1

Forthcoming in Journal of Happiness Studies 10.1007/s10902-011-9278-2

Pre-publication version

Neighborhood determinants of Quality of Life

Néstor Gandelman (Universidad ORT Uruguay)

Giorgina Piani (Universidad de la República)

Zuleika Ferre (Universidad de la República)

Abstract

In this paper we analyze various dimensions of the quality of life in Uruguay. The

results suggest that differences in overall happiness and in domain satisfaction

can partly be explained by different levels of access to public goods. We find that

the monetary equivalent value of public goods such as electricity, running water,

sewage system, drainage, waste disposal system, street lighting, sidewalks in good

condition, trees in the street, and the absence of air or noise pollution is

considerable.

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1. Introduction City authorities decide which neighborhood amenities and public goods to provide. In a context

of tight budget constraints, it is important that these allocation decisions should consider the best

way to improve the well-being of the population. In this paper, we study the impact of

neighborhood amenities and public goods on happiness and satisfaction with several life domains

in Uruguay.

There is little literature on Quality of Life (QoL) indexes for Latin American Cities.1 The

last and most comprehensive reference work is a recently published book by Lora et al (2010); it

contains several cases studies2 of the impact of house characteristics and public goods on rental

prices and housing satisfaction.

The starting point in life satisfaction methodology is to ask people how satisfied they are

with their lives and with specific life domains. Various authors use subjective indicators to

evaluate well-being (see for instance Winkelmann and Winkelmann 1998, and Frey et al 2004).

One advantage of this methodology is that since income is usually included as an explanatory

variable it is possible to compute the monetary values of significant variables as explained in the

methodological section of this paper.

Powell and Sanguinetti (2010) summarize the findings of the case studies presented in

Lora et al (2010). They point out that the results of these studies indicate that access to running

water, access to sewage facilities and the availability of piped gas are associated with higher

house prices. Other neighborhood variables that appeared to significantly affect house prices in

some of the studies include proximity to schools, proximity to parks or green spaces, and

security. The results of the relationship between public goods and life satisfaction are less robust

than their impact on real estate prices. Nevertheless, security, access to electricity, water, sewage

facilities, garbage collection and telephone services seem to be important factors behind

differences in people’s well-being.

A number of studies have also sought to measure the effect of the place where we live

(social, economical and political environment) on our reported life satisfaction. Dolan et al

(2008) carry out a detailed review of the “economic of happiness”. Some of the classical public

1 See for instance Amorin and Blanco (2003), Cavallieri and Peres (2008) and Acosta et al (2005). 2 Cruces et al (2010) for Buenos Aires, Medina et al (2010) for Bogotá and Medellin, Hall et all (2010) for Costa

Rica, Alcázar and Andrade (2010) for Lima and Ferre, et al. (2010) for Montevideo.

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bads that have been found to be negatively associated with subjective well-being have to do with

climate and the natural environment. For example, the paper by Welsch (2006) explores the

relationship between pollution and reported subjective well-being in ten European countries,

concluding that pollution plays a significant role as a predictor of inter-country and inter-

temporal differences in subjective well-being. In the same line, Van Praag and Baarsma (2005)

show that noise pollution can affect subjective well-being, Ferrer-i-Carbonell and Gowdy (2007)

suggest that environmental problems reduce life satisfaction, as well as living in an unsafe or

deprived area (Dolan et al. 2008). Ferrer-i-Carbonell and Gowdy (2004) go further to examine

the relationship between reported well-being and attitudes toward pollution and species loss and

find a negative relationship between well-being and concern about the ozone layer and a positive

relationship between well-being and concern about biodiversity loss.

On the other hand, there are other studies that examine the impact of non-market goods on

subjective well-being. Dolan and Metcalfe (2008) carry out a comparison between willingness to

pay and subjective well-being using a quasi-experimental design in the context of an urban

regeneration scheme in the UK. As the authors put it, regeneration does encompass some private

goods (e.g. new house fascias) but when the whole area becomes regenerated, and when the area

has a pleasurable aesthetic appeal, urban regeneration becomes a non-market public good. The

study finds reasonable evidence to show that living in the regenerated area significantly increases

life satisfaction.

In this paper we use a life satisfaction methodology similar to that of previous case studies

presented in Lora et al (2010) to focus on the impact of public goods on life satisfaction as a

whole and other life domains (leisure, social life, family, health, economic situation and work)

that are not obviously linked to public goods. We find that neighborhood amenities and public

goods do help explain differences in happiness and satisfaction in various life domains. The

equivalent monetary value of neighborhood amenities and public goods such as access to

electricity, access to running water, access to a sewage system, access to drains, the availability

of a garbage disposal system, street lighting, sidewalks in good condition, trees in the street, and

the absence of air and noise pollution is considerable.

This paper is structured as follows. In section 2 we present the survey especially conducted

for this study and the estimation methodology, in section 3 we present the results, and in section

4 we draw our conclusions.

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2. Data and Methodology

2.1.Measurement of Life Satisfaction

Although we acknowledge that there are a number of theories that draw a fine line among the

constructs “life satisfaction”, “well-being”, “happiness” and “quality of life”, according to the

affective or cognitive evaluation of the quality of one’s life, in this paper we follow Dockery

(2005) in taking a pragmatic approach and treat the terms as synonyms for the degree to which

an individual judges the overall quality of his or her life as favorable.

We tackle the measurement of life satisfaction from a “subjective” perspective insofar as

we focus on people’s self-assessment of their quality of life (QoL). Some economists are

skeptical about this methodological approach and question the validity and reliability of

subjective data. We recognize that this kind of reported statistical information has clear

limitations, but it would seem that if we are to analyze the nature of people’s happiness we

should include what they have to say about their well-being (Blanchflower and Oswald, 2002).

As we see it, this “subjective” or “psychological” approach complements the classic, more

“objective” concept of standard of living, and has shed light on new aspects of the phenomenon

and sparked a revival of interest in the QoL field.

For the purposes of measurement, we treat people’s perception of their QoL as an

“attitude”, which, according to Ajzen and Fishbein (1980), can be defined as a person’s

evaluation of any psychological object. The concept of attitude has played a major role

throughout the history of social psychology. In 1918 William Thomas and Florian Znaniecki

were the first to use it to explain social behavior; they viewed attitudes as individual mental

processes that determine a person’s actual and potential responses (Ajzen and Fishbein 1980).

Attitudes are important because they help us organize our relationship with our world in that they

reduce the vast amount of information we receive so we can process it into manageable units.

We also use attitudes as inputs to form judgments on new subjects. And finally, people’s

attitudes can sometimes be useful to predict behavior.

Because the concept of QoL is based on social, institutional and cultural factors,

expectations play a decisive role in determining people’s satisfaction with life. People receive

information about different life standards, they process it through their cultural and social

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frameworks and then they generate their own feelings, opinions and attitudes about their

individual and personal situation.

The measurement of subjective data such as attitudes, opinions, beliefs, feelings and

intentions is especially difficult as these terms refer to psychological aspects, which in principle

cannot be verified. In this paper, we use a subjective well-being survey to collect the views of

respondents about their perception of their quality of life. In order to minimize total survey error,

we designed the measurement instrument taking into account the methodological problems

mentioned above.

2.2.Data source

Our dataset is a population survey on quality of life that was a module of the 2007 International

Social Survey Program (ISSP) in Uruguay about “Leisure Time and Sports”3. The research team

designed, organized, conducted and analyzed the survey. The questionnaire was a tool to obtain

critical data on QoL neighborhood-specific characteristics and also individual characteristics.

The questions were formulated in English and later translated into Spanish and adapted to the

specificities of that language in Uruguay.

Our sampling design complies with the rigorous ISSP methodological requirements for a

general population representative survey. It is based on a multi-stage stratification procedure.

The population universe was all adults (aged 18 and over) living in urban areas (cities with at

least 5,000 inhabitants) and the sample frame was the 2004 population census. In order to avoid

self-selection bias, the questionnaire was answered by a randomly selected member of the

household through the Next-Birthday Method.

The interviews were conducted using a face-to-face, paper and pencil method. The

fieldwork was carried out from November 2007 to February 2008, with a break for the holidays

from December 23rd. to January 31st. The effective number of interviews obtained was 1437.

This sample has a confidence interval of +/- 3, with an approximate confidence level of

95% for a population proportion close to 0.5. The margin of error does not reflect other sources

3 The ISSP is a continuing annual program of cross-national collaboration on surveys covering subjects important

for social science research. Since 1983 it has brought together pre-existing social science projects and co-

coordinated research goals, thereby adding a cross-national, cross-cultural perspective to the individual country

studies.

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of error different from sampling (such as coverage error, non-response error, translation and

other measurement errors).

The resulting field work outcome rates, calculated in line with AAPOR (American

Association for Public Opinion Research 2011) Standard Definitions for RDD telephone surveys

and in-person household surveys, are as follows. The Total Response Rate (the number of

complete interviews divided by the number of eligible and unknown eligible units in the sample)

is 74.5%. The Total Refusal Rate (the number of refusals divided by the number of interviews

(complete and partial) plus the non-respondents (refusals, non-contacts, and others) plus the

cases of unknown eligibility) is 14.2%. And the Total Cooperation Rate (the number of complete

interviews divided by the number of interviews (complete plus partial) plus the number of non-

interviews that involve identification and contact with an eligible respondent (refusal and break-

off plus other) is 83.9%.

<TABLE 1 ABOUT HERE>

2.3. Question order

One of the most serious sources of non-random error in survey research is the impact of question

order. The question-order effect is the process whereby preceding questions may influence the

interpretation of subsequent ones because of the context they generate during the cognitive

question and answer process. In other words, question order may determine what type of

information respondents have in mind when they make an attitude judgment. This effect may

impair the validity of the measure (questionnaire) and therefore prevent the researcher from

knowing whether the response given reflects a true attitude of the survey respondent.

Sudman and Bradburn (1982) tackle this issue and point out that questions that are quite

closely related in terms of the attitude object may make particular aspects of the opinion appear

more salient or influence the interpretation of terms that might be taken into account by the

respondent when answering subsequent questions. The results of survey experiments show that

responses to more general questions relating to overall aspects are affected by the order in which

the questions appear, but this does not apply to specific questions.

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So as to reduce this source of error, we followed Sudman and Bradburn’s advice and

employed the technique of sorting the related questions according to their level of

generality/specificity. That is to say, we asked the more general questions first and then the more

specific ones, as shown below:

• If you were to consider your life in general these days, how happy or unhappy would you

say you are, on the whole …

1. Not at all happy

2. Not very happy

3. Fairly happy

4. Very happy

Cannot choose

• In general, would you say your health is …

1. Poor

2. Fair

3. Good

4. Very good

5. Excellent

Cannot choose

• How satisfied are you with your economic situation?

1. Very dissatisfied

2. Dissatisfied

3. Neither satisfied nor dissatisfied

4. Satisfied

5. Very satisfied

Cannot choose

The answer options for family satisfaction, social life and work satisfaction were the same as for

economic situation satisfaction

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2.4. Econometric Strategy

Overall happiness and other life domains such as leisure time, social life, family, health,

economic situation and work are evaluated with questions that have discrete distributions.

Ordinary least squares estimations would not be correct. The traditional approach is to postulate

a latent equation of the following form:

vZXconstantQoL ijjiijd +++= ''* γβ (1)

where QoL d * is a quality of life domain indicator, Xi is a vector of individual socioeconomic

characteristics (age, gender, schooling, etc.), Zj is a vector of neighborhood j amenities (crime

rate, green spaces, etc.) and vij is the composite error term which is a combination of a

neighborhood-specific error component and a house-specific error component ijij d v η+= . 4

The true valuation of the domain cannot be observed. For instance, the measure of happiness

takes four values (not at all happyl, not very happy, fairly happy, very happy), and it is implicitly

assumed that individuals whose happiness level is below a certain threshold 1µ will report they

are not happy at all, those between that value and a greater 2µ will report they are not very

happy, those between 2µ and an even greater

3µ will report they are farily happy, and finally

individuals with a happiness level above 3µ will say they are very happy.

happyVery if 4

happyFarily if 3

happyNot very if 2

allat happy Not if 1

3

*

3

*

2

2

*

1

1

*

µ

µµ

µµ

µ

≥=

≤≤=

≤≤=

≤=

id

id

id

id

id

id

id

id

QolQol

QolQol

QolQol

QolQol

(2)

Assuming that the error term is normally distributed across observations we have an

order probit (OP) model that implies the following probabilities:

4 The city-specific error component (dj) is common to all houses in the neighborhood and represents systematic

uncontrolled differences in amenity characteristics across sub-city areas. But it may also capture systematic

uncontrolled differences in housing quality across neighborhoods. Either of these two factors would mean that the

composite error term across houses within the same sub-city area will be correlated, making for a downwards bias in

the OLS-based standard errors (Moulton, 1987) that need to be corrected using clustered standard errors.

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( )( ) ( )( ) ( )

( )jiid

jijiid

jijiid

jiid

ZXµΦ)(QoL

,ZXµΦZXµΦ)(QoL

,ZXµΦZXµΦ)(QoL

,ZXµΦ)(QoL

''14Prob

''''3Prob

''''2Prob

''1Prob

3

23

12

1

γβ

γβγβ

γβγβ

γβ

+−−==

+−−+−==

+−−+−==

+−==

(3)

where ( ) Φ is the normal cumulative distribution function.

Van Praag and Ferrer-i-Carbonell (2008) argue that even in an order probit estimation

there is to a certain extent an implicit cardinalization of the variable under study. Expanding on

this idea, they propose a Probit Adapted OLS (POLS) method that is based on a transformation

of the data that allows discrete choice variables as if they were distributed along the whole real

line. The transformation consists first of deriving the values of a standard normal distribution

that correspond to the cumulative frequencies of the ordinal dependent variable

( )( )( )( ) 43214

3213

212

11

pppp

ppp

pp

p

+++=Φ++=Φ

+=Φ=Φ

µµµµ

(4)

where ip is the proportion whose domain lies in the i-th bracket. The final step in the POLS

methodology is to estimate the conditional means for the variables under study

( )iiiiQoLQoLEQoL µµ ≤<= −1 / and to regress it on the independent variables.

The main advantage of POLS is that it requires less computing time, allows the application

of more complex methods (systems of equations, fixed effects, etc.), and the estimated

coefficients are the marginal effects of the independent variables. The drawback is that for POLS

a harsher normality assumption is needed. The results reported in van Praag and Ferrer-i-

Carbonell (2008) suggest that the results under POLS and OP are almost the same up to a

multiplication factor.

2.5. Compensated differentials

Once we have estimated the determinants of a life domain we can use this regression to address

their relative importance and to provide their monetary value. Since the final step in the POLS

methodology is a linear regression the coefficients can be interpreted as marginal effects. The

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relative size of the estimated coefficients is a measure of their relative importance. For instance

we may find that

ijijijijd othersincomezQoL εββ +++= )log(ˆˆ

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where the hat indicates estimated coefficients, z is dummy variables indicating access to a public

good, income is measured in logarithms and ε is the error term.

We can think of the estimated equation as an indifference curve. There are many values

for the independent variables that would mean the same quality of life satisfaction. Therefore, we

can calculate how much one factor needs to increase to compensate for a decrease in the other.

For individuals without access to the public good, z takes the value 0; and for those that do have

access to the public good, z takes the value 1. Other things being equal, if an individual living

without a public good is given access to that good his satisfaction will not change if his income is

reduced according to the following equation:

0)log(ˆˆ31 =∆+ incomeββ

where ∆ stands for change. Therefore, the change in the log of income is 2

1

ˆ

ˆ)log(

ββ−=∆ income .

Expressing this in percentage changes, we have that giving access to the public good z will

compensate for a decrease in income of 12

1

ˆ

ˆ

−−

ββ

e . This figure can be interpreted as the maximum

tax that someone will be willing to pay for the provision of the public good. A similar analysis

can be carried out to compute how much income should increase to compensate for the loss of a

particular public good. In percentage terms this is 12

1

ˆ

ˆ

−ββ

e .

Since we are measuring various life dimensions, these values may differ among life

domains. For instance, it may be that having access to a waste disposal system has an effect on

health satisfaction but no effect on work satisfaction. Therefore the monetary interpretation of

the public good should be tied to the particular domain that is being considered.

The same type of compensated analysis can be performed in the OP regressions.

3. Results

3.1.Satisfaction Distribution

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Table 2 reports the distribution of overall happiness and satisfaction with the following specific

life domains: leisure, social life, family, health, economic situation, and current work. It shows

that four out of five respondents say they are “very happy” or “fairly happy”, which seems high

in absolute terms. In order to correctly interpret this figure we need to look at the data from an

international comparison perspective.

According to the subjective well-being study carried out in 97 countries using a

combined dataset of World Survey Values from 1995 to 2007, Uruguay ranks 39th. It is below

other Latin American countries like Colombia, El Salvador, Guatemala, Mexico, Venezuela,

Brazil and Argentina (survey results available at the NSF web page,

http://www.nsf.gov/news/newsmedia/pr111725/pr111725.pdf). In the World Database of

Happiness (Average Happiness in 146 Nations 2000-2009) Uruguay ranks 41st out of 146

countries, with a “Satisfaction with Life” value of 6.7 (measured on a 0 to 10 scale). Uruguay

ranks lower than other Latin American countries like Costa Rica (8.5), Mexico (7.9), Colombia

(7.7), the Dominican Republic (7.6), Brazil (7.5), Argentina (7.3), Guatemala, Venezuela (7.2),

Nicaragua (7.1) and Paraguay (6.9). Countries that report lower levels than Uruguay are El

Salvador (6.7), Chile (6.6), Bolivia (6.5), Ecuador (6.4) and Peru (6.2).

But what makes us more or less happy? The results in Table 2 seem to suggest that the

factors that underlie subjective well-being are associated with family (very satisfied + satisfied =

85.6%), social life (very satisfied + satisfied = 74.4%) and leisure (very satisfied + satisfied =

61.9%). Happiness seems to be highly associated with activities like spending time with family

and friends, well-spent leisure time and actions that combine socializing and physical exercise.

Many studies have argued that although these variables show a strong association with subjective

well-being, they account for only a small portion of its variance (e.g., Haring, et al 1984).

Psychologists tend to agree that happiness is largely due to personality traits and temperament

(Diener, 1984), rather than to circumstances in the real world that are more or less under our

control, like employment, education or money. Although, many studies show evidence that

personality does play a role for subjective well-being; we can not conclude that it is the only

important variable (DeNeve and Cooper, 1998).

Table 3 presents the Pearson correlations between happiness and other life domains. All

coefficients are positive and statistically significant at 5%. The positive correlation between all

life domains suggests that there are some common factors that affect them all. A common factor

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may be an unobservable optimistic (or pessimistic) attitude of individuals that makes them see

everything with positive (or negative) eyes. Alternatively, there may be some external force, like

the public goods we are focusing in this paper, that affect all life domains in a similar fashion.

<TABLE 2 ABOUT HERE>

<TABLE 3 ABOUT HERE>

3.2.Descriptive results: possible determinants of domain and overall life satisfaction

Information on neighborhood amenities and public goods is summarized in Table 4

disaggregated into two categories of reported happiness: “Very happy” + “Fairly happy” and

“Not very happy” + “Not at all happy”.

Public good coverage is larger among happier individuals for all public goods except for garbage

disposal. However, the mean figures for the two groups are very similar and without jointly

controlling for other covariates it is not possible to establish significant relations.

<TABLE 4 ABOUT HERE>

3.3.Determinants of Life Domains

In this section we report the estimation of the determinants of domain satisfaction.

Besides the public good variables we include several socio-demographic determinants of life

satisfaction, namely age, gender, marital status, education, employment status, family size and

income. One drawback of this analysis is that it lacks an empirical identification strategy to

pinpoint the causal relationship between public goods and life satisfaction. Therefore the

estimation results should be interpreted as descriptive.

Tables 5 and 6 show the estimations using the POLS and OP methodologies. Both show

the same picture in terms of significance values and in terms of the monetary value of public

goods.

In each regression we find that several public goods are associated with life and domain

satisfaction. The independent variables that seem to affect more domains are electricity in the

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block and absence of pollution. Electricity is associated with more satisfaction with social life,

health, economic situation, work, and more overall happiness. Air and noise pollution are public

bads. The absence of pollution improves leisure satisfaction (noise), social life satisfaction

(noise), health (air) and economic situation (air). In addition, people living in places with no

noise pollution are happier than those living in areas with noise pollution.

Individuals living in areas with running water are happier than those living without it but

we find no statistically significant association between water and other life domains. People

living in areas with access to drainage systems have better satisfaction with respect to their

family and health. Waste disposal is positively associated with satisfaction with health, economic

situation and work. Places with street lightening tend to have happier individuals who are more

satisfied with their economic situation. Living in a place where the sidewalks are in good

condition is associated with better social life. Living in a place with trees in the street is related

to better social life satisfaction.

When we examine public goods from the perspective on their monetary value, we find

that in general their value is very high. Electricity is so important that losing access to it is

equivalent to a decrease of more than 89% of income in all the regressions in which it turns out

to be significant. Living in an area with running water is also a key to happiness, and losing

access to it is equivalent to losing 92% of income.

Access to a drainage system is associated with more family and health satisfaction, and

losing access to it is equivalent to a decrease in income of 51% for family and 72% for health

satisfaction. For people living in an area without drainage system, having access to it is

equivalent to more than doubling their income (105% for family and 251% for health

satisfaction).

Losing access to a waste disposal system is equivalent to a reduction in income of 54%

for health satisfaction, 38% for economic situation satisfaction and 48% for work satisfaction.

On the other hand, the value of implementing a waste disposal system for people without one is

equivalent as an increase in income of 116% for health and 61% for economic situation and 91%

for work satisfaction.

Individuals living in areas with street lighting tend to be happier than those living without

it, and the latter group would need to multiply their income by four to compensate for the fact

that they do not have street lighting. From the opposite perspective, if people living in areas with

14

street lighting were to lose it, this would be equivalent to a decrease in 76% of income. Street

lighting is also associated with greater satisfaction with economic situation. People without this

public good need to be compensated with increases in income of 28% for economic situation.

Sidewalks in good condition are important for social life. For people who live in places

where the sidewalks are in bad condition or where there are no sidewalks at all this is equivalent

to decreases in income of 62% for their social life and satisfaction. Living in a place with trees in

the street also improves social life, and this improvement in social life satisfaction is equivalent

to a 86% increase in income.

Finally, the elimination of air pollution is equivalent to multiplying income by seven for

health satisfaction. The elimination of noise pollution is equivalent of multiplying income by six

for leisure satisfaction and by four for social life satisfaction. The effect of air pollution on

overall happiness is also considerable; for people living with noise pollution its elimination is

equivalent to multiplying their income by five.

<TABLE 5 ABOUT HERE>

<TABLE 6 ABOUT HERE>

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4. Conclusion

In this paper we analyze various dimensions of the quality of life of people living in Uruguay.

We conducted a specially designed survey to address these subject areas and we found that

satisfaction with various public goods and services plays a major role in the overall reported

well-being of individuals and in the satisfaction of life domains such as leisure, social life,

family, health, housing, neighborhood, economic situation and work. The monetary equivalent

value of public goods is very great for electricity, running water, a sewage system, a drainage

system, a garbage disposal system, street lighting, sidewalks in good condition, trees in the

street, the absence of air and noise pollution. Our results suggest that differences in overall

happiness and in domain satisfaction are in part explained by different levels of access to public

goods.

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18

Table 1: Details about the issued sample

AAPOR Standard Definitions Cases

1. Total number of starting or issued names/addresses (gross sample size) * 2105

2. Interviews (1.0) 1437

3. Eligible, Non-Interview

A. Refusal/Break-off (2.10)

B. Non-Contact (2.20)

C. Other

i. Language Problems (2.33)

ii. Miscellaneous Other (2.31, 2.32, 2.35)

484

275

178

31

0

31

3. Unknown Eligibility, Non-Interview (3.0) 9

4. Not Eligible

A. Not a Residence (4.50)

B. Vacant Residence (4.60)

C. No Eligible Respondent (4.70)

D. Other (4.10,4.90)

175

32

143

0

0

Source: Authors’ compilation using AAPOR Standard Definitions.

Based on Montevideo QoL Neighborhood Survey.

19

Table 2: Variables Expected to Explain Differences in QoL

(percentages of response)

Very happy

Fairly

happy

Not very

happy

Not at all

happy No answer

Overall

Happiness 29.9 51.0 15.8 2.9 0.4

Very

satisfied Satisfied

Not very

satisfied

Not at all

satisfied No answer

Leisure 12.9 49.0 25.3 12.7 0.2

Very

satisfied Satisfied

Neither

satisfied or

dissatisfied

Dissatisfied Very

dissatisfied No answer

Social life 18.7 55.7 15.7 6.5 1.5 1.8

Very

satisfied Satisfied

Neither

satisfied or

dissatisfied

Dissatisfied Very

dissatisfied No answer

Family 31.2 54.4 8.3 4.8 0.8 0.5

Excellent Very good Good Fair Poor No answer

Health 13.9 24.2 39.5 18.9 3.4 0.1

Very

satisfied Satisfied

Neither

satisfied or

dissatisfied

Dissatisfied Very

dissatisfied No answer

Economic

situation 4.9 35.5 23.2 26.2 10.1 0.2

Very

satisfied Satisfied

Neither

satisfied or

dissatisfied

Dissatisfied Very

dissatisfied No answer

Work 9.9 34.2 9.3 6.2 3.0 37.4

Source: Authors’ compilation based on Montevideo QoL Neighborhood Survey.

20

Table 3. Correlation between life domains

Happiness

Leisure

satisfaction

Life

satisfaction

Family

satisfaction

Health

satisfaction

Economic

situation

satisfaction

Work

satisfaction

Happiness 1.000

Leisure satisfaction 0.198 1.000

Life satisfaction 0.393 0.154 1.000

Family satisfaction 0.394 0.121 0.461 1.000

Health satisfaction 0.323 0.091 0.227 0.176 1.000

Economic situation

satisfaction 0.347 0.250 0.269 0.266 0.253 1.000

Work satisfaction 0.289 0.153 0.303 0.272 0.173 0.408 1.000

21

Table 4: Access to neighborhood amenities and public goods by level of happiness

Not very happy /Not at

all happy

Very happy / Fairly

happy Total

Mean Std. Dev. Mean Std. Dev. Mean Std. Dev.

Access to electricity 97.0% 17.0% 99.7% 5.9% 99.2% 9.1%

Access to running water 98.1% 13.6% 98.9% 10.5% 98.7% 11.2%

Access to sewage 71.7% 45.1% 75.2% 43.2% 74.5% 43.6%

Access to drainage 70.8% 45.6% 74.5% 43.6% 73.8% 44.0%

Access to garbage disposal 56.2% 49.7% 51.9% 50.0% 52.7% 49.9%

Access to street lighting 82.8% 37.8% 89.6% 30.5% 88.3% 32.1%

Sidewalks in good condition 23.5% 42.5% 24.7% 43.2% 24.5% 43.0%

Trees 65.1% 47.8% 73.6% 44.1% 72.0% 44.9%

Absence of air pollution 76.5% 42.5% 78.2% 41.3% 77.9% 41.5%

Absence of noise pollution 75.6% 43.1% 78.1% 41.4% 77.6% 41.7%

Source: Authors’ compilation based on Montevideo QoL Neighborhood Survey (2007).

22

Table 5. Determinants of domain satisfaction

Probit Adapted Ordinary Least Squares (POLS) estimations Happiness Leisure

satisfaction

Social life

satisfaction

Family

satisfaction

Health

satisfaction

Economic

situation

satisfaction

Work

satisfaction

Access to electricity 0.704 -0.244 0.631 0.182 0.383 0.833 0.953

(0.137)*** (0.314) (0.266)** (0.217) (0.217)* (0.120)*** (0.150)***

Access to running water 0.296 -0.153 0.225 0.346 0.186 0.385 0.066

(0.143)** (0.175) (0.278) (0.259) (0.135) (0.254) (0.222)

Access to sewage -0.063 0.060 -0.009 -0.039 -0.071 -0.054 -0.075

(0.053) (0.062) (0.066) (0.030) (0.043) (0.061) (0.079)

Access to drainage 0.047 -0.106 0.040 0.119 0.098 0.013 -0.059

(0.047) (0.063) (0.031) (0.031)*** (0.031)*** (0.035) (0.140)

Access to garbage disposal 0.029 0.052 0.004 0.063 0.060 0.184 0.172

(0.032) (0.033) (0.050) (0.041) (0.029)** (0.024)*** (0.076)**

Access to street lighting 0.160 0.056 0.035 0.078 0.129 0.096 -0.032

(0.057)*** (0.071) (0.030) (0.094) (0.092) (0.055)* (0.058)

Sidewalks in good condition -0.033 0.056 0.114 0.068 0.045 -0.005 0.144

(0.080) (0.103) (0.039)*** (0.049) (0.057) (0.033) (0.096)

Trees -0.008 0.019 0.073 0.024 0.008 -0.023 0.023

(0.030) (0.071) (0.031)** (0.030) (0.026) (0.041) (0.051)

Absence of air pollution 0.012 0.080 0.009 0.022 0.157 0.139 -0.086

(0.053) (0.051) (0.047) (0.050) (0.080)* (0.058)** (0.058)

Absence of noise pollution 0.201 0.155 0.182 0.032 -0.001 0.016 0.019

(0.034)*** (0.046)*** (0.091)* (0.052) (0.055) (0.071) (0.078)

Household income 0.113 0.085 0.118 0.166 0.078 0.385 0.266

(0.024)*** (0.046)* (0.029)*** (0.039)*** (0.023)*** (0.029)*** (0.023)***

Constant -1.334 1.304 -0.981 -2.535 0.352 -1.881 -3.622

(0.472)*** (0.627)** (0.608) (0.775)*** (0.310) (0.356)*** (0.211)***

Other individual controls Yes Yes Yes Yes Yes Yes Yes

Observations 1248 1248 1248 1248 1248 1248 802

R-squared 0.08 0.11 0.06 0.08 0.16 0.17 0.08 Note: Clustered standard errors in parentheses * significant at 10%; ** significant at 5%; *** significant at 1% The other individual controls are: age, gender, marital status, education and family size.

23

Table 6. Determinant of domain Satisfaction

Order probit estimations Happiness Leisure

satisfaction

Social life

satisfaction

Family

satisfaction

Health

satisfaction

Economic

situation

satisfaction

Work

satisfaction

Access to electricity 0.825 -0.310 0.729 0.196 0.484 1.061 1.093

(0.168)*** (0.403) (0.291)** (0.259) (0.248)* (0.184)*** (0.219)***

Access to running water 0.371 -0.195 0.286 0.452 0.225 0.464 0.053

(0.179)** (0.218) (0.332) (0.317) (0.172) (0.322) (0.271)

Access to sewage -0.083 0.075 -0.013 -0.051 -0.084 -0.067 -0.088

(0.067) (0.075) (0.081) (0.039) (0.054) (0.072) (0.094)

Access to drainage 0.061 -0.132 0.050 0.155 0.120 0.017 -0.069

(0.059) (0.078) (0.039) (0.039)*** (0.038)*** (0.043) (0.168)

Access to garbage disposal 0.035 0.064 0.004 0.079 0.073 0.239 0.207

(0.041) (0.040) (0.063) (0.054) (0.035)** (0.031)*** (0.093)**

Access to street lighting 0.204 0.076 0.043 0.099 0.160 0.129 -0.041

(0.069)*** (0.087) (0.037) (0.117) (0.114) (0.062)** (0.067)

Sidewalks in good condition -0.040 0.067 0.147 0.093 0.056 -0.012 0.175

(0.103) (0.126) (0.051)*** (0.065) (0.071) (0.044) (0.118)

Trees -0.008 0.023 0.092 0.032 0.010 -0.035 0.027

(0.039) (0.086) (0.038)** (0.039) (0.032) (0.055) (0.061)

Absence of air pollution 0.016 0.099 0.012 0.031 0.194 0.174 -0.104

(0.067) (0.061) (0.056) (0.062) (0.099)* (0.068)** (0.070)

Absence of noise pollution 0.263 0.188 0.225 0.041 0.001 0.019 0.026

(0.041)*** (0.055)*** (0.107)** (0.066) (0.068) (0.088) (0.092)

Household income 0.149 0.106 0.148 0.217 0.094 0.508 0.323

(0.031)*** (0.057)* (0.036)*** (0.050)*** (0.030)*** (0.035)*** (0.029)***

Other individual controls Yes Yes Yes Yes Yes Yes Yes

cut 1 -0.429 -2.809 -1.128 0.603 -2.537 1.120 2.549

(0.613) (0.817) (0.699) (1.005) (0.367) (0.428) (0.242)

cut 2 0.683 -1.914 -0.324 1.554 -1.303 2.141 3.207

(0.595) (0.801) (0.706) (0.942) (0.375) (0.438) (0.252)

cut 3 2.208 -0.405 0.401 2.130 -0.093 2.813 3.774

(0.620) (0.797) (0.710) (0.972) (0.389) (0.432) (0.244)

cut 4 2.050 3.774 0.770 4.394 5.348

(0.732) (1.000) (0.394) (0.431) (0.275)

Observations 1248 1248 1248 1248 1248 1248 802

Pseudo R-squared 0.04 0.05 0.03 0.04 0.06 0.07 0.03 Note: Clustered standard errors in parentheses * significant at 10%; ** significant at 5%; *** significant at 1%

The other individual controls are: age, gender, marital status, education and family size.