留學(xué)生MBAdissertation|發(fā)展中國(guó)家的服務(wù)業(yè)產(chǎn)出

Service Sector Output In Developing Countries 

 

本文著眼于全球79個(gè)發(fā)展中國(guó)家的人均國(guó)內(nèi)生產(chǎn)總值和服務(wù)業(yè)人均產(chǎn)出量。基于這兩個(gè)變量之間的關(guān)系，我們從我們的樣本數(shù)據(jù)中得出的異常值。這些孤立的國(guó)家有一個(gè)相對(duì)較低的服務(wù)部門(mén)的人均產(chǎn)出比人均國(guó)內(nèi)生產(chǎn)總值的百分比。

討論了樣品的特點(diǎn)，我們將做一個(gè)簡(jiǎn)短的描述約5的3個(gè)異常值在我們的樣品。我們將看看這些國(guó)家的一些關(guān)于他們的經(jīng)濟(jì)、服務(wù)業(yè)和勞動(dòng)力的一些一般性的信息。在第4章中的這些一般性信息后，我們將調(diào)查一些可能的因素，影響發(fā)展中國(guó)家的服務(wù)輸出。

這些因素發(fā)揮不同的重要性，在每個(gè)國(guó)家的服務(wù)部門(mén)的輸出，我們將在第5章闡述這些研究結(jié)果。我們發(fā)現(xiàn)有影響力的服務(wù)業(yè)產(chǎn)出的因素是：教育水平，就業(yè)服務(wù)部門(mén)占總就業(yè)的比例，人口生活在城市地區(qū)的百分比，國(guó)家貧困線，出口在服務(wù)業(yè)人均人均收入和人均收入。

 

This paper looks at the GDP per capita and service-sector output per capita for 79 developing countries across the globe. Based on the relationship of these two variables, we derive outliers from our sample data. These outlier countries have a relatively low percentage of service sector output per capita compare to their GDP per capita.

 

After discussing the sample characteristics we are going to make a short description about 3 of the 5 outliers in our sample. We will look at some general information of these countries about their economy, service sector and labor force. After these general information in chapter 4 we will investigate some of the possible factors which effect the service output in developing countries.

 

These factors play different importance in each country`s service sector output and we will elaborate these findings in chapter 5. The factors what we found influential for the service sector output are: education level, employment in service sector as percentage of total employment, percentage of population living in urban areas, national poverty lines, exports in the service sector per capita and finally income per capita.

 

Chapter 1 - Introduction

1.1 Motivation of the research

We tried to search for a reliable variable, which has important implications for developing countries. We decided to choose the service-sector output per capita as our variable of measure. The service sector includes all kind of sectors like banking sector, communication, consumer services and different government services. All of these need massive amount of capital to evolve and high level of coordination.

 

A service-dominated economy is a characteristic of a developed country (Encyclopedia Britannica) and these services are working on a much smaller scale if at all in undeveloped countries since they focused on other industries such as agriculture, mining or manufacturing.

 

The output of the service sector influences the economy in several ways such as employment, investment opportunities for foreign capital etc. Because of these reasons it is an interesting variable to compare it with the GDP per capita of a given country.

 

1.2 Research goals and relevance

Our goals is to analyze first the regression between GDP per capita and service sector output per capita for 79 developing countries and find outliers among these nations.

 

We will analyze 3 of the outlier countries in this sample and take a closer look at them. First we will search factors which influence the service sector output of a developing nation (chapter 4) and then investigate the importance of these factors for our outliers (Chapter 5). After investigating the relevant causes we will draw a conclusion.

 

One of the main points is to discuss the service sector output's impact and the relevant factors influencing it in developing countries around the world.

 

1.3 Problem definition

The problem definition of our research paper is as follows:

 

Which factors are the most significant in explaining the reasons why a country deviates significantly from its peers, when analyzing the relationship between national service sector output per capita and GDP per capita?

 

1.3.1 Research questions

1. Which countries deviate significantly from others when we compare their service sector output per capita to the GDP / capita they have.

 

2. What factors can influence the service sector output per capita of developing countries across the globe?

 

3. How do these factors change or what new factors come in the picture such that Angola, Azerbaijan and Thailand deviate significantly on the plot from other countries.

 

1.4 Demarcations

Our sample includes developing countries from across the globe from different continents. Our sample data is from the year 2007 for both GDP per capita and service sector output per capita, but some countries data had to be from earlier years since we could not find fresh enough input about them.

 

When collected the data for the factors affecting the service sector output per capita we used different sources, since not a single website could provide us all the necessary data needed. However, since all the website (we included them in the appendix) looked reliable, we consider their data reliable as well.#p#分頁(yè)標(biāo)題#e#

 

Since this is a rather big analyses of different economical factors we in used several website and reports for our research, which sources are all included in our paper.

 

1.5 Report Structure

Our report consist of 6 chapters. Our main goal is to analyse the outliers found by the regression analyses of GDP per capita and service sector output per capita. Each chapter logically follows the other. First we introduce our sample (information collection methods, characteristics, outliers).

 

After knowing the sample details we will take a closer look at 3 of the outlier countries from our regression analyses of GDP per capita and service sector output per capita.

 

In chapter 3, there is a short introduction these outliers. Chapter 4 and 5 will elaborate the factors which affect our second variable (service sector output per capita) and analyse their implications for outliers of the dataset.

 

In the end in chapter 6 we make a conclusion of our findings.

 

Chapter 2 - Sample

In this chapter we will introduce our sample in details. The method and sources of information gathering, the sample characteristics and the way of finding the outliers will be elaborated.

 

2.1. Chosen sample

For our sample we took a sample of 78 developing countries from around the world. Since we needed to have more than 30 developing countries we choose to sample countries from every continent possible, so they are included from Latin-America, Africa, Asia and Europe as well. Also we thought it would be interesting to compare countries from different parts of the world. We could find reliable data concerning the GDP / capita and Service sector output for these countries in the sample, from websites, which we included in the Appendix.

 

2.2 Data gathering and the reliability of data

Our data were collected mostly from the World Bank's website where we used the Data visualize tool to get all the numbers for our research variables. Our samples consist of data from 2007, unfortunately for some countries we only found older than 2007 data. Although since these data is not that much older we believe this can still give us reliable results.

 

Other websites we used to collect data for factors influencing the service sector output include:

 

UNDP.org, the CIA Fact Book and Intracen.org (International Trade Center).

 

2.3 Sample characteristics

Our sample consists of 78 developing countries from across the globe. Our table can be found in Appendix under Table 1. Our data is from 2007. The data includes the GDP / capita and the size of the service sector output per capita (value-added) and country names.#p#分頁(yè)標(biāo)題#e#

 

We used linear regression to compare and see the correlation between these two variables.

 

We investigated the characteristics of our sample and included it in the Appendix under Table 2 -sample characteristics. Based on these tables we can conclude the following sample characteristics:

 

For the service sector the mean is 481,3699, the median is: 295,3407. The standard deviation and variance are respectively 482,846 and 233140,827. The range of observation is about 2463,27 with 2490,62 as the maximum and 27,36 as the minimum.

 

For the GDP the mean is 931,5119 the median is 654,8650. The standard deviation and the variance respectively 771,88656 and 595808,863. The range of observation 3666,06 with 3760,00 as the maximum and 93,94 as the minimum.

 

2.4 Finding Outliers

The scatter plot of the data is included in our Appendix under point Table 2.6 - Scatterplot. The dependent variable is GDP per Capita the independent is Service sector output per capita. The outliers can be clearly seen on the plot.

 

We found our outlier countries using SPSS. First we made a simple linear regression analysis followed by creating a scatter plot (as mentioned above) with trend line.

 

Trend line is the line which minimizes the sum of squared errors of the observations deviating from the trend line itself. (McClave, Benson, and Sincich, 2001, p.462).

 

This data shows us that the best fit line has the equation (from Appendix - Table 2.3 Best fit line):

 

y = 1.517x + 0,058

In SPSS we found our outliers by running a linear regression and observe the outliers from the "Case wise Diagnostics" table (included in Appendix under Table 2.4 Case wise Diagnostics). Under the limitations of 2 standard deviations, the results from this analysis yielded five countries as outliers; Angola, Azerbaijan, China, North Korea and Thailand.

 

To further investigate which factors are the main determinants in the relationship between national services output per capita and GDP per capita, we concentrated in three of these countries, which are Angola, Azerbaijan and Thailand.

 

Chapter 3 - Outlier countries

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EXAMPLES OF OUR WORK

As we mentioned above we decided to analyze three outlier countries in this research. These are Angola, Azerbaijan and Thailand.

 

We will give further information about these three countries in this chapter and also about the correlation between the GDP / capita and service sector output.#p#分頁(yè)標(biāo)題#e#

 

3.1 Angola

3.1.1 Basic information:

Country name: Republic of Angola

 

Government type: Republic

 

Total land area: 1,246,700 km2

 

Population: 12,799,293 people

 

GDP: $61,400 million

 

GDP / capita: $1026

 

Service output/per capita : 213

 

3.1.2 Economy

The GDP of Angola is app. : $61,400 million, which means a per capita income of 5000 dollars. The GDP consist of the agriculture sector (85%), industry (15%) and service sector (15%). Oil export is the biggest contributors to the countries income (oil exports: 1,407 million bbl/day). Its biggest export partners are China, USA, South Africa and France.

 

3.1.3 Service sector

Angola is Africa's 3rd largest oil producer. Angola export more than 90% of its crude-oil to its main partners, China and US. Angola is a main player as the Major Oil Producer and exporter in Africa. Growth in the services sector in this period of 1980-2005 exceeded the gross domestic product (GDP) growth of 6.5 per cent per year. The strong growth in the services sector was contributed by the high growth in services sub-sectors such as finance, insurance, real estate and business services by 11.7 per cent per year; electricity, gas and water by 9.5 per cent; transport, storage and communication by 8.7 per cent; and wholesale and retail trade, hotels and restaurants by 7.3 per cent.

 

3.1.4 Labor force

There are 7.569 million labor forces in total and most of them are in agriculture sector (85%) and the rest are evenly in service and industry sectors (15% each).

 

3.1.5. Other information

As it said before that 85% of Angola labor force is in Agriculture. It shows that this country is still in the first stage of development, it is not a surprise that Angola has the most poverty among countries in Africa.

 

Diamond mining is also important industry in Angola. But it is still an issue about diamond smuggling, that hasn't been solved.

 

3.2 Azerbaijan

3.2.1 Basic Information

Country name: Azerbaijan

 

Government type: Republic

 

Total land area: 86,600 sq km

 

Population: 8,238,672 (July 2009 est.)

 

GDP: 31.24 billion dollar

 

GDP / capita: 9000 dollar

 

Service sector output / capita: 408.72 dollar

 

3.2.2 Economy

The GDP of Azerbaijan is app. 31.24 billion dollar, which means per capita income of 9500 dollars. The GDP consist of the agriculture sector (6%), industry (60.5%) and service sector (33.5%).#p#分頁(yè)標(biāo)題#e#

 

Oil exports has been growing through 2006 and 2008. This enhaned the economics growth of the country but it is worth mentioning that the non-energy sector, for example real-estate, also grew in high rates.

 

Oil exports are app.875,200 bbl/day, which makes Azerbaijan the 24th biggest oil exporting country. Thus the economic growth depends also on the world's oil prices. The major trading partners are Italy, Israel, Turkey, France, Russia, Iran, Georgia.

 

3.2.3 Service sector

The country's service sector developing, not surprisingly, with the biggest steps in the capital, Baku and where most of the oil fields can be found, around the peninsula of Absheron. Next to the oil-sector the mobile-telephone and communication sectors are worth mentioning. The latter services grew more than 35% in the year of 2004. The growth in tourism, mostly due to development and the oil sector also lifted the hotel and tourism sector which also contributed to the country's economic growth. In 2004 the service related industries made up approximately 30% of the GDP.

 

3.2.4 Labor force

By percentage 12% of the labor force is working in the service sector, Agriculture was the largest area of employment (34 %), followed by industry (16 %).In the industrial sector, the oil, chemical, and textile industries were major employers.

 

Minimum wage is currently 4,000 manats, a ridiculously low rate of about $1. The legal workweek is 40 hours. The minimum working age is 16 years.

 

3.2.5. Other information

Even though the economy grows really dynamically due to the point we mentioned before, the country has a long way to go concerning political and social reforms. Azerbaijan has to face difficulties in achieving these reforms and also in further economic development due to its diverse geography, bad infrastructure and its different climate zones across the country.

 

3.3 Thailand

3.3.1 Basic Information

Country Name: Thailand

 

Government type: Constitutional Monarchy

 

Total land area :

 

Population: 513,120 sq km

 

GDP: 173.15 billion

 

GDP per capita: 2710 dollars

 

Service output per capita: 1210,56

 

3.3.2 Economy:

The GDP of Thailand is 173.15 billion which it can approximately be interpreted as $2,710 dollars income per capita. The GDP of Thailand per sector is attributed to 11.4% in agriculture, 44.5% in industry and 44.1% in services. Its main industries are divided among automobile manufacturing (11%) financial services ( 9%), electrical equipments and computers (8%), tourism (6%) and others (textiles, tobacco, integrated circuits, tin production, jewelry, agricultural processing).#p#分頁(yè)標(biāo)題#e#

 

The country's exports account up to 175.3 billion (2008) and its imports are $157.3 billion (2008 est.)

 

Thailand's economy depends heavily on exports especially in the sector of automobile manufacturing industries and production of electronic goods. Foreign direct investment and consumer demands are crucial for its economy. Its main trade partners are the U.S, Japan, China, Singapore, Malaysia and Taiwan.

 

3.3.3 Service sector

The service sector in Thailand accounts for more than 40% of its GDP, meaning that its one of the biggest contributors to its economy and a key source for job creation. Employment in the service sector grew by 2.6 million between 2000 and 2005 contrast to 1.6 million in the industrial sector. However, Thailand's service sector has been characterized generally by a source of low wage and poor productivity performance. Since the financial crisis of 1997-1998 the labor productivity declined dramatically and remained so up until now. The service sector in Thailand consists of tourism, banking and finance. Tourism has major influence in its economy attributed to a 6% of GDP. In 2007 a total of 14 million people travelled to Thailand, and it shows and increasing pattern along the years. The banking and financial system of Thailand has been affected by the financial crisis from 97-98. However, it constitutes to 11% of GDP.

 

3.3.4 Labor force:

Thailand's labor force constitutes of 37.75 million people in 2008. This is attributed to 42.6% to agriculture. 20.2% industry and 37.1% services.

 

Around 12 million people are employed in the service sector. The minimum wage is around 203 baht per day (US$6.1) in the capital Bangkok and estimated to be less in the provinces around. The legal working age in Thailand is 13 and they are allowed to do light work. It is estimated that around 600.000 children between 13 and 15 are actively working.

 

Chapter 4 - Possible factors affecting the service output

4.1 Education Level

We consider the education level, literacy over total population, as relevant factor due to the fact that most of the service sector required their labors to be minimally possess literacy skill as a basic education. Therefore, we assume that in countries where service sector as tertiary economic sector contribute large percentage on their GDP per capita would employ most of the workers which have a literacy skills. As an impact of it, the demand of the workers with literacy skills will increase which motivate the labor producer to provide more of the labors with literacy skills.

 

Regarding our assumption above which mentioned the relation of service sectors percentage of GDP with literacy level over total population, we also test this relation using the statistical data (Appendix - Table 4 - Education Level):#p#分頁(yè)標(biāo)題#e#

 

Y = 8.856X + 2.292

 

R2 = 0,172

 

The formula above shows us that the relationship is positive and approximately 17, 2 % of the total sample variation is explained by this best-fit line. Therefore the two variables have strong relationship and our predicted is presumably predicted.

 

4.2 Employment in Service sector as percentage of Total Employment

We consider employment services percentage on total employment as one of the relevant factor due to the fact that employment in services are the main people who mainly contributed to the services sector percentage of GDP per capita with their economic activity. They are the chain drive or prime mover on the development in the services sector. Therefore we assume that with an increase in number of employment in services sector will increase the services sector percentage of GDP per capita.

 

We choose to compare the employment in services percentage on total employment with the services sector percentage of GDP per capita using the regression analysis which results (Appendix - Table 5 - Employment in Service Sector percentage of total employment):

 

Y = 7.439X + 3.509

 

R2 = 0,087

 

Related to the result above, we concluded that the relationship is positive with the correlation coefficients approaching 8, 7%. Therefore there is quite strong relationship between the two variables.

 

4.3 Percentage of population living in Urban Area

Services sector in most of the countries were developed in its urban areas where commonly located in their big cities. Therefore most of the workers on the service sector live in or near urban areas. As number of the population live in urban areas increased then it supposed to have a positive relation with the number of workers in services sector. As a result, there is possibility that this positive relation will also positively contribute to the value of the total service sectors income. This statement will be our considerate prediction towards variables which affect the number of service sectors total value.

 

We test this statement by using regression analysis comparing variables between percentage living in urban areas and total value of services sector. The analysis results (Appendix - Table 6 - Percentage of population living in Urban Area):

 

Y = 13,009x + 3,107

 

R2 = 0,203

 

There is positive relation between two variables and having 20, 3% correlation coefficient which comes to the conclusion that our prediction was quite proven.

 

4.4 National Poverty Line

The national poverty line is used to measure the population living under a certain minimum standard of living. This national poverty line may vary between nations, since different nations has different set of standards for a minimum life-style. This why the National poverty line should not be used to compare countries with very different economic background, however this case, since all the countries are developing (lower-middle and lower-income countries), this measure can be applied.#p#分頁(yè)標(biāo)題#e#

 

We expect countries with smaller percentage of the population living under the national poverty line to have better performing service sector. We are going to check this statement in our analysis. Results (Appendix - Table 3 - National Poverty Line):

 

Y = -8,033x + 2,892

 

R2 = 0,099

 

4.5 Export value in the service sector per capita

The services sector counts for a big percent of a developed country's economy and it os also important for developing countries. The world trade in service sectors had grown on an average of 5% percent in the past decade. Since obviously a higher export in services increases the size of the total service sector, it is important to check the relationship between the exports and the size of the service sector.

 

Not all services are equally important when talking about exports but there are a few areas which play a significant role in exports:

 

- Construction, design, and engineering

 

- Banking and financial services

 

- Insurance services

 

- Legal and accounting services

 

- Computer and data services

 

- Teaching services

 

We will analyze if exports indeed have a high impact on the size of the service sector and in the next chapter we will elaborate how this affects the outlier countries. Results: (Appendix - Table 7 - Export value in the service sector per capita)

 

Y = 1268,719x + 109,263

 

R2 = 0,646

 

4.6 Income per capita

Income per capita is a widely used measure to compare different countries wealth to each other. It is calculated by the national income of the country divded by the number of its population. Although income per capita can be misleading sometime since a relatively small amount but really wealthy class can raise the per capita income above the actual level of the rest of the population.

 

Since higher income per capita generally implies wealthier citizen who can afford consuming more products and services, that is why income per capita should have a strong affect on the service sector output. We will check this assumption using SPSS and consider its implications on the outliers in the next chapter. Results (Appendix - Table 8 - Income per capita):

 

Y = 0229x +0, 018

 

R2 = 0,702

 

Our data indeed shows, that income per capita explains a big part of the sample.

 

Chapter 5 - Analysis and discussion

5.1 Factors that influence the service sector output in Angola#p#分頁(yè)標(biāo)題#e#

Angola is one of the outlier we have chosen to be discussed further in the paper. Angola has a negative outcome in the regression. It is due to the significant low service sector output compared to high GDP, approximately only 15% contributed from the service sector to its GDP. There are some facts behind the high GDP although Angola is has still the biggest poverty among other African countries.

 

First of all Angola is still in the first stage of development which is agriculture sector. One of the important fact is that Angola is the biggest oil producer in Africa after Nigeria. Almost more than 50% of the countries revenue comes from oil production. This is strongly supported by the big investment given from the foreign major oil companies and also the government itself.  

 

The biggest non-oil productions are in agriculture sector due to the fact of Angola's fertile soil. Thus it is obvious that service sector is not yet Angola's main consideration. Although the services sectors such as tourism, real estate, financial services, retailing and commerce, have been increasingly dynamic. And so far the services sector accounted for 15% of GDP in 2007.

 

Furthermore we will elaborate more about factors that significantly influence the service sector output per capita compare to GDP per capita. However several factors are not relevant to Angola in regard to the current condition and facts about the country. Factors that are relevant in this context are education level ; employment in services percentage on total employment ; percentage people living in urban area ; national poverty.

 

Firstly education level is still low, yet there are other more crucial issues in the country that need to be solved before they think about their people education.

 

It is shown by the fact of 2.8 % of its GDP government spending on education. Also by its literacy over total population of 67.4%

 

Secondly Agriculture accounts for 50% of total employment in Angola. As it mentioned before due to its fertile soil and favorable climatic conditions, agriculture sector has the most productivity. Investment in farming is also increasing, shown by the growing numbers of farmers and fruit growing. In this sector coffee productions are one of the most profitable. In addition it is still expected to be increasing in the next 5 years.

 

Thirdly a percentage person living in urban area also influences the outcome in our regression. 50% of total population was living in urban areas in 2007. Most of the increased of income per capita came from the metropolitan areas, and most of them working in services sectors. While the rest of the population especially in rural area remains poor and partially unemployed. The high level of discrepancy is still the main issue here as in most of other developing countries. Lack of infrastructure mainly in rural area did not help the improvement of life in general for people consequently improvement in business climate as well. Without sufficient infrastructure it is hardly possible to distribute services or even to create new business. Thus other than people living in urban area, life remains difficult for the majority of Angolans.#p#分頁(yè)標(biāo)題#e#

 

Next factor that is relevant to the reason of why Angola is one of the outlier in our regression outcome is the high national poverty. Angola has the second highest poverty levels in Africa. In regard to this fact it is obvious that the main concern of the government is to solve the poverty problems.

 

5.2 Factors that influence the service sector output in Azerbaijan

Azerbaijan is located in Eurasia and its instituional form is republic. Azerbaijan's service sector output is significantly low among other countries if we look at the GDP/capita measure. It is due to the fact that the percentage of the service sector output to the GDP is only 20,96%, as a support to its industry sector which is counted for about 60,5%. Compared to Angola, Azerbaijan relies more on its service sector, although if we look at other countries it is far below from the average.

 

Azerbaijan shows a much higher GDP/capita than other countries. It is the result of the growth of oil and natural gas industry, as we know that Azerbaijan is rich in oil and natural gas. The country is also rich in other natural resources like silver, iron and magnesium. In 1994 September a contract was signed by SOCAR (State Oil Company of Azerbaijan Republic) and some major oil companies from around the world. These oil companies can tap on the deepwater oilfields which so far were unexplored. Due this unexplored fields, Azerbaijan is considered a really important and prosperous region for the oil indsutry right now. This could attract foreign direct investement which can be really helpful for economic growth. In other word, Azerbaijan concerns the development activity in its oil industry, not in its service sector. So the significance of the service sector output is lower in Azerbaijan.

 

In term of export in service sector, the service sector does not contribute highly to the total export, although it still exists. The oil and gas production becomes the major export product of the country. Based on our data what we gathered, the percentage of export in the service sector is quiet high, 72% which means another 28% goes to the internal consumption. Still, the whole percentage that the service sector contributes to its general export is not significant. This also appears in the Angola case. In our opinion this is one of the factors which contributes to the fact that these countries became outliers.

 

In addition, educational level also becomes in our consideration to determine the significance of the service sector output compared to the GDP. Since this country has relatively good regulations in education, high proportion of Azerbaijan's population has a high educational level. It is also due to the dependency on explorations in both the industry and service sectors, which require professionals and experts to support the,. Due to the significant role of the oil industry in Azerbaijan's economy, a relatively high percentage of Azerbaijanis have obtained some form of higher education, most notably in scientific and technical subjects. These findings lead to the conclusion that since the educational level of Azerbaijan is relatively high so the level of output in the service should be high as well.#p#分頁(yè)標(biāo)題#e#

 

In terms of the percentage of employment in the service sector which is 12%, compared to the agriculture 34% and industry 16%, gives the impression that Azerbaijan's levels of employment are not significantly different between the industry and service sector. But, in the agriculture sector, it is significant. This also adds to, them being outliers.

 

According to the data found, the national poverty line in Azerbaijan is 55%, which is half of its total population. Since our assumption states that if the national poverty line is quiet high, it leads to the consumption in service sector by the high level of income population being low. Therefore, this also leads Azerbaijan to become on of the outliers. This can also be seen in Angola's case.

 

Percentage living in urban area is one of the other factors what we take into consideration in the analysis of this outliers. Based on the data gathered (2000), 57% of its population is living in the urban area, compare to the world's percentage which is only 47%, we can thus conclude, that Azerbaijan has quiet high percentage of its population living in the urban area. This can also be an indicator that the service sector is developed highly in the urban areas.

 

5.3 Factors that influence the service sector output in Thailand

In a similar way to the two previous countries analyzed, Angola and Azerbaijan, Thailand was an outlier in our initial analysis where we found a positive and strong relationship between national services output per capita and GDP per capita. The case of Thailand is similar to its outlier peers, where the independent variable GDP per capita fails to explain the lack of variability b of national services output per capita.

 

From the descriptive statistics provided in the Appendix Table 1 - Countries, GDP/capita and Service Sector Output/capita, we can appreciate that from all the countries analyzed in our sample, Thailand has the highest level of GDP per Capita, meaning also that it is the most wealthy country when analyzed using this perspective. The level of output in the service sector per inhabitant (Service Sector Output/capita) is also among the highest in the sample. This suggests that Thailand is among the most developed economies in our sample and that its tertiary sector already constitutes a great percentage of the general economy as explained earlier were we mentioned that the tertiary sector constitutes 40% of Thailand's GDP.

 

The fact that Thailand appeared as an outlier in our sample, however, suggests that for the level of GDP per capita that the country presents, it would be expected the level of services output per capita to be much higher. Two possible explanatory reasons are that either the current level of services output is provided by a small percentage of the population, the primary and manufacturing sectors still play a much more important role in the economy, or both.#p#分頁(yè)標(biāo)題#e#

 

The data suggests both, as the primary and manufacturing sectors constitute up to 65 percent of Thailand's GDP

 

(The Economist, 2007), suggesting also that there are more inhabitants in percentage terms dedicated to either the primary and manufacturing sectors than to the tertiary sector.

 

Other factors that we consider possible explanatory variables which place Thailand as an outlier are the level of urban population and the constituents of total exports.

 

With a level of urban population at only 32.5% (The Economist, 2007), if analyzed in the context of our findings that suggest a strong relationship in urban population as a percentage of total population and services output per capita. These results suggest that in percentage of the population, higher number of inhabitants leave outside urban areas, therefore, more dedicated to economic activities others than the service sector. (Hence, this does not mean that some forms of services cannot be provided in rural areas.)

 

From our analysis, we found a strong relation between the percentage of exports in the service sector and the services sector output per capita. In the case of Thailand, however, as the main export products are from the manufacturing sector (Machinery and mech. Appliances 13.5%; Integrated circuits 13.2%; computer parts 9.35%; and electrical appliances 8.83%; The Economist, 2007); we consider that this characteristic, i.e. Thailand's main exports being from the manufacturing sector, significantly explains why Thailand resembled differently.

 

With relation to the level of education and poverty, we consider that for Thailand these two factors could contribute for the country to achieve a higher level of services output, as in both of these factors the country scores high. (Level of literacy, is above 96 percent) (The Economist, 2007).

 

To conclude our analysis of Thailand, we can stress out that the main reason why the country appeared as an outlier in our initial analysis is that it is expected from a country with such a high level of GDP per capita relative to its peer developing countries to have a higher level services output per capita. Thailand does not reflect this basically because the country also has a very well developed manufacturing sector which contributes to over half of its exports.

 

6 Conclusions

In this research we have analyzed the relation between the output per capita in the service sector and GDP per capita in developing countries. In this context, we have also analyzed possible factors that potentially can help explain this relationship and use this framework in explaining why some countries are outliers when service sector output per capita is regressed against GDP per capita.

 

In order to provide an answer to our research question,#p#分頁(yè)標(biāo)題#e#

 

Which factors are the most significant in explaining the reasons why a country deviates significantly from its peers, when analyzing the relationship between national service sector output per capita and GDP per capita?

 

we first analyzed using a regression analysis the relationship between these two variables.

 

We found, in a sample of 78 developing countries, a positive strong relationship between national service sector output per capita and GDP per capita, where over 90 percent of national service sector output per capita is explained by GDP per capita.

 

Subsequently, we tested for possible outliers, identifying five countries for which the level of national service sector output per capita felt short of expectations, given their high relative level of GPD per capita. These countries were Angola, Azerbaijan, Thailand, China and North Korea.

 

From these outliers, we further investigated the possible reasons why their level of service output was below expectations given their high level of GDP per capita, for Angola, Azerbaijan and Thailand.

 

Before analyzing our outliers individually, we analyzed the extent to which education level, employment in the service sector, population in urban areas, national poverty line, export value in serviced sector and income per capita have explanatory power and relate to national service sector output per capita.

 

We found that all these variables have a positive relation with national service sector output per capita, except for national poverty line, which has a negative relation.

 

We then used the results from this analysis to analyze our outlier countries individually.

 

We found that the most import factors why a country will deviate from its peers in the relationship between national service sector output per capita and GDP per capita; i.e. the country's national service sector output per capita will fall short of the expected relative to its GDP per capita, are a bigger primary and manufacturing sector relative to the service sector, and an export sector also mainly constituted by the primary and manufacturing sectors.

 

In the case of Angola and Azerbaijan, we found that these countries main economic driver is the extraction and production of oil. Also the main export product is, not surprisingly, also oil-related. This explains that GDP per capita¸ which is high for these countries relative to its other developing countries peers, is driven strongly by income from the oil industry, and that there is sufficient capacity to develop further the service sector.

 

In the case of Thailand, it's not the primary sector so strongly that constitutes the major contributor to GDP, but the manufacturing sector. Over half of Thailand's exports are manufactured products such as machinery and electronic parts and appliances.#p#分頁(yè)標(biāo)題#e#

 

Strangely enough, although we found originally that high levels of educational level are correlated with high levels of output in the service sector, these three countries also have relative high levels of literacy. The education programs, however, are to a high extent focused towards supporting the oil industry for Angola and Azerbaijan; and manufacturing for Thailand.

 

In a similar way population in urban area, national poverty line and income per capita, are not variables with a very high explanatory power of the low level of service output in these three countries. Population in urban areas is rather low, poverty is relatively low and income per capita is relatively high.

 

We suggest that in regards to Angola, Azerbaijan and Thailand; these three countries should develop programs that would help them develop their tertiary sector further, as their economies are underweighted in the tertiary sector, a potential source for further economic development and a source of income.