Probability and statistics research working age population & sustainable economic growth a basic statistics approach

FOREIGN TRADE UNIVERSITY FACULTY OF BASIC SCIENCES PROBABILITY AND STATISTICS RESEARCH Working Age Population & sustainable economic growth: A Basic Statistics Approach Author: Ph

FOREIGN TRADE UNIVERSITY FACULTY OF BASIC SCIENCES

PROBABILITY AND STATISTICS

RESEARCH

Working Age Population & sustainable economic

growth: A Basic Statistics Approach

Author:

Pham Duc Thang — ID: 1914450517 Nguyen Phuong Hang Nga — ID: 1914450512 MSc Phan Thi Huong

Hanoi - 2020

Table of Contents

Abtract 3

I Introduction 4

1 Definition 5

a Is working age population and GNI independent 0F HOỈP ce«ce<e<<< 6

within nations and how it differs among 3 differenf Caf€ØOTÌS -sss s55 8

b How income differs among 3 đị[ƒCF€HÍf CŒÍGĐOTIG e << eS<<s<< 10

Ill Estimation 12

V Conclusion 15

Abtract

This study examines the relationship between working age structure and economic growth of 51 countries in the world through a basic statistical method

till the year of 2019 The statistical tests indicate that all the figures in

employment-population ratio and the gross domestic income of each country have an intimate relationship A combination of box-plot, probability

distributions and normal curve distribution illustrate what range of income is

predominant in society and how the employment population ratio exerts an impact on 3 groups of countries’ gross income Similarity with estimation and hypothesis testing method, the working age-population has been proven to be remained unchanged in the future The implication of our findings 1s that the growth of working age portfolio 1s likely to increase our economic growth in the short run and also long run, so the policymakers need to pay special attention if they want to capitalize their country’s working age portfolio to boost the economic growth

Key word: economic growth, working age, statistical tests, intimate relationship

I Introduction

In many recent years, economic development is one of the top goals of all countries in the world, and to accomplish this goal, they have done a lot of research on the relationship between the employment-to-population ratio and the gross national income One of them, ILOSTAT (n.d) stated that an increase in the

working-age population creates opportunities for economic development but

simultaneously, it can also pose some difficulties such as creating jobs and

introducing new arrivals to the labor market On the other hand, a declining working-age population may generate several obstacles for growth in the

economy, competition, population reliance

The United Nations (n.d-a) stated the Sustainable Development Goals (SDG) 8 is to promote having a balanced and sustainable economic development,

having efficient jobs and better work for all A significant portion of the working

age population is considered as important for sustaining social and economic development (Ritchie, H & Roser, M 2019) According to the United Nations (2019), if the population from 25 to 64 years old in regions increases and their working-age population (also from 25-64 years) grows rapidly as compared to other age groups, these factors will give opportunities to boost growth in the domestic economy or demographic dividend They also stated that in order to gain from the demographic dividend, governments should focus on investing in education and health, particular for teenagers and young adults and establish

environments helpful for a sustainable economic growth From these, it can be

seen that the working age population is an important factor to consider in order to

achieve this goal

Since the working age population represents the number of qualified workers in a nation (Majaski, C 2020), the working age population is associated and linked to the gross national income (GNI) In developed countries, their working age population is increasing at a slow pace whereas in developing countries the workforce 1s increasing significantly and the task of creating stable jobs for everyone is becoming an obstacle (United Nations, 2013) This fact lets

us understand the impact of the working age population on a country’s GNI

In this research, we analyze key trends and implication by resorting to the Data of Working age population (2019 statistics) from the World Bank then

filtering and calculating it in our own worksheet For more information, please

visit: FINAL DATA SET

II Descriptive Statistics and Probability

1 Definition

According to the Organisation for Economic Co-operation and

Development (OECD), working age population is defined as those aged 15 to

64 This indicator measures the share of the working age population in total population The working age population measure 1s used to give an estimate of the total number of potential workers within an economy

However, in order to measure the economic health, the term

“employment-to-population ratio’, also known as the “employment-population ratio,” 1s a macroeconomic statistic that measures the civilian labor force currently employed against the total working age population (statistics are often

given for ages 15 to 64) of a region, municipality, or country Unlike the

unemployment rate, the employment-to-population ratio includes unemployed people not looking for jobs It 1s calculated by dividing the number of people employed by the total number of people of working age, and it 1s used as a metric

of labor and unemployment In general, a high ratio is considered to be above 70

percent of the working-age population whereas a ratio below 50 percent is considered to be low A low ratio suggests that a significant proportion of the population is not actively participating in business or economy related activities (United Nations, 2012)

2 Using statistics to show the relationship between employment- population ratio and the Gross National Income (GND

According to the data set, we can separate countries into three groups,

which are low income (LI, GNI less than $1000 per capita), middle income (MI, GNI between $1000 and $12500 per capita) and high income (HI, GNI greater than $12500 per capita) Moreover, they have also been placed into two

categories based on their employment to population ratio Countries with working age population between 50% and 70% are considered as high

employment to population ratio while countries with less than 50% are

considered as low employment to population ratio

Working age population

(>=50%)

a Is working age population and GNI independent or not?

In order to know if the statistics above are independent or not, we will review and check through the conditional probability

Let A denotes the event that the country selected has high proportion of working age population and let HI denotes the event that the country selected belongs to high income group

P (A | HD) is where event A 1s the high employment to population ratio while given the condition of HI 1s the high income Besides, the probability of

having high employment to population ratio of all countries (whether low, middle

or high income) is P(A)

= 88.235%

= 93.77%

In order for them to be statistically independent, P(A) must equal to P (A| HI) However, from the above calculations, it can be deducted that the two events are not statistically independent since P(A) does not equal P (A| HI ) The opportunity of a country having high proportion of working age population

depends on the country’s GNI In other words, income and working age

population are highly dependent on each other

Based on the results of calculation, all the countries which have high employment to population ratio tend to have higher income when compares to

countries with low employment rates, which is further explained in the following session

bh High-income countries are expected to have a high proportion of working age population

On the one hand, to explore the impact of income on working age population, we begin with the calculation of conditional probability, with the condition being country’s categories:

= = 100%

= = 83.32%

From the calculations, we can see that 1s extremely high with 93.77%, followed by at 83.32% Because of abnormal data, the proportion of working age

population in low income countries is higher than But as usual, given the

condition of income categories, the probability of working age population tends

to be higher in richer countries than impoverished countries

On the other hand, we also need to see the other way of this interaction by analyzing the effect that the working age population exert on income:

== 11.12%

= = 55.54%

From above calculations, we are able to identify that given the condition

of high proportion of working age population, the probability that the country selected is middle-income is highest, with P (MI | A) = 55.54% Therefore we can

conclude that middle-income countries are more likely to have a high proportion

of working age population, it is clear from this linking back to what the United

Nations (2019) have stated with the increased workforce in developing countries After some analysis, we can come to the finding that the probability of

high proportion of working age population within high-income country is highest

(P (A| HI) is highest) only in case of normal data, whereas the probability of middle-income country given the condition of high working age population is highest (P (MI | A) is highest) This 1s a two-way interaction between working age population and GNI As a result, every single country should try to increase the employment to population ratio in order to achieve sustanable economic goal

3 The impact of employment to population ratio on how income is distributed within nations and how it differs among 3 different categories

a The distribution of income within nations

The aformentioned statistics has illustrated the relationship between the

Employment to population ratio and the Gross National Income (GNI), usually it 1s a positive correlation which increasing one value will increase the other value,

but there still exist some outliers Now, we will have a deeper analysis of how the income how the gross income varies from country to country This table are

summarized data from 51 countries analyzed, which was grouped into 3 different

categories: High-Income Countries (GNI > 12500$), Middle Income (GNI

between 1000$ and 12500$) and Low-Income Countries (GNI < 1000$)

Countries

When measuring dispersion of one set of data and comparing different sets

of data, box-plot is considered a favorable method The median is the average

value from a set of data and is not subjected to outliers of a data set Therefore it

is the best measure of central tendency It is shown by the line that divides the box into two parts Half the scores are greater than or equal to this value and half are less Usually, box-plot divides the data into sections that each contain approximately 25% of the data in that set However, not all data are perfectly normal distributed and most box plots will not conform to this symmetry (where each quartile is the same length) There are three case that box-plot can fall into:

about the same on both sides of the box, then the distribution 1s symmetric

shorter on the lower end of the box, then the distribution is skewed nght

on the upper end of the box, then the distribution is skewed left, as can be

seen from Group 3’s figure

Below figures are examples of box-plots and how they are transformed to normal distribution:

<———> N z

@1 Q3 Min = Max Median

⁄ N

/ \

⁄ NS

ee ` `

Figure 1: High-Income countries

This is typical of skewed-nght distribution This graph depicts that the

median income of 16 high-income countries is 30090 US$ while around 50% of the income data collected in this group fall bewteen 20 905 US $ and 46180 USS

The still exists a large income gap bewteen the richest people and the relatively

lower-income populationn in this group

When graphing in normal curve, it is noticeable that the red box coincides

with the red-shaded area in the curve, meaning that the probability of income ranging between these two critical values mentioned above is roughly 50% The central tendency can be clearly seen from this Group

Figure 2; Middle-Income countries

<>

Q1 G3, „

Median

Normal Distribution: Pr(670<X<710)=0.076

Figure 3: Low-Income countries

This is typical of skewed-left distribution, but the data seems abnormal in this case The interquartile range (the length of the red box) is quite small

compared to the length of the line As showed by statistical information, the

probability of income ranging from two quartiles (Q1 and Q3) are small, around

7.6%

hb How income differs among 3 different categories

Comparing three box-plots illustrated in a single graph, we are capable of spotting the key difference between them and find out useful information

65000

55000

45000

3000

38000

25000

15000

10000

Figure 4: Three categories combination

Firstly, by looking and comparing the respective medians of each box plot,

several statistical information can be derived The median line of high-income

countries’ “box-plot” lies far above that of the compared middle and low-income countries, while the median line of middle and low-income countries are relatively close to one another This indicates that there are a huge discrepancy bewteen the high-income groups than the rest of the worlds, meanwhile the income gap bewteen the two other groups is insignificant

Another factor worth considering when making comparison is the

interquartile ranges (the length of the box) and the difference between two

extremes (the length of the line) in order to study how the data is dispersed The longer the box the more dispersed the data The smaller the less dispersed the data The interterquartile ranges of Group 1 is pretty large compared to the difference bewteen two extreme values, therefore when transformed into the normal curve, its distribution (the red area) is spread over a larger range of values, which can be infered that the variablity of income earned by each country

in this group is quite large By contrast, the box’s lenth of Group 2 is relatively small in comparison with the length of the line, which means that smaller range

indicates narrower distribution and thus less scattered data

By glacing at Group 3’s statistical data, while its median is many times

lower than that of Group 1, the difference between the interquartile ranges and

the extremes range are extremely noticeable, in this case the box’s length is too short and the line’s length 1s too long The data in this group is quite different

from the others Only a little portion of population live in the high classes of society (the area of the red box is pretty small), most of its population falls behinds and lives under the poverty line due to the lack of advanced knowledge, technical transfer and skilled labor workforce The society’s income gap is a huge problem for central planning authority to deal with

1 Estimation of world average of employment to population ratio

We have already analyzing each individual group’s employment to population ratio with known data Now it’s time to make an estimation of the population employment to population ratio This is a good method that world leaders, policy makers often turn to when they want to know whether the world economy are able to provide employment for those who want to work Note that the employment-to-population ratio 1s equal to the number of

people employed divided by the working-age population and multiplied by 100