CHAPTER 11.1 INTRODUCTIONEmployment is the fact of someone being paid to work for a company or organization. There are many factors of employment rate such as age, gender and academic achievement.Firstly, teenagers who already doing their part time job can develop their skills such as communication skill and survival skill. Based on Richard and Denise (2003), in early 1970s, the employment of men and women aged 50 and over in Britain has refused clearly. Blundell, Meghir and Smith (2002) have claimed that older workers lacked the necessary skills in the face of industrial change. For example, young workers nowadays have the basic technic of using information technology. People prefer to employ them because they have those basic skills of using computer and managing stuff.
Based on Zhou (2003) stated that male graduates find jobs more easily than female graduates in China. For example, the competitions between men are less than compared to women.In addition, people who have high qualification in academic and skills will be easier to get job. Based on study by Peter (2000) women are educated to the same level as the men in virtually all advanced countries. For example, graduates with high score who can do better at work will be considered by employers to be diligent and intelligent.
Next, having a higher degree does not mean that will be explained to a good career or a smoother search.Scenarios that happened in India which is related to employment rate is India’s employment elasticity, a common measure of how employment growth responds to Gross Domestic Product (GDP) growth, hovered around 0.3 between 1991 and 2007 was estimated by International Labor Organization (ILO). Basically, 1% of overall economic growth produced 0.
3% of employment growth. That number has been coming down quite alarmingly since, and now stands at only about 0.15%.
Furthermore, proficiency in English is also important because it impacts the effectiveness of our communication and ability to understand as well articulate subject matters. For an example, in the case of an investor relation officer who needs to communicate frequently with stakeholders of different nationalities, English would be the medium of communication. Other soft skills include teamwork, active listening and attention to details.1.2 PROBLEM STATEMENTEmployment is the state of having paid work and the utilization of something.
Nowadays, employer is very strict on being hired workers because there are many criteria are needed to fulfill. Besides, employers also take into consideration factors such as qualifications, academic results, critical skills for job success as well as extra—curricular activities. Based on department statistics, the employment rate for female is 54.1% and 80.6% for male in 2015.
In 2016, employment rate of female have been increased by 0.2% while male decreased by 0.4%. According to statistics, the rates of Malaysian unemployment have been increased from 2.6% in 1996 to 3.6% in 2003.
Nowadays, majority young people were facing difficulties in getting hired the job that related to their education. As a conclusion, this study will find out on which criteria those employers prefer to hire workers. The employment rate should be increase so that the unemployment rate will decrease. 1.3 RESEARCH OBJECTIVES1. To study the trend of employment rate in Malaysia. 2.
To study the forecasting of employment rate in Malaysia in the year 2017 to 2020. 1.4 RESEARCH QUESTIONS1. Is there any trend of employment rate in Malaysia?2.
Is there any forecasting employment rate in Malaysia between year 2017 to 2020? 1.5 SIGNIFICANCE OF STUDYThis study is important for the future of Malaysia, as when employment rate increase; it shows that the stability of economic growth in Malaysia increase hence can develop the economic value of Malaysia to a higher level. In addition, it can be used by other stakeholder in future planning. 1.6 SCOPE OF THE STUDYThis study has been done in Malaysia. The data of employment rate in Malaysia is started from the year 1982 until 2016. The data is obtained from Department Statistics.1.
7 LIMITATION OF STUDYThere were a few unavoidable limitations to this study. First, the information and data may not be accurate which is the sources of the data must always be checked. Besides, the way things are measured may change over time, making historical comparisons difficult.
Other than that, documents may lack authenticity which are parts of the document might be missing because of age, and we might not even be to verify who actually wrote the document, meaning that we cannot check whether it is biased or not. CHAPTER 2 LITERATURE REVIEW2.1 INTRODUCTIONThis chapter will be explain about the previous study between employment rate towards the three factors based on the journal, the variables that are related to the study and the most important thing is the result that are obtained from the study. For the past 25 years, in the Canadian labor market, the most dramatic changes observing the steady convergence of men and women’s employment ratios (Rowe, 2006).2.1.
1 GENDER Through setting out the changes in women participation at individual stages of academic staff in Japan and Australia, this survey sets the stage for future qualitative job exploring why differences in the numbers of women and men staff maintain. The aim for this study is to look at the rate of progress of men and ladies’ work as college scholastic staff in Australia and Japan (Kirsti, 2013). The result indicated that Both Australia and Japan have seen an expansion in female cooperation rates in scholarly work at all levels since the presentation of against sex separation regulation. Objective and subjective dimension of gender equity were appraised. The authors’ broad approach revealed that factors restraining gender equity run on multiple stage and highlights the relative importance of family aspects. The purpose of this study is to evaluate the situation for career advancement in healthcare management and consider factors that may be interfere gender equity (Tracey & Mary, 2012). 33% of ladies’ human services directors in our investigation detailed saw sex segregation in the previous five years. Not as much as half of male human services directors were steady of expanding the extent of ladies in senior administration positions, while more than 80 percent of ladies were.
Among those not yet advanced ladies were altogether more improbable than their male companions to try to senior administration positions. They concluded that ladies were altogether more averse to be elevated to senior administration, even in the wake of controlling for individual, authoritative and family level qualities. Based on Konarasinghe (2017), the data using the trend models such as Linear, Quadratic, Growth Curve and Pearl Read Logistic were tested. Besides, the data also analyzed by using Model De-trended series and testing the Validity of Hybrid models.
2.1.2 AGEEmployment rates are divided into three age groups which are those people just entering the labor market following education (aged 15 to 24), those people in their prime working lives (aged 25 to 54) and those people passing the peak of their career and approaching retirement (aged 55 to 64).
This indicator is seasonally adjusted and it is measured as a percentage in same age group. Employed people are defined that they have worked in gainful employment for at least one hour in the previous week or who had a job but were absent from work during the reference week while having a formal job attachment.When younger workers cannot supply enough labor to meet employer demands, older workers tend to experience higher demand during periods of economic growth.
Conversely, older workers are more likely than middle-aged workers to be laid off in periods of job cutbacks and experience longer periods of unemployment after job loss (Chan & Stevens, 2001).Businesses may not be able to rely on a young cohort of unemployed to fill new positions in future economic expansions occurring in the context of an aging workforce. Although a variety of strategies have been used including increased immigration and female labor force participation to compensate for a shortage of workers in the past, displaced older workers may become the new recruits (Burk Hauser & Quinn, 1997; Levine & Mitchell, 1993).There is a positive correlation over the lifecycle between age and income. The cost of workers’ wages will tend to be rejected by an aging labor force if the difference of current income retained by age (Johnson & Zimmermann, 1992). The impact on unit labor costs will depend on whether the average productivity increases with age.
An older workforce may be more productive, given that older workers lead to have more years of prior work experience (Disney, 1996). Currently the labor force participation rates for those of primed-aged and younger adults are well above of the older adults. Concerns have been expressed that the number of older adults who are out of work and on aggregate levels of economic activity will be affected due the rising share of older workers in the labor force (Cabinet Office, 2000). Nowadays, there is evidence that older workers more affected by involuntary job loss than are younger workers, although older workers are less likely to be displaced from jobs through redundancy, dismissal or the termination of a temporary contract (Gregg, Knight and Wadsworth, 1999), they face long-term unemployment risks following job losses.2.1.
3 ACADEMIC ACHIEVEMENTOne of the factors of employment rate is academic achievement. Based on previous study of other researchers, they also stated that there is a linear relationship between academic achievement and employment rate. According to Ma, Pender and Welch (2016), in 2015, the number of Bachelor’s Degree or higher is the highest with 82.6% compared to Associate Degree with 77.4%, some college with no degree with 72.2%, high school diploma with 67.
8% and less than a high school diploma as the lowest employment rate with 55.5%. It is also can be said that the higher the education, the higher the income earned. Graduates from doctorate after 1 year of graduating has the highest income with USD69,983 compared to Masters Degree with USD54,700, Bachelors degree with the income of USD46,169 and Diploma level with USD36,352 and the lowest income of employment after 1 year of graduating is Certificate at levels 1 to 3 with USD46,866 (Park et al, 2013).
Furthermore, it is said that different fields can affect the employment rate too. Engineering and Science and Management graduates are highly employed compared to Social science and Humanity graduates (Ariyawansa, 2008). On the other hand, Social science and Humanity degrees are of higher quality than some of the medical and engineering degrees (Sumanasiri et al., 2015). CHAPTER 3 RESEARCH METHODOLGY3.1 INTRODUCTIONThis chapter will clearly define the research methods used to conduct the study.
This study will explained how the necessary data and information to address the research objective and questions were collected, presented and analyze. 3.1 DATA DESCRIPTIONThis study using the secondary data which is the data have been collected by the researcher.
The data of this study was taken from the official website Department of Statistics which is about of the number of employed persons in Malaysia. The statistics is derived from Labour Force Survey (LFS) which is conducted every month using household approach. Employed persons are those between the working age of 15 until 64 years old who at any time during the reference week of LFS had worked at least one hour for pay, profit or family gain (as an employer, employee, own-account worker or unpaid family worker). This data referred to the year 1982 until 2016 which are contains 32 of data.
Unfortunately, LFS was not conducted during the years 1991 and 1994. The type of data sources used in this study is secondary data. Secondary data is a second-hand data from a previous research. Such data a cheaper and more quickly obtained than the primary data and also may be available when primary data cannot be obtained at all.
The examples of secondary data are data collected by a hotel on its customers through its guest history system, government health statistics and other. In this study, the data is obtained from the internet sources. These data was taken from Department Statistics (https://www.dosm.gov.
my/v1/). 3.4 PROCEDURE FOR DATA ANALYSIS3.4.1 Descriptive StatisticsIn this research study, the researchers collected data as the secondary data to conduct descriptive statistic and give a brief overview.
Based on descriptive statistics are used to represent or summarize data in ways that are worthwhile and helpful. To showcase the information got through diagram, table graph, chart and data. Beside, cross tabulation is also used to clearer result and better understanding.3.4.
2 Inferential Statistics3.4.2.2 Forecasting using the best model Data entry using Microsoft Excel.
To forecast the rate of employment while to observed the trends of employment in Malaysia, the analysis used is average change model.Average change modelThe average change model is widely used by organizations because of its stability and practicability. It is based on the premise that the forecast value is equal to the actual value in the current period plus the average of the absolute changes experienced up to that point in time. It is given as; Where Averages of Changes = ( )However, where significant changes are observed in the data series, then the average of more than two changes will be able to stabilize better the ‘Average of Changes’ value. This model is similar to the naïve with trend model, with the exception that it is less influenced by all historical observations and it response relatively quickly to changes in the actual time series. This model is most useful when the historical data being analyzed are characterized by period-period changes that are approximately of the same size.
On the other hand, this model is most suitable for short data series, a common phenomenon in most practical situations. Good examples are when forecasting for products recently introduced into the market or in a new firm in operation.Data partitionTable 1 shows data partition of estimation and evaluation partESTIMATION70%EVALUATION30% The table 2 shows calculation for estimation part using Microsoft ExcelYearEmployment rateFitted ValueError Error Squared Error Absolute19825249—-19835457—-19845566.7—-19855653.4=D6+((D6-D5)+(D5-D4))/2=D7-E7=F7^2=ABS(F7/D7)*10019865760.
1=D7+((D7-D6)+(D6-D5))/2=D8-E8=F8^2=ABS(F8/D8)*10019875983.9=D8+((D8-D6)+(D6-D5))/2=D9-E9=F9^2=ABS(F9/D9)*10019886157.2=D9+((D9-D8)+(D8-D7))/2=D10-E10=F10^2=ABS(F10/D10)*10019896390.9=D10+((D10-D9)+(D9-D8))/2=D11-E11=F11^2=ABS(F11/D11)*10019906685=D11+((D11-D10)+(D10-D9))/2=D12-E12=F12^2=ABS(F12/D12)*10019927047.8=D12+((D12-D11)+(D11-D10))/2=D13-E13=F13^2=ABS(F13/D13)*10019937383.4=D13+((D13-D12)+(D12-D11))/2=D14-E14=F14^2=ABS(F14/D14)*10019957645=D14+((D14-D13)+(D13-D12))/2=D15-E15=F15^2=ABS(F15/D15)*10019968399.3=D15+((D15-D14)+(D14-D13))/2=D16-E16=F16^2=ABS(F16/D16)*10019978569.
2=D16+((D16-D15)+(D15-D14))/2=D17-E17=F17^2=ABS(F17/D17)*10019988599.6=D17+((D17-D16)+(D16-D15))/2=D18-E18=F18^2=ABS(F18/D18)*10019998837.8=D18+((D18-D17)+(D17-D16))/2=D19-E19=F19^2=ABS(F19/D19)*10020009269.2=D19+((D19-D18)+(D18-D17))/2=D20-E20=F20^2=ABS(F20/D20)*10020019357.1=D20+((D20-D19)+(D19-D18))/2=D21-E21=F21^2=ABS(F21/D21)*10020029542.6=D21+((D21-D20)+(D20-D19))/2=D22-E22=F22^2=ABS(F22/D22)*10020039869.
7=D22+((D22-D21)+(D21-D20))/2=D23-E23=F23^2=ABS(F23/D23)*10020049979.5=D23+((D23-D22)+(D22-D21))/2=D24-E24=F24^2=ABS(F24/D24)*100200510045.4=D24+((D24-D23)+(D23-D22))/2=D25-E25=F25^2=ABS(F25/D25)*100200610275.4=D25+((D25-D24)+(D24-D23))/2=D26-E26=F26^2=ABS(F26/D26)*100 The table 3 shows calculation for evaluation part using Microsoft Excel200710538.1=D26+((D26-D25)+(D25-D24))/2=D29-E29=F29^2=ABS(F29/D29)*100200810659.
6=D27+((D27-D26)+(D26-D25))/2=D30-E30=F30^2=ABS(F30/D30)*100200910897.3=D28+((D28-D27)+(D27-D26))/2=D31-E31=F31^2=ABS(F31/D31)*100201011899.5=D29+((D29-D28)+(D28-D27))/2=D32-E32=F32^2=ABS(F32/D32)*100201112351.5=D30+((D30-D29)+(D29-D28))/2=D33-E33=F33^2=ABS(F33/D33)*100201212820.
5=D31+((D31-D30)+(D30-D29))/2=D34-E34=F34^2=ABS(F34/D34)*100201313545.4=D32+((D32-D31)+(D31-D10))/2=D35-E35=F35^2=ABS(F35/D35)*100201413852.6=D33+((D33-D32)+(D32-D31))/2=D36-E36=F36^2=ABS(F36/D36)*100201514067.7=D34+((D34-D33)+(D33-D32))/2=D37-E37=F37^2=ABS(F37/D37)*100201614163.7=D35+((D35-D34)+(D34-D33))/2=D38-E38=F38^2=ABS(F38/D38)*100 The table 4 shows that the error of estimation and evaluation part ESTIMATIONEVALUATIONMSE41,586.
44 107,403.60 MAPE0.203807 2.031336 3.5 SCOPE AND LIMITION OF STUDYThe study was conducted in the whole Malaysia. None of the states are specifically chosen as it will cover the whole Malaysia.
The data of number of employed of Malaysians is from the year 1982 until 2016.There are several limitations while this study was carried out. Since the data taken is a secondary data, there might be a possibility of getting a fake data. The variables are also limited hence; the researchers have to go through more thoroughly in finding other variables that suits the case.
Other than that, there are some missing years that the previous researchers did not conduct, for example the year 1991 and 1994.The data that have been obtained can only be analyzed with certain methods since the data is numerical. The analysis methods that are most suitable for the data are average change method. CHAPTER 4RESULT AND DATA ANALYSIS4.1 DESCRIPTIVE ANALYSIS Figure 1: The employment rate of Malaysia in the year 1982 until 2016 Figure 1 shows there is seasonal component.
Seasonal component also known as a seasonal variation. Seasonal component is regular fluctuation occurring within a specific period of time and it happen in a short-term memory effect. The type of seasonal component is fixed effect. Fixed effect is the regularity pattern of seasonal effect is assumed to occur at the short time period. 4.
2 INFERENTIAL ANALYSIS4.2.1 Best Model Figure 2: The actual and fitted values by using average change modelFigure 2 shows the employment rate versus average change model against year. The employment rate starts to increase by the year 1985 until 2006.
For average change model, it was starting increase in the year 1985 until year 1998 and starts to drop for a while until 1999. 4.2.
2 Forecasting of best modelTable 5 shows the rate of employment that have been forecast using average changeYearEmployment Rate201614328.85Forecast 201714319.25201828638.
5201942957.75202057277 Figure 3: The forecasting of the actual versus fitted by using average changeFigure 3 shows the prediction that increasing occurs in employment rate so that people gets employed will increase in future. CHAPTER 5CONCLUSION AND RECOMMENDATION 5.
1 Conclusion In this study, Average Change Model was chosen as the best model. It is chosen to forecast the employment rate in Malaysia. To choose the best model out of 5 models, the error measure such as Mean Square Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) are studied to compare the smallest value of error measure among the model. The result shown that Average Change Model has the smallest value of the error measure that is MSE for evaluation that is 107403.60. Since the researchers choose to forecast in this study, the number of employment rate in Malaysia is forecasted for 4 years ahead from the year 2017 until 2020.
It is found that the employment rate will increase year by year and estimated to be 57277 in the year 2020. Since it is forecasted that the employment rate will increase yearly, more people will be employed and Malaysia will hold a brighter future as it will increase the stability of the economic growth in Malaysia, hence can develop the economic value of Malaysia to a higher level. 5.2 Recommendation The recommendation for the future study is this study may apply for other researchers to determine factors of employment rate in Malaysia.
Besides, the researcher can further their study into a larger scope of population to get accurate result. The researcher recommended that, as for increasing the number employee and decreasing unemployed, behalf should do some campaign about how to increase the working skills among employees. Next, the future researcher should add more influential independent variable to get the significant data. Lastly, in future research, the suitable method for the data should be choose to improve the research study.
