Tables 4∼6 present the estimated results from using the determinant equation. The absolute t-values are put in parentheses. Because we use FSI = 1 as the benchmark, the explanations of the estimated results under columns FSI = 2 and FSI =3 are relative to this benchmark. Also, we attempt different specifications, denoted as 1A, 1B and 1C to examine robustness. Because CB, DC, LDC and CEE are our core explanatory variables, we do not remove them when we change the specifications.
Table 4 presents the estimated results when we take into consideration full the sample of countries. Specification 1A is the simplest model and includes only six explanatory variables, i.e., CB, DC, LDC, CEE, GDPper and POPULA. The coefficients of CB, which are respectively –1.954 and –1.973 in the FSI =2 and FSI =3 equations, are highly significant. This result strongly suggests that if a country’s central bank is in charge of bank supervision, it is prone to move away from partial and unified supervisions and is more likely to adopt sectoral supervision. Accordingly, there is a “reverse central bank effect”. In short, those countries which adopt sectoral supervision also tend to use the central bank to supervise banks. The coefficients of the DCs are insignificantly negative regardless of FSI, indicating that there is no DC effect on the choice of supervisory system when the whole sample is used. The coefficients of the LDCs are significantly positive when FSI = 2 but insignificantly positive when FSI = 3, implying that a LDC tends to adopt partial but not unified supervision. A significantly negative coefficient of CEE country when FSI= 2 but not when FSI= 3 equation means that a CEE is more inclined to adopt sectoral supervision over partial supervision. GDP per capita is significantly positive in both FSIs’, which has the implication that richer countries have less tendency to adopt sectoral supervision.
Specification 1C of Table 4 replaces GDP per capita by POPULA which shows a significantly negative sign when FSI =2 but an insignificant negative sign when FSI = 3.
This resembles the results for CEE, that is, a greater population decreases the tendency to adopt partial supervision. Results from population supports partially the “scale effect”
because coefficients of POPULA are significant only when FSI = 2. In the case of FSI = 3, the coefficients of DC, LDC and CEE all change from insignificant to significant. We find that this is owing to the extra explanation provided by POPULA. Specification 1C takes GDPper and POPULA into account and the results do not change.
Judging from the results of four specifications in Table 4, CB is overwhelmingly sig-nificantly negative, and this evidence does not change even if we use a different sample size in the tables which follows. Thus, there is a “reverse central bank effect”, in other words, a country with its central bank supervising banks, strongly prefers sectoral super-vision. This result holds for all specifications. The impact of DC is elusive with the sign changing in different specifications, and it is only significant in specification 1C. LDC, by contrary, is a rather robust factor because, except for 1B, its effect is overwhelm-ingly significant. Compared to partial supervision, a higher population prefers sectoral supervision. To sum up, the scale effect that a large country does not adopt sectoral supervision is quasi-supported when FSI = 2.
Table 5 expands the explanatory variables to consider five interaction terms, namely, CB×GDPper, CB×DC, CB×LDC, CB×DC×GDPper and CB×LDC×GDPper using the whole sample size. 17 There are three specifications, 2A, 2B and 2C. Because the sample size is the same for the three specifications (=98), the log likelihood ratio (LR) test can be performed. The log-likelihood values of the three specifications are –68.05,
17Our of interaction term of two variables always considers CB. Then, DC, LDC and GDPper are alternatively attempted. With respect to three variables, we simply pick CB, DC/LDC and GDPper.
–64.18 and –63.96, respectively, and the latter two are nested in the third, making the LRs 8.72 and 0.44. Consequently, we reject specification 2A, but we cannot reject 2B in a statistical sense. Specification 2B, therefore, is the grounds for the discussion below.
The coefficients of CEE are overwhelmingly significantly negative when FSI = 3.
Thus, CEE prefers sectoral supervision to unified supervision, which we could call “the CEE effect”. The coefficients of DC in specifications 2B and 2C are both significantly positive, which suggests that developed countries are inclined to adopt either partial or unified supervisions but not the sectoral one. It is inconsistent with the common notion that rich countries have a larger capacity, enabling them to adopt sectoral supervision.
Hence, a “reverse DC effect” is found. LDC has no effect on the choice of supervisory system. GDP per capita, to our surprise, changes its signs from positive in Table 4 to negative here, though mostly it is insignificant. One explanation for this change is probably related to the added interactive terms which mitigate the effect of GDP per capita per se. Population remains strongly negative when FSI = 2, supporting the “scale effect”.
The interactive terms also show particularly interesting results. Firstly, the coeffi-cients of CB×DC are significantly negative when FSI = 3. Thus, a developed country with its central bank supervising banks is strongly against the adoption of unified super-vision, but instead, prefers sectoral supervision. Given this evidence together with the above evidence from DC alone, what we can conclude is that developed countries when their central bank are not supervising banks tend to adopt unified supervision; conversely, they tend to adopt sectoral supervision when their central banks do not supervise banks.
Secondly, the coefficients of CB×LDC are significantly positive when FSI =2. This evidence along with the insignificant coefficient of LDC suggests that less developed
countries with their central bank supervising banks prefer partial supervision. If the central bank of a LDC does not supervise banks, it evidently has no effect whatsoever on the supervisory system. Thirdly, even among developed countries whose their central bank supervises banks the GDP per capita has a negative impact when FSI =3 but a positive effect when they are LDC. Thus, increasing GDP per capita decreases the probability of adopting unified supervision in DC×CB but increases the likelihood in LDC×CB.
Table 6 presents the estimated results using the subset samples. There are three specifications, 3A, 3B and 3C, in the table, and the sample size of each specification is different. Recall that only 46 countries have useable data when bank activity restriction variables are used. The sample is reduced to 41 further when both bank activity restric-tion variables and the OSPI and the PMI are used. As stated earlier, we thus do not conduct any LR tests when sample sizes are different.
Table 6 adds OSPI, PMI and GOODGOV into the model and also contains three specifications 4A, 4B and 4C. Because the sample size is not the same, no LR tests are conducted. None of coefficients of OSPI are insignificant, suggesting that whether official supervisory authorities have the authority to take specific actions to prevent and correct problems is not related to the choices of financial supervisory system. Coefficients of PMI are significantly positive regardless of specifications. Because PMI is more related to the concept of market discipline and self-correction, thus, a country with better pri-vate market discipline is inclined to adopt partial and unified, relative to the sectoral system, supervisory systems. The coefficients of GOODGOV are significant when FSI = 3 regardless of the specifications. This is not surprising because countries which adopt unified supervision, such as Denmark, Norway, Sweden and the U.K. have very good
gov-ernance. To a lesser degree, so do Japan and Korea.18 Thus, there is a good governance effect; countries with good governance are inclined to adopt unified supervision.
Also, when we use the small sample size of only 41 countries, the bank restriction variables become significant. For both FSI = 2 and 3, BANK-R is overwhelmingly significantly negative, whereas BANK-S and BANK-I are overwhelmingly significantly positive. Accordingly, a country which allows its bank to engage in real estate tends to adopt sectoral supervision. Furthermore, because of positive coefficients of BANK-S and BANK-I, a country which prohibits its banks to engage in securities and insurance are prone to adopt partial and unified supervisions. This contradicts the “blurring of the distinction effect” but supports the alternative. That is, most countries, which allow their banks to engage in security and insurance activities, adopt sectoral supervision. For example, Germany in our case, allowing banks to engage in these two non-bank activities but adopts only partial supervisory system. Thus, whether a country adopts a unified supervisory system clearly depends on many factors and are not completely linked to the mentioned blurring effect.
6 Conclusions
We classify the sample of countries on the basis of sectoral, partial and unified supervi-sions systems. The main findings of our study are the following.
First, the most striking results of our study is the evidence contradicting our earlier assertion. That is, those countries allow banks to engage in securities and insurance should be the most urgent to adopt unified supervision. Empirical results, however, lead little support for this assertion. That is, there is a “reverse central bank effect”.
18Taiwan has not yet been included in this group because it will join the unified supervision club in 2004.
Countries whose central bank also supervises banks tend to adopt sectoral supervision.
This result is robust regardless of sample size and specifications
Second, “the scale effect” is half-supported because coefficients of population are sig-nificant only when FSI = 2. Countries with a higher population prefer sectoral supervision to partial supervision. This is a marginal robust factor.
Third, “the poor country effect” is rejected because it is the rich countries, not the poor ones, that prefer unified supervision. Poor countries are prone to adopt sectoral supervision.
Fourth, similar to the conclusion drawn from the third determinant, developed coun-tries tend not to adopt sectoral supervision but prefer unified supervision. Less developed countries, on the other hand, tend to adopt partial but not unified supervision.
Fifth, central and eastern European countries prefer sectoral to unified supervision, which is our “CEE effect”.
As for the sixth determinant, a less developed country with its central bank (LDC×CB) supervising is are inclined to adopt partial supervision. A LDC without its central bank supervising banks has no effect on the choice of supervisory system.
Turning to the seventh determinant, Given this evidence together with the above evidence from DC alone, what we can conclude is that developed countries when their central bank are not supervising banks tend to adopt unified supervision; conversely, they tend to adopt sectoral supervision when their central banks do not supervise banks.
“The reverse blurring of distinction effect” is found when banks are restricted in engaging in securities business. That is, a country whose banks are not allowed to engage in security and insurance activities tends to adopt partial and unified supervisions. By contrast, when banks in a country are not allowed to engage in real estate activity, the
country tends to adopt sectoral supervisory system.
Ninth, in regard to the OSPI and the PMI, the former has no effect, whereas the latter is positively significant for both FSIs. A country with good private market discipline tends to adopt the partial and unified supervision.
Finally, “the good governance effect” does indeed exist. A country with good gover-nance tends to adopt the unified supervision, probably because this type of supervision desires full cooperation and coordination.
Our research is the first step systematically study the determinants of financial super-visory system. Future studies can study the performance of banks in different financial system.
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Table 1: Variable Definitions and Sources
Focused Variables
Variable Definition Contents Source
FSI Financial integration 1: no integration; Courtis (2002) of supervision 2: partial; 3: full
CB Central bank also 0-1; 1 is yes; and 0 is no Courtis (2002) supervising banks
Development of Country
DC Developed countries 1: yes; 0: no United Nation website
LDC Less Developed countries 1: yes; 0: no United Nation website
CEE Central and Eastern 1: yes; 0: no United Nation website
European countries Scale and Wealth of Country
GDPper GDP per capita WDI
POPULA Population IFS
Bank Restrictions
Bank-S Restriction on Bank’s 1: unrestricted; 2: permitted; Barth et al. (2001) Investment Activities 3: restricted; and 4: prohibited
Bank-I Restriction on Bank’s same as above Barth et al. (2001) Insurance Activities
Bank-R Restriction on Bank’s same as above Barth et al. (2001) Real Estate Activities
Institutional Variables
GoodGov Good Government 0-50, Sum of eff. of judic. LLSV (1998) Index system, rule of law,
corru-ption, risk of expropriation risk of contract repudiation
OSPI Official Supervision 0-16, Sum of prompt correc Barth et al. (2004) Power Index tive action, restructuring
power and declaring insolvency power
PMI Private Monitoring 0-10, Sum of a required certified audit Barth et al. (2004) Indexc required, percent of 10
big-gest bank rated, no explicit deposit insurance, bank acc-ounting, disclosure of off-sheet, disclosure of risk IFS: International Financial Statistics, 2000, IMF
WDI: World Development Indicators, 2000, World Bank
Table 2: Countries of Financial Integration of Supervision
Sectoral Integration (56)
CB Albania Algeria Argentina Bahamas Bahrain
supervises Bangladesh Barbados Botswana Brazil Bulgaria
banks (47) China1 Croatia Czech Rep. Egypt Ghana
Greece Hong Kong India Israel Italy
Jamaica Jordan Kazakhastan Lithuania Mauritius
Nepal Netherlands2 New Zealand Nigeria Oman
Pakistan Papua New Guin. Philippines Portugal Romania
Russian Rep. Slovenia Spain Sri Lanka Taiwan3
Tanzania Thailand Trinidad & Tunisia Ukraine
USA Zambia Tobago
CB does not Costa Rica France Germany4 Indonesia Latvia
supervise (9) Panama Poland Venezuela Turkey
Partial Integration (35)
Bank & Insurance (18)
CB supervises Colombia Ethiopia Gambia Honduras Macao
Paraguay Sierra Leone Suriname
CB does not Australia5 Austria Canada Cayman Is. El Salvador
supervise Gibraltar Guatemala Iceland Malaysia Peru
Bank & Security (14)
CB supervises Bermuda Cyprus Dominican Rep. Guyana Ireland
Saudi Arabia United Arab Emirates Mexico
CB does not Belgium Finland6 Hungary Ireland7 Luxembourg
supervise Switzerland
CB supervises Malta8 Netherlands Antilles8 Singapore Uruguay9
CB does not Japan10 Denmark Norway South Korea10 Sweden
supervise U.K.
Sources are mainly from Courtis (2002) and websites.
Sources are mainly from Courtis (2002) and websites.