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Risk Analysis of Thailand Essay

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Updated: May 31st, 2019

Background information on Thailand

Thailand is located on the South East Asia. The country boarders other countries such as Burma, Laos, Cambodia, Gulf of Thailand, Malaysia, Andaman Sea, Vietnam, India, and Indonesia. It ranks 51 in the world in terms of size with an area of 513,000 square kilometers.

The population of the country is approximately 64 million. Over seventy five percent of the population comprises of Thai. The other twenty five percent is made up of Thai Chinese, Malay, Mons, and other tribes. The key religion in the country is Buddhism. The economy relies on export business as a key component of the gross domestic product.

Exports accounts for more than sixty seven percent of the GDP. Further, industries and services are the key sectors in the country. The GDP per capita of the country is quite low. However, despite the low GDP per capita, the proportion of the population that lives below the poverty line is quite low, that is, approximately 13%. This can be attributed to the low unemployment rate. The economy is considered to be vibrant. Besides, the key development and social variables show that the economy is successful.

Further, the country has an impressive low level of unemployment rate. Currently, it ranks fourth after Cambodia, Monaco, and Qatar. Further, the government has maintained low levels of interest rate and inflation rate. The current inflation is at 2.70% while the current interest rate is 2.5%. In terms of politics, the country operates under the constitutional monarchy (Credit Suisse 1). The current economic statistics of Thailand show that it is a suitable country for investment.

Aim of the paper

The paper seeks to evaluate the economic health of Thailand. Several indicators of economic health will be used to evaluate the economy of Thailand. The main indicator that will be discussed is the gross domestic product (GDP). Further, the paper will make judgment on the macroeconomic performance of the country. This will focus on discussing the economic and investment potentials, external and domestic issues, and the economic strengths and weaknesses of the country.

Theoretical base and literature review

Various studies have been carried out to analyze the economic health and risk analysis of Thailand. These studies have been carried out by various scholars, groups and institutions. An example of such a research was carried out by the Credit Suisse. The author used a three factor model to forecast the GDP of the country. In the paper, the author separated the GDP into three components, these are, industrial, services, and agriculture.

The study showed that when the three components are used as input variables in the regression analysis, it generated a model with a high value of R-square. The analysis also showed that most of the variables used in the analysis were significant (Credit Suisse 2). In their study, they established that the real GDP of the country declined by 1.5% in the first quarter of the year 2012.

The study also showed that some economic variables such as real farm incomes, industrial production and volume of container shipping should be monitored closely. In the study, the author also indicated that the current account balance declined in 2012 as a result of the decline in export and an increase in consumption demand for imported products. Finally, the research also indicates that the investment is the main driver of GDP growth because investment will result in an increase in exports (Credit Suisse 3).

As noted above, Thailand is one of the largest economies in South East Asia. The economy of the country grew significantly before the 1997 financial crisis that hit the Asian countries. The country has a labor force of approximately 39 million people. The labor force is made up of well-educated and affordable workforce. Besides, the devaluation of the currency has made the exports of the economy cheap in the international market.

Further, the international market has a favorable perception on the products of the country. This has contributed significantly to the growth of export business. In the past ten years, the country has reported improved performance in key economic variables such as GDP, investment, savings, unemployment rate, and government spending. This shows that the economy of the country has improved significantly after the bailout by the International Monetary Fund (Credit Suisse 6).

From a political point of view, the political environment of Thailand has been stable in the past ten years. The country has not experienced any coup within the ten years. The political system of the system has improved significantly and now it is viewed as one of the most transparent economies in the world. This improved transparency was instigated by the Asian Financial Crisis. The government had to improve its transparency to enable it attract investors.

However, despite passing laws to eliminate corruption, bribery is eminent in the country. In general, the political environment of the country is quite conducive. From the social point of view, there is a wide gap between the rich and the poor in Thailand. The category of the rich tends to live a luxurious lifestyle and they tend to be responsive to forces of marketing and advertising than the category of the poor. This large disparity in income also impacts on consumption (Credit Suisse 4).

Methodology and Data

Scope of the methodology

The methodology is a systematic and a comprehensive method that entails the collection, grouping, and analyzing data (Mugenda and Mugenda 23). As a matter of fact, the result of this process combines outcome and the purpose of the research. As stated by Kothari (2004), a blueprint is transformed into a research design through a series of operational studies aimed at drawing a factual outcome comprising of evidence backed claims (Kothari 7).

According to Kothari (2004), research design resonates on the facets of research project blueprint that comprise of study of operations to create an efficient outcome that yield the desired information at minimal cost (Kothari 10). In order to achieve the desired outcome, a quality research should assimilate systematic investigation characterized by the phenomenon approach of a scenario through statistical and mathematical computations (Kothari 10).

Quantitative research in Secondary data

Reflectively, non-experimental quantitative research design determines existing or perceived relationship between dependent and independent variables of any given study population (Mugenda and Mugenda 23). In collective quantitative data, past survey instruments will be employed across the study.

The study opts for quantitative data collection method since it is economical on time, finance, and energy unlike qualitative method which may not be economical especially when the sample size is put into the picture. However, this method limits natural expression of ideas and attitude towards the study and objective especially in responding to questions asked or reflecting on a secondary thought.

The researcher will carry out comprehensive internet research as part of secondary research on the economy of Thailand. The data will be analyzed at all levels to present a comprehensive result. Reflectively, the context of secondary data will depend on online research on present, past, and projected future data on the state of the economy of Thailand.

A comprehensive approach to model design and interpretation will provide a general holistic framework to guide the study. To analyze the data, comparisons with previous studies will be conducted. In the analysis phase, the paper examines the health of economy of Thailand as discussed in the literature review section.

Data collection

The secondary data will comprise of GDP, population, unemployment rate, savings, investment, import, exports, interest rate, government spending, government debt. The expenditure model will be used to estimate the impact of the variables on the GDP of Thailand.

Specifically, the data will be retrieved from the International Monetary Fund website (International Monetary Fund 1). The source is reliable because it is updated by the renowned institution on a periodic basis. The data will cover a period of 10 years, that is, between 2003 and 2012.

Variable models

Regression analysis is a statistical tool that develops and approximates linear relationships among various variables. When coming up with the model, it is necessary to separate between dependent and independent variables. This section will discuss the various variables that will be used in the formulation of the regression model.

GDP

The dependent variable in this analysis is the GDP. The gross domestic product is a useful indicator of the status of the economy of a country. Measuring GDP is a challenge for economists in all the countries because it is often difficult to quantify all the economic activities in the region.

When analyzing the GDP of a country, it is important to distinguish between nominal and real GDP. Real GDP eliminates the effect of inflation. This section will analyze the GDP of the country for a period of ten years. The table presented below shows the values of GDP in US dollars for the period.

Billions 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
GDP 142.64 161.34 176.352 207.089 246.977 272.578 263.711 318.908 345.672 376.989

Source of data – International Monetary Fund 1

The table presented above indicates that there has been a continuous increase in the value of the gross domestic product over the 10 year period. However, in 2009, the country experienced a decline in the value of GDP due to the global recession. The graph presented below shows the trend of GDP over the years.

Unemployment

Unemployment rate, an explanatory variable, measures the number of people in a country who are not engaged in an economically productive activity even though they are willing and able to work. The table presented below shows the rate of unemployment in Thailand.

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Unemployment rate 2.17 2.08 1.85 1.52 1.38 1.39 1.5 1.04 0.68 0.68

Source of data – International Monetary Fund 1

There has been a continuous decline in the unemployment rate in the country. However, in 2009, there was a slight increase due to the global economic crisis. The rate is a good indication because it shows that the government is taking necessary measures to utilize the human capital available in the country. The unemployment rate is expected to have a negative relationship with the GDP. The graph presented below shows the trend of unemployment rate.

Population

In the regression analysis, population will be an explanatory variable. The table presented below shows the population of the country over the 10 year period.

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Population (millions) 63.08 61.974 62.418 62.829 63.038 63.39 63.525 63.878 64.076 64.46

Source of data – International Monetary Fund 1

It can be noted that the government has put in place measures to keep the population within a given range. The population is expected to have a positive relationship with GDP. The graph presented below shows the trend of the population.

Savings and investment

Savings and investment will be explanatory variables in the regression model. The graph below shows the values of savings.

Billions 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Investment 35.61436 43.2246 55.4486 58.59997 65.2859 79.38562 56.01485 82.70879 92.04208 117.9108
Gross national savings 40.3985 45.99158 47.80726 60.91523 80.96647 81.54443 77.91078 95.88607 103.909 117.1606

Source of data – International Monetary Fund 1

The values of savings and investment increased over the ten year period. Savings and investment will be positively related to the amount of GDP. The graph presented below shows the trend of the two variables.

Current account balance

The current account balance will be used as an explanatory variable. It will be a proxy for exports and imports. The table presented below shows the value of the current account balance.

billions 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Current account 4.784 2.767 -7.642 2.315 15.682 2.157 21.896 13.176 11.87 -0.749

Source of data – International Monetary Fund 1

In the model, current account balance is expected to have a positive relationship with the GDP. The graph below shows the trend of the values.

Interest rate

Evaluation of the effect of interest rate on GDP is also of great importance. The data for the real interest rate is presented below.

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Real interest rate (%) 4.5 2.3 1.2 2 3.5 3 2.9 2.2 2.6 5.7

Source of data – International Monetary Fund 1

The interest rate is expected to impact on GDP significantly through other variables such as investment and savings. The trend of interest rate is presented below.

Government spending and government debt

The final explanatory variable will be the government spending and debt. The values of these two variables are shown in the table below.

Billions 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Total expenditure 27.83334 33.34414 37.19616 41.59383 52.63574 57.85741 63.28273 74.04725 83.77015 90.74125
Gross debt 72.30279 79.80199 83.52207 86.96081 94.70827 101.5816 119.2422 135.9951 144.1176 166.5085

Source of data – International Monetary Fund 1

The total expenditure is expected to have a positive relationship with GDP. The trend of the values is presented below.

Model specification

To estimate the key determinants of GDP of Thailand, the expenditure model will be used. The expenditure model takes the form GDP = C + I + G + X – M. However, other variables will be included in the model to determine their effect. The equation can be transformed into linear form. When using the ordinary least squares method, the regression line takes the form. The regression equation can be simplified to take the form Y = b0 + b1X1 + b2X2 + b3X3 + b4X4. Thus, from the variables discussed above, the regression line takes the form presented below.

YGDP = b0 + b1Xunemployment rate + b2Xpopulation + b3Xinvestment + b4Xsavings + b5Xcurrent account + b6Xinterest rate + b7Xgovt.expenditure + b8Xdebt

Analysis and findings

Correlation analysis

Correlation coefficient measures the degree of association between two variables. The table presented below gives a summary of the correlation coefficients.

GDP Unemployment rate Population Investment Gross national savings Current account Real interest rate (%) Total exp. Gross debt
GDP 1
Unemployment rate -0.9768 1
Population 0.9041 -0.8669 1
Investment 0.9452 -0.9393 0.8283 1
Gross national savings 0.9942 -0.9738 0.8963 0.9409 1
Current account 0.295 -0.2518 0.3328 -0.0149 0.324 1
Real interest rate (%) 0.3446 -0.2674 0.5440 0.3937 0.391 0.054 1
Total expenditure 0.9937 -0.9642 0.9065 0.9248 0.982 0.319 0.344 1
Gross debt 0.9654 -0.9268 0.9000 0.9117 0.948 0.253 0.390 0.983 1

The results of correlation coefficient indicate that there is a strong negative relationship between the GDP and population (97.68%). Also, the results show that there is a strong positive relationship between GDP and variables such as population (90.41%), investment (94.52%), savings (99.42%), total government expenditure (99.37%), and gross debt (96.54%). Further, there is a weak relationship positive between GDP and variables such as current account balance (29.5%) and real interest rate (34.46%).

Regression results

The results of the coefficient of regression analysis are presented in the table below.

Coefficients
Intercept 710.5607569
Unemployment rate -5174.195933
Population -8.50800233
Investment -5083.059504
Gross national savings 5083.09199
Current account -5083.014842
Real interest rate (%) 478.7603194
Total expenditure 3.567519943
Gross debt -0.589457483

The regression equation can be written as shown below.

YGDP = 710.56 – 5,174.20Xunemployment rate 8.5Xpopulation 5,083.06Xinvestment + 5,083.09Xsavings – 5,083.01Xcurrent account + 478.76Xinterest rate + 3.57Xgovt.expenditure 0.59Xdebt

The negative coefficients imply that a unit change in the value of each variable result in a decline in the value of GDP. The positive coefficients on imply that a unit change in the value of the each variable result in an increase in the value of GDP.

Evaluation of the model

A t – test is used to evaluate the statistical significance of the explanatory variables. A two tailed t- test is carried out at 5% significance level.

Null hypothesis: Ho: bi = 0

Alternative hypothesis: H1: bi ≠ 0

The null hypothesis implies that the variables are not significant determinants of GDP. The alternative hypothesis implies that variables are significant determinants of GDP. The results of t – test are presented in the table below.

Variable t – values computed

(t at α 0.05 = 1.9432)

Decision
b0 0.899632 Do not reject
b1 -1.36399 Do not reject
b2 -0.76026 Do not reject
b3 -1.85037 Do not reject
b4 1.851125 Do not reject
b5 -1.85054 Do not reject
b6 0.922226 Do not reject
b7 2.777267 Reject
b8 -1.65828 Do not reject
b9 0.899632 Do not reject

From the results of the t – test, the interest rate is the only significant variable at 5% level of significance. The other variables are not statistically significant.

F–test of the regression models

The overall significance of the regression model can be evaluated using an F-test. The test is carried out 5% significance level.

Null hypothesis H0: β0 = β1
Alternative hypothesis H1: βj ≠ 0, for at least one value of j

The null hypothesis implies that the overall regression line is not significant. The alternative hypothesis implies that overall regression line is significant. The value of F-calculated is 963.23 while the value of F-tabulated is 3.8549. Thus, the null hypothesis is rejected. This implies that the overall linear regression line is significant and can be used in further analysis and predictions.

The value of R-square is 99.98%. This implies that the explanatory variables explain 99.98% of the variations in the explained variable. This indicates a strong regression model. Besides, it shows that the regression model fits the actual data well.

Conclusion

The regression analysis conducted above shows that the interest rate is the significant determinant of the GDP of Thailand. This implies that changes in interest rate will significantly affect the GDP. Further, the evaluation of the model indicates that all the variables in the model significantly explain the variation in the value of GDP.

Finally, analysis of the various economic data shows that there has been an upward trend on key variables such as GDP, savings, investment, government spending, and government debt. Also, the unemployment rate also declined significantly over the past ten years. The country reported positive performance in international trade. Further, the value of exports exceeded the value of imports in a number of years. Based on the analysis of the economic data above, it can be concluded that Thailand has a sound economic health.

Works Cited

Credit Suisse 2012, Thailand’s GDP: Quantifying the Bounce Back. Web.

International Monetary Fund 2013, Data and Statistics. Web. <>.

Kothari, Jaipur. Research Methodology: Methods and Techniques, New Delhi: New Age International (P) Limited Publishers, 2004. Print.

Mugenda, Olive, and A. Mugenda. Research Methods: Quantitative and Qualitative Approaches, Nairobi: Acts Press, 2003. Print.

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