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FORMAN JOURNAL OF ECONOMIC STUDIES
Volume: 8
2012
January-December
An Analysis of International Income Inequality
1
Muhammad Idrees and
Eatzaz Ahmad
Impact of Natural Disasters, Terrorism and Political News on
KSE-100 Index
13
Mian Ahmad Hanan, Saleem Noshina,
Saqib Ali Siddiqui and Shahid Imran
Performance of Alternative Price Forecast for Pakistan
31
Yaser Javed and
Eatzaz Ahmad
Impact of Trade Openness on Exports Growth,
Imports Growth and Trade Balance of Pakistan
63
M. Aslam Chaudhary and
Baber Amin
Determinants of Youth Activities in Pakistan
83
Rizwan Ahmad and
Ijaz Hussain
A Study of Implicit Tax in Pakistan’s Agriculture, with
Special Reference to the Case of Rice
107
Mohammad Aslam
Determinants of Residential Electricity Expenditure in Pakistan:
Urban-Rural Comparison
127
Ijaz Hussain and
Muhammad Asad
DEPARTMENT OF ECONOMICS
FORMAN CHRISTIAN COLLEGE (A CHARTERED UNIVERSITY)
FEROZEPUR ROAD, LAHORE, PAKISTAN
iii
Forman Journal of Economic Studies
Vol. 8, 2012 (January–December) pp. 1-11
An Analysis of International Income Inequality
Muhammad Idrees and Eatzaz Ahmad1
Abstract
This study measures and decomposes world income inequality between
world’s geographic regions during the past two decades using Theil’s two
measures of inequality. The study finds that the extent of income inequality
has been decreasing over the years mainly because of increasing per capita
income in China and to some extent India. Income inequality has been
highest, but declining sharply over time in East Asia & Pacific. Sub-Saharan
Africa and Middle East & North Africa show moderate income inequalities,
while other regions of the world show low inequality. The study finds that the
contribution of income inequality between regions has been substantially
larger than the contribution of inequality within regions.
Keywords: Income inequality; Geographical blocks; Theil’s Entropy
JEL classification: D63, O18, P25
1.
Introduction
With the passage of time and the world’s economies emerging as a
global village, the issue of world income distribution has gained importance.
A small number of countries are very rich, accounting for a significant
proportion of the world GDP. According to World Development Indicators
(WDI) data, based on the country-specific per capita incomes, the richest 20%
of the world population (those living in the richest countries) are found to
account for 80% of world income, while the share of poorest 20% of the
population has remained less than 2% of the world income. Table 1 indicates
that the income share of the middle 60% of world population has varied
between 13.78 and 26.6, signifying a skewed distribution with long tail of
poverty.
Quite a few studies have analysed income inequality across countries.
As noted in Heshmati (2004), the earlier work can be divided into two
categories. The first approach is to measure international inequality as
economic disparities between countries considering per capita GDP of each
1
The authors are Assistant Professor and Professor/Dean at School of Economics, Quaid-iAzam University, Islamabad, respectively.
Idrees and Ahmad
country as the unit of analysis [e.g. Andic and Peacock (1961), Rati R. (1979),
Berry et al. (1983), Chotikapanich (1997), Deininger and Squire (1996), Park
(2001), Podder (1993), Schultz (1998), Sala-i-Martin (2002), Firebaugh and
Goesling (2004) and Theil (1979, 1996), Theil and Seale (1994)]. This
approach is simple but ignores inequality within each country. The second
approach, which is to measure global inequality as economic disparity
between all individuals in the world, uses household income as the unit of
analysis by utilizing national level surveys [e.g. Milanovic (2002, 2005,
2010)]. But practical application of this approach is limited, as national
surveys of all countries in a given period are not easily available and the
household income measurement practices can vary considerably across
countries.
Table 1: Quintiles of Countries based on Per Capita GDP
Year
Share of Top 20%
Share of Bottom 20%
1960
1970
1980
1990
2000
2010
84.24
72.88
76.08
84.49
85.20
80.63
1.29
0.52
0.67
0.94
1.02
0.61
Note: Calculations are based on WDI.
The present study provides time series (1990 to 2010) of international
income inequality measures across countries of the world. In addition it also
decomposes international income inequality between different geographical
blocks of the world in order to observe the relative extent of inequality within
and between various regions.
2.
Analytical Framework and Data
The study carries out two tasks; a) measurement of international
income inequality over the period 1990 to 2010 based on per capita GDP at
PPP adjusted constant 2000 US$ and b) decomposition of the international
inequality into geographical blocks of the world.
Of all the measures of inequality, Gini index and Theil’s Entropy
measures have attracted much attention in empirical literature because of their
relative agreement with theoretically desirable properties of an inequality
measure. Since the decomposition of Gini index between regions would
2
Analysis of International Income Inequality
include, besides between and within components, a term called trans-variation
which has no straightforward interpretation [Dangum (1997)]; the study
employs Theil’s two well-known measures that are neatly decomposable.
Denoting per capita income of country i, per capita world income and the
number of countries by Yi , Y and n respectively, Theil’s measures are given
by:
Theil’s First Measure:
T1 =
1 n ⎛ Yi ⎞ ⎛ Yi ⎞
∑ ⎜ ⎟ ln ⎜ ⎟
n i =1 ⎝ Y ⎠ ⎝ Y ⎠
(1)
Theil’s Second Measure:
T2 =
1 n ⎡ ⎛⎜ Y
∑ ⎢ ln
n i =1 ⎢⎣ ⎜⎝ Y i
(2)
⎞⎤
⎟⎥
⎟⎥
⎠⎦
In case of perfect equality both T1 and T2 take the values equal to zero, while
in case of perfect inequality T1 and T2 take the values equal to log( n ) and
log(1 / n) n respectively.
According to Shorrocks (1980), the two measures are decomposable as
follows.
(3)
K
K
⎛ Yk ⎞
k
⎟
T 1 = ∑ s k .T 1 + ∑ s k ln ⎜
⎜ Y ⎟
k =1
k =1
1
424
3
⎝
1 4 2 4 3⎠
T
W
TB
K
K
⎛ Y ⎞
⎟
T 2 = ∑ p k .T 2k + ∑ p k ln ⎜
⎜Y ⎟
k =1
=1
1
42 4
3 k1
K ⎠
⎝
4
42 44
3
T
W
(4)
TB
Where sk and , pk are respectively the income and population shares
of group k, used as weights. The first term in each case measures weighted
inequality within the k sub-groups and the second term explains inequality
between the sub-groups. In order to calculate between groups inequality, Theil
measures set mean income of each country within each group equal the
respective group mean. The income inequality within groups is measured as
the weighted sum of inequalities within various groups.
Income inequality between countries can be based on GDP as the unit
of analysis, but in this case all countries are given equal weight irrespective of
their populations. Per capita GDP is obviously a better unit of analysis,
provided in the income inequality measures the income units (countries) are
3 Idrees and Ahmad
appropriately weighted by population or income shares as the case may be.
Furthermore, all per capita incomes have to be converted to one currency (e.g.
US$) and adjusted for PPP. The data for the present study are taken from
latest issue of World Development Indicators ((WDI)-2012, an annual
publication of World Bank. Since data for many countries are missing for the
earlier years, the present study covers the period 1990 to 2010 for 170
countries.
3.
Trends in International Inequality
The results of international income inequality presented in Figure 1
show a declining trend throughout the period of analysis. This indicates that
divergence between income and population share of different countries has
been decreasing with the passage of time. Furthermore, as expected, Theil’s
second measure that uses income shares rather than population shares as
weights shows greater degree of inequality and faster rate of decrease over the
years2.
Figure 1: Trends in International Income Inequalities
(….. Theil’s First Measure, ____ Theil’s Second Measure)
0.37
0.35
0.33
0.31
0.29
0.27
0.25
0.23
0.21
0.19
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
Our results are in harmony with those preseted in Firebaugh and
Goesling (2004) that if countries are weighted according to population, the
2
The estimates of Theil’s indices without PPP adjustment of per capita GDP show no
substantial difference in trend.
4
Analysis of International Income Inequality
international inequality shows declining trend. Chotikapanich et.al (2009) also
found that international income inequality declined between 1993 and 2000.
They emphasized that decline in inequality was largely attributable to
economic growth in China. Similar results were found in Sutcliffe (2004),
Warner et al. (2011) for China and Wolf (2004) for China and India.
China accounts for more than 21% of the world population and had
annual compound GDP growth rate at 9.5% during 1990-2000. India accounts
for more than 18% of the world population and had annual GDP growth rate
at 4.7% during the same period. In comparison to these two countries the
growth rate of rest of the world had been just 2.13%. Therefore, it is
worthwhile to determine how the trends in inequality are affected if one or
both of these countries are excluded from the sample. The results are shown in
figures 2 to 4.
As suspected the trends in international income inequality are almost
reversed when China is excluded from the sample (Figure 2). Both the indices
show an increasing trend in income inequality till 2000 and a mild decreasing
trend thereafter. Thus, the substantial decrease in income inequality over the
years may be attributed to inclusion of China, a relatively poor but fast
growing country, in the sample. On the other hand, exclusion of India (Figure
3) has no substantial effect on the general trends of income inequality across
countries, though the rate of decline in inequality is somewhat dampened.
Figure 2: Trends in International Income Inequalities (Excluding China)
(….. Theil’s First Measure, ____ Theil’s Second Measure)
0.37
0.35
0.33
0.31
0.29
0.27
0.25
0.23
0.21
0.19
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
5 Idrees and Ahmad
Figure 3: Trends in International Income Inequalities (Excluding India)
(….. Theil’s First Measure, ____ Theil’s Second Measure)
0.37
0.35
0.33
0.31
0.29
0.27
0.25
0.23
0.21
0.19
2009
2010
2009
2010
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
Figure 4: Trends in International Income Inequalities
(Excluding China and India)
(….. Theil’s First Measure, ____ Theil’s Second Measure)
0.37
0.35
0.33
0.31
0.29
0.27
0.25
0.23
0.21
0.19
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
This is so because the growth rate in India has not been as phenomenal
as in China. Both China and India account for close to one fifth of the world’s
population, both have per capita income less than the world average and the
GDP growth rate of China has been about twice as fast as that of India, which
in turn has been more than twice as fast as the growth rate of the rest of the
world. This explains why, as indicated in Figure 4, the presence of China and
6
Analysis of International Income Inequality
India in the sample have suppressed the income inequality across countries in
recent years to a reasonable extent.
4.
Decomposition of International Income Inequality
The decomposition is carried out with respect to the Wold Bank’s
classification of countries in seven geographic regions. The decomposition
statistics for all the years under consideration, not presented here, indicate
smooth trends over the years with no sudden jumps. Therefore in order to
preserve space, the statistics are presented with the gap of five years, that is,
for the years 1990, 1995, 2000, 2005 and 2010. The decomposition results are
reported in table 2.
The first two blocks of the table provide some indication of regional
income disparity. For example, as of the year 2010 the share of North
America in world income has been more than four times its share in world
population while the income share of Sub-Saharan Africa has been only one
fifth of its population share. This means that per capita income in North
America has been about 20 times the per capita income in Sub-Saharan
Africa. On the upper side of income distribution North America is followed
by Europe & Central Asia with per capita income about half of the former. On
the lower tail, Sub-Saharan Africa is closely followed by South Asia. The
income share of Latin America & Caribbean has been slightly higher than its
population share whereas the income share of East Asia & Pacific has been
somewhat lower than its population share.
The next two blocks show income disparity between countries within
each region. Both the measures show that the degree of inequality was highest
in East Asia & Pacific, which declined drastically after every five years. SubSaharan Africa and Middle East & North Africa show moderate income
inequalities, which remained quite stable over the years. The level of
inequality in the other four regions has been quite low and stable.
Coming now to the last two blocks, it is noticeable that Theil’s second
measure produces a larger contribution of inequality within regions than
Theil’s first measure. The reason is that Theils second measure assigns largest
weight (based on population) to the region of East Asia & Pacific where the
income inequality is the highest whereas Theil’s first measure assigns largest
weight (based on income) to the region of Europe & Central Asia and North
America where the income inequality is relatively quite low. In any case the
results show that the contribution of income inequality between regions has
7 Idrees and Ahmad
Table 2: Decomposition of World’s Per Capita Income Inequalities by
Geographic Regions
Inequality
statistics
Percentage
income shares
Percentage
population shares
Theil’s first
measure
Theil’s second
measure
Decomposition of
Decomposition of
Theil's 1st measure (%) Theil's 2nd measure (%)
Regions
1990
1995
2000
2005
2010
East Asia & Pacific
South Asia
Europe & Central Asia
Sub-Saharan Africa
Latin America & Caribbean
North America
Middle East & North Africa
East Asia & Pacific
South Asia
Europe & Central Asia
Sub-Saharan Africa
Latin America & Caribbean
North America
Middle East & North Africa
East Asia & Pacific
South Asia
Europe & Central Asia
Sub-Saharan Africa
Latin America & Caribbean
North America
Middle East & North Africa
East Asia & Pacific
South Asia
Europe & Central Asia
Sub-Saharan Africa
Latin America & Caribbean
North America
Middle East & North Africa
Contribution of Inequality within East Asia & Pacific
Contribution of Inequality within South Asia
Contribution of Inequality within Europe & Central Asia
Contribution of Inequality within Sub-Saharan Africa
Contribution of Inequality within Latin America & Caribbean
Contribution of Inequality within North America
Contribution of Inequality within Middle East & North Africa
Total Contribution of Inequality within All Regions
Contribution of Inequality Between All Regions
Contribution of Inequality within East Asia & Pacific
Contribution of Inequality within South Asia
Contribution of Inequality within Europe & Central Asia
Contribution of Inequality within Sub-Saharan Africa
Contribution of Inequality within Latin America & Caribbean
Contribution of Inequality within North America
Contribution of Inequality within Middle East & North Africa
Total Contribution of Inequality within All Regions
Contribution of Inequality Between All Regions
18.78
3.87
37.67
2.29
8.59
24.71
4.09
33.87
22.07
16.34
9.63
8.23
5.43
4.43
0.402
0.008
0.056
0.195
0.019
0.001
0.157
0.330
0.008
0.069
0.158
0.022
0.001
0.120
23.79
0.09
6.67
1.40
0.53
0.04
2.02
34.54
65.46
30.55
0.48
3.07
4.16
0.49
0.01
1.45
40.21
59.79
21.94
4.44
32.87
2.19
9.14
25.06
4.36
33.35
22.62
15.37
10.28
8.45
5.35
4.58
0.307
0.008
0.095
0.199
0.020
0.001
0.153
0.240
0.008
0.123
0.166
0.027
0.001
0.121
22.09
0.11
10.21
1.43
0.61
0.06
2.19
36.71
63.29
24.51
0.58
5.79
5.21
0.69
0.01
1.69
38.48
61.52
22.18
4.83
31.61
2.18
8.88
25.90
4.42
32.84
23.21
14.48
10.94
8.54
5.29
4.70
0.252
0.007
0.096
0.196
0.025
0.001
0.149
0.191
0.008
0.127
0.169
0.031
0.000
0.118
18.63
0.12
10.12
1.43
0.75
0.06
2.19
33.29
66.71
19.91
0.59
5.85
5.87
0.85
0.00
1.76
34.84
65.15
24.12
5.57
30.35
2.34
8.50
24.49
4.61
32.17
23.67
13.80
11.68
8.60
5.22
4.86
0.187
0.008
0.074
0.196
0.024
0.001
0.136
0.142
0.009
0.096
0.169
0.031
0.001
0.113
16.95
0.17
8.39
1.72
0.76
0.06
2.36
30.40
69.60
16.39
0.78
4.76
7.07
0.95
0.01
1.98
31.95
68.05
27.91
6.97
28.21
2.63
8.85
21.95
3.48
31.73
24.32
13.45
12.66
8.71
5.23
3.91
0.117
0.010
0.061
0.183
0.022
0.001
0.135
0.095
0.012
0.078
0.160
0.030
0.001
0.118
15.06
0.33
7.88
2.21
0.89
0.06
2.16
28.59
71.41
12.88
1.27
4.53
8.70
1.13
0.01
1.98
30.51
69.49
8
Analysis of International Income Inequality
been substantially larger than the contribution of inequality within regions.
Furthermore, the degree of inequality between regions has been increasing
almost steadily over the years. Another notable observation is that, as
expected, income inequality within East Asia & pacific has been the main
contributor of total inequality within regions.
5.
Conclusions
The study arrives at several interesting conclusions. It is shows that the
degree of inequality in income between countries has been decreasing steadily
over the years. However, this trend in income distribution does not mean that
economic conditions in poor countries are improving in most of the countries.
Far from it, if China alone is taken out of the picture, the trend is almost
reversed, showing slight deterioration in the 1990s and mild improvement in
the 2000s. Furthermore, if both China and India are taken out of the picture,
the trend would show a no net improvement in income over the past two
decades despite some improvement in recent years. Nevertheless, this need
not be viewed pessimistically. After all China and India account for about
40% of world’s population and, therefore, improvement of economic
conditions even in these two countries alone cannot be taken lightly. When it
comes to standards of living, what matters is the proportion of world
population, rather than the number of countries than show improvement.
Another useful finding of the study is that international inequality
mainly comprises of inequality between geographic regions while the
contribution of inequality within regions has been relatively small. Further,
the contribution of inequality between regions has increased consistently. This
pattern has serious implications for the way world economic cooperation
contributes to reducing inter-country economic disparity through free trade
and movement of production activities. Most formal efforts towards economic
cooperation are confined to regional economic cooperation in the form of
free-trade area, etc. Where there has been any cross regions (or cross
continental) economic cooperation, the effects on reduced disparity are
visible. This can be seen in the form of significant improvements in economic
conditions in China and India. While both countries have benefitted from free
trade and transfer of production facilities, China has reaped the maximum
gains. In any case, the study clearly indicates that there is substantial scope
foe bridging the gap between rich and poor countries of the world and
highlights the importance of international economic cooperation beyond
regional boundaries.
9 Idrees and Ahmad
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Wolf, M. (2004). States are cure and disease. World Economy, Special Report.
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Development Indicators (2012). http://data.worldbank.org/datacatalog/world-development-indicators, World Bank.
11 Forman Journal of Economic Studies
Vol. 8, 2012 (January–December) pp. 13-30
Impact of Natural Disasters, Terrorism and Political
News on KSE-100 Index
Mian Ahmad Hanan, Saleem Noshina,
Saqib Ali Siddiqui and Shahid Imran1
Abstract
This paper discusses the impact of news related to natural disaster, terrorism
and political on the Karachi Stock Exchange (KSE-100) index. This study is
based on Event Study Methodology. It focuses on 21 different news events:10
political, 9 terrorism and 2 natural disasters. It attempts to determine the
statistical relationship and effect of these events on the KSE -100 index by
using an 11 day stock market window. This paper concludes that news events
have a strong impact on the KSE-100 index and among political, terrorism
and natural disaster events; news related to terrorism has the most profound
influence on trend of KSE-100 index. It also shows that the bigger the news
event the greater is the impact on KSE-100 index. It also supports the notion
that good news has a positive and bad news has a negative impact on the
KSE-100 index. This study also reveals that the Karachi Stock Exchange is
“informationally efficient.”
Keywords: Good and bad news; Political, Terrorism and natural disasters
news; KSE-100 index
JEL classification: G14, H80, H84
1.
Introduction
Stock exchange is one of the key financial markets of every country
and considered as one of the imperative sources for the corporations to raise
capital. It has also made it easier for the investors including individual and
1
The authors are Professor/Chairperson at Department of Mass Communication, Forman
Christian College (A Chartered University), Lahore; Assistant Professor at Institute of
Communication Studies, University of the Punjab, Lahore and Research Assistants,
respectively. Corresponding Author’s Email: [email protected]
Hanan, Noshina, Siddiqui and Imran
institutions to invest in the highly liquid securities traded in the stock
exchange in contrast to invest in less liquid investments like real estate. Due to
its high liquidity characteristics, it is easy for investors to buy and sell their
securities quickly. It has become the key indicator of economic expansion and
strength of a country. It is widely believed that the performance of stock
market largely depends upon the arrival of information and news related to
different events. Chen and Siems (2004) describe the effect on news in these
words. “…global capital markets today are tightly intertwined; news spread
rapidly (especially bad news), with quick spill over or contagion effects” (p.
363). Furthermore, Goonatilake and Herath (2007) investigated the impact of
news on DJIA, NASDAQ and S&P 500 and found the association between the
news and stock market fluctuations. In addition, the efficient-market
hypothesis (EMH) asserts that financial markets are "informationally
efficient". There are three main versions of this hypothesis: "weak", "semistrong", and "strong". Weak EMH maintains that prices on traded assets (e.g.,
stocks, bonds, or property) already reflect all past publicly available
information. Semi-strong EMH claims both that prices reflect all publicly
available information and that prices instantly change to reflect new public
information. Strong EMH argue that prices instantly reflect even hidden or
"insider" information. There is evidence for and against the weak and semistrong EMHs, while there is powerful evidence against strong EMH. Oncu
and Aktas (2006) maintain:
An implication of EMH is that market prices reflect all available
information and expectations, and that any new information is
properly incorporated into prices without any delay. A stock market’s
speediness to incorporate new information into prices is referred to
informational efficiency. Market’s ability to reflect new information
properly is referred to market rationality (p. 233).
Therefore, the news of different types of events happening in the
country and around the world have a major impact on the performance of
stock market no matter it is directly linked to it or not. If the country’s
economic, political and law and order situation is unsatisfactory, it will also
have a deteriorating impact on the performance of stock market and vice
versa. Therefore, stock exchange is a key indicator of country progress and
economic development. News regarding political events has both constructive
and deteriorating impact on the stock market depending on its nature and
consequences. When the political condition of the country is stable, then it
14
Impact of Natural Disasters, Terrorism and Political News on KSE-100 Index
will have a positive impact on the stock market performance and attract more
investors to invest their capital and vice versa. Sandler and Enders (2002)
define terrorism as “the premeditated use, or threat of use, of extra normal
violence to obtain a political objective through intimidation or fear directed at
a large audience.” Barth et al (2006) conducted a research report on “Impacts
of Economic Terrorism: From Munich to Bali” and stated that approximately
20,000 terrorist activities and incident had been occurred in the world during
the past 35 years and concluded that “terrorism is associated with adverse
effects on overall economic activity” of a country (p. 3).
The basic objective of this paper is to examine the impact of news
related to politics, terrorism and natural disasters on the KSE-100 index. This
study is based on Event Study Methodology. It focuses on 21 different news
events including 10 political, 9 terrorism and 2 natural disasters and determine
the statistical relationship and effect of these events on the KSE -100 index by
analyzing the 11 days stock market trend. This paper concludes that news has
the strong impact on the KSE-100 index and among political, terrorism and
natural disaster news; news related to terrorism has profound influence on
trend of KSE-100 index. It also proves that bigger the news in terms of its
consequence, the more will be the impact on KSE-100 index. Finally, it also
claims that (1) Good news has an optimistic impact on the KSE-100 index; (2)
Bad news has a pessimistic impact on the KSE-100 index. In the end, this
study also supports the EMH that KSE is nearly efficient and security prices
fully reflect the available information and news.
2.
Literature Review
2.1.
Political News and its Impact on Stock Exchange
The political news always has a great impact on country’s economic,
social and political activities. Khan et al (2009) conducted the study about the
impact of news related to Pak-U.S. relations on the KSE-100 index by
applying the event methodology and concluded that the association between
political news and the KSE-100 index was highly significant. Moreover,
Fornari et al (2002) examined the impact of schedule and unscheduled news
on Italian financial market between the year 1994 and 1996 and found that
unscheduled news produced more volatility in the Italian financial market than
the schedule news (p. 611). Chan and Wei (1996) revealed that the favorable
and unfavorable news were correlated with the positive and negative returns
15
Hanan, Noshina, Siddiqui and Imran
of the Hang Seng Index. In the same way, Chan, Chui and Kwok (2001)
analyzed the impact on political news on Hang Seng Index and concluded that
impact of political news was more than the economic news. Zach (2003)
investigated the impact of political events on one of the major Israel Stock
Exchange namely Tel Aviv Stock Exchange Index from 1993 to 1997 by
applying different statistical techniques on the sample including statistical
test- location, statistical test- spread, regression analysis, cross-sectional
analysis, and News-intensive periods and found that political events had more
significant impact on the returns instead on the days when there were no
significant political events occurred (p. 243). Niederhoffer (1971) found that
the world events exert a discernible influence on the movement of the S&P
500. Returns following the world events tend to be larger in absolute value
than returns on other days (p. 193). Franck and Krausz (2009) analyzed the
impact of institutional reforms, political events and wars on the Israel Stock
Market between 1945 and 1960. They found that likelihood of war did not
have any effect on the stock market but it would increase the risk at the time
of skirmishes. Furthermore, domestic political instability also increased the
stock market risks (p. 141). In the Pakistan’s context, Khan and Ahmed
(2009) examined the relationship between events happening from December
2007 to October 2008 and their impact on aggregate stock market trading
volume and daily stock returns and maintained that political events noticeably
fluctuate the stocks returns and trading volume of KSE-100 index (p. 373).
Chan and Wei (1996), and Kim and Mei (2001) concluded that political news
produced more stock volatility in the Hong Kong. Niederhoffer et al (1970),
Peel and Pope (1983) and Gemmill (1992) found that stock market prices
were significantly affected by the elections both government and
congressional in different developed countries. They found changing in
governmental administration caused by elections tends to effect financial
policies or legislation, thereby significantly affecting stock prices (Chen, et al,
2005, p. 167). On the other hand, Aktas and Oncu (2006) tested the Efficient
Market Hypothesis with the case study of the refusal of controversial bill
allowing deployment of U.S troops in Turkey by Turkish Parliament and its
impact on the Turkish Stock Exchange performance and maintained that in
response to unfavorable political events, stock prices are expected to behave
differently in the efficient market since the new information will have
different economic impact on individuals firms (p. 78).
Chuang and Wang (2009) examined the impact of major political
changes related to U.S, U.K, France and Japan from the year 1979 to 2001 on
16
Impact of Natural Disasters, Terrorism and Political News on KSE-100 Index
the stock exchange indexes including Nikkei 225, SBF-250, FTSE 30 and
Dow Jones 30. They concluded that political changes negatively affected all
the countries indexes with the significance level of 5% and suggested that
political changes basically provided the opportunity for progress according to
the democracy but they had an inverse relationship with the stock return in the
developed countries (p. 948). Kaminsky, and Schmukler (1999) argued that
“in the chaotic financial environment of Asia in 1997–1998, daily changes in
stock prices of about 10 percent became commonplace. They found that
market movements were triggered by local and neighboring-country news,
with news about agreements with international organizations and credit rating
agencies having the most weight. In addition, the evidence suggests that
investors over-react to bad news” (p. 537). In the context of political election,
Pantzalis et al (2000) had used UIH to investigate the behavior of the stock
market indexes across 33 countries around the election days during the sample
period of 1974-1995 and found a positive impact of these elections on stock
market indexes resulting positive abnormal returns (p. 1575). Jones and
Banning (2009) analyzed the impact of U.S elections on the stock exchange
performance and found little relationship between both of them. They
concluded that elections and election cycle didn’t play much role in
forecasting stock market returns (p.273). Similarly, Goriaev and Zabotkin
(2006) study regarding the Russian Stock markets supported the argument that
economic and political events always influence the Stock Market trends (p.
380).
2.2.
Studies related to Terrorism Impact on Stock Exchange
Several studies support the argument that the terrorism incidents
always influence the stock exchange performance. Chen and Siems (2004)
applied the event methodology to measure the impact of 9/11 on global and
U.S share prices as well as to compare it with political, economic and natural
disasters impact and found that the 9/11 had less effect on financial markets as
compare to former terrorist events (p.349). Elder and Melnick (2004) analyzed
the impact of Palestinian terrorist attacks on the Israel’s Stock and Foreign
Exchange markets by using daily times series data from 1990 till 2003 and
establish the argument that the attacks had a permanent effect on both Stock
and Foreign Exchange markets but location of terrorist attacks had no effect in
either of the markets (p. 367; also cited in Sathye et al 2008, p. 6). In an other
study, Eldor and Melnick (2004) investigated the impact of terrorism on stock
and foreign exchange markets in Israel by using the data of 639 terrorist
17
Hanan, Noshina, Siddiqui and Imran
attacks classified into location, type, target, and causalities from the year 1990
to 2003 and argued that terrorist events had a permanent pessimistic impact
only on the stock markets. Johnston and Nedelescu (2006) revealed that
International Monetary Fund Report (2001b) regarding impact of September
11 attacks on the Standard and Poor’s 500 and NASDAQ indexes maintained
that the both indexes dropped to 11.6 percent and 16.1 percent respectively
between 17th and 21st September. In addition, the 9/11 attack had a significant
impact on the world major financial markets causing sharp declines and fall.
European Stock Market also had a severe decline after 17th September.
Correspondingly, Dow Jones STOXX index had dropped by 17.3 percent
during 11th and 21st September after the 9/11 terrorist attack (p. 12). Arin,
Ciferri and Spagnolo (2008) investigate impact of terrorists’ events on the
behavior of six financial markets including Indonesia, Israel, Spain, Thailand,
Turkey and U.K and concluded that the effects of terrorism not only on the
stock market, but also on the stock market volatility. In addition they found
that the magnitude of terrorist effects is larger in emerging markets (p. 164).
Barros et al (2009) studied the impact of Basque terrorism on the
Basque stock market. They took the data from July 2001 to 15th November
2005. They investigated the level of violence so called Kale Borroka (street
fighting) in this area, the police action and repressive policy measures by the
government against this violence and impact of this violence on the Stock
Market. They had found that they could reduce the violence by banning the
radical party Herri Batasuna and the occurrence of violence had a negative
effect on the stock market index. Ahmed and Farooq (2008) investigated the
impact of 9/11 terrorist attacks on the volatility of KSE-100 index and found
that this incident had a significant impact on the KSE. Their study also
negated that volatility was not due to the implementation of regulatory
reforms by the SECP as they had found the same qualitative results by
dividing the 9/11 period into pre and post reforms period (p. 71). Furthermore,
Chen and Wei (2005) investigated U.S capital market response to 7 major
terrorist and 7 military attacks from 1915-2001 through event methodology.
They applied their analysis to some other capital markets as well, but focus on
the impact of only two events: the 9/11 terrorist attacks and Iraq’s invasion of
Kuwait in 1990. They found that the U.S. capital markets rebound and
stabilized quicker after these two events compared to other markets, and U.S.
markets are more resilient now than in the past, which they explain by the
strength of the banking and the financial sectors in the U.S. One of the main
conclusions of their study is that the U.S. financial markets are efficient in
18
Impact of Natural Disasters, Terrorism and Political News on KSE-100 Index
absorbing the shocks caused by the terrorist attacks and can continue to
function in an effective way (p. 399).
2.3.
Natural Disasters and Stock Exchange
To explain the impact of natural disaster incidents on stock markets,
Worthington and Valadkhani (2004) investigated the effect of natural disasters
on the Australian equity market by studying the impact of 42 natural disasters
including storms, floods, earthquakes, cyclones etc on the All Ordinances
Index from the December 31, 1982 to January 1, 2002 by applying ARMA
model and concluded that bushfires, cyclones’, earthquakes had a major
impact on market returns as compared to severe storms and floods (p. 2177).
In another study, Worthington and Valadkhani (2005) compared the effects of
natural, industrial and terrorist disasters on the Australian Capital Markets by
applying Box and Tiao Intervention analysis on 10 market sectors: consumer
discretionary, consumer staples, energy, financial, health care, industrial
information technology, materials, telecommunication services and utilities
and found that “the sectors most sensitive to disaster of any type are the
consumer discretionary, financial services and material sectors. The most
significant single event during the past eight years would appear to be the
September 11 terrorist attacks” (p. 331). Shelor et al (1990) investigated the
impact of California Earthquake on the firms’ value dealing in Real Estate
industry. They found that this event had negative impact on the firm’s stock
returns that were operating in the area hit by the earthquake. To sum up,
various studies discuss in the literature related to different countries support
the argument that news has a significant impact on the stock markets.
2.4.
Conceptual Framework
The proposed conceptual model of this study is presented in Figure1.
The independent variable is News and dependent variable is KSE-100 index.
News is categorized into political, terrorism and natural disasters. One
dimensional arrow is demonstrating that political, terrorism and natural
disaster news have an impact on the KSE-100 index.
3.
Methodology
Event Study Methodology is applied in this study to analyze the
impact of each event on KSE-100 index. Purpose based sampling technique is
19
Hanan, Noshina, Siddiqui and Imran
used for the selection of news events. Three different types of 21 news
including 10 political, 9 terrorism and 2 natural disaster events (see Table 1)
were identified and determined the impact of them on the KSE -100 index by
analyzing the 11 days trend. The occurrences of these events have been
identified from the headlines appearing in the newspapers “The News” and
"the Dawn” respectively. These two newspapers are among the elite and most
trusted newspapers of Pakistan and have vast circulation across the Pakistan.
Statistical technique independent t-test is applied to analyze the impact of
political, terrorism and natural disaster news on stock exchange after testing
the equality of variances by Levene’s Test. If the variances turned out to be
unequal, in independent sample t-test, where variances were assumed to be
unequal had been used to compare the pre and post event mean values.
Figure 1
3.1.
Event Windows
Event Windows are divided into three parts: The five days has been
selected for the Pre-Event Window, one day for the Event, and five days for
the Post Event (Figure 5). Therefore, the trend of total eleven days has been
studied because of following reasons: (1) larger time span creates more noise
in the trend that may affect the results (2) accurate results are not possible
with shorter time span because impact is more dispersed. (3) The 11 days
trend presents more accurate variation in KSE-100 index. In the pre and post
20
Impact of Natural Disasters, Terrorism and Political News on KSE-100 Index
event windows, those days are not included when the stock market was closed
either for a weekend or on account of public holiday.
Figure 2
3.2.
Hypotheses
This study proposes following hypothesis to analyze the impact of political,
terrorism and natural disaster news on KSE-100 index.
H1: News has a significant impact on the KSE 100 Index
H2: Political News has a significant impact on the KSE 100 Index.
H 3: Terrorism News has a significant impact on the KSE 100 Index.
H 4: Natural Disaster News has a significant impact on the KSE 100 Index.
H 5: International news has a significant impact on the KSE-100 index.
H 6: News related to assassination of prominent political personalities has a
negative impact on KSE-100 index.
H 7: Good news has a positive impact on the KSE-100 index.
H 8: Bad news has negative impact on the KSE-100 index.
21
Hanan, Noshina, Siddiqui and Imran
3.3.
Research Questions
The study addresses three research questions: RQ 1: Which type of
news including political, terrorism or natural disaster has more impact on the
KSE 100 index? RQ 2: To what extent the bigger news in terms of its
consequence has a profound impact on KSE-100 index? RQ 3: How far the
news related to failed assassination attempts on prominent personalities
affects the stock market trends? RQ 4: Which type of bad news, local or
international, has more negative impact on KSE-100 index?
4.
Findings and Results
This section presents answers to the research questions as well as validating
hypotheses on the basis of quantitative analysis.
4.1.
Hypothesis 1
Table 1 determines that majority of news regarding politics; terrorism
and natural disaster incidents have a significant impact on the KSE-100 index.
For instance, political news 2, 3,4,5,6, and 9, terrorism related news 11, 13,
14,15,16,17 and 18, and natural disaster news 20 and 21 have a significant
impact on KSE-100 index. This validates the hypothesis (H1) as well as
supports the EMH. Therefore, these findings support that the KSE is
informational efficient.
4.2.
Hypothesis 2
Table 1 reveals that political news has a significant impact on the
KSE-100 index. Political news namely 2, 3,4,5,6, and 9 support this
hypothesis. For instance,
news item 2-“17th Amendment passed” is
statistically significant with respect to its impact on the KSE-100 index as t
(8)= -11.455, p<0.05 and there is an upward trend in the KSE-100 index as
Post Event KSE trend mean value rises sharply as compared to the mean value
of Pre-Event KSE trend.
4.3.
Hypothesis 3
The news related to terrorism incidents including 11, 13, 14,15,16,17
and 18 supports the hypothesis that terrorism news has significant impact on
KSE-100 Index. For example, News item 11- “9/11 attacks” has a significant
impact [t(8)= 4.377, p<0.05] on KSE-100 index and causes a radical change
of - 94.73 points between Pre-Event and Post Event mean values. Similarly,
22
Impact of Natural Disasters, Terrorism and Political News on KSE-100 Index
Table 1: News Impact on KSE-100 Index
23
Hanan, Noshina, Siddiqui and Imran
news item 17- “Benazir Bhutto’s assassination,” and 8- “Suicide attack on
Marriot Hotel Islamabad” have a negative impact on the KSE-100 index as t
(8)=4.595, p< 0.05 and t (8)= 3.885, p<0.05 respectively ( see Table 1).
4.4.
Hypothesis 4
The news related to natural disasters also has an impact on the KSE100 index. News items 20-“Massive earthquake” and 21-“Flood” had an
impact on the KSE-100 index as t (8) = -4.536, p<0.05 and t (8) = -4.324, p<
0.05 respectively (Table 1). However, both news incidents reflected that the
KSE-100 index went up because of the two factors: (1) these events had
provided an opportunity for the cement, manufacturing and other related
sectors to rebuild the infrastructure again in the disaster areas; and (2) the
KSE was not significantly affected because the earthquake (2005) and flood
(2010) largely devastated the non-industrial areas of Pakistan.
4.5.
Hypothesis 5
The news item 11 -“9/11 attacks” has a significant impact on the KSE100 index as t (8) = 4.377, p<0.05. The September 11 terrorist attacks have
brought a declining trend of (-94.73) points between the Pre-Event and PostEvent mean values. This international news likely brought fluctuations in
KSE-100 index because of two reasons: (1) it destabilized U.S. Pakistan
relations due to Pakistan’s support of Taliban regime at that time, and (2) U.S.
declared war against terrorism altered the geo-strategic situation of the region.
This shows that international news related to Pakistan has a significant impact
on the KSE-100 index (Table 1).
4.6.
Hypothesis 6
The news item 17- “Benazir Bhutto’s assassination” had a major
impact on the KSE-100 index as t (8)=4.595, p< 0.05 that caused a decline in
the Post Event KSE trend mean value to 13870.332 from 14663.622 because
Benazir was a renowned political leader of Pakistan and her killing caused a
law and order problem in the country. This declining trend is consistent with
the belief that assassination of a prominent political personality has a negative
impact on the stock market (Table 1).
4.7.
Hypothesis 7
The good news including news items 2, 3, 6, 7, 8, 9, and 10 (see Table
1) have a positive impact on KSE-100 index because (1) they increased the
investors’ confidence, and (2) stabilized the country politically that boosted
24
Impact of Natural Disasters, Terrorism and Political News on KSE-100 Index
the economic activities in the Pakistan. The quantitative findings support the
argument that good news has significant impact on the stock market.
4.8.
Hypothesis 8
Table 1 depicts that news item 5 “Emergency rule declared by
Musharraf” in Pakistan has a significant influence on the KSE-100 index as t
(8) = 5.313, p<0.05. It caused a declining trend of -591.668 points difference
between the Pre-Event mean value (14117.132) and Post Event mean value
(13525.464). The rationale behind this decreasing trend was the result of
President Musharraf‘s move to dissolve the parliament and introduction of
amendments in constitution to strengthen his power in the country. Moreover,
emergency rule destabilized the political and democratic institutions of
Pakistan due to which investors’ confidence diminished that resulted in
downwards trend. In addition, News item 11 “9/11 attacks” is statistically
significant with respect to its impact on the KSE-100 index as t (8) = 4.377,
p<0.05. There was a sharp declining trend of -94.728 in the KSE-100 index as
the Post Event KSE trend mean value (1149.736) decreased as compared to
the mean value (1244.464) of the Pre-Event KSE trend. This declining trend
was the result of the emerging severe tensions between the U.S. and the
Muslim countries, especially with Afghanistan and Iraq. U.S. declared the
War on Terror and divided world in to friends and foe. These U.S. moves
badly damaged the confidence of investors, business cycles and economies
around the world. Therefore, all the international financial markets including
the KSE demonstrated a sharp declining trend. Similarly, news items 17
“Benazir Bhutto’s assassination” and 18 “Suicide attack on Marriott Hotel
Islamabad” had also caused a negative impact on KSE-100 Index (Table 1).
Finally, news 1 “Musharraf wins presidential referendum” had no significant
impact on KSE-100 index as t (8) =0.532, p>0.05. But, it has caused a decline
in the KSE-100 index from 1875.546 to 1863.624 because this incident had
strengthened dictatorial regime in Pakistan. Hence, all the bad news including
this study support the argument that bad news has a negative impact on the
KSE-100 index.
4.9.
Research Question 1
On the basis of the higher mean values difference, three news items
were selected from political, terrorism and natural disaster categories
respectively resulted that news related terrorism has profound impact on KSE100 Index as compare to political and natural disaster news. Among the
political news, “Emergency rule declared by Musharraf” had created a
25
Hanan, Noshina, Siddiqui and Imran
significant downward mean value difference of -591.666. On the other hand,
the terrorism incident, “Benazir Bhutto’s assassination,” had a momentous
impact on the KSE-100 index because the pre and post event mean difference
was -793.29. Interestingly, “News item 20-Massive earthquake” in Pakistan
had stimulated a positive trend in the KSE-100 (Table 1).
4.10.
Research Question 2
Table 1 reveals that news item 5 -“Emergency rule declared by
Musharraf” had caused the decline of -591.668. Similarly, news item 9
“Supreme Court acquits Nawaz from hijacking charges” had increased the
KSE-100 index by difference of 144.48 from pre-event mean value 7644.108
to Post-Event Mean value of 7788.588 points. In addition, news related to
terrorist incidents including news items, 11- “9/11 attacks,” and 17-“Benazir
Bhutto’s assassination” have created a falling trend of -94.728 and -793.29
points respectively. These findings support the assumption that bigger news in
terms of its consequence has a strong impact on KSE-100 index.
4.11.
Research Question 3
Table 1 depicts that the news related to failed assassination attempts
on prominent political personalities, news items, such as 12, 13, 15 and 16
have no negative impact on the KSE-100 index because KSE is an efficient
market that absorb the shocks caused by these news incidents successfully.
Therefore, it appears that news related unsuccessful assassination attempts on
prominent personalities do not have a negative impact on KSE-100 index.
4.12. Research Question 4
The local news showed a greater negative impact on KSE-100 index
than international news. For instance, a local bad news -Benazir Bhutto’s
assassination (pre and post event mean value difference (-793.29) had
produced more volatility in KSE -100 Index than the international news
related to “9/11 attacks (-94.728) (see Table 1: news items 17 & 11).
5.
Conclusion
On the whole, it is concluded that news has the strong impact on the
KSE-100 index and news related to terrorism appears to have more influence
on the KSE-index as compared to political and natural disaster news. This
study has the following results. Firstly, political, terrorism and natural disaster
news has a significant impact on the KSE 100 Index. Secondly, the bigger
news in terms of its consequence has a profound impact on KSE-100 Index.
26
Impact of Natural Disasters, Terrorism and Political News on KSE-100 Index
Thirdly, this study also validates the view that good news has a positive
impact on the KSE-100 index and vice-versa. Finally, this study is consistent
with the notion that the Karachi Stock Exchange is “informationally
efficient.”
27
Hanan, Noshina, Siddiqui and Imran
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Forman Journal of Economic Studies
Vol. 8, 2012 (January–December) pp. 31-61
Performance of Alternative Price Forecast for Pakistan
Yaser Javed and Eatzaz Ahmad1
Abstract
To evaluate the price forecasts, we use two data frequencies i.e., annual and
quarter with two most demanding techniques, i.e., ARIMA and VAR models to
forecast the four index of inflation, named, Consumer Price Index (CPI),
Wholesale Price Index (WPI), GNP Price Deflator (GNPPD), and Implicit
Price Deflator of Total Domestic Absorption (DAPD).2 In order to test the
performance of price forecast for Pakistan, we found Consumer Price Index
(CPI) and Implicit Price Deflator of Total Domestic Absorption (DAPD)
better than Wholesale Price Index (WPI) and GNP Price Deflator (GNPPD).
In general more elaborate Vector Autoregressive (VAR) models outperform
the simplistic Auto Regressive Integrated Moving Average (ARIMA) models in
forecasting a price series. Another useful conclusion is that the quarterly data
provide better forecasts than the annual data. All these results support the
econometricians’ maintained hypotheses that, data observed at high
frequency and statistically more elaborate use of a given data set provides
better predictions than the data observed at low frequency and analyzed with
simplistic statistical tools.
Keywords: ARIMA models; Cointegration; ECM; VAR Models
JEL classification: C12, C13, C15, C22, E30, E31, E37, E58
1.
Introduction
Uncertainty about future events influence our present decision, the
main reason why expectations are made is that we want to incorporate that
uncertainty in our present decision to minimize the risk. For example, a
student does not know whether it will rain in the afternoon when he/she
returns from university. The student has to decide now on the basis of his/her
judgment or given knowledge about the pattern of climate whether carry
umbrella or leave it at home. A good decision about afternoon, in the morning
is important.
1
The authors are Ph.D. Student at Federal Urdu University of Arts, Science and Technology,
Islamabad and Professor/Dean at School of Economics, Quaid-i-Azam University Islamabad,
respectively.
2
Total domestic absorption price deflator is obtained from addition of imports and subtraction
of exports from GNP. This deflator was used by Ahmad and Ram (1991).
Javed and Ahmad
Macroeconomic policy makers are interested to know the inflation
rates for the coming years. If these figures are alarming then they suggests
monetary authorities that to tighten their steps towards monetary policy right
now, so that the remedy starts before occurring of the disease. Forecasting is
an important exercise in the context of time series analysis according to YinWong and Menzie (1997) a large industry is involved in the forecasting of key
macroeconomic variables.
None of the variable can be predict with certainty; decisions are made
on the basis of forecasts made by researches are individuals, but no forecast is
ever perfect there must be some errors. Importance of correct forecast is
obvious, from the observation of Blix et al. (2002) that a bad forecast can lead
to loss of business opportunities, loss of investment or to misguide
government macroeconomic policies; good forecast, on the other hand, can
lead to the opposite. So it is important to test the performance of such
forecasts.
The remaining portion of the study is organized as follows. In section
2, we review the existing literature on measuring performance of price
forecast and in section 3, empirical findings of the pertinent studies. In
section 4, we present data sources, estimation techniques and in section 5 we
present different type of performance hypothesis. In section 6, we present the
results of our performance tests. Finally section 7 concludes the study.
2.
Review of Literature
Analysis of time series started before the evolution of modern
macroeconomics, according to Yule (1927) forecasting has an even longer
history. Importance of time series analysis and forecast is obvious from the
observation of Ruey (2000) that objectives of the two studies may differ in
some situations, but forecasting is often the goal of a time series analysis.
Forecasting of economic time series is an important but difficult task;
especially in case of developing countries due to the poor quality of data, there
is also persistently destabilize economic and political environment.
Economics outcomes are often influenced by unanticipated events and data
may be inadequate, particularly in developing countries. According to Paula
(1996) economic forecasting is an art, not a science.
Granger (1996) points out that it is easy to find criticisms of economic
forecasts, both of their perceived quality and of the methods used in their
construction. No forecast can be properly evaluated in isolation and so it is
32
Performance of Alternative Price Forecast for Pakistan
worth noting that famous book by Box and Jenkins (1976) on univariate
models, has attracted substantial opponents in forecasting competitions.
According to Granger (1989) it is not possible to give a definite answer to the
question like ‘What is the best forecasting method?’. In any particular
forecasting situation some methods may be excluded either because of
insufficient data or because the cost is too high. If there are no such
limitations, it is still not possible to give a simple answer.
Importance of correct forecast is obvious, as according to Blix et al.
(2002) a bad forecast can lead to loss of business opportunities3, missed
investment or misguide government macroeconomic policies; good forecast,
on the other hand, can lead to the opposite. Accuracy of forecast is important
to policymakers, as several studies evaluate the forecasts, such as Gavin and
Mandal (2000), Oller and Barot (2000) and Batchelor (2001). As mentioned
Nordhaus (1987), given the heightened importance of forecasts and
expectations, it is natural to inquire into their accuracy and adequacy.
2.1.
Consistent Forecast
Generally a forecast having lower RMSE is considered better than the
ones having a higher value of RMSE. As mentioned by Yin-Wong and
Menzie (1997) when examining forecast accuracy researchers examine the
mean, variance and serial correlation properties of the forecast errors. The
issues of integration and cointegration are rarely addressed. These issues are
very important as pointed out by Clement and Hendry (1993) and Armstrong
and Fildes (1995) make a criticism on the RMSE, and mention that RMSE is
not a good benchmark.
After the rejection of conventional tools of analyzing the forecast, the
cointegration approach named ‘consistency’ was introduced, and this
technique was also used by Liu and Maddala (1992) and Aggarwal et al.
(1995) to assess the unbiasedness, integration and cointegration characteristics
of macroeconomic data and their forecasts.
2.2.
Efficient Forecast
Efficiency norm is defined by different researchers, and in different
ways. In a Congressional Budget Office Report (1999) efficiency indicates
the extent to which a particular forecast could have been improved by using
3
The expectations of the businessmen and investors play a key role in the business cycles
theories presented by Pigou (1927) and Keynes (1936).
33
Javed and Ahmad
additional information that was at the forecaster’s disposal when the forecast
was made. Nordhaus (1987) define efficiency in two ways i.e., ‘weak’4 and
‘strong’ efficiency. This kind of efficiency states by Beach et al. (1999).
Bonham and Cohen (1995) criticize the methodology used by Keane
and Runkle (1990) that directly tests conditional efficiency of forecast using
an approach that based on incorrect integration accounting. Their integrating
accounting errors result in trivial cointegration and improper distributional
assumption and, therefore, incorrect inference. Bonham and Cohen (1995)
claim that they correct the integration accounting errors and show that the
efficiency hypothesis is still rejected.5
2.3.
Rational Forecast
Doctrine of rationality is defined by Lee (1991) as follows,
expectations are said to be rational if they fully incorporate all of the
information available to the agents at the time the forecast is made. There are
many studies like Hafer and Hein (1985), McNees (1986), Pearce (1987) and
Zarnowitz (1984 and 1985) that places great weight on minimum mean square
error (MSE) but do not incorporate accuracy analysis convincingly in their
tests of rationality. However, there are many studies like Holden et al. (1987),
Ash (1990 and 1998), Artis (1996), Pons (1999, 2000 and 2001), Kreinin
(2000), Oller and Barot (2000) and Batchelor (2001), shows that the IMF and
OECD forecasts pass most of the tests of rationality.
Rather than simply compare forecast on the basis of RMSE, Bonham
and Douglas (1991) include a test for conditional efficiency6 in the definition
of strong rationality. In order to analyze the rationality of price forecast
Bonham and Douglas (1991) define a hierarchy of rationality tests starting
from ‘weak rationality’ to ‘strict rationality’. The level of rationality in
hierarchy is defined as, weak, sufficient, strong and strict.
4
Another notion of efficiency proposed by Bakhshi et al. (2003) is that current forecast errors
should be uncorrelated with past forecast.
5
In this study we are not much concern with the colliding debate of efficiency related to
Bonham and Cohen (1995) and Keane and Runkle (1990, 1994 and 1995) due to some flaws
with respect to comparative analysis between the forecasts obtained from ARIMA and VAR
models.
6
Granger and Newbold (1973), describe conditional efficiency as a forecast for which the
combination forecast does not produce a lower RMSE than its component forecast.
34
Performance of Alternative Price Forecast for Pakistan
2.3.1. Weak Rationality
Most of the applied work such as Evans and Gulmani (1984),
Friedman (1980), Pearce (1987) and Zarnowitz (1984 and 1985) view
rationality in term of the necessary conditions of unbiasedness and
information efficiency.7 According to the notion of weak rationality defines
by Bonham and Douglas (1991), the forecast must be unbiased and meet the
tests of weak information efficiency.
Ruoss and Marcel (2002) state that unbiasedness is often tested using
the Theil-Mincer-Zarnowitz equation. This is a regression of the actual values
on a constant and the forecast values. The null hypothesis to be tested is that,
the intercept is equal to zero and the slope is equal to one. Holden and Peel
(1990) pointed out that this null hypothesis is merely sufficient but not
necessary for unbiasedness. Clement and Hendry (1998) suggest, running a
regression of the forecast error on the constant, if the parameter estimate
deviates from zero, the hypothesis that the forecast is unbiased is rejected.
2.3.2. Sufficient Rationality
The forecast must be weak rational and must pass a more demanding
test of sufficient orthogonality, namely, that the forecast errors is uncorrelated
with any variable in the information set available at the time of prediction.
Rational expectation hypothesis played a critical role in
macroeconomic analysis and in the theory of economics decision-making.
Rational expectation assumes that economic agents are rational optimizers,
especially in making forecasts and in taking actions based on such forecasts.
Rational expectations hypothesis by Muth (1961) holds that predictions of
future inflation are formed in a manner that fully reflects relevant information
currently available.
2.3.3. Strong Rationality
The forecast must be sufficiently rational and pass tests of conditional
efficiency. Conditional efficiency requires a comparison of forecasts.8
Consider a sufficiently rational forecast as a benchmark. Combine benchmark
7
The same kind of unbiasedness and efficiency notion was build by Eichenbaum et al. (1988)
and Razzak (1997).
8
Started from the classic study of Bates and Granger (1969), a large literature on forecast
combination summarized by Clemen (1989), Diebold and Jose (1996) and Timmermann
(2005) has found evidence that combined forecasts tend to produce better forecast than
individual forecasting models.
35
Javed and Ahmad
with some competing forecast. Conditional efficiency refers to Granger and
Newbold (1973) that measures the reduction in RMSE, which occurs when a
forecast is combined with one of its competitors. Against such kind of notion
Granger (1989) suggest that combining often produces a forecast superior to
both components. Same kind of notion is build by Timmermann (2006). If
the combination produces an RMSE that is significantly smaller than the
benchmark RMSE, the benchmark forecast fails the test for conditional
efficiency because it has not efficiently utilize some information contained in
the competing forecast. Stock and Mark (2001) report broad support for a
simple combination of forecasts in a study of a large cross-section of
macroeconomic and financial variables.
2.3.4. Strict Rationality
According to Bonham and Douglas (1991) a statement about
rationality should not depend on arbitrary selection of time periods. A
forecast is strictly rational if it passes tests of strong rationality in a variety of
sub-periods, stated in section 5.3.4.
3.
Empirical Findings
Yin-Wong and Menzie (1997) concludes that the (final) Treasury bill
rate, housing starts, industrial production, inflation and most of their
respective forecasts appear to be trend stationary. The corporate bond rate,
GNP, the GNP deflator, unemployment and most of their respective forecasts
appear to be difference stationary. About half of the unit root pairs are
cointegrated. In only one of these cases the unitary elasticity restriction is
rejected the 1-quarter ahead GNP deflator forecast. In the study of Yin-Wong
and Menzie (1997) 30 out of 36 cases fulfill the requirement that forecast and
actual series possess the same order of integration. Surprisingly, the linkage
between forecasts and unrevised actual series is not unambiguously stronger.
However, while there is more evidence of cointegration, there is also a greater
rate of rejection of the unitary elasticity restriction.
The evidence from the study of Aggerwal et al. (1995) indicate that
there are significant deviations from the rational expectations hypothesis for
survey forecasts of a number of macroeconomics series. They find that
survey forecasts for the consumer price index and personal income are
stationary and consistent with the rational expectation hypothesis and that the
surveys of housing starts, the unemployment rate and the trade balance are
rational forecasts in the sense that the announced values and their survey
36
Performance of Alternative Price Forecast for Pakistan
forecasts are cointegrated. Aggerwal et al. (1995) suggests, that the quality of
forecast of industrial production and retail sales can be improved significantly
by using past values. These results have important implications for decisions
by many economic agents and for research based on these survey forecasts
and also favoring the univaraite methodology.
Results of weak efficiency hypothesis stated by Nordhaus (1987) are
that 50 of 51 tests, the forecast were found to be positively correlated. The
degree of correlation appears to be highest for institutional forecasts (such as
those made by international agencies) and lowest for professional forecasters
using time-series techniques. Nordhaus (1987) describes two reasons for this
kind of inefficiency. First, perhaps the true forecasts are indeed efficient,
while the published forecasts are not. Second, surely the high degree of
forecast inefficiency of international institutions must contain some element
of bureaucratically based forecast inefficiency.
Empirical results regarding the rationality of forecasts was explained
by Lee (1991) that forecast is fail to be rational in the strong sense even
though they are not rejected by the conventional test of weak rationality.
Ruoss and Marcel (2002) examine the forecast rationality of the Swiss
economy says that GDP forecasts in our sample do not pass the most stringent
test i.e., the test of strong informational efficiency, because, in some cases,
forecasts errors correlate with the forecasts of the other institutes.
Same kind of results is shown by Bonham and Douglas (1991) that the
most stringent criteria for testing rationality will not be useful for empirical
work. On these criteria there might not be a rational forecast of inflation.
Bonham and Douglas (1991) states that, rational forecast is getting by relaxing
the criterion that defines strict rationality.
Razzak (1997) and Rich (1989) test the rationality of National Bank of
New Zealand’s survey data of inflation expectation and SRC expected price
change data respectively. Both studies end up with a same conclusion, that
the results do not reject the null hypothesis of unbiasedness, efficiency and
orthogonality for a sample from their particular survey data series.
4.
Data Sources and Forecast Modeling
In order to test the performance of price forecast for Pakistan, we
forecast four proxies of prices, namely, Consumer Price Index (CPI),
Wholesale Price Index (WPI), GNP Price Deflator (GNPPD) and Implicit Price
Deflator of Total Domestic Absorption (DAPD). Annual data is taken from
37
Javed and Ahmad
various issues of Economic Survey of the Ministry of Finance, Government of
Pakistan, and Annual Reports of State Bank of Pakistan. Quarterly data is
taken from the IMF’s International Financial Statistics (2005) and World
Bank’s World Development Indicator (2006). Data of quarter GDP is taken
from the research paper of Kemal and Arby (2001). Data is taken on annual
and quarter basis for the period from 1972-73 to 2004-05 and 1972Q2 to
2005Q2, respectively.
For a better forecast, our estimation is based on univariate and
multivariate techniques. For the univariate technique, we use the Box-Jenkins
approach to modeling ARIMA models (Box and Jenkins, 1976). For the
multivariate technique, we use VAR approach presented by Sims (1980). In the
estimation of VAR we use price variable alternatively with the four other
variables, real GDP, Broad Money (M2), interest rate and exchange rate.
After three stages of identification, estimation and diagnostic
checking, we present the specification of ARIMA models in table 4.1. In table
4.2, we present the lag specification of VAR models.
Table 4.1: Specification of ARIMA Models
Annual Data
Quarterly Data
Consumer Price Index
Wholesale Price Index
GNP Price Deflator
Domestic Absorption PD
Consumer Price Index
Wholesale Price Index
GNP Price Deflator
Domestic Absorption PD
ARIMA (1,1,1)
ARIMA (0,1,1)
ARIMA (0,1,1)
ARIMA (0,1,1)
ARIMA (0,1,0)
ARIMA (4,1,0)
ARIMA (4,1,4)
ARIMA (4,1,4)
Note: ARIMA (p,d,q) stands for a model with autoregressive process of order p and
moving average process of order q applied to data integrated of order d.
Table 4.2: Specification of VAR Models
Annual Data
Quarterly Data
Consumer Price Index
Wholesale Price Index
GNP Price Deflator
Domestic Absorption PD
Consumer Price Index
Wholesale Price Index
GNP Price Deflator
Domestic Absorption PD
VAR (1)
VAR (1)
VAR (1)
VAR (1)
VAR (1,2)
VAR (1,4)
VAR (1,2,4)
VAR (1,4)
Note: The number in brackets show the lag periods specified in the VAR models.
38
Performance of Alternative Price Forecast for Pakistan
5.
Performance Hypothesis
After getting the forecasts we test the performance of price forecasts by
applying the different type of hypothesis under the definition of consistency,
efficiency and rationality.
5.1.
Consistency Test of Forecast
Consistent forecast states that the, observed price index and their
relevant forecast series are integrated of same order and they are cointegrated.
To test the existence of unit root we follow the spirit of Dickey and Fuller
(1979, 1981).
According to them if yt follows AR(p) process.
yt = φ1 yt −1 + φ2 yt −2 + ....... + φ p yt − p + ε t , a series yt is said to be
p
stationary, if the value of
∑φ
i =1
i
is less than unity. If the observed variable and
their forecast are of same level of integration, say I(1). Then the first condition
for consistency is met. Concept of cointegration was first introduced by
Granger (1981) and elaborates further by Engle and Granger (1987). The spirit
of the cointegration in this study is that observed price index (Po) is
cointegrated with their forecast (Pe). Both series posses same order of
integration, say I(1), then the linear combination9 of these two must be I(0).
We define it in following way.
ε t ≈ I (0)
P et = Φ 1 + Φ 2 P ot + ε t
(1)
Where {Φ1 ,Φ 2 } is the cointegrating vector producing a linear combination
{
}
of P e t , P o t , which is stationary. This will complete the proposition of
cointegration. After that there is a need to test the stability of long run
relationship through error correction models.
5.1.1. Error Correction Models
For the Error correction we estimate the following equations.
∆P
∆P
e
o
t
t
= α
1
+ α 2ε
t −1
= β 1 + β 2ε
t −1
+
m
∑
i=1
+
n
∑
i =1
δ i∆ P
γ i∆ P
e
t−i
o
t−i
+ ut
+ vt
9
(2)
(3)
We will apply Granger Causality test presented by Granger (1969), to determine dependent
variable in the linear combination of observed price series with their forecast series.
39
Javed and Ahmad
The selection of m and n in equation 2 and 3 depends on the significance of
lags under t-statistics. For a stable long run relationship between observed
price index with forecast the following feedback effect must be less than zero,
that is.
α
− Φ
2
2
β
2
< 0
(4)
If the above condition holds, it implies that disequilibrium in previous period
leads to adjustment in current time period, which counter balance the
disequilibrium forces.
5.2.
Efficiency Test of Forecast
Nordhaus (1987) define efficiency in the two classifications; weak
efficiency is the necessary condition for strong efficiency, but clearly not the
sufficient condition.
5.2.1
Weak Efficiency
{(
) }
A forecast is weakly efficient if it minimizes Ε ut 2 J t , where Jt is
the set of all past forecasts. Where Ut2 is the square of forecast error at time t.
In order to test weak efficiency of forecasts obtained from both techniques, we
estimate the following regression.
k
U t = α o + ∑ α i P e t −i + ε t
2
(5)
i =1
Selection of k depends upon the significance under t-statistics. Only significant
lags of expected price forecasts are included. Under this kind of efficiency
norm, a forecast is said to be weak efficient if we are unable to reject the null
that all the coefficients are simultaneously equal to zero.
5.2.2. Strong Efficiency
{( ) } is minimized, where I is
A forecast is strongly efficient if Ε ut 2 I t
t
all information available at time t. Strong efficiency requires that the square of
forecast error was not explained by the information set available at time t. The
information set in Univariate analysis is the past values of the variable itself, so
we regress the following equation, to test the strong efficiency for the forecasts
obtained from ARIMA models.
40
Performance of Alternative Price Forecast for Pakistan
n
U t = α o + ∑ α j P o t −i + ε t
2
(6)
j =1
Here Pot is the observed value of price variable at time t. A forecast fails to
pass the strong efficiency hypothesis if α0 and αj are significantly different
from zero. In order to test the strong efficiency of forecasts obtained from
VAR we estimate the following regression.
U t = α o + α 1 P o t − 1 + α 2 RGDP
2
t −1
+ α 3 M 2 t − 1 + α 4 R t − 1 + α 5 ER t − 1 + ε t (7)
A strongly efficient forecast obtained from VAR fail to reject the null
hypothesis that all the coefficients in equation 7 are simultaneously equal to
zero.
5.3.
Rationality Test of Forecast
Bonham and Douglas (1991) define a hierarchy of rationality tests
starts from ‘weak rationality’ to ‘strict rationality’ the level of hierarchy define
as follows:
5.3.1 Hypothesis of Weak Rationality
A forecast must be unbiased and meet tests of weak information
efficiency. Condition of unbiasedness and weak informational efficiency is set
after the estimation of following equation.
P
o
t
= α
o
+ α 1P
e
t
+ ε
(11)
t
A forecast is said to be unbiased if it satisfies the following conditions.
1. In equation 11, εt is serially uncorrelated.
2. In equation 11, αo and α1 are insignificantly different from zero and
one respectively.
Weak information efficiency means that the forecast errors Et = P t − P t are
uncorrelated with the past values of the predicted variables. To test the weak
efficiency hypothesis we estimate the following regression equation.
e
Et = α o +
m
∑α
i =1
i
P o t−i + ε t
41
(12)
o
Javed and Ahmad
If we fail to reject the following joint null hypothesis it implies that forecast
errors are systematically different from zero and/or past values of the observed
price series help to explain the forecast errors.
H
o
:α
o
=α
j
= 0
For all j = 1……….. m
(13)
Acceptance of such hypothesis represent that the forecast error at time t is
independent to the past information contained by relevant observed price
index.
5.3.2. Hypothesis Sufficient Rationality
The sufficient rationality requires that the forecast errors are not
correlated with any variable in the information set available at the time of
forecast. If Zt is a variable or a vector of variables used to build our forecast
model, then Zt is the exogenous variable in the following equation.
Et = α o +
m
∑α
i =1
i
Z t −i + ε t
(14)
Forecasts of ARIMA models have included only the lags of observed series as
the information set. For ARIMA forecasts two lags of associated price index
are used as information set. While forecasts obtained from VAR models
depend upon the lags of price variables, real GDP, M2, interest rate, and
exchange rate, so their lags with relevant price series are used to test sufficient
rationality. After estimating the equation 14 we test the following null
hypothesis.
H
o
:α
o
= α
j
= 0
For all j = 1……….. m
(15)
The rejection of above mentioned hypothesis states that the information
contained in the past values of related price series, real GDP, M2, interest rate
and exchange rate, has not been used efficiently in forming the forecast.
5.3.3. Hypothesis of Strong Rationality
A forecast is said to be strongly rational if it passes the test of
conditional efficiency introduced by Granger et al. (1973). Conditional
efficiency requires a comparison of forecasts. Call some sufficiently rational
forecast as benchmark; combine the benchmark with some competing forecast.
Estimate the following regression.
[
]
Dt = α + β S t − S t + ε t
(16)
42
Performance of Alternative Price Forecast for Pakistan
Where Dt and St are the difference and the sum of the benchmark and
combination forecast errors, respectively, and S t is the mean of the sum.
Under the null hypothesis of conditional efficiency (α=β=0) the combination
does not produce a lower RMSE. F test is appropriate if β>0 and the mean
errors of both forecasts have the same sign as α. If the mean errors of the two
forecasts do not have the same sign, then α cannot be interpret as an indicator
of the relative bias of the two forecasts.
5.3.4. Hypothesis of Strict Rationality
A forecast is strictly rational if it passes tests of strong rationality in a
variety of sub-periods. In this study only quarter forecasts of CPI can be
treated for strong efficient criterion, annual data do not have sufficient number
of observation to sub-divide in various sub-periods, so we estimate equation 16
in the sub-periods;1972-Q3 to1982-Q4, 1983-Q1 to 1994-Q2 and 1994-Q3 to
2005-Q2.
If a strongly rational forecast pass the same test based on equation 16 in
sub-periods mentioned above then according to Bonham and Douglas (1991)
that particular forecast is awarded as strict rational.
6.
Results and Discussion
We are not going to discuss the conventional tools for analyzing the
performance of forecasts, as lower RMSE and the maximum value of
covariance proportion etc. as Clement and Hendry (1993), Armstrong et al.
(1995) make a criticism on the RMSE, and mention that RMSE is not a good
benchmark. In general, we can say that forecast of Consumer Price Index
(CPI) is the best (among the others proxies of price variables used in this
study), while the forecasts of Wholesale Price Index (WPI) are not performing
well with reference to consistency, efficiency and rationality tests, because
forecasts from VAR models are not able to meet the tests of weak and strong
efficiency except for the quarterly CPI forecast that significantly accept the
weak efficiency hypothesis.
6.1.
Results of Consistency Tests of Forecast
As an initial condition of consistency, observed and expected price
variables should be the same order of integration. The results of unit root tests
of observed data series are given in table 6.1.1.
It is obvious from the results given in table 6.1 that the four price
series included in this study have unit root at levels form. Other variables also
43
Javed and Ahmad
have unit root except the annual series of interest rate that is stationary at
level. One variable i.e., WPI is stationary at 10% level of significant. But
following the general practice of considering the level of significant at 5%, we
conclude that all the annual and quarter observed data series except the annual
interest rate series are I(1). Simply the four annual and four quarter price
series are I(1), then in order to satisfies the conditions of consistency the
forecasts series must be I(1). The results of unit root test for the ARIMA and
VAR forecasts are shown in table 6.1.2 and 6.1.3 respectively.
The results in these tables shows that all the forecasts series obtained
in this study are I(1). For consistency the second condition is that the
observed price series must be cointegrated with their respective forecast. In
this study find the evidence on cointegration between observed
Table 6.1.1: Unit Root Tests of Observed Variables
Variables
Consumer Price Index
Wholesale Price Index
GNP Price Deflator
Domestic Absorption Price
Deflator
Real GDP
Interest Rate
Exchange Rate
M2
t-values (Annual Data)
t-values (Quarterly Data)
Level
-1.38
-0.79
-1.46
-1.18
First Diff.
-4.71***
-4.93***
-4.04***
-4.27***
Level
-1.53
-2.87*
-1.72
-1.39
First Diff.
-10.28***
-8.54***
-10.07***
-10.54***
-0.80
-3.66***
0.45
-0.39
-8.45***
------5.26***
-4.20***
-0.98
-1.97
0.98
-0.18
-21.02***
-10.13***
-11.81***
-18.23***
* Significant at 10% level of Significance and *** Significant at 1% level of significance.
Table 6.1.2: Unit Root Test of Forecasts from ARIMA Models
Variables
Consumer Price Index
Wholesale Price Index
GNP Price Deflator
Domestic Absorption Price
Deflator
t-values (Annual Data)
Level
-0.35
-2.15
-0.89
-0.81
First Diff.
-5.12***
-7.64***
-3.87***
-4.34***
t-values (Quarterly Data)
Level
-1.37
-1.72
-1.18
-0.90
First Diff.
-10.35***
-10.98***
-10.05***
-10.50***
*** Significant at 1% level of significance.
price series and their relevant forecasts, we first used Granger Causality test to
determine dependent variable in the linear combination of forecast and actual
44
Performance of Alternative Price Forecast for Pakistan
series. The series of linear combination are stationary at level form so we say
them I(0), except the annual WPI forecast series obtained from ARIMA
models as shown in the table 6.1.4.
One forecast out of sixteen is shown to be not consistent, that is the
annual forecast of WPI obtained from the ARIMA models. Otherwise all
remaining fifteen forecast series turned out to be consistent on the basis of
cointegration. This means that there exists a long-run relationship between
Table 6.1.3: Unit Root Test of Forecasts from VAR Models
Variables
t-values of the Root
t-values of the Root
with Annual Data
with Quarterly Data
Level
First Diff.
Level First Diff.
Consumer Price Index
-0.70
-5.20***
-1.36
-10.61***
Wholesale Price Index
GNP Price Deflator
Domestic Absorption Price
Deflator
-1.27
-0.43
-5.68***
-5.83***
-1.44
-0.95
-11.82***
-11.56***
-0.36
-6.58***
-0.78
-12.94***
*** Significant at 1% level of significance.
Table 6.1.4: Unit Root Test of Linear Combination of Observed
Variables with their Forecasts
Variables
Consumer Price Index
Wholesale Price Index
GNP Price Deflator
Domestic Absorption
Price Deflator
t-values (Annual Data)
Level
First Diff.
-4.14***
-10.13***
-2.74*
-11.40***
-7.48***
-10.57***
t-values (Quarterly Data)
Level
First Diff.
4.58***
-12.04***
-8.48***
-7.57***
-10.04***
-12.84***
-6.99***
-10.55***
-10.78***
-12.64***
* Significant at 10% level of Significance and *** Significant at 1% level of significance.
observed and forecasted price series. Now there is a need to check the
stability of the long run relationship that is to determine whether or not this
relationship is stable in the long run. For a stable long run relationship the
feedback effects obtained from the error correction mechanism should be
negative.
Table 6.1.5 shows that all the feedback effects are negative, implying
that all the consistent relationships between observed and forecasted price
series are stable in the long run. Thus disequilibrium between observed and
45
Javed and Ahmad
expected series in any period is eliminated in the subsequent period. In short,
we can say that we found fifteen out of sixteen forecast series consistent and
having a stable consistent long-run relationship with their relevant observed
price series.
Table 6.1.5: Feedback Effects10 of Forecasts
ARIMA
Annual Data
Quarterly Data
6.2.
Consumer Price Index
Wholesale Price Index
GNP Price Deflator
Domestic Absorption PD
Consumer Price Index
Wholesale Price Index
GNP Price Deflator
Domestic Absorption PD
-1.604
-2.279
-2.524
-2.442
-1.816
-1.865
-1.659
-1.626
VAR
-1.458
-2.664
-3.857
-3.936
-1.564
-1.732
-1.755
-1.511
Results of Efficiency Tests of Forecast
In the debate of efficiency we present the results of weak efficiency,
the concept represents by Nordhaus (1987) as a necessary but not the
sufficient condition for strong efficiency. Tables 6.2.1 and 6.2.2 represent the
results of weak efficiency of annual forecasts obtained from
Table 6.2.1: Weak Efficiency of Annual Forecasts (ARIMA Models)
U
2
t
= α
Equation
CPI
Equation
WPI
Equation
GNPPD
Equation
DAPD
Equation
o
+
k
∑
i=1
α
i
P
α0
9.39
(-0.98)
-2.11
(-0.56)
-16.62
(-1.08)
-15.61
(-1.03)
e
t− i
+ ε
Ho: All the coefficients are equal to zero
t
α1
-0.023
(-0.15)
-2.00
(-3.71)***
-9.821
(-2.79)***
-11.03
(-3.21)***
α2
----------2.35
(4.12)***
11.42
(3.07)***
12.72
(3.48)***
χ2 for Ho
3.002
(0.22)
35.89
(0.00)***
24.72
(0.00)***
25.398
(0.00)***
F-stat. for Ho
1.501
(0.24)
11.966
(0.00)***
8.239
(0.00)***
8.466
(0.00)***
Notes: t-statistics are in parentheses under the coefficients. Probabilities are in parentheses
under the test statistics. *** Significant at 1% level of significance.
10
We calculate the feedback effects using Engle and Granger (1987), procedure are defined in
section 5.1.1.
46
Performance of Alternative Price Forecast for Pakistan
ARIMA models and VAR models respectively. Annual forecasts obtained
from ARIMA are not good on the basis of weak efficiency test, except the
forecast of CPI. Results reported in the table 7 shows that only the CPI
forecast is weak efficient. Table 8 shows that the situation is worse for those
annual forecasts we obtained from VAR, where not a single forecast series is
able to pass the test of weak efficiency.
Table 6.2.2: Weak Efficiency of Annual Forecasts (VAR Models)
U
2
t
= α
o
+
k
∑
i =1
α iP
e
t−i
+ ε
t
Ho: All the coefficients are equal to zero.
Equation
α0
α1
χ2 for Ho
F-stat. for Ho
CPI
Equation
WPI
Equation
GNPPD
Equation
DAPD
Equation
3.04
(0.485)
-0.69
(-0.613)
-18.43
(-2.135)**
-15.78
(-2.220)**
0.09
(0.884)
0.07
(3.549)***
0.65
(4.084)***
0.57
(4.336)***
7.18
(0.03)**
29.38
(0.00)***
21.54
(0.00)***
24.39
(0.00)***
3.59
(0.04)**
14.69
(0.00)***
10.77
(0.00)***
12.2
(0.00)***
*** Significant at 1% level of significance. ** Significant at 5% level of significance.
Results presented in table 6.2.3 shows that the quarterly forecast of WPI
obtained from ARIMA models is not a weak efficient forecast, while forecasts
of CPI, GNPPD and DAPD accept the weak efficiency hypothesis.
Table 6.2.3: Weak Efficiency of Quarter Forecasts (ARIMA Models)
Ut =αo +
2
k
∑α
i =1
i
P e t−i + ε t
Ho: All the coefficients are equal to zero.
Equation
α0
α1
χ2 for Ho
F-stat. for Ho
CPI
Equation
WPI
Equation
GNPPD
Equation
DAPD
Equation
383.41
(1.06)
-99.61
(-1.40)
0.24
(-0.77)
-0.25
(-0.79)
-0.14
(-0.33)
0.41
(5.43)***
0.04
(1.82)*
0.04
(1.92)*
2.33
(0.31)
57.58
(0.00)***
4.72
(0.09)*
5.26
(0.07)*
1.16
(0.32)
28.79
(0.00)***
2.36
(0.10)*
2.63
(0.08)*
*** Significant at 1% level of significance. * Significant at 10% level of significance.
47
Javed and Ahmad
Table 6.2.4: Weak Efficiency of Quarter Forecasts (VAR Models)
Ut =αo +
2
k
∑α
i =1
i
P e t − i + ε t Ho: All the coefficients are equal to zero.
Equation
α0
α1
χ2 for Ho
F-stat. for Ho
CPI
Equation
WPI
Equation
GNPPD
Equation
DAPD
Equation
361.73
(1.13)
-116.39
(-1.69)*
-0.26
(-0.9)
-0.27
(-0.88)
-0.10
(-0.26)
0.46
(6.13)***
0.042
(2.03)**
0.05
(2.10)**
3.11
(0.21)
71.42
(0.00)***
5.70
(0.06)*
6.29
(0.04)**
1.55
(0.22)
35.71
(0.00)***
2.85
(0.06)*
3.14
(0.05)**
*** Significant at 1% level of significance. ** Significant at 5% level of significance. *
Significant at10% level of significance.
Quarter forecasts of GNPPD from both techniques are passing the test of weak
efficiency. These results are seems to be coherent with the Nordhaus (1987),
as they also find a few week efficient forecasts. After describing the results of
weak efficiency, we now present the results of strong efficiency test.
Table 6.2.5: Strong Efficiency of Annual Forecasts (ARIMA Models)
U
2
t
=α
o
+
k
∑α
i =1
i
P o t − i + ε t Ho: All the coefficients are equal to zero.
Equation
α0
α1
χ2 for Ho
F-stat. for Ho
CPI
Equation
WPI
Equation
GNPPD
Equation
DAPD
Equation
8.21
(0.91)
-4.13
(-0.98)
-22.72
(-1.51)
-19.66
(-1.26)
-0.005
(-0.04)
0.21
(2.92)***
0.91
(3.16)***
0.82
(2.78)***
3.00
(0.22)
13.98
(0.00)***
13.14
(0.00)***
10.32
(0.00)***
1.50
(0.24)
6.99
(0.00)***
6.57
(0.00)***
5.16
(0.01)***
*** Significant at 1% level of significance.
Results of strong efficiency presented in table 6.2.5, indicate that from
annual forecast computed by ARIMA models, no index passes the test of
strong efficiency except the forecast of CPI. The results of strong efficiency
reported in table 6.2.6 shows that quarter forecasts of CPI, GNPPD and
DAPD all pass the test of strong efficiency whereas the WPI forecast does not
48
Performance of Alternative Price Forecast for Pakistan
pass the test. We are not stating the results of strong efficiency of annual and
quarter forecasts, obtained from VAR models. These results show that neither
annual nor quarter forecast pass the test of strong efficiency.
Table 6.2.6: Strong Efficiency of Quarter Forecasts ARIMA Models
k
U t = α o + ∑ α i P o t − i + ε t Ho: All the coefficients are equal to zero.
2
i =1
Equation
F-stat. for Ho
α0
α1
χ2 for Ho
CPI
372.04
-0.13
2.31
1.16
Equation
(1.40)
(-0.31)
(0.31)
(0.32)
WPI
-103.42
0.42
59.08
29.54
Equation
(-1.47)
(5.55)***
(0.00)***
(0.00)***
GNPPD
-0.22
0.04
4.53
2.27
Equation
(-0.72)
(0.766)*
(0.10)*
(0.11)
DAPD
-0.23
0.04
5.05
2.52
Equation
(-0.73)
(1.862)*
(0.08)*
(0.08)*
*** Significant at 1% level of significance. * Significant at10% level of significance.
6.3.
Results of Rationality Tests of Forecast
In this section we discuss the results of the rationality tests of forecast we get
from ARIMA and VAR models. We estimate a hierarchy of rationality tests
starting from ‘weak rationality’ to ‘strict rationality’ presented by Bonham
and Douglas (1991).
In table 6.3.1 we present the results of weak rationality of ARIMA
forecast, which was the combination of unbiasedness and weak informational
efficiency present in the top panel. Where the first regression equation is the
famous Theil-Mincer-Zarnowitz equation. This is a regression of the observed
series on a constant and the forecast series, and their regression residuals must
be serially uncorrelated to fulfill the condition of unbiasedness as well as fail
to rejecting the null presented in front of first equation and the second
equation represents the weak informational efficiency, if the null in front of
that equation is accepted.
According to the results of weak rationality, the forecast of CPI and
DAPD pass this test in both time frequencies. Quarter forecast of GNPPD
also passes the test of weak efficiency, but annual forecast of GNPPD is found
to be biased. Annual forecast of WPI is biased and the null hypothesis of
weak informational efficiency is rejected. On the other hand the quarter
forecast of WPI is found weak efficient as shown in table 6.3.1.
49
Javed and Ahmad
Here we find the same kind of evidence about the forecasts of CPI and
DAPD as we result out in efficiency analysis, that these two series are better
than WPI and GNPPD. Annual and quarter forecasts of CPI and DAPD are
pass the two sets of tests for the rationality, therefore ARIMA models for the
two indices produce rational forecasts. Annual forecast of WPI fails in both
tests, while quarter forecast amazingly passes both the conditions for
rationality. This is the major breakthrough of this research, because according
to Bonham and Douglas (1991), Lee (1991) and Ruoss and Marcel (2002),
many of the forecasts were not able to pass these tests of rationality. In table
6.3.2 we present the same set of test applied on those forecasts obtained from
VAR model. There exist some similarities between results stated in table
6.3.1 and 6.3.2.
If we summarize the results of weak rationality tests of ARIMA
forecasts, we find that quarter forecasts pass the both tests, annual forecasts of
CPI and DAPD also passes both tests, while forecast of GNPPD is biased, but
it passes the weak informational efficiency hypothesis. Quarter forecasts
obtained from VAR models are found to be weakly rational, except the
forecast of WPI that is biased forecast. Forecasts of CPI from annual and
quarter data frequencies pass both the tests. Annual forecasts of WPI,
GNPPD and DAPD are biased, but they pass the test of weak informational
efficiency.
This is a hierarchy of rationality test, so we apply sufficient rationality
test, to those forecast series that pass both weak informational efficiency and
unbiasedness test, which are required for the weak rationality. The results of
sufficient rationality test are shown in table 6.3.3 indicate that the annual
forecast of CPI and DAPD obtained from ARIMA are able to pass the test of
sufficient rationality. Quarter forecast of GNPPD and WPI obtained from
ARIMA models passes this test of sufficient rationality. Annual and quarter
forecasts of CPI obtained from VAR are sufficiently rational, while quarterly
forecasts of GNPPD and DAPD do not pass the sufficient rationality test.
Strong efficiency depends on the concept presented by Granger and
Newbold (1973), requiring that a forecast is combined with one of its
competing forecast and the combination forecast does not produce a lower
RMSE. If we look at the quarter forecasts the WPI forecast obtained from
VAR is not found to be as weakly rational, GNPPD and DAPD forecasts do
not posses the same signs of mean forecast error, only one forecast i.e.,
50
Performance of Alternative Price Forecast for Pakistan
Table 6.3.1: Weak Rationality Tests of Forecasts (ARIMA Models)
Pot =αo +α1Pet +εt
m
(HoA: α0 = 0, α1 = 1)
E t = α o + ∑ α i P o t −i + ε t
(oB: α0 = αj = 0)
i =1
Dependent
Variable
Data
Frequency
α0
CPI
Annual
Forecast
Errors of CPI
CPI
Annual
0.19
(0.19)
-0.19
(-0.19)
0.42
(0.15)
-0.43
(-0.15)
0.81
(1.19)
-0.73
(-1.08)
2.88
(1.29)
-2.85
(-1.28)
0.42
(0.35)
-0.32
(-0.26)
1.00
(61.02)***
-0.00
(-0.05)
1.00
(302.3)***
-7.2e-04
(-0.21)
0.97
(89.2)***
0.02
(2.18)**
0.99
(418.5)***
0.005
(2.12)**
0.99
(47.54)***
0.01
(0.533)
-0.03
(-0.47)
0.032
(0.48)
1.00
(209.1)***
-0.004
(-1.01)
0.34
(0.56)
0.03
(0.24)
0.03
(0.02)
0.997
(49.68)***
0.001
(0.08)
2.56
(0.12)
-0.04
(-0.55)
0.04
(0.54)
1.01
(203.67)***
-0.005
(-1.13)
0.13
(0.72)
Quarter
Forecast
Errors of CPI
WPI
Quarter
Forecast
Errors of WPI
WPI
Annual
Forecast
Errors of WPI
GNPPD
Forecast
Errors of
GNPPD
GNPP
Annual
Quarter
Quarter
Annual
Annual
Quarter
Forecast
Errors of
GNPPD
DAPD
Quarter
Forecast
Errors of
DAPD
DAPD
Annual
Annual
Quarter
α1
F-stat.
(Ser. Corr.)
1.31
(0.26)
Null
Hypothesis
HoA
HoB
1.42
(0.24)
HoA
HoB
9.29
(0.00)***
HoA
HoB
0.07
(0.792)
HoA
HoB
4.49
(0.04)**
HoA
HoB
HoA
HoB
HoA
HoB
HoA
χ2 for Ho
F-stat.
(Ho)
0.22
(0.89)
0.22
(0.89)
0.47
(0.79)
0.47
(0.79)
6.73
(0.03)**
6.11
(0.05)**
5.17
(0.07)*
5.13
(0.08)*
0.48
(0.786)
0.35
(0.83)
0.11
(0.89)
0.11
(0.89)
0.23
(0.79)
0.23
(0.79)
3.36
(0.05)**
3.06
(0.06)*
2.58
(0.08)*
2.56
(0.08)*
0.24
(0.79)
0.17
(0.84)
1.35
(0.51)
1.35
(0.51)
0.67
(0.51)
0.67
(0.51)
0.04
(0.98)
0.02
(0.99)
0.02
(0.98)
0.01
(0.99)
1.65
(0.44)
1.63
(0.44)
0.82
(0.44)
0.82
(0.44)
Forecast
Quarter
HoB
Errors of
DAPD
*** Significant at 1% level of significance. ** Significant at 5% level of significance. *
Significant at 10% level of significance.
51
Javed and Ahmad
Table 6.3.2: Weak Rationality Tests of Forecasts Obtained from VAR
P
E
o
t
t
= α
= α
o
o
+
+ α 1P
m
∑
i=1
α
e
i
t
P
Dependent
Variable
CPI
Data
Frequency
Annual
Forecast
Errors of CPI
CPI
Annual
Quarter
Forecast
Errors of CPI
WPI
Quarter
Forecast
Errors of WPI
WPI
Annual
Annual
Quarter
Forecast
Errors of WPI
GNPPD
Quarter
Forecast
Errors of
GNPPD
GNPPD
Annual
Annual
Quarter
Forecast
Errors of
GNPPD
DAPD
Quarter
Forecast
Errors of
DAPD
DAPD
Annual
Annual
Quarter
+ ε
o
t − i
(HoA: α0 = 0, α1 = 1)
t
+ ε
(HoB: α0 = α1 = 0)
t
α0
α1
-0.14
(-0.14)
0.30
(0.30)
0.55
(0.19)
-0.39
(-0.14)
0.41
(0.81)
-0.39
(-0.77)
1.59
(0.68)
-1.42
(-0.61)
1.12
(1.15)
-1.09
(-1.09)
F-stat. for
F-stat.
Null
Ho
(Ser. Corr.) Hypothesis χ2 for Ho
0.07
0.04
1.00
0.42
HoA
(0.97)
(0.97)
(61.5)***
(0.52)
-0.003
HoB
0.11
0.05
(-0.19)
(0.95)
(0.95)
1.00
0.61
HoA
1.08
0.54
(296.8)***
(0.44)
(0.59)
(0.59)
-0.001
HoB
1.13
0.57
(-0.43)
(0.57)
(0.57)
0.99
6.95
HoA
1.64
0.82
(122.4)*** (0.01)***
(0.44)
(0.45)
0.010
HoB
1.56
0.78
(1.19)
(0.46)
(0.47)
1.00
5.75
HoA
1.51
0.75
(400.6)*** (0.00)***
(0.47)
(0.47)
-0.0003
HoB
1.52
0.76
(-0.120)
(0.47)
(0.47)
0.97
25.43
HoA
2.80
1.40
(59.48)*** (0.00)***
(0.25)
(0.26)
0.029
HoB
2.51
1.25
(1.55)
(0.28)
(0.30)
0.004
(0.05)
-5.e-04
(-0.007)
1.002
(208.5)***
-0.002
(-0.52)
2.60
(0.109)
0.95
(1.04)
-0.94
(-0.99)
0.98
(64.0)***
0.0233
(1.33)
30.29
(0.00)***
-0.005
(-0.069)
0.009
(0.13)
1.005
(201.2)***
-0.005
(-1.12)
2.03
(0.16)
Forecast
Quarter
Errors of
DAPD
*** Significant at 1% level of significance.
52
HoA
HoB
HoA
HoB
HoA
HoB
0.73
(0.69)
0.80
(0.67)
0.37
(0.70)
0.40
(0.67)
1.97
(0.37)
1.80
(0.41)
0.99
(0.38)
0.89
(0.42)
2.72
(0.26)
2.88
(0.24)
1.36
(0.26)
1.44
(0.24)
Performance of Alternative Price Forecast for Pakistan
forecast of CPI satisfying all the conditions for strong rationality. While from
annual forecast series obtained from VAR, only CPI passes the test of
sufficient rationality, and the forecast series obtained from the ARIMA also
passes this test, but the sign of mean forecast error of two series is not same.
Table 6.3.3: Sufficient Rationality Tests of Forecasts
Regress the forecast error to information set and set the null hypothesis that all the
coefficients are simultaneously equal to zero.
Forecast error of
Obtained
Data
F-stat. for Ho
χ2 for Ho
from
Frequency
CPI
ARIMA
Annual
DAPD
ARIMA
Annual
CPI
ARIMA
Quarter
WPI
ARIMA
Quarter
GNPPD
ARIMA
Quarter
DAPD
ARIMA
Quarter
CPI
VAR
Annual
CPI
VAR
Quarter
GNPPD
VAR
Quarter
DAPD
VAR
Quarter
1.44
(0.69)
3.50
(0.32)
1.97
(0.58)
5.10
(0.16)
1.53
(0.67)
1.65
(0.64)
20.07
(0.00)***
8.82
(0.18)
29.99
(0.00)***
36.26
(0.00)***
0.48
(0.69)
1.16
(0.34)
0.66
(0.58)
1.70
(0.17)
0.51
(0.67)
0.55
(0.65)
3.34
(0.01)***
1.47
(0.19)
5.00
(0.00)***
6.04
(0.00)
*** Significant at 1% level of significance.
We apply strong rationality test only to the quarter forecasts of CPI
from both techniques. The results of strong rationality are shown in table
6.3.4. Postulate that both the series posses the negative sign of mean forecast
error, when we take ARIMA forecast of CPI as benchmark and combined it
with VAR forecast, it gives us biased results because that the sign of α is
positive, as shown in panel A of table 6.3.4, so forecast series obtained from
ARIMA models do not pass strong rationality test. In panel B of the table
6.3.4 we take VAR forecast as benchmark and combine it with ARIMA
forecast. We found that the forecast series of CPI obtained from VAR, when
53
Javed and Ahmad
combined with ARIMA forecast does not produce lower RMSE. This result
means that the forecast of CPI obtained from VAR can be claimed to be as
strongly rational.
Table 6.3.4: Test of Strong Rationality
Benchmark Forecast
CPI from ARIMA
Panel A
Sign Mean Error
-ve
α
β
Prob.
Conclusion
CPI from VAR
Panel B
Sign Mean Error
-ve
α
β
Prob.
Conclusion
Data Sample: 1972Q3-2005Q2
When Combined With
CPI from VAR
-ve
0.38
-0.04
0.72
Bias
CPI from ARIMA
-ve
-0.38
0.04
0.72
Cannot Reject
Conditions of Strict rationality simply state that strongly rational
forecasts pass the same test of strong rationality with different sub-time
periods. We break the whole sample in three parts, when we check the sign of
mean forecast errors of both series while taking the sample from 1972Q3 to
1982Q4, the sign of mean forecast error is not the same, while from 1983Q1
to 1994Q2 and from 1994Q3 to 2005Q2, the sign of mean forecast error are
negative of both series. In the first time span that is from 1983Q1 to 1994Q2,
we are not able to find unbiased results, as the sign of α is positive. When we
take sample from 1994Q3 to 2005Q2, we find the CPI forecast passes the
conditional efficiency test that is the RMSE of combination is not lower than
the benchmark forecast as shown in table 6.3.5. But the condition of strict
rationality is not satisfied, because from the three sub-sample time periods,
forecast of CPI passes the test only for one sub-sample time periods that is
from 1994Q3 to 2005Q2. So we are not able to say that VAR produce a
strictly rational forecast of CPI.
54
Performance of Alternative Price Forecast for Pakistan
Table 6.3.5: Test of Strict Rationality
Panel A
1983Q1 1994Q2
Sign Mean Error
α
β
Prob.
Conclusion
Panel B
1994Q3 2005Q2
Sign Mean Error
α
β
Prob.
Conclusion
7.
Benchmark Forecast
CPI VAR
-ve
When Combined With
CPI ARIMA
-ve
4.59
1.78
0.00
Bias
Benchmark Forecast
CPI VAR
-ve
When Combined With
CPI ARIMA
-ve
-0.19
1.98
0.12
Cannot Reject
Conclusions and Policy Implications
In this section we rank the alternative price indicators on the basis of
performance test used in this study. Annual forecast of WPI obtained from
ARIMA is not found to be consistent. On the other hand although the quarter
forecast of WPI is not efficient but passes the tests of weak rationality and
sufficient rationality, it is a surprising results, because in empirical analysis
many forecasts are not able to pass these tests. Annual and quarter forecast of
CPI from ARIMA passes all the tests of consistency, efficiency and test of
weak and sufficient rationality.
Forecasts obtained from VAR shows same results about the forecast of
WPI in the context if efficiency and rationality but it is consistent. Annual
forecasts of WPI, GNPPD and DAPD are not pass the tests of weak rationality
i.e., unbiasedness test and weak information efficiency test. We rank CPI is
the best indicator of inflation from the forecasting point of view. Forecast of
DAPD stays at the second number, to satisfying the tests of consistency,
efficiency and rationality. Here we provide support to the observation of
Ahmad and Ram (1991) that DAPD is a better indicator of inflation as
compared to the other popular price indices. Forecasts of GNPPD are less
reliable, but the forecast of WPI is least reliable according to the findings of
this study.
55
Javed and Ahmad
So we can say that to get the best price forecast, the better
specification is VAR models with quarterly data, and we suggest CPI and
DAPD instead of GNPPD and WPI. For a VAR forecast, we rank WPI at
number third, better than GNPPD, while form ARIMA forecasts WPI is least
satisfactory price variable for forecasting point of view.
If we look at the construction procedure of the price indices like CPI
and WPI in Pakistan, there are also some facts that support results of the
study. For the construction of CPI, the price data are taken from the 71
markets of 35 cities of Pakistan. On the other hand, coverage of WPI is very
low. The wholesale price data are collected from a single market of 18 cities
each. The relatively poor forecasts of WPI compared with CPI suggest that
efforts need to be made to make the WPI more representatives by improving
the coverage in terms of markets, commodities and cities. There is also a need
to improve the skills of price collecting staff, especially for those enumerators
who collect the prices for the construction of WPI, so that the problem of low
coverage may be covered. In this way the qualities of survey indicators can
be improved with the improvement in the human capital that makes the survey
data a clear picture of the economy.
Although econometric forecasting is not yet very common among
policy makers and other agencies/institution, a movement in that direction is
in the making. For example, the State Bank of Pakistan has gone through
rigorous training programs on model building, econometrics and forecasting.
If econometric forecasts are used for policy making, they should also be aware
of limitations of the techniques. Our results show that in general more
elaborate VAR models outperform the simplistic ARIMA models in
forecasting a price series. Another useful conclusion is that the quarterly data
provide better forecasts than the annual data. All these results support the
econometricians’ maintained hypotheses that data observed at high frequency
and statistically more elaborate use of a given data set provides better
predictions than the data observed at low frequency and analyzed with
simplistic statistical tools.
Another implication of our findings is that researchers and policy
makers are likely to make better predictions and policy prescriptions if they
base their analyses on the price indices that have broader coverage like the
CPI as compared to WPI or the price deflator based on gross domestic
absorption as compared to gross domestic product or gross national product.
56
Performance of Alternative Price Forecast for Pakistan
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61
Forman Journal of Economic Studies
Vol. 8, 2012 (January–December) pp. 63-81
Impact of Trade Openness on Exports Growth,
Imports Growth and Trade Balance of Pakistan
1
M. Aslam Chaudhary and Baber Amin Abstract
This study mainly aims at analyzing the impact of trade openness on exports
growth, imports growth and trade balance of Pakistan. Pakistan has
undergone substantial trade openness measures during the last three decades.
The main objective behind the openness and liberalization has been to reap
the fruits of higher exports which contribute to higher economic growth. The
study analyzes the data from 1980-2008. The OLS and Auto Regressive
Distributive Lagged modeling approaches have been employed to find
empirical support. The results of the study reveal that trade openness affected
both exports growth and imports growth positively although the imports
growth increased more than exports one, which worsened the trade balance.
Nevertheless, trade openness played a limited role and remained constrained
in promoting economic growth through exports expansion. Thus, there is a
need to create a balance between exports and imports growth to reap the
fruits of openness.
Keywords: Export growth; Import Growth; Trade Balance; Trade Openness;
Pakistan
JEL classification: F41, F43, F15, F42
1.
Introduction
Trade liberalization as well as openness of economy is seen as driving
force to accelerate economic growth. Of course, openness of borders for trade
leads to reap the benefits of expanded demand for exports. For this reason,
most of the countries, particularly the developing ones, introduced reforms to
open up the foreign sector and also reformed the domestic economy too;
since the last three decades.2 The international financial institutions such as
1
The authors are Professor and lecturer at Forman Christian College (A Charted University),
Lahore and Lahore Leads University, Lahore, respectively. The paper is based on M. Phil
thesis of Amin B. (2011) and Working paper of Chaudhary, M. A. (2010). They are thankful
to Naeem Rashid for his valuable comments which helped to improve the paper.
2
The reforms process of opening up of the foreign sector started in late1980’s in Pakistan.
Chaudhary and Amin
WTO, World Bank and IMF also encouraged trade liberalization and
openness. In addition to above, one of the main objectives behind the
openness and liberalization has been to promote efficiency, competition and
discourage distortions.3 The more barriers on trade we have, the lesser will be
exports expansions. For a country like Pakistan, which introduced rapid
economic reforms and ended up with expanding imports and meager impact
on its exports expansion, the result is trade balance worsened.4 Thus, trade
openness might have beneficial, as well as harmful, effects for a country. If
trade openness leads towards higher exports and more efficient allocation of
resources, it is beneficial and could potentially accelerate growth by ensuring
needed foreign exchange and attracting foreign investment. Pakistan has not
generated efficiency and competition at domestic level and relied heavily on
imports which could turn out as worsening economic conditions. Pakistan is
suffering from twin deficits i.e. trade deficit and domestic budget deficit.5 So,
there is a need to analyze whether trade openness has really contributed to
accelerate economic growth of Pakistan or not. Most of the researchers
focused their research on the expansion of exports, due to openness; however
little attention has been paid towards increasing growth rate of imports which
ultimately could worsen and balance of trade. Furthermore, deficit in trade
balance again reflects as foreign borrowing which further aggravates the
problem of deficit. The important point to be noted here is that if trade
liberalization increases the import growth more than export growth, as it
happens in case of most of the developing countries, it might lead towards
creating worse conditions for the country. It is a well known fact that most of
the under developed countries are already suffering from foreign reserves
shortages, deficit in trade and low foreign direct investment. In this
environment, liberalization of foreign sector helps to improve economic
conditions. There is a limited research on these issues, particularly in the case
of Pakistan.
Given the above background, this study empirically analyzes the
impact of trade liberalization on both export and import growth. Moreover,
trade balance which was ignored is being analyzed to dig the roots of the
problem.
3
The Market Friendly Approach also conveyed the process of market competition,
international linkages which take place due to investment in human development.
4
See: Pakistan Economic Survey, 2011-12.
5
Pakistan’s budget deficit was as high as over 7% of GDP. It is even expected higher for the
current year i.e. 2012-13. For further details see: Pakistan Economic Survey, 2011-12.
64
Impact of Trade Openness on Exports Growth, Imports Growth and Trade Balance
The rest of the study follows a certain pattern. Section II describes the
performance of various variables in the post and pre-reform era regarding
trade openness and liberalization6. Section III, presents literature review and
the results of other important studies regarding the impact of trade openness
and liberalization on export growth, import growth and trade balance.
Theoretical background and model specification have been discussed in
Section IV. Section V presents the results of empirical estimation.
Conclusions and policy implications are provided in section VI.
2.
Economic Performance of Major Variables in Pre and PostReform Era.
Pakistan brought significant trade liberalization during the 1980s7.
Table 1 shows the average growth rates of various important variables before
liberalization i.e. pre-reform era 1980-1990 and post-reform era 1991-2008.
The table clearly indicates poor performance of all the indicators in the postreform era. The average GDP per capita growth rate was 3.2% in the prereform era while it reduced to 1.9% in the post-reform period. The average
real GDP growth was 6.3% in the pre-reform period, while it reduced to
4.36% in the post-reform era. In line with the pattern of the above-described
indicators, real exports slowed down in the post-reform period from 9.4% to
6.8% in the pre-reform period. In contrast to the above-discussed indicators,
average import growth increased from 4.37% to 5.28%; after liberalization.
The trade reforms thereof increased the average import growth while
decreased the export growth of Pakistan’s economy. Besides, the average
growth rate of trade deficit was minus 1.9% in the pre-reform period while it
increased to 26.8% in the post-reform period which indeed is a significant
increase in the trade deficit. The economic performance improved earlier
during the first decade of 2000’s and again deteriorated thereafter. It may be
noted that economic growth is still around 2.5%8. On the basis of above
discussion therefore, it may be inferred that trade reforms affected the
economic growth of Pakistan adversely.
6
In Pakistan, not only liberalization of the foreign sector took place but there were substantial
reforms to improve domestic economy such as privatization of the financial market etc.
7
See: Chaudhary M. A. (2004); Globalization: WTO, Trade and Economic Liberalization in
Pakistan.
8
See: for details, Pakistan Economic Survey, 2011-12.
65
Chaudhary and Amin
Table 1: Performance of Various Variables in Pre and Post-Reform Era
Variable
Pre-liberalization
Post-liberalization
GDP per capita
Real GDP
3.19
6.26
1.89
4.40
Real Exports
9.38
6.85
Real Imports
4.37
5.28
Trade Deficit
-1.85
26.8
Source: Pakistan Economic Survey, GOP (various Issues)
3.
Review of Literature
After the Washington consensus and emergence of WTO, the world
has been witnessing a continuous debate on the nexus between trade
liberalization and economic growth. It still remains to be seen if trade
liberalization and openness is growth promoting. If yes, we need to see the
channels through which it affects economic progress? There is an ample
literature available on the nexus between openness and export growth and
export led growth but very little attention has been paid on impact of trade
openness on import growth and trade balance. A brief literature review on the
issue is presented below:
Sherazi and Abdul Manap (2004) tested the ongoing issue that exports
growth enhanced economic growth. They also found that there is a feedback
impact on imports. However they have not tested this feedback impact. Our
study is focused to contribute to the literature in this context, which is
neglected so far.
Faini et al. (1992) analyzed the effects of trade policy on demand of
imports in developing countries. The study divided the imports into two
categories: under the quantitative restrictions and those which are freely
movable among countries. The results show that income elasticity was greater
than one among developing countries while the relative prices were proved to
be significant having elasticity less than one. The other important finding of
the study has been that the shortage of foreign exchange or when we have
restrictions on import flows then the estimated effects of income and price
elasticity becomes less prominent as compared with liberalized or more open
trade regime where this impact is prominent. The study suggests that while
interpreting the income and price elasticities in import demand studies, the
66
Impact of Trade Openness on Exports Growth, Imports Growth and Trade Balance
type of trade regime should be given special attention. It is the nature of trade
and goods which contribute to gains from the trade.
Santos-Paulino (2002) analyzed import demand function for twenty
two developing countries with special reference to their trade policy reforms,
particularly liberalization of trade. He utilized panel data, Fixed Effect (FE)
and Generalized Method of Moments (GMM), to draw empirical evidences.
The study presented estimates at both regional and panel level. The main
objective of this study was to observe the impact of trade liberalization
reforms on imports in developing countries. This study also used the
“Heritage Foundation Index of Economic Freedom” to categorize the
countries from very low to very high level of protection of traded goods.
Heritage Foundation Index of Economic Freedom classified countries into
five classes. In the estimation of fixed effect model, country specific dummy
was also used which takes into account the country specific factors and
environment. The dynamic panel data estimation is done through FE and
GMM method while the time series cross section analysis is based upon Two
Stages Least Square (2SLS) and Maximum Likelihood Method (MLM). The
results of the fixed effect model showed that all the variables had the sign
according to the prediction of economic theory and all the variables were
significant except the relative price indicator. The results also revealed that
short and long run price and income elasticities are same. However, the
variables of trade policy regime and import duty are statistically significant.
The study also showed that trade liberalization enhanced 100% in the imports
volume. The fixed effect estimates support the Melo and Vogt (1984)
hypothesis.9 Thus, based upon the study of Santos-Paulino (2002), it can be
stated that the affects of import duties vary from one region to the other region
while we do not have stable and consistent results for all the regions of the
world. Similarly, Income and price elasticities also differ among regions. Due
to 100% increase in imports after liberalization, the study suggested important
policy measures regarding the export promotion and current account deficit
problem of developing countries. However, there was not significant increase
in exports. The study suggested that liberalization should be carried out along
with export promotion strategy so that countries should not face the severe
problem of balance of payment which may reduce the fruits of liberalization
in terms of higher growth.
9
For more details on this hypothesis see: Melo, O., & Vogt, M. G. (1984) and Yanikhra,
(2003).
67
Chaudhary and Amin
In spite of using appropriate techniques of panel data estimation, they
missed important determinants of import growth in its function; like industrial
growth, exchange rate regime, law and order situation, incentives for
investors, institutional development and domestic environment etc., which
may have affected his results. Moreover, the study has found different results
for aggregate sample and regions. So, it is difficult for any country to fully
adopt the same policies based on these results because individual country
results might get different outcomes from the regional results. Thus a time
series comprehensive study for individual countries is also needed to provide
additional evidences for sound policy suggestions regarding liberalization and
export growth.
Santos-Paulino and Thrilwall (2004) studied and utilized different
measures for liberalization and openness. Their focus was on the impact of
trade liberalization on exports, imports and balance of payments problems of
developing countries. The study used the data set from twenty two developing
countries which brought significant changes and introduced liberalization in
the 1970s.They used two types of measures of liberalization which are: (a)
import and export duties (b) the dummy variable used for the year of
liberalization selected on the basis of world trade organization (WTO) and
World Bank’s criteria. The study used the Fixed Effect (FE) and Generalized
Method of Moments (GMM) for analyzing panel data for developing
countries using time series /cross sectional study for different regions of the
world.
The study analyzed and compared the impact of trade liberalization on
exports and imports growth for major developing countries. Besides, the
impact of liberalization on prices, income elasticity of demand for exports and
imports had also been estimated. Further, the impact of liberalization on
balance of payment and trade balance was highlighted.
The study supported the notion that trade liberalization enhanced both
exports and imports but the increase in former was greater than that of the
later, which worsened the problem of trade deficit. It is well known that most
of the developing countries have already been facing the problem of shortage
of foreign exchange reserves. Liberalization has therefore very important
policy implications for these because it may lead to growth below the
potential level. The results of the study also pointed out that import and export
duties have negative impact on import and export growth. The study
concluded that ten percentage point decrease in duties leads to 2% growth of
68
Impact of Trade Openness on Exports Growth, Imports Growth and Trade Balance
exports, while import growth increased between 2 to 4 %. Moreover,
liberalization increased the elasticity of demand for imports more than
exports. Thus the developing countries have to be careful in terms of
liberalization while remaining ready to handle balance of payment problem.
They need not become a victim of foreign exchange shortage. If it happens so,
a country may end up pilling up collusive amount of foreign debt10. This
trend has already been set for the Pakistan economy. Pakistan has not
borrowed as much as, in the first fifty five years since its birth, which it has
borrowed in the last five years.
Keeping in mind the outcomes of the above cited literature, our study
aims at exploring the important features and impact of liberalization,
particularly the impact of trade liberalization on exports, imports and balance
of payment. For this purpose, a model has been developed to draw empirical
evidences. The model is discussed below.
4.
Theoretical Background and Model Specification
4.1.
Trade Liberalization and Exports Growth
The demand for exports depends mainly upon relative prices and
world demand for exports (s). By keeping the price and income elasticity
constant and following Santos-Paulino and Thrilwall (2004), the export
demand function can be written as:
.
Where ‘X’ is the exports at time period t, pd/pf is the ratio of domestic to
foreign price in the same currency units, ‘W’ denotes the world income The
value of β2 indicates income elasticity of demand for exports, while β1 is the
price elasticity of demand for exports. After taking the logs and differentiating
with respect to time, the above equation may be written as: Now by adding trade openness variable (top), the equation becomes:
10
For other bottlenecks and trade contributions see Kroger (1978).
69
Chaudhary and Amin
Finally, by introducing dummy (lib) variable to capture the effect of the year
of liberalization as taken by Lopez (2003), Santos-Paulino and Thrilwall
(2004), the above equation will be: Here Xt is the export growth, pxt is the growth rate of relative price change,
/
is the growth rate of trade openness and libt is the
topt
liberalization dummy which considers the year 1991 as liberalizing year, as
commonly utilized in the literature.11
4.2.
Trade Liberalization and Imports
Most of the literature has focused on trade liberalization and export-led
growth12. However, there is limited body of literature which explored the
nexus of liberalization - import -growth phenomenon. Trade liberalization
may increase the growth of imports much more than growth of exports which
could create a problem of balance of payment deficit as well as that of
shortage of foreign exchange which may squeeze economic growth.
Therefore, it is also equally important to analyze the impact of trade
liberalization and openness on import growth.13 In order to analyze the impact
of trade liberalization on imports and economic growth by following SantosPaulino and Thrilwall (2004), the given equation is derived in the same way as
for exports growth model stated above. So the above described equation
becomes as following which is utilized to analyze the impact of trade
liberalization on import growth;
.
After taking the log, differentiating it with respect to time, and augmenting the
variable of trade openness, the above equation becomes as follow:
11
See: Santos-Paulino and Thrilwall (2004)
For details see: Balassa (1985), Ram (1987).
13
Santos-Paulino and Thrilwall (2004) studied the impact of trade liberalization on export,
import and trade balance growth in developing countries and proved that trade liberalization
increased the import growth more than export growth which created the balance of payment
problem too.
12
70
Impact of Trade Openness on Exports Growth, Imports Growth and Trade Balance
Now, after adding the dummy variable, the above equation may be written as:
Where we have Mt as the growth rate of imports, pmt the growth of import
price relative to domestic substitutes; yt the growth of domestic income, topt
the growth of trade openness, libt the dummy for the liberalization year i.e.
1991 in case of Pakistan.
4.3
Trade Liberalization and Current Account
The current account provides a good picture of a country’s position
regarding foreign exchange and foreign reserves. Thus, taking the difference
between exports and imports, as trade balance provides performance of trade
liberalization. To capture such impact this study will estimate the following
equation which is taken from Santos-Paulino and Thrilwall (2004):
Where ‘W’ is the world income, Y is the domestic income, P is real exchange
rate, TOP is the trade openness and TOT is the terms of trade.
5.
Empirical Estimation and Interpretation of Results
5.1.
Empirical Evidences: Trade Liberalization and Exports Growth
Two models discussed in the previous section were estimated by using
the OLS method. All the variables have been taken in growth rates and were
found stationary at level form. The results are presented in the following table
2. The shows that growth rates of all the variables are I(0) , so OLS can be
applied for empirical results. Table 3 shows the results of OLS regarding the
impact of trade liberalization on export growth.
The results of the regression analysis (table 3) show that trade
openness have significant and positive relationship with exports growth. The
results also reveal that 1% increase in trade openness leads to 1.06% increase
in exports growth while the world income growth and relative price change
variables remain insignificant.
By adding dummy variable to the model for capturing the affects of
liberalization, the following equation is estimated:
71
Chaudhary and Amin
Table 2: Results of the Unit Root Tests
Augmented Dickey Fuller
Philips Perron
Level
Level
Variable
Intercept
Intercept
Result
GX
-5.40708*
Trend &
Intercept
-5.3058*
-5.432890*
Trend &
Intercept
-5.306255*
I(0)
GRER
-4.710111*
-4.572675*
3.286711*
-3.134843***
I(0)
GTOP
-4.903515*
-4.820744*
-4.888834*
-4.798468*
I(0)
GW
-3.173418*
-3.444151
-3.076926*
-3.385888
I(0)
GY
-3.278274*
-3.467158**
-3.254017*
-3.467158**
I(0)
Note: *, ** and *** show level of significance at 1%, 5% and 10 %, respectively.
The results of the above equation are provided in appendix I. The
results indicate that trade liberalization (openness) has significant and positive
impact on export growth.
The variable is significant at 1% level of
significance. The above results reveal that one percent increase in trade
openness led to 1.17% increase in export growth. The world income growth
and liberalization dummy are also found significant. Interestingly, the sign of
liberalization dummy is negative but it is logical since after introducing the
trade reform policies and becoming liberalized, the openness squeezed exports
growth. The results are consistent with Santos-Paulino and Thrill wall (2004).
5.2
Empirical Evidences: Trade Liberalization and Imports Growth
In order to analyze the impact of trade liberalization (trade openness)
on import growth, the given equation is estimated14.
The results of the unit root test reveal that all the variables are integrated
at I(0). So, OLS can be applied and the results are given Table 2.
The results of the OLS have been given in Table 4 which shows that
the variable of trade openness is significant at 1% level of significance with
positive sign. It suggests that one percent increase in trade openness could
14
See Chapter 4, Amin B. (2011).
72
Impact of Trade Openness on Exports Growth, Imports Growth and Trade Balance
Table 3: Impact of Trade Liberalization on Exports Growth
(Dependent Variable: Growth of Real Exports)
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
6.740863
7.358236
0.916098
0.3700
GTOP
1.058946*
0.267205
3.963043
0.0007
GW
0.344935
1.956751
0.176280
0.8618
GPX
-0.134371
0.160071
-0.839446
0.4107
AR(1)
0.700776*
0.164761
4.253287
0.0004
MA(1)
-0.997480*
0.107147
-9.309462
0.0000
R-squared
0.557946
Adjusted R-squared
0.452695
F-statistic
5.301109
Prob.(F-statistic)
0.002646
Durbin-Watson stat
2.313301
Note: * indicates significant at 1% level of significance
lead to almost 1.2% increase in import growth.
positively related to trade liberalization.
The import growth is
Now by adding the dummy variable for capturing the affects of the
year of liberalization, the following equation is estimated.
Where libtt represents liberalization dummy. The results of the above
regression are given in appendix II. The results of the regression analysis
show that the variable of trade openness is still highly significant with positive
sign along with the co-efficient almost equal to one. The value of adjusted R2
is 0.81, while the value of DW is 1.8. The liberalization dummy is also found
significant with positive sign. Both the variables of trade openness and
liberalization dummy are significant at 1% level of significance, respectively.
73
Chaudhary and Amin
Thus trade liberalization is positively and significantly contributing to imports
growth. The liberalization dummy has 3.84 co-efficient which shows that 1%
increase in trade openness leads to 3.84% increase of imports. However, it
may be noted that positive association of trade liberalization and import
growth may not be very healthy for the economy. The increasing imports and
squeezing exports potentially create a serious problem of trade deficit which
Pakistan is being faced by Pakistan. Table 4: Impact of Trade Liberalization on Imports Growth
(Dependent Variable: Growth of Real Imports)
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
Y
PM
-2.538340
1.028560**
0.103221
3.285175
0.503367
0.114265
-0.772665
2.043360
0.903345
0.4476
0.0526
0.3757
TOP
1.199407*
0.161098
7.445208
0.0000
MA(1)
-0.574645*
0.187534
-3.064215
0.0055
F-statistic
D.W. stat.
15.15
1.78
Prob. (F-statistic)
R-squared Adjusted
0.01
0.73
Note: *, ** indicate significant at 1% and 5%, respectively.
5.3.
Impact of Trade Liberalization on Trade Balance
The following equation has been estimated in order to analyze the
impact of trade liberalization on trade balance following Santos-Paulino and
Thrilwall (2004).
First unit root tests have been conducted in order to determine the order of
integration of the variables. It helps to decide about the technique of
estimation.
The results of Table 5 indicate that some variables are I (0) while the
others are I(1) . In these circumstances econometric theory suggests that
Bounds Procedure and ARDL approach seem appropriate for determining
long and short run dynamics as described by Pesaran and Shin (1996, 1999
74
Impact of Trade Openness on Exports Growth, Imports Growth and Trade Balance
and 2001). The empirical estimations of long run and short run dynamics are
analyzed in the following section.
5.3.1. Estimation of ARDL Model
After analyzing the order of integration of the variables, the following
error correction version of the ARDL model has been used in order to
determine the short run and long run dynamics of the relationship among the
variables.
n
n
n
n
⎛ TB ⎞
⎛ TB ⎞
∆⎜
⎟ = α 0 + ∑ α1 ∆ ⎜
⎟ + ∑ α 2 ∆ LTOPt _ i + ∑ α 3 ∆LTOTt _ i + ∑ α 4 ∆ RERt _ i
i =1
i=0
i=0
⎝ GDP ⎠
⎝ GDP ⎠t _ i i = 0
n
n
i=0
i=0
+ ∑ α 5 ∆LWt _ i + ∑ α 6 ∆ LYt _ i + β1 LYt − 1 + β 2 LTOPt − 1 + β3 LTOTt − 1 + β 4 LRERt − 1
⎛ TB ⎞
+ β5 LWt − 1 + β 6 ⎜
⎟ + ξt
⎝ GDP ⎠t − 1
Where the parameters of ‘’α” show short run while of ‘’β “show long run co
efficient in the above equation.
Table 5:
Results of the Unit Root Tests
Augmented Dickey Fuller Test
Philips Perron Test
st
Level
1 Difference
Level
1st Difference
Variable
Trend Trend & Trend
Trend & Trend Trend &
Trend
Trend &
intercept
Intercept
intercept
intercept
X
0.7843 2.1526
6.0841* 5.9954* 0.7843 2.1988
6.0846* 5.9954*
M
1.0017 2.8549
5.4257* 5.3353* 0.8943 2.9114
5.6673* 5.6542**
TOP
3.75** 4.000** 5.1769* 5.0452* 3.09** 2.8427
5.2750* 5.1265*
TOT
1.4210 0.3873
4.7451* 4.9103* 1.4033 0.3502
4.7350* 5.0892*
TB/GDP 4.586* 4.4383
.1511
.8247
9.863* 10.2900* 4.5865* 4.4383*
RER
1.7119 0.6268
4.9808* 6.1885* 1.6933 0.6709
4.9863* 7.5889*
W
1.4574 2.98879 3.306** 3.25*** 0.9576 1.9568
5.4444* 4.2314**
Y
2.3383 2.0533
3.227** 3.46*** 1.9726 2.2736
3.2479* 3.4635**
Note: *, ** and *** indicate significant at 1%, 5% and 10%, respectively.
Result
I(1)
I(1)
I(0)
I(1)
I(0)
I(1)
I(1)
I(1)
5.3.2. Estimation of the F-Statistics
The joint significance test is applied to determine the existence of the
long run relationship among variables and then compared with the critical
bound values.15 The results show that calculated F- Stat is 5.18, which is
greater than the critical Bound values which are (3.23- 4.35) for 5% level of
15
For details see Pesaran & Shin (1996, 1999 and 2001).
75
Chaudhary and Amin
significance. So, we reject the null hypothesis of no co-integration at 5% level
of significance for the above model. The results of the estimated long run
elasticities are reported in table 6, given below:
Table 6: Estimated Long Run Elasticities Using the ARDL Approach
[ARDL (1, 0, 0, 1, 0, 0) Selected Based On Schwarz Bayesian Criterion]
Regressors
C
Dependent variable is TB/GDP
Coefficient
Standard Error
T-Ratio
32.6324**
14.8950
2.1908
Probability
.038
LTOT
.094994
.14383
.66045
.515
LTOP
-.59178*
.17621
-3.3584
.003
LRER
-.31419***
.17772
-1.7679
.090
LW
3.0325*
1.1545
2.6267
.015
LY
.66488*
.21938
3.0306
.006
Note: *, **, *** indicate significant at 1%, 5% and 10%, respectively
5.3.3. Error Correction Representation for Selected ARDL
Table 7 shows the results of error correction representation (ARDL)
model of the impact of trade liberalization on trade balance.
The results of both the short run and long run elasticities of ARDL
(table 6 and table 7) show that trade openness is significantly and negatively
related to trade balance. The variable is significant at 1% level of significance.
The results reveal that trade openness leads to the worsening of the trade
balance which means an increase in trade deficit. It may be noted that the
finding is in line with the previous findings that trade liberalization increased
the import growth more than export growth implying that it has negative
impact on trade balance. However, the variable of real exchange rate remains
insignificant while the world income growth has significantly positive impacts
on trade balance because it positively and significantly affects the exports of
Pakistan. The country’s income growth is negatively related to trade balance.
The variable is significant at 1% level of significance. It is quite logical to
have negative sign with it because we found in our previous analysis that the
domestic income growth leads to increased imports growth.
The results of the long run analysis show that 1% increase in trade
openness leads to 0.59% reduction in trade balance. The results of the error
correction representation show that the adjustment parameter is highly
76
Impact of Trade Openness on Exports Growth, Imports Growth and Trade Balance
significant at 1% level of significance with negative sign which is according
to theory. The co-efficient of the error correction term suggests that 67% of
the error will be adjusted in the first time period. It shows relatively fast
speed of adjustment. It also means that 67 % of the disequilibrium caused by
the previous period shocks will converge back to the equilibrium.
Table 7: Error Correction Representation for the Selected ARDL Model
[ARDL (1,0,0,1,0,0) Selected Based On Schwarz Bayesian Criterion]
Dependent variable is ∆TBGDP
Coefficient
Standard Error
T-Ratio
22.1739**
9.7532
2.2735
Regressors
∆C
Prob.
0.032
∆LTOT
0.064549
0.096428
.66940
0.509
∆LTOP
-0.40212*
0.098787
-4.0705
0.000
∆LRER
-0.0093804
0.090250
-0.10394
0.918
∆LW
2.0606*
0.76359
-2.6986
0.012
∆LY
-0.45179*
0.16608
-2.7203
0.012
-0.67951*
0.13226
-5.1378
0.000
Ecm (-1)
,
Note: * ** indicate significant at 1% and 5%, respectively.
6.
Conclusions and Policy Implications
The main objective of the study was to analyze the impact of trade
openness on export and imports growth. Moreover, trade balance has also
been analyzed by highlighting its determinants. As per our knowledge, such
analysis has been ignored in the previous literature. In other words, the issue
of deterioration in trade balance was the ultimate prime focus of this study.
For this purpose, the study analyzed the data form 1980 to 2008. The OLS and
ARDL approaches were applied to draw empirical investigations.
Most of the previous studies analyzed the impact of trade liberalization
on the performance of economic growth, exports, inequality and income
distribution etc. Hardly any study has analyzed the above-cited issue. The
impact of trade liberalization on trade balance and imports growth is very
important for a developing country like Pakistan. The general notion of
liberalization of trade, accelerating exports and bringing improvement to trade
balance may not be true for all. The liberalization may increase greater
growth of imports than exports and ultimately it might have serious effects on
country’s balance of payments. It may increase deficit of trade balance, which
77
Chaudhary and Amin
will affect foreign exchange reserves, foreign exchange rate and ultimately
economic growth unless the balance between imports and exports is
maintained. As a result, ultimately the economic growth is hampered.
The results of the study suggest that increase in trade openness and
liberalization has significant positive impact on the growth of imports and
exports where this influence on imports is greater. The results of the analysis
also show that exports growth is greater in the pre- reform era than the postreform era while the situation was vice versa for the imports growth. The
results of the study also revealed that trade openness and liberalization
worsened the trade balance.
The above cited findings have important bearings for policy
formulation. There is a need to review trade liberalization policy since it has
worsened the balance of payments. The increasing imports, more than exports,
could create further serious bottleneck for the economy. The trade deficit is
already on the verge of increase and it can pose a serious problem, if
appropriate measures are not taken. Pakistan must improve its exports and
also cut on imports to improve trade balance. There is also a need to review
trade openness policy and take additional necessary steps to reap the benefits
of trade liberalization.
78
Impact of Trade Openness on Exports Growth, Imports Growth and Trade Balance
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Impact of Trade Openness on Exports Growth, Imports Growth and Trade Balance
Appendix I
Impact of trade liberalization on export growth
Dependent Variable: Growth of Real Exports (GRX)
Variable
Coefficient
Std. Error
C
4.433517
8.317090
TOP
1.167103*
0.268145
W
3.378534***
1.863828
PX
0.705932
0.427948
LIBD
-8.306201**
3.264085
MA(2)
-0.942631*
0.034193
R-squared
Adjusted R-squared
F-statistic
Prob(F-statistic)
Durbin-Watson stat
t-Statistic
0.533061
4.352509
1.812685
1.649573
-2.544726
-27.56768
0.582761
0.487934
6.145515
0.001045
1.872891
Prob.
0.5993
0.0003
0.0836
0.1132
0.0185
0.0000
Note: *, ** and *** show level of significance at 1%, 5% and 10 %, respectively.
Appendix II
Impact of trade liberalization on import growth
Dependent Variable: Growth of Real Imports
Variable
Coefficient
Std. Error
C
-9.023977**
4.249876
GTOP
0.984337*
0.179387
GY
1.863871***
0.617836*
GPM
0.091508
0.074030
LIBD
3.843301**
1.383668**
MA(1)
-0.997458*
0.111083*
R-squared
Adjusted R-squared
F-statistic
Prob(F-statistic)
Durbin-Watson stat
t-Statistic
-2.123351
5.487229
3.016772
1.236087
2.777617
-8.979418
0.817887
0.776498
19.76088
0.000000
1.758968
Prob.
0.0452
0.0000
0.0063
0.2295
0.0110
0.0000
Note: *, ** and *** show level of significance at 1%, 5% and 10 %, respectively.
81
Forman Journal of Economic Studies
Vol. 8, 2012 (January–December) pp. 83-105
Determinants of Youth Activities in Pakistan
Rizwan Ahmad and Ijaz Hussain1
Abstract
This paper analyzes the youth labour market activities in Pakistan. Based on
micro data of Labour Force Survey (2006-07), the strength of analysis
presented in the paper is twofold. First, it highlights some issues of youth in
labour market, their attitude towards work and education in Pakistan and
second, the econometric analysis investigates the supply side determinants of
youth activities in Pakistan. Our descriptive analysis shows that a substantial
percentage of youth is neither in labour force nor enrolled as student which
shows the wastage of human resources in the society. Moreover, higher
unemployment among educated youth, poor level of education and skills,
predominance of informal economy are some of the major issues of youth
labour market in Pakistan. Results of multinomial logit model show that
being a female reduces the chances of full time work and full time students in
Pakistan. In general, young people with educated parents are more likely to
enroll in education, while those whose parents are working in agriculture or
informal sector are more likely to be full time workers. Similarly,
responsibilities within household as a head or having more number of siblings
also increase the economic participation of youth.
Keywords: Labor market; Youth activities; Education; Pakistan
JEL classification: J16, J21, J28
1.
Introduction
Since 1950s, the world’s population has gone through some major
changes. Some countries are facing the problem of ageing population while
some are having a larger share of youth2population. Pakistan is also one of the
countries which will have larger share of youth population in future.
According to United Nations Population Projections; by the year 2050, there
will be 50 million young people in Pakistan3. These young people can be
1
The authors are Assistant Professors at Department of Economics, Forman Christian College
(A Chartered University), Lahore and Department of Economics, Gomal University, D. I.
Khan, respectively.
2
In Pakistan, Youth constitutes a group of 15-24 years of age people.
3
World Population Prospects: The 2006 Revision.
Ahmad and Hussain
considering as an asset to produce demographic dividend4 for the society if
proper education and economic opportunities are provided to them. However,
unfortunately in Pakistan, these young people are facing number of challenges
and difficulties in their way to education and work (Nayab, 2008; Ahmad &
Azim, 2010). Early start of career, higher rate of unemployment, lack of
educational and vocational opportunities are some of the issues of youth in
Pakistan. On one hand, many young people start their career early which can
adversely affect their earnings later in life5 while on other hand, a substantial
percentage of youth especially females are not engaged in any economic
activity6. Usual status7 unemployment rate (22%) for youth in Pakistan is
almost three times higher than their official unemployment rate (7.5%).
Moreover, a substantial percentage (30.5 %) of youth in Pakistan is neither in
school nor in labour force. Only 27 percent of young people (22.1 percent
female and 31.7 percent of male youth) are currently enrolled as a student in
the age when they are supposed to complete their education. About 14.2
percent of employed youth work for less than 35 hours a week. Majority of
them (about 94.4 percent) are not available for additional work and those who
are available for additional work do not take serious measures to find work.
Only 2.6 percent of them are actively seeking for additional or alternative
work. Some of these figures are summarized in table 1.
All these facts and figures show that a substantial percentage of our
population will consist of young people in future and majority of them are
either sitting idle or facing difficulties in labour market. The opportunity cost
of sitting idle of these people can be very high and there is a need to analyze
what factors determine the outcomes of youth activities in Pakistan. This
paper is an attempt to analyze the supply side determinants of activities of
youth in Pakistan. For this purpose, we divide the activities of youth in four
categories8, i.e., those who are full time student, full time worker, combine
work with school and neither working nor enrolled as student. These
4
Different researchers have already claimed that countries with larger share of youth and
working age population may experience a boost in economic growth which is termed as
‘demographic dividend’. See for example, studies by Bloom et al., 2001; Lee et al., 2006.
5
Emerson and Souza, 2006; Faizunnisa, 2005.
6
Durrant, 2000.
7
Official unemployment rate is based on one week reference period while usual status
approach uses last twelve months status as reference period. For details about usual status
approach, see Ahmad (2010).
8
We follow the methodology used by Burki and Tazeen (1999) to divide youth activities in
four mutually exclusive categories.
84
Determinants of Youth Activities in Pakistan outcomes are then regressed on their personal, household and regional
characteristics to find out the factors affecting each outcome. Complete
descriptions of dependent and independent variables are given in table 4 while
remaining paper is organized as follows.
Table 1: Activities of youth (15-24 years)
Full time student
Full time work
Combine work with school
Out of school and out of labor force
Labour force participation rate (LFPR)
Unemployment rate
a)
Official
b)
Usual status
Vulnerable employment9
(as percentage of total employment)
21.8%
36.1%
1.2%
30.5%
47.6%
7.5%
21.7%
52.1%
Source: Calculated from LFS (2006-07)
Section two briefly presents some issues faced by youth in labour market,
section three gives a brief overview of literature survey and section four
discusses the data source and methodology for empirical analysis. Results of
empirical analysis are presented in section five while conclusions and
recommendations are discussed in section six.
2.
Issues of Youth Labour Market in Pakistan
Labour market in Pakistan is confronted with number of challenges.
Some of critical issues related to the youth labour are discussed below.
2.1.
Poor Level of Education and Skills
Education plays an important role in the development of a country. It
raises the productivity and efficiency of individuals in the labour market.
Unfortunately, Pakistan is lagging behind in the field of education and skills.
About one-third of the youth population, a total of 10.4 million (3.7 million
males and 6.7 million females) are illiterate (GOP, 2008). Statistics of
educational attainment of youth also do not present a good picture of situation.
In the year 2006, more than half of the youth labour force (62.2 per cent) had
9
Unpaid family helpers and own account workers are considered as vulnerable in labour
market, for detail see Pakistan Employment Trends for Youth ,2008.
85
A
Ahmad
and Hussain
either lesss than one year
y
or just primary
p
eduucation (Figuure 1). It is not
n hard to
imagine the
t skills and productiviity of the labbour force with
w such a loow level of
educationn.===
Figu
ure 1: Educa
ational Attaainment of the Youth Labour
L
Forcce (%)
Sourrce: Calculated
d from LFS, 2006-07
Technicaal and Vocaational Instiitutions alsoo play an important
i
roole in the
process of
o employm
ment generatiion especiallly for younng people. Returns
R
on
investmeent in educattion and trainning are verry high for youth
y
becausse they are
more moobile and flex
xible. A studdy by Nasir and
a Nazli (22005) has shoown that a
one yearr increase in
n technical education
e
reesulted in 2..4 percent inncrease in
income of individuaals in Pakistan. However, the staate of Techhnical and
onal Institutiions is not very satisfaactory in thee country.
Vocationnal Educatio
Accordinng to Econo
omic Surveyy of Pakistaan (2008-09)), there are just 1522
Technicaal and Vocattional Instituutions in Pakkistan (Tablee 2). Only 1.6
1 percent
students after matrriculation are
a
enrolledd in Technnical and Vocational
V
institutions as comp
pared to 8 percent
p
in developing
d
a 18 percent in the
and
developeed countries10. Moreoverr, the qualityy and structuure of these innstitutions
are also not
n very goo
od. Accordinng to Asian Developmen
D
nt Bank Survvey in year
2005, thee performancce of 28 perrcent of Voccational and Technical Innstitutions
10
Cited in UNESCO, 200
06
86
Determinants of Youth Activities in Pakistan in Pakistan was poor, 60 percent was fair and only 12 percent of institutions’
performance was ranked as good11.
Table 2: Trends in Technical and Vocational Education in Pakistan
Indicators
Number of Institutions
Total Enrollment
Percentage enrollment
(Technical and Vocational education)
1995-96
577
86000
0.56 %
2006-07
1522
314188
1.66 %
Source: GOP, 2008a. Economic Survey of Pakistan, 2007-08 and HDR, 2007
2.2.
Higher Unemployment12 among Educated Youth in Pakistan
There is an incidence of higher unemployment among educated people in
Pakistan which shows the mismatch between type of education and
opportunities available in labour market. It is clear from figure 2 that
unemployment rate among those with higher level of education is much
greater than those with lower level of education. It does not mean that
education is not good for labour market success in Pakistan. The main reason
is that in general, tertiary education in Pakistan is not providing required skill
for jobs in the labour market. Students generally do not have any practical
Figure 3: Unemployment on the basis of Education Level in Pakistan
Unemployment Rate (%)
(Youth, 2006-07)
20
15
10
5
0
Education Level
Source: Based on GOP, 2008. Pakistan Employment Trends
11
Cited in HDR, 2007
Figures presented in this section are based on weekly status approach and are official
figures.
12
87
Ahmad and Hussain
knowledge and skills during their education in schools. After education, they
usually demand comparatively higher wages as compared to people with low
level of education which results in the higher level of unemployment for them.
2.3.
Predominance of Informal Economy
In Pakistan, informal economy is formulated in terms of household
enterprises owned and operated by own-account workers (LFS 2006-07).
Share of informal economy in total GDP is 37 percent in Pakistan which is
much higher than the average share of informal sector in South Asian
economies (26 percent of GDP). It is considered as the primary source of job
generator after agriculture sector and provides more than half of the total
employment in urban areas of Pakistan13. The main reason of this may be its
biasness towards unskilled labour. Youth in early stage of their careers, get
involved in informal economic activities which may result in low wage and
productivity in future.
2.4.
Gender Gap in the Labour Market Outcomes
Last column of Table 3 highlights the gender gap in different labour
market outcomes for youth in Pakistan. All indicators show the biasness
against female youth in the labour market. Their LFPR is almost 51
percentage points lower than that of male LFPR. Similarly, literacy rate of
female youth is 19.7 percentage points less than that of male youth. Most of
employed females are working as unpaid family helpers, which show the lack
of proper work opportunities for them as compared to their male counterparts.
3.
Literature Review
There has been a debate among researchers over the effects of early
start of career. Their focus is on the impact of early start of career on
educational achievements, human capital accumulation, productivity and
finally on earnings later in life. In USA, a study by Michael and Nancy (1984)
has shown that early work experience of youth should not be ignored as it
does impact on labour market experience later in life.
For example, researchers like Elahi et al., (2005); Emerson and Andre
(2006) found that boys who enter labour market early earn less and more
likely to be in lowest income quintile later in life. Similarly, in Pakistan,
13
ILO, 2005.
88
Determinants of Youth Activities in Pakistan Faizunnisa (2005) found that, early start of career is often a phenomenon that
exists in poor families which adversely affect their life time earnings.
Table 3: Gender Gap in Labour Market Indicators (2006-07)
Indicators
Labour Force Participation Rate
Unemployment Rate
Employment-to-population Ratio (EPR)15
Literacy Rate
Share of Employment in Formal economy as
percentage of total employment
Share of Employment in informal economy
as percentage of total employment
Share of unpaid family helpers in total
employment
Male
69.2
7.1
64.0
77.2
14.0
Female
18.4
8.9
17.0
57.5
9.0
Gender Gap14
50.8
-1.8
47.0
19.7
5.0
53.0
32.0
21.0
35.0
56.5
-21.5
Source: Calculated from LFS, 2006-07
Some researcher analyzed the factors that can affect the decision of
schooling and work of young people in market. For example, Rosati and Rossi
(2003) analyzed the decision of household regarding the school attendance or
labour supply (hours worked) by young people in Pakistan. Using Household
Survey data, they applied Tobit model for the dependent variable of hours
worked per week and Probit model on the decision to school enrolment of
children. Independent variables include age, age squared (as a proxy variable
for experience), household income, household size, number of children in
household, and dummy variables for being female, and residence of rural
areas. Their results showed that household size and number of children
present in the household reduce the probability of school enrollment.
Similarly, children living in rural areas are also less likely to be enrolled. The
model of labour supply (hours worked per week) by children showed that
increase in the income of household reduces the number of hours worked by
children. Female children with larger household size worked fewer hours in
market, this may be due to the fact that they spend more time in household
work which increases in case of large household size.
14
Gender Gap is calculated by deducting the respective indicator of female youth from that of
male youth. For example, gender gap in literacy rate = male literacy rate – female literacy
rate.
15
EPR is taken from GOP (2008) “Pakistan Employment Trends for youth”.
89
Ahmad and Hussain
Female labour force participation in some developing countries like
Pakistan is very low. Majority of the women are engaged in household works
which are mostly unpaid and hidden. A study by Durrant (2000) showed that
45 percent of females aged 10–19 are apparently not engaged in any economic
activities in Pakistan. Similarly, Sathar (2005) also investigated women work
at home and found that at every age from 15-24, women work more hours
than men but their work is largely unpaid and hidden.
Lloyd and Monica (2004) used Adolescent and Youth Survey of
Pakistan (2001-02) and developed a model to analyze the determinants of
youth activities in Pakistan. They divided the youth activities into three
categories, i.e. household work, schooling, and paid work. Their study
concluded that the presence of children, elderly and young people in
household is associated with increase in the time of non-economic household
work by young females. Having literate parents decrease the time spend on
household work by young females especially in urban areas. Their study also
highlights that the availability of school, technical institution, and opportunity
for job (presence of factory in the area) are strongly associated with time use
pattern of young males and females in Pakistan. Availability of schools within
one kilometer of area reduces the chances of paid work among young females,
while presence of factory in the area increases the time spent by young males
and females on paid work.
Fafchamps and Wahba (2006) used labour force survey (1998-1999) in
Nepal and found that Children residing near or in urban areas attend school
more and work less. Moreover, higher education of parents reduces the
probability of child work. Kingdon and Soderbon (2008) by using Pakistan
Integrated Household Surveys (PIHS) (1998-99, 2001-02) found that along
with increase in education, the likelihood of involving in agricultural
production reduces for young men rather they prefer to quite labour force.
4.
Data Source and Methodology of the Study
This article is based upon micro data from Labour Force Survey of
Pakistan (2006-07), the survey provides information about 32,000 households
containing information of 224,000 individuals. From this data set, we selected
a sample of 44,902 individuals whose age was between 14 to 24 years, after
dropping the 4682 individuals with missing values we left with a sample of
40,220 to use for our empirical analysis. Table 4 describes the description of
dependent and independent variables of the study which is self explanatory.
90
Determinants of Youth Activities in Pakistan Table 4: Description of Variables
Dependent Variable
Description
Youth Activity
= 1 if full-time student
= 2 if combine work with school
= 3 if full-time worker
= 4 if neither enrolled nor economically active
(reference category)
Independent Variables
Covariates
Youth characteristics
Age
Age squared
Gender
Married
Training
Migration
Head
Education Level16
Sub-groups/Description
Age in completed years
Square of age (to capture the experience)
= 1 if female
= 0 if male (reference category)
= 1 if married
= 0 if not married at present (reference category)
= 1 if have some technical training and skills
= 0 if do not have technical training (reference
category)
=1 if migrated from one district to another
= 0 if did not migrate from one district to another
(reference category)
= 1 if head of the household
= 0 if not head of the household (reference category)
= 0 if education level is below primary17 (reference
category)
= 1 if education level is primary but below middle
and 0 otherwise
=1 if education is middle but below matric and 0
otherwise
=1 if education level is matric but below inter and 0
otherwise
=1 if education level is inter but below degree and 0
otherwise
=1 if education level is degree or above and 0
16
Different softwares require different methods to construct variables, we use STATA 9
which requires to generate variables as described in table 4
17
This category includes illiterate as well as those whose education level is below primary.
91
Ahmad and Hussain
otherwise
Regional Factors
Location
Province
= 1 if location is rural
= 0 if location is urban (reference category)
= 0 if province is Punjab (reference category)
= 1 if province is Sind and 0 otherwise
= 1 if province is KPK and 0 otherwise
= 1 if province is Baluchistan and 0 otherwise
Household Characteristics
Female head
Household size
Siblings
Head education
Head activity
4.1.
= 1 if head is female
= 0 if head is male (reference category)
Numbers of persons in household
Number of children under the age of 15 years in
household
= 0 if education level is below primary (reference
category)
= 1 if education level is primary but below middle
and 0 otherwise
= 1 if education is middle but below matric and 0
otherwise
= 1 if education level is matric but below inter and 0
otherwise
= 1 if education level is inter but below degree and 0
otherwise
= 1 if education level is degree or above and 0
otherwise
= 0 if head is unemployed or out of labour force
(reference category)
= 1 if head is working in formal sector and 0
otherwise
= 1 if head is working in informal sector and 0
otherwise
= 1 if head is working in agricultural sector and 0
otherwise
The Model
As our dependent variable has more than two categories, we estimate
multinomial logit model with maximum likelihood estimation procedure on a
set of explanatory variables to model the determinants of youth activities in
Pakistan
92
Determinants of Youth Activities in Pakistan Probabilities in the multinomial model18 are given by
|
∑
,
0,2, … ,
While J log-odds ratios are define as:
́
́
if k = 0
ln
0 ………(1)
………………………(2)
We assume that the odds ratio,
does not depend upon other choices.
As described by Green (2008), the log-likelihood can derived by
defining for each individual,
= 1 if alternative j is chosen by individual i,
and 0 if not, for the j-1 possible outcomes, then for each i, one and only one of
the ’s is 1. The log-likelihood is given by:
∑ ∑
ln
……………………………….(3)
ln
To interpret the effect of independent variables on the probabilities of each
choice we also calculate marginal effects of each outcome. By differentiating
equation (1) we find the marginal effects of the characteristics on the
probabilities are
∑
………………………(4)
Likelihood Ratio (LR) Chi-Square test is used to test the null hypothesis that
all the slope coefficients in the model are zero.
4.2.
Issues and Hypotheses
4.2.1. Age of Youth
Economic theory states that along with increase in age, people start
taking part in economic activities and enter in labor market. In the beginning
of career, a young person may experience unemployment due to lack of
experience and skills but as a young person gets experience, he or she
becomes less likely to be economically inactive. In our society, there is a great
emphasis on early start of career especially, in rural areas where children start
working with their families in fields, so chances to enroll as a student may
also decrease along with increase in age.
18
Multinomial Logit model described here is drawn from Green (2008).
93
Ahmad and Hussain
4.2.2. Gender
In a male dominant society, females are less likely to participate in
economic activities or enroll as a student. They are expected to engage in
household work.
4.2.3. Marital Status
It is assume that marriage brings some responsibility and a married
person is more likely to be engage in economic activities. However, this
relationship cannot be expected for females, rather it is assume that married
females are more likely to engage in household work and taking care of
siblings instead of enroll in educational institution or doing some job.
4.2.4. Education Level
Education level of a young person can also affect his/her activities in
two ways depending upon how we argue it. One can assume that investment
in human capital increases the chances of getting employment in the labor
market as educated people are more skillful and can better search for a job as
compare to those with low level of education. On the contrary, one can also
argue that young people with higher level of education usually have higher
expectations about pay and jobs. They become more status conscious and
prefer to wait for the time to get better and suitable employment instead of
being involved in low paid or informal economic activities.
4.2.5. Head of Household
It is assume that being the head of household increases the
responsibility of youth, they might start working earlier in their life, it is also
expected that there will be more chances of employment at early stage of life
if the young person is also having the responsibility of being the head of
household.
4.2.6. Location
It is assume that youth living in rural areas are more likely to engage in
economic activities instead of getting education as compare to their urban
counterparts.
4.2.7. Province
Due to diverse culture, cast system, and traditions, young people in
each province are expected to have different opportunities and attitude
94
Determinants of Youth Activities in Pakistan towards work and education. It is assume that more and better employment
and educational opportunities are available in Punjab and Sind as compared to
Baluchistan and KPK. So, youth living in Baluchistan and KPK are more
likely to be inactive as compare to youth living in Punjab and Sind.
4.2.8. Household Size and Number of Siblings
Household size may also affect the attitude of youth towards economic
activities. Generally, large families increase the burden on young persons to
engage in economic activities. This may have a positive impact on labor force
participation of youth in the household. One can also expect a negative impact
of household size and number of siblings present in house on female labor
force participation. As more children in household will require young females
to stay at home and take care of young siblings instead of going to work.
4.2.9. Status of Household Head
In our society, head is usually responsible to fulfill the financial
requirements of household. Therefore, status of household head may greatly
affect the activities of young persons in labor market. If head is unemployed
then other members of household especially young people will have to take
the responsibility to finance the household expenditures. Moreover, it is also
expected that if head is working in formal sector he would be better able to
finance his household and young persons may get their education instead of
participating in economic activities.
4.2.10. Gender of Household Head
Head are generally male in our society; it will be interesting to find out
whether youth living in female-headed household are more or less likely to
engage in economic activities. Youth living in female-headed household may
feel responsibility to manage their household and may start their career early.
4.2.11. Education Level of Household Head
Increase in the level of education of the household head is expected to
reduce the chances of youth to start their career early. A highly educated
person is expected to earn enough money that is sufficient to support their
families. Therefore, it is expected that higher the level of education of the
household’s head more will be the chances that youth will engage in
educational activities instead of economic activities.
95
Ahmad and Hussain
5.
Determinants of Youth Activities in Pakistan
Table 5 presents the results of multinomial logit estimates for youth
activities in Pakistan. For this purpose, we divide activities of youth in four
mutually exclusive categories, i.e. full-time students, those who combine work
with school, full-time workers and those who neither work nor go to school.
Using fourth category (neither work nor school) as our reference category we
estimate multinomial logistic coefficients with maximum likelihood
estimation. Results suggest that age has an important impact on the decision
about schooling and employment for youth in Pakistan. For example, in case
of full-time student, the estimated parameters of age and age squared show
that the probability of being a full-time student decreases at an increasing rate
and reached at its minimum point at the age of 26.59 years. Probability
derivative of age also indicates that a one year increase in age decreases the
probability of being a full-time student by 6.6 percentage points. Similarly, the
probability of combining work with school also decreases along with increase
in age while the probability of being a full-time worker increases by 11
percentage points. The main reason of this may be the increase in the cost of
education and opportunity cost of staying at school which rises with age.
Similar kind of results is reported by different researchers in Pakistan.
For example, studies by Naqvi and Shehnaz (2000) and Arif et al., (2002)
found that participation in economic activities increase with age for both male
and female youth in Pakistan. However, In Kuwait, Aly and Quisi (1996)
found that age is inversely related to women economic participation. The
results on the probabilities of female youth show that females are 0.2 percent
less likely to be full-time student, 0.9 percent less likely to combine work with
school and 71 percent less likely to be full-time workers than their male
counterparts. These results depict a traditional bias of society towards females
which are mainly considered to do household work instead of going to work
or school. Moreover, the probabilities of being a full-time student or full-time
worker also decrease if the young person is married. This may be due to high
rate of inactivity among female youth which are not expected to work or get
education after marriage. These results also confirm the results of earlier
studies of Durrant (2000) and Sathar (2005) which show that mostly females
in Pakistan are not economically active and their work is largely unpaid and
hidden.
As expected, migration, training and being the head of household have
positive impact on the probabilities of being a full-time worker. For example,
96
Determinants of Youth Activities in Pakistan probability of full-time work increases by 17 percentage points if the
respondent is the head of household, by 9 percentage points if have some
technical training and by 37 percentage points if migrates to earn his or her
living. Generally, in society like Pakistan, head of the household is considered
to take the responsibility of financial matters of family. Therefore, one can
expect an increase in economic participation and chances of full-time work by
young people as a head of household. Similarly, a person who gets some
training or migrates to earn living may also be expected to fully participate in
economic activities in order to maximize benefits of migration or technical
training.
To find the impact of education on youth activities we divide it in
different categories and take ‘below primary’ as our reference category.
Coefficients of this variable show some interesting results, along with increase
in the level of education, the probabilities of being full-time student or
combine work with school increase while that of full-time work decreases.
This may be so as youth with low level of education start their career early
(due to limited availability of options) and with higher level of education
prefer to get higher education instead of getting involved in low paid
economic activities. While on the other hand, all those who have education
level of primary or above are more likely to enroll for further education. The
highest enrolment rate is among those who have intermediate or degree level
education.
Household size and number of siblings present in the household do not
affect the first two outcomes (full-time student and combine work with
school). One can expect the household size to reduce the school enrolment
rate especially for female as concluded by Rosati and Rossi (2003). However,
in our results, only number of siblings presents in the household increases the
probability of full-time work by 2 percentage points.
Activity of head by sector of employment does not have much impact
on the decision of schooling or combining work with school but it does affect
the probability of being a full-time worker significantly. Our results suggest
that the probability of being full-time work increases by 17 percentage points
if the head is working in agricultural sector and by 6 percentage point if the
head is working in informal sector. It may be due to the fact that informal
sector in Pakistan is considered as the major source of employment in the
economy. It consists of households enterprises owned and operated by ownaccount workers or an enterprise owned and operated by an employer with
97
Ahmad and Hussain
less than ten persons involved in the business. Therefore, our results are not
surprising in the sense that youth living with the head who is either working in
informal or agriculture sector may be more likely to get involve in work with
their families in fields or in household enterprises.
In countries like Pakistan, one can expect that young people in femaleheaded households may start their career early. However, this variable does
not seem to have any impact on youth activities. It may be due to the limited
number of data points of this variable as only 0.1 percent of the households
are headed by female in our data set.
As expected, the education level of the head of household has strong
impact on youth activities in Pakistan. Our results confirm the hypothesis that
along with increase in the level of education of head of the household, the
probability of being full-time student increases and that of full-time work
decreases. A young person with the head’s qualification of degree or above is
about 7 percentage points more likely to be full-time student and 22
percentage points less likely to work as compared to a young man who lives in
house where head is illiterate or below primary. As far as regional variables
are concerned, our results show that young people living in rural areas are 2.6
percentage points more likely to work full-time; however, this variable does
not have much impact on other two outcomes (being full-time student or
combine work with school). Earlier study by Rosati and Rossi (2003) has also
shown that youth living in rural areas are less likely to be enrolled and more
likely to work. Provincial difference does not have much impact on the
probabilities of full-time student or combine work with school. However,
Punjab is the province where young people are more likely to work full-time
as compared to the youth in other three provinces.
6.
Conclusions and Recommendations
Based on micro data, this paper in descriptive terms shows that a
substantial number of our youth is neither in school nor working, moreover,
their attitude towards work and level of education also shows the areas need to
address. Empirically we investigated the supply side determinants of youth
activities in Pakistan. Our results show that being a female, reduces the
chances of full time work and full time students in our society. Results also
show that being a head of household increases the chances of full time work
substantially. The results of this paper show an overall pattern of youth
activities and the factors affecting them. In general, the young people with
educated parents are more likely to enroll in education, while on the other
98
Determinants of Youth Activities in Pakistan hand those whose parents are working in agriculture or informal sector are
more likely to be full time worker. Similarly, responsibilities within household
as a head or having more number of siblings also increase the economic
participation of youth. In general, we can conclude that region of residence,
personal and parent’s level of education, their employment status, and status
within household determine the outcomes of youth activities.
Being a labour abundant country, it would be fair to say that wellbeing of Pakistan, in future, will heavily depend upon the willingness of its
people to work. Unfortunately, the study highlights that a substantial
percentage of young people are inactive, neither have they worked nor they
study. One can expect high rate of inactivity among females due to household
responsibilities but higher inactivity rate among male youth shows the
wastage of human resources in the society. Current labour force survey
provides very little information about the activities of youth who are neither in
school nor in labour force. It is recommended that FBS (Federal Bureau of
Statistics) should set a questionnaire that evaluates what young people do in
their spare time. How much time they spend in family work, in schooling,
loafing and so forth? For this purpose, a time-use survey of youth can also be
initiated. The survey must provide information in much more comprehensive
way about youth time usage and activities instead of asking just few basic
questions. It would also be helpful to differentiate between those who are
discouraged workers from those who do not want to work or show any
commitment in finding work.
To reduce gender difference in labour market, a motivational
campaign is required to educate the society to change their attitude about
women work. Providing equal opportunities to young women in education and
labour market should be the focus of this campaign. Income generating
projects like handicrafts and other home based activities need to be identified
for young females in the informal sector. For this purpose, training and
educational programs should be launched. Government should also provide a
minimum social protection package to vulnerable youth especially for young
females in rural areas.
The study also highlights the fact that more than half of the youth
labour force (62.2 per cent) has either less than one year or just primary
education. Moreover, those who are educated face higher unemployment as
compared to those with low level or no education. It shows the need to
address the issues of relevance and practical application of education in
Pakistan. In order to identify the market requirements and needs, link between
99
Ahmad and Hussain
educational institutions and industry should be developed. We also observe
that parent’s education significantly affects the activities of youth. Having
educated parents improves the chances of youth to get higher education. Any
motivational campaign to educate the parents regarding the education and
work of their children could improve their economic participation and
enrollment in educational institutions.
100
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103
Forman Journal of Economic Studies
Vol. 8, 2012 (January–December) pp. 83-105
Appendix
Table A-5: Multinomial Logit Estimates of Youth Activities in Pakistan
Covariates
Subgroups
Full time Student
Coefficients
Odds
Ratios
Combine work
Work only
Marginal
Effects
Coefficients
Odds
Ratios
Marginal
Effects
Coefficients
Odds
Ratios
Marginal
Effects
Personal Characteristics
-2.638*
0.07
-0.066
-1.469*
0.23
-0.015
0.271*
1.31
0.111
0.050*
1.05
0.001
0.028*
1.03
0.000
-0.004**
1.00
-0.002
---2.214*
---2.618*
--0.942
---0.281
---0.007
--4.692*
7.058*
--0.11
--0.07
--2.56
--0.75
--0.99
--109.07
1161.64
---0.002
---0.034
---0.018
---0.010
---0.010
--0.530
0.870
---3.473*
---1.646*
--2.928*
--0.505
--0.566
--0.917*
2.875*
--0.03
--0.19
--18.69
--1.66
--1.76
--2.50
17.72
---0.009
---0.009
--0.008
--0.003
--0.001
--0.001
0.006
---3.830*
---0.575*
--2.817*
--0.404*
--0.778*
---0.019
-0.074
--0.02
--0.56
--16.72
--1.50
--2.18
--0.98
0.93
---0.714
---0.114
--0.374
--0.095
--0.171
---0.314
-0.519
Matirc
7.493*
1795.08
0.916
3.144*
23.19
0.004
-0.187*
0.83
-0.549
Inter
9.413*
12245.21
0.976
4.850*
127.77
-0.001
-0.099
0.91
-0.573
Degree or above
9.315*
11100.68
0.972
4.769*
117.75
-0.003
0.226*
1.25
-0.565
-0.003
1.00
0.001
-0.034
0.97
0.000
-0.048*
0.95
-0.012
Age
Age square
Gender
Married
Migrated
Training
Head of the
household
Educational
level
Male (ref)
Female
No (ref)
Yes
No (ref)
Yes
No (ref)
Yes
No (ref)
Yes
Below Primary(ref)
Primary
Middle
Household Characteristics
Household Size
Determinants of Youth Activities in Pakistan Covariates
Subgroups
Full time Student
Coefficients
Marginal
Effects
-0.001
Coefficients
-0.001
Odds
Ratios
1.00
Unemployed (ref)
Formal
Agricultural
--0.113
0.186*
--1.12
0.91
Informal
-0.089
Below Primary (ref)
Primary
Middle
--0.014*
0.296*
No. of siblings
Head activity
Head
education
Combine Work
Work only
Marginal
Effects
0.000
Coefficients
0.064**
Odds
Ratios
1.07
0.086*
Odds
Ratios
1.09
Marginal
Effects
0.021
--0.001
-0.007
--0.385*
1.285*
--1.47
1.79
--0.003
0.009
--0.142*
0.262*
--1.15
1.30
--0.031
0.174
1.20
-0.006
0.582*
3.62
0.004
0.784*
2.19
0.062
--1.01
1.34
--0.004
0.013
--0.006
-0.068
--1.01
0.93
--0.002
0.001
---0.289*
-0.307*
--0.75
0.74
---0.072
-0.080
Matric
0.336*
1.40
0.017
-0.083
0.92
0.001
-0.448*
0.64
-0.116
Inter
0.823*
2.28
0.045
-0.234
0.79
0.000
-0.615*
0.54
-0.168
Degree or above
1.105*
3.02
0.069
-0.250
0.78
0.001
-0.782*
0.46
-0.215
Urban (ref)
Rural
Punjab (ref)
Sind
NWFP
---0.176*
---0.434*
-0.085
--0.84
--0.65
0.92
---0.006
---0.005
0.012
--0.262*
---0.282*
-0.026
--1.30
--0.75
0.97
--0.002
---0.001
0.005
--0.104*
---0.385*
-1.053*
--1.11
--0.68
0.35
--0.027
---0.088
-0.256
Baluchistan
-0.654*
0.52
-0.010
-0.060
0.94
0.001
-0.184*
0.83
-0.038
Constant
25.904*
Regional Characteristics
Region
Province
Log Likelihood
LR Chi2
Pseudo R2
Observations
13.326*
-23012.35
45198.5
0.4955
40220
Note: * indicates significant at five percent level and ** indicates significant at ten percent level.
Omitted category is neither work nor school. 105
-1.034
Forman Journal of Economic Studies
Vol. 8, 2012 (January–December) pp. 107-125
A Study of Implicit Tax in Pakistan’s Agriculture, with
Special Reference to the Case of Rice
Mohammad Aslam1
Abstract
The study examined ‘implicit tax’ argument of the agriculturists’ lobby to
oppose imposition of an agricultural income tax. The paper discovered a
widening gap between procurement and export prices of both Basmati and
IRRI. The gap between procurement and consumer prices of the two varieties
also widened significantly. Thus while both producers and consumers
remained on the losing end, first government and then after the policy reforms
the exporters and other intermediaries, were the substantial gainers. Since
RECP has been disbanded and the Government has opted out of purchase and
export of rice, the margin now goes to the exporters instead of the
Government. Under the changed rice policy, the ‘implicit’ tax argument has
therefore lost much of weight and relevance.
Keywords: Agricultural prices policy; Basmati; IRRI; acreage; yield;
procurement price; consumer price; export price; implicit tax
JEL classification: Q11, Q17, Q18
1.
Introduction
The Agricultural Prices Policy in Pakistan has traditionally covered
both important inputs and outputs. The Input Price Policy is implemented
through provision of subsidized inputs to farmers. The Output Price Policy, on
the other hand, is implemented through fixation of procurement and support
prices of important food and cash crops such as wheat, rice, sugarcane,
potatoes etc. Although both tiers of the Agricultural Prices Policy are
important and interdependent too, the present study is limited to an analysis of
the output prices policy only.
The Output Prices Policy has been used by economic decision makers
in Pakistan since the early 1960s, as an incentive for growers and to expand
production frontiers of different crops. During the early years after
independence, the government did not use this policy due the widely held
1
The author is Professor of Economics at Lahore School of Economics (LSE), Lahore.
Aslam M.
view that subsistence farmers in developing countries are not responsive to
price incentive. It was a general perception that they produce only for self
consumption and are not influenced by prevailing market prices. The later
studies however showed that in Pakistan farmers do respond positively to
changes in prices of important food and cash crops and adjust their acreage
decision accordingly. This prompted the government to use the policy in the
1960s for expanding production.
1.1.
The Transition of the Policy and Problem Statement
The policy has undergone a change over a period of time and more
radically in the recent past and there is also reorganization of the institutional
framework. This is particularly true of the rice output price policy. Rice is a
crucial crop and occupies important place in the export economy of Pakistan
besides being a food supplement to wheat. The rice policy since late 1970s
and early 1980s has undergone many changes. Firstly, the compulsory
procurement policy was replaced with voluntary procurement policy.
Secondly, the ban on inter-district movement of rice was discarded. Thirdly,
the Rice Export Corporation of Pakistan (RECP) which was created in 1974
and was assigned the responsibility of procurement and export of rice in the
public sector was disbanded in 2000 and merged with the Trading Corporation
of Pakistan. The important thing however was that the government opted out
of rice export business and decided to discontinue fixation of procurement
price of rice. This was done to allow market forces to prevail in the area of
rice production and export. The Agricultural Prices Commission (APCOM)
responsible for recommending procurement and support prices was also recast
and renamed as the Agricultural Prices Institute (API).
Originally, the support price program covered crops like wheat, rice,
sugarcane, cotton, potatoes, onions, grams, and non-traditional oil seeds such
as sunflower, soybean, canola and safflower. In May 2001, on
recommendation of the MINFAL, the Economic Committee of the Cabinet
(ECC) reduced the coverage to wheat, rice, sugarcane and cotton crops. In
September 2002, the ECC decided to further limit it to wheat, rice and cotton
at the federal level while price of sugarcane was to be determined by the
provinces. The government opted out of the export of both rice and cotton and
specialized institutions created for the purpose i.e. Rice Export Corporation
(RECP) and Cotton Export Corporation of Pakistan (CECP) were disbanded
and merged with the Trading Corporation of Pakistan (TCP).
108
A study of Implicit Tax in Pakistan’s Agriculture Since then the thinking on fixation of prices has undergone a major
change with acceptance of an enhanced role for the markets. The coverage
thus was restricted to wheat and cotton only. At present support price system
stands discarded in case of almost all the crops. The prices fixed are only
‘indicative’ in character and provide growers a base level for negotiating
better prices for themselves.
The Rice Exporters Association of Pakistan (REAP) has taken the
place of RECP as regards procurement and export of rice. The TCP facilitates
fulfillment of orders in consultation with the REAP. The government has also
established a Quality Review Committee (QRC) that certifies the quality of
rice before shipment.
The agricultural sector has traditionally been exempted from levy of a
tax on agricultural incomes. The levy of the tax was opposed by the farming
community on grounds of paying an ‘implicit tax’ to the government. The
Government procured rice at prices arbitrarily fixed by her and then sold it
internationally at prices many times higher than prices paid to the farmers.
The margin accruing to the government was referred to as the so-called
‘implicit tax’.
1.2.
The Study Objective
The study examines ‘implicit tax’ plea advanced by farmers for
avoidance of an agricultural income tax, particularly in the background of
important institutional and policy changes referred to above.
2.
The Literature Review
There are many studies on the subject and its related matters. Aslam,
M. (1982) studied the rice economy of Punjab with a particular focus on
consumption aspect. The basic purpose of the study was to explore the
prospects of promoting rice consumption with a view of releasing pressure on
wheat. The study also analyzed the issue of the ‘implicit tax’. The important
finding of study pertaining to the ‘implicit tax’ issue was that the gap between
procurement and export price of rice had been widening over time and that
government was the real beneficiary and earned increasing revenue due to
this gap. The time series data of the three sets of prices for the period 1964-65
to 1979-80 was used for purpose.
Roberto Eliseu and Pastore Affonso (1978) studied the problem of
import substitution and implicit taxation of agriculture in Brazil. According to
them, industrialization in Brazil prior to the World War 2 had taken place at
109
Aslam M.
the cost of agriculture through a shift in resources to the industrial sector. In
the post World War 2 periods the same thing had happened through import
substitution industrialization. This was ensured through providing protective
devices, subsidized of credit and stable wages.
Chaudhry M. G. and Kayani N. N. (1991) discussed the issue of
implicit taxation of Pakistan’s agriculture. They compared import and export
parity prices of major agricultural commodities with their domestic
procurement prices and discovered that implicit tax argument was not without
substance. The implicit tax rate for some of the years under study 1970-71 to
1989-90 was as high as 75% in the case certain commodities.
Chaudhry, M. G. (2001) discussed the current tax policy in Pakistan’s
agriculture in the backdrop of the theory of optimal taxation. He quantified
total amount of implicit tax on agriculture that declined from Rs. 82 billion in
1989-90 to Rs. 65 billion in 1999-2000. Despite reduction, implicit tax,
calculated on the basis of parity and support prices, constituted 7-8.5% of
value added by agriculture.
Noor, P. K. (2002) reviewed implications of government intervention
in Pakistan’s wheat and cotton sectors. The study revealed overall transfers
from wheat and cotton producers to society. The study also showed that WTO
trade liberalization in wheat and cotton would have no significant impact on
wheat and cotton production.
Ronge, Eric; Wanjala Bernadette and others (2005) studied implicit
taxation of the agricultural sector in Kenya. They had concluded that
agriculture was being taxed implicitly through changes in macroeconomic
policies. They recommended that the government must ensure that this should
not have an adverse impact on Kenyan agriculture.
Lin, Justin Yifo and Liu, Mingsing (2007) examined the historical
evolution of China’s rural taxation system. The period under review was from
pre-reform period to the late 1990s.The study discovered excessive local
informal taxation on farmers. This necessitated a policy review that resulted in
a change in the traditional approach of implicit taxation.
Salam, A. (2010) studied recent trends in distortions in incentives for
production of major crops in Pakistan. The study compared domestic producer
prices between 1991 and 2008 with the corresponding international prices
with a view to measure nominal protection coefficients (NPCs). The study
110
A study of Implicit Tax in Pakistan’s Agriculture revealed that in the case of rice, average implicit tax per ton of Basmati paddy
was around $ 21.38.
3.
Methodology
To monitor relationships between procurement and export price, on the
one hand, and between procurement and consumer price, on the other, first
ordinary or actual curves were drawn. Then least square straight lines or trend
lines were estimated to examine the overall long term trend.
The actual curves generally exhibit wide fluctuations from one year to
the other and may not reveal much at first sight. That necessitated estimation
of the trend lines. The straight line equations and coefficients of variation
were also estimated for the three sets of prices.
3.1.
Data Collection
Secondary data was used for the study. This was collected mainly from
government of Pakistan publications such as Foreign Trade Statistics of
Pakistan, annual Economic surveys, Foreign Trade of Pakistan (an
EPB/TDAP publication), Agricultural Statistics of Pakistan and Pakistan
Statistical Yearbook. The data was also gleaned through publications and
studies of the international Rice Research Institute, Manila, Food and
Agricultural Organization (Rome) and Rice Research institute in Kala Shah
Kaku in District Sheikhpura. The time-series data used pertained to the period
1990 to 2008. This covered procurement, consumer and export prices of
Basmati and IRRI.
4.
Results and Interpretation
4.1.
Basmati Rice
4.1. 2. Actual Lines for Procurement and Export Prices of Basmati
The figure 1 shows actual lines for both procurement and export prices of
Basmati rice. The actual curve of export price of basmati shows more severe
fluctuations compared to the actual curve of procurement price of basmati. The last
two years of the period particularly show unusual and rapid upward trend in the
export price of basmati. There was phenomenal food inflation at the world level and
rice was no exception. The actual curve is almost flat and is shown increasing only
gingerly.
4.1.3. Trend Lines for Procurement and Export Prices of Basmati
The figure 2 shows trend lines of the procurement and export prices of
basmati.
111
Aslam M.
Price (per 40 kg)
Figure 1:Actual Lines for Procurement and
Export Prices of Basmati
4000
3500
Procurement
Export
3000
2500
2000
1500
1000
500
8
6
20
0
20
0
4
20
0
20
0
20
0
2
0
8
19
9
6
19
9
19
9
4
2
19
9
19
9
0
0
Years
Figure 2:Trend Lines of Procurement and Export
Prices of Basmati
2500
Procurement
Price (per 40 kg)
2000
Export
1500
1000
500
08
20
06
20
04
20
02
20
20
00
98
19
96
19
94
19
92
19
19
90
0
Years
The trend lines of procurement and export prices of basmati are shown
strongly drifting apart during the period under review. The closing years of
the period show even greater rapid divergence of the two curves. Under
assumption that overhead cost of exporters in terms of storage and
transportation charges did not increase abnormally, rapidly widening gap
112
A study of Implicit Tax in Pakistan’s Agriculture shows increasing profit margin for exporters. This also implies that while
exporters reaped huge profits, producers were the real losers.
The trend line linear equations for procurement and export prices of basmati
were estimated as under.
Yp = 105.965 + 20.1193t
(Procurement Price)
Ye = −14.8070 + 110.407t (Export Price)
The trend linear equation of procurement price shows an average
increase of Rs.20 per year while trend linear equation of export price shows an
average increase of Rs.110.4 per year.
4.1.4. The Impact on Consumer
In order to gauge impact on consumers in this process of production,
consumption and export of basmati, combined actual and trend graphs for the
three sets of prices were also constructed as in figure 3 and figure
4.
Figure 3: Actual Lines for Procurement, export and
consumer prices of basmati
4000
Price (per 40 kg)
3500
3000
2500
Procurement
Consumer
Export
2000
1500
1000
500
113
08
20
06
20
04
20
02
20
00
20
98
19
96
19
94
19
92
19
19
90
0
Years
Aslam M.
Actual lines for three sets of prices were combined in a single line
chart, the relative position of consumers became clear. The lines representing
export and consumer prices remained glued to each other over whole length of
the period and even submerged at times particularly starting early twenties.
When trend lines of the three sets of prices were jointly drawn in one
graph, its graph looked as in figure 4.The trend line representing consumer
goods is keeping pace with the export price trend line at a small distance. The
gap between trends lines of export and consumer prices on the one hand and
procurement price on the other is shown continuously widening over time.
This means both producers and consumers remained at a disadvantage
compared to the exporters of basmati.
Figure 4: Trend Lines for Procurement, Consumer
and Export Prices of Basmati
2500
Price (per 40 kg)
Procurement
2000
Consumer
Export
1500
1000
500
20
08
20
06
20
04
20
02
20
00
19
98
19
96
19
94
19
92
19
90
0
Years
The trend linear equation for consumer prices was estimated as under.
Yc = −90.5088 + 110.361t
(Consumer Prices)
This showed an average increase of Rs.110.4 per year in consumer
price of basmati over the period. Earlier average increase per year of export
price of basmati had also approximated to the same figure.
114
A study of Implicit Tax in Pakistan’s Agriculture 4.2.
IRRI Rice
4.2.1. Actual and Trend Lines for the Prices of IRRI
The figure 5 shows actual lines for both procurement and export prices
of IRRI rice. The actual curve of export price of IRRI shows more severe
fluctuations compared to the actual curve of procurement price of IRRI. The
last two years of the period particularly show rapid upward trend in the export
price of IRRI. There was severe food inflation at the international level and
rice was no exception. The actual curve is found increasing only modestly.
Figure 5: Actual Lines for Procurement and
Export Prices of IRRI
1800
Price (per 40 kg)
1600
Procurement
Export
1400
1200
1000
800
600
400
200
20
08
20
06
20
04
20
02
20
00
19
98
19
96
19
94
19
92
19
90
0
Years
The trend lines of procurement and export prices of IRRI are shown strongly
drifting apart during the period under review. The closing years of the period
show even greater rapid divergence of the two curves. Under assumption that
overhead cost of exporters in terms of storage and transportation charges did
not increase abnormally, rapidly widening gap shows increasing profit margin
for exporters. This also implies that while exporters reaped huge profits,
producers were the real losers. The trend line linear equations for procurement
and export prices of IRRI were estimated as under.
Yp = 29.3860 + 14.0561t
115
Aslam M.
Ye = −22.7018 + 49.1965t
The trend linear equation of procurement price shows an average increase of
Rs.14 per year while trend linear equation of export price shows an average
increase of Rs.49.2 per year.
Figure 6: Trend Lines of Procurement and Export Prices of IRRI
1000
Price (per 40 kg)
900
Procurement
Export
800
700
600
500
400
300
200
100
20
08
20
06
20
04
20
02
20
00
19
98
19
96
19
94
19
92
19
90
0
Years
4.2.2. Actual and Trend Lines for Procurement, Consumer and Export
Prices of IRRI
When actual lines for three sets of prices of IRRI were combined in a
single line chart, the relative position of consumers became clear. The actual
lines representing export and consumer prices are seen rising in close
proximity with one another but overtaking each other alternately during
certain intervals.
4.2.3. The Impact on Consumer
In order to gauge impact on consumers in this process of production,
consumption and export of IRRI, combined trend line graph for the three sets
of prices were also constructed as in and figure 8.
When trend lines of the three sets of prices were jointly drawn in one
graph, its graph looked as in figure 8. The trend line representing consumer
116
A study of Implicit Tax in Pakistan’s Agriculture goods is rising very close to the export price trend line and during certain
interval the two lines are seen coinciding with one another.
The gap between trends lines of export and consumer prices on the one
hand and procurement price on the other is seen continuously widening over
time. This means both producers and consumers remained at a disadvantage
compared to the exporters of IRRI.
Figure 7: Actual Lines for Procuremnt, Consumer and
Export Pricess of IRRI
Price (per 40 kg)
1800
1600
Procurement
1400
Consumer
1200
Export
1000
800
600
400
200
8
20
0
6
20
0
4
20
0
2
20
0
0
20
0
8
19
9
6
19
9
19
9
19
9
19
9
0
2
4
0
Years
Figure 8: Trend Lines for Procurement, Consumer and
Export Pricess of IRRI
1000
Price (per 40 kg)
900
800
700
Procurement
Consumer
Export
600
500
400
300
200
100
20
08
20
06
20
04
20
02
20
00
19
98
19
96
19
94
19
92
19
90
0
Years
The trend linear equation for consumer prices of IRRI was estimated as under.
117
Aslam M.
Yc = 34.5263 + 44.8579t
This showed an average increase of Rs.45 per year in consumer price of IRRI
over the period. Earlier average increase per year of export price of IRRI was
approximated to Rs.49 per year over the same period.
5.
Conclusions and Policy Implications
The study pertained to the three sets of prices and an examination of the
hidden tax argument. The important findings were as under.
1. During the period under study, the spread between
procurement/indicative price and export price of basmati kept on
widening.
2. This was also true of the spread between procurement and export
prices of IRRI although the spread was more pronounced in the case of
basmati due to its being premium quality rice.
3. The basmati and IRRI rice farmers receive prices that are many times
below the world prices. Thus on the face of it their contention of an
‘implicit tax’ being paid by them sounds logical.
4. This conclusion will not be significantly altered even after milling,
storage and transportation charges are duly accounted for and
adjustment made.
5. The consumers, on the other hand, pay quite high prices and in the
case of IRRI, consumer price even overtakes the export price. By
implication, it may be stated that exports of basmati and IRRI and
particularly the latter, do adversely impact upon domestic supply and
domestic prices of the two rice varieties.
6. Presently there are no exports in the Public sector. The Rice Export
Corporation of Pakistan was disbanded in 2000. The government now
only facilitates exports and exporters through Trade Development
Authority of Pakistan (former Export Promotion Bureau). The residual
is thus appropriated by the intermediaries including rice exporters.
7. Thus under changed circumstances, the ‘implicit tax’ argument is no
longer tenable. The government of late has opted out and does not fix
procurement prices in order to allow market forces to play their due
role.
8. After reversion to the market system, farmers are better advised to
form their own rice export associations in order to reduce the role of
intermediaries.
118
A study of Implicit Tax in Pakistan’s Agriculture References
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Hossein, A., & Cummings, J. T. (1977). Estimating agricultural supply
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A study of Implicit Tax in Pakistan’s Agriculture Umar, F., Trevor, Y. et al. (2001). The supply response of Basmati rice
growers in Punjab, Pakistan: Price and non-Price determinants.
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and Implications for the US field crops sector. USDA Economic
Research Service, Technical Bulletin No.1888.
121
Aslam M.
Appendices
Appendix Table 1: Actual and trend prices (per 40 kg) of Basmati (1990-2008)
Years ProcurementConsumer Export Trend
Trend
Price
Price
Price (procurement) (consumer,
linear)
1990 143
322
423
126.084
19.85
1991 150
366
411
146.204
130.21
1992 155
411
448
166.323
240.58
1993 175
454
498
186.442
350.94
1994 185
467
502
206.561
461.3
1995 211
452
558
226.681
571.66
1996 222
630
701
246.8
682.02
1997 255
648
793
266.919
792.38
1998 310
778
966
287.039
902.74
1999 330
905
1056 307.158
1013.11
2000 350
913
1002 327.277
1123.47
2001 385
990
1152 347.396
1233.83
2002 385
1187
1176 367.516
1344.19
2003 385
1205
1190 387.635
1454.55
2004 400
1300
1266 407.754
1564.91
2005 415
1350
1343 427.874
1675.27
2006 460
1509
1486 447.993
1785.64
2007 460
2255
2361 468.112
1896
2008 460
3107
3364 488.232
2006.36
122
Trend
(export,
linear)
95.6
206.01
316.41
426.82
537.23
647.64
758.04
868.45
978.86
1089.26
1199.67
1310.08
1420.48
1530.89
1641.3
1751.71
1862.11
1972.52
2082.93
Trend
(consumer ,
Quad)
479.74
436.81
411.91
405.04
416.21
445.42
492.66
557.93
641.24
742.58
861.96
999.38
1154.82
1328.31
1519.83
1729.38
1956.97
2202.59
2466.25
Trend
(export,
Quad)
558.48
514.59
488.86
481.28
491.85
520.57
567.44
632.47
715.65
816.98
936.46
1074.1
1229.89
1403.83
1595.92
1806.16
2034.56
2281.11
2545.81
A study of Implicit Tax in Pakistan’s Agriculture Appendix Table 2: Actual and trend prices (per 40 kg) of IRRI (1990-2008)
Years Procurement Consumer Export Trend
Trend
Price
Price
Price (procure) (consumer
,linear)
1990 66
166
156
43.442
79.384
1991 73
192
193
57.498
124.242
1992 78
214
215
71.554
169.1
1993 85
239
207
85.611
213.958
1994 90
231
239
99.667
258.816
1995 103
300
320
113.723 303.674
1996 112
429
315
127.779 348.532
1997 129
388
347
141.835 393.389
1998 153
433
418
155.891 438.247
1999 175
601
396
169.947 483.105
2000 185
423
347
184.004 527.963
2001 205
401
412
198.06
572.821
2002 205
453
412
212.116 617.679
2003 205
465
487
226.172 662.537
2004 215
549
524
240.228 707.395
2005 230
619
561
254.284 752.253
2006 300
623
612
268.34
797.111
2007 310
959
1151 282.396 841.968
2008 310
1494
1604 296.453 886.826
123
Trend
(export
,linear)
26.495
75.691
124.888
174.084
223.281
272.477
321.674
370.87
420.067
469.263
518.46
567.656
616.853
666.049
715.246
764.442
813.639
862.835
912.032
Trend
(consumer,
quad)
261.13
245.41
236.81
235.34
241
253.78
273.69
300.73
334.9
376.19
424.62
480.17
542.84
612.65
689.58
773.63
864.82
963.13
1068.57
Trend
(export
,quad)
295.66
255.13
225.17
205.75
196.89
198.59
210.84
233.65
267.01
310.93
365.4
430.43
506.02
592.16
688.86
796.11
913.92
1042.28
1181.2
Aslam M.
Table 1: Procurement, Consumer and
Export Prices of Basmati (1990-2008)
Years
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
Procurement
Price
143
150
155
175
185
211
222
255
310
330
350
385
385
385
400
415
460
460
460
Consumer
Price
322
366
411
454
467
452
630
648
778
905
913
990
1187
1205
1300
1350
1509
2255
3107
Export
Price
423
411
448
498
502
558
701
793
966
1056
1002
1152
1176
1190
1266
1343
1486
2361
3364
Sources:
1. Federal Bureau of Statistics, Statistics Division, Government of Pakistan
GOP): “Foreign Trade Statistics of Pakistan” (various years)
2. Export Promotion Bureau, Government of Pakistan: “Foreign Trade of
Pakistan
(Various years)
3. Economic Advisor’s Wing, Finance Division, Government of Pakistan:
“Pakistan Economic Survey (various years).
4. Economic Wing, Ministry of Food, Agriculture and Livestock, Government
of Pakistan (GOP): “Agricultural Statistics of Pakistan” (various years).
124
A study of Implicit Tax in Pakistan’s Agriculture Table 2: Procurement, Consumer and
Export Prices of IRRI (1990-2008)
Years
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
Procurement
Price
66
73
78
85
90
103
112
129
153
175
185
205
205
205
215
230
300
310
310
Consumer
Price
166
192
214
239
231
300
429
388
433
601
423
401
453
465
549
619
623
959
1494
Export
Price
156
193
215
207
239
320
315
347
418
396
347
412
412
487
524
561
612
1151
1604
Source:
1. Federal Bureau of Statistics, Statistics Division, Government of Pakistan (GOP): “Foreign
Trade Statistics of Pakistan” (various issues).
2. Export Promotion Bureau, Government of Pakistan (GOP): “Foreign Trade of Pakistan
(various issues).
3. Economic Advisor’s Wing, Finance Division, Government of Pakistan (GOP): “Pakistan
Economic Survey (various issues).
4. Economic Wing, Ministry of Food, Agriculture and Livestock, Government of Pakistan
(GOP):“Agricultural Statistics of Pakistan” (various issues).
125
Forman Journal of Economic Studies
Vol. 8, 2012 (January–December) pp. 127-141
Determinants of Residential Electricity Expenditure in
Pakistan: Urban-Rural Comparison
Ijaz Hussain and Muhammad Asad1
Abstract
In this study the authors attempted to find out the determinants of the
consumption expenditure on electricity by households. Explanatory variables
are income of household, family size, number of rooms in the house, region,
province and electricity consuming appliances like AC, fridge, freezer,
computer, washing machine and air cooler. The authors found out that
expenditure on electricity is income inelastic, increase in family size and
number of rooms increases the expenditure on electricity. Households living
in urban areas have more expenditure on electricity as compared to the rural
households. Households in urban and rural areas of Punjab have more
electricity expenditure as compared to the rest of the provinces. The
acquisition of electric appliances contributed positively towards the electricity
expenditure. A.C. and Freezer are the two most powerful contributors.
Keywords: Households; Electric expenditure; Electric appliances; Pakistan
JEL classification: Q4, Q41, Q43
1.
Introduction
At present, Pakistan is facing a power shortage ranging between 40005000 megawatts (MW), because supply of electricity is increasing much
slowly as compared with its demand. On overage demand for electricity has
increased at a rate of 9.5% per annum during last four years due to
urbanization, industrialization and electrification of the rural areas.. It is
projected to grow by 8.7% per annum.2
If we look at sectoral consumption of electricity by economic groups,
we find that domestic group is the largest consumer of electricity with average
annual share of 45%. In the last four years (2003-04 to 2006-07), on average,
consumption in domestic sector has increased by 8.9% annually. Number of
electricity consumers in March 2008 was 17.73 million, out of which 15.02
million were the domestic consumers. In 1997-98 domestic consumers were
1
The authors are Assistant Professor at Department of Economics, Gomal University, D I
Khan and Officer, State Bank of Pakistan, Lahore, respectively.
2
Source: Pakistan Economic Survey (PES) 2007-08.
Determinants of Residential Electricity Expenditure
8.4 million. Thus, the number of electricity consumers has doubled within 10
years as shown in table 1.2. If we look at the supply side we find that
projected supply is 2000-3000 MW lesser than demand.
Since the supply falls short of the demand and there is continuous
increase in the electricity consumption, it is highly desirable to conduct a
demand side analysis regarding domestic consumers of electricity, as they
constitute the largest group of electricity users (see table and figure, 1.1).
Instead of considering the supply side of electricity, the alternative option is to
study the demand side approach in electricity through demand management.
Table 1.1: The Share of Consumption of Electricity by End-Users (in %)
Year
Households
1997-98
42.2
1998-99
44.8
1999-00
46.9
2000-01
46.9
2001-02
45.8
2002-03
45
2003-04
44.9
2004-05
45
2005-06
45.4
2006-07
45.8
Average
45.27
July-March
2006-07
45
2007-08
45.6
Commercial
5.2
5.5
5.5
5.8
5.9
6.1
6.4
6.7
7
7.4
6.15
Industrial
27.6
27.7
29
29.4
29.8
30.8
30.3
30.3
29.3
29
29.32
Agriculture
15.5
12.9
9.9
10.1
11.1
11.4
11.7
11.4
11.7
11.3
11.7
7.3
7.4
29.7
28.4
11.5
11.8
Source: Pakistan Economic Survey: 2007-08
For this reason, detailed analysis of consumers’ electricity
consumption is necessary and is the focal point of this paper. In this paper we
intent to find out the determinants of domestic electricity expenditure (per
month) using micro-data. This is desirable because a household level study
can incorporate household characteristics and shed some light on the nature of
consumer responses [See Filippini & Pachauri (2004)]. Moreover, by
including different geographical factors we can see consumer behavior in
different sub-groups. Thus, use of micro-data provides more detail and depth
as compared to the aggregate level study. Unfortunately, all the studies which
128
Hussain and Asad
have taken up this topic in Pakistan have used aggregate level data. Therefore
there is a need for micro level study on electricity demand in Pakistan using
the micro data. Micro-data study is also important because it can suggest
something about the demand management policy. A demand management
policy could be a better solution, in the short run because changing the supply
of electricity will require a longer timeframe. And even after increase in
Table 1.2: Consumers by Economic Groups (Thousands)
Year
Households Commercial Industrial Agriculture
1997-98
1998-99
1999-00
2000-01
2001-02
2002-03
2003-04
2004-05
2005-06
2006-07
July-March
2006-07
2007-08
Other
Total
8455
8912
9554
10045
10483
11044
11737
12490
13390
14354
1397
1517
1654
1737
1803
1867
1935
1983
2068
2152
187
190
195
196
200
206
210
212
222
233
171
173
175
180
184
192
199
201
220
236
8
8
8
8
8
9
10
10
10
11
10218
10800
11586
12166
12678
13318
14091
14896
15910
16986
14069
15026
2132
2214
230
240
233
243
11
11
16675
17734
Source: PES 2007-08
Figure 1.1: Available Capacity and Computed Demand (in MW)
129
Determinants of Residential Electricity Expenditure
supply the demand management policy will ensure against a power crisis as is
faced by the country today.
2.
Brief Literature Review
Understanding the demand and supply forces and their determinants in
electricity sector is important because today our lives are directly affected by
it as we have become dependent on the use of appliances run by electricity. In
the light of current electricity crisis the topic of demand side management has
gained special significance. Despite its significance there has been no
considerable work regarding electricity demand, on household level data, in
Pakistan. Perhaps because the demand for electricity was considered as
“given” or predetermined. Whatever the reason may be, the demand side of
electricity is still waiting to be explored in Pakistan. We still have to develop
insight about the dynamics of electricity demand in our country. In this paper
our goal is to see; what are the major determinants of household expenditure
on electricity (demand) using household level data.
There are a host of studies that have taken up the topic of electricity
demand regarding domestic, industrial and commercial users. Some of the
studies focused on residential demand for electricity are mentioned here.
Houthakker (1951) has studied domestic demand for electricity in UK using
cross sectional data on 42 provincial towns for a period from 1937-1938. He
used OLS technique to estimate double log models which included variables
like; average annual electricity consumption of each household with a
decreasing two part tariff, average income , marginal price of electricity,
marginal price of gas, and average holding of electricity consuming
appliances per household.
Fisher and Kaysen (1962) have focused on both residential and
industrial demand for electricity in US. by using a dataset having observations
for 47 states for the period 1946 to 1957. They used OLS and analysis of
covariance techniques. The model they estimated was in log form and
included ex post average price and per capita income, both of them in real
terms. They explicitly differentiated between the short run and long run
domestic electricity demand, for the first time.
Houthakker and Taylor (1970) have analyzed the residential demand
for electricity using annual time series data on personal consumption
expenditure for the period 1947-1964. They used state adjustment model to
make an equation for personal consumption expenditure on electricity. They
130
Hussain and Asad
estimated both short run and long run elasticities. Other studies on US
residential demand for electricity include; Wilson (1971), Mount et al (1973)
and Anderson (1973) among others.
Researchers discussed in detail the issues involved in modeling the
demand for electricity including Houthakkar (1962), Fisher & Kaysen (1962),
Houthakkar & Taylor (1970), Wilson (1971), Cargil & Meyer (1971), Mount
et al (1973), Anderson (1973), Anderson (1971), Lyman (1973) and
Houthakkar et al (1973), Taylor (1975).
Moreover, Reiss and White (2001) have studied US household
electricity demand in the short run and have taken care of problems like nonlinearity of electricity prices, data aggregation and heterogeneity in
household’s price sensitivity. They used data of a representative sample of
1307 California households for year 1997. Estimation is done using
Generalized Method of Moments (GMM) technique.
Filippini and Pachauri (2004) have studied residential demand for
electricity for all urban areas of India. They have used cross section data
containing 30,000 households for the year 1993-94. They estimated three
demand functions in log form using monthly data for the summer, winter and
monsoon seasons. The variables they included were average price of
electricity, price of kerosene, price of LPG, total household expenditure,
covered area of the house, size of town, size of household and age of head of
the household. They did not include the information about the appliance held
by the households. Their results show that the residential electricity demand is
income and price inelastic in all three seasons whereas geographical,
household and demographic variables included, show significant impact on
electricity demand.
Other micro-data studies which have taken up this topic are; Halvorsen
(1975) for USA, Parti & Parti (1980) for San Diego, Barnes et al. (1981) for
USA, Murthy (2001) for India and Dubin & McFadden (1984) for USA.
Studies which have taken up this topic on the aggregate level and studied it in
the time series settings include; Holtedahl and Frederick (2004) for Taiwan,
Akmal & Stern (2001) for Australia, Zachariadis & Pashourtidou (2006) for
Cyprus, Halicioglu (2007) for Turkey, Dergiades and Lefteris (2008) for USA
and Hondroyiannis (2004) for Greece, among others.
In this study our goal is to conduct a detailed analysis regarding the
determinants of residential electricity demand in Pakistan by including the
131
Determinants of Residential Electricity Expenditure
relevant demographic and economic variables. In this respect, we have used
cross-section data discussed in detail in section 5.
3.
Residential Demand for Electricity in Pakistan
Electricity is a commodity which is not directly consumed by the
households. Households get utility from the use of electricity consuming
appliances, so the demand for electricity is a derived demand, originating
from the demand for services provided by electricity consuming appliances.
Use of the appliance may depend on the habits and preferences of the
consumers, which are different hence leading to heterogeneity. In our analysis
following literature [e.g. Taylor (1975)] we identify short run as a period in
which the appliance stock of a household is assumed to be constant, hence the
changes in electricity consumption occur due to changes in the utilization rate
of the existing appliances. In long run the appliance holding can change3.
In the short run the residential demand for electricity is mainly
determined by the price of electricity and the alternative forms of energy,
income of the household, family size, number of rooms in the house,
demographic factors like rural or urban area, temperature and seasonal factors
and the appliance holding of the household.
In Pakistan we have increasing block pricing, this makes modeling
demand difficult, hence in the our analysis we will drop the price variable. In
our analysis we will not address the complex issue of multistep block pricing;
there are two reasons for it. One is the data about the marginal price faced by
the consumers is not readily available. Second is the unit prices faced by
consumers are uniform thus, this variable lacks the required variability. In our
study we have not included the seasonal variable, because of unavailability of
data. Our study is thus prone to specification bias because of unavailability of
data.
This paper is arranged as follows: section 4 is about the methodology
used. Section 5 focuses on the data sources and sample details. Empirical
results are summarized in section 6. Analysis of the results is in section 7.
Section 8 gives the conclusion.
3
Long run analysis is skipped in this paper due to data availability constraint, since we have
only cross sectional data for the short run (SR).
132
Hussain and Asad
4.
Methodology
In our analysis we will see how the monthly expenditure by
households on electricity is related with a set of given variables, using the
OLS technique on cross section data of about 9,500 households. We will
estimate the following general form;
Q i = f (Yi , N Fi , N R i , D A Pik , D R G i , D P R i )
(4.1)
Where
Q = consumption expenditure by household on electricity (Rs/month)
Y = monthly income of the household.
NF = Number of family members.
NR = the number of rooms in the house
DR = dummy showing the region. 1 for Urban, and 0 for rural.
DAP = shows the presence of a particular appliances. Appliances selected are
freezer (fzr), fridge (frg), air conditioner (ac), air cooler (aclor), washing
machine (wm) and computer (comp). Value of each category is 1 for the
presence of the particular appliance, and is 0 otherwise.
DPR = dummy showing the province, i.e. Punjab, Sindh, NWFP and
Baluchistan. 1 if the household belongs to the specific province, 0 otherwise.
Following literature we estimate equation in double log form, because
in that case the coefficients of the variables will provide the respective
elasticities and semi-elasticities. We estimate the following equation:
ln Qi = α1 + α 2 ln Yi + α 3 NFi + α 4 NRi + α 5 DRi + α 6 Dsndhi + α 7 Dblchi + α 8 Dnwfp
+α9 Dfrzi + α10 Dfrgi + α11Daci + α12 Dacolri + α13Dwm + α14 Dcompi + µi (4.2)
The income elasticity of electricity demand α 2 is expected to be
positive, because as the income of the household increases their consumption
of electricity also increases by consuming more appliances. The semielasticities α 3 and α 4 are expected to be positive, because as the number of
family members and rooms in a house increases its electricity consumption is
also expected to increase. The coefficients α 5 through α14 cannot be
interpreted as semi-elasticities. The percentage effects of the dummy variables
133
Determinants of Residential Electricity Expenditure
on the electricity expenditure can be derived by exponential transformation of
the coefficients.
The electricity demand of a household depends on the demographic
factors. The households living in urban areas are expected to consume more
than those in rural areas. Similarly, there is expected to be province wise
differences in electricity consumption, to capture these differences we are
using dummy variables for each province by using Punjab as base category.
In the initial analysis we take a large sample which includes both
urban and rural households. Then we conduct separate analysis for rural and
urban regions to see the difference in response of electricity expenditure to the
selected set of explanatory variables. It is expected that there will be strong
heterocedasticity in the data because of its cross sectional nature. To counter
this problem we took the log of the consumption expenditure of electricity and
income. Other problems could be the presence of specification bias because of
the missing data about the season in which the households were surveyed.
5.
Data
All the data used are taken from Pakistan Social and Living Standard
Measurement Survey (PSLM) Round-1 (2004-05). This survey is conducted
by the Federal Bureau of Statistics. The survey following Core Welfare
Indicators Questionnaire (CWIQ) approach was conducted with the aim to
provide data for use by the government in formulating the poverty reduction
strategy as well as development plans at district level and rapid assessment of
programs.4
This is the first time that Federal Bureau of Statistics (FBS) has
conducted. The field work was carried out between September, 2004 and
March, 2005. Simultaneously FBS conducted Household Integrated Economic
Survey (HIES) by contacting more than 12000 households for the purpose of
collecting detailed information about the income and consumption
expenditure of the households. Hence, we have used the same households.
But after accounting for missing values and outliers we were left with 9,238
household observations, which include households from all four provinces and
from both rural and urban areas of Pakistan. Use of monthly data reduces the
possibility of aggregation bias over time.
4
A sample survey covering approximately 76,520 households to provide district level
indicators in the sectors such as Education, Health, Water Supply & Sanitation and Household
Economic Situation & Satisfaction by facilities and services use.
134
Hussain and Asad
The combined sample (Rural and Urban) has 4,898 households from
rural area and 4,340 households from urban areas. Province wise distribution
of households included in the combined sample is given in Table 5.1.
Table 5.1: Province wise distribution of households (combined).
Province
No. of Observations.
Percentage
Punjab
4075
44.1
Sindh
2215
23.9
Baluchistan
1151
12.5
NWFP
1797
19.4
The separate sample used for urban area includes 4,409 households’
observations. The province wise distribution of households included in this
sample is shown in table 5.2.
Table 5.2: Province wise distribution of households (Urban).
Province
No. of Observations.
Percentage
Punjab
1917
43.5
Sindh
1162
26.3
Baluchistan
586
16.8
NWFP
744
13.3
The separate sample used for rural areas include 4,997 household
observations. The province wise distribution of households included in the
sample is shown in table 5.3.
Table 5.3: Province wise distribution of households (Rural).
Province
No. of Observations.
Percentage
Punjab
2760
44.8
Sindh
1094
21.9
Baluchistan
572
11.4
NWFP
1094
21.9
135
Determinants of Residential Electricity Expenditure
6.
Empirical Results
The results of estimation of equation (4.2) for both the rural and urban
combined and separate samples are given in table 6.1 below.
6.1.
Analysis
When we are looking at a cross section data of 9,238 households, it is
obvious that the appliance holding will be having different from one
household to the other. Thus our estimated equation for consumption
expenditure on electricity will be encompassing the effects of variations in the
utilization rate and also the effect of intra-household change in appliance
stock. Keeping this in mind our estimated elasticities suggest something both
for short run and long run.5
Table 6.1: Estimated results of equation (4.2)
Variable
Coefficients
Overall
0.153*
3.52*
2.52*
0.14*
-0.02***
-0.22*
-0.23*
0.42*
0.35*
0.06*
0.16*
0.56*
0.20*
3.98*
Ln Y
NF
NR
DR
Dsndh
Dblch
Dnwfp
Dfrz
Dfrg
Dacolr
Dwm
Dac
Dcomp
C
_
Urban
0.167*
3.25*
3.76*
Rural
0.135*
3.86*
1.30***
-0.01
-0.29*
-0.25*
0.47*
0.31*
0.05***
0.14*
0.52*
0.22*
4.03*
-0.02
-0.14*
-0.22*
0.32*
0.40*
0.09**
0.19*
0.84*
_
R 2 = 0.38
R 2 = 0.426
4.11*
_
R 2 = 0.211
Note: *,**,*** represent significance at 1%, 5% and10% respectively.
In case of the combined sample, we see that income elasticity is about
0.15, which means that expenditure on electricity consumption is inelastic to
the income of the household. 100% increase in income of the household will
on average lead to only 15 % increase in the expenditure on electricity. The
coefficient associated with the number of family members give the semi5
See: Thomas (1987)
136
Hussain and Asad
elasticity. Its value in the case of combined sample is 3.52, which means if on
average family size increases by 1 unit i.e. member, the household
expenditure on electricity will increase by 3.52%. Similarly, the coefficient
associated with the number of rooms in the house, represent semi-elasticity.
Its value is 2.52, which means that a unit increase in number of rooms
i.e. one more room, will on average increase the electricity expenditure by
2.52%, this is because of increased expenses on lighting and air circulation.
The rest of the coefficients in our model are the dummy variables, and
because our dependent variable is in log form, we cannot interpret the
coefficients of these dummy variables as semi-elasticities. To find out the
percentage effect of the dummy variables on the dependent variable we have
to perform the exponential transformation of the coefficients of these dummy
variables. Nevertheless, the sign of the coefficients also explain the effect of
the dummy variables. The results show that the electricity expenditure is
significantly higher in the urban areas as compared to the rural areas. This is
probably because of more chances of electricity theft in rural areas as
compared to the urban areas. Another reason could be the greater hours of
load shedding in rural areas as compared to the urban areas. Also, there is less
trend of using electricity consuming appliances and because of lower income
in rural areas the appliance stock the households have is also limited.
Similarly, electricity expenditure is lesser in other provinces as
compared to Punjab. The coefficients for Sindh, Baluchistan and NWFP are 0.02, -0.22 and -0.23 respectively. Though we cannot tell about the percentage
changes in the electricity expenditure due to change in province but the
magnitude of the coefficients is comparable. For example we can see that in
Sindh electricity expenditure is slightly lower than Punjab, whereas electricity
expenditure of Baluchistan and NWFP is much lower than Punjab. We can
also see that the difference in electricity expenditure between Sindh and
Punjab is very small, and that too is significant on 10% level significance. The
results thus suggest that highest electricity expenditure is in Punjab, then
comes Sindh, then Baluchistan and lowest electricity expenditure is in NWFP.
This may be because of the non-payments of electricity dues in NWFP which
is a common practice in some areas of NWFP and Baluchistan. The
expenditure on electricity may also be lower because of more hours of load
shedding in those provinces. Also, because these provinces are less developed
as compared to Punjab and poverty is higher in those areas, appliance stock of
households would be lesser than that of the Punjab.
137
Determinants of Residential Electricity Expenditure
The dummies for the appliances included in the model show that
presence of an appliance always contributes positively towards the electricity
expenditure. The highest contributor towards the electricity expenditure is air
conditioner, followed by freezer, fridge, computer, washing machine and air
cooler, respectively.
If we compare the results of urban and rural areas we find that income
elasticity of expenditure is higher in urban areas as compared to the rural
areas. The income elasticity is about 0.17 in urban and 0.135 in rural areas. It
means a unit increase in the income of household living in urban area will
increase their expenditure on electricity consumption by 17% whereas by
13.5% in rural areas. This is because appliance stock is expected to be lesser
in rural households and trend of electricity consumption is comparatively
lesser in rural areas. So, an increase in income will only lead to increase in the
utilization rate of the existing lesser stock of appliances, thus showing lesser
income elasticity.
The semi-elasticity associated with the number of household members
is 3.25 in urban areas and 3.86 in rural areas. This results is different from
expected, and cannot be rationalized. The semi-elasticity associated with the
number of rooms in the house is 3.76 in urban areas and 1.30 in rural areas.
This result is according to the expectations. In rural areas the construction and
the degree of electrification of houses is different than those of the urban
areas. In urban areas the lighting and air circulation equipment is more
frequent and extensive in the rooms as compared to the rural areas. Thus an
increase in the number of rooms in an urban household leads to increase in
electricity expenditure of about 3.76% as compared to 1.30% in the rural
households.
If we look at the province wise differences we see that in case of urban
sub sample the expenditure on electricity is not significantly different in
Punjab and Sindh, but is lower in NWFP and much lower in the Baluchistan.
Thus, electricity expenditure is lowest Baluchistan in case of urban sub
sample. If we look at the province wise distribution of the household
electricity expenditure in the rural areas we find that there is no significant
difference in Punjab and Sindh, but electricity expenditure is lower in
Baluchistan and lowest in NWFP. Thus, in case of rural sample, NWFP shows
the lowest electricity expenditure, even lower than the Baluchistan, this may
be due to high electricity theft in rural areas of NWFP or due to lack of
electrification or load shedding.
138
Hussain and Asad
The appliance dummies we included in our model show that the
presence of an appliances always contributes positively towards the electricity
expenditure. The highest contributor in case of urban sample is AC, after it
come freezer, fridge, computer, washing machine and air cooler, respectively.
In case of rural areas the sequence of contribution in order of highest to lowest
is; AC, fridge, freezer, washing machine and air cooler respectively.
Computer was excluded in the case of rural sample because it appeared
insignificant, and only less than 1% of rural households had a computer.
7.
Conclusions and Policy Implication
In the current study it is attempted to explore the determinants of the
consumption expenditure on electricity by households on entire country as
well as on urban-rural basis. For this purpose, we have included the variables
including income of household, family size, number of rooms in the house,
region, province and electricity consuming appliances like air-conditioner
(AC), refrigerator, freezer, computer, washing machine and air cooler. It was
found that expenditure on electricity is income inelastic, increase in family
size and the number of rooms raises the expenditure on electricity on
household level. Households living in urban areas have more expenditure on
electricity as compared to the rural households. Households in urban and rural
areas of Punjab have more electricity expenditure as compared to those in
other provinces. Since the presence of electricity-consuming appliances
always contributes positively towards the electricity expenditure. The same
evidence is empirically proved here. Air-conditioner and Freezer are the two
most powerful contributors. Thus, to control or reduce the demand for
electricity, use of air conditioner and freezer must be reduced.
139
Determinants of Residential Electricity Expenditure
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