Accounting for Productivity and Spillover Effects in Emerging Energy Technologies

Accounting for productivity and spillover effects in emerging energy technologies: the case of wind
power
Richard Bowers
Proposal for Capstone Project
ECPA
Economics Department
UMBC
Advisor: Virginia McConnell
June 6, 2014
Abstract
The need to bring renewable energy sources into the market to replace more traditional sources in
order to reduce greenhouse gas (GHG) emissions are great, and will only increase over time. Many
renewable energy sources such as wind and solar photovoltaic (PV) currently have high costs relative to
traditional energy sources. The costs for new products tend to decline over time as the productivity
improves. This paper will examine the evidence for cost changes in the wind power industry as it has
emerged in its major market in California between 1985 and 1995. Productivity improvements over time
are hypothesized to occur for a number of reasons including technology improvements, scale economies,
learning within firms (intra firm learning), and learning across firms (inter firm learning) and the industry
as a whole. A Reason for policy intervention due to market failures such as learning spillovers, and R&D
are identified. An externality or spillovers can also exist on the demand side of the market and these will
be discussed.

Accounting for Cost Reductions and the Role for Subsidies in Emerging Energy Technologies
2
I. Introduction
There is a good deal of focus on emerging technologies for electric power generation around the
world because of the high rate of greenhouse gas (GHG) emissions from carbon based sources such as
coal, oil and natural gas. Many of these emerging technologies, such as wind and solar photovoltaic
(PV), are at early stages of development and have relatively high costs compared to more traditional
sources. While these high costs are prohibitive There is a great deal of pressure to subsidize these
emerging technologies with the hope of driving costs down, and allowing them to become mature
competitive sources in the future.
It is common for costs of new products to decline over time as the technology and manufacturing
processes mature. This paper will examine the reasons costs tend to decline, including factors within a
firm, and spillovers between firms. The arguments for declining costs will first be discussed, then
evidence from the literature on which of the various factors have been shown to be most important in
identifying learning will be presented.
In addition this paper will seek to establish when subsidies would be most appropriate. Economic
theory suggests that subsidies are appropriate as a correction to spillovers created in certain situations; 1)
research and development (R&D) and 2) learning by doing. Externalities on the demand side of the
market may also exist and will provide rationale on different types of subsidies. The focus of the paper is
then on the costs of wind power provision in California from 1985 to 1995. We attempt to identify the
existence and possible effect of operational learning by doing, or learning occurring from the day to day
use of a technology or process, by examining the increases in quarterly electricity output of individual
wind plants given wind speed changes and capital depreciation to determine if operational learning by
doing occurs in the wind energy production industry.
II. Arguments for Declining Costs for Emerging Industries
The issue of technology change and learning by doing has been a focus in economics as far back as
Arrow (1962) and the early development of knowledge accumulation and shifts in production (Arrow,

3
1962). Declining costs for emerging industries over time can occur for a number of reasons. We can
summarize them as:
A. Costs reductions within the firm: including R&D, Learning by Doing, and scale economies
B. Cost reductions due to spillovers from other firms within the same industry: including Learning
by Doing across the industry and R&D by other firms within an industry
C. Demand spillovers caused by increased usage of a given technology.
A. Cost Reductions within the Firm.

Research and development (R&D) is one method by which firms can attempt to reduce costs. While
internal firm learning involves optimizing the given inputs to minimize costs, R&D focuses on the
generation of new technologies, or process, to attempt to reduce costs. R&D, however, is often expensive
and resource consuming (Griliches, 1992). In deciding whether or not to engage in R&D, or deciding on
the level at which to engage in R&D, a firm will attempt to balance the general cost of R&D against
expected future profits that the effort might yield, adjusting for the risk of such an investment (Jaffe,
1996). In theory, firms would engage in R&D when its own expected benefits outweigh the costs.
However, due to R&D spillovers firms tend will tend not to realize benefits from R&D in excess of R&D
investments (Griliches, 1992). This can vary a greatly by industry.
Another way for firms to reduce costs is to improve at production methods. As a firm repeats the
same processes over and over again, it is generally well accepted economic fact that the costs associated
with such processes should decrease over time as the firm realizes efficiency improvements. This
behavior of knowledge accumulation (learning by doing) at the individual firm level has been
documented in numerous industries including farming, semi-conductor manufacturing, shipbuilding, and
aeronautics (Griliches, 1992; Wright, 1936; Thornton and Thompson, 2001; Irwin and Klenow, 1994).
Learning by doing is generally measured by a progress ratio or learning curve which represents the
decrease in production costs associated with a doubling of cumulative production at the individual firm
level (Epple, et al, 1991; McDonald & Schrattenholzer, 2001). Since learning is the process of

4
experience accumulation it only takes place through the attempt to solve a problem, which requires
activity (Arrow, 1962). No learning occurs from a firm sitting idle.
Learning can also take various forms. In a seminal paper, Bahk and Gort (1993) identify three
principle elements of knowledge accumulation by the firm, or learning by doing as:
- Organizational Learning: the matching of employees and tasks based on the knowledge and
experience of each employee or the accumulation of independent knowledge of each
employee which is not transferable from employee to employee; or managerial learning
reflected in improved scheduling and coordination among departments and in the selection of
external suppliers of services or products.
- Capital Learning: associated with the increase in knowledge about characteristics of psychical
capital (tolerance to which parts are designed, special tools or devices, and plant layout).
- Manual Task Learning: learning associated with repeating execution of manual and semi-
manual tasks (Bahk and Gort, 1993).

Finally, economies of scale is another reason why costs can fall within a firm. Economies of scale
are reached when a firm reaches a production volume over a given period of time which yields a
reduction in inputs due either to optimization of labor capital ratios, and/or a reduction of the price of
inputs brought on by higher volumes of production. The cost reductions derived from economies of scale
are reductions in average cost brought on by higher production volumes over a given period while
learning by doing reduces costs over time as total cumulative production increase (Arrow, 1962; Bahk
and Gort, 1993; Nemet, 2011).
B. Cost Reductions due to Industry Spillovers

In addition to firm-level reductions in costs, there can be cost reductions that can spillover over
from one firm to another. Innovation and development by one firm may affect design, production, and
the associated costs of these activities to other firms. This can be through R&D by one firm that allows
for improvements that other firms in the same industry can adopt. The key is that these are spillover
externalities – a single firm does not capture all of the returns to its own investment in R&D, or to

5
innovation in production strategies because some of the returns from innovation spillover to other firms
that produce similar products. Since a firm does not capture all the benefits of R&D itself, due to
spillovers into other firms, a firm is likely to invest into R&D at a level that is less than socially optimal,
preferring to free-ride on the R&D of other firms in the industry (Griliches, 1992) (Nemet, 2011). Such a
situation has occurred in the semiconductor industry in which many producers in the industry have
benefitted from the R&D advancements of an individual firm since the products produced are relatively
homogeneous (Irwin & Klenow, 1994).
Generally spillovers will refer to benefits accrued by a firm or industry which incurred minimal
or no costs obtaining those benefits. Spillovers differ slightly from free-ridership since spillovers are
generally derived from an action in which the costs are incurred by one firm, with the benefits initially
intended for that firm inclusively. Free-ridership normally occurs when a firm (or individual) obtains a
benefit without incurring any costs and when the benefits were intended for the larger society, at a cost.
Any time that a firm gains a benefit at the cost of another firm or a firm does not capture the full value of
an investment into new technologies it will be assumed that a spillover is present in the market.
C. Demand Spillovers

Increased production comes from an increased demand for the product. Thus, diffusion of the
new technologies on the demand side can also be important for reducing costs. (Rao, Keepo, & Riahi,
2006). Technology diffusion refers to the adoption of a technology and then the subsequent adoption of
that technology by additional users - in short it refers to the ability of a technology to ‘catch-on’ by users
resulting from the technology being visible in the market place and used by early adopters. This diffusion
of technology shifts the demand curve for the given technology outward overtime and allows production
to increase and costs to fall.
D. Spillovers and Wind Energy

Spillover effects are likely to be important for new technologies in the wind energy market, where
there is the need and potential for innovation, where there will likely to trial and error, and where there
are a relatively small number of players who can learn from each other. In addition to innovation and

6
spillovers in product development, installation siting, a major determinant to electricity output for wind
technologies, can also create spillovers. This is another case in which the actions of one firm can benefit
other firms even if not intended. Yet, another example for wind energy is the development a new
methodology for resource measurement, say wind speed measurement and predictions. If one firm begins
to place wind turbines in areas that are were once proven unprofitable, other firms will likely take notice
and can then benefit from that firm’s investments.
While technological change that lowers costs is brought on through learning, innovation and
R&D, costs are also reduced by increasing production.
III. Role of Subsidies for Emerging Energy Technologies
Subsidies are very common today for renewable energy firms. Here we focus on the economic
rationale for subsidies. The first argument for subsidies is the existence of un-priced externalities
associated with carbon dioxide (CO2) emissions from carbon-based fuels. In this case a better policy than
a subsidy might be a carbon tax, which would disadvantage carbon-based fuels relative to renewables.
But there are several economic rationales for subsidies based on the above discussion. There are two
possible spillovers or externalities that may result in renewables being under-supplied. One is the
spillover from R&D, as discussed above.
The second is the potential spillover in learning by doing, when those spillovers are not fully
accounted for by individual firms. Subsidization to account for these spillovers if it is equal to the
magnitude of the spillovers will increase social welfare. Developing some empirical estimates of those
spillovers is what this paper will address.
One important issue with spillovers from learning by doing is that both learning within firm and
learning between firms may be subject to diminishing returns. Information does not travel perfectly
between firms, due to information asymmetries between those who initially acquire the knowledge and
the adoption of that knowledge by others. Since information travels in an imperfect manner learning, both
between firms and within a firm, is subject to diminishing returns whenever the knowledge is
accumulated second-hand.

7
Another key issue is that knowledge gained from experience may depreciate over time, and
sometimes rapidly as learning occurs and knowledge accumulates certain learning is likely to be forgotten
as new knowledge is accumulated if not utilized often. This can occur for a variety of reasons including
employee turnover, change in corporate practice, and non-utilization of hyper-specialized knowledge
involved with a rare occurring event in project operation (Bahk and Gort, 1993; Nemet, 2011).
Also, since there are large spillovers involved with R&D, the level of R&D in which firms
engage in might not be the socially optimum level (Griliches, 1992). The inability of an individual firm to
capture all of the returns to R&D suggests the need for either public R&D or industry R&D subsidization.
Since the price paid by consumers is lower than the full price needed to capture the R&D value the
consumers receive a positive benefit from R&D spillovers. Non-optimal spending on R&D alone is not
enough to warrant government intervention in the form of subsidies, otherwise the government would
need to subsidize every industry. Even if government subsidization of R&D did occur, the generally
inelastic supply of scientists would result in higher salaries than more scientists, the latter of which would
be necessary to promote more R&D projects (Griliches, 1992).
IV. Application to Wind Power Generation
In order to determine the presence of learning in the wind energy sector in California between
1985 and 1995, this paper examines quarterly electricity output of wind projects and notes increases in
quarterly electricity output given wind speed and fixed but depreciating capital. These increases are
designed to reflect operational learning. Learning in this manner can be caused by wind project
owner/operators better adapting to changing wind conditions by controlling the pitch and yaw of the
turbine and scheduling maintenance during forecasted low wind periods. Accumulated knowledge
depreciates over time. Therefore we expect to see increases in electricity output at a given wind farm to
occur at diminishing rate, assuming that capital depreciation occurs in the manner which we will model it
(see below).
Early research on learning by doing utilized production theory to represent the occurrence of
learning. Rapping (1965) looked at shipbuilding output during World War II, and utilized a basic

8
production function to determine how labor inputs engaged in learning during this period to increase
output in the absence of capital gains or technology improvements (Rapping, 1965). The basic production
function is as given:
𝑋!" = 𝐴𝑀!"
!!
𝐾!"
!!
𝑉!", (1)
where X is the annual rate of ship output, M is the annual rate of physical labor inputs, K is the annual rate
of capital inputs, V is a random disturbance term, and i and t are production facility and time indices
respectively (Rapping, 1965). Rapping (1965) found an increase in X over time despite no increases in
capital or changes in technology, implying that labor learning occurred allowing for a higher output
(Rapping, 1965). The learning by doing literature expands on this basic production function approach.
Bahk and Gort (1993) use a standard production function and include a term that captures the
stock of accumulated knowledge:
𝑌! = 𝐹 𝐿!, 𝐾!, 𝐸! (2)
where, Y is output, L is labor, K is capital, and E refers to a stock of knowledge for a given relevant time
period t. The stock of knowledge for a given organization is a measure of the cumulated gross output
since the organization’s birth, or:
𝐸! = ℎ 𝑆! (3)
where, h’ > 0, 𝑆! = 𝑦!!!! , which is the cumulated gross output from the birth of the organization up
through the previous time period t.
Other research has extended this basic model to look at the wind energy production industry in
particular. This paper seeks to build upon the previous work of Gregory Nemet (2011) in looking at
knowledge accumulation spillovers in wind power for the state of California (Nemet, 2011). In
determining the presence and scale of knowledge accumulation spillovers Nemet utilized a modified
production function employed by Bahk and Gort (1993
In modifying the production function used by Bahk and Gort (1993) for general industries, Nemet
(2011), attempts to represent learning by doing in the wind energy production industry. His analysis

9
includes the addition of a W component presenting the wind energy available at the location of a wind
project and a Q, quality, component to account for exogenous improvements in the quality of equipment
that is not captured in higher purchase prices of capital (Nemet, 2011). An assumption is made of zero
returns to increasing labor inputs, so L is also dropped (Nemet, 2011). The assumption of zero returns to
increasing labor inputs is justified since wind turbine installation is a capital machine intensive process
and the ratio of labor to capital is unlikely to change unless there are intensive changes in the
methodology of installation.
The analysis here will focus not on installation but operation of turbines. To capture the effect of
learning E, we consider only operational learning at a given site, as discusses below. Given an individual
project, E represents the operational learning occurring over time which can take a variety of forms
including better maintenance scheduling and capturing more of the available wind energy from pitch and
yaw control of the turbine. When looking at operational learning, E, among different projects owned by
the same operator spillovers within a firm can be demonstrated. The resulting production function is:
𝑌! = 𝐹 𝐾!, 𝐸!, 𝑊!, 𝑄! , (4)
The estimated production function above provides the electrical output for each quarter t. Observing
changes in the electrical output over time at the same plant accounting for other changes over time will
support the hypothesis that gains from learning are occurring. In order to determine spillovers the
electrical output between projects operated by different owner/operators across quarter will support the
hypothesis that spillovers between firms exist since operating experience should be equal across all firms.
Output, intuitively is a product of inputs (labor, capital, materials), but the level of these inputs
can be changed due to other factors. The implementation of a policy can affect other things that may
make production easier. Implications of policy can be derived from the inclusion of a policy variable into
the production function:
𝑌! = 𝐹 𝐾!, 𝐸!, 𝑊!, 𝑄!, 𝑃! , (5)

10
where Pt is the indicator of an incentive policy in in effect during quarter t. Several policies exist for
rationale of increase output, but no single policy expands enough of the study period to be considered to
have an measurable effect on production.
This production function acts as the base model for determining the presence of operational
learning by estimating the effect of operational learning, E, on quarterly electricity output, Y. Spillovers
will be measured by comparing operational learning, E, for 1) different projects under the same
owner/operator to measure spillovers within a firm, and 2) different wind projects under different
owner/operators to measure spillovers between firms.
V. Data
This analysis will utilize a data set of wind turbine projects installed in California from 1985-
1995 of (n=144 projects) obtained from the Wind Performance Reporting System (WPRS) at University
of California Davis (eWPRS, 1985-1995). The WPRS dataset provides quarterly electricity output as well
as detailed turbine data for each project (eWPRS, 1985-1996). By providing information on every project
installed in California since the beginning of the industry locally in the state the survival bias is avoided.1
Figure 2, Figure 3, and Figure 4 depict the locations of turbines by capacity in the three wind
energy areas of interest for this analysis; Altamont, Tehachapi, and San Gorgino Pass. While not all
turbines depicted are included in the analysis due to the USGS map including turbines installed after
2003, the three figures gives a broad overview of the distribution of turbines across the roughly 40 km2
in
each figure.
1
The survival bias occurs when data points which are not present throughout the every year of the analysis are
dropped out of the data set prior to it being available which then biases results to those data points which lasted
(survived) the entire time period of the analysis.

11
Figure 1: Location of Three Wind Areas
Figure 2: Altamont Wind Turbine Locations

12
Figure 3: Tehachapi Wind Turbine Locations
Figure 4: San Gorgino Pass Wind Turbine Locations

13
VII. Estimation
To estimate the effects of learning and scale economies, we estimate equations above, using a
panel data set of quarterly data from wind power plants in California between 1985 and 1995. We rewrite
equation (6) including the subscript i as an indicator for individual wind projects. Estimating a
production function for wind we use equation (6) above. Here we explain in more detail the components
of equation (6) and the hypothesized coefficients of model.
𝑌!" = 𝐹 𝐾!", 𝐸!", 𝐸!", 𝑊!" (6)
Table 1 contains list of the variables used the analysis of the wind industry in California. While
most of the variables used in the analysis are measured fairly straightforward, a few of the defined
variables are detailed below.
Table 1: Variable Notation
We assume plants are installed at a point in time and then the capital depreciates over time. We
then define learning as an increase in quarterly electricity production given wind speed and depreciated
Symbol Description Units
t Calendar time; t = 1 in Q1-1985 Quarters
i Project identifier Category
Yit Electrical output by project i in quarter t kWh/qtr
υiτ Turbines installed in quarter τ by project i Turbines
λ Knowledge depreciation; remaining after 1 quarter %
δ Quarterly rate of knowledge depreciation; = − ln(λ) %
Eit Depreciated operating experience kWh
EIt Total depreciated operating experience for the industry kWh
Vit Average wind speed in quarter t at project i m/s
Wit Wind energy available in quarter t at project i kWh/m2
*qtr
Ut Dummy for windy season; 1= Q2 and Q3 Binary
h Number of hours per quarter Hours
Gi Generation capacity of each turbine at project i kW
Tit Number of turbines in quarter t at project i Turbines
ct Cost of wind turbine capacity in quarter t (2008$/kW)
γ Quarterly rate of capital depreciation %
Kit Depreciated capital stock in quarter t at project i (2008$)
Pt Value of policy dummies in quarter t Binary

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capital, and the other variables in equation (6). Since knowledge accumulation depreciates due to a
variety of reasons including, but not limited to; employee attrition; knowledge becoming less relevant due
to changes in demand, technology, or structure; or even ability to comprehend and adapt to indicators
from operation. Depreciated operating experience in the production function is modeled as: (Nemet, 2011)
𝐸!" = 𝑌!" 𝑒!! !!!!
!!! , (7)
where Yiτ is cumulative electricity for project i from the beginning of the data set until period t, where the
time period range is from τ = 1 until τ = t. The depreciation rate is denoted by δ and is estimated from
experience-derived knowledge depreciation values from other studies (we use 0.42).
The regression analysis includes both an Eit and an Eit
2
term to account for the possibility of
learning being non-linear over time as discussed above. The derivative of output Yit with respect to Eit,
!!!"
!!!"
, will yield the output change to the firm from operational experience gains. Therefore we hypothesize
the following:
HYPOTHESIS 1a. Operational experience at a firm is positively associated with output, but at a
diminishing rate over time.
HYPOTHESIS 1b. Operational experience across an industry is positively associates with output, but at
a diminishing rate over time.
Spillovers, as discussed above, occur when firms within an industry can readily adopt operational
practices that other firms in the industry have established. In order to measure the industry operational
learning variable will take the following form:
𝐸!! = 𝐸!"
!
!!! (8)
where, EIt is the summation of all depreciated operational experience for all projects in a given quarter. A
positive coefficient should suggest that spillovers are occurring within the industry, between firms. Due to
the nature of the wind power industry as suggested by the literature above, we hypothesize:
HYPOTHESIS 2: Industry learning will be positively associated with electrical output as firms should
learn from one another indirectly by observing behaviors and practices of projects.

15
Wind energy directly impacts the output and performance of a wind turbine project. The wind Wit
variable of the production function denoted available wind resource at project i in quarter t. Since
available wind is being measured by quarter the diurnal and smaller variation in wind speeds is averaged
away. Available wind for project i in quarter t is given as:
𝑊!" = 𝐹!"!
𝑅 𝑉!" ∙ ℎ,2
(9)
where R is a Rayleigh distribution with mean, Vit, h is the number of hours in a quarter, 2192, and 𝐹!"!
is
the power curve relationship between wind speed and electricity output for the given turbine used at
project i. There exists a physical cubic relation between wind speeds and electricity output, meaning that
electricity output increases by a power of 3 relative to the change in wind speed. This relationship only
holds between the cut-in speed (minimum necessary wind speed) and the rated power of the turbine
(given by the manufacturer). This relationship can be seen in Figure 5 Part C. This relationship
demonstrates the maximum rated power output of a turbine at a given wind speed, not actual achieved
power. The power curve of a wind turbine is based on the modeled output by the manufacturer and only
measures the potential output of the turbine.
The term capacity factor is often used to identify the relationship between actual power generated
at a plant and the potential power. Mathematically the capacity factor is the ratio of the actual output
achieved to the maximum potential output of the turbine determined by the manufacturer.
Increases in electricity output will demonstrate operational learning in the face of a depreciated
capital stock, with the electricity output gain likely derived from learning how to better position the yaw
and pitch of the turbine to maximize captured wind speeds. An increase in capacity factor for a given
project from quarter to quarter suggests operational learning of a different type.
Capacity factor can increase from operational learning in pitch and yaw control of the turbine, but
can also increase in learning from operational and maintenance (O&M) scheduling. Utilizing the
2
This equation captures the cubic relationship between wind speed and power output in the power curve for the
General Electric 2.5 MW turbine.

16
WindPower Program software, the power curve of each turbine model can be determined (output of the
software appearing in Figure 5). (WindPower, 2012).
Figure 5: WindPower Program Output for GE 2.5 MW turbine.A)Turbine name, rotor diameter, and cut-in/cut-out wind speeds,
B)Output (in kW) for given wind speed, C) graphically represented power curve, D) mean power output given a mean hourly
wind speed, E) wind speed probability distribution function, F) turbine output given increasing wind speeds.
The measurement of available wind maintains the cubic relationship between wind speed and
electricity output while not overestimating output due to high winds which cause cut-out of the turbine
(Nemet, 2011). Sensitivity analysis will be conducted on the available wind by utilizing different power
curves of turbines not at the technology frontier by utilizing the power curves for each turbine model at a
given project i provided by the WindPower Program. Since wind speed is the most impactful input in
electricity output we can hypothesize the following:
HYPOTHESIS 3. Available wind defines maximum potential electricity output and is therefore positively
associated with output.
Capital, like experience, depreciates over time. Following Nemet (2011) the capital stock
depreciation is measured by:
𝐾!" = 𝑐! 𝑇!" 𝐺!" 𝑒!! !!!!
!!! , (10)
A
B
C
D
E
F

17
where 𝑐! is the average cost of wind turbines sold in California each year, multiplied by the number of
turbines installed at project i in quarter τ, multiplied by the generation capacity of each turbine at project i
in quarter τ, depreciated by rate γ. Since the capital stock used for this analysis is a depreciated one, the
expected contribution of capital to electricity output from one quarter to another is expected to decrease.
As capital depreciates, other things the same we would expect output to fall over time, therefore we
hypothesize the following:
HYPOTHESIS 4. The greater the capital stock the greater the electricity output, therefore capital stock
will be positively associated with output, but at a decreasing rate. .
The analysis utilizes a basic linear form with project fixed effects, and four models that expand
upon each other:
MODEL 1: 𝑌!" = 𝛼 + 𝛽! 𝐾!" + 𝛽! 𝑊!" + 𝛽! 𝐸!" + 𝛽! 𝐸!" + 𝑢!" (11)
MODEL 2: 𝑌!" = 𝛼 + 𝛽! 𝐾!" + 𝛽! 𝑊!! + 𝛽! 𝐸!" + 𝛽! 𝐸!"
!
+ 𝛽! 𝐸!" + 𝛽! 𝐸!"
!
+ 𝑢!" (12)
MODEL 3: 𝑌!" = 𝛼 + 𝛽! 𝐾!" + 𝛽! 𝑊!" + 𝛽! 𝐸!" + 𝛽!" 𝐸!" + 𝛽! 𝐾!" 𝐸!" + 𝛽! 𝐾!" 𝐸!" + 𝑢!" (13)
MODEL 4: 𝑌!" = 𝛼 + 𝛽! 𝐾!" + 𝛽! 𝑊!" + 𝛽! 𝐸!" + 𝛽! 𝐸!"
!
+ 𝛽! 𝐸!" + 𝛽! 𝐸!"
!
+ 𝛽! 𝐾!" 𝐸!" + 𝛽! 𝐾!" 𝐸!" + 𝑢!" (14)
Under this form a positive coefficient of a variable will suggest an increase in electricity output of
a given wind project in a given quarter, other things the same. The coefficient on Wit should be positive
for the reasons stated above. Since capital depreciation, K, does occur we should see a larger capital
stock associated with larger electrical output with diminishing returns to this output as the capital stock
depreciates over time. Learning will be observed by the operational learning within the firm, Ei, and the
diminishing of maintained learning, Ei
2
.
The important spillover variable is industry experience, EIt. Industry experience should, in theory
show whether or not spillovers occur between firms. The inclusion of the variable EIt, which is the
summation of output of all projects in the region, should have a positive coefficient, as discussed above if
there are learning spillovers between firms within the industry. Two interaction terms are also included
between capital and operational experience within firms and capital and industry experience to deal with

18
the possible effect of project size on learning from experience. Small projects and large projects might
learn at differing rates. Smaller projects are likely to have higher rates of learning since the projects tend
to be less complex and easier to manage, although the output gains are not expected to be as large as
larger projects.
Table 3: Definition of Variables Used
VIII. Results
Equation (11) was estimated with project fixed effects in order to determine operational learning
between quarters, which is noted by increase electricity output, relative to wind speeds and capital
depreciation. In order to deal with large numbers all variables were converted from kilowatt-hour units to
megawatt-hour units and the variables were given the suffix “mw” to denote the difference. Spillovers are
measured by observing the coefficient of the industry learning variable. A positive coefficient suggests
spillovers are occurring between firms within the industry. Summary statistics for relevant variables are
provided in Table 4.
Symbol Variable Description Units
Yit outputmw Electrical output by project i in quarter t MWh/qtr
Kit capitalmw Depreciated capital stock in quarter t of project i (2008$)
Wit avalmw Wind energy available in quarter t at project i MWh/m2
*qtr
Eit expermw Depreciated operating experience in quarter t of project i MWh
Eit
2
expermw_squ Depreciated operating experience squared in quarter t of project i MWh
EIt ind_expermw Experience of the industry MWh
KitEit capital_exper Interaction between capital and experience (2008$)MWh
I Industry identifier Category
uit Error term

19
Table 4: Summary Statistics
Correlation coefficients for relevant variables are outlined in Table 5.
Table 5: Correlation Coefficients
We then ran the panel regression using project fixed effects and a linear form utilizing the
megawatt-hour unit versions of the variables. The output of the fixed effect linear model is provided in
Table 6.
Symbol Variable Description Units
Yit outputmw Electrical output by project i in quarter t MWh/qtr
Kit capitalmw Depreciated capital stock in quarter t of project i (2008$)
Wit avalmw Wind energy available in quarter t at project i MWh/m2
*qtr
Eit expermw Depreciated operating experience in quarter t of project i MWh
Eit
2
expermw_squ Depreciated operating experience squared in quarter t of project i MWh
EIt ind_expermw Experience of the industry MWh
EIt
2
ind_exper_squ Experience of the industry squared MWh
KitEit capital_exper Interaction between capital and experience (2008$)MWh
KitEIt capital_ind Interaction between capital and industry experience (2008$)MWh
I Industry identifier Category
uit Error term
plantsizemw 0.6425 0.8606 0.2513 0.8776 0.1415 1.0000
ind_expmw 0.1025 -0.0498 -0.0262 0.0781 1.0000
avalmw 0.6784 0.7406 0.1782 1.0000
expermw 0.4738 0.4757 1.0000
capitalmw 0.6066 1.0000
outputmw 1.0000
outputmw capita~w expermw avalmw ind_ex~w plants~w

20
Table 6: Linear Panel Regression with Project Fixed Effects
The regression equations has a fairly high degree of explanatory power, with all R2
being about
0.50, but model 4 provides the greatest explanatory power. Capital, which was hypothesized to have a
positive coefficient only holds up to that hypothesis in model 2 and model 4, both where it is significant.
In model 4 a $1000 increase in the size of the capital of the project is associated with gains of around 46
kWh increase in output. Since each megawatt of rated capacity costs $4500, as a project increases in size
by 10 megawatts, production should increase by 2070 MWh.
Experience with in a firm shows that learning is occurring when the coefficient is positive, which
is seen in model 1 and model 4. Positive experience within a firm suggests that from quarter to quarter the
individual project is learning from itself and increasing output. Although experience within a firm is
significant across all models it is not robust, and thus we only look at output results for model 4. As
cumulative output of a give plant increases by 10 MWh, on average a project, all else the same, increases
productivity by roughly 6 MWh from the quarter previous. The experience squared variable which was
hypothesized to capture diminishing returns to experience is positive and significant, rather than negative,
but only affects the output of a project after large gains to cumulative production.
capitalmw -‐0.026 *** 0.031 *** -‐0.0060863 0.0460995 ***
0.01 0.01 0.01 0.01
expermw 0.502 *** -‐0.353 *** -‐0.6553248 *** 0.6068446 ***
0.02 0.04 0.09 0.10
avalmw 0.028 *** 0.027 *** 0.0273408 *** 0.0260875 ***
0.00 0 0 0
ind_expmw 0.0000044 0.0002725 * -‐0.0001179 ** 0.0004135 ***
0.00 0.00 0.00 0.00
expermw_squ 0.0000035 *** 0.00000551 ***
0 0
ind_exper_squ -‐2.54E-‐11 ** -‐2.7E-‐11 **
0 0
capital_exper 0.00000319 *** -‐0.00000405 ***
0 0
capital_ind 6.89E-‐10 5.96E-‐09 ***
0 0
Constant -‐1718.241 *** -‐2334.112 *** -‐850.7861 * -‐4169.513 ***
331.85 346.54 364.75 388.14
R-‐sqr 0.5781 0.591 0.5724 0.6196
Model
4
b/se
*
p<0.05,
**p<0.01,
***p<0.001
Model
1
b/se
Model
2
b/se
Model
3
b/se

21
Spillovers between firms can be derived by looking at the coefficient of the industry experience
variable. Based on the regression, a 100 megawatt-hour increase in industry experience output is only
associated with a 4.135 kilowatt-hour increase in output of a given project. While this number seems
small relative to increases from project experience it still suggests spillovers between projects. Industry
experience across the models shows that industry experience is not robust and only positive and
significant in models 2 and 4, with model 4 providing the best fit overall. While the existence of
spillovers between firms is illustrated by the positive coefficient of industry experience, the rationale for
policy intervention is not as clear cut and will be discussed later.
The inclusion of two interaction terms, one between capital and experience within a project and
capital and industry experience provide some interesting, but possibly inconclusive results. The first point
about the interaction terms is that the coefficients are incredibly small, although both are significant in
model 4. The negative coefficient on the interaction between capital and project experience suggests that
as capital gets larger there are loses to experience gains, perhaps due to complexities of the project which
prevent larger projects from learning faster than smaller projects. The interaction between capital and
industry experience is positive however, which suggests that larger projects learn more from other firms
than smaller firms. This could be due to the ability of larger projects which might have more resources
obtaining market reports or perhaps being a larger member of the industry and having the ability to better
monitor other projects. There could also be some rationale not observed by the data that provides this
result.
IX. Conclusions
The goal of this project was assess whether and why there have been gains in productivity in the
wind industry. In particular we were interested in learning on the part of wind plants in California as
industry was getting started, and whether such learning spills over across firms. The data was drawn from
California plants during the period of 1985 to 1995. the conclusions drawn from the analysis suggest a
few things; 1) spillovers between firms do exists but they are rather small, 2) own firm learning is
important but these gains from own firm experience may not diminish over time as has been found for

22
other industries, and 3) while larger plants produce more there is not a strong relationship between
experience gains and project size.
One important question for new renewable energy industries is whether or not subsidies are
appropriate to promote learning by doing. The evidence here suggests that spillover effects have been
relatively small, and that large subsidies based on such spillover externalities would not be warranted.
This does not mean that subsidies are not appropriate at all, because they could be justified on grounds of
environmental externalities, for example, that wind energy has lower greenhouse gas emissions than
alternative sources of power. While the idea of a subsidy for the wind electricity production industry is
not ruled out by this analysis providing a subsidy, on the ground of learning by doing and encouragement
of spillover, does not seem strong enough given the analysis results. This analysis only utilized an
available data set from California during 1985-1995 due to the proprietary nature of the data of the
industry. In expanding this analysis, future considerations would be put into obtaining the national level
dataset from the National Renewable Energy Laboratory (NREL) provides resources available. In
continuing my education I intend to pursue this issue further while working on my PhD in Marine Policy
at the University of Delaware if able to obtain the national level dataset to determine how learning has
occurred on land based wind energy generation in the United States and how this learning can help reduce
the learning time for offshore wind energy generation.

23
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