Willingness to pay for the quality of drinking water.
Sattar, Abdul ; Ahmad, Eatzaz
This paper presents households behaviour in terms of willingness to
pay for safe drinking water in Hyderabad city, Sindh, Pakistan. The
multinomial logit model is estimated for averting water contamination
behaviour. Measures of awareness and households' wealth are
incorporated into the model to account for joint production of utility.
The study finds that measures of awareness such as different levels of
formal education of decision-makers and their informal exposure to mass
media have statistically significant effects on the willingness to pay
for drinking-water purification devices that can be adopted at home.
JEL classification: D12, D13, D31, Q21, Q25, Q51, R21
Keywords: Willingness to Pay, Quality of Drinking Water,
Multinomial Logit Model
1. INTRODUCTION
Willingness-to-Pay to avoid risks has long been recognised as an
important response to perceived environmental and health hazards.
Abdalla, et al. (1992) have documented the existence of consumer
averting behaviour in response to potential water contamination, while
Musser, et al. (1992) Smith and Desvouges (1986) and Courant and Porter
(1998) were among the first to provide a theoretical framework for the
averting behaviour in response to pollution. All these studies estimated
that averting behaviour formed a lower bound willingness-to-pay for
reduction in pollution under certain conditions.
In developing countries willingness-to-pay and demand for the good
quality drinking water is often low. The major causes are lack of
awareness regarding the contamination of drinking water and low levels
of household incomes.
The objectives of this paper are (a) to estimate the effects of
formal and informal awareness of households on the demand for the home
purification methods and b) to estimate willingness to pay for the safe
drinking water. To accomplish these objectives we develop a theoretical
framework of households' water purification behaviour by
incorporating the wealth and awareness indicators of households.
For this study primary data of households are collected from
Hyderabad city with known history of polluted water. Multinomial Logit
regression is used to analyse the effects of awareness and wealth on
safe drinking water practices among households. By using estimated
probabilities, costs of the different methods of purification are
calculated to arrive at the estimates of willingness to pay for the safe
drinking water. The estimated results show that the education level of
female decision-makers compared to that of male decision-makers has more
significant effect in using any or even more expensive method of
purification. Furthermore, on average education level of decision makers
is more important factor contributing to willingness to pay for safe
drinking water than the wealth of households.
2. THEORETICAL FRAMEWORK
The traditional demand functions are dependent on, besides income
and prices, several other factors capturing preference structure of
households like demographic composition, educational levels, profession
and residential status of households [see Deaton (1980)]. In cross
section data all consumers face the same set of prices and, therefore,
the variations is prices paid by different consumers do not represent
genuine price variations; these may be due to quality differentials and
the differences due to locations The budget allocation decision of
households in our context may be understood as a multi-stage budgeting
process. For example, at the upper stage budget is allocated to food,
health and other categories. Then at the lower stage the food
expenditure is allocated to clean drinking water and other food items,
while the lower-stage stage health expenditure is allocated to curing of
diarrhea and other waterborne diseases along with other health items.
Engel's law observes that the nature of preferences is such that
income-consumption curves are skewed, that is, as budget size increases,
the budget share of luxuries tends to rise and that of necessities tends
to decline. This implies that rich households are more likely to
allocate a larger share of their budget to more expensive water
purification devices as compared to poor households. In a typical
averting behaviour model developed by Courant and Porter (1998), water
purification practices enter into utility function through
households' production function of health. Thus denoting the
quantities of composites goods health and 'all other goods' by
H and Y respectively, the utility function of a consumer can be written
as
U = U [Z, H(A, [pi])] (1)
where Z is the Marshallian composite good, H is a measure of health
level, H(.) is the production function for health, A is the set of
averting activities, [pi] is an indicator of the perceived health-risk
associated with drinking contaminated water. It is assumed that
[U.sub.A] > 0 and [U/sub.[pi]] < 0. Here we have assumed that
households get utility through the drinking safe water and indirectly
through the health. We consider four water purification devices, which
are electric filter, ordinary filter, use of chlorine tablets and
boiling. If a household's uses two purification methods, we
consider the one that is the most effective, where effectiveness follows
the order: electric filter (most effective), ordinary filter, use of
chlorine tablets and boiling (least effective).
Further, household chooses between A and Z subject to budget
constraint. Y = Z + [P.sub.1] [A.sub.1] + [P.sub.2] [A.sub.2] +
[P.sub.3] [A.sub.3] + [P.sub.4] [A.sub.4] + C, where Y is income Pi is
the price of water alternative and C is the average cost of filtered
water. The price of the composite good is set equal to unity. Given that
the consumer chooses water alternative j, the conditional demand for
water practices can be solved as a function of wealth (a proxy of
permanent income), awareness (formal and informal) and other variables
like sex and occupation of decisionmaker and occurrence of diarrhea
among 0-5 age members of the household. That is,
[A.sub.j] = [A.sub.j] ([P.sub.j], Y, M, O) (2)
where M is the set of awareness variables and O is the set of other
variables.
To estimate willingness to pay for the safe drinking water we first
obtain the predicted probabilities of each choice from multinomial logit
model that determines the decision to adopt one of the four methods of
purifying drinking water vis-a-vis no water purification. The equation
for the multinomial regression model is given by
P([Y.sub.i] = j|[X.sub.i])= exp([[beta].sub.j][X.sub.i]) / 1 +
[4.summation over.m-1] exp([[beta].sub.m][X.sub.i]) j = 1, ..., 4 (3)
The predicted probabilities of each choice are then multiply by the
cost of adopted water purification device. The cost of adopting a
purification device is estimated as the annual cost reported by the
surveyed households.
Let [C.sub.ij] be the actual cost associated with jth method of
purification for ith household and P([A.sub.i] = j) be the probability
of the adoption of a purification method j by ith household predicted
from the estimated multinomial model. Then the ([WTP.sub.i]) by ith
household is given by
[WTP.sub.i] = [C.sub.1] P([A.sub.i] = 1) + [C.sub.2] P([A.sub.i] =
2) + [C.sub.3] P([A.sub.i] =3) + [C.sub.4] P([A.sub.i] = 4) (4)
Now from policy perspective, it is important to determine how this
WTP is affected by changes in various explanatory variables. For this
purpose we regress estimate WTP on a set of explanatory variables by
Ordinary Least Square (OLS) method, that is;
[WTP.sub.i] = [beta][X.sub.i] + [[epsilon].sub.i] (5)
From this willingness to pay we will calculate the mean willingness
to pay. Since there are four choices for water purification, the
multivariate logit model takes the form:
P([Y.sub.i] = j|[X.sub.i])= exp([[beta].sub.j][X.sub.i]) / 1 +
[4.summation over.m-1] exp([[beta].sub.m][X.sub.i]) j = 1, ..., 4
3. SURVEY DATA
The data used in this study are based on a survey conducted by
researchers themselves from Hyderabad city in the year 2006. The
stratified random sampling technique was used to collect the information
about various characteristics of 514 households, which consists of 3796
household members. The population of each stratum is taken form District
Census Report 1998, which shows that Hyderabad city is administratively
divided in four parts; the three Tehsils (Hyderabad city, Latifabad and
Qasimabad) and one cantonment. Total population of the city according to the Census is 1.473 million. The distribution of the sample is based on
population and the number of Union Councils of the area. Nine households
are chosen randomly from each union councils of city Tehsil, while ten
households are chosen, again randomly, form each of the rest of union
councils. Administratively cantonment has not been further divided into
union councils. However, its population is much higher than average
population of a union council in other parts of the city. So, the
authors made their own convenience strata based on number of households
in the area. The same treatment is given to the remaining areas of the
city.
Geographic distribution of population and the randomly drawn sample
are shown in Table 1, which also shows average size of households taken
from each region. Table 2 shows the distribution of sampled households
with respect to age, gender, and education levels of members of the
sampled households.
Geologically, the city is a flat-topped with subtropical, semi
desert type conditions. The main source of drinking water of the city is
surface water, which is served by five water supply systems. Since long
the quality of drinking water in Hyderabad has been poor. Mukesh and
Zeenat (2001) estimated the contents of metals in drinking water of
Hyderabad city by taking 18 water samples from different locations. The
results of the study reveal that water quality in the city is poor
against the standard health values.
The statistics shown in Table 3 indicate that out of 514
households, 35.02 percent households are not treating their drinking
water, while remaining 64.98 percent households are using some water
purification device with 32.68 percent using the boiling technique, 5.64
percent chlorine tablets, 11.87 percent ordinary filter and 14.79
percent electric filter at their homes.
For estimation, education will be used as a proxy for health
awareness of households regarding the drinking water contamination.
Education is classified into five categories, which are no education,
1-8 years of education, 9-12 years of education, 13-15 years of
education and 16 years of education or above. Table 3 shows that out of
65 illiterate decision-makers of households, 70.77 percent do not purify drinking water, while 16.92 percent boil water and only 1.54 percent use
the most expensive method of water treatment, that is, electric filter.
The percentage of households who do not purify drinking water reduces to
15.2 percent as decision-makers gets the highest level of education,
while for the same educational level, the percentage of boiling
increases to 31.2 percent and for electric filter to 29.6 percent.
The survey also collected the information on whether household
members read newspapers, watch television or listen to radio on regular
or irregular basis. Table 4 shows that decision-makers in 350 of the 514
households almost never listen to radio and among them 36.59 do not
purify their drinking water. Further, decision-makers in 469 of the 514
households watch television at least once in a week, out of which only
31.34 percent do not purify drinking water at their homes. Likewise
decision-maker in 335 households read newspaper at least once in a week
and out of these households only 26.28 do not purify drinking water.
It is expected that female decision-makers have greater willingness
to adopt safe drinking water practices than male decision-makers because
females are in general more intensively involved in the food related
household activities. The data show that among the male decision-makers
46.95 percent do not purify water, while this proportion reduced to only
16.75 percent among the female decision-makers. The most commonly
adopted device for safe drinking water among the female decision-makers
appears boiling of water. Data show that among the female
decision-makers 47.29 percent female use boiled water, which reduces to
14.29 percent for the expensive methods like electric filter.
One can also expect that the members of a household belonging to
medical profession have better stock of knowledge regarding water
contamination. The data in Table 3 show that among the 32 households in
which the decision makers are working in medical profession, only 12.50
percent do not purify drinking water at their homes, while 43.75 percent
use the most expensive and most effective device, that is, electric
filter.
The correct information on consumption, income, or wealth of
households cannot be collected accurately. However, the survey collects
information on households' ownership of various assets and
different characteristics of household dwelling. A wealth index is then
calculated from the given information by using first principle
component. (1) For the ease of interpretation, the analysis is carried
out on the basis of wealth quartiles rather than the actual wealth
index. Households of the lowest wealth quartile correspond to the
poorest units of the sample, while those belonging to the highest
quartile represent the richest units. Table 3 also shows the
relationship of water purification practices with the four wealth
quartiles. This relationship indicates some correlation between the two
attributes. In particular, households belonging to the lowest wealth
quartile tend to rely on the cheaper water purification device of
boiling, while among those belonging to the two upper wealth quartiles,
a larger percentage is found to use electric filters.
4. RESULTS AND DISCUSSION
Results from multinomial logit model are shown in Table 4, the
dependent variable consists of five categories i.e., no purification,
boiling, use of chlorine/alum tablets, ordinary filter and electric
filter. The no purification method is taken as the base category. The
marginal probability coefficient of the first educational level (1-8
years) of decisionmaker is significant only for boiling method. On
average the probability that households with 1-8 years of schooling
boils water for drinking is 23 percentage points higher as compared to
the households with illiterate decision-maker. The marginal probability
of boiling technique reduces as decision-makers become more educated.
The probability that the households with the most educated decision
makers (16 or above years) adopt the most expensive technology (electric
filter) turns to be 40 percentage points higher than the households with
illiterate decision-makers.
Among the media exposure variables radio listening habit of
decision-makers is statistically insignificant for water purification
techniques, while television-watching habit is only significant for the
use of chlorine tablet for water purification. The newspaper reading
habit of decision maker has significant influence on the probability of
adoption of all the water treatment methods.
The wealth quartile dummies have insignificant effect on the
households' purification behaviour except for the third and fourth
quartiles for the most expensive technique that is electric filter. The
estimated marginal probability coefficients show that on average the
probability of using electric filter to purify drinking water among the
third and the fourth wealth-quartile (richest) households is
respectively 21.1 and 25.8 percentage points higher than the first
wealth-quartile (poorest) households.
Other variables included in the set of explanatory variables are
the occurrence of diarrhea among 0-5 years old members of the house, sex
and occupation of decisionmakers. Sex of the decision makers is highly
significant for all the methods of purification. On average female
decision-makers are 36, 12 and 3 percentage points more likely to use
boiling, ordinary filters and electric filters at their home
respectively as compared to the male decision-makers.
Based on predicted probabilities of various purification methods
from multinomial logit model, we have calculated WTP. To relate this WTP
to household wealth, education, and media exposures, we have estimated a
linear regression equation by OLS. The results are reported in Table 5.
These results show that the two higher levels of education are
statistically significant at 5 percent level of significance and the top
educational level has the maximum WTP. On average, if a decision-maker
is has 16 and above years of schooling then his willingness to pay for
quality of drinking water will be 215.18 rupees higher than that of an
illiterate decision maker and 46.86 rupees higher than that of a
decision-makers who has 13-15 years of schooling.
Among the media exposure variables, only newspaper habit of
decision makers is statistically significant, and on average 69.14
rupees higher will the WTP, if household decision-maker reads newspaper
at least once in a week. The top two wealth quartiles are statistically
significant at 5 percent level of significance. The households who
belong to upper-middle and topmost wealth quartiles have on average 86.8
and 176.11 rupees higher willingness to pay than the households
belonging to the bottom wealth quartile.
Another variables included in the analysis is the dummy for
occupation of decision-makers, which is highly significant and shows
that households in which the decision makers belong to medical
profession are willing to pay 203.3 rupees more for safe drinking water
than the households in which decision-makers belong to non-medical
profession.
Sex of decision-makers is also significant and indicates that the
female decision makers are willing to pay on average 100.59 rupees more
than the male decision makers. The only variable with an unexpected sign
of its regression coefficient, which is also statistically significant,
is diarrhea.
5. SUMMARY AND CONCLUSION
The study measures WTP for safe drinking water practices among the
households in Hyderabad district, Sindh, Pakistan. The sample size is
514 households, which consists of 3796 household members. The study
estimates that there are statistically significant and quantitatively
non-negligible effects of formal education on the quality of safe
drinking water. The study also finds that there is a strong effect of
informal education like electronic and print media on the water
purification behaviour of households. The willingness to pay of a
better-informed household is more than an uninformed people, while study
finds that the willingness to pay of a better-educated person is 784
percentage pinots higher than that of an uneducated person. Thus better
level of formal and informal education, especially among the women,
about health hazards of contaminated drinking water may prevent
waterborne diseases, rather than focusing other strategies. Further, the
study also finds that female decision-makers are willing to pay more and
are more likely to adopt some water purification device than male
decision-makers.
Comments
(1) Due to the problem of clean drinking-water in growing urban
areas, this is a highly useful area of the research.
(2) Diarrhea is an effect than the cause of water purification.
But, the health conditions also affects the behaviour of the people.
Therefore, the variable diarrhea is endogenous. It needs to be taken
care.
(3) Income and wealth variables are taken interchangeably using
income in the theory and wealth in the model. Consistency is required.
(4) Some variables like occupation as medical professional is very
rare in the random sample. Special care need to be taken for such skewed
variables.
Description of the variables is desirable particularly for those
fitted in the model.
(5) Use of other means like bottled water and electric filter need
to be taken into account.
Krishna Prasad Pant
NARDF, Kathmandu, Nepal.
REFERENCES
Abdalla, C. W., B. A. Roach, and D. J. Epp (1992) Valuing
Environmental Quality Changes Using Averting Expenditures: An
Application to Groundwater Contamination. Land Economics 68, 163-169.
Courant, P. N., and R. C. Porter (1998) Averting Expenditure and
the Cost of Pollution. Journal of Environmental Economics and Management
8, 321-329.
Deaton, A., and J. Muellbauer (1980) Economics and Consumer
Behaviour. Cambridge, M.A: Cambridge University Press.
Mukesh, M. and K. Zeenat (2001) Content of Metals in Drinking Water
of Hyderabad. Pakistan Journal of Analytical Chemistry 2:2, 34-48.
Musser, W. N., L. M. Musser, A. S. Laughland, and J. S. Shortle
(1992) Contingent Valuation and Averting Costs Estimates of Benefits of
Public Water Decisions in a Small Community. Agriculture Economics and
Rural Sociology. Pennsylvania State University (Publication No. 238).
Smith, V. K. and W. H. Desvouges (1986) Averting Behaviour: Does It
Exits? Economics Letters 20: 3, 291-96.
World Health Organisation (2004) The World Health Report 2002.
Geneva: WHO, Switzerland.
(1) Consider a data matrix A consisting of m columns (variables)
and n rows (observations) on the m wealth indicators. Denote the
eigenvector associated with the largest eigenvalue of the
variance-covariance matrix of A by v. Then v'A = [m.summation
over.i=1][v.sub.i][a.sub.ij] is the defined to be the first principal
component of the matrix A. The first principal component is a linear
combination of the variables in the matrix A that captures the maximum
common variation in these variables.
Abdul Sattar <
[email protected]> is Research Officer,
Finance Division, Government of Pakistan, lslamabad. Eatzaz Ahmad
<
[email protected]> is Professor, Department of Economics,
Quaid-i-Azam University, Islamabad.
Table 1
Sample Profile
Number of Average
Name of Area Population Union Population of
(Tehsil) (Thousand) Councils Union Council
City 518 20 25.9
Latifabad 556 20 27.8
Qasimabad 114 4 28.5
Cantonment 85 3* 28.3
Remaining Areas 200 7* 28.6
Total 1.473 54 27.3
Number of Average
Name of Area Households Household
(Tehsil) Chosen Size
City 180 7.80
Latifabad 200 7.12
Qasimabad 40 7.05
Cantonment 30 6.03
Remaining Areas 64 7.89
Total 514 7.39
* District Census Report does not classify the total area into Union
Councils in these regions. The numbers are obtained on the basis of
the average size of a union council in the district. The numbers shown
are those retained after discarding some of the sampled households
due to incomplete and/or sketchy information.
Table 2
Education Profile of the Sample
Number of Males with Education
Age in Years Illiterate 1-8 9-12 13-15 16 +
0-5 172 34
6-10 14 195
11-20 40 155 346 71
21-30 30 26 122 115 95
31-40 3G 30 72 60 95
41-50 22 24 62 36 44
51-60 15 13 28 26 32
Above 60 16 14 9 6 7
Total 345 491 639 314 273
Percentage 16.73 23.81 30.99 15.23 13.24
Number of Females with Education
Age in Years Illiterate 1-8 9-12 13-15 16 +
0-5 157 20
6-10 24 148
11-20 58 136 210 66
21-30 51 15 109 125 76
31-40 63 25 85 38 24
41-50 49 25 51 27 15
51-60 29 18 20 11 6
Above 60 35 9 5 2 1
Total 466 396 480 269 122
Percentage 26.89 22.85 27.70 15.52 7.04
Table 3
Distribution of Purification Adoption Rates (Percentages)
by Households' Characteristics
Number of No
Household Characteristics households Purification Boiling
Education Level of Decision-maker
No Education 65 70.77 16.92
1-8 Years 59 54.24 32.21
9-12 Years 158 34.81 41.77
13-15 Years 107 26.17 30.84
16 Years or Above 125 15.20 31.20
Media Exposures of Decision-maker
Listening to Radio
Almost Never 350 34.29 34.00
At least Once a Week 164 36.59 29.88
Watching TV
Almost Never 45 73.34 17.78
At least Once a Week 469 31.34 34.12
Reading Newspaper
Almost Never 179 51.39 30.73
At least Once a Week 335 26.28 33.73
Sex of Decision-maker
Male 311 46.95 23.15
Female 203 16.75 47.29
Occupation of Decision-maker
Non Medical Professional 482 36.51 33.20
Medical Professional 32 12.50 25.00
Household Wealth
Bottom Quartile 129 33.33 40.31
Lower Middle Quartile 129 24.03 42.64
Upper Middle Quartile 130 31.54 34.62
Top Quartile 126 51.59 12.70
All Households 514 35.02 32.68
Chlorin/Alum Candle Electric
Household Characteristics Tablets Filter Filter
Education Level of Decision-maker
No Education 7.69 3.08 1.54
1-8 Years 8.47 1.69 3.39
9-12 Years 6.33 8.86 8.23
13-15 Years 3.74 17.76 21.49
16 Years or Above 4.00 20.00 29.6
Media Exposures of Decision-maker
Listening to Radio
Almost Never 6.57 10.85 14.29
At least Once a Week 3.66 14.02 15.85
Watching TV
Almost Never 0.00 4.44 4.44
At least Once a Week 6.18 12.58 15.78
Reading Newspaper
Almost Never 6.15 5.03 6.70
At least Once a Week 5.37 15.52 19.10
Sex of Decision-maker
Male 5.79 9.00 15.11
Female 5.42 16.25 14.29
Occupation of Decision-maker
Non Medical Professional 6.02 11.41 12.86
Medical Professional 0.00 18.75 43.75
Household Wealth
Bottom Quartile 5.43 11.63 9.30
Lower Middle Quartile 8.53 17.05 7.75
Upper Middle Quartile 3.85 9.99 20.00
Top Quartile 4.76 8.73 22.22
All Households 5.64 11.87 14.79
Table 4
Marginal Effects of Multinomial Logit Regression
Probabilities of Purification Methods
Boiling Chlorine/
Explanatory Variables Alum Tablets
Education of Decision-maker; 1-8 Years 0.230 * -0.001
(0.006) (0.351)
Education of Decision-maker; 9-12 Years 0.107 * -0.001
(0.005) (0.725)
Education of Decision-maker; 13-15 Years -0.046 * -0.002
(0.002) (0.821)
Education of Decision-maker; 16 Years -0.031 * -0.002
or Above (0.000) (0.562)
Radio Habit of Decision-maker 0.009 -0.001
(0.708) (0.295)
TV Habit of Decision-maker 0.010 0.012 *
(0.417) (0.000)
Newspaper Habit of Decision-maker 0.087 ** 0.000
(0.010) (0.163)
Second Wealth Quartile 0.055 0.001
(0.147) (0.220)
3rd Wealth Quartile -0.057 0.000
(0.366) (0.583)
Top Wealth Quartile -0.205 -0.001
(0.175) (0.641)
Diarrhea 0.108 * 0.000
(0.047) (0.283)
Sex of Decision-maker 0.357 * -0.001 *
(0.000) (0.019)
Occupation Decision-maker -0.026 -0.015
(0.854) (0.780)
Log Likelihood -596.172
Number of Observations 514
Candle Electric
Explanatory Variables Filter Filter
Education of Decision-maker; 1-8 Years -0.085 0.087
(0.903) (0.215)
Education of Decision-maker; 9-12 Years 0.003 0.173 *
(0.207) (0.046)
Education of Decision-maker; 13-15 Years 0.037 * 0.369 *
(0.018) (0.002)
Education of Decision-maker; 16 Years 0.045 * 0.39G *
or Above (0.007) (0.000)
Radio Habit of Decision-maker 0.036 -0.017
(0.288) (0.862)
TV Habit of Decision-maker 0.038 0.074
(0.360) (0.135)
Newspaper Habit of Decision-maker 0.101 * 0.042 *
(0.001) (0.030)
Second Wealth Quartile 0.021 0.036
(0.218) (0.234)
3rd Wealth Quartile -0.019 0.211
(0.551) (0.001)
Top Wealth Quartile -0.032 0.258 *
(0.631) (0.004)
Diarrhea 0.017 -0.032
(0.229) (0.963)
Sex of Decision-maker 0.117 * 0.029 *
(0.000) (0.000)
Occupation Decision-maker 0.030 0.069
(0.568) (0.327)
Log Likelihood
Number of Observations
Note: The probabilities vale's of the marginal effects are reported in
parentheses. The marginal effects significant at 5 percent and 10
percent levels are indicated by * and ** respectively.
Table 5
Parameters Estimates of the Willingness-to-pay Equation (in Pak Rupees)
Explanatory Variables Coefficients
Constant -249.95
(0.00)
Education of Decision-maker 1-8 Years 24.34
(0.54)
Education of Decision-maker 9-12 Years 42.90
(0.21)
Education of Decision-maker 13-15 Years 168.33 *
(0.00)
Education of Decision-maker 16 or Above Years 215.19 *
(0.00)
Radio Habit of Decision-maker 15.44
(0.46)
TV Habit of Decision-maker 53.19
(0.13)
Newspaper Habit of Decision-maker 69.14 *
(0.01)
Second Wealth Quartile 3.89
(0.89)
3rd Wealth Quartile 86.81
(0.00)
Top Wealth Quartile 176.11
(0.00)
Diarrhea 40.06 *
(0.05)
Sex of Decision-maker 100.59 *
(0.00)
Occupation Decision-maker 203.31
(0.00)
Number of Observations 514
F-statistic 19.29 *
R-squared 0.334
Note: The statistics significant at 5 percent and 10 percent levels
are indicated by * and ** respectively.