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European Journal of Economic Studies, 2015, Vol.(14), Is. 4

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EUROPEAN of Economic

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РЕДАКЦИОННАЯ КОЛЛЕГИЯ

Видищева Евгения – Сочинский государственный университет, Сочи, Россия

(Гл. редактор) Левченко Татьяна – Сочинский государственный университет, Сочи, Россия Симонян Гарник – Сочинский научно-исследовательский центр Российской академии

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РЕДАКЦИОННЫЙ СОВЕТ

Балацкий Евгений – Центральный экономико-математический институт РАН, Москва, Россия

Вишневский Валентин – Институт экономики промышленности Национальной академии наук Украины, Донецк, Украина

Гварлиани Татьяна – Сочинский государственный университет, Сочи, Российская Федерация Герасименко Виктор – Одесский государственный экономический университет, Одесса,

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C O N T E N T S

Articles and Statements

Agricultural Production Structure Optimization Scheme of Punjab (Province) Pakistan

Zeeshan Ahmad, Meng Jun, Muhammad Abdullah, Mazhar Nadeem Ishaq, Nguyen Nhu Bang, P.O. Bunnika, Majid Lateef ………………………………………………………….

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The Main Vectors of Educational Programs in Improving the Quality of Human Resources in People’s Republic of China

Gao Feng, Maria F. Mizintseva, Anna R. Sardaryan ……………………………………………….

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Export Potentials of Pakistan: Evidence from the Gravity Model of Trade Yasir Tariq Mohmand, Aneel Salman, Khurrum S. Mughal,

Muhammad Imran, Nedim Makarevic ………………………………………………………………………

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Liaison of Exchange Rate and Macroeconomic Variables: A Case Study of Pakistan

Aneel Salman, Nadia Asghar, Tahir Ul Mulk Kahlon, Iftikhar ul Husnain, Nedim Makarevic ………………………………………………………………………

221

Features and Tendencies of Human Resources Educational System Development in Ecuador

Zurita Jose Estalin Vergara, Maria F. Mizintseva, Anna R. Sardaryan ……………………..

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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. 14, Is. 4, pp. 198-205, 2015 DOI: 10.13187/es.2015.14.198

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Articles and Statements

UDC 33

Agricultural Production Structure Optimization Scheme of Punjab (Province) Pakistan

1* Zeeshan Ahmad

2 Meng Jun 1 Muhammad Abdullah

1 Mazhar Nadeem Ishaq 1 Nguyen Nhu Bang

1 P.O. Bunnika 3 Majid Lateef

1 College of Economics and management Sciences, Northeast agricultural university, Harbin 150030, Heilongjiang, China PhD Scholar 2 College of Economics and management Sciences, Northeast agricultural university, Harbin 150030, Heilongjiang, China Professor 3 Department of Agriculture Economics and management, Northeast Forestry University, Harbin, Heilongjiang, China PhD Scholar *Corresponding Author E-mail: [email protected]

Abstract This paper identifies the basis for agricultural development constraints. For this we have

taken 2013 year as the foundation period then a linear programming model has been established for Punjab Province’s agricultural production structure for year 2020. Later as response to the impact of the current situation of agricultural production structure in Punjab Province and the macroeconomic environment, three kinds of different emphases of production structural adjustment programs will be suggested. At the end, to provide a reference for the development of a specific quantity of agricultural production structural adjustment policies, will be conducting the optimization analysis.

Keywords: Punjab province, agricultural production structure optimization, linear programming model, DEA model.

Introduction The agriculture sector plays a very significant role in Pakistan’s economy in many different

ways. Roughly speaking almost 20 % of national income and 43 % of total employment are

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generated within this sector. Despite the accepted limitations about the neglection of agriculture in the country, the performance of the sector has been simply exciting. Keeping in view the current energy crisis agricultural structure adjustments have a considerable influence on energy efficiency In Punjab province, adjustments will make a positive impact on farmer’s income (Zeeshan A. 2015). Generally speaking, the agricultural structure includes horizontal (agriculture, forestry, animal husbandry, fishery and its internal structure) and vertical (proportional relationship between agro-processing and circulation) two levels. In a literary view in Punjab Province, regardless of acreage or production, food crops are dominant and internal grain production is still the main production. The agricultural structure based on planting mainly is not conducive to abundant labor resources into full play and advantageous to the local economy and it also limits provincial comparative proportion of the advantageous economic crops. Mean time in some other developed countries, the proportion of agriculture and animal husbandry is about 1:1. The proportion of livestock in some countries is as high as 60 % to 70 % (Khalid. I, 2012; Burki & Javed. S, 2010; Punjab’s economics importance, 2012). In 2013, Punjab Province farming output share was of 59.7 % and animal husbandry of 25.2 %, the ratio is about 1.6: 0.95, there is a large room for adjustment of farming and animal husbandry ration. Vertical perspective, along with worldwide surplus agricultural structural production, agricultural production is a gradual transform from the pursuit of quantity to quality type. In Punjab Province after several years, although the structural varieties of agricultural products have improved but the overall quality rate is still low and there is a huge room for improvement. Based on the above understanding, combined with the actual situation of agricultural production in Punjab Province, the use of linear regression and operation research methods in determining the basis for agricultural development constraints, taking 2013 as the base period, established linear programming model for Punjab Province’s agricultural production structure adjustment in 2020. Also proposed three kinds of structural adjustment cases to analyze main points of Punjab Province agriculture deomesitc production structure.

The establishment and estimation results of the agricultural structure optimization model (1). Variables Selection Based on the characteristics of the internal structure of agriculture, selected decision

variables for the paper are: planting acreage for major crop is variable for planting sector, the annual major livestock products is a variable for livestock sector, forest area in the province is variable for forestry sector, water aquaculture in province is variable for fishery sector. Set of

decision variables are , whereas, i take 1,2,3,4 respectively, agriculture, forestry, animal

husbandry and fishery industry, specific variables in Table-I. (2). Determining the objective function Objective of the model is to maximize total income of agriculture, forestry, animal husbandry

and fishery to ensure economic efficiency of agriculture, but will take advantage of social and ecological benefits and resources as constrains to meet the multi-objective optimization of agricultural production structure for balanced requirements. Optimization structure of agricultural single objective linear programming model is constructed as follows:

Whereas, is the jth decision variable for ith industrial sector, is the jth decision

variable for ith industrial sector’s net income per unit of primary products. Determining Constraints In general, the numbers of constraints which may be provided in the region includes amount

of natural and social resources, social demand for various industry products, maintaining the ecological virtuous cycle constraint, as well as coordination between the production structure of various sectors and interior distribution etc., or resource constraints, social demand constraints, eco-environmental constraints and industrial relations constraints. Combined with the actual


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situation in Punjab province, the constraint conditions are as follows (Lifang, Jiaqi Ge & Jun. M, 2005), parameters are shown in Table-II.

Resource constraint

i. The total area of arable land constraints

ii. Economic crop acreage constraints ≤

iii. Forage diets sowing acreage constraints ≤

iv. Aquaculture area constraints ≤

Social demand constraints

i. Wheat and rice acreage constraints (Iqbal. MJ, Ali. ZU & Ali. SS, 2012): In Punjab, Wheat and rice in local market has obvious advantages, food safety advantage, and further there is lager space required to store which may not reduce the cost. In addition, Punjab Province needs to be built as a big province of animal husbandry as there is no doubt that wheat and rice are the good source of feed, which is why ensuring the two crops acreage is necessary.

+ ≥

ii. Food security constraints

– d – g ≥ e

iii. Vegetable demand constraints

– g ≥ f

iv. Meat demand constraints

– g ≥ g

v. Eggs, dairy demand constraint

– g ≥ h

vi. Aquaculture demand constraints

– g ≥ j

Ecological and Environmental constraints Ecological environmental constraint is represented here as in the forest coverage rate:

≤ Π

The industry relation constraints

i. The forage demand balance constraint

≥

ii. Dietary demand balance constraint

d ≥

Table 1: Variables of agricultural structure optimization model

Industry Decision Variables

Plantation X1j Cereals planting area ( 0000 hm2) Wheat X11 Rice X12

Jowar X13 Maize X14

Bajra X15 Barley X16


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Pulses X17

Cash crops planting area ( 0000 hm2) Sugarcane X18 Cotton X19

Tobacco X110 Jute X111

Sugar beet X112

Guar Seed X113

Vegetables X114

Oil Seeds X115

Forage crop acreage (0000 hm2) Fodders X116

Animal husbandry X2j

Livestock and poultry products production (0000 Tons)

Milk X21 Beef X22

Mutton X23 Chicken X24

Eggs X25 Others X26

Fisheries X3j X31 aquaculture area (hm2)

Forestry X4j X41 forest area (hm2)

Table 2: Part of the technical parameters and their significance

Constraint Meaning Constraint Meaning

R1 Planning the final urban population forecast in the province

JG1j Crop yield per unit area of the straw from "j" crop

R2 Planning the final province rural population prediction

GF1j "j" crop straw usage ratio

LS1 LS2 Annual planning of food consumption for urban and rural residents

QC2j QC31

Green food consumption quantity by Every ten thousand tones of livestock and poultry products, hectares of aquatic products breeding area

SC1 SC2 Annual planning of vegetable consumption for urban and rural residents

HL2j HL31

Per unit Consumption of grain number by Livestock and poultry products, aquatic products

RL1 RL2 Planning urban and rural residents' annual average meat consumption

d The proportion of food products used for feeding livestock and poultry

DN1 DN2

Planning urban and rural residents average eggs, and dairy consumption

e Demand for food Processing, export and reserve seed for planting during the planning period

SP1 SP2 Annual planning of consumption of aquatic products for urban and rural residents

f g h j Corresponding products export quantity

Determining technical parameters and Establishment of the supplementary model In the optimization model Time-varying parameters are involved such as population growth,

demand for agricultural products etc., according to their variation over time, to establish appropriate supplementary module and to make reasonable predictions and estimates using a combination of qualitative and quantitative analysis methods. Among them, the social demand for agricultural products forecasting is an important constraint. It is needed to establish two types of supplementary models, one is population growth prediction model and another one is agricultural products per capita demand forecasting model. The former were analyzed using Logistic model, the latter based on forecast results of other documents, combined with analysis of Punjab Province actually needed to be amended.


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Table 3: The gray zone values of portion parameters

Constraint interval Constraint interval

S1 (Ten thousand hm²) 1754 ~ 2000 d (%) 7 ~ 12

S2 (Ten thousand hm²) 14 ~ 18 e (Ten thousand tons) 1600 ~ 2750

π (%) 34 ~ 38 f (Ten thousand tons) 110 ~ 190

a (%) 19 ~ 23 g (Ten thousand tons) 300 ~ 360

b (%) 11 ~ 23 h (Ten thousand tons) 927 ~ 1200

c (Ten thousand hm²) 684 ~ 750 j (Ten thousand tons) 10 ~ 15

Other data determining channels and parameters includes (1) Pakistan Bureau of Statistics

(Agriculture Statistics Section) 1993-2014, Government of Pakistan Ministry of Food, Agriculture and Livestock (Economic Wing) 2013, Pakistan Economic Survey (2005-06, 2012-13), Provincial Bureau Of Statistics , World Bank and FOA. (2) The use of existing historical data in Regression analysis.

Design and calculation of the agricultural structural optimization case 1. Design of the agricultural structural optimization case From a global perspective, an important trend in development of the agricultural structure is

the decrease of plantation proportion (but the level of productivity must gradually increase), increasing proportion of livestock production, with these changes growth rate is much higher than plantation. Along with this fishery get more attention and become an important source of food. Statistics show that in some developed countries animal husbandry output value generally accounts for more than 50 % of agricultural GDP, some as high as 60 % to 70 %, the individual reaches 90 %. Punjab Province is a major agricultural province of Pakistan and is the country’s major grain producing area. Potential livestock development, fishery and forestry development has advantages in resources. Agricultural restructuring must be based on local resource endowments, comparative advantageous performance, and fully learning from other province’s and country’s experience, and strive to optimize the allocation of resources. Based on Punjab Province’s agricultural situation and planning objectives, initially identified in the gray zone corresponding partial parameter values in the model (Table-III), making it drift over time in the corresponding gray interval, accordingly designed three different schemes of Punjab Province Agricultural production Structure Optimization with planning period (2020), the three kinds of programs or schemes are being developed by focusing the current situation in the province.

Method 1: Combination of crop and livestock. Particularly for: appropriately reduced crop acreage, adjust acreage of wheat and rice, take wheat as direction of structural adjustment optimization to perform local wheat advantageous quality and market, meanwhile as wheat and rice are effective support of animal husbandry therefore vigorously developing animal husbandry. The program focused on the development of farming and animal husbandry in parallel to ensure the production capacity of major grain producing areas of the province, meanwhile, making animal husbandry as economic core support with plantation and animal husbandry in parallel (Ahmad. M, 2001).

Parameter Value: S1= 1784, S2= 13, π = 34, a= 20, a’= 25, b= 21, b’= 23, c= 750, d= 5, e= 1700, f= 120, g= 310, h=1100, j= 12.

Method 2: Mainly livestock. Particular to expand the scale of raising cattle, sheep, poultry, livestock products to broaden domestic demand and increase foreign sales of livestock products. Expanding forage grass and forage crop acreage, increasing the proportion of animal husbandry output value within total agricultural output value. The case emphasized the pastoral farming, animal husbandry as agricultural economic pillar (Hai. AA, 1995).

Parameter Value: S1= 2000, S2= 13, π= 37, a= 20, a’=23, b= 24, b’= 24, c= 700, d= 12, e= 1600, f= 190, g= 330, h= 1150, j= 14.

Method 3: Characteristic Agriculture type. Particular for Internal food crops acreage reduction, increase cash crop acreage, the development of the province flax, oil, vegetables and


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other cash crops production capacity and market potential, in order to promote the planting of cash crop and animal husbandry and thus to increase the scale of industry. In particular, making full use and advantageous of poultry and dairy industry, establishes basic scale, improving yields and increase economic efficiency (Ali. M, 2010). According to estimation, the province’s freshwater fish industry has a high international comparative advantage therefore it should be ensured to maintain such production scale of the industry.

Parameter Value: S1= 1854, S2= 18, π= 38, a= 25, a’= 28, b= 23, b’= 22, c= 650, d= 10, e= 2200, f= 1 10, g= 210, h= 950, j=15.

2. Model solution results After inputting all the above parameter values and constraints into model and then using

optimization software LINGO to solve it, you can get the optimal solution of the decision variable and even be able to calculate the optimal solution of concerning economic indicators; the results are shown in Table-IV.

Table 4: Result of Agricultural Production Structural Optimization in Punjab Province for

planning period (2020) (ten thousand h m2, ten thousand t, hundred million Rs.)

Index Method 1 Method 2 Method 3

Wheat X11 714 700 690

Rice X12 208 310 171

Jowar X13 11 25 12

Maize X14 33 80 55

Bajra X15 32 62 40

Barley X16 4 6 4

Pulses X17 22 42 30

Sugarcane X18 84 90 86

Cotton X19 229 217 252

Tobacco X110 2 2 2

Guar Seed X111 4 6 12

Vegetables X112 21 25 29

Oil Seeds X113 183 286 291

Fodders X114 237 189 180

Milk X21 8209 8643 8634

Beef X22 231 212 203

Mutton X23 86 81 78

Chicken X24 103 97 96

Eggs X25 1606 1544 1476

Others X26 265 173 230

Forest area (0000 hectares) 399 480 483

Cereal crops 1024 1225 1002

Cash Crops 523 626 672

Fodders 189 237 180

Crop yields 9156 7210 7602

animal husbandry output (0000 tons) 10500 10950 10680

Fisheries aquaculture area product (0000 hectares) X31 83 87 97

Value of Planting (100 Million Rs.) 9465 9463 9455

Animal husbandry output (0000 tons) 10500 11750 10680

The fishery output (100 Million Rs.) 1372 1475 1434

Forestry output value (100 Million Rs.) 3639.0 3896.4 3830.0


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Conclusion As in terms, rationalization of the regional agricultural production structure is relative to the

region’s natural resources and social economic and technological conditions. Rational agricultural industrial structure can fully utilize resources, strengthen the comparative advantageous of resources and optimize coordination among the departments so as to maintain coordinated development of various industries to meet the needs of society.

Method 1 is suitable for weaken the basic crop planting status, food crop area will be adjusted to 1024 (0000 hm2), cash crop for 523 (0000hm2), forage crops for 189 (0000hm2), food and forage compared ratio reached 57.39 : 29.31 : 13.28, whereas plantation, forestry, animal husbandry and fishery output ratio reached 21.8 : 1 : 16.5 : 9.2.

Method 2 completely considers the development prospects of animal husbandry, fodder crop area will be adjusted to 2 37 (0000hm2), food and forage compared ratio reached 54.6 : 31.6 : 13.8 , whereas plantation, forestry, animal husbandry and fishery output ratio reached 16.3 : 1 : 18.2 : 8.1.

Method 3 focuses on the development of special industries and improve the proportion of crops and livestock, crop area will be adjusted to 1002 (0000hm2), economic crops 672 (0000hm2), food and forage compared ratio reached 52.04 : 33.2 : 14.76, whereas plantation, forestry, animal husbandry and fishery output ratio reached 14.8 : 1 : 16.5 : 9.2. This need to be noted that the effectiveness and limitations of the mathematical model should be an objective manner, the optimal solution exists only in the mathematical theory, and in the midst of real-world conditions, as long as you can find satisfied solution to help to increase benefit that would be sufficient.

References: 1. Akhtar A. Hai. The Impact of Structural Reforms on Environmental Problems in

Agriculture. The Pakistan Development Review, 1995, 34 : 4 Part II: 591—606 2. Burki, Shahid Javed. Economics and Extremism Dawn (newspaper) January 5, 2010. 3. Fu Lifang, Ge jiaqi, Meng Jun. Agricultural industrial structure adjustment optimization

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УДК 33

Сельскохозяйственная производственная структура – схема оптимизации, провинция Пенджаб, Пакистан

1* Зишан Ахмад

2 Менг Джун 1 Мухаммед Абдуллах

1 Мазшар Надим Ишак 1 Нгуен Нху Банг

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http://tribune.com.pk/story/378252/punjabs-economic-importance/


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1 П.О. Буника 3 Маджит Латиф

1

1 Колледж экономики и управления наук, Северный сельскохозяйственный университет, Харбин 150030, Хэйлунцзян, Китай PhD Scholar 2 Колледж экономики и управления наук, Северный сельскохозяйственный университет, Харбин 150030, Хэйлунцзян, Китай Профессор 3 Факультет сельского хозяйства, экономика и управления, Северный лесотехнический университета, Харбин, Хэйлунцзян, Китай PhD Scholar

Аннотация. В статье дается определение основ ограничений для развития сельского хозяйства. Для этого авторы рассматривали 2013 год в качестве основы периода линейного программирования, модель была создана для структуры сельскохозяйственного производства провинции Пенджаб в 2020 году. Затем, в ответ на воздействие текущих событий на структуру сельскохозяйственного производства и макроэкономическую среду в провинции Пенджаб были предложены три вида различной направленности производственной программы структурной перестройки. В заключение авторы приходят к выводу, что для разработки конкретного объема производства для структурной перестройки сельскохозяйственной политики должен будет проводиться анализ оптимизации.

Ключевые слова: провинция Пенджаб, сельскохозяйственные оптимизации структуры производства, линейная модель программирования, ДЭА модели.


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www.ejournal2.com UDC 33

The Main Vectors of Educational Programs in Improving the Quality of Human

Resources in People’s Republic of China

1 Gao Feng 2 Maria F. Mizintseva 3 Anna R. Sardaryan

1-3 Economics faculty’s department of management, Peoples' Friendship University of Russia, Moscow, Russian Federation 1 PhD student 2 Professor, Doctor of Economics 3 PhD, Associate Professor

Abstract The article considers the issues of government educational programs in China, determines

the role of Chinese universities in the world rankings and the impact of education programs on human resource development in organizations in China.

Keywords: China, the government programs of education, human resources, development, education, training, university, organization, employees.

Introduction People’s Republic of China takes the leading position in population ranking in the world.

Only since 2002, there have been introduced programs at the state level to improve the quality of human resources as a driving force in the conquest of the competitiveness and efficiency of the individual companies and the national economy in general. There have been initiated a policy aiming to establish “an educated society” in the country, setting a goal to achieve not only a high level of literacy, but to increase a number of citizens with the university education, persons with two diplomas, with science degrees etc. The article considers the issues of government educational programs in China, and the impact of education programs on human resource development not only in economic aspect, but also in demographic of the country.

Materials and methods Chinese population plays a major role in shaping the global demographic situation, which is

not constant: it changes and rebuilds the structure of the world’s population. The rates of population growth have slowed noticeably in recent years. According to the Sixth census, held in the 2014, the population of China was 1367000 million people.[1] The UN experts expect that natural growth of population stops by 2030 and it will be negative after 2035 [7].

With neck-breaking growth rates of up to 13 %, China has emerged in recent years as one of the major economies in the world. The motor that powers China’s fast paced development is its vast and diverse population. There are more than 1.3 billion people living in China today. 38 % of



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the population lives in the more developed coastal areas, while the central and western regions are more scarcely populated. The working-age population is 72 %. Literacy rates are as high as 96 %. Unemployment rate is 9 %. Capitalizing on China’s immense human resources, more than 263,250 (about 1.3 % of all) European Union small and medium size companies are now doing business in China [8].

A huge number of population of the country makes all problems in China bigger, deeper, and extra ordinary sharper and urgent. Hardly controlled population growth largely introduces an element of spontaneity in the development of social production. Now it is obvious that with failing to achieve a population stabilization it is impossible to achieve significant results in solving other socio-economic issues [5].

Many Foreign Investment Enterprises (FIEs) are now finding that the biggest obstacle to their growth in China is the acute shortage of high quality and experienced management. Foreign direct investment has soared in recent years and with it has the demand for quality management. As established businesses have grown, the need for new and far more complex management roles has emerged, which previously hardly existed in China's old state run enterprises.

Furthermore the rapid rise in the economy has resulted in a workforce with career aspirations and attitudes that have been forged solely from the experience of knowing a booming market. Job-hopping has become commonplace. With traditional local education still largely focused on academic knowledge to real life business situations, there is an acute shortage of practically skilled young people joining the workplace.

As a result, the HR function has become one of the key issues for any developing foreign business in China. Pressed with the need to fill positions quickly, HR departments are hiring it seems at any price, lowering the qualifications they are prepared to accept to dangerous levels. This does not only result in a wage spiral, but also with a far lower level of competence in high end management positions. HR departments are now being thrown into a new era. Whereas originally HR was mainly about recruitment, the focus has shifted increasingly to development and retention and there are indications that salary levels are already beginning to rise – particularly when fringe benefits are taken into account. Average employee life may be no more than a year and so HR departments are under increasing pressure to find ways to retain staff. Candidates are also more sophisticated these days. They are getting choosier and are far more particular about what a company has to offer [9].

According to the census of 2010, 119.6 million people had the higher education and 187.9 million had vocational education. It is expected that by 2020 to be more than 200 million people with higher education diplomas and 400 million people with vocational education finished (Table 1) [3]. People with higher education are concentrated in the major cities as in the other countries in the world as well, in cities like Beijing, Shanghai, and Chongqing etc. In 2010 6.1 million people with higher education lived in Beijing, 5.0 million in Shanghai, Tianjin – 2.2 million, Chongqing – 2.4 million [4].

Table 1: The population of high education in China in the 2010-2014

2010 2011 2012 2013 2014

Student with higher education 22317929 23085078 23913155 24680726 25476999 Graduates of the bachelor 5754245 6081565 6247338 6387210 6593671

Source: http://www.stats.gov.cn – National Bureau of Statistics of China The UN Development Programme’s statistics show that the literacy rate (calculated by the

index of literacy) China was at the 107 position in the world (Table 2). The index measures state’s achievements by two evaluation criteria – literacy among adults and the index of total enrollment of people studying basic, secondary and high education.

http://www.stats.gov.cn/

http://www.stats.gov.cn/english/


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Table 2: United Nations Development Programme: Education Index, 2013

Place in rating Country Index 1 Australia 0.927

2 New Zealand 0.917

3 Norway 0.910

4 Netherlands 0.894

5 USA 0.890

6 Ireland 0.887

7 Germany 0.884

8 Lithuania 0.877

9 Denmark 0.873 10 Czech Republic 0.866

26 Japan 0.808

36 Russia 0.780

41 Singapore 0.768 42 Hong Kong 0.767

106 Philippines 0.610

107 China 0.610

108 Albania 0.609

181 Mali 0.305

182 Sierra Leone 0.305

183 Guinea 0.294

184 Chad 0.256 185 Burkina Faso 0.250

186 Eritrea 0.228

187 Niger 0.198 Source: Compiled by the authors with the materials of “Countries ranking by the level of Education”. Humanitarian Encyclopedia // Humanitarian Researches Center. - 10.10.2009 (last edition: 18.03.2015) URL: http://gtmarket.ru/ratings/education-index/education-index-info

Discussion According a new “educated society” achieving program in the PRC, the government

emphasizes following directions in education – expanding the network of university education, increasing a number of secondary special educational institutions, improving the quality of educational services, the distribution of educational resources to the Central and Western regions of China. As well as establishing new educational centers of higher and secondary special education in the rural areas, increasing a quota for free higher education for graduates of rural schools from the poorest regions of China, active retraining of teachers in rural schools, information of the educational process and development of pre-school education. In addition, planned a large-scale reforming of the educational system, which affects schools and higher education institutions as well: the right of autonomy of the universities, establishing of private educational institutions, improving the system of vocational education.

The country has promoted a course for a brand new stage of development of the PRC with the unprecedented high percentage of educated people. The new higher education institutions were established, and the educational system’s reforming is in process. Recently, Beijing Tsinghua University and Fudan University in Shanghai took high positions in the world university ranks. Higher education institutions get autonomy, the government finances new studies and a

http://gtmarket.ru/ratings/education-index/education-index-info


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modernization, in the first time were introduced programs for people with disabilities and for the national minorities, providing scholarships for the education of young farmers from the poorest areas of the country.

As same as in organizations, each person brings a different talent, level of expectation, contribution, and sometimes problem to the country or to the organization. Organizations cannot purchase human resources en masse, nor can a day’s work be used twice; a day’s work lost is a day’s work lost and can only be replaced by another human resource doing that particular job. A day’s work low productivity is an even worse situation, since the person is costing resources but not turning out the fullest level of production. Although the organization can discharge the person under certain conditions, the person can leave the organization at any time; thus the organization must continually strive to maintain a positive environment to encourage each individual to continually do his or her most productive work. This benefits both the person and the organization.

The seven most common methods of recruiting new employees are job posting, search of existing files, advertising, employment agencies, referrals by current employees, and other organizations.

A career path is a progression of jobs linked together, each of which furnishes skills and/or experience necessary for advancement to the next job. A career path is composed of sequential jobs that are interrelated and that lead to higher positions within the organization.

Selection is the process by which the job applicants are screened and interviewed and a hiring decision made.

The job interview is the major selection device used by a business to determine the fit between the organization and the prospective employee. Job interviews demand a great deal of preparation, and those employees tasked with conducting an interview need to be trained and possess a high level of communication skills.

In breaking down the human resources management functions into its many components – including listing training and development as one of the major ones – there is an absence of the term “education”. An understanding of the key differences among training, education, and development is important in order to appreciate the relationships and meaning of these activities and their relevance in developing human resources [6].

One interesting approach is to think about the focus and evaluation of each of these activities. The focus of training is on the present job held by an individual. Evaluation of training is on the job. The focus of education is on a future job for which the individual is being prepared and the evaluation consequently will be on the future job. The focus of development, on the other hand, is on future organizational activities and evaluation is almost impossible.

All three activities are part of the human resources development concept, which means a series of organized activities conducted within a specified time, and designed to produce some type of behavioral change. Keeping this concept in mind and the differences among the activities involved in it, should provide a better understanding of the on-the-job methods of development and off-the-job methods that follow.

The development of employees can take place in many ways, some formal and some informal. This section outlines some on-the-job development methods and others that take place off the job.

Employees can be trained and develop new skills through several on-line-job methods. Such as:

- Orientation for new employees. This can be formal, such as an all-day program sponsored by the employer, of informal, such as a manager spending a half hour with each new employee. If there is no orientation program, the new employee will, by default, develop his own which may be more time consuming and less factual than a formal program.

- Apprenticeship training. Used widely in the trades, apprenticeship programs provide an opportunity for a person to work at less than the regular wage and to learn under the aegis of a skilled tradesperson.

- Internsbips, residencies, assistantsbips, clerksbips, and fellowsbips. These offer paid opportunities to work in an area under a controlled educational setting. Specific schooling may be required in order to be considered for these programs.


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- On-the-job rotation. Many companies rotate their personnel, especially their managers, every two or three years so that managers can gain a broad understanding of the entire organization.

- Coacbing. Managers must coach and counsel subordinates. - Departmental staff meeting. These can be powerful tools, when conducted appropriately,

to develop staff. A manager might choose to hold weekly or bi-weekly staff meetings where staff members can present status reports on each of their projects or activities. This can help develop presentation skills, keep the staff informed about the department’s activity, and create peer pressure on any members who may need to increase their production. A part of the staff meeting might be devoted to a short presentation or discussion of a new magazine article or pending legislation or regulation in your field or any other topic which can stretch the interests of manager and subordinates.

- Company sponsored courses. Companies frequently sponsor on site courses taught by inside experts. The purposes of these courses are to bring managers together for a common leaning experience, the application of the learning experience on the job is expected to increase productivity and/or enable the employee to assume greater duties.

Off-the-job methods may include off-site seminars and tuition reimbursement programs. Attendance at an off-site seminar may have several purposes: learning; communicating with peers in the same profession or industry; and perhaps, as a reward for performance.

Development activities generally are aimed at increasing job knowledge and improving interpersonal skills and understanding of the organization. Multiple techniques may be involved including simulation exercises, videotaped presentations, in-basket exercises, job instruction training, case studies and lectures.

Results The Plan, in about 27,000 words, is China’s first medium and long-term education plan in

the 21st century. It sets a series of concrete goals to be achieved by 2020, including universalizing preschool education, improving nine-year compulsory education, raising the senior high school gross enrollment rate to 90 %, and increasing the higher education gross enrollment rate to 40 %.

UNESCO has highly praised the Plan, saying that it shows China’s long-term determination to make its education one of the best in the world.

The Plan not only proposes concrete measures for education reform at various levels, but also addresses education issues of public concern. For example, in response to the problem of “parents competing to send their children to top schools” during the compulsory education phase, the Plan calls for balanced development of education within a certain region; to ease the homework burden of secondary and elementary school students, the Plan proposes to establish a homework burden monitoring and reporting mechanism, and stipulates that “exam scores and contest awards shall not be used as criteria for school admission in the compulsory education phase”; to solve the problem of using college entrance examination results as the sole criterion for college admission, the Plan suggests “trying multiple examinations in a year for certain subjects”, or “gradually introducing classified college entrance examinations”[10].

Conclusion China’s economic success in recent decades can be traced in large part to the mobilization of

the country’s enormous human-resource base. Improvements in the health and education of China’s huge working-age population have made a strong contribution to economic growth. As a result on active state programs, aimed an increase of education’s quality, we can see the growth of the popularity of Chinese universities within the country and abroad as well. Chinese high education institutions ranked in the list of world’s top universities. In the World University Rankings annually published by The Times, you can find Beijing Tsinghua University, Fudan University of Shanghai, Shanghai Jiao Tong University, and Nanjing University. According to the newly promulgated Outline of China's National Plan for Medium and Long-Term Education Reform and Development (2010-2020). China plans “to become a country with rich human resources ” by the year 2020.


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Amid growing concerns about inequality, the Chinese government has made substantial progress in initiating and improving public-sector programs to provide education, for all its citizens. The challenges are great, however, because China’s population is changing so dramatically.

References: 1. Communication by the results of the 6th All-China Census. 28.04.2011; Population

Division of the Department of Economic and Social Affairs of the United Nations Secretariat, World Population Prospects: The 2010 Revision. URL: http://esa.un.org/undp/wpp/index.htm.

2. Proceedings of the 5th and 6th All-China Census. China – 2030: forward to the general prosperity / Center of the studying situation in the country, Tsinghua University; ed. Hu Angang, Iluna Yan, Wen Sina. Beijing, 2011.

3. E.S. Bazhenova New aspects of the demographic situation in China // China: politics, economy, culture. Collected articles. M.: Publishing House: Forum, 2014. s. 192-209. URL http://ria.ru/society/20140618/1012412360.html#ixzz3s2u8edUi

4. Compiled by the authors with the materials of “Countries ranking by the level of Education”. Humanitarian Encyclopedia [Online] // Humanitarian Researches Center. 10.10.2009 (last edition: 18.03.2015)

5. Gao Feng. The role of government educational programs in China and their influence on the development of human resources. // Issues of Economics and Management. Izhevsk, 2015 No.12

6. Patrick J. Montana., Bruce H. Charnov. Management. Second edition. Barron’s business review series, 1993

7. www.statdata.ru – Site about countries and cities, population statistics 8. www.eusmecentre.org.cn – Eusme centre 9. www.dragonflygroup.com – Dragonfly group 10. www.moe.edu.cn – Ministry of Education of the People’s Republic of China 11. sinospaces.ru – Site "Chinese space"

УДК 33

Основные направления образовательных программ в повышении качества кадровых ресурсов в Народной Республике Китай

1 Гао Фенг

2 Мария Мизинцева 3 Анна Сардярян

1-3 Российский университет дружбы народов, г. Москва, Российская Федерация 1 PhD student 2 Доктор экономических наук, профессор 3 Кандидат наук, доцент

Аннотация. В статье рассматриваются вопросы государственных образовательных программ в Китае, определяется роль китайских университетов в мировых рейтингах и влияние образовательных программ по развитию людских ресурсов в организациях в Китае.

Ключевые слова: Китай, государственные образовательные программы, человеческие ресурсы, развитие, образование, подготовка, университет, организация, сотрудники.

http://www.eusmecentre.org.cn/

http://www.dragonflygroup.com/


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Export Potentials of Pakistan: Evidence from the Gravity Model of Trade

1 Yasir Tariq Mohmand

2 Aneel Salman 3 Khurrum S. Mughal 4 Muhammad Imran

5 Nedim Makarevic

1 COMSATS Institute of Information Technology, Islamabad, Pakistan E-mail: [email protected] 2 COMSATS Institute of Information Technology, Islamabad, Pakistan E-mail: [email protected] 3 COMSATS Institute of Information Technology, Islamabad, Pakistan E-mail: [email protected] 4 IQRA University, Islamabad, Pakistan E-mail: [email protected] 5 Embassy of Bosnia and Herzegovina in Pakistan, Pakistan E-mail: [email protected]

Abstract In this study, the gravity model of trade is used to analyze the export environment of

Pakistan. As clear from trade data, Pakistan’s share in world exports is marginal and imports dominate the trade balance. The inability of diversification both in terms of products and markets is regarded as the main cause behind this trade deficit. This research highlights the main influencing factors affecting the export environment of Pakistan. The results of the gravity equation are used to calculate the export potentials of Pakistan with its partner countries. The results suggest Pakistan still has plenty of export potential with most of the partner countries and as such Pakistan can possibly reduce or control the trade deficit by targeting these countries.

Keywords: international trade, Pakistan, gravity model. Introduction Most trade theories try to find answer to the qualitative question of the trade performance of

a country. Trade performance can be divided into three sub categories, i) How much a country trades? ii) What does it trade? and iii) With whom it trades? The answer to this question can vary across countries, owing to policy objectives against which the foreign trade of a country is being analyzed. In recent years, the controversy over globalization and particularly trade openness has made need for quantitative research and comprehensive policy information and analysis even more necessary. The increasing debate is over that whether the benefits of trade exceed the costs associated with it. Concerns regarding the distributional consequences of trade reforms have also been expressed.








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For sound policy formulation, the policy makers need to have access to comprehensive and credible information and analysis on the potential results of implemented or to be implemented trade policies. Such kind of information and analysis is required for various stages of policy implementation due to the reason that effects might differ as policy matures. These determining factors of trade and potential trade partners and markets can be highlighted using a detailed empirical research through the estimation of the gravity model of trade. The gravity model is used as a workhorse for analyzing international trade [1] because data for it is widely available, the model has high explanatory power, and there are established standard practices that facilitate the work of researchers.

Similar to the Newtonian theory of gravitation, Jan Tinbergen [2] stated that the magnitude of bilateral trade flows among countries can be explained by a law called the “gravity equation”, where countries trade in proportion to their respective GDPs and proximity. The stability of the gravity equation and its ability to account for bilateral trade flows prompted many new researches in this field to analyze the effects of different economic, social and cultural factors on the bilateral trade of countries. Some of the most prominent uses of this model exist in the modeling of Free Trade Agreements (FTA) effects on trade flows.

Located in South Asia, Pakistan is a semi industrialized developing country which in last 64 years, have gone through various levels of growth like prosperity, decline and recovery (Figure 1). The manufactured exports of Pakistan in the 1960s were more than those of Indonesia, Malaysia and Thailand combined [3]. Pakistan’s economy had also survived through various international catastrophes like Asian financial crisis, economic sanctions, global recession of 2001 – 2002 etc and showed incredible resilience. Pakistan has also suffered from difficult relations with neighboring India resulting into multiple conflicts, extensive domestic political disputes, a massive influx of immigrants from the neighboring country Afghanistan and being the frontline state in War in Afghanistan and the War on Terror, high population growth rate.

Figure 1. GDP Growth Rate Pattern Figure 2: Pakistan Trade Data The exports of Pakistan are primarily dominated by unprocessed or semi processed goods.

These include agricultural products, like textiles, cotton, food processing, sea food and leather products. The bilateral trade of Pakistan has increased from more than 19 billion US $ in 1995 to almost 70 billion US $ in 2011 (Figure 2). However, evident from Figure 2 is the fact that imports of Pakistan have increased considerably compared to exports and as a result, Pakistan is facing a trade deficit of more than 18 billion US $ in the year 2011. The term trade deficit is not new for Pakistan. Data from the United Nations Comtrade database reveals Pakistan of having trade deficit for as far as the year 1973. There can be two reasons for the mounting trade deficit. The major export products of Pakistan, more than 80 percent of the total exports, fall in the SITC category of Food and live animals (SITC 0), manufactured goods classified chiefly by material (SITC 6) and miscellaneous manufactured articles (SITC 8). This shows that the exports of Pakistan are highly concentrated in few types of commodities and are not diversified enough. This goes beyond the types of commodities issue and the data shows that the major export destinations of Pakistan has not changed much as well since last two decades with the exception of China.. More than 80 % of the total exports are directed towards only 26 countries and at the same time losing markets rapidly. Exports to the near South Asian countries, especially the neighboring countries of India


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and Iran are minimal. As a result of this trend of concentrated exports categories and markets, trade environment of Pakistan is extremely vulnerable to instabilities emanating from fluctuations in world prices, the socio political instabilities of the partner countries, and also the factors affecting the export promotion like poor infrastructure, out dated technology, non-tariff hurdles posed by the importer country, limited trade financing, etc. As such, this research primarily unveils the export scenario of Pakistan and export partners in an attempt to identify the factors influencing the bilateral trade of the country. Furthermore, the gravity model of trade is applied to the data and it is examined whether this model correctly explains Pakistan trade and trade partners. The results of the gravity model are then used to identify the potential trade partners of Pakistan, so as to provide policy makers with in-depth and accurate information for future policy making. Remaining paper is organized as follows: Section 2 contains explanation of the gravity model of trade, and review of literature on its theoretical underpinnings as well as empirical analysis, Section 3 discussed the sample data and its sources and the estimates, Section 4 discussed the results and finally Section 5 consists of conclusions deduced from our analysis.

The Gravity Model of Trade The gravity model of trade has been extensively applied in international economics and to

analyze the trade patterns of countries since its introduction by Tinbergen [2] and Poyhonen [4]. However, at first the gravity model was thought of only a depiction of an empirically stable relationship between the size of the economies, the distance between them and the amount of their trade. More popular theories at that time were the Ricardian Model and Heckscher-Ohlin (HO) model. The Ricardian model relies on differences in technology across different countries to explain the trade patterns between them whereas the HO model relies on differences in factor endowments among countries. But, because of the tremendous empirical success of the gravity model and the ability of the model to explain trade flow prompted many researchers to find theoretical justifications for it. A classical treatment of the gravity model was put forward by Linnemann [5], where partial equilibrium model of trade was estimated adding a variable to the model to reflect the trade flow constitution. Similarly, Leamer [6] added impact of income and population hence modifying the model. Anderson [7] developed a model using Armington assumption (where goods were differentiated by country of origin) and where consumers have preferences defined over all the differentiated products. Bergstrand [8, 9, 10] made several attempts in order to explore the theoretical determination of trade by employing Constant Elasticity of Substitution (CES) and monopolistic competition model. Helpman and Krugman [11] derived the gravity model under the assumption of increasing returns to scale in production. Deardorff’s [12] research proved that gravity model is consistent with Hecksher-Ohlin trade theory. Another particularly important contribution to this field has been Anderson and van Wincoop’s [13] paper, in which the authors showed that relative trade costs are very important if the gravity model is to be well-specified. The different applications of the gravity model provided with the explanation of the vast empirical applications possibilities of this model.

The following equation (1) gives a simple mathematical representation of the gravity model:

(1)

Where Xij = export of country i towards country j, Yi = GDP of country i Yj = GDP of country j, Dij is the geographical distance between countries i & j C, β, γ and δ are the coefficients which need to be derived empirically. A log-linear

transformation of equation (1) makes its convenient for estimation using regression analysis, therefore, after the transformation we can write equation (2).

(2)


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Equation (2) is taken as the standard gravity model. However, many recent studies have included a number of variables in the standard gravity model, which are generally used to proxy trade costs among countries. These include and are not limited to dummies for islands, landlocked countries, adjacency, and also cultural variables in the form of common language, colonial history, and common religion[14, 15, 16, 17, 18, 19]. The assumption here is that transport cost increases with distance and further that it would be higher for landlocked countries and islands. Additionally it is assumed that the transport cost would be lower for neighboring countries. Similarly, cultural variables are used to capture information costs. Another common use of the gravity model is the impact assessment of regional trade agreements[20, 21, 22, 23]. These dummy variables are represented by a vector of variables Zij included in equation (2) along with an error term εij representing other left out variables. Thus equation (2) can be written in a more general form as equation (3).

(3)

1. The Model, Sample and Data In order to construct the model, a sample of 142 countries is considered. Trade of Pakistan

with these countries accounts for more than 80 % of the total trade and hence can be considered to be a representative of the whole trade. The time period under study is 17 years, from 1995 – 2011. Equation (3) is modified for this particular study and hence the resultant equation becomes:

……………………………………………………………………………………………………………………………………….. (4) Where

Xijt Export of country i to country j in year t. GDPit GDP of country i in year t. GDPjt GDP of country j in year t. Dij Weighted distance between country i and j. Bij Border, dummy variable, which is given a value of 1 if country i and j shares a

common border or a value of 0 otherwise. RTAijt Dummy variable representing Regional Trade Agreement between country i and

country j in year t. Rij Religion, a dummy variable, given a value of 1 if country i and j have common

religion or 0 otherwise. Langij Common language, a dummy variable which is given a value of 1 if country i and j

shares a common official language or a value of 0 otherwise. TRGDPit Trade – GDP ratio of country i in year t. TRGDPjt Trade – GDP ratio of country j in year t. εij Error term.

As explained before, GDP and distance variables in the equation are the standard gravity

model variables. GDP is used to proxy the economic size of the trading partners. Theoretically it is assumed that larger the country size (size represented by GDP) the more it would trade. So if two countries have larger GDP, their trade would be higher. Hence this variable is expected to have positive significant impact on trade. The variable Distance captures transportation cost between the countries i & j, which in our case is Pakistan and its trading partner. Greater distance would simply mean greater transportation cost, hence the variable is appear with negative significant sign. A dummy variable named Border has been added which has a value of 1 if countries share a border. Countries with shared border have higher chances of stronger bilateral trade relations. Due to this it may be assumed that Border may have a significant positive impact on bilateral trade. RTA is another dummy variable which has been used to see the impact of Regional Trade Agreement on the trade of member countries. The variable has a value of 1 if country i and country j has a RTA and 0 otherwise. Since the sole purpose of a Trade Agreement is bolstering trade, therefore, RTA is expected to have a significant positive sign. Religion and language are used as cultural variables to capture information costs and the coefficients are expected to be positive. Trade – GDP ratio is used to proxy openness of an economy to trade and hence is also expected to be positive in this case. Data on exports are taken from the United Nations Commodity Trade Statistics


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Database (UN COMTRADE). Data on the rest of the variables are taken from World Bank Development Indicators (WDI) 2011.Distance is taken from CEPII with data in kilometers from capital of Pakistan to capitals of the trading partners. Data on regional trade agreements is taken from the World Trade Organization (WTO). Equation (4) is estimated using the Poisson Maximum Likelihood Estimator as it has been proved to give unbiased results as compared to OLS.

Estimation Results The estimated results of equation (4) are presented in Table 1. All the variables of the gravity

model possess their respective predicted signs apart from the border variable. The result shows numerically that a 1 % increase in the GDP of the importer and exporter country results in an increase of 2.44 % [exp (0.89)] and 3.21 % [exp (1.17)] in exports of Pakistan, respectively. The variable Distance has appeared with the expected sign which is negative and significant. The distance variable suggests that as the distance between the countries increases by 1 %, the export of Pakistan decrease 0.31 %. Interestingly, the variable “Border” variable is inversely related with Pakistan’s exports. Pakistan shares a common border with China, India, Iran and Afghanistan. Afghanistan has not been included in the analysis because its data is not available. Except for China, trade Iran and India is at a low level, and it is generally considered that this trade is unrecorded and underground. Hence the results suggested a negative sign for the border variable as there is limited trade amongst the neighboring countries of Pakistan. The rest of the variables are all in line with the gravity model theory and represent positive relationship with the exports of Pakistan.

The coefficients estimated from the gravity model are used to calculate the predicted exports of Pakistan, and then these predicted exports are compared to the actual exports to see whether or not export potential for Pakistan exist. Equation (5) provides the methodology used to calculate these potentials.

(5)

The plus one (+ 1) and minus one (- 1) in equation (5) are used to standardize the export

potential. Thus the reported potentials will be between minus one (- 1) and plus one (+ 1) where a positive index value (0 , 1) shows a higher bilateral trade than what is predicted by the model and that the exports have reached or exceeded the potential level whereas a negative index value (-1 , 0) reveals the opposite scenario.

Table 2 provides the export potential of Pakistan with the sample countries. The favorable results suggest that Pakistan still has the potential of increasing exports with 86 countries. The highest potential lies with countries of Iceland, Brunei Darussalam and Barbados whereas actual trade has exceeded with countries like Kenya, Bangladesh, Madagascar and United States. In fact these results show that Pakistan is currently focusing on trade with countries of exhausted potentials. United States, United Kingdom, United Arab Emirates and Germany are a few of the countries with which foreign trade of Pakistan is the highest, amounting to almost 33 percent Pakistan’s total exports in the year 2011, yet the results reveal that export potential with these countries have exhausted. Hence, although the exports of Pakistan are flowing towards developed countries, the real trade potential of Pakistan lies with developing countries which is highly unrealized.

Conclusion The export market scenario of Pakistan reveals a grim picture. During the past years, exports

of Pakistan are not increasing as much as the imports and this has led to the building of a large trade deficit for Pakistan and is hurting the economy of Pakistan severely. Trade data of Pakistan reveals that no step has been taken to diversify the exports commodities and markets which can be held responsible for the increase in trade deficit. As such, exports are gradually declining and losing markets, whereas at the same time, exports of the neighboring countries of India, China, Bangladesh etc. are increasing and capturing the potential markets of Pakistan.


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The main objective of this research is to highlight the countries with which export potential for Pakistan exist and highlight the important factors affecting the trade environment of Pakistan by using a gravity model. The coefficients of all the variables used in the model are in line with the theory of the gravity model, with the exception of the border variable, which is attributed to low levels of trade with the close proximity neighbors. The export potentials of Pakistan are bright as Pakistan still has potentials for improving its trade with the highlighted countries. The outcome of this study implies that Pakistan can bolster its exports by directing foreign trade to countries having potentials of export and as a result can control or possibly reduce the trade deficit that is damaging its economy.

Table 1: Estimation Results

Dependent Variable: Export / Method: PMLE

Variable Coefficient Std. Error Constant -30.206* 1.797

GDPi 0.892* 0.020 GDPj 1.168* 0.086

Distance -1.172* 0.068 Common Border -0.855* 0.202

RTA 0.626** 0.253 Common Religion 0.672* 0.086

Common Language 1.070* 0.069 TRADE-GDPi 0.846* 0.051 TRADE-GDPj 0.662** 0.305

R-squared 0.709 0.707 Adj. R-squared

Notes: All variables except dummies are expressed in natural logarithms. *, **, ***, denotes significance at 1%, 5% and 10% respectively.

Table 2: Export Potentials of Pakistan

Country Potential Country Potential Iceland -0.97 New Zealand -0.20 Brunei Darussalam -0.97 Panama -0.19 Barbados -0.97 Mexico -0.17 Gabon -0.96 Russian Federation -0.15 Suriname -0.95 Ukraine -0.10 Belize -0.95 El Salvador -0.09 Papua New Guinea -0.94 Mauritania -0.08

Albania -0.94 Belgium -0.07 Armenia -0.93 Zambia -0.05 Slovak Republic -0.92 Nepal -0.04 Burkina Faso -0.92 Zimbabwe -0.03 Guyana -0.90 Honduras -0.03 Bolivia -0.88 Burundi -0.02 Trinidad and Tobago -0.88 Netherlands -0.02 Bhutan -0.88 Canada -0.02 Libya -0.87 Thailand 0.03 Malta -0.87 Greece 0.06 Seychelles -0.86 Senegal 0.06 Slovenia -0.86 Japan 0.08 Turkmenistan -0.85 Brazil 0.09


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Mali -0.85 Australia 0.10

Iran, Islamic Rep. -0.83 Hong Kong SAR, China 0.11 Chad -0.83 Colombia 0.12 Kazakhstan -0.82 Argentina 0.13 Latvia -0.79 Saudi Arabia 0.13 Czech Republic -0.79 France 0.14 Switzerland -0.79 Uganda 0.15 Austria -0.78 Malawi 0.16 Jamaica -0.75 Korea, Rep. 0.18 Croatia -0.74 Ethiopia 0.18 Azerbaijan -0.74 Nigeria 0.19 Singapore -0.74 Congo, Dem. Rep. 0.20 Ireland -0.72 Guatemala 0.21 Hungary -0.71 Chile 0.21 Costa Rica -0.70 Portugal 0.23 Tajikistan -0.68 Cote d'Ivoire 0.24 Maldives -0.67 Cameroon 0.25 Kyrgyz Republic -0.66 Ghana 0.25

Georgia -0.66 Mauritius 0.28 Bulgaria -0.66 Indonesia 0.29 Fiji -0.65 United Arab Emirates 0.29 Norway -0.63 Liberia 0.31 Qatar -0.62 Germany 0.32 Finland -0.62 Cambodia 0.34 Algeria -0.62 Italy 0.36 Estonia -0.59 Sudan 0.36 Tunisia -0.58 Spain 0.38

Kuwait -0.57 Turkey 0.39 Lebanon -0.57 Philippines 0.40 Romania -0.56 Egypt, Arab Rep. 0.41 Ecuador -0.55 Vietnam 0.42 Malaysia -0.53 Guinea-Bissau 0.44 Cyprus -0.48 Niger 0.51 Denmark -0.48 Gambia, The 0.52 China -0.47 United Kingdom 0.57 Bahrain -0.46 Guinea 0.58 Lithuania -0.46 Yemen, Rep. 0.58 Uruguay -0.43 Togo 0.58 Poland -0.43 Sierra Leone 0.63 India -0.42 South Africa 0.65 Morocco -0.42 Comoros 0.68 Sweden -0.40 Djibouti 0.70 Syrian Arab Republic -0.36 Benin 0.70 Venezuela, RB -0.36 Afghanistan 0.72 Jordan -0.30 Mozambique 0.75 Uzbekistan -0.30 Sri Lanka 0.82 Paraguay -0.27 Tanzania 0.82 Oman -0.27 United States 0.84 Iraq -0.26 Madagascar 0.86

Dominican Republic -0.21 Bangladesh 0.89 Peru -0.21 Kenya 0.90


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Acknowledgments This paper is supported by 2011 Founded Project of National Natural Science Foundation of

China(71171084), 2011 Research Fund for the Doctoral Program of Higher Education of China (20110172110010) and The Fundamental Research Funds for the Central Universities(2012, x2gsD2117850).

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The Regionalization of the World Economy, Frankel J., ed., University of Chicago Press, 1997, 33-57. 2. Tinbergen J, An analysis of world trade flows. The Twentieth Century Fund, 1962 New

York. 3. World Bank, Pakistan Development Policy Review – A New Dawn? 2002 Washington,

D.C. 4. Poyhonen P. A tentative model for the volume of trade between countries.

Weltwirtschaftliches Archiv 90, (1963) 93-100. 5. Linnemann H. Modeling international trade flows: An econometric approach. 1966

Amsterdam, North-Holland. 6. Leamer E.E. The commodity composition of international trade in manufactures: an

empirical analysis. (1974)Oxford Economic Papers 26. 7. Anderson J.E. A theoretical foundation for the gravity equation. American Economic

Review 69(1), (1979) 106-116. 8. Bergstrand J.H. The gravity equation in international trade: some microeconomic

foundations and empirical evidence. Review of Economics and Statistics 67, (1985)474-481. 9. Bergstrand J.H. The generalized gravity equation, monopolistic competition and the

factor – proportions theory in international trade. Review of Economics and Statistics 71, (1989) 143-153.

10. Bergstrand J.H. The Heckscher-Ohlin-Samuelson model, the Linder Hypothesis and the determinants of bilateral intra-industry trade. Economic Journal 100, (1990) 1216-1229.

11. Helpman E., Krugman P.R. Market structure and international trade. 1985 MIT Press, Cambridge, MA.

12. Deardorff A.V. (1998). Determinants of bilateral trade: does gravity work in a neoclassical world? in: The Regionalization of the World Economy, Frankel J., ed., University of Chicago Press.

13. Anderson J.E., van Wincoop E. Gravity with gravitas: a solution to the border puzzle. American Economic Review93, (2003) 171-92.

14. Rose A.K. Do we really know that the WTO increases trade? American Economic Association 94, (2004) 98-114.

15. Glick R., Rose. A.K. Does a currency union affect trade? The timeseries evidence. European Economic Review 46, (2002) 1125-1151.

16. Melitz J. North, South and distance in the gravity model. European Economic Review 51, (2007)971-991.

17. Grant J.S., Lambert D.M., Do regional trade agreements increase members’ agricultural trade? American Journal of Agricultural Economics 90, (2008) 765-782.

18. Henderson D.J., Millimet, D.L. Is gravity linear? Journal of Applied Economics 23, (2008) 137-172.

19. Mohmand Y.T., Wang A.H. The Gravity of Pakistan’s Export Performance in Stratified Sampling. Pakistan Journal of Statistics, 29, (2013)203-216.

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23. Leitao N.C. The Gravity Model and United States’ Trade, European Journal of Economics, 21, (2010)92-100.


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УДК 33

Экспортный потенциал Пакистана: данные гравитационной модели торговли

1 Ясир Тарик Мохманд

2 Аниль Салман 3 Кхурум С. Мухгаль 4 Мухаммад Имран 5 Недим Mакаревик

1 COMSATS Institute of Information Technology, Исламбад, Пакистан E-mail: [email protected] 2 COMSATS Institute of Information Technology, Исламбад, Пакистан E-mail: [email protected] 3 COMSATS Institute of Information Technology, Исламбад, Пакистан E-mail: [email protected] 4 IQRA University, Исламбад, Пакистан E-mail: [email protected] 5 Посольство Боснии и Герцеговины в Пакистане, Исламбад, Пакистан E-mail: [email protected]

Аннотация. В этом исследовании гравитационная модель используется для анализа

экспортной среды Пакистана. Как свидетельствуют данные о торговле, доля Пакистана в мировом экспорте является незначительной, а импорт доминирует в торговом балансе страны. Неспособность диверсификации рынка сбыта рассматривается в качестве основной причины дефицита торговли. Это исследование подчеркивает основные факторы, влияющие на экспортную среду Пакистана. Результаты гравитационного уравнения используются для расчета экспортного потенциала Пакистана со странами-партнерами. Полученные результаты свидетельствуют, что Пакистан по-прежнему имеет достаточный экспортный потенциал с большинством стран-партнеров и, как таковой, возможно, может уменьшить или контролировать торговый дефицит, ориентируясь на эти страны.

Ключевые слова: международная торговля, Пакистан, гравитационная модель.







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Liaison of Exchange Rate and Macroeconomic Variables:

A Case Study of Pakistan

1 Aneel Salman 2 Nadia Asghar

3 Tahir Ul Mulk Kahlon 4 Iftikhar ul Husnain

5 Nedim Makarevic

1 COMSATS Institute of Information Technology, Islamabad, Pakistan E-mail: [email protected] 2 National Defence University, Islamabad, Pakistan E-mail: [email protected] 3 National Defence University, Islamabad, Pakistan E-mail: [email protected] 4 COMSATS Institute of Information Technology, Islamabad, Pakistan E-mail: [email protected] 5 Embassy of Bosnia and Herzegovina in Pakistan, Pakistan E-mail: [email protected]

Abstract Exchange rate plays a significant role in the economic growth of a country because it has also

a close relationship to some major macroeconomic variables like Gross Domestic Product (GDP), interest rate, current account and inflation etc. All these variables are adversely affected by uncertainty or fluctuations in exchange rate. The objective of this paper was to find out the relationship between the exchange rate and other above mentioned macroeconomic variables. The paper not only described the relationship but also defined the nature of the relationship between the selected variables. The results showed that exchange rate has a long run relationship to GDP, inflation, interest rate and current account. Granger Causality test concluded that there was unidirectional causal relationship between exchange rate and GDP and the direction of causal relationship run from exchange rate to GDP. There were also some policy implications suggested for the stability in exchange rate and removing the adverse effects of uncertainty in Pakistan.

Keywords: exchange rate, volatility, gross domestic product (GDP). Introduction The price currency of one country in terms of another is called exchange rate. It has a great

significance in macroeconomics perspective. There are two types of exchange rate; one is fixed exchange rate and second is called floating exchange rate. Both have some strength and weaknesses. In the year 1944, under Bretton Wood System fixed exchange rate was introduced. But it was collapsed in year 1973. Bretton woods system had some elegances and imperfections as








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well. For example, under Bretton Woods System, due to fixed exchange rate a country had to surrender its monetary policy autonomy but at the same time there were no fear of speculative attacks. In contrast with floating exchange rate, speculative attacks can destabilize the economy at any time. After the World War-II in the year 1944, fixed exchange rate was introduced in Bretton woods system and it was also collapsed in the year 1973. After that European countries made alliance and introduced a single currency “Euro” in all European countries in the year 1999.

Exchange rate plays a vital role in economic development of any country. It has direct relationship to price, interest rate and other macroeconomic variables. Pakistan is facing an increasing exchange rate against dollar since 1990’s. Exchange rate also affects Gross Domestic Products (GDP), Interest Rate, Current Account, Inflation and purchasing power of the people. Volatility in exchange rate adversely affects all these variables in the economy of any country. Pakistan adopted flexible exchange rate in the year 1982 but it was not steady in the economy and also had speedy fluctuation. Uncertainty of exchange rate badly affects the international trade. Consequently, Pakistan has to face the external balance deficit because exports of the country have become cheap while imports are expensive. In this study, exchange rate is measured against United States Dollar (US $).

Literature Review Hooper et al (1978) state that real exchange rate affects to trade inflows and out flows.

They conclude that fluctuations in exchange rate may badly affect the economies while the change is seriously affecting the developing countries as compare to the industrialized and the developed economies.

Warner and Kreinin (1983) explore that exchange rate volatility has serious concern to the trade volume. They also analyze that exchange rate can affect the national income of the country by affecting other major economic variables.

Dhawan and Kumar (1991) analyze that due to volatility in exchange rate balance of payments adversely affect. Exports become cheap and imports turn into expensive and this situation cannot be in the favor of any developing country. They conclude that uncertainty in exchange rate has negative effects on balance of payments and trade.

Arize (1996) suggests that uncertainty in exchange rate reduces the volume of trade. Due to high volatility in exchange rate, growth of trade flows becomes slow down due to uncertainty about future profits. He concludes that fluctuations in exchange rate adversely affect the international trade. It is because of the fluctuations in exchange rate may disturb other major macroeconomic variables and the whole economy as well.

Zhou (1996) concludes that interest rate is one of the major factors which causes of volatility in exchange rate. He says that the investors prefer to invest in those countries in which there is high interest rate with almost zero or near to zero inflation. Due to illusion of money people are less conscious about the real interest rate. So high interest rate or rate of return can prevent the investment to fly from the country, it makes the currency and exchange rate stable in the economy.

Baak et al (2002) conclude that exchange rate has a significant role in economic development of the country and due to volatility in exchange rate imports and exports of the country adversely effects.

Aizenman (2007) analyzes that exchange rate can affect the rate of return on assets because due to depreciation in currency real rate of return may be reduced. Consequently, investors avoid investing in such countries in which exchange rate fluctuations are common.

Jincai (2007) analyses that macroeconomic variables are so closely correlated to each other that a swing in one variable disturb the all others. Sometimes, it becomes difficult to find out that which cause is disturbing to other. However, exchange rate is a sensitive and significant variable which can cause disequilibrium. A little flux in exchange rate makes the people conscious and their trust on economy tremble. He concludes that people are much sensitive towards their interest. And volatility in exchange rate can cut the interest rate which leads to decrease in investment.

Wang (2011) concludes that exchange market is the basic pillar of any economy. Historical evidences show that crash of stock markets lead to collapse of economies. So stability in stock market is major task for economists. In the response of slight volatility in stock market, major economic variables react. So it is necessary to overcome the major fluctuations in exchange rate.

Ardakani et al. (2012) analyze that after the break through of Breton Woods System, exchange rate became the major factor to disturb the economies. Moreover, oil price shocks (if the


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oil is imported) also create fluctuation s in exchange rate and whenever the exchange rate fluctuates it may directly affect the GDP of the country. In response of that a budget deficit occurs which leads to disequilibrium in the economy. Investment is discouraged due to the depreciation of the currency and capital also escapes due to the fear of loss. The paper suggests that decrease in monetary policies and increase in fiscal policies may overcome the fluctuations in exchange rate.

Aurangzeb and Haq (2012) describe that the stability in exchange rate plays an important role in the economy. They also conclude that the fluctuated exchange rate is a major cause of economic destabilization of a country.

Shahbaz et al (2012) describe that exchange rate volatility has negative effects on investment. They also observed the negative relationship between exchange rate and economic growth in the long run.

Objectives of the Paper

To find out the nature of relationship between exchange rate and other variables (GDP, Inflation, Interest Rate and Current Account)

To find out the effects of exchange rate volatility on major macroeconomic variables (GDP, Inflation, Interest Rate and Current Account).

To draw some suggestions/policy implications on the basis of findings. Hypothesis of the Study H0: Exchange rate has no relationship to GDP. H1: Exchange rate has a relationship to GDP. H0: Exchange rate has no relationship to Inflation. H2: Exchange rate has a relationship to inflation. H0: Exchange rate has no relationship to interest rate. H3: Exchange rate has a relationship to interest rate. H0: Exchange rate has no relationship to current account. H4: Exchange rate has a relationship to current account. Methodology Research Type This is exploratory research based on secondary data which is collected from several

government reports and economic surveys of Pakistan. The selected time period for the study is from the year 1982-2012.

Data Analysis Granger Causality Test is used to analyze the data and also find out the long run relationship

between the variables Johansen Co-integration test is applied. Each hypothesis is tested independently because Exchange Rate is taken as independent variable in each hypothesis. The data is time series so there is a chance of unit root in the data. Therefore, Augmented Dickey Fuller (ADF) test is applied before running the Johansen Co-integration test.

Variables for Hypothesis # 1 Independent Variable: Exchange Rate (X) Dependent Variable: Gross Domestic Product (Y)

Ln + (1)

= Constant

= Slope of Coefficient

Error Term Variables for Hypothesis # 2 Independent Variable: Exchange Rate (X) Dependent Variable: Inflation (Y)

Ln + (2)

= Constant


Error Term


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Variables for Hypothesis # 3 Independent Variable: Exchange Rate (X) Dependent Variable: Interest Rate (Y)

Ln + (3)

= Constant


Error Term Variables for Hypothesis # 4 Independent Variable: Exchange Rate (X) Dependent Variable: Current Account (Y)

Ln + (4)

= Constant


Error Term Test for Stationary As the data is time series and there is a chance of unit root in the data so Augmented Dickey

Fuller (ADF) test is used to check the stationary. Simple autoregressive model can be represented as:

= α + (1)

The hypothesis of : α = 1 which means that series have unit root. It is measured against

the alternative hypothesis of : α < 1 which means that series is stationary. When the lags are added to ADF to avoid the problem of autocorrelation then the equation can be written in the general form as following:

= α + + ………. + (1.1)

Or

+ + + (1.2) Co-integration Technique Before the estimation of regression it is necessary to find the long run relationship between

the variables. It is necessary to obtain significance results the variables must have long run relationship. In order to find the long run relationship between the variables Johansen Co-integration test is carried out.

Results and Discussion

Table 1: Results of Augmented Dickey Fuller Test for Equation # 1

Var Level Ist Derivative Result

LNY -4.598768 -8.851817* I (1)

LNX (GDP) -2.154285 -4.337323** I (1)

*show stationerity at 5 % level of significance and **show stationerity at 10 % level of significance

The results in Table 1 imply that the variables are non-stationery at level, thus unit root is

carried out. When the unit root is tested at first difference, the data became stationery. The results show that the problem of unit root has been removed and the variables are integrated of order 1, I(1).


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LNY -2.767581 -3.431923* I (1)

LNX (Inflation) -4.722696 -5.228106** I (1)


The results in Table-2 imply that the variables are found stationery at 1st difference while at

level they are non-stationery.



LNY -5.176262 -7.328931* I (1)

LNX (Interest Rate) -2.312765 -3.124768 I(1)


The results in Table-3 indicate that the variables are found stationery at 1st difference.


Var Level Ist Derivative Result LNY -2.785412 -4.012785* I (1) LNX (CA) -0.987341 -3.491275** I (1) *show stationerity at 5 % level of significance and **show stationerity at 10 % level of significance

The results in Table-4 show that at 1st difference, unit root has been removed and data is now stationery.

Results of Johansen co-integration Test Results for Equation # 1

Table 1(a): Results of Johansen Co-Integration Test: (Trace Statistics)

Trace test shows 2 co-integrating eqn(s) at the significance level of 0.05 *indicates the significance level at 5 % ** P-Values Michelis et al. (1999)

Hypothesized Trace Statistics Significance Level

No. of CE(s) Eigenvalue Statistic Critical Value Probability**

None * 0.127865 26.56206 18.39771 0.0000

At most 1 * 0.984321 16.26277 3.851577 0.0001


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Table 1(b): Results of Johansen Co-Integration Test : (Maximum Eigenvalue)

Hypothesized Maximum Eigenvalue Significance Level

No. of CE(s) Eigenvalue Statistic Critical Value Probability**

None* 0.127865 41.78553 17.14769 0.0002

At most 1 * 0.984321 16.26277 3.841466 0.0001

Trace test shows 2 co-integrating eqn(s) at the significance level of 0.05 *indicates the significance level at 5 % ** P-Values Michelis et al. (1999) Source: researchers’ own calculations

Results for Equation # 2

Table 2(a): Results of Co-Integration Test: (Trace Statistics)

Trace test shows 2 co-integrating eqn(s) at the significance level of 0.05

*indicates the significance level at 5 % ** P-Values Michelis et al. (1999)

Table 2(b): Results of Co-Integration Test: (Maximum Eigenvalue)

Trace test shows 2 co-integrating eqn(s) at the significance level of 0.05 *indicates the significance level at 5% ** P-Values Michelis et al. (1999) Source: researchers’ own calculations


Table 3(a): Results of Co-Integration Test: (Trace Statistics) Trace test shows 2 co-integrating eqn(s) at the significance level of 0.05

*indicates the significance level at 5% ** P-Values Michelis et al. (1999)


No. of CE(s) Eigenvalue Statistic Critical Value Probability **

None * 0.341289 34.87231 18.39771 0.0000

At most 1 * 0.754391 12.76543 3.841466 0.0001


No. of CE(s) Eigenvalue Statistic Critical Value

Probability **

None * 0.341289 31.87632 17.14769 0.0002

At most 1 * 0.754391 12.76543 3.84166 0.0001



None * 0.564321 39.65328 18.39771 0.0000

At most 1 * 0.885482 26.98123 3.841466 0.0001


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Trace test shows 2 co-integrating eqn(s) at the significance level of 0.05 *indicates the significance level at 5% ** P-Values Michelis et al. (1999) Source: researchers’ own calculations


Table 4(a): Results of Co-Integration Test: (Trace Statistics)



None * 0.932142 33.32189 18.39771 0.0000

At most 1 * 0.761209 19.98432 3.841466 0.0001

Trace test shows 2 co-integrating eqn(s) at the significance level of 0.05 *indicates the significance level at 5% ** P-Values Michelis et al. (1999)




None * 0.932142 28.93164 17.14769 0.0002

At most 1 * 0.761209 19.98432 3.841466 0.0001

Trace test shows 2 co-integrating eqn(s) at the significance level of 0.05 *indicates the significance level at 5 % ** P-Values Michelis et al. (1999) Source: researchers’ own calculations

The results of Johansen co-integration in Table 1(a), and Table 1(b) for Equation No. 1,

Table 2 (a) and 2 (b) for equation No. 2, Table 3 (a), and 3 (b) for Equation No. 3 and Table 4 (a) and 4 (b) for Equation No. 4 show the trace and maximum eigenvalues. All the results indicate that all the variables are co-integrated hence have long run relationship.

Results of Ganger Causality Test

Table 1: Results of Granger Causality Test for Equation # 1

Null Hypothesis: Number of Observation F-test Probability Remarks

LN(Exchange Rate) does not Granger Cause LN(GDP) 30 3.50812 0.14351

Reject Null Hypothesis

LN(GDP) does not Granger Cause LN(Exchange Rate) 30 1.98732 0.07212

Accept Null Hypothesis

Source: researchers’ own calculations The above table shows unidirectional causality. The result shows that exchange rate causes

the GDP but GDP does not cause the exchange rate



None * 0.564321 21.76424 17.14769 0.0002

At most 1 * 0.885482 26.98123 3.841466 0.0001


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Null Hypothesis: Number of Observation F-test Probability

Remarks

LN(Exchange Rate) does not Granger Cause LN(Inflation) 30 4.52781 0.11254


LN(Inflation) does not Granger Cause LN(Exchange Rate) 30 1.23587 0.17485


Source: researchers’ own calculations The results depict two way casual relationships between the variables.



Remarks

LN(Exchange Rate) does not Granger Cause LN (Interest Rate) 30 3.21457 0.66251


LN(Interest Rate) does not Granger Cause LN(Exchange Rate) 30 3.41785 0.52478


Source: researchers’ own calculations The above mentioned results depict the bidirectional relationship between the variables.

The table shows exchange rate does cause of interest rate and interest rate also have causal relationship to exchange rate.



Remarks

LN(Exchange Rate) does not cause LN(Current Account) 30 5.68732 0.71451


LN(Current Account) does not cause LN(Exchange Rate) 30 4.37808 0.02145

Accept Null Hypothesis

Source: researchers’ own calculations The results show that the direction of casualty between exchange rate and current account

runs from exchange rate to current account. Conclusion The study has been intended to find out the relationship between exchange rate and different

macroeconomic variables. It is concluded from the findings that exchange rate has a long run relationship to GDP, inflation, interest rate and CA. Granger Causality test concludes that there is unidirectional causal relationship between exchange rate and GDP and the direction of causal relationship runs from exchange rate to GDP. Exchange rate and CA also have unidirectional causality, and the direction of causality runs from exchange rate to CA. There is bidirectional causality found between exchange rate and inflation. Bidirectional causality also exists between exchange rate and interest rate which indicates that fluctuations in exchange rate can cause change in interest rate and vice versa.

Policy Context In Pakistan, State Bank (Central bank of Pakistan) announces monetary policy. Pakistan is

facing the problems of high inflation rate, unemployment and current account deficit etc for the last several years. State bank is reducing the interest rate in each monetary policy. When interest rate reduces money supply increases in the economy which cause inflation. High inflation reduces the purchasing power of the people. Moreover, currency depreciates and exchange rate increases


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(in direct term). Due to which exports become cheaper while imports turn out to be expensive which cause the deficit in balance of payments. Pakistan exports the raw material and imports the final goods. The prices of raw material are cheaper as compare to final goods. Therefore, cheap exports are not in the favour of economy. As the above mentioned results, Johnson Co-Integration indicates that there is a long run relationship between the variables, so fluctuations in one variable may affect the other variables. The theory of Exchange Rate and Long Run Purchasing Power Parity (PPP) also supports these results. The results of Granger Causality Test depict that there is a unidirectional causality between some variables (exchange rate and GDP, exchange rate and CA) while bidirectional causality also exists between some variables (exchange rate and inflation and exchange rate and interest rate). Therefore, it is suggested that State Bank of Pakistan should adopted contractionary monetary policy. So that money supply may be controlled to remove inflation. Due to this currency will be appreciated and deficit in balance of payment may also be reduced.

Suggestions/Policy Implications State Bank may adopt the contractionary monetary policy. There is a need to increase the interest rate so that money supply may be controlled and

currency may be appreciated. State Bank may increase the supply of foreign reserve in the country in order to stabilize

the exchange rate. Tax may be imposed on the imports of luxury items so that the deficit in balance of

payment may reduce. Direct Foreign Investment may be encouraged in the country to overcome the

unemployment. Government may decrease its non-developing expenditure, so that dependency upon

foreign aid may become finish. Government may take solid steps against money laundering. Subsidies may be given by the government on goods of basic necessities so that the

purchasing power of the people may rise. Exchange rate may be stable to discourage the speculative attacks on economy. Fast and continuous fluctuations in exchange rate cause the capital flight from the

country, so necessary actions may be taken from the State Bank to overcome the problem. References: 1. Aizenman, J. (2007). Large Hoarding of International Reserves and the Emerging

Global Economic Architecture. Working Paper. 13277, NBER. 2. Ardakani, Z., Goudarzi, M. & Khanarinejad, K. (2012). Investigation of the factors

affecting real exchange rate in Iran. Acta Universitatis Danubius, 8(4), 55-67. 3. Arize. (1996). Co-integration test of a long-run relation between the trade balance and

the terms of trade in sixteen countries. North American Journal of Economics and Finance, 5, 203-215.

4. Aurangzeb, & Anwar, U. (2012). Factors Affecting The Trade Balance In Pakistan. Economics and Finance Review, 1(11), 25 – 30.

5. Baak, S., Al-Mahmood, A., & Vixathep, S. (2002). Exchange Rate Volatility and Exports from East Asian Countires to Japan and U.S. (Manuscript). University of Japan.

6. Chunsheng, Z. (1996). Stock Market Fluctuations and the Term Structure. Federal Reserve Report for the Year1996.

7. Dickey, D. A. & Fuller, W. A. (1979). Distribution of the Estimator for Autoregressive Time Series with a Unit Root. Journal of the American Statistical Association, 74, 427–431.

8. Hooper, P. & Kohlhagen, S. W. (1978). The Effects of Exchange Rate Uncertainty on the Price and Volume of International Trade. Journal of International Economics, 8, 483-511.

9. Kumar, R. & Dhawan, R. (1991). Exchange Rate Volatility and Pakistan’s Export to the Developed World, 1974-1985. World Development. No. 19, 1225-1240.

10. Muhammad, S., Abdul, J., & Faridul, I. (2012). Real Exchange Rate Changes and the Trade Balance: The Evidence from Pakistan. The International Trade Journal, 26, 139–153.

11. Warner, D. F., & Kreinin, M. E. (1983). Determinants of International Flows. The Review of Economics and Statistics. No. 65, 96-104.


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12. Xiufang, W. (2011). The relationship between stock market volatility and macroeconomic volatility: Evidence from China. Journal of Chinese Economics and Finance, 2(4), 67-77.

13. Xu, J. (2007). Interest Rate Uncertainty and Stock Market Volatility. Dissertations and Theses Collection (Open Access). Paper No. 25. УДК 33

Связи обменного курса и макроэкономических переменных:

социологическое исследование в Пакистане

1 Аниль Салман 2 Надим Азгхар

3 Тахир ул Мулк Кахлон 4 Ифтикхар ул Хуснайн

5 Недим Макаревик

1 COMSATS Institute of Information Technology, Исламбад, Пакистан E-mail: [email protected] 2 National Defence University, Исламбад, Пакистан E-mail: [email protected] 3 National Defence University, Исламбад, Пакистан E-mail: [email protected] 4 COMSATS Institute of Information Technology, Исламбад, Пакистан E-mail: [email protected] 5 Посольство Боснии и Герцеговины в Пакистане, Пакистан E-mail: [email protected]

Аннотация. Обменный курс играет важную роль в экономическом росте страны,

поскольку он взаимосвязан с некоторыми основными макроэкономическими переменными, такими как валовой внутренний продукт (ВВП), процентная ставка, текущий счет и инфляция и т.д. Все эти переменные зависят от неопределенности или колебания обменного курса. Целью данной работы было выяснить взаимосвязь между обменным курсом и другими упомянутыми выше макроэкономическими переменными. В статье описывается не только взаимосвязь, но также определяется характер отношений между выбранными переменными.

Ключевые слова: обменный курс, волатильность, валовой внутренний продукт (ВВП).







231




Features and Tendencies of Human Resources Educational

System Development in Ecuador

1 Zurita Jose Estalin Vergara 2 Maria F. Mizintseva 3 Anna R. Sardaryan

1-3 Economics faculty’s department of management, Peoples' Friendship University of Russia, Moscow, Russian Federation 1 PhD student 2 Professor, Doctor of Economics 3 Professor, PhD

Abstract The article is dedicated to the determining of the role of education as the most important

element influencing on economic growth in developing countries. Nowadays, Ecuador is among those countries. This article examines the problems and tendencies of education developing, provides the literacy rates. The article also reviews the main directions of Government’s activities in terms of the human resources educational level in Ecuador.

Keywords: education, labour-market, literacy rate, employment level, unemployment level, Ecuador.

Introduction Education has a power of transformation in any country, for this reason both universities and

society go together hand in hand. The aim of education is to promote productive development of the country, offering highly productive professionals, creative and independent, and at the same time society with their knowledge help to make the country more competitive.

Materials and methods Without human talent, Ecuador won’t have advance, for this reason since 2007, the

Ecuadorian government has supported the development of human talent to boost the country's development through scholarships already exceeding 10,000 [1].

According to the official statistics of the National Institute of Statistics and Population Census (INEC), out of 15 million people living in Ecuador, only 7 million Ecuadorians belonging to the employment rate, have higher education - 16,30 %.[2] In 2015 in Ecuador, adults literacy level (15 years and older) for the females is 93,7 % and for the males is 95,4 % (Table 1) [3].



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Table 1: The literacy level of Ecuador 1990–2015

Year Adults (15 years and older) Youth (15 to 24 years) Literacy rate Illiterate

population Literacy rate Illiterate

population MF M F GPI MF

(000)

% F MF M F GPI MF (000)

% F

1990 88.3 90.5 86.2 0.95 732 59.3 96.2 96.6 95.8 0.99 79 54.3 2001 91.0 92.3 89.7 0.97 746 57.5 96.4 96.4 96.5 1.00 90 48.6 2007 84.2 87.3 81.7 0.94 1.476 59.2 95.4 95.2 95.8 1.00 122 47.1 2009 84.2 87.1 81.5 0.93 1.554 59.3 96.8 96.8 96.8 1.00 85 49.1 2010 91.9 93.3 90.5 0.97 818 59.1 98.7 98.5 98.9 1.00 36 42.1 2011 94.6 93.1 90.2 0.97 860 58.8 98.7 98.6 98.8 1.00 36 45.1 2015 94.6 95.4 93.7 0.98 601 58.3 99.2 99.1 99.4 1.00 22 39.4

Source: Composed by the authors with the material of UNESCO Institute for Statistics./ ADULT AND YOUTH LITERACY National, regional and global trends, 1985-2015.- Published in 2013. Page 46.

Despite the fact that there is clearly a tendency to increase the prestige of education,

especially higher education, the education system is not sufficiently developed in Ecuador, and the proportion of students in institutions of higher education is still very low in comparison with other countries of the world and the Latin America.

In the world ranking by the Index level of education, Ecuador takes the 116th place (Table 2). The rating is calculated by the United Nations Development Programme (UNDP), and measures states achievements by two evaluation criteria – literacy among adults and the index of total enrollment of people studying basic, secondary and higher education. The last time the rating was made in 2013, 2014 and in 2015 the rating didn't occur. So in 2013, among the countries that are in the first places with the highest level are: Australia, New Zealand, Norway, USA, Germany and others. Among the countries with the lowest ratings - Chad, Niger, Eritrea and Burkina Faso. In Latin America the countries with the highest level in education are: Argentina, Chile, Barbados and Uruguay.

Table 2: Ranking countries by Index level of education (fragment rating)

№ Country Index 1 Australia 0.927 2 New Zealand 0.917 3 Norway 0.910

4 Netherlands 0.894 5 USA 0.890 6 Ireland 0.887

7 Germany 0.884 8 Lithuania 0.877

9 Denmark 0.873 10 Czech Republic 0.866

35 Argentina 0.783 49 Chile 0.746 50 Cuba 0.743

51 Barbados 0.740 61 Uruguay 0.712 75 Venezuela 0.682 78 Bolivia 0.674


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85 Brazil 0.661 86 Panama 0.657 90 Costa Rica 0.654

99 Mexico 0.638

113 Colombia 0.602

116 Ecuador 0.594

120 Surinam 0.588

121 Paraguay 0.587

134 Honduras 0.505 139 Nicaragua 0.484

140 Guatemala 0.484 164 Haiti 0.374 183 Guinea 0.294 184 Chad 0.256

185 Burkina Faso 0.250

186 Eritrea 0.228

187 Niger 0.198 Source: Composed by the authors with the materials Humanitarian encyclopedia [Electronic resource] // Centre of Humanitarian Technologies. Human Development Index 10.10.2009 (center 02.14.2015). Access: http://gtmarket.ru/ratings/education-index/education-index-info

Discussion The development of the knowledge economy is one of the main activities of the government –

to increase the level of education of human resources in Ecuador. Since education is a key element influencing the economic growth of developing countries, Ecuador is among these countries now. Ecuadorian government takes serious steps in improving education level.

During the academic year 2013-2014, in Ecuador only 12367 people decided to continue their higher studies. In the post – grade 28.9 % was registered and 71 % continues in the higher level. The establishment where these students have registered was mostly in the particular universities – 66.9 % and 33.1 % preferred the government universities. [5] Thus, we can make a conclusion that at present time the working-age population of the country is aspiring to increase their educational level.

There are some higher education institutions as Universities and Institutes in the country: The National Polytechnic School (EPN), The Higher Polytechnic School (Litoral), University San-Francisco de Quito (USFO), Pontifical Catholic University of Ecuador (PUCE), The Central University of Ecuador (UCE). In Ecuador there are 54 Universities in total: 9 of them provide higher education opportunities, 42 Universities provide higher education and professional development courses, 3 of them provide courses for improvement for specialists.[6] Top international consulting companies, such as Deloitte, Pricewaterhousecoopers, Great Place to Work and etc., also operate in Ecuador, and it's possible to improve qualification there. Medicine, mechatronics Software engineer, biomedical engineer and civil-engineer are among the most essential specialties in recent years, and also these are the most demanded by the students.[7]

The Government of the country takes important steps to increase the educational level of the Ecuadorians. There are a lot of programs, which allow to send young students abroad for studying the most perspective branches for Ecuador such as petroleum production, information technology, agriculture, industry and tourism. The Government thus hopes to increase country development standards level where high-tech and industry will prevail rather than agriculture. In Ecuador, agriculture sector have dominated for many years as well as it does nowadays. To reach its aim, Government was developing Yachay University, that could give the country hopes for research and innovation culture.

At the current moment in 2015, the project "city of knowledge» «Yachay» has already started to work for its implementation on a highlighted area of 4.2 thousand. Ha and $ 600 million initial


234

investment. In the city of knowledge being built several universities, including technological, scientific research centers, technology park, an experimental agricultural zone and Biotechnology Center. Also there are plans to create a special zone of preferential tax treatment, which should contribute to the development of biotechnology, nanotechnology, chemistry, pharmaceuticals, light industry [8].

Results The next Government's program for the human resources development in Ecuador is

stimulating the specialists to get the second higher education or MBA and Ph.D. The investment for these program has increased from 1,1 % in 2007 to 2,10 % in 2015 of GDP and USD 170 million. Ecuador is one of the countries, that has the largest capital investments in higher education in comparison with the other Latin American countries. [9]

In 2014, 13462 people left the country to study abroad. USA, Peru, Argentina, Colombia and Spain are the most popular countries where the Ecuadorian students study. Data is given according to a National Institute of Statistics and Census (INEC) statistics.[10] (Figure 1.)

Figure 1. The countries that Ecuadorians chosen for study.

Source: Composed by the authors with the materials of National Institute of Statistics and Census (INEC). Annual international entry and exit Ecuadorians. 2014. p. 2 - 365

In nowadays Ecuador, human resources management optimization study, as the driving force

for the national economic development, has a very big importance, especially in a contradictory economic activity.

Along with educational level increase, country carries out the procedures to stabilize unemployment level and to increase employment. In Ecuador, where there are about 11.3 million working-age population and the economically active population is 7.6 million, in September 2015 the registered unemployment rate was 4.3 %, this means a 0.4 % increase of unemployed, taking in consideration the level of 3.9 % , that could be seen during the same month in 2014. Data is given according to a National Institute of Statistics and Census (INEC) statistics [11] (Figure 2).


235

Figure 2. The working age population and economically active population at the national,

urban and rural levels (thousands people)

Source: Composed by the authors with the material of National Institute of Statistics and Census (INEC). Labour indicators in September 2015. C. 7. Internet resources – http://www.ecuadorencifras.gob.ec/documentos/web-inec/EMPLEO/2015/Septiembre

According to the statistics by gender feature, in 2015 working-age population in Ecuador is

made up of 5.5 million men and 5.8 million women. And the economically active population is distributed as follows: 4.4 million men and 3.2 million women. [11] (Figure 3.)

Figure 3. The working age population and economically active population by gender

(thousands people) Source: Composed by the authors based on materials of National Institute of Statistics and Census (INEC). Labour indicators in September 2015. C. 7. Internet resources - http://www.ecuadorencifras.gob.ec/documentos/web-inec/EMPLEO/2015/Septiembre

The figures presented were tabulated according to the conceptual framework to classify

population with employment into three groups: adequate employment, inadequate employment and non-classified employment. The characteristic “adequate or inadequate” is determined by two requirements: a) fulfilling a complete work day and b) earning at least the basic salary.

According to this survey, inadequate employment has reached 49,2 % in September 2015 in comparison to 48,0 % of the same month last year and adequate employment that reached 46,0 % in contrast to 47,8 % in September 2014. As a consequence, the unemployment rate has increased to 4.3% in 2015, while in September 2014 it was 3.9 %.[12] (Figure 4.)

http://www.ecuadorencifras.gob.ec/documentos/web-inec/EMPLEO/2015/Septiembre

http://www.ecuadorencifras.gob.ec/documentos/web-inec/EMPLEO/2015/Septiembre-


236

Figure 4. Inadequate, adequate and unemployment rate June 2010 – Sep 2015

Source: Composed by the authors with the materials of National Institute of Statistics and Census (INEC). Labour indicators in September 2015. C. 8. Internet resources - http://www. ecuadorencifras. gob.ec/documentos/web-inec/EMPLEO/2015/Septiembre

According to National survey of Employment, Unemployment and Underemployment

(ENEMDU) figures, In September 2015 the city with the highest unemployment percentage is Quito with 5,21 %, followed by Guayaquil with 4,93 % and Ambato with 4,8 %. While Cuenca and Machala present the lowest unemployment rates with 2,6 % and 4,1 % respectively [13].

Table 2: Unemployment rate for cities Sep 2007 – Sep 2015 (%)

YEAR QUITO GUAYAQUIL CUENCA MACHALA AMBATO

September 2007

7,5% 7,3% 6,1% 5,9% 3,7%

September 2008

6,2% 8,6% 5,8% 8,0% 4,0%

September 2009

6,1% 13,0% 6,5% 9,6% 3,7%

September 2010

5,8% 10,0% 4,0% 6,1% 3,7%

September 2011

4,2% 5,8% 4,1% 4,9% 4,7%

September 2012

3,4% 6,5% 4,4% 4,8% 4,2%

September 2013

4,7% 5,5% 4,3% 3,2% 3,6%

September 2014

4,9% 3,9% 4,2% 2,7% 4,9%

September 2015

5,2% 4,9% 2,6% 4,1% 4,8%

Source: Composed by the authors with the materials National Institute of Statistics and Census (INEC). Labour indicators in September 2015. C. 9. Access: http://www.ecuadorencifras.gob.ec /documentos/web-inec/EMPLEO/2015/Septiembre

However it's necessary to emphasize that the unemployed graduates are not considered in the

official unemployment statistics. The first month after graduating, high percentage of graduates


237

can hardly find employment. Thus, nowadays the young specialists having high potential, qualification or come back after graduating the Institutes of higher education are the most relevant among all young people in Ecuador. Today not only state and large private enterprises but also small and medium business companies rely on those specialists.

Conclusion Ecuador as a country with the active young people, searching for the opportunity to get a

quality education not only inside the country, but also abroad, as well as government’s understanding to implement human resources development programs and its importance, as a necessary competitive advantage of the country in the world market, has a huge potential of achieving economic and social efficiency. However, today the government of Ecuador is facing a number of the serious and actual problems, demanding immediate decision: labor population structure harmonization, decreasing of real unemployment level, education availability expansion, improvement of secondary and higher education quality. However, developing programs for resolving these problems, it is necessary to consider the real social and economic situation in the country, forecasts of developing of this situation in the near-term and long term perspective, the potential of human resources in the country during the next and long-term period.

References: 1. Andes news. Access: http://www.andes.info.ec/en/news/studying-abroad-dream-many-

ecuadorians-can-now-accomplish.html 2. Gabriela Quiroz. Let know the 11 characteristics of the Ecuadoran worker.// EL

COMERCIO.- 5 de mayo, 2015 // Access : http://www.elcomercio.com/datos/caracteristicas-trabajador-ecuador-educacion.html

3. UNESCO Institute for Statistics / Adult and youth literacy National, regional and global trends, 1985-2015. Published in 2013. Page 46.

4. Humanitarian encyclopedia [Electronic resource] // Centre of Humanitarian Technologies. Human Development Index 10.10.2009 (revised 02.14.2015). Access: http://gtmarket.ru/ratings/education-index/education-index-info

5. The Publisher's EKOS. Higher education. Ecuador in the search of the new professional generation // Ekos Ecuador. № 241, May 2014. p 34-35

6. Website Ecuador Today, The News of Ecuador. Access: http://rusecuador.ru/ecuador-novedades/diferente/12783-novaya-klassifikacziya-universitetov-ekvadora.html

7. EL COMERCIO/ The students prefere medicine, engineering and mechatronics..// Access: http://www.elcomercio.com/tendencias/colegiales-prefieren-medicina-ingenierias-y.html

8. Harlamenco A. Visit of the President of Ecuador Rafael Correa in Russia .// ILA RAN Latin American Institute of the Russian Academy of Sciences.- October 2013. Access: http://www. ilaran.ru/?n=891

9. Website Ecuador Universitario. Access: http://ecuadoruniversitario.com /noticias_destacadas/al-2015-el-ecuador-registra-una-fuerte-inversion-en-educacion-superior/

10. National Institute of Statistics and Census (INEC). Annual international entry and exit Ecuadorians. 2014. p. 2 – 365

11. National Institute of Statistics and Census (INEC). Labour indicators in September 2015. C. 7. Access: http://www.ecuadorencifras.gob.ec/documentos/web-inec/EMPLEO/2015 /Septiembr

12. National Institute of Statistics and Census (INEC). Labour indicators in September 2015. C. 8. Access: http://www.ecuadorencifras.gob.ec/documentos/web-inec/EMPLEO/2015/Septiembre

13. National Institute of Statistics and Census (INEC). Labour indicators in September 2015. C. 9. Access: http://www.ecuadorencifras.gob.ec/documentos/web-inec/EMPLEO/2015/Septiembre

http://www.andes.info.ec/en/news/studying-abroad-dream-many-ecuadorians-can-now-accomplish.html

http://www.andes.info.ec/en/news/studying-abroad-dream-many-ecuadorians-can-now-accomplish.html

http://www.elcomercio.com/datos/caracteristicas-trabajador-ecuador-educacion.html

http://www.elcomercio.com/datos/caracteristicas-trabajador-ecuador-educacion.html

http://rusecuador.ru/ecuador-novedades/diferente/12783-novaya-klassifikacziya-universitetov-ekvadora.html

http://rusecuador.ru/ecuador-novedades/diferente/12783-novaya-klassifikacziya-universitetov-ekvadora.html

http://www.elcomercio.com/tendencias/colegiales-prefieren-medicina-ingenierias-y.html




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УДК 33

Особенности и тенденции развития кадровых ресурсов по вопросам развития системы образования в Эквадоре

1 Зурита Джосе Эсталин Вергара

2 Мария Мизинцева 3 Анна Сардярян

1-3 Российский университет дружбы народов, Российская Федерация 1 PhD student 2 Доктор экономических наук, профессор 3 PhD, профессор

Аннотация. Статья посвящена определении роли образования как важнейшего

элемента, влияющего на экономический рост в развивающихся странах. В настоящее время, Эквадор является одной из таких стран. В статье рассматриваются проблемы и тенденции развития образования, обеспечения уровня грамотности. В статье также рассматриваются основные направления деятельности правительства с точки зрения развития образовательного уровня кадровых ресурсов в Эквадоре.

Ключевые слова: образование, рынок труда, уровень грамотности, уровень занятости, уровень безработицы, Эквадор.

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