Information and risk perception: Evidence from a randomized experiment

Sonia Akter Lee Kuan Yew School of Public Policy National University of Singapore

Research Questions

Lack of concern about the negative impacts of climate change acts as a barrier to adaptation

How people’s risk perception can be shifted? Can information work as a catalyst?

Study Area: Barguna District

District

5 Sub-District

18 Unions

50 Villages

121 Hamlets

408 Households

Primary male/female

Randomly selected

Randomly selected

Randomly selected

Randomly selected by tossing a coin

Randomly selected

A household survey was conducted in Barguna district from April to May 2016

Treatment Control

Information on CC NO Information on CC

The Treatment Card 1

Card 2

Card 3

• Two separate questionnaires were designed

• Treatment and Control questionnaires were same in all respects except for the section on CC information

• The treatment questionnaire was accompanied by an information booklet

• Each enumerator was given a set of questionnaires in random order

• After the selection of the respondent during the field interview, the enumerator randomly chose a questionnaire from the available set

Randomization Process

1. Concern about the harmful impacts of climate change • Do you worry or are you concerned about the harmful impacts

of climate change on your life and livelihood? (1=no or slightly worried; 2=moderately worried; 3=very or extremely worried)

2. Concern about the harmful impacts of cyclone • Do you worry or are you concerned about the harmful impacts

of cyclone on your life and livelihood? (1=no or slightly worried; 2=moderately worried; 3=very or extremely worried)

3. Likelihood of future cyclone • How frequently do you think a cyclone like Sidr might take

place in your area in the future? Once every ________ year • 68% replied “I don’t know”

Outcome Variables (1)

4. Change in the extent of salinity intrusion • What percentage of your cultivable land will be unusable due to

salinity intrusion in 10 years from now?______%

• Perceived change in salinity intrusion in farmland in 10 years from now

• Difference between perceived proportion minus current proportion

• 1 If perceived is greater than current

• 0 if perceived is less than or equal to current

Outcome Variables (2)

Empirical Model

𝑌𝑖𝑗𝑘 = 𝛽0 + 𝜷𝟏𝑰𝒏𝒇𝒐𝒓𝒎𝒂𝒕𝒊𝒐𝒏𝒊𝒋𝒌 + 𝛽2𝑋𝑖𝑗𝑘 + 𝛽3𝑉𝑘 + 𝛽4𝐸 + 𝜀𝑖𝑗𝑘

• i stands for individual and j stands for hamlet and k stands for

village • 𝑌𝑖𝑗𝑘 is risk perception indicators

• Information =1 if respondent is in treatment group and zero otherwise

• 𝑋𝑖𝑗𝑘 is a vector of individual and household characteristics

• 𝑉𝑘 is village fixed effect and E is enumerator fixed effect • β is a parameter vector, and 𝜀𝑖 a normally distributed error

term • Standard errors are clustered at the hamlet-level

Regression Results (1): Climate Change

CC Risk Perception Indicators Coeff (SE)

Information 0.31**

(0.15) Enumerator fixed effects

Village fixed effects Y

Individual characteristics Y

Household characteristics Y

N 397

Pseudo R2 0.23 Notes:

• Ordered probit model.

• Dependent variable=CC risk perception: 1=not at all or slightly

worried; 2=moderately worried; 3=extremely worried

• Robust standard errors clustered at the hamlet level are reported in

parentheses.

• p<0.01***, p<0.05**, p<0.10*

Regression Results (2): Cyclone

Cyclone Risk Perception Indicators Coeff (SE)

Information 0.15

(0.18) Enumerator fixed effects

Village fixed effects Y

Individual characteristics Y

Household characteristics Y

N 398

Pseudo R2 0.32 Notes:

• Ordered probit model.

• Dependent variable=Cyclone risk perception: 1=not at all or slightly worried;

2=moderately worried; 3=extremely worried

• Robust standard errors clustered at the hamlet level are reported in

parentheses.

• p<0.01***, p<0.05**, p<0.10*

Regression Results (3): Salinity

Salinity Risk Perception Indicators (1) (2)

Information 0.40** 0.49**

(0.16) (0.25)

Enumerator fixed effects

Village fixed effects Y Y

Individual characteristics Y Y

Household characteristics Y Y

N 265 265

Pseudo R2 0.13 0.45

Column (1): Ordered probit model. Dependent variable=Expected extent of salinity

intrusion in terms of percentage of farmland unusable due to soil salinity

Columns (2): Probit model. Dependent variable=Perceived change in the extent of

salinity intrusion. Decrease=0; increase=1

Robust standard errors clustered at the hamlet level are reported in parentheses.

p<0.01***, p<0.05**, p<0.10*

Conclusions

• Exposure to information can shift risk perceptions about CC [at least temporarily]

• Measuring risk perception is challenging • Small sample size is a limitation of the study • Future research needs to be carried out:

• Large sample size • Temporary vs permanent shift