Fairness in water sharing under climate change

The intervention consists in varying the context in which respondents express their perception of fairness for different water sharing rules.
We consider in particular variations in water scarcity, variation in the composition of the water user group among which water must be shared and variations in the water needs expressed by water users.

2022-12-20

2023-01-31

Perceived level of fairness associated to different ways of distributing water among competitive users

Using distributive justice criteria, different ways of distributing water among competitive users are proposed to respondents. We consider in particular the following water allocation rules: equal award, constrained equal award, equal loss, proportional, Talmud, priority. Our primary outcomes are the perceived level of fairness associated to these different ways of distributing water among competitive users.

We use an internet-based study to investigate in a conflicting claim problem how fairness drives individual choices to allocate water among competitive users. Respondents to the survey are randomly allocated to different treatments in which we vary: 1/ the level of water scarcity (moderate water scarcity, high water scarcity, very high water scarcity); 2/ the composition of the water user group among which water must be shared (heterogenous water user group that may include a household, a farmer, a firm, and the environment); and 3/ the water needs expressed by water users.

We rely on an online survey implemented by a private firm which offers access to large first-party data. This survey is conducted in France and in India (1000 respondents in each country following a pre-test survey. In the online survey, we ask respondents to consider the following setting. In city alpha, there are three users sharing a water resource. Usually, 1200 hm3 are needed to satisfy all the basic water needs of these three users. Due to unfavorable weather conditions, the alpha commune is not able to deliver the 1200 hm3 needed, and some users will therefore have to be rationed. Using distributive justice criteria, we then propose different ways of rationing the three water users and we ask respondents the extent to which they consider the proposed allocations fair or, on the contrary, unfair.
We use a randomized control trial where we vary 1/ the level of water scarcity (3 levels), 2/ the composition of the water user group among which water must be shared (3 possibilities) and 3/ the water needs expressed by water users (3 possibilities).

We use an internet-based study conducted by a private professional firm. Randomization is based on computer random draws. Respondents are allocated to the different treatments at the beginning of the survey.

Individuals

No

1000 respondents for the Indian sample.
1000 respondents for the French sample.

1000 respondents for the Indian sample. 1000 respondents for the French sample.

111 respondents per treatment

To conduct the power analysis, we rely on the study conducted by Schmid et al. (2021) which tests the adhesion to different principles of distributive justice in a context of public infrastructure planning. Although our context differs from the one studied by Schmid et al. (2021) we argue that it may be relevant for the following reason. First, they study fairness in a water-related context (ageing of wastewater systems). Second, their analysis is conducted in Switzerland, a country sharing similarities with France. Third, Schmid et al. (2021) focuses on three principles of distributive justice –equity, equality, and need– which are also at the core of our analysis. Our central outcomes of interest are the subjective perception of fairness associated to the different water sharing rules which are measured using a scale going from 0 (Totally unfair) to 10 (Totally fair). In Schmid et al. (2021), perception of fairness associated to the three principles of distributive justice is measured using a 6-points scale (Not at all fair, Not fair, Rather not fair, Rather fair,Fair, Totally Fair). In order to match our way of measuring fairness (quantitative scale from 0 to 10), we have converted this 6-points qualitative scale into a quantitative scale going from 0 (Not at all fair) to 10 (Totally Fair). We have then computed a fairness score for each principle of distributive justice with its associated standard deviation. Table: Descriptive results on adhesion to fairness principles Mean score Standard deviation Equality 5,01 2,71 Equity 5,70 2,59 Need 7,07 2,68 Source: Author’s computation based on Schmid et al. (2021) We conduct a power analysis to detect significant differences between fairness scores associated to the three principles of distributive justice. We compute sample sizes, power and minimal detectable effects using two-sample means tests. All tests are implemented using the power function in Stata version 15.1. First, we determine the sample sizes needed to obtain a power of 0.8 given an alpha of 0.05 (5%-level two-sided test). For Equality and Need (resp. Equity and Need ; Equality and Equity), the power analysis indicates that we require a sample size of at least n = 28 (resp n=60 ; n=233) in each treatment. Based on our sample size (n=222) we get a power for Equality and Need (resp. Equity and Need ; Equality and Equity) equal to 100% (resp. 99.9% ; 78.1%). This translates into effect size for Equality and Need (resp. Equity and Need ; Equality and Equity) equal to 0,76 (resp. 0,52 ; -0.26). Schmid, S., Vetschera, R., & Lienert, J. (2021). Testing Fairness Principles for Public Environmental Infrastructure Decisions. Group Decision and Negotiation, 30(3), 611-640. Appendix: STATA code for power analyzes * Sample size for a two-sample means test power twomeans 5.01 7.07, sd1(2.71) sd2(2.68) power twomeans 5.01 5.70, sd1(2.71) sd2(2.59) power twomeans 5.70 7.07, sd1(2.59) sd2(2.68) * Computing power power twomeans 5.01 7.07 , n(222) sd1(2.71) sd2(2.68) power twomeans 5.70 7.07 , n(222) sd1(2.59) sd2(2.68) power twomeans 5.01 5.70 , n(222) sd1(2.71) sd2(2.59) * Effect size power twomeans 5.01, n(222) power(0.8) direction(lower) sd1(2.71) sd2(2.68) power twomeans 5.70, n(222) power(0.8) direction(lower) sd1(2.59) sd2(2.68) power twomeans 5.01, n(222) power(0.8) direction(lower) sd1(2.71) sd2(2.59)