10. Characteristics of banking systems around the world Solutions

These are not model answers. They are provided to help students, including those doing the project outside a formal class, to check their progress while working through the questions using the Excel, R, or Google Sheets walk-throughs. There are also brief notes for the more interpretive questions. Students taking courses using Doing Economics should follow the guidance of their instructors.

Note

These solutions are based on the June 2017 version. Your solutions will differ slightly if using other versions.

Part 10.1 Summarizing the data

  1. Explanations of why each indicator may be a good measure or may give misleading information of that category follow:

Depth

  • Private credit by deposit money banks to GDP (%). The amount of outstanding credit extended by banks to the non-financial private sector by deposit money banks measured relative to a country’s GDP is a measure of the size of the financial sector. (Deposit money banks are resident banks, called commercial banks in Unit 10 of Economy, Society, and Public Policy, which have liabilities in the form of deposits payable on demand, transferable by electronic transfer, cheque, or otherwise usable for making payments.) However, countries vary in terms of the government’s role in financial service provision and in the extent of ownership of enterprises by the government rather than the private sector. This measure may therefore underestimate financial depth in countries where the government is more dominant.
  • Deposit money banks’ assets to GDP (%). This is a broader measure and whilst it includes lending to government-owned enterprises, for example, it also includes other assets (such as government bonds) that are not directly related to bank lending in the economy.

Access

  • Bank accounts per 1,000 adults. Countries with more bank accounts per 1,000 adults would have better access, ceteris paribus. However, countries differ in terms of the preference for accessing formal sources of finance, the credibility of the institutions, and the nature of the services. The cross-country differences in this measure may reflect differences in these factors rather than in access. Also, this measure does not account for the possibility that one person can have multiple bank accounts.
  • Bank branches per 100,000 adults. Bank branches provide an easy, efficient and trustworthy platform for people to access financial services. The number of branches reflects financial institutions’ dedication to and presence in a country. If the additional branches are located in different areas, more people would be able to physically reach them and access the services. However, if the additional branches are all concentrated in the same area, then the effect on access would be small. Given the variation across countries of the distribution of branches, this measure should be used with caution. Also, many banking services can be done online rather than at a physical branch, requiring fewer physical branches in a country. This feature of modern banking is not captured by this measure.
  • Firms with a bank loan or line of credit. Firms, compared with other economic actors, have a greater preference for and hence demand for financial services. The advantage of using this measure is that the variations arising from varying preferences for loans across countries would be lower. Once again, however, the measure is determined by many other factors which may vary across countries.
  • Small firms with a bank loan or line of credit (%). Smaller firms, including those that are starting up, are more likely to face credit rationing or exclusion for the reasons discussed in Unit 10 of Economy, Society, and Public Policy, making this measure of particular interest to policymakers.

Stability

  • Bank Z-score. This is a measure of the probability of a default in a country’s banking system and is calculated as a weighted average (using the total assets of individual banks as the weights) of the Z-scores of the individual banks. The Z-score links a bank’s capitalization with the rate of return it is making and the volatility of those returns. A higher Z-score indicates greater banking stability. This measure does not, however, take account of the interconnectedness of banks.
  • Bank regulatory capital to risk-weighted assets (%). The more regulatory capital banks have, relative to their assets, the more capable they are of withstanding negative shocks. However, due to cross-country differences in accounting and policies, the data is not directly comparable across countries.
  1. The box and whisker plots are shown in Solution figures 10.1–10.8. For most indicators, the data are quite tightly clustered together (as shown by the narrow width of the box). Extreme values appear to be an issue for most indicators (except ‘Firms with a bank loan or line of credit’, and ‘Small firms with a bank loan or line of credit’). One possible reason for a large number of outliers is that the way banking is done can vary greatly across countries, for example, countries that rely heavily on online banking would have far fewer bank branches per 100,000 adults than countries in which transactions are mostly done in-person.
Box and whisker plot: Private credit by deposit money banks to GDP (%).
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Solution figure 10.1 Box and whisker plot: Private credit by deposit money banks to GDP (%).

Box and whisker plot: Deposit money banks’ assets to GDP (%).
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Solution figure 10.2 Box and whisker plot: Deposit money banks’ assets to GDP (%).

Box and whisker plot: Bank accounts per 1,000 adults.
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Solution figure 10.3 Box and whisker plot: Bank accounts per 1,000 adults.

Box and whisker plot: Bank branches per 100,000 adults.
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Solution figure 10.4 Box and whisker plot: Bank branches per 100,000 adults.

Box and whisker plot: Firms with a bank loan or line of credit (%).
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Solution figure 10.5 Box and whisker plot: Firms with a bank loan or line of credit (%).

Box and whisker plot: Small firms with a bank loan or line of credit (%).
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Solution figure 10.6 Box and whisker plot: Small firms with a bank loan or line of credit (%).

Box and whisker plot: Bank Z-score.
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Solution figure 10.7 Box and whisker plot: Bank Z-score.

Box and whisker plot: Bank regulatory capital to risk-weighted assets (%).
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Solution figure 10.8 Box and whisker plot: Bank regulatory capital to risk-weighted assets (%).

  • ‘Deposit money banks’ assets to GDP (%)’ and ‘Bank accounts per 1,000 adults’ are used as examples here. Averages are rounded to two decimal places and the number of observations is listed underneath the average value.
High income: non-OECD High income: OECD Low income Lower middle income Upper middle income
2000 63.89 89.67 15.26 28.10 45.60
25 32 26 45 44
2001 67.17 90.06 14.96 27.85 46.79
25 32 26 47 45
2002 68.69 91.16 14.98 27.61 45.17
26 32 27 48 46
2003 67.29 92.75 15.71 27.76 44.80
26 32 27 48 46
2004 63.35 94.17 15.20 28.61 44.92
27 32 27 48 46
2005 62.19 98.79 15.25 30.36 46.68
27 32 27 48 45
2006 62.77 105.78 15.86 30.35 48.37
27 32 26 48 46
2007 65.25 110.92 16.57 32.02 50.24
27 31 26 48 47
2008 68.73 117.73 18.28 34.73 54.25
27 31 26 48 47
2009 79.37 123.16 19.11 37.65 58.59
25 30 25 47 47
2010 78.77 120.75 20.30 37.23 58.52
26 30 25 48 47
2011 78.00 118.81 21.58 37.88 58.91
26 29 25 46 47
2012 78.85 117.64 21.19 38.63 59.95
26 29 23 47 47
2013 80.12 115.07 22.87 40.28 61.48
26 29 23 46 47
2014 83.81 112.49 23.56 42.46 64.68
25 29 22 44 45

Solution figure 10.9 Deposit money banks’ assets to GDP (%), 2000–2014, by income group.

East Asia and Pacific Europe and Central Asia Latin America and Caribbean Middle East and North Africa North America South Asia Sub-Saharan Africa
2000 67.56 58.61 46.93 60.92 68.46 28.12 20.64
25 45 33 17 2 7 43
2001 67.25 58.10 48.89 62.51 83.10 29.39 20.53
25 46 33 18 2 7 44
2002 62.32 58.67 49.10 61.92 94.57 32.06 20.50
26 47 33 19 2 7 45
2003 61.24 60.47 48.25 59.39 92.08 33.15 21.43
26 47 33 19 2 7 45
2004 60.62 62.51 46.69 57.02 91.04 36.02 20.99
26 47 33 20 2 7 45
2005 62.69 67.64 47.28 56.14 94.11 38.77 21.48
25 46 33 20 2 7 46
2006 63.32 73.50 47.17 56.31 100.84 34.78 22.12
25 47 33 20 2 8 44
2007 64.10 79.04 48.88 55.71 101.98 37.70 22.78
25 46 33 21 2 8 44
2008 68.48 85.71 51.88 57.94 104.46 41.53 24.38
25 46 33 21 2 8 44
2009 74.95 92.90 55.29 65.27 66.73 43.88 26.32
25 44 33 21 1 8 42
2010 75.56 91.18 54.30 64.12 60.50 45.36 26.15
25 46 32 21 1 8 43
2011 75.97 90.08 54.39 65.47 59.34 45.63 26.94
23 46 32 20 1 8 43
2012 75.11 89.98 55.87 65.36 58.29 45.79 27.45
24 46 32 20 1 8 41
2013 79.25 88.83 56.44 66.47 58.08 46.44 28.39
24 46 32 20 1 8 40
2014 86.66 87.06 57.33 75.13 60.28 45.50 29.01
23 46 31 18 1 8 38

Solution figure 10.10 Deposit money banks’ assets to GDP (%), 2000–2014, by region.

High income: non-OECD High income: OECD Low income Lower middle income Upper middle income
2000
2001 21.80 0.36 265.15 9.96
1 2 1 1
2002 28.47 38.91 285.32 10.46
1 2 1 1
2003 39.22 366.82 10.52
2 1 1
2004 592.08 1,095.45 78.59 243.98 406.29
9 2 15 22 12
2005 607.45 1,055.63 79.36 366.78 466.04
10 3 16 26 17
2006 657.89 1,172.50 80.45 394.09 485.32
10 3 18 27 20
2007 697.26 1,258.88 70.21 405.92 546.63
10 3 19 29 21
2008 778.63 1,511.63 89.02 412.18 594.75
11 4 20 31 21
2009 830.02 1,579.37 96.29 427.92 623.42
12 4 21 32 22
2010 956.33 1,590.49 121.95 475.07 634.92
14 4 22 31 22
2011 973.43 1,429.95 105.51 519.34 745.30
14 3 21 32 23
2012 982.11 1,184.27 122.68 546.57 692.47
13 5 20 30 22
2013 1,058.99 1,211.82 122.68 509.85 714.89
13 5 18 31 21
2014 1,101.30 1,230.74 101.69 632.79 805.33
13 5 11 23 18

Solution figure 10.11 Bank accounts per 1,000 adults, 2000–2014, by income group.

East Asia and Pacific Europe and Central Asia Latin America and Caribbean Middle East and North Africa North America South Asia Sub-Saharan Africa
2000
2001 265.15 8.12
1 4
2002 285.32 29.19
1 4
2003 366.82 29.65
1 3
2004 580.53 537.83 521.01 368.07 425.56 126.12
5 8 8 6 4 29
2005 575.46 843.54 473.32 380.89 452.45 139.72
6 12 11 8 4 31
2006 516.70 919.94 528.12 388.46 487.81 145.62
9 12 12 8 4 33
2007 557.99 982.22 600.95 420.53 525.75 150.74
9 12 12 9 4 36
2008 713.03 1,092.63 651.44 463.48 458.24 167.85
10 12 12 11 5 37
2009 738.29 1,053.40 695.64 523.62 428.60 185.96
10 13 13 13 4 38
2010 764.47 1,116.05 750.63 523.34 431.42 216.89
10 15 13 13 5 37
2011 973.88 1,159.48 647.61 531.78 545.29 235.62
10 15 13 14 4 37
2012 796.89 1,156.74 695.38 536.23 560.50 277.71
11 14 12 14 4 35
2013 744.74 1,097.12 730.90 529.63 580.13 320.58
12 14 12 13 4 33
2014 863.20 1,161.06 797.02 542.29 672.26 413.40
11 14 11 8 4 22

Solution figure 10.12 Bank accounts per 1,000 adults, 2000–2014, by region.

  • Solution figures 10.13 to 10.16 show the line charts, and comments on these are provided.

    Richer countries tend to have larger financial institutions and markets. Depth measure by deposit money banks’ assets to GDP has been increasing in all countries except in non-OECD high-income countries.

    Richer countries have better access as measured by bank accounts per 1,000 adults. Access displays an upward trend in all groups except in high-income OECD countries.

    The values of both indicators increase over time for all groups except the high-income OECD group in which access and depth fell for several years after the global financial crisis.

Deposit money banks’ assets to GDP (%), 2000–2014, by income group.
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Solution figure 10.13 Deposit money banks’ assets to GDP (%), 2000–2014, by income group.

Deposit money banks’ assets to GDP (%), 2000–2014, by region.
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Solution figure 10.14 Deposit money banks’ assets to GDP (%), 2000–2014, by region.

Bank accounts per 1,000 adults, 2000–2014, by income group.
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Solution figure 10.15 Bank accounts per 1,000 adults, 2000–2014, by income group.

Bank accounts per 1,000 adults, 2000–2014, by region.
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Solution figure 10.16 Bank accounts per 1,000 adults, 2000–2014, by region.

Note: No data is available for North America over this period.

  • No solution is provided.
  • No solution is provided.
  • Values are rounded to two decimal places, and shown in Solution figure 10.17. North America is omitted due to missing data in all years.
East Asia and Pacific Europe and Central Asia Latin America and Caribbean Middle East and North Africa South Asia Sub-Saharan Africa
2004 253.86 637.40 504.48 301.03 529.82 64.48
2005 336.63 1,262.14 471.72 324.82 531.27 76.57
2006 81.43 1,324.44 484.82 351.77 549.06 79.62
2007 85.34 1,380.43 520.49 393.35 573.51 132.21
2008 87.54 1,525.15 561.82 418.40 614.92 149.09
2009 90.62 1,501.88 633.35 436.77 227.03 195.23
2010 94.60 1,371.62 686.92 431.48 267.47 220.34
2011 105.83 1,419.89 688.62 420.16 334.88 236.72
2012 96.85 1,269.52 736.57 426.43 373.07 277.92
2013 101.48 1,043.82 755.75 427.68 399.85 309.17
2014 106.33 1,059.82 780.49 524.85 411.35 350.80

Solution figure 10.17 Population-weighted averages of the indicator ‘Bank accounts per 1,000 adults’, 2004–2014.

  • Where the weighted average is smaller than the simple average, countries with larger populations will generally have poorer access than countries with smaller populations, as is the case with East Asia and Pacific. The reverse holds when the weighted average is larger than the simple average, as is generally the case for Latin America and the Caribbean.
  • The number of bank accounts per 1,000 adults is used as an example: In 2010, the 5th percentile is 27.59, and the 95th percentile is 1,604.69.
  • No solution is provided.
  • Solution figure 10.18 is provided for income groups. Values are rounded to two decimal places.

    The simple averages of Winsorized values are lower. The differences are large, suggesting that the average values before the Winsorization were driven by extreme values.

Income group 2010 average (Winsorized) 2010 average (non-Winsorized)
High income: non-OECD 916.90 956.33
High income: OECD 1,356.47 1,590.49
Low income 123.06 121.96
Lower middle income 422.65 475.07
Upper middle income 635.71 643.92

Solution figure 10.18 Bank accounts per 1,000 adults: Winsorized averages for 2010.

Part 10.2 Comparing financial stability before and after the 2008 global financial crisis

  1. There has been a rapid increase in the number of regulations since the global financial crisis. Banks are now required to hold more capital and liquid assets against the risks they take. Investment banks are forced to focus on facilitating client trades rather than on trading using their own capital. In many countries, regulators require banks to be prepared to survive future financial crises. These changes have raised the capital–asset ratio and lowered the probability of default.
  1. Solution figures 10.19 to 10.22 provide the separate tables for the relevant indicators by region and income group. Values are rounded to two decimal places.
2007 2014
Income group Mean N SD Mean N SD Diff in means SD (diff in means) CI width
High income: non-OECD 12.14 32 6.67 11.57 25 6.60 –0.57 9.38 3.87
High income: OECD 11.66 32 8.27 11.83 31 6.81 0.17 10.71 3.62
Low income 7.75 25 4.74 9.49 9 4.50 1.73 6.54 3.98
Lower middle income 12.88 46 8.12 12.82 31 8.92 –0.05 12.07 4.06
Upper middle income 11.90 47 8.33 11.42 32 9.35 –0.49 12.52 4.16

Solution figure 10.19 Bank Z-score, by income group.

2007 2014
Income group Mean N SD Mean N SD Diff in means SD (diff in means) CI width
High income: non-OECD 15.07 15 2.74 17.30 19 2.71 2.23 3.86 2.00
High income: OECD 12.01 32 1.43 16.79 31 4.27 4.79 4.50 1.70
Low income 21.58 4 7.97 21.0 6 5.18 –0.57 9.50 13.70
Lower middle income 18.36 24 6.23 17.03 30 4.67 –1.34 7.78 3.20
Upper middle income 15.83 27 3.58 16.25 32 2.29 0.42 4.25 1.60

Solution figure 10.20 Capital to asset ratio, by income group.

2007 2014
Region Mean N SD Mean N SD Diff in means SD (diff in means) CI width
East Asia and Pacific 12.81 24 7.66 12.48 18 7.16 –0.34 10.48 4.77
Europe and Central Asia 8.92 51 7.12 7.98 43 6.29 –0.94 9.50 2.78
Latin America and Caribbean 13.09 35 6.64 12.60 28 6.80 –0.49 9.50 3.47
Middle East and North Africa 20.17 20 7.41 21.76 9.13 15 1.59 11.76 6.11
North America 19.37 3 3.15 17.78 3 6.26 –1.59 7.01 15.93
South Asia 10.85 8 8.49 10.38 6 2.53 –0.47 8.86 7.74
Sub-Saharan Africa 8.25 41 5.16 9.46 15 5.10 1.21 7.25 3.27

Solution figure 10.21 Bank Z-score, by region.

2007 2014
Region Mean N SD Mean N SD Diff in means SD (diff in means) CI width
East Asia and Pacific 14.58 12 4.98 16.45 15 4.17 1.86 6.49 3.88
Europe and Central Asia 15.12 44 5.48 17.85 45 3.79 2.73 6.66 2.02
Latin America and Caribbean 14.93 17 2.34 15.28 17 1.77 0.35 2.93 1.50
Middle East and North Africa 15.21 10 3.65 15.27 15 2.68 0.06 4.53 3.01
North America 13.80 2 1.00 14.30 2 0.10 0.50 1.00 12.19
South Asia 12.30 2 0.00 15.36 5 3.19 3.06 3.19 4.43
Sub-Saharan Africa 17.76 15 5.65 19.05 19 4.83 1.29 7.43 3.88

Solution figure 10.22 Capital to asset ratio, by region.

Confidence intervals for Bank Z-score, by income group.
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Solution figure 10.23 Confidence intervals for Bank Z-score, by income group.

Confidence intervals for Capital to asset ratio, by income group.
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Solution figure 10.24 Confidence intervals for Capital to asset ratio, by income group.

Confidence intervals for Bank Z-score, by region.
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Solution figure 10.25 Confidence intervals for Bank Z-score, by region.

Confidence intervals for Capital to asset ratio, by region.
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Solution figure 10.26 Confidence intervals for Capital to asset ratio, by region.

  • The means for bank Z-scores were estimated quite imprecisely (wide confidence intervals) so it is likely that the observed differences between 2007 and 2014 are due to chance. The size of the difference is also small for all countries and regions (recall that the vertical axis is measured in percentage points).

    In high-income countries, the bank regulatory capital to risk-weighted assets ratio increased between 2007 and 2014, especially for OECD countries, and Europe and Central Asia. For these countries/regions, the mean is quite precisely estimated. The sample data, therefore, is not very compatible with pre- and post-crisis population distributions which have identical means. For all other countries, the observed differences are small and imprecisely estimated (either because there are few countries in that group, or because there is huge variation within the group).

    The global financial crisis affected some countries more than others (for example, in North America, the US economy was affected more severely than the Canadian economy). As an extension, you could instead group countries according to how affected they were by the crisis (e.g. not much, moderately, severely) and recalculate the difference in means before and after the crisis. We might expect that countries that were affected more severely would also place stricter banking system regulations than less-affected countries.