rafmacalaba/datause-encoder-data huggingface dataset:
# **Box 2. Financial inclusion in the European Union** : **Insights from the 2017 Global Findex Survey**

- Account ownership varies across EU member states. In Western European countries, such as France, Germany, and the Netherlands, account ownership is virtually universal. Account ownership is lower in some Eastern and Central European economies. The share is roughly 80 percent in the Czech Republic and the Slovak Republic and about 75 percent in Bulgaria and Hungary. In Romania just 58 percent of adults have an account, the lowest share in the European Union.

- Gender gaps are rare in the European Union. In the Czech Republic, Hungary, and Romania, men are roughly 6 percentage points more likely than women to have an account, a gap similar to the average global gender gap.

- Income gaps are evident in some EU countries with relatively low account ownership. Among adults in Bulgaria from the poorest 40 percent of households, 55 percent have an account; for adults in the richest 60 percent of households, the share is 84 percent. In Romania 71 percent of wealthier adults have an account—nearly twice the share among poorer adults.

- Use of digital payments varies widely within the European Union. In Denmark and the Netherlands, virtually all adults make or receive digital payments. The share is about 80 percent in Croatia, Cyprus, and the Czech Republic; about 70 percent in Greece and Hungary; about two‐ thirds in Bulgaria; and about half in Romania. Use of mobile phones and the Internet to make digital financial transactions is highest in Denmark, Finland, and Sweden (80 percent) and lowest in Greece (18 percent) and in Bulgaria and Romania (about 10 percent), among ECA countries with available data.

- With the European Union, the share of adults formally saving is highest in Sweden, at 75 percent. About 60 percent of adults formally save in Denmark, Luxembourg, and the Netherlands. In contrast, only about half of adults in France and Italy and a quarter of adults in Bulgaria and Hungary save formally, and the share in Romania is only 14 percent.

- Digitizing payments could increase account ownership. Many unbanked adults have mobile phones, making it easy to adopt digital financial products. In Romania roughly 1 million unbanked adults work in the private sector, get paid in cash, and have a mobile phone. Increasingly, these workers are employed by multinationals or in global value chains. In Bulgaria and Hungary, more than 40 percent of unbanked adults receive government payments—wages, social benefits, or pensions—in cash. In Romania about 3 million unbanked adults fall into this category.

- There is also room to increase the use of accounts among adults who are already financially included. In Italy about 9 million adults pay utility bills in cash despite having an account and owning a mobile phone. The number is about 3 million in Greece and in Germany.

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characteristics that affect firms’ financial constraints (inferred from the firm’s investment Bellman equation). Beck et al (2006), using the same World Bank Enterprise Surveys as we use, follow a different approach that looks at firms’ self-reporting financial obstacles. They show that older, larger and foreignowned firms report lower financing obstacles. In addition, firms in countries with higher levels of financial intermediary development, stock market development, legal system efficiency and higher GDP per capita report lower financing obstacles. In another paper with the same dataset (Beck et al, 2008), they examine the disadvantages of small firms in the access to a broad spectrum of financing sources, including leasing, supplier, development and informal finance.

The remainder of the paper is organized as follows. Section 2 describes the data and presents summary statistics. Then, section 3 explores factors that affect the likelihood of having to put collateral when borrowing. Next, section 4 studies determinants of the required amount of collateral and finally, section 5 concludes.

# **2 Data and descriptive analysis**

We use the World Bank’s Enterprise Surveys (WBES), a rich, firm-level survey database that provides information about firms’ characteristics. These characteristics are: ownership, size, sector, region in which it is located, annual sales, capacity utilization, employment, length in operation, whether it has loans, and in cases that it has, whether collateral was required, type of assets used as collateral and how much collateral was needed. The database also contains information about lenders such as the type of lending institutions: private commercial banks, state banks or non-bank institutions which include microfinance institutions, credit cooperatives, credit unions or financial companies. The database covers many small and medium size firms, thereby allowing us to study the issue of collateralized borrowing across countries and firm characteristics.

We restrict our analyses to 22,263 firms across 131 countries between 2008 and 2017 (the list of countries is given in Table A1). These 22,263 firms are those that were currently borrowing, and the loan was issued within three years before the survey. For example, if the firm is surveyed in 2009 and the loan was issued earlier than 2006, then, we drop the firm. In the data, some existing loans were granted as far back as 1930s. Loans that were granted more than three years from the time of the survey were dropped because they were too far back to be determined by firms’ current characteristics. Effectively, the loan and collateral data are from 2005 to 2017, although the survey years are from 2008 to 2017. The dataset is effectively cross-sectional since the firms do not repeat, i.e. each firm only appears once in the dataset. The dataset includes countries from six regions of the world: Sub-Saharan Africa (SSA), East Asia and Pacific (EAP), Europe and Central Asia (ECA), Latin America and the Caribbean (LAC), Middle East and North Africa (MENA), South Asia (SAR). The summary statistics of all the variables are presented in Table A3.

5
after the conflict has ended, its fallout in the form of declining age at marriage could persist due to fear of rapes, unwanted pregnancies, or marginalization.

- (b) **Economic Role of Culture** : In patriarchal and patrilineal societies, where brides move in with the groom’s family after marriage, parents often view daughters as burdens and sons as insurance for old age. The tradition of dowry payments from the bride’s family to the groom’s family at the time of the wedding in South Asian countries, including India, also makes having daughters more expensive. Additionally, studies suggest that the dowry payment increases monotonically with the age of the bride since men prefer younger brides (Field and Ambrus, 2008; Sarkar, 2021). Therefore, in periods of negative income shocks, such as communal conflicts, parents may prefer to marry off their daughters as soon as possible at a younger age.

- Economic theory also suggests that dowry or marriage payments can be used as a coping mechanism during periods of negative income shocks. When earnings are already low, men are more likely to marry younger after an adverse wealth shock to earn some wealth from dowry (Corno et al., 2020; Khanna and Kochhar, 2020). Additionally, since men prefer to marry younger women, this would also reduce women’s age at marriage. We find some support for this mechanism since we find that after the riots, men’s age at marriage decreased by nine months, and the probability of child marriage increased by 2.8 percentage points (Appendix F, Table F.1).

- (c) **Distorted Sex Ratios** : Imbalances in sex ratios are typical in the aftermath of wars and conflicts. Studies suggest that if there are fewer surviving men after conflicts, women’s position in the marriage market deteriorates due to the scarcity of grooms, which increases men’s bargaining power (La Matthina, 2017; Abramitzky et al., 2011). On the contrary, when a shock leads to fewer women relative to men, there is an increase in female bargaining power in the marriage market (Angrist, 2002). Therefore, when sex ratios are distorted, the more prevalent sex is generally induced to delay marriage, marry individuals of less appropriate age, or remain unmarried due to greater competition for spouses (Hacker et al., 2010). We test this hypothesis using Census data of 2001 (before Gujarat riots in 2002) and 2011 (post-riots), using the following difference-in-difference model to examine if the sex ratios changed after the riots for specific age groups:

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_23_

# **9. Experience in pricing payment services: banks, payees, and central banks**

**_Bank payment costs and prices._** Bank costs per transaction for different types of U.S. payment instruments are shown in Table 2. These range from $.15 for an electronic (ACH) payment to a weighted average of $.41 per check. Credit card costs are higher still ($2.45) but include the extra service of extending credit as well as being a payment, and so is not directly comparable with the other instrument **s** hown. Deposit account maintenanc **e** xpenses are shown separately since they would apply regardless of which payment instrument is used. As noted earlier, consumer payors do not face these differential bank payment costs as explicit fees when choosing an instrument (although business payors do because of their larger payment volume).

**Table 2: Average bank cost of payment services** (United States, 1994)

|_Payment instrument_|_Average cost_|_Percent of_check<br>_operating expense_|
|---|---|---|
||_(In US$)_|_(%)_|
|Account maintenance (monthly)|$7.42|36%|
|**Paper-based (check):**|||
|Payor bank activity|||
|On-us debit|0.27|28|
|Check cashing|0.48|10|
|Issue official check|0.80|1|
|Payee bank activity|||
|Deposit|0.55|11|
|Transit check deposit|0.15|14|
|Volume weighted average|$0.41|1|
|**Electronic:**|||
|Credit card')|2.45|na|
|ACH|0.15|na|
|ATM withdrawal|0.43|na|

- _Source: Federal Reserve System (1994), demand deposit and other functions. Data are for the average medium sized bank with deposits of $200 million to $1 billion._

- _(1) Credit card expenses include both payment processing and a loan component associated with outstanding balances (which incorporates loan losses and the opportunity cost of funds needed to finance unpaid balances)._

Norway has gone further and implemented policies designed to shift consumer as well as business use of paper-based (giro and check) payments to lower cost electronic transactions (giro and point-of-sale). Norwegian banks have been encouraged by the central bank to institute per transaction fees that increasingly reflect the differential bank costs of producing different types of payments. Currently, 48% of bank payment instrument expenses are covered through transaction fees whereas only 13% was so covered in 1988 (Flatraaker and Robinson, 1995). Previously, bank
first decile increased to over 66% in 1998, from 58% in 1994.

**Table 4: Distribution of welfare across Socioeconomic Groups**

||_Head_<br>_in_|_count_<br>_dex_|_Povert_<br>_ind_|_y Gap_<br>_ex_|_Rel_<br>_Contri_|_ative_<br>_bution_|
|---|---|---|---|---|---|---|
|_Socioeconomic Groups_|_1994_|_1998_|_1994_|_1998_|_1994_|_1998_|
|**Wage Public**|2.2|5.9|.40|1.6|_.20_|.50|
|**Wage Private**|6.7|11.1|2.2|2.5|.40|.70|
|**Artisans/Commerce**|9.8|12.7|2.8|2.7|1.4|1.6|
|**Other Actives**|19.5|29.3|6.4|7.0|.30|.40|
|**Export**Farmers|50.1|42.4|13.8|12.5|11.8|15.7|
|**Subsistence Farmers**|51.5|53.4|16.3|16.5|78.9|77.1|
|**1nactive**<sup>**23**</sup>|41.5|38.7|14.5|12.1|7.1|4.0|
|**National**|44.5|45.3|13.9|13.9|-|-|

Sources: Authors! calculations.

More surprising is the rising contribution of export crop producers to national poverty amid overall decline of poverty incidence in this particular group. The rising contribution of this SEG is largely attributed to the increased number of export crop farmners, particularly cotton producers, following the post-devaluation boom. Expressed as a percentage of total population, the share of export crops farmers increased by over 40%; and correlatively, the number of export crop farmers which accounted for about 10% of all poor in 1994 increased by over 5 percentage points, to nearly 15% in 1998. The number of non poor deriving their income from this source increased by nearly 4 percentage points, from 7 to 11%. The overall increase in the relative contribution to national poverty may reflect both the net effect exacerbated by population increase, and the much lower wealth or income redistribution effects.

Figure 2 shows the probability P of being poor in a cross-section of SEG over time at the national level. This probability increased across most SEG, including wage income earners from public and private sectors which are traditionally better off, and export crops farmers, where a significant decline in poverty incidence occurred. Top among the SEG exhibiting a high rate of increased probability are food crop producers and subsistence farmers. The conditional probability of being poor in this SEG, though already high, rose to .61. And to the extent that this SEG represents the largest group of active population, a magnitude of probability of this size represents a proportionate increase in poverty.

More revealing also is the similarity in the pattern and trend of these conditional probabilities between 1994 and 1998, which suggests relative stability in rank ordering of SEG on the welfare scale. Figure 2 in Annex III shows the distribution of all poor and non poor across SEG over time.

> 23 Income received by household falling in this socioeconomic group is in the form of capital income (dividends, interest, rent income, imputed rent from residing in own dwelling), pensions, public transfers and remittances which represent important sources of income, particularly in the southem and northem regions of Burkina Faso.

13
||(1.58)|(2.80)|-1.8|(0.22)|-2.68|-1.01|
|---|---|---|---|---|---|---|
|Services|0.201***|0.0532|0.158***|0.129|0.187***|0.0397|
||(4.49)|(1.85)|-3.89|(1.47)|-3.55|-0.85|
|Agriculture|0.0834*|0.0436|0.077|-0.0707|0.0626|-0.0395|
||(1.98)|(1.56)|-1.93|(-0.95)|-1.47|(-0.97)|
|Households received wages|||||||
|(dummy)|0.143***|0.0419***|0.107***|-0.0790|0.00279|0.00597|
||(6.63)|(3.32)|-5.55|(-1.89)|-0.13|-0.28|
|Household receives pensions|||||||
|(dummy)|0.0985|-0.0543|-0.015|0.304*|-0.063|0.125|
||(0.93)|(-1.07)|(-0.17)|(2.07)|(-1.14)|-1.39|
|Household receives remittances|||||||
|(dummy)|0.0898|0.0983|0.161|0.0250|0.124***|-0.00554|
||(0.35)|(0.70)|-0.73|(0.49)|-5.02|(-0.24)|
|Household receives social benefits|||||||
|(dummy)|0.0978|0.0410|0.0926|-0.0390|0.0159|-0.0345|
||(1.67)|(0.85)|-1.76|(-0.64)|-0.52|(-1.04)|
|Constant|0.313***|0.110**|0.309***|0.879***|0.316***|0.464***|
||(5.66)|(2.87)|-6|(8.18)|-4.61|-7.03|
|Observations|2746|3449|3641|631|1982|2909|

Source: Calculations based on HBS data. Note: Regression model includes region- fixed effects and accounts for weights.

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**Bloom, Howard S.** 2005. “Randomizing Groups to Evaluate Place-Based Programs.” _Learning More from Social Experiments: Evolving Analytic Approaches_ , 115–172. Russell Sage Foundation.

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28
Finally, in February 2001, the asset swap mechanism was terminated but the limit on total foreign assets was retained at 15% of total assets for long-term insurers, pension funds and fund managers and 20% for unit trusts. New foreign investments were, however, still limited to 10% of the preceding calendar year net inflow. In November 2001, the cash flow dispensation was extended to registered fund managers, who were also authorized to make new foreign investments up to 10% of their net calendar 2000 inflow, subject to the 15% asset limit.

Institutional investors utilized the swap facilities to achieve some international diversification of their assets. The SARB indicated that ZAR 60 billion of asset swaps had been effected by February 1999 and the total reached ZAR 100 billion in February 2001. However, the foreign assets acquired through swap arrangements or outright have not been separately identified in the statistics published by SARB. For insurance companies, foreign assets are shown with other assets, while for pension funds, they are included with domestic equities or bonds, as the case may be. Data for long-term insurers show that between 1995 and 2001, other assets grew from 3.7% to 17.1% of assets.<sup>8</sup> In general, market practitioners claim that institutional investors made full use of the permitted 15% limit by 2001.

The South African experience underscores the benefits of a gradual approach. In fact, both of the intermediate solutions mentioned in section II above were used. The authorities allowed limited use of asset swaps and permitted small but gradually increasing amounts of outright foreign investments as special limits for institutional investors. In this way, significant risk diversification was achieved, while averting a capital flight.

However, several questions arise with regard to the South African experience. How did the asset swap mechanism work? Who were the counterparties? What were the costs? How was settlement effected? What was the treatment of local equities that were also listed in overseas markets? Were there any collateral requirements and how was

> 8   As already noted, short-term insurers are not classified as institutional investors in South Africa and did not qualify for use of the asset swap mechanism. Their other assets also grew from 9.4% to 15.4% of their total assets between 1995 and 2001 but this must have been caused by other developments and not by acquisition of foreign assets.

13
32

|**Country**|**V**<br>**1999**|**V**<br>**2008**|**V**<br>**2009**|∆**V**<br>**99–08**|∆**V**<br>**99–09**|**Data**<br>**source**|
|---|---|---|---|---|---|---|
|Ukraine|3.9|4.2|3.6|0.3|-0.3|IEA|
|United Arab Emirates|-15.1|-27.6|-17.8|-12.5|-2.7|IEA|
|United Kingdom|-0.5|0.2|0.1|0.7|0.6|IEA|
|United States|0.7|2.9|1.6|2.2|0.9|IEA|
|Uruguay|1.1|5.5|3.6|4.4|2.5|IEA|
|Uzbekistan|-0.9|-0.6|-0.4|0.3|0.5|IEA|
|Vanuatu|1.1|4.0|3.8|3.0|2.7|EIA|
|Venezuela, RB|-18.9|-26.8|-15.7|-7.8|3.2|IEA|
|Vietnam|-3.6|0.4|0.1|4.0|3.7|IEA|
|Yemen, Rep.|-26.6|-21.0|-14.0|5.5|12.6|IEA|
|Zambia|2.1|2.9|2.3|0.8|0.2|IEA|
|Zimbabwe|2.9|10.4|4.6|7.5|1.7|IEA|

_Source_ : Author’s calculations.

_Note_ : Reporting to the first decimal point is for formatting purposes and does not imply that numbers exceeding 10 percent have three significant figures.

Table A1.2: Median vulnerability and changes in vulnerability by income and oil trade status

|||**Num**|**ber of coun**|**tries**||**Median i**<br>|**n percentag**<br>|**e of GDP**||
|---|---|---|---|---|---|---|---|---|---|
||**Oil**||||**V**|**V**|**V**|∆**V**|∆**V**|
|**Income**|**trade**|**1999**|**2008**|**2009**|**1999**|**2008**|**2009**|**99–08**|**99–09**|
|Low|Import|31|30|30|1.9|7.1|4.4|4.3|1.7|
||Export|1|2|2|-2.0|-28.2|-19.7|-27.5|-19.0|
|Lower<br>|Import|36|36|38|2.6|6.9|4.5|4.2|1.9|
|middle|Export|12|12|10|-10.7|-6.4|-9.2|1.8|8.9|
|Upper<br>|Import|33|33|31|2.4|6.9|4.8|4.9|2.4|
|middle|Export|17|17|18|-16.6|-21.5|-12.6|-3.5|-0.8|
|High|Import|37|38|38|1.5|3.4|2.2|2.1|0.9|
||Export|13|12|12|-17.1|-32.8|-23.9|-4.8|-1.0|

_Sources:_ Table A1.1 and author’s calculations. _Notes:_ The oil trade status refers to being a net oil importer or exporter in the year specified for V and in the terminal year for ∆ V. Reporting to the first decimal point is for formatting purposes and does not imply that numbers exceeding 10 percent have three significant figures.