rafmacalaba/data-use-mentions-extended huggingface dataset:
issues that are influencing firm issuance. Finally, a detailed study into the costs of issuance across many countries could help augment the literature concerning listings and issuance on foreign markets over domestic markets, by identifying whether costs drive the firm’s decision.

This paper takes a first step by studying the primary markets for firms in Brazil, Chile and Mexico, and calculates the components of issuing costs for corporate debt and equity domestically. In addition, the paper highlights specific features of the demand and supply of these assets, which have an impact on both the costs of issuance and the nature of the debt and equity markets in the aggregate. Finally, the paper also presents the costs to these firms of accessing international debt markets, in order to investigate the relative incentives (especially pertaining to cost) that a firm faces when deciding to issue securities.<sup>1</sup> The paper is organized as follows. Section II documents the methodology of the data collection and discusses the types of costs incurred by firms when issuing. Sections III, IV and V present primary market characteristics of Chile, Mexico and Brazil, and the cost data. International issuance costs are shown in Section VI, while Section VII concludes with a comparison across countries.

# **II.  Measuring the Costs of Issuance**

# **A. Types of Costs**

When contemplating issuing debt or equity securities, a firm faces several types of direct and indirect costs. The firm typically contacts an investment bank at the beginning of the process which will advise the company through the necessary steps, and will make it more aware of the out-of-pocket expenses which will be incurred. The company will already have undergone an internal process of analysis, where it has determined that the raising of outside sources of capital is a good alternative, given the nature of the business and the capital requirements. It will normally have decided to pursue either a debt or equity issue, but will rely on the investment bank to help in its decision of
measure is intended to capture the cost of transporting intermediate inputs it may also, to some extent, re fl ect local protectionism.

The importance of intermediate supplies from the rest of the world is proxied by trade weighted tariffs imposed by China on imported intermediate inputs, weighted by the I/O coefficient aik, to re fl ect the fact that the relative importance of inputs varies by industry, The information on trade weighted tariffs on products corresponding to the I/O codes comes from the World Bank’s WITS database. It should be noted that many industries in China have access to duty free intermediate inputs through duty drawbacks and hence would not be affected by tariffs on intermediate inputs. Nonetheless, there are many industries that do pay these tariffs and thus it is important to include this variable in the estimation.<sup>10</sup>

# 3.4. Market Access

We construct two measures of market access to re fl ect that fi rms can supply other fi rms and households within their own province and in other provinces. The own market access measure is de ned as where Yp,t<sup>kistheoutputofindustrykproducedinprovincepattimet.Itisdividedby</sup> the total output of industry k produced in China, to get the share of industry k’s output produced in each province. The share is then weighted by bik, which is the fraction of

> 10Approximately 40 per cent of imports are subject to tariffs.

14
3) defining M l as equal to one for banks with a Lerner Index above the median and zero otherwise. The results of this exercise are summarized in Panel A of Table A3 in the Appendix. We confirm the main finding: after issuing an earmarked loan, banks with more market power tend to increase the interest rate of new working capital loans to riskier borrowers. Notably, these banks do not reduce their lending volume to these firms.

Exploiting the fact that asymmetric information is a natural source for imperfect competition, we use the number of bank relationships for each firm as an alternative measure of market power. The intuition behind this measure is that a bank can extract rents for firms that are more informationally opaque because of the limited ability of these firms to find new lenders. For instance, if a bank is the sole provider of credit to a firm, it gains valuable information about the borrower’s risk through its longstanding relationship, which can be then used to extract rents from the client (Greenbaum, Kanatas, and Venezia, 1989; Sharpe, 1990; Petersen and Rajan, 1994; Bharath et al., 2007; Ioannidou and Ongena, 2010). We estimate Equation (3) defining M l as equal to one if the firm only receives credit from a single bank and zero if the firm has credit relationships with multiple banks prior to obtaining earmarked credit. The results of this check, displayed in Panel B of Table 4 in the Appendix, show that while price mark-ups in working capital loans are more pronounced for riskier firms that obtain their credit from a single bank, their loan volumes remain unchanged.

> 15 There are at least two advantages of this measure. First, banks set their loan provisions according to this rating. Thus, this variable provides useful information about the risk assessment of the firm at the exact time of its first earmarked loan. Second, different from our baseline risk variable, this measure does not require information about prior working capital loans of a firm;
c. _Gini Index of equivalized disposable income, Bulgaria, 2009-2021_ d. _Gini Index of equivalized disposable income, EU27, 2021_ <!-- Start of picture text --> 43.0<br>45.0<br>38.4<br>41.0 40.0<br>39.0 35.0 29.6<br>37.0 30.0<br>25.0<br>35.0<br>20.0<br>33.0<br>15.0<br>31.0 10.0<br>29.0 5.0<br>27.0 0.0<br>25.0<br>Bulgaria EU-27<br>Gini Index Ginin Index<br>SlovakiaSloveniaCzechiaBelgiumNetherlandsPolandFinland HungarySwedenDenmarkAustriaIrelandCroatia GermanyCyprusLuxembourg France Malta GreeceEstoniaPortugalSpainRomaniaItalyLatviaLithuaniaBulgaria<br>Euro area – 20 ...<br>European Union<br><!-- End of picture text --> Source: Panel a and b: World Bank estimates based on 2007-2021 EUSILC. The income year is reported here, not the survey year. Panel c and d: Eurostat.

Note: For panels a and b figures, 2021 estimates are still unavailable. All figures reflect income year, not survey year.

**Moreover, while there has been a slight reduction in inequality in recent years, the country still retained**

**the highest income inequality among EU countries in 2021.** Between 2015 and 2018, income inequality in the country increased, but it has been decreasing since then. The per adult equivalent Gini Index, a measure of income inequality, rose from 37.7 in the income year 2015 to 40.8 in the income year 2018, in contrast to the overall stable trends of inequality among EU countries (figure 1, panel c). Nevertheless, while there has been a recent decline in income inequality, the level of inequality remains persistently high, positioning
(ii) Results: We start by examining whether districts that received the most FONCODES expenditure for school improvements achieved the largest gains in school attendance.<sup>4</sup> Although the LSMS surveys can be used to construct measures of district-level attendance, district-level measures based on larger numbers of children from more districts can also be obtained from the 1993 census and the 1996 INEI survey. While we do not have household-level census data, we do have information on district-level attendance rates for children aged 6 to I 15 Figure 6.1 shows the relationship between school attendance rates and a modified version of the FONCODES index. We modify the FONCODES index so that we exclude one variablethe fraction of children aged 6-11 who are not attending school.<sup>6</sup> Keeping this variable in the FONCODES index would have produced a purely mechanical negative relationship between the index and school attendance in 1993. Each line in Figure 6.1 corresponds to a nonparametric regression of the attendance rate on the index in each of the two years, 1993 and 1996. Figure 6.1 shows that the relationship between attendance rates and the FONCODES index ilas changed during the period. There is an obvious negative relationship between school attendance and high levels of unmet basic needs in 1993, but this is no longer apparent in 1996. In other words, worse-off districts had large gains in school attendance, but better-off districts did not. Figure 6.2 graphs the _change_ in the school attendance rate and the per capita FONCODES expenditures on education-both as a function of the FONCODES index. This figure shows that the worse-off districts which experienced greater gains in school attendance also received more funding for school improvements. The degree of co-movement between attendance gains and school funding is striking. It is possible that the relative gains of children in poorer districts were due to some (unobserved) factor which is coincidentally associated with FONCODES expenditures. But the data are also consistent with a causal relationship between expenditures and attendance gains.

14 Throughout section 6, we
by consumption expenditure in the underlying household survey data. Other income concepts are therefore computed through backward and forward calculations. Individual level expenditure is estimated based on intra-household resource allocation (Belete, Menon, and Perali 2019; Calvi et al. 2020) and equivalence scales (Browning, Chiappori, and Lewbel 2013). The allocation approach of expenditures to household members is based on consumption patterns and the availability of individual-specific information in the data. For example, the 2018/19 Ethiopia Socioeconomic Survey (ESS) collects clothing expenditures for boys and girls, as well as individual expenditures on education and health. Some expenditures, such as alcoholic drinks and cigarettes, are assignable only to adults. Non-assignable expenditures are allocated to each child based on equivalence scales.

5
P. Verwimp. 2014. “Winners and Losers among a Refugee-Hosting Population.” _Economic Development and Cultural Change_ 62 (4): 769–809.

Melander, E., and M. Öberg. 2007. "The Threat of Violence and Forced Migration: Geographical Scope Trumps Intensity of Fighting." _Civil Wars_ 9 (2): 156–73.

Mixed Migration Hub. 2015. “Detained Youth: The Fate of Young Migrants, Asylum-Seekers and Refugees in Libya Today.” Study 1, July 2015. MHUB. www.mixedmigrationhub.org/wpcontent/uploads/2015/07/MHUB_2015_Detained-Youth_Exec-Summary_EN.pdf Moran P. A. P. 1950. “Notes on Continuous Stochastic Phenomena.” _Biometrika_ 37 (1): 17–23.

Ortega, F., and G. Peri. 2012. “The Role of Income and Immigration Policies in Attracting International Migrants.” _IZA Discussion Paper_ 6655.

Otto A. H., and M. F. Steinhardt. 2014. “Immigration and Election Outcomes—Evidence from City Districts in Hamburg.” _Regional Science and Urban Economics_ 45: 67–79.

Peres M., H. Xu, and G. Wu. 2016. “Community Evolution in International Migration Top1 Networks,” _PloS One_ 11 (2): e0148615.

Raleigh C., A. Linke, H. Hegre, and J. Karlsen. 2010. “Introducing ACLED: An Armed Conflict Location and Event Dataset: Special Data Feature.” _Journal of Peace Research_ 47 (5): 651–60.

Reichardt J., and Bornholdt S. 2006. “Statistical Mechanics of Community Detection,” _Physical Review E_ 74, 016110.

Rotte R., M. Vogler, and K. Zimmermann. 1997. “South-North Refugee Migration: Lessons for Development Cooperation.” _Review of Development Economics_ 1 (1): 99–115 Ruiz I., and C. Vargas-Silva. 2013. “The Economics of Forced Migration.” _The Journal
The results of this experiment are shown in Table 2. The direct effect of the increase in aid is on the budget. On the one hand it lowers the fiscal deficit (because it adds to resources “above the line”), and on the other it increases it, because it raises overall public investment, as discussed in the previous section. The increase in public investment is initially of the order of 0.7 percent of GDP, but it rises over time (as a result of the increase in tax revenue), becoming almost one to one. The initial reduction in the budget deficit therefore gradually disappears. The increase in public investment in infrastructure “crowds in” private investment and leads to a higher growth rate in output of about 0.2 percentage points in the long run. The increase in private capital accumulation tends to raise the demand for educated labor, given the low degree of substitution between these factors. Because the increase in public investment is allocated across all components of government capital formation (according to initial shares), the greater demand for educated labor is matched at least in part by an increase in supply. Over time, the increase in the stock of public capital in health raises the efficiency of educated labor, whereas the increase in public capital in infrastructure raises the marginal productivity of all other production factors (including “effective” labor). These productivity gains contribute also to higher domestic output, which in turn raises consumption spending and lowers poverty. The growth rate of real disposable income per capita rises at its peak by almost 0.6 percentage points; the poverty rate, based on survey data, falls by 3.6 percentage points by 2015, whereas the estimate based on a partial elasticity of -1.8 falls by about 4.3 percentage points by 2015. The order of magnitude is thus similar in both cases. However, with a partial elasticity of -0.5, poverty falls by only 1.1 percentage points at the same horizon
Findex surveys, only 11 percent of people aged 15 years and above borrow to start or expand a business.

Improving opportunities for safe long-term saving outside housing in the form of government-sponsored or subsidized old-age savings instruments could yield greater independence in old-age and reduce the burden on younger families. We find that average net worth accumulation accelerates midway through the working years, roughly around age forty. While some of this accumulation may reflect patterns in inheritances, we show that 19
but concentrated in high-risk areas such as the coast or river basins. In this section we present flood exposure estimated disaggregated to subnational regions, the number of which can vary across countries. World Bank household surveys are sampled to be statistically representative at different subnational levels – in this study we adopt these statistically representative subnational units which enable us to compare flood exposure estimates with socio-economic characteristics, such as income levels and poverty (Section 3.4).

The disaggregation of exposure estimates highlights several subnational regions with high risks (Figure 9). Punjab, Pakistan, with a population of 120 million people, frequently experiences heavy flooding and ranks highest in terms of the absolute population headcount exposed to significant flood risk; approximately 48 million people are exposed, which is 38% of the total population. Several of the populous regions in China and India are also among the world’s subnational regions with the highest number of people exposed to flood risk. In China for instance, high population exposure tends to be close to the coast and around the Yellow River valley. However, the sub-national region with the highest population exposure in China is Guangdong in the southeast, on the South China Sea, where 43 million people are exposed across Hong Kong SAR, China; Macao SAR, China; Shenzhen; and the rest of the region. In India, the three sub-regions with the highest population exposure to flood risk (Uttar Pradesh, Bihar, and West Bengal) are all situated in the Ganges basin. Maharashta, which spreads across the central and western regions of India, experiences heavy monsoon seasons and ranks fourth in India in terms of absolute population exposure.

|Punjab (Pakistan)|48.0m (37.6%)|
|---|---|
|Guangdong (China)|43.1m (34.5%)|
|Uttar Pradesh (India)|41.1m (17.4%)|
|Jiangsu (China)|37.4m (45.0
Table 1: Venezuelan Net Population Displaced Abroad Over the Years

|2010|2015|2019|
|---|---|---|
|Worldwide<br>556,641|695,551|4,326,330|
|South America<br>62,240|86,964|3,239,730|
|North America (US+Canada)<br>196,910|273,418|371,919|
|Europe (Portugal, Spain, Italy)<br>203,117|224,328|309,170|
|Central America & Mexico<br>21,260|33,065|166,974|
|Carribbean<br>19,629|21,74|119,333|
|Others<br>53,485|56,702|119,204|
|South America|||
|2010|2015|2020|
|Colombia<br>43,511|48,714|1,717,352|
|Peru<br>3,504|4,129|1,043,460|
|Chile<br>8,095|54,787|475,702|
|Ecuador<br>6,120|8,901|415,835|
|Brazil<br>2,844|3,425|261,441|
|Argentina<br>1,236|1,240|217,562|

Source: Author’s calculation from UN Data This paper attempts to fill this gap by investigating how integrated the Venezuelan refugees and migrants are in the education, formal labor market and social protection sectors of Brazil<sup>4</sup> and how di↵erent economic and social factors accelerate or hinder the process of integration. By integration, this paper refers to the definition advocated by OECD (2011) that defines integration as a two-way process of adaptation by migrants and host societies that
the capital stock drives down the marginal product of capital, i.e., it drives down the rental rate on capital, until the rate of return on capital falls back to the initial level.<sup>12</sup> To analyze trade policy, this comparative steady state approach has been employed by many authors, including Harrison, Rutherford and Tarr (1996, 1997) and Baldwin et al. (1999) and Francois et al. (1996). The approach, however, dates back to the 1970s, when both Hansen and Koopmans (1972) and Dantzig and Manne (1974) used it. The approach ignores the foregone consumption necessary to achieve the higher level of investment and thus, is an upper bound estimate on the long run gains within the framework of the model assumptions.

# **IV.  Data of the Model and Evidence for Key Elasticities**

# **Input-output matrix**

The core of the model data consists of an input-output table. No official recent inputoutput table for Armenia exists, so we produced the table based on data provided by the National Statistical Office of the Republic of Armenia. Our data sources include an unbalanced supply-use table with 16 sectors for the year 2006 and detailed data on GDP for 2007 by types of income, expenditure, and production. The supply-use table contains all the elements we need for the input-output table, but supply deviates significantly from use in most of the sectors. We therefore develop a balancing procedure to arrive at a balanced input-output table. The procedure involves an optimization problem in which the elements of the table are adjusted such that the sum of the squared deviations from the initial values are minimized and subject to a number of side constraints, including supply-use balance. As part of the procedure, we also use detailed GDP data to update the dataset to the year 2007. Finally, we disaggregate two services sectors to get more details on transport, communication and financials sectors. The final table contains 21 sectors. Details of the
_The spouse left behind gains from migration of the spouse, in terms of shares of income spent on his own private consumption, only when the migrant spouse is “generous enough_ ” _Children always receive a larger share of income when none of their parents migrates, because either of them has incentives to shift resources onto own private consumption when alone._ These results should not be interpreted as suggesting that, for example, children are always worse off when either of their parents migrates, or that the spouse left behind would prefer no migration to take place, because the allocations compared are in terms of shares of the total household budget available, which is generally larger than the budget available in the case of no migration, as suggested in proposition 1. Yet they highlight how, in the absence of a moral hazard problem, children of migrant parents would instead receive a larger share of the household income.

# II. DATA

The empirical analysis is based on data from the Indonesia Family Life Survey (IFLS), an ongoing longitudinal survey of Indonesian households, which started in 1993, was repeated in 1997, 2000, and 2007, and contains a sample that is representative of about 83% of the total Indonesian population, including over 30,000 individuals living in 13 of the 27 provinces of the country.<sup>11</sup> By tracking individuals over time, these data allow detecting migration. Indeed, for all individuals who appeared in the first wave of the survey the IFLS roster provides information on where they currently are (if they are not in the household anymore), why and when they left and how much they earned in the past twelve months.

I define migrants as those adult people who have left the household and are reported to having done so for work reasons or explicitly to help the family. Table 1 shows that from the time of the first interview in 1993, fourteen years later more than one household out of four had at least one member that had migrated and not come
Table S2.6 Relative Supply of Workers with 20 or More Years of Experience

|||Evolution of the 20 or more<br>years of experience ratio|
|---|---|---|
|||20 or more years of<br>experience / total|
|Country|1995|2002<br>2015|
|Argentina|0.528|0.502<br>0.490|
|Bolivia|0.507|0.498<br>0.496|
|Brazil|0.565|0.559<br>0.574|
|Chile|0.486|0.511<br>0.531|
|Colombia|n.a.|0.519<br>0.523|
|Costa Rica|0.500|0.537<br>0.548|
|Ecuador|n.a.|0.503<br>0.506|
|Guatemala|n.a.|0.522<br>0.534|
|Honduras|0.529|0.506<br>0.525|
|Mexico|0.474|0.508<br>0.535|
|Nicaragua|0.521|0.507<br>0.496|
|Panama|0.460|0.492<br>0.532|
|Peru|0.490|0.491<br>0.544|
|Paraguay|0.525|0.528<br>0.492|
|El Salvador|0.528|0.520<br>0.523|
|Uruguay|0.599|0.584<br>0.569|

_Source_ : Authors’ calculations based on data from the Socioeconomic Database for Latin America and the Caribbean, World Bank and Center for Distributive, Labor and Social Studies of the Universidad Nacional de La
**|
|Household head is a female|0.14|0.16|0.13|-0.03***|
|Household head is currently married|0.89|0.88|0.89|-0.01|
|Household size|5.85|5.63|6.05|-0.42***|
|The household head was employed or<br>operating a business in Syria before|0.25|0.25|0.23|0.02|
|the crisis|||||
|The household head was studying and<br>working before the crisis|0.002|0.0025|0.0016|0.001|
|Household asset index in Syria|2.60|2.71|2.49|0.22***|
|Household asset index in Syria<br>squared|8.93|9.83|8.04|1.80***|
|Share of household that earn enough<br>for basic needs in 2010 (in Syria)|0.87|0.86|0.87|-0.01|
|<br>Number of months in Jordan|44.60|39.23|49.43|-10.20***|
|Number of months in Jordan squared|2182|1724|2633|-909***|
|Number of observations|2399|1191|1208||

N/B differences were tested using t-test; *** means significant at 1%, ** means significant at 5% and * means significant at 10% 18