rafmacalaba/datause-encoder-data huggingface dataset:
it was in the summer for the _Eurobarometer_ . On the other hand, ESS samples are much larger, and, as mentioned above, the quality of ESS field work in the Southern Europe, as well as in Ireland and UK seems to be better.

<!-- Start of picture text --> Sectoral Composition of Formal and Informal Employees<br>In European Countries, by Region. 2004-2009<br>LV RU UA BG PL EE SK HU SI CZRO SE FI NO DK ES IT GR PT CY IL CH UK IE AT DE NL FR BE<br>East North South West<br>LV RU UA BG PL EE SK HU SI CZ RO SE FI NO DK ES IT GR PT CY IL CH UK IE AT DE NL FR BE<br>East North South West<br>Construction, Trade, Hospitality & Pers. Serv. Transport, Finance & Bus. Serv<br>Manufacturing Agriculture<br>Education, Health & Social Care, Public Administration, Utilities, Post & Communications<br>Formal: Employee with a contract; Informal: Employee without a contract. Self-employed excluded<br>Source: Calculation with ESS data<br>100<br>75<br>50<br>25<br>0<br>100<br>75<br>50<br>25<br>0<br><!-- End of picture text -->

# **Figure 12 Sectoral composition of formal and informal employees, by country**

Figure 12 compares, for each country, sectoral distribution of informal employees and their formal sector counterparts (self-employed are not considered). Taking into account that the number of observations on informal employees for seven of the countries examined is less than 100, we report distribution among just five broad sectors. The ―usual suspects‖ (construction, trade, auto repair, hotels and restaurants, personal and household services) together account for [almost] 40% to 70% of all non-contracted employees in all countries except Belgium and France; with few exceptions, this share is one-and-a-half to two times as big as among formal employees.

Another sector which accounts for a substantial share of informal employees in most countries includes services such as education, health and social care, post and communications, as well as energy and water supply. This share ranges between 15% and 32% in most cases, but reaches 45% in France and Belgium, whilst it is below 10% in Latvia, Russia, Ukraine, Bulgaria, Poland and Italy. In every country except Estonia, 6% to 20% of non-contracted employees work in transportation, finance, and business services. The same is 32
in median household post-tax income and individual post-tax income is identical. Then we take the 2019 welfare vector and change each quintile’s income share to match the published change. Subsequently, we grow the entire distribution such that the change in median welfare equals the published change.

For Vietnam, we rely on growth rates in consumption per capita by decile between the 2018 and 2020 Vietnam Household Living Standards Survey (VHLSS). We apply those growth rates to the 2018 distribution we have for Vietnam in PIP, which is from to the 2018 VHLSS.

# **A.2 Estimates from the literature**

For 21 countries, we use Eurostat’s flash estimates of income inequality and poverty, which are based on a microsimulation building on the work of Rastrigina et al. (2016). These flash estimates contain lower and upper limits of five points on a growth incidence curve. We take the midpoint of those two limits and linearly interpolate between them to generate a full growth incidence curve. Though these estimates are based on adult equivalent income, we apply them to our 2019 welfare vector which is in per capita terms.

For South Africa, we use the results from Barnes et al. (2021). This study contains decile growth rates comparing April-June 2020 with March 2020 and uses a measure of disposable income. To use these results, we assume that disposable incomes did not change after June and apply three-fourth of the April-June growth rates to the 2019 welfare vector (essentially assuming no change to welfare from 2019 to March 2020). We also ignore the fact that the 2019 welfare vector is based on consumption rather than disposable income.

For the Islamic Republic of Iran, we have decile specific growth rates reported in per capita terms that simulate the impact of the pandemic from the 2018/19 survey from Rodriguez and Atamanov (2021). We apply these growth rates to our distribution for the Islamic Republic of Iran for 2019. For <mark>Türkiye,</mark> we have percentile specific simulated per capita growth rates in income from 2019 to 2020 from Baez and Celik (2021). We collapse these to ventile specific growth rates to smoothen out some variance and apply them to our 2019 vector, noting that the 2019 vector is based on a consumption aggregate rather than an income aggregate. For Ethiopia, from Wieser et al. (2022) we have predicted consumption levels from a survey-to-survey imputation from the 2018/19 household survey to a high frequency phone survey carried out in 2020. We convert these predictions to per capita percentile growth rates from 2018/19 to 2020 and apply them to our 2019 vector for Ethiopia. For Tanzania, we use estimates of the growth in mean disposable income due to the pandemic by income quartile from Lastunen et al. (2021). The growth rates are relative to a counterfactual dataset created to reflect the situation right before the pandemic started. We apply these growth rates to our 2019 distribution, noting that the 2019 distribution is based on a consumption aggregate.

37
Finally _,_ we control for ownership type. _Private domestic_ is a dummy variable equal to one if a bank is owned by private Kenyan interests, while _Government_ is a dummy equal to one if a bank is owned by the Kenyan government. In some models, we further differentiate between banks that are majority-owned by the government and those in which the government owns only a minority stake (which we call ‘government-influenced’ banks). The coefficients on these ownership variables are therefore intended to capture any differences in spreads relative to banks owned by foreign interests (our omitted ownership category) that are not accounted for by the other explanatory variables.

The base results in Table 9 suggest that overhead costs are the driving factor of interest rate spreads. Both models are estimated via OLS, and model 2 includes bank-specific fixed effects.<sup>15</sup> In model 2, the estimated coefficients therefore reflect departures from each bank’s average spread for the period. The main results from the base models is that spreads are highly sensitive to overhead costs: the estimated coefficient indicates that a one percentage point increase in the ratio of overheads to total assets is associated with a 1.8 percentage point increase in interest spreads. The fixed effects models also indicate that increases in equity and provisions for loan losses were associated with higher spreads during this period, while an increase in the intermediation ratio was associated with lower spreads. All of these results are in line with our hypotheses.

> 15 In model 1, standard errors are clustered at the bank level.

20
organisation of fishers in Tanzania where the _Tajiri_ (proprietor) in charge of a fleet of vessels is able to negotiate directly with a fish processor.

**Figure 9. Capacity and operating volumes of operational fish processing facilities prior to ban on exports to the EU, 1997:**

<!-- Start of picture text --> 80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>A B C D E F G H I J K L<br>Capacity Operating volunes<br><!-- End of picture text --> _Source: Abila and Jansen (1997)._ The sector as a whole continues to be characterised by low levels of value added; most exports are in the form of block frozen bulk packs of semi-processed filets. This is a particular problem given the increasing constraints on the supply of raw fish. Some processors have explored opportunities for value-added products and/or have made attempts to diversify into other sectors, for example bakery products, meat and ice cream. However, these remain the exception and gross margins remain both low and extremely sensitive to the landed price of Nile perch.

The industrial fish processing sector produces a number of by-products and wastes that also generate value. The fish maws have a high value as an export product to China and Japan. These are dried and sold on to traders. Waste products, including the fish skeleton (frame), skin, off-cuts and fat are sold to local artisanal processors, either directly or through traders (Gibbon, 1997; Abila and Jansen, 1997; Jansen, 1997) or to industrial fish meal plants.

The growth in the industrial fish processing sector was mirrored by the establishment and expansion of a separate sub-sector that processed these waste by-products (Abila, 1995; 1996; Abila and Jansen, 1997; Jansen, 1997). This sub-sector is generally located in direct proximity to industrial fish processing facilities. Much of the processing is undertaken in the open involving rudimentary techniques such as flash frying and using fish fat and skins as fuel. The processed product is bundled and supplied to urban markets throughout Kenya. Abila and Jansen (1997) estimate that 600 people (75% of which are women) are employed in the processing of Nile perch frames and other by-products in Kisumu and a further 400 in Homa Bay and Nairobi. However, this activity is being threatened by competition for fish processing waste 26
<u>Table 7 Comparison of teacher hourly earnings by contract type</u>

|||<br>Teachers on<br>|<br>apermanent<br>|<br>contract<br>Inter-||Tea|chers on a fix<br>|ed term/temp<br>|orarycontra<br>Inter-|ct||
|---|---|---|---|---|---|---|---|---|---|---|---|
||Median|Standard<br>deviation|Inter-<br>quartile|quartile/<br>mean|N|Median|Standard<br>deviation|Inter-<br>quartile|quartile/<br>mean|N|Ratio of<br>medians|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|
|Burkina Faso|4.0|4.9|3.9|0.94|247|1.5|3.8|4.5|1.3|24|0.38|
|Côte d'Ivoire|7.6|10.0|7.1|0.62|47|4.0|4.4|3.0|0.6|30|0.53|
|DR Congo|0.7|0.6|0.4|0.44|537|0.7|0.5|0.4|0.6|367|1.00|
|The Gambia|||N.A.|||||N.A.||||
|Ghana|||N.A.|||||N.A.||||
|Liberia|||N.A.|||||N.A.||||
|Malawi|||N.A.|||||N.A.||||
|Namibia|14.8|6.4|7.6|0.51|291|9.6|6.4|10.8|1.0|58|0.65|
|Niger|4.2|14.1|4.6|0.59|78|2.4|2.1|0.7|0.2|36|0.57|
|Nigeria|||N.A.|||||N.A.||||
|Senegal|5.9|5.8|5.2|0.67|224|4.5|6.9|3.5|0.6|54|0.76|
|Sierra Leone|2.6|60.2|2.2|0.13|127|4.0|87.2|4.3|0.1|11|1.51|
|Tanzania|5.0|2.3|2.7|0.51|363|4.0|2.8|2.7|0.6|20|0.80|
|Uganda|2.3|3.2|1.3|0.40|92|1.5|1.9|1.6|0.7|62|0.64|
|Zambia|11.2|4.4|4.5|0.39|411|8.3|5.0|8.5|1.1|59|0.75|
|**Average **|**5.8**|**11.2**|**3.9**|**0.48**|**242**|**4.1**|**12.1**|**4.0**|**0.7**|**72**|**0.69**|

Notes: Earnings are in PPP ($,2011). Ratio of medians is the ratio of the median earnings for teachers on a fixed term or temporary contract to that for teachers on a permanent contract. Teachers include survey respondents who self-identified as a primary or secondary school teacher and a wage worker. All teachers have secondary or secondary or post-secondary education. The top and bottom 1% earnings are trimmed. Surveys in The Gambia, Ghana, Liberia, Malawi, and Nigeria did not report whether a worker was on a permanent or fixed term/temporary contract. Source: Surveys listed in Table 1.

40
_31_

**_Invoicing method:_** Before cutover, new customers are asked to provide S.W.I.F.T. with the results of a traffic survey. On the basis of this survey and S.W.I.F.T.'s estimated costs related to the customer's country, S.W.I.F.T. informs the customers whether or not the traffic is expected to cover costs, and if not, gives an estimate of the amount which will have to recovered.

After cutover, the costs are reviewed in the light of, for example, exchange rate, contract prices, local taxes, and the amended annual amount is divided by four to give the direct quarterly costs. The costs fixed at this point remain in force for the rest of the budget year.

At the end of each quarter, the following calculation is performed by S.W.I.F.T.: A-B-C-D-E-F = R

|where|A|=|direct quarterly costs|
|---|---|---|---|
||B|=|joining fees (divided by 5)|
||C|=|quarterly revenue from traffic sent|
||D|=|quarterly revenue from traffic received|
||E|=|new joining fees, if any (divided by 4)|
||F|=|subsidy for traffic received (if any)|
|and|R|=|the amount (if any) to be recovered.|

If the direct costs are lower than the revenues (that is, R is negative), no reimbursement will be made to the user.

If the direct costs are higher than the revenues (that is, R is positive), an additional invoice will be sent to each user to recover his share, calculated according to the allocation formula defined by the User Group Chairperson.

At the beginning of each subsequent year, the annual costs are reviewed in the light of the actual costs.

**_S.W.I.F.T. connection and supplied equipment_ costs:** If it is necessary for S.W.I.F.T. to establish a connection between a S.W.I.F.T. Access Point (SAP) and a user by means of either a PSTN connection or a PDN connection, the cost of doing so is charged to the receiving customers. Invoicing frequency depends on usage; it may be yearly, half yearly or quarterly.

The price for PSTN connections are identical for all customers and will be based on a per minute price that includes all charges incurred by S.W.I.F.T.

PDN prices can either be based on a flat rate or can be usage based. A flat rate implies a fixed periodic subscription fee regardless of the number of messages transmitted. A usage based price as applied by S.W.I.F.T. can include the following:
shown in Table 3, a higher proportion of poor male Ninis in the 15 to 18 age group were studying a year earlier than the corresponding proportion of the non-poor (39.7 percent versus 25.7 percent). Although less marked, the same tendency is observed for male 19 to 24 yearolds (20.8 percent versus 13.8 percent). For young women in both age groups the proportion of Ninis who were studying a year earlier is roughly constant across income quintiles. In terms of urban/rural dynamics, almost a third of male Ninis and a quarter of female Ninis aged 15 to 18 years old and living in urban centers were studying a year earlier. For both genders, a higher proportion of urban youth transition from school into Nininess between the first and the fifth interview relative to rural youth.

Figure 2. Which states do Ninis come from? Status at time of first interview for those who were Ninis in the second interview (15 to 18 year olds.as % of Ninis in second interview) <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>Work only<br>50%<br>Study only<br>40% Work + Stud<br>Nini<br>30%<br>20%<br>10%<br>0%<br>2005 2009 2012 2005 2009 2012<br>Males Females<br>Year in which cohort Year in which cohort<br>had the 1st interview had the 1st interview<br><!-- End of picture text --> _Notes_ : Weights are used in the estimation. A person is considered a _Nini_ if s/he is not working nor studying. _Source_ : Authors' calculations based on the _Encuesta Nacional de Ocupación y Empleo_ (ENOE).

Where do Ninis come from in terms of their study and work time use? Figure 2 answers this question. It identifies Ninis in the second interview and goes back to the first interview to calculate the percentage of Ninis (in the second interview) that come from each of four states: work only, study only, work and study, and Ninis. This exercise is conducted for three different 13
contrasting evidence presented suggests that we could be overestimating the capacity of satellite connectivity in policy strategies, meaning the cost of universal broadband deployment is being underestimated. This provides justification for focusing on universal broadband strategies based on terrestrial technologies, such as 3G and 4G. Having reviewed literature pertinent to the research questions, a method will now be presented.

# 3. Method

Myriad high-level policy reports have attempted to quantify the costs of infrastructure delivery for connecting (only) unconnected communities (International Telecommunication Union, 2020). The majority use high-level spreadsheet methods to estimate the required investment, leaving substantial uncertainty embedded within the results which is rarely portrayed to policy makers. The method developed here takes a new approach by drawing on a range of analytical tools rarely utilized in telecom policy research, including remote sensing and least-cost network designs, derived from infrastructure simulations. Figure 1 illustrates how these approaches are combined to produce demand and supply estimates to quantify universal broadband strategies. The open-source codebase adheres to scientific computing best practices (fully-tested and fully-documented) (Wilson et al., 2017, 2014) and is openly available from the Policy Options for Broadband Infrastructure Strategies (pobis) repository.

8
# **Annex 2. Ahmedabad and Mumbai: Computation of Income Distributions and Allocation of Households Across Housing Stocks**

## **A. Income Distribution**

Data about income distribution in Ahmedabad and Mumbai is reported in the recent NCAER publication.<sup>19</sup> The income distribution in 2007-8 represented in 2004-5 prices is shown in Table 1.

### **Table 1: Income Distribution: Ahmedabad and Mumbai**

|**Income Distribution 2007**<br>|**-8, Mean Income in**<br>**Ahme**<br>|**2004-5 Prices**<br>**dabad**<br>||**Mu**|**mbai**<br>|
|---|---|---|---|---|---|
|**Class**|**% of Total**<br>**Households**|**Annual Total**<br>**Income of the**<br>**Class INR in**<br>**Million**|**% of Total**<br>**Income**||**Annual Total**<br>**Income of the**<br>**Class INR in**<br>**Million**|
|**Low Income**|**13.6**|**38,906**||**10.8**|**213,372**|
|**Aspirants**|**43.9**|**125,585**||**41.1 **|**811,998**|
|**Middle Class**|**39.8**|**113,856**||**41.9**|**827,804 **|
|**High Income**|**2.7 **|**7,724**||**6.2 **|**122,491 **|
|**Total **|**100**|**286,070**||**100**|**1,975,665**|
|||**286,070**|||**1,975,665**|
|**Mean Household Annual**|**Income**|**317,856**|||**459,457**|
|**Households million**||**0.9**|||**4.3**|
|**Note: Outputs of the Stud**<br>**Source: NCAER (2010)**|**y are shaded**|||||

It may be noted that NCAER has furnished Mean Household Annual Income and Number of Households. From this total household income is calculated (Households x Mean Income). The total household income of the city is distributed across the four income classes according to the shares provided by NCAER.

## **B. Distribution of Households according to Income Classes**

NCAER (2010) provides the household income range for each class indicated above in USD. These have been converted to INR at an exchange rate of 1USD=INR 46. By dividing the total income of the class shown in Table 1 by the estimated household income the number of households in each class is arrived at. This is shown in Table 2.

> 19 NCAER (2010)

55
# **I Introduction**

A sound tax system and a healthy business environment are crucial for the welfare of an economy (Easterly and Rebelo, 1993; Aterido et al., 2011). However, many African and Latin American economies face major challenges on both fronts. Indeed, high levels of tax evasion are ubiquitous in these countries. On average, 25% of total sales are not reported for tax purpose in African and Latin American countries compared with only 7% in OECD countries.<sup>1</sup> This shortfall has important social and economic consequences, as evaded taxes reduce a government’s ability to invest in productive infrastructures and institutional quality, and to provide public goods and services. Moreover, insufficient government resources result in an inefficient business environment as various distortions (power outages, corruption, etc.) hinder firms’ performance and their ability to create jobs (Besley and Burgess, 2004; Aterido et al., 2011; Buera et al., 2013; Goyette and Gallipoli, 2015). As such, African and Latin American economies fare poorly in terms of ease of doing business.<sup>2</sup> The coexistence of tax evasion and inefficient business environments thus raises the question about a potential link between these variables in developing countries. This paper aims to examine how distortions stemming from the business environment and institutional quality, i.e., a tax agency’s monitoring capacity, affect firms’ production and tax evasion decisions. We focus on two specific mechanisms. First, entrepreneurs, knowing that institutional quality is low, also anticipate low monitoring of taxes and have, therefore, more incentive to evade their taxes. Second, we argue that a poor business environment generates costs for firms and that this creates a wedge between firms’ potential and realized profits. As a result, firms have an incentive to under-report their sales for tax purposes in order to compensate for the losses incurred due to the distortions in their business environment.

Using firm-level data from the World Bank Enterprise Surveys (WBES) as well as data from the World Governance Indicators (WGI), we provide supporting evidence consistent

> 1See Table A.1 in Appendix A.

> 2See Appendix A.

2
those from developed ones across the entire FSD of debt issuers. Within country groups, the largest firms capture the bulk of the debt financing through both bonds and syndicated loans and typically borrow at longer terms than smaller corporations.

The short-termism in developing countries’ debt observed in firm balance sheets is partly explained by a lower proportion of firms using bond and syndicated loan markets, which are longer term than other debt instruments not analyzed here. We arrive to this conclusion by matching the transaction-level data on bond and syndicated loan issuances with balance sheet data for publicly listed firms. Balance sheet data show that the median firm from developing countries holds a lower ratio of long-term debt to total liabilities than firms from developed countries (15 percent and 19 percent, respectively). However, when considering only firms that use bond or syndicated loan markets, the ratio of long-term debt to total liabilities goes up and becomes similar across developing and developed countries (30 and 31 percent, respectively). These patterns are consistent with the findings in this paper using transaction-level data, but contrast with the evidence in the existing literature based on balance sheets from firms (or banks) that argues that developing country firms borrow shorter term than developed country ones. Moreover, when focusing on firms that do not use bond or syndicated loan markets, the ratio of long-term debt to total liabilities drops to 12 and 14 percent for developing and developed countries, respectively. This indicates that other debt instruments (not explicitly analyzed here) are relatively shorter term. Additional evidence suggests that the ratio of listed firms using bond and syndicated loan markets to all listed firms correlates positively with the level of GDP per capita, reinforcing the idea that a greater proportion of firms from developing countries use these shorter-term instruments.

Overall, our results suggest that debt short-termism in developing countries is rooted in certain areas. One source is domestic bonds, which firms from developing countries issue at shorter maturity than firms from developed countries. Another one lies in more traditional bank loans or other types 4