With the restrictions of equity financing of Chinese listed companies, debt dimensions are increasing, and the issue of corporate financial structure and financing constraints influence on capital misallocation has become an important practical problem which Chinese listed companies face. This paper is concerned with a model about capital misallocation and its influencing factors of integrated financing, capital operation, and investment performance. We take 7096 observations of 646 Chinese listed companies during fiscal years 2003 to 2014 for A-shares on the Shanghai and Shenzhen stock exchange, for instance, to empirically test the microscopic influencing factors of capital misallocation under different external financing dependence. The study illustrates the following: (1) in descriptive statistics of different industries capital misallocation, more than half of firms experience the circumstance of capital misallocation; (2) although Chinese listed companies are faced with financing constraints, capital market inefficiency, and other issues, most companies still depend on external financing; (3) the main factors that affect capital misallocation of the listed companies are financial liquidity and financial pledgeability; (4) the firms with high innovation abilities generally have stronger profitability, superior financial liquidity, and better financial pledgeability, thus reducing corporate capital misallocation; (5) the Chinese listed companies with large-scale assets and strong profitability easily obtain bank loans and equity financing, while violating the principle of assets matching.
Chinese capital market has assumed proportions of a minor industry, but there are still structural defects because of economic characteristics existing in the transition, for example, lack of effectiveness in the stock market, the deformed development of corporation bonds market, excessive policy intervention, and credit discrimination in bank loans. As a result, Chinese listed companies often face financial constraints, and the microcosmic factors of capital misallocation are much complicated [
In that context, conducting on the degree of capital misallocation of Chinese listed companies, this paper develops a model for measuring capital misallocation by referencing the study of Hsieh and Klenow, Sordi et al., and Uras [
Capital market imperfections will lead to capital flow barriers, capital congestion, and scarcity between regions or industries, the allocation of capital in the absence of equalizing marginal revenue products of capital across plants, and thus capital misallocation [
In recent years, the studies of phenomenon and factors about capital misallocation in Chinese listed companies have made a breakthrough. Song et al. and Li et al. suggested that state-owned enterprises have easier access to capital than non-state-owned enterprises, while the investment performance is opposite. The fact that the resource is controlled by government in China leads to capital misallocation [
There are still some disputes about the microcosmic factors influencing the capital misallocation in listed companies from China. The first is a problem about the impact of financial structure on capital misallocation. Domestic scholars generally believe that the differences about corporate financing constraints, capital adjustment cost and unreasonable debt term structure, corporate governance mismatches with investment, and financing maturity are the main reasons [
The capital misallocation of listed companies is a systematic performance of financing, capital operation, and investment process. How to integrate financing and how to measure the dimension of capital operation are discussed. Available literatures focus on the effects of financial structure on capital misallocation and do empirical research of capital allocation efficiency under financing constraints and external financial dependence, while the studies about microcosmic affection factors of capital misallocation are very rare [
The model includes the following variables:
For firms as rational people, investment decisions are at the trade-off between benefits and costs. The purpose of company production is to maximize profit; the profit of firm
Suppose that the capital required for producing of firm
Every firm’s constraint condition has the same cumulative distribution function
Two types of firms exist in each industry, denoted as investors and lenders. In equilibrium, it is impossible for any firm that is both a lender and an investor at the same time. Lender firms possess enough internal assets (capital and industrial consumables) to reach the optimum scale of capital investment; however, investor firms demand external finance to cover the optimum scale of investment.
A competitive equilibrium consists in the financial market where lender firms are indifferent between lending and selling the capital. This no-arbitrage condition leads to the equalization of the rental rate and the purchase prices of capital good, namely, to
In the equilibrium state (there is no arbitrage,
The function of profit for a financial constraints firm is
The objective function intimates that capital distortion and financing constraints affect the marginal cost of capital. The higher
Not only do internal financing, financial structure, and financial pledgeability influence the cost of financing, but also they affect capital-labor ratio.
According to (
Hence, profit maximizing capital-labor ratio for a nonfinancial constraints firm,
According to (
Using (
The data covers fiscal years 2003 to 2014 for A-shares of Chinese nonfinancial firms that were listed on the Shanghai and Shenzhen stock exchange. And we screen and process the data according to the following standards: (
The number of samples in different industries.
Industry | |||||||
---|---|---|---|---|---|---|---|
Circulation | Petrochemical | Electron | Medicine and medical | Mechanical and electronic | Mining | Comprehensive | |
Observations | 788 | 1203 | 856 | 985 | 1321 | 627 | 1316 |
Table
Statistical description of capital misallocation (
Industry | |||||||
---|---|---|---|---|---|---|---|
Circulation | Petrochemical | Electron | Medicine and medical | Mechanical and electronic | Mining | Comprehensive | |
Mean | 1.19 | 0.9 | 0.61 | 0.64 | 0.58 | 0.58 | 0.44 |
Median | 0.4 | 0.48 | 0.36 | 0.24 | 0.2 | 0.32 | 0.01 |
Standard deviation | 2.9 | 1.91 | 1.84 | 1.93 | 2.07 | 2.23 | 2.12 |
Minimum | −6.57 | −6.43 | −6.48 | −5.94 | −6.85 | −6.93 | −6.47 |
Maximum | 7.82 | 5.82 | 5.19 | 6.94 | 6.77 | 6.68 | 7.31 |
Quartile 50% | 0.4 | 0.48 | 0.36 | 0.24 | 0.2 | 0.32 | 0.01 |
Quartile 75% | 2.33 | 1.84 | 1.52 | 1.48 | 1.43 | 1.7 | 1.36 |
Observations | 788 | 1203 | 856 | 985 | 1321 | 627 | 1316 |
Note: to calculate capital misallocation,
Equation (
Table
Sensitivity analysis table of capital misallocation (
Cost of capital | Financial pledgeability | |||||
---|---|---|---|---|---|---|
12 | 10 | 8 | 6 | 4 | 2 | |
|
|
|
|
|
|
|
0.1 | −0.55 | −0.45 | −0.35 | −0.25 | −0.15 | −0.05 |
0.11 | −0.09 | −0.079 | −0.065 | −0.048 | −0.023 | 0.019 |
0.12 | −0.107 | −0.094 | −0.078 | −0.057 | −0.028 | 0.022 |
0.13 | −0.125 | −0.11 | −0.091 | −0.067 | −0.033 | 0.026 |
0.14 | −0.146 | −0.128 | −0.106 | −0.078 | −0.038 | 0.03 |
0.15 | −0.167 | −0.147 | −0.121 | −0.089 | −0.043 | 0.035 |
0.16 | −0.178 | −0.156 | −0.13 | −0.095 | −0.046 | 0.037 |
0.17 | −0.202 | −0.177 | −0.147 | −0.108 | −0.053 | 0.042 |
Note: in the calculation, assume that the higher the cost of capital is, accordingly the lower the liquidity ratio is. For example, when cost of capital changes from 0.12 to 0.11, liquidity ratios change from 0.5 to 0.55. Numerous domestic and international studies assume the cost of capital is 10%, so we assume it starts from 0.1.
When cost of capital is 0.1, financial pledgeability is poor, and capital misallocation usually does not exist, while it rarely happens. When financial pledgeability is 2, all values are positive except the case that capital rental rate is 0.1 (for details, see Table
Tendency chart of capital misallocation (
Figure
First of all, in order to control the problem of multicollinearity between variables, this paper does descriptive statistics for each variable and investigates the correlations of variables. After that, we employ panel data models to empirically analyze the impact of microcosmic factors, financial liquidity, financial pledgeability, and other controlled variables on capital misallocation and test whether different external finance dependence affects capital misallocation.
According to available literatures and theoretical analysis, all the variables used in this paper for studying the impact of financial pledgeability on capital misallocation can be found in Table
Variables’ definitions.
Variables | Name | Description | Calculation |
---|---|---|---|
Dependent variable | KLDistort
|
Capital misallocation ratio |
|
|
|||
Independent variables | liquidity
|
Financial liquidity | (Current assets − current liabilities)/(current liabilities)
|
pledgeability
|
Financial pledgeability | See details in ( | |
|
|||
Control variables | Ln( |
Firm size | LN (total assets (ten thousand yuan)) |
Profitabilityit | Profitability | (Total operating incomes – total operating costs)/total assets | |
Debt structure | Debt structure | Total long-term debts/total debts | |
IFA | Investment in fixed assets |
| |
EFD | External finance dependence | See details in ( | |
Industry | Industry | Dummy, if firm belongs to this industry, industry = 1; otherwise, industry = 0 | |
Year | Year | Dummy, if data belongs to this year, year = 1; otherwise, year = 0 | |
IP | Innovation performance | See details in ( |
It is popular in many overseas researches that measure firm’s financial pledgeability using Standard & Poor’s Credit Rating data (according to the Standard & Poor’s Compustat Database, firm credit ratings in the sample range from
A classification of firms on the basis of their solvency and liquidity conditions.
A firm’s liquidity at time
This paper measures it in each period t as the surplus of financial inflows
The solvency of a firm may be measured by its net worth (
In order to explore the dynamics of
The vertical dashed line drawn at
Table
Distribution of companies at various
Time | Description | Region 1 | Region 2 | Region 3 | Region 4 | Region 5 | Region 6 |
---|---|---|---|---|---|---|---|
|
Number | 1245 | 2899 | 1863 | 1197 | 962 | 14 |
Proportion (%) | 15.22 | 35.44 | 22.78 | 14.63 | 11.76 | 0.17 | |
|
|||||||
|
Number | 1107 | 2632 | 1976 | 1237 | 1203 | 25 |
Proportion (%) | 13.53 | 32.18 | 24.16 | 15.12 | 14.71 | 0.31 | |
|
|||||||
|
Number | 936 | 2249 | 2138 | 1344 | 1441 | 72 |
Proportion (%) | 11.44 | 27.49 | 26.14 | 16.43 | 17.62 | 0.88 |
By comparing the numbers at
Financing constraints are relative to firm’s external finance dependence. Rajan and Zingales considered firm’s external finance dependence as (operating cash flow − capital expenditure)/capital expenditure [
Financial structure is relative to research and development ability (generally, poor R&D capability is associated with labor intensive production). The characteristics of research and development investments are long cycle, high extent of asymmetric information, and great uncertainty. Research and development investments which mainly come from firm’s own capital holdings are more sensitive to financing constraints than fixed investments. With binding financial constraints, the rational firms allocate more capital to production, which leads to the lack of research and development investments as well as the unbalanced investment.
To measure the innovation performance, we draw lessons from Vassalou and Apedjinou and assume that the production function is a Cobb-Douglas production function [
Assume that there are no intermediate goods in a competitive labor market, and then the gross profit margin (GPM) is
If all
In order to overcome the difference of variables’ units, we can obtain
In order to test the microcosmic factors that influence the allocation of resources under different external financing dependence, this paper constructs
Table
Descriptive statistics of main variables.
Variables | EFD = 3 (high level of external finance dependence) | EFD = 2 (intermediate level of external finance dependence) | EFD = 1 (low level of external finance dependence) | |||
---|---|---|---|---|---|---|
Mean | Observations | Mean | Observations | Mean | Observations | |
Financial liquidity | 0.322 | 3542 | 0.547 | 1293 | 0.862 | 2256 |
Financial pledgeability | 5.63 | 3543 | 6.38 | 1294 | 8.74 | 2255 |
Firm size | 11.75 | 3543 | 12.26 | 1294 | 13.72 | 2255 |
Profitability | 5.37 | 3543 | 9.21 | 1294 | 12.33 | 2257 |
Debt structure | 0.672 | 3544 | 0.534 | 1295 | 0.39 | 2257 |
Investment in fixed assets | 6.34 | 3544 | 7.66 | 1295 | 5.46 | 2257 |
Innovation performance | 0.121 | 3537 | 0.377 | 1286 | 0.614 | 2245 |
Table
Pearson correlation coefficient between main variables.
Variables | Financial liquidity | Financial pledgeability | Firm size | Profitability | Debt structure | Investment in fixed assets | Innovation performance |
---|---|---|---|---|---|---|---|
Financial liquidity | 1 | ||||||
|
|||||||
Financial pledgeability | 0.922 |
1 | |||||
−4.265 | |||||||
|
|||||||
Firm size | 0.434 |
0.563 |
1 | ||||
−2.015 | −2.236 | ||||||
|
|||||||
Profitability | 0.722 |
0.566 |
0.618 |
1 | |||
−3.565 | −4.912 | −2.025 | |||||
|
|||||||
Debt structure | 0.119 |
−0.721 |
0.794 |
0.093 |
1 | ||
−1.945 | (−3.653) | −4.135 | −2.987 | ||||
|
|||||||
Investment in fixed assets | −0.279 |
0.493 |
0.831 |
0.435 | 0.447 |
1 | |
(−3.339) | −3.756 | −5.343 | −1.484 | −3.351 | |||
|
|||||||
Innovation performance | 0.637 |
0.344 |
0.239 |
0.776 |
−0.311 |
−0.213 |
1 |
−4.895 | −2.248 | −3.776 | −5.322 | (−2.214) | (−4.674) |
Note: (1)
On the basis of
Regression results of
Variables | |
|||||
---|---|---|---|---|---|---|
liquidity
|
0.003 |
0.0017 |
||||
|
||||||
liquidity
|
−0.0016 |
−0.0011 |
||||
|
||||||
liquidity
|
−0.3973 |
−0.3318 |
||||
|
||||||
|
−0.346 |
−0.388 |
−0.712 |
|||
|
||||||
|
−0.0613 |
−0.173 |
−0.337 |
|||
|
||||||
Debt structure | 0.526 |
0.673 |
0.706 |
|||
|
||||||
IFA | 0.1176 |
0.2396 |
0.489 | |||
|
||||||
IP | −1.004 |
−0.952 |
−0.894 |
|||
|
||||||
Industry | Yes | Yes | Yes | Yes | Yes | Yes |
|
||||||
Year | Yes | Yes | Yes | Yes | Yes | Yes |
|
||||||
|
0.436 | 0.497 | 0.46 | 0.532 | 0.482 | 0.519 |
Rho | 0.568 | 0.608 | 0.577 | 0.635 | 0.559 | 0.6384 |
chi2 | 275.77 | 316.39 | 189.55 | 289.33 | 179.84 | 211.02 |
Hausman | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
Note: (1)
On the basis of
Regression results of
Variables | |
|||||
---|---|---|---|---|---|---|
pledgeability
|
−0.037 |
−0.028 |
||||
|
||||||
pledgeability
|
−0.059 |
−0.076 |
||||
|
||||||
pledgeability
|
−0.4673 |
−0.3726 |
||||
|
||||||
|
−0.782 |
−0.815 |
−1.123 |
|||
|
||||||
|
−0.345 |
−0.399 |
−0.476 |
|||
|
||||||
Debt structure | 0.717 |
0.730 |
0.365 |
|||
|
||||||
IFA | 0.396 |
0.479 |
−0.563 |
|||
|
||||||
IP | −0.779 |
−0.812 |
−1.041 |
|||
|
||||||
Industry | Yes | Yes | Yes | Yes | Yes | Yes |
|
||||||
Year | Yes | Yes | Yes | Yes | Yes | Yes |
|
||||||
|
0.516 | 0.612 | 0.590 | 0.627 | 0.482 | 0.519 |
Rho | 0.627 | 0.665 | 0.682 | 0.703 | 0.559 | 0.6384 |
chi2 | 178.66 | 226.17 | 351.06 | 211.84 | 179.84 | 211.02 |
Hausman | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
Note: (1)
To test the reliability of regression results, this paper has carried out the following robustness test: Select the data between the sample data of 20%–80% to do regression analysis. To clarify external financing dependence, exclude firms whose debt is invariant or small changes. Divide the original sample data into two parts, 2003–2007 and 2008–2014. Regression results hint that the basic conclusions are equal. From Tables
Robustness test of panel data
Variables | |
|||||
---|---|---|---|---|---|---|
liquidity
|
0.109 |
0.0923 |
||||
|
||||||
liquidity
|
−0.0352 |
−0.0209 |
||||
|
||||||
liquidity
|
−1.039 |
−0.867 |
||||
|
||||||
|
−0.557 |
−0.519 |
−0.673 |
|||
|
||||||
|
−0.225 |
−0.316 |
−0.619 |
|||
|
||||||
Debt structure | 0.432 |
0.597 |
0.576 |
|||
|
||||||
IFA | 0.225 |
0.791 |
1.023 | |||
|
||||||
IP | −0.877 |
−1.21 |
−1.049 |
|||
|
||||||
Industry | Yes | Yes | Yes | Yes | Yes | Yes |
|
||||||
Year | Yes | Yes | Yes | Yes | Yes | Yes |
|
||||||
|
0.555 |
0.617 |
0.567 |
0.655 |
0.598 |
0.703 |
Note: (1)
Robustness test of panel data
Variables | |
|||||
---|---|---|---|---|---|---|
pledgeability
|
−0.041 |
−0.037 |
||||
|
||||||
pledgeability
|
−0.212 |
−0.103 |
||||
|
||||||
pledgeability
|
−0.597 |
−0.556 |
||||
|
||||||
|
−0.563 |
−0.703 |
−0.903 |
|||
|
||||||
|
−0.419 |
−0.417 |
−0.391 |
|||
|
||||||
Debt structure | 0.635 |
0.663 |
0.441 |
|||
|
||||||
IFA | 0.773 |
0.559 |
0.947 | |||
|
||||||
IP | −0.904 |
−0.724 |
−0.891 |
|||
|
||||||
Industry | Yes | Yes | Yes | Yes | Yes | Yes |
|
||||||
Year | Yes | Yes | Yes | Yes | Yes | Yes |
|
||||||
|
0.571 |
0.639 |
0.607 |
0.649 |
0.530 |
0.677 |
Note: (1)
This paper consults researches of Antunes et al. and Uras to establish a model about capital misallocation and their influencing factors of integrated financing, capital operation, and investment performance and empirically test the microscopic influencing factors of capital misallocation under different external financing dependence and the critical influencing factors of capital misallocation taking Chinese listed companies, for instance, [ In descriptive statistics of different industries capital misallocation ( Although Chinese listed companies are faced with financing constraints, capital market inefficiency, and other issues, most companies still depend on external financing. In 7096 observations, the proportion for the companies under high level of external financing dependence is about 49.8%; the proportion for the companies under intermediate level of external financing dependence is about 16.148%; the proportion for the companies under low level of external financing dependence is about 31.8%. The main factors that affect capital misallocation of the listed companies are financial liquidity and financial pledgeability. The empirical analysis explains that, under different external financing dependence, the regression coefficient is diversified, but the regression coefficient is significantly negative. Innovation ability of the labor intensive firms is usually poor, which easily causes capital misallocation. In empirical analysis, the firms with high innovation abilities generally have stronger profitability, superior financial liquidity, and better financial pledgeability, thus reducing corporate capital misallocation. A lot of domestic researches point out that the Chinese listed companies with large-scale assets and strong profitability easily obtain bank loans and equity financing, while violating the principle of assets matching.
All the authors of this paper declare that there are no competing interests in connection with the work submitted.
This work is funded by Zhejiang Social Sciences Planning Project (no. 14NDJC099YB), the National Statistical Scientific Research Projects (no. 2014078), the National Social Science Fund Projects in China (stage results) (no. 15FJY009), the National Natural Science Foundation of China (nos. 71171176 and 71471161), and the Key Program of the National Natural Science Foundation of China (no. 71631005).