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European Journal of Economic Studies, 2015, Vol.(11), Is. 1

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EUROPEAN

of Economic

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ournal

Studies

Has been issued since 2012.

ISSN 2304-9669. E-ISSN 2305-6282

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C O N T E N T S

Growth and Instability Analysis of Rice Production and Export of Pakistan Muhammad Abdullah, Jia li, Sidra Ghazanfar,

Jaleel Ahmed, Imran Khan ………. 4

Agri. Industrial Structure and its Influence on Energy Efficiency: a Study of Pakistan

Zeeshan Ahmad, Meng Jun, Imran Khan ……… 16

Business Competitive of Tourist Destination: the Case Northeastern Montenegro

Jelisavka Bulatović, Goran Rajović ………. 23

The Analysis of Dynamics and Structure of the Consolidated Budget of the USA

Bella V. Kazieva, Leila B. Baisultanova, Lina H. Shokarova ………. 39

Additional Services as a Factor in the More Complete Satisfaction of Client Needs

Irina N. Markaryan ……….. 45

The Cluster Organization of Entrepreneurial Activity Within the Tourism and Recreation Sphere

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Copyright © 2015 by Academic Publishing House

Researcher

Published in the Russian Federation

European Journal of Economic Studies

Has been issued since 2012.

ISSN: 2304-9669 E-ISSN: 2305-6282

Vol. 11, Is. 1, pp. 4-15, 2015

DOI: 10.13187/es.2015.11.4

www.ejournal2.com

UDC 33

Growth and Instability Analysis of Rice Production and Export of Pakistan

1 Muhammad Abdullah 1 Jia li

1 Sidra Ghazanfar 2 Jaleel Ahmed

3 Imran Khan

1 College of Economics and Management, North East Agricultural University, P.R China

Harbin 150030

2 School of Management, Harbin Institute of Technology, P.R China

Harbin 150001

3 The Islamia University of Bahawalpur & PhD scholar at School of management, Harbin Institute

of technology, China

Department of Management Sciences Lecturer

E-mail: neau2010@outlook.com

Abstract

Rice is an important crop for Pakistan because it is a source of earning foreign exchange for its economy. This study was conducted with two objectives; (a) to analyze the pattern of growth and instability in production, area, yield, export quantity, and export value of Pakistani rice and (b) to determine the relationship between production, export quantity and area in Pakistan for rice crop. For the analysis, the time series data from the year of 1972 to 2011 regarding rice production, area under rice, rice export quantity and value were obtained to calculate the compound growth rate, coefficient of variation for the stated variables and to find relationship between the stated variables. The results revealed that the overall compound growth rates for rice production, area and yield were found to be positive and showed annual growth rate of 6.81%; 5.43% and 1.30% respectively. The instability analysis was applied by using the coefficient of variation calculation and the results showed that the rice production, area and yield were found to be considerably instable as the overall coefficient of variation values were found to be 30.06 %, 15.62% and 14.48% respectively. In terms of growth rate of rice exports quantity and rice export value, the compound growth rates for the overall period were found to be 15.80% and 30.06% respectively. The instability analysis was also applied by computing coefficient of variation for the rice export quantity and rice export value in dollars terms and the results revealed that in the overall period very high instability in rice export quantity and rice export value was observed as the values were found to be 58.76% and 89.32% per year respectively.

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Introduction

Pakistan is located in South Asia on the border of the Arabian Sea. Pakistan has border with China on the north, Afghanistan and Iran on the west and India on the east (The World Fact book. 2014). Agriculture plays a key role in the economic growth & development in Pakistan. Being a key sector in the economy of Pakistan, it contributes 21.4 % to gross domestic production (GDP), provides employment to 45 % of the labor force of the country and also plays a significant role in the growth and development of other sectors of the economy (Govt. of Pakistan, 2013). Total area of Pakistan is 79.61 million ha. Among this area, 27.10 million ha is classified as agricultural area and 1.644 million ha is classified as forest area (FAO stats country/region, 2014). Wheat, rice, cotton, maize and sugarcane are the important crops in Pakistan. The contribution of these important crops in GDP is 5.4% and these account for 25.2% of the value added in overall agriculture. Rice occupies a unique position among the important crops in Pakistan. Among the staple food grain crops in Pakistan, rice is ranked as second after wheat. Contribution of rice to value added in agriculture and GDP of Pakistan is 27% and 0.60% respectively (Govt. of Pakistan, 2013).

Rice is a cash crop in Pakistan and cultivated on 10% of the total cropped area (Shaikh et al 2011). Since rice is not a staple food in Pakistan so every year a considerable quantity of rice produced is exported to earn foreign exchange (Govt. of Pakistan, 2013). Pakistan is famous for its Basmati rice which is a type of aromatic rice. Appearance, aroma and taste are the three main factors upon which the fragrant rice is usually identified. It has superfine grain which possess pleasant aroma (Chaudhary, Tran and Duffy 2003). Rice is a staple food for almost 62.8% population of the world and a source for the 20 % of the caloric intake for the population of the world while in Asia this rate is 29.3 % (Timmer 2010). Since rice is a staple food for majority of the people in the world and a great source to ensure food security so international trading of rice produced is very low; only 7.13 % of the total rice produced in the world. Fragrant rice which includes basmati and jasmine verities accounts for 15 to 18 percent of the rice trade in the world (Baldwin and Childs 2011; FAO 2012; Young and Wailes 2003).

Like other developing countries the economy of Pakistan is heavily dependent on exports of agricultural commodities. Exporting is very important for the development of any economy and the economic health of any country largely depends upon exporting because it creates employment opportunities, help to maintain the trade balance, contributes towards economic growth and contributes towards the improvement of the standard of living of people (Lee & Habte Giorgis, 2004). In order to enhance the exports of rice from Pakistan, several market oriented steps have been taken by government of Pakistan. Until 1987-88, Rice Export Corporation of Pakistan (RECP) had the authority to exclusively handle the rice exports of Pakistan but in 1988-1989, the government of Pakistan shifted the responsibility of exports of rice to private sector in order to create more competitive environment to support the exports. This private sector works under a platform of an organization which is called Rice Exporters Association of Pakistan (REAP) that performs its activities with the interaction and cooperation of different government departments i.e. ministry of commerce and Ministry of Food, Agriculture and Livestock (REAP, 2010).

The measurement of instability regarding the agricultural production is important because it is helpful to understand various food issues and the issues arisen by the fluctuation in output, their impact on prices and resultantly the fluctuation of the producer’s return (FAO, 1998). Due to many reasons there occur fluctuations in the production of agricultural commodities and same is in Pakistan. The performance of the agriculture sector and especially the crop (as sub sector) is effected due to unfavorable conditions i.e. weather and other natural hazards like flood and drought (Govt. of Pakistan, 2013). The fluctuations in the rice production in Pakistan can affect the domestic prices of rice as well can affect the competiveness of Pakistan rice in the international market and resultantly the export quantities of rice from Pakistan can fluctuate . Such fluctuations also affect the returns of the farmers and exporters. The analysis of instability is useful for producers because in the light of such analysis they can better decide what to grow as well as this analysis can provide policy makers an insight to pinpoint the issues and causes of instability and variability in order to reduce the possible effects of instability.

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Materials and Methods

In this study, the secondary data regarding the rice production, area under cultivation and quantity of rice exported covering the period from 1972 to 2011 was used. This data were extracted from the official website of the food & agriculture organization of the UN; FAO. The 40 years period (1972-2011) is broken into 8 equal periods for convenience hence among the eight period, each period consists on five years data and each period represents the mean value of production and area of rice as well as export quantity and value of rice earned via rice exports.

1- For the trend analysis of production, area, yield and exports; the average production and average area were analyzed. The yield was calculated by dividing the mean production of each production by the mean area of the same period.

2- Per year growth rates for the stated variables were calculated and the geometric mean of the growth rate values was taken to find the compound growth rate for each period. In order to obtain the values of overall compound growth rate for the stated variables, the geometric mean of the compound growth rate values of all the respective periods was taken to obtained overall compound annual growth rate. Below formulas were used for this purpose

Growth rate = Current year/base year*100 to find growth rate per year within a period

[ ] 1/n -1 to find compound growth rate

Where n= number of years

r = compound annual growth rate value

3- Coefficient of variation has been used by different researchers for the studies regarding the instability analysis of agricultural production i.e. Hazell (1982) used coefficient of variation for the estimation of instability in Indian agriculture production. Later on Farih (1996) applied same technique in Sudan, Singh (1989) & Gangwar and Singh (1991) adopted coefficient of variation during measuring the instability and poverty in India. So for the instability analysis in this study, the measurement of coefficient of variation was made for production of rice, area under cultivation of rice as well as yield and the rice quantity exported and value earned against the export of rice from Pakistan. The measurement of coefficient of variation for the stated variables was done using the below formula.

4-CV= ζ / x̅ * 100

Where ζ = standard deviation

x̅= mean

5- In order to find the relationship between the exports (as a dependent variable) and area, production as independent variables, we applied regression analysis using SPSS. Due to multi correliniarity effect and higher score of VIF value, Ridge regression was used to draw the relationship between exports of rice from Pakistan and production & area.

Results and discussion Trend analysis

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Table 1: Rice Production area, quantity and yield in Pakistan (based on 5 years average)

Periods Area (000 ha) Production(000

tones) Yield (Tons/ha)

1972-1976 1977-1981 1982-1986 1987-1991 1992-1996 1997-2001 2002-2006 2007-2011

1610.9532 1973.660 1980.780 2064.240 2139.600 2349.420 2481.560 2659.520

3735.9412 4797.0912 4951.500 4849.540 5648.580 6854.2434 7600.950 9107.3352

2.32 2.43 2.50 2.35 2.64 2.92 3.06 3.42

Historical data shows that average area under rice cultivation in Pakistan was 1.61 million hectare during 1972-1976 and in the following sub period of 1977-1981 it increased to 1.973 million hectares with a positive change of 362.70 thousand hectares. In the same period the average yield increased with a minor increase and production of rice reached to 4.797 million tones with a positive increase of 1.061 million tones. Later on positive trend in the area was observed that shows that farmers considered the rice growing activity as a profitable business. During the period of increase in area, there was seen an increase in the production too except 1987-1991 when a decline was observed in production. From 1992-1996, the production increased rapidly and yield too as from this period, yield reached at 3.42 tons/ha in 2007-2011 from 2.64 tons/ha as a result of increase in production from 5.64 million tons to 9.10 million tons in 2007-2011.During the world food crisis, a sharp increase in the rice prices at international level was observed which motivated the farmers to produce more and more rice. Rice is not the staple food of Pakistan people so every year a considerable quantity of rice produced is exported and hence the farmers can get the better price of rice because of the growing demand of rice in international market. The overall picture of historical data shows a continuously increase in the area under rice cultivation in Pakistan (using 5 years average method), a continuously increase in yield except during 1987-1991 and a continuously increase in production except in the period of 1987-1991 (by considering the previous period as a base period). Results are also shown in figure 1.1 and figure 1.2

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Figure (1.2) showing the trends in yield of rice in Pakistan

Growth rates in Area, Yield and Production of rice

In order to observe the changes in the production, area, yield, export quantity and export value; during the stated periods, compound annual growth rate values were calculated and given in table 2.

Table 2: Compound annual growth rates values (%) for area, production and yield of rice

Periods Area Production Yield

1972-1976 1977-1981 1982-1986 1987-1991 1992-1996 1997-2001 2002-2006 2007-2011

Overall

1972-2011

8.65 3.89 0.08 5.12 8.32 1.22 11.35 5.35

5.43

6.66 8.29 -4.36 -0.25 20.05 4.97 12.81 8.13

6.81

-1.83 4.23 -4.43 -5.10 10.83 3.71 1.31 2.64

1.30

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by just 1.31% per year because the increase in production was mostly due to increase in the area. For the last period of 2007-2011, area under cultivation of rice increased by 5.35% per year and production increased by 8.13% per year. Since the increase in production was greater than that of increase in area so the rate of growth in yield increased and it reached 2.64% per year. The overall compound annual growth rate values for area, production and yield were found to be 5.43%, 6.81% and 1.30% per year respectively.

Instability analysis for area, yield and production

In order to analyze the instability in the rice production in Pakistan in terms of area, production and yield, the instability analysis was conducted. This analysis was conducted using the sub periods defined earlier in this paper. For this purpose the values of coefficient of variation were calculated for the respective fields as given in table 3. The greater values of coefficient of variation represent a higher degree of instability or variation while the smaller values of coefficient of variation show stability in the respective field i.e. a stable growth was observed during the periods when the less coefficient of variation was found. The results shown in table 3 reveal that in terms of production, the first 4 sub period starting from 1972 and ending at 1991, showed relatively less instability (stable growth) as compared to last 4 sub periods where a greater instability was observed in terms of production. The greater instability found in last four periods was due to the sharp increase in production. The instability analysis in terms of area showed that the 1st period

(1972-1976) and the last three sub periods showed greater instability as compare to other periods which were recorded as 7.35%, 6.39%, 6.28% and 9.53% per year respectively. This higher instability was due to the relatively greater increase in the area. The periods where less coefficient of variation was observed, it was due to the fact that area increased in those periods but at a slower pace than with the periods with higher coefficient of variation value.

Table 3: Coefficient of variation for production, area and yield of rice (%)

Periods Production Area Yield

1972-1976 1977-1981 1982-1986 1987-1991 1992-1996 1997-2001 2002-2006 2007-2011 Overall (1972-2011) 7.39 5.57 6.82 0.72 12.65 10.59 8.63 14.88 30.06% 7.35 2.96 3.71 3.06 4.85 6.39 6.28 9.53 15.62% 4.36 4.30 3.81 3.11 8.30 4.78 3.29 6.58 14.48%

The instability analysis in terms of yield showed that relatively little deviation was observed in the yield. Only in the period of 1992-96 and 2007-2011, there existed greater instability i.e. 8.30 % per year and 6.58 % per year and for the other periods there was not observed much instability. This showed that except the two stated periods, there were not greater shocks in the yield and a consistency in the growth of yield was observed. In terms of overall instability analysis from 1972-2011, the coefficient of variation values for production, area and yield were found to be 30.06 %, 15.62 % and 14.48 % respectively which state that in Pakistan the rice yield was found to be stable (static) as compare to area and area was found to be more stable than the production.

These phenomena can also be stated in terms of quantitative measures i.e. the overall increase in production of rice in Pakistan was recorded as higher than that of the increase in area and the increase in the area was found to be greater than that of the increase in the yield.

Trends in rice export from Pakistan:

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Table 4: Trends in export of rice from Pakistan (based on 5 years average)

Periods Export quantity (tons) Value (000 $)

1972-1976 1977-1981 1982-1986 1987-1991 1992-1996 1997-2001 2002-2006 2007-2011

571,259 1,016,416 1,031,106 1,056,676 1,396,218 1,994,026 2,381,436 3,256,120

171,069 367,262 333,419 312,076 390,244 538,544 746,058 1,782,992

In 1988-1989, Pakistan decentralized the rice marketing and export system as a result the private sector took over the control of rice export and this step facilitated the competition in the rice production and export in Pakistan (REAP, 2010) resultantly the market forces started determining the price in local market and this healthy competition facilitated the exports. The greater demand of Basmati rice (a type of fragrant rice) which is a premium variety of rice in Pakistan also played a vital role in the earning of foreign exchange as this variety is sold at a higher price than the other verities of Pakistan rice. Another reason for growing exports quantity of rice is that rice is not a staple food for Pakistani people so much surplus rice is available for export. The results are also shown in figure 3 and 4

Figure 3. Graphic presentation of milled rice export quantity and export value

Figure 4. Export quantity and export value (1972-2011)

Analysis of growth rate of rice export from Pakistan

Table 5 presents the analysis of growth in export quantity of rice and exported value of rice from Pakistan. The data of rice exported and value of exported rice was divided in a 5 years average from 1972 to 2011. The average increase in export quantity within the 1st period of 1972-1976 was

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monetary terms was observed to be 181.48%. The positive growth in this period shows an increase in exported quantity and exported value for the economy of Pakistan. During 1970 and 1971 there was political unrest in the country which ended in 1971 on the division of country so after the political situation came on normal routine, exports from country came on normal routine and a sharp increase in exports of rice was observed during the preceding years.

Table 5: Growth rate analysis of rice export from Pakistan (%)

Periods Exported rice quantity (%) Value (%)

1972-1976 1977-1981 1982-1986 1987-1991 1992-1996 1997-2001 2002-2006 2007-2011 Overall (1972-2011) 158.93 4.62 5.76 -18.68 -10.43 12.09 34.47 2.82 15.80% 181.48 39.78 -17.12 3.67 -8.61 11.96 53.03 54.69 30.06%

For the next period of 1977-1981, the growth rate in exported quantity was observed positive with an average increase of 4.62 % and an average increase in exported value of rice was observed 39.78 %. This indicates that although the growth rate in exported quantity was not higher but the average growth rate in exported quantity was relatively much higher and it was due to increase in the price of rice at international market. In the third period of 1982-1986, although there was observed a growth in the milled rice exported but a decline in the value was observed and it was due to decline in the prices of rice. In the coming period of 1987-1991, a decline in the growth of export of rice was seen but the increasing prices provided support to Pakistan economy. During the last period of 2007-2011, the growth rate in export of rice was just 2.82% but the average growth in value of rice was observed to be 54.69% and it was due to the global food crisis when the prices of rice increased tremendously in international markets. The overall CGR of export quantity and export value of rice were found to be 15.80% and 30.06% which states that overall there was an increase in the prices of rice in the international market.

Analysis of Coefficient of variation in rice export:

The analysis of coefficient of variation for rice export quantity and dollar value earned was made. The results are shown in table 6.

Table 6: Coefficient of variation for milled rice exported and value earned (%)

Periods Exported rice quantity

(%) Value earned (%)

1972-1976 1977-1981 1982-1986 1987-1991 1992-1996 1997-2001 2002-2006 2007-2011 Overall (1972-2011) 43.41 16.84 24.53 22.69 26.93 13.23 36.86 17.82 58.76% 50.48 35.75 24.07 15.24 28.11 7.99 38.33 22.98 89.32%

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period. The table 6 shows that the most instable period for the milled rice exported quantity were 1972-1976, 2002-06 and 1992-96 with coefficient of variation values of 43.41%, 36.86% and 26.93% respectively. Among the three stated periods with higher instability, the reason for the instability for first two is the increase in export quantity and for the third period of 1992-96, the reason for instability is decline in export quantity and a decline in the prices received by the exporters. In terms of dollar value earned for export of rice, the periods of 1972-76, 1977-81 and 2002-06 were found most instable with the CV values of 50.48%, 35.75% and 38.33% respectively. The instability in value earned is due to fluctuations in prices received by the exporters due to increase in export quantity and increase/decrease in the export price.

Regression analysis

At first, we applied the OLG regression by taking export quantity of rice as a dependent variable while production and area of rice as the independent variables but the variables were found to be highly correlated and in such cases the OLS does not reflect better relationship between the stated variables. The results of correlation and OLS are given in table 7 and table 8.

Table 7: Correlation calculation Correlations

exp_qty area production exp_qty Pearson

Correlation 1 .748

** .828**

Sig. (2-tailed) .000 .000

N 40 40 40

Area Pearson

Correlation .748

** 1 .965**

Sig. (2-tailed) .000 .000

N 40 40 40

Production Pearson

Correlation .828

** .965** 1

Sig. (2-tailed) .000 .000

N 40 40 40

**. Significant at 0.01 level (2-tailed).

Table 8: OLS regression Coefficients

Model

Unstandardized

Coefficients Standardized Coefficients

T Sig.

Co linearity Statistics

B Std. Error Beta Tolerance VIF

(Constant) 395368.0

29 568424.648 .696 .491

Area -1.156 .508 -.751 -2.273 .029 .068 14.656

Production .451 .096 1.553 4.699 .000 .068 14.656

a. Dependent Variable: export quantity

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Ridge Regression Analysis:

The variable of export quantity was considered as dependent variable and the independent variables were taken as area under rice cultivation and production of rice. Data of 40 years (1972-2011) were used in this analysis. The results are below.

Results for the Ridge Parameter = 0.35

Parameter Estimate VIF

CONSTANT -970298.

Area 0.363579 0.300596

Production 0.128904 0.300596

Estimation

N 40

MSE 1.03789E11

MAE 194447.

MAPE 36.0288

ME -6.40284E-11

MPE -12.2506

R-Squared = 54.4752 percent, R-Squared (adjusted for d.f.) = 52.0144 percent, Standard Error of Est. = 322162, Mean absolute error = 194447, Durbin-Watson statistic = 0.394339, Lag 1 residual autocorrelation = 0.694303

Below is the fitted regression model:

Export Quantity = -970298. + 0.363579*area + 0.128904*production

In this study the ridge parameter value was set to be 0.35 and this value is usually set between zero and one. To determine a better value of ridge parameter, we examine the standardized regression coefficients and the variance inflation factors. The R-Squared statistic indicates that the model as fitted explains 54.4752% of the variability in export quantity. The adjusted R-Squared statistic is 52.0144%. The standard error of the estimate shows the standard deviation of the residuals to be 322162. The mean absolute error (MAE) of 194447 is the average value of the residuals. The Durbin-Watson (DW) statistic tests the residuals to determine if there is any significant correlation based on the order in which they occur in your data file and the result shows that there is no autocorrelation in our data.

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Conclusion

On the basis of five year average, there has been observed a continuously increase in the area of rice in Pakistan since 1972, a continuously increase in yield except during 1987-1991 and a continuously increase in production except in the period of 1987-1991(by considering the previous period as a base period). This increase has been result of green revolution, introduction of new rice verities with higher promising yields, better supply of agricultural inputs, sharp increase in the international rice prices and many other factors. The privatization of the export sector of rice has also helped a lot to promote rice exports from Pakistan which motivated farmers to grow rice as the better prices were expected.

The growth rate analysis suggests that if the increase in area is greater than the increase in production, it would have a negative impact on the growth of yield. For area under rice, the compound growth rates have been positive for all periods but variations occurred at production level and as a result the compound growth rates for yield were affected. The overall compound growth rate values for area, production and yield were found to be 5.43%, 6.81% and 1.30% respectively which state that the growth in production was greater than the area and this was the main reason of growth in yield but since the gap between the growth in production and area was narrow so a short increase in the yield was observed. So in order to increase the yield, efforts should be made to get maximum production by utilization of minimum area. Except the two periods of 1987-1991 and 1992-1996, a positive growth rate was observed for the export quantity of rice from Pakistan while 1982-1986 and 1992-1996 were the two periods when there was observed a decline in the growth of export value. The overall compound growth rates of export quantity and export value of rice were found to be 15.80% and 30.06% which states that overall there was an increase in the prices of rice in the international market.

In terms of instability, there were three periods; 1992-96, 1997-2001 and 2007-2011 which were found to be much instable regarding area, production and yield. The overall coefficient of variations for production, area and yield were found to be 30.06%, 15.62% and 14.48% respectively which states that instability in production was greater than area and yield and this implied that the positive variation in production was found to be greater than the positive variation in area and yield. The difference between the CV of production and area was greater and as a result the value of CV of yield was lower. For the instability analysis of rice export and value of rice exported the periods of 1972-1976, 2002-06 and 1992-96 were found to be the most instable period for the milled rice exported quantity while the periods of 1972-76, 1977-81 and 2002-06 were found to be the most instable with the CV values of 50.48%, 35.75% and 38.33% respectively.

While the overall coefficient of variation values for export quantity and export values were found to be 58.76% and 89.32% respectively. The higher coefficient of variation value for export value earned showed a higher positive instability in the rice value earned which was a positive sign for the exports. The positive sign in the coefficient of variation in export quantity showed that the instability was there in export quantity which states that increase in export quantity was observed.

Application of Ridge regression using the historical data, the regression intercept was found to be 970298 and production and area both were found to be the factors which affect the export quantity of rice of Pakistan. Area was found relatively more influencing factor as compare to production which means more export is possible by increasing the area under rice cultivation but it is not easily possible so focus should be on the increase in production and in this way the yield gaps of rice with other rice growing countries can be reduced and export of rice can be increased.

The yield in Pakistan is lower as compare to many other rice producing and exporting countries. These yield gaps are due to differences in management practices and such gaps can be narrowed by applying efforts in research and development and facilitating the extension services. In order to bridge the yield gaps several steps can be taken. For this purpose stable performing verities can be introduced and as well as the hybrid rice verities are very useful and this experience has been very successful in china ,Vietnam, Indian and Philippine. The effective measures to prevent the weeds can be taken to increase the production and reduction of the losses. Better irrigation facilities can also be very useful to increase the production and yield.

References:

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Production and Marketing. FAO books, Roma, 358.

3. FAO (2014) Last retrieved on 30, November 2014 from

http://faostat3.fao.org/browse/area/165/E

4. FAO, 1998. ―On the measurement of instability of agricultural production and the associated risk of insecurity‖. : A Paper Presented on Sixth IWG. Agri. Seminar on Agri. Statistics, Russia, June 29–July 3, 1998.

5. FAO. 2012. Rice Market Monitor. XV (4): November 37.

6. Farih, A.A., 1996. Instability in agricultural production and its effects on farmers income. M.Sc. Thesis, University of Khartoum, Sudan.

7. Gangwar, A.C., Singh, S.P., 1991. Instability in cereal production in

Haryana: a decomposition analysis. The Recent Advances in

Agricultural Statistics Research. Wiley Eastern, New Delhi, p. 130.

8. Government of Pakistan. (2013). Economic survey of Pakistan. Islamabad: Ministry of Finance, Pakistan

9. Hazell, Peter B.R., 1982. Instability in Indian food grain production. Research Report No. 30, International Food Policy Research Institute, Washington, DC.

10. Lee, J., and Habte-Giorgis, B., 2004, ―Empirical approach to the sequential relationships between firm strategy, export activity and performance in U.S. manufacturing firms‖, International Business Review, 13, 101-129.

11. Rice Exporters Association of Pakistan (REAP). ―Introduction.‖ Rice Exporters Association of Pakistan’s web page http://reap.com.pk/links/introduction.asp (Last accessed on 23-11-2014)

12. Shaikh FM, Jamali MB, Shaikh K, Abdi AR. 2011. WTO reforms and rice market in Pakistan. Int. J. Asian Soc. Sci. 1(3):45-51.

13. Singh, I.J., 1989. Agricultural instability and farm poverty in India. Indian J. Agric. Econ. 44 (1), 1.

14. The World Factbook. 2014. Washington, D.C.: Central Intelligence Agency Retrieved 24 November, 2014 from https://www.cia.gov/library/publications/ the-worldfactbook/geos/pk.html

15. Timmer, C.P. 2010. The Changing Role of Rice in Asia’s Food Security. Asian Development Bank, Working paper series, 15, September, 19.

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Copyright © 2015 by Academic Publishing House

Researcher

Published in the Russian Federation

European Journal of Economic Studies

Has been issued since 2012.

ISSN: 2304-9669 E-ISSN: 2305-6282

Vol. 11, Is. 1, pp. 16-22, 2015

DOI: 10.13187/es.2015.11.16

www.ejournal2.com

UDC 33

Agri. Industrial Structure and its Influence on Energy Efficiency: a Study of Pakistan

1 Zeeshan Ahmad 2 Meng Jun 3 Imran Khan

1 College of Economics and Management, Northeast Agriculture University, Harbin, China

PhD Scholar

E-mail: zeeshanahmad_83@yahoo.com

2 Northeast Agriculture University, Harbin, China

Professor at Faculty of Sciences

3 School of Economy and Management, Harbin Institute of Technology, China

PhD Scholar, Lecturer, Department Of Management Sciences, the Islamia University Of Bahawalpur, Bahawalnagar Campus, Pakistan

Abstract

For last few years Pakistan has been suffering from energy crisis, under these circumstances to encourage efficiency and increase farmer’s income is very important and for this proposes the optimization of agricultural industrial structure is an important way. We collected 18years data on agricultural industrial structure measurements, energy efficiency indicators in 4 provinces from 1993-2010, and established regression models to look for the influence of agricultural structure on energy efficiency of Pakistan. We tried to find out the relationship between energy efficiency and agricultural industrial structure. Empirical results have shown that agricultural structure optimization has positive effect on social stability, agricultural economic development and energy efficiency in Pakistan’s rural areas. Therefore agriculture industrial structure deserves the attention of researchers as well as government.

Keywords: agricultural industrial structure; energy efficiency; economics growth; optimization; Pakistan; regression model; rural development.

Introduction

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region has also made space for smuggling of livestock from all provinces to war affected areas where it is sold on high prices.

As per the Pakistan Economics survey 2011, the percentage share of agriculture to total output is 21.4 % which shows that agriculture is of vital importance in country’s economic development. Furthermore, considering current energy crisis situation, continuously increasing energy costs and international environmental challenges, energy efficiency is considered to be of utmost importance for sustained growth in Pakistan. As per the Hydrocarbon Development Institute of Pakistan 2011, coal, oil, LPG (liquid Petroleum Gas), natural gas and electricity accounted for 10%, 29%, 1%, 44% and 16% respectively of the total energy consumption in Pakistan. Agriculture sector of Pakistan largely depends on electricity and oil but this is mostly associated to low efficiency and the oil usage also results in pollution. As agriculture is the source of bread and butter for rural areas of Pakistan and its economic development, considering the current energy crisis situation and short fall of energy production in the country, energy consumption is a key constraint in the future development.

Hence, modifications in agricultural structure are of key importance for the economic growth of the country. Since 1993 to 2010 in Punjab and Sindh province, plant cultivation was the most important sub-sector in agriculture and accounted for 57% of agricultural output. As agriculture is the main industry in the country, if there are any kinds of structural changes within the agricultural industry then it would be most important factor for increase in incomes and living standards of the people related to this sector in the region. On the contrary, as energy efficiency depends mostly on technology advancement to decrease the energy intake and energy amount in the production process, it might be possible to make agriculture as energy efficient sector by adjusting its industrial structure to best optimized one. Therefore to improve energy and resource efficiency, agricultural industrial structure alteration can be of vital importance.

Although a few studies of energy use patterns in Pakistan have been carried out but these were mostly for a specific area or a specific crop (Khan, 1994; Khan and Singh, 1996; Khan and Singh, 1997). Pakistan is continuously going through technology adoption and for this reason the energy output/input ratio has fluctuated over this period. Therefore it is not rational to consider this only by taking few crops into consideration. Energy use in the Pakistan agriculture has significantly increased over the last several years. This trend will continue in the future. Therefore the policy makers are required to put in order energy use policies which are environment friendly energy and guarantee a sustainable growth of agriculture sector (Mohammad A. K et. al. 2009).

In this research the analysis are conducted initially to study systematic influence of the agriculture industrial structure adjustment on Energy Efficiency of Pakistan’s four provinces (Punjab, Sindh, Khyber Pakhtunkhwa (KPK) and Balochistan). In addition, our work distinguishes itself from earlier qualitative studies by conducting quantitative analysis using regression analysis to apprehend the heterogeneity in each sub sector and four provinces, to enhance the energy efficiency of the county’s rural areas.

This research initially gives insight to selective literature overview on development and structural adjustment/change, later quantitatively analyzes the relationship between agricultural industrial structure and efficient energy consumption. Finally the research discusses some repercussions and future guidelines in policy making, based on our study, to make sure a sustainable growth in sector by reducing the energy cost of the sector by enhancing energy efficiency.

Literature review

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production quota slows own structural change and hinders efficient adjustment processes (Colman, 2000).But is this also true if quotas could be tradable (Barichello, 1995)?

Previous research work in this area has studied agricultural industrial structure from other angles. Most of the researches in the area have been carried out since 1990’s with a goal to improve the agriculture industrial structure. As energy efficiency relies mainly on technology advancement to reduce the energy intake and energy amount in the production process, it might be possible to make agriculture as energy efficient sector by adjusting its industrial structure to the best optimized one, increasing the proportion of energy saving sub sectors, and reducing the share of energy consuming sub sectors (Zha et. al. 2009). This can also be achieved by looking at the Sector-wise energy conservation potential. The change of both attitudes and of life styles towards the use of energy is needed at the national level to conserve electricity which will help in reducing the present run-way demand. Energy conservation is the only short term measure which can fill the gap between demand and supply (M. S. Khan 2012). Agricultural production system which includes input output ration of the sector is also an important factor to energy efficiency. In recent years Pakistan’s Agricultural production systems could not increase the resulting output. Importance of integrated planning in achieving desired outputs while using all the available technologies for improving agricultural production has increased and the planners need to formulate policies which are environmental friendly and sustainable in longer terms (Mohammad A. K. et. al. 2009).

Pakistan is a developing country and is under a process of technology adoption in every area. Agriculture is also taking a modern shape. This means the use of modern technology and the use of more energy. But it is still far behind than the other developing countries in the region like India or China. Although agriculture sector is not using huge energy supplies but still there is huge potential for energy efficiency and through proper implementation Pakistan can save around billions of dollar per annum (F. U. Khan 2011).

The weakness in the previous study works are related to the lack of detailed quantitative data, therefore, some of the conclusions are absent of strong empirical evidences. Several studies have used environmental factors and discussed their impact on agricultural development (Karkacier and Goktolga 2005; Downing et al. 2005). This research aims to address some of the weaknesses by (a) establishing a regression analysis using historical data from four provinces of Pakistan to observe the whole influence of agricultural industrial structure adjustment on energy efficiency, and (b) applying regression analysis to measure the role of different sub sectors of agriculture to energy efficiency.

Data and pre-analysis treatments

The data which is used in this study covers all four provinces of Pakistan namely Punjab, Sindh, Khyber Pakhtunkhwa, and Balochistan. We build two models for analysis of the Agri-Industrial Structure Adjustment and its influence on Energy Efficiency.

In underlying study the dependent variable of these two models is output per unit of energy consumption. Higher the energy efficiency the better it is. The independent variable in our study, for both of the models, is Agri-industrial structure. Whereas it is further divided into four sub sectors namely; Plant cultivation, Livestock/animal husbandry, Forestry and Fisheries. The industrial structure is measured in terms of share in GDP of the above mentioned four sub sectors.

For consistency reasons, estimates of GDP are taken at constant factor cost of (1999-2000). We calculate gross output values as the sum of output from plant cultivation, livestock/animal husbandry, forestry, fisheries, and service sector. Province-wise data has been calculated as the average of provincial outputs. For output per unit of energy consumption we first calculated the per acre production of every province then per unit output of energy consumption was calculated by the proportion of each agriculture sector. Data for 4 provinces are obtained from different sources i.e. Pakistan Bureau of Statistics (Agriculture Statistics Section, 1993-2012), Government of Pakistan Ministry of Food, Agriculture and Livestock (Economic Wing) 2006, Pakistan Economic Survey (2005-06, 2012-13), Hydrocarbon Development Institute of Pakistan, Pakistan Energy Yearbooks (2001, 2003, 2006), Provincial Bureau of Statistics , World Bank and Food & Agriculture Organization (FAO).

Data analysis and discussion

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development. To check the impact of agricultural factors on energy efficiency we use regression analysis. Separate regression models were built for each province because energy supply and efficiency is different in each province. For our analysis we have not taken forestry into consideration and supposed that it has very minimal effect on energy conservation.

To check the energy efficiency of Punjab we develop the following model:

Et= β0+ β1mt+ β2nt+ β3ot+εit (Model-1)

Where E is energy efficiency and mt, nt, ot represents the ratio of the agri. sub sector variables

cultivation, livestock and fisheries in the GDP respectively. β 1, β2, β3 are the slopes of the sub sector variables and β0 is intercept. ε is the error term with zero mean and constant variance. t denotes the different time periods. Following are the SPSS output tables:

Table 1

Model No. R2 R2 –

Adj. Std Error of Estimate F (sig)

1 0.787 0.742 1.383 0.000

2 0.794 0.750 3.17684 0.000

Table 2

Model No. 1 TOL VIF Model

No.2 TOL VIF

Constant (β0)

-12.579 63.912

Cultivation (β1)

1.141 0.178 5.621 1.222 .621 1.611

Std. error 0.523 0.199

Sig. 0.047 0.000

Livestock (β2)

-0.465 0.205 4.868 2.175 .629 1.591

Std. error 0.270 0.421

sig 0.106 0.000

Fisheries (β3)

1.50 0.648 1.544 -4.191 .905 1.105

Std. error 0.383 1.110

Sig. 0.002 0.002

First table shows the model summary, according to R2 the model is fitted good with the value of (0.787) which means that 78.7% of the variation is explained by the model. Significant result of ANOVA also supports that our model is fitted good with significant value of f-test is (0.000). Most of the variation is explained by the model. There is no multi-collinearity in the model as our variance inflation factor (VIF) values are below 10.

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Government should focus on these sectors by taking serious initiatives to enhance the GDP share of cultivation and fisheries as it has positive relationship with energy efficiency in Punjab province.

Energy efficiency of Sindh province:

Same model is used to measure the energy efficiency of Sindh province Et= β0+ β1mt+ β2nt+ β3ot+εit (Model-2)

The explanation of the parameters are same but in consideration of Sindh province.

This model is also fitted well with the R2 value of (0.794) which means that 79.4% of the variation is explained by the model. Significant value of F-test (0.000) also shows that the model is fitted well. Model doesn’t have any violation of the regression assumption as VIF and tolerance (TOL) values are in the acceptable range. Beta coefficients of the variables cultivation, livestock and fisheries are 1.22, 2.175 and -4.191 respectively. Cultivation and livestock shows the positive effect on energy efficiency but fisheries shows negative impact on energy efficiency. This negativity is because most of the fisheries industry of Sindh is associated with sea.

Energy efficiency of KPK province:

To validate the simple model we modify the model by applying the natural logarithm of both independent and dependent variables. So the new model is as under:

lnEt= δ0+ δ 1lnmt+ δ 2lnot+ δ 3lnqt +εt (Model-3)

δ0 is constant, δ 1, δ 2 and δ 3 are parameters and m, o, q are variables for cultivation, livestock

and fisheries respectively. Where t is time period.

Table 3

Model

No. R2 R2 – Adj. Std Error of Estimate F (sig)

3 0.503 0.397 0.4459 0.018

4 0.357 0.219 0.4722 0.000

Table 4

Model

No. 3 TOL VIF Model No.4 TOL VIF

Constant (δ0) -3.205 9.943

Cultivation (δ1)

.349 0. .417 2.399 1.721 0.583 1.715

Std. error 0.413 0.767

Sig. 0.000 0.041

Livestock (δ2) 1.350 0.477 2.095 -2.584 .576 1.738

Std. error 1.431 1.074

sig 0.000 0.030

Fisheries (δ3) -.537 0. 281 3.560 -1.488 .983 1.017

Std. error .287 1.815

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As the results show that this model fitted well. Both R2 and F-value support the argument

(0.50 and 0.018 respectively). Model doesn’t have any violation of the regression assumption as VIF and TOL values are in the acceptable range. Beta coefficients of all independent variables are significant (Cultivation and livestock are significant at 0.01 and fisheries is significant at 0.10). Beta coefficient of livestock indicates that 1% increase in ratio of livestock in GDP will increase energy efficiency by 1.35%. Beta coefficient of cultivation shows that 1% change of cultivation ratio in GDP will result an increase of 0.35% in energy efficiency. While the fisheries has negative effect on energy efficiency. As results shows that 1% increase in fisheries ratio in GDP will decease energy efficiency by 0.54%. Policy makers have to focus more on livestock and cultivation in KPK province to enhance energy efficiency. KPK is the province with more feasible conditions for livestock as well but previously it was considered that it is only suitable for cultivation. Many of the fruits are being cultivated in KPK. As the empirical results show, Government should support farmers to enhance livestock share in KPK GDP.

Energy efficiency of Balochistan province:

To validate the simple model, we modify the model by applying the natural logarithm of both independent and dependent variables as applied for KPK. So the new model is as under:

lnEt= δ0+ δ 1lnmt+ δ 2lnot+ δ 3lnqt +εt (Model-4)

δ0 is constant, δ 1, δ 2 and δ 3 are parameters and m, o, q are variables for cultivation, livestock

and fisheries respectively. Whereas ―t‖ represents the time period.

Goodness of fit of the model is good as F-value is significant (0.000). Models don’t have any violation of the regression assumption as VIF and TOL values are in the acceptable range. Beta coefficients of all independent variables are significant except fisheries. Cultivation and livestock is significant at 0.05 level of significant. According to results livestock has negative impact on energy efficiency. One percent increase in ratio of livestock in GDP will result a decline in energy efficiency by 2.58%. It is because that the rural area of Balochistan province is lacking behind in some fundamental agriculture facilities. The Population is very scattered. While cultivation has positive impact on energy efficiency as results indicates that 1% increase in cultivation ratio in GDP will improve energy efficiency by 1.72%. Balochistan is an important fruit producing province in Pakistan. If government focuses more in enhancement of its share in GDP it will increase energy efficiency.

Conclusion

Agriculture sector plays a significant role in Pakistan’s economy in diverse ways. Roughly 20% of national income and 43 % of total employment are generated within this sector. Despite the accepted lamentations about the neglection of agriculture in the country, the performance of the sector has been simply exciting. Pakistan is a developing country and is under a process of technology adoption in every area. Agriculture is also taking a modern shape. This means use of modern technology and use of more energy. But it is still far behind than the other developing countries in the region like India or China. Although agriculture sector is not using huge energy supplies but still there is huge potential for energy efficiency and through proper implementation, Pakistan can save around billions of dollar per annum. According to National Energy Conservation Centre (Enercon) Pakistan under these energy crises situation sector-wise energy conservation potential includes 25 per cent for industry, 20 per cent for transport, 20 per cent for agriculture and 30 per cent for buildings. Which means agriculture sector is playing an important role in energy efficiency of the country.

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These adjustments will make a positive impact on farmer’s income which makes a room for further research in this area. Based upon our results one can concluded that agriculture sector has potential for energy efficiency and therefore needs further attention of government authorities to make policies in this regard. There is one limitation of this research that we didn’t differentiate among different sources of energy like coal, oil, natural gas etc. while modeling the influence of agri. Structural adjustment, since they produce varying levels of pollutions which may influence our results as discharge of pollution also influences energy efficiency.

References:

1. Bai, W., Hao, J. M., Zhang, Q. P., & Guo, W. H. (2007). Impacts of policy related to structural adjustment of agriculture on grain supply in China. International Journal of Sustainable Development & World Ecology, 14(3), 287–298.

2. Barichello, R. R. (1995). Overview of Canadian agricultural policy systems. in Understanding Canada/United States Grain Disputes: Proceedings of First Canada/U.S. Agricultura land Food Policy Systems Information Workshop, ed. by R. Loyns, R. Knutsen, and K. Meilke pp. 37-59.

3. Chang, T. Y. (2011). The influence of agricultural policies on agriculture structure adjustment in Taiwan an analysis of off-farm labor movement. China Agricultural Economic Review, 3(1), 67–79.

4. Chen, G. Q., Jiang, M. M., Chen, B., Yang, Z. F., & Lin, C. (2006). Energy analysis of Chinese agriculture. Agriculture Ecosystems & Environment, 115(1–4), 161–173.

5. Colman, D. (2000). Ine_ciencies in the UK milk quota system. Food Policy 25: pp. 1-16. 6. Downing, M., Volk, T. A., & Schmidt, D. A. (2005). Development of new generation cooperatives in agriculture for renewable energy research, development, and demonstration projects. Biomass & Bioenergy, 28(5), 425–434.

7. Farid Ullah Khan. (2012). Energy efficiency: Pakistan can save massive energy through conservation Published in The Express Tribune, June 7th, 2012.

8. Gomes, E. G., Mello, J. C. C. B. S., Souza, G. D., Meza, L. A., & Mangabeira, J. A. D. (2009). Efficiency and sustainability assessment for a group of farmers in the Brazilian Amazon. Annals of Operations Research, 169(1), 167–181.

9. Karkacier, O., & Goktolga, Z. G. (2005). Input-output analysis of energy use in agriculture. Energy Conversion and Management, 46(9–10), 1513–1521.

10. Khan MA and Singh G (1996). Energy inputs and crop production in Western Pakistan. Energy., 21(1): 45-53.

11. Khan MA and Singh G (1997). Energy inputs and potential for agricultural production in western Pakistan. Agricultural Systems; 54(3): 341-356.

12. Mohammad A. K., Shahbaz K. and Noman L. (2009). Analysis of Energy Inputs and Outputs in Pakistan Agriculture, The Gomal University Journal of Research, VOL: 25 NO.2 DECEMBER, 2009.

13. Muhammad Saghir Khan. (2012). Prospects of Energy Efficiency Business, In Pakistan. Energy Associates (Pvt.) Limited.

14. S. Q. Memon, M. S. Mirjat, A. Q. Mughal, and N. Amjad. 2012. Evaluation of Inputs And Outputs Energy For Maize Grain Yield, Sarhad J. Agric. Vol.28, No.3, 2012: pp. 387-393.

15. Singh, G. (1999). Relationship between mechanization and productivity in various parts of India. A paper presented during the XXXIV annual conservation, Indian Soc. of Agric. Engineers, CCSHAU, Hisar, pp: 16-18.

16. Tariq Husain. (2010). Pakistan’s Energy Sector Issues: Energy Efficiency and Energy Environmental Links. The Lahore Journal of Economics 15: SE (September 2010): pp. 33-59.

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Copyright © 2015 by Academic Publishing House

Researcher

Published in the Russian Federation

European Journal of Economic Studies

Has been issued since 2012.

ISSN: 2304-9669 E-ISSN: 2305-6282

Vol. 11, Is. 1, pp. 23-38, 2015

DOI: 10.13187/es.2015.11.23

www.ejournal2.com

UDC 33

Business Competitive of Tourist Destination: the Case Northeastern Montenegro

1 Jelisavka Bulatović 2 Goran Rajović

1 College of Textile Design, Technology and Management, Serbia

Street Starine Novaka 20, Belgrade E-mail: jelisavka.bulatovic@gmail.com

2 Street Vojvode Stepe 252, Belgrade, Serbia

E-mail: dkgoran.rajovic@gmail.com

Abstract

Business tourism northeastern Montenegro, in terms of tourist valorization is insufficiently explored. Hence the paper discusses the business competitiveness of tourism destinations.

The

research results show that the two determinants of destination management and Qualifying Determinants ("at the same level in competing destinations") weakest determinants of competitiveness northeastern Montenegro, while the highest grade awarded determinants of key resources and attractions. How a key competitiveness factors vary depending on the destination, northeastern Montenegro must avoid the universal solution, and must have specific policies and strategies to improve competitiveness, based on the nature of the competitive set. The research results can be of benefit management organizations, tourism policy makers to better understand and identify the strengths and weaknesses of the business tourism northeastern Montenegro, and to help formulate strategies to effectively manage tourist destinations.

Keywords: Northeastern Montenegro; destination competitiveness; business tourism.

Introduction

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