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on the text below the content, author: Liu Yuwen, Loughborough economics and finance, communication mail: [email protected]
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Karthik Krishnan, Debarshi K. Nandy, Manju Puri, Does Financing Spur Small Business Productivity? Evidence from a Natural Experiment, The Review of Financial Studies, Volume 28, Issue 6, June 2015, Pages 1768–1809, https://doi.org/10.1093/rfs/hhu087
We analyze how increased access to financing affects firm total factor productivity (TFP) by exploiting a natural experiment following interstate banking deregulations that increased access to bank financing. We find that firms' TFP increases after their states implement these deregulations. Using a regression discontinuity approach based on the Small Business Administration's funding eligibility criteria, we show that TFP increases following the deregulations are significantly greater for financially constrained firms. Our results suggest that greater access to financing allows financially constrained firms to invest in productive projects that may otherwise not be taken up .
Can increased financing stimulate the productivity of small businesses? The results of a quasi-natural experiment
1. Introduction
The author uses the quasi-natural experiment of increasing bank financing channels after the deregulation of interstate banks to analyze the impact of increasing financing channels on the company’s total factor productivity (TFP). The study found that after the deregulation of Intercontinental Bank, the total factor productivity of enterprises increased.
In this article, the author takes the Interstate Banking and Branch Efficiency Act of 1994 (IBBEA) as the node, and uses the RD method comprehensively to determine whether to meet the threshold of the U.S. Small Business Administration (SBA) to provide financial support, that is, different financial constraints as the only variable. (Using the method of regression discontinuity based on the funding eligibility criteria of the Small Business Administration) measure the different degrees of deregulation, and apply the SRD (Sharp Regression Discontinuity) method to explore the impact of deregulation on TFP, and the results Shows that companies below the SBA threshold (that is, without SBA authorization and financial constraints) have a greater degree of TFP growth than companies above the threshold. Finally, this article applies the Quasi Regression Discontinuity method (ie, the breakpoint regression discontinuity method) to measure the relationship between deregulation and TFP (affected by financial constraints). Here Quasi regression is a DID method that combines the deregulation index (IBBEA deregulation index) with dummy variables (above or below the SBA threshold) of SBA financial constraints, and its test results are consistent with the SRD method. The results show that for companies with financial constraints, TFP growth after deregulation is significantly greater. This is because more financing channels allow companies subject to financial constraints to invest in productive projects that cannot be used during tightening.
2. Background introduction
In 1994, the Interstate Banking and Branch Efficiency Act (IBBEA) was promulgated, announcing the relaxation of the supervision of interstate banks within its borders. After the deregulation of interstate bank branches, the number of state bank branches and out-of-state ownership increased. This supports the premise that in deregulated states, IBBEA allows companies to increase their access to bank financing.
According to Rice and Strahan (2010), this paper created a variable called the IBBEA deregulation index, which increases with the degree of deregulation of interstate branches within a state.
3. Data sources and models
Data source: The main data used in this study comes from LRD (Large Microdatabase), which contains detailed data on small and large manufacturing companies in the United States in the long-term series from 1976 to 2005, which facilitates the calculation of company-level productivity and performance indicators . The database is maintained by the Economic Research Center of the U.S. Census Bureau. In order to verify the vertical links between companies in the census data, this article also uses two alternate data sources maintained by the U.S. Census Bureau, namely the Standard Statistical Agency List (SSEL) and the Vertical Business Database (LBD).
Model establishment:
TFP (Company Total Factor Productivity) measurement: By estimating the log-linear Cobb-Douglas production function for each industry and year, we can obtain the TFP measurement at the infrastructure level.
The industry is the three-digit NAICS code defined at this level. Individual factory i, industry j, year t,
Y: Output-factory sales; L: Labor input; K: Values for capital stock; M: Material input.
Note: In addition to the company-specific and industry-wide controls mentioned above, We also use separate variables for "sharp" regression discontinuity and "quasi" regression discontinuity analysis. We also created a standardized employment variable, which is defined as the company’s total employment in the most recent year of deregulation (used to create SBA-eligible variables) divided by the NAICS industry’s three-digit SBA employment threshold. And we define the standardized employment logarithm as the natural logarithm of standardized employment. Finally, in the RD analysis, the change in the average TFP is also used, which is defined as the difference between the average TFP in the three years immediately after the deregulation year and the average TFP in the three years immediately before the deregulation year.
The regression model is as follows:
Yit dependent variables, such as the company’s TFP; the logarithm of Xit assets, where assets = the one-year lag value of total capital stocks; Deregulation_index, is the main variable to measure the control of intercontinental banks; where Before(4,1) is a dummy variable, and 1 represents In the four years before deregulation, 0 was reversed.
4. Validation analysis
1) The impact of financial constraints and increased financing opportunities on productivity: regression discontinuity analysis (RD)
increases the channels for obtaining funds can increase total factor productivity. We assume that after the deregulation of interstate banks, the increase in total factor productivity is mainly driven by companies subject to financial constraints. Due to lack of sufficient funds, these companies cannot carry out other productive projects, so they cannot grow or improve performance. However, since the deregulation of interstate bank branches leads to an exogenous increase in financing availability, companies subject to financial constraints will benefit the most after deregulation, as this will enable them to undertake other projects, thereby increasing productivity.
We use the external discontinuities in the business qualifications that can apply for Small Business Administration (SBA) financing to distinguish between financially restricted and unrestricted businesses. Companies that are eligible for SBA support can get more sources of financing, so they have fewer financial constraints than companies that are not eligible for SBA support. The important deterministic assumption here is that in addition to being eligible for SBA financing, companies above and below the SBA financing threshold are similar in most other respects. This allows us to compare companies that are unlikely to have significant differences in characteristics except for additional sources of financing (SBA financing). As a result, compared to SBA-qualified companies (that is, immediately below the SBA threshold), SBA-qualified companies (that is, immediately above the SBA threshold) have greater financial constraints. Therefore, if financial constraints do promote performance, we will find that if companies that do not originally have SBA qualifications increase financing opportunities, productivity will be greatly improved.
2) Accurate regression discontinuity analysis, SRD (sharp regression discontinuity) method:
Construct the change of the average TFP before or after deregulation.
Where ei is an error term and Xs is the standardized employment point. These regressions are weighted by the Epanechnikov kernel function and are estimated for each standardized employment point. The intercept term of this regression (ie, term a in equation (4)) is plotted in the figure below. 
The figure above shows the change in the SBA financial support threshold of the 3-year average TFP after the deregulation of IBBEA
. The figure above shows that after the deregulation of interstate bank branches, the change in the average TFP will "jump up", just above the SBA qualification threshold (in The x-axis in the figure above is 1). In other words, compared with companies with more financing options, after the deregulation of bank branches, companies with relatively few alternative financing options have a greater growth in TFP.
3) Quasi regression discontinuity analysis (Quasi regression discontinuity):
This analysis uses SBA discontinuity to determine the impact of deregulation changes on the treatment outcome (TFP). The method here will combine the dummy variables of the SBA threshold and the IBBEA deregulation index.
The sample is companies with 10% employment around the SBA threshold. The model considers the interaction terms between the IBBE deregulation index and the SBA authorization. In addition, it also includes the interaction terms between the standard employment logarithm and the IBEA deregulation index to control the sensitivity of continued employment in the TFP.
The following table is: TFP's changes in the IBBEA deregulation index, supported by the company's SBA financial authorization.
The results of column (1) in the above table show that companies that are not authorized by the SBA have a greater increase in their productivity rate than those authorized by the company. Columns (2) (3) (4) (5) all get the same result: greater financial constraints lead to increased financing availability caused by deregulation of interstate bank branches, and there is a positive correlation between the company's total factor productivity . And the interaction term between the IBBEA deregulation index and Log standardized employment is negative. In states that loosen the control of interstate bank branches to a greater extent, the total factor productivity of smaller companies will be greater. That is, smaller companies that are usually financially constrained will benefit more from more financing channels.
The above figure is aimed at the dynamics of the company's TFP in the sample of 10& near the SBA qualification threshold. We use decentralized TFP to measure the company's fixed effect: subtract the company's average TFP for all years from the total TFP. The company is divided into two categories: SBA authorized and non-authorized. In the figure above, year 0 is the last year before the deregulation of state banks.
The conclusion shows that companies not authorized by the SBA have increased after deregulation, which is similar to the previous conclusions.
in conclusion
This article uses a large number of samples of manufacturing companies obtained from the U.S. Census Bureau's Longitudinal Research Database (LRD) to analyze how the increase in financing channels affects corporate productivity, and uses the deregulation of interstate bank branches in the 1990s to transfer foreign financing. Deregulation factors are linked to total factor productivity at the enterprise level. The results of the study show that after states allow out-of-state banks to establish local bank branches, the total factor productivity of enterprises has increased.
We have found that companies that are close to but do not meet the U.S. Small Business Administration’s financial support eligibility criteria (and therefore are subject to more financial constraints) have all been deregulated after the deregulation of bank branches and in states where the degree of deregulation of state branches is greater. Factor productivity growth is higher than that of companies that meet the eligibility criteria (with less financial constraints). After the deregulation of bank branches, companies subject to financial constraints, such as smaller companies, have a greater increase in TFP than companies in industries subject to greater financial constraints. The findings of this article support this view, that more financing opportunities may increase financially constrained companies to obtain other production project opportunities and investment opportunities that may not be able to afford. Finally, it is also emphasized that financing channels are not only important for entrepreneurial activities, but also for the success of existing entrepreneurs and small companies. 
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