rafmacalaba/data-use-mentions-extended huggingface dataset:
1.2 cross-country evidence on revenue recovery

7

- Inflation: reflects the extent to which revenue is generated from seigniorage, which needs to be controlled for.

- VAT: a modern VAT regime that is strictly enforced is associated with increased domestic revenue collection; however, a weakly enforced VAT system with widespread exemptions could be revenue-reducing compared to taxes collected at fixed border points.

# 1.2.2 _Data_

The IMF’s Government Finance Statistics is the best publicly accessible source for cross-country data on tax revenue, but it is incomplete and suffers from mismeasurement. I therefore use the same panel data as that used by Baunsgaard & Keen (2010) who adjust the GFS data by cross-checking numbers with internal IMF figures obtained through (“Article IV”) consultations with individual countries. They try to correct a common flaw in many countries where tariff and VAT revenues are conflated if they are both collected at the border. This would be problematic for the exercise in this paper because the aim is to find out whether decline in tariff revenues are made up for by domestic sources like VAT and excise.

I make three modifications to Baunsgaard and Keen’s data set. First, their data on VAT is only a binary variable of whether the country had VAT in place in the year concerned. I use in its place actual ad valorem rates, compiled from three different sources as follows: Krever (2008), Ernst & Young (2008) and World Bank 2011 _a_ . Second, I confine my analysis to 40 low-income countries over a shorter time period of 25 years, from 1982 to 2006.<sup>11</sup> Third, I use two new measures for a country’s abundance in natural resources as an additional explanatory variable. The first measure is the per capita natural resource-based exports (belonging to

> 11 Five countries drop out of the regression because of incomplete data on inflation and per capita income, as follows: Comoros, Guinea, Myanmar, Sao Tome and Principe, and the
**<u>Table 3: Regional Decomposition of Public Education Spending Changes (%), 1998–2017</u>**

||Real change in<br>|Source of<br>|changes in public e<br>|ducation spending<br>|
|---|---|---|---|---|
||public education<br>spending|Economic<br>growth|Total public<br>spending as a<br>share of GDP|Education<br>prioritization –<br>education spending as<br>a share of the total<br>government budget|
||(1)|(2)|(3)|(4)|
|East Asia and Pacific|251|215|69|-33|
|Europe and Central Asia|42|34|6|2|
|Latin America and the Caribbean|137|86|37|14|
|Middle East and North Africa|15|37|-8|-14|
|North America|134|189|13|-68|
|South Asia|81|75|22|-16|
|Africa|116|91|15|9|

_Source:_ World Bank calculations based on UIS and IMF data. _Note:_ Education spending is measured in PPP (constant 2015 dollars). Average percentage growth in all variables **– –** calculated as the percentage change from the four-year average in 1998 2001 to the four-year average in 2014 17, for all countries with data available in at least one year across both four-year periods. Column 1 shows the total percentage change in education spending. Columns 2 – 4 break down the overall change into its component parts (see Box 5). Column 1 is equal to the sum of columns 2 – 4 with small differences due to rounding errors.

Boosting the priority given to education in government
eradicated from Tanzania, and the government reduced testing and monitoring efforts and ordered schools to reopen.<sup>17,18</sup> Case counts, hospitalization, and mortality data were unavailable; however, informal reports suggest that there were multiple waves of respiratory illness and deaths in the subsequent months. Despite this, the government did not endorse mask-wearing or vaccination.<sup>18</sup> In March 2021, President Magufuli died and was replaced by President Samia Suluhu Hassan, who announced plans to develop a coronavirus task force and urged the citizens of Tanzania to take preventive measures, including social distancing.<sup>17</sup> The President warned of a third wave in June 2021, though masks and vaccination efforts were not publicly endorsed. The Ministry of Health, Community Development, Gender, Elderly and Children (MOHCDGEC) then changed course, submitting a request to the global COVAX program, an initiative that is co-led between Gavi, the Vaccine Alliance, WHO, and the Coalition for Epidemic Preparedness Innovations to improve global equitable access to COVID-19 vaccines. Government officials subsequently reported 176 incident cases and 29 deaths during the “third wave”, recommended mask use, precautionary measures, and began a vaccination campaign.<sup>19</sup> By June 1, 2022, 33,928 cumulative cases and 803 deaths had been reported,<sup>15</sup> likely a significant underestimation of the true toll.

# **Study population and design**

The _Rewarding STI Prevention and Control in Tanzania II_ (“RESPECT II”) study<sup>20</sup> was conducted from May 2018 to January 2022 in Dar es Salaam, Tanzania, which has an estimated FSW population of 155,450 and an HIV prevalence of 26% for FSW.<sup>21</sup> The primary aim of the study was to investigate the effect of a lottery intervention on combined HIV/STI incidence among FSW. In May 2018,
For total consumption, food and non-food consumption, respectively. The imputation process requires not only these shares but also the consumption of comparable items ( ) of those in previous rounds. ii2022 Second, the two-step random hot deck imputation methodology is implemented following Fernandez, yy Olivieri, and Sanchez (2023), whose analysis is based on the approach proposed by Lucchetti et al. (2020). This method utilizes PMM (Predictive Mean Matching), LASSO regression, and resampling techniques to predict the 2010 and 2016 consumption aggregates comparable to the data collected in the 2022 HIES. This methodology aims to accurately rebuild the poverty and inequality trends that Bangladesh has experienced over the last 12 years while avoiding some assumptions behind parametrical alternatives. The implicit approach behind PMM-like methods (Little and Rubin 2002) does not require defining an explicit model for the distribution of the missing values. Because of this, predictive mean matching is less vulnerable to model misspecification than traditional parametric methods (Van Buuren, 2018).

Morris, White, and Royston (2014) conducted simulation studies to contrast the results of PMM versus a parametrical approach under different data missingness scenarios (entirely at random, at random, and not at random). They demonstrated that PMM-like methods are effective when the underlying imputation model is accurately specified. Furthermore, this approach offers an enhancement compared to parametric imputation in scenarios where the imputation model is not correctly specified. These findings are consistent with Schenker and Taylor (1996), who compared partially parametric (PMM-like) and fully parametric regression-based multiple-imputation methods. They found that a fully parametric approach yields marginally more efficient estimates of the outcome's marginal distribution function than the partially parametric methods. However, when there is a misspecification in the mean structure, all methods remain effective in estimating the marginal mean. However, the fully parametric approach exhibits a slight rise in bias and variance. In estimating the marginal distribution function, the fully parametric method encounters difficulties in various scenarios, while the PMM-like methods continue to perform well. In addition
that fell into poverty by the third wave.<sup>10</sup> A household is resilient if it was never poor in all three survey waves or escaped from poverty by the third wave.<sup>11</sup> Because we can only observe whether a household is vulnerable or resilient at the third survey wave, we generally use covariates in the third wave in a cross-sectional sense.

# 4.2.2 Independent variables

In addition to the usual socioeconomic and demographic factors that may influence whether a household is vulnerable and its level of resilience, we computed variables to measure rainfall variability and exposure to climate shocks. We computed a _Standard Precipitation Index_ (SPI) that measures rainfall variability following Patel et al. (2007). Using 30-year spatial rainfall data, we computed the SPI as in Equation 1: where _SPIit_ is the SPI for household _i_ in year _t_ , _cagrainit_ is total rainfall for the agricultural season in year _t_ , _bar_cagrain30_ is the average seasonal rainfall over the last 30 years, and _sdagrain30_ is the standard deviation for seasonal rainfall over the last 30 years.

We adapt the approach of Azzarri et al. (2020) to compute a _drought risk variable_ that measures exposure to climate risk as seasonal rainfall that is less than the average seasonal rainfall at the enumeration area level minus two standard deviations of the enumeration area seasonal rainfall. We define _flood risk_ as seasonal rainfall that is more than the average seasonal rainfall at the enumeration area level plus two standard deviations of the enumeration area seasonal rainfall. We measured _rainfall variability_ using the coefficient of variation of the 30-year rainfall and a measure of _rainfall stress_ . Following agronomic recommendations as implemented in Ngoma et al. (2015), we define _rainfall stress_ as the number of consecutive 20-day periods with less than 40mm of rainfall.<sup>12</sup> We also include _30-year average rainfall_ to measure the long-term effects, and the current growing season corresponding to the survey reference period to measure short-term effects. In addition to
5 grow more rapidly at the center and if growing local spending requirements are to be met, there will be the need for increased flows of funds from higher to lower levels of government.

Whether or not gram.s ave been taking on a more prominent role in developing countries during the recent past is an empirical; unfortunately, as is so often the case with financial information on local governments in developing countries, the data necessary ta examine these trends are scarce. Furthermore, there are some important conceptual issues surrounding the definition of intergovernmental grants.

Measurement Issues Since different revenue administration arrangements are utilized in developing countries, issues arise concerning what is, in fact, an intergovernmental transfer. Taxes imposed and collected by a local Jurisdiction are most reasonably considered own source revenues even though it is often the case that lower level governments have no autonomy in determining the tax rates imposed or in defining the tax base. The issue is less clear cut when one level of government collects the tax and then distributes the proceeds to another governmental body. The International Monetary Fund, which probably provides the most extensive and complete set of data on public sector finances throughout the world, uses the following guideline: "...it may be useful to attribute tax revenues to noncollecting beneficiary governments (1) when they have exercised some influence or discretion over the setting of
Policy Research Working Paper 10907

# **Abstract**

How reliable are public debt statistics? This paper quantifies the magnitude, characteristics, and timing of hidden debt by tracking ex post data revisions across a comprehensive new database of more than 50 vintages of World Bank debt statistics. In a sample of debt data covering 146 countries and 53 years, the paper establishes three new stylized facts: (i) debt statistics are systematically under-reported; (ii) hidden debt accumulates in boom years and tends to be revealed in bad times, often during IMF programs and sovereign defaults; and (iii) in debt restructurings, higher hidden debt is associated with larger creditor losses. The novel data is used to numerically discipline a quantitative sovereign debt model with hidden debt accumulation and an endogenous monitoring decision that triggers revelations. Model simulations show that hidden debt has adverse effects on default risk, debt-carrying capacity and asset prices and is therefore welfare detrimental.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at shorn1@worldbank.org; dmihalyi@worldbank.org; philipp.nickol@vwl.uni-due.de; and csosapad@nd.edu.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of
10 An effective innovation system provides an environment that nurtures research and development (R&D), which results in new goods, new processes and new knowledge, and hence is a major source of technical progress. The OECD (OECD, 1993) defined R&D to “comprise of creative work undertaken on a systemic basis in order to increase the stock of knowledge and the use of this stock of knowledge to devise new applications”.

There have been a number of studies that show that innovation or the generation of technical knowledge has substantial positive effects on economic growth or productivity growth. For example, Lederman and Maloney (2003), using regressions with data panels of five-year averages between 1975 to 2000 over 53 countries, finds that a one-percentage point increase in the ratio of total R&D expenditure to GDP increases the growth rate of GDP by 0.78 percentage points. Guellec and van Pottelsberghe (2001) investigated the long-term effects of various types of R&D on multifactor productivity growth using panel data for the OECD over the period 1980-98. They find that business, public<sup>11</sup> and foreign<sup>12</sup> R&D all have statistically significant positive effects on productivity growth. Adams (1990), using the number count of academic scientific papers of various scientific fields<sup>13</sup> to proxy for the stock of knowledge, finds that technical knowledge contributed significantly to the total factor productivity growth of U.S. manufacturing industries for the period 1953-1980.

Currently, the majority of technical knowledge is produced in the developed countries: more than 70 percent of patenting and production of scientific and technical papers are accredited to researchers in industrialized countries. The disparity in the production of technical knowledge per capita between developed and developing countries is even greater than the disparity in income. However, note that domestic technological innovation is not the sole source of generation of technical knowledge. There are many ways for developing countries to avoid reinventing the
cognitive scale, discussed further in Section 4.4 below.<sup>15</sup>

> 13 Two nomadic clusters (one control and one treatment cluster, including 6 villages in total) were originally drawn into the evaluation sample but had to be dropped from the baseline survey due to communities being entirely inaccessible. One of the two clusters could be added back into the sample at follow-up as part of the additional booster sample (see discussion in Section 3.2).

> 14 Based on this household listing, screening criteria were applied to exclude ineligible households, defined by the program as those with self-reported income higher than a pre-set threshold. Approximately 20% of households were deemed ineligible based on this criterion. (Premand and Schnitzer (forthcoming) show that the use of exclusion criteria does not contribute to targeting inefficiency in a later phase of the program). The sample is representative of households eligible for the program at the commune level. Therefore, the evaluation sample is representative of eligible households in communes eligible to the cash transfer program. Note that after clusters were drawn into the impact evaluation sample, random sampling was used to draw households. When the household sample was drawn, 13 small villages among the full sample of clusters had no observation drawn into the sample: (i) 4 villages in the control group; (ii) 6 villages in the cash transfers group and (iii) 3 villages in the cash transfers and parenting training.  A few small clusters of less than 30 households remained, particularly among nomadic villages.

> 15 In practice, household and child survey teams followed each other in the field. Household survey teams were responsible for referring children eligible for the child survey to the child survey teams. To ensure a smooth transition between the two surveys, the household survey teams pre-filled information on household location and children’s identification in the child questionnaire, before passing them to the child survey team. Child survey teams were then responsible to verify children’s age and collect data
41.8|16.6|
|4|371,419|4.0|35.6|60.4|1,660.2|661.4|26.1|21.3|52.5|20.9|
|5|371,509|3.7|35.5|60.0|1,975.4|787.0|27.6|24.2|64.1|25.5|
|6|371,100|3.5|36.2|61.0|2,344.2|934.0|28.8|25.6|81.6|32.5|
|7|371,906|3.3|31.9|59.7|2,772.3|1104.5|29.9|25.9|89.9|35.8|
|8|371,362|3.2|33.1|59.3|3,339.8|1330.6|30.4|27.7|103.2|41.1|
|9|371,589|3.0|34.9|57.8|4,266.4|1699.7|27.7|25.5|107.3|42.7|
|10|371,558|2.5|39.5|55.9|7,896.6|3146.0|31.4|30.4|154.1|61.4|
|All|3,714,823|3.5|34.9|58.7|2,734.0|1089.2|27.6|23.7|74.9|29.8|

_Source:_ Household Income and Expenditure Survey (HIES) 2017 and World
||**in**<br>**2020**<br>**Euros**|
|---|---|
|Single Person (Average Monthly, Euros)|148.2|
|Single Parent and One child (Average Monthly, Euros)|294.0|
|Single parent and Two Children (Average Monthly, Euros)|439.9|
|Family of two adults and one child (Average Monthly, Euros)|442.2|
|Family of two adults and two children (Average Monthly, Euros)|577.7|
|Single Person (Average Monthly, Euros)- Including physical activity and other functions|180.6|
|Family of two adults and two children (Average Monthly, Euros)- Including physical activity and other<br>functions|669.1|

**Panel c. Average Monthly Cost per Capita, 2020 Euros**

|||**Food Basket (with non-food**|
|---|---|---|
|**Currency**|**Food Basket**|**components / other functions)**|
|Euros|126.0|180.6|
|RON|610|874|

_Source:_ Panel a. European Commission (2015). Panel b and c. Own estimates based on European Commission (2015).

**The second step involves defining the household type of the target interest group and obtaining EUSILC survey estimates necessary to estimate living wages.** One needs to define what a typical Romanian household living in relative poverty with positive labor income<sup>18</sup> looks like. Based on the 2021 EU-SILC, a household living below the relative poverty line has, on average, 2.51 household members. This allows us to get to the cost of the food basket for a family of this average size or how much is needed
|||0.263||
||||(2.916)||||(2.804)||
|MOB × ln(Distance from CBD) ×|||<br>-0.136||||<br>0.014||
|<br>Self-employed|||(0.309)||||(0.296)||
|MOB × Poor||||-4.856||||-5.000|
|||||(3.916)||||(3.940)|
|MOB × ln(Distance from CBD) ×||||<br>0.536||||<br>0.552|
|<br>Poor||||(0.408)||||(0.411)|
|Individual FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Round FE|No|No|No|No|No|No|No|No|
|Round × Ind/HH characteristics|No|No|No|No|No|No|No|No|
|MOB × Ind/HH characteristics|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Obs.|5139|5016|5016|5016|3552|3466|3466|3466|

_Note:_ Cluster-robust standard errors in parentheses.<sup>*</sup> _p_ < 0.1,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01. MOB indicates the Facebook mobility index with the mobility level in February 2020 as the baseline. Individual and household characteristics include age, sex, education, household size, dependency ratio, self-employment, industries, internet access, and
IBRD 47748 | MARCH 2024

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Misperception is determined by the disparity between the Community perceived norm (on a 0-100 scale) and the Aggregate Level ofAgreement with the norm within the country (on a 0-100 scale). Here we present the actual values of the misperception — some countries havea positive value (overestimation) andsome a negative value (underestimation) suggesting varying degrees of misperception. Darker shades ofthe map suggest an overestimation of the male breadwinnernorm — individuals think their community is more gender conservative or stereotypical than actual beliefs in that country. Lighter shades ofthe map suggest an underestimation of the male breadwinner norm — individuals think their community is more gender progressive or counter-stereotypical than actual beliefs in that country. Source: Gender Equality at Home Data, 2020.
This makes it hard to interpret the existing evidence on the effects of demand side policies such as subsidies and CHW. For instance, one RCT in Ghana and a difference-in-difference analysis from South Africa document positive effects of user fee removal on some health outcomes for children (Tanaka, 2014; Powell-Jackson et al., 2014).<sup>1</sup> On the other hand, randomized trials of health insurance subsidies in Nicaragua, Mexico, and, most recently, India have resulted in few health or utilization effects, with often low uptake and retention (King et al., 2009; Thornton et al., 2010; Malani et al., 2021). As Malani et al. note in addition, even very large health insurance trials may not be powered to detect mortality reductions. Given this mixed evidence and the challenge of analyzing effects on rare events, information on _utilization conditional on the child’s health status_ is central to interpreting the demand response to policy interventions. If utilization is low, such data can for example disentangle whether there is no significant unfilled need for care or whether an intervention failed to curb underuse, perhaps due to unobserved supply-side effects. If utilization is high, it can help understand what share constitutes overuse vs. appropriate use, and whether complementary interventions such as CHW visits can help moderate any misallocation.

In this study, we measure effect of subsidies and CHW visits on healthcare demand conditional on illness, using novel health data collected at home. In partnership with the NGO Mali Health, we conducted a randomized control trial of a health care subsidy, which reduced primary care cost per formal care visit for children under five years by 72% on average, and a community healthworker (CHW) program, for which CHWs visit families biweekly and teach them basic health knowledge. The interventions were cross-randomized at the compound (dwelling) level, with a final sample of 1768 children from 642 compounds. Comprehensive quality controls at Mali Health’s partner clinics and the small sample