This paper re-examines the empirical validity of the hysteresis hypothesis in unemployment rates in terms of education level in 17 OECD countries. To this end for unbalanced panel, we employ Pesaran’s Cross Sectional Dependence (CD) and Cross-Sectionally Augmented ADF (CADF) tests. Our empirical findings provide that the evidence is favorable to the non-stationary of the unemployment rates by primary and secondary education attainment in total unemployment, and therefore the existence of hysteresis while there is no evidence of hysteresis for unemployment rates by tertiary education.Hysteresis, unemployment, the natural rate hypothesis

Aslan, Alper

Kula, Ferit

English

Research Papers in Economics

  1
Hysteresis vs. natural rate of unemployment: One, the other, or both? 
 
Abstract 
This  paper  re-examines  the  empirical  validity  of  the  hysteresis  hypothesis  in 
unemployment rates in terms of education level in 17 OECD countries. To this end for 
unbalanced panel, we employ Pesaran’s Cross Sectional Dependence (CD) and Cross-
Sectionally Augmented ADF (CADF) tests. Our empirical findings provide  that  the 
evidence is favorable to the non-stationary of the unemployment rates by primary and 
secondary education attainment in total unemployment, and therefore the existence of 
hysteresis while there is no evidence of hysteresis for unemployment rates by tertiary 
education.  
 
I. Introduction 
 
There  has  been  an  intense  and  lively  academic  and  political  debate  on  the 
unemployment  in  world  economies  during  the  last  25  years,  notably  for  European 
economies. It can be distinguished two major hypotheses on the time series properties 
of  unemployment:  the  natural  rate  hypothesis  (NRH)  and  the  hysteresis  hypothesis 
(HH). NRH characterizes unemployment dynamics as a mean reverting process, which 
means that the unemployment rate tends to revert to its equilibrium in the long run. On 
the other hand, HH states that cyclical fluctuations have permanent effects on the level 
of unemployment therefore; the level of unemployment can be characterized as a non-
stationary process. 
 
Despite a blooming literature on testing HH and NRH (e.g., Blanchard and Summers, 
1986; Mitchell, 1993; Song and Wu, 1998; Leon-Ledesma, 2002; Chang et al., 2005) by 
the  time  series  and  panel  data  unit-root  methodology,  there  are  still  some 
methodological debates associated with empirical literature.  
 
First of all, the dynamics of the aggregate unemployment rate need not reflect that of 
joblessness is neglected in many studies (Gustavsson and Osterholm, 2006). Due to 
discouraged-worker and added-workers effects particularly in less skill workers, new 
empirical works have started to turn their attention to examining variations in the labor   2
force participation rate and employment rate
1 (e.g., Gustavsson and Osterholm, 2007; 
Madsen et al., 2008). One other issue that has been addressed in time-series analyses of 
HH is whether there has been a structural break in the unemployment series. The several 
studies illustrate that structural breaks could provide an explanation for hysteresis or 
persistence in the equilibrium rate of unemployment (e.g., Papell et al. 2000; Summers, 
2003;  Lee  and  Chang,  2008).  Last  methodological  problem  is  the  cross-sectional 
dependencies are not taken into account in panel data analysis of HH (e.g., Camarero et 
al., 2006; Berger and Everaert, 2008). This problem is stated expressly by Christopoulos 
and Ledesma (2007). They applied a battery of second-generation panel unit root tests 
that allow for cross-sectional correlation. Although the data set was the same used in 
Ledesma  (2002),  contrary  to  ledesma  findings,  the  hypothesis  of  unemployment 
hysteresis in the EU is rejected. The study shows that, contrary to previous empirical 
literature, hysteresis does not characterise EU unemployment 
 
In this paper, we re-examine the informational value of unemployment rates in studies 
of  hysteresis  from  disaggregated  perspectives.  In  particular,  it  is  applied  second 
generation panel data unit root methodology to investigate the differences between on 
unemployment among workers categorized by their level of educational attainment for 
17 OECD countries. This approach allows us to abstract away from changes in the 
composition of the unemployed labor force by focusing on particular educational groups 
and accounting at the same time for the presence of cross sectional dependence. 
 
The paper is organized as follows: In Section II presents the data used. The econometric 
techniques  and  the  empirical  results  are  discussed  in  Section  III.  The  final  section 
concludes the paper. 
 
II. Data 
This study employs unemployment indicators in which are the percentage distribution 
of a country’s total unemployed according to level of educational attainment. Data for 
both indicators were collected from International Labour Organization-ILO (2007) and 
World  Bank’s  World  Development  Indicators  (WDI)  online  database.  The  major 
classifications used in the databases are unemployment with primary education (UPE), 
unemployment  with  secondary  education  (USE)  and  unemployment  with  tertiary 
                                                 
1 Labor force skill level and expansion of the educational system may effect employability of workers and 
then cyclicality  of both employment and  unemployment rates (Murphy  and  Topel, 1997; Keane and 
Prasad, 1993; Hoynes, 1999; Gustavsson and Osterholm, 2007; Camarero et al., 2008).   3
education  (UTE).  Sample  is  an  unbalanced  panel  data  that  comprises  of  17  OECD 
countries with a time length that varies between 12 to 27 years. For details about data, 
please see the Appendix. 
 
III. Methodology and Analysis 
 
A traditional testing procedure in which to empirically examine HH is to apply unit root 
tests on the unemployment rate. Because hysteresis is consistent with non-stationary 
unemployment rates, unit root tests provide a convenient methodological framework. 
Starting with Levin and Lin (1992), much work has also been done on testing for unit 
roots in panels, including papers by Maddala and Wu (1999), Choi (2001), Im et al., 
(2003)  and  others.  Besides,  as  shown  in  two  simulation  studies  by  Banerjee  et  al. 
(2004a, 2004b) if panel members are cross-correlated, all these tests experience strong 
size distortions and restricted power. For this reason, panel unit root tests relaxing the 
assumption  of  cross  sectional  independence  have  recently  been  proposed  in  the 
literature by Choi (2002), Bai and Ng (2003), Moon and Perron (2003), Pesaran (2003), 
Phillips and Sul (2003) and Peseran (2005). 
 
To check  if our  sample is characterized by cross-section  dependence, the Pesaran’s 
cross sectional dependence test is applied. 
 
Pesaran  (2004)  presents  a  simple  cross-sectional  dependence  test  (CD)  that  can  be 
applicable to both balance and unbalanced panels. The test is based on the average of 
pair-wise  correlation coefficients ( ˆ ij σ ) of the residuals obtained from  the individual 
augmented Dickey-Fuller (ADF) regression. The CD statistics for an unbalanced panel 
is computed as: 
 
1 1
2 ˆ
( 1)
ij ij
N N
i j i
CD T
N N
σ
= = +
 
=   −   ∑ ∑             (1) 
 
Table 1 contains CD statistics that obtain residuals from ADF estimations with intercept 
and linear trend regression. The hypothesis of zero cross section correlation is rejected 
for all series at the 1%-level of significance.  
 
   4
 
Table 1. Pesaran’s cross sectional dependence test 
Test results  UPE  USE  UTE 
CD statistic 
p-value 
17.171 
(0.000) 
22.647 
(0.000) 
38.377 
(0.000) 
Notes: The CD statistic is asymptotically normally distributed. The p-values refer to a two-sided test. 
 
The  rejection  of  the  non-cross-sectional  dependence  null  hypothesis  implies  that  it 
should be taken this dependence into account when we test the unit root null hypothesis. 
To this end it can be adopted second generation panel unit root tests that rejects the 
cross-sectional independence include Phillips and Sul (2003) and Pesaran (2005), Bai 
and Ng (2004), Moon and Perron (2004). In this paper, we consider that the test defined 
in Pesaran (2005) can be helpful for small panels
2. 
 
Pesaran (2005) proposes the following ADF regression with the cross section averages 
of lagged levels and first differences of the data:  
 
it
p
j
j t ij t i
p
j
j t i ij t i i i i it u y y y y t c c y + + + + + + = ∑ ∑
=
− −
=
− −
0
1
1
, 1 , 0   η ϕ   γ β       (2) 
 
where  N y y
N
i it t /
1 ∑ = = . The t-ratio of βi is used as the test statistic for a unit root and it 
is called the cross-sectionally augmented ADF (CADF) statistic. Its critical values have 
been  generated  by  Monte  Carlo  and  are  tabulated  in  Pesaran  (2005).  The  results 
reported  are  the  ( , ) Z N T   version  in  which  is  normally  distributed  under  the  null 
hypothesis of the unit root defined as: 
 
1
1
1
( , ) ( ) iT
N
Z N T P
N
φ
− = ∑               (3) 
where  iT P  is the p-value corresponding to the unit root test of the 
th i  individual cross 
section unit. 
 
Table 2 shows the CADF statistics for UPE, USE and UTE series within our samples. 
The null hypothesis of a unit root cannot be rejected for the UPE and USE series with 
                                                 
2 Because Peseran’s method does not require the direct estimation of idiosyncratic components from the 
data it can be beneficial for small panels where estimation of factors is difficult (Moon and Peron, 2007).    5
all lag specifications. But we are able to reject the null hypothesis of a unit root in for 
UTE series with 0 and 1 lag specifications.  
 
    Table 2. Pesaran’s CADF test 
p  UPE  USE  UTE 
0  -0.161 
(0.436) 
-1.283 
(0.100) 
-2.696* 
(0.004) 
1  0.880 
(0.810) 
0.992 
(0.839) 
-1.545** 
(0.061) 
2  3.856 
(1.000) 
2.717 
(0.997) 
1.836 
(0.967) 
    Notes: p is average lags. p-values in brackets. * and ** indicates significant at the 1% and %10 
level respectively.  
 
Our results from CADF statistics are consistent with the HH for UPE and USE series. 
However, the empirical evidence not to favor the HH for UTE series for our sample. 
These results indicate that shocks have permanent effects on the unemployment with 
lower levels of educational attainment while the unemployment with higher level of 
educational attainment tends to revert to its equilibrium in the long run after a shock.  
 
IV. Conclusion 
 
We  have  applied  CADF  unit  root  tests  to  the  unemployment  rates  by  educational 
attainment in total unemployment for 17 OECD countries during the period 1980–2007 
with  unbalanced  panel  data.  After  controlling  for  educational  attainment  we  find 
significant differences between unemployment rates. More specifically we can conclude 
that  the  evidence  is  favorable  to  the  nonstationary  of  the  unemployment  rates  by 
primary and secondary education attainment in total unemployment, and therefore the 
existence of hysteresis, in these parts of unemployed labor force.  
 
But we also find that, there is no evidence of hysteresis for unemployment rates by 
tertiary  education  in  total  unemployment.  These  results  show  that  the  aggregate 
unemployment rate should be superior to tests for hysteresis. The results also point to 
the  importance  of  considering  some  degree  of  heterogeneity  with  educational 
differences in labour markets. 
   6
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   9
Appendix 
Country  Age  Data 
availability 
Australia  15+  1989-2007 
Austria  15+  1985-2007 
Belgium  15+  1994-2007 
Canada  15+  1980-2007 
Denmark  15-66  1994-2007 
Finland  15-77  1995-2007 
Germany  15+  1996-2007 
Italy  15+  1993-2007 
Japan  15+  1987-2007 
Netherlands  15-64  1995-2007 
New Zealand  15+  1990-2007 
Norway  16-74  1996-2007 
Spain  16+  1980-2007 
Sweden  16-64  1987-2007 
Switzerland  15+  1991-2007 
United 
Kingdom 
15-64  1987-2007 
United States  25+  1994-2007 
 
 

A PANIC attack on unit roots and cointegration”,

A simple panel unit root test in the presence of cross-section dependence, Cambridge Working Papers in Economics No.0346,

An empirical analysis of nonstationarity in a panel of interest rates with factors,

An empirical note on testing hysteresis in unemployment for ten European countries: panel SURADF approach,

Combination unit root tests for cross-sectionally correlated panels,

Cyclical dynamics in the new economy,

Does unemployment hysteresis equal employment hysteresis?,

Dynamic panel estimation and homogeneity testing under cross section dependence,

General diagnostic tests for cross section dependence in panels, CESifoWorking

Hysteresis unemployment: evidence from OECD countries,

Key indicators of the labour market (KILM), Fifth edition.

Nonlinear IV unit root tests in panels with cross-sectional dependence,

Skill levels and the cyclical variability of employment, hours,

Some Cautions on the Use of Panel Methods for Integrated Series of Macroeconomic Data&quot;,

Testing for hysteresis in unemployment in OECD countries: new evidence using stationarity panel tests with breaks,

Testing for PPP: Should we use Panel Methods?&quot;,

Testing for unit roots and persistence in OECD unemployment rates,

Testing for unit roots in heterogeneous panels,

The employment, earnings, and income of less skilled workers over the business cycle.”

The informational value of unemployment statistics: a note on the time series properties of participation rates,

The structure of unemployment,

Unemployment and nonemployment,

Unemployment hysteresis in EU countries: what do we really know about it?,

Unemployment hysteresis in the U.S. states and the EU: a panel approach,

Unemployment hysteresis in transition countries: evidence using stationarity panel tests with breaks,

Unit root tests in panel data: asymptotic and finite-sample properties, U. C. San Diego Discussion Paper 92–23

Hysteresis vs. natural rate of unemployment: One, the other, or both?

Hysteresis vs. natural rate of unemployment: One, the other, or both?

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