feed-in tariff econometric analysis
TRANSCRIPT
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.
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