rafmacalaba/data-use-mentions-extended huggingface dataset:
example, income that is retained in a closely held company will typically not be realized (and taxed) at the personal level until distributed, enabling corporate structures to be used for tax avoidance. In this regard, Alstadsaeter et al. (2016) link individual and firm level data in Norway in order to attribute business income to individuals as it accrues, finding that this significantly increases the share of total income held by top income earners.

3 There remains debate in the literature regarding the extent to which recurrent property taxes exhibit features of a benefits tax and hence are closer in nature to a payment for services (and consequently have no distributional impact) as opposed to a tax. For a summary, see Zodrow (2007).

4
teaching and during which there is not a “large group” or “all” students—which per the Stallings manual is defined as 6 or more students—that are off-task, namely being “disciplined by the teacher,” who are involved in “social interaction” or who are “uninvolved;” we call this Share of Time Teaching and Learning. The instrument also records the availability of materials in the classroom, infrastructure and the classroom environment, from which we create two aggregates: Availability of Materials and Classroom Infrastructure (again, which we shorten to “Materials and Infrastructure”), and Classroom Environment (see Box 3; more details are in Appendix 1 Table 2).

|**Box 3: Summary of variables derive**|**d from Stallings observation system**|
|---|---|
|**Time-on-task (Level 1)**<br>||
|Share of Time Teaching|Share of 10 snapshot observations in which teacher is engaged in<br>teaching activity.|
|Share of Time Teaching and Learning|Share of 10 snapshot observations in which teacher is engaged in<br>teaching activity and in which there are not 6 or more students who<br>are off-task.|
|**Teacher practices and classroom enviro**|**nment (Level 1)**|
|Classroom Environment|Average of 2 0/1 variables capturing whether pupil work or other<br>materials are displayed on the walls.|
|Availability of materials and Classroom<br>Infrastructure|Average of 5 0/1 variables capturing whether the classroom has a<br>blackboard and chalk; whether there are reading books in the<br>classroom; whether the classroom has electricity; whether students<br>have textbooks/other printed material; or whether students have a<br>notebook/writing material.|
|Source: Authors and World Bank (2015)||

# **
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- L. Pierce, A. Rees-Jones, and C. Blank. The negative consequences of loss-framed performance incentives. Technical report, National Bureau of Economic Research, 2020.

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- K. E. Semrau, K. A. Miller, S. Lipsitz, J. Fisher-Bowman, A. Karlage, B. A. Neville, M. Krasne, J. Gass, A. Jurczak, V. Pratap Singh, S. Singh, M. Marx Delaney, L. R. Hirschhorn, B. Kodkany, V. Kumar, and A. A. Gawande. Does adherence to evidence-based practices during childbirth prevent perinatal mortality? a post-hoc analysis of 3,274 births in uttar pradesh, india. _BMJ Global Health_ , 5(9), 2020.

- S. Sexton. Automatic bill payment and salience effects: Evidence from electricity consumption. _Review of Economics and Statistics_ , 97(2):229–241, 2015.

- T. Sherry. A note on the comparative
%) Estimated difference<br>C13.3. SOSD: Social Sustainability Index C13.4. Rank correlations<br>Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.92*** 0.98*** 0.93***<br>H<br>Kendall tau-b 0.78*** 0.94*** 0.83***<br>Spearman 0.95*** 0.9*** 0.93***<br>A<br>Kendall tau-b 0.89*** 0.78*** 0.83***<br>Spearman 0.98*** 0.98*** 0.98***<br>M0<br>Kendall tau-b 0.94*** 0.94*** 0.94***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br>1 .05<br>.8 0<br>.6 -.05<br>CCDF .4 Difference -.1<br>.2 -.15<br>0 -.2<br>.5<br>.4<br>.3<br>.2<br>Social Sustainability Index .1<br>0<br><!-- End of picture text --> _C14. Change in the indicators included for each
br>0 80 100<br>0<br>-20 80<br>60<br>-20<br>-40 60<br>40 -40<br>-60 40<br>-60<br>20<br>-80 -80 20<br>-100 0 -100 0<br>03/01 05/01 07/01 09/01 03/01 05/01 07/01 09/01<br>Stringency of<br>Mobility around<br>Period of interview lockdown<br>transit stations<br>restrictions<br>Google mobility Stringency of Google mobility Stringency of<br>index (baseline = 0) lockdown measures index (baseline = 0) lockdown measures<br>Google mobility Stringency of Google mobility Stringency of<br>index (baseline = 0) lockdown measures index (baseline = 0) lockdown measures<br>Google mobility Stringency of Google mobility Stringency of<br>index (baseline = 0) lockdown measures index (baseline = 0) lockdown measures<br>Google mobility Stringency of Google mobility Stringency of<br>index (baseline = 0) lockdown measures index (baseline = 0) lockdown measures<br><!-- End of picture text --> Data from Google (2020) for the mobility trends and Hale et al. (2020) for the lockdown restrictions. The mobility trends are smoothed using 7-day moving averages.

35
Column 11).

# **4.3 Implied leakage rates**

We restate the baseline estimates so they express the leakage rate: the dollar increase in haven deposits associated with a 1 dollar aid disbursement. This step helps assess the likely scale of elite capture through offshore accounts. Since our empirical model does not deliver the leakage rate directly, we need to rely on simple transformations to obtain it.

> 14Changes in exchange rates can cause changes in our deposit measures because they aggregate different currencies into USD equivalents using contemporaneous exchange rates. The model in Column (9) controls for exchange rate movements by including a variable that expresses the mechanical change in deposits following from exchange rate changes. We construct this variable as the average percentage change in exchange rates (relative to USD) weighted by country-specific currency shares in deposits (obtained from the BIS Locational Banking Statistics).

> 15Andersen et al. (2017) show that rents from petroleum production are associated with money flows to havens in countries with poor democratic governance. We control for resource rents by including the interaction between the time dummies and an indicator for petroleum producing countries.

13
ante or ex-post than other firms raising equity or bonds. Fourth, the results are robust to splitting the sample in 2007. Fifth, the results are qualitatively similar when using the Worldscope data, as reported in Appendix Table 6.

# **4.4.  Implications for the firm size distribution**

The evidence on firm size and firm growth has important implications for the FSD and its evolution across publicly listed firms. To study this, we estimate four density estimates: two for the distribution of firm size as of 2003 (one for users and one for non-users of capital market financing) and two analogous ones as of 2010. Figure 5A shows the results for total assets as a proxy for firm size, whereas Figure 5B reports the estimates for sales. In this sub-section, users are the firms that raise equity (Panels A and C) or bonds (Panels B and D) between 2004 and 2010; all firms with capital raising activity in 2003 are excluded from the sample in this figure. Non-users are the other firms in our sample. The figure shows that the nonparametric density estimates of the Kernel distributions of firm size, measured by total assets, are indeed different between users and non-users of capital markets.

The distribution of users of capital markets as of 2003 is located to the right of that of the non-users, indicating that the firms that use capital markets are typically larger than the other firms. The differences in the density functions between equity or bond users and non-users in both China and India are all statistically significant according to the Kolmogorov-Smirnov tests (Figure 5, bottom panel).<sup>30</sup> These patterns indicating differences in firm size at the beginning of our sample are consistent with the evidence presented thus far that larger firms are more likely to use equity or bond financing.

> 30 Among the largest issuers (in terms of total assets) from China are a number of financial corporations, such as China Merchants Bank and Bank of Communications, as well as non-financials
|
|Philippines; 2008 , 2013|Timor Leste; 2009, 2010|
|Rwanda; 2014, 2015|Uganda; 2006, 2011|
|Sao Tome and Principe; 2008, 2009|Ukraine; 2007|
|Tajikistan; 2012|Zambia; 2007 , 2013, 2014|
|Tanzania; 2009, 2010, 2015, 2016||
|Timor Leste; 2009, 2010||
|Uganda; 2006, 2011||
|Ukraine; 2007||
|Zambia; 2007 , 2013, 2014||

_Source:_ Demographic and Health Surveys (DHS).
to construct an export productivity measure defined as exports per worker. Since exports and exports per worker are heavily skewed and contain many zeroes, our pre-analysis plan stated that we would take the inverse hyperbolic sine transformation of these outcomes. Finally, we also construct an overall export performance index, defined as the average of standardized z-scores of these different export measures. Appendix C defines each outcome in more detail.

We supplement these export and employment data with a combination of data from the program, a survey, and with linking the firms to other government datasets in Colombia. We use data from the application forms to describe the characteristics of firms at baseline, for stratifying the random assignment, and for balance checks. Program records provide information on take-up and usage of the intervention. A follow-up survey (described in section 5.2) is used to examine impacts of the program on export-specific and general management practices. Finally, in Appendix E we report impacts on secondary outcomes of interest such as sales, employment, survival, and productivity, by using data from 20182020 annual filings of firms in the Mercantile Registry (RUES) and to data from 2016 to 2019 in the Annual Manufacturing Survey (EAM). We did not elicit priors for these secondary outcomes, and so provide only frequentist and not Bayesian impact evaluation results for those outcomes.

# **4.2 Estimation of Treatment Effects using Frequentist Methods**

Our frequentist estimation follows the approach standard in the literature. We use the following pre-specified Ancova linear regression specification to estimate the intention-totreat effect. Our estimating equation for the ITT impact on outcome _Y_ of firm _i_ being assigned to treatment versus being assigned to control takes the form: where _Yi,t−s_ is the _s_ th pre-intervention lag of the outcome of interest; _δj_ are randomization strata fixed effects (following Bruhn and McKenzie (2009)); and _β_ is the intent-to-treat effect. Robust (Eicker-White) standard errors are then used.

In addition to the standard hypothesis test that the average effect of being
reservations in local areas through the set-aside programs increased the desire of women for more educational attainment and stronger careers. It is possible that our results are a reflection of these greater aspirations. An inspiration stimulus would again be mostly neutral, and thus could align well with the industry patterns we observe. A strong factor favoring this channel is that an inspiration account would also naturally connect to the smaller-scale nature of our entry patterns given that the inspiration stimulus is plausibly most likely to influence entry choices at the smaller end of establishment sizes (i.e., those who would not have otherwise entered without the inspiration are likely to be those starting smaller firms than those who would enter regardless).

While both of these hypotheses are appealing, we are cautious to note that we cannot differentiate between them. We likewise cannot completely separate them from other state-year factors that could be tied to the reservations (e.g., women-oriented policies promoted by local governments). Our comparative advantage in this project compared to the emerging literature stream overall is that we are able to quantify the economic patterns for the full Indian manufacturing sector. Administrative datasets, however, have natural limits for measuring subtler factors like women-oriented public goods or inspiration. We hope to encourage future research at the village level to pick up on these entrepreneurship dimensions. To date, we are not aware of any village-level studies that have collected information about women’s entrepreneurship, and our study highlights the high potential return for doing so.

# **7.  Conclusions**

The implementation of political reservations had strong effects for empowering women in India in many spheres. While we do not see much evidence that this increased women’s employment in manufacturing, we do identify that women are more likely to start new establishments in the unorganized sector after the reforms. This growth in entrepreneurship was concentrated in industries that women have traditionally been active in and at the household-establishment scale.

This study provides among the first evidence of how political reservations can affect economic outcomes for women. This
Figure 5: Validation with SICONFI data - payment <!-- Start of picture text --> CE MG<br>1500<br>600<br>1000<br>400<br>200 500<br>0 0<br><-25 -20 -15 -10 -5 0 5 10 15 20 25> <-25 -20 -15 -10 -5 0 5 10 15 20 25><br>PB PE<br>300<br>1500<br>200<br>1000<br>500 100<br>0 0<br><-25 -20 -15 -10 -5 0 5 10 15 20 25> <-25 -20 -15 -10 -5 0 5 10 15 20 25><br>PR RS<br>600<br>1000<br>400<br>500<br>200<br>0 0<br><-25 -20 -15 -10 -5 0 5 10 15 20 25> <-25 -20 -15 -10 -5 0 5 10 15 20 25><br>SP % difference from SICONFI data<br>6000<br>4000<br>2000<br>0<br><-25 -20 -15 -10 -5 0 5 10 15 20 25><br>% difference from SICONFI data<br>Frequency<br>Frequency<br>Frequency<br>Frequency<br><!-- End of picture text -->

**Notes:** This figure presents the percentage deviation in total amount of budget payments, at the municipality-year level, between our dataset and SICONFI, the public finance dataset of the Brazilian Treasury. Values are positive whenever the total amount in our dataset, aggregated from individual payments, is larger than that of SICONFI. See Table 1 for more details on our data coverage. We
, such as schooling costs, which may or may not be actually binding, for example if the schooling cost can be overcome with a scholarship.

A handful of studies with measures of both aspirations and expectations suggest that they are different concepts. Given that aspirations are ideal outcomes and expectations are most likely ones, expectations are on average lower or at maximum equal to aspirations. In rural Ethiopia, a sample of adults had on average only slightly lower expectations for their children’s education than aspirations — 12.4 and 12.9 years, respectively — but had much lower expectations in terms of income, wealth, and social status, than the level they aspired to (Bernard et al. 2014).<sup>2</sup> Likewise, in a sample of poor households in Colombia, 14 percent of parents expected to complete university themselves while 33

> 2 Measures of expectations in the four dimensions were based on a survey question with a time horizon of ten years, while the aspirations question were based on wished achieved level in life, which may have influenced differential aspired and expected levels.

6
densities as recommended by the United Nations Statistical Commission and defined according to stage I of the Degree of Urbanization (Eurostat, 2021). The built-up surface, land layer, and a 1km<sup>2</sup> population spatial raster dataset are the inputs for the GHSL SMOD. As shown in panel (b) of **Figure 3** , the GHS SMOD classifies the 1km<sup>2</sup> grid cell at the first hierarchical level into three spatial entities, including “urban center”, “urban cluster”, and “rural grid cells” (Schiavina et al., 2023).

**Figure 3: Global human settlement layer**

_Notes_ : Panel (a) presents the built-up area, and panel (b) shows the SMOD classes. Basemap: ESRI Satellite Imagery.

# **3.1.4 Administrative Boundary**

It is necessary to nest the produced enumeration area (EA) boundary inside administrative borders. The generated EA boundary should be nested within administrative boundaries. The HDX website <u>(https://data.humdata.org/) provided the necessary administrative border, which is based on the 2011</u> census, for Armenia.

# **3.2 Semi-automatic Creation of pre-EAs**

This section describes the semi-automated process employed to generate the first digital national sampling frame in Armenia. The national sampling frame is automatically created, although some manual adjustments 12
# **2.1. Data Description**

We study four complementary types of investment: bank loans and deposits, portfolio investment in debt and equity, FDI, and international reserves. These investment types make up the bulk of the IIP for most countries in the world and are known as functional categories in the balance of payments (IMF 2009). They represent (together with financial derivatives, not covered here) countries’ external assets in the External Wealth of Nations database (Lane and Milesi-Ferretti 2001, 2007). Instead of using aggregate international investment at the country level, we work with the underlying bilateral data. That is, for each country in the sample, we analyze the value of investments held by that country vis-à-vis each individual counterparty country.

Our data sets comprise bilateral year-end stocks of cross-border loans and deposits, portfolio investment, and FDI. The appendix provides detailed explanations of how we construct the data for each investment type. Because no bilateral data are available for international reserves, we construct a bilateral data set at the country-to-region level, that is, the international reserves each country holds in assets from different regions. We combine data on the level of international reserves from the IMF’s IFS with data on the currency denomination of reserves from the IMF’s COFER. Reserve assets denominated in a given currency typically consist of government debt owed by the sovereign issuing that currency. We thus assign as destination of reserve investments the region of the country in whose currency reserves are denominated. For example, we assume that euro denominated reserves are issued by the North.

For loans and deposits and FDI, we take advantage of “mirror data” to maximize sample coverage. For many country pairs, we have assets reported by the source country as well as liabilities reported by the destination country. Thus, whenever a source country A does not report its asset holdings in a country B, we use the liabilities reported by country B as the investment for the country pair A→B. Using this
a ground-truthing exercise to verify assumptions, followed by listing of the selected segments (Figure 5.D).

This process described had to accommodate two detours: First, for some rural northern mauzas the availability of demarcated buildings in OSM data was low, so in those cases a different approach was used. Since enumeration teams would in any case be sent to the field to list, segments were created based on the assumption that the population was uniformly distributed across the area. Enumeration areas were then created that field teams would be asked to list using field protocols.

Second, the mauza shapefiles less accurate in urban areas: In general, the shapefiles available were not a perfect match to the actual areas; with this in mind, the team proceeded with listing in the selected mauzas using the available boundaries. In two cases (Light House Para and Nazirar Tek) in Cox’s Bazar Sadar, the location of the mauzas on the shapefile did not coincide with the actual location of the areas.<sup>33</sup> The field team

> 29 In 13 cases the coverage was low, so a different approach was adopted to segment within those mauzas.

> 30 For example, in Shahparir Dwip, the 2011 census reports 4,351 households, and 2,081 buildings were found. To obtain 100 household segments, it was necessary to create 44 segments. Assuming that the distribution of population is uniform, each segment will have around 47 buildings (2081/44).

> 31 In a sense, the listing process was able to validate the assumption, which worked surprisingly well in rural areas.

> 32Although a set number of segments had to be created, this process needed to run with some flexibility. The number of buildings had an upper and a lower bound. For example, if 200 buildings are observed and two segments are needed, the method will use 99 as a lower bound and 101 as an upper bound as a first try, then proceed until a solution to the problem
countries in Latin America, are in this category. Thirteen countries had a minimum loan size requirement of ten times or more GNI per capita. Saudi Arabia (USD 1.3 million), Jordan (USD 1.4 million) and Germany (EURO 1.5 million) are the countries with the largest cutoff requirements where the credit registry is used mostly for bank supervision and systemic risk monitoring. Regarding the countries analyzed in this paper, Argentina has a very low minimum cut-off, of 50 Argentine pesos (previously US$50 and now roughly US$18), Brazil has a minimum of US$ 2,100 down from US$ 50,000 several years ago and Mexico begins at US$ 20,000.

The supervisory agenda is also often apparent in the type of data collected in a PCR. The most obvious example of this is the lack of transparent data on payment status for loans, which is the heart of most private credit bureaus. Only about one-third (18/57) of PCRs collect a straightforward borrower payment history which shows the number of on-time and late payments and defaulted loans and which indicates the extent of arrears (30 days, 90 days, 180 days late, etc.). To some extent this data is contained in ratings as described above, since payment status is a key determinant of a rating. However, since other more subjective factors can bear on the rating, such as a bank’s estimate of the probability of default, ratings data are less transparent and more difficult to verify than objective payment data. Another variable which is very valuable for credit evaluation but often neglected by public bureaus is address information, which is lacking in about 40% of PCRs. Other data which are important for assessing creditworthiness, such as employment data and demographic data are also absent from a significant proportion of PCRs.

# **_3. Economic research, macroeconomic policy and control_**

Credit bureau information can also be important for macroeconomic intelligence. Credit bureau information can