The value of banks' political and business connections in the Russian industrialization
Determination of the consequences of the financial crisis in Russia after the decline in the inflow of foreign capital. Analysis of the activities of banks that were more distressed during the crisis, with personal ties to heavy industry companies.
Рубрика | История и исторические личности |
Вид | статья |
Язык | английский |
Дата добавления | 10.10.2021 |
Размер файла | 77,5 K |
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As a further preview to my results, I examine the distribution of interlocks among banks. During the crisis banks located in Saint Petersburg, the capital, were more connected to heavy industry than banks located in Moscow and the provinces. 'Their 53 personal linkages resulted in the number of connections per board member being three times the number of those in Moscow or the provinces. Given that Saint Petersburg banks sustained the greatest financial losses of the three banking groups, their high interconnectedness with heavy industry implies that personal ties may have played a role in bank distress. Saint Petersburg's banks were also the group most connected with the Finance Minister's circle. Their 22 connections were three times the number of those in MoscowThe share of banks with connections to the Finance Minister's circle was 28 percent. For comparison, [Grossman, Imai, 2016. P. 80] find that in 1900 the share of British banks connected to members of the Parliament was about 24 percent..
Taking one bank as an example, the Russian Bank for Foreign Trade incurred the largest investment loss during the crisis among all banks, equivalent to 44.8 percent of the value of its corporate securities. This bank employed five board members with connections to the Finance Minister's circle, more than any other bank; nine members connected to heavy industrial companies, falling behind only one bank on this measure; but only two members with ties to light and non-industrial firms. During the prosperous period of 1894-1898, the bank gained 44.4 percent on its corporate securities, which was the fourth largest gain among its peers.
My core evidence is presented in Table 1, which reports the results of nine regression models, the only difference between each specification being the type of the interlock variable used. Note that all models incorporate both direct and indirect interlocks, unless otherwise specified. Robust standard errors are used in all models. Model (1) shows that, with each additional bank board member connected to a variety of officials, both close to the Minister of Finance and not directly related to his circle, a bank experienced 2.2 percent of extra investment portfolio losses during the crisis, though the result is statistically insignificant at standard levels. Model (3) tests exclusively for bank board members connected to officials close to the Minister of Finance. The model reveals that a bank lost much more, or 5.2 percent. It may be concluded that the banks which sustained greater losses were the financiers of heavy industry, because the value of heavy industrial equities declined by substantially more than that of the light industrial or non-industrial equities, as discussed above.
Table 1 Investment Losses 1899-1901, OLS Model
1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
||
Member connected to government |
0.022 (0.014) |
|||||||||
Interlocks with government |
0.012 (0.010) |
|||||||||
Member connected to the FM's circle |
0.052* (0.029) |
-0.022 (0.031) |
||||||||
Interlocks with the FM's circle |
0.040** (0.015) |
|||||||||
Member connected to light and non-industrial firms |
0.004 (0.007) |
|||||||||
Interlocks with light and non-industrial firms |
0.010* (0.005) |
|||||||||
Member connected to heavy industrial firms |
0.026** (0.010) |
0.013* (0.007) |
||||||||
Interlocks with heavy industrial firms |
0.010*** (0.003) |
|||||||||
Member FM's circle * heavy industrial firms |
0.010** (0.004) |
|||||||||
Bank age |
0.001 |
0.001 |
0.001 |
0.000 |
0.000 |
0.000 |
-0.000 |
-0.001 |
-0.000 |
|
Number of locations |
-0.000 |
-0.000 |
-0.003 |
-0.003 |
0.000 |
0.000 |
-0.002 |
-0.002 |
-0.004* |
|
Leverage |
-0.105 |
-0.090 |
-0.028 |
0.002 |
0.007 |
-0.007 |
0.069 |
0.096 |
0.098 |
|
Liquidity |
0.686 |
0.623 |
0.107 |
-0.081 |
0.522 |
0.635 |
0.249 |
0.214 |
-0.393 |
|
Asset growth |
0.095 |
0.093 |
0.081 |
0.076 |
0.100 |
0.101 |
0.109* |
0.087 |
0.081* |
|
Board size |
-0.004 |
-0.003 |
-0.003 |
-0.004 |
0.003 |
0.001 |
-0.004 |
-0.002 |
-0.009** |
|
Constant |
0.062 (0.055) |
0.048 (0.054) |
0.062 (0.049) |
0.071 (0.051) |
-0.036 (0.057) |
-0.035 (0.053) |
-0.014 (0.050) |
-0.021 (0.050) |
0.074* (0.043) |
|
Observations |
39 |
39 |
39 |
39 |
39 |
39 |
39 |
39 |
39 |
|
Adjusted R2 |
0.057 |
0.031 |
0.257 |
0.320 |
-0.070 |
0.039 |
0.288 |
0.312 |
0.536 |
Notes. 1. Interaction term is member connected to the Finance Minister's circle ' member connected to heavy industrial firms: 2. Robust standard errors in parentheses: * --p < 0.10, ** -- p < 0.05, *** -- p < 0.01.
Model (5) in Table 1 provides evidence of the presence of a bank board member connected to non-financial firms other than heavy industrial resulting in 0.4 percent of additional investment losses, though the result is statistically insignificant. Model (7) narrows the range of connections to bank board members connected specifically to heavy industrial companies. The model reveals that a bank lost much more, or 2.6 percent. This suggests the particularly unfortunate effect of personal ties to heavy industrial firms, those that were highly stimulated by government. Model (7) also shows that the banks that increased their assets by 10 percent in 1898 experienced an additional investment loss of 10.9 percent during the crisis.
Models (2), (4), (6) and (8) in Table 1 use another set of interlock variables, or the aggregate number of a bank's connections. Remember that one bank board member could have multiple personal connections to government or corporate entities. These models confirm the results of the core models discussed above.
Model (9) in Table 1 adds an interaction term between bank board members connected to the Finance Minister's circle and heavy industrial companies. First, the model shows that, for the bank with no connections to the Finance Minister's circle, the independent effect of a heavy industry interlock is an investment loss, just as expected. Second, the model reveals that, for the bank with no connections to heavy industry, the effect of an interlock with the Finance Minister's circle is statistically insignificant. This suggests that banks had to have heavy industry connections to sustain investment losses. Third, the positive and statistically significant interaction term in model (9) suggests that the effect of having a heavy industry interlock is strengthened by an interlock with the Finance Minister's circle.
The models in Table 1 capture observable official connections. However, banks also established unofficial ties that these models do not capture. For example, the St. Petersburg Discount and Loan Bank collaborated with Femistokl I. Petrokokino, who searched for profitable underwriting and lending opportunities for the bank [Bovykin, 1967. P. 216-219]. Because the models do not capture unofficial ties, the coefficients on interlock variables are likely biased upward.
To test whether bankers acted in the best interests of their clients or abused their positions to extract rents, my sample is restricted to companies that were established in or after 1893, the period when banks had many opportunities to offer excessive loans, equity and debt securities to newly established enterprises. If bankers acted as rent seekers, I would expect banks with all types of connections to sustain losses during the crisis. 'This is because it is unlikely that banks extracted rents only from new heavy industrial firms and did not extract any from new light and non-industrial firms. I find that banks did not incur statistically significant losses on their light industrial and non-industrial connections, but they did sustain losses on heavy industrial tiesI do not report these results to conserve space, but they are available upon request.. These results are consistent with the view that bankers acted as delegated monitors, rather than as rent seekers.
Models (10) to (13) in Table 2 include bank and time fixed effects to address the issue of omitted variable bias. There is enough within- subject variability in the interlock variables for fixed effects models to work. For example, in 1896 the average number of connections to the Finance Minister's circle was 0.87. Between 1896 and 1898 this number changed, on average, by 0.33.
Table 2 Investment Performance 1894-1901, OLS Model with Bank and Time Fixed Effects
10 |
11 |
12 |
13 |
||
Member connected to the FM's circle |
0.205** (0.093) |
||||
Interlocks with the FM's circle |
0.171** (0.072) |
||||
Member connected to heavy industrial firms |
-0.031 (0.034) |
||||
Interlocks with heavy industrial firms |
-0.037** (0.014) |
||||
Number of locations |
0.002 |
0.006 |
0.002 |
0.012* |
|
Leverage |
-0.938*** |
-0.949*** |
-0.916** |
-0.855*** |
|
Liquidity |
2.445 |
2.060 |
2.532 |
2.305 |
|
Asset growth |
0.102 |
0.102 |
-0.001 |
0.018 |
|
Board size |
-0.049 |
-0.043 |
-0.030 |
-0.002 |
|
Russian for Foreign Trade |
-0.421** |
-0.786* |
-0.123 |
0.242 |
|
Russian for Trade and Industry |
0.094 |
0.247 |
-0.207 |
0.250 |
|
St.Petersburg-Azovsky |
0.335 |
0.437 |
-0.434** |
-0.082 |
|
St.Petersburg International |
0.533 |
0.445 |
0.186 |
0.560** |
|
St.Petersburg Muscovy |
0.347 |
0.470 |
-0.519** |
0.028 |
|
St.Petersburg Discount and Loan |
0.209 |
0.380 |
-0.224 |
0.331 |
|
St.Petersburg Private |
0.187 |
0.296 |
-0.200 |
0.490 |
|
Credit Lyonnais |
0.153 |
0.312 |
-0.730** |
-0.184 |
|
Moscow Merchant |
1.172** |
1.069** |
0.291 |
0.293 |
|
Moscow International Trade |
-0.019 |
0.075 |
-0.515** |
-0.326* |
|
Moscow Trade |
1.032* |
1.100* |
-0.093 |
0.074 |
|
Moscow Discount |
1.053* |
1.122** |
-0.062 |
0.124 |
|
South-Russian Industrial |
0.157 |
0.279 |
-0.720** |
-0.229 |
|
Azovsko-Donskoy |
0.533 |
0.557 |
-0.458** |
-0.348** |
|
Commercial in Bialystok |
-0.001 |
0.124 |
-0.884*** |
-0.444 |
|
Commercial in Warsaw |
0.622 |
0.687 |
-0.311* |
-0.007 |
|
Warsaw Discount |
0.492 |
0.607 |
-0.451** |
-0.067 |
|
Vilnius Private |
0.551 |
0.638 |
-0.399 |
-0.087 |
|
Voronezh Commercial |
0.353 |
0.477 |
-0.584** |
-0.180 |
|
Ekaterynoslavsky Commercial |
0.386 |
0.523 |
-0.533* |
-0.050 |
|
Kazan Merchant |
0.595 |
0.693 |
-0.456** |
-0.157 |
|
Kiev Private |
0.806 |
0.893 |
-0.149 |
0.26 |
|
Commercial in Kostroma |
0.543 |
0.640 |
-0.459*** |
-0.186 |
|
Lodz Merchant |
0.040 |
0.155 |
-0.880*** |
-0.437 |
|
Trade in Lodz |
0.597 |
0.673 |
-0.249 |
0.291 |
|
Minsk Commercial |
0.350 |
0.453 |
-0.512* |
-0.029 |
|
Nizhny Novgorod Merchant |
0.824 |
0.917 |
-0.216 |
0.057 |
|
Odessa Discount |
0.412 |
0.549 |
-0.516* |
-0.075 |
|
Orlovsky Commercial |
-0.043 |
0.045 |
-0.906*** |
0.536** |
|
Pskov Commercial |
0.446 |
0.563 |
-0.467* |
-0.042 |
|
Riga Commercial |
0.452 |
0.554 |
-0.431 |
-0.033 |
|
Rostov-on-Don Merchant |
0.208 |
0.370 |
-0.765** |
-0.312 |
|
Siberian Trade |
0.512 |
0.510 |
-0.395* |
-0.172 |
|
Central Asian Commercial |
0.288 |
0.372 |
-0.717** |
-0.375 |
|
Tiflis Commercial |
0.828 |
0.917* |
-0.145 |
0.154 |
|
Kharkov Trade |
0.486 |
0.609 |
-0.456 |
-0.023 |
|
Russo-Asian |
0.064 |
0.163 |
-0.574** |
-0.083 |
|
Time fixed effects |
-0.332*** |
-0.338*** |
-0.291*** |
-0.242*** |
|
Constant |
0.638 (0.678) |
1.423*** (0.487) |
0.497 (0.677) |
0.735* (0.431) |
|
Observations |
76 |
76 |
76 |
76 |
|
Adjusted R2 |
0.425 |
0.211 |
0.435 |
0.471 |
Notes. 1. The omitted bank is Volzhsko-Kamsky. 2. Robust standard errors in parentheses: * -- p < 0.10, ** -- p < 0.05, *** -- p < 0.01.
Model (10) in Table 2 shows that, with each additional bank board member connected to the Finance Minister's circle, a bank experienced an extra 20.5 percent in portfolio outperformance. Model (11), which uses the aggregate number of connections at a bank, confirms this result. These findings suggest that the benefits of being politically connected before the crisis overweighed the losses sustained during the crisis.
Model (13) in Table 2, which also uses the aggregate number of connections, shows that, with each additional connection to heavy industrial firms, a bank sustained an extra 3.7 percent loss. Model (12), which tracks the number of bankers connected to heavy industry, is statistically insignificant. However, I am not concerned about this result. In those cases when a single banker maintained multiple industrial connections, the model could become statistically insignificant. Taken together, econometric findings suggest that political and heavy industry interlocks had a positive effect on bank performance during prosperous times and a negative effect during turbulent times.
My main results are robust to additional testsI do not report these results, but they are available upon request.. Specifically, the results are robust to excluding one and two banks that were most heavily connected to the Finance Minister's circle. 'The results are also robust to randomly excluding one top government institution and one heavy industry. The results hold to using the bank share price as the dependent variable. They also hold when two different sets of interlock variables are used: those capturing only direct interlocks and, separately, only indirect interlocks. Finally, the results are robust to using propensity matching analysis.
Lastly, models (14) to (17) in Table 3 test whether banks' connections in 1896 led to additional investment profits in the years leading up to the crisis (1894 to 1898). Model (14) shows that, with each additional bank board member connected to the Finance Minister's circle, a bank experienced an extra 10.5 percent in portfolio outperformance. Model (16) reveals that, with each additional bank board member with ties to heavy industrial firms, a bank's portfolio appreciated by an extra 4.4 percent. Models (15) and (17), which use the aggregate number of a bank's connections, confirm these results.
Table 3 Investment Profits 1894-1898, OLS Model
14 |
15 |
16 |
17 |
||
Member connected to the FM's circle |
0.105** (0.049) |
||||
Interlocks with the FM's circle |
0.071* (0.036) |
||||
Member connected to heavy industrial firms |
0.044** (0.017) |
||||
Interlocks with heavy industrial firms |
0.024** (0.010) |
||||
Baseline controls |
? |
? |
? |
? |
|
Constant |
0.285 (0.175) |
0.313 (0.196) |
0.306* (0.154) |
0.320* (0.157) |
|
Observations |
38 |
38 |
38 |
38 |
|
Adjusted R2 |
0.309 |
0.254 |
0.086 |
0.047 |
Notes. 1. Baseline controls are locations, leverage, liquidity, asset growth, and board size. 2.
Robust standard errors in parentheses: * -- p < 0.10, ** -- p < 0.05, *** -- p < 0.01
Conclusions
Banks' personal connections played an important role in Russia's industrialization of the 1890s, when there were high information asymmetries between firms and their lenders and corporate regulation was weak.
The evidence presented in this paper suggests that the banks which experienced greater distress during the final financial crisis in the Russian Empire had more connections to those government officials who were closest to the epicenter of policymaking and to those companies which had been most stimulated by state policies to expand production in the lead-up to the crisis. During the prosperous times of the 1890s, the reverse was true. Banks' personal ties with the Finance Minister's circle and heavy industrial companies were positively related to bank performance.
These findings suggest that, in a crisis, well-connected bankers might fail to provide the valuable expertise and foresight that is expected from them. Instead, the presence of such bankers might harm bank performance when decisive action is required to adapt to rapidly changing economic circumstances.
A key remaining question is what caused the banks to excessively finance heavy industrial companies in the first place. Based on the narrative evidence presented in this paper, I would argue that it is the government policies targeted at the development of the real economy that enticed banks to expose themselves financially to new technology companies. These policy incentives culminated in disastrous bank performance when heavy industry experienced a slowdown.
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