Effectiveness of Subsidizing Energy Saving Technologies ... - CiteSeerX

Effectiveness of Subsidizing Energy Saving Technologies: Evidence from Dutch panel data

Rob F.T. Aalbers Erasmus University Rotterdam and OCFEB, Rotterdam H.L.F. de Groot Dept. of Spatial Economics, Vrije Universiteit, Amsterdam Herman R.J. Vollebergh Erasmus University Rotterdam and OCFEB, Rotterdam July 30, 2004

Abstract Using panel data on investment of firms in energy saving technology, we measure the impact of the payback period of the technology on the probability to adopt the technology in the absence of tax and subsidy incentives. We find that the decision rules to adopt energy saving technology differs substantially between different types of firms, i.e. for-profit/not- for-profit as the decision to adopt energy saving technologies by not- for-profit firms does not depend in a systematic way on the payback period of the technology. Moreover, firms using an explicit investment criterion are less likely to adopt energy saving technology compared to firms not using an explicit investment criterion. Based on our preferred model, on average 45.5% would also have bought the technology without the subsidy or tax credit. This percentage differs markedly between different types of firms. Of all not-for-profit firms using an explicit investment criterion only 34.6% would have bought the technology, whereas of all not-forprofit firms not using an explicit investment criterion 66.1% would have bought the technology. Finally, the increase in the return on investment that is achieved by subsidies and tax credits only explains in 44 out of a total of 466 cases why firms have changed their decision and bought the energy saving technology. JEL classifications: D21; H25; H32; O33; Q48

Keywords:

Tax expenditure programs; Technology adoption; Energy subsidies; Probit analysis

Correspondence: Herman Vollebergh, Department of Economics, Erasmus University Rotterdam. Tel: (+31)104081498; Email: [email protected].

1. INTRODUCTION Whether and how subsidy treatments affect economic behavior of agents and whethe r such treatment s are efficient is still an open question. For instance, in order to be effective the stimulus for a given environmental policy objective should shift polluting behavior into less polluting behavior, and to be efficient this stimulus should minimize the cost to society. It is often claimed, however, that the use of subsidies is not be effective, and therefore by implication not efficient. The transfer of public money to subsidized agents who would have invested in environmentally friendly or abatement technologies anyway is a waste of scarce public resources. Moreover, subsidies stimulate entry in an already polluting sector and might therefore precisely give the wrong signal if pollution externalities are involved (Baumol and Oates, 1988). Moreover, empirical research has had difficulties in finding a significant effect of residential energy-conservation credits for a long time (Hassett and Metcalf, 1995), which has further weakened the case for using subsidies, like energy tax credits. 1 The asymmetry in the (a priori) evaluation of instruments by economists is striking. Indeed, economists more or less take it for granted that firms explicitly and carefully compare the cost and benefits of investment projects before deciding whether or not to invest. Interestingly, little is actually known about how subsidies influence economic behavior of firms, in particular small firms. Evaluations of subsidies are typically restricted to households. How firms actually evaluate their investments and how subsidies affect these decisions in practice is still a black box despite recent advances in investment theory. 2 This paper employs unique micro-data from a Dutch tax rebate scheme in the profit sector and a subsidy program in the non-profit sector to evaluate the effectiveness and efficiency of both programs that aim to stimulate the adoption of energy-efficient technologies by firms. Using a probit model we analyze 862 subsidized investment decisions in 20 technologies across 57 sectors. Interestingly, we find that agents indeed tend to claim that the subsidy has had no effect on their behavior, but also that the likelihood of this claim is significantly affected by the way in which firms make their investment decisions. Firms using an explicit investment criterion are less likely to invest in energy saving technology. Moreover, we find evidence that suggests that different types of firms use different rules of thumb when evaluating investments decisions. In particular, the effect of the energy saving characteristics of the technology on the adoption of that technology depends on how firms evaluate these characteristics. As one may expect for-profit firms using an explicit investment criterion, i.e. critical payback period or internal rate of return, are less likely to invest in energy saving technology when the payback period of the technology is longer. For-profit firms not using an explicit investment criterion appear to be using a cruder rule of thumb. The data suggest that for these firms the likelihood of investment depends on the physical energy saved compared to the level of investment. Finally, the investment decisions made by not- for-profit firms are unrelated to the payback period of the technology. These findings are robust across different specifications. Accordingly, it is important that governments control for these differences when designing or advertising subsidy schemes.

1

Interestingly, subsidization of the adoption of more environmentally efficient technologies is nowadays increasingly recognized as an instrument to alleviate positive externalities associated with technology adoption (e.g. Jaffe, Stavins and Newell (2003)). 2 Graham and Harvey (2001) present results of a survey among CFOs about capital budgeting methods. Large firms rely heavily on present value techniques and the capital asset pricing model, while small firms are relatively likely to use the payback criterion.

The structure of the paper is as follows. Section 2 describes the subsidy schemes in detail and discusses the data. In section 3 a simple investment model is presented. Section 4 presents our findings and section 5 concludes.

2. SUBSIDY SCHEMES IN DETAIL In 1997 the Dutch government launched two programs to stimulate investments in energysaving technologies, the EIA (Energy Investment Deduction) 3 for-profit firms and the EINP (Energy Investments in Non Profit sectors) for not- for-profit firms. 4 Firms investing in energy-saving technology may apply for a tax-deduction (EIA) or subsidy (EINP) provided the technology is eligible, i.e. it appears on the so-called ‘Energy-List’. Once a year, this list is updated on the request of firms that supply energy technologies. Criteria for admission are that: (i) the use of the technology should result in a substantial reduction in the consumption of energy used; (ii) the technology is not common. Technologies on the list, which no longer meet these criteria, are removed. The Energy-List differs for the EIA and EINP, as not-forprofit firms typically invest in different technologies as for-profit firms. Indeed, only a small number of technologies, like insulation, energy efficient lightning systems and frequency converters, appear on both lists. The total number of techniques on the ‘Energy-List’ has varied from year to year. For example, the number of techniques on the EIA Energy List has varied from 80 to 110. 5 Table 1 presents information from Senter on the number and size of the applications for the EIA between 1997 and 2003. Since 1997 the number of applications has grown steadily until on September 2002 the EIA was temporarily shut down because of over drafting. 6 In order to prevent over drafting in the future the criteria for eligibility have been tightened up as of 2003. Table 1: Number and size of applications under the EIA

1997 1998 1999 2000 2001 2002 2003

Number of applications 10,366 14,145 17,408 25,815 28,139 17,228 15,518

Amount claimed (in mln euros) 430 656 587 695 1,058 1,344 834

Average investment (in euros) 41,500 46,400 33,700 26,900 37,600 78,000 53,700

Over the years around 90% of the EIA-applications have been submitted by small companies, i.e. companies with a maximum of 100 employees. The share of investments by small companies has risen from 32% in 1998 to 87% in 2003. In 1998 35% of the EIA-applications were submitted by companies with a Long-Term-Agreement (LTA). 7 In 2002 this percentage was down to 29%. 3

Energie InvesteringsAftek. Energie Investeringsaftrek Non-Profit sector en bijzondere sectoren. 5 The current Energy-List can be downloaded from www.senter.nl (in Dutch). 6 This was due to a small number of extremely large applications which would have nearly doubled the 2002 expenditures under the program. 7 Companies that have signed an LTA commit themselves to a certain amount of energy reduction. The reduction is voluntary, i.e. there are no sanctions in case of non-compliance. 4

In order to prevent fraudulent claims Senter checks all applications made by companies. These checks may include company visits in order to check whether the technology filed is indeed installed. On average Senter approves 80-85% of the amount claimed. Moreover, some applications are withdrawn voluntarily representing around 3-6% of the amount claimed. In 1997 the tax deduction granted under the EIA varies between 52% for investments up to 29,000 euro and 40% for investments larger than 224,000 euro. The investment figures are updated on a yearly basis with the consumer price index. As of 2002 the tax deduction is 55% irrespective of the size of the investment. As marginal tax rates in the Netherlands vary between 35% (lowest corporate tax) and 60% (highest income tax), the net-advantage obtained by firms under the EIA has varied between 14 en 33%. Finally, firms may carry-over any net-operating losses to the three past and seven subsequent years. In most instances this will allow them to cash the tax deduction up to any present value considerations. Under the EINP, not-for-profit firms may obtain a subsidy, depending on the size of the investment, between 14.5 and 18.5% of the amount invested. Typically, the number of EINPapplications is about 10% of the number of EIA-applications. The EINP was terminated in 2001. Table 2 presents the 20 technologies that were drawn from the Senter-database, 10 belonging to the EIA program only, 7 to the EINP program only, and 3 to both the EIA and EINP programs. These technologies were selected on the basis of the total amount invested in them. A second criterion was that the number of applications submitted was larger than 20. 8 Each of these technologies has been continuously on the Energy-List between 1997 and 1999. In total, we obtained 4967 and 513 records for the EIA and EINP respectively. The Senter-database contained information on the type of technology, the level of the investment, the year the application was submitted and the sector of the firm in question. Senter also provided us with so-called reference-technologies, i.e. the technology the firm would have invested in, if it had not invested in the subsidized technology. These referencetechnologies, their costs and energy use were determined by Senter on the basis of a 1999 survey and expert opinion. Notice that for 12 out of the 20 investigated technologies the reference-technology is characterized by no investment. In order to supplement the information from Senter a survey was sent to 2353 firms that had obtained a tax deduction under the EIA and 513 firms that had obtained a subsidy under the EINP. Firms were asked questions about their turnover, their legal form, the method they used for evaluating this specific investment project (internal rate-of-return, pay back period, or other), the critical values used for evaluating investments (minimal internal rate-of-return, critical pay-back period), the level of other subsidies, if any, that were obtained for the technology subsidized by the EIA or EINP, the specific time the firm had information about the technology and subsidy program respectively, the prime motive for investing in the technology, and whether the investment subsidized by the EIA or EINP would have been

8

This procedure was followed on request of the Dutch Ministry of Finance in order to evaluate the efficiency of the energy investment programs.

made at the same time, if the technology was not subsidized. Finally, we obtained energyprices per sector from the Netherlands Bureau of Economic Policy Analysis. 9 Table 2: Technologies included in our sample Technology Combined Heat and Power (miscellaneous items) Condenser Draught sealing Energy blinds Energy conserving cooling and freezing equipment, type A Energy conserving cooling and freezing equipment, type B Energy efficient lightning Frequency converter Generic construction techniques Generic equipment and processing techniques Heat-buffer Heat pump Heat recovery from ventilation air Heat registration system High efficiency boiler High efficiency glass Insulation Lightweight semi-trailer Weather dependent optimization of non-residential heating Wind turbine

Subsidy Scheme EIA EIA EINP EIA EIA EIA EIA and EINP EIA and EINP EIA EIA EIA EINP EINP EINP EINP EINP EIA and EINP EIA EINP EIA

Reference-technology No Yes No No Yes Yes Yes No No No No Yes No No Yes Yes No Yes No No

Table 3 summarizes the characteristics of the sample taken form the Senter-database and the subsequent survey. The response was 32.9% for the EIA-survey and 46.2% for the EINPsurvey. 86.7% of the EIA-surveys and 79.7% of the EINP-surveys proved to be useful. The response was equally divided over the technologies with the exception of insulation and energy conserving cooling and freezing equipment, type B, which were underrepresented. Table 3: Characteristics of the sample Program EIA Records from Senter-database 4917 Survey 2353 Returned 776 (32.9%) - of which are useful 673 (86.7%)

EINP 513 513 237 (46.2%) 189 (79.7%)

Sample statistics for the data set are provided in Table 4. Respondents were asked which method they used for evaluating this specific investment : 41% indicated that they used payback, 4% internal rate-of-return. However, 43% indicated that they did not use an explicit method for the evaluation of this investment, whereas 12% didn’t know if and what criterion their firm used. That such a large share of firms uses pay back as a method for evaluating investments is surprising. For example, in a survey by Graham and Harvey (2001) the internal rate-of-return was used relatively more than payback. Graham and Harvey remark, however, that ‘very small’ firms are much more likely to use payback than large firms. However, even a substantial share of ‘very small’ firms in the survey of Graham and Harvey uses internal rateof-return. The discrepancy may be explained by the fact that ‘very small’ firms in the survey 9

Sectoral energy prices are likely to be a good proxy for energy prices paid by individual firms in our sample for two reasons. First, the gas market was not liberalized prior to 2001. Second, the electricity market was liberalized in July 1999 for large users only. Hence, only an estimated 1.3% of energy prices paid in our sample was subject to negotiation.

of Graham and Harvey have a turnover of less than $100 million, whereas in our sample firm’s turnover is much smaller. For example, in our sample 11.5% of the firms have a turnover of less than 0.12 million euro, 24.8% have of turnover between 0.12 and 0.45 million euro, and 41.5% have a turnover between 0.45 and 4.5 million euros. Table 5 reports the results of a separate probit analysis on the likelihood of using an explicit investment criterion. This reveals that large firms are significantly more likely to use explicit methods for evaluating investment projects (75%) than small firms (50%), which may be the explanation that in our sample we have a relatively small percentage of firms us ing explicit investment criteria. Table 4: Sample characteristics Variable Decision not altered by subsidy Pay back period (excl. subsidy) - monetary - non-monetary Pay back period (incl. subsidy) Investment Investment reference technology, if any Energy saved - monetary - non-monetary Price electricity Price of gas Economic lifetime Critical payback period, if any Obtained vamil Obtained other subsidies EIA or EINP subsidy as % of investment No attention value % small to medium companies % medium companies % large companies

Number of observations 862

Mean

Minimum

Maximum

51%

Standard deviation n.a.

0

1

862 862 862 862 310

9.8 149.7 9.4 172.8 159.3

9.1 120.7 9.6 487.5 538.8

0.8 15.0 -66.5 56.6 2.4

41.7 445.0 41.7 6,872.5 5,841.6

862 862 862 862 862 384 862 862 862 862 862 862 862

27.4 1.91 16.9 37.5 22.2 7.0 0.18 0.11 19.9% 39.4% 24.8% 41.5% 22.2%

112.6 6.54 2.7 12.2 16.5 3.9 n.a. n.a. n.a. n.a. n.a. n.a. n.a.

3.2 1.7e-04 7.6 18.6 10.0 1.0 0 0 13.7% 0 0 0 0

2,492.7 100.3 26.5 63.3 50.0 31.4 1 1 26.4% 1 1 1 1

As a result of this finding a natural question is whether or not investment behavior would differ between firms that (i) use explicit investment criteria and firms that do not, 10 and (ii) between for-profit and not- for-profit firms. Hence, Table 6 contains sample statistics for these four different groups. Table 5: Which firms use explicit investment criteria? Variable Probability of using explicit investment criteria Food industry 29% Agriculture 30% Industry 28% Trade 25% Transport 21% Commercial services 25% Non-commercial services 28% Turnover between 0.45 and 4.5 mln euro 66% Turnover larger than 4.5 mln euro 75%

10

We will use the following terminology. Firms using an explicit investment criterion are called explicit firms. Firms not using an explicit investment criterion are called implicit firms.

Just over half of the firms indicated that they would have bought the technology at the exact same time without the EIA or EINP subsidy. This differs markedly between groups. Only 33% of the explicit, not-for profit firms would have bought the technology irrespective of the technology compared to 65% for implicit, not- for-profit firms. A possible explanation may be that policy response bias, deliberately giving biased responses in the hope of affecting the outcome of the analysis, may be different between different types of firms. We will return to this possibility when discussing the results. 11 Using data on investments, reference technologies, energy savings, operating and maintenance costs and energy prices we were able to calculate the payback periods of the technologies invested in (see Figure 1). Average calculated payback periods do not differ substantially between the different types of firms with the exception of implicit, for-profit firms, of which a substantially higher percentage invested in insulation. The percentage of not- for-profit firms acquiring non-EINP subsidies was 5 times higher than for-profit firms acquiring non- EIA subsidies. However, when acquired, for-profit firms obtained higher subsidies (measured as a percentage of investment) than not- for-profit firms. For-profit firms, however, extensively made use of the possibility to deduct the investment at will, called VAMIL, which is not available for not-for-profit firms. Table 6: Sample statistics for different groups of firms Variable Explicit For-profit Not-for -profit Decis ion not altered by subsidy 46.2% 32.9% Pay back period (excl. subsidy) - monetary 7.6 9.2 - non-monetary 123.0 131.9 Pay back period (incl. subsidy) 7.3 8.5 Investment 223.6 115.7 Investment standard technology 36.1 55.0 % no investment 73.6% 39.2% Energy saved - monetary 52.5 12.5 - non-monetary 3.61 0.79 Price electricity 15.93 18.84 Price of gas 37.31 32.06 Economic lifetime 19.3 16.7 Critical payback period 6.63 8.48 Dummy vamil 0.30 -Dummy other subsidies 0.07 0.30 Other subsidies as % of investment (if 48% 12% obtained) Dummy no attention value 33.8% 45.9% % small to medium companies 23.8% 8.9% % medium companies 52.1% 21.5% % large companies 19.2% 53.2% % EIA or EINP subsidy 20.0% 17.8% Number of observations 305 79 384

11

Implicit For-profit Not-for -profit 54.8% 64.5% 12.1 186.4 12.0 165.0 74.6 75.3%

8.7 114.4 7.2 99.2 60.0 18.0%

16.1 1.16 16.60 40.36 27.3 -0.18 0.05 27%

6.1 0.46 18.87 32.30 16.6 --0.27 29%

25.3% 32.3% 41.6% 16.8% 20.9% 368

43.6% 14.4% 26.1% 26.1% 18.0% 110 478

Another common problem with surveys is that they do not necessarily measure actions as the respondents may feel inclined to give desirable answers. Since all respondents have actually bought the energy-saving (desirable) technology, this type of behavior is unlikely to have affected the response.

Figure 1: Empirical cumulative density function of payback periods of adopted technologies for different types of firms

We asked whether or not firms knew about the EIA or EINP subsidy before or after they decided to invest in the energy-saving technology. It turned out that 39.4% of firms were familiar with the technology before they were familiar with the subsidy. For these firms the subsidy could not have had attention value. The percentage of not- for-profit firms not receiving attention value was higher than the percentage of for-profit firms. Explicit, forprofit firms use on average lower critical payback periods, 6.6 years, than explicit, not-for-

Figure 2: Empirical cumulative density function of critical payback periods for different types of firms

profit firms, 8.5 years (see Figure 2). Finally, explicit, not- for-profit firms in our sample have higher turnover than for-profit firms (explicit and implicit), which, in turn, have a higher turnover than implicit, not- for-profit firms.

3. THE MODEL To motivate the regressions that follow, we sketch out the simplest of investment models. Consider a firm having two options, investment in a standard technology with cost, I 0 , and investment in an energy-saving technology with cost, I 1 . Firms, when investing in the energy saving technology may obtain a subsidy or net tax deduction of size, S . Investment in the energy saving technology results in energy savings, ∆E = E 0 − E1 , where E0 , E1 are the energy use of the standard and energy-saving technology respectively. Energy prices are constant and given by Pe. When firms use a critical pay back period, CPBP , for evaluating investments projects they should invest if −

∆I S + + CPBP > 0 . Pe ∆E Pe ∆E

(1)

where we have assumed that energy prices stay constant during the lifespan of the technology. Parameterizing the model and allowing for optimizing error we can rewrite this equation as y * = −αPBP + βS * + γCPBP + ε ,

(2)

where PBP is the payback period of the technology, S * the subsidy as a percentage of the yearly energy savings and ε is a random variable reflecting errors in optimization. 12,13 We construct a discrete choice model in which the firm invests in the energy saving technology ( I = 1) or does not invest in the energy saving technology ( I = 0) according to

 1, if y * > 0 I = 0, otherwise.

(3)

Under the assumption that the distribution of ε is symmetric, the probability that I = 1 is given by

Pr( I = 1) = F (−α PBP + βS * + γCPBP )

(4) From (4) we may deduce the expected signs of the coefficients. A higher pay back period of the technology, PBP , should lead to a decrease of the investment. Higher subsidies, S * , should lead to an increase in the probability of investment. A higher critical payback period should increase the probability of investment. 12

Notice that for firms using net-present-value we could deduce a similar formula. The −n

CPBP would in that

case have to be replaced by a ‘pseudo critical payback period’, (1 − (1 + r ) ) / r , where r is the required rateof-return and n is the economic lifetime of the technology. See Sarnat and Levy (1969) for a discussion on the relationship between the payback period and the internal rate-of-return. 13 If firms assume that energy prices rise exponentially, the ‘pseudo critical payback period’ can be accordingly modified.

4. RESULTS Our first regression result is for the pooled sample, and is reported in Table 7. The dependent variable is the dummy indicating whether the firm would have bought the energy-saving technology in the absence of the EIA or EINP-subsidy. The probability of investment decreases with the payback period of the technology as expected. Companies that obtained knowledge about the subsidy at or after the time of the decision to invest are more likely to invest then companies that had knowledge about the subsidy prior to the time of investment. A possible explanation of this result is that the investments made by companies who knew about the EIA or EINP subsidy may be investments that have been ‘discovered’ by looking on the ‘Energy-List’. These investments may receive less support during the decision process or may be – on average – less profitable. The percentage of other subsidies received is not significant which is probably due to the fact that we have insufficient variation in the dataset as only 11% of all respondents received other subsidies. Moreover, it has the wrong sign. Finally, note that the critical payback period has the wrong sign as well and is significant. Higher critical payback periods should increase the probability of investment. One reason why the critical payback period may not be explaining investment very well, is that investment behavior may differ between firms using an explicit investment criterion and firms not using an explicit investment criterion on the one hand, and between for-profit and not- for-profit firms on the other hand. Column 2 presents a regression in which we differentiate between four groups of firms: (i) explicit, for-profit firms; (ii) explicit, not-forprofit firms; (iii) implicit, for-profit firms; and (iv) implicit, not- for-profit firms. Investment behavior differs substantially between these different types of firms. The probability of investment by for-profit firms decreases with the payback period of the technology, which is not the case for not- for-profit firms. Moreover, explicit, not- for-profit firms are substantially less likely to invest in any energy-saving technology irrespective of its payback period. Large companies are more likely to invest in energy-saving technology. Fina lly, the coefficient of the critical payback period no longer has the wrong sign. It is, however, insignificant. 14 We now turn the possibility of policy response bias. As noted in the previous paragraph firms may be inclined to answer that they would not have bought the energy-saving technology in the hope of affecting the analysis. We have asked firms what their prime reason was for investing in the energy-saving technology. ‘Obtaining the subsidy’ was the response of 14% of the firms. 15 We suspect that firms, which are primarily interested in obtaining the subsidy, are more likely to respond strategically. The results, reported in column 3, suggest that policy response bias has indeed played a role, in particular for for-profit firms. The coefficient of the payback period of the technology diminishes for both explicit and implicit, for profit firms, as is to be expected. However, for implicit, for-profit firms the coefficient of the payback period becomes insignificant, while the dummy is positive and significant, suggesting that implicit, for-profit firms have an on-average higher probability of investing in energy-saving equipment which is unrelated to the payback period of the technology.

14

A formal test that investment behavior differs only between for-profit and not-for-profit firms is rejected at the 99% level. Moreover, in the restricted model the coefficient of the critical pay back period has again the wrong sign and is significant. 15 Other answers were: an upgrade of a previous similar investment (25%), an upgrade of the entire production process and a cleaner environment (8%), regulation (3%) and better image (2%).

Table 7 : Regression results Variable (1) PBP -0.0137a (0.004) - explicit, for-profit - explicit, not-for-profit - implicit, for-profit - implicit, not-for-profit PBP (non-financial) - explicit, not-for-profit - implicit, for-profit - implicit, not-for-profit Constant - explicit, for-profit - explicit, not-for-profit - implicit, for-profit - implicit, not-for-profit % other subsidies Large company* No attention value* CPBP - when economic lifetime of investment is 10 year - when economic lifetime of investment is 15 year - when economic lifetime of investment is 50 year Prime reason subsidy (for-profit firms )* Prime reason subsidy (not-for-profit firms )* Wind turbine*

-

(2)

(3)

(4)

(5)

(6)

-

-

-

-

-

a

a

a

-0.0170 (0.311) 0.012 (0.533)

-0.042a (0.003) 0.002 (0.919)

-

-

-

-

0.013 (0.530)

0.006 (0.742)

-0.039 (0.000) -0.001 (0.942) -0.011b (0.069) 0.006 (0.776)

-

-

-

-

-

-

-0.036 (0.001) 0.002 (0.917) -0.009 (0.150) 0.006 (0.742)

-0.037 (0.001) -

-0.004 (0.856) -0.027a (0.001) 0.004 (0.866)

-

-

0.001 (0.965)

-0.027a (0.001)

-

-

-0.001 (0.998) -0.897a (0.003) 0.425a (0.001) 0.233 (0.290) -0.012 (0.741) 0.175 (0.120) 0.216a (0.021)

b

0.140 (0.089)

0.004 (0.918) 0.142 (0.174) 0.262a (0.003) -0.036a (0.000)

0.001 (0.997) -0.688a (0.009) 0.123 (0.263) 0.183 (0.398) -0.007 (0.833) 0.185b (0.090) 0.240a (0.008) 0.011 (0.529)

0.057 (0.701) -0.702a (0.008) 0.207b (0.065) 0.233 (0.290) -0.010 (0.776) 0.185b (0.093) 0.207a (0.024) 0.017 (0.363)

0.047 (0.752) -0.660a (0.025) 0.416a (0.001) 0.245 (0.289) -0.008 (0.825) 0.196b (0.076) 0.223a (0.016) 0.017 (0.356)

-0.112 (0.471) -0.960a (0.000) 0.228 (0.120) 0.155 (0.491) -0.006 (0.864) 0.221b (0.054) 0.203a (0.031) 0.026 (0.155)

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-0.727a (0.000) -0.362 (0.219)

-0.697a (0.000) -0.349 (0.238)

-

0.053a (0.038) 0.015 (0.506) 0.035 (0.104) -0.725a (0.000) -0.355 (0.230)

-0.666a (0.000) -0.334 (0.261) -0.991a (0.007) * a Energy blinds 0.380 (0.001) * a Frequency converters 0.474 (0.010) * a Lightweight semi-tra iler -0.544 (0.031) * Insulation -0.369 (0.210) a Significant at the 95 percent level; b Significant at the 90 percent level Note: T-probabilities are reported in parentheses. An asterisk on a variable indicates a dummy variable. -

-

Column 4 presents a regression in which we replaced the payback period of the technologies with their non-financial counterpart (measured in TJ). These non-financial pay back periods are a measure of the amount of energy saved per euro invested. Two out of the three coefficients, both for not- for-profit firms, are insignificant, but the coefficient for the implicit, for-profit firms is highly significant. This suggests that explicit, for-profit firms use a cruder ‘rule of thumb’ than pay back period: they seem to trade off the energy saved against the level of investment. For these firms a higher energy savings per euro invested increase the probability of investment. We check the robustness of our model by including fixed effects for the technologies. The results are reported in column 5. 16 The fixed effect for insulation affects the coefficients of the pay back period for explicit, for profit firms and the non- financial pay back period for implicit, for profit firms. In order to check whether insulation drives our results we reestimated the model after deleting all observations on insulation from our dataset. 17 Without insulation, the results are similar to the results reported in column 4. 18 So we conclude that insulation does not drive our results. Note that the critical payback period is still not significant. One reason for this is that firms may be using another investment criterion than they have reported or that they may take account of other characteristics besides the payback period of an investment, like its economic lifetime. Finally, column 6 presents a regression in which we interact the critical payback period reported by firms with dummy variables indicating the economic lifetime of the investment, respectively 10, 15 or 50 years. The critical payback period for investments with an economic lifetime of 10 years is significant at the 5% level, the other two not. 19 Table 8 presents evidence on the effectiveness of the subsidy programs. Column 1 contains the probabilities that a certain type of firm would have bought the technology in the absence of the subsidy according to their own answers. These probabilities differ substantially between different types. Whereas only 32.9% of explicit, for-profit firms would have bought the technology without the subsidy, 64.6% of the implicit, not- for-profit firms indicated that they would have done so. On average explicit firms are much less likely to invest in energysaving technologies in the absence of a subsidy than implicit firms. Column 2 reports the predicted, in sample, probabilities that a certain type of firm would have bought the energysaving technology in absence of the subsidy. Note that these predicted probabilities are close to the reported probabilities. Column 3 presents the probabilities that a certain type of firm would have bought the energy-saving technology in the absence of the subsidy program, if they would have reported truthfully. These probabilities were calculated by putting the respective dummies to zero. Depending on the category between 1.5 and 3.6 percentage points is expected to have given a strategic answer. Column 4 presents the probabilities that firms would have bought the technology if the effect of the subsidy is to make the technology more profitable only, i.e. to shorten its payback period. These probabilities were evaluated by subtracting the EIA and EINP subsidy from the investment and recalculating the pay back

16

Fixed effects that are not significant and do not affect other coefficients are not reported. The number of observations is reduced from 862 to 655 by deleting al observations on insulation. Another way to check for robustness would be to interact the dummy for insulation with the four previously defined groups of firms. One would then have 8 instead of 4 categories of firms. The data however, do not permit this, as some of the groups have insufficient observations. 18 Available on request from the authors. 19 The critical payback period for investments with a lifetime of 50 years is nearly significant at the 10% level. 17

periods of the technologies. Note that the impact of the subsidy as shortening the payback period is between 3.6 and 4.7 percentage points. 20 Table 8: % of firms that would have bought the technology without subsidies under various conditions Type of firm (1) (2) (3) (4) Implicit, for-profit 46.2% 46.3% 49.9% 53.5% Implicit, not-for-profit 32.9% 32.8% 34.6% 34.3% Explicit, for-profit 54.9% 55.0% 58.6% 63.3% Explicit, not-for-profit 64.6% 64.6% 66.1% 65.1% All firms 43.3% 43.3% 45.9% 48.7% Percentages in column 1 have been computed directly from the data. Percentages in column 2 have been calculated directly from the as displayed in column 6 of Table 7. Percentages in column 3 have been calculated as the percentages in column 2 with ‘prime reason subsidy’ set to zero for both for-profit and not-for-profit firms. Percentages in column 4 have been calculated as in column 3 with recalculated payback periods.

Finally, figure 1 shows how the probability of buying the energy-saving technology in the absence of the subsidy, depends on the pay back period of the technology for the firms in our sample. 21 For explicit and implicit, for-profit firms the probability of buying the technology in the absence of the subsidy is decreasing in the payback period of the technology. Moreover, for each level of the payback period implicit, for-profit firms have a higher probability of buying the technology than explicit, for-profit firms. Note that there is no significant relationship between the level of the pay back period and the probability that not- for-profit firms, implicit and explicit, buy the technology. Again implicit, not-for-profit firms are much more likely to have bought the technology in the absence of the subsidy as compared to implicit, not- for-profit firms.

Figure 3: Probability of buying energy saving technologies for different types of firms

20

Subtract the percentages in column 4 from those in column 3. Probabilities have been evaluated at the mean of the respective variables. For implicit, for-profit firms, the probability of buying the technology in the absence of the EIA subsidy has been calculated by converting the non-financial payback period into a financial payback period. Since prices differ between firms and between types of energy (gas, electricity), one obtains a region of probabilities instead of a unique probability. The lines shown are calculated at the minimum and maximum prices of gas and electricity paid in our sample. 21

What may we conclude from Table 8 about the effectiveness and efficiency of the EIA and EINP programs? First, note that the effectiveness 22 of the EIA and EINP program is 45.4% and 47.1% respectively. 23 The effectiveness is lower for implicit firms (39.7%) than for explicit firms (53.3%). Unfortunately, a government does not observe the type of firm, implicit or explicit, and hence is unable to target a subsidy program on the type of firm. From figure 1 one may hypothesize that the effectiveness of the programs will differ substantially per technology as the technologies typically have different payback periods. Table 9 reports the effectiveness of the program per technology. It shows that the effectiveness per technology differs from 34.4% for energy conserving cooling and freezing equipment, type A to 58.9% for heat recovery from ventilation air. Hence, by removing technologies from the ‘EnergyList’ the government may increase the effectiveness and efficiency of the programs. Table 9: Effectiveness of the subsidy program per technology Technology Subsidy Scheme Combined Heat and Power (miscellaneous items) EIA Condenser EIA Draught sealing EINP Energy blinds EIA Energy conserving cooling and freezing equipment, type A EIA Energy conserving cooling and freezing equipment, type B EIA Energy efficient lightning EIA and EINP Frequency converter EIA and EINP Generic construction techniques EIA Generic equipment and processing techniques EIA Heat-buffer EIA Heat pump EINP Heat recovery from ventilation air EINP Heat registration system EINP High efficiency boiler EINP High efficiency glass EINP Insulation EIA and EINP Lightweight semi-trailer EIA Weather dependent optimization of non-residential heating EINP Wind turbine EIA

Effectiveness 45.2% 41.9% 44.5% 38.6% 34.4% 37.3% 52.8% 40.3% 35.4% 38.1% 47.6% 47.8% 58.9% 44.0% 43.4% 46.8% 55.1% 44.3% 42.2% 50.7%

5. CONCLUSION In this paper we showed that investment behavior of small firms differs markedly between different types of firms. Using a data set on individual investments in energy saving technology we showed that (i) the probability that for-profit firms will invest in energy saving technology decreases in the payback period of the technology, whereas the probability that not- for-profit firms will invest does not depend on the payback period of the technology; (ii) that firms using an explicit investment criterion are less likely to invest than firms not using an explicit investment criterion. Moreover, we showed that almost half of the firms would have bought the technology even in the absence of the subsidy. This result contributes to the existing empirical literature by shedding new light on the question why firms invest in certain technologies and what role is played by subsidies and tax credits. For one, subsidies and tax credits make the investment more profitable, but this effect 22

The effectiveness of a program is measured as the percentage of firms that would not have bought the technology in the absence of the subsidy. 23 The percentage for the EIA program was calculated as follows form the percentages in column 3: 100% (305*49.9%-368*58.6%)/(305+368). The percentage for the EINP program was calculated in the same way.

of subsidies and tax credits can only explain that an estimated 44 out of 466 firms changed their investment decision. Apparently, subsidies must have important other effects, like alleviating liquidity constraints. This is especially true for firms that do not use explicit investment criteria. Whether and in what way government may exploit this information and optimize tax (subsidy) incentives is another matter and cannot be resolved in this paper. One obvious way is, of course, to make technologies with a low effectiveness no longer eligible for subsidization. However, this fails to exploit the fact that investment behavior differs markedly between different types of firms. Not until we fully understand the way in which subsidies change behavior it is possible to adapt government policy accordingly.

REFERENCES Baumol, W.J. en W.E. Oates, 1988, The Theory of Environmental Policy. Cambridge: Cambridge University Press Graham, J.R., and C.R. Harvey, 2001, The Theory and Practice of Corporate Finance: Evidence form the field, Journal of Financial Economics, Vol. 60, pp. 187-243. Hassett, K.A., and G.E. Metcalf, 1995, Energy Tax Credits and Residential Conservation Investment: Evidence from panel data, Journal of Public Economics, Vol. 57, pp. 201-17 Jaffe, A.B, R.N. Stavins, and R.G. Newell, 2003, Technological Change and the Environment. Chapter 11 in Handbook of Environmental Economics, Volume 1, K.G. Mäler and J.R. Vincent, eds, pp. 161-516, Elsevier Science, Amsterdam. Sarnat, M. and H. Levy, 1969, The Relationship between Rules of Thumb to the Internal Rate of return: a Restatement and generalization, The Journal of Finance, Vol. 24, pp. 479-90.