Abstract
The average number of hours we spend at work varies dramatically by country. Previous research focuses on tax policy, social security, and labor market regulation to explain the differences. This paper builds on previous work by focusing on politics. Specifically, it examines the relationship between democracy and the average number of hours worked per person employed. Using data on the supply of labor from the Penn World Tables 9.1, I find there is an important difference between democracies and dictatorships: as GDP/capita increases, individuals in democracies spend fewer hours at work than their counterparts in dictatorships. The results are robust to various specifications of the model that account for selection bias and data that are missing not at random (MNAR). These findings imply that the elections, civil rights, and the political liberties associated with democracy influence the amount of time people spend at work.
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Notes
A comprehensive review of the literature on institutions and culture or the new cultural economics can be found in a piece by Alesina and Giuliano (2015).
It is important to remind that in terms of democracy and the average number of hours worked, the important differences in this study are between the USA and Venezuela or Russia, not between the USA and France or Switzerland.
This assumes, of course, that the dictator is not the median voter or does not arbitrarily choose the same raft of policies.
I use the variable “polity2” from the polity data which combines the “democ” and “autoc” scores that ranges from − 10 representing the most authoritarian regimes and + 10 representing the most democratic regimes. Throughout this paper, I classify the polity2 score of seven and above as democratic. This is slightly different in comparison to the cutoff adopted by Epstein et al. (2006) to distinguish between full democracies and all other regime types. I check the stability of the main results with respect to the cutoff point by varying it between four and eight (Table 2). I find the statistical and substantive significance of the estimates hold regardless of where the cutoff is made.
The countries that remained at 10 throughout the sample are as follows: Australia, Austria, Canada, Denmark, Finland, Germany, Italy, Lithuania, Luxembourg, Netherlands, New Zealand, Norway, Slovenia, Sweden, and Switzerland.
There is an important restriction in this particular formulation of the more general ARDL (1,1) model (De Boef and Keele 2008). In the Koyck model, the estimate for the lagged form of the independent variable is restricted to be zero. Invalid restrictions pose serious problems for the coefficients on the independent variable of interest. De Boef and Keele recommend testing whether the lagged form of the independent variable of interest is statistically significant coefficient. A lagged version of the democracy dummy variable was included in the basic model and the null hypothesis could not be rejected: the estimate could be zero.
For a nice treatment of how to calculate and present substantive results from distributed lag models, see Williams and Whitten (2012).
I want to thank one of the anonymous reviewers for suggesting this as a possibility.
The measure of capacity used throughout this paper is a function of mining, exports, and social security contributions as a percentage of GDP. Added to that is GDP/capita, the economically active population, education, and a dummy indicator for OECD members.
Including country and year dummies together resulted in computational problems that stem from matrices that were singular and could not be inverted.
So that I can produce estimates across all models using the sample selection methods, I use the “simulated annealing” stochastic global optimization algorithm proposed by Bélisle (1992).
To preserve the dichotomous nature of the democracy measure, when calculating the means across years to construct different cross-sections, I count countries with a majority (over .50) of years as democratic as democracies. I varied the cutoff between .40 and .60 and found it had no significant impact on the findings or the conclusions drawn from them.
For the imputation, in the selection equation I, use the democracy dummy variable, GDP/capita (logged), the interaction between democracy and GDP/capita and state capacity (the “ape1” variable).
To measure TFP I use the variable “ctfp” from the Penn World Tables data (9.1).
Another possible avenue for future research: what role does democracy play in the labor share of income? If democracy does, in fact, influence the power dynamics between labor and capital, it stands to reason regime type could influence the share of GDP output going to labor and capital. I want to thank one of the anonymous reviewers for pointing to this possibility for future investigation.
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Brown, D.S. Democracy and the Supply of Labor. St Comp Int Dev 57, 149–170 (2022). https://doi.org/10.1007/s12116-021-09331-y
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DOI: https://doi.org/10.1007/s12116-021-09331-y