Abstract
In this paper, we analyze the effect of the time spent on reading for leisure (when the motivation is intrinsic) on the time devoted to reading for job-related or educational purposes (when the motivation is extrinsic). To do so, we use the Cultural Habits and Practices Survey conducted by the Ministry of Education and Culture of Spain in 2014–2015. As the main determinants of the time spent on intrinsic reading, we consider the time devoted to extrinsic reading, sociodemographic characteristics, labor situation, participation in other leisure activities and an index of cultural capital at home. We estimate a Heckman model that allows us to control for self-selection. Results show that the time devoted to intrinsic reading mainly depends on the time spent on reading with extrinsic motivation, human capital and cultural background. Reading with extrinsic motivation increases the likelihood of intrinsic reading but reduces the time allocated to it. Therefore, our results suggest a substitution pattern between time spent reading for leisure and reading with extrinsic motivation.
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Notes
This implies that the MRS between leisure activities j and k within leisure decision is independent of working hours.
As our dependent variable, Intrinsic Reading (IR) is heavily skewed and has considerable non-normal kurtosis, we firstly estimate the λ parameter of the Box–Cox transformation in order to test if the regression model for the time devoted to reading is better in logs than in levels. Given that \(\hat{\lambda } = 0.13\), we have greater support for expressing this variable in logs (Cameron and Trivedi 2009). The same holds for Extrinsic Reading (ER), so we also take its natural logarithm (Ln_ER).
We assume that physical cultural capital is highly correlated with household income.
Several attempts to split PCA into two or three groups of variables were made, in order to check if different types of cultural capital could affect reading habits differently. However, the use of a single index of cultural capital provides the best model fit, so we it was the preferred option.
This transformation sets the dependent variable to missing as Ƭ = 0; so, it is necessary to substitute the censoring point Ƭ by another small value close to zero but different from it. This allows ln Ƭ to exist.
There have been some attempts to move away from the normality assumption (Martins 2001). Nonetheless, the empirical literature is dominated by the joint normal distribution assumption.
As individuals were questioned about their reading habits within the last 3 months, we consider that when a respondent declares that he/she has not read during the considered period, we cannot deem him as an actual reader. This validates the election of the Heckman model which assumes that all the zeros are generated by the non-participation decision.
As the error terms are assumed to be correlated and the structure of the model is recursive, it is recommended that at least one variable in Z does not appear in X.
Estimates were conducted using the heckman module in Stata 14.
When the error assumptions are met, the FIML estimator will always be more efficient than the Heckman two-step alternative (Puhani 2000).
To examine the potential existence of multicollinearity, we have computed the Variance Inflation Factor (VIF) after an OLS regression. All the values are below 10, which is normally taken as the threshold point, so we consider that there is no such problem in our model.
To allow for possible non-linearities, squared term was introduced. Since it was not statistically significant, it was finally omitted. We also defined a set of age groups. The results of this alternative specification are consistent: Age does not explain the amount of time reading with intrinsic motivation. Results are available upon request.
Since retired and those younger than 13 years old have not been considered in our sample, age range is substantially narrower than usually, which could partially explain why age issues do not arise.
For the sake of parsimony, we only report the AME for ln PRW, Male, Age, PCA Cultural Capital and Cultural Participation. The rest of AME are available from the authors upon request.
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This study did not receive any specific grant from funding agencies in the public, commercial or not-for-profit sectors. All errors in fact or interpretation are ours. We would like to thank Víctor Fernández-Blanco and Juan Prieto-Rodríguez, the editor and two anonymous referees for their constructive comments
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Appendices
Appendix 1: Principal component analysis for cultural capital
Following the practice of Fernández-Blanco and Prieto-Rodríguez (2009), we conducted a PCA for cultural capital in order to proxy household income. We included the number of books, e-books, encyclopedias, vinyl CDs, DVDs, CDs and Blue Rays, and other audio devices, as well as the number of computers. Descriptive statistics of each variable can be seen on Table 5.
The coefficient for the first factor is positive for all considered variables and, consequently, first factor analysis predicts that the cultural capital is positively correlated with the variables that we contemplate. The eigenvalue of the first factor is 1.025, and it explains the 62.68% of the total variance.
Appendix 2: Heckman estimates by labor status
See Table 6.
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Suárez-Fernández, S., Boto-García, D. Unraveling the effect of extrinsic reading on reading with intrinsic motivation. J Cult Econ 43, 579–605 (2019). https://doi.org/10.1007/s10824-019-09361-4
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DOI: https://doi.org/10.1007/s10824-019-09361-4