rafmacalaba/data-use-mentions-extended huggingface dataset:
. In a sample of 36 LMICs, the survey data statistically significantly exceeds figures reported in administrative sources in 35 out of 57 survey rounds and by 47% on average. This pattern is particularly striking and consistent in Sub-Saharan Africa.

We investigate a number of potential explanations for this discrepancy, focusing on possible errors of representation and errors of measurement in phone survey data from five Sub-Saharan African countries. The gap between both data sources shrinks when accounting for selection bias at the respondent level but remains substantial in most cases. While we cannot experimentally probe the administrative data in the same way as the survey data, we present indicative evidence pointing to some weaknesses and inaccuracies in administrative data.

Our findings make several substantial contributions.

First, we show that substantial misalignment between survey-based and administrative vaccine coverage rates plagues COVID-19 data, the largest vaccination effort in history. Our study is the first to document these discrepancies in a cross-country sample and a low- and lower-middle 30
|Improvements to Land|||
|Other Assets|10|...|

_Source:_ Based on OECD (2001b) and expert consultation.

# **4.4 Quality Indices of Capital and Labor**

Based on the previous calculations, quality indices can be elaborated. The quality index of the labor force will only be used in the later growth accounting exercise since it already reflects improvements in human capital. However, the estimate for the quality of capital enters into the regressions. The following paragraphs are concerned with the construction of the indices for the quality of capital and labor, respectively, and a brief comparison over both indices for 1970-2002. The capital and labor quality indices yield interesting outcomes.

# **_Quality of Capital_**

One can calculate a quality index of capital by using the disaggregated capital stock data. The estimate follows the methodology advocated by Laurits et al. (1980) and

-25-
scores and other sentiment indicators (Ribeiro et al., 2016).

Using GDELT data, we construct the variable _Tone News Destinationsi,m−_ 1 _,t_ , which measures the average tone of all migration-related news articles published in month _m-1_ in year _t_ in the destination countries _j_ of migrants in FMP _i_ . This is done with the formula: where _Nji_ is the number of top destination countries indicated by migrants in FMP _i_ , _xz,ji,m−_ 1 _,t_ is the tone (i.e., the sentiment) of migration-related news article _z_ published in country _j_ in month _m-1_ in year _t_ , and _Nz,ji_ is the number of migration-related news article published in destination country _j_ in that period of time.<sup>18</sup> The variable _Tone News Destinations i,m−_ 1 _,t_ has mean -2.39 and standard deviation 0.59.

We construct the same index also for different subsets of topics in migration-related news published in destination countries. To this end, we calculate the following index: where _xztopic,ji,m−_ 1 _,t_ is the sentiment of a migration-related news article discussing a specific topic published in destination country _j_ of migrants in FMP _i_ in month _m-1_ in year _t_ , while the other terms are the same as in the previous formula. In our analysis, we consider the following topics: 1) discrimination (at destination); 2) sea incidents; 3) economic conditions (at destination); 4) crime (at destination); 5) government announcements (related to migration). Finally, we create a variable measuring the tone of the migration-related news articles referring to any other topic not included in this list.<sup>19</sup> Summary statistics for these variables are shown in Table A1.

# **4.3 Other data**

**Economic activity in Libya** To compute a proxy for economic activity around
69. While past tax reforms have made Slovakia ́s tax system simpler and easier to obey, the system of tax collection has not kept pace, which makes it still cumbersome for taxpayers to comply. In Slovakia a typical firm needs 325 hours per year for the compliance of all business taxes while in Estonia only 81 hours are neded. As a result, Slovakia ranks much lower (126<sup>th</sup> ) with respect to paying taxes in the World Bank‘s Doing Business report than Estonia (34<sup>th</sup> ) despite similar tax rates. The government is now in cooperation with the World Bank preparing a reform of tax collection (UNITAS), which after full implementation (planned for 2014) will unify the collection of general taxes and social insurance contributions, as is already the case in Estonia and some other NM-EU countries (Hungary, Bulgaria, Latvia, Romania, and Slovenia).

# **8.3 The Case of the Czech Republic**

70. The Czech Rebublic appears to be among those NM-EU countries that have made some progress in containing undeclared work. Acccording to the Statistical Office, undeclared work in the Czech Republic was 9-10 percent of GDP in 2006/2007, the second lowest after Estonia, with no change in recent years. According to the business surveys for the IMD World Competitiveness report, in the Czech Republic tax evasion is a less important factor for hampering business activity than in the other NM-EU countries.

71. Some characteristics of the Czech economy and economic policy have helped to contain undeclared work. The relatively highly skilled workforce amd moderate wage policies with a view towards maximizing employment have eased structural adjustments. As a result, employment in the formal sector has - prior to the recent economic crisis - remained at a relatively high level, which reduced the pressure on workers to work informally. The relatively low unemployment during and after economic transformation has led people to speak of a ―Czech miracle‖.<sup>
is compensated by the benefit from indirect subsidies (equivalent to 17 percent of market income for Decile 1). Nonetheless, there are a number of vulnerable households who live just above the poverty line, so the net impact on the cost of living from indirect taxes affects poverty (as felt by households if not measured officially). As is standard practice, we do not report the poverty impact of in-kind transfers, since these benefits are not cash (as all the taxes and other spending benefits are), nor are they considered when constructing the poverty line.<sup>53</sup> Even a progressive fiscal system does not necessarily imply net positive transfers for all poor households. The Fiscal impoverishment indicator quantifies how many people were pre-fiscal poor and made poorer by the fiscal system or were non-poor and made poor. Excluding in-kind transfers in health and education and using the national poverty line as benchmark, 4.2 percent of individuals have been impoverished by fiscal policy. In other words, they were poor to begin with and were made poorer because they paid more in taxes than they have received in direct or indirect transfers, or their market income was above the poverty line but their consumable income fell below. Jordan’s fiscal impoverishment rate is low among middle-income developing countries. Using the international poverty line for lower-middle-income countries at Purchasing Power Parity (PPP) ($ 3.2), the fiscal impoverishment rate of Jordan (0.2 percent) is close to that in Chile and Brazil (see Lustig and Higgins, 2017).<sup>54</sup> On the other hand, close to 15 percent of individuals were made richer by fiscal policy, that is they were lifted above the poverty line because received more in cash or near cash benefits than they paid in taxes and contributions, or, even if they remained poor, they were better off after taxes and transfers.

> 51 We measure marginal contributions to consumable income, except for in-kind benefits, for which the marginal
_._<br>0.102<br>_(0.131)_|
|Rural (yes=1)|-0.143|
||_(0.157)_|
|Household characteristics||
|Proportion of households who are self-employed farmers|0.363<br>_(0358)_|
|Proportion of income generated by wages|_._<br>-0.518<br>_(0.413)_|
|Proportion of population with some secondary education|<br>0.568<br>_(0.459)_|
|Household assets (excluding land)|<br>0.065<br>_(0150)_|
|Proportion who have a loan from a formal bank|_._<br>0.772**<br>_(0.304)_|
|Proportion of borrowers who are women|-0.288<br>_(0.420)_|
|Expenditure per capita|<br>-0.219***<br>_(0.049)_|
|Loan terms and conditions||
|VF would borrow more if funds were short|2.076***<br>_(0.170)_|
|VF could lend out another 1m baht safely|<br>0.937***<br>_(0.182)_|
|VF wants to borrow more from BAAC, GSB, etc.|<br>0.853***<br>|
||_(0.130)_|
|Memo items:||
|Number of observations|2,203|
|p-value for equation|0.00|

_Source:_ Village Fund Survey 2010 and Thailand SES 2009. 2,203 observations. Robust standard errors in parentheses. * p<0.05,
Corporate income tax|91.6|19.7|3.7|No|
|Taxes on property|21.1|4.5|0.9|No|
|Indirect taxes|155.0|33.3|6.3||
|Sales tax (goods and services)<sup>b</sup>|65.5|14.1|2.7|Yes*|
|Excise taxes<sup>c</sup>|41.4|8.9|1.7|Yes*|
|Customs duties (stamp taxes)|7.7|1.7|0.3|No|
|Taxes on exports|21.9|4.7|0.9|No|
|Other taxes<sup>d</sup>|18.53|4.0|0.8|No|
|Nontax revenue|133.8|28.8|5.5||
|Sales of goods/services|26.5|5.7|1.1|No|
|Property income/ fines, penalties/ voluntary<br>transfers|83.2|17.9|3.4|No|
|Miscellaneous revenues|24.2|5.2|1.0|No|
|Grants<sup>e</sup>|25.4|5.5|1.0|No|

_Source:_ Ministry of Finance.

*Revenue collections included may not be fully allocated within the HIECS 2015 for various reasons. See the section on methodology for detail on the allocative methods and assumptions.

a Personal income tax includes taxes from employment, other activity, and capital gain taxes. b Sales of goods represent 53.4 LE billion.

c Includes 39.7 LE billion of excise taxes on domestic goods.

d Other taxes include an 8.3 EGP billion discrepancy in MoF accounts.
. Global growth has slowed in recent years, and it is far from clear that the high rates of economic growth observed in the developing world during the past decade can be sustained for the next 15 years. Therefore, the pace of poverty reduction depends critically on engendering growth that reflects broadly shared prosperity and improves the living standards of the poorest. Evidence on where the extreme poor live, in which sectors they work, their demographic characteristics, and how they differ from the non-poor can help inform the strategies of country governments, multilateral development organizations and non-government organizations committed to reducing extreme poverty.

Serious knowledge gaps remain, however, about the characteristics of the extreme poor in the developing world, mainly due to the lack of globally harmonized household survey data. Countryspecific poverty profiles are valuable inputs into national poverty reduction strategies, but are not internationally comparable because each country adopts a unique national poverty line. Studies that combine data from multiple countries therefore convert welfare to a common currency using Purchasing Power Parity (PPP) exchange rates, which despite their many conceptual and practical shortcomings remain the consensus method to compare welfare across countries.<sup>3</sup> Cross-country studies based on PPP exchange rates, however, often have limited geographic coverage. Banerjee and Duflo (2010), for example, provide an extensive analysis of the poor’s economic behavior, including levels of saving, and investment in education and health, based on data for 13 low- and middle-income countries. Olinto et al. (2013) provide a preliminary analysis of the characteristics

> 2 Ferreira et al. 2015, Jolliffe et al. 2014, WDI 2016, Yoshida et al 2014.

> 3 See Deaton (2016) for a detailed discussion of the issues associated with using PPPs to make welfare measures comparable across countries.

2
57 The ratio of retail kerosene to diesel prices in each country in January 2012 is shown in figure A2.1. The fuel pricing policy in each country is described briefly in table A2.2.

Figure A2.1: Ratio of kerosene prices to diesel prices in January 2012

|0.49<br>0.36<br>0.31<br>Malawi<br>India<br>Nigeria|
|---|
|0.56<br>0.51<br> <br><br>Indonesia<br>Ghana<br>|
|0.59<br><br>Gabon|
|0.65<br>Angola|
|0.67<br>Sri Lanka|
|0.67<br>Cameroon|
|0.68<br>Zambia|
|0.72<br>Madagascar|
|0.72<br>Japan|
|0.73<br>Bolivia|
|0.75<br>Uruguay|
|0.76<br>Mozambique|
|0.77<br>South Africa<br>|
|0.78<br>Togo|
|0.78<br>Ethiopia<br>|
|0.79<br>Senegal<br>|
|0.80<br>Tunisia<br>|
|0.81<br>Argentina|
|0.81<br>Kenya<br>|
|0.84<br>Uganda<br>|
|0.84<br>Honduras<br>|
|0.86<br>Turkey|
|0.86<br>Niger<br>|
|0.86<br>Costa Rica<br>|
|0.90<br> <br>Pakistan<br>|
|091<br> <br>Dominican Republic<br>|
|0.97<br>.
Another well-known model-based approach, which is the one we adopt, is known as the behavioral equilibrium exchange rate.<sup>8</sup> This methodology, proposed by MacDonald (1997), is based on reduced form estimation of a long-run equilibrium relationship between the real exchange rate and a set of fundamentals, using Johansen’s (1995) cointegration techniques. The current value of fundamentals can be used to determine equilibrium exchange rates, and the difference with the actual exchange rate defines the magnitude of the misalignment.

The major advantage of these equilibrium exchange rate models, over the simple PPP framework, is that they relax the assumption of the equilibrium being static and allow for it to vary according to changes in fundamentals. However, they also have their caveats. For example, the FEER approach removes speculative capital flows from the medium-term capital account, making it difficult to account for short-term changes in the UIP condition that affect the dynamic adjustment path of the exchange rate. Moreover, the FEER approach is critically sensitive to trade elasticity estimates. Unlike the FEER, the BEER considers how changes in fundamentals affecting the capital account may impact the “behavior” of the exchange rate. This is critical for “countries that are experiencing substantial variation in short-term fundamentals (for relatively stable economies operating in the neighborhood of internal and external balance, the BEER would converge to the FEER)” (IMF 2007). However, the BEER approach relies on appropriate historical data availability, and is therefore difficult to implement in countries that have undergone substantial structural change or for which longer-term data are scarce. Sustained misalignment may also lead to misinforming results.<sup>9</sup> Another drawback is that very little economic theory guides the choice of economic fundamentals considered in the econometric analysis.

<u>Table 1: Equilibrium Exchange Rate: Alternative Concepts</u>

||**Definition of equilibrium exchange rate**|
|---|---|
|**Estimation-based appr**|
violent incidents as our main indicators of violence. However, for 2016 and on, we are also able to disaggregate each indicator by major types of violence- ground battles, remote violence (e.g., airstrikes), and thus we are unable to identify the share of non-responses related to active phone numbers. 9The survey captures the number of times households in the week before the survey have consumed from important food groups, the number of times households have resorted to five common food coping strategies, and whether the household received food assistance in the past 30 days. These questions form the basis for computing the household Food Consumption Score (FCS) and reduced Food Coping Strategies Index (rCSI).

> 10For households that were surveyed only after displacement, we can identify the origin governorate but not the specific district. Governorates are very large and similar in size to a state in the United States, while districts are much smaller and more similar to a county in the United States.

9
Kraay, A. 2004. “When is Growth Pro‐Poor? Evidence from a Panel of Countries.” _World Bank._ Kwon, H. and Yi, I. 2009. “Economic Development and Poverty Reduction in Korea: Governing Multifunctional Institutions.” _Development and Change,_ 40(4): 769‐792.

Legislative Council of Hong Kong. Undated. “Information Note: Poverty Combating Strategy in South Korea.” Research and Library Services Division, IN34/04‐05.

Leipziger, D. and Thomas, V. 1993. _Lessons of East Asia._ Washington, DC: World Bank.

<mark>Levy, S. and Walton. M. 2009.</mark> _<mark>No Growth without Equity? Inequality, Interests, and Competition in Mexico.</mark>_ <mark>New York: Palgrave Macmillan.</mark> <mark>Lipton, M. 2014. “Farming, Fertility, Food: Why Has Bangladesh Done Better than Expected? Can It Continue to Do So?” 1st Bangladesh Economic Forum Paper, Dhaka.</mark> <mark>Liu, Y. and Deininger, K. 2010. “Poverty Impacts of India's National Rural Employment Guarantee Scheme: Evidence from Andhra Pradesh.” Agricultural & Applied Economics Association 2010 AAEA,CAES, & WAEA Joint Annual Meeting Paper.</mark> <mark>Lobell, D.B. and Burke, M. 2009.</mark> _<mark>Climate change and food security: Adapting Agriculture to a Warmer World</mark>_ <mark>. New York: Springer Science & Business Media.</mark> Lopez‐Acevedo, G. 2008. “Mexico: Quality of Public Expenditure.” Note No. 4 – June 2008.

McCaig, B. and Pavcnik, N. 2013. “Moving out of Agriculture: Structural Change in Vietnam.” _National Bureau of Economic Research
13% among those living in solid houses (column 8), compared to the control. Again, we do not find impacts on respondents living in non/semi-solid dwellings (column 7). We also explicitly asked about malaria cases and testing behaviors. However, Annex Table D9 shows no significant impact of the ads on these outcomes. The general lack of effect on fever and malaria incidence throughout the campaign period can be attributed to the necessity of maintaining protective actions (such as consistently using bed nets) over an extended duration for them to yield health benefits. This justifies our decision to enlist a fresh cohort of respondents and gather data on malaria incidence after the campaign concluded.

# **5.2.2 Self-reported malaria incidence after the campaign**

Table 4 shows the impact on malaria incidence in the household since the launch of the campaign in August 2020, self-reported in the cross-sectional survey conducted 1-3 months after the end of the campaign. The table reports incidence both at the extensive (if any household member had malaria, columns 1-4) and intensive margins (share of household members who had malaria, columns 5-8). In both cases, the estimates suggest null impact of the ads on overall malaria incidence (columns 1-2 and 5-6). Yet, columns (3-4) and (7-8) unmask substantial heterogeneity in ad effectiveness, showing a significant effect on households living in solid dwellings. For them, the ad campaign reduced the probability that any member reported malaria by 2.4 p.p., a 44% reduction compared to control and, consequently, lowered the share of infected members by 0.8 p.p., a 53% decrease.

Figure 4 provides evidence about the role of bed net ownership on moderating the effectiveness of advertisements. The left panel shows that the reduction in malaria cases is more substantial and statistically significant for households living in solid dwellings equipped with at least one bed net. The right panel shows that among households with bed nets, the decrease is
|**_ES9_**|10.06|9.82|||||
|**_ES16_**|11.88|12.13|||||

Notes: (1) The data used are CFPS 2016 with first-born children aged 18-35. (2) IGMEK is Intergenerational Marginal effect when father has K years of schooling. (3) ESK is expected schooling when father has K years of schooling. K=0,6,9,16. (4) Robust standard errors in parentheses; standard errors clustered at province-level in square brackets; *** p<0.01, ** p<0.05, * p<0.1.
- -71

Based on a rating of one to five, the performance of utilitles In the 1908s appears, on average, about the same as during the late 19608 and 19709. The average utility falls In the "fair" range. In the mid-1970s, the percentage of utilltles with poor performance Increased quite sharply (33%), due In part to the Bank lending for the first time to new Borrowers with limited resources and experience.

W. Africa had the lowest overall rating of Institutional performance and E. Asia the highest. These findings should be viewed with great caution because of the different circumstances of utilities In the 1980s, especially In terms of the Increased magnitude of operatlons and changing external environment, and because of the subjective nature of the evaluation of pe'ormance.

The project audit reports Include a rating of the extent to which utilities achieved the Institutional targets set at appraisal. About 74% of projects were assesed as fair to good at having achieved institutlonal targets and another 6% met such targets to a llmited extent. Twenty percent of projects were judged Ps having not met them at all. Unfortunately, Institutional targets are often not clearly stated In many appraisal reports and more often than not the completion reports and appraisal reports were prepared by different project staff. It Is likely, therefore, that institutional performance has been assessed as poor only In those cases where there were obvious Institutional problems rather than when there were continuing but less glarlng Inefficiencies.

# 2.9 Conformitv With Loan Covenants

Conformity with loan covenants was assessed for projects Included In the two sample surveys of projects Implemented from 1968-83. Rating of the utnitles' conformity wlth loan covenants was based on a scale of one to five where one equalled 100% conformity and five no conformity at all. On average, there were seven malor covenants per project. Those covenants that are repeatedly found In loan agreements mostly concern
# 3. Methods

## 3.1 Data

The analyses described in this paper are drawn from two recent large-scale assessments of education: TIMSS 2019 and Saudi Arabia’s NALO 2018. TIMSS provides a robust nationally representative sample, while the NALO study was conducted to provide regionally representative information (requiring a much larger sample size) as well as national-level data. Although the paper focuses on the national level in the analyses described below, it should be noted that subsequent analyses at the regional level would be possible using NALO data.<sup>5</sup> In addition, there is a substantial degree of overlap between the content covered by the TIMSS and NALO contextual questionnaires, although some variables appear in one study but not the other, or they are presented in slightly different formats. The primary analysis, reported in depth in the next section, is conducted using TIMSS 2019 data. Given the high degree of overlap between the two studies’ focus on mathematics and science, at the same two grade levels (grades 4 and 8), data from NALO 2018 are used to supplement this primary analysis by drawing on variables of particular interest to Saudi Arabia that have no equivalents in TIMSS. In other words, NALO 2018 data are used as supplementary information to shed additional light on questions arising from the multilevel analysis of TIMSS data. For this reason, NALO data are described in text, with full tabulation of the NALO analyses presented in appendix A1.

### _3.1.1 TIMSS 2019_

TIMSS is a study of the International Association for the Evaluation of Educational Achievement (IEA). It assesses mathematics and science achievement at two grade levels, grades 4 and 8. TIMSS has been carried out every four years since 1995. In 2019, 64 countries participated in TIMSS, including Saudi Arabia. In addition to providing countries with robust data on mathematics and science achievement, TIMSS collects a wealth of contextual data from students, parents, teachers, and school principals. In Saudi Arabia, 5