rafmacalaba/data-use-mentions-extended huggingface dataset:
.S., et al., 2020. Impact of Covid-19 on remittance inflows to Samoa. Asian Economics Letters 1, 17894.

- Clemens, M.A., McKenzie, D., 2018. Why don’t remittances appear to affect growth? Econ J 128, F179–F209.

- Demirg ̈uc ̧-Kunt, A., C ́ordova, E.L., Martinez Per ́ıa, M.S., Woodruff, C., 2011. Remittances and banking sector breadth and depth: Evidence from Mexico. J Dev Econ 95, 229–241.

- Docquier, F., Rapoport, H., Salomone, S., 2012. Remittances, migrants’ education and immigration policy: Theory and evidence from bilateral data. Reg Sci Urban Econ 42, 817–828.

- El Economista, 2020. UBS espera contracci ́on de 7.6% para M ́exico este a ̃no — El Economista. URL: https://www.eleconomista.com.mx/economia/UBS-espera-contraccion-de-7. 6-para-Mexico-este-ano-20200413-0146.html.

- El Financiero, 2020. Remesas ’destrozan’ pron ́osticos y tienen en marzo su mejor mes desde septiembre de 2003. URL: https://www.elfinanciero.com.mx/economia/ remesas-tienen-en-marzo-su-mayor-incremento-desde-2001.

14
# **Figure 2: CDBP vs. bank risks, GDP growth outlook, and fiscal space**

**Panel A: Increase in NPLs that will wipe out capital buffers for at least 20% of total banking system assets (in percentage points)**

**Panel B: Increase in NPLs that will wipe out capital buffers for at least 20% of total banking system assets (in percentage points) vs. EIU banking risk index**

<!-- Start of picture text --> 45 Upper middle income Lower middle income Low income<br>45<br>40<br>40<br>35<br>35<br>30<br>30<br>25 25<br>20 20<br>15 15<br>10<br>10<br>5<br>5<br>0<br>0 30 40 50 60 70 80<br>EAP ECA LAC MENA SAR SSA EIU banking risk indicator (high=100)<br>(in percentage points)<br>(2019, in percentage points)<br>Consolidated Distance to Break Point (CDBP)<br>Consolidated Distance to Break Point (CDBP)<br><!-- End of picture text -->

**Panel C: Increase in NPLs that will wipe out capital buffers for at least 20% of total banking system assets (in percentage points) vs. real GDP growth (2021 forecast)**

**Panel D: Increase in NPLs that will wipe out capital buffers for at least 20% of total banking system assets (in percentage points) vs. general government gross debt**

<!-- Start of picture text --> Upper middle income Lower middle income Low income Upper middle income Lower middle income Low income<br>45 45<br>40 40<br>35 35<br>30 30<br>25 25<
able to raise capital). Because we have limited information on firm-level characteristics before the IPOs, we include in our data set only firm-level balance sheet information for the post-IPO years. Our final matched data set comprises 2,458 firms from China and 4,305 firms from India. Of these firms, 1,915 Chinese firms and 3,428 Indian firms did not have any capital raising issue through equities or bonds in domestic or foreign markets between 2003 and 2011.<sup>15</sup> The number of firms with capital raising activity in our final matched data set is smaller than the number of firms included in the SDC Platinum database alone because several firms that raised capital through security issues do not have balance sheet data available from Orbis.

We focus the analysis on some key performance indicators (described in Appendix Table 1).<sup>16</sup> Specifically, we focus on the level and growth rate of total assets, sales, and the number of employees to shed light on the relation between firm size, growth, and their use of domestic and international markets.<sup>17</sup> We also examine firm profitability and financial indicators such as return on assets (ROA), leverage (including bank and other types of financing), the maturity profile of the liabilities, and retained earnings. These indicators allow us to shed light on how healthy firms are and to what extent access to capital markets might affect firm mismatches and their dependence on the more expensive internal financing.

To obtain information on firm investments, which is not available in the Orbis database, we match the SDC Platinum database on the use of capital markets with the Thomson Reuters’ Worldscope database, which also contains balance sheet information for listed firms. The data

> 15 Our merged data set comprises both financial and non-financial firms. The results in this paper are quantitatively and qualitatively robust to the exclusion of financial firms.

> 16 We deflate the nominal variables (measured in US dollars)
suspension, and the worker’s previous formal employment spells. PNAD-C collets information about individuals who report to have received the insurance in the period of the survey. It also asks about the time individuals had been actively searching for a new job, though it does not collect precise information about the time when the termination ocurred and about the previous labor history. Unemployment benefit amounts are reported in the survey and used in our estimations. F<sup>23</sup>

- _Salário Familia._ The benefit is granted to formal workers who have children or stepchildren aged 14 or less and receive a salary up to a limit (BRL 1,364.43 in 2019) . In case of disabled children, there is no age limit to the benefit. PNAD-C does not have information on disability, so we consider individuals as disabled when they declare to have received BPC and to be under 65 years old. It is important to note that both parents can receive Salário Família if their salary is below the threshold and they have a child under 14 years old or with a disability. Workers receive a benefit that is proportional to the number of children that match these conditions.

- _Abono Salarial._ This benefit is paid to formal workers who earn up to a limit (two minimum wages). To be eligible, workers must satisfy two criteria: i) have been registered in PIS/PASEP for five years or more and ii) have worked for at least one month in the current calendar year. 23F<sup>24</sup> Because PNAD-C does not provide information on enrollment in PIS/PASEP database, we assume that a person needs to have five years of potential experience to be considered a beneficiary of Abono Salarial, defined as age minus years of schooling minus 5. The benefit value is calculated based on the number of months worked in the previous calendar year considering all formal jobs. Given that this information is not available in PNAD-C data, we use the number of months worked
the fate of the magnitude of the coefficient of the dummy variable representing the Vietnamese students in the sample. However, we also need to investigate structural differences in the effects of endowments between Vietnam and Dev7 countries. In Section 4, we adopt an approach first used to explain variation in PISA performance between Germany and Finland by Andreas Ammermueller [Ammermueller, 2007]. This is an adaptation of the popular Oaxaca-Blinder decomposition of the wage earnings equation to uncover evidence of discrimination on the basis of gender [Blinder, 1973] and [Oaxaca, 1973]. In this section, we examine closely the structural differences between Vietnam and the Dev7 countries, including the contribution of differences in endowments and the coefficients to the gap in test scores.

Even a multi-variate regression approach only proves correlation with nothing more than a hint regarding causation, and so far we have only one year (2012) of PISA data for Vietnam. Even though we cannot uncover causality, there are useful policy related conclusions that we can derive from the analysis presented in this paper. There is a veritable industry of papers 4
|[0.062]|[0.058]|[0.057]|
|Constant|0.04<br>[0.005]***|0.01<br>[0.002]***|||
|Observations|791|432|784|418|
|R-squared|0.050|0.190|||
|Number of countries|39|18|39|18|
|Country Fixed Effect|Yes|Yes|Yes|Yes|
|Adjusted R-squared|||-0.337|0.108|
|**First Stage IV Estimates**|||**First Stage **|**First Stage **|
|Trade Weighted Growth * Exp|||1.43<br>[.21]***|1.32<br>[.08]***|
|Trade Weighted Growth * Rec|||1.72<br>[.29]***|1.39<br>[.15]***|
|**Underidentification - Weak Identification - Wea**|**k Instruments Tes**|**ts**|||
|LM test statistic for underidentification|||41.87|30.31|
|p-value of underidentification LM statistic (Anderso<br>F statistic for weak identification (Cragg-Donald or|n or Kleibergen-P<br>Kleibergen-Paap)|aap)|9.77e-11<br>21.85|3.68e-08<br>51.14|
|Anderson-Rubin chi-sqared test of significance of e|ndogenous regress|ors|64.05|22.66|
|p-value of Anderson-Rubin chi-sqared test of endog|enous regressors||0|1.20e-05|

Source: Authors’ calculations based on World Economic Outlook database 2019.

Notes: Robust standard errors in brackets. *** p<0
experience an absolute decline in the “statistical capacity index”- an index of data transparency (Arezki et al. 2020). More data transparency may improve political trust and create more social cohesion. Releasing micro data to the public requires balancing two fundamental principles of statistics: confidentiality and access. An agency not committed to data transparency could argue (erroneously) that privacy considerations –captured in every Statistics Act, prevent it from releasing anonymized micro data.

To assess access to microdata we take the perspective of an everyday data user and visit the public facing websites of all National Statistical Offices (NSOs) in the MENA region as well as microdata libraries maintained by the World Bank, International Household Survey Network (IHSN), IPUMS and Eurostat. We also visit the web-portals of the MICS and DHS surveys. Though, as World Bank staff, we often already have access as part of our official duties, we opt to follow a ‘mystery client approach’ and explore which data can be accessed through public channels. We verify if up to date microdata across several data categories is available for download either immediately or after a request is made by the user.

Informed by this exercise, we do two things. First, we make suggestions aimed at improving NSOs’ ability to provide up to date microdata online. Second, we present a new Microdata Access Indicator (MAI) that measures the degree of openness of available microdata across MENA countries and the degree to which microdata users can <mark>access, understand, and navigate the websites of MENA NSOs.</mark> The new MENA MAI provides insights on microdata accessibility in MENA and complements the Open Data Access Indicator (ODIN)<sup>3</sup> published by Open Data Watch that focuses on NSOs’ ability to provide access to produced statistics and indicators (as opposed to anonymized raw data). Together, both the MENA MAI and the ODIN provide a robust picture of data accessibility in MENA and can over time serve as a useful tool
US$1,328.3 million for the initial baseline. A major reason for this is that price is 18.8 percent lower. However, overall consumption falls by 3 percent. This is driven by the lower consumption from rice producing households, which suffer a loss in income as a result of the lower price for rice. The simulation also suggests that given the current technical and economic parameters, in order to maximize consumer/producer surplus, the total domestic production of rice (524,520 Mt) should only be produced in the Mangrove (351,600 Mt) and Riverain (172,920 Mt) ecologies and no rice should be produced in the Upland, IVS and Boli ecologies. This reflects a dramatic shift for the current spatial distribution of the crop. Furthermore, the marginal value (shadow price) of the upland land acreage in the model results is US$ - 166.28, suggesting that if an additional hectare of upland rice was included, the overall consumer/producer surplus would fall by US$166.28. In contrast, the marginal value of Mangrove land is US$ 191.70, suggesting that if one more hectare of Mangrove land could be added beyond the current bound the total consumer/producer surplus would be increased by US$191.70.

**Table 11: Results of the model with Acreage Upper bounds compared with survey data**

|Scenario|Consumer<br>&<br>Producer<br>Surplus<br>(US$Mn)|Domestic<br>Consumption<br>(Mt)|Domestic<br>Production<br>(Mt)|Imports<br>(Mt)|Exports<br>(Mt)|Price<br>(US$/Mt)|Trade<br>Balance<br>(US$ Mn)|Employment<br>(Numbers)|
|---|---|---|---
Policy Research Working Paper 10587

# **Abstract**

This paper examines the pervasiveness of tax evasion among firms in Indonesia and the characteristics associated with higher levels of noncompliance. Tax evasion is estimated through a randomized, double-list experiment embedded in a nationally representative survey of 2,955 registered firms. This revealed whether firms pay all the taxes they owe without them having to disclose this directly. Across both list experiments, around a quarter of the firms indirectly reveal that they have evaded taxes. Firms that do not export, face intense competition from informal firms, and believe tax administration is a major obstacle to their business are the most likely to evade taxes. These findings help to inform the enforcement activities of tax authorities in middle-income countries, which face substantial challenges in estimating levels of tax evasion and identifying noncompliant taxpayers.

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 choy@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility. worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage
micro data have the potential to present much more accurate comparisons of wage convergence and labor market integration.

# **B. The Microeconomic Data**

To control for the differences in demographic changes across countries, we draw upon Latin American household surveys from 16Latin American countries: Argentina, Brazil. Chile, Colombia, Dominican Republic, Costa Rica, Bolivia, Ecuador, El Salvador, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, and Uruguay. We restrict the samples to just years after 2000 and before 2014. We impose other sample restrictions as well, including limiting the age to the 18-65 year range and, in this first pass, we limit the sample to males with positive earnings. We also drop observations of income in the top and bottom 1% in each country.

There is a great deal of variation in sample size across countries. Table 4 shows the number of observations by country (and average per year) after the sample restrictions are imposed. There is much variation in the size of the sample, with the smallest sample being in Nicaragua. Mexico has the largest sample. The main point of Table 4, however, is that even the relatively small sample sizes are large enough to have confidence in the estimates of mean wages.

As is well-known, the demographic distribution differs across countries. Figure 3 demonstrates one aspect of this by presenting the age distribution by country (before the sample restrictions are imposed) for the latest year that the data are available for each country. In general, the poorer countries have larger population spikes at the younger ages. This is important because differences in demographic distributions across countries can affect aggregate comparisons (such as GDP per capita) and therefore helps motivate our focus on matching demographic groups.

To generate the mean wage estimates, we identify five age groups (18-26, 27-35, 37-45,4613 | P a g e
with country-province-specific hazards. In a second step, we narrow our physical risk assessment to the portion of assets that banks allocate to households for home loan purposes, i.e. mortgages. We therefore combine the flood hazard mapping information with the banking system's outstanding mortgages within each country-province. In a third and final step, we use geographical nonperforming loan (NPL) data at the country-province level and natural disaster information to study possible changes in banks’ asset quality in the aftermath of large-scale natural disasters. Our baseline empirical methodology consists of a difference-in-difference method that exploits quasi-exogenous variations in provinces affected by natural disasters.

We estimate credit risk exposure to transition risks by relying on a sectoral exposure analysis. To obtain a rough measure of LAC banks’ exposure to transition risks, we focus on exposures to highly CO2-intensive and environmentally damaging industries and assets. We focus on industries most affected by transition risks, including fossil fuel and energy production, heavy industry, transportation, agriculture and real estate. Moreover, using firm-level data, we analyze the financial health of LAC firms operating in transition-sensitive sectors.

Our data set is composed by a cross section from 9 countries (Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Mexico, Peru, and Dominican Republic) for the estimation of physical risks, and from 12 economies (the nine countries cited above excluding Ecuador plus Costa Rica, El Salvador, Paraguay, and Uruguay) for the estimation of transition risks. Our physical risk assessment is complemented by an empirical estimation comprising a panel of 5 countries (Argentina, Brazil, Chile, Mexico, and Peru) over a period of 35 quarters (2011q3 and 2020q1). We take all data from public sources, with data availability determining our sample composition.

Our results are the following. In terms of physical risks, we find that exposures to floods represent the most important source of credit risk for the LAC banking sector. This is compounded by high loan concentration in and around LAC capital cities, which, ceteris paribus
# _Part I. Scorecard_

|**Africa Sector Database Sector**<br>**Description**|
|---|

|Agriculture|Agriculture, Hunting and Forestry, Fishing|
|---|---|
|Mining|Mining and Quarrying|
|Manufacturing|Manufacturing|
|Utilities|Electricity, Gas and Water supply|
|Construction|Construction|
|Trade services|Wholesale and Retail trade; repair of motor vehicles, motorcycles and personal and household goods,<br>Hotels and Restaurants|
|Transport services|Transport, Storage and Communications|
|Business services|Financial Intermediation, Real Estate, Renting and Business Activities|
|Government services|Public Administration and Defense, Education, Health and Social work|
|Personal services|Other Community, Social and Personal service activities, Activities of Private Households|
|Aggregate services|Trade services & Transport services & Business services|

## **_Key_**

|Initial Output:|Gross value added at constant local prices (millions), 1970. Value added in constant prices is calculated from estimated series of<br>nominal data and price deflators, and rebased to express values in 2005 prices.|
|---|---|
|Initial Employment Share:|The ratio of persons employed in sector to total persons employed , 1970. Employment is defined as all persons engaged, aged 15|
|Initial Productivity:|The ratio of output to number of persons engaged in sector, 1970.|
|Aggregate Services:|Wholesale and Retail trade; repair of motor vehicles, motorcycles and personal and household goods, Hotels and Restaurants;|
|Employment Share Change (1970s-2000s)|Average employment share in the 2000s minus average employment share in 1970s.|
|Employment Share<br>Growth(1970s to2000s)|(Average employment share in the 2000s - average employment share in 1970s) / average employment share in the 1970s|

Annex: Page 2
# **8 Figures**

**Figure 2.1 Percentage of the labor force by maximum educational level achieved, Latin America, 1950-2010**

Source: Barro and Lee (2010).

**Figure 2.2 Change in average years of education. 16 Latin American countries, c.1989-c.2009**

<!-- Start of picture text --> 3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>ARG, 92-09 BOL, 93-05 BRA, 92-09 CHL, 90-09 COL, 92-04 CRI, 90-09 ECU, 94-09 HND, 91-09 MEX, 89-08 NIC, 93-05 PAN, 91-09 PRY, 90-09 PER, 97-09 SLV, 91-08 URY, 89-09 VEN, 89-06<br><!-- End of picture text --> Source .Cruces _et al_ (2011), based on SEDLAC (CEDLAS and World Bank).

23
As Rodrik (2008) and Ravallion (2008) have argued, there is a trade-off in policy evaluation between external and internal validity. As traditional identification of causal effects through instrumental-variable strategies never completely eliminates confounding influences, these strategies always suffer from an internal-validity problem. However, when based on crosscountry evidence, they pick up average effects that can be relatively stable — provided they are consistent with some sort of theory — because induction, even on cross-country samples, may fail to produce generalized results.

By contrast, IE purges out confounding influences, but generates results that are empirical and case-dependent. Such results may fail to carry over to different settings. Limited external validity of any study would not be a problem if we could replicate it easily. With enough replications, the sheer mass of evidence would provide the desired generality (although the method would still be inductive and would thus suffer from the general critique of inductive methods in science). But some kinds of IE can be costly. For instance, the World Bank reckons that household surveys cost on average $300 per household. At that rate, a baseline and final survey of 500 households would cost $300,000. This is a lot for studies with only internal validity.<sup>40</sup> However, costs can often be contained by working with local institutions, which has the added advantage of building capacity in a key area.

Some trade-related programs target limited numbers of firms, so their evaluation is less costly than that of poverty-reduction programs. For instance, in a middle-income country, the cost of surveying 500 firms can be substantially lower than $100,000. Moreover, the data may exist prior to and independently of the IE in the form of census or industrial surveys and customs records. In that case, the cost of the IE goes down dramatically. The problem then is no longer one of cost but more of securing buy-in from the agencies possessing the data so that they share it. However, it should also be
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the poor quality of national accounts rather than missing top incomes. Blanchet et. al (2022) note that in LMICs tax data is almost always in the form of aggregated tables rather than microdata, which limits what it can be used for and point out that even when microdata is available it contains few covariates, which can limit how the data can be used to improve estimates of the top tail from household surveys.

Kerr (2021) highlights that researchers using and comparing tax and survey data for South Africa created yearly earnings from household surveys obtained by multiplying monthly earnings by 12 for those employed at the time the survey was undertaken. This is very different conceptually to tax data on earnings in the last year, since the surveys exclude those employed at other points in the year, who are more likely to be low earners. Kerr (2021) show that, in the South African case, this error makes 21