1 ^ Tables 2.9 and Table 2.10 for each information change variable respectively The

estimation o f marginal effects for interaction variables is less straightforward in non-linear models. The approach taken follows the applied methodology presented in Karaca-M andic et al. (2012) and we com pare the change in the predicted probability for a one unit change (zero to one) in the categorical demographic variable being analysed (further details/examples provided below in Section 2.4.2).

A difference-in-difference (DID) approach is employed to investigate the effects that improvements/deteriorations in the self-reported stock o f information may have had on electricity demand (research question two). The DID model can be employed to explore the effects o f a policy change when data from two periods (pre and post-policy) and two

'■ The model is estimated using STATA version 11.2.

Estimating these interactions simultaneously is not possible due to the size o f the dataset. The results are summarized as it would not be possible to present full results from the 24 models (six separate demographic interactions by overall treatment and by stimulus for both knowledge statements).

groups (treated and untreated) are available. The DID model is generally estimated by

pooled Ordinary Least Squares and is described by (see W ooldridge (2010), for example):

Vit = Po +

where is electricity demand for household i in period t, Y2 is the period two dummy

and T is the treatment dummy {Ui^ the usual noise term). The coefficient describes the

temporal change in demand for the control group and ^ 2 describes the difference in

demand between control and treatment groups in period one. The main coefficient o f

interest is the interaction term ^3, which describes how the demand o f the treatment group

changed in period two (compared to the control group), or more formally:

P3 {yT,Y2 ~ J T y i ) ~ {yc,Y2 ~ y c ,Y i) (2 )

where subscript C represents the control group. The m odel can also be estimated in a panel

data setting by adding a fixed effect (Cj) to the error term and using a Within Regression

Estimator. H owever, all time-invariant terms, such as Ti, are swept away by this time-

invariant unobserved heterogeneity term and the model then reduces to:

y u = ^ 0 + + e i + Uit (3)

In Section 2.4.3, this model is employed to explore the effects o f treatment on electricity

demand (for comparison against the original CER reports). Section 2.4.4 then explores i f

improvements in the self-reported stock o f information help explain these reductions by

adding fiirther interactions (the information change variables) with each treatment group.

2.4

Results

2.4.1

The effects of treatment on the households’ stocks of information

The M NL results and marginal effects o f treatment on K1 change (general energy reducing information) are displayed by overall treatm ent (‘TREAT’ - all feedback stimuli combined) and by individual feedback in Tables 2.5 and 2.6 respectively. Section 2.2 showed that, prior to the trial, almost 60% o f the sample felt they had a sufficient understanding o f electricity reducing actions (either agreed or strongly agreed with the statement). Results show that trial participation has increased this proxy for the stock o f information further, with improvements significantly larger in the treatm ent groups. Overall (Table 2.5), the marginal effects demonstrate that treatment significantly increases the probability o f improving the stock o f infonnation ( ‘moved to agree’) by 8.9 percentage points and reduces the probability o f lowering the stock o f information (move to disagree) by 7.3 percentage points compared to the control group. This effect is highest for the MST and the IHD (Table 2.6), where households are 9.7 and 11.5 percentage points more likely than the control group to show improvements.

Table 2.5: M logit Results - Effect o f Treatment (overall) on K1

K1 "I know w hat I need to do in order to reduce electricity usage"

C oef. Std. Err. D Y /D X Std. Err.

O utcom e 1 — M oved to D isagree:

TR EA T (D) -0.291** 0.120 -0.073*** -0.017

Constant -0.207** 0.098 - -

O utcom e 2— N o Change (base):

TR EA T (D) - - -0.016 -0.021

Constant - - - -

O utcom e 3 - M oved to A gree:

TR EA T (D) 0.245** 0.105 0.089*** -0.022

Constant 0.167** 0.090 - -

M odel Stats:

N 2445 LR chi test stat. 22.07

Log-Likelihood -2565.403 P > chi 0.000

Pseudo R-Squared____________________0.0043_____________________________________________ N ote: ‘TREA T ’ is a dum m y variable capturing overall tre a tm e n t ‘D Y /D X ’ indicates m arginal effect a n d significance levels are highlighted by '* * * ’ (1%), “* * ’ (5%) an d (10%).

Table 2.6: M logit Results - Effect o f Feedback Stimuli on K1

K1 "I know what I need to do in order to reduce electricity usage"

Coef. Std. Err. D Y /D X Std. Err.

O utcom e 1 - M oved to D isagree;

BI-MST (D) -0.309* 0.024 -0.070*** 0.024

MST (D) -0.19 0.023 -0.067*** 0.024

IHD (D) -0.389** 0.000 -0.097*** 0.023

Constant -0.207** 0.000 - -

O utcom e 2 — N o Change (base):

BI-MST (D) - - 0.009 0.027

MST (D) - - _{-0.03 0.026}

IHD (D) - - _{-0.018 0.027}

Constant - - - -

O utcom e 3 - M oved to A gree:

BI-M ST (D) 0.112 0.000 0.058** 0.028 MST (D) 0.314** 0.028 0.097*** 0.027 IHD (D) 0.311** 0.027 0.115*** 0.028 Constant 0.168* 0.000 - - M o d el Stats: N Log-Likelihood Pseudo R-Squared 2445 -2562.77 0.0053

LR chi test stat. P > chi

27.330 0.000

N ote: ‘B I-M S T ’ refers to the bi-m onthly statem ent, ‘M S ’ to m onthly statem en t a n d ‘I H D ’ to the in-house display.

A household’s self-reported stock of information on appliance consumption is explored in