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Financial Constraints: Do They Matter to Allocate R&D Subsidies?

Filipe Silva and Carlos Carreira ORCID logo

Abstract

We examine whether subsidies are allocated to financially constrained firms and if they effectively alleviate these constraints. We claim that in addition to the usual “public good” arguments behind the allocation of subsidies, the extent to which firms are able to obtain external funding should not be overlooked. Overall, our results question both the allocation and the effectiveness of subsidies in alleviating financial constraints of firms willing to innovate. Additionally, the decision criteria for allocating public funds seem to be similar from those used by the private investors. These results have important implications on the design of future innovation policy.

JEL Classification: G32; H25; L53; O32; O38

Appendix

Figure 4: Kernel density estimates of propensity scores for treated and untreated companies.

Figure 4:

Kernel density estimates of propensity scores for treated and untreated companies.

Table 10:

Structure of dataset.

FUE and IEHCIS waves
PatternFrequencyPercentPatternFrequencyPercent
00000000173110.33001398656.31
10110111172610.26100110215.57
1011111102253.18010108015.26
0000000112052.900113585.06
0000011111922.711012193.09
1111011101852.611101922.71
0000100001742.461111422.01
0011011111692.39
1011111111572.22
Other431560.95
Total7079100Total7079100

  1. Note: Before exclusion of missing observations or unreasonable values (negative values and outliers).

Table 11:

Definition of variables.

VariablesDescription
(i) Generic information (FUE)
AgeComputed as the difference between the current year and the year of establishment of the firm plus one, in logs.
IndustryPortuguese industrial classification (CAE rev 2.1). Different industry codes are converted into dummy indicators.
LocationEuropean regional classification (NUT). Different region codes are converted into dummy indicators.
Public capitalPercentage of capital owned by the public sector.
Foreign capitalPercentage of capital owned by non-nationals.
(ii) Balance sheets variables (IEH)
SizeMeasured as log of the number of employees.
Capital (K)Total assets.
Investment (I)Measured as additions to plant, property and equipment- gross investment, scaled by total assets.
Cash- flow (CF)Computed as net income before taxes plus depreciation, scaled by total assets.
Cash stockMeasured as total cash holdings, scaled by total assets.
Sales GrowthMeasured as changes in total sales from previous period.
Debt and equity issuancesSum of debt and equity issuances, scaled by total assets. For the year 2001 equity issuances are reported as missing. The reason lies in legal changes that took place with the introduction of Euro (most firms adjusted their equity, not necessarily meaning issuing equity).
Non-cash net working capitalDifference between non-cash current assets and current liabilities, scaled by total assets.
Interest paymentsInterest payments of a firm, scaled by total assets. It can be argued to proxy for the credit rating of the firms.
LeverageMeasured as the ration of liabilities to the total value of a firm.
Returns on financial investmentsReturns on financial investments of firms, scaled by assets.
Intangible assetsComputed as intangible assets, scaled by total assets. In the absence of a better alternative, this variable is intended to proxy the knowledge stock, through R&D stock and the patent stock of firms (we do not have detailed information neither on patents, nor on highly disaggregated firm accounts);
ExportsFirm exports, scaled by assets.
Market shareThis variable is constructed as a firm’s sales over total sales of the corresponding firm’s industry – at maximum level of industrial classification disaggregation (5-digit).
(iii) Innovation variables (CIS)
Public Finance (SUB)Binary variable for firms that received public funding and those that did not. It includes financial support to innovation activities provided by the Portuguese local or central administration, as well as by the EU (through the “Framework Programs”). This support may take the form of subsidies strictu sensu, credit guarantees and tax benefits (from the CIS survey we are not able to distinguish them). For the sake of this paper and simplicity we will refer it as “subsidies”.
Share of subsidised firms-industryComputed as the ratio of number of subsidised firms in each industry (2-digit, CAE rev 2.1) to the total number of subsidised firms.
Share of subsidised firms-regionComputed as the ratio of number of subsidised firms in each region (NUT2). Both of these variables serve as instruments for subsidies. The rationale is that, in the absence of information on public policy budgets, the share of subsidies by industry and region will reflect policy goals for certain industries or regions (see Schneider and Veugelers 2010).
CooperationBinary variable that indicates if a firms cooperated with other firms or institutions for the purpose of innovation activities.
PatentBinary indicator of whether a firm registered any patent during the wave period.
R&D workersPercentage of employers in the firm that work on R&D.

  1. Note: All continuous variables of interest were winsorised at the 1 % level (0.5 % each tail) in order to avoid problems with outliers in the estimation procedures. Deflators used include the Industrial Production Price Index and Labour Cost Index, both drawn from INE, and the GDP deflator, drawn from the Portuguese Central Bank (BdP). Nevertheless, no deflators were used when a variable was constructed as a ratio of two nominal values (normalised). In such cases we assume that the price growth rates are homogeneous.

Table 12:

Descriptive statistics and characteristics of subsidy recipient vs. non-recipient firms.

Means and Standard DeviationsNonparametric tests
SUB=0SUB=1K-S (D)F-P (U)
(1)(2)(3)(4)
Size4.665 (1.168)5.304 (1.268)0.227 [0.000]‒10.254 [0.000]
Age3.018 (0.716)3.105 (0.746)0.085 [0.007]‒2.530 [0.011]
Foreign capital (%)0.679 (0.826)0.764 (0.751)0.127 [0.000]‒1.870 [0.062]
R&D employees (%)0.133 (0.526)0.611 (1.162)0.192 [0.000]‒2.793 [0.005]
Cooperation0.105 (0.307)0.524 (0.500)0.418 [0.000]‒6.036 [0.000]
Exports0.266 (0.512)0.361 (0.516)0.204 [0.000]‒6.026 [0.000]
Share of subsidies by industry (%)0.038 (0.067)0.166 (0.161)0.546 [0.000]‒27.287 [0.000]
Share of subsidies by region (%)38.429 (44.150)38.786 (38.818)0.108 [0.000]0.001 [0.999]
Market share0.120 (0.179)0.125 (0.169)0.063 [0.092]‒1.994 [0.046]
Patents0.221 (0.558)0.481 (0.670)0.233 [0.000]‒3.610 [0.000]
Intangibles0.034 (0.075)0.055 (0.084)0.257 [0.000]‒11.715 [0.000]
Observations3,142424

  1. Notes: Comparison of main explanatory variables between recipient and non-recipient firms (columns 1 and 2). Mean values and standard deviations in parentheses. The values of Kolmogorov-Smirnov (D) and Fligner–Policello (U) statistics are reported in columns (3) and (4), respectively. The associated p-values are in brackets. Rejection of the null means that the two distributions are stochastically different.

Table 13:

Variables measuring financial constraints.

MeasureNatureComments
Self-assessmentOrdinalSubjective
Firm-specific
Varies across waves
HH índexContinuousAssumes ICFS holds
Firm-specific
Time invariant
MS índexOrdinalAssumes same level of constraints across industries
Firm-specific
Varies across years
Weighted MS indexOrdinal (assumed continuous)Firm-specific
Varies across years
Table 14:

Distribution of financial constraints measures.

FrequencyPercentage
(a) Self-assessment (ordinal Measure)
0 (unconstrained)1,98255.58
144612.51
255115.45
3 (high level)58716.46
Total3,566100
(b) MS index (ordinal Measure)
1 (unconstrained)67820.51
244413.43
32788.41
43079.29
52958.92
62868.65
72617.89
834610.47
92668.05
10 (high level)1454.39
Total3,306100
(c) Weighted MS index (continuous measure)
Min0.046
25 %0.318
50 %0.418
75 %0.568
Max1.875
Mean0.457
Std. Dev.0.223
Observations3303
(d) HH index (continuous measure)
Min‒6.666
25 %‒0.001
50 %0.000
75 %0.001
Max3.415
Mean‒0.005
Std. Dev.0.207
HH>01692(54.4 %)
Observations3110
Table 15:

Testing hypothesis 1–subsidy allocation (endogenous financial constraints).

VariablesSelf-assessmentMS indexWeighted MS indexHH index
(1)(2)(3)(4)
FC0.021 (0.251)0.082 (0.052)0.388 (1.157)3.258 (3.862)
Size0.062* (0.037)0.101*** (0.039)0.081** (0.036)0.078* (0.041)
Age0.056 (0.055)0.037 (0.051)0.050 (0.063)0.030 (0.046)
Foreign capital0.178** (0.077)0.257*** (0.084)0.224 (0.150)0.137 (0.106)
R&D employees‒0.008 (0.055)‒0.015 (0.055)‒0.032 (0.056)‒0.054 (0.046)
Cooperation0.167*** (0.049)0.163*** (0.048)0.176*** (0.052)0.136 (0.100)
Exports0.987*** (0.091)0.964*** (0.095)0.970*** (0.105)0.814* (0.476)
Share sub. by industry6.795*** (0.559)6.887*** (0.579)6.994*** (0.590)5.904* (3.291)
Share sub. by region‒0.007*** (0.002)‒0.008*** (0.002)‒0.008*** (0.002)‒0.007* (0.004)
Market share‒0.926*** (0.299)‒0.853*** (0.305)‒0.969*** (0.312)‒0.804* (0.444)
Patents0.075 (0.058)0.084 (0.059)0.095 (0.065)0.059 (0.072)
Intangibles0.133 (0.414)0.248 (0.415)0.219 (0.490)0.255 (0.388)
ρ0.016 (0.353)‒0.225 (0.158)‒0.115(0.255)‒0.621 (1.011)
Observations3,1803,0593,0562,956
Log-likelihood‒2108‒3599‒224.9‒89.80
(Pseudo) R0.520.51..
AIC4314.657302.55517.86287.59

  1. Notes: Dependent variable: dichotomous subsidy variable. Regression of simultaneous equations model (2), assuming α2=0 and normalised variance of the errors, and using different measures of financial constraints: self-assed (column 1), MS index (column 2), industry weighted MS index (column 3) and HH index (column 4). The parameter ρ ≠0 can be used to test endogeneity hypothesis. The instruments used are those corresponding to variables in the vector X2. Robust standard errors in parentheses. ***, **, and * denote statistical significance at the 0.01, 0.05, and 0.10 levels, respectively.

Table 16:

Testing hypothesis 1–subsidy allocation (innovation investment opportunities control).

VariablesMS indexWeighted MS index
(1)(2)
FCt0.05 (0.06)1.71 (1.54)
FCt1‒0.09 (0.06)‒2.23 (1.73)
Size0.11 (0.11)0.09 (0.12)
Age0.11 (0.12)0.09 (0.12)
Foreign capital‒0.20 (0.14)‒0.16 (0.14)
R&D employees‒0.08 (0.10)‒0.09 (0.11)
Cooperation1.23*** (0.23)1.25*** (0.24)
Exports0.33* (0.19)0.32* (0.19)
Share sub. by industry5.59*** (0.87)5.53*** (0.87)
Share sub. by region‒0.02*** (0.00)‒0.02*** (0.00)
Market share‒0.84 (0.56)‒0.81 (0.55)
Patents0.02 (0.25)0.01 (0.25)
Intangibles0.99 (1.24)0.74 (1.26)
Innovation investment opportunities (R&D investmentw+1)0.07 (0.06)0.07 (0.06)
Constant‒1.93** (0.98)‒1.75* (1.04)
Observations323323
Log-likelihood‒48.68‒48.68
(Pseudo) R0.440.44

  1. Notes: Dependent variable: dichotomous (lagged) subsidy variable. Main explanatory variable MS index (column 1) and industry weighted MS index (column 2). Robust standard errors in parentheses. ***, **, and * denote statistical significance at the 0.01, 0.05, and 0.10 levels, respectively.

Table 17:

Testing hypothesis 2–subsidy effectiveness (endogenous subsidies).

VariablesSelf-assessmentMS indexWeighted MS indexHH index
(1)(2)(3)(4)
SUB0.476*** (0.175)0.450*** (0.135)0.004 (0.028)0.015* (0.009)
Size‒0.062** (0.024)‒0.128*** (0.023)‒0.013** (0.006)‒0.003 (0.002)
Age0.048 (0.039)0.045 (0.034)‒0.026*** (0.007)‒0.008 (0.008)
Public capital‒0.002 (0.001)‒0.002 (0.001)0.001*** (0.000)0.000 (0.000)
Foreign capital‒0.003*** (0.001)‒0.005*** (0.001)‒0.000 (0.000)0.000 (0.000)
Sales growth‒0.115 (0.102)0.392*** (0.093)0.021 (0.018)‒0.014 (0.010)
Cash stocks‒1.005*** (0.283)
Cash-flow‒0.639** (0.317)
Leverage0.226** (0.107)‒0.096 (0.066)
Issuances‒0.356** (0.169)‒0.720*** (0.139)‒0.061** (0.027)0.035 (0.044)
Δ interest paid12.542*** (3.717)‒4.359 (3.548)‒0.513 (0.786)1.431 (0.907)
Returns finan. invest.‒10.751 (11.981)‒15.537* (8.386)‒3.885*** (1.142)0.222 (0.342)
Exports‒0.056 (0.060)‒0.307*** (0.051)‒0.118*** (0.008)‒0.003 (0.003)
Market share‒0.179* (0.101)‒0.604*** (0.094)‒0.096*** (0.021)0.004 (0.007)
‒0.227* (0.119)‒0.251*** (0.090)‒0.041 (0.076)‒0.031* (0.016)
Observations3,1803,0593,0562,956
Log-likelihood‒2105‒3596‒273.7‒98.13
(Pseudo) R0.520.51
AIC4308.267295.69599.34

  1. Notes: Dependent variable: self-assed financial constraint (column 1), MS index (column 2), industry weighted MS index (column 3) and; HH index (column 4). Regression of simultaneous equations model (2) in columns 1–2 and corresponding treatment effects model in columns 3–4, assuming α1=0 and normalised variance of the errors. The parameter ζ ≠ 0 can be used to test endogeneity hypothesis. The instruments used are those corresponding to variables in the vector X1. We deliberately omit variables that are highly correlated with the measure of constraints by construction (columns 2–4). Robust standard errors in parentheses. ***, **, and * denote statistical significance at the 0.01, 0.05, and 0.10 levels, respectively.

Table 18:

Testing hypothesis 2 – the effects of subsidies given in 2000 upon financial constraints (differences-in-differences).

OutcomeMS indexWeighted MS index
Effects(1)(2)
Differences in treated‒0.79*** (0.17)‒0.14*** (0.01)
[‒1.12; ‒0.45][‒0.16; ‒0.12]
Differences Post-20001.46*** (0.02)0.11*** (0.00)
[1.42; 1.51][0.10; 0.11]
Differences-in-Differences0.36 (0.26)0.07*** (0.02)
[‒0.14; 0.87][0.02; 0.11]
Constant4.55*** (0.02)0.50*** (0.00)
[4.51; 4.59][0.50; 0.51]
No. firms10,84110,841
(of which treated)295295
Observations86,45586,437

  1. Notes: This table shows the results of differences-in-differences calculations for the impact of subsidies provided in the year 2000 upon financial constraints. Measures of financial constraints include the MS index (column 1) and industry weighted MS index (column 2), both time-variant. Treated firms are those that received subsidies in 2000. The table reports the differences-in-differences (row 5) as well as the differences in financial constraints between treated and non-treated firms (row 1) and the differences in constraints for all firms before and after 2000 (row 3). Coefficients are calculated using OLS. Robust standard errors in parentheses. ***, **, and * denote statistical significance at the 0.01, 0.05, and 0.10 levels, respectively.

Table 19:

Propensity score estimation.

VariablesSubsidiesFinancial Constraints
(1)(2)
Size‒0.12*** (0.02)0.23*** (0.03)
[‒0.16; ‒0.08][0.18; 0.28]
Age0.04 (0.03)0.07 (0.04)
[‒0.02; 0.10][‒0.02; 0.15]
R&D employees‒0.05 (0.04)
[‒0.12; 0.02]
Cooperation0.21*** (0.06)
[0.09; 0.33]
Exports‒0.08* (0.04)0.09 (0.06)
[‒0.16; 0.00][‒0.02; 0.20]
Share sub. by industry‒0.14 (0.24)
[‒0.61; 0.32]
Market share‒0.24** (0.12)0.17* (0.10)
[‒0.48; ‒0.01][‒0.03; 0.37]
Patents0.20*** (0.04)
[0.13; 0.27]
Intangibles0.39 (0.28)
[‒0.17; 0.94]
Public capital0.00 (0.00)
[‒0.00; 0.00]
Foreign capital‒0.00** (0.00)
[‒0.00; ‒0.00]
Sales growth‒0.01 (0.12)
[‒0.25 ‒ 0.23]
Cash stocks‒0.98*** (0.32)
[‒1.61; ‒0.34]
Cash-flow1.26*** (0.36)
[0.56; 1.96]
Leverage‒0.46*** (0.14)
[‒0.73; ‒0.19]
Issuances‒0.70*** (0.19)
[‒1.08; ‒0.32]
Returns finan. invest.‒5.75 (10.82)
[‒26.96; 15.46]
Constant0.29** (0.12)‒2.30*** (0.22)
[0.05; 0.52][‒2.74; ‒1.86]
Observations3,6593,382
Log-likelihood‒2,469.30‒1,236.70
Pseudo- R0.020.06

  1. Notes: Propensity scores were estimated using a Probit model and matching was performed with replacement. Robust standard errors in parentheses. ***, **, and * denote statistical significance at the 0.01, 0.05, and 0.10 levels, respectively.

Table 20:

Testing the balancing property in propensity score matching.

Meant-test
VariablesTreatedControl%biastp>|t|
Size5.3795.2897.51.040.297
Age3.1373.1222.00.280.779
Public capital8.1847.4053.50.430.665
Foreign capital11.92812.209‒0.9‒0.130.895
Sales growth‒0.0485‒0.0531.60.230.821
Cash stocks0.0550.057‒2.4‒0.380.703
Cash-flow0.1040.105‒1.0‒0.150.884
Leverage0.6130.614‒0.2‒0.040.972
Issuances‒0.049‒0.047‒1.0‒0.140.892
Returns finan. invest.0.0010.0010.70.100.921
Exports0.3660.3562.00.270.786
Market share0.2450.2411.40.190.846

  1. Notes: Propensity scores were estimated using a Probit model and matching was performed with replacement. Additional tests of the balancing property by blocks of the propensity score distribution are available from the authors upon request.

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Published Online: 2017-8-18

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