Energy saving incentives and institutional environment: a cross country analysis. Part 2 Текст научной статьи по специальности «Экономика и бизнес»

УДК 338.242, 338.24.01 JEL C51, D02, O33

N. I. Suslov

Novosibirsk National Research State University 2 Pirogov Str., Novosibirsk, 630090, Russian Federation

Institute ofEconomics and Industrial Engineering of Siberian Branch ofRussian Academy of Sciences 17 Lavrent'ev Ave., Novosibirsk, 630090, Russian Federation

nsus@ieie.nsc.ru

ENERGY SAVING INCENTIVES AND INSTITUTIONAL ENVIRONMENT: A CROSS COUNTRY ANALYSIS PART 2 *

We suggest as a hypothesis that the main reason why energy efficiency of production in Russia is substantially lower than in developed economies is the weakness of energy saving incentives brought about by shortcomings of institutional system. To demonstrate this we present a theoretical model of energy consuming economic sector and show that the higher is transaction costs caused by facing firms the lower is a probability that any energy saving measures would be undertaken as a respond to a energy price rise. We included into regressions for energy intensity coefficients for 77 World economies constructed for a period of the middle of the previous decade institutional variables which made it possible to estimate demand for energy price elasticities differentiated by individual economies. Average of these indices for CIS economies (by its absolute values) showed itself to be almost four times lower than that for OECD countries.

Keywords: energy intensity, energy conservation, World economies, price elasticity, institutions, Cournot competition

Model analysis:

energy consuming sector under price shock

Now consider a P - fold rise in the energy price with p > 1. The proper reaction of a firm, which we term «the adjustment project», is adjusting its cost to the new price combination by substituting any other production factors for energy. We assume that firms take this course if transaction cost of adjusting is low, and avoid it if the expect transaction cost to be high. Thus, each of the firms considered has to solve the following problem:

max jpe - F (E,...) - pB - Ept - X - tc- e° |, (7)

Pe = G - H-fx Q! + F(E,-) 1, (8)

where in addition to previous designation, pt ~ denotes the price of a non-energy factor i, Xt - stands

E0 E0

for the input of the non-energy factor i, the initial output energy intensity being e0 = —_, e0 = —_,

Qe is an output expected by the firm considered from another firm l, I is a set of indices denoting all the used non-energy factors, L - set of indices designating all the firms other than a considered one. Thus, the sum ^Q1 means the expectation of a given firm about the outputs of all (n -1) oth-

lsL

* Продолжение статьи. Начало см.: Вестник Новосибирского государственного университета. Серия: Социально-экономические науки. 2014. Т. 14, вып. 2. С. 61-70.

Suslov N. I. Energy Saving Incentives and Institutional Environment: A Cross Country Analysis. Part 2 // Вестн. Ново-сиб. гос. ун-та. Серия: Социально-экономические науки. 2014. Т. 14, вып. 3. С. 46-55.

ISSN 1818-7862. Вестник НГУ. Серия: Социально-экономические науки. 2014. Том 14, выпуск 3 © N. I. Suslov, 2014

ers and, so, P is expected output price depending on this expectation. Function ..) is a Dirichlet type function with x) = 1 if x > 0 and x) = 0 in other cases, and the variable tc = (0,h) has only two values - of low and high levels of transaction cost. For simplicity, we assume that low transaction cost does not affect the company's activity at all and it is therefore taken to be zero. If it is high, its level is designated by h, and it is high enough to stop an adjustment project (specifically by the implicit fraction).

After the increase in the energy price, the general set of the firms breaks into two subsets. The first one includes k firms, which face low transaction cost and thus implement the adjustment projects. The second subset consists of (n - k) firms facing high transaction cost and therefore rejecting adjustment behavior.

For convenience of the further discussion, we provide a solution for the situation when transaction costs are high for all the n firms. In this case, all the firms experiencing a rise in the energy price also face a rise in the cost per unit of output, indicated by Ac1. Thus, the cost per unit of output when a firm faces high transaction cost is c1 = c + Ac\ In this case, the equilibrium solution Q1, Y1 = n-Q1, P1 differs from the initial one as follows: Y1< Y0, Q1< Q0, P1> P0 due to the new unit cost c1 is higher than initial level c.

Now consider a case when k < n and, so, k firms adjust their production factors combinations to new the price structure. Each of them reduces the energy intensity of its output by Ae and the cost per unit of output by Ac2, thus, its unit cost is c1 - Ac2 = c + Ac1 - Ac2 1.

Designate by AQ+ (k ) the output increment from the level Q1 of a firm, which implements the adjustment project , and by AQ-(k) from this level for the company rejecting it, the argument k in brackets means that k firms implement the adjustment project. The increment of the solution with respect to the level (y1,Q1,P1 ) can be calculated as follows:

AQ + (k) = (n H ++1}- Ac2, (9)

H - (n +1)

-k•Ac2

AQ- (k) = H-k( 1), (10)

H - (n +1)

k . Ac2

AY (k) = -HtT-^ , (11)

H - (n +1)

-k.Ac2

AP(k ) =JL+iL, (12)

n+1

where AY(k) and AP(k) stand for the increments of the total sector output and the market price correspondingly, given that k firms undertake the adjustment project. It would be natural to assume that Q1 (k ) + AQ(k ) > 0 for each k, which is guarantied if

G - c1 - n-Ac2 = G - c-Ac1 - n-Ac2 > 0. (13)

This condition seems fairly natural. For instance, if firms' production functions have unit elasticity of substitution with a standing for energy intensity parameter, then

An2 =[(P-1)-a-(Pa-1) ]-n,

with p, as before, standing for the index of energy price increase 2.

Our basic assumption is that the number of the firms facing low transaction cost k is strongly dependent on the quality of institutions: the higher the quality, the higher the number k is. We assume it is zero if institutions are very bad and provide for no incentives for adjustment to energy price change and it is close to n if institutions provide for strong incentives.

PROPOSITION: Let n symmetrical firms having production function of type (1) 3 operate in a given sector and the sector demand function be as (4). Let the sector to reach Cournot equilibrium.

1 Obviously a condition Ac1 - Ac2 > 0 holds.

2 Under a = 0,2 doubling of real energy price (P = 2) leads to the value of Ac2 = 0,0513.

3 Sector index i is omitted.

After an increase in the price for the energy factor pE, each firm solves the problem (7)-(8), k firms face low transaction costs associated with the implementation of adjustment projects, and (n - k) firms face high transaction cost. Then the final value of the sector energy intensity, measured as

n

IE

j=1

Y

is the lower, the higher k is.

PROOF: Let the energy intensity of a firm before the price shock be e1, then after the energy price increase, the firms, which undertake adjustment projects have energy intensity e2 with e2 < e1 which holds true by the property of the production function. The value of energy intensity of the firms not undertaking adjustment projects stays e1. Thus, the value of the sector energy intensity under condition that k firms undertake adjustment projects e(k) is:

e(k) = 5 (k) • e2 + (1 - s(k)) • e1 (14)

with s(k) is a share of the firms undertaking adjustment projects in total sector output. At the same time, obviously, the value of the sector energy intensity derivative by s is negative because e2 < e1.

ds(k)

So, it is necessary to prove that-> 0, which is not obvious since the rising number of the firms

dk

implementing the project reduces individual outputs of these firms (due to the equation (9)).

s(k) .

Construct the value

1 - s(k )

k • Q1 + (n - k +1)

k •Ac2

s (k ) H • (n +1)

1 - S(k) (n - k) • Q1 - (n - k) • k •AC

(15)

H • (n +1)

k • (n - k) • Ac2

Indicate 0(k) = k • Q1 +--and substitute for into (15):

H • (n +1)

9(k)+ k^

s (k ) H • (n +1)

(16)

1 - s(k) n • Q1 -0(k) Due to the condition (4) the derivative of 0(k) with respect to k is positive:

d0W = Q1 + (n - 2k)—Ac!-= C - c1 +(n - 2k) ^ > 0. (17)

d (k) H • (n +1) H • (n +1)

Taking into account (17) one can find that the numerator in (16) grows and at the same time the de-

s(k)

nominator reduces as k increases. Thus, the value - is a function growing by k. The latest

1 - s(k)

s (k) de(k) fact can be true if and only if-> 0. This means that-< 0 and the proposition is proved.

1 - s (k) dk

At this point, we conclude that the weak institutional environment could be considered an important factor undermining the efficiency of energy use. Weak incentives for change of technologies may result from the high cost of market operation. We assume that the value h may be associated with both the market performance itself and its interaction with the government including the quality of the policy measures and the degree of corruption. As we specified before, this value probably includes some monetary component, such as bribes and higher taxes, and the non-monetary component are additional attempts of entrepreneurs for establishing and maintaining agreements.

Another important suggestion we make based on the theoretical framework discussed in this section is a dependence of the demand for energy price elasticity prevailing in a given economy on the quality of institutions. In the model considered, we showed that the sector demand for energy reaction to the energy change is the stronger, the more firms react adequately to the change in the real energy price and, therefore, the stronger the economic institutions are. Thus, it is to be expected that

in a given national economy the reaction of the aggregate demand for energy from the production sphere as a whole will demonstrate the same property. For this reason we advance a hypothesis, which will later be tested in the next section of this paper, that the price elasticity of the energy demand of the production sphere is a function dependent on the quality of economic institutions. More specifically, we construct a model of energy intensity of production sector for a particular economy and, therefore, specify the price elasticity of output energy intensity. This coefficient in general differs from the former one.

The first index, at list given the production function with constant returns to scale, captures only the substitution effect. The second one except for the substitution effect allows for wealth effect, i.e. the demand change due to change of the output.

First, if the technology considered actually has constant returns to scale it allows only for the substitution effect of the real price change. Secondly, if some inputs of the production factors cannot change - which is a short-term case - output and total cost change may vary not equally and, therefore, the output energy intensity may change not only due to substitution of other factors for energy, but also due to the variation of the production scale. Moreover, in this situation substitution effect itself may be weaker than in the long run because a certain component of the energy input may be a quasi-fixed factor.

Specification

We use the following specification:

ln(e) = p0 + P1 • DISTE + p2 • INST • ln

f p \

pe

-Ps- ln

f p \

pe

(18)

though the variable INST may designate different institutional variables from their total list presented in the part 1. We used in our analysis both several individual variables and their combinations but present in our paper the most satisfactory version of this variable being a sum of two institutional indices - Government Effectiveness and Control of Corruption:

INST = GE + CC. (19)

is

f p ^

The variable of a combined influence of the real energy price and institutions INST • ln

.pe

called the interaction term, which we use following Polterovich and Popov [1]. If it proves significant, one could suggest that the institutions affect energy intensity through the price system. On the other hand, a simple transformation in (18) helps to see that the value P2 •INST + P3 is the price elasticity of output energy intensity as a function of the institutional strength index, which fit our theoretical model.

Estimation Results:

What are the Main Reasons for High Transaction Cost?

We estimated the model (18) keeping (19) for 5 years: 2002 trough 2006. The reason why we omitted the year of 2001 is absence of institutional indices for it in the World Bank databases. The main results are presented in the Table 1.

Using in the regression a variable of seasonal temperature fluctuation which we consider a good reflection of climate severity rather than a mean annual temperature one is caused by the fact that the first indicator works better in all the regressor's combinations we tried. We address this phenomenon to two things. First, it represents better technologic specifics brought about by the climatic conditions in the country: equipment should fit to both low and high temperature regimes; on the other hand more enduring technologies are more energy intensive. Secondly, the variable of seasonal temperature fluctuation is at the same time a measure for a geographical continentality of the countries taking into account that the economies located more distantly from the sea shores incur additional (energy) cost of the world economic integration.

Table 1

Estimated Energy Intensity of Production in the World Countries (dependent variable: ln[Energy Consumption in production sphere per a unit of GDP PPP],

White covariance matrix method)

Variables 2002, 75 observ. 2003, 77 observ. 2004, 74 observ. 2005, 75 observ. 2006, 77 observ.

Constant term -0,1718 ¿-Value = -1,30 -0,1665 ¿-Value = -1,25 -0.1511 ¿-Value = -1,26 -0.2771 ¿-Value = -2,30 -0,2872 ¿-Value = -2,49

Variable of climate conditions: DISTE 0,0025 ¿-Value = 4,84 0,0023 ¿-Value = 4,30 0,0019 ¿-Value = 3,97 0,0021 ¿-Value = 4,48 0,0022 ¿-Value = 4,15

Real energy price for previous year: ln(P/pE)-i 0,5155 ¿-Value = 5,13 0,4592 ¿-Value = 4,95 0,4429 ¿-Value = 4,94 0,2536 ¿-Value = 2,56 0,2841 ¿-Value = 2,67

Interaction term: ln(P/pE)-i INST * 0,1153 ¿-Value = 3,29 0,1005 ¿-Value = 2,49 0,1133 ¿-Value = 2,76 0,1124 ¿-Value = 2,96 0,1239 ¿-Value = 2,54

^-squared 0,4835 0,4231 00,3979 0,3189 0,3343

F-value 19,75 18,90 16,40 10,96 8,73

Root MSE 0,38297 0,39872 0,36507 0,36192 0,37684

Hausman test, Chi2 ** 0,00, Prob>chi2 = 0,9999 0,03, Prob>chi2 = 0,9984 0,76, Prob>chi2 = 0,8582 0,27, Prob>chi2 = 0,9661 0,90, Prob>chi2 = 0,8246

* Combination of Government Effectiveness and Control of Corruption indices. Instrumental variables are logarithm of import cost of oil in the previous year for the real energy price variable and in addition a combination of latitude degree and infant mortality variables for the interaction term; IVS and OLS models are compared for the samples of economies for which is the data on import cost of oil accessible.

Table 2

Coefficients of Price Elasticity of Production Energy Intensity by the Economies and the Groups Economies of the World

2002 2003 -2004 2005 2006 In average

World in Average, 118 economies -0,546 -0,519 -0,506 -0,278 -0,317 -0,433

OECD, 26 economies -0,889 -0,838 -0,910 -0,596 -0,666 -0,780

Former Socialist, 27 economies -0,451 -0,436 -0,406 -0,212 -0,243 -0,349

East Europe and Baltic, 14 economies -0,559 -0,540 -0,551 -0,322 -0,362 -0,467

CIS, 11 economies -0,318 -0,308 -0,234 -0,082 -0,102 -0,209

Russian Federation -0,374 -0,374 -0,320 -0,124 -0,128 -0,264

* Without new members.

Endogeneity of regressors problem is expected to be present with respect of use in the regression of both the institutional and energy price variables which could be affected with the energy intensity one. Trying to soften it for the real price of energy factor we used in the regression a variable for the

( p ^

( p ^

previous year rather than for current one: ln — instead of ln — . Besides this a proper

V PE 7_1 V PE 7

method to treat the problem of endogenity is application of IVLS estimator in addition to OLS employing Hausman test. A serious difficulty here is existence of consistent instrumental variables for energy price. The only possible one which we could imagine was crude oil import cost for corresponding economies. We applied the data from IEA database containing statistics on only 25 OECD countries. Thus the sample used to test the problem was of only this dimension what, of cause reduced the reliability of the estimates which we obtained. Nevertheless we present the results of Hausman test suggesting that the effective model should be preferred. Institutional index was instrumented with the help of latitude degree and infant mortality variables.

In order to confirm or even strengthen the results described using the data for the last year of the period under consideration we constructed similar regressions for two economic sectors - goods production and services (see Table A2 in Appendix). Since using transaction term in these cases provided for less convincing results, we present the estimations for both regressions which still include this variable and ones with the use of only institutional index - without its combining with the price variable. As an institutional variable we chose Control of Corruption index. One more distinction is a climate variable. For energy intensity in services sector it found itself insignificant and thus was removed. In the regressions for goods production sphere a variable of mean annual temperature provided for more robust estimations.

As it could be seen, significance of institutional variables is still well preserved and for the services sector proves o be even higher than for overall energy intensity. However, transaction term visibly loses its explanation power in the regressions for the goods production sector. This fact has a transparent explanation: share small and medium-sized enterprises in services sector is essentially higher than in goods producing one. At the same time small and medium-sized business, at least in economies with not good enough institutions, suffer from overregulation and corruption considerably higher than large enterprises. Thus, the implicit transaction cost burden for it is higher as well.

We provide our calculations of price elasticity of production energy intensity both by the groups of the economies (Table 2) and for each country from the sample (Table A1 in Appendix). One can see that these results confirm our theoretical assumption: the better the institutions the stronger consumption of per output unit responds to changes in real energy price. Particularly, in CIS countries, adjustment of energy demand to changes in real energy prices is to be regarded as weak: the absolute value of average price elasticity coefficient of energy intensity is about one third of that in OECD countries; in the East European and Baltic countries this value is also visibly lower than in the developed countries though not so crucially (it is "only" one half of the OECD level). This fact means weak incentives of firms for energy conservation and, thus, serves an important reason for the higher energy intensity of production.

Conclusion

The energy intensity in the most of the world countries was falling down during the last decades of the previous century. At the same time in former socialist economies it stays essentially higher than the developed countries. In order to explain this phenomenon we suggest a theoretical model of an economic sector including a certain number of firms, which consume energy and face the necessity of implementing energy conservation projects under the condition of uncertain transaction cost associated with this implementation. The high transaction cost completely stops the project but low transaction cost does not affect the behavior of the firms. We show that the inadequate institutional environment leading to a high probability for a firm to be faced with adverse external conditions resulting in the high transaction cost brings about the lack of incentives for energy conservation. Thus, under such a condition, the substitution effect of energy price change is weaker than in tough market environment. This fact means that higher transaction costs worsen the incentives for energy conservation.

Our econometric model permits one to calculate energy price elasticity of production energy intensity, which is a value similar to price elasticity of conditional demand for energy. Analysis showed that two institutional variables from their common list provided in [2; 3] have high significance levels. They are - "government effectiveness" and "control of corruption". Using the estimates results we provided coefficients of energy price elasticity of production energy intensity both by the groups of the economies and for each economy from the sample. We show that the average of these coefficients for the group of CIS economies is about four times lower than that for the OECD economies (by their absolute values); in the East European and Baltic countries this value is also visibly lower than in the developed countries though not so crucially ("only" approximately two times). This fact means weak incentives of firms for energy conservation and, thus, serves an important reason for the higher energy intensity of production.

References

1. Polterovich V. and Popov V. 'Accumulation of Foreign Exchange Reserves and Long Term Growth'. In: Tabata S. and Iwashita A. (Eds.), Slavic Eurasia's Integration into the World Economy. Slavic Research Center, Hokkaido University, 2004.

2. Kaufmann D., Kraay A., and Zodio-Lobaton P. 'Governance Matters'. World Bank Research Working paper, 1999, vol. 2, p. 196.

3. Kaufmann, D., Kraay, A., and Mastruzzi M. Governance Matters VII: Aggregate and Individual Governance Indicators, 1996-2007. World Bank Policy Research Working Paper, 2008, no. 4654,June.

Appendix

Table A1

Estimated Coefficients of Price Elasticity of Energy Intensity in the Years of 2002-2004

2002 2003 2004 2005 2006 In average

Albania -0,352 -0,339 -0,317 -0,103 -0,148 -0,252

Algeria -0,355 -0,361 -0,315 -0,163 -0,173 -0,273

Angola -0,244 -0,234 -0,125 -0,001 0,021 -0,117

Argentina -0,385 -0,409 -0,388 -0,182 -0,223 -0,317

Armenia -0,412 -0,392 -0,356 -0,178 -0,186 -0,305

Australia -0,947 -0,924 -1,045 -0,688 -0,768 -0,874

Austria -0,978 -0,924 -1,008 -0,658 -0,738 -0,861

Azerbaijan -0,298 -0,290 -0,189 -0,066 -0,077 -0,184

Bangladesh -0,315 -0,280 -0,173 -0,015 -0,031 -0,163

Belarus -0,288 -0,263 -0,156 -0,023 -0,038 -0,154

Belgium -0,931 -0,861 -0,928 -0,604 -0,677 -0,800

Benin -0,362 -0,378 -0,329 -0,084 -0,149 -0,261

Bolivia -0,382 -0,356 -0,288 -0,076 -0,133 -0,247

Bosnia and Herzegovina -0,351 -0,353 -0,327 -0,148 -0,171 -0,270

Brazil -0,487 -0,502 -0,474 -0,219 -0,247 -0,386

Bulgaria -0,513 -0,480 -0,497 -0,281 -0,288 -0,412

Cameroon -0,296 -0,328 -0,214 -0,020 -0,056 -0,183

Canada -0,991 -0,938 -1,021 -0,688 -0,785 -0,884

Chile -0,829 -0,752 -0,851 -0,547 -0,590 -0,714

China -0,457 -0,431 -0,385 -0,166 -0,217 -0,331

Colombia -0,409 -0,413 -0,422 -0,218 -0,259 -0,344

Congo -0,242 -0,236 -0,179 0,015 0,010 -0,127

Table Al continued

2002 2003 2004 2005 2006 In average

Costa Rica -0,664 -0,621 -0,596 -0,337 -0,361 -0,516

Croatia -0,585 -0,531 -0,558 -0,328 -0,355 -0,471

Cyprus -0,769 -0,720 -0,729 -0,463 -0,537 -0,644

Czech republic -0,661 -0,612 -0,624 -0,417 -0,456 -0,554

Denmark -1,025 -0,978 -1,112 -0,746 -0,869 -0,946

Dominican Republic -0,430 -0,383 -0,318 -0,133 -0,158 -0,284

Ecuador -0,309 -0,316 -0,242 -0,051 -0,052 -0,194

Egypt -0,428 -0,401 -0,371 -0,157 -0,154 -0,302

El Salvador -0,406 -0,424 -0,397 -0,184 -0,233 -0,329

Eritrea -0,443 -0,378 -0,281 -0,111 -0,086 -0,260

Estonia -0,695 -0,696 -0,751 -0,481 -0,547 -0,634

Ethiopia -0,349 -0,318 -0,274 -0,064 -0,127 -0,226

Finland -1,053 -0,990 -1,101 -0,758 -0,869 -0,954

France -0,859 -0,820 -0,876 -0,576 -0,630 -0,752

Gabon -0,413 -0,374 -0,272 -0,100 -0,087 -0,249

Georgia -0,303 -0,312 -0,327 -0,161 -0,223 -0,265

Germany -0,954 -0,865 -0,933 -0,639 -0,718 -0,822

Ghana -0,446 -0,421 -0,390 -0,202 -0,269 -0,346

Greece -0,679 -0,636 -0,661 -0,372 -0,406 -0,551

Guatemala -0,385 -0,355 -0,302 -0,087 -0,111 -0,248

Haiti -0,150 -0,140 -0,036 0,071 0,064 -0,038

Honduras -0,352 -0,348 -0,290 -0,102 -0,117 -0,242

Hong kong -0,830 -0,801 -0,909 -0,627 -0,727 -0,779

Hungary -0,700 -0,651 -0,678 -0,407 -0,454 -0,578

Iceland -1,012 -0,992 -1,105 -0,783 -0,851 -0,949

India -0,450 -0,443 -0,417 -0,206 -0,246 -0,352

Indonesia -0,314 -0,318 -0,282 -0,103 -0,133 -0,230

Iran -0,419 -0,395 -0,317 -0,106 -0,129 -0,273

Ireland -0,895 -0,839 -0,898 -0,628 -0,694 -0,791

Israel -0,740 -0,697 -0,740 -0,450 -0,556 -0,637

Italy -0,712 -0,668 -0,649 -0,364 -0,386 -0,556

Ivory coast -0,308 -0,274 -0,123 0,045 0,037 -0,125

Jamaica -0,453 -0,431 -0,413 -0,187 -0,257 -0,348

Japan -0,755 -0,741 -0,789 -0,526 -0,632 -0,689

Jordan -0,534 -0,544 -0,544 -0,294 -0,342 -0,452

Kazakhstan -0,288 -0,294 -0,218 -0,085 -0,111 -0,199

Kenya -0,310 -0,310 -0,257 -0,049 -0,090 -0,203

Korea, south -0,668 -0,616 -0,640 -0,422 -0,461 -0,562

Kuwait -0,654 -0,616 -0,631 -0,391 -0,414 -0,541

Kyrgyz republic -0,344 -0,320 -0,231 -0,034 -0,052 -0,196

Latvia -0,597 -0,587 -0,597 -0,365 -0,419 -0,513

Lebanon -0,438 -0,403 -0,346 -0,164 -0,132 -0,297

Lithuania -0,622 -0,616 -0,629 -0,385 -0,404 -0,531

Luxembourg -1,022 -0,919 -1,041 -0,680 -0,748 -0,882

Macedonia -0,373 -0,391 -0,382 -0,168 -0,223 -0,308

Malaysia -0,649 -0,611 -0,655 -0,396 -0,444 -0,551

Malta -0,738 -0,732 -0,783 -0,477 -0,583 -0,663

Mexico -0,517 -0,480 -0,442 -0,212 -0,256 -0,381

Moldova -0,339 -0,318 -0,214 -0,086 -0,095 -0,210

End of the Table A1

2002 2003 2004 2005 2006 In average

Morocco -0,494 -0,465 -0,447 -0,214 -0,247 -0,373

Mozambique -0,391 -0,356 -0,322 -0,143 -0,158 -0,274

Namibia -0,532 -0,505 -0,476 -0,267 -0,320 -0,420

Nepal -0,427 -0,399 -0,277 -0,061 -0,100 -0,253

Netherlands -1,007 -0,935 -1,040 -0,698 -0,774 -0,891

New Zealand -0,986 -0,957 -1,101 -0,720 -0,807 -0,914

Nicaragua -0,370 -0,362 -0,331 -0,097 -0,073 -0,247

Nigeria -0,236 -0,248 -0,156 -0,023 -0,033 -0,139

Norway -0,999 -0,940 -1,047 -0,710 -0,811 -0,901

Pakistan -0,351 -0,343 -0,253 -0,083 -0,119 -0,230

Panama -0,482 -0,455 -0,446 -0,227 -0,254 -0,373

Paraguay -0,248 -0,249 -0,178 -0,016 -0,040 -0,146

Peru -0,437 -0,420 -0,345 -0,133 -0,179 -0,303

Philippines -0,436 -0,413 -0,357 -0,176 -0,180 -0,312

Poland -0,620 -0,587 -0,558 -0,336 -0,368 -0,494

Portugal -0,811 -0,757 -0,787 -0,498 -0,525 -0,676

Romania -0,460 -0,436 -0,415 -0,220 -0,257 -0,358

Russia -0,374 -0,374 -0,320 -0,124 -0,128 -0,264

Saudi Arabia -0,540 -0,486 -0,435 -0,219 -0,253 -0,387

Senegal -0,506 -0,423 -0,415 -0,215 -0,202 -0,352

Singapore -1,031 -0,978 -1,107 -0,744 -0,832 -0,938

Slovak Republic -0,586 -0,587 -0,635 -0,411 -0,443 -0,532

Slovenia -0,714 -0,693 -0,747 -0,465 -0,536 -0,631

South Africa -0,633 -0,596 -0,637 -0,413 -0,432 -0,542

Spain -0,890 -0,834 -0,854 -0,562 -0,551 -0,738

Sri Lanka -0,480 -0,443 -0,399 -0,178 -0,230 -0,346

Sudan -0,268 -0,214 -0,123 0,067 -0,003 -0,108

Sweden -1,013 -0,954 -1,058 -0,708 -0,815 -0,909

Switzerland -1,022 -0,951 -1,080 -0,724 -0,826 -0,920

Syria -0,379 -0,320 -0,234 -0,048 -0,060 -0,208

Tajikistan -0,263 -0,246 -0,152 -0,011 -0,043 -0,143

Tanzania -0,355 -0,351 -0,327 -0,129 -0,181 -0,269

Thailand -0,497 -0,482 -0,481 -0,277 -0,280 -0,403

Togo -0,295 -0,257 -0,143 0,004 0,048 -0,129

Trinidad and Tobago -0,548 -0,544 -0,539 -0,287 -0,299 -0,443

Tunisia -0,642 -0,592 -0,575 -0,301 -0,349 -0,492

Turkey -0,472 -0,471 -0,458 -0,272 -0,303 -0,395

Ukraine -0,322 -0,328 -0,250 -0,142 -0,142 -0,237

United Kingdom -0,980 -0,917 -1,012 -0,663 -0,750 -0,865

United States -0,939 -0,870 -0,964 -0,609 -0,657 -0,808

Uruguay -0,677 -0,620 -0,629 -0,408 -0,443 -0,555

Uzbekistan -0,271 -0,254 -0,159 0,013 -0,028 -0,140

Venezuela -0,283 -0,265 -0,202 -0,046 -0,073 -0,174

Vietnam -0,382 -0,377 -0,300 -0,134 -0,144 -0,268

Yemen -0,346 -0,330 -0,221 -0,068 -0,075 -0,208

Zambia -0,313 -0,299 -0,236 -0,067 -0,104 -0,204

Zimbabwe -0,263 -0,234 -0,132 0,058 0,048 -0,105

Table A2

Estimated Energy Intensity of Production in Separate Economic Sectors in the World Economies, (2004, 72 observations, White covariance matrix method)

Variables Dependent variable sumption per a unit goods production s : ln[Energy Con-of Value Added in phere] Dependent variable: ln[Energy Consumption per a unit of Value Added in services sphere]

Using interaction term Using institutional variable Using interaction term Using institutional variable

Constant term 0,1900 ¿-Value = 1,37 0,1760 ¿-Value = 1,48 0,0708 ¿-Value = 0,80 0,0548 ¿-Value = 0,75

Mean annual temperature: MEATE -0,0025 ¿-Value = -3,60 -0,0027 ¿ -Value = -3,60

Real energy price: ln(P/pE) 0,5017 ¿-Value = 5,25 0,4075 ¿ -Value = 4,90 0,5022 ¿ -Value = 4,56 0,4243 ¿ -Value = 5,07

Interaction term: ln(P/pE) CC04 0,1996 ¿-Value = 1,85 0,2029 ¿ -Value = 3,58

Control of Corruption Index: CC04 -0,1145 ¿ -Value = -2,10 -0,1503 ¿ -Value = -4,36

^-squared 0,3295 0,3439 0,3501 0,4604

F-value 14,52 14,83 12,36 14,84

Root MSE 0,45613 0,45121 0,31151 0,28384

Hausman test, Chi2 * 1,79, Prob>chi2 = 0,6164 0,60, Prob>chi2 = 0,896 0,46, Prob>chi2 = 0,7932 0,27, Prob>chi2 = 0,8756

* Instrumental variables are logarithm of import cost of oil for the real energy price and in addition latitude degree for the interaction term. IVS and OLS models are compared for the samples of economies for which is the data on import cost of oil accessible.

Material received 01.03.2014

Н. И. Суслов

Новосибирский государственный университет ул. Пирогова, 2, Новосибирск, 630090, Россия

Институт экономики и организации промышленного производства СО РАН пр. Акад. Лаврентьева, 17, Новосибирск, 630090, Россия

nsus@ieie.nsc.ru

СТИМУЛЫ К ЭНЕРГОСБЕРЕЖЕНИЮ И ИНСТИТУЦИОНАЛЬНЫЕ УСЛОВИЯ:

ОПЫТ МЕЖСТРАНОВОГО АНАЛИЗА ЧАСТЬ 2

Наша гипотеза состоит в том, что главной причиной значительного отставания России от передовых стран по энергоэффективности является слабость стимулов к энергосбережению, обусловленная недостатками институционального механизма. Чтобы это продемонстрировать, мы строим теоретическую модель энергопотребляющего сектора экономики и показываем, что чем выше транзакционные издержки, вызванные недостатками рынка и регулирования, тем ниже вероятность осуществления энергосберегающих мероприятий в ответ на рост цены энергии. Включение для 77 стран мира на период середины предыдущего десятилетия в регрессии для коэффициентов энергоемкости институциональных переменных позволило нам построить коэффициенты эластичности спроса на энергию по цене, дифференцированные по различным экономикам. Такие показатели (по абсолютной величине) для стран СНГ оказались почти вчетверо ниже, чем в странах ОЭСР.

Ключевые слова: энергоемкость, энергосбережение, экономики мира, эластичность по цене, институты, конкуренция по Курно.