Download Relationship of Rate of Unemployment and Inflation in Pakistan

IJE : Volume 6 • Number 1 • June 2012, pp. 111-130
Relationship of Rate of Unemployment and Inflation in Pakistan
(Empirical Status Estimation of NAIRU)
Mazhar-ul-Haq Baluch* & Azam Amjad Chaudhry**
Abstract: The NAIRU is defined as the rate of unemployment at which there is no tendency for
inflation to change (Weiner, 1993). The constant NAIRU as well as Time Varying-NAIRU was
estimated by applying appropriate needed techniques associated with Hodrick Prescott filter.
The NAIRU was estimated considering uni-variate model. The results reflected weak model
specification, but expected negative relationship between unemployment and inflation. The
results reflected unstable long run relationship between inflation and unemployment since the
estimated constant NAIRU was different for every considered periodical segment. The time
varying NAIRU was estimated by following the Ball and Mankiw (2002) approach and finally
Hodrick Prescott (HP) filter technique. The estimated results were according to the expectations,
since the inflation rate remained high during 1981 to 2000. However inflation started declining,
the estimated NAIRU also declined and it was lower than inflation for the period. In 2006 the
inflation and the unemployment remained up the NAIRU but inflation was relatively higher
than the unemployment. Finally, the derived conclusion confirmed the existence of trade off
between inflation and unemployment and this trade off ascertain the Phillips curve. Despite
measurement problems, NAIRU can be used as a forecasting tool to assess its relationship with
unemployment and inflation for policy implication and provide base for inflation targeting and
keep unemployment as low as possible without accelerating inflation.
1. INTRODUCTION
The rate of unemployment below which rate of inflation tends to accelerate is labelled as the
Non-Accelerating Inflation Rate of Unemployment—NAIRU (Shostak. F, 2005). Low
Unemployment refers to expansion in production activities in an economy. This implies indirectly
strongdemand for goods and services. Under the phenomenon of low unemployment the
expansion in demand of goods and services by workers put the pressure to increase the wages,
which adversely affect the prices of goods and services due to upward shift of cost of production
.This leads to rise in inflation. Rising inflation rate associated with low level of unemployment
becomes a challenging component of sound macro-economic policies. In Pakistan inflation and
cyclical unemployment pattern have negating effects on the economy as a whole and on individual
as well, since the consumption increases without upward shift in production due to challenging
increase in cost following the increased input prices.
Basically the changes in the inflation rate depend upon labour market slack or the
unemployment rate. Actually the direction of movement of inflation could be predicted by
*
**
Senior Research Fellow ([email protected])
Dean, Department of Economics
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unemployment level. Conceptually, changes in aggregate demand push inflation and
unemployment in opposite directions specifically in the short run.
Paul Samuelson and Robert Solow (1960) phrased the term “Phillips Curve” at the 1959
AEA (American Economic Association) meetings to describe that relationship, reacting to the
publication of Phillips (1958). A few years later, Melton Friedman (1968) termed it as “natural
rate of unemployment,” which more recently has come to be known by the acronym “NAIRU,”
standing for the Non-Accelerating Inflation Rate of Unemployment (Gordon, 1997).This leads
towards the conclusion that Tobin’s concept “the NAIRU, is expansion of Phillips curve as it
provides the base for NAIRU. The unemployment that occurs at full employment, when the
economy is producing at potential output, is called the natural rate of unemployment
.Consequently the NAIRU is defined as that rate of employment at which there is no tendency
for inflation to change (Weiner, 1993).
The main problem encountered with the NAIRU is that it was not observed but estimated.
Thus considering the prevalent economic environment of the country, inflation and
unemployment status, it is essential to assess the relationship of inflation and unemployment in
the light of Phillips curve and NAIRU to chalk out the relevant policies theoretically suitable
for smoothing economic concern functions.
The principal Objective of the study is to assess the relationship between inflation and
actual unemployment in the introduced economic environment and estimate the gap between
NAIRU and actual unemployment and their relationship with inflation in Pakistan.
2. REVIEW OF LITERATURE
Staiger, D; Stock, J. H; and Watson, M .W (1997); in their article titled, “The NAIRU,
Unemployment, and Monetary Policy,” focused on “state of the art” models that allows the
NAIRU to change overtime. There is evidence that the NAIRU has declined by approximately
1 percentage point over the past 10 years. They also concluded that a natural rate does not exist.
This argument could either be based on behalf on a belief that the NAIRU has shifted, or on the
wide confidence intervals surrounding the estimates. A theoretical justification for such a position
could be that the hysteresis that has been proposed as a description of European unemployment
(Blanchard and Summers, 1986) is present in the U.S. economy as well, so that there is no rate
of unemployment that is general consistent with constant inflation. We do not believe that the
evidence supports this view. Although there is evidence that the NAIRU has shifted, the shifts
have been relatively minor over the past three decades. Using total civilian unemployment and
the GDP deflator, the NAIRU moved from a low of 4.9 in 1966 to a high of 7.0 in 1978. A more
useful, if more difficult, task is to focus on the general problem of forecasting inflation. Certainly,
the recent history of the unemployment rate helps to predict inflation over the next year, although
it is less valuable over the next two years but other variables are as good or better, including the
capacity utilization rate, over labour market variables, interest rates and, at longer horizons,
some monetary aggregates. The results suggested that monetary policy should be informed by
a wide range of variables, not just unemployment.
Wanningen (2000) In his working paper, “NAIRU and Inflation: An Empirical Analysis for
Netherlands,”The empirical results show that it is difficult to obtain robust estimates of the
NAIRU for the Netherlands. Moreover for short horizons (two years or less) inflation forecasts
Relationship of Rate of Unemployment and Inflation in Pakistan…
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turn out to be quite invariant to changes in thelevel of NAIRU. It is concluded that the NAIRU
is not a very reliable information variable for monetary policy.
Whelan, K. (1999) In his paper titled, “Real Wage Dynamics and the Phillips Curve,” has
examined the role of real wage dynamics in determining aggregate inflation and shown that the
Phillips Curve relationship between the change in price inflation and the unemployment rate
does not depend on a specific assumption about the pattern of auto-regression in micro level
real wages. He also concludes the accelerationist relationship between the change in price
inflation and the unemployment rate is consistent with any type of microeconomic real wage
dynamics. However these dynamics will determine how supply shocks affect inflation. Evidence
on supply shocks and inflation indicates against the traditional real wage formulation.
Implications for the recent behaviour of the NAIRU are explored.
Llaudes. R, (2005), In his paper titled, “The Phillips Curve and Long –term Unemployment,”
studies the role of long-term unemployment in the determination of prices and wages. Labour
market theories such as insider- outsider models predict that such type of unemployed are less
relevant in the wage formation process than the newly employed. He looks for evidence of this
behaviour in a set of OECD countries. He proposes a new specification of the Phillips Curve
that contains different unemployment lengths in a time – varying NAIRU setting. He constructs
an index of unemployment that assigns different weights to the unemployed based on the length
of their spell. The results show that unemployment duration matters in the determination of
prices and wages, and a smaller weight ought to be given to the long term unemployed. This
modified model has important implications for the policy makers; it produces more accurate
forecasts on inflation and more precise estimates of the NAIRU.
Rodenburg, P. (2007) In his research article titled, “Derived Measurement in
Macroeconomics: Two Approaches for Measuring the NAIRU Considered,” investigates
two different procedures for the measurement of the NAIRU; one based on structural
modelling while the other is a statistical approach using Vector Auto Regression (VAR)models. Both measurement procedures are assessed by confronting them with the dominant
theory of measurement, the Representation Theory of Measurement, which states that for
sound measurement a strict isomorphism (strict one-to-one mapping) is needed between
variations in the phenomenon (the NAIRU) and numbers. The paper argues that shifts of
the Phillips-curve are not a problem for the structural approach to measurement of the
NAIRU, as the NAIRU itself is a time-varying concept. It is however, the impossibility to
identify the exact shape of the Phillips-curve that causes problems of multiple empirical,
relational forms and hence non-unique isomorphic mappings for measurement. While VARmodels are being accused of being ‘theoretical macro econometrics’ in the literature, the
Wold decomposition theorem applied to the VAR brings out a stable correspondence between
variance of the phenomenon (the NAIRU) and numbers and turns the set of equations into
isomorphic mapping that can serve as a useful foundation for the construction of a measuring
instrument.
3. METHODOLOGY
The Phillips curve is briefly an inverse relationship between the rate of unemployment and rate
of increase in money wages or in other words there is a trade-off between wage inflation and
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unemployment. Phillips (1958) and Friedman (1968) consider the shift of the curve due to the
supply side factors and demand side factors. So it is defined as:
πt = πte = ε (Ut - UNAIRU) + ε
(1)
πt = Inflation Rate
πet = Expected Inflation Rate
Ut = unemployment Rate
UNAIRU =Non-accelerating Inflation Rate of Unemployment
ε = Parameter that measures the responsiveness of inflation to unemployment
The series for inflationary expectations, as is given in equation–1 require for empirical
assessment. Following Tootell (1994), Fuhrer (1995) and Eisner (1995) “random walk” model
for inflationary expectations is πte = πt-1, so πt -πte=∆πt. When the lags of π t -πte are included in the
right hand side of equation-1, this is equivalent to specifying the Phillips relation in the level of
inflation and imposing the restriction that sum of the lags add to one i.e. 1 (Staiger, Stock and
Waston, 1996).
According to Dornbusch and Fisher, 1990; Lipsey et al , 1993, as is given in equation (1)
inflation (πt) will decline relative to the expected inflation (πte), if the actual unemployment rate
(Ut) exceeds the non-accelerating inflation rate of unemployment (UNAIRU ). In words equation
(1) states that the level of price is falling (πt 0) when the unemployment rate exceeds the nonaccelerating inflation rate of unemployment (Ut>UNAIRU), rising (πt> 0) when the unemployment
rate is less that the non-accelerating inflation rate of unemployment (Ut > UNAIRU) and is stable
(πt = 0) when the unemployment rate is equal to the non–accelerating inflation rate of
un- employment (Ut =UNAIRU). Equation (1) has been modified into the ‘expectations augmented
and change –in –unemployment extended Phillip curve’
πt= πet- ε( Ut - UNAIRU ) – (Ut – Ut-1)
(2)
Where,
= is a parameter that measures the extent to which changes in unemployment (Ut – Ut-1)
affect the level of inflation. This parameter indicates that the larger is , the more important is
the effect of changing unemployment on the inflation rate.
Equation (2) the consideration reflects the situation where in the case that the unemployment
gap (Ut-UNAIRU) is negative, and increasing in absolute terms, then actual unemployment is
decreasing, meaning that Ut – Ut-1< 0. On the contrary, in the case that the unemployment gap
(Ut - UNAIRU) is negative and decreasing in absolute terms, then actual unemployment is increasing,
meaning that Ut – Ut-1> 0. Usually are expected higher inflationary pressures or less rapidly
falling inflation in the former case than in the latter (Pekos et al., 2004).
The NAIRU is generally defined as the level of unemployment that is consistent to a stable
expectations-augmented Phillips relation. Setting aside the lagged effect, the expectationsaugmented Phillips relation can be estimated as:
πt= πet- ε (Ut -1-UNAIRU ) + xt+ vt
(3)
where,
Ut = the unemployment rate
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π = the rate of inflation
πte = the expected rate of inflation = πt-1
UNAIRU = the NAIRU
vt = error term
X = the additional regressors
These regressors are included in some of the empirical specifications to control for
supply shocks to the prices of food and energy, which some have argued would shift the
intercept of the Phillips curve (Gordon, 1990). As already described above, the expected
rate of inflation is πet = πt-1, so πt -πet ∆πt. Consequently expected inflation, considered in this study
is previous year inflation rate. Moreover unem01 or unemployment Ut-1 is used in order to
accommodate the assumption that unemployment causes inflation in time 1 or time 2. Thus
the equation will be:
∆πt = ε (Ut -1 - UNAIRU ) + xt +vt
(4)
The above equation describes the natural rate of unemployment where the inflation rate is
constant and leads towards Phillips curve in the absence of supply shocks.
Due to the introduction of new methods of estimation in econometrics, the estimation of
the NAIRU has drawn attention as important topic of research in economics (Katos et al, 2004).
Accordingly, equation (2) has been extended in to the following:
πt= πet- ε (Ut - UNAIRU) – (Ut – Ut-1)
(5)
Where,
UtNAIRU = time varying non- accelerating inflation rate of unemployment.
4. ESTIMATION OF TIME VARYING (TV) NAIRU
According to available literature time varying NAIRU is estimated considering the estimated
Phillip curve or estimating constructed equations in methodology by applying ordinary least
square method. Relaxing the estimated constant NAIRU, the estimated values ofY on the basis
of estimated coefficients is finally estimated.
Finally, the Ball-Mankiw approach for estimating the time varying NAIRU postulates the
following standard expectations-augmented specification for the Phillip curve:
πt= πet - a(Ut - UNAIRU) + vt
(6)
*
v is the supply shock and NAIRU is U (Ball and Mankiw 2002)
As described above in the methodology expected inflation equals the last period actual
inflation. Under this assumption the Phillip curve equation will be as under:
πt= πt-1- α(Ut - U* ) + vt
(7)
Equation-6 can be written as:
 ∆π
Ut +  t
 a
Where
πt - πt-1= ∆πt

 νt 
*
 =U + a 
 

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For a given of the slope of the Phillip curve (α) the left side of the above equation can be
νt
derived from the data of the study yielding an estimate of U* + ( ) .
a
νt
νt
* (
*
) , the element U represents long term trend, while ( ) is
Within the sum U +
a
a
proportional to the short term supply shocks.
νt
Ball and Mankiw (2002) suggested that estimates of U*could be extracted from U*+ ( )
a
by means of a standard approach for estimating a time series trend. Then they proceeded to use
the Hodrick – Prescott (HP) filter for further final estimation. Consequently, the HP filter was
applied to separate the value of U* from that sum.
The value of the HP smoothing parameter applied in the study on hand was 1000.
5. HODRICK- PRESCOTT FILTER
Hodrick Prescott Filter is a mathematical tool used for estimating cyclical and trend component
of a time series using a frequency cut off or smoothness parameter. It is widely used specifically
in real business cycle theory to have smooth estimate of the long term components of the series.
It allows the slope of linear time trend to change gradually overtime. It formally minimizes the
sum of square deviation between the trend and the actual series. The Hodrick Prescott Filter
was also applied by Ball and Mankiw (2002) to estimate the NAIRU in US economy.
Consequently the HP Filter has been applied to estimate the NAIRU. They applied the Phillips
standardized equation to calculate the NAIRU as follows:
∆π = αU* - αU + υ
The parameter ‘α’ indicates the slope of the inflation and unemployment relationship, the
above equation has been rearranged such as:
U* + V/α = U +∆π/α
In this case the estimated U*+ V/α presents the shift in the Phillips curve. U* represents the
long term trends and V/α was proportional to the short term supply shocks. To estimate the
trend in applied series or to separate U* from U* +V/α Hodrick Prescott filter was applied
following Ball and Minkaw (2002) to derive estimates regarding time varying NAIRU.
The Phillips curve is briefly an inverse relationship between the rate of unemployment and
rate of increase in money wages or in other words there is a trade-off between wage inflation
and unemployment.
Phillips (1958) and Friedman (1968) consider the shift of the curve due to the supply side
factors and demand side factors. So it is defined as:
πt = πte = ε (Ut -UNAIRU)
(8)
where Under prevalent economic environment of Pakistan, the behaviour of the labour market
created critical situation for policy makers to develop practically feasible reforms for the
betterment of human beings. Generally, the unemployment trends directly or indirectly affect
the economic activities of all the sectors of the economy by affecting production costs as well
as product prices. Theoretically unemployment reflects inverse relationship with inflation,
however, when labour market reaches equilibrium point and became stable with sustainable
Relationship of Rate of Unemployment and Inflation in Pakistan…
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117
situation in the goods and services markets, it becomes about complicated to adhere the situation
for isolating the effects of both these considered variables. The role of both these variables
becomes more crucial when it is determined by supply shocks or exogenous factors exclusive
to own emerged relationship. In addition the supply shock can persuade the policy.
6. RESULTS OF THE STUDY
Inflation (%)
A person refersan un-employed when he/she must be out of job and actively looking for work.
However when ones stops looking for a job than considered out of the labour force and is no
longer counted as unemployed. It is also worth noting that even if certain economy is reflecting
at or near full employment, the un-employment rate will never be zero. The inflation rate in
economics is referred to the rate of change of price index positively in percentage term or
percentage rate of change in price level overtime. Inverse to it the rate indicating decrease in
the purchasing power of money will also approximately similar change and leads towards changes
in price level. Consequently in simple term the rate of change of prices or as indicated by a
price index calculated on specific time period (monthly or annually) is referred as inflation rate.
There are three major categories of unemployment. These are frictional, structural, and cyclical.
Beyond economic environment of the country concerned there will always be frictional and
structural unemployment. Frictional unemployment occurs from movement of people between
jobs, careers and location, while structural unemployment refers to absence of the demand for
workers that are available. However, cyclical unemployment rate moves in the opposite direction
as the GDP growth rate. So when GDP growth rate is small or negative unemployment is high.
The relationship between un-employment and price level is generally negative, though there
is an inconsistency with the classical theory that there appear to be a systematic relationship
between the rate of change of wages and unemployment. Theoretically, the movement of production
pattern towards capacity output reaches close to it, an increase in demand will result upward
change in the overall price level. Since the production process or resource use pattern specifically
had a little option to change frequently and thus unemployment pattern also following the production
adjustment results in slow adjustment responding to inflation or rising wage prices. This
responsiveness measure of inflation to unemployment however supports the Phillips curve—the
higher the rate of unemployment, the lower the rate wage inflation. Considering all these theoretical
aspects, trend of relationship has been developed applying data and presented in the Figure 1.
Figure 1: Relationship of Inflation and Unemployment in Pakistan (1980 to 2007)
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Mazhar-ul-Haq Baluch & Azam Amjad Chaudhry
Generally, the unemployed workers would prefer that the firms should reduce the wages
to create demand for jobs. In other wordsthere existed a Phillips curve supporting relationship
between inflation and unemployment. However there remained the data problem regarding
the unemployment, which has been manipulated or forecasted for several years except that of
the labour survey periods. Despite this data ambiguity, the displayed reflection of inflation
and unemployment supports the Phillips curve in the initial period of 1980s. However in
certain cases both the products and prices rise in short and medium periods, while this shift in
aggregate supply leads to higher unemployment and higher inflation as well. The policy
makers in such case of supply shocks also respond to it by increasing aggregate demand.
Consequently this resulted in unfavourable trade off between inflation and unemployment
for certain periods. Since the supply shocks directly affect production cost and ultimately
this situation shift the Phillips curve, as it was in 1990s. Consequently during the census
periods the results lead towards provision of support favouring Phillips curve. With the
consequent there existed Phillips curve relationship between unemployment and inflation
during the considered series period except that of affected supply shocks behaviour in certain
years, but there remained generally Phillips curve supporting situation throughout the
considered periods.
Moreover to delineate the historical trend indicating the relationship of unemployment
rate and inflation the considered periods (1980 to 2007) was confined by grouping it into six
observations. Basically the observations concerning to inflation were arranged in ascending
order to trace minimum and maximum level for the whole considered period. Since the inflation
recorded was the lowest at 3.1 per cent, it was given the level < 4.0 per cent in grouping the
data, whereas the other considered group was 4.0 to <6 percent. The next group considered
was at 6 to < 8 percent. Similarly the last group was ≥ 12.0. Subsequently, within the considered
ranges of inflation the variations in movement of unemployment rate were indicated in the
graph. In the Figure 2 below there was found a specific relation observed in linear movement
of inflation and respective unemployment rate. It was also obvious that up to inflation
<8 percent the theoretically acceptable inverse relationship existed there between
unemployment rate and rate of inflation, since with increased inflation rate unemployment
decreased at every increased periodical step. However this relation deviated and reflected
positive relationship up to <10 per cent inflation rate. The emerged situation led towards
existence of NAIRU in between 8 to 10 per cent in case of Pakistan. Beyond 10 per cent
inflation rate the unemployment rate indicated again inverse relation to inflation rate in the
prevalent economic environment of the country. Ultimately there existed theoretically Phillips
curve supporting relationship between inflation and unemployment rate. By this could be
concluded that unemployment may prove a valuable instrument to forecast the future inflation.
However the above given precise assessed value of NAIRU by applying bi-variate graph
must be the intersecting point of both the observations, which would be ambiguous and
uncertain in real value term. Though hypothetically the NIARU was suggested precisely, but
it was not a reliable approach, since it does not capture the effects of additional lags and
specifically the effects of supply shocks.
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119
Figure 2: Relationship of Unemployment with Linear Inflation (1980 to 2007)
7. RELATIONSHIP OF INFLATION, UNEMPLOYMENT AND ECONOMIC GROWTH
Centrally focussed point of discussion on any economic activity has been the economic
betterment or economic growth. Economic growth has mainly concerned with growth of Gross
Domestic Product of the country or GDP growth. Since the concerned activities may contribute
positively, negatively or have no effect for the considered period, assessment of the relationship
has been made by graphic presentation in Figure 3.
Figure 3: Relationship of Inflation, Unemployment and Economic Growth in Pakistan (1980 to 2007)
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Mazhar-ul-Haq Baluch & Azam Amjad Chaudhry
The above figure reflected uneven relationship of the considered variables with all the time
volatile and disposition situation. However in 1991 and 2004 all the variables were found
intersecting each other indicating equilibrium situation under the available economic
circumstances. However beyond this, the theoretical concerning made the GDP growth rate
down with relative upward inflation rate creating exceptional downward position of
unemployment. Ultimately no determined relationship of the concerned variables could be
weighted.
8. EXPECTATIONS-AUGMENTED PHILLIPS CURVE
Empirical evidence on the expectations-augmented Phillips curve in the absence of supply
shocks has been reflected in the Figure 4. This reflected year-wise changes in the inflation
(consumer price index) by plotting symbols only against the annual unemployment of the last
year in Pakistan for the considered period (1980 to 2007). This figure reflected negative
relationship between both the considered variables, since lower unemployment is associated
with higher inflation and vice versa at every considered unit of the period. This gave indication
that both the variables (Inflation and Unemployment) remained fluctuated during the considered
period, but visionary assessment considers this graphic method an uncertain and incredible
primary reflection concerning the assessment of NAIRU.
Figure 4: Annual Changes in Inflation Vs unemployment (t-1)
8.1. Inflation and Unemployment Relationship
Very firstly an effort has been made to estimate the relationship of both the considered variables
i-e.inflation and unemployment by applying regression model of single variable equation
such as:
∆inf = c +unem01
∆inf = Change in inflation (Consumer Price Indx)
Unem01 = Unemployment lagged by one year
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By applying the above given equation the estimates were assessed by distributing the
considered in various segments. The overall period of the study concerned pertained to 1980 to
2007. The overall period was classified into 4 random segments i.e (1980 to 2007), (1985 to
2007), (1990 to 2007) and (1995 to 2007).The results are given in the Table 1. The results in
this table reflected expected negative relationship between unemployment and inflation. However
the results—derived
reflected weak model specification of the considered variables on the basis
—
of R2 and R2 . R is generally negative in all the considered periodical segments. The estimated
values of various test statistics specifically F-ratio in all the periodical segments were found
statistical insignificant. The NAIRU was estimated considering un-ivariate model ignoring all
other weak specification of model and the derived unjustified results were presented in the
Table 1. However the estimated environment indicated that to derive relative reliable estimates
certain theoretically accepted additional variables and supply shocks are essentially needed to
estimate Phillips curve for Pakistan. Consequently, by including certain additional variables
strength of the estimates would be determined.
Table 1
Results of Models by Applying Uni-variate Model for all the Considered Periodical Segments
—
Periodical Segment
Constant
UNEM01
R2
R2
NAIRU
1980 to 2007
1985 to 2007
1990 to 2007
1995 to 2007
1.0804
1.1498
0.4182
-3.2119
- 0.2061
- 0.2082
-0.0938
+0.4503
0.014
0.017
0.002
0.054
-0.023
-0 .029
-0.060
-0.032
5.24
5.52
4.46
7.13
8.2. Constant NAIRU Estimates by Multi-Variables
The constant NAIRU was estimated by applying regression analysis by considering the model
estimated coefficients. Since the study’s principal objective is to determine the relationship of
inflation and unemployment, the constant NAIRU has been derived by applying the value of
constant (a0) and the coefficient lagged unemployment (unem01). By this it was concluded that
NAIRU was the ratio of a0 and a1 .
To estimate the NAIRU, as is given in methodology certain additional variables and supply
shock were considered appropriate to estimate the constant NAIRU. Consequently food and
energy price inflation were included in the models to derive relatively reliable results. The
following equations were found theoretically appropriate to include in the model to derive
reliable results. Ultimately the following equations were applied to estimate the coefficients for
further estimation of NAIRU for the considered periodical segments.
∆inf = a0 + a1unem01 +a2 cpi01+a2finf +a3 engy +ε
(9)
∆inf = a0 + a1unem02 +a2cpi01 +a2cpi02 +a3 finf +a4 engy 02 + engy + ε
(10)
The estimated results of equation-1 considering various periodical segments have been
presented in the Table 2.
In this Table, beyond presentation of the results of equation-1, the constant NAIRU was
estimated on the basis of regression with not only unemployment level but also considering
expected shocks to the prices of food and energy, which are expected to shift the intercept of the
Phillip curve (Gordon, 1990). SSW (1997), proposed changes in energy prices and term of
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Table 2
The Results of Equation-1 on the Basis of Considered
‘Periodical Segments
∆inf = a0 + a1unem01 +a2cpi01 +a2finf +a3 engy +ε
Periods
1980 to 2007
1985 to 2007
1990 to 2007
1995 to 2007
—
Constant
Unem01
Cpi01
finf
engy
R2
R2
1.3005
1.2629
4.2632
1.9673
-0.1084
-0.1244
-0.4330
-0.1644
-0.8586
-0.8111
—0.9094
-0.7948
0.6264
0.5872
0.5710
0.5255
0.0747
0.8295
0.0781
0.0664
0.80
0.76
0.82
0.89
0.77
0.70
0.77
0.0.84
NAIRU
12.0
10.15
9.84
11.97
trade as supply shocks. A relative price of food inflation and relative price of import inflation
has been used as supply and exchange rate shocks respectively (Julio, 2001). Consequently,
two variables ( Food price and energy price inflation) were included in regression controlling
supply shocks.
The constant NAIRU was estimated on the assumption of constant NAIRU and random
walk expectations. The NAIRU was estimated on the basis of estimated constant (a0 ) and
coefficient of lagged unemployment (a1). Since a0 = a1U*, the U* = a0/ a1. Thus the NAIRU is the
ratio of a0/a1.
The Table presents estimated NAIRU was 12.0 percent during 1980 t0 2007, 10.17 percent
during 1985 to 2007, 9.84 per cent during 1990 to 2007 and 11.97 during 1995 to 2007.
These results reflected unstable long run relationship between inflation and unemployment
since the estimated NAIRU was different for every considered periodical segment under the
existing environment of the country. It was about equal during 1980 to 2007 and 1995 t0 2007.
However, the difference in estimated NIARU for various periodical segments could be attributed
to non existence of formal stable relationship between inflation and economic growth (Haq and
Shahid, 2009). Since both the variables remained fluctuated indifferently, but the results were
supporting to the Phillip’s Curve indicating negative relationship between inflation and
unemployment for all the periodical segmented equations. On the basis of Equation -2 the
results of the model has been interpreted in the Table 3.
Table 3
The results of Equation-2 on the Basis of Considered Periodical Segments (1980 to 2007)
∆inf = a0 + a1unem02 +a2cpi01 +a2cpi02 +a3 finf +a4 engy02 + engy + ε
Period
1980- 2007
1985 - 2007
1990 - 2007
1995 - 2007
Constant Unem02
1.4862
1.8698
4.1158
0.3701
-0.1552
-0.1113
-0.4304
0.0483
—
Cpi01
Cpi02
Finf
Engy
Engy02
R2
R2
NAIRU
-0.8986
-0.9144
-1.0328
-0.7848
0.1202
0.2087
0.1558
0.0622
0.6091
0.5389
0.5750
0.5147
0.0705
0.0746
0.0869
0.0667
-0.0338
-0.0977
-0.0307
-0.0223
0.826
0.805
0.856
0.895
0.776
0.732
0.777
0.790
9.58
16.80
9.56
7.68
The above table reflected relatively better results to interpret the Phillip’s curve, since
with the inclusion of additional variables of unemployment, inflation and supply shocks, the
relationship of inflation and unemployment was changed. By adding lagged variables of
unemployment and inflation and other supply shocks variables, it was found that change in
inflation was negatively to its first lag and positively related to its second lag. This could be
Relationship of Rate of Unemployment and Inflation in Pakistan…

123
attributed to short and long run behaviour of the considered variables with respect to contribution.
The inverse results were obvious in case of change in unemployment pattern. The results derived
indicated that the change in unemployment lagged by one year was positively related to change
in inflation except 1995-2007, and negatively related to its second lag. However Phillips’ curve
specifications were also assessed for the various periodical segments. The estimated coefficients
of unemployment lagged by second year were positive for the period 1995 to 2007, while the
unemployment parameter lagged by one year confirmed the Phillip’s relationship with negative
contribution (-0.450) to change in inflation during the same period. However from certain
different derived situation, this could be concluded that the data though was taken from
governments’ publication, the data on both the variables (Inflation and Unemployment) created
complicated situation. Unemployment data was correct only for the census year or the labour
survey year. The similar situation was observed in case of inflation, since the inflation data has
not been regularly collected by concerned agencies. However, estimated NAIRU was different
for periodical segments and it was the highest (16.8 per cent) during 1985 to 2007. A more
flexible technique is available to estimate time- varying NAIRU.
8.3. Estimation of Time–Varying NAIRU
As described above the estimated constant NAIRU was different, while estimated on various
periodical segments in economic environment of Pakistan. Consequently, relaxing the assumption
of constant NAIRU, the time varying NAIRU was estimated by following the Ball and Mankiw
approach applying the above described equations and finally Hodrick Prescott (HP) filter
technique. The equations used was as follows :
∆inf = a0 + a1unem02 +a2cpi01 +a3cpi02 +a4 finf +a5engy02 + ε
To apply HP filter technique for the estimation of Time Varying NAIRU, the above described
equation was practically processed as under:
a
U*+v/α =
(∆cpi) + unem02+α2/α1(cpi01)+α3/1(cpi02) +α4 /α1(finf) +α5 /α1(engy02)
α1
After processing the equations the HP filter technique was applied. However various
equations were practised but the results were relatively better by considering the above equation
than other equations. So finally this equation was considered for the results.
U*+v/α showed the shift in Phillip curve. Within this sum U* represents the TV-NAIRU
(long term trend ) while v/α is proportional to the short term supply shocks. HP filter technique
was applied to separate U* from the sum following Ball and Mankiw, 2002.The details regarding
estimated NAIRU were presented in Figures 5 & 6.
The figures reflected that Time-varying NAIRU supported overtime changing occurring in
NAIRU. The sum U* +v/α1 remained fluctuatingon the basis of NAIRU plus trend (proportional
to the short term supply shocks. However the U* was separated by applying Hodrick Prescott
(HP) filter technique by applying the value of HP smoothing parameter adopted in this exercise
for annual data was λ = 100 and λ = 1000 considering the above equation.
The graph presented indicated the results concerning the U*+v/a estimates derived by
applying the above described procedure and separating the U* and V/a by applying HP filter
technique by using two different values of smoothing parameter. The sum was, though,
124

Mazhar-ul-Haq Baluch & Azam Amjad Chaudhry
fluctuating on the basis of inclusion of trend line, however the estimated NAIRU was derived
by separating the value of U* from the sum. Consequently the sum U*+ v/a and U* and
v/a were given separately. Firstly the considered value of smoothing parameter λ = 100 in
Figure 5.
Figure 5: Hodrick- Prescott Filter ( = 100)
inf = a0 + a1unem02 +a2cpi01 +a2cpi02 +a3 finf +a4 engy02 + 
The estimated NAIRU reflected more volatility by considering the value of Hp smoothing
parameter (λ = 100) ranging from 2.50 per cent to 14.96 percent. In this case the estimated
NAIRU fluctuated over time with upward and downward movement with specific trend
for the period concerned ,as was in case of value of HP smoothing parameter λ = 1000 in
Figure 6.
Figure 6: Hodrick- Prescott Filter ( = 1000)
inf = a0 + a1unem02 +a2cpi01 +a2cpi02 +a3 finf +a4 engy02 + 
Relationship of Rate of Unemployment and Inflation in Pakistan…

125
Similarly, the HP filter technique was applied by considering the value of HP smoothing
parameter (λ = 1000) following Ball and Mankiw (2002) to impose greater smoothing of the
considered series and presented in Figure 6.
The similarity was generally observed in reflected trend by separating the sum (U*+ V/a).
The estimated NAIRU reflected more volatility by considering the value of Hp smoothing
parameter (λ = 100), but considering the HP smoothing parameter (λ = 1000) the range expectedly
squeezed to 6.45 to 9.57. Moreover, the results reflected that the estimated NAIRU decreased
from 9.57 per cent in 1999 to 6.45 percent in 2007. It was 8.75 per cent in 1980 and then
decreased to7.63 per cent in 1991. It then again rose to 9.57 percent in 1999. The estimated
confidence interval bound around time varying NAIRU at 95% precision level and it ranged
between 5.13 lower bound and 13.87 upper bound (Annexure-1).
8.4. Relationship of Unemployment, Inflation and NAIRU
The relationship of inflation (t-1), unemployment (t-1) and estimated NAIRU was presented in
the Figure 6a. It was worth noting that the actual rate of unemployment remained below the
estimated NAIRU throughout the considered period except that of 2004-07. The estimated
results were according to the expectations, since the inflation rate remained higher than
unemployment during 1981 to 2000. However inflation started declining and remained relatively
lower than unemployment during 2000-05. The estimated NAIRU also declined and it was
lower than inflation up to considered period. In 2006 the inflationand the unemploymentremained
up the NAIRU but inflation was relatively higher than the unemployment. The actual rate of
unemployment remained below the estimated NAIRU throughout the considered period except
that of 2004-07. The estimated results were according to the expectations, since the inflation
rate remained high during 1981 to 2000. However inflation started declining and remained
relatively lower than unemployment during 2000-05. The estimated NAIRU also declined and
it was lower than inflation for the period. In 2006 the inflationand the unemploymentremained
up the NAIRU but inflation was relatively higher than the unemployment. From 2003 NAIRU
Figure 6a : Relationship of Inflation, Unemployment and Tv-NAIRU (U*) (1980 to 2007)
126

Mazhar-ul-Haq Baluch & Azam Amjad Chaudhry
(U*=100) decreased from 10.55 percent to 3.1 percent in 2007. However inflation touched
the bottom (3.1 percent) in 2004 and rose to 9.3 in 2006 and then fell monotonically to 7.9 per
cent in 2007. Theestimated figures by considering both the values of HPsmoothing parameter
(U* = 100 and U* = 1000) reflected the similar trend (Annexure-1).
8.4.1. Relationship of Unemployment and NAIRU
Generally, adoption of any simple or complex strategy, the estimation of the NAIRU remained
a question, since the joint behaviour of inflation, increased wages and disturbance in
unemployment ratio caused the concept ofNAIRU seriously disrupted. However,the relationship
of unemployment and the estimated NAIRU has been reflected in a relatively better way in
Figure 7. It was observed that NAIRU was below in 1980 and beyond that the NAIRU remained
higher then unemployment throughout the remaining period up to 2004. Both the variables i.e.
NAIRU and actual unemployment showed a continuous increase but this increase was relatively
far more in case of NAIRU. However there remained short term fluctuations in them. NAIRU
tended touched the peak in 2000. During 2004 and 2006 the NAIRU remained below the
unemployment and inflation.
Figure 7
Estimated NAIRU (= 100) and Unemployment
5. CONCLUSIONS
Considering the prevalent economic environment of the country, the relationship of inflation,
unemployment and estimated NAIRUhas been assessed in the light of Phillips curve to chalk
out the relevant policies, theoretically suitable for smoothing economic concern functions. The
results derived applying uni-variate model, reflected weak specification,however, expected
negative relationship between unemployment and inflation. However the estimates indicated
that certain theoretically accepted additional variables and supply shocks are essentially needed
to derive reliable estimates regarding Phillips curve for Pakistan. Consequently food and energy
price inflation were included in the models to derive relatively reliable results.
Relationship of Rate of Unemployment and Inflation in Pakistan…

127
The results reflected unstable long run relationship between inflation and unemployment
since the estimated constant NAIRU was different for every considered periodical segment
under the existing environment of the country by adding lagged variables of unemployment
and inflation and other supply shocks variables.
Relaxing the assumption of constant NAIRU, the time varying NAIRU was estimated by
following the Ball and Mankiw (2000) approach applying the described equation and finally
Hodrick Prescott (HP) filter technique. The actual rate of unemployment remained below the
estimated NAIRU throughout the considered period except that of 2004-07. The estimated
results were according to the expectations, since the inflation rate remained high during 1981 to
2000. However inflation started declining and remained relatively lower during 2000-05. The
estimated NAIRU also declined and it was lower than inflation for the period. In 2006 the
inflationand the unemploymentremained up the NAIRU but inflation was relatively higher than
the unemployment. From 2003 NAIRU decreased from 10.55 percent to 3.1 percent in 2007.
However inflation touched the bottom (3.1 percent) in 2004 and rose to 9.3 in 2006 and then
fell monotonically to 7.9 per cent in 2007. NAIRU remained higher then unemployment
throughout the considered period up to 2004. The NAIRU and actual unemployment showed a
continuous increase but this increase was relatively far more in case of NAIRU. However there
remained short term fluctuations in them. NAIRU tended touched the peak in 2000.
Lastly, the NAIRU remained changing overtime and supported the idea of time varying
NAIRU. The effecting factors such as public policies developed and implemented overtime,
labour productivity, and behavioural attitude of local and abroad marketing of the products
were responsible for the prevalent economic situation and NAIRU as well.
Finally, the derived conclusion confirmed existence of trade off between inflation and
unemployment and this trade off ascertains the Phillips curve. However, despite encountering
measurement problems, NAIRU can be used as a forecasting tool to assess its relationship with
unemployment and inflation for policy implications in future.It provides base for policy makers
to use other available tools for inflation targeting and keep unemployment as low as possible
without accelerating inflation.
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Mazhar-ul-Haq Baluch & Azam Amjad Chaudhry
ANNEXURE—1
Years
U**
Lower
Upper
1980
8.747915
7.422326
13.05091
1981
8.749066
7.423477
13.05207
1982
8.718333
7.392744
13.02133
1983
8.611094
7.285505
12.91409
1984
8.410447
7.084858
12.71345
1985
8.173149
6.84756
12.47615
1986
7.921245
6.595656
12.22425
1987
7.685816
6.360227
11.98882
1988
7.512515
6.186925
11.81551
1989
7.447891
6.122302
11.75089
1990
7.497735
6.172146
11.80074
1991
7.627332
6.301743
11.93033
1992
7.851594
6.526005
12.15459
1993
8.125696
6.800107
12.4287
1994
8.436328
7.110739
12.73933
1995
8.778385
7.452795
13.08138
1996
9.120357
7.794768
13.42336
1997
9.395095
8.069506
13.6981
1998
9.561712
8.236123
13.86471
1999
9.568768
8.243179
13.87177
2000
9.414288
8.088699
13.71729
2001
9.111969
7.78638
13.41497
2002
8.705865
7.380276
13.00887
2003
8.231356
6.905767
12.53436
2004
7.736955
6.411366
12.03996
2005
7.272285
5.946696
11.57529
2006
6.856095
5.530505
11.15909
2007
6.452958
5.127368
10.75596
U** = Estimated NAIRU.