rafmacalaba/data-use-mentions-extended huggingface dataset:
# **E MEDIA DISSEMINATION OF THE PEP PROGRAM**

Table E.1. News in National and Regional Newspapers Mentioning the Permiso Especial de Permanencia

|Main topic of the news|Number of the news|
|---|---|
|Information about PEP program and RAMV census|33|
|Border Enforcement and Migration Control|14|
|Causes of Migration and Venezuelan Context|5|
|Consequences of accepting Velenzuelan Migrants in Colombia|22|
|Government benefts for Venezuelans in Colombia|10|
|Situation of Venezuelan Migrants in Colombia|21|
|Statistics of Venezuelan Migration|15|
|Total Number|120|

_Notes:_ This table presents the total number of news articles found when we searched the _Permiso Especial de Permanencia_ or the acronym PEP in the principal newspapers of Colombia in 2018. The newspaper sources used in the analysis were El Tiempo, El Espectador and Publimetro. To complement the analysis, we used the Google news tool to check for regional newspapers.

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Social Liberal. Evangelicals and white Brazilians were much more likely to vote for the Partido Social Liberal than other religious and ethnic groups. Although the Gallup World Poll does not contain an ethnicity variable, a further examination of the religion variable using the Gallup World Poll reveals that the decline in SWB is not larger for respondents with strong religious beliefs.<sup>26</sup> Table 7 (column 2) also shows that that the middle aged, evangelicals, and people with a partner were more likely to switch parties and withdraw support from the incumbent party. Overall, these results suggest that the rise of the Partido Social Liberal was associated not only with the voting choices of groups that experienced a larger decline in SWB, but also with the support of groups that espouse certain values.

**Table 7: Socio-Demographic Characteristics Associated with the Bolsonaro Vote – Logistic Regression**

||**Dependent variable:**|**Dependent variable:**|
|---|---|---|
||Voted for Bolsonaro in Second<br>Round of Presidential Elections|Switched from voting Dilma in<br>2014 to Bolsonaro in 2018|
||(1)|(2)|
|Age|||
|<30|Ref.|Ref.|
|30-45|0.96|1.37*|
|45-60|1.30#|1.23|
|60+|1.13|0.55**|
|Female|0.64**|1.06|
|Has a partner|1.34**|1.49**|
|Other persons in household|0.85|0.88|
|Evangelical|2.11*|1.34*|
|White (Branco)|1.28*|0.94|
|Immigrant|0.32|1.62|
|Lives in a rural area|1.01
evolution of Central Bank Net Foreign Assets is shown in Figures 8a, 8b.) The Treasury, after having canceled the auctions in November 1993 on the basis of high rates (for example 73% in November) would return to the domestic borrowing market in January 1994, having used up half of the 1994 legal limit on STA's in only the first 3 weeks of the year. this time by fixing the maximum interest rate at 94% in simple annual terms. Yet the demand for these bills were extremely low. The announced budget for 1994 contained no measures for fiscal correction, price increases on SEE goods were delayed but expected to take place immediately. after the local elections in the first couple of days of April 1994, thus inflationary expectations were high (Figure 10 shows the evolution of WPI and CPI ) and residents were abandoning TL denominated assets in favor of foreign ones. Currency substitution, as proxied by the ratio of M2Y to M2 where M2Y is a broad money measure including foreign currency deposits is shown in Figure 11. The share of domestic currency deposits in M2Y fell from 53 percent to 42 percent between December 1993 to April 1994 **9.** The evolution of foreign currency deposits at commercial banks is shown in Figure 12. The decline the stock amount of deposits during the third and fourth months of 1994 corresponds to the period of large withdrawals, and then the return to TL denominated assets, mainly government paper, in May 1994. The domestic borrowing market would in fact disappear until May 1994, when the Treasury managed to borrow substantial amounts again, but at compounded annual rates around 400%. Rates on 3, 6 and 9 montly bills is shown in Figure 13.

Returning to the situation in early 1994, as the domestic borrowing market of the Treasury collapsed, the mechanism through which commercial banks borrowed at foreign markets, and at 90ECD Economic Survey on Turkey, 1995, p.<sup>2 3</sup> .

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assess what can be salvaged and what needs to be fixed.

As always when it comes to divorces, the story is more complex than it seems. Facts tend to be ignored, reinterpreted or rewritten by each partner in the couple and each one gets a wagon of supporters and detractors who tend to be happy to help rewrite history in favor of the side they want to support. Facilitating the subjective and emotional positioning of observers are the many gaps in the common knowledge of crucial basic facts. Indeed, many of the facts are actually unknown simply because many of the fights and “peace treaties” made within the couple are not shared with the outsiders—e.g. who knows about the side deals between governments and private operators or even between host governments and the government of origin of the operators? who knows about the deals between governments and the unions?.

Recognizing that there are common knowledge gaps, this paper tries to provide a fair overview of basic documented facts which could explain why the rejection of PPI seems to be increasing in many countries while at the same time, new marriages still generated close to US$50 billion in 2003. The main focus of the paper is on the various dimensions of the distributional effects of PPI. The paper draws a lot on my direct personal experiences and research as a witness of reforms and crisis in PPI in a large number of developing countries and does probably not do justice to a lot of the ongoing research in the field.

The paper is organized as follows. Section 2 briefly reviews the historical context that eased the initial flirting of many governments with PPI. Section 3 gives a sense of the actual

> 1 The data is from the World Bank PPI database.

1
Yet, there is no evidence that longer-term impacts of the program identified in this paper are driven by differences in pre-program trends. Two main checks were conducted to directly investigate this. The first uses pre-baseline and baseline data from PIHS (2001) and MICS (2003) to run double-difference models on schooling, labor market, marriage, and fertility outcomes for the same cohorts of girls.<sup>36</sup> In principle, one should not expect to see impacts of the FSSP since the program began after the period covered by this _placebo test_ . In fact, results of this analysis show that all outcomes examined in the paper were progressing in similar trends for stipend and non-stipend districts at the preprogram time. The second check consists of doubledifference models using data from MICS (2007-08) to examine the trends of school attainment for two slightly older cohorts of girls (ages 20-22 and 23-25) that, by definition, finished school before the program started and, thus, did not benefit directly from it. Again, results of these exercises do not reveal any differential pre-program trends that may contaminate the main findings of the paper (Table 12).

Another important concern stems from the fact that the empirical analysis is based on a proxy measure of the level of exposure of girls to the program. Consequently, program duration for the synthetic cohorts used in the analysis is likely measured with some error. This measurement error in the indicator of treatment could produce imprecise or biased estimates of program impacts. If this is the case, the parameters that measure the effects of the FSSP on the longer-term outcomes would be biased toward zero (also known as ―attenuation bias‖). Although there is no sensible way to correct for this type of bias in the context of this evaluation, the results of the analysis are still very informative as they can be interpreted as lower bounds of the true impacts.

There are various problematic compositional changes in the sample that could take place. An example is that the FSSP likely induces the
time trends. These tests are appropriate for panel data settings with short timeframes. The results of the tests provide significant evidence against the existence of a unit-root process, and therefore a random walk, for the tested series. These results provide support to the notion that the benchmarking data are consistent over time.

For each statistic we study, we subtract the corresponding benchmark data sourced from survey or administrative data from the claim of the respondent. For questions related to the rules of the service, which are uniform across all respondents regardless of sector and tier, the claim is subtracted from the benchmark data and the absolute value generated. For the demographic and sector claims, the format of these differ slightly by tier. District officials were asked directly about their district, and the relevant benchmark indicators subtracted from these responses. The regional officials were asked about the 25th, 50th and 75th percentile districts of individual variables separately. The federal officials were asked, “Which region, out of the following options, do you work on most/think that you know best/feel most comfortable answering questions about?” They were then asked about the 25th, 50th and 75th percentile districts of individual variables within that region. This ensures that each official is responding to questions on the districts that they are most familiar with, and work most closely on. Federal officials across sectors chose 36
Figure B.4: **Land Distributions by Bank Type and Source of Landholding Data** <!-- Start of picture text --> 1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br>−.5 −.4 −.3 −.2 −.1 0 .1 .2 .3 .4 .5 −.5 −.4 −.3 −.2 −.1 0 .1 .2 .3 .4 .5<br>Hectares from acre or hectare cut−off Hectares from acre or hectare cut−off<br>Non−matching land records Matching land records Non−matching land records Matching land records<br>Cumulative density Cumulative density<br><!-- End of picture text --> Notes: Panel (a) plots the cumulative density of land records by source of land data (bank data or audit of electronic land records) for all commercial bank accounts in the sample. Panel (b) plots the cumulative density of land records by source of land data for all cooperative bank accounts in the sample.

Finally, Figure B.4 plots bank-reported and audit-derived landholding distributions for landholdings between 4 and 6 acres. By ignoring whether land matches or not, this allows for a comparison of the raw land distributions, as considered from bank and government sources. The distributions are visually indistinguishable, and a Kolmogorov-Smirnov test fails to reject the equality of distributions with p=0.357. This suggests that the bank-reported distribution is almost certainly a case of natural bunching, rather than the result of deliberate bank or borrower manipulation.

54
Although fiscal cadasters have long been a key and growth-enhancing element of state capacity (D’Arcy _et al._ 2024),<sup>1</sup> land and property taxation also provides the basis for the social contract between taxpayers and the state (Besley and Persson 2014). Even in fragile, low-income and conflict-affected settings, increased property tax collection triggered a large “participation dividend” by raising citizens’ political participation and improving their perception of government responsiveness to their concerns (Weigel 2020). Provision of public goods increased property tax payments even by those not affected in Argentina (Carrillo _et al._ 2017) with willingness to pay property taxes enhanced by better service delivery (Kresch et al. 2023).

Public goods, including transport infrastructure, are generally capitalized in land prices: For example, in mid-19th century Chicago, public provision of water and sewer access doubled property values, generating a total benefit about 60 times construction costs (Coury et al. 2021). <mark>In New York, infrastructure extension increased land prices by 8 percent, creating US$ 5.5 billion in property value, though less than one-third of the private value created was captured via higher property tax revenue, pointing to considerable unexploited potential from property taxes to allow governments to capture a greater share of the added value from such investment</mark> (Gupta _et al._ 2022).

Experimental evidence suggests that infrastructure improvements, in this case first-time asphalting of residential streets, will result in appreciation of local land values in an amount that is approximately equal to construction costs (Gonzalez-Navarro and Quintana-Domeque 2016). In Colombia, higher own tax revenue had a bigger impact on public good delivery than an equally sized increase in central government transfers while increasing accountability by local officials (Martínez 2023).

Land taxes are not only an effective way to finance local public goods, but also one of the least distortive ways of raising public revenue as land is immobile and land rents are a true scarcity rent
workers with less than a college degree, which is sensible if a significant degree of offshoring takes place through exports for further processing. Likewise, the negative and significant coefficient on total factor productivity suggests that productivity growth has been labor saving for most educational categories.<sup>13</sup> Productivity growth in manufacturing has been achieved in conjunction with falling employment.

The results in Table 5 suggest that productivity growth, export growth, and import competition have been associated with (sometimes significant) declines in domestic manufacturing employment and that the effects of offshoring have been smaller in magnitude and mixed in sign. These results are important insofar as they suggest a fluid labor market where changes in other factor prices and global competition lead to employment reallocation. Furthermore, these results provide an explanation for our finding in Table 2 that the within-industry wage effects of trade and offshoring are smaller than the within-occupation effects. If trade and offshoring lead some workers to shift sectors (in particular, to exit high-wage jobs in manufacturing), then it is possible that the wages of those who retain their jobs or find new jobs in the same industry are not significantly affected by offshoring, while those who stay within their occupation but shift sectors are negatively affected. We examine this conjecture in the next section.

- C. Wage Implications of Switching Within and Between Sectors

In Table 6 we examine how (a) switching industries within manufacturing, (b) leaving manufacturing, or (c) leaving manufacturing and switching occupations entirely affects a worker’s wages. Our sample is composed of manufacturing workers observed in CPS samples in consecutive years between 1983 and 2002. We regress the change in log wages between period _t_ and _t_ +1 for a given worker on an indicator for switching industry or occupation, including a rich set of controls

> 13 Table 5 results are robust to excluding total factor productivity as a control variable.
--|
|Cereals and cerealpreparations|863,917|10.7%|
|Telecommunications & sound recording|330,136|4.1%|
|Fish,crustaceans,mollucs,preparation|276,152|3.4%|
|Other transport equipment<sup>9</sup>|255,846|3.2%|
|Medicinal andpharmaceuticalproducts|241,312|3.0%|
|Manufactures of metal|214,157|2.7%|
|Artif.resins,plastic mat.,cellulose|151,868|1.9%|
|Essential oils &perfume material|104,932|1.3%|
|Professional,scientific & controllingequipment|101,065|1.3%|
|Dairy products and birds'eggs|99,125|1.2%|
|Miscellaneous manufactured articles|98,169|1.2%|
|Rubber manufactures,n.e.s.|96,489|1.2%|
|Paper,paperboard,articleofpaper|91,269|1.1%|
|Textileyarn,fabrics,made-upart.|82,120|1.0%|
|Beverages|58,480|0.7%|

_Source: COMTRADE database, 4-digit STIC Revision 2._

> 9 Since transport equipment may be protected by patents, Nigeria could start by producing generic products.

20
|4.42|Apply Math/Sci/Engg know.|4.07|Written Communication|4.07|
|Teamwork|4.41|Creativity|4.07|Reading|4.04|
|Willingness to learn|4.40|Problem solving|3.93|Technical Skills|4.02|
|Entrepreneurship|4.35|System design to needs|3.84|Experiments/data analysis|4.01|
|Self-discipline|4.26|Contemporary issues|3.83|Verbal Communication|4.00|
|Self-motivated|4.22|Customer Service|3.51|Basic computer|3.95|
|Flexibility|4.15|||Advanced computer|3.71|
|Understand/take||||||
|directions|4.14|||||
|Empathy|3.92|||||
|**Average**|**4.27**|**Average**|**3.91**|**Average**|**4.01**|

Employers rated _Professional Skills_ the lowest on average among the three factor skills. This may be partly because employers think that engineering related skills can be partly remedied through in-house training even after graduation while _Core Employability Skills_ would require longer timeframe to be acquired.

Communication in English is ranked the most important skill under _Communication Skills_ . This could be explained by English being the preferred language in many economic sectors and firms. Azam, Chin, and Prakash (2010) also find that employers demand English skills. Specifically, they estimate based upon a large household survey that English communication skills increase the hourly wages of men by a whopping 34%. The return mainly accrues to young highly educated workers (such as engineers). As Indian economic activities go global, better command of English language is desired. In addition, the high ranking of Communication in
Conditions (EU-SILC), available for the 27 EU countries, and can be disaggregated for vulnerable groups that can be identified in the household surveys.

## **Despite the advantages, AROPE has faced scrutiny as a multidimensional poverty measure,**

**with critics highlighting various shortcomings.** For example, the AROPE framework is still limited to economic dimensions (employment, income, and material deprivation) without considering other dimensions that were already included in other multidimensional poverty measurements. Some indicators of access to basic services and affordability, or education, which are highly policy-relevant, are not included in this framework. Furthermore, it fails to acknowledge that the concurrence of several indicators can lead to the accumulation of difficulties and consequently result in more severe situations of social exclusion.<sup>22</sup> From this perspective, an individual is labeled as at risk of poverty or social exclusion regardless of the number or extent of dimensions in which they fall short. The approach also disregards the depth of poverty, as it is not possible to determine the intensity of shortfall from each threshold and prioritize those below the poverty line (see, e.g., Barcena-Martín et al., 2020 **;** Nolan and Whelan, 2011a, 2011b; Copeland and Daly, 2012; Maître et al., 2013; Fusco, 2015).

**<mark>The specific target population of this study includes primarily those at high risk of poverty or social exclusion in recent years, measured with the AROPE framework, and the chronically poor and materially and socially deprived in Bulgaria, with a focus on Roma.</mark>** <mark>This means we focus on the multidimensionally poor (a snapshot of who is deprived in a given year). Additional analysis of chronic poverty and chronic material and social deprivation (capturing who is consistently monetary poor/consistently deprived over time) is also undertaken in Bulgaria. Within this group, attention will be given to the Roma to
. We nevertheless show geometric returns throughout our paper and researchers using our dataset can compute holding period returns for any period of their choosing.

Eichengreen and Portes (1988, 1991) focus on the interwar years and show that these were a bad period for investing in external bonds. Over the decade of the 1920s, nominal rates of return were around 4%5%, which is similar to what we find in the 1920s and only slightly higher than the returns on UK or US government bonds.<sup>16</sup> For an era that predates our Waterloo starting point, Drelichman and Voth (2011) also calculate IRRs and find substantive (profitable) returns on short-term loans to King Philipp II of Habsburg Spain, despite his notorious serial defaults.<sup>17</sup> Klingen, Weder, and Zettelmeyer (2004) study the return performance of sovereign debt in a large sample of developing countries between 1970 and 2000, including, importantly, the dominant form of lending in that era, syndicated bank loans. To estimate returns they use aggregate bank and bond flows, public and

> 16 These averages combine sovereign, sub-sovereign (e.g., regional), and corporate bonds. Less than 20% of bonds in Eichengreen and Portes (1988, 1991) were issued by a sovereign.

> 17 There is also a literature studying the overall portfolio return of British overseas investments before WW1 using price data, but with no emphasis on sovereign debt. Edelstein (1982) finds that British investors gained a higher return abroad than at home, using returns on 566 foreign stocks and bonds (private and sovereign), 1870-1913. Goetzman and Ukhow (2006) use the same data but apply modern portfolio theory. Chabot and Kurz (2010) compute returns on more than 4,000 stocks and bonds (private and sovereign) trading in the United Kingdom and the United States during 1866-1907. They report significant excess returns on foreign government bonds compared with UK government bonds. None of
Gender and spatial differences as well as differences between people with and without a partner are more limited, although people in the North of Brazil have a significantly lower SWB (6.4) than the South (6.7) and the Southeast (6.7). The average difference in SWB between the pre-crisis (2010-2014) and post-crisis (2015-2020) period is 0.6 points.

Table 2 presents results from different specifications of model (1) in Brazil. Model specification (1), shown in column (1), includes only personal characteristics, based mainly on objective data. In line with the descriptive statistics provided in the previous section, we find a U-shaped relationship between age and SWB. However, this relationship loses its significance and becomes less pronounced in model specification (2) where we control for subjective economic situation. The respective differences between males and females and singles and individuals with partners are statistically significant and these results are robust to the inclusion of different controls. In all 13