産 業 關 係 硏 究

第24卷 第4號, 2014. 12. pp.55-92

ⓒ 韓 國 雇 傭 勞 使 關 係 學 會

논문접수일 : 2014년 7월 7일, 심사의뢰일 : 2014년 7월 23일, 심사완료일 : 2014년 12월 4일

**** This paper is based on the correspondent author’s Ph.D thesis, which is revised and supplemented according to the comments by anonymous two reviewers.  We would like to give thanks to them.

****  (Correspondent Author) Sinchul JANG, Ministry of Employment and Labor, 010-3163-9734, marathonjang@hanmail.net

**** (Co-Author 1) Soobong UH, Korea Tech, 010-9033-9137, soobong@kut.ac.kr

**** (Co-Author 2) Kisung CHOI, Korea Tech, 010-7461-7942, ckisung92@kut.ac.kr

産業關係硏究 제24권 제4호

Survival Analysis on the labor mobility of irregular ~ (Sinchul JANG ․ Soobong UH ․ Kisung, CHOI)

Survival Analysis on the labor mobility of irregular workers by employment types in Korea*

Sinchul JANG**․Soobong UH***․Kisung CHOI****

On the study of irregular employment in Korea, its scale shows sizable variance and empirical results are not consistent as researchers use different definitions of irregular workers and data. Further, previous studies took blanket approach without classifying irregular workers by employment types and failed to present heterogeneity among them.

Using two years of Panel Data of irregular workers, this study analyzes 1) whether labor mobility of irregular workers is a trap or a stepping stone; and 2) which explanatory variables bring impacts on the mobility. Double counting is eliminated among six types of irregular workers. Extended Cox Hazard Model and Competing Risk Model are applied for analysis.

Findings show that durable trapping hypothesis is supported. Compared with fixed-term workers, the transition rate of other irregular workers was lower and women, the aged, the low-educated, workers with shorter length of service, unionized workplaces significantly show less transition rate to regular workers.

▶ Keywords: irregular workers, employment types, extended Cox Hazard Model, Competing Risks Model, fixed-term workers, Temporary Agency Workers

Ⅰ. Introduction

‘Dualism of two employment types’ (regular vs. irregular employment) is the words that typically represent the change in labor markets since the 1980s in many countries. The increase of various employment types and softened employment contracts deviated from standard employment (single employer, standard working hours and unlimited employment contracts) have challenged labor market in various angles. Matters regarding irregular employment are becoming more of an ideological and conflicting labor issue than past debates on dualism associated with gender, education, company size, etc. Studies on irregular employment have been done extensively in Korea and abroad, and covered the issues like the instability of employment and social exclusion, the increase of the working poor and a polarized labor market. Many studies have attempted to verify labor market divides between insiders having regular sratus with high level of wage & employment protection and outsiders having employment instability, low wages and poor working conditions (Doeringer, P. and Piore, M., 1971; Atkinson, J., 1984; Emmenegger et al., 2012). In Korea, as irregular employment increased rapidly in the wake of the economic crisis caused by the disastrous exchange rate in 1998 and 1999, studies on irregular employment began in earnest regarding its scale, the reasons for such an increase in the early 2000s and labor mobility from the mid-2000s (Hyeonju Min et al., 2005; Sigyun Lee, 2007; Jaeryang Nam, 2008, 2011, Yongseong Kim, 2009).

This study conducts quantitative analysis focusing on two topics; 1) whether irregular employment is limited for upward mobility and functions as a trap shuttling unemployment and non-Economically Active Population (EAP) 2) which variables determine the labor mobility to regular status, the unemployed or non-EAP, etc. Most studies in Korea conclude that an irregular employment acts as a 'trap' rather than stepping stones to a regular jobs; Likelihood for regular status is very little and even when converted to a regular position, the employment period is short. Such a trap is more noticeable among females, older people and workers in small & medium sized businesses. There are very few studies that viewed irregular employment as stepping stones.

Even though there are many studies that analyzed labor mobility of irregular workers, this study deals with some of the same issues again based on the following reasons. Firstly, this study tries to observe internal heterogeneity of irregular workers. When we treat irregular workers as a lump, it is difficult to look at the different aspects of labor mobility among various employment types. Under the name of “irregular workers”, most of the previous studies employed a blanket-approach, without looking at this point. Secondly, in order to exactly capture the size of each irregular-worker-type, this study eliminates ‘double counting’ among irregular workers and recounting the number of each type of irregular workers by applying hierarchical structure among employment types. Thirdly, the most recent data was used to reflect the changes in ꡔEmployment Protection Legislationꡕ(EPL) for irregular workers, enacted in 2007. Lastly, this study applies a new methodology which reflects the time-varying aspect of labor mobility as well as the potential multiple destinations of labor mobility. It is clear that a binary model (exit or non-exit from irregular status) cannot show various aspects of the labor mobility in each type of irregular workers.

Ⅱ. Theories related to labor mobility and review on previous studies

1. Theories related to labor mobility

(1) Labor mobility and institutional environment

The quantity and quality of labor mobility is determined by the interaction of various factors such as labor-management relation systems, the EPL, the strength of the existing social safety net, and the distribution of the industrial and occupational population, etc. Legal and institutional characteristics of each country can be represented by the term 'welfare regimes' and the most well-known examples include ‘Welfare State model’ (liberal, corporatist, social democratic model) by Esping-Anderson (1990) and ‘Two models of Capitalism’ (Coordinated Market Economies vs. Liberal Market Economies) by Hall & Soskise (2001). The way firms utilize labor is affected by such institutions beyond companies. In case of countries, especially with strict labor market regulations, supra-organizational institutions causes range of variations in organization of work and employer's labor utilization practices.. The interplay between functional and quantitative flexibility would be affected by such institutions (Kalleberg, 2001).

According to Muffels et al. (2002), the quantity and quality of labor mobility in Anglo-Saxon countries or liberal regimes are better, and those in corporatist systems (continental European type) or southern Europe(Mediterranean type) are relatively less and low. Among other things, many studies have been interested in the EPL, which is thought to be a strong factor that has significantly affected labor mobility in each welfare regime. The OECD (2006, 2007) and the European Commission (2012) made a study that the existence of a gap in the EPL between regular and irregular employment creates more incentives for employers to use irregular workers, which causes negative(-) relation to transition towards regular status. Kahn (2007), on the other hand, in the analysis of the European Community Household Panel (ECHP) between 1996 and 2001, revealed that mitigation of EPL in European countries increased the possibility for regular workers to be replaced by temporary workers and it caused a negative effect on workers’ transitions to regular status.

Since this is not a comparative study among countries, the EPL on irregular employment is not a variable included in the analysis. However, it is necessary to point out that Korea enacted the Fixed-term Act and new Temporary Agency Workers (TAW) Act in July 2007, which allows employers to use fixed-term workers and TAW within only two years, and Korean labor market thereafter has been adjusted by the increased EPL in using irregular workers. LEE & Eun (2011) analyzed that the legal restriction on maximum period of use by Fixed-term Act and TAW Act had positive effect on the transition into regular jobs, whereas Nam et al.(2011) showed that Fixed-term Act had negative impact in the near term and such effect maintained in the long-term though the intensity of the impact lessened. Yoo & Kwon (2012) maintains that irregular workers’ Acts of 2007 showed positive effect on the transition into regular status  in early stage, but no big difference after 2009.

When employers use them more than two years, the Acts regard them as unlimited contracts, and it is forbidden to dismiss those workers without just cause.

(2) Durable trap hypothesis

This hypothesis is in line with Segmented Labor Market Theory and Transaction Cost Theory. The Institutional school claims that labor market is segmented into non-competitive groups due to various institutional forces and non-economic barriers, which results in discontinuity and incompleteness in the labor market. The Segmented Labor Market Theory assumes that free labor mobility is limited since the labor market is segmented by various social elements and factors such as imperfect information, internal labor markets, education level, gender, company sizes, labor unions, class or discrimination, culture and customs. These segmented labor markets show differences in terms of wage and employment stability. The Segmented Labor Market Theory is based upon Dual labor market theory (Doeringer & Piore, 1971; Reich et al., 1973) which focuses on the disconnection between internal and external labor markets; and Core vs. Peripheral theory (Atkinson, 1984; Osterman, 1988) for securing functional and numerical flexibility.

Transaction Cost Theory assumes that the parties doing a transaction put priority on cost reduction & internal structure, and that governance or institutions are established accordingly (Williamson, 1975; 1996). This theory takes different perspective from Segmented Labor Market Theory in explaining how irregular workers find it hard to be included in the primary labor market and continuously remain as peripheral workforces. According to this theory, companies have to decide on making or buying workforces from a transaction based on the nature of the labor transaction; as the internal labor market is the 'hierarchy' consisting of the code of conduct and rules of the organization, it is distinguished from the labor forces in an external labor market. Cost needs to be minimized in maintaining such a hierarchy; a company chooses a labor transaction type depending on asset-specific qualities and the availability of easy performance measurement. For example, when a job requires general or low level skills, the level of the uncertainty of the task is low and performance measurement is easy and the transaction cost is low. In this case, employers tend to choose an externalization and indirect employment strategy. However, when a job requires firm-specific or high level skills and the level of uncertainty on the task is high, employers tend to choose direct employment for easy control and supervision over workers' opportunistic behaviors and to internalize labor transaction to reduce transaction cost.

(3) Stepping stone hypothesis

This hypothesis considers irregular employment as a transitional status for moving toward better jobs by acquiring new skills and experiences. This hypothesis is based on the competitive labor market which assumes free mobility of labor and no existence of an institutional barrier in the labor market. In this hypothesis, workers decide to take part in the labor market or not by comparing their reservation wages with market wages. Here, labor mobility is understood as a rational choice to realize more profit by workers, and wage imbalance is supposed to be cleared by labor mobility. The Screening theory and the Signaling theory introduce a reasonable decision process on the part of companies and workers (Spence, M., 1973; Stiglitz, 1995). Those two theories suggest that irregular employment works as a bridge to move to regular positions since irregular employment is a temporary status to resolve asymmetry of information between employers and employees. Screening theory supposes that employers know through experience that employees’ educational background itself is not an enough signal to verify “good workers”. So, employers often use temporary employment to test job aptitude, personal reliability, employee loyalty to the company etc., before hiring an individual as a regular employee. Whereas, the signaling theory focuses on irregular workers who try to give good signals to their employers in order to become regular workers. Hopp & Minten (2012) support the signaling theory by showing that workers are likely to become regular workers when working as irregular staff, rather than remaining unemployed because working as an irregular staff for a longer term can help establish a positive relationship with a prospective employer.

2. Review on previous studies

(1) Studies on trap analysis

A. Studies supporting durable trap hypothesis

Among studies undertaken overseas, Shikata (2012) analyzed the Keio Household Panel Survey data between 2004 and 2007 on fixed-term workers and TAW aged between 25-64 in Japan. The results showed that 17.5% of the workers were converted to regular positions between 2004~2005, and 24.9% between 2004~2007. When contrasted against the lower percentages of transition to regular status than in European countries, these results suggest a high likelihood of a trap in Japan. It is noticeable, however that only 10.4% of fixed-term workers and TAW for 1 year and 15.4% of them for 3 years in Japan became the unemployed or non-EAP. These percentages show their stronger attachment to the labor market than in some European countries (UK, France, Italy).

Givord & Wilner (2012) analyzed the Labor Force Survey (2006-2010) of France and found out that fixed-term workers were highly likely to become regular workers compared to the unemployed, whereas TAW were less likely to become regular workers than the unemployed. Hopp & Minten (2012) studied TAW in Germany and revealed that companies used TAW for screening for recruitment and employers took it as a bad sign if workers had some working experiences as TAW. This suggests job experience of TAW might function as a trap.

<Table 1> International comparison of labor mobility of fixed-term, TAW

	1998 → 1999(year)			1998 → 2001
	Converted to regular	Stay temporary	Became non-employed	Converted to regular	Stay temporary	Became non-employed
Austria	55.9	35.3	8.8	67.5	22.6	9.9
UK	51.9	29.9	18.3	63.4	15.1	21.5
Netherlands	49.1	40.9	10.0	69.9	17.6	12.5
Germany	46.6	38.5	14.9	60.0	23.5	16.4
Denmark	35.2	46.2	18.6	61.3	20.5	18.2
Italy	27.2	49.7	23.1	47.2	30.3	22.5
France	18.1	52.1	29.9	45.3	30.6	24.1
Japan (fixed-term, TAW)	17.5	72.1	10.4	24.9	59.7	15.4
Japan(entire irregular workers)	7.5	81.5	11.0	10.7	71.5	17.8
* Re-recited from Shikata(2012) * The case of Japan only is based on 2004-2007 data.

In Korea, most studies before 2008 showed irregular employment worked as a trap. Jaeryang Nam and Taegi Kim (2000) used an EAP Survey for 2 years on contingent and daily workers. The result revealed that more than 80% of the workers returned to irregular jobs in less than 2 years, even after they exited from irregular employment, and 60% had deviated from a regular position in less than 2 years after they moved to a regular status, which suggested only 0.5-0.7% of irregular workers settled in regular jobs. Juyeop Ahn, Junmo Jo and Jaeryang Nam (2002) analyzed the EAP survey data and suggested that most irregular employment functioned as traps and this tendency applied to all groups sorted by gender, age and education rather than just to specific groups. They said such tendency was not a temporary phenomenon caused by the foreign exchange crisis of Asia in 1997-1999, but a structural labor market trends. Hyeonju Min and Eunjong Shin (2005) conducted a survival analysis using both contingent and daily workers from the Korean Labor & Income Panel Study (KLIPS) undertaken between 1998-2001 and their results suggested that once employed as irregular workers, the chance for them to move to regular jobs was very slim and the stepping stone hypothesis was not very convincing. Similarly, Sigyun Lee (2007) analyzed the labor mobility of contingent and daily workers of the EAP after controlling unobserved heterogeneity. The result showed a strong trap of irregular employment with only 8.8% transition to regular jobs.

According to the manual of the EAP Survey, contingent workers are those ① who are supposed to work more than 1 month and less than 1 year or ② in case there is no contract about employment period, those who do not receive severance pay, bonus, allowances or those who are not regulated by a firm’s employment rules. Daily workers are ① those who are supposed to work less than 1 year or ② in case there is no contract about employment period, those who are hired and paid on daily basis.

Daily workers are ① those who are supposed to work less than 1 year or ② in case there is no contract about employment period, those who are hired and paid on daily basis.

B. Studies supporting stepping stone hypothesis

Overseas studies supporting the stepping stone hypothesis are found mostly in countries where the EPL is not strict. Booth et al.(2002) revealed that fixed-term workers in the UK experienced some wage loss, but 80% of temporary workers moved to regular jobs within 5 years and, for these individuals, temporary employment worked as stepping stones; however this was not the case for seasonal & daily workers. Zijl et al.(2004) conducted quantitative analysis and revealed that those who chose temporary positions over unemployment were more likely to transit to regular status and shorten their period of unemployment. After studying the cases of Germany, the UK, Italy, Netherlands and Spain, Zijl and Leeuwen (2005) showed that with the exception of Spain temporary employment worked as intermediate position between unemployment and regular status, though sometimes it delayed job search activity for obtaining a regular job or sent negative signals on a worker's capability to potential employers;.

In Korea, the study by Jaewoo Ryu and Jaehong Kim (2000) is the first research that revealed contingent & daily employment worked as stepping stones for regular jobss. They used the 2-year data of the Korean Labor & Income Panel Study between 1998 and 1999, and showed that 51.0% of those who moved from contingent & daily status to regular one moved within the same workplaces, implying that contingent & daily employment worked as stepping stones for joining regular jobs. On the other hand, Jaeryang Nam (2011) analyzed state dependence of irregular employment and he revealed that many studies so far might have over-estimated state dependence by using the controversial concept of contingent & daily workers or self-declared irregular workers and the trap of irregular employment was highly likely to have been over-estimated.

(2) Studies on determinants of labor mobility of irregular employment

The study by the European Commission (2012) on 27 EU member countries delved into the determinants of transition from irregular to regular positions. There were some variations among countries, but in general more male than female workers, younger than older workers, more educated workers and more experienced workers were likely to be employed and hold regular jobs and single householders were more likely to become regular workers than married householders by 5〜6%.

Among studies about Korean cases, Sigyun Lee (2007) conducted an analysis by using the concept of contingent & daily workers of additional EAP survey; the result showed peripheral workers such as females and low-skilled workers and low-paid workers were rarely to become regular workers. Since the wage gap between regular and irregular employment influenced negatively in becoming regular workers, it was expected that resolving wage difference would have positive effect on the transition to regular workers. Also, it was shown that the possibility for obtaining a regular job was higher when an irregular worker stayed working in the same workplace for a longer period. This indicates that well-developed firm-specific skills contribute positively for irregular workers to acquire regular status; and it was revealed that workplaces with labor unions showed a higher ratio of transition from irregular to regular positions than workplaces without labor unions. Yongseong Kim (2009) utilized a Competing Hazards Model in his study based on additional EAP survey between 2003 and 2008; the result showed that more male than female workers moved to regular positions and the ratio of irregular employees moving to regular position decreased as workers got older. The construction and service industries showed fewer transitions to regular jobs than in manufacturing industry, while smaller businesses experienced more difficulty in transitioning workers into regular positions. It was also discovered that longer continuous service year has a positive effect on the transition to a regular position. Nakhyeok Choi and Hyeokju Gweon (2010) used the Korean Labor & Income Panel Study data between 2001-2007 and studied fixed-term workers only by setting continuous service year as an independent variable. The result showed that continuous service year had a positive effect on transition to regular status, while it had no significant effect on the transition to irregular status and had a negative effect on transition to unemployment. Female workers and production workers were at a disadvantage in becoming regular workers; and the manufacturing industry was more favorable to transition to regular jobs than other industries. Jaeryang Nam (2011) analyzed the Korean Labor & Income Panel Study data between 2002-2006, and suggested that males, younger and more-educated workers were likely to move to regular positions. The study also suggested that the ratio of transition to regular positions was higher in workplaces with labor unions and among irregular workers with longer periods of continuous service year; but marital status did not affect the ratio.

However, most studies in Korea treated irregular employment as a lump due to limited data; therefore not only fixed-term and TAW type but also daily workers, service contract workers and independent contractors were included in their analysis. This kind of approach results in a very low ratio of moving to regular position and makes it hard to show the determinants of labor mobility by each employment type

3.  Limitation of the concept regarding irregular employment and
blanket approach on "irregular employment"

(1) Limitation of the concept regarding irregular employment

Concerning the study on irregular workers in Korea and abroad, inconsistent definition on irregular employment gives troubles in understanding the scale of each type of irregular workers and doing comparative studies between countries (Kalleberg, 2001). In Korea, the concept of regular vs. contingent vs. daily workers in the EAP survey has been used since 1963 and the last two have been used for the proxy variables of irregular workers. However, many scholars have pointed out its limitation and warned of the over-estimation in the number of irregular workers. Gyeongsu Choi(2001) was the first one to suggest that not all contingent & daily workers could be considered as irregular labor and the EAP survey is not on employment types but on the status of workers. Juyeop Ahn et al.(2001) also mentions that the EAP survey is not about irregular workers and the number of non-regular workers is over-estimated since the contingent & daily workers are residual concept left after strict definition on regular workers. Yuseon Kim (2001; 2003; 2010) on the other hand, insists that contingent & daily workers should be considered irregular labor since those best represented unstable employment, low wages and bad working condition; and the scale of irregular labor in Korea should be identified by a sum of contingent & daily workers plus other irregular employment types (domestic labor, service contract workers, part-timers, etc.).

The fact that the percentage of contingent & daily workers since the year 2000 is decreasing, shows the limitation of using contingent·daily workers as a proxy for irregular laborers. The ratio of contingent & daily workers in the EAP survey peaked at 52.1% in the year 2000. It then dropped to 37.4% in 2012 and continues to drop. Compared to the slight decrease of irregular workers’ ratio to 33.3% in August 2012 from 37.0% in 2004, the decrease of contingent·daily workers was quite considerable. This shows the concept of contingent·daily workers in the EAP survey does not reflect the labor market trend well. Accordingly, we can assume that precedent studies using the concept of contingent & daily workers have much possibility of overstatement regarding the trap of irregular labor.

The definition and scope of irregular employment prepared by the Economic and Social Development Commission in 2002 also has the problem of double counting among irregular workers because the definition considers both the contract period and the ways of providing labor. The TAW, for example, is supposed to be used within 2 years, therefore all TAWs overlap with fixed-term workers. Part-time workers who account for 32.2% of irregular workers overlap with all the other employment types such as TAW, service contract workers, independent contractors and daily workers. Statistics may vary depending on whether the overlaps are removed or not. Therefore, in order accurately to capture the labor mobility of each type of irregular worker, it is crucial to design a method to remove overlaps mutually and exclusively among irregular workers

(2) Blanket approach treating ‘irregular employment’ as a lump

Even though the seven types of irregular employmentwere included following the agreement of the Tripartite Commission in 2002, most studies in Korea treat irregular workers as a lump. Workers are simply divided into binary code (regular vs. irregular) and accordingly different characteristics of each type of irregular workers are ignored. It stands to reason that labor mobility and determinants affecting ratio of transition to regular positions can vary depending on each type of irregular workers. Such a blanket approach treating ‘irregular workers’ as a lump makes it easy to overlook internal heterogeneity among irregular workers. This can mislead and oversimplify their labor mobility.

They are fixed-term workers, part-timers, TAW, service contract workers, independent contractors, domestic workers and daily workers.

Ⅲ. Study method

1. Removing overlaps among irregular workers

Refer to JANG(2012), “A Study on the Definition of Non-regular workers and its Scale in Korea"

(1) Data for Analysis

For the analysis of labor mobility of irregular workers, ꡔPanel Data for Irregular Workersꡕ by Ministry of Employment and Labor is used. The first survey was made with over 20,000 irregular workers in April 2010 and regularly surveyed (a quarter or a half-year) thereafter. This large-scale panel survey focusing only on irregular workers is the first of its kind in Korea. The sample size alone is enough to perform dynamic analysis on diverse types of irregular employment, including TAW whose portion is below 2% of wage workers in Korea. The downsides of the Panel Data include a short period of observation (only two years) and an absence of samples on regular workers for comparison.

TAWs in Korea are allowed in 32 trades only such as computer related work, translators, technicians, drivers, janitors, delivery-related work, telephone operators, talents, office work assistants, etc.

This study used two year data ranged from the first Panel Data (April 2010) to the 8th survey (April 2012); 20,583 workers were included in the first survey and 16,044 panels provided responses without missing any 1th~8th surveys, which holds 77.9% of total 20,583 panels. Panel Data are divided into job history and personal data; job history data includes responses on labor mobility, industry, occupation, employment types, contract period, working hours, wage and information on the company, education/training experience and personal information. Personal data include opportunity to become a regular worker and intention on filing petition to correct on discrimination.

(2) How to remove overlaps among irregular workers

To resolve the overlaps among irregular workers in Panel Data, it is necessary to get rid of double-counting by setting a priority to make each irregular type mutually exclusive and the sum of each type of workers 100%. It seems valid to set priority in the order of stronger irregularity which is farther from standard employment type (indefinite employment contract, single employer). Firstly, independent contractors are, in many cases economically dependent on one employer only, but most of them are still legally recognized as self-employed, not employees. They bear the economic risk of their businesses and they control their own work. So, the nature of work is farthest from the regularity. In case of TAW and service contract workers, they are regarded as ‘employees’, but they are under tripartite employment relations. They have a “user employer” who utilizes their labor and they have a “real employer” who hired them. Therefore, TAW and service contract workers show stronger irregularity than directly hired workers such as daily workers or fixed-term workers. Daily workers frequently move workplaces and their employment relations with a certain employer is often very loose, but it hard to deny that they are hired under a direct employment relationship. Therefore, daily workers do not seem to have stronger irregularity than TAW, service contract workers or independent contractors. Here, [Figure 1] shows the above-mentioned priority in removing overlaps among irregular workers.

As aforementioned, part-time workers are those who work less than 36 hours a week and they belong to any one of the irregular employment types. In theory, part-time workers with no definite employment contract do not overlap with other irregular employment types and there are only 2.9% of such part-timers in the Panel Data. According to the OECD and Eurostat survey on labor force, part-time workers with no definite employment contract are classified as regular workers, but in Korea they are included in the irregular category and it would be interesting to trace their labor mobility. This study refers to these employees as ‘part-time-exclusive’ and categorizes these workers as another type of irregular employment. As a result, this study divides irregular labor into 6 types: 1) fixed-term workers, 2) TAW, 3) service contract workers, 4) daily workers, 5) independent contractors, 6) part-time-exclusive.

Both OECD and Eurostat labor force survey collect data on fixed-term workers, TAW and daily workers under the name of “temporary workers”.

[Fig. 1] Priority when removing overlaps among irregular labor

Independent contractors (Indirect employment)	≫	TAW, service contract (Indirect employment)	≫	Daily workers (Directment employment)	≫	Fixed-term workers (Direct employment)
Regarded as self-employed, not employees		Regarded as employees. Working at the user's work place		Direct employment but frequent move to other workplaces		Major difference from regular labor is that it is limited contract

* Regularity increases to the right.

(3) Characteristics of the samples after removing overlaps

<Table 2> shows the composition ratio before and after removing overlaps among irregular workers based on the first Panel Data. Before removing the overlaps, 41.7% of workers were overlapped compared to the actual number of workers, 20,556 in the sample. Once the overlaps are removed, the ratios by each employment type show some differences. Independent contractors and TAW which have strongest irregularity, are classified as they are and the ratio does not change. However, when part-time labor is dissolved into other employment types, only 2.9% of part-time-exclusive is left. For fixed-term labor, the composition ratio was 64.4% before removing overlaps; however, it was reduced to 54.9% after re-categorization into service contracts, independent contractors and TAW. The appendix in this paper shows distribution of irregular employment types of the samples after removing overlaps and they are sorted by variables regarding individuals and labor market.

<Table 2> Composition ratio before and after removing overlaps among irregular labor

	Before removing overlaps		After removing overlaps
	Number of workers	Ratio(%)	Number of workers	Ratio(%)
Total	29,133	141.7	20,556	100.0
Fixed-term	13,232	64.4	11,290	54.9
TAW	417	2.0	417	2.0
Service contract	1,881	9.2	1,881	9.2
Daily workers	5,762	28.0	5,110	24.9
Independent contractors	1,255	6.1	1,255	6.1
Part-time	6,586	32.0	(PT-exclusive) 603	2.9

2. Analysis models

Labor mobility is change of statuses in economic activity via time (t) dimension. The status of economic activity are divided into regular, irregular workers and non-working (unemployed or non-EAP). To determine whether a trap exists, we should consider several dimensions in terms of time (t) such as how long irregular workers stays in the same irregular employment, the ratio of employees becoming regular workers, the ratio of workers staying at the same regular status after becoming regular workers and how soon workers who moved from irregular to the unemployed or non-EAP status can move to non-working. For dynamic analysis on labor mobility, this study uses Markov transition probability model (Marston, 1976) which is the most commonly used .

To analyze the determinants of irregular labor mobility, survival analysis is used to show the difference in the survival ratio among groups over time, which makes more exquisite analysis than Panel Logit. Survival analysis uses hazard rate and it indicates the rate at which subjects fail by t given that the subjects have survived until t (Park, 2006; Mills, 2011). Depending on how the distribution of the hazard rate is assumed, we can use exponential, Weibull, Compertz or Log-logistic distribution. However, it is hard to know how hazard functions are distributed and in such a case Cox proportional Hazard model is used. Since hazard may change over time, Cox model uses Hazard Ratio (HR) instead of hazard. Cox's proportional hazard model can be expressed as follows.

[eqn: h _{i} (t)`=`h _{0} (t)exp( beta _{1} x _{i1`} +.......+ beta _{k} x _{ik} )]	(Formula-1)

Where [eqn: i] represents individuals, [eqn: x _{1,`} x _{2,`} .......x _{n}] are variables affecting [eqn: h _{i} (t)]. [eqn: h _{0} (t)] is the baseline hazard rate, indicating the HR at point t of all values are 0; [eqn: beta _{k}] is a survival regression coefficient related to [eqn: x], similar to that of the regression coefficient of OLS and a vector of [eqn: x _{i}]. Algebra transition of Formula-1 is shown below:

[eqn: log {h _{i} (t)} over {h _{0} (t)} = beta _{1} x _{i1} + beta _{k} x _{ik}]	(Formula-2)

where the hazard ratio [eqn: h _{i} (t)/h _{0} (t)] is a relative hazard of th individual vs. the baseline hazard ratio [eqn: h _{0} (t)] at point [eqn: t] and it is always constant. This indicates that the HR remains constant even when the hazard changes over time. Using this method, we can calculate the HR, [eqn: h _{i} (t)/h _{j} (t)] between individual [eqn: i] and [eqn: j]. If covariate is [eqn: x], the hazard ratio can be expressed as Formula-3; even with multiple covariates, the HR is proportionate to the difference of the observed value of each variable.

[eqn: {h _{1} (t)} over {h _{0} (t)} =exp` beta _{1} (x _{i1} -x _{j1} )+ beta _{k} (x _{i1} -x _{j1} )]	(Formula-3)

However, if there is a time-varying variable among explanatory variables of the model, the 'extended Cox model' should be used for analysis. The Extended Cox model is a non-proportional hazard model and can be expressed as the below formula:

[eqn: h _{i} (t)`=`h _{0(t)} exp beta _{1} x _{i1`} +` beta _{2} x _{i2} (t)......+ beta _{k} x _{ik}]	(Formula-4)

where [eqn: x _{1}] is a time-independent variable and [eqn: x _{2} (t)] is a time-varying variable at point t.

As censored data exist in the Cox model, Partial Likelihood (PL) is used instead of Maximum Likelihood. That is, the [eqn: beta] value of the covariate neglects when censoring occurs and only considers likelihood at the point of event. PL is calculated as follows:

[eqn: PL( beta )`=` prod _{i=1} ^{n} L _{j} (j=1,2,3,......k)]	(Formula-5)

The limitation of Cox model, however, is that it assumes certain individual experiences only one event. For a Cox model to have validity there should be no competing events. But in the real world, it would be more realistic to assume that there would be competing risks among multiple events at a certain point. When competing risks are present, the occurrence of 'interesting events' can be affected by competing events. As a result, the Cox model tends to over-estimate the occurrence of a certain event since it is a single-exit model over time (Wolbers et al., 2009). Therefore, it is better to employ the Competing Risk Model (CRM) for the analysis based on the assumption of competing events. Algorithm of this model is expressed in Formula-6 and the HR of each event can be obtained. The CRM, like the Cox model, has a hazard as a dependent variable, but  unlike the Cox model, the CRM has the advantage of estimating Subhazard Ratio (SHR) of each event under the condition of competing risks. The exit rate can be obtained by comparing the SHR of the reference group and the comparison group; if SHR ratio is >1, it is interpreted that exit rate (death rate) of the compared group is higher than that of the reference group.

[eqn: SHR=exp( beta _{1} )= {h(t□`comparison`group)} over {h(t□`reference`group)}]

[eqn: h _{j} (t)= lim _{TRIANGLE t-> 0} {{P(t LEQ T<t+ TRIANGLE t,`D=j□T GEQ )} over {TRIANGLE t}}]	(Formula-6)

The Partial Likelihood (PL) of CRM is expressed in Formula-7 where [eqn: d _{ij}] indicates the death of i caused by  j at point [eqn: t _{i}]. Since likelihood is hazard function brought by a certain cause, [eqn: beta] value can be estimated (Bakoyannis & Toulourmi, 2010).

[eqn: PL( beta )`=` prod _{j=1} ^{k} ` prod _{i=1} ^{n} lambda _{j} (t _{i} ;`x _{i,`} beta ) ^{d _{ij}} exp LEFT [ - int _{0} ^{t _{i}} {lambda _{j} (u;`x _{i} ,` beta )} du RIGHT ]]	(Formula-7)

3. Setting Variables

<Table 3> presents an operational definition of each variable in this analysis. Based on precedent studies, explanatory variables affecting labor mobility are chosen. Variables related to personal attributes include gender, age, education, training experience, marital status, etc. Results from precedent studies show that in general more males than females, younger rather than older people, more educated(associate degree or higher) than less educated and trained than untrained workers show a higher exit rate to regular workers, whereas marital status and period of irregular employment vary among studies. Additionally, to verify 'duration dependency' of irregular employment regarding whether transition to regular job is more difficult when irregular work experience is longer, total period of irregular employment in the same workplace before the first survey was also included in the analysis.

Labor market factors include firm size, occupations, industries, the presence of labor unions, wage and 6 irregular employment types. Wage levels are divided into two groups: the high wage group with a monthly wage of over 2,285,000 KRW and low wage group with monthly wage below 2,285,000 KRW, based on the Working Condition Survey by employment types in 2010. Irregular employment types are divided into 6 categories with overlaps removed: fixed-term, TAW, service contract, daily workers, independent contractors and part-time-exclusive. Fixed-term workers are chosen as the reference group since they show higher ratio of transition to regular jobs and better working conditions compared to other irregular workers.

Explanatory variables are divided into time-invariant and time-varying variables. In theory, all explanatory variables can change during surveys except gender. Here, age is treated as time-invariant because it can be measurable at any time. Since irregular employment period at the current workplace and education are also unlikely to change in the values during two years of observation, these were also categorized as time-invariant variables. The rest are treated as time-varying variables. And according to the precondition of causality analysis that the change of explanatory variables precedes change of dependent variables, [eqn: t-1] value was used for explanatory variable to apply lag-time.

<Table 3> Operational definitions of variables

Variable name	Operation definition
1) Gender	Male=0, Female=1
2) Age	Age 15-29, 30-39, 40-54 and over 55 (based on the first survey)
3) Education	High school degree or lower, college degree(including associate degree), master's degree
4) Education/training	Having experience Yes=1, No=0
5) Marital status	Married=1, Non-married=0
6) Irregular employment period	Length of service as irregular workers before the first survey (at current workplace)
7) Firm size	Employees less than 30, 30-99, 100-299 and over 300
8) Occupation	Management(1,2), Office work(3), Service/sales(4,5), Agriculture/fishery(6), manual work/machine assembly(7～9)
9) Industry	Agriculture/fishery(1),Mining/manufacturing(2,3), Electricity/gas/water/construction (4～6), Wholesale/retail food and lodging(7～9), Transportation and communication(8～10), Finance/insurance(11), Real estate/service/education (12,14,16), Welfare/private business/service(13,15,17〜21)
10) labor unions	Yes = 0, No = 1
11) Wage level	Below monthly average wage of regular workers (2,285,000 KRW) = 0, over 2,285,000 KRW = 1
12) Employment types	Fixed-term, TAW, service contract workers, daily workers, independent contractors, part-time-exclusive

Ⅳ. The results of analysis

1. Results on trap analysis

<Table 4> provides an overall trend of labor mobility for two years from April 2010 to April 2012. While the transition from irregular to regular positions was only 4.4%, 71.8% stayed in irregular employment, 8.0% became unemployed and 15.8% moved to non-EAP, showing high exit rate to non-working status. Although there was some difference depending on employment types, ‘closed chain of irregular employment⇋ unemployed ⇋non-EAP’ was dominantly observed. This corresponds to the results of previous studies showing that many irregular workers were in the closed path between unemployed and non-EAP status.

<Table 4> Labor mobility by irregular employment types for 2 years

(Unit : person, %)

		Total	Employment types in April 2012
			Regular	FT	TAW	SC	DW	IC	PT	SE	Unemp	Non-EAP
Employment types in April 2010	FT	8,753	562	4,832	65	230	426	160	69	353	691	1,365
		(100.0)	(6.4)	(55.2)	(0.7)	(2.6)	(4.9)	(1.8)	(0.8)	(4.0)	(7.9)	(15.6)
	TAW	341	9	61	101	41	27	5	2	5	40	50
		(100.0)	(2.6)	(17.9)	(29.6)	(12.0)	(7.9)	(1.5)	(0.6)	(1.5)	(11.7)	(14.7)
	SC	1,477	25	218	22	783	94	10	2	31	127	165
		(100.0)	(1.7)	(14.8)	(1.5)	(53.0)	(6.4)	(0.7)	(0.1)	(2.1)	(8.6)	(11.2)
	DW	4,050	65	668	20	109	1,748	68	24	259	345	744
		(100.0)	(1.6)	(16.5)	(0.5)	(2.7)	(43.2)	(1.7)	(0.6)	(6.4)	(8.5)	(18.4)
	IC	951	20	92	1	7	21	581	4	94	44	87
		(100.0)	(2.1)	(9.7)	(0.1)	(0.7)	(2.2)	(61.1)	(0.4)	(9.9)	(4.6)	(9.1)
	PT	441	31	163	5	4	16	10	46	23	30	113
		(100.0)	(7.0)	(37.0)	(1.1)	(0.9)	(3.6)	(2.3)	(10.4)	(5.2)	(6.8)	(25.6)
Total		16,013	712	6,034	214	1,174	2,332	834	147	765	1,277	2,524
		(100.0)	(4.4)	(37.7)	(1.3)	(7.3)	(14.6)	(5.2)	(0.9)	(4.8)	(8.0)	(15.8)

In the ratio of transition to regular status ⅰ) fixed-term and part-time-exclusive group and ⅱ) TAW, the service contract workers, daily workers and independent contractors group showed a noticeable difference. Part-time-exclusive workers showed the highest transition ratio of 7.0%, while the transition ratio of fixed-term workers under direct employment was 6.4%, the ratio of TAW under indirect employment was 2.6%, and that of service contract workers was 1.7%. It is notable that the ratio of daily workers was also very low (1.6%). To summarize, with the exception of independent contractors, all the other types of irregular workers showed 20%〜30% of transition to unemployed or non-EAP status for two years and it overwhelmed the ratio of transition to regular status.

When it comes to the ratio of staying in the same irregular employment for 2 years, independent contractors showed the highest retention rate of 61.1%, followed by fixed-term employees(55.2%), service contract workers(53.0%), TAW(29.6%) and daily workers(43.2%). It was also demonstrated that 37.0% of part-time-exclusive workers moved to fixed-term positions for 2 years and only 10.4% stayed in the same positions, which suggests it functions as a bridge to move to other irregular employment types. The ratios of transition to indirect employment (TAW, service contract) from fixed-term contracts were 0.7%, 2.6% respectively, which showed that a ‘crowding-out effect’ replacing direct employment to indirect employment was not found.

<Table 5> Labor mobility of fixed-term and part-time-exclusive every half-year

(Unit : %)

Start point : April 2010		at	aa1	aa2	as	au	an
Fixed-term	After 6 months	3.1	72.4	4.1	1.2	7.7	11.4
	After 12 months	5.7	60.9	4.9	2.6	10.4	15.5
	After 18 months	7.7	55.7	4.7	3.9	9.5	18.5
	After 24 months	7.9	53.5	4.7	4.9	9.8	19.3
Part-time-exclusive	After 6 months	4.4	34.3	25.8	1.3	7.2	26.9
	After 12 months	5.8	21.9	27.3	3.6	11.7	29.7
	After 18 months	7.8	12.2	32.5	4.9	9.4	33.2
	After 24 months	9.0	8.7	34.0	6.7	8.7	32.8
* at : Fixed-term/part-time-exclusive → regular, aa1: Fixed-term/part-time-exclusive → fixed-term/part-time-exclusive, * aa2: Fixed-term/part-time-exclusive → other irregular, as: Fixed-term/part-time-exclusive → self-employed, * au: Fixed-term/part-time-exclusive → unemployed, an: Fixed-term/part-time-exclusive → non-EAP

Now, let’s examine the labor flow of each employment type by using Markov Transition Probability Model. Here, the analysis focuses on fixed-term and part-time-exclusives onlydue to their characteristics of providing labor. The ratio of transition to regular position of TAW, service contracted workers, daily workers and independent contractors is less than 4%. <Table 5> shows labor mobility of fixed-term and part-time-exclusive workers every half-year. In case of fixed-term workers converted to regular status, the transition ratio increases over time; up to 7.9% after 24 months. aa1(indicating moving from fixed-term to fixed-term) decreased in ratio as time progresses; it dropped to 53.5% after 24 months and half of the workers exited from fixed-term workers. aa2 (indicating moving from fixed-term to other irregular employment) stayed around 4% for 24 months, showing not much labor mobility. However moving from fixed-term to unemployed (au) did not show noticeable increase (10.4% after 12 months and 9.8% after 24 months) as time went on. Moving to non-EAP (an), on the other hand, increased up to 19.3% after 24 months, which suggests exit from the labor market was accelerated. To summarize, half of the fixed-term workers stayed in the position for 2 years and only 4.7% of the workers who exited from fixed-term position moved to other irregular employment, while the ratio of moving to unemployed/non-EAP reached 29.1%. This suggests that there is a solid moving path between fixed-term and unemployed/non-EAP.  In case of part-time-exclusive, 8.7% moved to unemployed (au) and 32.8% moved to non-EAP (an) after 24 months, showing 41.5% of moving to non-working status. This suggests part-time-exclusive is used for temporary economic activity only when there is a job.

<Table 6> Retention rates and success rates of regular position by employment types

(Unit : %)

	All irregular				FT				PT				SC				DW				IC
Start point : Apr2011	RR		SR		RR		SR		RR		SR		RR		SR		RR		SR		RR		SR
After 3 months	77.2		4.6		77.4		7.3%		66.7		17.8		90.3		5.2		77.3		3.4		85.0		1.8
After 6 months	68.0		4.0		68.8		6.5%		58.3		15.6		81.4		4.7		61.2		2.7		67.5		1.4
After 9 months	Survey was not conducted in January 2012.
After 12 months	54.5	3.2		55.6		5.2%		54.2		14.5		68.3		3.9		49.2		2.2		40.0		0.8
* The workers who moved to regular jobs up until April 2011 were included in the analysis; retention rate of regular positions = the number of workers remaining in regular positions checked every 3 months ÷ the number of regular workers at the start point in April 2011; success rate of regular positions = retention rate × rate of entering regular positions at the start point in April 2011.

The next analysis focuses on retention rate (RR) and success rate (SR) designed by Hyosu Lee (2002). The RR in <Table 6> indicates the ratio of irregular workers staying in regular status over time and the SR of regular position' is the value that multiplies the RR by ratio of joining regular status from irregular status at the starting point in April 2011. April 2010 was not set as the starting point because observation period is needed to identify workers who moved to regular job since all samples of the Panel Date at the first survey in April 2010 were irregular workers. Therefore, this study can observe only one year of RR and SR on workers who moved to regular status between April 2010 and April 2011.

In the case of fixed-term workers, the likelihood of workers who moved to regular jobs up until April of 2011 and who stayed in the same status after 12 months diminished over time and the RR was 55.6%. Whereas, the SR was only 5.2% of all fixed-term workers who succeeded in keeping regular positions, and this means that 5.2 out of 100 fixed-term workers could keep their regular position for 12 months after exiting to a regular position. Compared to the blanket approach treating irregular employment as a lump, the RR among fixed-term and irregular workers does not show much of a gap once fixed-term and other irregular workers joined regular positions. The SR of fixed-term workers, on the other hand, ranged from 7.3% to 5.2% for 3~12 months, which was higher by 40% than 4.6%~3.2% for the entire irregular worker group. It indicates that the trap of fixed-term workers was over-estimated when compared with the blanket approach treating irregular employment as a lump.

When it comes to the RR for 12 months, service contract workers showed an unexpectedly high 68.3%, whereas the RR of daily workers and independent contractors were lower than that of fixed-term employees. As seen in the descriptive statistics, the SR of fixed-term workers were higher than other types of irregular workers; and notably independent contractors showed only 0.8% of SR for 12 months, suggesting that only a small number of independent contractors move to and keep regular positions due to the nature of providing labor.

<Table 7> Retention Rate and Success Rate of unemployed and non-EAP

(Unit : %)

Start point : April 2011	Retention Rate of the unemployed & non-EAP	Rate of failed re-employment of the unemployed & non-EAP
After 3 months	70.7	19.9
After 6 months	60.5	17.0
After 12 months	51.2	14.4
* The analysis was conducted on all irregular workers who moved to unemployed and non-EAP until April 2011 1) Retention rate (RR) of the unemployed and non-EAP = the number of workers checked every 3 months who remain unemployed and non-EAP÷ the number of workers who became unemployed and non-EAP until April 2011 2) Success rate (SR) of failed re-employment of the unemployed and non-EAP = retention rate × rate of entering unemployed and non-EAP status from irregular employment for 1 year since the starting point in April 2011.

<Table 7> shows the RR and SR of becoming the unemployed and non-EAP, meaning how long they stay in the same status (RR) and how long they fail to get jobs again (SR). Higher RR means that they stay in the same status for longer period because they did not succeed in getting a new job, suggesting they are in a trap. Lower SR means they fail to maintain unemployed and non-EAP status and they succeed in getting jobs again. In case of workers who moved to unemployed or non-EAP status between April 2010 and April 2011, the RR is 70.7% after 3 months, 60.5% after 6 months and 51.2% after 12 months, showing the chances of moving to employed status again increases over time. The SR of the unemployed and non-EAP decreased over time and only 14.4% remained after 12 months. These two facts suggest that the unemployed and non-EAP in Korea do not completely withdraw from labor market but continue to move among irregular workers ↔ unemployed ↔ non-EAP. This result corresponds to Yungyu Yun and Jaemin Seong (2011).

2. Results of analysis on determinants of irregular worker’s labor mobility

Firstly, variables affecting labor mobility are analyzed through extended Cox model. Here, even with change of workplace, if irregular status is maintained, it is not considered exit. <Table 8> shows the result of a model for all ages. If other conditions are equal, the HR of female workers was 1.105. This means female workers were more likely to exit from irregular employment than male workers by 10.5%, which was statistically significant. As discussed earlier in this study, more female workers than male became unemployed and non-EAP, females tend not to keep the same irregular status longer than men. For age variable, with 40-54 as the reference group, the HR of 15-29 age group was 1.401, that of over 55 aged group was 1.422. This means youths and aged people experience exit from the same irregular jobs by 40.1% and 42.2% higher than the reference group. For education variable, HR of workers with college/master's degree is lower than that of workers with high school degree, suggesting that higher level of education increases possibility to maintain the same irregular employment; however it was not statistically significant in case of workers with master's degree. For marital status, workers with spouses were more likely to maintain the same irregular employment(significant at 90% level).

For industry variable, with mining/manufacturing industry as the reference group, HR of finance/insurance was less than 1, which is the only one industry with higher rate of maintaining the same irregular status than the reference group. The HR of other industries is over 1, suggesting high exit rate and great variability in employment types. For occupation, the HR of manager/specialist/office worker is less than 1 compared to that of technician/machine operator/manual workers, showing higher rate of maintaining the same irregular status. The HR of service/sales and agriculture/fishery worker was over 1 and HR of only manager/specialist group shows statistical significance. For firm size, the HR of firm size of 30〜99 only is less than 1 compared to that of firm size of less than 30 employees, suggesting significantly higher rate of maintaining the same irregular status. For employment types, the HR of service contract and independent contractors are 0.770 and 0.793 respectively, suggesting significantly high rate of maintaining the same employment types compared to fixed-term workers. the HR of TAW/daily workers/part-time-exclusive

<Table 8> Result on exit determinants from the same irregular employment (extended Cox model)

Explanatory variable	Explanatory variable category	HR	Std. Err.	z	P>z
Gender	Male=0, Female=1	1.105	0.025	4.500	0.000
Age (Reference group = Age 40-54)	Age 15-29	1.401	0.048	9.860	0.000
	Age 30-39	1.024	0.029	0.810	0.416
	Over 55	1.422	0.036	14.030	0.000
Education (Reference group =High school degree)	College degree	0.927	0.028	-2.510	0.012
	Master's degree	0.827	0.097	-1.620	0.105
Marital status	Non-married=0, married=1	0.963	0.022	-1.660	0.098
Industry (Reference group = Mining/manufacturing)	Agriculture/fishery	1.571	0.084	8.430	0.000
	Electricity/gas/water/construction	1.157	0.047	3.590	0.000
	Wholesale/retail/lodging/food	1.130	0.043	3.210	0.001
	Transportation/communication	1.099	0.058	1.800	0.072
	Finance/insurance	0.851	0.068	-2.040	0.042
	Real estate/service/education	1.290	0.046	7.200	0.000
	Welfare/private business/service	1.092	0.044	2.190	0.029
Occupation (Reference group = manual work, machine assembly)	Manager, specialist	0.789	0.035	-5.420	0.000
	Office work	0.925	0.039	-1.820	0.069
	Service/sales	1.005	0.031	0.160	0.869
	Agriculture/fishery	1.006	0.095	0.060	0.949
Firm size (Reference group = less than 30 employees)	30-99 employees	0.939	0.028	-2.110	0.035
	100-299 employees	0.974	0.042	-0.620	0.537
	Over 300 employees	1.075	0.053	1.480	0.138
Employment types (Reference group = Fixed-term)	TAW	1.117	0.073	1.700	0.089
	Service contract workers	0.770	0.029	-6.870	0.000
	Daily workers	1.363	0.037	11.300	0.000
	Independent contractors	0.793	0.042	-4.380	0.000
	Part-time-exclusive	1.964	0.094	14.110	0.000
Education/training experience	Yes=0, No=1	1.030	0.042	0.710	0.476
Labor union	No=1, Yes=0	1.030	0.018	1.670	0.095
Length of service at current workplace	Unit: month (before the first survey)	0.999	0.000	-10.550	0.000
Wage	Below average wage of regular workers=0, Over average wage of regular workers=1	0.893	0.043	-2.370	0.018
Cox regression Breslow method for ties		No. of obs=20556		No. of subjects=20556
Log-pseudolikelihood= -109898.7		No. failed=11464		LR chi2(30)=1353.91

is over 1, showing lower survival rate than that of fixed-term. The HR of part-time-exclusive especially is 1.964 showing two times higher exit rate than that of fixed-term workers, suggesting very low rate of maintaining the same employment type..

Workers with education/training experience showed positive exit rate, but not significant statistically and presence of labor union is also not statistically significant. The length of service as irregular status before the first survey point shows significantly negative effect on exit from the same employment type. This shows that longer length of service as irregular workers at the same workplace increases the possibility of maintaining the same employment type, which tells the existence of duration dependency. When it comes to wage levels, the HR of high-paid irregular workers, whose earning is more than average wage of regular workers, is 0.893 and the rate of maintaining the same irregular status is also higher by 10.7%, suggesting that high-paid irregular workers tend to work longer at the same job compared to other irregular workers who show big scale of transition to unemployed and non-EAP status.

To move on, <Table 9> illustrates results on the determinants of transition to regular status by using a Competing Risks Model. For gender variable, the Subhazard Ratio (SHR) of female workers is 0.744, which means female workers’ transition rate to regular status is 25.6% lower than that of males. For age, with 40-54 as the reference group, the rate of transition to regular status within the 15-29 age group is 1.786 and that of the 20-39 age group is 1.463, showing 1.5〜1.8 times higher than 40-54 age group.  However, the SHR of workers over 55 dropped to 0.395, showing a drastically low rate of transition to regular jobs. For education variable, we can see that workers with college & master's degrees show a higher rate of transition to regular status, 1.357 (statistically significant) and 1.425 (not significant) respectively. For marital status, married workers are more likely to become regular workers, but it was not statistically significant. For industry variable, with mining/manufacturing as the reference group, the SHR of all the other industries is less than 1, showing highest rate of transition to regular position in mining/manufacturing. The SHR of agriculture/fishery, electricity/gas/water/ construction and real estate/service/education is less than 0.5 and the transition rate to regular status in the said industries is less than 50%. The SHR of welfare/private business/home service is 0.540 and that of wholesale/retail/food/lodging is only 0.625, showing very low and statistically significant rate of transition to regular position, compared to fixed-term workers. For firm size, businesses with over 30 employees shows higher rate of transition to regular jobs

<Table 9> Results of the determinants of transition to regular status (Competing Risks Model)

Explanatory variable	Explanatory variable category	SHR	Std. Err.	z	P>z
Gender	Male=0, Female=1	0.744	0.059	-3.700	0.000
Age (Reference group = Age 40-54)	Age 15-29	1.786	0.210	4.930	0.000
	Age 30-39	1.463	0.138	4.030	0.000
	Over 55	0.395	0.064	-5.720	0.000
Education (Reference group=High school degree)	College degree	1.357	0.125	3.320	0.001
	Master's degree	1.425	0.405	1.240	0.214
Marital status	Non-married=0, married=1	1.002	0.090	0.020	0.983
Industry (Reference group = Mining/manufacturing)	Agriculture/fishery	0.194	0.115	-2.770	0.006
	Electricity/gas/water/construction	0.453	0.071	-5.030	0.000
	Wholesale/retail/lodging/food	0.625	0.075	-3.890	0.000
	Transportation/communication	0.804	0.112	-1.560	0.119
	Finance/insurance	0.761	0.152	-1.370	0.171
	Real estate/service/education	0.456	0.055	-6.560	0.000
	Welfare/private business/service	0.540	0.072	-4.610	0.000
Occupation (Reference group = manual work, machine assembly)	Manager, specialist	1.060	0.145	0.420	0.672
	Office work	1.584	0.185	3.940	0.000
	Service/sales	0.985	0.116	-0.130	0.897
	Agriculture/fishery	0.593	0.412	-0.750	0.452
Firm size (Reference group = less than 30 employees)	30-99 employees	1.018	0.101	0.180	0.861
	100-299 employees	1.264	0.166	1.780	0.074
	Over 300 employees	1.219	0.179	1.340	0.179
Employment types (Reference group = Fixed-term)	TAW	0.273	0.104	-3.410	0.001
	Service contract	0.188	0.048	-6.510	0.000
	Daily workers	0.316	0.050	-7.240	0.000
	Independent contractors	0.287	0.064	-5.640	0.000
	Part-time-exclusive	1.011	0.190	0.060	0.955
Education/training experience	Yes=0, No=1	0.791	0.092	-2.010	0.044
Labor union	No=1, Yes=0	1.266	0.147	2.030	0.042
Length of service	Unit: month(before the first survey)	1.002	0.001	3.320	0.001
Average wage	Below average wage of regular position=0, Over average wage of regular position=1	1.643	0.186	4.400	0.000
No. of obs=298205		No. censored=9781		No. of subjects=20556
Log pseudolikelihood = -7428.9423		Wald chi2(30)=635.05		No. failed=791

than that of businesses with less than 30 employees (not statistically significant). For occupation variable, the SHR of office workers is 1.584, showing the rate of transition to regular position is 1.6 times higher than that of technicians, machine operators, manual workers; and other occupations do not show significant result.

As for irregular employment types, all irregular employment types except part-time-exclusive showed SHR ranged from 0.188 to 0.316, showing low rate of transition to regular position. This implies fixed-term labor is the most advantageous irregular type for transition to a regular position. In case of part-time-exclusive, the rate of transition to regular positions is slightly higher than that of fixed-term workers, but it is not statistically significant. Workers with no experience in education or training shows a 20.9% lower rate of transition to regular position than workers with education or training experience, which corresponds to the results of the studies by Sigyun Lee (2007) that education/training experience increases possibility to convert to regular position.

For presence of labor union, the SHR of workplaces without labor unions is 1.266 showing a 26.6% higher rate of transition to regular status than that of workplaces with a labor union and it is statistically significant. Since the mid-2000s Korean labor unions have succeeded in drawing social attention to the issue of irregular labor and they have campaigned hard for “an equal pay for equal work”, the prohibition of discrimination and the abolition of TAW. Yet, the interests of regular union workers are not always in line with that of irregular workers, and accordingly, regular workers in some labor unions oppose the inclusion of irregular workers as union members. Therefore, it is hard to say unequivocally whether labor unions are conducive to the transition of irregular workers to regular employment.  Regarding this point, it is especially important to keep in mind that when labor unions (for regular workers) consider using irregular workers as a buffer at the time of business reductions, it would be more difficult for irregular workers to become regular workers. Previous studies also show contrasting views about the effect of the labor unions in transition to a regular position, therefore more in-depth analysis is needed.

In case of length of service before the first survey point at current workplace, a longer length of service increased the possibility of transition to a regular position, which is statistically significant. Such a result corresponds to that of studies by Yongseong Kim (2009) and Jaeryang Nam (2011). They suggest that working continuously at the same work place is more advantageous than moving to another work place for transition to regular position. By wage levels, high-paid workers whose earning is higher than average wage of regular workers are 1.6 times more likely to become regular workers and it is statistically significant. In extended Cox analysis, as discussed earlier, high-paid workers are highly likely to keep the same employment types

In <Table 10> regarding determinants of moving to ‘non-employed’ (unemployed and non-EAP status), the SHR of female workers is 1.174 and as expected, the rate of female workers becoming non-employed was 17.4% higher than male workers, which was statistically significant at a 95% level. This result corresponds to the results of similar studies that showed female workers who considered their irregular work as second jobs tended to transit more often into non-employed status. For the age variable, with the 40-54 age group as the reference group, the SHRs at the age of 15-29 and the over 55 age group were over 1.6 and they were 60% more likely to move to become non-employed. It seems because in case of 15-29 age group, many of them manage both work and classes, and tend to shuttle between being employed and non-employed status, while workers over 55 tend to exit from the labor market due to mostly retirement or difficulty in finding re-employment. As for the education variable, the SHRs of workers with college degrees and master's degrees were 0.839 and 0.581 respectively. This suggests higher level of education increases survival rate in continuing economic activity. When it comes to marital status, the rate of exit to non-employed status was 7.1% lower in married workers than non-marred workers (statistically significant). Male workers with spouses are less likely to become non-employed because they are usually main breadwinners in Korea. For industry variable, with mining/manufacturing as the reference group, the SHRs of other industry groups than transportation/communication and finance/insurance group were over 1 and they were statistically significant. This suggests that these industries show higher rate of moving to non-employed as well as lower rate of transition to regular position as seen earlier. Notably, the SHR of the agriculture and fishery industry was 1.974, showing almost 2 times higher rate into non-employed, compared to workers in mining and manufacturing. For the occupation variable, the SHR of managers & the specialist group was 0.823, suggesting a statistically lower rate of exit to non-employed status than the technicians/machine operators/manual workers; no statistical significance was found in other occupations, which implies that exit to unemployment or non-EAP is more prevailing in all other occupations than manager/specialist group.

<Table 10> Determinants of transition to unemployment or non-EAP (Competing Risks Model)

Explanatory variable	Explanatory variable category	SHR	Std. Err.	z	P>z
Gender	Male=0, Female=1	1.174	0.030	6.180	0.000
Age (Reference group = Age 40-54)	Age 15-29	1.629	0.065	12.250	0.000
	Age 30-39	1.012	0.036	0.340	0.734
	Over 55	1.620	0.047	16.490	0.000
Education (Reference group=High school degree)	College degree	0.839	0.031	-4.780	0.000
	Master's degree	0.581	0.092	-3.420	0.001
Marital status	non-married=0, married=1	0.929	0.024	-2.800	0.005
Industry (Reference group =Mining/manufacturing)	Agriculture/fishery	1.974	0.123	10.920	0.000
	Electricity/gas/water/construction	1.404	0.068	6.960	0.000
	Wholesale/retail/lodging/food	1.339	0.060	6.470	0.000
	Transportation/communication	0.930	0.065	-1.040	0.296
	Finance/insurance	0.968	0.092	-0.350	0.730
	Real estate/service/education	1.496	0.063	9.540	0.000
	Welfare/private business/service	1.176	0.057	3.360	0.001
Occupation (Reference group = manual work, machine assembly)	Manager, specialist	0.823	0.043	-3.780	0.000
	Office work	0.907	0.046	-1.950	0.051
	Service/sales	0.982	0.034	-0.530	0.594
	Agriculture/fishery	1.008	0.103	0.080	0.934
Firm size (Reference group = less than 30 employees)	30-99 employees	0.965	0.032	-1.060	0.288
	100-299 employees	0.966	0.048	-0.700	0.481
	Over 300 employees	1.091	0.060	1.580	0.115
Employment types (Reference group = Fixed-term)	TAW	0.941	0.072	-0.790	0.428
	Service contract	0.775	0.033	-5.920	0.000
	Daily workers	1.285	0.040	8.050	0.000
	Independent contractors	0.667	0.046	-5.890	0.000
	Part-time-exclusive	0.960	0.065	-0.600	0.547
Education/training experience	Yes=0, No=1	1.119	0.050	2.520	0.012
Labor union	No=1, Yes=0	1.010	0.029	0.340	0.735
Length of service at current workplace	Unit: month (before the first survey)	0.998	0.000	-10.300	0.000
Average wage	Below average wage of regular workers=0, Over average wage of regular workers=1	0.693	0.045	-5.640	0.000
No. of obs=298205		No. censored=9781		No.of subjects=20556
Log pseudolikelihood = -72612.301		No. failed=7559		Wald chi2(30)=1613.34

When it comes to the firm size, the SHR of businesses with over 30 employees were over 1 or less than 1, which was not statistically significant. For the employment types variable, the SHR of daily workers was 1.285, which showed a higher rate of people moving to non-employed status than fixed-term workers. The SHRs of service contract workers and independent contractors were only 0.775 and 0.667 respectively, showing a 22.5% and a 33.3% higher survival rate than that of fixed-term workers and this suggested that they are more likely to continue economic activity and not to exit to non-employed. Workers without education and training experience are 11.9% more likely to become non-employed than workers with education and training experience, suggesting that education and training experience increases the possibility of transition to a regular position and reduces moving to non-employed status at the same time. The presence of labor unions in workplaces did not have a statistically significant effect on the transition to non-employed status. In the case of length of service at current workplaces before the first survey point, the rate of exit of workers becoming non-employed was significantly low with longer length of service, suggesting that the length of service of irregular employment not only increases the rate of transition to regular positions, but it also reduces the rate into non-employed status, as discussed earlier.

Therefore, it can be interpreted that irregular employment is inductive to keep the position to increase possibility to settle in the better jobs. For rate of moving to regular position according to wage level, the SHR of high-paid group of workers whose earnings are higher than average wage of regular workers was only 0.693 and the rate of maintaining economic activity was 30.7% higher.

In summary, females, workers over 55, workers with only a high school degree or lower, workers without education or training experience and lower-paid workers showed a higher rate of exit to becoming non-employed, which corresponds to the result of the precedent studies. For the employment types variable, service contract workers and independent contractors showed a lower rate of exit compared to fixed-term employees, showing a higher rate of maintaining the same economic activity. The firm size and the presence of labor unions, however, did not have any effect on workers moving into becoming regular status.

Ⅴ. Summary and conclusion

This study starts from the awareness that studies on irregular employment so far use different definitions among researchers and the number of irregular workers shows sizable variance, which affected study results. Also previous studies considers irregular employment types as a lump and such a blanket approach leads to overlooking internal heterogeneity among various types of irregular workers.

Closely examining two years of data from the Panel Survey of Irregular Workers, undertaken by Ministry of Employment and Labor, this study analyzes whether the labor mobility of irregular workers is a trap or a stepping stone, and which explanatory variables impact the workers’ mobility. To figure out the scale of each irregular employment type,  double counting was eliminated and the number of each type of irregular workers was recounted with hierarchical structure of: independent workers > TAW and contract workers for a specific task > daily workers > fixed-term workers. The part-time workers who do not belong to any other types of irregular workers are referred to as ‘part-time-exclusive’ and they are included in the analysis.

Based on the work above, several aspects of labor mobility are reviewed to verify the durable trapping hypothesis and it is analyzed first as to whether labor mobility of irregular workers is actually a trap. The overall rate of transition from irregular to regular positions between April 2010 and April 2013 was only 4.4%; by employment types, part-time-exclusive was 7.0%, fixed term 6.4%, TAW 2.6%, independent contractors 2.1%, daily workers 1.6% and service contract workers 1.7%, respectively. The average rates among the 6 employment types in moving to unemployed or non-EAP were 8.0% and 15.8% respectively, showing a closed path of 'irregular labor⇋unemployment⇋ non-EAP'. In analysis on how many workers who move to regular positions stay in the same status by using Markov’s transition probability model, 77.2% maintained their status for 3 months, 68.0% for 6 months and 54.5% for 12 months; a similar trend is found in each irregular employment type. Among workers who moved to a non-employed status, 51.2% of workers were not employed after 12 months, but the rest were re-employed. These findings supports durable trapping hypothesis, and sizable irregular workers move between employment and non-employed without withdrawing from labor market for good.

Determinants of irregular labor mobility are analyzed by using an extended Cox Hazard model and a Competing Risks Model which draw time-varying variables. Through an extended Cox model, it is analyzed which variables promoted exit from irregular employment; female workers, workers aged between 15 and 29 and workers over 55, workers with high school or lower degree, workers with short service years before the first survey point and workers paid lower than average wage of regular workers are more likely to exit from irregular employments. Compared to that of fixed-term workers, the HR of service contract workers and independent contractors are 0.770 and 0.793 respectively, showing high likeliness of maintaining the same irregular employment types; in case of TAW and part-time-exclusive, hazard rate>1 shows greater variability than fixed-term workers.

Since destinations of labor mobility is not binary, Competing Risks Model (a multi-exit model) is used to analyze determinants of transition to a regular position; female workers, older workers, workers with high school or lower degrees, workers without education or training experience, workers at workplaces with labor unions, workers with short service years at a current workplace before the first survey point are more unlikely to transit to a regular position. The rate of transition to a regular position from other irregular employment is ranged between 18.8% and 31.6%. The rates of exit to non-employed are high among female workers, workers aged between 15 and 29, workers over 55, workers with high school or lower degrees, workers without education or training and workers with a wage lower than the average wage of regular workers, which corresponded to the results of the precedent studies.

These results reveal the heterogeneity among irregular workers and their different paths of labor mobility. As a result, any government measures to cope with the issue of irregular workers should avoid implementing a one-size-fits-all approach. As TAW’s labor mobility is way worse than fixed-term workers, more favorable policy consideration is needed to TAW.  In case of independent workers, service contract workers for a specific task and daily workers, main policy should focus on improvements of working conditions including the application of social insurances instead of transition into regular workers, reflecting their employment characteristics. It is necessary to make a law for independent workers by selectively applying individual and collective labor laws. More ladders for smooth transition are crucial to reduce related social risks and to cut the vicious circle of “irregular workers⇌unemployment⇌non-EAP”. Also, the surveys concerning irregular workers need to be changed to construct mutually exclusive data, and part-time workers who do not have fixed-term contracts should be classified as regular workers in Korea.

As this study is based on labor-supply side analysis, it is necessary to look into previous researches which have delved into the labor-demand side to fully understand the labor mobility of irregular workers. Also, there are limitations in this study that labor mobility in terms of midium to long-term and  comparative analysis between regular vs. irregular workers cannot be analyzed because dataset used in this study contains two years of information on irregular workers only. Thus, future researches are invited to analyze labor mobility by using Panel Survey with longer survey duration and to study the reasons for irregular workers to join unemployment and non-EAP in order to boost employment rate up to 70% in Korea.

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<Appendix 1> Descriptive statistics of samples

Explanatory variable	Group	Frequency	Rate	Avg length of service	log-rank test
Total		20,556	100.0	15.41
Gender***	Male	9,822	47.8	15.82	chi2(1)= 47.93
	Female	10,734	52.2	15.04
Age group***	Age 15-29	3,255	15.8	14.27	chi2(3) = 87.79
	Age 30-39	4,097	19.9	16.75
	Age 40-54	7,801	38.0	16.44
	Over 55	5,403	26.3	13.62
Education***	High school degree or lower	16,376	79.7	15.09	chi2(2)=87.79
	College degree	3,985	19.4	16.65
	Master's degree	195	1.0	17.88
Industry***	Agriculture/fishery	951	4.6	11.28	chi2(7)=410.93
	Mining/manufacturing	2,753	13.4	16.67
	Electricity/gas/water/construction	3,015	14.7	15.24
	Wholesale/retail/lodging/food	4,335	21.1	14.91
	Transportation/communication	1,085	5.3	16.85
	Finance/insurance	623	3.0	18.96
	Real estate/service/education	5,128	25.0	15.19
	Welfare/private business/service	2,666	13.0	15.65
Occupation***	Manager, specialist	2,063	10.0	17.19	chi2(4)=84.51
	Office worker	1,543	7.5	16.25
	Service/sales	4,449	21.6	15.30
	Agriculture/fishery	184	0.9	13.90
	munual workr, machine assembly	12,317	59.9	15.08
Firm size***	Less than 30 employees	13,871	67.5	15.15	chi2(4)=114.03
	30-99 employees	2,966	14.4	16.73
	100-299 employees	1,210	5.9	16.50
	Over 300 employees	906	4.4	15.88
	No response	1,603	7.8	14.19
Types of Irregular employment***	Fixed-term	11,290	54.9	15.92	chi2(5)=707
	TAW	417	2.0	15.31
	Service contract	1,881	9.2	17.13
	Daily workers	5,110	24.9	13.59
	Independent contractors	1,255	6.1	18.37
	Part-time-exclusive	603	2.9	10.12
Average wage***	Below average wage of regular workers	19,311	93.9	15.24	chi2(1)=114.98
	Over average wage of regular workers	1,245	6.1	18.09
※ Note : *** indicates significance level, 0.01.

abstract

생존분석을 통한 근로형태별 비정규직의 노동이동 분석

장 신 철․어 수 봉․최 기 성

한국에서의 비정규직 연구는 연구자마다 상이한 비정규직 개념을 사용했고 사용한 데이터도 달라서 비정규직 규모에 상당한 차이가 나타났으며 그에 따라 연구결과가 영향을 받는 문제가 있었다. 또한 기존 연구들은 비정규직들을 하나의 덩어리로 연구했기 때문에 비정규직간에 존재하는 내적 이질성도 보지 못하였다.

본 연구는 고용노동부의 「비정규패널」을 활용하여 2개년의 관찰 값을 토대로 ⅰ) 비정규직의 노동이동이 함정인지의 여부 ⅱ) 비정규직의 노동이동에 어떤 변수들이 영향을 미치는가를 분석하였다. 6개의 비정규직 고용형태간에 존재하는 중복계산(double counting)을 제거하였고, 확장형 Cox 해자드 모형과 경합적 위험 모형을 사용하였다.

분석 결과에 의하면 지속함정가설이 지지되었고, 기간제와 비교하여 다른 비정규직들의 정규직 전환율이 저조하였다. 또한 여성, 고령자, 저학력자, 근속이 짧은 자, 유노조사업장의 정규직 전환율이 낮았다.

주제어: 비정규직, 근로형태, 기간제, 파견, 확장형 Cox 해자드 모형, 경합적 위험 모형