Proceedings of 4th European Business Research Conference
9 - 10 April 2015, Imperial College, London, UK, ISBN: 978-1-922069-72-6
Forecasting Regional Unemployment
Yuwaorn Pradubmook* Udechukwu Ojiako** Alasdair Marshall*** and
Maxwell Chipulu****1
This In light of the on-going global economic crisis, unemployment remains
a key area of concern to European governments. Most scholars maintain
an interest in understanding unemployment drivers due to its multiplier
impact and by implication, major social and political consequences. In line
with this interest, utilising models, the authors seek to gain a deeper
understanding of the determinants of these regional variations on
unemployment. To facilitate this study, we employ time series analysis
(specifically LES and ARIMA models) to examine factors driving regional
unemployment. Our study shows that two factors, GDP and labour force,
are important variables impacting unemployment.
JEL Codes: Management: Management Science
1. Introduction
In Europe, following increasing concern among scholars of the long term effect of the debt
crisis on first, the ‗at risk‘ countries such as Portugal, the Republic of Ireland, Greece and
Italy (BNP Paribas, 2010; Maurer, 2010), and now an overall concern about the survival of
the Euro-zone (Eichengreen, 2007; 2010), the attention of scholars has moved to the
question of how the debt crisis may be best addressed (Arghyrou and Tsoukalas, 2011).
While this debate has raged, most governments in Europe have sought to enact and
implement various policies which in a majority of cases have involved drastic and
substantial reductions in the public sector workforce. In the United Kingdom, the story has
not been any different. For example, according to earlier projections by the OBR in 2010
(OBR, 2010a, 2010b), cuts in public sector spending which were being designed to reduce
the UK‘s public debt would deliver a reduction of the number of public sector workers by
610,000 (between 2010/11 and 2015/16).
Perhaps most worrying for the government and scholars is not necessarily that job cuts
are being implemented within the public sector, but that the private sector appears unable
to create enough jobs to match the number of people being made redundant from the
public sector. Scholars such as Elsby and Smith (2010), Elsby et al. (2011), Marinescu
(2011) and Smith (2011), point out there are several reasons for this including the fact that
the demand for goods and services produced by the private sector has declined as people
spend less. Secondly, there are problems of adjustment to structural change, such as a
shift in labour supply. To ensure flexibility in labour supply, most European UK
government has sought to ensure labour market flexibility primarily through legislation
which reduces the cost of firing employees. Governments have however had to balance
the need for flexibility against a cold recognition of the political ‗importance‘ of
1
* Yuwaorn Pradubmook, Southampton Business School, University of Southampton, UK; email:
** Dr Udechukwu Ojiako, Faculty of Engineering & IT, British University in Dubai & Hull University Business
School, UK; email: uojiako@buid.ac.ae
*** Alasdair Marshall, Southampton Business School, University of Southampton, UK: email:
a.marshall@soton.ac.uk
**** Dr Maxwell Chipulu, Southampton Business School, University of Southampton, UK: email:
m.chipulu@soton.ac.uk
Proceedings of 4th European Business Research Conference
9 - 10 April 2015, Imperial College, London, UK, ISBN: 978-1-922069-72-6
unemployment in politics (Baxandall, 2002), and arguably, its threat to European
democracy. This is because the question of unemployment forms a significant element of
the wider question of the prevailing health of a national economy with governments
presiding over countries with high unemployment likely to lose political legitimacy and
control over their citizens. As Piven and Cloward (199; pp. 6–7) suggest ―mass
unemployment serves that bond, loosening people from the main institution by which they
are regulated and controlled‖, in effect, according to Baxandall (2002; p. 471), since
unemployment ―dilutes social control‖, its effect can destabilize governments because not
only are regular ―work routines crucial for maintaining social order‖ (p. 471), however
unemployment ―diminishes the capacity of other institutions to bind and constrain people‖
by removing people from conformity to occupational behaviors and outlooks (1993, p. 7).
Unemployment is particularly destabilizing because political horizons can be constrained
through regular work routines and the framing of private interests (p. 471) due to its
perceived benefits. As the recent Arab-spring shows, unemployment has the ability to
unseat even dictatorships (Springborg, 2011). At this juncture we highlight that although
substantial studies have examined the challenges associated with unemployment, a large
number of studies on unemployment utilize ‗average‘ rates which hides perhaps a more
difficult challenge with unemployment, that is; how to deal with regional unemployment
within an country. It is this question of regional unemployment that is of particular interest
to us, especially due to its ability to radically re-shape traditional political clusters and
boundaries.
From a regional unemployment perspective, we seek in this study to examine not only the
factors that may impact on regional unemployment in the UK, but specifically, to examine
through correlations, how these factors may be related. We acknowledge the existence of
prior works of scholarship that have sought to model unemployment patterns (see Nickell
et al., 2002; Elhorst, 2003). These include for example the Beveridge Curve which seeks
to explain the relationship between the rate of unemployment rate and vacancy rate
(Nickell et al., 2002) and the NAIRU model (see Elhorst, 2003). However, for example, the
NAIRU model is limited in that it examines only age and unemployment rate.
This paper is organized as follows. In the second section, we undertake a review of prior
work on regional unemployment. This is followed by section three which focuses on
presenting a range of forecasting models which may be employed in the study. In the
fourth section, we undertake the forecasting, in particularly showing the measures of
accuracy of each model. We present a brief discussion and conclusion in section five.
2. Literature Review
Scholars have historically argued that unemployment is one of the major economic
challenges facing national governments (Marston, 1985; Ma and Weiss, 1993; Matthews
et al., 2008; Domenech and García, 2008; Ljungqvist and Sargent, 2008; Dutt et al., 2009;
Chaudhuri, 2011). In Europe, studies seeking to model unemployment allude to the fact
that regional differences exist in unemployment not only among European countries
(Demertzis and Hallett, 1998; Balakrishnan and Michelacci, 2001; Munich and Svejnar,
2007), but also within these countries (Brunello et al., 2001; Walsh, 2003; Bande et al.,
2008). In general, unemployment trend in Europe appears to suggest high unemployment
rates over the last two decades in ‗Southern‘ European countries such as Spain, Italy and
Portugal.
Proceedings of 4th European Business Research Conference
9 - 10 April 2015, Imperial College, London, UK, ISBN: 978-1-922069-72-6
Our review of a wide selection of literature on regional unemployment (see Marston, 1985;
Brunello et al., 2001; Overman et al., 2002; Walsh, 2003; Bande et al., 2008), does not
appear to indicate the existence of a single and generally applied underlying theory that is
able to provide explanations to spatial unemployment differences. However, from classical
economic literature (see Harris & Todaro, 1970; Blanchard and Katz, 1992), scholars had
been able to suggest that regional unemployment was as a result of the failure of policy
makers to understand the supply-demand relationship that exist between three
fundamental variables (i) wages, (ii) migration and (iii) re-generation. Although it is difficult
to articulate a single theory of regional unemployment, its multiplier impact on social
factors such as health, education and housing are well documented. We are also aware
that although a decrease in regional unemployment may bring about benefits (such as
lower inflation rates), its benefits may be diminished because of resultant migration
(Epifani and Gancia, 2005; Eggert et al., 2010). In effect, one could argue that
unemployment does have a paradoxical impact, thus serving as a rationale why scholars
have continued to retain a broad interest in studying this topic.
In the United Kingdom (UK), a substantial number of scholars (Elsby and Smith, 2010;
Elsby et al., 2011; Marinescu, 2011; Smith, 2011), have focused their attention on the
question of regional unemployment. In general, most of these studies suggest that
regional unemployment rate in the UK differs considerably with Northern England having
the highest rate of unemployment, while the South East and East Anglia have the lowest
unemployment rates. We also find a pattern of unemployment in the UK which appears to
be closely associated with economic activity.
3. The Methodology and Model
Data Sample/collection
Seasonally adjusted (to the nearest thousand) unemployment data between January 1993
and June 2010 (210 months) was obtained from Office for National Statistics (ONS). The
data was spread again eleven regional areas of mainland United Kingdom (basically, not
including Northern Ireland). As an example of the unemployment rate in a time series, a
time series is denoted by xt for the value at the time period t, over the period from t = Jan
2010 up to t = n, where n = Dec 2010. The data obtained from the ONS were of two types;
monthly (between 1993 and 2001) and quarterly data (between 2001 and 2010). The
monthly data had no missing data. However, the quarterly data had three missing vacancy
and wages variables. We added an estimated value where the data was missing.
Forecasting from the time series
Linear exponential smoothing: We used the linear exponential smoothing (LES) method
because of the need to autocorrelate a stationary time series model. This involved
checking for seasonality. Due to the fact that the data has been seasonally adjusted, thus
it can be found that all of the regions are non-seasonal in 11 regions of the UK. Figure 1
shows an example of non-seasonality; checked by the autocorrelation function plots.
Proceedings of 4th European Business Research Conference
9 - 10 April 2015, Imperial College, London, UK, ISBN: 978-1-922069-72-6
Figure 1: Example of Autocorrelation Function Plots
Autocorrelation Function (ACF)-London
Autocorrelation Function (ACF)-East
1.2
1.2
1
1
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0.2
0
0
1
6
11
16
21
26
31
36
41
46
51
1
56
6
11
16
21
26
31
36
41
46
51
56
-0.2
-0.2
Holt‘s (1960) LES method is used for forecasting because of its robustness forecasting
non-seasonal data. Holt‘s model is represented mathematically as;
Lt
=
αYt + (1-α) (Lt-1 + bt-1)
b t = β(Lt – Lt-1) + (1 - β)bt-1
F t+m = Lt + btm
(1)
(2)
(3)
Where α, β = smoothing constants (α > 0, β < 1)
Lt
= the level of the series at time t
b t = the linear trend
F t+m = the linear forecast from t forwards
Equation (3) can imply that F t+m is a forecast of unemployment in the 12 month (m = 12)
period ahead.
ARIMA model: In this method, we considered a particular set of models used in advanced
forecasting and time series analysis based on the autocorrelation function (ACF), as used
with the LES model. The ARIMA modelling commenced with the identification of
unemployment data in each region in order to select possible models. Firstly, we made a
time plot, and then looked at patterns of data. It was found that time plots were nonstabilize variance and non-stationary because the ACF plots decreased slowly in all 11
regions. Then, we took the first and second differences of data used. After stationary were
completed, we observed a pattern of the ACF plot and selected a possible model where
the patterns suggested autoregressive (AR) or moving average (MA). Finally, we selected
MV models in order to predict the regional unemployment rate in the UK, and a possible
identification of model for data used. In the next phase of the ARIMA modelling, we used
the X-12-ARIMA program to estimate the parameters of the ARIMA model, i.e.
(p,d,q)(P,D,Q), for testing and forecasting the unemployment rate in each region. Then,
we forecasted the 12 months ahead (from April 2010 to March 2011). Finally, we used
Akaike‘s information criterion (AIC) (Akaike, 1974), and X-12-ARIMA to generate generate
AIC values for each UK region of ARIMA model processed in order to achieve the lowest
AIC.
Proceedings of 4th European Business Research Conference
9 - 10 April 2015, Imperial College, London, UK, ISBN: 978-1-922069-72-6
AIC = -2logL + 2m
Where
(4)
L = likelihood, m = p+q+P+Q
In the last phase, we compared the LES and ARIMA models by considering measures of
accuracy. The ARIMA models provided a powerful approach to time series analysis that
pays especial attention to correlation between observations. Finally, we chose the best
model to forecast the unemployment rate in each region, and then calculated the total
unemployment rate in the UK.
Forecasting using regression: Regression analysis was utilised to examine the
relationships between regional unemployment and explanatory variables impacting on
unemployment such as population, minimum wages, migration and GDP. Data was
typically quarterly in which seasonal components of uncertain effect were shown. In effect,
an indicator variable, Di, for each of the four quarters was employed to represent the effect
of categorical variables (See Table 1), thus;
Ŷunemployment = β0 + β1 (GDP) + β2 (labour force) + β3 (vacancy) + β4 (wages)
Table 1 Description variables used in multiple linear regression
Variables
Y
Description
The unemployment rate
in the UK
The GDP in the UK
X1
(million pounds)
The labour force
X2
(thousands)
The vacancies
X3
X4
(thousands)
The amount of wages
(pounds/week)
Variables
Description
β0
Interception
β1
β2
β3
β4
Co-efficient,
GDP
Co-efficient,
labour force
Co-efficient,
vacancy
Variable
s
Description
D1
1st quarter
D2
2nd quarter
D3
3rd quarter
D4
4rd quarter
Co-efficient,
wages
Measure of accuracy
In order to select the most accurate approach for forecasting unemployment rates, we
refer to earlier work by Hyndman and Koehler (2006), by comparing the measures of
accuracy of LES and the ARIMA models in order to select the model that provided the
lowest error value. We considered the general forecast errors in terms of the mean square
error and mean percentage, defined as:
et = Yt - Ft
(5)
Where , Yt
= Unemployment values at year t
Ft = Unemployment values at year t+1
Proceedings of 4th European Business Research Conference
9 - 10 April 2015, Imperial College, London, UK, ISBN: 978-1-922069-72-6
In equation (5), Ft is estimated by proceeding of Yt -1, so the forecast value (Ft) is predicted
by one step forward. Therefore, in this case, Ft is called one step forecast, and et is called
one step forecast error. If errors are estimated such et from n values, it will be measure
errors as:
MSE =
(6)
Where , MSE = the mean square error
And,
PEt =
(7)
MAPE =
(8)
Where, MAPE = the mean percentage error
4. The Findings
Time and Seasonal Plots
From Figure 2, the trend of the unemployment data from all eleven regions appears
similar. Checking for seasonality, because of seasonal adjustments, we deduce that
unemployment data has no seasonal effect.
Figure 2 Time plot of unemployment rate in the UK by regions (thousand)
Unemployment Rates in the UK by 11 regions
600
London
500
East
400
East Midland
West Midland
300
North East
North West
200
South East
100
South West
1993 Jan
1993 Aug
1994 Mar
1994 Oct
1995 May
1995 Dec
1996 Jul
1997 Feb
1997 Sep
1998 Apr
1998 Nov
1999 Jun
2000 Jan
2000 Aug
2001 Mar
2001 Oct
2002 May
2002 Dec
2003 Jul
2004 Feb
2004 Sep
2005 Apr
2005 Nov
2006 Jun
2007 Jan
2007 Aug
2008 Mar
2008 Oct
2009 May
2009 Dec
2010 Jul
2011 Feb
0
Scotland
Wales
York& the Humber
Model Selection
In Table 2, we show the estimates of alpha (α) and beta (β). It can be found that the
smoothing constants are estimated as being between 0 and 1. Most regions have the
smoothing constant, alpha (α) that equals to 1, however the estimates of alpha ( α) in the
North West, Wales and York and Humber are 0.99908, 0.87523 and 0.97841 respectively.
Proceedings of 4th European Business Research Conference
9 - 10 April 2015, Imperial College, London, UK, ISBN: 978-1-922069-72-6
Table 2 LES selection
Values of Parameters
Regions
α (Alpha)
β (Beta)
London
1.00000
0.02825
East
1.00000
0.02829
East Midland
1.00000
0.01863
West Midland
1.00000
0.22180
North East
1.00000
0.00000
North West
0.99908
0.03584
South East
1.00000
0.02934
South West
1.00000
0.02465
Scotland
1.00000
0.04032
Wales
0.87523
0.02179
York and the
Humber
0.97841
0.02931
In Table 3, we show the results using the appropriate forecasting model. Columns two and
three present that the selection model in term of (p, d, q) (P, D, Q) 12 and the Akaike‘s
Information Criterion (AIC) on unemployment in each region. Four regions were largely
consistent with the model as (0,2,1)(0,1,1) in North East, (0,2,1)(0,1,2) in South East,
South West and York and Humber, (0,2,2)(0,1,2) in London, East, East Midlands, North
West, Scotland and Wales. The last fitted model is (0, 2, 3) (0, 1, 2) in the West Midlands.
This was because these models provided the lowest values of AIC (see Appendix IV).
Column 4 shows residuals.
Table 3 ARIMA selection
Regions
(p,d,q)(P,D,Q)1
2
AIC
Are residuals
white noise?
Residuals
Justification
London
(0,2,2)(0,1,2)
1426.969
Yes
Good
Acceptable
East
(0,2,2)(0,1,2)
1246.693
Yes
Good
Acceptable
East
Midland
(0,2,2)(0,1,2)
1203.48
Yes
Good
Acceptable
West
Midland
(0,2,3)(0,1,2)
1275.869
Yes
Good
Acceptable
North East
(0,2,1)(0,1,1)
1156.776
Yes
Good
Acceptable
North West
(0,2,2)(0,1,2)
1306.51
Yes
Good
Acceptable
Proceedings of 4th European Business Research Conference
9 - 10 April 2015, Imperial College, London, UK, ISBN: 978-1-922069-72-6
South East
(0,2,1)(0,1,2)
1330.636
Yes
Good
Acceptable
South West
(0,2,1)(0,1,2)
1226.581
Yes
Good
Acceptable
Scotland
(0,2,2)(0,1,2)
1282.892
Yes
Good
Acceptable
Wales
(0,2,2)(0,1,2)
1132.495
Yes
Good
Acceptable
York and
the Humber
(0,2,1)(0,1,2)
1298.484
Yes
Good
Acceptable
Measurement of Accuracy
The mean square error (MSE) which is shown in Table 4 shows forecast error across the
eleven regions. By comparing the MSE values of both approaches, the ARIMA models
appear to outperform the LES model because of their ability to calculate smaller values
over the data period being used for evaluation. For example, the MSE value of ARIMA
model in London is 61.67988, which is smaller than the estimated MSE values of LES at
72.14212. We also observe that the MAPE values for the ARIMA model are lower than
that of the LES. This enables us conclude that the ARIMA model out performs the LES
model in terms of accuracy.
Table 4 LES and ARIMA Summary measures
Mean Square Error (MSE)
Mean Absolute Percentage
Error (MAPE)
Regions
LES
ARIMA
LES
ARIMA
London
72.14212
61.67988
2.24691
2.04264
East
28.38846
22.63298
2.93441
2.59780
East Midland
22.07552
20.01268
3.14214
3.02490
West Midland
35.42630
27.55003
2.53138
2.32870
North East
16.53110
15.57939
3.30050
3.18110
North West
37.59745
32.68708
2.27820
2.17719
South East
46.74427
35.28771
2.84387
2.46309
South West
26.43936
21.86923
3.48546
3.28565
Scotland
32.60272
28.18887
2.70852
2.51254
Wales
14.67444
14.29529
3.39834
3.38483
York and the
Humber
35.85076
34.08104
2.93800
2.88600
Forecasting Values by using ARIMA models
Proceedings of 4th European Business Research Conference
9 - 10 April 2015, Imperial College, London, UK, ISBN: 978-1-922069-72-6
The results of the ARIMA modelling show that there are several regions such as London
that have a high rate of unemployment (per thousand). Table 5 shows a total number of
unemployment rates of ARIMA models when comparing with the actual data used from
April 2010 to March 2011 while Table 6 shows a total number of unemployment rates of
ARIMA models when comparing with the actual data used from April 2010 to March 2011.
Table 5 Forecasting Values of regions in the UK using ARIMA models
Regions
Forecasting Values (Thousands), ARIMA models
APR
10
MAY
10
JUN
10
JUL
10
AUG
10
SEPT
10
OCT
10
NOV
10
DEC
10
JAN
11
FEB
11
MAR
11
London
387.1
386.7
390.5
392.2
398.0
404.9
408.8
406.1
411.8
410.0
414.3
416.7
East
201.6
206.6
210.1
214.0
213.3
214.2
217.0
221.3
222.6
224.9
226.2
228.9
East
Midland
170.8
172.7
174.5
177.7
180.0
179.6
180.2
184.0
186.8
189.6
189.6
192.7
West
Midland
229.4
237.4
240.6
248.6
255.0
257.6
258.2
260.3
264.9
269.7
276.9
278.6
North
East
119.5
120.9
121.2
121.8
121.1
120.9
122.7
124.1
125.3
125.9
127.6
128.4
North
West
281.5
285.0
292.0
293.9
295.9
299.5
302.0
302.2
308.2
312.6
319.4
322.2
South
East
274.9
277.1
274.0
278.7
278.5
284.2
288.7
292.6
293.2
296.4
301.9
309.6
South
West
162.4
165.6
167.3
170.6
174.0
179.8
177.3
180.2
184.2
186.0
187.2
189.7
Scotland
237.8
243.4
249.8
251.0
254.8
258.4
262.9
265.8
270.0
275.2
277.1
278.7
Wales
120.0
122.7
123.2
125.2
125.6
126.0
128.1
128.5
132.6
131.9
134.0
138.5
York and
the
Humber
249.6
252.7
253.5
257.6
262.0
264.8
265.2
266.2
270.7
277.8
281.6
284.3
Total
31,198.3
Table 6 Comparison unemployment rate between Actual data and ARIMA models
Total Unemployment
Actual Data
ARIMA models
April 2010-March 2011
28,941,000
31,198,300
Multiple regression models for forecasting
The multiple regression models were used to predict unemployment based on significant
independent variables. We first tested the statistical significance of explanatory variables,
which are used to explain the behaviour of the dependent variable, Yunemployment. A null
hypothesis is put forward:
Proceedings of 4th European Business Research Conference
9 - 10 April 2015, Imperial College, London, UK, ISBN: 978-1-922069-72-6
H0 : β1 = β2 = β3 = β4 = 0
H1 :
one or more of the regression co-efficients are not zero
We show the results of the P- value in Table 8 (P- value = 0.0001), which is lower than 0.5
thus we reject the null hypothesis. We next sought to establish the co-efficient of
determination, R2 which explains variation between the dependent and explanatory
variables. The result shows that the R2 equals 0.9777, this independent variables (GDP,
labor force, wages and vacancy) may explain a relatively high ratio of variation in terms of
unemployment.
Table 7 Regression Results for Analysis of Variance
Analysis of Variance
Source of
Variation
SS
df
MS
Regression
5186957.4752
7
740993.9250
Error
118564.8998
32
3705.15312
Total
5305522.3750
39
R2
F-Ratio
199.9900
Pr > F
< 0.0001
0.9777
The third stage in the process involved interpretation of the regression co-efficient, βi. The
regression model is presented by running regression (shown in Table 7), which can be
represented mathematically as;
Ŷunemployment= -19496.5-0.0246(GDP) +0.7510(labour force)-0.0928(vacancy)-0.1124(wages)
The equation illustrates that unemployment rates can be explained by four independent
variables GDP, labour force, vacancy and wages. In other words, the coefficients of βi
signify the average change in unemployment rate associated with a unit change in one
independent variable when other independent variables are constant. The final stage of
the multiple regressions involved examining the significance of explanatory variables from
which it can be concluded that there is a regression co-efficient that do not equal to zero
(βi ≠ 0). The t distribution is used to determine the hypothesis of individual coefficients, at
0.5 of significant level. The null hypothesis is expressed as:
H0 : βi = 0 ; i = 1, 2, 3, 4
H1 :
βi ≠ 0 ; i = 1, 2, 3, 4
In Table 8, the results the P- values of GDP and labour force, 0.0001 are lower than 0.5.
So, the null hypotheses are rejected. Whereas, the P- values of vacancy and wages are
0.3898 and 0.7536 respectively, which are higher than 0.5 and so that the null hypotheses
cannot be rejected. As the results of this, it can be concluded that there are two variables
i.e. GDP and labour force that can be used in the regression equation in order to forecast
unemployment rate in the UK. Conversely, vacancy and wages variables cannot explain
the unemployment in the regression model.
Proceedings of 4th European Business Research Conference
9 - 10 April 2015, Imperial College, London, UK, ISBN: 978-1-922069-72-6
Table 8 Regression Results for Fit of Regression Model
Regression Coefficients
Variables
Coefficients
Estimated
Coefficients
Estimated
Standard
Deviation
t-values
Pr > I t I
Intercept
β0
-1,9496.485
722.2621
- 26.9936
< 0.0001
GDP
β1
– 0.0246
0.0015
- 16.4950
< 0.0001
Labour force
β2
0.7510
0.0280
26.8465
< 0.0001
Vacancy
β3
0.0928
0.1065
- 0.8719
0.3898
Wages
β4
0.1124
0.3550
- 0.3166
0.7536
Based on the outcome of the multiple regressions, we posit that an effective regression
model for unemployment may be best developed based on four independent variables
(GDP, labour force, vacancy and wages), which can be summarised mathematically as;
Ŷunemployment= -19496.5 - 0.0246*(GDP) + 0.7510*(labour force) (9)
From equation (9), GDP and labour force can be calculated the forecasting values in the
first quarter in 2011 using Excel. The values of GDP and labour force are 329,760.66 and
40,142.82 respectively (See Table 9). Therefore, the unemployment rate in the first
quarter 2011 can be forecasted as in equation (10).
Ŷunemployment = -19496.5 - 0.0246*(329,760.66) + 0.7510*(40,142.82)
(10)
= 2,538.66
Table 9 Forecasting values of unemployment in 2011
Quarterly
Unemployment
(thousands)
GDP(£ million)
Labour force
(thousands)
Q1 2011
2,538.66
329,760.66
40,142.82
Q2 2011
2,576.54
330,331.67
40,211.96
Q3 2011
2,614.42
330,902.69
40,281.10
Q4 2011
2,652.29
331,473.70
40,350.24
Selection of the best model and the comparison models
Based on the outcome of the fitting of the forecasting models shown in Table 10, the
ARIMA model is selected for accuracy because its values of error measures are less than
the LES model.
Proceedings of 4th European Business Research Conference
9 - 10 April 2015, Imperial College, London, UK, ISBN: 978-1-922069-72-6
Table 10 The fitting criterion of forecasting models
Model
Fitting Criterion (the smallest value)
s
MSE
MAPE
LES
X
X
ARIMA


Table 11 shows the forecasting values in the first quarter of 2011 between the ARIMA
model and regression models. This shows that the unemployment rate forecast value
obtained from the regression models is closer to the actual value compared to the ARIMA
model. The multiple regression models are adopted to classify factors that impact on the
unemployment rate. In addition, we use two significant factors, GDP and labour force, to
forecast values.
Table 11 Comparing forecasting values between ARIMA and regression models
Forecasting values of unemployment (thousands)
Quarterly
Actual
Q1 2011
2,393.33
Regression models
2,538.66
ARIMA models
2,734.70
5. Summary and Conclusions
Regional unemployment is a critical economic factor of significant political and social
importance. Although a substantial amount of scholarship has been focused on
understanding the economic and political ramifications from regional unemployment, its
social impacts in terms of factors such as migration and the dismantling of traditional ways
of living and local communities, is yet to attract considerable and sustained interest in
Europe, perhaps because of the current trend of government to ‗fund‘ unemployment
through the benefit and social security systems. However, it is likely that the situation will
change, even with the mass redundancies within the public sector. The signs appear
ominous, if one considers concerted effort among European governments to reform
national pensions. It remains uncertain at present in light of the on-going debt crisis what
the true consequences of employment will be on the UK regions. Available data is
however not encouraging. For example, according to the North East Public Observatory
(NEPHO), the North-East of England, a region traditionally characterised by high
unemployment currently exhibits not only prevalence for psychiatric disorder of 17.5%
(higher than the English average of 13.2%), but the region has the highest death rates
from suicide in England (NEPHO, 2011). In the absence of a single underlying theory that
is able to provide explanations to spatial unemployment differences, one is still left with
the question of why these regional differences? Certainly, all regions within the UK will be
impacted by the current economic climate sweeping through Europe, however as
observed, based on major differences (as observed in our findings). We posit that a
Proceedings of 4th European Business Research Conference
9 - 10 April 2015, Imperial College, London, UK, ISBN: 978-1-922069-72-6
possible approach to explaining these variations in regional unemployment in the UK
could be related to the traditional industrial base. The hard hit areas such as the North
East had been an area with deep rooted traditions in steel works, manufacturing and
mining, industries which are traditionally associated with family and heritage (Townsend
and Taylor, 1975), and therefore less likely to migrate. Not surprisingly, studies by Forrest
and Naisbitt (1988), found that regional industrial heritage may significantly impact
sensitivity to national economic trends.
The viability of application of models to understand socially constructed norms has
remained an area of considerable interest to scholars over a number of years. In most
cases, scholars have employed such models (see Woodhouse, 2006; Rutten and
Boekema, 2007), as a means of rationalising social behaviour which might have otherwise
posed considerable challenges to be applied to standard economic models. In the case of
generating an understanding of regional unemployment, we observe that the utilisation of
such models has played a considerable role in ensuring that policy makers gain an
understanding of the supply-demand relationship that exist between key unemployment
variables. Our proposition is that despite the severity in terms of importance of the
question of unemployment, there appears to be very few empirical studies in this area that
have sought to understand the societal impact of regional unemployment. The question
then arises as to the implications of this considering that the crucial social impact of
unemployment is mainly psychological.
Scholars such as Smith (2011) and Blazek and Netrdova (2012), generally tend to share
the opinion that unemployment in Europe is high and that associated regional
unemployment remains a major cause of societal imbalance and migration. Our study
showed that regional unemployment in the UK was driven by four factors; GDP, labour
force, vacancy and wages. Generally speaking, the conventional thinking in the various
works of scholarship examining regional unemployment (see Blundell and MaCurdy,
2000), is that of a positive correlation between unemployment and labour supply. In effect,
that in regions where we experience high unemployment, there is a high supply of labour,
and conversely, in regions where there are labour shortages, it is likely that full
employment is obtainable. One therefore argues that convalesce will exist between
government seeking to ensure that its working population are in employment (and
therefore likely to pay tax), and employers, in the form of companies seeking to expand
their operations and businesses. The argument could therefore be that such convalesce
will deliver on achieving a balance in a more equitable distribution of employment. The
impact will therefore be that employers will obtain more readily labour from regions with
high supply of labour through increased mobility, not of labour, but of firms and
businesses more willing to move their operations to areas with higher unemployment, and
most likely, lower wages. The challenge faced by government (in terms of policy), is that
national costs in terms of labour generally appear similar across regions, even those
across the employment continuum. However, one possible approach that can be adopted
to address the question of regional disparities in unemployment may well be to encourage
limit the power of labour unions. One possible means of achieving this objective may well
be to limit the bargaining powers of unions through legislation.
In conclusion, the purpose of this study was to undertake a forecast of regional
employment based on available (January 1993 to June 2010). We also sought to identify
key factors likely to impact on regional unemployment for which we employed multiple
regression modelling. As expected of studies engaged in forecasting, the study was not
without limitations of which the most significant being that we did not employ a large
Proceedings of 4th European Business Research Conference
9 - 10 April 2015, Imperial College, London, UK, ISBN: 978-1-922069-72-6
number of explanatory variables such as migration, interest rates, and house prices in the
multiple regression models.
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