in the Post-Crisis Period (1998-2009)
3.4 Econometric application and data
Econometric application
As I mentioned in the previous section, banks are assumed to be price-takers when acquiring deposit but they are not necessarily price-takers when supplying loan. As a result of their profit maximization behavior, their price-quantity relationship is given by equation (5). I assume that there are two factors of production, labor and physical capital; then equation (5) can be rewritten in a specific form as follow,
12 1 2 1 2 1 2 11 22 12 1 2 1 2 2 2 H v v v v v v M QH QH p p p p p (7) where v and 1 v are prices of labor and physical, respectively. 2The unknown parameters in equation (7) consisting of , , 11, 22, 12, 1 and 2 can be econometrically estimated. Nevertheless, note that the endogenous
variable, Q, appears in the RHS. Consequently, a model for Q is required. In fact, outside the perfectly competitive markets, firms do not have supply curves given by P = MC(Q). Instead, price or quantity-setting conduct follows more-general supply relation that is MR = MC16. Hence, I need to specify only the aggregate-demand-for- loan function. In so doing, I adopt a log-linear demand function depending on its own price, price of a substitute, and aggregate income as follow:
0
1
2
ln Q l ln p l ln pa l ln y
(8)
where p is the interest rate of loan from an alternative source, and a
y is a variable representing level of income or aggregate production.
Theoretically, demand function must be linearly homogenous in all prices and income, so the demand function (8), after imposing the condition of homogeneity of degree zero, can be rewritten as the following17,
0 2 ln ln ln a a p y Q l l p p (9)As an empirical strategy, equation (7) and (9) are to be estimated simultaneously by using nonlinear three-stage least-squares (N3SLS)18 because of the endogeneity of the quantity and price variables, Q and p. All exogenous variables
consisting of v ,1 v , 2 w , H, p and a y are used as instrumental variables.
Data
The data set is cross-sectional, which covers the four largest commercial banks in Thailand consisting of Bangkok Bank, Kasikorn Bank, Siam Commercial Bank, and Krung Thai Bank, during the period of 1998 – 200919. These four banks are selected in accordance with the bank of Thailand’s peer-group comparison in which large-size banks are defined as banks with market share of total assets greater than 10%20. Data were acquired from the quarterly financial statement of each individual bank, and the
17 See Varian (1992) Chapter 12.
18 3SLS combines two stage least squares (2SLS) with seemingly unrelated regression (SUR). The first deals with endogeneity problem, while the latter is relevant to the correlation of error terms across equations. I use SAS programs to perform the N3SLS estimation in this study.
19 Kubo (2006) adds two more banks in his study including Bank of Ayudhaya and Thai Military Banks. His data span over the period of 1992-2004.
20 Medium-bank group includes 4 banks with market share of total assets between 3% and 10%. Small- bank group consists of 6 banks with market share less than 3%.
financial statistics published on the bank of Thailand’s website21. The definition of variables and their sources are provided in table 3.1 below.
Table 3.1: List of Variables and Their Definitions
Definition Source
p Minimum loan rate (MLR) Bank of Thailand’s financial statistics
w 3-month time deposit interest rate Bank of Thailand’s financial statistics
M Interest-rate margin
pw
p1
v Expenses on personnel/ the number
of employees Individual bank’s Quarterly financial statements
2
v Expenses on premises and equipment/ value of premises and equipment
Individual bank’s Quarterly financial statements
i
q Loan and accrued interest receivables
Individual bank’s Quarterly financial statements
Q Total credits of all commercial banks
Bank of Thailand’s financial statistics
H CR4 (concentration ratio of the
biggest four firms) i I i
, , ,
q Q I BBL KBANK SCB KTB
a
p State enterprises’ bond rate22 Bank of Thailand’s financial statistics
y Real GDP Bank of Thailand’s financial statistics 12 Weighted average interest rates of new issues in each month. Almost all of the state
enterprise bonds have initial maturities of 3-10 years.
With regard to the price of output (p), I use the minimum loan rate (MLR),
the interest rate at which each bank charge its most favored customers, i.e. those with high creditability; higher-risk customers are charged MLR plus their specific risk premium; it can be comparable to the prime rate in the US. I choose the interest rate paid on 3-month time deposit to represent the price of material input ( w ). Regarding the cost variables, I assume that banks use labor and physical capital, in addition to
deposits, in the production of loans. The factor prices, wage (v ) and rent (1 v ), are 2 computed from the expenses on personnel, and expenses on premises and equipment divided by the number of employees, and value of premises and equipment, respectively. Since information on the Herfindahl index (H) for the loan market is not
available, I use the four-firm concentration ratio for the loan market (CR4) as a
substitute23.
In estimating the aggregate demand for loans, the total amount of loans and accrued-interest receivables issued by commercial banks registered in Thailand (Q) is obtained from commercial banks’ credits reported on the bank of Thailand’s website. Interest rate on state enterprises’ bond is adopted as the price of a substitute for bank loans; it is believed that this rate is a good measure (up to monotone increasing transformation) of the private-corporate bonds’ rate. Real GDP is chosen to be a variable representing aggregate production (y). Finally, note that all nominal
variables like wage and loan are adjusted to be in the real terms by the consumer price index (CPI) 24. Table 3.2 below shows the descriptive statistics of variables used in estimation.
23 Azzam (1997) argued that there are close correlation between CR
4 and the Herfindahl index. Also, strong correlation between CR3 and the Herfindahl index was found by Bikker and Haff (2002). 24 2007 is the base year.
Table 3.2: Summary of Descriptive Statistics of Variables
Variable Mean S.D. Max. Min.
p 7.59 2.29 15.50 5.50 w 2.80 2.18 10.00 0.65 M 0.67 0.14 0.89 0.34 1 v 145,168 34,321 257,326 88,623 2 v 0.05 0.01 0.08 0.03 Q 5,826,683 596,432 7,127,449 4,559,200 H 0.52 0.03 0.58 0.46 a p 5.29 2.68 14.82 2.48 y 893,576 143,695 1,144,005 658,899
Note: unit of Q and y in million Baht; unit of v1 in baht.