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Comparison between Agriculture output and Manufacturing Industry outputTRANSCRIPT
Comparison between Agriculture output and Manufacturing Industry output
Md.Taijul Islam
Business Administration Department, Major in finance, East West University
1/15/2014
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Abstract
This paper examines the comparison between the agriculture and manufacturing
industry outputs and how much impact of credit, employment, land used, energy
consumption and Inflation have on their outputs. Secondary data was collected available
in various sources penetrating from year 1983—2012.Data was analyzed using Linear
Regression Model to find out correlations among variables. Results indicated that
Agriculture credit, employment, land, energy consumption and inflation have positive
correlation to Agricultural Gross Domestic Product. Manufacturing industries
employment, energy consumption and inflation have positive correlation to
Manufacturing Gross Domestic Product. Further it also revealed that average
manufacturing output was higher than average agriculture output.
Keywords: Bank credit, Scheduled banks, output, GDP.
1.0 Introduction
Bangladesh economy mostly depends on agriculture and manufacturing industries GDP which
contributed 45.9% of total GDP. Agriculture is the most important sector of Bangladesh which
contributed about 17.3 percent of the total GDP in the year 2012-2013. This sector has been
playing a vital role in socio-economic advancement and sustainable economic development
through gradual improvement of the rural economy, ensuring food security and alleviating
poverty. About 48 percent of the total labor forces of the country are engaged in agriculture
(BBS 2009). Though the contribution of the agriculture sector has decreased over time, it has an
indirect contribution to the overall growth of GDP. The growth in the service sector, particularly
the growth in wholesale and retail trade, hotel and restaurants, transport and communication
sectors are strongly supported by the agriculture sector. To uphold the role of agriculture sector
and rural areas in the overall socio-economic development of the country, the government has
been pursuing distribution programs of agriculture and rural credit through Bangladesh Krishi
Bank, Rajshahi Krishi Unnayan Bank, Nationalized Commercial Banks, specialized banks,
foreign banks, private commercial banks.
Bangladesh Manufacturing industry now is becoming the key sector of Bangladesh economy
which contributed 28.6% of total in the year 2012-2013.In the year 1981-1990 and 1991-2000
industry contribution to Bangladesh economy was 12% and 15% respectively. But it was
increased in the year 2001 to 2011 by 30% of total economy. Its contribution increases every
year. To grow this sector there is a need for financial help. Banks as the credit providers have
crucial role in the production facilities in the industrial sector. Bangladesh bank provides credits
to this sector through its schedule banks. One of the major reasons for growing this sector is
cheap labor. Especially the female women are the main source of manufacturing industry. So to
develop this sector Bangladesh bank provides more credit than any other sectors. In the
beginning, state-owned development finance institutions (DFIs) were major provider‘s of long-
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term funds to industrial enterprises at concessional and directed interest rates. Bangladesh shilpa
bank (BSB) and Bangladesh shilpa rin sangstha (BSRS) provided long-term capital by way of
loans, equity participation, etc. for setting up of new industries as well as for balancing,
modernisation, replacement and expansion (BMRE) of existing ones, both in the public and
private sectors. Later, BSB and BSRS, BSCIC are another institution that provides medium- and
long-term loan to small industries, either directly, or through consortium of commercial banks.
2.0 Literature review:
Modern agriculture is essential for economic development. Employing modern agriculture is
possible when farmers are provided credit for purchasing modern inputs (Schultz, 1964; Zuberi,
1989). Easy and cheap credit is the quickest way for boosting agricultural production
(Abedullah, 2009). The need of credit for smooth operation of agricultural farms is widely
recognized and the need is more for small and marginal farmers (Hakim 2004). He also argues
that access of small and marginal farmers to micro credit can significantly help them to avoid
sliding down the poverty ladder. Agricultural credit has a significant effect on standard of living
(Sanoy and Safa 2005). Masawe (1994) argued that agricultural credit stimulates agricultural
production, particularly among small farmers. Many developed countries had recognized the
benefits of using modern farm technology. But application of modern farm technology to
increase agricultural output had increased financing needs of farmers (Mellor, 1966). Credit is
provided for relief of distress and for purchasing seed, fertilizer, cattle and implements (Yusuf,
1984). Use of modern technology increased demand for credit and resulted in increase in
agricultural productivity of small farmers (Saboor et al, 2009) Access to credit promoted the
adoption of yield-enhancing technologies. Governments used credit programs to promote
agricultural output, (Adams and Vogel, 1990).(Rahman et al., 2011), all scheduled financial
intermediaries, under the instruction of the Bangladesh Bank (BB), are required to offer different
short and long-term credit options to agricultural sector. According to the latest records of BB
around 63% & 37% of the enlisted banks‘ credit lending (Rahman et al., 2011) has been directed
towards helping the agro-based community develop through short-term & long term loans,
respectively. The small-scale farmers and the countryside poor are often to decide between
taking monetary assistance from either social or institutional sources. The social source may
include friends, family members, shop owners, agents etc., whereas, the institutional source has
banks, micro finance institutions and other financing organizations (Bashir et al., 2010; Okojie et
al., 2010). Faruqee (2010), in his working paper, has identified three major sources of official
loan providers in the rural context of Bangladesh. They are the ―formal, informal and the quasi-
formal‖ sources of fund providers. As cited in the paper, the friends, relatives and family fall in
the category of informal loan providers, and result to a significant 8% - 21% of rural people
seeking fund from this. Interestingly, the loan repayment record also happens to be satisfactory
from this source (Faruqee, 2010).When it comes to financing tools, there is also a variety in
options that are available to the growers. Several former researches have suggested a various mix
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of easy credit plans that can aid the new farmers, beginners or small-scale agricultural
entrepreneurs to start-up production on their own (Saboor et al., 2009). If it is availed to the
small-scale farmers, with lesser overhead costs, it can motivate many unemployed farmers or
growers to get on board as well. Nevertheless, several studies (Badiru, 2010; CDF, 2006 as cited
in Rahman et al., 2011; Rahman, 2004; Gloy et al., 2005) have shown a common practice that,
both the availability and accessibility of finance is more in Nongovernmental Organizations-
Microfinance Institutions (NGO-MFIs) due to higher presence of branches of the same in
village-levels, in contrast to mainstream Commercial Banks, which only operate in Metropolitan
locations. General loans, program loans, housing loans etc. are few of the popular loan products
issued by financial institutions like the MFI‘s in Bangladesh‘s rural sector (Faruqee, 2010). In his
working paper, Faruqee (2010) has identified, agricultural activities, operating poultry, livestock,
sericulture, fisheries and forestry are the major deeds that are covered in the program loans
issued by the NGOs-MFIs. However, the down-fold of this system, is the sheer amount of added
interest that is being charged to the farmers, in oppose to the Commercial and Private Banks.
Dantwala (1989) estimated demand and supply of credit and its role in poverty alleviation in
India. He emphasized on supply of credit and to increase technical assistance to farmers to
increase agricultural productivity. Developing countries improved their agricultural output by
introducing modern agricultural technology such as chemical fertilizers, recommended seeds,
tractors and modern irrigation facilities etc. But modern agricultural technology was capital
intensive and hence increased demand for credit (Johnson and Cownie, 1969). Nosiru (2010)
proved in his research article on the topic ―Micro credits and Agricultural Productivity in Ogun
State, Nigeria that micro credit enabled farmers to buy the inputs they needed to increase their
agricultural productivity. However, the sum of credit obtained by the farmers in the study area
did not contribute positively to level of output. This was as a result of non-judicious utilization,
or distraction of credits obtained to other uses apart from the intended farm enterprises.
The impact of institutional credit, fertilizers, seeds, and irrigation on agricultural production was
found positive and significant (Zuberi, 1983, 1990; Sohail et al, 1991 Iqbal et al., 2001, 2003;
Waqar et al, 2008).Credit had been only a meek cause of agriculture sector growth in Nepal
(Shrestha, 992). Credit as an independent variable showed insignificant impact on production but
chemical fertilizers, high quality seeds, labor and tractors were found significant (Zuberi,1989;).
Mean input expenditures per hectare was significantly higher for the farmers who participated in
credit. Higher input expenditures were presumably associated with higher productivity growth
(Saeed et al., 1996).Chaudhry (1986) stated that combined effect of irregation, fertilizers, seeds
and pesticides etc. was positively on crop production. Strong correlation exists between the
amounts of institutional credit and the real gross domestic product agriculture sector in a given
time period (Carter 1988; Carter and Weibe 1990; Feder et al, 1990; Shrestha, 1992; Binswanger
and Khandker 1995; Pitt and Khandker 1996). Positive relationships exist between institutional
credit and productivity (Bernstein and Nadiri, 1993; Nickell and Nicholitsas, 1999; Schiantarelli
and Sembenelli, 1999; Schiantarelli and Jaramillo, 1999; Schiantarelli and Srivastava,
1999).Ahmad et al, (2006) analyzed the impact of advancing in-kind credit in the form of
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fertilizer and seed to smallholder farmers in the Ethiopian. They found that in kind input credit of
fertilizer and seed increased crop output reasonably. Zuberi (1989) found that 70 percent of total
formal credit was used for the purchase of seed and fertilizer and concluded that most of the
increases in agricultural output could be explained by changes in the quantity and quality of seed
and fertilizer.
Selim Raihan (2012) stated that almost half of all workers in Bangladesh are employed in the
agricultural sector and labor productivity in the agricultural sector remains very low.
Hossain (2001) argues that inflation increases the amount of agriculture output. Food price
inflation has dominated the increase in overall inflation since FY 03. A positive feature of
Bangladesh‗s inflation is its low and declining volatility. The paper notes that there is conclusive
evidence internationally of a negative correlation between the level of inflation and income
growth for all but low inflation countries. High inflation distorts decisions private agents make
about investment, saving and production. High energy intensity indicates a high cost of
converting energy into GDP, while lower energy intensity indicates higher GDP per unit energy
use. The energetic efficiency declines with increasing energy input, and the result indicates that
input energy increases faster compared to energy output (Khosruzzaman, S., Asgar, M.A.,
Karim, N. and Akbar, S. (2010).)
Industrial production has been assuming a very significant role in our GDP. In the recent fiscal,
manufacturing contributed around 18% in GDP which was around 13% in the 1993-94 fiscal.
Advances by the banks seem play a great role in this enhancement of the contribution of the
industrial sector in GDP (khan, rahman, Islam 2001). Bulir (1998) shows that industrial
production is integrated with various measures of bank credits between 1976 and 1990. Almost
all the scheduled banks have shown a positive trend in financing industrial production in the
form of manufacturing advances or industrial advances (Khan, Rahman Islam 2011).Patrick
(1966) argues that financial sector contributes significantly to industrial growth in emerging
markets, while the industrial growth increases demand for financial sector services in advanced
economies.
The industrial production and inflation for each country is very closely linked in the long-run
(chaudhry, khan, boldin 2010). It is almost standard in the theoretical literature to envisage that
inflation and productivity growth are negatively related (Bardsen 2007). Recognition and strong
evidence of real wages, inflation and productivity interrelationships can help shape policy
formation for productivity enhancement, inflation control or consumption stimulation. Many
conceive that inflation and productivity growth are negatively related (Jaret and Selody, 1982;
Clark, 1982; Hondroyiannis and Papapetrou, 1997). Bitros and Panas (2001) examined the effect
of inflation on total factor productivity across Greek manufacturing industries between 1964 and
1980. They found that the acceleration of inflation from the period 1964-1972 to 1973-1980 led
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to a significant slowdown in total factor productivity in 16 out of 20 manufacturing industries.
Tsionas (2003) also found a negative relationship between inflation and productivity for fifteen
European countries over the period 1960-1997.
Thus Most of the research focused only the impact of agricultural credit on productivity, impact
of industrial credit on productivity, impact of inflation on production. There is a little research
connected to the comparison between agriculture and industry output. Therefore this small study
was intended to fill this gap. The focus of this study is to find out the comparison between
agricultural output and industrial output in terms of credit provided by Bangladesh bank. I also
tried to examine how Agriculture GDP depends on the amount of credit provided, amount of
land used, energy consumption, inflation, employment and how industrial GDP depends on
credit, energy consumption, employment, inflation. The major objectives are too observe the
correlations among agriculture GDP, agriculture credit, used land, employment, energy
consumption and inflation and manufacturing industry GDP, credit, employment, energy
consumption and inflation.
3.0 Methodology of the study
In this study secondary data was collected available in various sources. Secondary data is
penetrating from 1983—2012. Published Data has been collected mainly from Bangladesh bank,
the annual budget of Bangladesh Government, Bangladesh Bureau of Statistics, Bangladesh
Economic review and different published reports by the government. Besides, more information
has been obtained from academic books and a variety of Journals. Financial figures have been
taken from relevant literature survey, observation method were used extensively. The study
represents all the Scheduled banks in Bangladesh. The banks are divided on the basis of
ownership pattern like nationalized commercial banks, private commercial banks, and foreign
commercial banks. There is no use of primary data in the research.
Statistical software SPSS has been very helpful in finding correlations among different
Variables, growth rate, trends, etc. Linear Regression Model is used to find out correlations
among variables. Agricultural Gross Domestic Product (AGDP) was used as the dependent
variable and agricultural credit, land, employment, energy consumption, inflation used as
independent variables. On the other hand, manufacturing industry Gross Domestic Product
(MGDP) used as the dependent variable and the manufacturing industry credit, employment,
energy consumption, Inflation used as independent variables. Numerical data have been analyzed
and interpreted with concentration and relation to the main issue. Data and information collected
from different sources were critically compared and found negligible mismatching. Theoretical
analysis along with numerical evidences has been used to substantiate the findings of the paper.
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4.0 Analysis and interpretation
4.1 Comparisons between agriculture gross domestic product and agriculture credit:
From 1983 to 2012, a constant increase noticed in the credit provided by all scheduled banks in
the agriculture sector. The amount of credit was at its peak in the year 2012 when the amount
was US$ 2493.81million. From 1983 to 2005 except some positive growth, agriculture GDP has
shown negative growth during these years. But after that the overall trend is upward. The amount
of agriculture GDP was at its peak in the year 2012.
Graph-1: agriculture GDP & credit
4.2 Comparisons between manufacturing industry gross domestic product and
manufacturing industry credit:
During the year 1983 to 2012, we can notice a constant increase in the credit provided by all
scheduled banks in the manufacturing industry sector. It has shown positive trend throughout the
year. From 1983 to 1992 manufacturing GDP has shown positive trend. But after that the overall
trend is downward till 1999. The growth rate again was upward trend during the year 2000 to
2012.
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Graph-2: manufacturing industry GDP & credit
4.3 Comparisons between agriculture gross domestic product and manufacturing industry
gross domestic product:
It can be seen from the graph there was no regular trend in the change in agriculture gross
domestic product and manufacturing industry gross domestic product. During the year 1983 to
1999 agriculture GDP was higher than manufacturing industry GDP. During these years average
agriculture GDP was 125.31% higher than manufacturing industry GDP. But the situation started
to change from the year 1999. From 1999 to 2012 manufacturing GDP was increased with
respect to previous year. During these years average manufacturing GDP was 83.33% higher
than agriculture GDP.
Graph-3: agriculture GDP & manufacturing industry GDP
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4.4 Comparisons between agriculture credit and manufacturing industry credit:
From 1983 to 1987, we noticed that credit provided by all scheduled banks in the manufacturing
industry sector was higher than the agriculture sector. After that the growth rate manufacturing
credit has been positive with massive increase. The amount of manufacturing industry credit was
at its peak in the year 2012 when the amount was US$ 8719.34 million. Throughout the
observation period the average manufacturing industry credit was US$ 3056.588 million and the
average agriculture credit was US$ 1495.163 million which was 104.43 % higher than
agriculture credit.
Graph-4: agriculture credit & manufacturing industry credit
4.5 Comparisons between agriculture employment and manufacturing industry
employment:
From 1983 to 2012, agriculture employment has been shown overall positive trend. The amount
of agriculture employment was at its peak in the year 1999 when the amount was 50.77 million.
From 1983 to 2012 manufacturing industry employment has been shown much higher trend than
agriculture employment. Throughout the observation period the average agriculture employment
was 22.09 million and the average manufacturing industry employment was 3.62 million which
was 610.67 5 % higher than manufacturing industry employment.
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Graph-5: agriculture employment & manufacturing industry employment
5.0 Statistical findings:
5.1 The following table describes correlation among agriculture GDP, credit, employment,
land, energy consumption, inflation
Model Summary
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
1 .544a .296 .149 1180.32189
a. Predictors: (Constant), INFL, AEC, ALAND, AEMP, ACRE
ANOVA
a
Model Sum of Squares df Mean Square F Sig.
1
Regression 14035460.439 5 2807092.088 2.015 .113b
Residual 33435834.105 24 1393159.754
Total 47471294.544 29
a. Dependent Variable: AGDP
b. Predictors: (Constant), INFL, AEC, ALAND, AEMP, ACRE
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Coefficients
a
Model Unstandardized Coefficients Standardized Coefficients
t Sig.
B Std. Error Beta
1
(Constant) 6777.911 1308.209 5.181 .000
ACRE 1.374 1.520 .434 .904 .375
AEMP -.477 31.494 -.005 -.015 .988
ALAND 35.414 31.005 .248 1.142 .265
AEC -.220 2.366 -.028 -.093 .927
INFL 93.553 92.184 .183 1.015 .320
a. Dependent Variable: AGDP
[AGDP= agriculture domestic growth, ACRE=agriculture credit, AEMP=agriculture employment, ALAND=agriculture land used,
AEC=agriculture energy consumption, INFL=inflation]
Agriculture employment and energy consumption were found strong positive correlation to
Agricultural Gross Domestic Product. Agriculture land was found weak positive correlation to
Agriculture GDP. Agriculture credit was found weak positive correlation to Agriculture GDP.
Inflation was found weak positive correlation to Agriculture GDP.
5.2 The following table describes correlation among agriculture GDP, credit, employment,
land, energy consumption, inflation
Model Summary
Model R R Square Adjusted R Square
Std. Error of the Estimate
1 .912a .832 .806 3423.35244
a. Predictors: (Constant), INFL, MEMP, MCRE, MEC
ANOVA
a
Model Sum of Squares df Mean Square F Sig.
1
Regression 1454510857.783 4 363627714.446 31.028 .000b
Residual 292983547.980 25 11719341.919
Total 1747494405.763 29
a. Dependent Variable: MGDP b. Predictors: (Constant), INFL, MEMP, MCRE, MEC
Coefficients
a
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1
(Constant) 1192.678 3681.084 .324 .749
MCRE 2.458 .432 .709 5.697 .000
MEMP -10.813 463.006 -.003 -.023 .982
MEC 10.498 5.962 .222 1.761 .090
INFL -658.463 253.911 -.213 -2.593 .016
a. Dependent Variable: MGDP
[MGDP= manufacturing industry gross domestic product, MCRE=manufacturing industry credit, MEMP= manufacturing industry employment,
MEC=manufacturing industry energy consumption, INFL=inflation]
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Manufacturing industries employment was found strong positive correlation to Manufacturing
Gross Domestic Product. On the other hand, manufacturing credit was found no correlation to
Manufacturing Gross Domestic Product. Manufacturing industries energy consumption and
inflation were found weak positive correlation to Manufacturing Gross Domestic Product.
6.0 Major findings
From this study the major findings are:
In aggregate Agriculture GDP has shown an overall positive trend during the year 1983
to 2012.
From 1983 to 1987, a constant increase noticed in the financing provided by all
scheduled banks in the agriculture sector in the form of advances. But after that the
overall trend has been downward.
Agriculture GDP has shown higher growth trend than agriculture credit.
Tremendous growth has been envisaged in the manufacturing industry GDP.
A constant increase noticed in the financing provided by all scheduled banks in the
manufacturing industry sector in the form of advances. Except some meager negative
trend it has shown overall positive trend throughout the observation period.
The average manufacturing GDP was 83.33% higher than agriculture GDP.
The average manufacturing industry credit was 104.43 % higher than agriculture credit.
Throughout the observation period the average agriculture employment was 610.68 %
higher than manufacturing industry employment.
Correlation exists among Agriculture gross domestic product, credit employment, land,
and energy consumption. Agriculture employment and energy consumption were found
strong positive correlation to Agricultural Gross Domestic Product.
Correlation exists among Manufacturing Industry gross domestic product, credit
employment, and energy consumption. Credit was found no correlation to Manufacturing
Industry gross domestic product. Agriculture employment and energy consumption were
found strong positive correlation to Agricultural Gross Domestic Product.
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7.0 Conclusions
Agriculture and Manufacturing industry are the key sector of Bangladesh economy and
contributed 45.9% of total GDP. It is no wonder that GDP of the country would be dependent
with the performance of these sectors. These sectors have been playing a vital role in socio-
economic advancement and sustainable economic development through gradual improvement of
the economy, ensuring food security and alleviating poverty. Throughout the analysis, I have
come up with the findings that the trend of Bangladeshi economy is more reliable on industrial
production to agricultural production. Average credit providing rate to the manufacturing sector
was higher than agricultural sector thus average manufacturing GDP is higher than average
agriculture GDP. The availability of employment increased output in both sectors. Since
agriculture and manufacturing output largely depends on credit, land used, employment, energy
consumption, to increase more output must be consider these factors. To robust output in these
sectors as well as our economy, government and Bangladesh bank can play vital role in terms of
financing or appropriate policy making.
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Appendix
Table 1.1: Agriculture GDP, credit, employment, land, energy consumption, inflation.
Agriculture
year AGDP (MN $)
ACRE (MN $)
AEMP (MN )
ALAND (MN)
AEC (US $)
INFL
1983 10210.83 1066.12 4.90 29.66 410.22 9.53
1984 10245.92 1065.22 5.50 20.16 765.18 10.41
1985 9229.31 969.46 5.60 23.65 553.43 10.47
1986 9356.64 997.35 8.80 23.65 403.40 10.18
1987 9493.73 1028.35 6.30 29.66 629.13 10.83
1988 10132.14 972.51 5.90 28.64 582.20 9.67
1989 11713.97 1082.69 6.10 39.61 503.68 8.73
1990 11113.85 1024.18 9.30 40.12 541.62 10.52
1991 10541.39 1307.08 10.30 43.56 564.67 8.29
1992 10770.67 1243.08 11.20 45.66 514.41 3.62
1993 11141.45 1246.55 12.30 23.65 637.15 2.98
1994 11260.15 1484.71 13.20 21.20 738.43 6.15
1995 11391.80 1443.45 16.80 20.32 737.63 10.12
1996 10997.35 1491.27 34.00 17.77 694.57 2.46
1997 10549.23 1463.49 39.30 10.24 612.71 4.96
1998 10123.26 1481.20 45.69 18.70 617.67 8.65
1999 9911.73 1584.92 50.77 17.86 468.32 6.18
2000 9448.93 1600.69 19.00 19.87 734.82 9.00
2001 8978.30 1626.83 24.85 21.45 789.85 9.00
2002 9001.64 1638.89 26.30 23.64 919.88 5.80
2003 8877.74 1578.68 22.90 31.25 966.80 5.80
2004 8538.72 1638.96 23.90 30.21 646.38 3.10
2005 8373.20 1615.88 25.30 31.26 599.81 5.60
2006 9013.95 1651.79 22.80 32.16 706.59 7.00
2007 10222.19 1634.64 28.90 33.65 724.04 7.20
2008 11685.06 1830.51 28.67 36.67 746.57 8.90
2009 11593.16 2004.76 43.53 36.67 800.29 6.66
2010 12835.71 2265.85 25.70 36.67 937.65 7.31
2011 11669.94 2321.95 39.20 36.67 884.69 8.80
2012 13769.87 2493.81 45.69 41.02 1070.61 10.62
[AGDP= agriculture domestic growth, ACRE=agriculture credit, AEMP=agriculture employment,
ALAND=agriculture land used, AEC=agriculture energy consumption, INFL=inflation]
Page | 16
Table 1.2: manufacturing GDP, credit, employment, energy consumption, inflation
Industry
year MGDP (MN. $)
MCRE (MN. $)
MEMP (MN.)
MEC (MN. $)
INFL
1983 1036.37 863.76 1.10 410.22 9.53
1984 1073.38 906.06 1.60 765.18 10.41
1985 1186.62 769.06 1.80 553.43 10.47
1986 1162.94 727.16 2.30 403.40 10.18
1987 1106.34 946.29 2.50 629.13 10.83
1988 2361.36 1009.70 2.60 582.20 9.67
1989 3276.58 1477.89 3.70 503.68 8.73
1990 3463.06 1364.78 3.60 541.62 10.52
1991 6177.82 1568.98 3.80 564.67 8.29
1992 8315.48 1616.12 3.90 514.41 3.62
1993 7905.35 1864.41 3.20 637.15 2.98
1994 7874.18 2154.19 3.60 738.43 6.15
1995 7655.68 2231.62 3.80 737.63 10.12
1996 6165.20 2359.31 4.10 694.57 2.46
1997 6956.75 2508.94 2.10 612.71 4.96
1998 7604.12 2981.04 3.60 617.67 8.65
1999 5761.92 3027.97 0.51 468.32 6.18
2000 14708.65 3088.04 3.70 734.82 9.00
2001 14347.48 3126.00 4.10 789.85 9.00
2002 16264.97 2995.99 4.20 919.88 5.80
2003 17047.98 2909.39 4.30 966.80 5.80
2004 16952.08 3016.93 3.90 646.38 3.10
2005 16727.64 2988.99 4.20 599.81 5.60
2006 18371.68 3631.88 5.20 706.59 7.00
2007 20316.65 4563.46 4.50 724.04 7.20
2008 21086.80 5578.09 3.10 746.57 8.90
2009 22947.95 6728.81 0.21 800.29 6.66
2010 23047.32 7972.58 6.70 937.65 7.31
2011 21029.92 8000.87 7.70 884.69 8.80
2012 22832.47 8719.34 8.90 1070.61 10.62
[MGDP= manufacturing industry gross domestic product, MCRE=manufacturing industry credit, MEMP=
manufacturing industry employment, MEC=manufacturing industry energy consumption, INFL=inflation]
Page | 17
Table 1.3: Agriculture GDP, credit, employment, land, energy consumption, and inflation. (growth
%)
growth
year AGDP (mn $) %
ACRE (mn $) %
AEMP (mn ) %
ALAND (mn) %
AEC (US $) %
1983
1984 0.34 -0.08456 12.14898 -32.0334 86.52643
1985 -9.92 -8.9891 1.905264 17.3466 -27.6731
1986 1.38 2.876517 57.14286 0.002883 -27.1099
1987 1.47 3.108074 -28.4091 25.37802 55.95849
1988 6.72 -5.43009 -6.34921 -3.43084 -7.45895
1989 15.61 11.32961 3.389831 38.28317 -13.488
1990 -5.12 -5.40463 52.45902 1.301133 7.533496
1991 -5.15 27.62277 10.75269 8.578889 4.254983
1992 2.18 -4.8963 8.737864 4.814195 -8.90013
1993 3.44 0.279261 9.821429 -48.1993 23.86039
1994 1.07 19.10539 7.317073 -10.3473 15.89623
1995 1.17 -2.77899 27.27273 -4.1666 -0.10809
1996 -3.46 3.312343 102.381 -12.5472 -5.83865
1997 -4.07 -1.8625 15.58824 -42.4036 -11.7847
1998 -4.04 1.21006 16.25954 82.65402 0.808034
1999 -2.09 7.002639 11.11841 -4.48837 -24.1796
2000 -4.67 0.994653 -62.5763 11.24871 56.90717
2001 -4.98 1.632962 30.78947 7.99649 7.4884
2002 0.26 0.741685 5.83501 10.16768 16.46273
2003 -1.38 -3.67379 -12.9278 32.21724 5.100414
2004 -3.82 3.818003 4.366812 -3.31304 -33.1426
2005 -1.94 -1.40776 5.857741 3.448705 -7.2047
2006 7.65 2.222165 -9.88142 2.882936 17.80327
2007 13.40 -1.03815 26.75439 4.6483 2.469117
2008 14.31 11.98253 -0.79585 8.963896 3.111973
2009 -0.79 9.5191 51.83118 0 7.195487
2010 10.72 13.02322 -40.9603 0 17.16377
2011 -9.08 2.476031 52.52918 0 -5.64819
2012 17.99 7.401673 16.55612 11.87627 21.01506
Page | 18
-15.00
-10.00
-5.00
0.00
5.00
10.00
15.00
20.00
19
83
19
85
19
87
19
89
19
91
19
93
19
95
19
97
19
99
20
01
20
03
20
05
20
07
20
09
20
11
agriculture gdp growth %
AGDP(mn $)
-20
-10
0
10
20
30
19
83
19
85
19
87
19
89
19
91
19
93
19
95
19
97
19
99
20
01
20
03
20
05
20
07
20
09
20
11
agriculture credit growth %
ACRE(mn $)
-100
-50
0
50
100
150
19
83
19
85
19
87
19
89
19
91
19
93
19
95
19
97
19
99
20
01
20
03
20
05
20
07
20
09
20
11
agriculture employment growth % AEMP
(mn )
-60
-40
-20
0
20
40
60
80
100
19
83
19
85
19
87
19
89
19
91
19
93
19
95
19
97
19
99
20
01
20
03
20
05
20
07
20
09
20
11
agriculture land uses growth %
ALAND(mn)
Page | 19
Table 1.4: manufacturing GDP, credit, employment, energy consumption and inflation.(growth %)
year MGDP
(mn. $) %
MCRE
(mn.$) %
MEMP
(mn.) %
MEC
(mn. $) %
1983
1984 3.571595 4.897723 45.45455 86.52643
1985 10.54941 -15.1213 12.5 -27.6731
1986 -1.99498 -5.44712 27.77778 -27.1099
1987 -4.86754 30.13449 8.695652 55.95849
1988 113.439 6.700413 4 -7.45895
1989 38.75836 46.3698 42.30769 -13.488
1990 5.69133 -7.65336 -2.7027 7.533496
1991 78.39204 14.96169 5.555556 4.254983
1992 34.60212 3.004545 2.631579 -8.90013
1993 -4.93216 15.36373 -17.9487 23.86039
1994 -0.3943 15.5427 12.5 15.89623
1995 -2.77487 3.594027 5.555556 -0.10809
1996 -19.469 5.722213 7.894737 -5.83865
1997 12.83907 6.341788 -48.7805 -11.7847
1998 9.305586 18.81709 71.42857 0.808034
1999 -24.2264 1.574115 -85.8333 -24.1796
2000 155.2737 1.983812 625.4902 56.90717
2001 -2.4555 1.229379 10.81081 7.4884
2002 13.36464 -4.15915 2.439024 16.46273
2003 4.814068 -2.89045 2.380952 5.100414
2004 -0.56255 3.696427 -9.30233 -33.1426
2005 -1.32396 -0.92617 7.692308 -7.2047
2006 9.828287 21.50843 23.80952 17.80327
2007 10.58677 25.65024 -13.4615 2.469117
2008 3.790759 22.23367 -31.1111 3.111973
2009 8.826113 20.62935 -93.2258 7.195487
2010 0.433048 18.48431 3090.476 17.16377
2011 -8.75331 0.35485 14.92537 -5.64819
2012 8.571366 8.979785 15.58442 21.01506
-40
-20
0
20
40
60
80
100
19
83
19
85
19
87
19
89
19
91
19
93
19
95
19
97
19
99
20
01
20
03
20
05
20
07
20
09
20
11
agriculture energy consumption growth %
AEC (US $)
Page | 20
-50
0
50
100
150
200
19
83
19
86
19
89
19
92
19
95
19
98
20
01
20
04
20
07
20
10
m.industry gdp growth %
MGDP (mn. $)
-20
-10
0
10
20
30
40
50 m. industry credit growth %
MCRE (mn.$)
-1000
0
1000
2000
3000
4000 m. industry employment growth %
MEMP (mn.)
-40
-20
0
20
40
60
80
100 m. industry energy consumption growth %
MEC (mn. $)