rafmacalaba/data-use-mentions-extended huggingface dataset:
capital adequacy requirement. Two banks would fall under the insolvency threshold of 2% and would be considered ”failing” banks. All other banks are able to withstand the assumed country-specific Stress scenario. To recapitalize all banks in the system to the extent that a 2% ERW-CAR would be maintained in the Stress VAR scenario requires about 18% of the annual TTC profits generated by the entire banking system. This figure is arrived at by assuming a 3% TTC Return on Assets (ROA) during normal economic conditions. This figure can also be calculated as a simple arithmetic average on historical ROA data.<sup>46</sup> Profits for each bank are then calculated as ROA times Total Assets and are subsequently summed to provide a banking system wide measure of profits. We use TTC profits here as the base, because banks would normally use retained profits to gradually build up their capital buffers to the required level.<sup>47</sup> The results of the Stress scenario based on the historical cross-country crisis experience under the ”Stress Int.” heading indicate that, on average, the capital buffers of the banking system would get reduced to 0.4%, should this scenario occur. More specifically, nine banks would use their capital buffers, falling below the 8% minimum regulatory CAR level, and six banks would become insolvent with their ERW-CARs falling below 2%. Nevertheless, four banks would still have capital buffers strong enough to withstand such a severe credit risk shock. Recapitalizing all banks so that they would withstand this Stress scenario, i.e., bringing their ERW-CARs above the 2% level, requires about 130% of the annual TTC profits generated by all banks.

The extent of the recapitalization needs at the system level is substantial at close to 1%

> 46This corresponds to through the-cycle profits because the (unconditional) averages are computed over different stages of the business cycle and also preferably over a number of different business cycles. 47However, note that capital injections,
example, individuals living in large families (comprising five or more members)<sup>44</sup> have a significantly higher likelihood of experiencing multidimensional poverty. Conversely, in Croatia, households with a pensioner are notably more vulnerable. Though Roma are presumably one of the most vulnerable groups in these countries, unfortunately, it is not possible to identify them and measure their deprivations, as the EU-SILC survey does not contain ethnicity identifiers.

**Table 4.** AROPE rates for individuals living in vulnerable households (%) by country (2022)

|**vulnerable households**|**Bulgaria**|**Croatia**|**Poland**|**Romania**|
|---|---|---|---|---|
|household with unemployed|65.7|36.5|39.0|75.7|
|single-elderly household|67.7|57.9|35.2|51.0|
|female-headed household|34.7|22.9|17.1|38.2|
|household with 3 or more children|69.1|27.8|22.2|68.9|
|household with 5 or more members|43.5|17.7|16.7|47.9|
|household with disability recipient|30.8|18.4|16.7|38.9|
|household with pensioner|35.1|25.6|16.0|33.0|
|household with social aid|69.1|66.5|53.5|-|

> 44 The type of households composed by 3 or more children and by 5 or more members capture different populations, given that only a portion of the families with 5 or more members have 3 or more children – this proportion varies between 26% (Poland) and 38
12**|**4.0**|
|Kyrgyz R.|39.8|45.8|12|13|2.2|
|Kazakhstan|43.2|44.6|11|14|2.3|
|Russia|46.1|44.4|9|15|2.7|
|Moldova|45.1|41.4|14|16|2.3|
|Georgia|67.3|39.3|7|17|2.1|
|Belarus|48.1|39.2|17|18|4.1|
|Ukraine|52.2|34.6|20|19|2.3|
|Azerbaijan|60.1|34.5|18|20|1.9|

Note: CPI—Corruption perception index, the average for 2000-04. It assumes values between 1 (maximum incidence of corruption) and 10 (minimum) Source: Own calculations based on data in WB 2005a and Web site of Transparency International.

The change in ranking is rather dramatic and brings it more in line with expectations. Slovenia moves up 11 positions to join other new EU member states that have moved decisively to the top eight positions in terms of real business friendliness of regulatory regime taking into account the extent to which entrepreneurs prefer to stay in the shadow economy. So has Romania by six positions. Bulgaria’s ranking, however, has barely changed.

> 7 The correlation between the values of Corruption Perception Indices, as reported by Transparency International, and the size of informal sector in percent of GNI for CEEC/CIS states is 67 percent. For a discussion on why and how this indicator is derived, see Kaminski (2005a).
‘commercial’, education and health, which are taken up _seriatim_ . The first lends itself much more readily to estimation than the other two; but even in the commercial sphere, there are considerable uncertainties. Indeed, the empirical basis for estimating the main effects leaves much to be desired. The only available primary sources that provide reasonably reliable evidence are field-notes on a tour of Assam, Himachal Pradesh and Orissa (Bell, 2009a), a special re-survey of 240 households drawn from 30 villages in Orissa’s uplands (Bell and van Dillen, 2012), and materials on agricultural marketing provided by Grahame Dixie.<sup>2</sup> A useful secondary source is Khandker et al. (2009), which yields estimates of certain effects of rural roads in Bangladesh. The secondary literature is otherwise disappointingly thin for present purposes. In keeping with the aim of developing a robust estimate of the Project’s social profitability, we adopt some extreme alternatives, as near polar as possible.

# **4.1 Commercial**

The household is endowed with labor, land and other resources. As a consuming unit, it has preferences over goods and leisure. Faced with the prices **p** ruling in the marketplaces in which it trades, it draws up a production plan **y** and a consumption plan **x** . The difference **y** _−_ **x** is the vector of its net exports, and its total outlay on transportation is **c** _· |_ **y** _−_ **x** _|_ , where the vector of associated unit costs of transporting goods to and fro is **c** . If there is complete market integration, the farmgate price of a good that is exported (imported) will be _qi ≡ pi − ci_ ( _qi ≡ pi_ + _ci_ ). An all-weather feeder road is now built, which
**The pressure on credit ratings is leading to a renewed focus on “fallen angels”.**<sup>**5**</sup> JP Morgan, for example, expects about 14 percent of EM corporate investment grade (IG) bonds in the CEMBI Broad and EMBIG quasi to migrate to non-investment grade in 2020. This compares to the previous peak reached in 2015, when the sovereign downgrades of the Russian Federation and Brazil triggered a large magnitude of “fallen angels”. So far in 2020, over 7 percent of the investment grade universe has fallen to non-investment grade, which has also been driven by the downgrade of Pemex, the Mexican state-owned oil company in April 2020. A more adverse case assuming all BBB-rated bonds fall to non-investment grade would result in circa 20 percent of investment grade issuers migrating to non-investment grade.

**The downgrade to non-investment grade can lead to forced selling by investors whose investment decisions are indexed to various investment indices. Based on the latest JP Morgan figures,** benchmarked investors hold around 15 percent of EM corporate BBB bonds outstanding or about US$ 93 billion. In a downgrade scenario, and assuming investors could retain some of these now non-investment grade bonds, one can estimate that forced selling for the remainder of year could amount to at most US$ 6 billion.<sup>6</sup>

**The number of downgrades during negative rating cycles tends to be dominated by highyield (HY) issuers, which is not surprising given the greater vulnerability in times of stress (Exhibit 1) that generates higher risk premiums.** This was the case in both major downgrade periods of 2008/2009 and 2015/2016 and is also evident in the current cycle.<sup>7</sup> Given the latest changes in market conditions, investment banks and credit rating agencies expect the average EM corporate HY default rate over the next 12 months to be between 5 and 13 percent, which compares to the 10.5
previous section, guerrilla-controlled areas were more likely to experience land fragmentation and remain divided today. Yet, even though these areas are poorer, there may be less income inequality. We explore this notion by evaluating reported income from household surveys and the wealth index from the demographic census.

Table D.18 presents results for different measures of inequality. We find that while individuals have less income in former guerrilla areas (Columns 1 and 2), there is no evidence of lower in36
children, Operation Timbang (OPT), requires villages, or barangays, to measure and record the weight of young children in their areas. The survey team gathered data from barangay health officers on the number of children participating in OPT weighing, and the number of these that were recorded as having weight well below normal measures of good health. On average across the barangays in the sample, the records indicate that close to 3 percent of the children weighed under OPT were found to have a weight well below the normal, healthy range. There is wide variation across barangays in this proportion—half of the barangays report 1 percent or fewer children with weight well-below normal; however, 13 barangays report between 5 and 17 percent of children with weight well-below normal. These 13 barangays include those where the largest percentage of household respondents indicated instances of vote buying in the municipality. This is the correlation the analysis below will examine in greater detail.

The barangay-level data collection also includes other measures of availability of health services—the number of barangay health workers appointed by the municipality, and the number of health projects undertaken. The health projects typically involve rehabilitating and equipping the village health facility. On average, a barangay in the sample has 3 health workers assigned to it, and 1 health project recently completed or ongoing. However, these averages mask large variation across barangays—some barangays have 0 health workers, whilst others have more than 10; some barangays have 0 health projects, whilst others have more than 3.

15
# ANNEX TABLE 1. LABOR MARKET DATA FOR OLDER ADULTS

||**Brazil**|**Chile**|**Colom-**<br>**bia**|**Costa**<br>**Rica**|**Mexico**|**Russian**<br>**Federati**<br>**on**|**South**<br>**Africa**|**EU27**|**OECD**|
|---|---|---|---|---|---|---|---|---|---|
|**Demographic situation**||||||||||
|-- Old-age dependency ratio|14%|<br>18%|13%|13%|11%|23%|<br>9%|31%|<br>64%|
|-- Effective labour force exit age (years) Men|65|<br>68|70|70|70|63|<br>62|64|<br>66|
|Women|62|<br>63|63|64|66|61|<br>57|63|<br>64|
|-- Gender gap in exit (years)|3|5|7|6|4|2|<br>5|1|<br>2|
|**Employment**||||||||||
|-- Employment rate, 50-74 (% of the age group)|36|<br>49|51|45|50|38|<br>31|48|<br>51|
|_of which_50-54|62|<br>66|66
14 Table 4: Ukraine Evolution of the Official and Unofficial Economy (unitary elasticity scenario)

||1989|199Q|1291|192|199|1994|
|---|---|---|---|---|---|---|
|Official Economy Index|88.0|84.7|73.3|60.5|_51.5_|38.9|
|Unofficial Economy Index|12.Q|16.3|25.5|32.2|_34.0Q_|_M_|
|Overall Economy Index|100.0|100.0|98.8|92.7|85.4|75.4|

On the basis of the index numbers in Table 3, which show the extent of the decline of the economy in Ukraine, it is also easy to calculate the relative shares of the official and unofficial economies, as shown in Table 5.

Table 5: Ukraine Relative Shares of Official and Unofficial GDP (unitary elasticity scenario)

||12M2|199|1991|1992|1993|1994|
|---|---|---|---|---|---|---|
|Official Economy Share|88.0|83.8|74.2|65.3|60.3|_51.5_|
|Unofficial Economy Share|12.0|16.2|25.8|_347_|397|48.5|
|Overall Economy Share|100.0|100.0|100.0|100.0|100.0|100.0|

Following the same approach for the other two more conservative scenarios, we can calculate the unofficial economy index and shares, respectively, and present all three scenarios together in Tables 6 and 7. The data of Table 7 is depicted graphically in Figure 2.

Table 6:
For example, while out of 51 firms in the top productivity decile, 62 percent are formal (32 firms), this ratio drops to 6 percent (3/53) in the bottom decile. Further evidence on the average gaps in performance between formal and informal firms is provided in section 4.

# **_3.4 Formal versus informal employment_**

Informal workers are typically defined as those who do not have a formal employment contract. The RIA data allow a description of the prevalence of this type of firm as well as a characterization of their performance relative to firms that offer at least some of their workers a written employment contract. Out of the 517 firms in the full sample, just under one-half (249 or 48%) employ at least one full-time employee. Of these, as highlighted in Table 3, just over 10 percent (33 out of 249 or 13%) offer a written employment contract, denoted as “formal worker-employing firms”. Interestingly, roughly 20 percent of these firms (7 out of 33 or 21%) are informal firms, that is, unregistered at local and/or national levels.

**Table 3: Distribution of employment (in)formality variable across worker-employing firms**

||Has at least 1<br>employee with formal<br>contract?||
|---|---|---|
|For firms with 1+ workers|No<br>Yes|Total|
|informal firms|110<br>7|117|
|formal firms|106<br>26|132|
|Total|216<br>33|249|

Note: Unweighted data. The question is: “How many of your employees have a written employment contract?” 10 Quantile regressions were carried out at finer intervals as well. For log productivity, when regressions are carried on the quintiles of the distribution (20 intervals), results suggest that the productivity gap between formal and informal firms
**Table 1. Summary statistics, 2021**

|**Variable**|**Number of**<br>**Observations**|**Weighted**<br>**mean**|**Standard**<br>**Deviation**|**Min**|**Max**|
|---|---|---|---|---|---|
|Women|81,106|50%|0.5|0|1|
|Poorest 40% of within-country household<br>income distribution|81,106|40%|0.5|0|1|
|Age (years)|81,106|37.25|16.2|15|99|
|Primary education only|81,106|51%|0.5|0|1|
|Household size|81,106|4.61|2.8|1|26|
|Unemployed|81,106|9%|0.3|0|1|
|Out-of-workforce|81,106|39%|0.5|0|1|
|Self-employed|81,106|25%|0.5|0|1|
|Employed for wage|81,106|26%|0.5|0|1|

Source: Global Findex database 2021.

Note: Summary statistics are shown for 79 low- and middle-income countries. The full list of countries is shown in Appendix table A.1. The summary statistics exclude China due to missing data on employment status.

# **4. Who are the unbanked?**

Our measure of financial inclusion is ownership of an account—that can be used to store money and send or receive payments—at a regulated financial institution, including mobile money service providers. Looking at all countries covered in the database
To determine the sample size, we use several target variables: (i) nominal per capita expenditure; (ii) nominal total household expenditure; and (iii) poverty headcount ratios. We conducted an assessment of how the sample changes using these different targeted variables but our final estimates are based on total nominal household expenditure in order to be consistent with the sampling design of the HIES 2010.

We use three accuracy statistics to assess the performance of the sample: (i) RSEs of less than 10 percent from the mean nominal per capita expenditure for each district and division (urban/rural), as well as for each quarter at the national level; (ii) RSEs of less than 10 percent from the mean nominal household expenditure for each district and division (urban/rural), and for each quarter at the national level; (iii) SEs of less than 5 percent of the poverty headcount ratio for each district and division (urban/rural), and for each quarter at the national level.

Given that the sample for the HIES 2016/17 was designed to provide reliable poverty and welfare statistics at three different levels, accuracy in the estimation of statistics for all levels cannot be obtained simultaneously without compromising on the cost of the survey (i.e. on the sample size). The final allocation of the sample was selected to minimize the sampling errors at the district level, produces reliable and unbiased estimates of poverty for each quarter, and achieves the level of precision of poverty statistics for most of the sampling strata.

For the HIES 2016/17, BBS decided to prioritize the production of reliable annual districtlevel poverty and welfare statistics over annual division-level statistics for rural, urban, and CC. This decision stems from the fact that district-level statistics correspond to clear administrative policy making units and estimation of reliable statistics at the district level is in general more challenging. As a result, we first compute the optimal allocation of the sample at the district level and then modify it slightly for cases
with the export tariff exposure.<sup>17</sup> The coefficient of the imported input tariff exposure does not change at all, while the export tariff exposure coefficient is marginally reduced in magnitude. There is some weak evidence that the subsidy directly had a positive impact on job postings when included alongside the interaction term in column (3), but this effect does not appear to be any stronger for commuting zones that were more exposed to retaliatory tariffs as was the declared intention of the US Administration.<sup>18</sup> Here a one-standard-deviation increase in subsidies ($25,100,000), led to a 2.5 percent increase in postings.

The weakness of the subsidy results may be due to several factors. The first is that the effect is small and difficult to identify, especially given the few months included in the sample after the announcement of the subsidies. The second is that the online job adverts data may insufficiently capture farm labor, and particularly informal farm labor. Third, due to the construction of the export tariff exposure measure using CBP data, which excludes farm labor, the results may insufficiently capture the part of retaliatory tariff exposure which affected farm labor. Hence, the agricultural subsidies would be less effective at pushing back against this negative impact in this specification.

# **5.3 Impact on different skill groups and occupations**

Table 5 displays the baseline regressions run separately for high skilled and low skilled job adverts. The classification here categorises 2-digit SOC codes into skill levels based on whether the average number of years of education required is greater than or equal to 14 (discussed further in Section 3). Panel A shows the results for low skilled job adverts, while Panel B shows the results for high skilled job adverts.

Relative to the aggregate baseline coefficients, presented for reference in column (6), the low skilled results remain significant and increase slightly in magnitude for both imported input and export tariff exposure measures. The opposite is seen
by taking the average of those households sampled from each unit. Gunther and Harttgen (2009) include as community-level variables an infrastructure index using fourteen characteristics estimated via principal component analysis (bus stop, community road, provincial road, national road, secondary education facility, tertiary education facility, hospital, safe water, electricity, veterinary, agricultural services, fertilizer, market, bank), population density through a city dummy, the mean educational level, the percentage of households working in the formal sector, and the percentage of households possessing an enterprise within the community. The data on household characteristics are taken from the national household survey and the others from the census. Meanwhile, Rude and Robayo-Abril (2023) control for municipality characteristics including the employment rate, the unemployment rate, the share of self-employed and entrepreneurs, the share of public sector employees, the share of children attending school, the share of the population with secondary education, the crime index, the security index, and the environmental (natural hazards) index. The source of data for municipality level variables is the household survey data.

> 10 The mean and standard deviation correspond to the total period of 1901-2021.

12
_Sustainabilitv._ Data on default and arrears are problematic because BKDs only write off loans in default if the borrower moves, or if the loan is more than 5 years overdue. Table 31 ) I estimates default and

**Table 31**

**Incremental Annual Default and Arrears Rates for BKD**

|,|**19929**|**.1993**|**1994**|**1995**|**1996**|
|---|---|---|---|---|---|
|**Loan volume in default**|**6.0**|**_4.4_**|**2.0**|2 6|4**9**|
|**percent of outstanding loans**||||||
|<br>**Loan volume in arrcars**<br>**percent of outstanding loans**|22.9|17 8|15.9|17.6|**21.2**|

arrears rates for the program by simulating the program writin.g off all loans in default each year.<sup>5</sup> ' Using this approach, annual default rates ranged from 2 percent to 6 percent in the mid-1990s. Loans in default accounted for approximately 4.9 percent of loans in 1996, up from 2.6 percent in 1995. The loan volume in arrears was equal to 16 to 23 percent of outstanding credits during the mid- 1990s. The volume of loans in arrears in 1996 was 21 percent of outstanding loans, up from 16 percent in 1994.

BKDs do not adequately provision for bad debt. To partially adjust for the reporting inaccuracies that this creates, BRI reported the BKD system's profitability if the units wrote off 100 percent of their loans in arrears past the final due date each
325***|-0.071|0.070|-1.039***|
||(0.104)|(0.091)|(0.063)|(0.070)|
|Number of observations<br>* Significant at the 10 percen<br>_Source:_Authors' estimations<br>_Note:_Standard errors (in par<br>Model:_FC_=_b_0 +_b_1Small<br>_b_9Foreign +_b_10Govt +_e_.|34,916<br>t level. ** Significa<br>based on data fro<br>entheses) are rob<br>+_b_2Medium +_b_3|35,663<br>nt at the 5 percent level.<br>m World Bank Enterpris<br>ust to heteroskedasticity<br>Large +_b_4Mature +_b_5|36,746<br>*** Significant at the 1<br>e Surveys (2006-10).<br>and clustered on cou<br>Older +_b_6Multi +_b_7|36,746<br>percent level.<br>ntries.<br>Manuf +_b_8Exporter +|

28
even banked persons request loans from informal lenders, as personal loans may be harder to obtain from formal lending institutions than business loans or subsidized housing loans. However, as discussed in Chapter 1, consumer lending in Colombia has grown significantly over the past two years.

**Table 3. 4  Reasons for requesting a loan**

|**Income percentile**|**0-20%**|**21-40%**|**41-60%**|**61-80%**|**81-00%**|
|---|---|---|---|---|---|
|Personal Expenses|34.5%|32.2%|23.4%|17.5%|21.9%|
|Education or Health|8.3%|7.7%|17.4%|20.2%|17.9%|
|Vehicle|7.4%|11.1%|2.1%|6.7%|16%|
|Housing, including equipment and furniture|25.6%|31.4%|30.3%|28.5%|13.5%|
|Business, investment, equipment and repairs|14.0%|9.2%|12.3%|10.6%|20.3%|
|Payment of debts|8.0%|6.5%|10.6%|13.2%|9.1%|
|Other|2.3%|2%|4%|3.3%|1.4%|
|_Totals_|_100.0%_|_100.0%_|_100.0%_|_100.0%_|_100.0%_|

_Source: DANE survey, 2003_ 25. **More people seek loans