2. International Research Journal of Finance and Economics - Issue 22 (2008) 180
Since independence, notably the economy of Pakistan shows high Economic Growth in three
different periods: 1958 to 1970, 1977 to 1988 and 1999 to 2008; surprisingly all the time periods are
military-led governments. By keeping in view the type of government and high or low economic
performance, the sixty-three years are divided in seven periods:
1. Post-Independence era (1947-1958)
2. Military-led government era (1958-1971)
3. Elected governments era (1971-1977)
4. Military-led government era (1977-1988)
5. Elected governments era (1988- 1999)
6. Military-led government era (1999- 2008)
7. Elected governments era (2008 to present)
In the above mentioned periodical categories, first eleven years were just the foundation time
period. During military regime in second (1958-71), forth (1977-1988) and the sixth period (1999-
2008) high economic growth trend was observed and unfortunately after every military government the
elected government did not even maintain the economic growth, on the contrary the economic
conditions became that much worse that sometimes the country was near to default.
On the political side, since its birth Pakistan could never enjoy a stable political scenario. It has
experienced almost all types of political controls from democracy-presidential and parliamentary- to
repeated military coups with economic theses from capitalism to socialism and a mix of these theoretic.
Pakistan experienced military government for almost 33 years and other 30 years practiced different
forms of government including democracy, which is approximately always dissolved before time. In
these 30 years the early 9 years was without any defined political system (1947-1956).
In the current study, by keeping in view the fluctuations in the economic development with the
political scenario, it is hypothesized that besides other factors political instability plays a vital role in
the fluctuation of the economic development of Pakistan.
2. Literature Review
The relationship between political instability and economic growth is not new, a good number of
studies are present in this area. In the coming lines the most prominent literature is reviewed.
Alesina, Ozler, Roubini and Swagel (1992) investigate the connections between economic
growth and political instability. For the period of 1950-82, he uses the sample of one hundred and
thirteen countries. The study uses simultaneous equations model and measure economic growth and
political instability simultaneously by using Amemiya’s Generalized Least Square technique (AGLS).
The authors conclude that where there are high chances of government collapse, growth is considerably
lower and vice versa, further they do not find any indication that economic growth is significantly
different when authoritarian regimes are compared to democracies.
Barro and Lee (1994) observe the growth rates in 116 economies from 1965 to 1985. They
estimate five different variables impact on economic growth including political instability that is
measured by number of revolutions per year. The authors conclude that Political instability negatively
affects economic growth.
Haan and Siermann (1996) examine whether lack of political stability and lack of political
freedom are negatively correlated with economic growth. By using a sample of 97 countries (excluding
major oil-exporters) for the period of 1963-88, the authors estimate cross-section model based on a
simple neo-classical production function. In conclusions, the study shows that the political instability
reduces economic growth in Africa and in continuation in Asia political instability hampers
investment.
Feng (1997) examines the interrelationships among democracy, political instability and
economic growth. The author uses the technique of three-stage least square on the cross country data of
ninety six countries for the time period of 1960 to 1980 by using simultaneous equation system. The
estimation facts point out either there is constitutional government change or regime change, both have
3. 181 International Research Journal of Finance and Economics - Issue 56 (2010)
the significant reverse effects on growth; on the other hand the growth is affecting regime change
negatively and affecting positively the probability of the ruling party staying in power.
Guillaumont, Jeanneney and Brun (1999) examine the role of instabilities on Africa’s low rates
of growth during 1970 to 1990 on a sample of African and non-African countries by using cross-
section statistical estimates. Among other instabilities, the authors estimate impact of political
instability on economic growth. The findings depict that the political instability lowered the rate of
GDP growth more through their effect on total factor productivity growth than by diminishing the rate
of investment.
Gyimah-Brempong and Traynor (1999) investigate the linkages between economic growth and
political instability. He uses a big sample of 39 Sub-Saharan African countries. The researchers use
simultaneous equations model and a dynamic panel estimation method for the estimation of time-series
cross-national data for the period of 1975-88. In the findings the negative relationship is identified
between economic growth and political instability.
Haber, Razo and Maurer (2000) presents rather surprising conclusions, clearly that it is not
compulsory that Political instability hamper the economic growth significantly by focusing on a single
country, Mexico, for the period of 1910-1934 (this time period in Mexico was noticeable by
revolutions, counter-revolutions, civil wars, military coups, and presidential assassinations). The
research is basically a discussion on the said topic on the basis of facts and figures over the said period.
They analyze descriptively and extensively and also with some regression analysis. Then they exert
that either there is political instability or not, the government can flourish the economic performance
just protecting the private property rights. Opposing the general phenomena that political instability
always worsens the economic performance.
Asteriou and Price (2001) test the influence of political instability on economic growth for
United Kingdom for the period of 1961-1997 by using time-series data. They use GARCH and
GARCH-M models and OLS regression technique. Besides the direct use of proxies, they construct the
political instability index by employing principal component method. The researchers find that
political instability and growth of UK GDP per capita are strongly negatively related to each other.
Fosu (2001) scrutinizes the inter-linkages between political instability and economic growth.
He uses the Cobb–Douglas production function. He uses the data of different events of coups d’etat of
the Sub-Saharan African countries for the period of 1960–1986. In conclusion, the author says that
political instability greatly hampers the economic growth in the Sub-Saharan African countries.
Further, he adds that political instability index is giving much more appropriate results than the coup
variables separately.
Berthelemy, J.C., Kauffmann, C., Renard, L. and Wegner, L. (2002) observe that how
Government behaves on the political and economic level when social and political disturbances take
place. The researchers work on the dataset of twenty-two African countries for the time period of 1996
to 2001. They analyzed on quarterly basis from January 1996 to December 2001 to understand the
interlinks between nature of political regime and political unrest; while annual data is also used to
observe the economic and political dynamics. Besides other conclusions, the researchers come to the
points that not only political events controlled the political developments, the economic factors also
responsible to them.
Fosu (2002) examines the degree to which a range of coup events of political instability in the
thirty one Sub-Saharan African countries bothered the economic growth in the post-colonial period for
the period of 1960-86. The author uses augmented production framework that integrates political
instability events. The study uses OLS technique for the analysis. Political instability is measured as of
three events, they are as given: Abortive coups, Coup plots and Successful coups. Initially, the impact
on economic growth of Abortive coup, Coup plots and Successful coups are observed separately and
then re-estimated with all three political instability events together. But the results remain same that
Abortive coups are found largest adverse effect on GDP growth and Coup plots effects are found
adverse and significant but less than Abortive coups and at last Successful coup, surprisingly, are
4. International Research Journal of Finance and Economics - Issue 22 (2008) 182
observed as insignificant. McGowan & Johnson (1984) find that successful coup was the most
destabilizing event to economic growth but on the contrary this work finds that it is insignificant in
case of 31 SSA countries.
Ghura and Mercereau (2004) provide econometrical proof that through deteriorations in terms
of trade and low tax revenues, the chances of political instability in the Central African Republic has
been augmented. Although econometrically the direct impact of political instability on economic
growth is not significant. With the intention to test the proposition, the researchers estimate a probit
model for the Central African Republic annual data series for the period of 1967- 2002. In conclusion,
it is said that political instability and social unrest adversely affect the Central African Republic
economy from time to time.
Institute of Integrated Development Studies, Katmandu, Nepal (2005) conduct research to
observe the very active topic of today’s research, i.e. how political instability affect the economic
growth. The study is conducted particularly for the country Nepal. They construct two political
instability indexes to capture the political unstable environment of Nepal. For the construction of
indexes, they use Principal component method for one and for the other index Arbitrary method. The
researchers used following six variables (Demonstrations, Politically motivated killings, general
strikes, the sum of the total numbers of political prisoners, Change of government and Change of
cabinet) in the construction of Political instability indexes. Through the econometric analysis, the study
reveals that economic growth is badly affected by the political instability in Nepal. The impact of
political instability on the economy of Nepal is extensive.
Zureiqat (2005) studies the connections between political instability and economic
performance, where Polity II democratization score2 is used as the political instability measurement
variable, while GDP per capita is used for the measurement of economic performance. The author uses
the data of twenty five countries of five different regions: Africa, Central and Eastern Europe, Latin
America, the Middle East, and Southeast Asia. The sample is of 1985 to 2002. The results of the
empirical models demonstrate significant evidence supporting the proposition that political instability
slow down the economic growth and, on the other hand, there is no significant sign observed that
economic growth and political instability are endogenous and mutually determined. Regarding the
question that how different economies behave with political instability, vary greatly among the regions.
It is found that Southeast Asia with the lowest Polity II score has the highest GDP per capita, while the
Middle East with the highest Polity II score has the highest GDP per capita.
Campos and Karanasos (2008) analyze the impact of political instability on the economic
growth of by using time series data for Argentina over a long time period of 1896 to 2000. They use
Power-ARCH framework for the study. They find strong negative impact of political instability on the
economic growth of Argentina.
Through the literature review, we come to the conclusion that political instability exclusively
hampers the economic growth either there is time series analysis or the panel data analysis.
3. Research Design and Sample
This research is associated with the relationship between political instability and economic
development of Pakistan. To examine this relationship econometric tools have been used. The sample
of the study is 1971 to 2008. Annual time series data of this period has been used.
2
Polity2 was published in the Polity IV dataset project by Monty G. Marshall of the University of Maryland, College Park
and Keith Jaggers of Colorado State University in 2002. The score ranges from -10 (strongly autocratic) to +10 (strongly
democratic).
5. 183 International Research Journal of Finance and Economics - Issue 56 (2010)
4. Models
To observe the overall impact of political inability on the economic development in Pakistan, some
single equation models are established to check our hypothesis.
We are going to observe the relationship of dependent variables with the political instability;
wherever necessary we convert dependent variable series in the log form, because our variable Political
Instability which is independent variable here, is in the form of index with mean 10. The political
instability index is self-constructed and discussed in the coming lines. So, now equations in the
functional forms are:
1. Manf = f (PI)
2. LInv = f (PI)
3. Exp = f (PI)
4. Inf = f (PI)
5. LExtdt = f (PI)
6. Emp = f (PI)
Where political instability (PI) is independent variable and manufacturing production growth
rate (Manf), log of total investment (LInv), total exports growth rate (Exp), inflation rate (Inf), log of
external foreign debt (LExtdt) and employment rate (Emp) are the dependent variables, manufacturing
production is used as a proxy of domestic output.
5. Sources of the Data
All the data is taken from the World Development Indicators 2010, except total investment data is
taken from the Government of Pakistan, Pakistan Economic Survey, Various issues and for political
instability an index is constructed.
The data for all the variables of political instability is self-calculated on the basis of quality data
taken from the book, Razi and Shakir (2005) “Pakistan 58 years”; which is based on a research of all
leading newspaper news.
6. Construction of Political Instability Index
Quantitative studies of the relation between political instability and economic growth have to tackle
one major issue that is how to define political instability.
Researchers have defined and measured political instability in two ways. One way is to use one
or more variables suitable for study i.e. some studies used only one variable as an indicator of political
instability. Haan and Siermann (1996), Benhabib and Spiegel (1997) focuses on executive turnover,
namely, on the frequency of government collapses as an indicator; Alesina et al. (1992) and Alesina et
al. (1996) used an estimated probability of government termination; Guillaumont, Jeanneney and Brun
(1999) and Stevens (2000) used Government longevity; Fosu (2002a), Fosu (2002b), Fosu (2004), and
Ghura and Mercereau (2004) used coup events and Zureiqat (2005) used Polity II democratization
score as an indicator. Some researchers used two or three different variables to define political
instability, such as Easterly and Rebelo (1993) used assassinations and war casualties; Barro and Lee
(1994) used revolutions, assassinations war casualties; Sala-i-Martin (1997) used revolutions, coups,
and war dummy; Ley and Steel (1999) used revolutions, coups, and war dummy; Berthelemy et al.
(2002) used occurrence of strikes, demonstrations, violence and coup d’etat; etc. The other way is to
use an index of socio-political unrest that summarizes several indicators of more or less violent forms
of political protest and social violence such as riots, political prisoners, demonstrations against the
government, strikes against government, purges, political motivated killings and assassinations etc.
Earlier, Hibbs (1973), Vaniers and Gupta (1986) and Gupta (1990) used Principal Component
Method for the construction of Political Instability index, but this method become prominent, when it
is used by Alesina and Perotti (1996). Later on by following them some of the researchers also used the
6. International Research Journal of Finance and Economics - Issue 22 (2008) 184
Principal Component Method to construct an aggregate Political Instability index i.e. Perotti (1996),
Campos and Nugent (1999a), Gyimah-Brempong and Traynor (1999), Annett (2000), Asteriou and
Simon Price (2001) and Ponzio (2005) and Institute of Integrated Development Studies, Katmandu,
Nepal (2005). Although, all of them used same technique, but definitely of different indicators as
suited them for the study.
Political instability in Pakistan normally observed only through democratic or non-democratic
government, but in fact, this criterion is failed to describe political stability in Pakistan, There is
democracy or not, this is only one factor of political stability. As it is mentioned in earlier that in
military regimes Pakistan showed much more good performance in economic indicators than in
democratic era. Although, it is believed that it is only due to government longevity, so government
longevity is another factor of political stability. Some believe that frequent government changes plays
vital role in political instability. So we cannot only justified by one of these phenomenons. For proper
measurement of political instability, following the methodology of Alesina and Perotti (1996) but
different indicators that can best define the political instability in Pakistan are being used to construct
political instability index for Pakistan for the year 1971-2008.
The total number of general strikes, demonstrations, riots, government longevity, change of
government including coups, war and regime type have all been used as indicators of political
instability and thus are used to construct a composite index of political instability. This Political
Instability Index is constructed by Principle Component Method by using computer software “Minitab
13 for Windows”.
Above-mentioned studies, which used Principal Component technique for constructing index,
used only first principal component index for the analysis except Asteriou and Simon Price (2001)
(who used all principal component indexes as that study scope was). Therefore, we also use the first
principal component index as the index of political instability.
6.1. Definitions of Political Instability Index Variables
Although detailed description of variables is given in Appendix D, for general view brief definitions
are outlined here.
General Strikes
General strikes called by political actors for a complete closedown or blockade of economic activities
that can last anywhere from a single to several days.
Demonstrations
It is an assembly of people or procession with an explicit political purpose.
Riots
It is explained as any violent demonstration or clash of more than 100 citizens involving the use of
physical force.
Government Longevity
Years of Government longevity.
Government change
An instance of change of government including military coups.
War
Dummy.
Regime Type
Democracy, Military led democracy or Military government.
7. 185 International Research Journal of Finance and Economics - Issue 56 (2010)
6.2. Summary of Political Instability Index
As earlier mentioned that political instability is measured as the sum of the total numbers of general
strikes called by political actors, demonstrations, riots, government longevity, government change
including military coups, war and regime type. We consider that these particular above mentioned
parameters, rather than any other set of variables, are able to better represent the politically unstable
environment of Pakistan.
The rational in selecting these particular parameters is that they, not only, are able to capture
politically unstable situations but also their possible effect on economic growth in Pakistan. General
statistical features of these indicators are given below in table 6.1 and the brief summary of first
principal component is given below in table 6.2.
Table 6.1: Summary of the Political Instability Indicators
S.No. Variables Observations Min Max
1 General Strikes 38 0 69
2 Demonstrations 38 0 31
3 Riots 38 13 258
4 Government Longevity 38 0 10
5 Government change 38 0 1
6 War 38 0 1
7 Regime Type 38 1 3
Source: Self calculation
Min: Minimum value of variable in a year
Max: Maximum value of variable in a year
Table 6.2: Factor Loadings of PC1
Variables PC1
General Strikes 0.511*
Demonstrations 0.384**
Riots 0.329**
Government Longevity -0.326**
Government change 0.378**
War 0.457*
Regime Type -0.158
Note:
* Denotes statistical significance at 1% level.
** Denotes statistical significance at 5% level
Critical values from Koutsoyiannis (1977)
PI = 0.511 E1 + 0.384 E2 + 0.329 E3 – 0.326 E4 + 0.378 E5 + 0.457 E6 – 0.158 E7
Where
E1 = General Strikes E2 = Demonstration
E3 = Riots E4 = Government Longevity
E5 = Government change including coups E6 = War
E7 = Regime Type
On the basis of above mentioned equation political instability is calculated and standardized as
required by the method for unbiased results, then the index is transformed with mean 103 for better
results.
3
Institute of Integrated Development Studies, Katmandu, Nepal (2005), the index constructed transformed for better
analysis
8. International Research Journal of Finance and Economics - Issue 22 (2008) 186
7. Definitions of all Variables
Some of the variables in above-mentioned model are quite clear, but for a clear-cut understanding
summary of specification is given below for all of the variables.
PI = Political Instability: Measured by political instability index which is self
constructed by using seven different variables (General Strikes, Demonstration,
Riots, Government longevity, Government change, War and Regime type) by
using Principal Component Method for the year 1971-2008.
Manf = Manufacturing production: Manufacturing production growth rate.
LInv = Log of Investment: Log of Total investment.
Exp = Total exports: Annual Growth rate of total exports.
Inf = Inflation: Inflation rate.
LExtd = Log of total external foreign debt.
Emp = Employment rate
8. Estimation and Explanation
For observing the effects of political instability on the economic development of Pakistan for the time
period of 1971 to 2008, in this section we are going to present results and their explanation
All the equations are estimated by the method of OLS and the base year for all observations is
same. All the equations and their parameters are already discussed. If autocorrelation is found in an
equation, it is re-estimated by Auto regression or Moving average; AR (1) or AR (2) or MA (1) or MA
(2). The results of the estimations are reported below.
8.1. Manufacturing Sector and Political Instability
• Dependent Variable: Manufacturing Sector Growth Rate
• Independent Variable: Political Instability Index
In the table 8.1, the results show that the impact of political instability on manufacturing sector
is highly significant at 1%, and the relationship is negative which was expected. The found
autocorrelation is removed by using AR(1) and MA(1). High political instability reduces the growth in
the manufacturing sector. This is well proven fact that the manufacturing sector is directly inter-linked
with the government trade policy and if the overall political situation is poor in the country, it will
negatively affect the manufacturing organizations too, as it is depicted through our results.
8.2. Investment and Political Instability
• Dependent Variable: Log of total Investment
• Independent Variable: Political Instability Index
In the investment equation, the political instability is negative and significant which shows that
the overall investment gets also hampered due to political instability in Pakistan (see table 8.2). The
result to this variable is significant at 10 percent level. The autocorrelation is removed through AR(1),
AR(2) and MA(1). High political instability adversely affects the overall investment in Pakistan. High
political instability puts off the coming investment and also disturbs existed investment; this is, in fact,
a long run loss in terms of loosing investors’ confidence for both: existed and expected future
investors. Hence, we can say on the basis of our results that stability in the political setup improves
investors’ confidence, which ultimately develop investment culture in the country.
9. 187 International Research Journal of Finance and Economics - Issue 56 (2010)
Table 8.1: Summary of results for single equations regressions Sample 1971-2008
Manufacturing sector Equation Dependent variable:
Manufacturing growth rate
Variables Coefficient (t-statistics in parenthesis)
Ordinary Least Square Method
Intercept 20.86701
(3.946685)
Political Instability -1.473978*
(-2.758455)
AR(1) -0.306533
(-1.279711)
MA(1) 0.512507
(1.965698)
R2 0.289149
Adjusted R2 0.218064
F-Statistics 4.067640
Sum of squared residual 327.0565
Durbin-Watson stat 1.857588
Inverted AR Roots -.31
Inverted MA Roots -.51
Note:
* Denotes statistical significance at 1% level or better.
** Denotes statistical significance at 5% level
*** Denotes statistical significance at 10% level
8.3. Export Growth and Political Instability
• Dependent Variable: Export Growth Rate
• Independent Variable: Political Instability Index
On the basis of investment equation results, we can deduce that if investment is hampered due
to unstable political setup, the export will also be disturbed due to political instability; because if due to
high political instability, there is no good environment for investment how there could be good export
growth. So, the results in table 8.3 prove our hypothesis in the export equation that the exports in
Pakistan adversely affected due to high political instability. The result is significant at 5% level and
there is no autocorrelation found. F-statistics and T-statistics are quite significant in the equation.
Table 8.2: Summary of results for single equations regressions Sample 1971-2008
Investment Equation Dependent variable:
Log of total Investment
Coefficient (t-statistics in parenthesis)
Variables
Ordinary Least Square Method
15.78419
Intercept
(12.38268)
-0.025031***
Political Instability
(-1.740548)
0.260973
AR(1)
(0.967450)
0.674779
AR(2)
(2.602460)
0.839183
MA(1)
(3.301599)
R2 0.996242
Adjusted R2 0.995664
F-Statistics 1723.289
Sum of squared residual 0.163992
10. International Research Journal of Finance and Economics - Issue 22 (2008) 188
Durbin-Watson stat 2.022875
Inverted AR Roots .96 -.70
Inverted MA Roots -.84
Note:
* Denotes statistical significance at 1% level or better.
** Denotes statistical significance at 5% level
*** Denotes statistical significance at 10% level
Table 8.3: Summary of results for single equations regressions Sample 1971-2008
Export growth Equation Dependent variable:
Total export growth rate
Coefficient (t-statistics in parenthesis)
Variables
Ordinary Least Square Method
31.93331
Intercept
(2.484981)
-2.558662**
Political Instability
(-2.018532)
R2 0.109900
Adjusted R2 0.082927
F-Statistics 4.074470
Sum of squared residual 5149.681
Durbin-Watson stat 2.267069
Note:
* Denotes statistical significance at 1% level or better.
** Denotes statistical significance at 5% level
*** Denotes statistical significance at 10% level
8.4. Inflation and Political Instability
•Dependent Variable: Inflation Rate
•Independent Variable: Political Instability Index
When we find out the impact of political instability on the inflation in Pakistan, our results
depict the positive relationship between the two variables; results are shown in table 8.4. When there is
high political instability in the country, there will be higher inflation and the results are highly
significant. There was autocorrelation found in the results which was removed through AR(1) and
AR(2). Fstatistics and T-statistics are also showing complete significance.
For the country like Pakistan where inflation remains a constant issue to control over the
history, during 90s seven out of ten years, the inflation remains in two digits, that mean more than
10%4, then slow down during 1998 to 2003 and again has increasing trend since then. The 90s was
considered as unstable political environment too, due to frequent government changes and bad law and
order situation in the country. So, we can say that if there is high instability in the political
environment, the overall price level will also increase unexpectedly.
4
Government of Pakistan, Pakistan Economic Survey 2005-06.
11. 189 International Research Journal of Finance and Economics - Issue 56 (2010)
Table 8.4: Summary of results for single equations regressions Sample 1971-2008
Inflation Equation
Dependent variable: Inflation Rate
Variables Coefficient (t-statistics in parenthesis)
Ordinary Least Square Method
Intercept -10.25975
(-2.628662)
Political Instability 1.940467*
(5.141241)
AR(1) 1.069568
(8.728792)
AR(2) -0.618196
(-5.645178)
R2 0.711400
Adjusted R2 0.681545
F-Statistics 23.82842
Sum of squared residual 286.0604
Durbin-Watson stat 2.045941
Inverted AR Roots .53+.58i .53-.58i
Note:
* Denotes statistical significance at 1% level or better.
** Denotes statistical significance at 5% level
*** Denotes statistical significance at 10% level
8.5. Foreign Debt and Political Instability
• Dependent Variable: Log of Total Foreign Debt
• Independent Variable: Political Instability Index
In external debt equation, we hypothesize that when there is higher instability in the political
environment, there will be higher external debt burden. Our results prove our hypothesis and the
political instability is quite positively significant (see table 8.5); which reveals that whenever there is
instability in the political setup in the country, the overall debt start increasing more. The result is
significant at 5% level. The found autocorrelation is removed through AR(2). T-statistics and F-
statistics are at significant level. External foreign debt remains a big burden on the Pakistan economy
since 1970s; no serious efforts were made to get rid of foreign loans. In 1998-99, it has reached
maximum to 61.48%1 of total GDP of Pakistan. Although, since then, it has a declining trend, but even
then it is still very high up to 32.6% of total GDP.
12. International Research Journal of Finance and Economics - Issue 22 (2008) 190
Table 8.5: Summary of results for single equations regressions Sample 1971-2008
Foreign Debt Burden Equation Dependent variable:
Log of Total Foreign Debt
Coefficient (t-statistics in parenthesis)
Variables
Ordinary Least Square Method
24.76518
Intercept
(87.11755)
0.013747**
Political Instability
(2.022953)
0.905690
AR(2)
(58.71782)
R2 0.992414
Adjusted R2 0.991908
F-Statistics 1962.289
Sum of squared residual 0.101145
Durbin-Watson stat 1.540473
Inverted AR Roots .95 -.95
Note:
* Denotes statistical significance at 1% level or better.
** Denotes statistical significance at 5% level
*** Denotes statistical significance at 10% level
8.6. Employment Growth Rate and Political Instability
• Dependent Variable: Employment Growth Rate
• Independent Variable: Political Instability Index
The employment rate is always a big question in Pakistan’s economy. The economist always
would like to find out the root causes of disturbances in employment generation. In the employment
growth equation (see table 8.6), political instability is negative, as it was expected. The result is highly
significant at 1% level. There was autocorrelation found in the results which was removed through
AR(1) and MA(1). In Pakistan, political instability highly disturbs the employment rate. The results
significance can be observed by T-statistics and F-statistics.
Table 8.6: Summary of results for single equations regressions Sample 1971-2008
Employment Growth Rate Equation Dependent variable:
Employment Growth Rate
Coefficient (t-statistics in parenthesis)
Variables
Ordinary Least Square Method
-22.07097
Intercept
(-1.153483)
-0.325529*
Political Instability
(-2.626680)
1.012564
AR(1)
(229.0833)
-0.957090
MA(1)
(-51.00997)
R2 0.990045
Adjusted R2 0.989050
F-Statistics 994.5317
Sum of squared residual 15.11551
Durbin-Watson stat 1.762263
Inverted AR Roots 1.01
Inverted MA Roots 0.96
Note:
* Denotes statistical significance at 1% level or better.
** Denotes statistical significance at 5% level
*** Denotes statistical significance at 10% level
13. 191 International Research Journal of Finance and Economics - Issue 56 (2010)
Conclusion
Throughout the analysis, the impact of political instability on the economic development is quite
significant and robust. Political instability badly hampers most of the areas of the economy. It is
evident from the history that economic growth in Pakistan averages to a good throughout the past that
is approximately 5% per annum5. Yet the study has concluded a profound impact of political scene on
the economic development of the country that is a bad political situation is found to be a development
retarding one and the development pattern is highly volatile in the years of political instability that
spans almost over the half history. In fact, the decision makers are political actors, the policies are
made, executed but disturbed due to political instability, and most of the policies go in vain due to
government change particularly irregular government change. So, if the political scene is made stable
then at least the same pace of economic development can sum to the average number very easily and
even this contribution may lead to higher levels of economic development. Here the recommendation
of the study is to smooth the political scene of the country which broadly can contribute to the present
development and average growth rate of the country.
The most important for Pakistan is, let any of the political system be flourished; there must not
be any discontinuation of it because every thing needs some time to grow up. The most harmful for the
development of Pakistan is non-stability of political system, which is actually the centre of decision, so
stable the political setup for the long term prosperity of the country.
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