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Occasional Papers
No. 19
The inverse relationship between
inflation and unemployment
in Romania.
How strong was it after the crisis?
Occasional papers
No. 19
January 2016
NotE
The views expressed in this paper are those of the author and do not
necessarily reflect the views of the National Bank of Romania.
All rights reserved. Reproduction for educational and non-commercial
purposes is permitted provided that the source is acknowledged.
ISSN 1584-0867 (online)
ISSN 1584-0867 (e-Pub)
The inverse relationship between
inflation and unemployment
in Romania.
How strong was it after the crisis?
Ștefania Cristina Iordache
Mădălina Militaru
Mihaela Luiza Pandioniu
The authors thank Elena Iorga, Daniela Bordei and Răzvan Stanca for their suggestions and remarks formulated while the
paper was drafted.
Contents
Abstract7
1. Introduction
9
2. Literature review 11
3. “Triangle” model
14
4. Empirical results
18
4.1. Time-varying NAIRU
4.2. Goodness of fit
18
20
5. Inflation determinants
21
6. Structural unemployment rise
22
7. The stability of the relationship between inflation and unemployment
25
8. Conclusions
29
References32
Annex 1. The Kalman Filter Annex 2. The Extended Kalman Filter
Annex 3. Principle of Dynamic Contributions
35
36
38
Abstract
The inverse relationship between inflation and unemployment or the Phillips
curve, as it is known in the literature, is fundamental in explaining the price setting
mechanism. After the crisis, however, macroeconomic models grounded on this
theory consistently underestimated price developments, possibly due to an increase
in structural unemployment, but also to the weakening intensity of the relationship.
A similar phenomenon took place in Romania, as the financial and economic
crisis led to a significant loss in terms of jobs, while inflation remained relatively
elevated. Therefore, this paper aims to estimate a reduced form of the Phillips curve,
not only to test the validity and strength of the relationship, but also to give an
estimate of structural unemployment in Romania – which is why we opted for the
“triangle” model, underpinning the estimates of the natural rate of unemployment
of international institutions such as the OECD, the IMF and the EC. The chosen
specification, which includes the unemployment gap, supply-side shocks and
adaptive expectations proved suited for capturing the inflation trajectory in Romania.
Also, our estimates validate the empirical inverse relationship between inflation and
unemployment gap, and reveal an expansion in structural unemployment starting
2011, a trend highlighted by qualitative indicators as well – increase in long-term
unemployment and less efficient search&matching process. However, the intensity
of the relationship between inflation and unemployment might have also weakened,
given the relatively high downward wage rigidity, and the growing importance of
external developments in companies’ pricing policy on the domestic market due to
globalization. Thus, a simple empirical exercise of relaxing the assumption of linearity
in our model revealed the outline of a flattening trend of the Phillips curve starting
2007, but the approach is still preliminary, given the small sample size, covering only
one full business cycle.
Keywords: structural unemployment, NAIRU, Phillips curve, Kalman filter,
search&matching
JEL classification codes: J21, J23, J24, J50, J64
NATIONAL BANK OF ROMANIA
7
1.Introduction
The recent economic and financial crisis has brought to the fore, for both academics
and policy makers, the relationship between inflation and unemployment, in the
context of significant losses at the employment level, not accompanied by price
adjustments of the magnitude predicted by macroeconomic models grounded on
the Phillips curve theory. Possible explanations included, in addition to challenging
the relationship per se, the increase in structural unemployment (referred to in the
literature as the natural rate of unemployment or NAIRU – the level of unemployment
consistent with stable prices)1, and also the weakening intensity of the relationship
between inflation and unemployment after the crisis (WEO – IMF, 2013).
Given the significant losses on the Romanian labour market (dismissal of about
700 thousand employees) in the 2009 and 2010 recession years, during which the
annual inflation rate remained relatively high, and the subsequent emergence of
signals pointing to an increase in structural unemployment (expansion of long-term
unemployment, low efficiency in matching the right candidate with the right job),
this paper not only aims to test the validity and strength of the inverse relationship
postulated in theory between inflation and unemployment, but also to obtain an
assessment of structural unemployment. Therefore, our empirical approach started
from the estimation of a reduced-form Phillips curve, namely the “triangular” model
proposed by Gordon (1997), underpinning the NAIRU estimates made by international
institutions such as the OECD, the IMF and the EC. Estimating an alternative form of the
Phillips curve, less popular nowadays among central bankers, can provide additional
information in the process of evaluating the cyclical position of the economy, being
well known that the monetary policy impulse can only influence this component. In this
framework, an increase in aggregate demand could lead to an expansion in economic
activity and, hence, in the demand for labour, which can only be accommodated to
the extent that the available supply meets companies' requirements. Thus, structural
unemployment refers to the people who look for a job, but lack the adequate skills and,
therefore, do not really compete for the existing jobs. Therefore, these unemployed
people can influence neither the wage-setting, nor the price-setting process.
Estimating the Phillips curve remains, however, a challenge for most economists,
alternative solutions being proposed in the literature, depending on the treatment of
inflation expectations (adaptive, forward-looking or hybrid forms), the measurement
of the cyclical position of the economy (starting from unemployment, GDP or real
marginal cost), and the explicit inclusion of supply-side shocks (commodity or
import prices). The approach adopted in this paper, known as the “triangular” model,
includes, in addition to the deviation of unemployment from its natural rate (as a
measure of the deficit or excess demand in the economy), adaptive expectations
and supply-side shocks. Explicitly incorporating the latter in our model is of major
importance in the attempt to model inflation dynamics in Romania, given that,
in recent years, its trajectory has been profoundly marked by such shocks – large
increases (or decreases) of raw material prices (agricultural and oil) and fiscal
measures (changes in indirect taxes).
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In this paper, the terms “structural unemployment”, “natural rate of unemployment” and “NAIRU” are used interchangeably.
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Occasional Papers ■ No. 19
Our estimates empirically validate the inverse relationship between the
unemployment gap and the dynamics of the annual inflation rate in Romania and,
in addition, the model performs well in capturing inflation dynamics regardless of
the price index chosen (CPI, HICP and adjusted CORE2). Regarding the natural rate of
unemployment, our estimates indicate an increase starting 2011, a tendency revealed
by other economic indicators as well (long-term unemployment, the Beveridge
curve).
The analysis of labour market developments after the crisis outbreak shows that the
strong contraction in the economic activity (aggregate GDP decrease by almost 8
percent during 2009-2010) was initially reflected by higher short-term unemployment
(less than one year), which mostly affected the youth and people with a low education
level. The worrying fact about this evolution is that some of these people retained
the unemployed status even after the economy resumed positive growth rates.
This caused the share of long-term unemployment in active population to double
between 2010 and 2014, thus signalling an increase in structural unemployment.
The phenomenon, referred to as ”the hysteresis effect” in the literature, is driven
by the fact that the longer the period a person stays unemployed, the smaller
the chances to find a job, as a result of both the skill depreciation and the change
in companies’ requirements concerning the training of candidates. The latter is
particularly relevant in the context of the economy post-crisis repositioning on a more
competitive structure, oriented towards more technology-intensive sectors.
Similarly, the evolution of structural unemployment is reflected by the Beveridge
curve, which illustrates the relationship between labour demand, approximated by
the job vacancy rate, and the excess labour supply, shown by the unemployment
rate. The multiple outward shifts (simultaneous rise in the job vacancy rate and
unemployment), which took place in Romania starting 2011, indicate a more
pronounced inefficiency in the search&matching process and, consequently, a rise in
structural unemployment.
The increase in structural unemployment, revealed by both econometric estimates
and qualitative indicators (long-term unemployment, the Beveridge curve) could
provide an explanation for the reduced influence of the economic slack on price
dynamics in the post-crisis period in Romania. However, we cannot ignore that a
weakening of the relationship between inflation and unemployment might have
occurred during a period of weak labour market conditions – actually, a nonlinear
relationship between (wage) inflation and unemployment has been postulated
from the beginning by Phillips (1958), who observed that during periods of low
unemployment, its reduction causes a much broader increase in wages, as compared
to periods with high unemployment. In addition, frequent references are made in the
literature with regard to the flattening of the Phillips curve as a result of globalization,
implying an increased influence from external developments in the price-setting
process of internal agents. In this context, a simple empirical exercise of relaxing the
linearity assumption in the relationship between unemployment and inflation reflects
the shaping of a weakening trend starting 2007, in possible correlation with Romania’s
accession to the EU. However, one should bear in mind the reduced available dataset
that includes a recession and early recovery period, during which the relationship
10
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January 2016
weakening may have only been a temporary phenomenon caused by the presence
of downward nominal wage rigidity, which is higher in Romania as compared to EU
regional peers2. Therefore, a complete evaluation of the amending trade-off between
inflation and unemployment should be carried out over several economic cycles (data
for Romania cover a single business cycle).
The paper is divided into eight sections. Section 2 reviews the literature on the
influence of economic activity on inflation in the context of the Phillips curve,
with emphasis on developing the concept of structural unemployment. Section 3
describes the “triangular” model specification and the reasons for choosing this
model, while Section 4 presents the results and tests the model’s ability to capture the
dynamics of the annual inflation rate. Section 5 provides an economic interpretation
of the results, in terms of inflation determinants in the post-crisis period. Section 6
analyses the factors leading to an increase in structural unemployment in the
post-crisis period, whereas Section 7 evaluates the stability of the relationship
between inflation and unemployment in the same interval. The main conclusions are
formulated in Section 8.
2.Literature review
The relationship between inflation and unemployment is known in the literature as
the Phillips curve, after A.W. Phillips who, in 1958, observed a negative correlation
between unemployment and the rate of change of money wages in the UK. Thus, in a
Keynesian spirit, Phillips offered empirical evidence that wage inflation is determined
by the excess or deficit demand on the labour market. In essence, he noticed that,
when the unemployment rate is low (there is excess demand for labour), employers
are constrained to grant ample wage increases. When unemployment is high, wages
stagnate or even grow moderately, on the back of the occurrence of downward wage
rigidity. Therefore, Phillips not only noted an inverse relationship between inflation
and unemployment, but also showed that this relationship is nonlinear and depends
on the cyclical position of the economy.
Subsequently, economists’ interest in studying the Phillips curve grew when
Samuelson and Solow (1960) formulated the implications of this relationship from the
perspective of economic policies. They found that the Phillips curve is a useful tool in
formalizing the two existing theories at the time about the causes of inflation, namely
demand-pull and cost-push inflation. Thus, demand-pull inflation was associated
with movements along the curve, while cost-push inflation was seen as generating
shifts in the Phillips curve. Therefore, the idea of a trade-off between inflation and
unemployment is introduced for the first time, implying that monetary policy makers
can reduce inflation at the cost of higher unemployment and vice versa.
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Bulgaria, Hungary, Poland and Slovenia.
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Occasional Papers ■ No. 19
Friedman (1968) and Phelps (1967) criticised this theory, arguing that, in the
long-run, money is neutral and the unemployment tends to a natural rate,
determined by fundamental factors. In their argumentation, the two economists
turned to standard economic theory according to which firms base their employment
decisions on real wages. Moreover, they argue that price increases pass through to
unemployment and not vice versa, as shown empirically by Phillips. Assuming that
the economy is in equilibrium (W0, L0), an expansionary monetary policy will stimulate
an increase in aggregate demand and, thus, in prices of goods and services, leading
further to an expansion in the aggregate demand for labour (D curve shifts to the
right in Chart 1) and supporting job creation. However, at some point, workers realise
that price increases negatively affect their purchasing power and, therefore, demand
higher wages, which leads to a decrease in aggregate labour supply (S curve shifts to
the left). Under these circumstances, equilibrium is restored at a higher nominal wage,
but at unchanged levels of output, employment and real wage. Therefore, although
in the short-run monetary policy can stimulate economic activity, in the long-run the
economy returns to equilibrium and unemployment to its natural rate.
Chart 2. Trade-off between inflation
and unemployment
Chart 1. Effects of monetary policy
on the labour market according
to Friedman and Phelps
W
W0
S
Inflation Rate
W1
D
Rata inflației
W2
Long-term
S1
D1
Short-term
L0
L1
Source: History and Theory of the NAIRU: A Critical Review,
Espinosa-Vega, Russell, 1997
L
NAIRU
Unemployment Rate
Source: History and Theory of the NAIRU: A Critical Review,
Espinosa-Vega, Russell, 1997
Following this criticism, the theory on the relationship between inflation and
unemployment has been reconsidered, a consensus being reached in the literature
regarding the existence of a level of unemployment consistent with the concept
of price stability – NAIRU (Chart 2). In the short-run, unemployment can fall
below NAIRU, generating inflationary pressures or it can increase above this level,
determining a reduction in inflation. However, in the long-run the unemployment
rate gets closer to NAIRU, thus being consistent with the idea of a natural rate of
unemployment.
A bit later, Lucas (1972, 1973) developed the model proposed by Friedman and
Phelps, incorporating the hypothesis of rational expectations according to which
economic agents learn from past experience and anticipate future developments,
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January 2016
thus avoiding the repetition of past mistakes. In this framework, Lucas points to
a reduced efficiency of predictable monetary policy, including in the short-run, a
hypothesis that was subsequently invalidated by Gordon (1982) and Mishkin (1982)
who showed that anticipated changes in the monetary policy stance may have (quite
significant) effects on economic activity in the short term.
Since the ’70s, following the large supply-side shocks that were strongly felt by both
inflation and unemployment, a positive correlation emerged between inflation and
unemployment in the US which seemed to indicate that the short-run trade-off
between the two indicators no longer exists. In this context, the Phillips curve was
called into question once again.
From this moment on, two schools of thinking on the nature of the relationship
between inflation and unemployment emerged in the literature. On the one hand,
Gordon (1975) proposed a triangular model, as described in equation (1), in which
inflation
depends on its lags and both on demand-side
and supply-side
The introduction of inflation lags reflects the assumption of adaptive
factors
expectations of economic agents and price inertia in general, due to the implicit and
explicit contracts between agents and the lagged transmission of shocks along the
production chain.
.
(1)
On the other hand, the New Keynesian Phillips Curve (NKPC), shown in equation (2), is
based on the concepts developed by Kydland and Prescott (1977) and Sargent (1982)
and assumes that inflation is determined by demand-side factors and forward-looking
expectations that respond automatically to current and future changes in fiscal and
monetary policy stance. Thus, the credibility of policy makers is very important to New
Keynesians because it influences the trade-off between inflation and unemployment
by anchoring inflation expectations. Since NKPC models failed to simulate the
inflation inertia often observed in practice, Gali and Gertler (1999) proposed a hybrid
version of the Phillips curve that takes into account the fact that agents’ expectations
are not purely forward-looking, they also include a backward-looking component.
.
(2)
In recent years, multiple versions of the Phillips curve were developed in the literature,
which differ by the measure chosen to quantify the influence of economic activity on
prices, by the type of economic agents’ expectations and by the selection of relevant
indicators to assess the impact of supply-side shocks. Thus, Gordon (2013) recently
proposed a specification of the triangular model with backward-looking expectations,
short-term unemployment gap (proxy for aggregate demand deficit) and a number of
variables such as import prices, the growth trend in productivity and the variation of
food and energy prices to incorporate supply-side shocks. Halka and Kotlowski (2013)
estimate a Phillips curve for the prices of each CPI basket component, also using a
model with backward-looking expectations, but which includes the output gap as a
factor to illustrate the influence of demand and the nominal effective exchange rate,
foreign inflation, and food and oil prices to capture the supply influence. Moreover,
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Occasional Papers ■ No. 19
Baxa, Plasil and Vasicek (2013) and more recently the ECB (2014, 2015) estimate
various forms of the Phillips curve, testing a variety of relevant indicators to assess
the influence of demand on inflation, including the output gap, the unemployment
gap (including short-term), the real unit labour cost and survey data regarding the
production capacity utilisation and factors limiting production in terms of labour
shortages. All these studies show, however, that the Phillips curve estimation is
sensitive to model specification, so that the size of the impact of economic activity
on inflation varies depending on the measure used to quantify the demand deficit.
Moreover, none of the measures performs systematically better in capturing price
developments in the economy. Under these circumstances, it seems that there is no
unique concept of the Phillips curve at present; therefore, using a single specification
in grounding monetary policy decisions proves insufficient, a more comprehensive
analysis being necessary, ECB (2014, 2015).
Also, more recently, the debate on the existence of nonlinearities in the Phillips curve
was brought forward. The relative price stability that characterised most economies
in the aftermath of the international financial crisis, a period marked by a severe
contraction in economic activity, has led many economists to postulate a weakening of
the negative relationship between inflation and unemployment. In this regard, recent
empirical evidence (whose detailed presentation is provided in Section 7) indicates
that the relationship between the two economic indicators seems, indeed, nonlinear,
the slope of the Phillips curve changing in line with the characteristics of the economy,
under the influence of wage rigidity, monetary policy stance or even globalization.
3. “Triangle” model
As one of the objectives of this paper is to obtain an evaluation regarding the level
and evolution of structural unemployment after the outbreak of the economic crisis,
our empirical approach starts from a reduced-form of the Phillips curve, based on
the triangle model of inflation described by Gordon (1997). The model underpins
the structural unemployment estimates made by international institutions such as
the OECD, the IMF and the EC. In addition, the particularities of this model, namely
the adaptive expectations assumption and the explicit incorporation of supply-side
shocks, make it suitable to describe inflation dynamics in Romania.
Thus, despite the recent change in the process of inflation expectation’s formation,
meaning that a forward looking component is also taken into account, the
expectations of economic agents in Romania remain, however, rather adaptive
(Bojeșteanu, Manu and Stanca, 2011; Iordache and Pandioniu, 2015). Furthermore,
as shown in Chart 3, in recent years, inflation dynamics was marked by numerous
and wide supply-side shocks, such as (i) VAT rate changes (2010 and 2013); (ii) the
agricultural production (below the long-term average in 2010, 2012 and exceptional
in 2013, 2014) led to sizeable price changes (especially in the case of a negative
shock), as food items account for about one third of the household consumption
basket; (iii) international oil price fluctuations.
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January 2016
Chart 3. Inflation dynamics
10
8
percent
2009 Q1:
- leu depreciation
2010 H2:
- ↑VAT rate
- ↑food item prices, oil price
2011 H1:
- ↑domestic and non-domestic agri-food prices
- ↑oil price
2012 H2 – 2013 Q1:
- agricultural production < long-term average
6
multi -annual flat inflation target:
2.5% ±1 pp
4
2
0
2013 H2 – 2014 H1:
- agricultural production > long-term average
- ↓VAT rate on bread and bakery products
-2
-4
Dec.
2008
Dec.
2009
Dec.
2010
Dec.
2011
Dec.
2012
2014 H2:
- record high agricultural production
- ↓oil price
Dec.
2013
Dec.
2014
Source: NIS, NBR calculations
In this context, the model estimated in this paper quantifies the impact of three
factors on inflation rate dynamics in Romania, namely cyclical unemployment
(as a proxy for the deficit/excess demand in economy, defined as the difference
between the unemployment rate and NAIRU), commodity prices (oil and agricultural
commodity prices), import prices (in order to capture the supply's influence) and
inflation inertia, derived from assumptions of adaptive expectations and price
rigidities in general. The model also includes dummy variables in order to isolate the
impact of VAT rate changes from 2010 and 2013.
The structural rate of unemployment or NAIRU is the unemployment rate that
ensures a stable level of inflation, representing an unobservable variable that might
change over time. An unemployment rate below NAIRU points to inflationary
pressures generated by excess demand, while an unemployment rate above NAIRU
shows a demand deficit causing disinflationary pressures. The evolution of the
NAIRU is assumed to be smooth, this hypothesis being supported, in theory, by the
fundamental nature of the factors that might influence the NAIRU, in association with
the labour supply and demand mismatch generated by structural changes in the
economy (i.e. demographic or technological). In this respect, the explicit inclusion of
supply-side shocks plays a key role, as, due to their incorporation, the model is able
to better capture price dynamics in the economy and, at the same time, ensures a
less volatile path for NAIRU, which corresponds to the concept of the natural rate of
unemployment.
The model was estimated based on the Kalman filter, which the literature considered
to be the appropriate tool in this case. The econometric technique assumes the
existence of two equations: the measurement equation, i.e. relation (3), specifying
the inflation dynamics based on the previously-described factors, and the transition
equation, i.e. relation (4), which captures the evolution of NAIRU (the state variable)
using a random walk process. The volatility of NAIRU is assumed a priori to be lower
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Occasional Papers ■ No. 19
than that of inflation, by imposing a restriction known as the signal-to-noise ratio
Gordon (1997), OCDE, CE3.
.
(3)
(4)
– change in the annual inflation rate (adjusted CORE2, CPI, HICP);
– cyclical unemployment (unemployment gap);
– natural rate of unemployment (NAIRU or structural unemployment);
– supply-side shocks;
and
Data description
Unemployment rate
. In Romania, there are two available indicators that measure
unemployment: the unemployment rate calculated based on the International Labour
Organization (ILO) methodology and the registered unemployment rate determined
by the National Employment Agency (NEA). The difference between the two rates is
that the first measure includes the unemployed persons who actively sought a job
in the past four weeks, whereas the second takes into account only those people
registered in the NEA records.
Chart 4. Unemployment rate in Romania
10
percent, seasonally adjusted data
8
6
4
2
0
registered unemployment rate
ILO unemployment rate
2004
Q1
2006
Q1
2008
Q1
2010
Q1
2012
Q1
2014
Q1
The analysis of the two indicators
reveals slightly different paths.
As shown in Chart 4, in 2010-2011,
the ILO unemployment rate remained
relatively stable, while the registered
unemployment rate dropped sharply
by about 3 percentage points.
This was due to a change in labour
market legislation, which became
effective as of 2011 and provided for
unemployment benefits to be cut off
in case of jobseekers’ refusal of a job
offer consistent with their training or
education.
Source: NIS, NEA, authors' calculations
Therefore, these people were removed
from the NEA records, as they no longer
renewed their registration under non-claimant unemployed, thereby bringing the
3
16
For further details on the estimation technique, see Annex 1.
NATIONAL BANK OF ROMANIA
January 2016
unemployment rate down, without an improvement in the situation of these persons.
Given that the ILO unemployment rate seems to better reflect real labour market
developments, in order to estimate the structural unemployment rate, we used this
measure, thus also ensuring comparability with estimates for other countries.
Inflation rate
. In this paper, three different measures of inflation were used,
depending on the chosen price index (Chart 5). The first, namely the annual growth
rate of adjusted CORE2 has the advantage that exogenous components (volatile
prices, administrated prices, and also the prices heavily influenced by the excise
policy) are excluded, thus better reflecting the influence of demand factors.
Our analysis also includes the annual CPI inflation rate, which is the current
benchmark in the formulation of the monetary policy objective. The third measure
relates to the annual change of the Harmonised Index of Consumer Prices (HICP),
which is relevant in light of the assessment of the Romanian economy convergence,
ensuring further comparability of our results to the estimates of various international
institutions.
Chart 5. Inflation rates
14
Chart 6. Commodity and import prices
real annual percentage change*
annual percentage change
adjusted CORE2
HICP
CPI
12
10
120
80
40
8
0
6
-40
4
2
2004
Q1
0
-2
2004
Q1
2006
Q1
2008
Q1
Source: NIS, Eurostat, NBR
2010
Q1
2012
Q1
2014
Q1
2006
2008
2010
2012
2014
Q1
Q1
Q1
Q1
Q1
crude oil price
wheat price
import prices (excl. wheat and crude oil)
-80
*) deflated by CPI
Source: Bloomberg, Eurostat, authors' calculations
Supply-side shocks
. The real annual growth rate of oil and wheat prices (proxy
for energy and agricultural commodity prices), expressed in lei, and the real annual
growth rate of import prices, excluding the two above-mentioned components
(Chart 6) were chosen as representative explanatory factors for inflation dynamics.
Estimates were made using quarterly data, covering the 2004 Q1 – 2014 Q2 period.
NATIONAL BANK OF ROMANIA
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Occasional Papers ■ No. 19
4.Empirical results
Regardless of the measure chosen to describe the evolution of consumer prices
(CPI, HICP or adjusted CORE2), our empirical results validate the inverse relationship
between the unemployment gap and the annual inflation rate dynamics in Romania
(Table 1). The negative value estimated for the coefficient of unemployment deviation
from its natural rate is statistically significant, at around -0.7.
Table 1. Model estimates
KF(adjusted CORE)
∆πt-i
ΔInflation ratet-1
-0.39***
-0.37***
-0.66***
Unemployment gap
-0.75***
-0.7***
ΔWheat pricet
0.02***
0.01***
0.01***
0.01***
0.01***
ΔOil pricet
ΔOil pricet-2
∆zt
KF (HICP)
-0.26***
ΔInflation ratet-4
ut - u*t
KF (CPI)
∆πt
Dependent variable
0.01***
-0.04***
ΔImport pricest
0.06***
ΔImport pricest-2
0.06***
dummy 2010 Q3
1.66***
3.14***
3.13***
dummy 2011 Q3
-1.16***
-2.43***
-2.51***
dummy 2013 Q4
-1.27***
0.16***
0.16***
Signal-to-noise ratio
0.16***
2004 Q1 – 2014 Q2
Estimation period
Log Likelihood
-26.10***
-43.08***
-53.36***
Note: *, ** and *** significance level at 99%, 95%, 90%.
Source: Authors' estimates
Moreover, the model reveals a relatively pronounced impact of supply-side factors on
CPI dynamics, as a 10 percent increase in agri-food and oil commodity prices would
entail, caeteris paribus, a rise in the annual inflation rate by 0.1-0.2 percentage points
in the first quarter since the shock occurred. The transmission is slower for the other
import prices.
4.1.Time-varying NAIRU
The estimates for structural unemployment, based on the three models, indicate close
values and similar paths for NAIRU, which reflects the robustness of the results
(Chart 7). Moreover, robustness is also confirmed by the immaterial change in
coefficients obtained when using different specifications (Table 1).
The analysis of the natural rate of unemployment reveals a relatively stable
path at around 6.0 percent during 2005-20104. Subsequently, the natural rate of
unemployment followed an upward trend, reaching approximately 6.5 percent in
2014 Q2. Under these circumstances, the post-crisis demand deficit in the Romanian
4
18
Chart 7 shows smoothed NAIRU estimates, as they use the full information set (for further details, see Annex 1).
NATIONAL BANK OF ROMANIA
January 2016
economy, reflected by the unemployment rate rising above NAIRU, fostered
disinflation, despite its visible narrowing towards the end of the period.
Chart 7. NAIRU estimates and unemployment gap
8
percent
percentage points
8
4
4
0
0
unemployment gap (adjusted CORE2, rhs)
NAIRU (CPI)
NAIRU (HICP)
-4
2005
Q1
2006
Q1
2007
Q1
2008
Q1
2009
Q1
ILO unemployment rate
NAIRU (adjusted CORE2)
2010
Q1
2011
Q1
2012
Q1
2013
Q1
-4
2014
Q1
Source: NIS, Eurostat, NBR, authors' estimates
Chart 8. Hysteresis effect
8
Chart 9. Structural unemployment
percent
percent
6
10
8
4
6
Euro area
Romania*
2
0
2008
Q1
unemployment rate
short-term unemployment rate
long-term unemployment rate
2009
Q1
2010
Q1
2011
Q1
Source: Eurostat, authors' calculations
2012
Q1
2013
Q1
2014
Q1
2008
Q1
2009
Q1
2010
Q1
2011
Q1
2012
Q1
2013
Q1
2014
Q1
4
*) based on HICP
Source: Eurostat, OECD, authors' estimates and calculations
This trend is similar to that of long-run unemployment (Chart 8), which points to a
hysteresis effect. A possible explanation of this phenomenon lies with the gradual
depreciation of human capital while becoming detached from the labour market, with
opportunities to find jobs growing smaller over time (Ball, 2009). Moreover, there is
empirical evidence indicating that this hysteresis effect was also seen at the European
level, being amplified by strong economic activity contraction in construction sector,
as these employees’ skills cannot easily be applied or transferred to another sector
(Chart 9). In this regard, the available estimates from international institutions such as
the OECD, the IMF and the EC show an increase in structural unemployment by
1 percentage point in the euro area on average during 2008-2013, mostly as a result
of the widening skill mismatch between labour demand and supply.
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Occasional Papers ■ No. 19
The Beveridge curve – a complementary approach
on structural unemployment
An alternative in the analysis of structural unemployment is provided by the Beveridge
curve, which captures the relationship between labour demand, approximated by
the job vacancy rate, and the available supply, illustrated by the unemployment rate.
Movements along the curve generally reflect business cycle developments, when the
unemployment rate and the number of vacancies post opposite developments, while
shifts in the curve’s slope may be generated by structural factors. Specifically, more
efficient search&matching process brings an inward change of the Beveridge curve,
while the simultaneous rise of the two indicators signals a more inefficient process,
reflected by an outward shift of the Beveridge curve.
Chart 10. The Beveridge curve
2.0
The path of the Beveridge curve
in Romania (Chart 10) reflects the
recession between 2009 and 2010,
a period when unemployment rate
increased, concurrently with the fall in
the job vacancy rate.
recession (2008 Q4 – 2010 Q3)
recovery (2010 Q4 – 2014 Q4)
1.6
1.2
0.8
0.4
5.5
6.5
Ox: Unemployment rate, s.a. (%)
Oy: Job vacancy rate, s.a. (%)
Source: Eurostat, authors' estimates and calculations
7.5
However, starting 2011, the outward
shifts have been indicative of a
more inefficient search&matching
process and, as a result, of a rise
in structural unemployment.
Accordingly, at the same job vacancy
rate, the unemployment rate was
0.3 percentage points higher, on
average, in 2014 than in 2009.
Nevertheless, estimates of the structural unemployment level by means of the
Beveridge curve remain a topic for future research, at least for the time being, given
the small data sample concerning the job vacancy rate (available as of 2008).
4.2.Goodness of fit
In order to evaluate goodness of fit, Gordon (1997, 2013) reviews the actual price
dynamics in relation to those simulated based on the model. A similar exercise was
also made in this paper. Chart 11 shows both inflation rate actual developments, on
the basis of the three price indices chosen, and the evolution generated by dynamic
simulations resulting from the triangle model5. The differences between the actual
change in the annual inflation rate and the simulated ones for CPI, HICP or adjusted
CORE2 are insignificant.
5
20
The method is described in Annex 3.
NATIONAL BANK OF ROMANIA
January 2016
Furthermore, besides the visual analysis confirming models’ ability to capture effective
inflation patterns, the root mean-square error per sample proves a relatively high
performance of the three models (about 0.5 points) and it gets close to the results
obtained from similar studies (Gordon, 2013).
Chart 11. Annual inflation rate simulations
5
Adjusted CORE2
HICP
CPI
percentage points
5
percentage points
percentage points
5
effective
2014 Q1
2013 Q1
2012 Q1
2011 Q1
2010 Q1
2009 Q1
2013 Q3
2012 Q3
2011 Q3
2010 Q3
2009 Q3
2014 Q1
-5
2013 Q1
-5
2012 Q1
-5
2011 Q1
0
2010 Q1
0
2009 Q1
0
simulated
Source: NIS, Eurostat, NBR, authors' estimates and calculations
Table 2. Root mean squared error
CPI
HICP
Adjusted CORE2
0.56
0.58
0.44
Source: Authors' estimates
5.Inflation determinants
Despite the volatility induced by the occurrence of supply-side shocks deriving from
commodity prices (oil and agricultural commodity prices), along with those related to
fiscal measures (mainly changes in the VAT rate, such as the rise in the standard VAT
rate from 19 percent to 24 percent in July 2010, followed by a decrease to 9 percent
in the VAT rate for bread and some bakery products in September 2013), inflation
decreased gradually between 2009 Q1 – 2014 Q2.
Chart 12 illustrates the contributions of determinants to the annual CPI inflation
rate change, computed based on dynamic simulations. Thus, it appears that the
post-crisis demand deficit in the Romanian economy, indicated by the positive
unemployment gap (unemployment rate above the NAIRU), supported disinflation,
although in the latter part of the reviewed period its contribution decreased, as a
result of the improved cyclical position of the economy and the increase in structural
unemployment.
NATIONAL BANK OF ROMANIA
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Occasional Papers ■ No. 19
The disinflation process was temporarily interrupted during 2010 H2 – 2011 H1
and 2012 H2-2013 H1. Apart from the direct effect of the increase in the VAT rate
(whose contribution of about 2.4 percentage points to the annual rate movements in
2010 Q3 have subsequently dissipated), these episodes are triggered by significant
contractions in agricultural production (at global level as well) that led to significant
increases in consumer prices, as food items make up about one third of the household
consumption basket. These shocks were immediately and strongly felt by volatile
prices (vegetables, fruit), while the transmission in the case of processed food items
was gradual. In addition, international oil price hikes in the second half of 2010
stemming from higher global demand, especially from emerging economies, fed
through directly, rapidly and strongly to fuel price inflation in Romania, and indirectly,
but more slowly and with a lower impact on production costs, including on economic
operators’ transportation costs. Either way, the transmission of an increase in raw
material prices along the production chain is faster and wider in the case of an
adverse shock, Militaru (2014), Inflation Report (February 2015).
Chart 12. Contributions to the annual CPI inflation rate
5.0
percentage points
percentage points
5.0
2.5
2.5
0.0
0.0
-2.5
-2.5
-5.0
2009
Q1 Q2
Q3
2010
Q4 Q1 Q2
Q3
2011
Q4 Q1 Q2
Q3
2012
Q4 Q1 Q2
VAT direct effect
wheat price
unemployment gap
∆ CPI inflation rate (rhs)
Q3
2013
Q4 Q1 Q2
Q3
2014
Q4 Q1 Q2
-5.0
import prices (excl. oil and wheat prices)
oil price
other factors*
*) supply-side factors, difficult to model - VFE, administered prices
Source: NIS, Bloomberg, authors' estimates and calculations
Moreover, an important contribution to consumer price dynamics is attributable
to import prices (excluding crude oil and agricultural commodity prices). If the leu
depreciation put pressure on domestic prices at the onset of the crisis, the demand
deficit in the European economy, enhanced by the outbreak of the sovereign debt
crisis, subsequently resulted in low euro area inflation levels that were partly visible in
Romania by means of import prices.
6.Structural unemployment rise
The rise in structural unemployment in Romania starting 2011, also revealed by
the increase in long-run unemployment, probably reflects the hysteresis effect, the
precise mechanism whereby it manifests in the economy remaining yet unclear in the
22
NATIONAL BANK OF ROMANIA
January 2016
dedicated literature. The most frequently used explanation lies with the depreciation
of human capital, insofar as workers remain detached from the labour market for a
longer time period, making them less attractive for employers
(Ball, 2009).
In addition, the growth of structural unemployment is also revealed by shifts in the
Beveridge curve, which shows the worsening correlation between labour demand
and supply (skill mismatch). At the same time, the economy’s capacity to create
job openings diminished after the crisis, due to restrictive labour market institutions,
such as minimum wage policy or collective bargaining agreement coverage, and
to the economic activity reorientation towards more technology-intensive
industries. Furthermore, a significant role was played by demographic factors, as
migration or ageing population for instance lower the chances to find appropriately
skilled candidates.
Skill mismatch
The loss of a large number of jobs (around 700 thousand) between 2009 and 2011
was concentrated in sectors with the largest shares of unskilled workers on their
payrolls, namely in industry (about a half ) and construction. Afterwards, job recovery
was slow, only half of the loss being regained by the end of 2014, as the process was
hampered by the high mismatch between the skills required by companies and those
offered by potential candidates. This was due, on the one hand, to the low level of
transferability of workers’ skills from
Chart 13. Obstacles to hiring – shortage
construction, whose activity shrank
of skilled workers
after the crisis, to other sectors, and, on
the other hand, to the development of
percentage of firms
100
more competitive sectors such as the
automotive industry and IT&C services,
75
where specific skills are required.
50
Therefore, demand for skilled workers
25
(programmers, engineers) in these
0
two sectors increased, in a context of
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
increased difficulties in finding qualified
11 - construction
1 - beverages
12 - electrical equipment and
2 - transport and storage
staff (Chart 13)6.
3 - crude oil processing
4 - printing and recording
5 - non-metallic mineral products
6 - other manufacturing
7 - chemical and pharmaceutical
industry
8 - wood
9 - -publishing
10 trade
electronic products
13 - IT and communications
14 - automotive industry
15 - food industry
16 - accommodation and food
services
17 - light industry
18 - metallurgy
Moreover, this discrepancy is also visible
in the case of people with medium
educational level. Thus, companies in
food and light industries (accounting for
Source: NBR survey on the firms' behaviour on the labour market
around a quarter of the manufacturing
sector), as well as in accommodation and food services encountered more
pronounced difficulties in finding appropriately skilled workers.
6
NATIONAL BANK OF ROMANIA
Data on firms’ behaviour on the labour market are based on a survey conducted by the NBR in 2014, whose results were
presented in ”Tendinţe comportamentale pe piaţa muncii – o perspectivă microeconomică” (Behavioural Trends on the Labour
Market. A Microeconomic Perspective) at the 8th edition of the Monetary Policy Colloquia, Iordache, Militaru, Pandioniu (2015).
23
Occasional Papers ■ No. 19
Overall, more than half of private companies in Romania consider that one of the
major obstacles in hiring is the lack of qualified personnel.
At the same time, major deficiencies are identified from the perspective of the
educational system's ability to generate appropriately skilled staff. On the one hand,
international tests place the general level of knowledge of the Romanian students
significantly below the OECD average, the result being attributed to an elevated
school dropout rate and the lower preference of graduates for a career in education7,
which further leads to a shortage of teachers in disadvantaged socio-economic
environments. On the other hand, the preference of the Romanian students for the
social science background (more than half, 18 percentage points above the EU-27
average) to the detriment of exact sciences and the severely contracting vocational
education signal the need for active policies to guide the youth towards areas of
interest to the economy (Chart 14). Therefore, the difficulty of young people to get a
job is reflected by the relatively high unemployment rate in this age group, regardless
of the level of training (Chart 15).
Chart 14. Higher education graduates
Chart 15. Youth unemployment rate
percent
Education
60%
Services
40%
Humanities
and arts
20%
Health
and
welfare
30
20
Social
science,
business
and law
0%
10
Science
Agriculture
Engineering,
manufacturing
and construction
Source: Unesco
Romania
EU-27
unemployment rate (15-24 years)
2008
Q1
2009
Q1
2010
Q1
2011
Q1
2012
Q1
2013
Q1
2014
Q1
0
Source: Eurostat
Labour market institutions
A role in inhibiting job creation had institutional features, such as labour taxation,
minimum wage policy or the existence of collective wage agreements. Thus,
according to the labour market survey conducted by the NBR in 2014, 75 percent of
private companies consider high labour taxation as the most important obstacle to
hiring. Moreover, future hiring is limited by the accelerated growth of gross minimum
wage in the last two years, given that productivity gains in recent years were lower
than the growth rate of wages (NBR, Inflation Report, May 2015). Another source of
pressure on recruitment is the relatively high degree of real wage rigidities
7
24
Only 2 percent of the students chose a teaching career as compared to 10 percent at EU level.
NATIONAL BANK OF ROMANIA
January 2016
(about 32 percent of companies stated they indexed wages to inflation between
2010 and 2013) considering the widespread use of collective labour contracts – in
Romania, approximately 60 percent of private firms apply such an agreement,
which represents, in fact, the main cause of real wage rigidity (Iordache, Militaru and
Pandioniu, 2015).
Demographic factors
In addition to the above-mentioned qualitative labour supply factors, the mismatch
between the required skills and those of potential employees is also the result of
demographic changes. In this respect, a negative contribution is made by ageing
population, as the share of people aged under 40 in the total population continued to
decline in recent years (from more than half before 2008 to about 45 percent in 2014),
coupled with a drop in the birth rate.
7. The stability of the relationship
between inflation
and unemployment
Based on Phillips’ observation of a negative correlation between inflation and
unemployment, empirical studies have shown that the relationship between the two
indicators is nonlinear, its intensity varying under the influence of changes in the
economic environment. This feature of the Phillips curve was brought to the attention
of economists in the aftermath of the crisis, in the context of significant losses in
terms of jobs, accompanied by persisting positive inflation rates. On the one hand,
the hypothesis of a reduction in the trade-off between inflation and unemployment
is supported by the fact that companies pricing policy depends, to a larger extent,
to external factors since the intensification of globalization. On the other hand, this
weakening is likely to be only temporary, occurring in periods of high unemployment,
as a consequence of wage rigidity.
Given the relatively high wage rigidity observed in Romania, the national economy
may have been characterised by a temporary flattening of the Phillips curve in the
post-crisis period, that was marked by the difficult conditions on the labour market.
Also, we cannot ignore the effects of the increasing exposure of economic agents to
external developments in the context of EU integration and globalization, with the
potential to generate structural changes in the relationship between inflation and
unemployment.
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Occasional Papers ■ No. 19
How is globalization reflected by domestic price dynamics?
The influence of globalization on the slope of the Phillips curve was shown empirically
by the IMF in a 2006 study that points to increasing responsiveness of domestic prices
to external fluctuations, given that the increasing openness of the economy enhances
the impact of import prices. Benigno and Faia (2010) obtain similar results, showing
that the rise in the share of imported goods in the consumption basket automatically
generates an increase in the magnitude of exchange rate pass-through to domestic
prices. Going beyond the influence of import prices, Borio and Filardo (2007) show
that the Phillips curve in developed economies (including the eurozone) improves
considerably its ability to depict
Chart 16. Imports of consumer goods (CG) inflation developments by simply
and domestic demand
introducing a proxy for global output
gap in its estimation.
220
percent
index, 2004=100
20
15
180
10
140
5
0
the ratio of the value of CG imports
to household consumption (rhs)
volume of CG imports
volume of retail trade (excl. auto)
Source: NIS, Eurostat, authors' calculations
2014
2013
2012
2011
2010
2009
2008
2007
2006
2005
2004
100
Although the openness of the
Romanian economy remains below
the EU average, the international
trade increased in recent years, so that
the openness of the economy was
10 percent higher in 2014 than in 2007,
reaching about 82 percent. At the same
time, the share of imported goods
in final consumption is going up, as
revealed by the faster dynamics of
consumer goods imports as compared
to domestic trade developments
(Chart 16).
The importance of external factors influencing domestic price-setting behaviour in
Romania is supported empirically by recent studies and analyses that reveal a full and
rapid transmission of movements in international prices of important raw materials
(oil and agricultural commodity prices) to domestic prices, (Militaru, 2014; NBR
Inflation Report, February 2015), the costs of raw materials being the most important
factor in firms’ decision to change prices (Iordache and Pandioniu, 2015). While in the
case of oil prices, an explanation lies with Romania's status as a net importer of crude
oil, in the case of agricultural commodities, where Romania is a net exporter, the final
price is imposed by international traders to a large number of domestic farmers. This
last element is particularly relevant, given that food items hold about one third of
the consumption basket and that the transmission of raw material costs in the prices
of processed food items is nonlinear, larger and faster in the case of adverse shocks,
(Militaru, 2014).
The idea of different exposures of goods to external influences was studied by
Chmielewski and Kot (2006) who, on the basis of a simple empirical exercise for
the Polish economy, showed that, even though the relationship between core
inflation and the measure of aggregate demand deficit appears to be not statistically
significant at first, the exclusion from the consumption basket of some tradable
26
NATIONAL BANK OF ROMANIA
January 2016
goods, strongly correlated with external prices, lead to a significant strengthening
of the relationship. Subsequently, Halka and Kotlowski (2013) develop the idea and
identify a number of products included in the consumption basket that react more to
external developments rather than to domestic economic conditions. These products
include food items, such as milk and dairy products and vegetable oils affected by the
Common Agricultural Policy, and a number of durables (household appliances and
cars) and semi-durables (wearing apparel, footwear and books, among others).
Another transmission channel of the effects of globalization refers to the impact of
increased competition on changes in the structure of the economy. In this regard,
a recent paper by Guilloux – NeFussi (2015) points out that, in the context of
globalization, stronger competition on the domestic market favours a concentration
of the distribution of companies around the largest and most productive ones, that
tend to be more rigid in changing prices and transfer with a lag and to a lower extent
the marginal cost increases in the final price to defend their market share. Referring to
Romania, almost all companies experienced a strong competition and large firms are
indeed more rigid in changing prices (Iordache, Pandioniu, 2014). However, isolating
the external influences in this process remains an open research topic.
The role of wage rigidity
Another important factor which may cause a change in the slope of the Phillips
curve is related to wage rigidities. Thus, as shown by Phillips (1958), if nominal wages
display downward rigidity, the relationship between inflation and unemployment is
nonlinear – prices rise slowly and moderately during periods of demand deficit, but
increase rapidly and broadly in times of a demand surplus. In this regard, Meier (2010)
and Yellen (2012) state that the weakening of the relationship between inflation
and unemployment at the onset of the crisis may be caused by downward nominal
wage rigidity, which prevented a decline in real unit labour costs in an economic
environment characterised by low inflation and increased significantly the likelihood
of labour market adjustments through the reduction in the number of employees.
In Romania, the number of employees fell by over 10 percent in the aftermath of the
crisis, while the gross average wage in the economy, both nominal and real, remained
on an upward trend, except for the 25 percent cut in public sector wages in 2010,
following the implementation of a series of fiscal measures needed to balance the
state budget (Charts 17 and 18).
Beyond the manifestation of composition effects on average wages, given that the
layoffs during the crisis were concentrated in economic sectors with a higher share
of low-skilled employees and hence lower wages, a recent microeconomic analysis
reveals that downward nominal wage rigidity played a major role in explaining
labour market developments (Iordache, Militaru and Pandioniu, 2015). According to
the aforementioned analysis, the role of wage rigidity is especially important given
that during 2010-2013, 18 percent of companies have frozen base wages (proxy for
downward nominal wage rigidity), which led to an increased likelihood of a company
to reduce the number of employees by individual and/or collective layoffs.
NATIONAL BANK OF ROMANIA
27
Occasional Papers ■ No. 19
Chart 17. Number of employees
5,000
Chart 18. Domestic gross average wages
index, 2005 Q1=100, s.a.
thousand persons, s.a.
private - nominal
private - real
public - nominal
public - real
300
250
4,600
200
4,200
150
2014
2013
2012
2014
2013
2012
2011
2010
2009
2008
2007
2005
Source: NIS, authors' calculations
2011
2010
2009
2008
2007
2006
2005
3,400
2006
100
3,800
Note: The proxy for public sector wages was
determined by aggregating wages in public
administration, education, healthcare and
recreational activities.
Source: NIS, authors' calculations
Empirical exercise
As we have seen in Section 5, the contribution of economic slack to inflation in
Romania decreased in recent years, not only due to the decrease in unemployment,
but also to the increasing structural unemployment. Although the expansion of the
latter is revealed both by econometric estimates and other indicators (long-term
unemployment, the Beveridge curve), we cannot ignore the fact that a weakening of
the relationship between inflation and unemployment might have occurred given the
growing importance of external influences in companies’ pricing policy and also the
relatively high degree of wage rigidity in the post-crisis period.
In this context, we conducted a simple empirical exercise that allowed us to assess
whether there is evidence of a change in the slope of the Phillips curve in the
analysed period. To this end, we explicitly incorporated time-varying coefficients
in our model, starting from the assumption that they, similar to NAIRU, follow a
random walk process. Since the introduction of time-varying coefficients generated
nonlinearities in the Phillips curve, in our estimation we used an extended version of
the Kalman filter8, similar to the approach used in a recent IMF study by Matheson
and Stavrev (2013). Mention should be made, however, that this is a preliminary
estimate, the assessment of a change over time in the relationship between inflation
and unemployment requiring a higher sample size, our dataset covering a single
economic cycle so far.
Chart 19 illustrates the evolution of the unemployment gap coefficient during
2005 Q1 – 2014 Q2 estimated with the nonlinear model and shows that the intensity
of the relationship between inflation and cyclical unemployment changes over time.
Our estimates point to early signs of a possible reduction in the slope of the Phillips
curve starting 2007, with a more pronounced decrease occurring after 2010.
8
28
For details, see Annex 2.
NATIONAL BANK OF ROMANIA
January 2016
Chart 19. The slope of the Phillips curve – adjusted CORE2 inflation
0.4
0.0
-0.4
-0.8
-1.2
unemployment gap coefficient*
-1.6
I II III IV I II III IV I II III IV I II III IV I II III IV I II III IV I II III IV I II III IV I II III IV I II
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
*) ±1 st. dev.
Source: Authors' estimates
However, it remains difficult to assess to what extent the weakening relationship
between inflation and unemployment in the post-crisis period is the result of
structural factors with a persistent influence, such as globalization, or of transitory
factors, such as wage rigidity that manifest itself during recessions and early recovery
periods or of monetary policy stance, the effects of which are conditional on firmly
anchored inflation expectations. In this respect, as shown by Stevens (2013), there is
a possibility that the flattening of the Phillips curve is a temporary phenomenon or it
depends to a large extent on the credibility of the central bank to achieve its objective
of price stability.
Also, we cannot ignore the fact that, in the related literature, estimates of the intensity
of this relationship are sensitive to both the measure chosen to quantify the degree of
slack in the economy and the employed estimation technique. At present, Baxa, Plasil
and Vasicek (2013) and ECB (2014, 2015) show the existence of various forms of the
Phillips curve. Each of them manages to explain, to some extent, price dynamics in the
economy, but neither systematically outperforms the others. Thus, they recommend
a more comprehensive analysis in grounding monetary policy decisions, since using a
single specification proves insufficient.
8.Conclusions
The economic and financial crisis has led to a significant loss in the labour market
in Romania (cut of about 700 thousand jobs), however inflation remained relatively
elevated. Subsequently, although the economy has resumed positive growth
rates, job recovery was rather slow (only half by the end of 2014 as compared
with the full recovery in terms of output), suggesting a possible extension of
structural unemployment. In the context of the intensifying debates at European
and even at global levels on the weakening influence of the unemployment gap
NATIONAL BANK OF ROMANIA
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Occasional Papers ■ No. 19
on price dynamics after the crisis, with direct reference to the increase in structural
unemployment, we aimed in this paper not only to test the validity and intensity
of the relationship between inflation and unemployment in Romania, but also to
get an estimate of structural unemployment. Therefore, we opted for a reduced
form of the Phillips curve, namely that proposed by Gordon, a reference model in
estimating structural unemployment, underpinning estimations made in this regard
by international institutions such as the OECD, the IMF and the EC. The specification
has proved suitable to capture the dynamics of inflation in Romania, as it includes,
in addition to the deviation of unemployment from its natural rate (as a measure of
the cyclical position of the economy), adaptive expectations of economic agents and
explicit supply-side shocks. The model was estimated for three measures of inflation
(CPI, HICP, adjusted CORE2) and managed to capture quite accurately consumer price
developments in Romania.
Our results empirically validate the relationship between inflation and unemployment,
the unemployment gap coefficient standing at around -0.7. Regarding the natural
rate of unemployment, our estimates suggest a relative stability in the pre-crisis
period (around 6.0 percent), followed by an increase in the early recovery phase of
the business cycle, reaching 6.5 percent in 2014. The estimated trajectories for the
structural unemployment rate are similar, regardless of the particular price index
used in estimations. Although, in general, the estimation of unobservable variables is
subject to a certain degree of uncertainty, a similar trend in structural unemployment
is highlighted by the increase in long-term unemployment and the higher inefficiency
in the search & matching process, as reflected by the Beveridge curve. Thus, the
manifestation of a hysteresis effect after the outbreak of the crisis, consisting in a
depreciation of human capital with the extension of the unemployment period,
made some jobseekers less attractive to employers. In addition, the Romanian
economy repositioned on a more competitive structure and, therefore, companies
have become more demanding vis-à-vis the candidates’ skills. From this point of
view, higher mismatches appeared in the automotive and IT sectors, where about
70 percent of the companies noted the difficulty of finding appropriately qualified
personnel (according to a labour market survey conducted by the NBR in 2014), as
the available supply does not meet the requirements. Moreover, this mismatch is
compounded by the inability of the education system to generate personnel with
appropriate qualifications suitable for the needs of the economy, reflected in a high
youth unemployment rate. Also, the development of more technology intensive
sectors, along with the inhibitory action of institutional factors, such as the increase
in the minimum gross wage in the past two years, high labour taxation and/or the
existence of collective agreements (via the wage rigidity channel), resulted in a lower
capacity of the economy to create jobs.
Increasing structural unemployment can be an important element for the evaluation
of the cyclical position of the economy (relevant from the monetary policy
perspective), as those people looking for a job but who don’t have the adequate skills
do not actually compete for existing jobs. Therefore, these people cannot influence
the wage-setting process and, therefore, neither the price-setting process.
30
NATIONAL BANK OF ROMANIA
January 2016
An extension of the structural unemployment from the onset of the crisis was
observed at the European level as well, drawing attention to the need for corrective
structural reforms, as the resilience of European economies to shocks is a key
element in the functioning of the euro area or in the prospect of it. In Romania,
possible courses of action to streamline the search & matching process could include
reforms of the education system, an active policy on the labour market, namely the
development of programs for the reinstatement or re-training in accordance with the
needs of the economy, and improving the transport infrastructure in order to facilitate
employers’ access to potential candidates.
Switching to an inflation perspective, the annual growth rate of consumer prices
remained on a downward path, except for some episodes associated to
supply-side shocks. During the recession and the early recovery phase of the
business cycle, disinflation was supported by the positive unemployment gap, its
influence diminishing in recent years, partly on account of the increase in structural
unemployment. However, we cannot ignore that a weakening of the relationship
between inflation and unemployment may have occurred in the context of weak
labour market conditions, the nonlinearity in the relationship driven by the cyclical
position of the economy being empirically observed by Phillips since 1958. In addition,
there is a growing literature pointing to a flattening of the Phillips curve due to
globalization. Therefore, to test this hypothesis we carried out an empirical exercise,
by incorporating a form of time variability in the coefficients used in our model.
However we need to specify that this is a preliminary step, given the small sample
covering just one business cycle. The results provide some clues on the diminishing
trade-off between inflation and unemployment in the short term in Romania, starting
2007, in possible correlation with the European Union accession. However, a full
assessment of this phenomenon should be carried out throughout several business
cycles. In this respect, it is difficult to separate the action of a structural factor such
as globalization, which led to a more pronounced response of domestic prices to
international developments, the effects of monetary policy, subject to anchoring
inflation expectations, or the short-lived influence coming from the downward
nominal wage rigidity. The latter was actually a barrier to reducing unit labour costs,
enhancing labour market adjustment by decreasing the number of employees.
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Occasional Papers ■ No. 19
References
Ball, L. M.,
Mankiw, G. N.
The NAIRU in Theory and Practice, Journal of Economic Perspectives,
16 (4), pp. 115-136, 2002
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Annex 1
The Kalman Filter
The empirical exercise conducted in this paper starts by estimating a state-space
model, which consists in a system with two equations: a measurement equation and a
transition equation. The measurement equation shows the link between the variation
of the annual inflation rate (CPI, HICP and adjusted CORE2) and its determinants such
as inertia, unemployment gap (the difference between unemployment and NAIRU –
the unobservable variable) and supply-side shocks:
or more generally
. (1)
The transition equation illustrates the random walk process followed by the
unobservable variable, NAIRU in our case, also known as the state variable:
with
(2)
.
Also, given the numerous recommendations in the literature (Gordon, the OECD and
the EC among others) a signal-to-noise ratio
type of restriction was imposed
in the model, which takes into account the fact that NAIRU volatility is lower than
inflation volatility; the value used for the signal-to-noise ratio was 0.16.
Given that, the Kalman filter is a recursive algorithm with a two-step filtering
procedure from to namely prediction and update, in a first step was necessary to
provide initial values for NAIRU and for the variance of its prediction erro for model
coefficients and for the variance-covariance matrices of and
and respectively.
The initial values were obtained by estimating with OLS the measurement
equation using the whole sample (2004-2014) and a Hodrick-Prescott trend of the
unemployment rate as a proxy for NAIRU.
Subsequently, the following steps were taken recursively:
1.Prediction
Assuming we are at
, in a first step a forecast of NAIRU at is obtained with
information available at
,
(3)
with the covariance of the prediction error:
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Occasional Papers ■ No. 19
.
(4)
2.Update
In this step, given that new informationa , becomes available, the previous forecast
is updated. Initially the prediction error of the variation of the annual inflation rate is
obtained:
,
(5)
then the Kalman gain is computed by minimising the mean squared a posterior errors
of NAIRU:
.
(6)
The updated forecast for NAIRU is obtained by adjusting the initial forecast and
covariance of the prediction error with this Kalman gain:
.
(7)
(8)
Kalman smoothing
In the end, the estimates obtained with the recursive algorithm described above are
smoothed from to by using the Kalman smoother:
(9)
.
(10)
Annex 2
The Extended Kalman Filter
The incorporation of time-varying coefficients generates nonlinearities in the Phillips
curve and increases the number of state variables.
In this context, the measurement equation becomes
with a nonlinear function with the first order derivative
,
(1)
and the transition equation is
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with the first order derivative
(2)
and shows the dynamics of both NAIRU and model coefficients.
In this case, the Kalman filter was initialised with values of NAIRU and coefficients
obtained by estimating the linear model described in Annex 1, and with values of
the variance-covariance matrices resulted from the rolling-window non-linear least
squares estimation of the measurement equation based on a narrowed sample
(2004 Q1 – 2009 Q4).
The following steps are similar with those described in Annex 1, with additional two
intermediate steps stemming from the first order linearization of the model.
1.Prediction
First intermediate step:
(3)
(4)
.
(5)
2.Update
Second intermediate step:
(6)
(7)
(8)
.
(9)
Kalman smoothing
(10)
(11)
(12)
. (13)
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Occasional Papers ■ No. 19
Annex 3
Principle of Dynamic Contributions
The principle of dynamic contributions is used in the case of models with inertia
because it takes into account the structure and the delays (lags) with which the
explanatory variables influence the dependent variable.
Let be an endogenous variable,
the explanatory variables and the error term.
A model with inertia that describes the influence of
on can then be written as
follows:
.
(1)
The full dynamics can be represented by using polynomials of the lag operator :
,
where
and
(2)
.
The dynamic contributions of each explanatory variable are obtained by inverting
:
38
(3)
.
(4)
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The inverse relationship between
inflation and unemployment
in Romania.
How strong was it after the crisis?