The importance of service, manufacturing, and agriculture on economic growth: a regional concentration on 77 provinces of Thailand

Abstract

This project studies the importance of service, industrial activity (manufacturing), and agriculture on per capita GDP in 77 provinces of Thailand in 2011 and 2020. The three key sectors are examined whether they have any relationship and positive impacts with per capita GDP, especially the service and manufacturing. In the history of economic devlopment, agriculture and manufacturing are key driver of economic growth. However, the trend has changed to deindustrialization where many cities transform from agriculture and manufacturing to service. In Thailand, the share of service sector accounted for more than half in GDP in 2020. The project investigates the importance and contribution of each key sectors to per capita GDP and the result shows that manufacturing has a positive impact on per capita GDP while the result of of service is insignificant and has a negative impact on per capita GDP. However, it cannot be concluded that agriculture has any relationship and impact on per capita GDP.

1) Introduction

This project is organized into four parts; introduction, data and methodology, exploratory data analysis, regression analysis, and concluding remarks.

2) Data and Methodology

2.1 Data

The dataset comes from the government agency of Thailand namely the Office of the National Economic and Social Development Board of Thailand or NESDB.

Year of analysis is 2011 and 2020.

The observation covers 77 provinces of Thailand.

df = pd.read_csv("/work/shareandcombineRegionProvincialDataTH.csv") df

regionobject

Northeastern25%

Northern22.4%

5 others52.6%

provinceobject

Khonkaen1.3%

Udonthani1.3%

74 others97.4%

Northeastern

Khonkaen

Northeastern

Khonkaen

Northeastern

Khonkaen

Northeastern

Khonkaen

Northeastern

Khonkaen

Northeastern

Khonkaen

Northeastern

Khonkaen

Northeastern

Khonkaen

Northeastern

Khonkaen

Northeastern

Khonkaen

df.columns

2.1.1 Descriptive Statistics

df1.describe().round(2)

yearfloat64

agrifloat64

count

760.0

mean

2015.5

17462.57

std

2.87

12299.89

min

2011.0

1978.61

25%

2013.0

8626.37

50%

2015.5

14794.1

75%

2018.0

21499.77

max

2020.0

80165.12

2.1.2 Description

df_def = pd.read_csv("/work/Definition2.csv") df_def

variable object

gdp3.7%

gdppc3.7%

25 others92.6%

description object

gross domestic product in baht3.7%

gross domestic product per capita in baht3.7%

25 others92.6%

gdp

gross domestic product in baht

gdppc

gross domestic product per capita in baht

agri

agriculture, forestry and fishing

nonagri

industrial and services

indu

industrial activities including 1) mining and quarrying 2) manufacturing 3) Electricity, gas, steam, and air conditioning supply 4) Water supply (sewerage, waste management and remediation activities

minq

mining and quarrying

manu

manufacturing

elec

electricity, gas, steam, and air conditioning supply

wats

water supply (sewerage, waste management and remediation activities

serv

services including 1) constuction 2) wholesale and retail trade and repair of motor vehicles and motorcycle 3) transportation and storage 4) accommodation and food service activities 5) information and communication 6) financial and insurance activities 7) real estate activities 8) professional, scientific and technical activities 9) administrative and support service activities 10) public administration and defence; compulsory social security 11) education 12) human health and social work activities 13) arts, entertainment and recreation 14) other service activities

/shared-libs/python3.9/py/lib/python3.9/site-packages/pandas/core/arraylike.py:358: RuntimeWarning: divide by zero encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)

2.1 Methodology

The project employs simple OLS regression to investigate the relationship between dependent and independent variables. The equation is as follows;

3) Exploratory data analysis

4) Regression analysis

Regression result

The result shows shindu is significant as p-value is less than 5 percent and the coefficient has a positive sign meaning that there is a relationship between log_gdppc and shindu. It can be understood that, in 2011, 1 percent increase in shindu, the log_gdppc increases by 1.86 percent.

However, the shserv is insignificant as p-value is more than 5 percent and the confident interval includes zero, and the coefficient has negative sign.

The regression result of agriculture cannot be interpreted.

In 2011

In 2020

Agriculture

In 2011

In 2020

Error found

smf.ols(formula='log_gdppc ~ shserv + shindu + shagri', data=df1.query("year == 2011")).fit().summary()

mod1 = smf.ols(formula='log_gdppc ~ shserv', data=df1).fit() mod2 = smf.ols(formula='log_gdppc ~ shserv + shindu', data=df1).fit() mod3 = smf.ols(formula='log_gdppc ~ shserv + shindu + shagri', data=df1).fit() Stargazer([mod1,mod2,mod3])

Prediction

results = smf.ols(formula='log_gdppc ~ shserv+shindu', data=df).fit() b = results.params

myX1s = np.array([0.2, 0.5]) myX1s

myX2s = np.array([0.5, 0.2]) myX2s

y_hat = b[0] + b[1]*myX1s+b[2]*myX2s y_hat

5) Conclusion remarks

The industrial activities (manufacturing) are concentrated in some regions namely Eastern, Central and Bangkok and vicinities. And these regions likely to contribute to higher per capita GDP.

Most provinces in which there are higher agricultural output tend to have lower per capita GDP.

Based on the regression result, manufacturing is still a key to achieve higher per capita GDP in Thailand.

Service has a negative impact on per capita GDP and it is insignificant. More analysis should be included in order to explain the relationship.

References

Otsubo, S. T., & Otchia, C. S. Designing Integrated Industrial Policies Volume I.

Tipayalai, K., & Mendez, C. (2022). Regional convergence and spatial dependence in Thailand: global and local assessments. Journal of the Asia Pacific Economy, 1-28.