The first is free: do employee stocks incentivize stock market participation?

Figures & data

Table 1. Summary statistics.

Categorical variables	Has employee stocks		No employee stocks
Stock market participation (excl. employee stock)	35.65%		24.37%
Individual stocks (excl. employer stock)	24.83%		18.33%
Mutual funds	3.75%		2.49%
Both mutual funds and individual stocks	7.06%		3.55%
Male	54.35%		49.89%
Homeowner	75.17%		60.00%
Has kids	26.93%		27.39%
Married	65.43%		55.36%
Divorced	8.51%		9.37%
Single	25.03%		34.06%
Widowed	1.02%		1.21%
Capital region	43.51%		30.67%
Central Denmark region	20.02%		21.82%
Nortern Denmark region	8.22%		10.58%
Zealand region	16.04%		14.95%
Southern Denmark region	12.22%		21.98%
Lower than high school	8.85%		17.42%
High school	50.19%		45.41%
Short higher education	11.69%		5.01%
Bachelor’s degree	13.37%		21.31%
Master’s degree or higher	15.90%		10.85%
Danish	96.07%		93.88%
Immigrant	3.22%		5.39%
Descendant of immigrants	0.71%		0.73%
Management	5.86%		4.87%
Work requiring knowledge high level	39.65%		29.05%
Work requiring knowledge medium level	27.06%		13.00%
Office and customer service	13.78%		8.55%
Service and sales	3.31%		19.00%
Agriculture	0.07%		0.61%
Artisan	3.06%		9.89%
Operators	5.03%		5.92%
Other manual labor	2.14%		8.09%
Military	0.04%		1.03%
Continuous variables	Mean	Std.	Mean	Std.
Disposable income (100,000 DKK)	3.32	1.26	2.60	1.06
Age	43.64	10.19	42.12	11.91
Wealth in stocks (100,000 DKK)	1.16	2.39	0.16	1.10
Non-stock financial assets (100,000 DKK)	1.88	3.61	1.20	2.74
Property value (100,000 DKK)	10.24	10.00	7.06	9.38
Debt (100,000 DKK)	8.94	8.43	6.47	7.53
Nr. of observations	82,830		1,783,230

This table shows, for those with and without an employee stock, the means and standard deviation (fractions in each category for categorical variables), for the various control variables and the dependent variable. All DKK variables measured in 100,000 DKK and are winsorized at a 0.5% level. Stock market participation is defined as owning a mutual fund or an individual stock, excluding employee stocks.

Table 2. Stock market participation.

	Stock market participation
	LPM		Logit
	(1)	(2)	(3)	(4)
Has employee stocks	0.113*	0.047*	0.542*	0.213*
	(0.002)	(0.002)	(0.007)	(0.008)
Avg. partial effect	0.113*	0.047*	0.113*	0.036*
	(0.002)	(0.002)	(0.002)	(0.002)
Avg. partial effects controls
Male		0.036*		0.034*
		(0.001)		(0.001)
Income		0.026*		0.022*
		(0.001)		(0.001)
Deposits		0.046*		0.035*
		(0.000)		(0.000)
Has masters’ degree		0.069*		0.070*
		(0.001)		(0.001)
Age		0.010*		0.009*
		(0.000)		(0.000)
Control variables	No	Yes	No	Yes
Nr. of observations	1,866,060	1,866,060	1,866,060	1,866,060
Pseudo R^2	0.00	0.11	0.00	0.11

This table shows the results of a linear probability model (1–2) and a logit (3–4). The dependent variable is a binary variable equaling 1 if an individual participates in the stock market, and 0 otherwise. Standard errors (in parenthesis) are robust. Standard errors for the average partial effects (APE-s) are calculated using the delta method. * Significant at 1% level. The variables deposits and income are measured in 100,000 DKK. Has Masters Degree is a dummy equal to 1 if an individual has a masters’ degree or higher education, and is the only one out of 5 education categories displayed: the baseline category is not having completed high school.

Table 3. Stock market participation mode.

	Stock market participation mode
	Individual stocks		Mutual funds		Both
	(1)	(2)	(3)	(4)	(5)	(6)
Has employee stocks	0.465*	0.181*	0.571*	0.281*	0.849*	0.392*
	(0.008)	(0.009)	(0.019)	(0.021)	(0.014)	(0.017)
Avg. partial effect	0.065*	0.021*	0.013*	0.004*	0.035*	0.011*
	(0.002)	(0.001)	(0.001)	(0.001)	(0.001)	(0.001)
Control variables	No	Yes	No	Yes	No	Yes
Nr. of observations	1,866,060	1,866,060	1,866,060	1,866,060	1,866,060	1,866,060
Pseudo R^2	0.00	0.11	0.00	0.11	0.00	0.11

This table shows the results of a multinomial logit model. The dependent variable is a categorical variable for the mode of stock market participation, where the possible categories are to invest in individual stocks, mutual funds, both, or none (which is the baseline). Thus, the odd numbered columns, are the results from the multinomial logit without control variables, and the even numbered columns are the results of the multinomial logit with controls. Standard errors (in parenthesis) are robust. Standard errors for the average partial effects (APE-s) are calculated using the delta method. * Significant at 1% level.

Table 4. Number of stocks and fraction of wealth in stocks.

	Number of individual stocks		% Wealth in stocks
	Poisson	Hurdle Poisson	Fractional logit	Hurdle fractional logit
	(1)	(2)	(3)	(4)
Total effect
Has employee stocks	0.308*		0.301*
	(0.009)		(0.009)
Avg. partial effect	0.159*	0.142*	0.016*	0.012*
	(0.006)	(0.005)	(0.001)	(0.001)
Extensive margin
Has employee stocks		−0.227*		−0.235*
		(0.008)		(0.009)
Avg. partial effect		−0.038*		−0.039*
		(0.001)		(0.001)
Intensive margin
Has employee stocks		0.210*		0.087*
		(0.011)		(0.009)
Avg. partial effect		0.300*		0.015*
		(0.007)		(0.003)
Control variables	Yes	Yes	Yes	Yes
Nr. of observations	1,866,060	1,866,060	1,866,060	1,866,060

This table shows the results of Poisson and fractional regressions. In columns 1–2 the dependent variable is a count variable, the number of non-employee stocks an individual holds. In columns 3–4 the dependent variable is a fractional variable, the fraction of wealth, that is invested in equity (excluding employee stocks). In columns 2 and 4 I also model the extensive margin separately, i.e. the decision to participate; this is a logit model using the same covariates, where the dependent variable is a binary variable taking on the value 1 if an individual is not participating. Thus, the signs are flipped from Table 1. The combined effect is the effect on the dependent variable resulting from both the intensive and extensive margins. Standard errors (in parenthesis) are robust. Standard errors for the average partial effects (APE-s) are calculated using the delta method. * Significant at 1% level.

Table 5. Poisson fit.

Pr(Nr. Stocks = x)	Actual	Poisson	Hurdle Poisson
0	77.68%	67.73%	77.68%
1	15.13%	23.75%	10.25%
2	3.18%	6.08%	6.33%
3	1.31%	1.59%	3.15%
4	0.77%	0.48%	1.43%
5	0.51%	0.18%	0.63%
6	0.35%	0.08%	0.28%
7	0.25%	0.04%	0.13%
8	0.18%	0.02%	0.06%
9	0.14%	0.01%	0.03%
Maximum deviation	0.00%	9.95%	4.88%

This table shows the predicted probabilities of the various Poisson regression for the first 10 count outcomes, compared to the actual fractions in the data.

Data availability statement

The data underlying this article cannot be shared publicly due to data-restriction policies of Statistics Denmark, who hosts the data used in the article.