rafmacalaba/data-use-mentions-extended huggingface dataset:
This paper seeks to make a first contribution to the literature in this direction by exploring the relationship between data transparency and economic growth for developing economies for a unique time period of considerable uncertainty- during and after the global financial crisis.<sup>1</sup> Countries that have credible and timely data systems are likely to perform better during periods of uncertainty than countries that have poor data systems – being transparent is a good antidote for uncertainty. Our work builds on the study by Hodelin (2022) that explores how data density – availability of country-level data in the World Development Indicators (WDI) increases long term economic growth. We define data transparency as the regular publication of credible statistics by the state. Beyond just data density at the country level, data transparency also encompasses the availability of micro data and the credibility of data through adherence to international standards. Our choice of time period is largely dictated by the availability of the data transparency indicator. At the time of writing, we are not aware of other existing empirical analyses of the link between data transparency and subsequent economic growth in developing economies during and after the period of global financial crisis.

Historical data collection can be traced to the first recorded census undertaken by Babylonians around 3800 BC (Grajalez et al., 2013). The purpose and nature of data collection have evolved over time. Early data collection served rulers with the aim of accounting for wealth and power. Information was gathered for taxation purposes, counting of men for military recruitment and workforce, and ascertaining conquered populations and territories (World Bank 2021). The data were kept secret from the public and not meant to improve their lives. This raised general distrust of data collection activities among the populace. In contrast, the enlightenment ideals in eighteenth century Europe emphasized objective scientific inquiry. The role of data evolved to a means of examining society and became more public. In the late eighteenth century, statistical agencies were set up in Europe and North America to publish official statistics and inform the public
in Spanish), they find that Nini rates have decreased since the 1960s. Moreover, they find that recessions increase the prevalence of Ninis by 3 to 4 percentage points (p.p.), and that the majority of Ninis are women whose personal lives have affected their schooling and labor force participation decision (e.g. 61 percent of women aged 19 to 23 have children). Notably, the authors conclude that Ninis are present in all levels of society and regions of the country, but that being a Nini does not necessarily mean being unproductive, as many of them are involved in activities that are socially useful.

Several authors have questioned the adequacy of the definition of Ninis and its implications for social policy precisely because of the assumed unproductivity of these youth under the definition. This assumed unproductivity of Ninis is clearly reflected in newspapers and public opinion as referenced by Arceo-Gómez and Campos-Vázquez (2011). Two papers, NegretePrieto and Leyva-Parra (2013) and Leyva-Parra and Negrete-Prieto (2014), carefully analyze the numbers of Ninis in a cross-section of Mexican youth aged 14 to 29 from the Mexican Labor Force Survey (ENOE by its acronym in Spanish), the authors show that different definitions for identifying Ninis yield groups of young people with very different characteristics. For example, using the traditional definition of Ninis, the majority (around 70 percent) of Ninis are women. However, this percentage reduces substantially when housework and searching for work are excluded from the definition of Nininess. Leyva-Parra and NegretePrieto (2014) also exploit the longitudinal characteristics of ENOE data to show that most Ninis change their status in every quarter for which they are observed in the survey.<sup>6</sup> Other authors have also called for a better understanding and measurement of Ninis, in particular with regards to housework, family composition, schooling decisions, and the differential policy responses that these may point to (Pederzini, 2011; Rivero and Perderzini, 2014). This research calls for a better understanding of decisions to
/sup> In Canada, data from the Labor Force Survey show that COVID-19 induced a 32 percent decline in aggregate weekly work hours among workers aged 20-64, alongside a 15 percent decline in employment.<sup>35</sup> In the United Kingdom, the collapse has been twenty times faster and much deeper than the Great Recession. More than a quarter of workers have lost at least half of their income due to the COVID-19 crisis.<sup>36</sup> Low income workers in developing countries may face a higher risk of income loss during the COVID-19 lockdown as it is less feasible to conduct their jobs from home.<sup>37</sup> Predicted earnings losses due to COVID-19 in India appear substantial.<sup>38</sup> According to a recent survey in the U.S., students have internalized these losses, as they expect less education, fewer job opportunities and lower earnings in the future.<sup>39</sup>

# **Theoretical framework**

We use human capital theory.<sup>40</sup> Time and money spent on education builds human capital, hence one should be able to estimate the rate of return on such investment in a way similar to investment in physical capital. The costs incurred by the individual are the foregone earnings while studying, plus the resource costs of schooling. Since in a majority of countries, basic education is available to children tuition-free, in practice the only economic cost to individuals is the foregone earnings. The private benefits amount to what a more educated individual earns above a control group of individuals with less education. "More" and "less" in this case usually refers to adjacent levels of education, for example, university graduates versus secondary school graduates (see Figure 1).

The private rate of return to an investment in a given level of education in such a case can be estimated by finding the rate of discount (r) that equalizes the stream
; they can be lower. The third column in Table 1 lists the share of imported products over which the country has agreed to take on a tariff commitment. The US, the EU, Saudi Arabia, Argentina, Brazil, China, Mexico, Russia, Democratic Republic of Congo, and Vietnam have agreed to bind tariffs for 100 percent of their of well-defined exceptions that we describe below. On the other hand, WTO members establish their second (set of products with any cap) and third (level of the binding tariff) commitments individually. These second and third commitments have resulted from either decades of interaction with other WTO members under GATT negotiating rounds or the negotiated process of accession.

> 9More details on these data are described in the Data Appendix. First, the applied tariffs are the tariffs that governments set, and the measure derives from policy data that either the government reports itself (to the WTO) or which are collected via official government publications by other international organizations; typically these are reported on an annual basis. Put differently, these measures of tariffs are _not_ imputed from data on customs revenue collections. Furthermore, these applied tariff measures do _not_ include other border charges or taxes, including safeguards tariffs and antidumping duties that we describe in Section 3.1 below. Second, throughout this analysis we only utilize simple average tariffs; the alternative of constructing trade-weighted average tariffs can lead to the well-known problem of downward bias due to products with high tariffs receiving low weights (because of small import volumes). The intuition is provided by the limiting example of a prohibitive tariff level which receives zero weight in the averaging calculation. Third, the calculations in Table 1 derive from data that does include consideration of ad valorem equivalent estimates for products over which the import tariff that the country applies is a specific duty. We describe data on the prevalence of import tariffs applied as specific duties in Section 2.2 below. In other areas of this chapter - such as when we wish to focus on changes in
.e. removing any time invariant components of the model).

When we move to Columns 3 and 4, regression results from the Fixed Effects Model, GDP and population are still significantly positive, while property tax rate and governance now turn to insignificant. The reason that coefficients of property tax rate and governance change to insignificant mainly comes from controlling for time-invariant unobserved state characteristics. When we run OLS regressions, property tax rate and governance have important impacts on the tax revenues because we consider the variations across states. But when we use the Fixed Effects Model, we estimate the impacts within states, so that impacts from the tax rate and governance diminish significantly, since tax rates and governance within each state maintain very similar levels across time. The most important changes are in the observed coefficients of federal transfer and household size. Now, both of them show negative impacts on property tax revenue as expected and are statistically significant. Since Fixed Effects Models are more sophisticated to deal with the

> 11 Some plausible indicators can be cited—especially those related to the indexation applied in perception surveys to reveal the intrinsic pricing of local benefits provided by property taxes.

9
time required for clearance (figure 5b). New port investments are on going on Kribi and Limbé but a comprehensive logistics strategy is still missing to address the growing demand for transport services (which may further accelerate in the mid-term if some major mining projects materialize).

Other nonphysical barriers and logistical inefficiencies play a major role in travel times. 2009 surveys of corridor operations reported between 70 and 150 checkpoints (legal and illegal) between Douala and N’Djamena and 45 between Douala and Bangui (World Bank 2011c). On a return trip between Douala and N’Djamena, transporters pay on average the equivalent of $580 in legal charges and illegal bribes. Another major obstacle to trade facilitation in the CEMAC region is institutional weakness at the regional level and to some extent at national level (World Bank 2011c).

Due to the physical and non-physical barriers, trade in the subregion is the lowest in Sub-Saharan Africa—but it is slowly increasing. With interregional trade accounting for just 0.5 to 1 percent of the total trade of its member states, the CEMAC subregion is the least integrated in Sub-Saharan Africa, far behind the Common Market for Eastern and Southern Africa (COMESA, 5 percent), the Southern African Development Community (SADC, 10 percent), the Economic Community of West African States (ECOWAS, 10 percent), and the West African Economic and Monetary Union (UEMOA, 15 percent).Between 2005 and 2008, annual flows of freight between Cameroon, Chad, and the Central 11
Technology transfer may come from public or private sources. Public funding includes aid from governments or non-governmental organizations (NGO), typically in the form of official developmental assistance (ODA). Compared to private investment, ODA flows are low, but are important in areas of the world that receive little foreign investment (Gupta _et al_ . 2007). Technology transfer from private sources may come via international trade, foreign direct investment, or licensing. Spillovers are possible through private transfers, but depend both on the nature of technology flow (e.g. spillovers are often less likely via FDI, which allows multinational firms to maintain control over their technology) and the absorptive capacity of the country. Absorptive capacity describes a country’s ability to do research to understand, implement, and adapt technologies arriving in the country. Absorptive capacity influences the speed at which a newly arriving technology diffuses through a developing country. It depends on the technological literacy and skills of the workforce, and is influenced by education, the strength of governing institutions, and financial markets.<sup>18</sup> FDI is important for environmental technology transfer, as multinationals are usually the first to bring new environmental technologies to a country (see, for example, Dasgupta _et al_ . 2002). In many cases, it is easier for a multinational firm to use the same equipment and processes that it uses at home, rather than develop a dirtier process for use in developing countries. Transfer via FDI is likely to be particularly important for integrated process solutions to reduce pollution. Although currently unexplored, this notion is a fruitful topic for future research.

> 18 World Bank (2008b) provides a discussion of the role of absorptive capacity in technology transfer.  They use data on education, governance and macroeconomic stability to construct an index of absorptive capacity.  In addition to the importance of an educated workforce, they provide evidence that a stable economy and strong business environment improve adaptive capacity.

20
Economists have used remotely sensed information since at least the 1930s, but digital technology advancements have allowed economists to start taking significant advantage of this source of information (Donaldson & Storeygard, 2016). Geospatial data cover wind speed, night light, precipitation, forest cover, crop choice, economic activities, urban development, building type and size, roads, pollution, beach quality, and many other difficult-to-measure statistics. The advantage of using geospatial data is amplified by the fact that they are usually available at a much higher degree of spatial resolution than traditional data, addressing a problem frequently found in MENA countries.

For example, National Aeronautics and Space Administration (NASA) data on nighttime lights offers a good proxy for settlement patterns and wealth (Henderson, Storeygard, and Weil, 2012). In fact, estimating economic activity using night lights can more reliably approximate real GDP since it accounts for informal economic activity, and does not depend on government infrastructure to measure. Nighttime light data can play a key role analyzing economic activity at the subnational level, especially in countries with poor quality or non-existent information.

Geospatial information has also been used to estimate welfare indicators. This non-traditional source of information not only allows new ways to explore topics, but also proxies for wealth and poverty data. Marx, Stoker, and Suri (2015) used satellite data to detect better-maintained or new roofs as indicators of higher quality housing in Nairobi. Kudamatsu, Persson, and Stromber (2012) studied how weather fluctuations affect infant mortality in Africa. Daytime imagery has even been used to estimate and replace bad quality poverty data in developing countries (Xie et al., 2016). As with phone surveys, geospatial data have also proved to be an excellent option in persistent conflict and unsafe zones, or for data on illegal and informal activities not usually reported. For example, remote satellite sensing can capture spectral reflectance of different crops, thereby allowing calculation of land surfaces devoted to poppy cultivation (Lind, Moene, and Willumsen, 2014).
to 87.14 percent in 2019, followed a constant increasing trend except for the period after the 2008 crisis (Figure 2.2). After entry into force of the EU agreement in particular, trade as a percentage of Moroccan GDP increased from 59.1 percent (26.8 percent from exports and 32.3 from imports) in 2000 to 87.1 percent (39.1 percent from exports and 48 percent from imports) (WITS, 2022).

**Figure 2.1. Tariff rates considerably Figure 2.2. Trade openness almost declined doubled**

Moroccan tariff rate of all products in Total trade as percentage of GDP percentage <!-- Start of picture text --> 50 90<br>40 80<br>30 70<br>20 60<br>10<br>50<br>0<br>40<br>1993 1996 1999 2002 2005 2008 2011 2014 2017 2020<br>1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 2019<br><!-- End of picture text -->

**Source:** Authors’ elaboration using data from Our World in Data (https://ourworldindata.org/)

**Source:** Authors’ elaboration using data from Our World in Data

(https://ourworldindata.org/) 3 Weighted mean applied tariff is the average of effectively applied rates weighted by the product import shares corresponding to each partner country.

3 | P a g e
cessor publications, the Global Development Finance reports (1997–2012) and the World Debt Tables (1973–1996). While the name of the publication has changed over the recent decades, all reports build on the same underlying data series from the World Bank’s DRS and on highly similar variable definitions (see Appendix B.1 for details).

The latest vintages of the debt statistics are readily available in machine-readable format on the World Bank’s website. For the majority of vintages, however, we need to digitize the data from PDFs or hard-copy reports. Specifically, we download all vintages of the International Debt Statistics (2013–2023) and eight vintages of the Global Development Finance reports (2005–2012) from the World Bank website. For all editions prior to 2005, we obtain PDFs or hard copies from different libraries and rely on a combination of optical character recognition (OCR) software and manual coding to render reports into machinereadable formats. Finally, we verify and reconcile the consistency of the data series and merge the data from all vintages into a single dataset (see Appendix A.2 for details).

**Scope of database:** Our new database covers debt statistics for an unbalanced sample of 146 developing and emerging market countries covering 53 years of data from 1970 to 2022. Appendix Table C6 provides a full list of all countries in our sample. For each country, the database combines the full reporting history with up to 51 different vintages so that each statistic can be compared across dozens of different debt reports.

**Table 1:** New database of debt data revisions: Scope and coverage

|Number of vintages|51|
|---|---|
|Number of variables|49|
|Number of countries|146|
|Time coverage|1970–2022|
|Number of observations|3,315,950|

_Notes:_ This table provides details on the scope and coverage of our new database on debt data revisions
concentration. Most of the included countries were able to increase diversification to access different markets. China’s performance stands out because of the significant number of new destinations.

In terms of products, the picture is not so favorable. As in many countries, Brazilian exports showed increased concentration for products in recent years. Commodity products gained significant relevance. Other commodity exporters, such as Chile and Russia, also showed similar performance with nonnegligible increases in the degree of concentration. China presented a slight deterioration, but from a low level of concentration. Anyway, as shown in figure 5, despite some worsening in recent years, Brazil still shows low levels of concentration when considering the level of GDP per capita.

Figure 5. Herfindahl Index and GDP per Capita (2008–10) <!-- Start of picture text --> R U S<br>C H L<br>P E RC O L<br>IN D J OR B R AU R YA R GM E X K O R<br>C H N<br>0 1 0 0 0 0 2 00 00 3 00 0 0 4 0 0 0 0<br>G D P p e r ca p ita (P P P , a v .)<br>.3<br>.2<br>H H In d e x<br>.1<br>0<br><!-- End of picture text --> _Source_ : World Development Indicators; authors’ calculation.

Given the recent trend toward more concentration, a key question is how Brazil is dealing with international competition. Figure 6 shows the main products exported, as introduced in table 2, between 2006 and 2011. The circle size shows the representativeness of this specific product in total exports. If above the line, it shows that Brazilian sales grew more than exports of this specific product in international trade between 2006 and 2011. Therefore, among the top 20 products exported, Brazil
The MCB is used as a criterion of access to certain privileges, in particular privileges on real estate construction or purchase for young families.

In 2017, one‐third of the population had disposable incomes below the MCB, compared to 18.7 percent of the population in 2014. The scale of this poverty increase corresponds with the results of the poverty analysis based on the cost of basic needs approach, presented in Mazol (2017). An increase in the risk of moderate poverty was observed for every social group, irrespective of the initial level of poverty.<sup>4</sup> Nevertheless, the magnitude of the increase of the poverty risk was lower than average for residents of Minsk, for the working age population with high educational attainment, and for households without children (see Annexes, Table A3). On the contrary, residents of small cities (defined as cities with a population not exceeding 100,000) faced the highest moderate poverty risk increase. As a result, the level of moderate poverty in small cities caught up with the one of rural areas in 2017.

_Figure 10: Official and moderate poverty rates, 2014‐2017_ <!-- Start of picture text --> 40% 8%<br>35% 7%<br>30% 6%<br>25% 5%<br>20% 4%<br>15% 3%<br>10% 2%<br>5% 1%<br>0% 0%<br>Moderate poverty Official poverty<br>2014q1 2014q2 2014q3 2014q4 2015q1 2015q2 2015q3 2015q4 2016q1 2016q2 2016q3 2016q4 2017q1 2017q2 2017q3 2017q4<br><!-- End of picture text --> Note. See methodological notes on definition of poverty concepts. Source: Authors’ estimates based on quarterly HBS data.

# 3.3. Vulnerability

While official poverty did not change substantially during the crisis, this need not indicate that the degree of vulnerability to poverty risk
Note that we write the specification in equation (3) as a nested model, whereby CASH = 1 for predicted beneficiary households in any of the treatment groups, and BCC=1 for predicted beneficiary households in the treatment group assigned to cash transfer and BCC. This specification implies that the estimates of γ in equation (1) and of β in equation (3) both capture the value-added of the behavioral change component. The consistency of estimates for the two specifications provides an indication of the robustness of the results. We also report the p-value from a F-test for the significance of the overall treatment effect ( _α + β = 0_ ) in villages assigned to cash transfers and behavioral change promotion.

Importantly, estimates from specifications (1) and (2) provide precise and unbiased estimates of average treatment effects (ATE) of the value-added of the BCC intervention for cash transfer beneficiaries, respectively of spillovers among cash transfers non-beneficiaries. In contrast, estimates from specification (3) provide intent-to-treat (ITT) estimates for the relative effect of the behavioral change and cash transfer interventions on predicted beneficiary households. As such, estimates from specification (1) are more precise than estimates from specification (3).

# <u>4.3 Baseline Balance</u>

Table 2 documents baseline balance for households surveyed at follow-up. Panel A considers all baseline households surveyed at follow-up. Panel B focuses on actual beneficiaries, and Panel C on households predicted to be beneficiaries when applying the PMT targeting formula on baseline data. Balance is documented for variables from the household survey (top panel, first for household-level variables, then for individual-level variables), as well as variables from the child survey (bottom panel). Results show that the randomization achieved balance between control and treatment groups in the full sample and in the sample of predicted beneficiaries, as well as between actual beneficiaries in the two treatment groups. Observed differences are not more frequent than would be expected, and generally either weakly significant or of small magnitude (less