ANALYZING THE IMPACT OF FEED-IN TARIFF POLICY ON RENEWABLE

ENERGY SOURCES OF ELECTRICITY

HAJIME ALABANZA UNIVERSITY OF CALIFORNIA, SAN DIEGO

DECEMBER 16, 2015

1Source: REN21, Renewables 2015 Global Status Report (2015), http://www.ren21.net/wp-content/uploads/2015/07/REN12-GSR2015_Onlinebook_low1.pdf.

2Source: Elsevier, Policy Differences in the Promotion of Renewable Energies in the EU Member States, Danyel Reiche and Mischa

Bechbeger (2005), https://www.aub.edu.lb/fas/pspa/politics-sports/Documents/eu-member.pdf.

3Source: Greenpeace, Japanese Utilities Hinder Clean Energy,

http://www.greenpeace.de/sites/www.greenpeace.de/files/publications/final_engrid_report_jan2015.pdf.

ABSTRACT:

Feed-in Tariff (FIT) policy has been one of the most popular mechanisms to galvanize renewable

energy (RE) deployment; however, the results of FIT policy have been widely inconsistent. My

paper seeks to address two questions: 1) whether FIT policy is successful in accelerating the

growth of renewable energy sources of electricity (RES-E) 2) if electricity market regulation

hinders the efficacy of FIT policy. Utilizing a fixed-effects and propensity score matching

estimator, I examine the causal relationship between FIT policy and RES-E in 35 OECD

countries plus 4 non-OECD countries. Both models provide strong evidence that FIT policy is

effective in increasing RES-E in these countries. Furthermore, results show that market

liberalization is a necessary component in ensuring the success of FIT policy.

1: Introduction

Issues of global warming, energy independence, and oil conflict have propelled clean

energy onto the global agenda. Feed-in Tariff (FIT) policy has been one of the most popular

policy measures to galvanize the development of RE sources—as of date, over 80 countries have

employed FIT policy. 1 Although FITs have a variety of designs (detailed by Klein et al.), all FIT

policies seek to accomplish two goals: 1) enable RE generators to run projects at competitive

costs 2) obligate utilities to purchase electricity generated by these RE projects. Evidence shows

that FIT policy has been met with substantial increases in RES-E in many countries; however, in

other countries, like Israel and South Africa, the same cannot be said.2 This can in part be

attributed to the fact that utilities in regulated electricity markets have the power to restrict access

to the grid.3 For example, three years after FIT policy was enacted in Israel and South Africa,

RES-E on average fell -5% and increased 1%, respectively.

Figure 1: Relationship between RES-E growth (excluding hydroelectricity) and market structure

(6 denotes high regulation) for four countries, approximately three years after FIT launch

Source: EIA (2015)

Hungary

Turkey

IsraelSouth Africa

-50%

0%

50%

100%

150%

200%

250%

0 2 4 6

Gro

wth

Rat

e (3

Yea

r A

ver

age)

Market Regulation Score (3 Year Average)

4Source: NREL, Integrating Variable Renewable Energy: Challenges and Solutions (2013), http://www.nrel.gov/docs/fy13osti/60451.pdf

First, this paper seeks to address the question of whether FIT policy is successful in

increasing RES-E (excluding hydroelectricity) in OECD countries. Second, it determines if

electricity market liberalization is necessary for the growth of RES-E. The methodology of my

study first utilizes a fixed effects model. The fixed effects estimator should control for time

invariant factors such as the size of a country and technological know-how; hence, along with

my control variables, this should mitigate selection bias. In addition to a fixed effects model, I

implement a propensity score matching (PSM) analysis to estimate the efficacy of FIT policy on

increasing RES-E. PSM should eliminate selection bias by pairing several countries that are

similar to one another (based on the control variables), with the only difference being that one

country implemented FIT policy and the other did not. I hypothesize that FIT policy has a

positive impact on the development of RES-E. Furthermore, I believe that the magnitude of FIT

policy is even greater when implemented in countries with high levels of electricity market

liberalization.

2. Literature Review

There are numerous studies that analyze the impact of RE policy on RE supply and/or

capacity. Yin/Powers (2009), for instance, analyze the efficacy of RPS policy on RES-E.

However, like Yin/Powers’ piece, many studies focus on Renewable Portfolio Standards (RPS)

in the United States. Of those papers that focus on FIT policy at the country level—which I am

interested in—the variable of interest is RE capacity (i.e. Groba et al. (2011)). In this paper, my

primary concern is not the effect of FIT policy on RE capacity, but, rather, the impact of FIT

policy on RES-E. In certain countries like Israel, Luxembourg, and France, RE capacity growth

was not followed by a similar rate of growth in RES-E; hence, the impact of FIT policy could be

both misleading and overestimated if measured on RE capacity alone. The discrepancy between

RES-E and RE capacity could be true for a number of reasons, including the inability of grids to

handle the variability of RE resources and/or utilities restricting access to the grid.4 The figure

below illustrates this discrepancy:

Figure 2: Difference between the growth of RES-E and RE capacity, approximately three years

after FIT launch

Source: EIA (2015)

-5%

4%8%

43%

50%

26%

-10%

0%

10%

20%

30%

40%

50%

60%

Israel Luxembourg France

RESE Growth (3 Year Average) RE Capacity Growth (3 Year Average)

5The Hausman test rejects the null hypothesis of no unit heterogeneity, meaning that some of the independent variables are correlated

with the unit effects; hence, the unobserved differences among countries must be controlled for.

Furthermore, because this study is assessing the impact of FIT policy across countries, it is

important to account for the different electricity market structures since utilities in monopolistic

markets have the power to restrict access to the grid. Failing to account for electricity market

structure could distort causal inference.

As mentioned previously, I will use a fixed effects estimator to analyze the causal

relationship between FIT and RES-E. Because counties are fundamentally different from one

another (i.e. Germany likely has more potential for solar energy than Luxembourg, due to greater

land area), it is important to account for these differences. Five other studies—Carley (2009),

Yin/Powers, Marques et al. (2010), Shrimali/Kneifel (2011), and Grobal et al.—utilized fixed

effects estimators to account for these time invariant differences.

Furthermore, this study will assess the causal relationship between FIT policy on RES-E

in 34 OECD countries plus 5 non-OECD countries (Brazil, China, Indonesia, India, and South

Africa). Similarly, Marques et al. and Groba et al. assessed RE policy in many European OECD

member countries, but did not include OECD countries outside of Europe. I chose to conduct this

study on OECD countries—plus the additional four emerging economies— because FIT policy

has a strong history within these countries. Moreover, as OECD members, these countries have

relatable economic/political environments and objectives, which helps to limit selection bias.

3: Empirical Framework and Data

The main objective of this study is to ascertain the causal relationship between FIT policy

and RES-E (excluding hydroelectricity) in 35 OECD countries plus 4 non-OECD countries. In

other words, all else equal, how much of the growth in RES-E in these countries can be

attributed to FIT policy? In addition to this, I would like to ascertain whether electricity market

structure impacts the performance of FIT policy.

Using panel data, I observe the behavior of these 39 countries across 23 years (1990-

2012)—a total of 897 observations. First, I run a pooled cross-section regression in order to

establish a baseline. Then, I conduct a country-level fixed effects estimation to analyze the

effects of FIT policy on RES-E. The fixed effects estimator is necessary to control for

heterogeneity across countries. According to Groba et al., fixed effects models will control for

any country level characteristics that stay constant over time. This includes a country’s potential

for generating electricity from RE sources (i.e. strength and consistency of wind, amount and

intensity of sunlight, amount of geothermal reserves, etc.), time-invariant environmental

propensity, and the capacity of RE in a country prior to 1990.5 For country i, in year t, my fixed

effects model takes this form:

(1) RESEit = 𝛼𝑖 + 𝛽𝐹𝐼𝑇𝑖𝑡 + 𝜉𝑅𝑃𝑆𝑖𝑡 + 𝛿𝑊𝑖𝑡 + 𝑒𝑖𝑡

where RESE is the total amount of non-hydro electricity generated in billions of kWh, i denotes

a country-specific intercept, 𝐹𝐼𝑇𝑖𝑡 is a binary treatment variable that represents whether a country

has implemented FIT policy or not, 𝑅𝑃𝑆𝑖𝑡 is a binary variable indicating whether a Renewable

Portfolio Standard (a regulation that requires more production of RE sources) exists in a country

or not, and 𝑊𝑖𝑡 is a variable that captures social and economic factors that influence RES-E. It is

also important to note that I transformed all of my variables in this study to deal with non-

normality. Information about this is provided in Table 2.

The dependent variable in this study is the annual amount of electricity generated from

RE sources (excluding hydroelectricity) by a country from the years 1990-2012, in billions of

kWh. This data was procured from the U.S. Energy Information Administration (EIA). As

previously mentioned, this study examines electricity generated from RE sources opposed to

installed RE capacity since RE capacity has the potential to bias the impact of FIT policy

upwards. Furthermore, I chose to forgo using RES-E as a ratio of total electricity generation,

because the goal of FIT policy is not to increase the proportion of RES-E to total electricity

generation; rather, its goal is to increase RES-E.5

The policy/treatment variable for my study is a binary variable and is equal to 1 if a FIT

exists in that country for any given year or a 0 if a FIT does not exist in that country for any

given year. Again, I am curious to assess the impact that FIT policy has on RES-E since the

success of the policy has thus far been inconsistent. This data was gathered from the Renewable

Energy Policy Network for the 21st Century (REN21). Provided below, is a table showing the

years that countries used in this study enacted FIT policy:

Table 1: FIT Phase-in

FIT Policy Implemented That

Year

Country Cumulative

Number

1990 Germany; United States 2

1991 Switzerland 3

1992 Italy 4

1993 Denmark; India 6

1994 Luxembourg; Greece; Spain 9

1999 Norway; Portugal; Slovenia 12

2001 France 13

2002 Austria; Brazil; Czech Republic;

Indonesia

18

2003 Estonia; Hungary; South Korea;

Slovakia

22

2004 Israel 23

2005 China; Turkey; Ireland 26

2007 Australia; Finland 28

2009 Japan; South Africa 30

2010 United Kingdom 31

2011 Netherlands 32 Source: Renewable Energy Policy Network for the 21st Century (2015)

I also included Renewable Portfolio Standard (RPS) as a control variable. RPS is a

regulation that requires countries to increase the production of energy from RE sources, so it

heavily influences RES-E levels within a country. The data is gathered from REN21.

The economic and social factors included either directly or indirectly impact RES-E. They

are described in detail below:

Electricity Market Regulation: Countries with higher levels of electricity market

liberalization elicit a higher level of competition; hence, RE generators should fare better

in these countries. Giving equal weight to four factors (entry regulation, public

ownership, vertical integration, and market structure), a country score from 0 to 6, where

6 denotes the highest level of regulation or lowest level of competition, is created to

measure the level of electricity market regulation with a country. This metric is denoted

as EMR in this study. This data is gathered from the OECD.

Economic Output: Countries transitioning from conventional sources of electricity to

RES-E will likely realize higher electricity prices since RE has not yet reached grid parity

in most countries. As a result, electricity consumers residing in countries with strong

economic performance should be able to better handle an increase in electricity prices

resulting from greater RES-E. To account for this, GDP per capita (current US$) is

included in this study and is denoted as GDP. This data is gathered from the World Bank.

Availability of Domestic Credit: Like any project, RE projects require a great deal of

financing—especially since RE technology is costly in many parts of the world. Investors

in countries with higher levels of commercial credit and lending should more readily be

able to procure financing for RE projects, which most likely leads to higher RES-E. To

account for this, I include a variable that represents domestic credit provided by the

financial sector (% of GDP) within a country and is denoted DC. This data is gathered

from the World Bank.

Electricity Consumption: Countries that consume more electricity overall will likely have

more total RES-E. To account for this, I include a variable that captures the total amount

of electricity consumed in billions of kWh within a country and is denoted EC. This data

is gathered from the U.S. Energy Information Administration (EIA).

Fossil Fuel Generation: For a majority of countries, fossil fuels are the main resources

used to generate electricity. As a result, a country using more fossil fuels to generate

electricity might require less RE as a source of electricity. I have included total fossil

fuels electricity net generation in billions of kWh to account for this. This variable is

denoted as FF. This data is gathered from the U.S. Energy Information Administration

(EIA).

Table 2: Control Specifications

Variable Description Unit Obs. Source Also used

in

FIT (Treatment) FIT Policy Binary 897 REN21 Groba et al.

(2011)

RPS RPS Regulation Binary 897 REN21 Yin and

Powers (2010)

Electricity Market

Regulation (EMR)

Square root of Electricity Market

Regulation score

Score from 0

to 6

811 OECD

GDP per capita (GDP) Natural logarithm of GDP per

Capita, Current US$

Current US

Dollars

886 World

Bank

Groba et al.

(2011)

Availability of Domestic

Credit (DC)

Square root of domestic credit

offered by financial sector

% of GDP 839 World

Bank

Total Electricity

Consumption (EC)

Natural logarithm of supply of

total electricity generated

Billions of

kWh

886 EIA Yin and

Powers

(2010),

Groba et al.

(2011)

Fossil Fuel (FF) Natural logarithm of supply of

electricity generated by FF

Billions of

kWh

886 EIA Groba et al.

(2011)

In addition to the fixed effects model, I will use a Propensity Score Matching (PSM)

estimator. The effectiveness of FIT policy on RES-E can be measured by finding the average

treatment effect. Or, in other words, we can measure the impact of FIT policy by taking the

difference in average outcomes between countries that implemented FIT policy and countries

that did not. This can be illustrated mathematically:

(2) E(RESE1 – RES-E0|FIT=1) = E(RESE1|T=1) – E(RESE0|T=1)

However, the fundamental problem of causal inference indicates that it is impossible to perceive

the individual treatment effect. That is, a country cannot both implement and not implement FIT

policy. As a result, a counterfactual for E(RESE0|T=1) must be created. E(RESE0|T=1) can be

interpreted as the outcome that countries receiving FIT policy would have realized, on average,

had FIT policy not been implemented. PSM will allow me to procure an accurate counterfactual

by matching countries that are similar in all aspects (observable variables) with the exception of

FIT policy implementation. Formally, the matching estimator can be illustrated as so:

(3) RESE0 FIT | X

This says that differences between the treatment and control groups are captured by the

observable variables X that I provide—𝑅𝑃𝑆𝑖𝑡 and 𝑊𝑖𝑡. Furthermore, it is also necessary that a

control group be selected from countries that did not implement FIT policy in a way that the

distribution of the observable variables is very close to the distribution of the countries that did

implement FIT policy. For this, it is necessary that:

(4) 0 < Prob{FIT=1 | X=x} < 1 for x∈ �̃� Finally, using the two equations above, propensity score matching is expressed formally below:

p(x) ≡ Prob{FIT=1 | X=x}

(5) RESE0 FIT | p(x) for X in �̃�

We then pair the countries that received FIT policy to comparable countries that did not receive

FIT policy:

(6) 𝐺(𝑃𝑖) = min𝑗

|𝑃𝑖|𝑃𝑗|

where 𝐺(𝑃𝑖) depicts the group of control countries j matched to treated countries i (on the

estimated propensity score), 𝑃𝑖 is the estimated propensity score for treated countries i (countries

that implemented FIT policy), and 𝑃𝑗 is the propensity score for control countries j (countries that

did not implement FIT policy). This type of matching is known as nearest neighbor matching

since the absolute difference between the estimated propensity scores for FIT and non-FIT

countries are minimized. Once the treatment and control groups are matched, the average

treatment effect can be found.

Provided below are summary statistics of both non-transformed and transformed

variables:

Table 3: Summary Statistics

Variable Mean Standard Deviation Min Max

RESE 8.955545 21.61234 0 232.1202

Log_RESE .6355298 2.081176 -4.60517 5.447299

FIT .4202899 .4938808 0 1

RPS .1059086 .3078923 0 1

EMR 3.936001 1.668107 .871875 6

Sqrt_EMR 1.933681 .4439852 .9337425 2.44949

GDP 23616.07 18885.37 308.5348 113738.7

Log_GDP 9.602321 1.178606 5.731834 11.64166

DC 108.5998 61.97828 1.67601 348.0478

Sqrt_DC 10.00403 2.920496 1.294608 18.65604

EC 294.4252 648.254 4.065 4467.923

Log_EC 4.610567 1.462513 1.402414 8.404679

FF 202.9653 505.7655 .00188 3675

Log_FF 3.564928 2.427309 -6.276484 8.209309 In order to take logarithm of RESE, a value of 0.01 was added to all observations of RESE where RESE it = 0.

4: Results

Table 4 details the results of the baseline pooled cross-section regression. The results

reveal a positive relationship between FIT policy and RES-E. More specifically, it shows that

countries that implement FIT policy generate approximately 7% more RES-E than countries that

do not implement FIT policy. However, this relationship is not statistically significant due to the

omission of variables (i.e. country level characteristics); hence, the results of this model cannot

be accepted as causal.

Table 4: Pooled Cross-Section OLS Results

Log_RES-E Coef. Std. Err.

FIT .0765071 .1061878

RPS .1283716 .1710668***

Sqrt_EMR -1.427153 .1306722***

Log_GDP .468518 .0744805

Sqrt_DC .0299484 .0216943

Log_EC .8195714 .055038***

Log_FF -.0655351 .0300057**

Number of obs 729

Adj R2 0.6028 *Significance 10%, **Significant at 5%, ***Significant at 1%

Table 5 illustrates the results of my main model—the fixed effects model. This time,

country level characteristics are controlled for. As hypothesized, the results reveal that FIT

policy has significant and positive effects on RES-E. The regression indicates that countries with

FIT policy can expect RES-E to be approximately 35.56% higher (remember that 0.01 was added

to RES-Eit=0 in order to take the logarithm, so it must be subtracted from final value). Moreover,

the variable representing Electricity Market Regulation indicates that for a one-unit increase in

EMR score, RES-E falls approximately 77%. The EMR variable is statistically significant and

provides strong evidence that electricity market regulation greatly hinders the growth of RES-E.

In addition to these two variables, all other control variables in this model are statistically

6Source: Harvard, How Robust Standard Errors Expose Methodological Problems They Do Not Fix, and What to Do About It (2014). Gary

King and Margaret Roberts, http://gking.harvard.edu/files/gking/files/robust_0.pdf.

significant. I should also mention that I include robust standard errors in order to confirm the

validity of my model and results. According to King and Roberts, a model in which standard

errors and robust standard errors differ significantly, estimators are inefficient and model

misspecification is likely.6 As can be seen in the table, the variables in my model remain

statistically significant—albeit at different levels—even after applying robust standard errors.

Table 5: Fixed Effects Results

Log_RES-E Coef. Std. Err. Robust Std. Err.

FIT .3655634 .0739562*** .1431638**

RPS .2945525 .0964799*** .1725297*

Sqrt_EMR -.7695064 .0966433*** .228897***

Log_GDP .9149807 .0945171*** .2018576***

Sqrt_DC .0760919 .0160915*** .0368983**

Log_EC 1.219851 .1670577*** .4424084***

Log_FF -.3594651 .0797203*** .1872557*

Number of obs 729

Number of groups 38

R2 0.5181

*Significance 10%, **Significant at 5%, ***Significant at 1%

Next, I will perform my PSM estimator. Recall that the PSM estimator seeks to assess the

impact of FIT on RES-E by matching countries (three countries in this case) that are most similar

to one another with the only difference coming from FIT implementation. The results show that

implementing FIT policy increases RES-E by approximately 36.56% (again, I subtracted 0.01

from the final value) in these countries. Moreover, the results are shown to be statistically

significant. This figure is similar to the 35.56% coming from the fixed effects estimator. The

results are located below:

Table 6: Propensity Score Matching Results

Log_RESE Coef. AI Robust Std. Err.

FIT vs No FIT .37557 .1238862***

Number of obs 729

Matched 3 *Significance 10%, **Significant at 5%, ***Significant at 1%

5: Conclusion

This paper provides a comprehensive econometric analysis of the efficacy of FIT policy

in OECD countries. By measuring FIT policy’s impact on RES-E instead of RE capacity, a more

accurate measure of the strength of this policy is addressed. Furthermore, this research takes into

account the regulatory environment of a country’s electricity market—a critical variable that is

missing in other studies. The results indicate strong evidence of the efficacy of FIT policy on

RES-E in OECD countries. Countries that implement FIT policy generate approximately 36%

more RES-E than countries that do not. Furthermore, electricity market liberalization is proven

to be a significant factor in ensuring the success of FIT policy. That is, all else equal, a one-unit

move towards electricity market liberalization results in approximately 77% more RES-E within

a country. This indicates that FIT policy should be implemented in concert with policy measures

that aim to liberalize a country’s electricity sector. In future analyses, I hope to (1) test the

efficacy and impact of different FIT policy designs on RES-E (i.e. fixed-price and premium-

price) (2) examine the external validity of the study.

References

Bechbeger, Mischa & Reiche, Danyel. (2005), ‘Policy Differences in the Promotion of

Renewable Energies in the EU Member States’, Elsevier, 5-9.

Bird, L.; Lew, D. & Milligan, M. (2013), ‘Integrating Variable Renewable Energy: Challenges

and Solutions’, NREL, 1-10.

Carley, S. (2009), 'State Renewable Energy Electricity Policies: An Empirical Evaluation of

Effectiveness', Energy Policy 38(8).

Faber, Thomas; Held, Anne; Klein, Arne; Ragwitz, Mario & Resch, Gustav. ‘Evaluation of

Different Feed-in Tariff Design Options – Best Practice Paper for the International Feed-in

Coorporation’, 10-55.

Greenpeace, (2015), ‘Japanese Utilities Hinder Clean Energy’, 1-4.

Groba, Felix; Indvik, Joe & Jenner, Steffen. (2011), ‘Assessing the Strength and Effectiveness of

Renewable Electricity Feed-in Tariffs in European Union Countries’, Deutsches Institut fur

Wirtschaftsforschung’.

International Energy Agency Statistics. (2015), IEA. N.p., n.d. Web.

King, Gary & Roberts, Margaret. (2014), ‘How Robust Standard Errors Expose Methodological

Problems They Do Not Fix, and What to Do About it’, 161-177.

Marques, A.; Fuinhas, J. & Manso, J. (2011), 'A Quantile Approach to Identify Factors

Promoting Renewable Energy in European Countries', Environmental & Resource Economics

49(3).

Marques, A. C.; Fuinhas, J. A. & Pires Manso, J. (2010), 'Motivations Driving Renewable

Energy in European Countries. A Panel Data Approach', Energy Policy 38(11).

Organization of Economic Co-operation and Development Statistics. (2015), OECD N.p., n.d.

Web.

Renewable Energy Policy Network for the 21st Century, (2015), ‘Renewables 2015 Global Status

Report’, 153.

Shrimali, G. & Kneifel, J. (2011), 'Are Government Policies Effective in Promoting Deployment

of Renewable Electricity Resources?', Energy Policy 39(9).

The World Bank Statistics. (2015), The World Bank. N.p., n.d. Web.

Yin, H. & Powers, N. (2009), 'Do State Renewable Portfolio Standards Promote In-State

Renewable Generation?', Energy Policy 38(2).