Training during recessions: recent European evidence

	Observations	Mean	SD
Participated in training	43,173,984	0.066	0.248
Participated in training – employed	30,372,367	0.079	0.271
Participated in training – not employed	12,801,617	0.034	0.181
Training hours	43,173,984	1.113	9.205
Training hours – employed	30,372,367	1.090	7.664
Training hours – not employed	12,801,617	1.170	12.100
Age (years)	43,173,984	45.38	11.07
Male	43,173,984	0.486	0.500
Has a tertiary education degree or higher	43,173,984	0.249	0.432
Employed	43,173,984	0.703	0.457
Unemployment rate – cyclical component	1,620	0	1.128
Unemployment rate – trend	1,620	0.092	0.041
Employment rate – cyclical component	1,620	0	0.994
Employment rate – trend	1,620	0.643	0.059

Table 2

Descriptive analysis

	Training participation	Training hours
Gender: male	−0.020^*** (0.001)	−0.030^*** (0.007)
Age × 10	−0.005^*** (0.000)	−0.038^*** (0.002)
Upper secondary education	0.008^*** (0.002)	0.212^*** (0.031)
Tertiary education	0.058^*** (0.003)	1.301 ^*** (0.054)
Not employed	−0.029^*** (0.001)	0.645^*** (0.047)
Second quarter	−0.001 (0.004)	0.038 (0.084)
Third quarter	−0.028^*** (0.003)	−0.367^*** (0.073)
Fourth quarter	0.003 (0.004)	0.068 (0.083)
Western and Central Europe	0.051^*** (0.003)	1.342^*** (0.049)
Southern Europe	0.018^*** (0.002)	0.660^*** (0.065)
Northern Europe	0.123^*** (0.006)	1.783^*** (0.120)
Observations	43,173,984	43,173,984
R-squared	0.048	0.011

Notes: Less-than-secondary education, female gender, employed, first quarter, and Eastern Europe are the omitted reference categories.

Standard errors clustered by country and time period are reported in parentheses.

* p<0.1;

** p<0.05;

*** p<0.01.

Table 3

The effects of the business cycle (U_cycle) on training participation and employment status

	(1)	(2)	(3)	(4)
Dependent variable	Training participation	Employment probability	Training participation	Training participation
Sample	All	All	Employed	Not employed
Cyclical component of unemployment rate × 10	0.015^** (0.006)	−0.038^*** (0.006)	0.024^*** (0.007)	0.006 (0.005)
Demand shock: Z_ct		0.007^*** (0.001)
Inverse Mills ratio			−0.108^*** (0.010)	0.051^*** (0.005)
Male gender	−0.015^*** (0.001)	0.140^*** (0.002)	−0.045^*** (0.003)	0.006^*** (0.001)
Age	−0.001^*** (0.009)	−0.008^*** (0.001)	0.001^*** (0.001)	−0.003^*** (0.001)
Has tertiary education	0.058^*** (0.002)	0.154^*** (0.002)	0.036^*** (0.003)	0.052^*** (0.002)
R-squared	0.076	0.098	0.077	0.062
Observations	43,173,984	43,173,984	30,372,267	12,801,617
Estimation method	OLS	Probit	OLS	OLS

Notes: The table reports the effects of the business cycle on employment and training participation. The dependent variable is listed in the heading of each column. Estimates refer to the full sample in Columns (1) and (2), to the employed in Column (3), and to the not employed in Column (4). Column (2) reports the marginal effects from a probit specification. Each regression also includes year and country dummies and country-specific employment trends. For the probit specification, the pseudo-R-squared value is reported instead of the R-squared value. Standard errors clustered by country and time period are reported in parentheses. OLS: ordinary least squares.

* p<0.1;

** p<0.05;

*** p<0.01.

Table 4

The effects of the business cycle (U_cycle) on training participation and employment status (excluding the inactive)

	(1)	(2)	(3)	(4)
Dependent variable	Training participation	Employment probability	Training participation	Training participation
Sample	Labor force	Labor force	Employed	Unemployed
Cyclical component of unemployment rate × 10	0.018^** (0.008)	−0.026^*** (0.006)	0.026^*** (0.008)	0.036^*** (0.008)
Demand shock: Z_ct		0.005^*** (0.001)
Inverse Mills ratio			−0.127^*** (0.026)	−0.121^*** (0.031)
Observations	32,998,703	32,998,703	30,372,367	2,626,336
Estimation method	OLS	Probit	OLS	OLS

Notes: The table reports the effects of the business cycle on employment and training participation. The dependent variable is listed in the heading of each column. Estimates refer to the labor force in Columns (1) and (2), to the employed in Column (3), and to the unemployed in Column (4). Column (2) reports the marginal effects from a probit specification. Each regression also includes age, gender, a dummy for tertiary education, year and country dummies, and country-specific employment trends. Standard errors clustered by country and time period are reported in parentheses. OLS, ordinary least squares.

* p<0.1;

** p<0.05;

*** p<0.01.

Table 5

The effect of selection into employment on the estimates of the effects of the business cycle on training participation

	(1)	(2)	(3)	(4)
Dependent variable	Training participation	Training participation	Training participation	Training participation
Sample	Employed	Not employed	Employed	Not employed
Cyclical component of unemployment rate × 10	0.024^*** (0.008)	0.006 (0.005)	0.017^** (0.007)	0.010^* (0.005)
Inverse Mills ratio	−0.108^*** (0.009)	0.055^*** (0.005)
Observations	30,372,267	12,801,617	30,372,267	12,801,617
Estimation method	OLS	OLS	OLS	OLS

Notes: The table reports the effects of the business cycle on training participation of the employed and the not employed. Estimates in Columns (1) and (2) include the inverse Mills ratio, while estimates in Columns (3) and (4) do not. Each regression also includes age, gender, a dummy for tertiary education, year and country dummies, and country-specific employment trends. Standard errors clustered by country and time period are reported in parentheses. OLS, ordinary least squares.

* p<0.1;

** p<0.05;

*** p<0.01.

Table 6

The effects of the business cycle (U_cycle) on training participation (by gender, age, education and industry)

	(1)	(2)	(3)
Cyclical component of unemployment rate × 10	All	Employed	Not Employed
Males	0.011^* (0.006)	0.011^* (0.006)	−0.002 0.006
Females	0.018^** (0.007)	0.030^*** (0.009)	0.011^** (0.006)
Age 25–44 years	0.015^** (0.007)	0.026^*** (0.008)	0.015^*** (0.005)
Age 45–64 years	0.015^** (0.006)	0.038^*** (0.007)	0.015^** (0.005)
Less-than-upper-secondary education	0.002 (0.006)	0.022^*** (0.008)	−0.007 (0.006)
Upper secondary education	0.011^** (0.005)	0.009 (0.005)	0.005 (0.005)
Tertiary education	0.039^*** (0.001)	0.042^*** (0.015)	0.034^*** (0.012)
Manufacturing		0.011 (0.008)
Private services		0.023^** (0.009)
Public services		0.039^** (0.015)

Notes: The table reports the effects of the business cycle on training participation. The dependent variable is listed in the heading of each column. Estimates refer to the full sample in Column (1), to the employed in Column (2), and to the not employed in Column (3). Each regression also includes year and country dummies, country-specific employment trends and, when appropriate, age, gender, a dummy for tertiary education. Columns (2) and (3) include also the estimated inverse Mills ratio. Standard errors clustered by country and time period are reported in parentheses.

* p<0.1;

** p<0.05;

*** p<0.01.

Table 7

The effects of the business cycle (U_cycle) on training hours and employment status

	(1)	(2)	(3)	(4)
Dependent variable	Training hours	Employment probability	Training hours	Training hours
Sample	All	All	Employed	Not Employed
Cyclical component of unemployment rate × 10	−0.038 (0.133)	−0.004^*** (0.001)	0.006 (0.011)	−0.783^*** (0.022)z
Demand shock: Z_ct		0.007^*** (0.001)
Inverse Mills ratio			0.099 (0.147)	7.073^*** (0.386)
Male	−0.116^*** (0.011)	0.140^*** (0.002)	−0.130^*** (0.039)	1.916^*** (0.095)
Age	−0.034^*** (0.001)	−0.008^*** (0.001)	−0.025^*** (0.002)	−0.159^*** (0.007)
Has tertiary education	1.044^*** (0.032)	0.154^*** (0.002)	0.970^*** (0.037)	3.341^*** (0.142)
R-squared	0.018	0.098	0.019	0.028
Observations	43,173,984	43,173,984	30,372,267	12,801,617
Estimation method	OLS	Probit	OLS	OLS

Notes: The table reports the effects of the business cycle on employment and training hours. The dependent variable is listed in the heading of each column. Estimates refer to the full sample in Columns (1) and (2), to the employed in Column (3), and to the not employed in Column (4). Column (2) reports the marginal effects using a probit specification. Each regression also includes year and country dummies and country-specific employment trends. For the probit specification, the pseudo-R-squared value is reported instead of the R-squared value. Standard errors clustered by country and time period are reported in parentheses. OLS, ordinary least squares.

* p<0.1;

** p<0.05;

*** p<0.01.

Table 8

The relationship among employment protection, public training expenditure on GDP, and the sensitivity of training participation to the business cycle: second step estimate

Dependent variable: the sensitivity of training participation to the business cycle
Employment protection index	0.022^** (0.010)
Public expenditure for training as % of GDP	0.045^* (0.023)
Observations	22
R-squared	0.25

Notes: Robust standard errors. The regression is weighted using the reciprocal of the variance of first-stage coefficients. GDP: gross domestic product.

* p<0.1;

** p<0.05;

*** p<0.01.

Table A1

The effects of the business cycle (U_cycle) on training participation by employment status – 5% random sample by country and time period

	(1)	(2)
Dependent variable	Training participation	Training participation
Sample	Employed	Not employed
Cyclical component of unemployment rate × 10	0.021^*** (0.008)	0.007 (0.007)
Inverse Mills ratio	−0.110^*** (0.012)	0.045^*** (0.012)
Observations	1,518,752	639,964

Notes: Bootstrapped standard errors. The table reports the effects of the business cycle on training participation in a 5% random sample by country and time period. Column (1) is for the employed, and Column (2) represents the unemployed. Each regression also includes age, gender, a dummy for tertiary education, year and country dummies, and country-specific employment trends. Standard errors reported in parentheses are obtained from 200 bootstrap replications (clustered by country and time period). In each replication, we estimate the probit specification for selection into employment, the inverse Mills ratio, and the effect of U_cycle on training for the employed and the not employed (OLS regressions). OLS, ordinary least squares.

* p<0.1;

** p<0.05;

*** p<0.01.

Table A2

The effects of the business cycle (U_cycle) on training participation and labor force participation status

	(1)	(2)	(3)	(4)
Dependent variable	Training participation	Labor force participation	Training participation	Training participation
Sample	All	All	Active	Inactive
Cyclical component of unemployment rate × 10	0.015^** (0.006)	−0.006^** (0.003)	0.019^** (0.007)	0.005 (0.005)
Demand shock: Z_ct		0.004^*** (0.001)
Inverse Mills ratio			−0.062^*** (0.005)	0.086^*** (0.035)
Male	−0.015^*** (0.001)	0.145^*** (0.002)	−0.035^*** (0.002)	0.015^*** (0.001)
Age	−0.001^*** (0.000)	−0.010^*** (0.001)	0.001^*** (0.000)	−0.002^*** (0.001)
Has tertiary education	0.058^*** (0.002)	0.126^*** (0.001)	0.048^*** (0.002)	0.052^*** (0.002)
R-squared	0.076	0.098	0.077	0.062
Observations	43,173,984	43,173,984	30,372,267	12,801,617
Estimation method	OLS	Probit	OLS	OLS

Notes: The table reports the effects of the business cycle on employment and training participation. The dependent variable is listed in the heading of each column. Estimates refer to the full sample in Columns (1) and (2), to active workers (employed plus unemployed) in Column (3), and to the inactive in Column (4). Column (2) reports the marginal effects from a probit specification. Each regression also includes year and country dummies and country-specific employment trends. For the probit specification, the pseudo-R-squared is reported instead of the R-squared value. Standard errors clustered by country and time period are reported in parentheses.

OLS, ordinary least squares.

* p<0.1;

** p<0.05;

*** p<0.01.

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DOI: https://doi.org/10.2478/izajolp-2022-0007 | Journal eISSN: 2193-9004

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Language: English

Accepted on: Jul 17, 2022