_ parasite rate (PfPR), a commonly reported index of malaria transmission intensity: PfPR < 5% as _low_ endemicity, PfPR 5%-40% as _medium/intermediate_ endemicity, and PfPR > 40% as _high_ endemicity. In the _low_ endemicity category, there may not be much use of DDT as other measures of vector control can be generally adequate to eliminate malaria. In the _medium/intermediate_ endemicity range one can expect to see the use of Insecticide Treated Nets (ITN)/Long-lasting insecticide treated nets (LLIN) along with targeted IRS to interrupt malaria transmission. In the _high_ endemicity range, however, DDT is likely to have widespread use in IRS.

In this study, global mapping techniques were used to estimate population exposed to DDT. This involves combining spatial information of endemicity areas and administrative areas and then summarizing the estimates of population by the combined areas.<sup>15</sup> First, the malaria endemicity maps were overlaid on global population maps from Landscan 2005<sup>16</sup> (Dobson, 2000) and country-level population exposure in the three endemicity areas were then computed. Figure 1 presents country level estimates of the population exposure to DDT that is expressed by Malaria endemicity categories in 71 countries known for malaria prevalence. Table 3 summarizes the vulnerable population estimates for each category of malaria intensity by presenting the top-10 malaria cases based on total exposed population.

# **[Insert Figure 1]**

> 15 Due to the spatial reference of the data and the number of observations in the
(presented in Table A1) depending on the type of status chosen. Most of these potential benefits are related to the possibility to apply for bank services, or to access new markets like government and large companies’ contracts. The _entreprenant_ status gives access to all advantages except the rights to export and to access large public contracts. It explicitly targeted micro and small businesses managing one type of activity with a limited turnover.<sup>3</sup> Businesses with multiple activities or with turnover greater than a threshold in two consecutive years will lose _entreprenant_ status and have to adopt the individual enterprise status.

When they formalize, businesses get a unique fiscal identifier and are registered with the tax administration. Accordingly, the main potential cost of formalization is related to taxes. In Benin, the link between formalization and taxes is complex and varies according to the business. In theory, all businesses with a fixed location would pay taxes even if they were informal. Before the reform of the tax system affecting microentrepreneurs was introduced in 2015,<sup>4</sup> which will be used to calculate the tax owed in 2016, there were four different tax regimes that could apply to informal businesses in Cotonou, depending on their location and economic activity.<sup>5</sup> The regime most commonly applicable to micro, informal businesses was the TPU ( _“Taxe Professionnelle Unique”)_ and was calculated based on the rental value of the business premises. However, in the majority of cases taxpayers did not have a lease contract, the only official and opposable proof of rental value. As a result, the law assigned the tax administration the responsibility for assessing the rental value. This assessment often left a door open for discretion. In practice, tax inspectors estimated businesses’ ability to pay based on their appearance and on discussion with business owners.

Data from our baseline survey (see Table 1) show that more than 70 percent of firms think it is difficult to know in advance
# **3. Violent Conflict and Oil**

24. A large number of quantitative studies have examined the role of mineral wealth in general, and petroleum wealth in particular, on the onset, duration, and intensity of violent conflict.<sup>9</sup> A country‘s petroleum wealth – measured as the value of oil and gas produced per capita – is strongly correlated with the likelihood that a conflict will begin. The relationship between oil and conflict risk is non-linear, as Collier and Hoeffler (2004) have suggested: while modest sums of petroleum wealth can increase a country‘s conflict risk, exceptional oil wealth – enough to raise per capita incomes above about $5000 – makes countries sufficiently rich that their conflict risk begins to drop. To put it differently, oil wealth only raises the risk of conflict in countries with relatively low incomes; if they are rich enough – even if they became rich through their oil – insurgencies become less likely.<sup>10</sup> Fjelde (2009) notes that political corruption may not necessarily be associated with higher conflict risks in oil-rich states, because this is inherent to the stability of the regimes themselves. But political corruption in turn generates particular symptoms and outcomes that pose challenges for more ―virtuous transitions.‖ 25. To describe conflict trends globally and in the MENA region (Section 4), we use the Armed Conflict Dataset (ACD), originally produced by Gleditsch et al. (2002) and updated on the website of the Peace Research Institute of Oslo; it is the most transparent and complete list of armed conflicts around the world.<sup>11</sup> It records both ―minor‖ conflicts (which cause from 25 to 1000 battle-related deaths in a calendar year) and ―major‖ conflicts (which cause more than 1000 battle-related deaths). Table 4 displays some simple cross-tabulations, showing the annual conflict rate among countries in different categories.<sup>12</sup>
2.57|-0.04|2.67|3.65|0.98|
|Sri Lanka|1.99|1.26|3.77|-1.78|3.50|3.75|0.25|
|Tajikistan|2.73|0.38|2.89|-0.15|-0.21|2.66|2.88|
|Tanzania|2.68|1.11|4.66|-1.99|1.56|3.26|1.70|
|Togo|3.35|1.25|5.28|-1.93|-2.91|1.44|4.35|
|Uganda|2.54|0.83|4.21|-1.67|0.43|3.36|2.93|
|Ukraine|2.49|0.14|2.62|-0.13|-0.31|2.30|2.60|
|Uzbekistan|4.23|0.60|5.22|-0.99|-1.37|3.67|5.04|
|Vietnam|1.80|0.75|4.13|-2.33|-1.76|0.82|2.58|
|Yemen, Rep.|2.10|0.28|3.07|-0.97|0.14|2.14|2.00|
|Zambia|7.44|0.46|8.61|-1.17|0.72|7.84|7.12|

Notes: Authors’ calculations based on HIT data. The table presents the gains associated with the elimination of country’s own agricultural import tariffs.

32
therefore, to strengthening the powers, resources, and independence of statutory regulators. IOSCO and other multilateral organizations, including the World Bank, have supported this effort by producing international standards and benchmarks in numerous areas. Market scandals have been another catalyst to strengthening government regulators, in response to pressure to increase standards of conduct in the markets. Government bodies have much broader jurisdiction and powers than SROs, and they are seen as stronger, more effective regulators.

- _Trend to consolidation of financial regulators:_ A global trend toward consolidation of financial regulators is another factor in reducing reliance on self-regulation. Some governments believe that regulation is more efficient if fewer government agencies, rather than multiple public and private agencies, carry it out on a centralized basis. Others argue that the system benefits from the checks and balances inherent in the involvement of SROs, exchanges, the industry, and the regulators in response to the issues, which improves the content of new initiatives.

_-_ A survey sponsored by the World Bank (de Luna Martinez and Rose 2003) found that even five years ago 46 countries had consolidated some financial regulatory supervision (although the scope of regulatory powers of those agencies varied significantly). In the most advanced form of consolidation, some countries have created universal financial regulators that extend to banking, insurance an other sectors, such as the UK’s Financial Services Authority.<sup>13</sup> The FSA supplanted a number of self-regulatory bodies, including the London Stock Exchange (LSE), which even lost its responsibility for listing rules.<sup>14</sup> The creation of a universal regulator does not necessarily mean the elimination of selfregulation, but its use is often reduced significantly in such cases, especially if a country aims to adopt the UK model. Other areas of the financial services industry, such as banking, do not share the securities industry’s tradition of self-regulation. Finally, major reforms such as creation of a new central agency invariably include expanding the power and authority of
2 .15<br>.15<br>.1<br>Financial Products<br>Subsidized Healthcare .1<br>.05 .05<br>0 0<br>.25<br>.2<br>.15<br>.1<br>Transfers from Government .05<br>0<br><!-- End of picture text --> _Notes:_ Each graph depicts the bias-corrected estimator 200 days around June 8, 2018, when the RAMV census closed, and the mean squared error (MSERD) optimal bandwidths. Confidence intervals are at the 95% significance level.

87
individuals are deemed poor. This threshold pertains to the minimum amount of Nepalee Ruppe to satisfy minimum caloric requirements and basic needs for nonfood goods and services. In 1995, the official poverty rate of 42 percent was based on the poverty line of Rs. 5,089 per person per year. In 2003, the poverty line of Rs. 7,696 was used after adjusting for inflation between the two survey rounds.<sup>25</sup> This is to ensure that the same standard of living is used to identify the poor across the two survey rounds so that Rs. 7,696 in 2003 should afford the same basket of goods and services Rs. 5,089 afforded in 1995.

If the original poverty line was used to estimate poverty in 2010 after inflation adjustments, the poverty line would have been Rs. 14,316 and the resulting poverty rate would have been as low as 12.5 percent. This, however, was not adopted as the official poverty rate because of the growing perception and mounting evidence about improving living standards in Nepal. Overall economic well-being in Nepal had improved so much that the definition of what it meant to be poor in 1995 became obsolete by 2010. Some of what used to be luxury became necessity. For example, in 2010, Nepalis on average consumed more expensive food items such as meat and eggs and less cereals than 15 years ago. They also spent more on nonfood goods and services than they did in 1995.

In order to reflect the evolving way of life and the current consumption patterns in Nepal, a new poverty line was estimated using consumption data from the 2010 NLSS.<sup>26</sup> The new poverty line was set at Rs. 19,262, an increase in real value by 35 percent compared to the original poverty line in 1995. The poverty rate using the new poverty line was 25 percent in 2010. This is based on

> 24 Another notable change in the 2010/11 NLSS was the
5.2|8.7|5.8|3.6|3.3|
|Secondary sector|71.1|52.4|44|38.1|36.2|
|Tertiary sector|23.7|38.9|50.2|58.3|60.5|
|_Shanghai_||||||
|_Employees_||||||
|Primary sector|34.4|11.1|9.8|10.8|11.6|
|Secondary sector|44.0|59.3|54.5|44.3|41.2|
|Tertiary sector|21.6|29.6|35.7|44.9|47.2|
|_GDP_||||||
|Primary sector|4.0|4.3|2.5|1.8|1.7|
|Secondary sector|77.4|63.8|57.3|47.6|47.6|
|Tertiarysector|18.6|31.9|40.2|50.6|50.7|

_Source_ : Beijing Statistic Yearbook 2002; Shanghai Statistic Yearbook 2002.

**Table 3:The employees in state-owned sectors in Beijing and Shanghai in selected years**

|% ofemployees|1978|1990|1995|2000|2001|
|---|---|---|---|---|---|
|Beijing|54.2|57.1|53.8|45.7|40.4|
|Shanghai|86.2|71.9|59.7|55.7|54.4|

_Source_ : Beijing Statistic Yearbook 2002; Shanghai Statistic Yearbook 2002.

10
with an analysis of declaration patterns is useful to identify and target some high-risk economic operators.

Third, in order to assess the compliance of a declaration, investigators should use all sources of available information, whether internal or external.<sup>38</sup> Abnormal statistics (inconsistent unit values, etc.) suggest fraud. The use of the internal information, that is, using declaration patterns of "competing importers", can help identify non-compliance. Similarly, the use of external sources of information, such as information gleaned from sources other than customs databases, can also reveal bad practices. For instance, based on a comparison of declared unit values with international market prices, we have highlighted probable misclassifications for fertilizers.

Fourth, only effective inspections have a deterrence effect. As highlighted for rice imports, non-compliance may be frequent despite an inspection rate close to 100 percent.

> 38 See Chalendard (2015).

17
a possible research agenda moving forward.

# II. Data

# A. Sources and definitions

The data on social spending come from a variety of sources and adopt different definitions that are discussed below. Information on the variables and the sources used for our analysis is summarized in Table 1, and the countries included in the analysis are listed in Table A.1. We combine 3 sources of data to make the first compiled data set of social sector spending for human capital:

- 1) **Health** : Government health expenditures are aggregated and refined by the World Health Organization (WHO), resulting in the Global Health Expenditure Database (GHED) (World Health Organization 2018), commonly considered the most reliable source. The data set used for this analysis is curated by the World Bank’s Health Financing Global Solutions Group (Tandon et al. 2018). The data include total government spending on all aspects of health, not distinguishing by age group or malady. WHO provides ample warning against combining estimates of current and capital health expenditures due to the deemed extremely low quality of the capital expenditure

> 2 Moreover, private spending might fulfill different functions – an indispensable substitute when public services fall short of minimum quality (which is often the case in low income countries) or simply allow users who can afford it to buy the preferred bundle of services.

3