Using Sakerna's data years, this paper analyzes the phenomenon of job shifting, both the direction of worker movement and the characteristics of the shifting work. How the real phenomenon of job shifting in Indonesia is the topic analyzed in this paper. The issue of labor shifts is the transition of labor between sectors and regions that is discussed in this paper.
Policies can influence labor utility intensity without affecting labor shifts between regions and industries. On the other hand, the labor supply per household is specified on the basis of the real wage - wio /P, and time off - H. The real wage consists of main wage, support, bonus and other components that can be included in the money interest. Holzer (1989) argues that types of labor shifts have different effects on labor absorption and unemployment levels.
Meanwhile, the value of the negative static displacement effect indicates the phenomenon of labor displacement in the sector with the lowest level of labor productivity.
METHODOLOGY
Characteristics of labor shifts in normal economic conditions may differ from characteristics of labor shifts in crisis conditions. In normal circumstances, the shift in labor can be caused by changes in sectoral productivity while it is in a crisis situation; labor shifts tend to move. Labor shift analysis conducted by Permata (2008) shows that under normal circumstances labor tends to shift in a more promising sector with a higher level of productivity, reflected by a positive value of the static shift effect.
Thus, job shifting is expected to have a positive impact on the overall increase in labor productivity, which will ultimately provide positive support to the increase in economic growth. Non-labour force = persons of working age who are not part of the labor force and engaged in activity, such as students, household or others. Respondents who meet the four criteria: (a) are of working age, (b) are not working within the past week, (c) do not have a job during unemployment, and (d) are looking for a job, or;.
From all the data of the respondents according to the filter above, then the coding is carried out to be able to detect job displacement. For i, j = U means jobs are unemployment, so cell mUU shows the state of job status from unemployment to unchanged, while mio shows that the previous job in the sector becomes unemployment. For i, j = 1, ..., 9 so mi j indicates the volume of labor displacement from sector - i to sector - j , while mii, for example, indicates the workers who are still working in the same sector, namely sector - i .
This dependent variable is a binary variable Y = 1, showing respondents who shift, while Y = 0 shows respondents who don't. Vector Xj shows a range of employment characteristics, namely (i) sex coded SEX = 1 for men with female category SEX = 0, as a measure, (ii) working age (AGE), which is continuously variable, (iii) education level8 coded EDUC_CAT= 1 for employees with a high level of education and category EDUC_CAT=0 as benchmark, (iv) work experience status with code FORMAL_CAT=1 for employees with previous work experience in the formal sector, and category FORMAL_CAT=0 as benchmark , (v) wages with code WAGE_CAT =1 for high wage with low wage category (WAGE_CAT=0) as benchmark. The regression is not performed in a panel, but over a period of time to see how the likelihood of shifting work is based on the characteristics (gender, age, education, origin of the formal sector, wages and white collar)9.
RESULT AND ANALYSIS
Structural Break in Labor Market in Indonesia
As described earlier, there is a huge discharge during the crisis, but in fact labor absorption increased by 2.7% in 1998 (Table II.1). This means that the level of labor absorption remains broadly the same during the crisis and there is a shift of labor, especially to the informal sector. During the crisis, a shift of labor to the informal sector is taking place in most sectors, with the exception of the agricultural sector.
Using primary data from a study by the Bank of Indonesia10, the DSM research result shows the decline in labor growth between 2007 and the first quarter of 2009, even with negative growth of minus 2.48% in the first quarter of 2009 (Figure II .6). That shows that the companies are trying to reduce the high labor costs, which emerged when the companies implemented labor cuts. 11 Definition used: PERMANENT WORK is work with a fixed working hour every day and for which a pension fund is obtained. CONTRACTED LABOR is labor hired on the basis of a particular contract/project and for which no pension fund is obtained, and NON-PERMANENT LABOR is labor with specific working hours and no pension fund or company facility.
95% Confidence 95% Confidence 95% Confidence 95% Confidence 95% Confidence Interval Interval Interval Interval Interval Difference DifferenceDifferenceDifferenceDifference Std. The workforce reduction is mainly for contract jobs, with permanent reduction (laid off) in both 2008 and 2009. Based on the survey result, the main reason for a firm to reduce working hours is the efficiency of cost (37.61%), the contraction of global demand (34.19). %) and decrease in domestic demand (19.66%).
During the crisis, export had negative growth from November 2008 to July 2009 (See Figure II.10). The description from the labor supply side shows that, during the crisis, people of working age tend to be unemployed due to the lack of new job vacancies. This phenomenon is consistent with the paired sample test, which shows that there is no structural break in the Indonesian labor market.
Labor Shifting Determinant
And the largest marginal effect is in the transport sector, where the displacement probability for men is 21.9% higher than the female workforce. Meanwhile, the age at birth (AGE) does not have a significant effect on the probability of labor displacement. The previous formal work experience (FORMAT_CAT) has a significant and high marginal effect on the probability of job change for all sectors.
The result of the assessment shows that, except for the wage level (WAGE_CAT), all variables affect the probability of the labor force shifting to the construction sector12. The result of the assessment also shows that educated workers have a lower transfer probability of 5.1% than uneducated workers. Previous work experience has the greatest marginal effect among job characteristics on the probability of job transfer; workers who once worked in the formal sector are 41.02% more likely to switch out of the construction sector.
Based on the estimated result, the major marginal effect on labor displacement in the agricultural sector is previous formal work experience. On the other hand, white-collar workers have a higher displacement probability of 4.0% than blue-collar jobs. In this Trade sector, workers with previous formal work experience have a higher displacement probability of 40.58%.
After the formal work experience variable (FORMAL_CAT), the second highest marginal effect is sex (SEX), with men in the transportation industry having a higher probability of 21.59% than women. Statistically, the inferential test shows that low-paid work has a higher probability of change of 3.82% than high-paid work. For 35-year-old and highly educated male employees in the financial sector, in a managerial position, with a high wage and with previous formal work experience, there will be a 55.8% higher chance of staying and working in the financial sector.
The explanatory variables that greatly influence the probability of job relocation in the Finance sector are previous formal work experience (FORMAL_CAT), education (EDUC_CAT) and wage level (WAGE_CAT), with a sequential marginal effect of 13.75%. The effect of education levels and wages on the probability of job relocation in the finance sector is the largest among all sectors observed.
CONLCUSION AND SUGGESTION
In addition, the estimation result also shows a relatively lower marginal effect of previous formal work experience in agriculture compared to other sectors. Fifth conclusion, the educational factor does not affect the probability of labor relocation in the electricity and transport sectors. In these sectors, the male workforce has a higher probability of relocation than the female workforce, with the greatest tendency occurring in the transport sector with a 21.9% higher probability.
The seventh conclusion, the age of the workforce does not have a significant effect on the probability of job change. Statistically, the variable age only affects the possibility of moving labor in the industrial sector, but with a very low marginal effect value of 0.12%. This means that high-wage workers have a displacement probability of 13.7% and 19.7% lower than low-wage workers.
The ninth conclusion, the financial sector is the most dynamic sector of nine existing sectors, with the highest migration target being to the trade sector (1.22%), service sector (0.56%), industry (0.49%) and agriculture ( 0.49 %). The most influencing explanatory variables in the direction of the tendency to change jobs from the financial sector are previous formal work experience (FORMAL_CAT), education (EDUC_CAT) and wage level (WAGE_CAT) with a subsequent marginal effect of and 13.75%. The effect of education and wage levels on the probability of labor displacement in Finance is the largest among all observed sectors.
In addition, the effect of formal work experience on shifting probability in the financial sector is the second highest after the industrial sector. In addition, the modeling can be developed to enable the internalization of the structural factors such as sectoral growth, the exposure level of each sector and other variables with a strong theoretical basis and or strong empirical connection with the labor displacement phenomenon. 2008.∆Job-to-Job Transitions: Greater Mobility and Security in the Workforce∆∆Center for Data Analytics 08-06.The Heritage Foundation.
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