Trabajo fin de grado Título Nombre Apellido

EUE Empresariales / Enpresa IUEVitoria / Gasteiz

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Nombre Apellido

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Trabajo fin de grado

Título

Nombre Apellido



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2

1) Objetivo2) Revisión de la literatura

3) Datos4) Resultados

5) Conclusiones

Ínidice

1) Objetivo

2) Revisión de la literatura

3) Datos

4) Resultados

5) Conclusiones



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5) Conclusiones

Objetivo del estudio

Building of new wind turbines is not universally accepted action.

The objective of the survey was to analyse the preferences of German citizens regarding wind power generation.

Programme A Programme B Programme C

Size of the wind farms large farms medium farms small farms

Height of the turbine 200 metres 150 metres 110 metres

Effect on red kite population 10 % 15 % 5 %

Minimum distance to village 750 metres 1,100 metres 1,500 metres

Surcharge to energy bill per month € 0 € 6 € 1

I choice



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Revisión de la literatura – modelos híbridos



5) Conclusiones

Hybrid model combining discrete and continuous heterogeneity representations of tastes has been applied e.g in Bujosa et al. (2010)(Bujosa, A., Riera, A. and Hicks, R. ,2010, Combining Discrete and Continuous Representations of Preference Heterogeneity: A Latent Class Approach, Environmental and Resource Economics)

Hess et al. (2011) combines continuous MXL with LC model using two classes for each coefficient, where one value is constrained to zero but the value in the second class is allowed to vary across respondents using a continuous distribution.(Hess, Stathopoulus, Campbell, O’Neill, Caussade, 2011, It’s not that I don’t car, I just don’t care very much: confounding between attribute non-attendance and taste heterogeneity)

(Boeri M., D. Campbell, A. Longo, 2012, Discrete mixture of sets of continuous distributions: a mixed finite-continuous approach)



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5) Conclusiones

Revisión de la literatura – modelos híbridos

Fosgerau and Hess (2009) analyse various ways of specifying random taste heterogeneity in DCM: base distribution with Legendre polynomials and discrete mixture of multiple continuous distributions. The performance of two parameter distributions is poor in comparison to FB or MOD.(Fosgerau and Hess, 2009, A comparison of methods for representing random taste heterogeneity in discrete choice models)

Campbell et al. (2010) compare different combinations of discrete and continuous mixing approaches to uncover the unobserved heterogeneity in price sensitivities. They show that single continuous distribution may not adequately represent the true distribution.

(Campbell, Doherty, Hynes, van Rensburg, 2010, Combining discrete and continuous mixing approaches to accommodate heterogeneity in price sensitivities in environmental choice analysis.)



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5) Conclusiones

Revisión de la literatura – que aportamos

Hess and Hensher (2010) have proposed inferring attribute-processing strategies through the analysis of respondent-specific coefficient distributions obtained through conditioning on observed choices.(Hess, S. & Hensher, D.A., 2010: Using conditioning on observed choices to retrieve individual-specific attribute processing strategies, Transportation Research Part B)

We propose a heuristic approach based on mean values of the conditional distributions, which should be valid indicators of the coefficient sign, nevertheless . . .

. . . it is important to recognise that it is impossible to retrieve the actual processing strategy used by a given respondent with certainty and we can only state that a respondent has positive or negative coefficient up to a probability.



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5) Conclusiones

Datos: descripción del lugar

Localización del parque El Espino



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5) Conclusiones

Datos: La encuesta

Percentages of stated choice-card non-attendance

Attribute Stated choice-card non-attendance

Size of the wind farms (WindF) 10%

Height of the winmill (Height) 12%

Effect on red kite population (Red) 13%

Minimum distance to village (Min) 10%

Surcharge to energy bill per month (Cost) 9%

The survey: 353 interviews, 5 choice cards per respondent (Meyerhoff et al. 2010).

The use of information on stated choice-task non-attendance can lead to endogeneity bias, given the likely correlation between the answers to non-attendance questions and other unobserved components. (Hess, 2011: Impact of unimportant attributes in stated choice surveys)

(Beck , Rose, and Hensher, 2011: Consistently inconsistent: the role of certainty, acceptability and scale in automobile choice )



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Datos: Subgrupos



5) Conclusiones

Histograms of choice probabilities of 20% of individuals not included in the reduced sampleFull Attendance

Fre

qu

en

cy

0.0 0.2 0.4 0.6 0.8 1.0

05

01

00

15

02

00

Stated non-attendance

Fre

qu

en

cy

0.0 0.2 0.4 0.6 0.8 1.0

05

01

00

15

02

00

Inferred non-attendance

Fre

qu

en

cy

0.0 0.2 0.4 0.6 0.8 1.0

05

01

00

15

02

00

Threshol

d

Subsample 1

full attendance

Subsample 2

non-attendance

Subsample 3

inferred non-attendance

0.5 78.23% 73.23% 79.11%0.6 73.52% 69.70% 75.29%0.7 69.11% 64.41% 71.17%0.8 62.35% 57.05% 63.52%0.9 52.35% 43.82% 52.94%

Frequency of choice probabilities higher than 0.5, 0.6, 0.7, 0.8, and 0.9 for three different subsamples



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5) Conclusiones

Modelo RPL

Model 1: RPL

full attendance assumed

Attributes only

Model 2: RPL full attendance assumed

Attributes + Soc.-dem. variables

Parameter t-value

St. Dev. t-value

Parameter

t-value

St. Dev. t-value

WindF 0.2257 1.08 0.2097*** 7.13 -0.0496* -1.67 0.1963*** 6.52 High 0.0004 0.16 0.0216*** 6.39 -0.0004 -0.15 0.0228*** 6.93 Red -0.2299*** -8.00 0.2413*** 6.67 -0.488*** -6.00 0.2385*** 6.50 Min 0.0009*** 2.59 0.0042*** 8.79 0.0001 0.27 0.0046*** 8.26 Cost -0.9673*** -8.32 1.1274*** 8.30 -0.960*** -8.38 1.1019*** 9.62 ASC1 -0.2441 0.97 -0.2894 -1.13 ASC2 0.8175*** 6.70 0.8159*** 6.60 WindF * Gender

0.7333** 2.05

WindF * Urban

0.0995*** 2.68

High*Close Turbine

0.0084 1.63

Red*Age 0.0049*** 3.41 Red* Close Turbine

0.1048* 1.87

Min*High Income

0.0019*** 2.62

LogL -1353.73 -1333.71 AIC 2731.63 2703.82 BIC 2797.17 2801.99 N 1765 1765

Model 1: RPL

full attendance assumed

Attributes only

Model 2: RPL full attendance assumed

Attributes + Soc.-dem. variables

t-value

Parameter

t-value

St. Dev. t-value

7.13 -0.0496* -1.67 0.1963*** 6.52 6.39 -0.0004 -0.15 0.0228*** 6.93 6.67 -0.488*** -6.00 0.2385*** 6.50 8.79 0.0001 0.27 0.0046*** 8.26 8.30 -0.960*** -8.38 1.1019*** 9.62 -0.2894 -1.13 0.8159*** 6.60 0.7333** 2.05

0.0995*** 2.68

0.0084 1.63

0.0049*** 3.41 0.1048* 1.87

0.0019*** 2.62

-1333.71 2703.82 2801.99 1765



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5) Conclusiones

Distribución de los coeficientes

-1.0 -0.5 0.0 0.5 1.0

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

-0.5 0.0 0.5 1.0

01

23

45

67

-0.5 0.0 0.5 1.0

01

23

45

67

Sin no-atención Con no-atención Con atributos controversiales



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5) Conclusiones

Atributos separados

Table 9 Percentages of individuals with positive and negative coefficientsAttribute Positive Negative

Size of the wind farms (WindF) 50.9% 49.0%

Height of the windmill (Height) 54.1% 45.9%

Effect on red kite population (Red) 0.00% 100.00%

Minimum distance to village (Min) 48.7% 51.2%

Cost 0.00% 100.00%

Controversial attributes may lead to the existence of two clearly distinct groups of respondents: one experiencing positive and the other negative impacts of these attributes on their utilities, while maintaining rather homogeneous preferences inside these groups.



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Atributos separados: RPL



5) Conclusiones

RPL Full attendance RPL Stated non-attendance RPL Inferred non-attendance

Parameter t-value Std Dev. t-value Parameter t-value Std Dev. t-value Parameter t-value Std Dev. t-value

Size (+) 0.2044*** 5.52 0.1256*** 3.17 0.2116*** 5.54 0.1068** 2.52 0.2125*** 5.44 0.1082** 2.21

Size (-) -0.1366*** -5.78 0.0640** 2.28 -0.1290*** -7.10 0.0540** 1.97 -0.1544*** -6.47 0.0839*** 2.82

High (+) 0.3234*** 7.67 0.0695 1.10 0.0271*** 7.74 0.0047 0.62 0.3422*** 7.95 0.0762 1.11

High (–) -0.1882*** -7.40 0.1164*** 3.43 -0.0172*** -8.84 0.0021 0.33 -0.1813*** -7.31 0.0856 1.49

Red -0.4691*** -6.33 0.2279** 2.05 -0.2297*** -6.16 0.1956* 1.72 -0.5153*** -6.85 0.2460*** 4.99

Min (+) 0.3725*** 9.43 0.2045*** 4.23 0.0030*** 9.54 0.0017** 2.04 0.3714*** 9.09 0.1960*** 4.14

Min (–) -0.3839*** -7.32 0.0966 1.36 -0.0031*** -7.76 0.0007 1.23 -0.3838*** -7.49 0.0667 0 .99

Cost (-) -0.5369*** -7.55 0.5011*** 7.06 -0.0002*** -2.96 0.0001 0.26 -0.7059*** -10.41 0.2085** 2.14

ASC1 -0.1898 -.85 1.2437*** 8.55 -0.5137** -2.36

ASC2 0.7212*** 6.56 .6411*** 6.69 0.7558*** 6.79

Size * Gender

0.0263 1.06 .0174 0.92 0.0455* 1.86

Size * Urban

0.0708*** 2.81 .0519*** 2.60 0.0685*** 2.74

High*Close Turbine

0.0244 .67 .0252 0.87 0.0159 0.43

Red*Age 0.0048*** 3.72 .0002 0.45 0.0056*** 4.17Red* Close Turbine

0.0862* 1.72 .0857** 2.05 0.0877* 1.70

Min*High Income

0.2100*** 4.66 .1779*** 4.81 0.2084*** 4.58

LogL -923.7 -1030.4 -864.3AIC 1951.5 2164.8 1832.6BIC 2236.2 2449.5 2117.3N 1765 1765 1765

RPL Full attendance RPL Stated non-attendance RPL Inferred non-attendance



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La heterogeneidad reducida



5) Conclusiones

Unobserved preference heterogeneity is reduced

RPL Inferred non-attendance

Parameter t-value Std Dev. t-value

Size (+) 0.2125*** 5.44 0.1082** 2.21

Size (-) -0.1544*** -6.47 0.0839*** 2.82

High (+) 0.3422*** 7.95 0.0762 1.11

High (–) -0.1813*** -7.31 0.0856 1.49

Red -0.5153*** -6.85 0.2460*** 4.99

Min (+) 0.3714*** 9.09 0.1960*** 4.14

Min (–) -0.3838*** -7.49 0.0667 0 .99

Cost (-) -0.7059*** -10.41 0.2085** 2.14

. . . .

. . . .

. . . .



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Disposiciones a pagar



5) Conclusiones

WTP for Min and Height attributes

-2 -1 0 1 2

Height of the windmill (Height)

WTP (in Euros)

Inferred non-attendance (split)Full attendance (split)Full attendance

-2 -1 0 1 2

Minimum distance to village (Min)

WTP (in Euros)

Inferred non-attendance (split)Full attendance (split)Full attendance



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Conclusiones



5) Conclusiones

1) Respondents state that they have not attended to certain attributes when they have actually taken them into account.

2) Controversial attributes lead to difficulties in calculation of WTP measures because their coefficients are distributed around zero.

3) Latent class allowing for continuous taste heterogeneity within classes (second approach) leads to similar results obtained by the method based on respondent-specific coefficient distributions (first approach).

4) The treatment of controversial attributes with the two presented approaches leads to expected reduction of the unobserved preference heterogeneity.



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Muchas gracias por su atenció[email protected]

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