2.6 Estimation and Results
2.6.2 Commercial Loans
I analyse commercial loans in the similar way I do for development loans 25. The
findings of commercial loans would be a robustness check for the findings of develop- ment banks, especially from spatial dimension and political connection perspectives. I expect that spatial dimension of lending should be effective for commercial loans. Commercial banks reduce informational asymmetry and moral hazard by allocating loans to close periphery that would also minimises the cost of funding. The political connection, as defined in this study, may not be effective in commercial banking. Mayors may not attract commercial loans by exploiting their political affiliation.
24The clear reason behind this decision still needs confirmation, yet, this institutional change in
Development Bank of Turkey suggests a reasonable explanation. To have more information about the scope of the Bank’s activities please visit: http://www.kalkinma.com.tr/
25According to the definition of BAT (2008), the loans are classified as commercial (ticari) loans,
development (kalkinma) loans and expertise (ihtisas) loans. Expertise loans are the sectoral loans that focus on specific sectors of economy, e.g. mining, agriculture, maritime etc.
This is especially valid when commercial banks are private–owned.
I explore the impact of distance, political connection, and elections on commer- cial loan provision. I run several regressions with different estimators as I run for development loans. Table 2.8 presents the estimation results with Hausman–Taylor and one–step system GMM estimator. Once commercial loans are examined, the results underpin the findings of development loans in terms of spatial dimension. Distance from the political centre negatively and significantly impacts commercial loan provision 26. Political connection does not have a statistically significant as-
sociation with commercial loans, as opposed to the findings of development loans. Khwaja and Mian (2005) find that politically connected borrowers do not have much incentive to borrow from private banks where the loan has to be repaid. Commercial banks are better at assessing the credit risk of their customers and can perform suc- cessful governance practices. The results of commercial loans in this study provide evidence that political connection of mayors with the ruling party was not a motive for commercial lending.
In terms of the impact of elections, I follow the same strategy pursued for devel- opment loans and estimate separate models for general and local elections. Similar to the findings of development loans, the results do not provide exact and clear relationship. The results of commercial loans confirm the findings of Akalın and Erki¸si (2007) and Baum et al. (2010) who examine election cycles and their impacts on Turkish economy. Baum et al. (2010) find no particular effect of election cycles on deposit–to–asset and bond–to–asset ratios of Turkish banks during 1963–2007. They conclude that election cycles do not render any change on the behaviour of depositors and governments. Akalın and Erki¸si (2007) also find no evidence of elec- tion cycle in Turkey during 1950–2006. They particularly could not find evidence
26Although the period I study corresponds to heavy state dominance in banking activity, I
have computed the same distance measures simply taking the financial center as Istanbul (see e.g. Onder and Ozyildirim, 2011), the most populous city having larger industry and commercial base. In doing so, I explore as to whether Istanbul dominates in commercial lending activity. I do not present the results to save space but the results do not show significant relationship. This is just in line with the arguments of spatial dimension of lending, since during the sample period, the headquarters of the great portion of commercial banks were also in Ankara (BAT, 2008).
of governmental policy shift around election years.
Regarding the control variables, the estimate of the P ost coup dummy is neg- ative but statistically insignificant. Recession dummy enters into the regressions with negative and statistically significant signs which is quite expected. It is reason- able that during recession times economic activity slows down, supply and demand shocks lead bank lending to dwindle. The effect of urbanisation on commercial loans is statistically insignificant. Black and Henderson (1999) find a high corre- lation between urbanisation and economic growth in developing countries, because urbanisation contributes to efficient functioning of goods, labour and financial mar- kets and information spillovers amongst producers. During the sample period, with the majority of the population still rural, the relationship between urbanisation on bank lending might not be clear. I find that fixed capital investments have positive but insignificant impact on commercial loans. Motivated from the discussion on the findings of development loans, capital formation in the regions was not triggered by commercial lending. This can be explained by the fact that the composition of commercial loans mainly exclude those expertise loans, e.g. agricultural loans, vocational loans, trade loans, that are open to capital accumulation (see e.g. BAT, 2008).
Overall, the results of commercial loans suggest that spatial dimension of lending is also crucial in the provision of commercial loans. Growing costs and agency problems with increasing remoteness also matter for commercial lending. Intuitive and interesting finding is that political connection does not matter. Mayors’ political influence is not effective in commercial lending. The association between commercial loan distribution and the elections is still fuzzy as is found in development loans.
Table 2.8: Estimation Results of Commercial Banking
Dependent variable : Real regional per capita commercial loans
Hausman-Taylor One-Step System GMM
Variables General Election Local Election General Election Local Election
Log(loan)t−1 0.728*** 0.699*** 0.618*** 0.494** (0.0446) (0.0457) (0.188) (0.201) Mean distance -0.00154* -0.00174* -0.00771* -0.0103** (0.000823) (0.000905) (0.00415) (0.00429) Political connection 0.247 0.226 -1.673 -2.047 (0.278) (0.284) (1.682) (1.488) Pre-election dummy 0.154*** -0.0863* -0.134 -0.0411 (0.0491) (0.0492) (0.150) (0.129) Election dummy 0.0171 -0.150*** -0.151 -0.100 (0.0497) (0.0484) (0.154) (0.125) Post-election dummy -0.0937* -0.0149 -0.218 -0.219* (0.0496) (0.0486) (0.142) (0.133)
Post coup dummy 0.0824 0.0711 0.0201 -0.0879
(0.0595) (0.0631) (0.179) (0.201)
Log(fixed capital investment) 0.00104 0.0331 0.0935 0.117
(0.0695) (0.0725) (0.234) (0.244) Urbanisation -0.151 -0.383 -0.267 -0.358 (0.538) (0.558) (1.534) (1.602) Recession -0.195*** -0.208*** -0.450** -0.325* (0.0644) (0.0610) (0.175) (0.169) Constant 0.224 0.0165 2.737 3.634* (0.822) (0.876) (1.950) (2.129) Observations 279 279 279 279 Number of regions 9 9 9 9 Number of instruments 35 35 Sargan p-value† 0.959 0.985 AR(2)† 0.922 0.470 F test p-value‡ 0.00 0.00 0.00 0.00
Notes: * p<0.10, ** p<0.05, *** p<0.01. Robust standard errors are given in parentheses. ‡
indicates Wald test for overall significance. † indicates the invalidity of the instruments is rejected by using Sargan test of over–identifying restrictions for the models with no heteroscedasticity or autocorrelation. The second order serial correlation is also removed. Inverse distance, mean distance, and squared distance measures of region i are, M eanDistance =
ni P k=1 distancek n , InverseDistance = ni P k=1 1
distancek and SquaredDistance =
ni P
k=1
distance2
k, respectively. Political connection is measured by
P oliticalConnectionit=
n
P
k=1
(P opulationitk∗M ayork) n
P
k=1
P opulationitk
where k denotes the cities in region i and n denotes the number of cities in region i on year t. M ayor is a dummy that takes value 1 if the mayor of the province k is from the ruling party and takes 0 otherwise. P opulation is the population figure for the city k in region i on year t. I estimate the population between censuses based on Turkstat assumption, the population of Turkey grows exponentially between any two censuses, P opulationt=
P opulation0∗ ert. Here, 0 represents the base year, i.e. between two censuses the base year is the
earlier census. Pre-election, election, and post-election dummies take the value of 1, one year before election, election and one year after election respectively, and 0 otherwise. 1988 dummy takes the value of 1 on the year 1988 and 0 otherwise. Log(fixed capital investment) is the logarithmic transformation of annual fixed capital investment figure. The urbanisation figure is the ratio of urban population in total population. The analysis follow Baum et al. (2010) in the creation of post coup and recession dummies. Post coup dummy takes the value of for the three consecutive years after 1980 when the coup took place, and 0 otherwise. The recession dummy takes the value of 1 when the years were 1979, 1980 and 1994 when there occurred economic downturn in the country, and 0 otherwise.