This assessment will provide certain questions which are like:
- What is the significant areas undertaken for labour force participating?
- Give the effective recommendations and the issues is relation to the fluctuations of the company and the educational purposes.
TITLE
'Economics of Education- The effects of business cycle on educational attainment'
INTRODUCTION
It is very important part by the central and state government that they are spending too much on education of youth and its citizens (The Heterogeneous Impacts of Business Cycles on Educational Attainment, 2018). But there is higher exposure of unemployment rate on educational attainments that is seen on many of them. This educational attainment is that which is referred by USA Census Bureau Glossary meaning highest degree of education which is completed by individual person. So, there are many studies which show that changes and fluctuations within business cycle will be having its impact on this educational attainment. This means that at time when there is scarcity of market job opportunity, wages are also stagnant then there will be decrease into opportunity cost for undertaking or pursuing education. While in certain previous studies it was also shown that there is opposite reaction meaning that higher unemployment rate will be linked with the increase in educational attainments. So, the objective of this topic is to find out effect of business cycle on educational attainment which is very important to study.
Aim:
To analysis the impact of business cycle on educational attainment.
Objective:
- To analysis relationship between education and employment rate within people
- To find out relationship between rate of college attendance and labour force
Research hypothesis:
Hypothesis 1
- H0: There is significant relationship between fluctuation within business cycle and attainment of education
- H1: There is no significant relationship between fluctuation within business cycle and attainment of education
Hypothesis 2
- H0: There is significant relationship between rate of college attendance and labour force participating.
- H1: There is no significant relationship between rate of college attendance and labour force participating.
REVIEW OF PREVIOUS LITERATURE
There are many other studies which are covered within the framework of topic which includes the impact of economic crisis on educational outcome. One of the research done by M. Najeeb Shafiq which was on measuring impact of economic crisis on educational outcome. This was based on effects of economic crisis on children’s education whether it is positive or negative. According to Kieling, Ulkuer and Rahman, (2011) it was concluded within this research that various factors will be helping in moderating the effect of economic crisis one education of children. These factors will be those of condition of cash transfer, block grants, reduction in fee and media campaigns.
It is evident from the economic crisis it was seen that there was reduction in working hours, wage rates and the public funds from government that were available. Then there are certain policies which intervened for helping moderating the impact of economic crisis onto education of children. There is exposer of child which will be having negative impact on them during the time of economic recession that is directly linked with business cycle fluctuations. These negative impacts could be the pressure on parents to not educate their children during the time when their income is low or cut down. Then the other factor being parents relaying on child labour so this would result into loss of education to children. As per the view of Lusardi and Mitchell, (2014) the last factor which is recognised by them could be that of parents not allowing their children to continue their studies in good and prominent schools or colleges if their income is lower down or reduced.
Whereas on other side it could be included by the view of Colander, Lux and Sloth, (2014) there are also some positive factor which child is exposed to when there is economic crisis. The wage rate of child will be lower down in this period this would not attract parents to send their child on labour. While the other being if the parent is convinced that less educated child will not be getting good work then they could turn supportive for their children education. There are very less studies which shows the effect of economic crisis or business cycle on the outcome of educational attainment. As per the view of Boffy-Ramirez, (2017) within one of the study which was done on Costa Rica on crisis of 1980-83 it was noted that there was less decline of primary school enrolment as compared to that of secondary school enrolment. This will vary from characteristic of family to which the person is belonging their upbringing, parental education, their occupation and status in society based on income.
Then as compared to the other studies also showed which was held in Indonesia that poor children suffer more than that of rich from the economic crisis and business cycle as well. The main reason behind this economic crisis is related to problematic monetary and fiscal policy which will attract changes in export import policy and even natural disaster. Further it was included that these studies which are done is mainly identifying the relationship between child labour and their education during time of economic crisis. But our present research is done on effect of business cycle on educational attainment. So, both the study’s results could differ to great or some extent and conclusion will also be different from each other.
Livingstone, (2018) included that government are the main link between people who are not able to attain education further and their lack of employment. So, it is the duty of government that they are formulating some easy policies for these people who are impacted the most by this business cycle or economic crisis as well. So, within the study it could be included that there were mostly 3 type of intervention which will help to lower down the impact of business cycle and economic crisis as well on educational attainment.
Government could make their impact on lowering down the fee of school or colleges and can also give some monetary help to these poor people. Then other will be protecting educational outcome at time of business fluctuation and crisis which will be through help of media campaign. Like during time of crisis of 1990 government of Indonesia launched the campaign named stay in school which was great step in this movement. The last one is that of government providing monetary benefits to school which is called to as block grants.
DATA DESCRIPTION
The data which is that on educational attainment is basically drawn from the National Longitudinal Survey of Youth in year 1979 (NLSY79) this was based on individuals who are on boundary between the pursuing and not pursuing additional education. The individuals were of the age 17 and matching with the national and state unemployment rate. The study includes about 9 cohorts in which 1 is oldest and 9th is youngest all of them facing national unemployment on various different rate. This will be showing the range of age of each cohort, their size in 1979, national unemployment rate and educational attainment of each cohort in 2010. All the data regarding rate of unemployment was taken by Bureau of Labour Statistics (BLS).
The results showed that the participants who were of older age have conducted their higher education and having experience of job as well so the AFQT score was standardised with this age group.
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EMPIRICAL RESULTS
As per analysing the outcomes there have been use of various statistical tools that will help in analysing proper outcomes. It consists of identifying relationship between unemployment and labour force age between 15-24 of Australia. There has been consideration of data base since 1990 to 2017. Moreover, there will be application of various tests on the data base which were made in respect with identifying accurate analysis such as descriptive statistics, correlation and regression analysis.
Descriptive
| Descriptive Statistics | |||||||||||||
| N | Range | Minimum | Maximum | Mean | Std. Deviation | Variance | Skewness | Kurtosis | |||||
| Statistic | Statistic | Statistic | Statistic | Statistic | Std. Error | Statistic | Statistic | Statistic | Std. Error | Statistic | Std. Error | ||
| Unemployment rate | 19 | 3 | 4 | 7 | 5.58 | .192 | .838 | .702 | -.277 | .524 | -.178 | 1.014 | |
| Labor Force | 19 | 3494315 | 9416369 | 12910684 | 11189345.79 | 259406.828 | 1130728.150 | 1278546148273.509 | -.056 | .524 | -1.350 | 1.014 | |
| Valid N (listwise) | 19 | ||||||||||||
Interpretation: On the basis of above listed determination of the outcome which are mainly summary of the data base that have been considers. There have been use of Labour force under age 15-24 and unemployment rate in Australia. The consideration of data base has presented the descriptive analysis as there is average rate of unemployment which is 5.58 while in labour there are 11189345.9 mean labour force analysed over the period 1990 to 2017. Moreover, in analysing the standard deviation from the data base which demonstrated the standard deviation in the unemployment rate of Australia is around 0.838 while in labour force it represents 1130728.150 respectively.
Regression
In analysing the relationship between labour force of Australia and unemployment rate there has been use of regression test over such variables. However, it will be helpful in identifying the appropriate outcomes which are to be measured for analysing their significance differences.
|
Descriptive Statistics |
|||
|
Mean |
Std. Deviation |
N |
|
|
Labor Force |
11189345.79 |
1130728.150 |
19 |
|
Unemployment rate |
5.58 |
.838 |
19 |
|
Correlations |
|||
|
Labor Force |
Unemployment rate |
||
|
Pearson Correlation |
Labor Force |
1.000 |
-.171 |
|
Unemployment rate |
-.171 |
1.000 |
|
|
Sig. (1-tailed) |
Labor Force |
. |
.242 |
|
Unemployment rate |
.242 |
. |
|
|
N |
Labor Force |
19 |
19 |
|
Unemployment rate |
19 |
19 |
|
| Model Summaryb | |||||||||
| Model | R | R Square | Adjusted R Square | Std. Error of the Estimate | Change Statistics | ||||
| R Square Change | F Change | df1 | df2 | Sig. F Change | |||||
| 1 | .171a | .029 | -.028 | 1146313.794 | .029 | .514 | 1 | 17 | .483 |
| a. Predictors: (Constant), Unemployment rate | |||||||||
| b. Dependent Variable: Labor Force | |||||||||
| ANOVAa | ||||||
| Model | Sum of Squares | df | Mean Square | F | Sig. | |
| 1 | Regression | 675230308521.078 | 1 | 675230308521.078 | .514 | .483b |
| Residual | 22338600360402.086 | 17 | 1314035315317.770 | |||
| Total | 23013830668923.164 | 18 | ||||
| a. Dependent Variable: Labor Force | ||||||
| b. Predictors: (Constant), Unemployment rate | ||||||
| Coefficientsa | ||||||||
| Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | 95.0% Confidence Interval for B | |||
| B | Std. Error | Beta | Lower Bound | Upper Bound | ||||
| 1 | (Constant) | 12479226.083 | 1818512.820 | 6.862 | .000 | 8642499.407 | 16315952.760 | |
| Unemployment rate | -231204.958 | 322533.403 | -.171 | -.717 | .483 | -911690.956 | 449281.039 | |
| a. Dependent Variable: Labor Force | ||||||||
| Residuals Statisticsa | |||||
| Minimum | Maximum | Mean | Std. Deviation | N | |
| Predicted Value | 10860791.00 | 11554406.00 | 11189345.79 | 193682.201 | 19 |
| Residual | -1488912.375 | 1818687.625 | .000 | 1114016.765 | 19 |
| Std. Predicted Value | -1.696 | 1.885 | .000 | 1.000 | 19 |
| Std. Residual | -1.299 | 1.587 | .000 | .972 | 19 |
| a. Dependent Variable: Labor Force | |||||
Interpretation:
In analyzing the data base of Labour force and unemployment rate in Australia for the period 1990 to 2017 which insist several outcomes. Thus, as per considering the R square of data bae which presents that 0.029 that is 2.9% of the relationship has been made among such variables. Moreover, as per analyzing the significance value which is 0.483 as it is higher than the level of P value such as 0.05. Thus, in this case there are weak evidences against the null hypothesis. Therefore, there will be acceptance to H0 which presents that, there is no significant relationship between fluctuation within business cycle and attainment of education. Moreover, there will be no relationship among the Labour rate and the unemployment rate in Australia.
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Correlations
|
Descriptive Statistics |
|||
|
Mean |
Std. Deviation |
N |
|
|
Unemployment rate |
5.58 |
.838 |
19 |
|
Labor Force |
11189345.79 |
1130728.150 |
19 |
| Correlations | |||
| Unemployment rate | Labor Force | ||
| Unemployment rate | Pearson Correlation | 1 | -.171 |
| Sig. (2-tailed) | .483 | ||
| Sum of Squares and Cross-products | 12.632 | -2920483.684 | |
| Covariance | .702 | -162249.094 | |
| N | 19 | 19 | |
| Labor Force | Pearson Correlation | -.171 | 1 |
| Sig. (2-tailed) | .483 | ||
| Sum of Squares and Cross-products | -2920483.684 | 23013830668923.160 | |
| Covariance | -162249.094 | 1278546148273.509 | |
| N | 19 | 19 | |
CONCLUSIONS & RECOMMENDATIONS
Conclusion
On the basis of above research, it has been concluded that, the appropriate educational policies and labour force performing the environment will not affect the unemployment rate in Australia. There had various analyses and tests made over data base from historical data such as descriptive statistics, regression analysis as well as correlation of data. Therefore, there is no significant relationship between fluctuation within business cycle and attainment of education. Moreover, there will be no relationship among the Labour rate and the unemployment rate in Australia.
Recommendation
As per analysing the outcome which presents that, there have been various operational analysis made on gathered data that have presented that, there is no significant relationship between fluctuation within business cycle and attainment of education. Thus, to make necessary improvement in the Labour market of Australia and educational system there will be requirement of making appropriate policies. Therefore, there are several recommendations which will help Australia in improving employment opportunities such as:
- Creating new job opportunities for freshers and young employees that will help in rasi9ng the per capita income.
- Encouraging the literacy rates with the help of creating more scholarships and grants in educational system.
Developing schemes for SME’s operating in environment which will benefits in improving domestic production as well as created reduction in inflationary rates.
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