rafmacalaba/data-use-mentions-extended huggingface dataset:
# **A.1 Example of data extraction and analysis**

In this section we exemplify how researchers can use our dataset for generating descriptive statistics about public procurement and budget execution across Brazilian municipalities. The full documentation on using datasets hosted on the Data Basis platform can be found at https://basedosdados.github.io/mais (in Portuguese), including video tutorials and details on how to create projects at Google BigQuery.

Here we provide a simple example using the ‘basedosdados‘ R package. This package allows users to connect to their BigQuery projects (and billing ID) and directly make SQL queries on BigQuery datasets, which are then loaded in R.

The SQL query presented below does the following: it connects to the _licitacao_ (tender) table including tender-level information for all municipalities and years covered in our data (over 2.3 million unique tenders); it creates an indicator for when the modality is defined as categories 8 (waiver) or 10 (non-requirement); then it computes the average share of tenders that are non-competitive and the total number of tenders at the municipality-year level. The resulting table, loaded in the ‘data‘ object, includes 19,033 municipality-year observations. When executed, this query took 3 seconds to process the 2.3 million observations and provide us these summarized statistics.

We then can simply plot a histogram of the share of non-competitive tenders across municipality - year (in this case, we filter the dataset for observations with at least 50 tenders). We present the results in Figure A.7. We can see that the average we describe in Table 2 of 30%-40% masks substantial heterogeneity: while a substantial amount of municipalities in a given year do not use non-competitive modalities, in some municipalities the share of these is above three-quarters.

Using this dataset and auxiliary tables also easily accessible in the ‘basedosdados‘ package, we could also explore additional questions, such as whether the use of non-competitive tenders vary systematically over time
prone to face a larger informal competition.<sup>20</sup> Further evidence supporting our argument can be established when dividing our sample between the firms that never exit in our period of analysis (which are part of the balanced panel) and the firms who exit at some point. Our main estimates use the unbalanced panel of firms (all firms), as such, the results observed in Tables I and II correspond to the firms that are in the market each period of analysis (but may exit in the future). The size, in terms of production, of the firms who exit anytime between 1995 and 2010 is smaller relative to the firms that stay during the whole period of analysis. In particular, the mean production of the firms in the balanced panel is 20,300,000 (6,376 observations) and the mean production of the firms who exit sometime between 1995 and 2010 is 10,900,000 (115,885 observations). To clarify how the sample composition is affecting our main results we estimate the effects of IDP migration for the sample of firms in the balanced panel (i.e., the largest firms) in Appendix IV. We find no effects of IDP migration on any of the outcomes that we study. These results imply that the negative effects that we observe on Tables I and II are driven by the smaller firms, which ultimately exit the market at some point during our period of analysis. These results also support our argument as smaller firms should be disproportionately affected by the competition of an enlarged informal sector relative to larger firms.

# **VI.1 Do other sources of data support our hypothesis?**

We also use the Colombian household surveys to test how higher IDP inflows affect the size informal sector. Particularly, we examine whether larger IDP inflows are indeed affecting the size of the informal sector by classifying all workers age 15 and 62 into two groups according to whether their primary job is part of a sectors that
20)|(0.19)|
|Child unschooled=1 2011<br>(proportion)|0.390|0.324|0.236|0.124|0.065|0.168***|0.157***|0.325***|
|Literacy raw score 2011|2.22|4.40|3.68|2.73|10.20|-6.52**|-1.46*|-7.98***|
|if not in school|(3.64)|(6.45)|(5.05)|(2.85)|(11.26)|(2.08)|(0.81)|<br>(1.64)|
|Literacy raw score 2011|11.50|16.08|20.67|25.47|36.69|-16.05**|-9.17****|-25.19***|
|if in school|(13.72)|(15.89)|(18.07)|(20.42)|(19.39)|(1.87)|(1.82)|(1.89)|
|Lit 2011|-0.591|-0.350|-0.133|0.175|0.813|-0.944***|-0.460***|-1.404***|
|eracy z-score|(0.626)|(0.724)|(0.860)|(1.011)|(1.013)|(0.087)|(0.072)|(0.082)|
|Math raw score 2011|13.78|17
available, we also captured outcomes of teachers, such as classroom practice or self-reported measures of wellbeing. We extracted a total of 798 outcomes at the child and teacher levels. In one study that relied on a very large administrative dataset with a panel structure (Rossin-Slater & Wust, 2020), outcomes were aggregated to combinations of geographic unit and birth cohort. Sample sizes

> 10 These categories included (i) teacher professional development, (ii) subsidized or free access, (iii) change in curriculum, (iv) change in pedagogy, (v) provision of materials, (vi) provision of new staff, (vii) provision of health and nutrition services, (viii) preschool construction, (ix) parental engagement, (x) community outreach, (xi) preschool day extension, and (xii) teacher payments.

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11 Sexual violence has particularly serious consequences for women’s sexual and reproductive health. It may lead to gynecological problems, unwanted pregnancy, chronic pelvic pain, unsafe abortion, and sexual dysfunction; intimate partner violence has been linked to similar outcomes, as well as complications during pregnancy, miscarriage and low birth-weight (Campbell, 2002). Sexual abuse in childhood and adolescence has been linked to a higher risk of subsequence victimization, early sexual activity, substance abuse, and multiple sexual partners (Felitti et al., 1998; Heise, Ellsberg, and Gottemoeller, 1999; Walker et al., 1999) _._ Many women consider the psychological consequences of physical, sexual and emotional abuse to be even more serious than its physical effects. Recent results from the WHO MultiCountry Study on Women’s Health and Domestic Violence found that women with a history of physical and/or sexual partner abuse were as much as three times more likely to consider and/or attempt suicide. In all 10 countries surveyed, women who had experienced intimate partner violence at any point in their lives were significantly more likely to be suffering from symptoms of emotional distress at the time of the interview (World Health Organization, 2004).

Researchers have also documented negative outcomes among children of women who experience violence. For example, researchers in Nicaragua found that children of women who were physically and sexually abused by their partners were six times more likely than other children to die before the age of five, with one third of all child deaths in this setting being attributed to partner violence (Åsling-Monemi et al., 2003). Boys and girls who witness violence in the home may be at increased risk for emotional and behavioral problems, such as anxiety, depression and violence towards their peers (Jaffe and Sudermann, 1995) and, in the case of boys, for perpetrating intimate partner violence and/or sexual violence as adults (Straus and Gelles 1986; Ellsberg, Peña et al. 1999; Kishor and Johnson 2004).

#
Table 2: Profile** **<u>of Partner Countries which Participated in the 2011 PD survey</u>**

||T|otal|o/w IDA<br>(incl.|eligible<br>blend)|F|CS|
|---|---|---|---|---|---|---|
||No.|%|No.|%|No.|%|
|Africa (AFR)|37|48%|32|42%|10|13%|
|EastAsia andPacific|13|17%|10|13%|2|3%|
|Europe and Central Asia|8|10%|6|8%|2|3%|
|Latin America and Caribbean|11|14%|4|5%|1|1%|
|Middle East and North Africa|4|5%|0|0%|1|1%|
|South Asia|4|5%|4|5%|1|1%|
|**Total**|**77**|**100%**|**56**|**73%**|**17**|**22%**|

_Source: OECD (2011), World Bank classification_

**Table** **<u>3: Profile of Selected Development Partners which Participated in the 2011 PD survey</u>**

|**Group**|**Number **|**Country / Organization **|
|---|---|---|
|Bilateral|22|Australia, Austria, Belgium, Canada, Denmark, Finland, France,<br>Germany, Ireland, Italy, Japan, Korea, Luxembourg, Netherlands,<br>New
natural sources of variation that a study such as this can exploit analytically; and (3) Its recent political history features dramatic changes in institutional structure and accountability.

Ethiopia has achieved impressive development results in recent years. That progress includes rapid and significant improvements in basic service delivery indicators. An Overseas Development Institute study (2010) found that Ethiopia was making the third-fastest improvements of any country towards reaching the Millennium Development Goals (MDGs). The latest Ethiopia Demographic and Health Survey data show that child mortality has fallen from 123 per thousand in 2005 to 88 in 2010, and primary net enrollment rates rose from 68 percent in 2004/2005 to 82 percent in 2009/2010. Such progress on basic service delivery is coupled with an impressive growth record over the past decade. GDP grew on average 11 4
>More equal Income inequality Less equal<br>(Gini coefficient)<br><!-- End of picture text --> _Note:_ Data on Gini coefficients are for 2009 or the latest available year. _Source:_ Demirguc-Kunt and Klapper 2012; World Bank, World Development Indicators database.

# VARIATION BY INDIVIDUAL CHARACTERISTICS

Financial inclusion also differs in important ways by individual characteristics such as gender, education level, age, and rural or urban residence. There are significant disparities in account penetration along gender lines. In developing economies 46 percent of men report having an account at a formal financial insti<!-- Start of picture text --> Non-account-holders in the poorest quintile<br>in selected high-income economies<br>1.6 FIGURE<br><!-- End of picture text --> <!-- Start of picture text --> Adults in the poorest quintile without an account<br>at a formal financial institution (%)<br>26<br>WITHOUT<br>ACCOUNT<br>9<br>3 3<br>AUSTRALIA UNITED CANADA UNITED<br>Gini = 33.6 KINGDOM Gini = 32.4 STATES<br>Gini = 34.5 Gini = 37.8<br><!-- End of picture text --> tution, while only 37 percent of women do. These shares reflect the use of both individually and jointly owned formal accounts, as the Global Findex survey captures the use of an account together with a family member.

The gender gap is particularly large in South Asia and the Middle East and North Africa (figure 1.7). But it is relatively small in Sub-Saharan Africa, where 27 percent of men and 22 percent of women report that they have an account.<sup>8</sup> The gender gap is statistically significant in all regions, even when controlling for education, age, income, and countrylevel characteristics.

_Note
in amounts lower than requested (the difference between PACS and MFIs exceeds 30 percentage points) and less likely to issue new loans even if the previous ones were fully repaid. PACS were less trustworthy of savings and more likely to have a share of the loan deducted as a bribe. Not surprisingly, the loans most likely to suffer from this problem were those issued by PACS. MFI loans, on the contrary, were perceived as less likely to do so. There was a nearly 20 percentage point gap between the two loan sources.

> 5 The international evidence shows the same patterns. Dinç (2005), for example, shows that increased lending by government-owned banks right before elections is not specific to India but can be observed in data from 22 developing countries. Khwaja and Mian (2005) also find that in Pakistan, politically connected firms are able to secure larger and cheaper loans from state-owned banks and default on these loans much more than other nonconnected firms.

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we use. The advantage of using vital statistics data to learn about fertility in the population comes from the universal coverage of the data for the entirety of Brazilian municipalities over the period of interest. Information about the residence of the mother during pregnancy is particularly important, as information on the place of birth may be misleading if there is a discrepancy between place of residence of the mother and the place of occurrence of birth, which is more likely for relatively small municipalities that do not have clinics with birth facilities.

# _Population estimates_

The Brazilian Census Bureau (IBGE) provides official population estimates for each municipality based on the 1990 and 2000 census and the 1996 and 2006 population counts. These data provide population estimates by sex and age group that we use in all the regressions to account for cohort sizes.

# _Municipality controls_

We use a rich set of municipality-level time-varying controls on the characteristics of the municipalities from a variety of sources. These include municipality GDP, and the fraction of municipality level expenditure on education, health, welfare, transportation and housing, provided by IBGE. In addition, we use information on the number of _Bolsa Famíli_ a<sup>15</sup> recipients and the total amount of _Bolsa Família_ payments in the municipality. These data are available annually for the 1997–2009 period.<sup>16</sup> We provide details on the source of these data in the appendix (Table S1.2).

# **4. Empirical Strategy**

> 15 Bolsa Família is a cash transfer program of the Brazilian government for poor Brazilian families conditional on meeting requirements regarding school attendance and completion of vaccination schedules.

> 16 The Bolsa Família programme was introduced in 2004. Data for municipal GDP is available from 1999.

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aid to productive capacity is insignificant. When considering sectorally targeted aid, the authors again find that aid to infrastructure has a significant impact on export values, but aid to productive capacity does not, controlling for country-sector fixed effects that account for comparative advantage differences.

Brenton and von Uexkull (2009) examine the response of product-level exports from developing countries to product-level export-development aid, combining mirrored product-level (HS4) export data with export-development aid data from the German cooperation agency GTZ and from the OECD/WTO Trade Capacity Building Database for 48 developing countries. Using a matching difference-in-differences (DID) approach (discussed in section 4) they show insignificant effects of contemporaneous and lagged aid on product-level exports after controlling for lagged exports, and country and year-product fixed effects, and eliminating outliers.<sup>10</sup> However, the authors do show strong positive effects in a simple comparison of product-level exports before and after receiving export development aid. This finding suggests an important attribution problem — namely, export growth may not be due to the aid received but instead may reflect the fact that aid targets sectors with promising prospects. The authors go on to argue that, in evaluating the impact of technical assistance for exports, it is essential to identify what would have happened in the absence of the policy intervention. This is a primary concern in this paper.

As the literature stands, it is fair to say that the effect of AfT on the export performance of beneficiary countries has not been established on the basis of aggregate numbers. Ferro, Portugal-Perez, and Wilson (2011) advance the analysis of the effectiveness of AfT revisiting the data from OECD’s Creditor Reporting System. The authors exploit the differential intensities of service use across manufacturing sectors (based on input-output tables from the U.S. and Argentina) to evaluate the impact of aid for trade flows directed at five services sectors — transport, communications, energy, banking/financial services, and business services — on the exports of downstream manufacturing sectors in 106 aid-recipient countries
**

<!-- Start of picture text --> 12 5<br>10.8 4.4<br>10 4<br>3<br>8<br>6.9 2<br>6 1.5<br>1<br>4 4.1 -0.2 4.0 0<br>2 1.9 -0.2 -0.8 -1<br>0 -2<br>MPO-Mar WEO-Apr MPO-June MPO-Oct GEP<br>2020 2021<br>Student Dropout Estimates by Growth Projection<br>GDPpc Growth Projections for 2020 and 2021 (p.p.)<br>Number of dropouts<br>(in millions of students)<br>GDPpc Growth Projections (p.p.)*<br><!-- End of picture text --> _Note: The macro number used in this paper reflect what was available at the time of the Global Learning Poverty 2022 update in June/2022._

# Outcome 3. Percent below Minimum Proficiency on PISA

Globally, the percentage of 15-year-olds below minimum proficiency on PISA could rise by 12.3 percentage points in the intermediate scenario (see Figure 6 below). These results imply a rise in the share of students who are not able to “identify the main idea in a text of moderate length, find information based on explicit though sometimes complex criteria, and reflect on the purpose and form of texts when explicitly directed to do so” (PISA’s definition of a minimum level of proficiency).

> 21 Figure B2.

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Therefore, their presence in the group of exporters is also likely to generate a downward bias in the effect of learning-by-exporting.

The previous discussion suggests two ideas to truly capture learning-by-exporting effects in the data. A first idea is to focus on young plants. Young plants are much more likely than established plants to face new stimulus situations, which require managers and workers to find solutions to new technical and organizational problems. This is the reasoning underlying the strong evidence of learning-by-exporting found by Delgado et al. (2002) and Baldwin and Gu (2003) for young Spanish and Canadian manufacturing plants, respectively. In our paper, we seek to confirm this evidence for young Colombian manufacturing plants. A second idea is to focus on measures of export experience, rather than on export participation, to capture learning-by-exporting effects. Our export experience measures, the number of years a firm has exported and an index of cumulative exports, require that we observe plants’ complete production and export histories. This provides us with a second rationale for focusing on young plants born in 1981 or later as 1981 is the first year when plant-level export data is available in the annual Colombian manufacturing surveys.

Our results show robust evidence of learning-by-exporting effects for young plants, using both traditional export participation measures and our export experience measures. The average annual rate of TFP growth for young entrants into export markets is around 3% to 4% higher than that for young nonexporters. Each additional year of export experience increases plant TFP between 4% and 5%, even controlling for a dummy indicating whether the plant is currently exporting. In extensions to our main results we show that learning-by-exporting is significantly more important for young plants than for old plants. Also, we find that the relationship between 4
|0.082|
|Admin./Managers|0.177|0.037|0.007|0.269|0.102|0.037|
|<br>Clerical Related|0.219|0.058|0.020|0.331|0.114|0.060|
|Sales Workers|0.235|0.071|0.027|0.263|0.103|0.058|
|Service Industry|0.259|0.079|0.030|0.298|0.109|0.056|
|Agric./Forestry|0.473|0.175|0.079|0.357|0.151|0.080|
|Product./Transp.|0.265|0.112|0.051|0.396|0.121|0.057|
|Manufact./Process.|0.351|0.096|0.037|0.233|0.111|0.064|
|Others|0.305|0.087|0.032|0.292|0.100|0.054|
|Student/Apprentice|0.414|0.087|0.029|0.379|0.157|0.086|
|_All Urban_|_0.317_|_0.124_|_0.051_|_0.304_|_0.140_|_0.076_|
|Source: NCS, 1985/86 a|nd 1992/93.||||||

Most rural areas depend on the labor market for their sustenance. Focusing on the link between urban and rural patterns of welfare as mediated through the labor market, an ILO study based on data from 1964 to 1985 demonstrated that except for intermittent increases, real wages in Nigeria declined sharply between 1973 and 1985 (Jamal and Weeks, 1988).
stage), as well as accounting and legal services (back‐office stage). These sectors may be indicative of the broader restrictiveness of services within each production stage, as many more services fall under each stage as shown in Table A2 in Annex 1. However, the regulatory databases used for this analysis only include a subset of the entire range of services.

**Figure 4.4: Domestic regulatory restriction in services for Turkey and production stages (2014)**

<!-- Start of picture text --> Manufacturing Post-manufacturing<br>Gas 0.4 Rail 1.0<br>Electricity 0.4 Road 0.6<br>Egnineering 0.2 Airlines 0.3<br>Architecture 0.2 Retail 0.2<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Post-sales Back-office<br>Legal 0.9<br>Postal service 0.4 Accounting 0.8<br>Telecoms 0.0<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br><!-- End of picture text --> Source: OECD PMR; authors’ calculations. The scale of the regulatory restriction index is 0‐1.

**Turkey shows a mix of entry and conduct barriers.** Rail transport services are completely restricted, affecting both firm entry and conduct. Air transport services mainly have restrictions related to public ownership, which affects the entry of foreign firms, but some conduct barriers also exist. The road transport sector has high entry barriers, while operations regulations are significantly lower. Gas and electricity services (manufacturing stage) have high conduct regulations, such as vertical integration policies, public ownership regulations, and uncompetitive market structures, whereas they are fairly open to firm entry. Architectural and engineering services (manufacturing stage) have high entry barriers while accounting and legal
), Tanja Goodwin (co-TTL), and Mariem Malouche (co-TTL).

> 37 The CPSD Knowledge note (Sanchez-Navarro, Goodwin, & Kikeri, 2021) and the forthcoming toolkit on state footprint and private sector development offer a good set of tools for practitioners on how to implement the findings of the global BOS database and the taxonomy of sectors to design SOE reforms including the sequence, prioritization of sectors, as well as the definition of the right instrument of reform (beyond privatization).

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and insignificant. Facebook mobility data from cellphone locations indicates that restricted mobility is a plausible channel. Monthly household data point to lower household income but not consumption as an important channel for these findings. More developed districts with above median population density, share of employment in services, credit per capita, and mean age experienced larger impacts of the restrictions.

Our study contributes to the growing literature on the economic impacts of government interventions to mitigate pandemics. Deb et al. (2020) find large effects of containment measures to slow the spread of COVID-19 on economic activity across countries. Goolsbee and Syverson (2020) examine the drivers of pandemic-related economic decline in the United States and compare consumer behavior across commuting zones to distinguish between government restrictions and the role of fear – both of which matter. Kong and Prinz (2020) quantify the employment impact of different state-level containment measures in the United States and Petroulakis (2020) shows that high non-routine jobs reduce the probability of job losses. While

> 2While data from the United States Air Force Defense Meteorological Satellite Program (DMSP) using the Operational Linescan System (OLS) are only available at annual frequency, recent data from the Suomi National Polar Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) are monthly.

3