ESTIMATING INCOME ELASTICITIES OF LEISURE ACTIVITIES … · Financial support from research group CREVALOR (at the Faculty of Economics and Business Administration of the University

DTECONZ 2015-02: J. González

1

Documento de Trabajo 2015-02

Facultad de Economía y Empresa

Universidad de Zaragoza

Depósito Legal Z-1411-2010. ISSN 2171-6668

ESTIMATING INCOME ELASTICITIES OF LEISURE ACTIVITIES USING

CROSS-SECTIONAL CATEGORIZED DATA

Jorge González Chapela*

Centro Universitario de la Defensa de Zaragoza

Address: Academia General Militar, Ctra. de Huesca s/n, 50090 Zaragoza, Spain

Email: [email protected] – Tel: +34 976739834

Abstract

The empirical classification of daily activities into luxuries, necessities, or inferior activities is

useful for predicting the impact of economic development, the life cycle, or social mobility on

the organization of people’s time. This paper conducts an empirical examination of three

broad leisure categories plus their main subcategories using a cross-section of time-use

observations for the United States. Estimation takes account of the form of the data in which

the income variable was recorded. Comparison of income elasticities with those reported by

previous studies is also made.

JEL codes: D12, J22.

Keywords: Engel aggregation, empirical time-demand function, time-use income elasticity,

American Time Use Survey.

* I am grateful to Dan Hamermesh, Robert Hill, Nancy Mathiowetz, and Frank Stafford for

helpful comments. Financial support from research group CREVALOR (at the Faculty of

Economics and Business Administration of the University of Zaragoza), funded by the

Diputación General de Aragón and the European Social Fund, is gratefully acknowledged.

mailto:[email protected]


2

ESTIMATING INCOME ELASTICITIES OF LEISURE ACTIVITIES USING

CROSS-SECTIONAL CATEGORIZED DATA

Abstract

The empirical classification of daily activities into luxuries, necessities, or inferior activities is

useful for predicting the impact of economic development, the life cycle, or social mobility on

the organization of people’s time. This paper conducts an empirical examination of three

broad leisure categories plus their main subcategories using a cross-section of time-use

observations for the United States. Estimation takes account of the form of the data in which

the income variable was recorded. Comparison of income elasticities with those reported by

previous studies is also made.

JEL codes: D12, J22.

Keywords: Engel aggregation, empirical time-demand function, time-use income elasticity,

American Time Use Survey.


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1. INTRODUCTION

Ever since the seminal works of Mincer (1963) and Becker (1965), the notion that the

consumption of market goods calls for the consumer’s time has spread among economists to

reach, nowadays, the status of a common research tool. Coinciding with the diffusion of that

idea, leisure per adult in the United States increased dramatically (Aguiar and Hurst 2007),1

and demand analysis, which was fundamentally concerned with the demand for market goods,

became increasingly interested in the demand for leisure (see for example Owen 1971,

Gronau 1976, Wales and Woodland 1977, Juster and Stafford 1985, Kooreman and Kapteyn

1987, Solberg and Wong 1992, Robinson and Godbey 1997, Hamermesh 2002, Kimmel and

Connelly 2007, Datta Gupta and Stratton 2010, Mullahy and Robert 2010, and Sevilla et al.

2012).

Still, certain aspects of the demand for leisure are not well understood. Americans, for

example, have preferences about the way their leisure time is spent, preferring as a rule

talking with friends to watching television (Juster 1985c) and socializing after work to using

the computer at home (Kahneman et al. 2004). Hence, one would expect that, ceteris paribus,

the demands for different leisure activities reacted differently to improvements in the standard

of living, moving as a whole towards a more enjoyable composition of total leisure.

Nevertheless, the empirical verification of this conjecture has proved elusive. Stafford and

Duncan (1985) developed estimates of income effects on time use to different activities for

208 working males included in Juster et al.’s (1978) 1975-76 Time Use Study (TUS). Their

standard errors appeared generally large relative to the coefficients except for meals out,

whose estimated elasticity at the mean was .20. Also using the TUS, Kooreman and Kapteyn

(1987) found negligible income responses in the demand for seven types of non-market

activities by 242 couples, and Biddle and Hamermesh (1990) obtained no evidence of income

1 The increase over the whole 20th century was smaller (Ramey and Francis 2009).


4

effects in the demand for non-market time by 706 individuals. For non-disaggregated leisure,

but also considering cross-sectional household data, Kimmel and Connelly (2007) estimated a

positive effect of husband’s earnings on his wife’s leisure in a large sample of 4,552 mothers

drawn from the American Time Use Survey (ATUS). While husband’s earnings played the

role of nonlabor income from the wife’s point of view, it could be also capturing a cross-price

substitution effect whereby the true income effect would be larger if husband’s and wife’s

leisure were complements (as found in Connelly and Kimmel 2009).2 In any case, if the

quantity of leisure increases with the standard of living, how can the demand for many

specific leisure activities appear generally as unaffected?

Additional research on the demand for disaggregated leisure was conducted by Juster

(1985a), Robinson and Godbey (1997), and Dardis et al. (1994). Juster (1985a) analyzed the

amount of investment time (i.e., time whose satisfaction derives from the activity’s end

product and not from the process of carrying it out) present in active, passive, and social

entertainment, concluding that investment time increased with household income. Table 17 of

Robinson and Godbey (1997) arrayed major background variables related to the allocation of

time. Household income appeared as a significant predictor of free time activities, but as the

authors recognized its predictive power could be the result of composition effects. Dardis et

al. (1994) considered a related issue. Using 1988-89 Consumer Expenditure Survey data on

active, passive, and social entertainment, they estimated expenditure elasticities in the range

of .40 to .72, indicating that goods consumed in the course of those activities (hereafter,

2 Solberg and Wong (1992) found that the husband’s and the wife’s leisure were negatively

related to commuting times. Although in the time allocation model of Gronau (1977)

increases in commuting time cause negative income effects, the critical predictions of

Gronau’s model were contradicted by Solberg and Wong (1992).


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recreation goods) were necessities.3 But unless goods and time are consumed in fixed

proportion, the analysis of consumer expenditure is of limited utility for assessing the

variation of activity times. Thus, for example, there is evidence that expenditure on recreation

goods increases, but time allocated to leisure production decreases, with the consumer’s

education (Gronau and Hamermesh 2006).

This study is aimed at estimating income elasticities of demand for several leisure

activities in the United States. The knowledge of income elasticities is useful for predicting

which leisure activities will grow or decline on average as the economy develops, over an

individual’s life cycle, or across a nation’s income strata. This study departs from previous

disaggregated leisure analyses in two respects. First, the data source is the ATUS, which

allows a much larger sample than the 1975-76 TUS. Second, the form of our income measure

plus its treatment in estimation avoids some problems of parameter estimation. The income

variable used in Kooreman and Kapteyn (1987) was constructed by adding up the amounts

given to questions about the size of several nonlabor income sources. As the authors

recognized, the resulting measure could contain substantial measurement error. Stafford and

Duncan (1985) and Biddle and Hamermesh (1990) computed midpoints of income intervals

(as I also did in a previous version of this paper),4 but estimators computed from midpoints

are biased (e.g., see Haitovsky 1973), and Beaumont’s (2005) corrections are not workable

when intervals are of uneven width. Instead, the approach used here is that of Hsiao and

Mountain (1985a) in their study of the income elasticity of demand for electricity: To

approximate the distribution of categorized income by a continuous probability function,

3 Pawlowski and Breuer (2012) estimated expenditure elasticities of demand for leisure

services in Germany.

4 I thank Dan Hamermesh and Frank Stafford for clarification on the form of their income

measure.


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using it to evaluate the conditional mean or compute the covariance between income and

other explanatory variables. In this way, the resulting regression output is known to be

consistent.

The rest of the paper is organized in four sections. Section 2 briefly discusses two

theoretical underpinnings to this investigation: A straightforward implication for the

allocation of time of the linear time-budget constraint that is analogous to the Engel

aggregation condition for commodity demand functions, and some issues involved in the

specification and estimation of a time-demand regression function. Section 3 describes the

data and the estimation method. Estimation in particular will be conducted assuming that all

consumers faced the same recreation goods prices, but, as in Mincer (1963), it will hold

constant consumers’ opportunity cost of time to avoid misinterpreting the estimated income

effects. Results are presented in Section 4. The final section summarizes the main

conclusions.

2. PRELIMINARIES

2.1 An Engel aggregation condition for the allocation of time

Suppose a consumer purchases goods and combine them with time to maximize satisfaction.

The allocation of time on a given day must obey the constraint

1

0

1, 440J

j J

j

t t

, (1)

where jt is minutes spent on activity j and Jt working time. Assume that demand functions

exist:

, , , 0, , 1j jt t x j J p q , (2)

1

0

1,440 , , , ,J

J j J

j

t t x t x

p q p q , (3)


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where (3) is the derived labor supply function. In these expressions, p represents a vector

with the unit prices of the goods consumed, q a vector with other relevant characteristics, and

x full household income. For simplicity, the same determinants are assumed to appear in

each activity (which will be true in our empirical examination).

Since (2) and (3) must satisfy (1), changes in x will cause rearrangements in the

consumer’s activities such that

1

0

, , , ,0

Jj J

j

t x t x

x x

p q p q. (4)

Defining

, ,j

jx

j

t x xe

x t

p q, (5)

, ,J

Jx

J

t x xe

x t

p q, (6)

jb as the share of x spent indirectly (i.e. through the foregoing of money income) on activity

j , and Jb as the share of labor earnings, (4) leads to the following elasticity formula:

1

0

0J

j jx J Jx

j

b e b e

. (7)

This restriction expresses that the weighted sum of income elasticities is zero, whereby either

all elasticities are zero or there must be at least one positive and one negative elasticity.

Estimates of Jxe tend to be negative (e.g., see Juster and Stafford 1991, Blundell and

MaCurdy 1999, Klevmarken 2004, and Kimmel and Connolly 2007), whereby we would

expect at least one jxe to be positive. By analogy with the analysis of expenditure patterns

(e.g., see Deaton and Muellbauer 1980), if 1jxe activity j would be a luxury. Since jb will

increase with x if and only if 1jxe , a luxury is therefore an activity that takes up a larger

share of x as x increases. When an activity takes up a lower share of x as x increases it is a


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necessity. In other words, a necessity is an activity for which 0 1jxe . Inferior activities are

those which take up a lower quantity of time as x increases. In that case, 0jxe . This study

focuses on estimating income elasticities of demand for leisure or free-time activities. As

argued by Robinson and Godbey (1997), these are activities that allow maximum

opportunities for choice, pleasure, and personal expression, and facilitate recovery from work-

related effort.

2.2 Specification and estimation of a time-demand regression function

The 'sjxe cannot be derived from time-use observations in share form when the share’s

denominator does also react to changes in the explanatory variables. If, for example, total

non-market time were in the share’s denominator, the share elasticity would be given by jxe

minus the elasticity of total non-market time, so that jxe could not be identified without

knowing the latter. Hence, I shall work with observations in level form.

Choosing a specification and estimation method for , ,jE t xp q is complicated by

the presence of diaries with zeros. Presumably, zeros pertain to two kinds of individuals:

those who never do j (non-doers), and doers who, on the observation day, spent no time on

j (called reference-period-mismatch zeros by Stewart 2013). As shown by Stapleton and

Young (1984), the latter type introduces measurement error in jt , which renders the Tobit

estimator inconsistent. Stapleton and Young’s (1984) alternative estimators relied on the

possibility of separating doers from non-doers, which is not feasible in this study. Two-part

and exponential Type II Tobit models (e.g., see Wooldridge 2010) were also discarded

because those models’ first-stage regression represents whether j was done on the

observation day, which is quite different from whether j is done or not.


9

While the ordinary least squares (OLS) estimator is inconsistent in the Tobit context,

Stoker (1986) found that with normally distributed regressors OLS consistently estimates

Tobit’s marginal effects. The same conclusion was reached by Greene (1981), whose Monte

Carlo study further suggested that that result is robust in the presence of uniformly distributed

and binary regressors, but is distorted by the presence of skewed regressors.5 The reason

behind the apparent robustness of OLS is that the presence of (random) measurement error in

jt is inconsequential when the estimating model is linear. The combination of a linear

specification for , ,jE t xp q with an OLS estimator is therefore a reasonable choice when

the regressors adopt the format recommended by Greene (1981) and Stoker (1986).

3. DATA AND METHODS

3.1 Data selection and construction of key measures

The data for this study come from the ATUS. The ATUS is drawn from a subset of

households that have completed their participation in the Current Population Survey (CPS). In

each selected household, an individual aged 15 or older is interviewed over the phone, who is

asked to report on her activities over the previous 24 hours, beginning at 4 am. The ATUS

also asks for basic labor market information (including labor force status, earnings, and hours

of work), but an important range of socio-demographic measures (such as household income)

are carried over from the final CPS interview, which takes place two to four months before

the ATUS interview. Hamermesh et al. (2005) offer a more complete description of the

ATUS.

5 Stewart (2013) has simulated the behavior of the OLS estimator with time-diary data and

produced results consistent with Greene’s (1981). The regressors in Stewart’s data-generating

process were a dummy and two uniformly distributed variables.


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The ATUS data selected for this study were collected evenly during 2011. Particular

of that year in the United States was that the price of recreation goods remained virtually

constant. I assume that this fact plus the inclusion of within-year and spatial controls allows

us to treat the relationship between income and leisure in isolation from p . The final size of

the 2011 ATUS was 12,479 individuals. In any study of the allocation of time, a crucial

control is the opportunity cost of time. Since this is generally approximated by the hourly

wage rate in the case of workers, only wage-earners aged 23-64 were included in this analysis

(the 2011 ATUS did not ask for earnings of the self-employed). I also removed individuals

whose household income was imputed or whose earnings were updated in the ATUS

interview. (Since household income was imputed primarily from longitudinal assignments, it

may be so far apart that might have changed; also, household income may become

mismeasured when earnings were updated.) After discarding cases with other missing or

inconsistent data, the usable sample comprised 3,239 persons. Of these, 1,907 did not live

with a spouse/partner or lived with a spouse/partner who was not working (for brevity, “single

earners”), and 1,332 lived with a spouse/partner who was also working (“dual earners”).

I focus on three broad leisure aggregates: active leisure, passive leisure, and social

entertainment, plus their main component activities. These three aggregates were identified as

major types of leisure by Hill (1985) and Juster (1985b), and were also studied by Kooreman

and Kapteyn (1987) and Dardis et al. (1994). The following definitions are taken from Hill

(1985) (the specific activities involved are listed in Appendix A). Active leisure includes a

wide assortment of recreational activities requiring active physical or mental exertion, plus

some domestic crafts. Passive leisure comprises television viewing plus a variety of activities

including relaxing, exposure to other media, and communication with others. Social

entertainment is composed of spectator and participation-oriented activities, the latter


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TABLE 1. SAMPLE DESCRIPTIVE STATISTICS (3,239 INDIVIDUALS)

Variable (minutes per day) Mean S.D. Min Max % = 0

Active leisure 45 93 0 1050 60.2

Requiring physical exertion 20 61 0 860 82.2

Requiring mental exertion 9 48 0 1050 92.7

Domestic crafts 16 52 0 580 78.3

Passive leisure 224 178 0 990 7.3

TV viewing 139 151 0 990 23.9

Other passive leisure 85 116 0 800 35.1

Social entertainment 41 94 0 1075 71.6

Spectator activities 11 56 0 1075 95.0

Participation-oriented activities 30 74 0 750 74.5

Variable ($) Mean S.D. Min Max

Average hourly earnings 23.30 13.64 4.12 72.12

Spouse’s average hourly earnings* 23.55 13.01 4.50 72.12

Variable (%) Mean Variable (%) Mean

Male 48.6 Summer 25.7

Age 23-30 15.6 Autumn 23.2

Age 31-40 30.1 Sunday 25.7

Age 41-50 27.5 Friday 9.5

Age 51-64 26.8 Saturday 24.6

Less than high school 4.9 Work day 53.1

Exactly high school 24.4 Household income below $5,000 0.8

Some college 28.4 between $5,000 and $7,499 0.6

College graduate 42.3 between $7,500 and $9,999 0.9

Presence of spouse/partner 55.5 between $10,000 and $12,499 1.6

Presence of children 0-5 21.4 between $12,500 and $14,999 1.6

Presence of children 6-12 26.2 between $15,000 and $19,999 3.3

Presence of other adults 18.9 between $20,000 and $24,999 4.5

Black 12.4 between $25,000 and $29,999 4.8

Hispanic 12.8 between $30,000 and $34,999 6.3

Disabled 3.2 between $35,000 and $39,999 5.6

Northeast 16.9 between $40,000 and $49,999 9.8

Midwest 26.6 between $50,000 and $59,999 9.8

South 34.1 between $60,000 and $74,999 11.6

West 22.4 between $75,000 and $99,999 15.3

Metropolitan area 84.3 between $100,000 and $149,999 14.7

Winter 25.2 above $150,000 8.6

Spring 25.8

Notes: *: Persons living with a spouse/partner who is also working (1,332 individuals).

including meals out. All uses of time include the associated travel and are measured in

minutes.


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Table 1 presents descriptive statistics on the dependent and the explanatory variables.6

The controls included in q are: The respondent’s sex, age, educational attainment,

race/ethnicity, disability status, and w , the ratio of usual weekly earnings to usual hours of

work;7 indicators for the presence of a spouse/partner in the household, of children aged 0-5

and 6-12, and of other adults beyond the spouse/partner; and indicators for region of

residence, metropolitan status, day of the week, work day, and season of the year. Pursuant to

6 All income measures are recorded before payments.

7 The wage rate was assumed to be exogenous in Solberg and Wong (1992), but was treated

as endogenous in other time-use studies. I tested for the endogeneity of ln w using two

different sets of instrumental variables. The first set was taken from Biddle and Hamermesh

(1990) and included dummy variables for union membership and for one-digit occupation and

industry. The second set was taken from Kimmel and Connelly (2007) and Connelly and

Kimmel (2009), and included age squared, education squared, age times education, and the

state-month unemployment rate. In our case, both sets of instruments appeared to be weakly

related to ln w (particularly the second set), though their validity was hardly questioned by

Hansen’s (1982) J test of overidentification restrictions. The endogeneity of ln w was tested

using Hayashi’s (2000, p. 220) C statistic and having dummy variables for the observed

income categories in place of the unobserved x . Overall, instrumenting received little

empirical support. As to the first set of instruments, and with a 95% of confidence, ln w was

endogenous only in the regression for spectator activities among single earners and in the

regression for activities requiring mental exertion among dual earners. In both cases, its

estimated coefficient was positive (ranging from 14 to 17 minutes, S.E. around 6.5).

Considering the second set, ln w was endogenous only in the regressions for passive leisure

and TV viewing among dual earners. In both cases, the estimated wage effect was huge

(around -300) but measured imprecisely (S.E. around 160).


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Greene (1981) and Stoker (1986), w is included in log form and the remaining controls as

(sets of) binary variables. When the respondent lived with an employed spouse/partner, q

also includes the (log of the) spouse’s average hourly wage so as to control for possible cross-

substitution and power effects within the couple (e.g., see Solberg and Wong 1992, Kimmel

and Connelly 2007, and Datta Gupta and Stratton 2010). Region, season, and metropolitan

status are intended to control for possible differences in the price of recreation goods. As in

Datta Gupta and Stratton (2010), a work day is a day on which the respondent spent more

than 2 hours working.

The ATUS does not ask for the amount of nonlabor income, which complicates the

construction of a measure of full household income. Hence, x will be measured as (the log

of) annual household income. This differs from full income in the inclusion of the number of

actual hours worked, instead of the maximum possible hours of work. But because of the

short period of time analyzed, there is little reason to believe that the number of hours worked

in the year is related to jt , especially after controlling for the work/non-work character of the

observation day. The income level of each household was recorded in one of 16 intervals of

uneven width. Figure 1 shows that its distribution in the sample is skewed to the right.

3.2 Estimation method

Estimation of ,jE t xq is conducted using the conditional mean (CM) and pseudo-

instrumental variable (PIV) methods developed by Hsiao and Mountain (1985a). Let

,jE t xq be specified in error form as

j j j j jt x u γ q , (8)

where , ,j j j γ are unknown parameters and ju is iid with mean zero and variance 2

ju .

x falls in one of 16 mutually exclusive intervals, indicated by the dummy variables hz ,


14

Figure 1. Household income distribution (wage earners aged 23-64)

Notes: Author’s calculations with data from the 2011 ATUS. When the original interval

width was greater than $10,000, households were assigned assuming that they were

uniformly distributed within the original interval.

1, ,16h . To facilitate the interpretation of the income response, Hsiao and Mountain

decided against replacing x in (8) with these dummies. Instead, they approximated the

marginal distribution of household income by a lognormal distribution (as suggested for

example by Aitchison and Brown 1957), using it to evaluate the mean of x in each interval or

to compute the covariance between x and q . In this way, the log of household income has a

format recommended by Stoker (1986). Additionally, with the inclusion of x and w in log

form, our estimating equation adopts the semi-log specification of Kooreman and Kapteyn


15

(1987) and Biddle and Hamermesh (1990),8 which facilitates the comparison with previous

time-use studies.9

Let θ denote the mean and variance 2

x that characterize the marginal distribution

of x , and let θ̂ be its interval regression estimator. Then, ˆˆ 1,h hm E x z θ is a consistent

estimate for 1h hm E x z . The CM method replaces x in (8) with ˆhm , and then regresses

jt on a constant, q , and ˆhm . Unless ˆ

hx m and q are correlated, the resulting CM

estimates of , ,j j j γ are consistent and have as asymptotic variance matrix the expression

given in (2.6) of Hsiao and Mountain (1985a). When ˆhx m and q are correlated, ˆ

hm is not

substituted for x in (8), but ˆ, hmq are used as instruments for , xq . Although the sample

covariance estimates ˆxq

Σ and ˆmx cannot be directly computed because x is unobserved,

they can be approximated by observed quantities, yielding

1

2

ˆˆˆ ˆˆ

ˆ ˆ ˆˆ

j

j

PIV

tj m

PIV

j mtm m

qqq q

q

Σγ Σ Σ

Σ, (9)

ˆˆ ˆPIV PIV PIV

j j j jt x γ q , (10)

8 Stafford and Duncan (1985) used a log-linear model because their time-use data presented

almost no zeros: The TUS obtained four time diaries at three-month intervals from each

respondent, which were then combined into “synthetic weeks”.

9 Nevertheless, the shape of activity Engel curves could vary, for example, by activity or with

the amount of leisure available, as the variety of expenditure Engel curves suggests (see

Lewbel 2008). It is hoped that this analysis will stimulate research on the shape of activity

Engel functions.


16

where 2 2ˆ ˆ ˆx m and jt , q , and x are sample means. The PIV estimator is consistent and

has asymptotic variance matrix given by expression (2.13) of Hsiao and Mountain (1985a).10

The interval regression estimates of and 2

x were 10.9564 and .6203. I tested the

appropriateness of the lognormal assumption using a chi-squared goodness-of-fit test. The test

statistic was 243.53. The critical value at 10% significance level with 13 df is 19.81. Clearly,

the null hypothesis that the distribution of household income in our sample is lognormal

cannot be accepted. The largest contributor to the criterion was the lowest income class (see

Table 2, which was constructed analogously to Table 1 of Hsiao and Mountain 1985a).

Following Hsiao and Mountain, I proceeded by removing observations in that class and

approximating the remaining observations’ income distribution by a lognormal curve. After

adjusting a truncated interval regression and redoing the test, the result was still a strong

rejection of the null, which stemmed again primarily from the lowest surviving income class.

I repeated the process eliminating each time the lowest/highest surviving income class that

contributed the most to the criterion. I stopped when the 5 lowest and the highest original

income classes had been removed. Then, the truncated interval regression estimates for

and 2

x were 11.0706 and .5526. The predicted number of households in each income

category is given in Table 2 under the heading of Model 2. The chi-squared statistic was 9.87.

Since the critical value at 10% significance level with 7 df is 12.02, the lognormal assumption

cannot be rejected. Estimations, therefore, will ignore cases with household income below

$15,000 or

10

An element of that matrix was corrected in Hsiao and Mountain (1985b). The asymptotic

variance of ˆ PIV

j is calculated as 2ˆ

, var ,ˆj

PIV

j

u PIV

j

N x x

γq q , N being the sample size.


17

TABLE 2. COMPARISON OF FITTED AND ACTUAL DISTRIBUTIONS

Logarithm of income

range Actual

Model 1, original

income

categorizationa

Model 2, removing the

5 lowest and the highest

income categoriesb

8.5172x 25 3 ‒

8.5172 8.9227x 21 13 ‒

8.9227 9.2103x 30 27 ‒

9.2103 9.4335x 51 43 ‒

9.4335 9.6158x 52 58 ‒

9.6158 9.9035x 108 150 108

9.9035 10.1266x 145 179 144

10.1266 10.3090x 155 193 166

10.3090 10.4631x 204 194 178

10.4631 10.5966x 183 189 180

10.5966 10.8198x 319 347 348

10.8198 11.0021x 319 298 313

11.0021 11.2252x 376 358 391

11.2252 11.5129x 495 410 465

11.5129 11.9184x 477 418 488

11.9184 x 279 359 ‒

Total 3,239 3,239 2,781

2

statistic 243.53 9.87

102 (critical value)

102

13df 19.81 102

7df 12.02

Notes: a: 10.9564, .6203x N .

b: 11.0706, .5526x N

above $150,000.11

The surviving sample comprised 2,781 persons, of whom 1,127 lived with

a spouse/partner who was also working.

4. EMPIRICAL RESULTS

This section presents the estimated income elasticities at the means of the data ˆj jt for

active leisure, passive leisure, social entertainment, and their main component activities. The

complete set of time-demand regression estimates is given in Appendix B. The income

11

Aitchison and Brown (1957, p. 116) discuss the systematic discrepancy from lognormality

at the ends of the income scale.


18

elasticities are shown in Table 3 separately for single and dual earners, as well as for the PIV


19

TABLE 3—INCOME ELASTICITIES OF DEMAND

Single earners Dual earners

(1)

PIV

(2)

CM

(3)

Midpoint

(4)

PIV

(5)

CM

(6)

Midpoint

Leisure activity Estimate S.E. Estimate S.E. Estimate Estimate S.E. Estimate S.E. Estimate

Active leisure .20 .15 .19 .16 -.01 .17 .19 .17 .17 .05

Physical exertion .47** .22 .46* .26 .14 .05 .30 .05 .26 -.01

Mental exertion .22 .36 .21 .39 -.11 -.19 .50 -.18 .42 -.14

Domestic crafts -.20 .25 -.19 .20 -.15 .45 .29 .44 .29 .18

Passive leisure -.09* .05 -.09* .05 -.01 -.04 .07 -.04 .07 -.02

TV viewing -.11 .07 -.11* .06 .01 -.04 .09 -.04 .10 -.10

Other passive -.05 .09 -.05 .10 -.06 -.06 .13 -.05 .13 .13

Social entertainment .30* .17 .29* .17 .21 .45** .20 .44** .20 .14

Spectator .61 .41 .60* .36 .64 .25 .46 .25 .44 .16

Participation .19 .18 .19 .19 .06 .51** .22 .50** .23 .13

Notes: Elasticities are calculated at the means of the data. Standard errors are computed using the delta method. *: Significant

at 10%. **: Significant at 5%.


20

and CM estimation methods. Since the covariance of , xq was similar to that of ˆ, hmq ,

differences between the PIV and CM estimates were very small.1 For comparison purposes,

Table 3 also presents elasticities obtained using the midpoint of an income interval as a proxy

for x .

The estimated income elasticities for the three leisure aggregates ranged from -.09 to

.45. For their main component activities, they ranged from -.20 to .61. The highest elasticity

values were obtained for social entertainment: .30 and .45 for single and dual earners,

respectively. These estimates attained statistical significance at or around 5%. For single

earners, the main contributor to the reaction of social entertainment was the set of spectator

activities (.61). For dual earners, however, the main contributor was the category of

participation-oriented activities (.51). The magnitude of the income elasticity for active leisure

was similar for single (.20) and dual earners (.17). However, for the former group the main

contributor to the response was the set of activities requiring physical exertion (.47), whereas

for the latter it was domestic crafts (.45). Passive leisure was slightly inferior for single (-.09)

and dual earners (-.04). For single earners this response attained statistical significance at

10%, and TV viewing (-.11) was the main contributor to the reduction. The estimated

elasticities yielded by the midpoint technique were, as a rule, substantially smaller, and some

of them were of incorrect sign.

Our estimated income elasticities for passive leisure and social entertainment agree

with the claim that Americans prefer talking with friends or socializing after work to watching

TV or using the computer at home. The elasticity for participation-oriented activities among

single earners (.19) is remarkably similar to that obtained by Stafford and Duncan (1985) for

1 Expression (2.10) of Hsiao and Mountain (1985a) shows that xqΣ can be approximated as

ˆˆmq

Σ . In this study, ˆ 1.0188 .


21

meals out among working males (.20). However, the presence in the household of an

employed spouse/partner increased that elasticity to .51. The expenditure elasticities for non-

salary income observed by Dardis et al. (1994) in a sample of households where two thirds

had income from salary from the household head, were higher and ranged from .40 for active

leisure to .59 for passive leisure and .72 for social entertainment. In combination with ours,

their results suggest that recreation goods and leisure time are not consumed in fixed

proportion, but that, holding other factors fixed, consumers endowed with more income

increase the quality (i.e., the goods intensity) of the leisure activities consumed.

The coefficient associated to ln w is representing both a substitution and a traditional

income effect, the latter created by variation in the consumer’s real full income when w

changes. The estimated effect of ln w was generally small and insignificant, with the

exception of the demand for passive leisure among single earners, which shrank 16 minutes

per week when the wage rate increased by 10%. As in Connelly and Kimmel (2009), the

results showed little effect of the spouse/partner’s wage on the individual’s leisure demands.

The estimated effect of education on the demand for leisure was also insignificant, which

suggests that the positive association between education and physical activity among

working-age individuals found by Mullahy and Robert (2010) stemmed from a positive

income effect.

5. CONCLUSION

A straightforward implication of the linear time-budget constraint is that the weighted sum of

income elasticities of demand for the set of daily activities has to be zero, whereby either all

the elasticities are equal to zero or at least some of them is positive and other is negative. This

paper focused on estimating income elasticities of demand for leisure activities. The results of

fitting a linear model with a categorized income variable to a sample of workers taken from

the 2011 ATUS suggest that social entertainment increases moderately with income (the


22

elasticity ranged from .30 to .45). The effect however is larger for spectator activities among

single earners (.61) and for participation-oriented activities among individuals living in dual-

earner couples (.51). Active leisure is slightly normal (.17 to .20), although the effect is larger

for activities requiring physical exertion among single earners (.47) and for domestic crafts

among dual earners (.45). Passive leisure is slightly inferior (-.04 to -.09), including TV

viewing among single earners (-.11). These estimates are larger than those of previous studies

in which a measure of income obtained either by adding up several nonlabor income sources

or by computing midpoints of income intervals was utilized. They are, however, smaller than

the corresponding leisure expenditure elasticities, and suggest that consumers endowed with

more income consume leisure activities of higher quality.


23

A COMPONENTS OF ACTIVITIES WITH ATUS ACTIVITY CODES

1st-tier 2nd-tier 3rd-tier

Active Leisure (ACT)

Requiring physical exertion (PHY)

Sports and exercise as part of job 05 02 03

Participating in sports, exercise, or recreation 13 01

Travel related 18 13 01

Requiring mental exertion (MEN)

Taking class for personal interest 06 01 02

Playing games 12 03 07

Hobbies 12 03 09,10,11

Writing for personal interest 12 03 13

Travel related 18 06 (12) 01 (03)

Domestic crafts (DOM)

Sewing, repairing, and maintaining textiles 02 01 03

Lawn, garden, houseplants, animals, and pets 02 05,06

Travel related 18 02 05,06

Passive Leisure (PAS)

Television viewing (TV) 12 03 03,04

Other passive leisure (COM)

Conversations with family/friends/neighbors/acquaint. 12 01

Relaxing, thinking 12 03 01

Tobacco and drug use 12 03 02

Listening to/playing music 12 03 05,06

Computer use for leisure (exc. games) 12 03 08

Reading for personal interest 12 03 12

Phone calls to/from family/friends/neighbors/acquaint. 16 01 01,02

Travel related 18 12 01,03

Social Entertainment (SOC)

Spectator activities (SPE)

Arts and entertainment (other than sports) 12 04

Attending sporting/recreational events 13 02

Travel related 18 12 (13) 04 (02)

Participation-oriented activities (PAR)

Attending social events with coworkers/bosses/clients 05 02 01

Eating/drinking at others’ home, bar, or restaurant* 11

Attending or hosting social events 12 02

Travel related 18 11 (12) 01 (02)

*: Time spent eating/drinking at a bar/restaurant is included whenever the respondent was not

alone.


24

B COMPLETE ESTIMATION OUTPUT

TABLE B1.a—TIME USE (MINUTES). PSEUDO-INSTRUMENTAL VARIABLE ESTIMATES. SINGLE EARNERS

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

Independent variables ACT S.E. PHY S.E. MEN S.E. DOM S.E. PAS S.E. TV S.E. COM S.E. SOC S.E. SPE S.E. PAR S.E.

Constant -21 57 -44 39 -15 31 38 32 572 106 388 97 184 77 -46 57 -40 36 -6 45

ln w 3 6 -3 4 2 3 4 4 -23 12 -17 11 -6 9 -2 6 -3 4 1 5

Male 17 5 7 3 4 2 6 3 33 8 37 8 -4 6 -4 5 -2 3 -3 4

Age 31-40 -7 7 -6 5 -2 4 1 4 34 13 11 12 24 9 -15 7 1 4 -16 5

Age 41-50 -12 7 -14 5 -3 4 4 4 39 13 26 12 13 10 -19 7 3 4 -22 6

Age 51-64 -3 7 -10 5 -5 4 12 4 54 13 33 12 21 9 -22 7 -3 4 -19 5

Exactly high school 0 10 0 7 1 6 -2 6 -7 19 -3 18 -4 14 2 10 3 7 -1 8

Some college 4 11 0 7 0 6 4 6 -18 20 -21 18 3 14 -1 11 2 7 -3 8

College graduate 5 11 2 8 0 6 3 6 -33 20 -39 19 6 15 7 11 4 7 3 9

Pres. of spouse/partner 3 6 1 4 -3 3 5 3 3 11 6 10 -3 8 -9 6 -5 4 -4 5

Pres. of children 0-5 -16 7 -5 5 -3 4 -9 4 -28 13 -12 12 -17 10 -8 7 -3 5 -5 6

Pres. of children 6-12 -12 6 -3 4 -4 3 -5 3 -44 11 -32 10 -12 8 8 6 1 4 6 5

Pres. of other adults -6 6 0 4 0 3 -5 3 -5 10 1 10 -6 8 -4 6 -3 4 -1 4

Black -18 6 -10 4 -2 3 -6 4 28 12 30 11 -1 8 -12 6 -4 4 -8 5

Hispanic -9 7 -1 5 -5 4 -4 4 -22 13 5 12 -26 9 -11 7 -7 4 -4 5

Disabled 0 11 -3 8 8 6 -5 6 22 21 14 19 8 15 -8 11 3 7 -11 9

Work day -34 6 -16 4 -9 3 -9 3 -149 10 -91 9 -58 7 -32 6 -10 3 -22 4

Sunday -6 6 -10 4 -2 3 6 4 17 12 16 11 0 9 5 6 -4 4 9 5

Friday 0 8 -2 5 0 4 1 4 15 14 0 13 15 10 25 8 5 5 20 6

Saturday 9 6 1 4 4 4 4 4 4 12 -9 11 13 9 34 7 10 4 24 5

Winter -28 6 -14 4 -2 3 -11 3 29 11 43 10 -13 8 -15 6 -8 4 -7 5

Spring -15 6 -10 4 -1 3 -4 3 29 11 19 10 10 8 -11 6 -3 4 -8 5

Autumn -18 6 -11 4 -2 3 -6 3 26 11 35 10 -9 8 -11 6 -7 4 -4 5

Midwest 9 7 3 5 8 4 -2 4 -5 13 -5 12 0 9 1 7 2 4 -1 5

South 17 6 9 4 4 4 4 4 -3 12 -5 11 2 9 -1 6 2 4 -3 5

West 19 7 10 5 5 4 3 4 -17 13 -19 12 2 9 -5 7 0 4 -6 5

Metropolitan area -14 6 -6 4 -1 3 -7 4 15 12 12 11 2 9 7 6 3 4 4 5

ln household income 9 6 9 4 2 3 -3 4 -22 12 -17 11 -5 9 11 6 6 4 5 5

Notes: The number of observations is 1,654 in all columns. Unreported age: 23-30. Activity abbreviations are defined in Appendix A.


25

TABLE B1.b—TIME USE (MINUTES). CONDITIONAL MEAN ESTIMATES. SINGLE EARNERS

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)


Constant -20 58 -43 41 -14 33 37 28 568 103 385 89 183 79 -44 59 -39 33 -5 47

ln w 3 7 -3 5 2 3 4 2 -24 11 -18 9 -6 9 -2 6 -3 3 1 5

Male 17 5 7 3 4 3 6 2 32 9 37 8 -4 6 -4 4 -2 3 -3 4

Age 31-40 -7 6 -6 5 -2 3 1 3 34 12 11 11 24 9 -15 7 1 4 -16 6

Age 41-50 -12 6 -14 5 -3 4 4 3 39 13 26 12 13 9 -19 8 3 5 -22 6

Age 51-64 -3 7 -10 5 -5 4 12 3 54 13 33 12 21 9 -22 7 -3 3 -19 6

Exactly high school 0 10 0 8 1 4 -2 4 -7 20 -3 19 -4 14 2 8 3 3 -1 8

Some college 4 11 0 8 0 4 4 5 -18 20 -21 18 3 15 -1 9 2 4 -3 8

College graduate 5 11 2 7 0 5 3 5 -33 20 -39 18 6 15 7 10 4 5 3 8

Pres. of spouse/partner 3 6 1 5 -3 2 5 4 3 10 6 9 -3 8 -9 5 -5 3 -4 4

Pres. of children 0-5 -16 7 -5 5 -3 3 -9 3 -28 13 -11 11 -17 9 -8 6 -3 4 -5 5

Pres. of children 6-12 -12 6 -3 4 -4 3 -5 3 -44 11 -32 9 -12 8 8 6 1 4 6 5

Pres. of other adults -5 5 0 5 0 2 -5 2 -5 10 1 9 -6 8 -4 5 -3 3 -1 4

Black -18 5 -10 3 -2 3 -6 3 29 13 30 13 -1 10 -13 6 -4 4 -8 5

Hispanic -9 6 -1 5 -5 3 -4 3 -21 12 5 11 -26 9 -11 6 -7 3 -4 5

Disabled 0 13 -3 6 8 12 -5 4 22 23 14 24 8 17 -8 10 3 8 -11 5

Work day -34 5 -16 4 -9 2 -9 3 -149 11 -91 10 -58 8 -32 5 -10 3 -22 4

Sunday -6 6 -10 4 -2 3 6 4 17 12 16 11 0 9 5 6 -4 4 9 5

Friday 0 6 -2 5 0 3 1 3 15 13 0 11 15 10 25 7 5 4 20 6

Saturday 9 7 1 5 4 3 4 3 4 13 -9 11 13 10 34 7 10 5 24 5

Winter -28 6 -14 5 -2 3 -11 3 29 11 43 10 -13 8 -15 6 -8 4 -7 5

Spring -15 7 -10 4 -1 4 -4 3 29 11 19 10 10 9 -11 6 -3 4 -8 5

Autumn -18 6 -11 5 -2 3 -6 3 26 11 35 10 -9 8 -11 6 -7 4 -4 5

Midwest 9 6 3 4 8 3 -2 3 -5 13 -5 12 0 9 1 7 2 4 -1 6

South 17 6 9 4 4 2 4 3 -3 12 -5 11 2 8 -1 7 2 4 -3 5

West 19 6 10 4 5 3 3 3 -17 12 -19 11 2 9 -5 7 0 4 -6 6

Metropolitan area -14 7 -6 5 -1 3 -7 4 15 12 12 11 2 9 7 5 3 3 4 4

ln household income 8 7 9 5 2 4 -3 3 -21 11 -16 10 -5 9 11 7 6 4 5 5

R-squared .26 .14 .06 .15 .72 .55 .42 .23 .06 .21

Notes: See notes to Table B1.a.


26

TABLE B2.a—TIME USE (MINUTES). PSEUDO-INSTRUMENTAL VARIABLE ESTIMATES. DUAL EARNERS

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)


Constant -27 83 17 56 23 39 -67 54 385 130 272 113 113 92 -203 80 -34 45 -169 67

ln w 5 7 6 5 -2 3 0 5 -9 11 -6 9 -2 8 11 7 2 4 8 6

ln w of spouse/partner 2 7 4 5 2 3 -4 5 -12 12 -15 10 3 8 7 7 2 4 5 6

Male 21 6 12 4 5 3 3 4 30 9 31 8 -1 7 -11 6 -7 3 -4 5

Age 31-40 -17 9 -12 6 -10 4 5 6 32 14 20 12 12 10 13 9 1 5 12 7

Age 41-50 -15 10 -14 7 -10 5 10 7 23 16 21 14 2 11 -23 10 -9 6 -14 8

Age 51-64 -4 11 -10 7 -9 5 15 7 42 17 35 15 7 12 -10 11 -7 6 -2 9

Exactly high school 20 16 1 10 9 7 10 10 32 24 19 21 13 17 1 15 6 8 -5 13

Some college 18 16 1 11 7 7 11 10 -14 25 -28 22 14 18 13 15 8 9 5 13

College graduate 8 17 -1 11 7 8 3 11 -6 26 -32 22 26 18 9 16 12 9 -2 13

Married -8 11 -3 7 -1 5 -4 7 -5 16 -6 14 1 12 -9 10 4 6 -13 8

Pres. of children 0-5 -13 7 -4 5 -2 3 -8 5 -46 11 -28 10 -18 8 -12 7 -11 4 -2 6

Pres. of children 6-12 6 6 2 4 2 3 2 4 -24 10 -17 9 -7 7 2 6 0 3 3 5

Pres. of other adults -9 8 -8 6 2 4 -3 5 2 13 -2 11 4 9 5 8 4 4 1 7

Black -31 12 -11 8 -6 5 -14 7 33 18 14 16 19 13 1 11 -4 6 4 9

Hispanic 0 10 3 6 -4 4 1 6 -13 15 -3 13 -9 11 12 9 5 5 7 8

Disabled 0 21 -12 14 0 10 12 13 5 32 -12 28 17 23 37 20 16 11 21 17

Work day -48 7 -21 5 -9 3 -19 5 -115 11 -71 10 -43 8 -33 7 -8 4 -25 6

Sunday -10 8 -7 6 -4 4 1 5 50 13 43 11 7 9 3 8 5 5 -2 7

Friday 0 10 -9 7 12 5 -3 6 13 15 3 13 10 11 13 9 6 5 7 8

Saturday -3 8 -4 6 -1 4 2 5 15 13 -2 11 17 9 55 8 16 4 39 7

Winter -17 8 -8 5 4 4 -13 5 32 12 34 11 -2 9 -18 8 -10 4 -8 6

Spring 2 8 -5 5 2 4 5 5 2 12 -1 10 3 8 -6 7 0 4 -6 6

Autumn -6 8 0 5 4 4 -10 5 1 12 9 11 -8 9 -4 8 -2 4 -2 6

Midwest 3 9 2 6 0 4 1 5 -2 13 -12 12 10 9 6 8 0 5 6 7

South -2 9 1 6 0 4 -2 5 -9 13 -18 12 9 9 -1 8 0 5 -1 7

West 4 9 -3 6 5 4 2 6 -21 14 -17 13 -4 10 4 9 6 5 -2 7

Metropolitan area -9 7 -11 5 3 3 -2 5 8 12 8 10 -1 8 -13 7 -2 4 -11 6

ln household income 8 9 1 6 -2 4 9 6 -9 14 -5 12 -4 10 20 9 3 5 17 7

Notes: The number of observations is 1,127 in all columns. Unreported age: 23-30. Activity abbreviations are defined in Appendix A.


27

TABLE B2.b—TIME USE (MINUTES). CONDITIONAL MEAN ESTIMATES. DUAL EARNERS

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)


Constant -26 76 17 49 23 32 -65 51 384 140 271 123 113 90 -200 85 -33 46 -166 71

ln w 5 7 6 4 -2 3 1 5 -9 10 -6 9 -2 7 11 7 2 4 9 5

ln w of spouse/partner 2 7 4 5 2 3 -3 4 -12 12 -15 11 3 8 8 7 2 3 6 6

Male 21 6 12 4 5 3 3 4 30 9 31 8 -1 6 -11 6 -7 3 -4 5

Age 31-40 -17 11 -12 9 -10 5 5 4 32 14 20 11 12 10 13 8 1 5 12 7

Age 41-50 -14 12 -14 9 -10 7 10 5 23 16 21 13 2 11 -23 9 -9 6 -13 7

Age 51-64 -4 13 -10 10 -9 7 15 7 42 18 35 15 7 13 -10 10 -7 6 -2 9

Exactly high school 20 11 1 8 9 4 10 6 32 29 19 29 13 16 1 11 6 4 -5 10

Some college 19 12 1 8 7 5 11 7 -14 29 -28 29 14 17 14 12 9 6 5 10

College graduate 8 12 -1 9 7 4 3 7 -7 29 -32 29 26 17 9 12 12 5 -2 11

Married -8 11 -3 7 -1 6 -4 7 -5 16 -6 13 1 11 -9 10 4 4 -13 9

Pres. of children 0-5 -13 8 -4 5 -2 5 -8 3 -46 11 -28 9 -18 8 -12 7 -11 4 -2 6

Pres. of children 6-12 6 6 2 4 2 3 2 4 -24 10 -17 8 -7 7 2 6 0 3 3 5

Pres. of other adults -9 8 -8 4 2 3 -3 6 2 14 -2 13 4 8 5 8 4 5 1 6

Black -31 6 -11 4 -6 2 -14 4 33 21 14 20 19 16 1 10 -4 5 4 9

Hispanic 0 10 3 6 -4 5 1 7 -13 15 -3 14 -9 10 11 10 5 7 6 8

Disabled 0 21 -12 6 0 8 12 20 5 41 -12 35 17 30 37 31 16 15 21 30

Work day -48 8 -21 5 -9 4 -19 5 -115 12 -71 11 -43 8 -33 6 -8 3 -25 6

Sunday -10 10 -7 6 -4 3 1 7 50 14 43 12 7 9 3 7 5 4 -2 6

Friday 0 8 -9 4 12 6 -3 4 13 13 3 10 10 9 13 8 6 4 7 7

Saturday -3 10 -4 6 -1 4 2 6 15 14 -2 13 17 10 55 9 16 5 39 8

Winter -17 7 -8 4 4 3 -13 4 32 13 34 11 -2 9 -18 7 -10 4 -8 7

Spring 2 8 -5 5 2 3 5 6 2 11 -1 10 3 9 -6 8 0 5 -6 6

Autumn -6 9 0 6 4 4 -10 5 1 12 9 11 -8 9 -4 8 -2 5 -2 7

Midwest 3 8 2 6 0 4 2 5 -2 14 -12 12 10 10 6 8 0 4 6 8

South -2 8 1 6 0 4 -2 5 -9 14 -18 12 9 9 -1 8 0 4 -1 7

West 4 10 -3 7 5 6 2 6 -21 14 -17 12 -4 9 4 10 6 5 -2 8

Metropolitan area -9 8 -11 6 3 2 -2 5 8 12 8 10 -1 9 -13 7 -2 3 -11 5

ln household income 8 8 1 5 -2 4 9 6 -9 15 -5 13 -4 10 19 9 3 5 17 7

R-squared .27 .14 .06 .16 .70 .57 .38 .30 .10 .25

Notes: See notes to Table B2.a.


28

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32

DOCUMENTOS DE TRABAJO

Facultad de Economía y Empresa

Universidad de Zaragoza

Depósito Legal Z-1411-2010. ISSN 2171-6668

2002-01: “Evolution of Spanish Urban Structure During the Twentieth Century”. Luis Lanaspa,

Fernando Pueyo y Fernando Sanz. Department of Economic Analysis, University of Zaragoza.

2002-02: “Una Nueva Perspectiva en la Medición del Capital Humano”. Gregorio Giménez y Blanca

Simón. Departamento de Estructura, Historia Económica y Economía Pública, Universidad de

Zaragoza.

2002-03: “A Practical Evaluation of Employee Productivity Using a Professional Data Base”.

Raquel Ortega. Department of Business, University of Zaragoza.

2002-04: “La Información Financiera de las Entidades No Lucrativas: Una Perspectiva

Internacional”. Isabel Brusca y Caridad Martí. Departamento de Contabilidad y Finanzas, Universidad

de Zaragoza.

2003-01: “Las Opciones Reales y su Influencia en la Valoración de Empresas”. Manuel Espitia y

Gema Pastor. Departamento de Economía y Dirección de Empresas, Universidad de Zaragoza.

2003-02: “The Valuation of Earnings Components by the Capital Markets. An International

Comparison”. Susana Callao, Beatriz Cuellar, José Ignacio Jarne and José Antonio Laínez. Department

of Accounting and Finance, University of Zaragoza.

2003-03: “Selection of the Informative Base in ARMA-GARCH Models”. Laura Muñoz, Pilar

Olave and Manuel Salvador. Department of Statistics Methods, University of Zaragoza.

2003-04: “Structural Change and Productive Blocks in the Spanish Economy: An Imput-Output

Analysis for 1980-1994”. Julio Sánchez Chóliz and Rosa Duarte. Department of Economic Analysis,

University of Zaragoza.

2003-05: “Automatic Monitoring and Intervention in Linear Gaussian State-Space Models: A

Bayesian Approach”. Manuel Salvador and Pilar Gargallo. Department of Statistics Methods,

University of Zaragoza.

2003-06: “An Application of the Data Envelopment Analysis Methodology in the Performance

Assessment of the Zaragoza University Departments”. Emilio Martín. Department of Accounting and

Finance, University of Zaragoza.

2003-07: “Harmonisation at the European Union: a difficult but needed task”. Ana Yetano Sánchez.

Department of Accounting and Finance, University of Zaragoza.


33

2003-08: “The investment activity of spanish firms with tangible and intangible assets”. Manuel

Espitia and Gema Pastor. Department of Business, University of Zaragoza.

2004-01: “Persistencia en la performance de los fondos de inversión españoles de renta variable

nacional (1994-2002)”. Luis Ferruz y María S. Vargas. Departamento de Contabilidad y Finanzas,

Universidad de Zaragoza.

2004-02: “Calidad institucional y factores político-culturales: un panorama internacional por niveles

de renta”. José Aixalá, Gema Fabro y Blanca Simón. Departamento de Estructura, Historia Económica y

Economía Pública, Universidad de Zaragoza.

2004-03: “La utilización de las nuevas tecnologías en la contratación pública”. José Mª Gimeno

Feliú. Departamento de Derecho Público, Universidad de Zaragoza.

2004-04: “Valoración económica y financiera de los trasvases previstos en el Plan Hidrológico

Nacional español”. Pedro Arrojo Agudo. Departamento de Análisis Económico, Universidad de

Zaragoza. Laura Sánchez Gallardo. Fundación Nueva Cultura del Agua.

2004-05: “Impacto de las tecnologías de la información en la productividad de las empresas

españolas”. Carmen Galve Gorriz y Ana Gargallo Castel. Departamento de Economía y Dirección de

Empresas. Universidad de Zaragoza.

2004-06: “National and International Income Dispersión and Aggregate Expenditures”. Carmen

Fillat. Department of Applied Economics and Economic History, University of Zaragoza. Joseph

Francois. Tinbergen Institute Rotterdam and Center for Economic Policy Resarch-CEPR.

2004-07: “Targeted Advertising with Vertically Differentiated Products”. Lola Esteban and José M.

Hernández. Department of Economic Analysis. University of Zaragoza.

2004-08: “Returns to education and to experience within the EU: are there differences between wage

earners and the self-employed?”. Inmaculada García Mainar. Department of Economic Analysis.

University of Zaragoza. Víctor M. Montuenga Gómez. Department of Business. University of La Rioja

2005-01: “E-government and the transformation of public administrations in EU countries: Beyond

NPM or just a second wave of reforms?”. Lourdes Torres, Vicente Pina and Sonia Royo. Department of

Accounting and Finance.University of Zaragoza

2005-02: “Externalidades tecnológicas internacionales y productividad de la manufactura: un análisis

sectorial”. Carmen López Pueyo, Jaime Sanau y Sara Barcenilla. Departamento de Economía Aplicada.


2005-03: “Detecting Determinism Using Recurrence Quantification Analysis: Three Test

Procedures”. María Teresa Aparicio, Eduardo Fernández Pozo and Dulce Saura. Department of

Economic Analysis. University of Zaragoza.

2005-04: “Evaluating Organizational Design Through Efficiency Values: An Application To The

Spanish First Division Soccer Teams”. Manuel Espitia Escuer and Lucía Isabel García Cebrián.

Department of Business. University of Zaragoza.


34

2005-05: “From Locational Fundamentals to Increasing Returns: The Spatial Concentration of

Population in Spain, 1787-2000”. María Isabel Ayuda. Department of Economic Analysis. University of

Zaragoza. Fernando Collantes and Vicente Pinilla. Department of Applied Economics and Economic

History. University of Zaragoza.

2005-06: “Model selection strategies in a spatial context”. Jesús Mur and Ana Angulo. Department of

Economic Analysis. University of Zaragoza.

2005-07: “Conciertos educativos y selección académica y social del alumnado”. María Jesús

Mancebón Torrubia. Departamento de Estructura e Historia Económica y Economía Pública.

Universidad de Zaragoza. Domingo Pérez Ximénez de Embún. Departamento de Análisis Económico.


2005-08: “Product differentiation in a mixed duopoly”. Agustín Gil. Department of Economic

Analysis. University of Zaragoza.

2005-09: “Migration dynamics, growth and convergence”. Gemma Larramona and Marcos Sanso.

Department of Economic Analysis. University of Zaragoza.

2005-10: “Endogenous longevity, biological deterioration and economic growth”. Marcos Sanso and

Rosa María Aísa. Department of Economic Analysis. University of Zaragoza.

2006-01: “Good or bad? - The influence of FDI on output growth. An industry-level analysis“.

Carmen Fillat Castejón. Department of Applied Economics and Economic History. University of

Zaragoza. Julia Woerz. The Vienna Institute for International Economic Studies and Tinbergen Institute,

Erasmus University Rotterdam.

2006-02: “Performance and capital structure of privatized firms in the European Union”. Patricia

Bachiller y Mª José Arcas. Departamento de Contabilidad y Finanzas. Universidad de Zaragoza.

2006-03: “Factors explaining the rating of Microfinance Institutions”. Begoña Gutiérrez Nieto and

Carlos Serrano Cinca. Department of Accounting and Finance. University of Saragossa, Spain.

2006-04: “Libertad económica y convergencia en argentina: 1875-2000”. Isabel Sanz Villarroya.

Departamento de Estructura, Historia Económica y Economía Pública. Universidad de Zaragoza.

Leandro Prados de la Escosura. Departamento de Hª e Instituciones Ec. Universidad Carlos III de

Madrid.

2006-05: “How Satisfied are Spouses with their Leisure Time? Evidence from Europe*”. Inmaculada

García, José Alberto Molina y María Navarro. University of Zaragoza.

2006-06: “Una estimación macroeconómica de los determinantes salariales en España (1980-2000)”.

José Aixalá Pastó y Carmen Pelet Redón. Departamento de Estructura, Historia Económica y Economía

Pública. Universidad de Zaragoza.

2006-07: “Causes of World Trade Growth in Agricultural and Food Products, 1951 – 2000”. Raúl

Serrano and Vicente Pinilla. Department of Applied Economics and Economic History, University of

Zaragoza, Gran Via 4, 50005 Zaragoza (Spain).


35

2006-08: “Prioritisation of patients on waiting lists: a community workshop approach”. Angelina

Lázaro Alquézar. Facultad de Derecho, Facultad de Económicas. University of Zaragoza. Zaragoza,

Spain. Begoña Álvarez-Farizo. C.I.T.A.- Unidad de Economía. Zaragoza, Spain

2007-01: “Deteminantes del comportamiento variado del consumidor en el escenario de Compra”.

Carmén Berné Manero y Noemí Martínez Caraballo. Departamento de Economía y Dirección de

Empresas. Universidad de Zaragoza.

2007-02: “Alternative measures for trade restrictiveness. A gravity approach”. Carmen Fillat & Eva

Pardos. University of Zaragoza.

2007-03: “Entrepreneurship, Management Services and Economic Growth”. Vicente Salas Fumás &

J. Javier Sánchez Asín. Departamento de Economía y Dirección de Empresas. University of Zaragoza.

2007-04: “Equality versus Equity based pay systems and their effects on rational altruism motivation

in teams: Wicked masked altruism”. Javier García Bernal & Marisa Ramírez Alerón. University of

Zaragoza.

2007-05: “Macroeconomic outcomes and the relative position of Argentina´s Economy: 1875-2000”.

Isabel Sanz Villarroya. University of Zaragoza.

2008-01: “Vertical product differentiation with subcontracting”. Joaquín Andaluz Funcia. University

of Zaragoza.

2008-02: “The motherwood wage penalty in a mediterranean country: The case of Spain” Jose

Alberto Molina Chueca & Victor Manuel Montuenga Gómez. University of Zaragoza.

2008-03: “Factors influencing e-disclosure in local public administrations”. Carlos Serrano Cinca,

Mar Rueda Tomás & Pilar Portillo Tarragona. Departamento de Contabilidad y Finanzas. Universidad de

Zaragoza.

2008-04: “La evaluación de la producción científica: hacia un factor de impacto neutral”. José María

Gómez-Sancho y María Jesús Mancebón-Torrubia. Universidad de Zaragoza.

2008-05: “The single monetary policy and domestic macro-fundamentals: Evidence from Spain“.

Michael G. Arghyrou, Cardiff Business School and Maria Dolores Gadea, University of Zaragoza.

2008-06: “Trade through fdi: investing in services“. Carmen Fillat-Castejón, University of Zaragoza,

Spain; Joseph F. Francois. University of Linz, Austria; and CEPR, London & Julia Woerz, The Vienna

Institute for International Economic Studies, Austria.

2008-07: “Teoría de crecimiento semi-endógeno vs Teoría de crecimiento completamente endógeno:

una valoración sectorial”. Sara Barcenilla Visús, Carmen López Pueyo, Jaime Sanaú. Universidad de

Zaragoza.

2008-08: “Beating fiscal dominance. The case of spain, 1874-1998”. M. D. Gadea, M. Sabaté & R.

Escario. University of Zaragoza.

2009-01: “Detecting Intentional Herding: What lies beneath intraday data in the Spanish stock

market” Blasco, Natividad, Ferreruela, Sandra (Department of Accounting and Finance. University of


36

Zaragoza. Spain); Corredor, Pilar (Department of Business Administration. Public University of

Navarre, Spain).

2009-02: “What is driving the increasing presence of citizen participation initiatives?”. Ana Yetano,

Sonia Royo & Basilio Acerete. Departamento de Contabilidad y Finanzas. Universidad de Zaragoza.

2009-03: “Estilos de vida y “reflexividad” en el estudio del consumo: algunas propuestas”. Pablo

García Ruiz. Departamento de Psicología y Sociología. Universidad de Zaragoza.

2009-04: “Sources of Productivity Growth and Convergence in ICT Industries: An Intertemporal

Non-parametric Frontier Approach”. Carmen López-Pueyo and Mª Jesús Mancebón Torrubia.


2009-05: “Análisis de los efectos medioambientales en una economía regional: una aplicación para la

economía aragonesa”. Mónica Flores García y Alfredo J. Mainar Causapé. Departamento de Economía

y Dirección de Empresas. Universidad de Zaragoza.

2009-06: “The relationship between trade openness and public expenditure. The Spanish case, 1960-

2000”. Mª Dolores Gadea, Marcela Sabate y Estela Saenz. Department of Applied Economics. School of

Economics. University of Economics.

2009-07: “Government solvency or just pseudo-sustainability? A long-run multicointegration

approach for Spain”. Regina Escario, María Dolores Gadea, Marcela Sabaté. Applied Economics

Department. University of Zaragoza.

2010-01: “Una nueva aproximación a la medición de la producción científica en revistas JCR y su

aplicación a las universidades públicas españolas”. José María Gómez-Sancho, María Jesús Mancebón

Torrubia. Universidad de Zaragoza

2010-02: “Unemployment and Time Use: Evidence from the Spanish Time Use Survey”. José

Ignacio Gimenez-Nadal, University of Zaragoza, José Alberto Molina, University of Zaragoza and IZA,

Raquel Ortega, University of Zaragoza.

2011-01: “Universidad y Desarrollo sostenible. Análisis de la rendición de cuentas de las

universidades del G9 desde un enfoque de responsabilidad social”. Dr. José Mariano Moneva y Dr.

Emilio Martín Vallespín, Universidad de Zaragoza.

2011-02: “Análisis Municipal de los Determinantes de la Deforestación en Bolivia.” Javier Aliaga

Lordeman, Horacio Villegas Quino, Daniel Leguía (Instituto de Investigaciones Socio-Económicas.

Universidad Católica Boliviana), y Jesús Mur (Departamento de Análisis Económico. Universidad de

Zaragoza)

2011-03: “Imitations, economic activity and welfare”. Gregorio Giménez. Facultad de Ciencias

Económicas y Empresariales. Universidad de Zaragoza.

2012-01: “Selection Criteria for Overlapping Binary Models”. M. T Aparicio and I. Villanúa.

Department of Economic Analysis, Faculty of Economics, University of Zaragoza

2012-02: “Sociedad cooperativa y socio cooperativo: propuesta de sus funciones objetivo”. Carmen

Marcuello y Pablo Nachar-Calderón. Universidad de Zaragoza


37

2012-03: “Is there an environmental Kuznets curve for water use? A panel smooth transition

regression approach”. Rosa Duarte (Department of Economic Analysis), Vicente Pinilla (Department of

Applied Economics and Economic History) and Ana Serrano (Department of Economic Analysis).

Faculty of Economics and Business Studies, Universidad de Zaragoza

2012-04: “Análisis Coste-Beneficio de la introducción de dispositivos ahorradores de agua. Estudio

de un caso en el sector hotelero”. Barberán Ramón, Egea Pilar, Gracia-de-Rentería Pilar y Manuel

Salvador. Facultad de Economía y Empresa. Universidad de Zaragoza.

2013-01: “The efficiency of Spanish mutual funds companies: A slacks – based measure approach”.

Carlos Sánchez González, José Luis Sarto and Luis Vicente. Department of Accounting and Finance.

Faculty of Economics and Business Studies, University of Zaragoza.

2013-02: “New directions of trade for the agri-food industry: a disaggregated approach for different

income countries, 1963-2000”. Raúl Serrano (Department of Business Administration) and Vicente

Pinilla (Department of Applied Economics and Economic History). Universidad de Zaragoza.

2013-03: “Socio-demographic determinants of planning suicide and marijuana use among youths: are

these patterns of behavior causally related?”. Rosa Duarte, José Julián Escario and José Alberto Molina.

Department of Economic Analysis, Universidad de Zaragoza.

2014-01: “Análisis del comportamiento imitador intradía en el mercado de valores español durante el

periodo de crisis 2008-2009”. Alicia Marín Solano y Sandra Ferreruela Garcés. Facultad de Economía y

Empresa, Universidad de Zaragoza.

2015-01: “International diversification and performance in agri-food firms”. Raúl Serrano, Marta

Fernández-Olmos and Vicente Pinilla. Facultad de Economía y Empresa, Universidad de Zaragoza.

2015-02: “Estimating income elasticities of leisure activities using cross-sectional categorized data”.

Jorge González Chapela. Centro Universitario de la Defensa de Zaragoza.

ESTIMATING INCOME ELASTICITIES OF LEISURE ACTIVITIES … · Financial support from research group CREVALOR (at the Faculty of Economics and Business Administration of the University

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