Unemployment and vacancy flows in spatial labour market matching at the regional level. The case of a transition country

Figures & data

Figure 1. Distribution of the regions that attract and produce the most commuters.

Note: Data for 2011.Source: Own elaborations based on CSO data.

Table 1. Correlation between the inflow of commuters to the region (up to 11 orders of contiguity) and the employment in that unit.

Order of the contiguity of commuters’ inflow	Correlation coefficient
1	0.39***
2	0.24***
3	0.12**
4	0.05
5	−0.02
6	−0.12**
7	0.03
8	0.18***
9	0.10*
10	0.01
11	0.05***

Note: Significance levels: α = 0.10*, 0.05 **, 0.01 ***.

Source: Own elaborations based on CSO data.

Table 2. Summary statistics (mean values, 2003–2014) of unemployment, vacancy stocks and inflows, and outflow from unemployment to employment.

	LAU-1
Variable	Mean	Std. Dev.	Min	Max	Median
unemployment inflow	592 (−7)	443 (−14)	60 (−15)	6584 (−1)	486 (−7)
unemployment stock	5941 (−38)	4793 (−31)	268 (−53)	67,647 (−17)	4876 (−38)
vacancy inflow	208 (48)	239 (95)	2 (0)	5500 (149)	149 (41)
vacancy stock	119 (282)	257 (172)	0 (0)	6601 (345)	54 (529)
outflow from unemployment to employment	270 (−1)	205 (−7)	13 (−39)	3325 (−1)	221 (0.4)

Note: n = 379, T = 144, N = 54,576. In parentheses, we computed the changes between 2003 and 2014 to show diversity over time (in %).

Source: Own elaborations based on public employment offices data.

Figure 2. Vacancy inflow to unemployment stock ratio (a), and vacancy stock to unemployment stock ratio (b), LAU-1, 2003–2014 mean value.

Note: Labour market tightness indices: vacancy inflow to unemployment stock (on the left) and vacancy stock to unemployment stock (on the right). The labour market tightness index reflects how many job offers there are per worker. The darker the district, the tighter the labour market.Source: Own elaborations based on public employment offices data.

Figure 3. Exit rate at LAU-1, averaged over the years 2003–2014.

Note: The exit rate from unemployment: the ratio of the outflow from unemployment to employment to unemployment stock.Source: Own elaborations based on public employment offices data.

Table 3. Spatial model estimates, balanced panel: n = 379, T = 144, N = 54,576.

Independent variable/statistics	Parameters
	Random matching	Stock-flow	Job-queuing
	0.02***	0.01***	–
${\mathit{\alpha }}_{\mathbf{2}}\left({\mathit{U}}_{\mathit{i}\mathbf{,}\mathit{t}}\right)$	0.63***	0.51***	0.61***
${\mathit{\alpha }}_{\mathbf{3}}\left(v_{i,t}\right)$	–	0.07***	0.08***
${\alpha }_4\left(u_{i,t}\right)$	–	0.25***	–
${\gamma }_1\left(\mathbf{W}{V}_{i,t}\right)$	0.01***	0.01***	–
${\gamma }_2\left(\mathbf{W}{U}_{i,t}\right)$	−0.03**	−0.01	−0.04***
${\gamma }_3\left(\mathbf{W}{v}_{i,t}\right)$	–	0.07***	0.05***
${\gamma }_4\left(\mathbf{W}{u}_{i,t}\right)$	–	−0.20***	–
	0.89***	0.87***	0.88***
(pseudo)${\mathit{R}}^2$	0.83	0.85	0.84
log-likelihood	521.99	658.15	549.35
spatial Chow test	4355.53***	3503.74***	5507.97***
Moran’s I of residuals	−0.02	−0.01	−0.01
log-likelihood ratio test	137.64***	149.28***	111.28***

Note: Significance levels: α = 0.10*, 0.05 **, 0.01 ***.

Source: Own elaborations based on public employment offices data.

Table 4. Random matching, stock-flow matching, and job-queuing model estimates panel non-spatial models.

Independent variable/statistics	Random matching	Stock-flow	Job-queuing
${\alpha }_1\left(V_{i,t}\right)$	0.08***	0.05***
${\alpha }_2\left(U_{i,t}\right)$	0.52***	0.48***	0.42***
${\alpha }_3\left(v_{i,t}\right)$		0.17***	0.19***
${\alpha }_4\left(u_{i,t}\right)$		0.14***
(adj. $R^2$)	0.82	0.84	0.83
ADF Fisher test for residuals	9155.02***	9125.52***	9110.53***
redundant fixed effects F test	72.79***	48.84***	71.16***
sum of the parameters	0.60	0.84	0.61
Moran’s I of residuals	0.11***	0.07***	0.09***

Note: As the Levin–Lin–Chu test results implied that the null hypothesis on the unit root in the series should be rejected, we assumed the series to be I(0).

Source: Own elaborations based on public employment offices data.

Table 5. Comparison tests for spatial models, balanced panel: n = 379, T = 144, N = 54,576.

Comparison Tests	Spatial Models
	Random matching	Stock-flow	Job-queuing
SDEM
(pseudo) ${\mathit{R}}^2$	0.83	0.85	0.84
log-likelihood	521.99	658.15	549.35
log-likelihood ratio test	137.64***	149.28***	111.28***
Moran’s I of residuals	−0.02	−0.01	−0.01
SDM
(pseudo) ${\mathit{R}}^2$	0.63	0.77	0.68
log-likelihood	523.83	658.03	552.27
log-likelihood ratio test	141.32***	149.05***	117.11***
Moran’s I of residuals	−0.02	−0.003	−0.01
SLX
(pseudo) ${\mathit{R}}^2$	0.43	0.61	0.47
log-likelihood	453.17	583.51	493.71
log-likelihood ratio test	97.51*	100.01**	99.06*
Moran’s I of residuals	0.43***	0.46***	0.38***
GSM
(pseudo) ${\mathit{R}}^2$	0.63	0.71	0.70
log-likelihood	568.15	611.01	599.05
log-likelihood ratio test	139.63***	143.02***	140.00***
Moran’s I of residuals	−0.02	−0.01	−0.03

Source: Own elaborations based on public employment offices data.

Figure 4. Local specific influence on the outflow from unemployment to employment (fixed effects).

Note: The darker the region, the stronger the influence on the outflow from unemployment to employment.Source: Own elaborations based on public employment offices data.