rafmacalaba/datause-encoder-data huggingface dataset:
(“hothouse world”). We also investigate the inverse scenario which reflects a hothouse world scenario in 2050 compared to an orderly transition. The main difference between the two scenarios is the time horizon over which increased credit losses are expected. For the transition scenario these are mainly expected in the years between 2025 and 2030 and would not persist thereafter. For the hot house world scenario losses increase gradually until 2050 and are persistent. To obtain quantitative estimates we employ a standard expected loss (EL) formula which reflects the annual expected losses in a credit risk portfolio, for _n_ sectors _i_ and for scenarios _j_ : Baseline estimates for PD and LGD were obtained from several European CGSs, averaged, and rounded. We assume constant balance sheets and constant LGDs over time for most sectors.<sup>7</sup>

# 4. Results

## _4.1 Transition risk vulnerability_

## **European CGSs provide guarantees to all sectors, with an allocation that differs from the value-**

**added shares to the overall economy.** Figure 2 shows the distribution of CGSs’ guarantees at the first level of sectoral disaggregation (NACE level 1). Labels indicate the percentages of the total outstanding guarantee portfolio in each sector. The five sectors with the largest outstanding guaranteed amounts are wholesale and retail trade (20 percent), manufacturing (11 percent), agriculture (10 percent), accommodation and food services (10 percent), and construction (10 percent). When comparing the shares of guarantees per sector (as percentage of the overall portfolio) to the share of value added in Europe, we find that CGSs are relatively highly exposed to agriculture (a multiple of 5.6 times), accommodation and food (5.6 times) and administration and support services (2.1 times). Sectors with relatively low fractions of guarantees to value added are real estate (0.3 times), education (0.3 times) and financial services (0.4 times). CGSs are also relatively underexposed to capital-intensive sectors including mining, manufacturing, and utilities, probably reflecting their focus on SMEs that are not typically found in those sectors. Annex 1 provides details for the sectoral shares of CGSs’ guarantees and sectoral value added.

**We estimate that about one-third of CGSs’ guarantee portfolios are towards sectors that have elevated exposure to a disorderly energy transition.** Using our largest sample, we find that 34 percent of guarantees are towards sectors that are identified to be most sensitive to an energy transition, namely agriculture (A), mining (B), manufacturing (C), utilities (D), water, sewerage,

> 7 Baseline PD is set at 3 percent. We furthermore set an LGD of 25 percent in most cases. For mining and utilities, we increase the LGD to 75 percent in our transition scenario since the residual value of the capital will likely be very low, as assets in those sectors become mostly stranded (Cahen-Fourot et al., 2021).

10
on the decrease while cohort size is on the increase, potentially creating offsetting effects in terms of resources available to children and youth.

Using the micro data from the Brazilian censuses of 1960, 1970, 1980, 1991, and 2000, Lam and Marteleto analyze the number of siblings born to different cohorts of young people. These estimates are made by matching young people to their mothers in the census and using the mothers’ reports of children ever born and children surviving. Figure 13 shows the average number of siblings ever born and siblings still surviving at the time of the census for youth aged 12-14 in each of these Brazilian censuses. Note that the surviving number of siblings increases slightly between 1960 and 1970, even though the number of siblings ever born declines. This represents the effect of rapidly falling infant and child mortality, which more than offset the decline in fertility that had already begun to appear. Between the 1970 and 1980 censuses the impact of falling fertility more than offset the impact of declines in infant and child mortality, with young people in 1980 having fewer surviving siblings than their counterparts in 1970. The number of surviving siblings fell by almost one child between the 1980 and 1991 censuses and again between the 1991 and 2000 censuses. The cumulative decline between 1960 and 2000 was 2.3 siblings, a 44% decline. As shown by Lam and Marteleto, this decline was driven in particular by a decline in the prevalence of large families. The result was a significant decline in the competition for resources within families, potentially an important factor affecting their health and schooling. At the same time, most of these young people were being born at a time when cohort size was rapidly increasing, implying increased competition for resources at the macro level. The pattern observed in Brazil is similar to that in most developing countries. Many of today’s young people grew up in a period in which cohort size was increasing but family size was decreasing, implying that they have had to compete with rising numbers of 15
and 3 were conducted after September 2014. The time lapse between the first and the second visits was much greater than originally planned (3 months in average instead of 2 weeks) because of logistical constraints, and because it often required several trips to the business to complete the second visit successfully.

During the two years following program launch, 302 businesses registered with CGA (13 percent of the total in treatment group 2 and 15 percent of the total in treatment group 3), and 272 businesses participated in a group training session at CGA (12 percent of the total in treatment group 2 and 14 percent of the total in treatment group 3). Since businesses had first to register for the _entreprenant_ with GUFE in order to be eligible to register at the CGA, and thus receive the trainings, the percentage of eligible businesses that did register with CGA is sizeable. In fact, 83 percent of the businesses in groups 2 and 3 that formalized (362 businesses in total) decided to register with the CGA, and 75 percent decided to obtain trainings. Business owners in groups 2 and 3 who decided to register as _entreprenant_ had also the possibility to open a bank account at BoA or Orabank. After two years, 131 businesses opened an _entreprenant_ bank account (6.2 percent of total).<sup>8</sup> Panel A of Table 2 summarizes achievement for each program implementation step.

Qualitative information collected with beneficiaries during program implementation suggests that the program was implemented following the study protocol and in particular that the formalization process with the _entreprenant_ status was considered as simple and fast (in addition to being free of charge). Panel B of Table 2 shows quantitative data from the follow up survey and confirms that the formalization process was fast and cheap for businesses in treatment groups. 82 percent of businesses that benefited from the program and formalized declared that they did not pay anything in the process (those who paid something in the treatment groups formalized with a different status than the _entreprenant_ status).

Qualitative work conducted few days or weeks after the businesses received a visit from the CGA suggests that the program understanding was relatively good, given the complexity of the intervention. However, data from our endline survey suggest that one and a half to two years

> 8 Bank data did not include sufficient information besides names that could be used for the matching. As a result, matching between study data and bank data was not perfect and only 70 percent of the _entreprenant_ accounts were found in the study data. Therefore, 6.2 percent represents a lower bound of the number of _entreprenant_ bank accounts opened by study participants.

13
Following the selection of the EAs, a fresh household listing was conducted, and 15 households were then randomly selected from each EA and interviewed for the study. This gave a total of 2,000 households and over 7,700 individuals. Labor-related data were collected for individuals aged 15 years and older, in accordance with the legal employment age in Ghana. Overall, 4,497 individuals successfully provided this information. Gender role attitudes and personality traits information was collected only from individuals that were available and able to answer for themselves during the survey. Within each household, up to 4 individuals aged 15-64 were randomly selected to provide gender roles attitudes and personality traits information. Given that one of the primary study objectives was to investigate the impact of gender role attitudes and personality traits on labor market participation and employment choices, we further restricted the sample to include only individuals aged 15 years or older who provided complete information on gender role attitudes and personality traits modules. This additional restriction resulted in an analytical sample of 2,478 individuals. In the subsequent analyses and results, the 4,497 individuals constitute the _full survey sample_ , while the 2,478 restricted sample constitutes the _analytical or pooled sample_ .

# **3.2. Definition and construction of analytical variables**

Table 1 outlines the definitions of variables used in the analysis. The demographic variables are those found in the literature to be main drivers of labor market participation and employment choice (Baah-Boateng, 2014; Contreas et al., 2010; Naudé and Serumaga- Zake, 2010). In this section, we provide additional clarity on the construction of some of these variables. An individual (15 years or older) is classified as employed if they worked for at least one hour in the 7 days prior to the day of interview or have been temporarily absent from their job. _Self-employed_ pertains to individuals (> 15 years of age) who either operate their own business or contribute to another household member’s business. _Wage employment_ refers to working for pay as an employee, trainee or intern for someone else outside the household.

Asset-based wealth quintiles were constructed from household ownership of durables including furniture, sewing machine, stove, fridge and freezer, fan, radio, computer, TV, vehicles, among others. A principal component analysis (PCA) technique was used to categorize households into one of five (5) asset-based wealth quintiles- poorest, poorer, middle, richer, richest. This was further reclassified to poor (comprising poorest and poorer quintiles) and non-poor (i.e., middle, richer, richest) households.

7
Finally, too often formalization is perceived as a one-way process. Yet, as the benefits of formality are not necessarily evident for SMEs, entrepreneurs are not likely to know whether going formal will lead to greater benefits. Hence, many SMEs are likely to experiment with some aspects of formality, learn about the realized benefits, and choose whether to pursue further formalization or return to informality. A continuously changing business environment and fluctuations in enterprise performance and revenues may also affect the decision to become or remain formal.

The design of effective formalization strategies requires going beyond the traditional formal/informal dichotomy and gaining a better understanding of the nuances of the formalization process. Although some of these aspects have been documented in the literature, to the best of our knowledge there has been no attempt to analyze the pathways to formalization, looking jointly at business and labor formalization processes and how they influence one another.

In this paper, we use a unique panel data set of 888 Peruvian micro enterprises to document the joint process of business and labor formalization. The sample draws from small firms that registered with the Tax Authority (SUNAT). An interesting fact emerges: registration with SUNAT does not necessarily imply compliance with tax legislation. Matching the data set with actual tax data shows that some small firms, despite having registered, do not file taxes and appear in the tax database as inactive.

Because of the sampling design, we do not use tax status as an indicator of business formality; instead, our business formality indicator relies on whether in a given year the firm acquired the municipal licenses required for formal operation. Since licenses need to be renewed, the indicator has the benefit of allowing the tracking of movements toward formality and informality. The survey also contains information on whether firms have registered their workers with the Ministry of Labor in the Electronic Payroll Registry, which obliges firms to comply with labor regulations such as minimum wage legislation, health insurance payments, and social security. Again, registration in the Electronic Payroll Registry is not a one-off process: each month, firms are required to submit a listing of their workers —we observe a significant number of firms that stop providing such information while remaining active. Accordingly, we use worker registration in the Electronic Payroll Registry as our indicator of labor formality. It may appear surprising that active firms can stop acquiring licenses and submitting information about their workers. Nevertheless, overall levels of enforcement remain 3
# Globally Engaged Firms in the COVID-19 Crisis<sup>∗</sup>

Cristina Constantinescu (World Bank) Ana Margarida Fernandes (World Bank) Arti Grover (World Bank)<sup>†</sup> Stavros Poupakis (University College London) Santiago Reyes (World Bank)

## **JEL classification** : D22, F14, L20, L25, O10.

**Keywords** : COVID-19, Crisis, Firms, Recovery, Trade, Exporters, Global Value Chains.

∗The authors are immensely grateful to the many World Bank Group colleagues and counterpart institutions that have supported the Business Pulse Survey (BPS) data collection, without whom this work would not have been possible. Likewise, we thank the Enterprise Analysis Unit of the Development Economics Global Indicators Department of the World Bank Group for making the Enterprise Survey data available. We are thankful to the members of the EITFE team for their efforts in coordinating BPS data collection. We thank Gene Grossman, Mona Haddad, Denis Medvedev, Antonio Nucifora, Marcelo Olarreaga and Daria Taglioni for their guidance and strategic support of the BPS initiative, and the Umbrella Facility for Trade trust fund (financed by the governments of the Netherlands, Norway, Sweden, Switzerland and the United Kingdom), as well as the USAID, the IFC, and the World Bank for their financial support. We thank Bishakha Barman, Yewon Choi, and Shwetha Eapen for their research assistance. The authors are also grateful to the following colleagues for their feedback at the concept stage of this project: Asya Akhlaque, Caroline Freund, Mary Hallward-Driemeier, Leonardo Iacovone, Michele Ruta, Daria Taglioni, Gonzalo Varela and Erik von Uexkull. We thank Beata Javorcik for kindly sharing the data on letter of credit intensity and Arthur Johannes Giesberts for his generosity in sharing his tool for converting text variables into 4-digit industry codes. The views expressed in this paper are solely those of the authors and do not necessarily reflect those of the World Bank, its Executive Directors, or the countries they represent.

> †Corresponding author, email: agrover1@worldbank.org
Table 6. TSLS Estimation Results for Components<sup>of Private.\Investment</sup>

|Dependent variable<br>is Real Priv. Investment/GDP|Build|ings|Equipm|ent|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Constant|-4.699|-4.806|-2.416|-2.396|
|Flow of real credit to private<br>sector as a ratio to GDP|-0.014<br>(-0.75)|--|0.106*<br>(3.70)|* 0.106**<br>(3.91)|
|Foreign exchange reserves as<br>a ratio to GDP<br>|0.291**<br>|0.264"'<br>|0.335*<br>|* 0.337*"<br>|
|(2-year sum, lagged 1 period)|(5.79)|(5.83)|(4.42)|(4.63)|
|Real exchange rate|0.586"'|= 0.672**|0.472*<br>|0.469*<br>|
|(trade weighted, 1985-<br>100)|(3.59)|(5.13)|(1.92)|(1.97)|
|Public infrastructure<br>capital|0.845'<br>|1.049<br>|1.419*<br>|* 1.413"<br>|
|stock as a ratio to GDP|(2.01)|(2.89)|(2.24)|(2.31)|
|Lagged growth of GDP|1.826'<br>|1.586<br>|0.166<br>|--|
||(1.78)|(1.58)|(0.11)||
|R-BAR SQUARED|0.92|0.92|0.90<br>|0.91<br>|
|F STATIFT'C|43.4<br>|58.8<br>|34.9<br>|46.7<br>|
|DURBIN-irSON|2.29|2.15|0.98|0.97|
|N|20|21|20|20|

Though public infrastructure capital stock is significant for both components of private investment, the coefficients<sup>are significantly higher</sup> for equipment investment. This is quite plausible<sup>since manufacturing</sup> dominates equipment investment and the profitability<sup>of manufacturing</sup> investment appears to be more dependent on infrastructure. On the other hand, changes in the availability of credit does not affect buildings<sup>investment.</sup> This is surprising. One possibility is that it<sup>reflects the effect of</sup> restrictions on credit flows to housing and real<sup>estate investment. However,</sup> 22
# **Appendix 5: Derivation of GVC indicators**

This derivation is based on Hummels and others (2001) and follows the exposition of Kummritz (2016). To derive the indicators for the analysis, Hummels and others (2001) suggested applying the standard Leontief’s (1936) insight to ICIOs and in order to derive a decomposition of gross exports into value added along the four dimensions: source country, source industry, using country, and using industry. This means in a simple example for a given year with two countries, _k_ and _l,_ and two industries, _i_ and _j_ , that we multiply the value-added multiplier, _V (I-A)_<sup>_-1_</sup> , with country-industry-level gross exports, _E_ , to deduce their valueadded origins<sup>10</sup> : i where vc<sup>s</sup> gives the share of domestic value added, dvac<sup>s</sup> , in output, yc<sup>s</sup> , and ek indicates gross exports. su su Finally, acf denotes the share of inputs, inpcf , in output. Accordingly, the elements of the V (I−A)<sup>−1</sup> E matrix are estimates for the industry-level value-added origins of each industry’s exports. These estimates can be summed to get industry-country totals for _FVAX_ and _DVAR_ . Note that for _FVAX_ we exclude sourcing from the mining sector to avoid spurious effects driven by variations in raw material prices such as oil.

> 10 The decomposition was technically implemented using the R package _decompr_ described in Quast and Kummritz (2015), which automates the calculation of GVC indicators.

43
As Table 2 illustrates, the modest values of Tanzanian exports and imports of maize are concentrated on a small set of partners. Western Europe and the USA are the major trading partners in terms of both exports and imports. Tanzania trades very little with other African countries, with total exports to other Sub-Saharan African countries accounting for less than 22% of all maize exports and 25% of all maize imports. Among African countries, Uganda and Malawi are important sources of maize, supplying 15% of imported maize. These trade patterns reflect a number of factors, including: historical ties, logistical costs, Tanzania’s myriad trade agreements and preferences, the barriers that Tanzania places on its own imports and exports, as well as those barriers faced by its exports in other markets.

**Table 1: Maize Production, Consumption and Trade in Tanzania (thousand MT)**

|**Year**|**Production**|**Imports**|**Exports**|**Consumption**|
|---|---|---|---|---|
|1990/1991|2430|0|14|2636|
|1991/1992|2300|37|0|2557|
|1992/1993|2220|35|0|2415|
|1993/1994|2300|207|0|2507|
|1994/1995|2150|78|8|2320|
|1995/1996|2570|8|0|2428|
|1996/1997|2650|43|0|2693|
|1997/1998|1900|92|0|1992|
|1998/1999|2750|80|0|2630|
|1999/2000|2450|80|0|2600|
|2000/2001|2000|32|0|2250|
|2001/2002|2500|9|20|2450|
|2002/2003|2700|35|0|2600|
|2003/2004|2320|76|0|2600|
|2004/2005|3230|41|0|2900|
|2005/2006|3300|163|0|3400|
|2006/2007|3373|21|50|3450|
|2007/2008|3660|0|50|3650|
|2008/2009|3634|55|15|3750|
|2009/2010|3326|5|5|3450|
|2010/2011|3600|5|25|3550|
|2011/2012|3600|5|5|3550|

Source: USDA (2011) Note: Marketing years are July to June with 2011/12 referring to the year beginning in July 2011 5
**Table 6: Regression Framework including Correlates of Reporting Accuracy**

|Dependent var.|Differenc|e(= survey-|boxes)||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||One if|One if|Per-day|Per-day|Per-day|Per-day|One if|No. of|Per-day|One if|Per-day|Per-day|
||have|have|hours|hours|hours<br>|hours<br>|have|days|hours|have|hours|hours|
||outage|outage|of outage|of outage|of<br>outage|of<br>outage|outage|having|of outage|outage||of outage|
||1 day|2 days|1 day|2 days|1 day|2 days|over|outage<br>|on|||(6pm &|
||ago|ago|ago|ago|(6pm &|(6pm &|10 days|over 10<br>days|average|||10pm)|
||||||10pm)|10pm)|||||||
||(a)|(b)|(c)|(d)|(e)|(f)|(g)|(h)|(i)|(j)|(k)|(l)|
|Work age|-0.005|-0.028|0.004|-0.036|-0.026|-0.004|-0.037|-0.084|-0.046|-0.016|-0.016|-0.015|
||(0.024)|(0.023)|(0.329)|(0.343)|(0.055)|(0.056)|(0.036)|(0.195)|(0.371)|(0.021)|(0.323)|(0.053)|
|No tertiary education|0.001|0.002|0.302|0.175|0.011|0.007|-0.035|-0.206|-0.127|0.002|0.238|0.009|
||(0.019)|(0.020)|(0.255)|(0.253)|(0.043)|(0.043)|(0.031)|(0.166)|(0.332)|(0.017)|(0.242)|(0.041)|
|Sick|0.049***|0.028*|0.446**|0.490**|0.075**|0.063*|0.128***|0.309***|0.872***|0.039***|0.469**|0.069**|
||(0.015)|(0.015)|(0.195)|(0.195)|(0.033)|(0.034)|(0.022)|(0.117)|(0.221)|(0.013)|(0.185)|(0.031)|
|Head\spouse unemployed|-0.012|-0.006|-0.140|-0.047|0.002|-0.026|-0.018|-0.067|-0.314|-0.009|-0.094|-0.012|
||(0.021)|(0.021)|(0.284)|(0.290)|(0.049)|(0.049)|(0.030)|(0.174)|(0.324)|(0.019)|(0.276)|(0.047)|
|Household size|-0.004|-0.001|-0.016|-0.004|-0.008|-0.003|-0.002|-0.030|-0.037|-0.002|-0.010|-0.006|
||(0.003)|(0.003)|(0.035)|(0.040)|(0.006)|(0.007)|(0.004)|(0.024)|(0.045)|(0.003)|(0.036)|(0.006)|
|Per capita income|0.004|0.008|0.003|0.056|0.003|0.003|0.003|0.008|-0.075|0.006|0.030|0.003|
||(0.007)|(0.007)|(0.097)|(0.096)|(0.016)|(0.016)|(0.010)|(0.050)|(0.107)|(0.006)|(0.091)|(0.015)|
|Have migrant|0.037|0.036|0.201|0.324|0.047|0.094*|0.106***|0.559***|0.738*|0.037|0.262|0.071|
||(0.025)|(0.028)|(0.321)|(0.346)|(0.052)|(0.057)|(0.032)|(0.177)|(0.389)|(0.023)|(0.317)|(0.051)|
|Urban|-0.095***|-0.065***|-0.881***|-0.735***|0.028|0.016|-0.016|0.258*|0.095|-0.080***|<br>-0.808***|0.022|
||(0.018)|(0.019)|(0.229)|(0.238)|(0.038)|(0.038)|(0.030)|(0.150)|(0.276)|(0.016)|(0.223)|(0.036)|
|2 day recall|-|-|-|-|-|-|-|-|-|0.017**|0.119**|0.007|
|(refgp: 1 dayrecall)||||||||||(0.007)|(0.048)|(0.009)|
|Interview date FE|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|R-squared|0.186|0.176|0.164|0.155|0.138|0.130|0.112|0.140|0.133|0.158|0.152|0.123|
|No. of obs.|6447|6453|6447|6453|6453|6450|6457|6457|6456|12900|12900|12903|

14