rafmacalaba/datause-encoder-data huggingface dataset:
children and adolescents. BPC, Continuous Benefit Provision targets the elderly (those above 65 years) and the people with disabilities who cannot support themselves. Displaced Venezuelans benefitted from both the programs. For example, while 400 Venezuelans benefitted from PBF in February 2017, about 6,000 Venezuelans benefitted in December 2018 and about 48,000 in April 2020. In response to the Coronavirus pandemic, the federal government, initiated two main policies to alleviate the impact of the pandemic. They expanded the PBF to include eligible families and granted a financial benefit of R$ 600 (USD 117), Auxilio Emergencial, to those, whose occupations were seriously affected by the crisis, including informal workers, individual micro-entrepreneurs, self-employed and unemployed. Eligibility depends on having a per capita household income of less than R$ 522.5 (USD 101) monthly or a total household income of R$ 3,135 (USD 609). As long as one is registered in the Single Registry or receives Bolsa Familia benefit, the receipt of Auxilio Emergencial is automatic, but one can also register for the benefit online and a substantial number of Venezuelans benefited from the program between April and November 2020.

The influx of forcibly displaced may be followed by an increase in government revenue, which may be due to a jump in indirect taxes and consumption. Figure 7 reveals that the total public revenue in Roraima jumped down in the second bimonth of 2018 but then jumped up in the sixth bi-month of 2018. Income taxes seem have a declining trend in 2018 and then increase from the second bimonth of 2019. Social contribution revenue, which is contributed by employees on their Brazilian-source income and ranges between 7.5% to 14% depending on income and family composition in Brazil, also maintains its declining trend before spiking in the fifth bimonth of 2018. Indirect taxes increased from the fouth bimonth of 2018 and continued its increasing trend till the end of 2020. This is consistent with FGVDAPP (2020), who reports an increase in ICMS (tax on circulation of goods and services) about 25% and an increase in international trade of about 10% in Roraima. Part of this increase in indirect taxes may be contributed by Venezuelan refugees and migrants paying VAT while buying good and services for consumption and paying custom taxes for the goods that they are bringing into Roraima. The Federal transfers constitutes about 40% of municipal revenues and in the state of Roraima, transfer revenue consists of about 63% of all revenue between 2015 and 2020. They are allocated according to population thresholds and the overall increase in transfers, especially the jump seen in the last bi-month of 2018 may be stemming from the population increase in municipalities like Pacaraima, which is the main gateway for Venezuelan influx in to Brazil. In addition, the Supreme Court in Brazil ruled on October 2020 to reimburse half of the 180 million Brazilian reais that the Government of Roraima requested for the extra spending on the Venezuelan influx, which is not reflected in the data as it ends in 2020, when the transfer have not been made.

11
(AF), agricultural raw materials (ARM), minerals, ores and metals (MOM) plus a fourth group for manufactures or final good (FG).

We provide below a review of the existing literature on commodity prices volatility.

|**Author**|**Research Question**|**Data**|**Methodology**|**Results**|
|---|---|---|---|---|
|Moledina A., Roe|The paper tries to determine|Monthly prices (1957-|GARCH (or ARIMA)|The paper attributes most of|
|L., Shane M.|the most appropriate measure|2001) for selected|model fitted to|the volatility in commodity|
|(2004)|of commodity prices volatility<br>and investigates the presence<br>of a linear time trend in the<br>conditional volatility series.|agricultural<br>commodities from<br>IMF’s_IFS_. CPI data<br>from Bureau of Labor<br>Statistics are used to<br>deflate each commodity<br>series.|compute conditional<br>volatility.|prices to macroeconomic and<br>political factors. Moreover,<br>the median volatility over<br>time for all commodities does<br>not show consistent increases<br>or decreases.|
|Cuddington J.T.,<br>Jerrett D. (2008)|Do metal prices exhibit any<br>long-term upward or<br>downward trends? Is there<br>evidence of a strong super-<br>cycle component for each<br>series? Is there any strong co-<br>movement of the super-cycle<br>components? How<br>pronounced are the shorter<br>cycles?|Annual data series (in<br>some cases from 1850)<br>on the LME-traded<br>nonferrous metals from<br>Heap (2005). US CPI<br>(2006=100) used to<br>deflate the series.|Band-pass filters<br>developed by Baxter<br>and King (1999) and<br>Christiano and<br>Fitzgerald (2003).|Considerable evidence of<br>three past super-cycles in real<br>metal prices. Moreover it<br>finds evidence for high<br>correlation of the six LME<br>metal super-cycles.|
|Jacks S., O’Rourke|Has commodity price|Monthly local city|The paper presents|No evidence of an increase of|
|K.H., Williamson|increased over time? Have|prices (UNCTAD|descriptive statistics|commodity prices volatility|
|J.G. (2009)|commodities always shown<br>greater price volatility than<br>manufactures? Does market<br>integration breed more or less|classification) from<br>different sources,<br>including Bezanson<br>(2005), Posthumus|of a selected measure<br>of volatility. Prices<br>volatility is defined as<br>the standard deviation|though time. It also<br>concludes that higher<br>commodity price volatility<br>compared to manufactures|
||commodity price volatility?|(1946), Friis and<br>Glamann (1958), Gayer,|of the price ratio.|has been a constant since 18<sup>th</sup><br>century. It finds evidence|
|||Rostow, Schwartz||that economic isolation has|
|||(1953), IMF and<br>UNCTAD.||been associated with higher<br>commodity price volatility.|

5
2003 and 2013 in manufacturing as a whole and in four subcategories of manufacturing, namely heavy manufacturing, light manufacturing, clothing, and food.<sup>8</sup> The rate of growth of firms and employees in the clothing industry is significantly higher in the north than in the south. Figure 3 (panels d, e, and f) provides corroboratory evidence based on district maps. District hubs for heavy and light manufacturing activities are scattered evenly between the north and the south. However, the top one-third of districts experiencing employment growth in the clothing sector between 2003 and 2013 are predominantly located in the north. Overall, the growth of manufacturing employment in the north is predominantly led by the clothing sector.

# **3. Spatial Differences in Intersectoral Linkages**

To estimate spatial differences in intersectoral linkages, we construct five-sector national I-O tables based on 2004 and 2013 supply and use tables for Ghana (GSS 2006, 2021). The five sectors are agriculture; mining; “other industry,” which includes industrial subsectors other than mining; wholesale and retail trade (WRT) services; and “other services,” comprising services subsectors other than WRT services.<sup>9</sup> This five-sector classification allows us to examine the changing patterns of intersectoral linkages between mining and other sectors. Table 2 reports five-sector national I-O tables for 2004 and 2013. The unit for inputs and outputs are in billion 2004 cedis for 2004 and million 2013 cedis for 2013. In 2007, due to inflation, the Ghana cedi was devalued. The current cedi is 10,000 times the old cedi (before 2007). We converted the figures to million cedis in both years, and then applied the sectorlevel deflators from the ETD to have them in 2004 constant prices. In addition, GSS rebased Ghana’s national accounts series from the 1993 base year to 2006. In our analysis, both 2004 and 2013 I-O tables use the rebased figures. For robustness, we compare the value-added shares from our I-O tables against the ones available from the ETD. The values closely match for “other industry” and WRT services but appear somewhat different for the rest of the sectors. This is mainly because several adjustments have been made to ensure consistency over time of sectoral value-added and employment shares in the ETD.

Appendix B discusses how we constructed national I-O tables from the supply and use tables for Ghana. To construct I-O tables for the north and the south of Ghana for 2004

> 8 Since independence in 1960, Ghana’s district boundaries have changed multiple times. NIC 2003 follows a classification of 138 districts, and IBES 2014 follows a classification of 216 districts.

> 9 In our five-sector classification, “other industry” encompasses manufacturing, construction, and utilities, and “other services” include transport, communications, finance, commerce, government services, and private services.

10
Figure 8: GIRFs for a one percent decline in US’s GDP growth rate <!-- Start of picture text --> china usa euro japan<br>.12 0.0 .0 .0<br>.08 -0.4 -.1 -.1<br>.04 -.2<br>-0.8 -.2<br>.00 -.3<br>-1.2 -.3<br>-.04 -.4<br>-.08 -1.6 -.4 -.5<br>-.12 -2.0 -.5 -.6<br>5 10 15 20 5 10 15 20 5 10 15 20 5 10 15 20<br>austlia india indns kor<br>.00 .08 .00 .0<br>-.04 -.05 -.1<br>-.08 .04 -.10 -.2<br>-.12 .00 -.15 -.3<br>-.20 -.4<br>-.16 -.04 -.25 -.5<br>-.20 -.08 -.30 -.6<br>-.24 -.35 -.7<br>-.28 -.12 -.40 -.8<br>5 10 15 20 5 10 15 20 5 10 15 20 5 10 15 20<br>mal nzld phlp sing<br>.0 .00 .0 .0<br>-.1 -.1<br>-.2 -.04 -.1 -.2<br>-.3 -.08 -.3<br>-.4 -.2 -.4<br>-.5 -.12 -.3 -.5<br>-.6 -.16 -.6<br>-.7-.8 -.20 -.4 -.7-.8<br>-.9 -.24 -.5 -.9<br>5 10 15 20 5 10 15 20 5 10 15 20 5 10 15 20<br>thai oil agri metal<br>.0 0 0.0 0<br>-.1 -1 -0.5 -2<br>-2<br>-.2 -3 -1.0 -4<br>-4 -1.5<br>-.3 -5 -2.0 -6<br>-.4 -6 -2.5 -8<br>-7<br>-.5 -8 -3.0 -10<br>-.6 -9 -3.5 -12<br>5 10 15 20 5 10 15 20 5 10 15 20 5 10 15 20<br><!-- End of picture text --> Note: Please refer to Table A1 in the Appendix for a glossary of acronyms. The lines correspond to the responses of 1985( _red dots_ ), 1995( _short dash_ ), 2005( _long dash_ ), and 2013( _line_ ).

three commodities model suggests a larger negative impact in the long-term. Although the magnitude is much smaller than that of Indonesia, the negative impact of a Chinese shock on Malaysia and Thailand are also larger with three commodity models. These results indicate the usefulness of including the multiple commodity prices in order to capture the different channels of shock transmission.

# **6 Conclusions and remarks**

In this paper, following CPRX, we estimated a GVAR model using a time-varying trade weight matrix. China’s economy has been growing fast and its presence in the global economy 32
parental education, on the other hand, suggests credit constraints and role model effects as potential causal channels.<sup>38</sup> The recent literature has underscored the importance of schooling expansion and returns to education as major factors in determining trends in educational mobility (and more generally economic mobility including income mobility).<sup>39</sup> The evidence indicates that educational mobility improves when government invests heavily in educational infrastructure to ensure access at low costs. The lack of a significant improvement in educational mobility in post reform India thus immediately raises the issue of access to schools and its quality.

After the independence, India began its journey with a low level of literacy and limited schooling infrastructure. In 1951 only 9 percent of women and 27 percent of men were literate. The number of primary schools grew from 215036 in 1951 to 641615 in 2001. By 2002, 87 percent villages had a primary school within 1 km. The effects of the recent expansion in primary schooling on educational mobility will probably become evident in the coming years. The number of secondary schools is, however, much smaller; there was only 1 secondary school for every 5 primary schools in 2002. Private schools have become increasingly important in India, especially in the urban areas (World Bank (2006)). The evidence also indicates that many public schools are plagued with teacher absence and fail to offer quality education and thus the learning outcomes are very poor (ASER Report (2006), Das and Zajonc (2010)). The growth in private schooling has taken place more in those places where public school quality is poor. While the recent expansion of primary schooling has been successful in achieving near universal enrollment, improvements beyond primary schooling remain limited. The returns to secondary and tertiary education have experienced the most increase, but inequality in access to secondary schooling remains high.<sup>40</sup> The increasing role of private schools and private tutoring has raised concerns about inequality in educational opportunity (see, for example, Kingdon (2007)).<sup>41</sup> A private market for education can be especially inequalizing in a developing country such as India where the credit market is underdeveloped in general, and the student loan market is almost non-existent.

> 38 For evidence that parent‘s may be important role models, especially for women in Nepal, see Emran and Shilpi (2011).

> 39 See, for example, the discussion on the role of inequality in access of higher education and increasing wage premium for higher education in explaining the observed decline in mobility in UK by Blanden et al. (2008).

> 40 Inequality of access measured as the difference between the top and bottom quintiles of the income distribution. See World Bank (2006).

> 41 Banerjee et al. (2007) show that private tutoring improves learning outcomes in India.  For an analysis of the role of private schools in decreasing mobility in UK, see Green et al. (2010).

26
intensive sectors will lead to a contraction of other sectors, further moderating the emissions impact.

<u>Figure 2a: Impact of Unilateral Emissions Reductions by Industrial Countries on Global Emissions ( % change: relative</u> <u>to BAU in 2020)</u> <!-- Start of picture text --> Reduction in emissions<br>0.0%<br>5% 10% 15% 20% 25% 30% 35% 40% 45%<br>‐2.0%<br>‐4.0%<br>‐6.0%<br>‐8.0%<br>‐10.0%<br>BTAFU<br>‐12.0%<br>‐14.0%<br>NBTA17<br>‐16.0% BTADU<br>‐18.0% BTADE<br>Countries( % change: relative to BAU in 2020)<br>Reduction in emissions<br>4.0%<br>3.0%<br>NBTA17<br>2.0%<br>1.0%<br>BTADU<br>0.0%<br>5% 10% 15% 20% 25% 30% 35% 40% 45%<br>‐1.0%<br>BTADE<br>BTAFU<br>‐2.0%<br>‐3.0%<br>Change relative to business‐as‐usual in 2020<br>Change relative to business‐as‐usual in 2020<br><!-- End of picture text --> <u>Figure 2b: Impact of Unilateral Emissions Reductions by Industrial Countries on Emissions of Low and Middle Income</u> Figure 3a illustrates the competitiveness impact in industrial countries. The larger the emissions reductions the greater the negative impact on competitiveness (all the curves – except in the scenario BTADE - are downward sloping and the scenario with no offsetting trade action is the most steep). The milder form of trade action (BTADU) can claw back most of the competitiveness effects (the curve associated with this scenario are closer to the x-axis). The extreme form of trade action (BTAFU) is even more successful in clawing back the competitiveness loss from unilateral emissions reduction. But it is the efficient border tax adjustment (BTADE) that is successful in more-than-offsetting the impact of unilateral emissions reductions. The main reason is that the BTADE regime 16
Figure 4 summarizes the breakdown of spending between durable and tradable categories. Each rectangle corresponds to one unit of spending. The blue rectangles towards the right, denoted as uncategorized, correspond to the 10% of spending that cannot be categorized. Among the transactions that can be categorized (90% of all spending), 31% are categorized as tradable and the remaining 69% as nontradable. Among the transactions that can be categorized, 32% are durable and 68% nondurable. Figure 4 also shows substantial orthogonal variation between the two categorizations. That is, not all tradables are durables and vice-versa.

Table 2 also shows average spending statistics for each key spending category used in our analysis. Specifically, column (2) shows that customers who participated in the experiment used their credit cards to spend average monthly amounts of $371 (33% of the $1,135 total credit card spending) on durables and $275 (24% of total spending) on tradable durables. On average, subjects had $1,805 in outstanding credit card debt, equivalent to 1.5 months of spending.

# **6 Results**

## **6.1 Spending: Survey versus Administrative Data**

Existing research has generally studied the impact of economic expectations on consumption using survey data, which may suffer from a number of well-known limitations, such as measurement error, selection problems, and surveyor demand effects. To assess whether this is a source of concern in our study, we explicitly test the relationship between self-reported consumption plans and _actual_ future consumption in our data. If survey measures of consumption track actual consumption closely, measuring consumption in administrative data has few benefits. If, however, there is a disconnect between survey responses and actual spending, this would suggest that using administrative data could be crucially important to avoid spurious results.

Our survey elicited expectations about future credit card spending by asking respondents whether they expect their credit card spending to increase, decrease, or remain the same. Comparing these self-reported consumption plans to actual spending can reveal the extent to which survey measures predict actual consumption. Figure 5 presents the results. The x-axis corresponds to the actual change in monthly credit card spending in the 3 months after the survey completion. The y-axis corresponds to self-reported consumption plans on a 3-point scale from -1 (“go down”) to +1 (“go up”).

We find a statistically significant (p-value=0.020) relationship between the expected change in credit card expenditures and the actual change in spending, indicating that self-reported consumption plans do have some information content. This relationship is, however, weak. The estimated slope (0.034) implies that a one-standard-deviation increase in actual future expenditures is associated with an increase in expected future expenditures of only 0.03 standard 17
# **5 Model**

To quantify the effects of hidden debt and its revelation on default risk and asset prices and to explore the welfare implications of transparency policies, we develop a quantitative model that builds on the traditional framework of the sovereign debt and default literature (following Eaton and Gersovitz, 1981, Arellano, 2008, and Aguiar and Gopinath, 2006) with long-term debt and positive recovery rates (as in Hatchondo et al., 2016 and Hatchondo et al., 2021). Our main modifications to this framework are motivated by the insights derived from our new data, particularly the stylized facts presented in Section 4. Our model features a hidden debt accumulation process and allows for international lenders to monitor the country’s accounts, thereby triggering endogenous revelations of hidden debt. In the model, monitoring is costly, but investors have an incentive to monitor because hidden debt reduces their claims in the case of default through a diluting effect on their recovery rate. Our new data is used to inform the statistical properties of all of these model innovations.

To keep the framework tractable and to be able to make full use of our rich new dataset, our model abstracts from other potentially important features of hidden debt. Most notably, in the model, hidden debt accumulates exogenously and is not driven by a strategic borrower decision.<sup>23</sup> In this sense, our model economy is best understood as an economy in which the government has limited control over the borrowing decisions of the broader public sector, e.g. the country’s state-owned enterprises. Therefore, the model primarily reflects the case of countries with weak institutions and large uncertainty about the true level of indebtedness (see Figure 6).

Furthermore, our model features international lenders who form expectations about the level of hidden debt, and these expectations depend on the time that has passed since the last revelation. However, we abstract from investors learning from the country’s actions by assuming that investors arrive in overlapping generations. In addition to helping to keep the model tractable and allowing us to use the rich new data, the idea of having different generations of investors without learning is consistent with the “this time is different” narratives of past crisis episodes (Reinhart and Rogoff, 2009, 2011b). New generations of investors often repeat past mistakes by believing that financial crises are a phenomenon of the past.

## **5.1 Environment**

**Preferences and income process.** The representative agent in the borrowing economy has preferences given by

> 23For work on the strategic use of hidden debt, see Alfaro and Kanczuk (2022), Gamboa (2023) or Guler et al. (2022).

22
a-vis that driven by voluntary ‘exit’ decisions resulting from private cost-benefit calculations that lead firms to opt out of formal institutions. The former views informality as resulting from either a “necessity” that creates a class of subsistence low-productive firms that would otherwise exit the market to be profitable enough to survive (La Porta and Shleifer, 2014). Alternatively, such informal firms may have untapped potential but excluded from the formal market due to burdensome regulations (De Soto, 1989). By comparison, the latter perceives informal firms as “parasites” that have productivity comparable to their formal counterparts but still take advantage of the lower costs of informality to remain more profitable (Levy Algazi et al., 2018). Recent works by Ulyssea (2018, 2020) allow for these views of informality to coexist and explain the heterogeneity and complexity of the informality problem in developing countries.

# **2.4 Crisis and Informality**

Several studies have analyzed the role of the informal sector in the context of crises (e.g., Elgin et al., 2021, 2022) and specifically if it can serve as a safety net in the face of employment losses during shocks. Studies suggest that informality may indeed be a safety net (Loayza and Rigolini, 2011; Colombo et al., 2019; Fernández and Meza, 2015) although it may depend inversely on the size of informality. To the best of our knowledge, so far the evidence on the impact of the pandemic on the informal economy has been limited to macro measures (Ohnsorge and Yu, 2022) or on informal employment or workers (Alfaro et al., 2020, ILO, 2020). The closest work is related to firm-level evidence on the impact of the COVID-19 shock on small firms (Bartik et al., 2020; Humphries et al., 2020; Apedo-Amah et al., 2020; Cirera et al., 2021; Brucal et al., 2021).

# **3. Data and Methodology**

# **3.1 Data**

To analyze the role of informality in the context of the COVID-19 crisis, we use data from the World Bank BPS. This survey has collected data for thirty-three months for over 150,000 firms in 87 countries. The BPS survey instrument includes questions that have focused on the different shocks that firms suffered in the aftermath of COVID (change in sales, uncertainty, expectations, supply shocks, financial fragility), the adjustment mechanisms they had to implement (employment adjustments, digitalization, and innovation efforts), as well as the amount and characteristics of the public support received.<sup>5</sup> 5It also contains detailed information about firm-level baseline characteristics such as firm size, sector, age, gender of the owner, indebtedness, access to credit, and pre-COVID sales, among other indicators. For details on the first three waves, see Apedo-Amah et al. (2020) and Cirera et al. (2021).

6