12 research outputs found

    An analysis on the determinants that lead provincial college graduates to get jobs at regional small and medium enterprises and leave the jobs

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    ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ง€๋ฐฉ 4๋…„์ œ ๋Œ€ํ•™ ์กธ์—…์ž๋“ค๊ณผ ์ „๋ฌธ๋Œ€ํ•™ ์กธ์—…์ž๋“ค์˜ ์ง€๋ฐฉ์ค‘์†Œ๊ธฐ์—… ์ทจ์—… ๊ฒฐ์ •์š”์ธ๊ณผ ์ด์ง๊ฒฐ์ •์š”์ธ์„ ๊ฐ๊ฐ ๋ถ„์„ํ•˜์˜€๋‹ค. ๋จผ์ € ์ง€๋ฐฉ์ค‘์†Œ๊ธฐ์—… ์ทจ์—…๊ฒฐ์ •์š”์ธ์„ ๋ถ„์„ํ•œ ๊ฒฐ๊ณผ ์ง€๋ฐฉ 4๋…„์ œ ๋Œ€ํ•™ ์กธ์—…์ž๋“ค๊ณผ ์ „๋ฌธ๋Œ€ํ•™ ์กธ์—…์ž๋“ค ๋ชจ๋‘ ์ง€๋ฐฉ์ค‘์†Œ๊ธฐ์—… ์ทจ์—…์— ๊ฐ€์žฅ ๊ฒฐ์ •์ ์ธ ์š”์ธ์€ ์ถœ์‹  ๊ณ ๋“ฑํ•™๊ต์˜ ์ง€์—ญ์ด์—ˆ๋‹ค. ์ฆ‰ ์ˆ˜๋„๊ถŒ ๊ณ ๋“ฑํ•™๊ต๋ฅผ ์กธ์—…ํ•˜๊ณ  ๋Œ€ํ•™์„ ์ง€๋ฐฉ๋Œ€ํ•™์œผ๋กœ ์ง„ํ•™ํ•œ ๊ฒฝ์šฐ ์กธ์—… ์ดํ›„ ๋Œ€๋ถ€๋ถ„์ด ์ˆ˜๋„๊ถŒ์œผ๋กœ ๋Œ์•„๊ฐ„๋‹ค๊ณ  ๋ณผ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋‹ค์Œ์œผ๋กœ ์ง€๋ฐฉ๋Œ€ํ•™ ์กธ์—…์ž๋“ค์˜ ์ฒซ ์ง์žฅ์—์„œ์˜ ์ด์ง๊ฒฐ์ • ์š”์ธ์„ ๋ถ„์„ํ•˜์˜€๋Š”๋ฐ, ๋ถ„์„๋Œ€์ƒ์„ ์žฌ์ง๊ธฐ๊ฐ„ 3๋…„ ์ด์ƒ๊ณผ 3๋…„ ๋ฏธ๋งŒ์œผ๋กœ ๋‚˜๋ˆ„์–ด์„œ ๋ถ„์„ํ•˜์˜€๋‹ค. ๋ถ„์„๊ฒฐ๊ณผ 4๋…„์ œ ๋Œ€ํ•™ ์กธ์—…์ž๋“ค์˜ ๊ฒฝ์šฐ ๋‘ ์ง‘๋‹จ ๊ฐ„์— ๋…ธ๋™์‹œ์žฅ ์ •์ฐฉ์„ฑ์—์„œ ์œ ์˜๋ฏธํ•œ ์ฐจ์ด๋ฅผ ๋ฐœ๊ฒฌํ•  ์ˆ˜ ์—†์—ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ง€๋ฐฉ ์ „๋ฌธ๋Œ€ํ•™ ์กธ์—…์ž๋“ค์˜ ๊ฒฝ์šฐ ์žฌ์ง๊ธฐ๊ฐ„์ด 3๋…„์„ ์ดˆ๊ณผํ•œ ์ด๋“ค์˜ ๊ฒฝ์šฐ ๋Œ€๋ถ€๋ถ„ ๋ณ€์ˆ˜์— ๋Œ€ํ•ด 3๋…„ ๋ฏธ๋งŒ ์žฌ์ง์ž๋“ค๋ณด๋‹ค ์ง์žฅ์— ์ •์ฐฉํ•˜๋ ค๋Š” ๋” ๋†’์€ ์„ฑํ–ฅ์„ ๊ฐ€์ง€๊ณ  ์žˆ๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋˜ํ•œ, ์ด๋“ค์˜ ์ด์ง ๊ฒฐ์ •์—์„œ ์ž„๊ธˆ๊ณผ ๊ทผ๋กœ์‹œ๊ฐ„์„ ํ†ต์ œํ•œ ์ƒํƒœ์—์„œ๋„ ์ผ์ž๋ฆฌ์— ๋Œ€ํ•œ ๋งŒ์กฑ๋„๊ฐ€ ๊ฐ€์žฅ ์ค‘์š”ํ•˜์˜€๋‹ค. ๋”ฐ๋ผ์„œ ์ด๋“ค์˜ ์ง์žฅ ์ •์ฐฉ์„ฑ์„ ์ง€์†์ ์œผ๋กœ ๋†’์ด๊ธฐ ์œ„ํ•ด์„œ๋Š” ์ž„๊ธˆ๊ณผ ๊ทผ๋กœ์‹œ๊ฐ„ ์™ธ์—๋„ ์ผ์ž๋ฆฌ์˜ ์งˆ์„ ๋†’์ด๊ธฐ ์œ„ํ•œ ๋‹ค๋ฐฉ๋ฉด์˜ ์ •์ฑ…์  ๋…ธ๋ ฅ์ด ํ•„์š”ํ•  ๊ฒƒ์œผ๋กœ ์ƒ๊ฐ๋œ๋‹ค.This study analyzes the job choices of provincial college graduates and their decisions to leave jobs. The analysis on job choices reveals that the high school region of graduates is very important. Many graduates from high schools near Seoul go back to Seoul after completing college. Next, we studied their decisions to leave jobs by dividing them into two groups. One was those who have worked for more than three years, and the other was those who worked less than three years. There was no difference between two groups among college graduates. However, for vocational college, the former group's adherence to current jobs is stronger. In addition, the level of job contentment is the most important in their decision to leave jobs, even after controlling for wages and working hours. This means that improving job quality, not just wages and working hours, is good way to increase their job retention

    The Employment Effect of Meister School Education: Compared to Specialized High School

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    ๋ณธ ์—ฐ๊ตฌ๋Š” ํŠน์„ฑํ™”๊ณ ์™€ ๋น„๊ตํ•˜์—ฌ ๋งˆ์ด์Šคํ„ฐ๊ณ  ๊ต์œก์ด ์กธ์—…์ƒ๋“ค์˜ ์ทจ์—…์— ๋ฏธ์น˜๋Š” ํšจ๊ณผ๋ฅผ ํŒŒ์•…ํ•˜๋Š”๋ฐ ๋ชฉ์ ์ด ์žˆ์—ˆ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ์ฒซ ๋งˆ์ด์Šคํ„ฐ๊ณ  ์กธ์—…์ƒ์ด ๋ฐฐ์ถœ๋œ 2013๋…„์— ํ•œ๊ตญ๊ณ ์šฉ์ •๋ณด์›์ด ์กฐ์‚ฌํ•œ โ€˜๊ณ ์กธ์ž์ทจ์—…์ง„๋กœ์กฐ์‚ฌ(HSGES)โ€™ ์ž๋ฃŒ๋ฅผ ํ™œ์šฉํ•ด CEM ๊ธฐ๋ฒ•์„ ํ†ตํ•œ ์ทจ์—…ํšจ๊ณผ๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ์‚ฌ์ „์ฒ˜์น˜๋ณ€์ˆ˜(pre-treatment variables)๋กœ ์„ฑ๋ณ„, ๋ถ€๋ชจ์˜ ํ•™๋ ฅ, ๊ฐ€๊ตฌ์†Œ๋“, ์กธ์—…์ „ ์ทจ์—…๋ชฉํ‘œ ์„ค์ •์—ฌ๋ถ€, ์ž๊ฒฉ์ทจ๋“์—ฌ๋ถ€, ํ•™๊ต์„ฑ์ , ํ˜„์žฅ์‹ค์Šต ๊ฒฝํ—˜, ์ „๊ณต๊ด€๋ จ ์ˆ˜์ƒ๊ฒฝ๋ ฅ, ์žฌํ•™์ค‘ ์ผ๊ฒฝํ—˜์— ๋Œ€ํ•ด ๋‘ ์ง‘๋‹จ ํ‘œ๋ณธ์„ ๋งค์นญํ•˜์˜€๋‹ค. ์ทจ์—…์„ฑ๊ณผ๋กœ๋Š” ์ทจ์—…์—ฌ๋ถ€, ๊ทผ์†์—ฌ๋ถ€, ์ •๊ทœ์ง ์—ฌ๋ถ€, ์ผ์ž๋ฆฌ ๋งŒ์กฑ๋„, ์›”ํ‰๊ท  ์ž„๊ธˆ, ์—…๋ฌด-์ „๊ณต ์ผ์น˜๋„์— ๋Œ€ํ•ด ๋ถ„์„ํ•˜์˜€๋‹ค. ๋ถ„์„๊ฒฐ๊ณผ ๊ณ ์กธ์ž์˜ ์ทจ์—…์„ฑ๊ณผ๋ฅผ ํŒ๋‹จํ•˜๊ธฐ ์œ„ํ•ด ์„ค์ •ํ•œ 6๊ฐœ ๋ณ€์ˆ˜ ๋ชจ๋‘์—์„œ ๋งˆ์ด์Šคํ„ฐ๊ณ  ํšจ๊ณผ๊ฐ€ ์œ ์˜๋ฏธํ•˜๊ฒŒ ๋†’๊ฒŒ ๋‚˜ํƒ€๋‚ฌ์œผ๋ฉฐ, ํŠน์„ฑํ™”๊ณ ์— ๋น„ํ•ด 10๏ฝž23% ์ •๋„์˜ ํ”„๋ฆฌ๋ฏธ์—„์„ ๊ฐ€์ง€๊ณ  ์žˆ์—ˆ๋‹ค. ์ด๋Ÿฌํ•œ ๊ฒฐ๊ณผ๋Š” ๊ทธ๋™์•ˆ์˜ ๋งˆ์ด์Šคํ„ฐ๊ณ  ์œก์„ฑ ์ •์ฑ…์ด ๊ณ ์กธ์ทจ์—…์— ๊ธ์ •์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์ณค๋‹ค๋Š” ๊ฒƒ์„ ๋ณด์—ฌ์ค€ ๊ฒฐ๊ณผ๋ผ ํ•  ์ˆ˜ ์žˆ๋‹ค.The purposes of this study are to identify the effects of Meister high schools compared to specialized high schools on employment effect in graduates. In order to analyze average treatment effect on the treated (Meister high school graduates), the study employed the data of the High School Graduates Employment Survey (HSGES) conducted by Korea Employment Information Service in 2013, using the coarsened exact matching method. Meister school first produced graduates in 2013. The pre-treatment variables for matching experimental group with control group included gender, parent's education level, household income, career plan, acquisition of qualifications, school grades, field practice experience, awards related to major, and part-time work experiences during school years. According to the results of this study, the Meister school graduates had advantages of between 10 to 23% in the six employment indexes compared to specialized high school graduates. These results implied that Meister school education had significantly positive effects on the employment of high school graduates

    A Cohort Study of Career Types of Korean Youth during and after the Financial Crisis in the Late 1990s

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    ์ด ์—ฐ๊ตฌ๋Š” ์™ธํ™˜์œ„๊ธฐ ๋‹น์‹œ ๋ฐ ์ดํ›„ ๋…ธ๋™์‹œ์žฅ์œผ๋กœ ์ง„์ž…ํ•œ ์ฒญ๋…„์ธต์˜ 10๋…„ ๊ฒฝ๋ ฅ์„ ์ข…๋‹จ์ ์œผ๋กœ ์œ ํ˜•ํ™”ํ•˜์—ฌ ๋น„๊ต๏ผŸ๋ถ„์„ํ•˜๊ณ , ๊ฐ ๊ฒฝ๋ ฅ ์œ ํ˜•์˜ ์ธ๊ตฌํ†ต๊ณ„ํ•™์ , ์ง„์ž…, ๊ณผ์ •, ๊ฒฐ๊ณผ ํŠน์„ฑ์„ ์ด์ฒด์ ์œผ๋กœ ๋ถ„์„ํ•˜์˜€๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ์ฒญ๋…„์ธต์˜ ๊ฒฝ๋ ฅ์„ ์ง์—…๊ฒฝ๋ ฅ๊ณผ ๊ณ ์šฉ๊ฒฝ๋ ฅ ์œผ๋กœ ๊ตฌ๋ถ„ํ•˜์—ฌ ๋‹ค์ฑ„๋„์‹œํ€€์Šค๋ถ„์„์„ ์‹ค์‹œํ•˜์˜€๊ณ , ๊ฒฝ๋ ฅ ์œ ํ˜•์˜ ์ธ๊ตฌํ†ต๊ณ„ํ•™์ , ์ง„์ž…, ๊ณผ์ •, ๊ฒฐ๊ณผ์— ๋Œ€ํ•ด ๊ฐ ์ฝ”ํ˜ธํŠธ ๋‚ด ๊ฒฝ๋ ฅ ์œ ํ˜•๋ณ„ ์ฐจ์ด ๊ฒ€์ •๊ณผ ๊ฒฝ๋ ฅ ์œ ํ˜•๋ณ„ ์ฝ”ํ˜ธํŠธ ๊ฐ„ ์ฐจ์ด ๊ฒ€์ •์„ ์‹ค์‹œํ•˜์˜€๋‹ค. ์ด ์—ฐ๊ตฌ์˜ ๊ฒฐ๊ณผ ์ฒซ์งธ, ์™ธํ™˜์œ„๊ธฐ ๋‹น์‹œ ๋ฐ ์ดํ›„ ์ฒญ๋…„์ธต์˜ ๊ฒฝ๋ ฅ์€ ์•ˆ์ •์  ์‚ฌ๋ฌด์งํ˜•, ์ง„์ž…์œ ์˜ˆํ˜•, ๋‹ˆํŠธํ˜•, ์„œ๋น„์Šค์ง ๋ฐ ์ƒ์‚ฐ์ง ํ˜ผ์žฌํ˜•, ์•ˆ์ •์  ์ค€์ „๋ฌธ(๊ธฐ์ˆ )์งํ˜•, ์•ˆ์ •์  ์ „๋ฌธ์งํ˜•์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋‘˜์งธ, ์™ธํ™˜์œ„๊ธฐ ๋‹น์‹œ ์—ฌ์„ฑ์˜ ๊ฒฝ๋ ฅ๊ฐœ๋ฐœ ์ƒํ™ฉ ๋ฐ ์—ฌ๊ฑด์€ ์ทจ์•ฝํ•œ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๊ณ , ๋Œ€์กธ์ž๋Š” ํ•˜ํ–ฅ์ทจ์—…์„ ํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์…‹์งธ, ์™ธํ™˜์œ„๊ธฐ ๋‹น์‹œ ์ฒซ ์ผ์ž๋ฆฌ ์ทจ์—… ์†Œ์š” ๊ธฐ๊ฐ„์˜ ํŽธ์ฐจ๊ฐ€ ์ฒญ๋…„์ธต์˜ ๊ฒฝ๋ ฅ ์œ ํ˜• ๊ฐ„ ํฐ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๊ณ , ์ฒซ ์ผ์ž๋ฆฌ์˜ ์ž„๊ธˆ์€ ์ €์ž„๊ธˆ์œผ๋กœ ๋™๊ฒฐ๋˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋„ท์งธ, ์ฒญ๋…„์ธต์˜ ์ด์ง์€ ๊ฒฝ์ œ์ƒํ™ฉ๊ณผ ๋ฌด๊ด€ํ•œ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋‹ค์„ฏ์งธ, ์ฒญ๋…„์ธต์˜ ๊ฒฝ๋ ฅ ์œ ํ˜•์— ๋”ฐ๋ผ ์ทจ์—…์—ฌ๋ถ€ ๋ฐ ๊ทผ์†๋…„์ˆ˜ ์ฐจ์ด๊ฐ€ ๋‚˜ํƒ€๋‚ฌ์œผ๋ฉฐ, ์™ธํ™˜์œ„๊ธฐ ๋‹น์‹œ ์ž…์ง์ž์˜ ๊ฒฝ์šฐ 10๋…„ ๊ฒฝ๊ณผ ์‹œ์ ์—์„œ๋„ ๊ฒฝ๋ ฅ ์œ ํ˜• ๊ฐ„ ์ž„๊ธˆ ์ฐจ์ด๊ฐ€ ํฌ์ง€ ์•Š์•˜๋‹ค.The purpose of this study is to investigate career types of Korean youth during and after the financial crisis in the late 1990s. To accomplish the research purpose, the following questions are addressed in this study: 1. What are Korean youth's career types during and after the financial crisis in the late 1990s? 2. Can career types of Korean youth be described by demographic and entry-process-outcome phase characteristics? The findings of the study include the following: first, career types of Korean youth were identified as stable-office-workers type, deferment type, NEET (i.e. not in employment, education, or training) type, service & production-workers- mixture type, stable-technicians type, and stable- professions type. Second, women and university graduates suffered unstable and mismatched career during the financial crisis. Third, the youth had different preparation periods for their first job according to the career types, and experienced wage freeze during the financial crisis. Fourth, changing jobs was not related to financial circumstances. Fifth, depending on the career types, there were differences in employment status and years of continual services. For those who entered the labor market during the financial crisis, there were not wide wage gaps between the career types even in 10 years

    The Effects of Selection of College and Major on Entry into the Labor Market

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    ๋ณธ ์—ฐ๊ตฌ๋Š” ๋Œ€์ž…์‹œ๊ธฐ ๊ฐœ์ธ๋“ค์˜ ๋Œ€ํ•™ ๋ฐ ์ „๊ณต ์„ ํƒ์ด ์ดํ›„ ๊ทธ๋“ค์˜ ๋…ธ๋™์‹œ์žฅ ์ง„์ž…์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ํ™•์ธํ•œ๋‹ค. ๋Œ€ํ•™ ๋ฐ ์ „๊ณต ์ง€ํ–ฅ ์„ ํƒ์€ ๋‹ค์Œ์˜ ๋„ค ๊ฐ€์ง€ ์œ ํ˜•์œผ๋กœ ๊ตฌ๋ถ„๋œ๋‹ค. ์ฒซ์งธ, ํ•™๋ ฅ์ฃผ์˜์— ๊ธฐ์ดˆํ•œ ๋Œ€ํ•™ ์ง€ํ–ฅ ์„ ํƒ, ๋‘˜์งธ, ๋…ธ๋™์‹œ์žฅ์ด ์„ ํ˜ธํ•˜๋Š” ํ•™๊ณผ๋ฅผ ์šฐ์„ ์ ์œผ๋กœ ์„ ํƒํ•˜๋Š” ์ทจ์—…์œ ๋ฆฌ-์ „๊ณต์ง€ํ–ฅ ์„ ํƒ, ์…‹์งธ, ์ž์‹ ์˜ ๊ด€์‹ฌ๊ณผ ํฅ๋ฏธ์— ๊ธฐ์ดˆํ•œ ์ทจ์—…๋ถˆ๋ฆฌ-์ „๊ณต์ง€ํ–ฅ ์„ ํƒ, ๋„ท์งธ, ๋Œ€ํ•™๊ณผ ์ „๊ณต์˜ ํŠน์„ฑ์„ ๋™์‹œํ•œ ๊ณ ๋ คํ•œ ์„ ํƒ์ด๋‹ค. ํ•œ๊ตญ์‚ฌํšŒ์˜ ํ•™๋ฒŒ์ฃผ์˜์™€ ํ•™๊ณผ ์„ ํ˜ธ์˜ ํŽธํ–ฅ์„ฑ์„ ๋ฐ˜์˜ํ•˜๋Š” ์ด ๋„ค ๊ฐ€์ง€ ์œ ํ˜•์€ ๋…ธ๋™์‹œ์žฅ ์ง„์ž…์—์„œ์˜ ์œ ๋ฆฌํ•œ ์œ„์น˜๋ฅผ ์ ์œ ํ•จ์œผ๋กœ์จ ๊ตฌ์กฐ์  ํšจ์šฉ์„ ์ตœ๋Œ€ํ™”ํ•˜๊ณ ์ž ํ•˜๋Š” ๊ฐœ์ธ๊ณผ ๊ฐ€๊ตฌ์˜ ๋ณตํ•ฉ์ ์ธ ์ „๋žต๋“ค์„ ๋‚˜ํƒ€๋‚ธ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๋Œ€ํ•™-์ „๊ณต์— ๋Œ€ํ•œ ์„ ํƒ์˜ ๋ฐฐ๊ฒฝ์  ํŠน์„ฑ๊ณผ ๊ทธ๊ฒƒ์ด ํ˜„ ํ•œ๊ตญ์‚ฌํšŒ์˜ ๋Œ€ํ•™๊ต์œก ๊ตฌ์กฐ ๋‚ด์—์„œ ์–ด๋– ํ•œ ์œ„์น˜๋ฅผ ์ ์œ ํ•˜๋Š”์ง€๋ฅผ ํ™•์ธํ•œ๋‹ค. ๊ทธ ํ›„ ์ด๋Ÿฌํ•œ ์„ ํƒ์œ ํ˜•๋“ค์ด ๋Œ€์กธ์ž๋“ค์˜ ๋…ธ๋™์‹œ์žฅ์ง„์ž…์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๋ถ„์„ํ•˜์˜€๋‹ค. ๊ตฌ์ฒด์ ์œผ๋กœ๋Š” ํ•œ๊ตญ๊ต์œก๊ณ ์šฉํŒจ๋„ 1๏ฝž11์ฐจ ์ž๋ฃŒ๋ฅผ ํ™œ์šฉํ•˜์—ฌ, ์ฒซ ์ทจ์—… ์œ ํ˜•๋ณ„๋กœ ๊ฒฝ์Ÿ์œ„ํ—˜๋ถ„์„(competing risk analysis)์„ ์‹ค์‹œํ•˜์˜€๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ๋ณธ ์—ฐ๊ตฌ๋Š” ํ•œ๊ตญ์‚ฌํšŒ์—์„œ์˜ ๋…ธ๋™์‹œ์žฅ๊ณผ ๋Œ€ํ•™๊ตฌ์กฐ๊ฐ€ ๊ฐœ์ธ๋“ค์˜ ์„ ํƒ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ๊ณผ ๊ทธ ํšจ๊ณผ๋ฅผ ํ™•์ธํ•ด๋ด„์œผ๋กœ์จ ํ•™๋ ฅ์ฃผ์˜์˜ ์–‘์ƒ์„ ๋˜์งš์–ด๋ณด๊ณ  ์ด๊ฒƒ์˜ ํ•จ์˜์— ๋Œ€ํ•ด ๋…ผํ•˜์˜€๋‹ค.This study examines the effects of individuals' selection of college and major on their entries into the labor market afterwards. The college/major-oriented selections are divided into four types, including the college-oriented selection based on credentialism, the major-oriented selection preferentially choosing departments advantageous for getting jobs, the major-oriented selection based on individual interests despite low job prospects, and the selection considering the characteristics of both college and major. This study analyzes the effects of such selection types on college graduates' entries into the labor market. Using the data of 1st to 11th Korean Education & Employment Panel (KEEP), we analyzed the competing risk model by type of intial employment. By examining the influence and effects of the labor market and college structure of Korean society on individuals' selection, this study aims to review the aspect of credentialism and also to discuss its implications

    ํŠน์„ฑํ™”๊ณ ๋“ฑํ•™๊ต ํ•™์ƒ์˜ ์ทจ์—…ํฌ๋ถ€์™€ ํ•™์ƒ ๋ฐ ํ•™๊ธ‰ ๋ณ€์ธ์˜ ์œ„๊ณ„์  ๊ด€๊ณ„

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๋†์—…์ƒ๋ช…๊ณผํ•™๋Œ€ํ•™ ๋†์‚ฐ์—…๊ต์œก๊ณผ, 2018. 2. ์ตœ์ˆ˜์ •.์ด ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์€ ํŠน์„ฑํ™”๊ณ ๋“ฑํ•™๊ต ํ•™์ƒ์˜ ์ทจ์—…ํฌ๋ถ€์™€ ํ•™์ƒ ํŠน์„ฑ ๋ฐ ํ•™๊ธ‰ ์ˆ˜์ค€ ๋ณ€์ธ์˜ ๊ด€๊ณ„๋ฅผ ๊ตฌ๋ช…ํ•˜๋Š”๋ฐ ์žˆ์—ˆ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ํŠน์„ฑํ™”๊ณ ๋“ฑํ•™๊ต ํ•™์ƒ์˜ ์ทจ์—…ํฌ๋ถ€์— ๋Œ€ํ•œ ํ•™์ƒ ๋ฐ ํ•™๊ธ‰ ์ˆ˜์ค€์˜ ๋ณ€๋Ÿ‰์„ ๊ตฌ๋ช…ํ•˜๊ณ  ๊ฐ ๋ณ€์ธ๋“ค์˜ ํšจ๊ณผ๋ฅผ ๊ตฌ๋ช…ํ–ˆ๋‹ค. ๊ทธ ํ›„ ์œ ์˜๋ฏธํ•œ ์˜ํ–ฅ๊ด€๊ณ„๋ฅผ ๋ณด์ธ ๋ณ€์ธ์„ ๋Œ€์ƒ์œผ๋กœ ์ƒํ˜ธ์ž‘์šฉ ํšจ๊ณผ์— ๋Œ€ํ•ด ๊ฒ€์ฆํ•˜์˜€๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” ํŠน์„ฑํ™”๊ณ ๋“ฑํ•™๊ต์— ์žฌํ•™ ์ค‘์ธ 3ํ•™๋…„ ํ•™์ƒ์„ ๋Œ€์ƒ์œผ๋กœ ์ˆ˜ํ–‰๋˜์—ˆ๋‹ค. 2016๋…„ ๊ธฐ์ค€ ํŠน์„ฑํ™”๊ณ ๋“ฑํ•™๊ต์— ์žฌํ•™ ์ค‘์ธ 96,419๋ช…์˜ 3ํ•™๋…„ ํ•™์ƒ์„ ๋ชจ์ง‘๋‹จ์œผ๋กœ ์„ค์ •ํ•˜์˜€๋‹ค. ํ‘œ๋ณธ์˜ ํŠน์„ฑ๊ณผ ์ž๋ฃŒ ๋ถ„์„ ๋ฐฉ๋ฒ•์„ ๊ณ ๋ คํ•˜์—ฌ ํ‘œ์ง‘ ์ธ์›์€ 40๊ฐœ ํ•™๊ธ‰์˜ 20๋ช…์˜ ํ•™์ƒ์„ ๋Œ€์ƒ์œผ๋กœ ์ด 800๋ช…์œผ๋กœ ์„ค์ •ํ•˜์˜€๋‹ค. ํ‘œ์ง‘์€ ๋‹ค์ธต ๋ถ„์„์˜ ํƒ€๋‹น์„ฑ์„ ๋†’์ด๊ธฐ ์œ„ํ•˜์—ฌ ๋น„๋ก€์ธตํ™”ํ‘œ์ง‘์„ ํ™œ์šฉํ•˜์—ฌ ์‹ค์‹œํ•˜์˜€๋‹ค. ์กฐ์‚ฌ๋„๊ตฌ๋Š” ํŠน์„ฑํ™”๊ณ ๋“ฑํ•™๊ต ํ•™์ƒ์˜ ์ทจ์—…ํฌ๋ถ€ ๋ฐ ํ•™์ƒ ๋ฐ ํ•™๊ธ‰ ์ˆ˜์ค€ ๋ณ€์ธ์„ ์ธก์ •ํ•  ์ˆ˜ ์žˆ๋Š” ๋ณ€์ธ๋“ค๋กœ ๊ตฌ์„ฑ๋œ ์„ค๋ฌธ์ง€๋ฅผ ํ™œ์šฉํ•˜์˜€๋‹ค. ํ•™์ƒ ๋Œ€์ƒ ์„ค๋ฌธ์ง€๋Š” ์ทจ์—…ํฌ๋ถ€์™€ ์ธ๊ตฌํ†ต๊ณ„ํ•™์  ํŠน์„ฑ, ์ง์—…๊ต์œก ํฅ๋ฏธ, ํ•™๊ต์ƒํ™œ ๋งŒ์กฑ๋„, ํ‰๊ท ์„ฑ์ , ๊ต์œกํฌ๋ถ€, ๊ฐ€์ •ํ™˜๊ฒฝ ํŠน์„ฑ, ๊ฐ€์ •์˜ ์ง„๋กœ์ง€์ง€, ์ทจ์—…์ง€์› ์ •๋„๋กœ ์ด 78๊ฐœ์˜ ๋ฌธํ•ญ์œผ๋กœ ๊ตฌ์„ฑ๋˜์—ˆ๋‹ค. ํ•™๊ธ‰ ์ˆ˜์ค€ ๋ณ€์ธ ์ค‘ ๊ต์‚ฌ์˜ ์ง์—…๊ต์œก ์ธ์‹๊ณผ ๊ด€๋ จํ•œ ๋ฌธํ•ญ์€ ๊ธฐ ๊ฐœ๋ฐœ๋œ ๋ฌธํ•ญ์ด ์—†์–ด ๋ฌธํ•ญ๊ฐœ๋ฐœ ์ ˆ์ฐจ์— ๋”ฐ๋ผ ๋ฌธํ•ญ์„ ๊ฐœ๋ฐœํ•˜์—ฌ ์ง„ํ–‰ํ•˜์˜€๋‹ค. ๊ทธ ์ด์™ธ์˜ ๋ฌธํ•ญ์œผ๋กœ๋Š” ๊ต์‚ฌ์˜ ์ธ๊ตฌํ†ต๊ณ„ํ•™์  ํŠน์„ฑ, ์ทจ์—…์ง€๋„ ๋Šฅ๋ ฅ ๋“ฑ ์ด 50๊ฐœ์˜ ๋ฌธํ•ญ์œผ๋กœ ๊ตฌ์„ฑ๋˜์—ˆ๋‹ค. ์ž๋ฃŒ ์ˆ˜์ง‘์€ ๋ฌธํ•ญ์˜ ์‹ ๋ขฐ๋„์™€ ํƒ€๋‹น๋„ ์กฐ์‚ฌ๋ฅผ ์œ„ํ•œ ์˜ˆ๋น„์กฐ์‚ฌ๋Š” 2017๋…„ 9์›” 25์ผ๋ถ€ํ„ฐ 10์›” 10์ผ์— ์ด๋ฃจ์–ด์กŒ๊ณ , ๋ณธ ์กฐ์‚ฌ๋Š” 10์›” 13์ผ๋ถ€ํ„ฐ 29์ผ๊นŒ์ง€ ๋ฐฉ๋ฌธ์กฐ์‚ฌ ๋ฐ ์šฐํŽธ์กฐ์‚ฌ๋ฅผ ํ†ตํ•ด ์ด๋ฃจ์–ด์กŒ๋‹ค. ๋ฐฐํฌ๋œ ์„ค๋ฌธ์ง€ 800๋ถ€ ์ค‘ 765๋ถ€(ํšŒ์ˆ˜์œจ 97.0%)๊ฐ€ ํšŒ์ˆ˜๋˜์—ˆ๋‹ค. ์ž๋ฃŒ ๋ถ„์„์€ SPSS 23.0 for Windows ํ”„๋กœ๊ทธ๋žจ๊ณผ HLM 6.08 for Windows ํ”„๋กœ๊ทธ๋žจ์„ ์ด์šฉํ•˜์˜€๋‹ค. ์ด ์—ฐ๊ตฌ์˜ ๊ฒฐ๊ณผ๋ฅผ ์š”์•ฝํ•˜๋ฉด ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ํŠน์„ฑํ™”๊ณ ๋“ฑํ•™๊ต ํ•™์ƒ์˜ ์ทจ์—…ํฌ๋ถ€์˜ ์ „์ฒด ๋ณ€๋Ÿ‰ ์ค‘ ํ•™๊ธ‰ ๊ฐ„ ์ฐจ์ด๋กœ ์„ค๋ช…๋˜๋Š” ๋ณ€๋Ÿ‰์€ 14.3%, ํ•™์ƒ ๊ฐœ์ธ ํŠน์„ฑ์˜ ์ฐจ์ด๋กœ ์„ค๋ช…๋˜๋Š” ๋ณ€๋Ÿ‰์€ 85.7%๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋‘˜์งธ, ํŠน์„ฑํ™”๊ณ ๋“ฑํ•™๊ต ํ•™์ƒ์˜ ์ทจ์—…ํฌ๋ถ€ ์ˆ˜์ค€์— ์ „๋ฌธ๊ต๊ณผ ์ˆ˜์—… ํฅ๋ฏธ์™€ ํ‰๊ท  ์„ฑ์ , ๊ฐ€์ •์˜ ์ง„๋กœ์ง€์ง€๊ฐ€ ์œ ์˜๋ฏธํ•œ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ฆ‰, ์ „๋ฌธ๊ต๊ณผ ์ˆ˜์—… ํฅ๋ฏธ๊ฐ€ 1 ๋†’์„ ๋•Œ ์ทจ์—… ํฌ๋ถ€ ์ˆ˜์ค€์ด 0.172 ์ฆ๊ฐ€ํ•˜๊ณ , ํ‰๊ท  ์„ฑ์ ์ด 1 ์ฆ๊ฐ€ํ•  ๋•Œ, ์ทจ์—…ํฌ๋ถ€ ์—ญ์‹œ 0.078 ๋†’์•„์กŒ๋‹ค. ๊ฐ€์ •์˜ ์ง„๋กœ์ง€์ง€ ์ˆ˜์ค€์ด 1 ์ฆ๊ฐ€ํ•  ๋•Œ๋„ ์ทจ์—…ํฌ๋ถ€ ์ˆ˜์ค€์ด 0.190 ์ฆ๊ฐ€ํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ํ•™์ƒ ๊ฐœ์ธ ์ˆ˜์ค€ ๋ณ€์ธ ์ค‘ ํ•™๊ธ‰๋ณ„ ์ฐจ์ด๊ฐ€ ์žˆ๋Š” ๋ณ€์ธ์„ ๋ถ„์„ํ•œ ๊ฒฐ๊ณผ ํŠน์„ฑํ™”๊ณ  ์„ ํƒ ์ด์œ , ์ „๋ฌธ๊ต๊ณผ ์ˆ˜์—… ํฅ๋ฏธ, ํ‰๊ท  ์„ฑ์ , ๊ต์œกํฌ๋ถ€, ์‚ฐ์—…์ฒด ๊ฒฌํ•™ ๋ฐ ์ฒดํ—˜, ์‚ฐ์—…์ฒด ํŒŒ๊ฒฌ ํ˜„์žฅ์‹ค์Šต, ๋ฐฉ๊ณผ ํ›„ ํ•™๊ต ๋ฐ ๋™์•„๋ฆฌ ํ™œ๋™, ํ•œ๋ถ€๋ชจ ๊ฐ€์ • ์—ฌ๋ถ€, ๊ฐ€์ •์˜ ์ง„๋กœ์ง€์ง€๊ฐ€ ์œ ์˜๋ฏธํ•œ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ํŠนํžˆ ๊ต์œกํฌ๋ถ€์™€ ์ทจ์—…๊ด€๋ จ ๊ฒฝํ—˜์€ ํ•™์ƒ ๊ฐœ์ธ์˜ ์ˆ˜์ค€์—์„œ๋Š” ์ทจ์—…ํฌ๋ถ€์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์ด ์œ ์˜ํ•˜์ง€ ์•Š์•˜์ง€๋งŒ ํ•™๊ธ‰๋ณ„ ์ฐจ์ด๋Š” ์œ ์˜๋ฏธํ•œ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์…‹์งธ, ํ•™๊ธ‰๋ณ„ ํ‰๊ท  ์ทจ์—…ํฌ๋ถ€ ์ˆ˜์ค€์€ ๋ถ€์žฅ๊ต์‚ฌ ์—ฌ๋ถ€, ๋‹ด๋‹นํ•™๊ธ‰์˜ ๊ณ„์—ด(๊ณต์—…), ์ทจ์—…์ง€์› ์ •๋„์— ๋”ฐ๋ผ ๋‹ฌ๋ผ์ง€๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋‹ด์ž„๊ต์‚ฌ๊ฐ€ ๋ถ€์žฅ๊ต์‚ฌ์ธ ๊ฒฝ์šฐ ํ‰๊ต์‚ฌ์ธ ๊ฒฝ์šฐ์— ๋น„ํ•ด ์ทจ์—…ํฌ๋ถ€๊ฐ€ 0.177 ๊ฐ์†Œํ•˜๊ณ , ํ•™๊ธ‰ ๊ณ„์—ด์ด ๊ณต์—…์ธ ๊ฒฝ์šฐ ๋‹ค๋ฅธ ๊ณ„์—ด์— ๋น„ํ•ด 0.156 ๊ฐ์†Œํ•˜์˜€๋‹ค. ์ทจ์—…์ง€์› ์ •๋„๊ฐ€ 1 ์ฆ๊ฐ€ํ•˜๋ฉด ์ทจ์—…ํฌ๋ถ€๋Š” 0.551 ์ฆ๊ฐ€ํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ํ•™์ƒ ์ˆ˜์ค€ ๋ณ€์ธ๊ณผ ํ•™๊ธ‰ ์ˆ˜์ค€ ๋ณ€์ธ์˜ ์ƒํ˜ธ์ž‘์šฉ ํšจ๊ณผ ๋ถ„์„ ๊ฒฐ๊ณผ ํŠน์„ฑํ™”๊ณ ๋“ฑํ•™๊ต ํ•™์ƒ์˜ ์ทจ์—…ํฌ๋ถ€์™€ ํ‰๊ท  ์„ฑ์ ์˜ ๊ด€๊ณ„์—์„œ ์ทจ์—…์ง€์› ์ •๋„๊ฐ€ ์ƒํ˜ธ์ž‘์šฉ ํšจ๊ณผ๋ฅผ ๊ฐ€์ง€๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋˜ํ•œ ํŠน์„ฑํ™”๊ณ ๋“ฑํ•™๊ต ํ•™์ƒ์˜ ์ทจ์—…ํฌ๋ถ€์™€ ์‚ฐ์—…์ฒด ๊ฒฌํ•™ ๋ฐ ์ฒดํ—˜์˜ ๊ด€๊ณ„์—์„œ ๋‹ด๋‹นํ•™๊ธ‰์˜ ๊ณ„์—ด(๊ณต์—…)๋„ ์ƒํ˜ธ์ž‘์šฉ ํšจ๊ณผ๋ฅผ ๋ณด์ด๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ด ์—ฐ๊ตฌ์˜ ๊ฒฐ๋ก ์€ ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ํŠน์„ฑํ™”๊ณ ๋“ฑํ•™๊ต ํ•™์ƒ์˜ ์ทจ์—…ํฌ๋ถ€๋Š” ํ•™์ƒ ๋ฐ ํ•™๊ธ‰ ์ˆ˜์ค€ ๋ณ€์ธ์— ๋”ฐ๋ผ ์˜ํ–ฅ์„ ๋ฐ›๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋”ฐ๋ผ์„œ ํŠน์„ฑํ™”๊ณ ๋“ฑํ•™๊ต ํ•™์ƒ์˜ ์ทจ์—…ํฌ๋ถ€๋ฅผ ๋ถ„์„ํ•˜๋Š”๋ฐ ์žˆ์–ด ํ•™๊ธ‰ ์ˆ˜์ค€ ๋ณ€์ธ์˜ ์˜ํ–ฅ์„ ํ•จ๊ป˜ ๊ณ ๋ คํ•ด์•ผ ํ•œ๋‹ค. ๋‘˜์งธ, ํŠน์„ฑํ™”๊ณ ๋“ฑํ•™๊ต ํ•™์ƒ์˜ ์ทจ์—…ํฌ๋ถ€์— ์žˆ์–ด์„œ ์ „๋ฌธ๊ต๊ณผ ์ˆ˜์—… ํฅ๋ฏธ, ํ‰๊ท  ์„ฑ์ , ๊ฐ€์ •์˜ ์ง„๋กœ์ง€์ง€๊ฐ€ ์˜ํ–ฅ์„ ์ฃผ๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์…‹์งธ, ํŠน์„ฑํ™”๊ณ ๋“ฑํ•™๊ต ํ•™์ƒ์˜ ํ•™๊ธ‰๋ณ„ ํ‰๊ท  ์ทจ์—…ํฌ๋ถ€์— ๋ถ€์žฅ๊ต์‚ฌ ์—ฌ๋ถ€, ๋‹ด๋‹นํ•™๊ธ‰์˜ ๊ณ„์—ด(๊ณต์—…), ์ทจ์—…์ง€์› ์ •๋„๊ฐ€ ๋ฏธ์น˜๋Š” ํšจ๊ณผ๊ฐ€ ์žˆ๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ด๋Š” ๋‹ด์ž„๊ต์‚ฌ๊ฐ€ ๋ถ€์žฅ๊ต์‚ฌ์ผ์ˆ˜๋ก ํ•™์ƒ๋“ค์˜ ์ทจ์—…์ง€๋„ ์ด์™ธ์˜ ์—…๋ฌด๊ฐ€ ๋งŽ์•„ ์ทจ์—…์ง€๋„์— ์†Œํ™€ํ•  ์ˆ˜ ์žˆ์–ด ํ•™์ƒ์˜ ์ทจ์—…ํฌ๋ถ€๊ฐ€ ๋‚ฎ์•„์ง€๋Š”๋ฐ ์˜ํ–ฅ์„ ๋ฏธ์น  ์ˆ˜ ์žˆ๋‹ค. ํŠนํžˆ ํ•™์ƒ์ด ์ทจ์—…์ง€์› ์ •๋„๊ฐ€ ๋†’๋‹ค๊ณ  ์ธ์‹ํ• ์ˆ˜๋ก ์ทจ์—…ํฌ๋ถ€๊ฐ€ ๋†’๊ฒŒ ํ˜•์„ฑ๋˜๋ฏ€๋กœ ํ•™์ƒ๋“ค์—๊ฒŒ ์ ์ ˆํ•œ ์ทจ์—…์ง€์›์ด ์ด๋ฃจ์–ด์งˆ ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ์ทจ์—…์ง€์› ์ •๋„์™€ ๋‹ด๋‹นํ•™๊ธ‰์˜ ๊ณ„์—ด(๊ณต์—…)์ด ์ƒํ˜ธ์ž‘์šฉ ํšจ๊ณผ๋ฅผ ๊ฐ€์ง€๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ํ›„์† ์—ฐ๊ตฌ๋ฅผ ์œ„ํ•˜์—ฌ ์ œ์–ธ์„ ํ•˜๊ณ ์ž ํ•œ๋‹ค. ์ฒซ์งธ, ํŠน์„ฑํ™”๊ณ ๋“ฑํ•™๊ต ํ•™์ƒ์˜ ์ทจ์—…์˜ ์งˆ์— ๋Œ€ํ•œ ์ธก์ • ๋ฐ ์ทจ์—…ํฌ๋ถ€์— ๋Œ€ํ•œ ์‹ค์ฆ์ ์ธ ์—ฐ๊ตฌ๊ฐ€ ์ง€์†์ ์œผ๋กœ ์ถ•์ ๋  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ๋‘˜์งธ, ํŠน์„ฑํ™”๊ณ ๋“ฑํ•™๊ต ํ•™์ƒ๋“ค์˜ ์ทจ์—…์ง€๋„์— ์žˆ์–ด์„œ ์ทจ์—…ํฌ๋ถ€๋ฅผ ๊ณ ๋ คํ•œ ์ฒด๊ณ„์ ์ธ ์ง„๋กœ์ง€๋„๊ฐ€ ์ด๋ฃจ์–ด์ ธ์•ผ ํ•œ๋‹ค. ์…‹์งธ, ํŠน์„ฑํ™”๊ณ ๋“ฑํ•™๊ต ํ•™์ƒ๊ณผ ๋‹ด์ž„๊ต์‚ฌ์˜ ์˜ํ–ฅ๊ด€๊ณ„์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๊ฐ€ ๋” ์ˆ˜ํ–‰๋˜์–ด์•ผ ํ•œ๋‹ค. ํŠนํžˆ ๋‹ด์ž„๊ต์‚ฌ์˜ ์˜ํ–ฅ์„ ๊ตฌ๋ช…ํ•  ์ˆ˜ ์žˆ๋Š” ํ•™๊ธ‰ ์ˆ˜์ค€์˜ ๋ณ€์ธ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๊ฐ€ ์ถ”๊ฐ€๋กœ ์ด๋ฃจ์–ด์งˆ ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ๋„ท์งธ, ํŠน์„ฑํ™”๊ณ ๋“ฑํ•™๊ต์˜ ์ทจ์—…์ง€์› ํ™œ๋™ ์ดํ›„ ์ ์ ˆํ•œ ํ”ผ๋“œ๋ฐฑ ์ œ๊ณต์ด ๋ฐ˜๋“œ์‹œ ์ด๋ฃจ์–ด์ ธ์•ผ ํ•œ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, ํ•™๊ต ์ˆ˜์ค€ ๋ณ€์ธ๊ณผ ํ•™๊ธ‰ ์ˆ˜์ค€ ๋ณ€์ธ์˜ ์ฐจ์ด์— ๋Œ€ํ•œ ์ถ”๊ฐ€์ ์ธ ๋ถ„์„์ด ํ•„์š”ํ•˜๋‹ค.I. ์„œ๋ก  1 1. ์—ฐ๊ตฌ์˜ ํ•„์š”์„ฑ 1 2. ์—ฐ๊ตฌ์˜ ๋ชฉ์  3 3. ์—ฐ๊ตฌ ๋ฌธ์ œ 4 4. ์šฉ์–ด์˜ ์ •์˜ 5 II. ์ด๋ก ์  ๋ฐฐ๊ฒฝ 9 1. ํŠน์„ฑํ™”๊ณ ๋“ฑํ•™๊ต ํ•™์ƒ์˜ ์ทจ์—…ํฌ๋ถ€ 9 2. ํŠน์„ฑํ™”๊ณ ๋“ฑํ•™๊ต ํ•™์ƒ์˜ ์ทจ์—…ํฌ๋ถ€์— ๋Œ€ํ•œ ํ•™์ƒ ์ˆ˜์ค€ ๋ณ€์ธ์˜ ์˜ํ–ฅ์š”์ธ 28 3. ํŠน์„ฑํ™”๊ณ ๋“ฑํ•™๊ต ํ•™์ƒ์˜ ์ทจ์—…ํฌ๋ถ€์— ๋Œ€ํ•œ ํ•™๊ธ‰ ์ˆ˜์ค€ ๋ณ€์ธ์˜ ์˜ํ–ฅ์š”์ธ 42 III. ์—ฐ๊ตฌ ๋ฐฉ๋ฒ• 53 1. ์—ฐ๊ตฌ ์„ค๊ณ„ 53 2. ์—ฐ๊ตฌ ๋Œ€์ƒ 56 3. ์กฐ์‚ฌ ๋„๊ตฌ 60 4. ์ž๋ฃŒ ์ˆ˜์ง‘ 75 5. ๋ถ„์„ ๋ฐฉ๋ฒ• 75 IV. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ 81 1. ์ธก์ • ๋ณ€์ธ์˜ ๊ธฐ์ดˆํ†ต๊ณ„ ๋ถ„์„ ๊ฒฐ๊ณผ 81 2. ์ทจ์—…ํฌ๋ถ€์— ๋Œ€ํ•œ ํ•™์ƒ ์ˆ˜์ค€ ๋ณ€์ธ์˜ ํšจ๊ณผ 85 3. ์ทจ์—…ํฌ๋ถ€์— ๋Œ€ํ•œ ํ•™๊ธ‰ ์ˆ˜์ค€ ๋ณ€์ธ์˜ ํšจ๊ณผ 95 4. ์ทจ์—…ํฌ๋ถ€์— ๋Œ€ํ•œ ํ•™์ƒ ์ˆ˜์ค€ ๋ณ€์ธ๊ณผ ํ•™๊ธ‰ ์ˆ˜์ค€ ๋ณ€์ธ์˜ ์ƒํ˜ธ์ž‘์šฉ ํšจ๊ณผ 97 5. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ์— ๋Œ€ํ•œ ๋…ผ์˜ 104 V. ์š”์•ฝ, ๊ฒฐ๋ก  ๋ฐ ์ œ์–ธ 111 1. ์š”์•ฝ 111 2. ๊ฒฐ๋ก  113 3. ์ œ์–ธ 115 ์ฐธ๊ณ ๋ฌธํ—Œ 119 [๋ถ€๋ก 1] ์˜ˆ๋น„์กฐ์‚ฌ ์„ค๋ฌธ์ง€(ํ•™์ƒ์šฉ) 135 [๋ถ€๋ก 2] ์˜ˆ๋น„์กฐ์‚ฌ ์„ค๋ฌธ์ง€(๊ต์‚ฌ์šฉ) 143 [๋ถ€๋ก 3] ๋ณธ ์กฐ์‚ฌ ์„ค๋ฌธ์ง€(ํ•™์ƒ์šฉ) 149 [๋ถ€๋ก 4] ๋ณธ ์กฐ์‚ฌ ์„ค๋ฌธ์ง€(๊ต์‚ฌ์šฉ) 157Maste

    The Relationship between Employment preparation behavior, Anxiety, Parental Socioeconomic Status, Teacher-Student Relationships and Online Class Satisfaction of Specialized Vocational High School Students

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๋†์—…์ƒ๋ช…๊ณผํ•™๋Œ€ํ•™ ๋†์‚ฐ์—…๊ต์œก๊ณผ, 2022.2. ๊น€์ง„๋ชจ.The purpose of this study was to explore the relationship between employment preparation behavior, anxiety, parental socioeconomic status, teacher-student relationships, and online class satisfaction of specialized vocational high school students. To satisfy the research goal, the specific research objectives were as follows; First, the direct effect of anxiety, parental socioeconomic status, teacher-student relationships, online class satisfaction on employment preparation behavior of specialized vocational high school students was explored. Second, the mediating effect of online class satisfaction in relationship between anxiety, parental socioeconomic status, teacher-student relationships, and employment preparation behavior was examined. The target population for this study was the entire students in domestic specialized vocational high school students and 2nd- and 3rd-grade students who have experienced online learning for more than one year after the COVID-19 pandemic. The 30 schools were selected of the whole specialized vocational high schools by region and department, and 600 students were selected as the sample group. The data were collected from October 21, 2021 to November 15, 2021 via mail and face-to-face survey. A total of 580 responses from specialized vocational high schools were collected, of which 554 responses were analyzed after removing careless responses. As the research instrument, the survey consists of 86 questions, among which were 23 items on employment preparation behavior, 20 items on anxiety, 21 items on teacher-student relationship, 15 items on online class satisfaction, 3 items asking about parental educational background and monthly household income and 4 items on general characteristics. Moreover, parental socioeconomic status was used as a single scale by assigning scores to the educational background of fathers and mothers, and to the monthly income of the household, standardizing them, and adding them together. As for the research instrument, face validity was confirmed from 3 teachers of specialized high schools. For analysis, correlation analysis, difference analysis and regression analysis were conducted. To evaluate the level of perceptions on each variable, frequency, percentage, average and standard deviation were utilized. Furthermore, the analysis of correlation was conducted, to investigate the correlation among the variables. In order to identify the direct effect of the independent variable on the dependent variable, regression analysis was conducted. To identify the mediating effect, Hayes (2013)โ€™s PROCESS macro model 4 was adopted. All the statistical significance was determined at .05. The results from the analysis were as follows. First, anxiety had no relationship with employment preparation behavior(p>.05). Parental socioeconomic status had a relationship with employment preparation behavior(ฮฒ=.081, p<.05). Teacher-student relationships had a statistically significant relationship with employment preparation behavior(ฮฒ=.154, p<.001). Online class satisfaction had a statistically significant relationship with employment preparation behavior(ฮฒ=.281, p<.001). Second, anxiety of specialized vocational high school students had a full mediating effect through online class satisfaction on employment preparation behavior(-.0474). Additionally, parental socioeconomic status(.0113), teacher-student relationship(.1053) turned out to have a partial mediating effect via online class satisfaction. Drawing upon the results from the analysis, concluding results are as follows. First, although anxiety does not directly affect employment preparation behavior, it has a full mediating effect that negatively affects employment preparation behavior through online class satisfaction. Therefore, in order to increase students' employment preparation behavior, psychological support to reduce anxiety should be prepared. Parental socioeconomic status, teacher-student relationships have a positive effect on employment preparation behavior, they have a partial mediating effect that affects employment preparation behavior through online class satisfaction. This result demonstrates that the more the students feel close to the teacher, the more reliable and competent they are, the more active their employment preparation behavior is. Between anxiety, parental socioeconomic status, teacher-student relationships, and online class satisfaction, online class satisfaction has the biggest explanation. And although anxiety does not directly affect employment preparation behavior, it has an indirect effect through online class satisfaction. The importance of online learning is growing, especially in specialized vocational high schools. This suggests that it is important to increase studentsโ€™ satisfaction by improving the quality of the online classes. The suggestions for future researches are as follows. First, it is necessary to explore the variables that influence online class satisfaction. The variables that affect online class satisfaction such as individual motivation and immersion, design, and execution of online classes need to be dealt with in a different dimension from the general variables of class satisfaction. Second, the parental socioeconomic status, an independent variable in this study, is not only difficult to overcome through individual efforts of parents or children, but also devalues โ€‹โ€‹the importance of parental support and parenting attitudes. In addition, as the economic support for low-income students has been strengthened as online class has become a universal class, it is necessary to expand the parental socioeconomic status to broader family variables such as parental support in future researches. Third, it is important to compare and analyze the difference in satisfaction between face-to-face and non-face-to-face classes for the same class. Fourth, it is necessary to conduct a comparative study between specialized high school students and Meister high school students who have advanced online class by making the curriculum flexible. Moreover, the implications for the field are as follows; First, as this study confirmed the effect of online class satisfaction of students of specialized vocational high schools on employment preparation behavior, it is necessary to make technical and institutional efforts to enhance the sense of reality of online practice classes. Second, an online learning environment should be improved. For example, the proper equipment such as the functioning computers and the Internet should be provided in addition to digital literacy education. Third, although anxiety did not directly affect employment preparation behavior, it was confirmed that it had an indirect effect through online class satisfaction. Therefore, it can be said that the emotional stability of students is more important in the online learning situation.์ด ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์€ ํŠน์„ฑํ™”๊ณ ๋“ฑํ•™๊ต ํ•™์ƒ์˜ ์ทจ์—…์ค€๋น„ํ–‰๋™๊ณผ ๋ถˆ์•ˆ, ๋ถ€๋ชจ์˜ ์‚ฌํšŒ๊ฒฝ์ œ์  ์ง€์œ„, ๊ต์‚ฌํ•™์ƒ๊ด€๊ณ„ ๋ฐ ์›๊ฒฉ์ˆ˜์—…๋งŒ์กฑ๋„์˜ ๊ด€๊ณ„๋ฅผ ๊ตฌ๋ช…ํ•˜๋Š” ๋ฐ ์žˆ์—ˆ๋‹ค. ์ด๋ฅผ ์œ„ํ•˜์—ฌ ๋‹ค์Œ๊ณผ ๊ฐ™์€ ์—ฐ๊ตฌ๋ชฉํ‘œ๋ฅผ ์„ค์ •ํ•˜์˜€๋‹ค. ์ฒซ์งธ, ํŠน์„ฑํ™”๊ณ  ํ•™์ƒ์˜ ๋ถˆ์•ˆ, ๋ถ€๋ชจ์˜ ์‚ฌํšŒ๊ฒฝ์ œ์  ์ง€์œ„, ๊ต์‚ฌํ•™์ƒ๊ด€๊ณ„ ๋ฐ ์›๊ฒฉ์ˆ˜์—…๋งŒ์กฑ๋„๊ฐ€ ์ทจ์—…์ค€๋น„ํ–‰๋™์— ๋ฏธ์น˜๋Š” ์ง์ ‘์ ์ธ ์˜ํ–ฅ ๊ตฌ๋ช…, ๋‘˜์งธ, ํŠน์„ฑํ™”๊ณ  ํ•™์ƒ์˜ ๋ถˆ์•ˆ, ๋ถ€๋ชจ์˜ ์‚ฌํšŒ๊ฒฝ์ œ์  ์ง€์œ„, ๊ต์‚ฌํ•™์ƒ๊ด€๊ณ„์™€ ์ทจ์—…์ค€๋น„ํ–‰๋™์˜ ๊ด€๊ณ„์—์„œ ์›๊ฒฉ์ˆ˜์—…๋งŒ์กฑ๋„์˜ ๋งค๊ฐœํšจ๊ณผ ๊ตฌ๋ช…์ด์—ˆ๋‹ค. ์ด ์—ฐ๊ตฌ์˜ ๋ชจ์ง‘๋‹จ์€ ๊ตญ๋‚ด ํŠน์„ฑํ™”๊ณ  ํ•™์ƒ ์ „์ฒด์ด๋ฉฐ ์ฝ”๋กœ๋‚˜19 ์ดํ›„ 1๋…„ ์ด์ƒ ์›๊ฒฉ์ˆ˜์—…์„ ๊ฒฝํ—˜ํ•œ 2, 3ํ•™๋…„ ํ•™์ƒ์„ ์—ฐ๊ตฌ๋Œ€์ƒ์œผ๋กœ ํ•˜์˜€๋‹ค. ์ „๊ตญ์˜ ํŠน์„ฑํ™”๊ณ ๋ฅผ ์ง€์—ญ๋ณ„, ๊ณ„์—ด๋ณ„ ํ•™๊ต ์ˆ˜ ๋น„์œจ์— ๋”ฐ๋ผ ๋‚˜๋ˆ„๊ณ  30๊ฐœ๊ต๋ฅผ ์„ ์ •ํ•˜์—ฌ, ํ•™์ƒ 600๋ช…์„ ํ‘œ๋ณธ ์ง‘๋‹จ์œผ๋กœ ์ •ํ•˜์˜€๋‹ค. ์ž๋ฃŒ์˜ ์ˆ˜์ง‘์€ 2021๋…„ 10์›” 21์ผ๋ถ€ํ„ฐ 2021๋…„ 11์›” 15์ผ๊นŒ์ง€ ํ•™๊ต๋ฐฉ๋ฌธ๊ณผ ์šฐํŽธ์„ ํ†ตํ•ด ์ด๋ฃจ์–ด์กŒ๋‹ค. ์ด 580๋ถ€๊ฐ€ ์ˆ˜์ง‘๋˜์—ˆ์œผ๋ฉฐ, ์ด ๊ฐ€์šด๋ฐ, ๋ถˆ์„ฑ์‹คํ•˜๊ฒŒ ์‘๋‹ตํ•œ ์„ค๋ฌธ์„ ์ œ์™ธํ•˜๊ณ , 554๋ถ€๋ฅผ ๋ถ„์„์— ์‚ฌ์šฉํ•˜์˜€๋‹ค. ์กฐ์‚ฌ๋„๊ตฌ๋Š” ๋ชจ๋‘ 86๋ฌธํ•ญ์œผ๋กœ ๊ตฌ์„ฑ๋˜์—ˆ๋‹ค. ์ทจ์—…์ค€๋น„ํ–‰๋™ 23๋ฌธํ•ญ, ๋ถˆ์•ˆ์€ 20๋ฌธํ•ญ, ๊ต์‚ฌํ•™์ƒ๊ด€๊ณ„๋Š” 21๋ฌธํ•ญ, ์›๊ฒฉ์ˆ˜์—…๋งŒ์กฑ๋„๋Š” 15๋ฌธํ•ญ์œผ๋กœ ๋ชจ๋‘ 5์  ๋ฆฌ์ปคํŠธ ์ฒ™๋„๋กœ ๊ตฌ์„ฑํ•˜์˜€๊ณ , ๋ถ€๋ชจ์˜ ์‚ฌํšŒ๊ฒฝ์ œ์  ์ง€์œ„์˜ ํ•˜์œ„์š”์ธ์ธ ์•„๋ฒ„์ง€์˜ ํ•™๋ ฅ๊ณผ ์–ด๋จธ๋‹ˆ์˜ ํ•™๋ ฅ, ๊ฐ€๊ตฌ์˜ ์›” ์ˆ˜์ž…์„ ๋ฌป๋Š” 3๋ฌธํ•ญ๊ณผ ์ธ๊ตฌํ†ต๊ณ„ํ•™์  ํŠน์„ฑ ๋ณ€์ธ 4๋ฌธํ•ญ์ด ํฌํ•จ๋˜์—ˆ๋‹ค. ์ด๋•Œ ๋ถ€๋ชจ์˜ ์‚ฌํšŒ๊ฒฝ์ œ์  ์ง€์œ„๋Š” ์•„๋ฒ„์ง€์™€ ์–ด๋จธ๋‹ˆ์˜ ํ•™๋ ฅ, ๊ฐ€๊ตฌ์˜ ์›” ์ˆ˜์ž…์— ๊ฐ๊ฐ ์ ์ˆ˜๋ฅผ ๋ถ€์—ฌํ•˜๊ณ  ํ‘œ์ค€ํ™”ํ•œ ๋’ค ํ•ฉ์‚ฐํ•˜์—ฌ ๋‹จ์ผ ์ฒ™๋„๋กœ ์‚ฌ์šฉํ•˜์˜€๋‹ค. ๋„๊ตฌ๋Š” ํŠน์„ฑํ™”๊ณ  ๊ต์‚ฌ 3์ธ์œผ๋กœ๋ถ€ํ„ฐ ์•ˆ๋ฉดํƒ€๋‹น๋„๋ฅผ ํ™•์ธํ•˜์˜€๋‹ค. ์ž๋ฃŒ๋ถ„์„์„ ์œ„ํ•ด ์ฐจ์ด๋ถ„์„, ์ƒ๊ด€๊ด€๊ณ„๋ถ„์„, ํšŒ๊ท€๋ถ„์„์„ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ๋Œ€์ƒ์˜ ์ผ๋ฐ˜์  ํŠน์„ฑ์„ ๊ตฌ๋ช…ํ•˜๊ธฐ ์œ„ํ•ด ๋นˆ๋„, ๋ฐฑ๋ถ„์œจ, ํ‰๊ท , ํ‘œ์ค€ํŽธ์ฐจ ๋“ฑ์„ ์‚ฌ์šฉํ•˜์˜€๋‹ค. ๋˜ํ•œ ๊ฐ ๋ณ€์ธ๋“ค ์‚ฌ์ด์˜ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๊ตฌ๋ช…ํ•˜๊ธฐ ์œ„ํ•ด ์ƒ๊ด€๋ถ„์„์„, ์ข…์†๋ณ€์ธ์— ๋Œ€ํ•œ ๋…๋ฆฝ๋ณ€์ธ์˜ ์ง์ ‘ํšจ๊ณผ๋ฅผ ํ™•์ธํ•˜๊ธฐ ์œ„ํ•ด ํšŒ๊ท€๋ถ„์„์„ ์ˆ˜ํ–‰ํ•˜์˜€๊ณ , Hayes(2013)๊ฐ€ ๊ฐœ๋ฐœํ•œ PROCESS macro๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ๋งค๊ฐœํšจ๊ณผ๋ฅผ ๊ฒ€์ฆํ•˜์˜€๋‹ค. ์ถ”๋ฆฌํ†ต๊ณ„์—์„œ ํ†ต๊ณ„์  ์œ ์˜์„ฑ์€ .05๋ฅผ ๊ธฐ์ค€์œผ๋กœ ํŒ๋‹จํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ๊ฒฐ๊ณผ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ๋ถˆ์•ˆ์€ ์ทจ์—…์ค€๋น„ํ–‰๋™์— ์œ ์˜ํ•œ ์˜ํ–ฅ์„ ๋ฏธ์น˜์ง€ ๋ชปํ•˜์˜€๋‹ค(p>.05). ๋ถ€๋ชจ์˜ ์‚ฌํšŒ๊ฒฝ์ œ์  ์ง€์œ„๋Š” ์ทจ์—…์ค€๋น„ํ–‰๋™์— ์œ ์˜ํ•œ ์ •์ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๊ฒƒ์œผ๋กœ ํ™•์ธ๋˜์—ˆ๋‹ค(ฮฒ=.081, p<.05). ๊ต์‚ฌํ•™์ƒ๊ด€๊ณ„๋„ ์ทจ์—…์ค€๋น„ํ–‰๋™์— ์œ ์˜ํ•œ ์ •์ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค(ฮฒ=.154, p<.001). ์›๊ฒฉ์ˆ˜์—…๋งŒ์กฑ๋„ ์—ญ์‹œ ์ทจ์—…์ค€๋น„ํ–‰๋™์— ์œ ์˜ํ•œ ์ •์  ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๊ฒƒ์œผ๋กœ ํ™•์ธ๋˜์—ˆ๋‹ค(ฮฒ=.281, p<.001). ๋‘˜์งธ, ์›๊ฒฉ์ˆ˜์—…๋งŒ์กฑ๋„๋Š” ๋ถˆ์•ˆ๊ณผ ์ทจ์—…์ค€๋น„ํ–‰๋™์˜ ๊ด€๊ณ„๋ฅผ ์™„์ „๋งค๊ฐœํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค(-.0474). ๋˜ ๋ถ€๋ชจ์˜ ์‚ฌํšŒ๊ฒฝ์ œ์  ์ง€์œ„(.0113)์™€ ๊ต์‚ฌํ•™์ƒ๊ด€๊ณ„(.1053)๋Š” ์›๊ฒฉ์ˆ˜์—…๋งŒ์กฑ๋„๋ฅผ ๋งค๊ฐœ๋กœ ์ทจ์—…์ค€๋น„ํ–‰๋™์— ๊ฐ„์ ‘์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์นจ์œผ๋กœ์จ ๋ถ€๋ถ„๋งค๊ฐœํ•˜๋Š” ๊ฒƒ์ด ํ™•์ธ๋˜์—ˆ๋‹ค. ์ด ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ์–ป์€ ๊ฒฐ๋ก ์€ ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ๋ถˆ์•ˆ์€ ์ทจ์—…์ค€๋น„ํ–‰๋™์— ์ง์ ‘์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์น˜์ง€ ๋ชปํ•˜์ง€๋งŒ, ์›๊ฒฉ์ˆ˜์—…๋งŒ์กฑ๋„๋ฅผ ๋งค๊ฐœ๋กœ ์ทจ์—…์ค€๋น„ํ–‰๋™์— ๋ถ€์ ์˜ํ–ฅ์„ ๋ฏธ์นœ๋‹ค. ๋”ฐ๋ผ์„œ ํ•™์ƒ๋“ค์˜ ์ทจ์—…์ค€๋น„ํ–‰๋™์„ ๋†’์ด๊ธฐ ์œ„ํ•ด์„œ๋Š” ๋ถˆ์•ˆ์„ ๋‚ฎ์ถ”๋Š” ์‹ฌ๋ฆฌ์ง€์›๊ณผ ๋”๋ถˆ์–ด, ์›๊ฒฉ์ˆ˜์—…๋งŒ์กฑ๋„๋ฅผ ๋†’์ด๊ธฐ ์œ„ํ•œ ๋ฐฉ์•ˆ์ด ๋งˆ๋ จ๋˜์–ด์•ผ ํ•  ๊ฒƒ์ด๋‹ค. ๋ถ€๋ชจ์˜ ์‚ฌํšŒ๊ฒฝ์ œ์  ์ง€์œ„์™€ ๊ต์‚ฌํ•™์ƒ๊ด€๊ณ„๋Š” ์ทจ์—…์ค€๋น„ํ–‰๋™์— ์œ ์˜ํ•œ ์ง์ ‘ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋ฉฐ, ๋˜ํ•œ ์›๊ฒฉ์ˆ˜์—…๋งŒ์กฑ๋„๋ฅผ ๋งค๊ฐœ๋กœ ๊ฐ„์ ‘ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๊ฒƒ์ด ํ™•์ธ๋˜์—ˆ๋‹ค. ์ด๋Š” ํŠน์„ฑํ™”๊ณ  ํ•™์ƒ ๋ถ€๋ชจ์˜ ์‚ฌํšŒ๊ฒฝ์ œ์  ์ง€์œ„๊ฐ€ ๋†’์„์ˆ˜๋ก, ๋˜ ํ•™์ƒ์ด ๊ต์‚ฌ๋ฅผ ์นœ๋ฐ€ํ•˜๊ฒŒ ๋Š๋ผ๊ณ  ์œ ๋Šฅํ•˜๋‹ค๊ณ  ์—ฌ๊ธธ์ˆ˜๋ก ์›๊ฒฉ์ˆ˜์—…๋งŒ์กฑ๋„๋ฅผ ๊ฒฝ์œ ํ•ด ์ทจ์—…์ค€๋น„ํ–‰๋™์ด ๋” ํ™œ๋ฐœํ•ด์ง์„ ์˜๋ฏธํ•œ๋‹ค. ๋ถˆ์•ˆ, ๋ถ€๋ชจ์˜ ์‚ฌํšŒ๊ฒฝ์ œ์  ์ง€์œ„, ๊ต์‚ฌํ•™์ƒ๊ด€๊ณ„, ์›๊ฒฉ์ˆ˜์—…๋งŒ์กฑ๋„ ๊ฐ€์šด๋ฐ ์›๊ฒฉ์ˆ˜์—…๋งŒ์กฑ๋„๋Š” ์ทจ์—…์ค€๋น„ํ–‰๋™์— ๊ฐ€์žฅ ํฐ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋ฉฐ, ํŠนํžˆ ๋ถˆ์•ˆ์€ ์›๊ฒฉ์ˆ˜์—…๋งŒ์กฑ๋„๋ฅผ ๋งค๊ฐœ๋กœ ํ•˜์—ฌ, ์ทจ์—…์ค€๋น„ํ–‰๋™์— ๊ฐ„์ ‘์ ์ธ ๋ถ€์ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๊ฒƒ์ด ํ™•์ธ๋˜์—ˆ๋‹ค. ํŠน์„ฑํ™”๊ณ ์—์„œ ์›๊ฒฉ์ˆ˜์—…์˜ ์ค‘์š”์„ฑ์ด ํŠนํžˆ ์ปค์ง€๊ณ  ์žˆ๋‹ค. ์›๊ฒฉ์ˆ˜์—…์˜ ์งˆ์„ ๊ด€๋ฆฌํ•จ์œผ๋กœ์จ ํ•™์ƒ์˜ ์ˆ˜์—…๋งŒ์กฑ๋„๋ฅผ ๋†’์ด๋Š” ์ผ์ด ์ค‘์š”ํ•จ์„ ์‹œ์‚ฌํ•œ๋‹ค. ๋™์‹œ์— ๋ถˆ์•ˆ์„ ๋‚ฎ์ถ”๋Š” ๋…ธ๋ ฅ๋„ ์‹œ๋„๋˜์–ด์•ผ ํ•  ๊ฒƒ์ด๋‹ค. ์ด ์—ฐ๊ตฌ๊ฒฐ๊ณผ๋ฅผ ํ† ๋Œ€๋กœ ํ›„์†์—ฐ๊ตฌ๋ฅผ ์ œ์–ธํ•˜๋ฉด ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ์›๊ฒฉ์ˆ˜์—…๋งŒ์กฑ๋„์— ์˜ํ–ฅ์„ ์ฃผ๋Š” ์„ ํ–‰๋ณ€์ธ์„ ํƒ์ƒ‰ํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ๊ฐœ์ธ์˜ ๋™๊ธฐ์™€ ๋ชฐ์ž… ๋ฐ ์›๊ฒฉ์ˆ˜์—… ์„ค๊ณ„์™€ ์‹คํ–‰ ๋“ฑ ์›๊ฒฉ์ˆ˜์—…๊ณผ ๊ด€๋ จํ•œ ๋ณ€์ธ์„ ํ†ตํ•ฉ์ ์œผ๋กœ ๊ณ ๋ คํ•˜์—ฌ ์ผ๋ฐ˜์ ์ธ ์ˆ˜์—…๋งŒ์กฑ๋„์˜ ์„ ํ–‰๋ณ€์ธ๊ณผ๋Š” ๋‹ค๋ฅธ ๋ณ€์ธ์„ ํƒ์ƒ‰ํ•ด์•ผ ํ•œ๋‹ค. ๋‘˜์งธ, ์ด ์—ฐ๊ตฌ์˜ ๋…๋ฆฝ๋ณ€์ธ์ธ ๋ถ€๋ชจ์˜ ์‚ฌํšŒ๊ฒฝ์ œ์  ์ง€์œ„๋Š” ๋ถ€๋ชจ๋‚˜ ์ž๋…€์˜ ๊ฐœ์ธ ๋…ธ๋ ฅ์œผ๋กœ ๊ทน๋ณตํ•˜๊ธฐ ์–ด๋ ค์šธ ๋ฟ ์•„๋‹ˆ๋ผ, ๋ถ€๋ชจ์˜ ์ง€์ง€๋‚˜ ์–‘์œกํƒœ๋„ ๋“ฑ์˜ ์ค‘์š”์„ฑ์„ ํ‰๊ฐ€์ ˆํ•˜ํ•˜๊ธฐ๋„ ํ•œ๋‹ค. ๋˜ํ•œ ์›๊ฒฉ์ˆ˜์—…์ด ๋ณดํŽธ์ ์ธ ์ •๊ทœ์ˆ˜์—…์œผ๋กœ ์ง„ํ–‰๋˜๋ฉด์„œ, ์ €์†Œ๋“์ธต ํ•™์ƒ์— ๋Œ€ํ•œ ๊ฒฝ์ œ์  ์ง€์›์ด ๊ฐ•ํ™”๋œ ๋งŒํผ, ์ถ”ํ›„ ์—ฐ๊ตฌ์—์„œ๋Š” ๋ถ€๋ชจ์˜ ์‚ฌํšŒ๊ฒฝ์ œ์  ์ง€์œ„๋ฅผ ๋ถ€๋ชจ ์ง€์ง€์™€ ๊ฐ™์€ ๋ณด๋‹ค ํญ๋„“์€ ๊ฐ€์ •๋ณ€์ธ์œผ๋กœ ํ™•๋Œ€ํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ์…‹์งธ, ๋™์ผํ•œ ์ˆ˜์—…์„ ๋Œ€์ƒ์œผ๋กœ ํ•œ ๋Œ€๋ฉด, ๋น„๋Œ€๋ฉด ์ˆ˜์—…์˜ ๋งŒ์กฑ๋„ ์ฐจ์ด๋ฅผ ๋น„๊ต ๋ถ„์„ํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ๋„ท์งธ, ์ทจ์—…์„ ๋ชฉํ‘œ๋กœ ๊ต์œก๊ณผ์ •์„ ์œ ์—ฐํ™”ํ•ด ์›๊ฒฉ์ˆ˜์—…์„ ๊ณ ๋„ํ™”ํ•œ ๋งˆ์ด์Šคํ„ฐ๊ณ  ํ•™์ƒ๋“ค๊ณผ ํŠน์„ฑํ™”๊ณ  ํ•™์ƒ๋“ค์— ๋Œ€ํ•œ ๋น„๊ต์—ฐ๊ตฌ๊ฐ€ ํ•„์š”ํ•˜๋‹ค. ์—ฐ๊ตฌ์˜ ํ™œ์šฉ์„ ์œ„ํ•œ ์ œ์–ธ์€ ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ์ด ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ํŠน์„ฑํ™”๊ณ  ํ•™์ƒ๋“ค์˜ ์›๊ฒฉ์ˆ˜์—…๋งŒ์กฑ๋„์˜ ์ค‘์š”์„ฑ์ด ํ™•์ธ๋˜์—ˆ๋‹ค. ์ด์— ์›๊ฒฉ ์‹ค์Šต์ˆ˜์—…์˜ ์‹ค์žฌ๊ฐ์„ ๋†’์ผ ์ˆ˜ ์žˆ๋„๋ก ๊ธฐ์ˆ ์ , ์ œ๋„์  ๋…ธ๋ ฅ์ด ํ•„์š”ํ•˜๋‹ค. ๋‘˜์งธ, ์›๊ฒฉ์ˆ˜์—…์ด ์ •๊ทœ๊ต์œก์œผ๋กœ ์ œ๊ณต๋˜๋Š” ๋งŒํผ, ๋ชจ๋“  ํ•™์ƒ์„ ๋Œ€์ƒ์œผ๋กœ ํ•œ ๊ธฐ๊ธฐ์™€ ์ธํ„ฐ๋„ท, ๋ฏธ๋””์–ด ๋ฆฌํ„ฐ๋Ÿฌ์‹œ ๊ต์œก ๋“ฑ ๊ต์œก์ง€์›์ด ๊ฐ•ํ™”๋˜์–ด์•ผ ํ•  ๊ฒƒ์ด๋‹ค. ์…‹์งธ, ํŠน์„ฑํ™”๊ณ  ํ•™์ƒ์˜ ๋ถˆ์•ˆ์€ ์ทจ์—…์ค€๋น„ํ–‰๋™์— ์ง์ ‘์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์น˜์ง€ ์•Š์Œ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ , ์›๊ฒฉ์ˆ˜์—…๋งŒ์กฑ๋„๋ฅผ ๋งค๊ฐœ๋กœ ๊ฐ„์ ‘์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๊ฒƒ์œผ๋กœ ํ™•์ธ๋˜์—ˆ๋‹ค. ์ด๋Š” ๋น„๋Œ€๋ฉด ์ƒํ™ฉ์—์„œ ํ•™์ƒ๋“ค์˜ ์ •์„œ ์ง€์›์„ ๋”์šฑ ๊ฐ•ํ™”ํ•ด์•ผ ํ•จ์„ ์‹œ์‚ฌํ•œ๋‹ค.I. ์„œ๋ก  1 1. ์—ฐ๊ตฌ์˜ ํ•„์š”์„ฑ 1 2. ์—ฐ๊ตฌ์˜ ๋ชฉ์  6 3. ์—ฐ๊ตฌ๊ฐ€์„ค 6 4. ์šฉ์–ด์˜ ์ •์˜ 7 II. ์ด๋ก ์  ๋ฐฐ๊ฒฝ 11 1. ํŠน์„ฑํ™”๊ณ  ํ•™์ƒ์˜ ์ง„๋กœ๋ฐœ๋‹ฌ 11 2. ์ทจ์—…์ค€๋น„ํ–‰๋™ 20 3. ๋ถˆ์•ˆ, ๋ถ€๋ชจ์˜ ์‚ฌํšŒ๊ฒฝ์ œ์  ์ง€์œ„, ๊ต์‚ฌํ•™์ƒ๊ด€๊ณ„, ์›๊ฒฉ์ˆ˜์—…๋งŒ์กฑ๋„ 32 4. ๋ณ€์ธ ๊ฐ„์˜ ๊ด€๊ณ„ 61 III. ์—ฐ๊ตฌ ๋ฐฉ๋ฒ• 79 1. ์—ฐ๊ตฌ๋ชจํ˜• 79 2. ์—ฐ๊ตฌ๋Œ€์ƒ 80 3. ์ธก์ •๋„๊ตฌ 83 4. ์ž๋ฃŒ์ˆ˜์ง‘ 90 5. ์ž๋ฃŒ๋ถ„์„ 92 โ…ฃ. ์—ฐ๊ตฌ๊ฒฐ๊ณผ ๋ฐ ๋…ผ์˜ 97 1. ์—ฐ๊ตฌ๋ณ€์ธ ์ผ๋ฐ˜ํ†ต๊ณ„๋Ÿ‰ 97 2. ์ทจ์—…์ค€๋น„ํ–‰๋™๊ณผ ๋ถˆ์•ˆ, ๋ถ€๋ชจ์˜ ์‚ฌํšŒ๊ฒฝ์ œ์  ์ง€์œ„, ๊ต์‚ฌํ•™์ƒ๊ด€๊ณ„ ๋ฐ ์›๊ฒฉ์ˆ˜์—…๋งŒ์กฑ๋„์˜ ์˜ํ–ฅ๊ด€๊ณ„ 105 3. ๋ถˆ์•ˆ, ๋ถ€๋ชจ์˜ ์‚ฌํšŒ๊ฒฝ์ œ์  ์ง€์œ„, ๊ต์‚ฌํ•™์ƒ๊ด€๊ณ„์™€ ์ทจ์—…์ค€๋น„ํ–‰๋™์˜ ๊ด€๊ณ„์—์„œ ์›๊ฒฉ์ˆ˜์—…๋งŒ์กฑ๋„์˜ ๋งค๊ฐœํšจ๊ณผ 107 4. ๋…ผ์˜ 112 โ…ค. ์š”์•ฝ, ๊ฒฐ๋ก  ๋ฐ ์ œ์–ธ 117 1. ์š”์•ฝ 117 2. ๊ฒฐ๋ก  119 3. ์ œ์–ธ 121 ์ฐธ๊ณ ๋ฌธํ—Œ 127 ๋ถ€๋ก 165 Abstract 175์„

    The Hierarchical Linear Relationship between Organizational Adjustment and Individual and Organizational Variables of Early-career Workers Graduated from Meister High School

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๋†์—…์ƒ๋ช…๊ณผํ•™๋Œ€ํ•™ ๋†์‚ฐ์—…๊ต์œก๊ณผ, 2022.2. ๋‚˜์Šน์ผ.์—ฐ๊ตฌ์˜ ๋ชฉ์ ์€ ๋งˆ์ด์Šคํ„ฐ๊ณ ์กธ ์ดˆ๊ธฐ๊ฒฝ๋ ฅ์ž์˜ ์กฐ์ง์ ์‘๊ณผ ๊ฐœ์ธ ๋ฐ ์กฐ์ง ๋ณ€์ธ๋“ค์˜ ์œ„๊ณ„์  ๊ด€๊ณ„๋ฅผ ๊ตฌ๋ช…ํ•˜๋Š”๋ฐ ์žˆ์—ˆ๋‹ค. ๊ตฌ์ฒด์ ์ธ ์—ฐ๊ตฌ ๋ชฉํ‘œ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™์ด ๋‹ค์„ฏ ๊ฐ€์ง€์ด๋‹ค. ์ฒซ์งธ, ๋งˆ์ด์Šคํ„ฐ๊ณ ์กธ ์ดˆ๊ธฐ๊ฒฝ๋ ฅ์ž์˜ ์กฐ์ง์ ์‘ ์ˆ˜์ค€๊ณผ ๊ทธ์™€ ๊ด€๋ จ๋œ ๊ฐœ์ธ ๋ฐ ์กฐ์ง ์ฐจ์› ๋ณ€์ธ๋“ค์˜ ์ˆ˜์ค€์„ ๊ตฌ๋ช…ํ•œ๋‹ค. ๋‘˜์งธ, ์กฐ์ง์ ์‘์— ๋Œ€ํ•œ ๊ฐœ์ธ ๋ฐ ์ง‘๋‹จ ์ฐจ์› ๋ณ€๋Ÿ‰์„ ๊ตฌ๋ช…ํ•œ๋‹ค. ์…‹์งธ, ์ง๋ฌดํšจ๋Šฅ๊ฐ, ํ•™์Šต๋ฏผ์ฒฉ์„ฑ, ๊ฒฝ๋ ฅ๊ณ„ํš, ์ž…์‚ฌ์ „ ์ง€์‹, ์ง๋ฌด์ ํ•ฉ์„ฑ, ์ง๋ฌด์ž์œจ์„ฑ ๋“ฑ์˜ ๊ฐœ์ธ ๋ณ€์ธ๋“ค์ด ์กฐ์ง์ ์‘์— ๋ฏธ์น˜๋Š” ํšจ๊ณผ๋ฅผ ๊ตฌ๋ช…ํ•œ๋‹ค. ๋„ท์งธ, ๊ฒฝ๋ ฅ์„ฑ์žฅ๊ธฐํšŒ, ์กฐ์ง์ง€์›์ธ์‹, ์กฐ์ง๊ณต์ •์„ฑ ๋“ฑ์˜ ์กฐ์ง ๋ณ€์ธ๋“ค์ด ์กฐ์ง์ ์‘์— ๋ฏธ์น˜๋Š” ํšจ๊ณผ๋ฅผ ๊ตฌ๋ช…ํ•œ๋‹ค. ๋‹ค์„ฏ์งธ, ์กฐ์ง์ ์‘์— ๋Œ€ํ•œ ๊ฐœ์ธ ๋ณ€์ธ๊ณผ ์กฐ์ง ๋ณ€์ธ์˜ ์ƒํ˜ธ์ž‘์šฉ ํšจ๊ณผ๋ฅผ ๊ตฌ๋ช…ํ•œ๋‹ค. ๋ชจ์ง‘๋‹จ์€ ๋งˆ์ด์Šคํ„ฐ๊ณ ๋ฅผ ์กธ์—…ํ•˜๊ณ  ์ทจ์—…ํ•œ ์žฌ์ง๊ธฐ๊ฐ„ 3๋…„ ์ด๋‚ด์˜ ์ดˆ๊ธฐ๊ฒฝ๋ ฅ์ž์ด์—ˆ๋‹ค. ๋งˆ์ด์Šคํ„ฐ๊ณ  ์กธ์—…์ƒ์ด 5์ธ ์ด์ƒ ์ทจ์—…ํ•œ 40๊ฐœ ๊ธฐ์—…์— ๋Œ€ํ•ด ๊ธฐ์—…๋ณ„ 10๋ช…์”ฉ ์ด 400๋ช…์„ ํ‘œ์ง‘ํ•˜์˜€๋‹ค. ๋‹ค๋งŒ, 2021๋…„ 2์›” ์กธ์—…์ƒ๋“ค์€ ์ทจ์—… ๊ธฐ๊ฐ„์ด 2๊ฐœ์›” ์ •๋„๋ผ์„œ ์ œ์™ธ๋˜์—ˆ๋‹ค. ์กฐ์‚ฌ๋„๊ตฌ๋Š” ์กฐ์ง์ ์‘, ๊ฐœ์ธ ๋ณ€์ธ(์ง๋ฌดํšจ๋Šฅ๊ฐ, ํ•™์Šต๋ฏผ์ฒฉ์„ฑ, ๊ฒฝ๋ ฅ๊ณ„ํš, ์ž…์‚ฌ์ „ ์ง€์‹, ์ง๋ฌด์ ํ•ฉ์„ฑ, ์ง๋ฌด์ž์œจ์„ฑ), ์กฐ์ง ๋ณ€์ธ(๊ฒฝ๋ ฅ์„ฑ์žฅ๊ธฐํšŒ, ์กฐ์ง์ง€์›์ธ์‹, ์กฐ์ง๊ณต์ •์„ฑ) ๋“ฑ์œผ๋กœ ๊ตฌ์„ฑ๋œ ์งˆ๋ฌธ์ง€๋ฅผ ํ™œ์šฉํ•˜์˜€๋‹ค. ์กฐ์ง์ ์‘ ์ธก์ •๋„๊ตฌ๋Š” ์˜ˆ๋น„์กฐ์‚ฌ ๋ฐ ๋ณธ์กฐ์‚ฌ ๋‹จ๊ณ„์—์„œ ๋‚ด์ ์ผ์น˜๋„ ๊ณ„์ˆ˜๋ฅผ ํ†ตํ•ด ์‹ ๋ขฐ๋„๋ฅผ ํ™•๋ณดํ•˜์˜€๋‹ค. ์ž๋ฃŒ์ˆ˜์ง‘์€ 2021๋…„ 5์›” 14์ผ๋ถ€ํ„ฐ 5์›” 28์ผ๊นŒ์ง€ ์˜จ๋ผ์ธ ์กฐ์‚ฌ๋ฅผ ํ†ตํ•ด ์ด๋ฃจ์–ด์กŒ์œผ๋ฉฐ, ์ด 400๋ถ€์˜ ์งˆ๋ฌธ์ง€ ์ค‘ 237๋ถ€๊ฐ€ ํšŒ์ˆ˜๋˜์–ด ํšŒ์ˆ˜์œจ์€ 59.3%์ด์—ˆ๋‹ค. ํšŒ์ˆ˜๋œ 237๋ถ€ ์ค‘ ๋ถˆ์„ฑ์‹คํ•œ ์‘๋‹ต๊ณผ ๊ธฐ์—…๋ณ„ 5์ธ ๋ฏธ๋งŒ์œผ๋กœ ํšŒ์ˆ˜๋œ 5๊ฐœ ๊ธฐ์—…์˜ ์‘๋‹ต์„ ์ œ์™ธํ•˜๊ณ , ์ด 35๊ฐœ ๊ธฐ์—… 213๋ช…์˜ ์ž๋ฃŒ๋ฅผ ์ตœ์ข… ๋ถ„์„์— ํ™œ์šฉํ•˜์˜€๋‹ค. ์ž๋ฃŒ๋ถ„์„์€ SPSS for Windows 26.0 ํ”„๋กœ๊ทธ๋žจ์„ ์ด์šฉํ•œ ๊ธฐ์ˆ ํ†ต๊ณ„ ๋ถ„์„๊ณผ HLM 8.1 for Windows ํ”„๋กœ๊ทธ๋žจ์„ ์ด์šฉํ•œ ์œ„๊ณ„์  ์„ ํ˜•๋ชจํ˜• ๋ถ„์„์„ ์‹ค์‹œํ•˜์˜€๋‹ค. ํ†ต๊ณ„์  ์œ ์˜์ˆ˜์ค€์€ 0.05๋กœ ์„ค์ •ํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ๋งˆ์ด์Šคํ„ฐ๊ณ ์กธ ์ดˆ๊ธฐ๊ฒฝ๋ ฅ์ž์˜ ์กฐ์ง์ ์‘ ์ˆ˜์ค€์€ ํ‰๊ท  4.13์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋˜ํ•œ, ์กฐ์ง์ ์‘์˜ ํ•˜์œ„์š”์ธ์— ๋”ฐ๋ผ ์—ญํ• ๋ช…ํ™•์„ฑ 4.42, ์‚ฌํšŒ์ ํ†ตํ•ฉ 4.17, ์ง๋ฌด์ˆ™๋‹ฌ 3.78 ์ˆœ์ด์—ˆ๋‹ค. ๋‘˜์งธ, ์กฐ์ง์ ์‘์˜ ์ „์ฒด ๋ณ€๋Ÿ‰ ์ค‘ ์†Œ์† ๊ธฐ์—… ๊ฐ„ ์ฐจ์ด๋กœ ์„ค๋ช…๋˜๋Š” ๋ณ€๋Ÿ‰์€ 7.8%, ๊ฐœ์ธ ๊ฐ„ ์ฐจ์ด๋กœ ์„ค๋ช…๋˜๋Š” ๋ณ€๋Ÿ‰์€ 92.2%์ธ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋˜ํ•œ, ์ธ๊ตฌํ†ต๊ณ„ํ•™์  ํŠน์„ฑ, ์ง๋ฌด์ ํ•ฉ์„ฑ, ํ•™์Šต๋ฏผ์ฒฉ์„ฑ, ๊ฒฝ๋ ฅ๊ณ„ํš, ์ž…์‚ฌ์ „ ์ง€์‹, ์ง๋ฌด์ ํ•ฉ์„ฑ, ์ง๋ฌด์ž์œจ์„ฑ ๋“ฑ ๊ฐœ์ธ ์ฐจ์› ๋ณ€์ธ๋“ค์„ ํ†ต์ œํ•œ ํ›„ ์žฌ์งํ•˜๋Š” ๊ธฐ์—… ๊ฐ„ ์ฐจ์ด๋กœ ์„ค๋ช…๋˜๋Š” ๋ณ€๋Ÿ‰์€ 54.6%, ๊ฐœ์ธ ๊ฐ„ ์ฐจ์ด๋กœ ์„ค๋ช…๋˜๋Š” ๋ณ€๋Ÿ‰์€ 45.4%์ธ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์…‹์งธ, ์กฐ์ง์ ์‘์— ๊ด€ํ•œ ๊ฐœ์ธ ๋ณ€์ธ์˜ ์„ค๋ช…๋Ÿ‰์€ 80.6%์ด์—ˆ๋‹ค. ๊ฐœ์ธ ๋ณ€์ธ ์ค‘ ์ง๋ฌดํšจ๋Šฅ๊ฐ(ฮฒ=0.408, p<0.01), ์ง๋ฌด์ ํ•ฉ์„ฑ(ฮฒ=0.138, p<0.05), ์ง๋ฌด์ž์œจ์„ฑ(ฮฒ=0.113, p<0.01) ๋“ฑ์€ ์กฐ์ง์ ์‘์— ๋ชจ๋‘ ์ •์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์ณค๋‹ค. ๋„ท์งธ, ์ธ๊ตฌํ†ต๊ณ„ํ•™์  ํŠน์„ฑ, ์ง๋ฌด์ ํ•ฉ์„ฑ, ํ•™์Šต๋ฏผ์ฒฉ์„ฑ, ๊ฒฝ๋ ฅ๊ณ„ํš, ์ž…์‚ฌ์ „ ์ง€์‹, ์ง๋ฌด์ ํ•ฉ์„ฑ, ์ง๋ฌด์ž์œจ์„ฑ ๋“ฑ ๊ฐœ์ธ ์ฐจ์› ๋ณ€์ธ๋“ค์„ ํ†ต์ œํ•œ ํ›„ ์กฐ์ง์ ์‘์— ๋Œ€ํ•œ ์กฐ์ง ๋ณ€์ธ์˜ ์ˆœ์ˆ˜ ์„ค๋ช…๋Ÿ‰์€ 39.8%์ด์—ˆ์œผ๋ฉฐ, ์กฐ์ง ๋ณ€์ธ ์ค‘ ๊ฒฝ๋ ฅ์„ฑ์žฅ๊ธฐํšŒ(ฮฒ=0.243, p<0.05)๋Š” ์กฐ์ง์ ์‘์— ์ •์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์ณค๋‹ค. ๋‹ค์„ฏ์งธ, ๋งˆ์ด์Šคํ„ฐ๊ณ ์กธ ์ดˆ๊ธฐ๊ฒฝ๋ ฅ์ž์˜ ์ง๋ฌด์ ํ•ฉ์„ฑ์€ ๊ฒฝ๋ ฅ์„ฑ์žฅ๊ธฐํšŒ(ฮฒ=0.211, p<0.05)์™€ ์ƒํ˜ธ์ž‘์šฉ์„ ํ†ตํ•ด ์กฐ์ง์ ์‘์— ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜๋ฏธํ•œ ์˜ํ–ฅ์„ ์ฃผ๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ์— ๋Œ€ํ•œ ๋…ผ์˜๋ฅผ ํ†ตํ•ด ๋‚ด๋ฆฐ ๊ฒฐ๋ก ์€ ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ๋งˆ์ด์Šคํ„ฐ๊ณ ์กธ ์ดˆ๊ธฐ๊ฒฝ๋ ฅ์ž์˜ ์กฐ์ง์ ์‘ ์ˆ˜์ค€์€ ๋†’์€ ํŽธ์ด๋ฉฐ, ๋งˆ์ด์Šคํ„ฐ๊ณ  ์กธ์—…์ƒ๋“ค์˜ ์ทจ์—… ํ›„ ์กฐ์ง์ ์‘์€ ์›ํ™œํ•˜๊ฒŒ ์ด๋ฃจ์–ด์ง€๊ณ  ์žˆ๋‹ค. ๋‘˜์งธ, ๋งˆ์ด์Šคํ„ฐ๊ณ ์กธ ์ดˆ๊ธฐ๊ฒฝ๋ ฅ์ž ์กฐ์ง์ ์‘ ์ˆ˜์ค€์˜ ์ฐจ์ด๋Š” ๋™์ผ ๊ธฐ์—…์— ์†ํ•œ ๊ฐœ์ธ๋“ค ๋ณด๋‹ค๋Š” ์žฌ์งํ•˜๋Š” ๊ธฐ์—…๋“ค ์‚ฌ์ด์—์„œ ๋” ๋งŽ์ด ๋ฐœ์ƒํ•œ๋‹ค. ์…‹์งธ, ์ฃผ์–ด์ง„ ์ง๋ฌด๋ฅผ ์œ ๋Šฅํ•˜๊ฒŒ ์ˆ˜ํ–‰ํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ๋ฏฟ์Œ, ์ž์‹ ์˜ ๋Šฅ๋ ฅ ๋ฐ ์ ์„ฑ๊ณผ ์ผ์น˜ํ•˜๋Š” ์ •๋„, ์กฐ์ง์—์„œ ๋ถ€์—ฌ๋œ ์ž์œจ์„ฑ ๋“ฑ ์‚ฌํšŒ์‹ฌ๋ฆฌ์  ์š”์ธ์ด ๋งˆ์ด์Šคํ„ฐ๊ณ ์กธ ์ดˆ๊ธฐ๊ฒฝ๋ ฅ์ž์˜ ์กฐ์ง์ ์‘์„ ๋†’์ธ๋‹ค. ์กฐ์ง์ ์‘์— ์ •์ ์ธ ์˜ํ–ฅ์„ ์ค„ ๊ฒƒ์œผ๋กœ ์˜ˆ์ƒํ•œ ํ•™์Šต๋ฏผ์ฒฉ์„ฑ, ๊ฒฝ๋ ฅ๊ณ„ํš, ์ž…์‚ฌ์ „ ์ง€์‹ ๋“ฑ์˜ ๊ฐœ์ธ ๋ณ€์ธ๋“ค์€ ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜๋ฏธํ•œ ์˜ํ–ฅ๋ ฅ์„ ๋ฏธ์น˜์ง€ ์•Š์•˜๋‹ค. ๋„ท์งธ, ๊ฐœ์ธ์˜ ๊ฒฝ๋ ฅ์„ฑ์žฅ์— ๋„์›€์ด ๋˜๋Š” ์ผ์„ ์ˆ˜ํ–‰ํ•˜๋„๋ก ๊ธฐ์—…์—์„œ ์ง€์›ํ•˜๋Š” ๊ฒƒ์ด ๋งˆ์ด์Šคํ„ฐ๊ณ ์กธ ์ดˆ๊ธฐ๊ฒฝ๋ ฅ์ž์˜ ์กฐ์ง์ ์‘์„ ์ด‰์ง„ํ•œ๋‹ค. ์กฐ์ง์ ์‘์— ์ •์ ์ธ ์˜ํ–ฅ์„ ์ค„ ๊ฒƒ์œผ๋กœ ์˜ˆ์ƒํ•œ ์กฐ์ง์ง€์›์ธ์‹, ์กฐ์ง๊ณต์ •์„ฑ ๋“ฑ์˜ ์กฐ์ง ๋ณ€์ธ๋“ค์€ ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜๋ฏธํ•œ ์˜ํ–ฅ๋ ฅ์„ ๋ฏธ์น˜์ง€ ์•Š์•˜๋‹ค. ๋‹ค์„ฏ์งธ, ๋งˆ์ด์Šคํ„ฐ๊ณ ์กธ ์ดˆ๊ธฐ๊ฒฝ๋ ฅ์ž์˜ ์กฐ์ง์ ์‘์— ๋Œ€ํ•œ ๊ฐœ์ธ์˜ ์‚ฌํšŒ์‹ฌ๋ฆฌ์  ์š”์ธ์˜ ์˜ํ–ฅ๋ ฅ์„ ๊ทน๋Œ€ํ™” ํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ๊ธฐ์—…์˜ ๊ธ์ •์  ์ง€์›์ด ํ•„์š”ํ•˜๋‹ค. ์—ฐ๊ตฌ์˜ ๊ฒฐ๋ก ์— ๊ธฐ์ดˆํ•œ ์ œ์–ธ์€ ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ๋งˆ์ด์Šคํ„ฐ๊ณ ์กธ ์ดˆ๊ธฐ๊ฒฝ๋ ฅ์ž์˜ ์ง๋ฌด๋Šฅ๋ ฅ์„ ๋†’์ด๊ธฐ ์œ„ํ•ด ๋งˆ์ด์Šคํ„ฐ๊ณ  ๊ต์œก๊ณผ์ •์„ ๋”์šฑ ์ง๋ฌด ์ค‘์‹ฌ์œผ๋กœ ๊ฐœํŽธํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ๋‘˜์งธ, ๊ธฐ์—…์— ๋”ฐ๋ผ ๋งˆ์ด์Šคํ„ฐ๊ณ ์กธ ์ดˆ๊ธฐ๊ฒฝ๋ ฅ์ž์˜ ์กฐ์ง์ ์‘ ์ˆ˜์ค€์ด ๋‹ฌ๋ผ์งˆ ์ˆ˜ ์žˆ์œผ๋ฏ€๋กœ ์ธํ„ด์‰ฝ, ํ˜„์žฅ์‹ค์Šต ๋“ฑ์„ ํ†ตํ•ด ์ฑ„์šฉ ๊ธฐ์—…์„ ์‚ฌ์ „์— ๊ฒฝํ—˜ํ•˜๋Š” ๊ธฐํšŒ๋ฅผ ์ œ๊ณตํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ์…‹์งธ, ๋งˆ์ด์Šคํ„ฐ๊ณ ์—์„œ ์‚ฐํ•™ํ˜‘๋ ฅ ๊ธฐ์—…์„ ์„ ์ •ํ•  ๋•Œ, ๋ฉด๋‹ด, ์„ค๋ฌธ์กฐ์‚ฌ, ๋งŒ์กฑ๋„ ๋“ฑ ์กธ์—…์ƒ ๋Œ€์ƒ์˜ ๊ธฐ์—…ํ‰๊ฐ€๋ฅผ ํ™•์ธํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ๋„ท์งธ, ์ง๋ฌด ๊ด€๋ จ ๊ฐœ์ธ ๋ณ€์ธ์„ ์กฐ์ง ๋ณ€์ธ์œผ๋กœ ํˆฌ์ž…ํ•˜๋Š” ๊ฒƒ์„ ๊ณ ๋ คํ•ด ๋ณผ ์ˆ˜ ์žˆ๋‹ค. ๋‹ค์„ฏ์งธ, ์ž…์ง ์ดํ›„ ์ดˆ๊ธฐ๊ฒฝ๋ ฅ์ž์˜ ์กฐ์ง์ ์‘์— ์ง์ ‘์ ์ธ ์˜ํ–ฅ์„ ์ฃผ๋Š” ์ง๋ฌด ๊ด€๋ จ ๋ณ€์ธ์„ ์ถ”๊ฐ€๋กœ ์„ ์ •ํ•˜์—ฌ ํ›„์† ์—ฐ๊ตฌ๊ฐ€ ์ˆ˜ํ–‰๋  ํ•„์š”๊ฐ€ ์žˆ๋‹ค.The purpose of this study was to identify the hierarchical linear relationship between the organizational adjustment, individual variables and organizational variables of early-career workers graduated from meister high school. The specific objectives were to identify the level of organizational adjustment, individual and organizational variables, to identify the variance on individual and organizational dimension about organizational adjustment, to identify the effects of individual variables on organizational adjustment, to identify the effects of organizational variables on organizational adjustment, and to identify the interaction effects of individual and organizational variables on organizational adjustment. The population of this study was those who graduated from meister high school and had less than three years of employment. Using purposive sampling method, a total of 400 people, 10 for each company, were selected. A survey questionnaire was conducted to measure the variables of this study. The survey questionnaire was consisted of organizational adjustment scale, individual variables scale(job self-efficacy, learning agility, career planning, pre-entry knowledge, person-job fit, job autonomy) and organizational variables scale(career growth opportunity, perceived organizational support, organizational justice). Through pilot test and final survey, the reliabilities and validity of these scales were examined. The data collection was conducted through an online survey from May 14 to May 28, 2021, with 237 of the total 400 questionnaires retrieved, with a returning rate of 59.3%. A total of 213 data from 35 companies were used for the final analysis, except for responses that unfaithful or less than five employees per company. Data analysis was conducted using the SPSS for Windows 26.0 program to analyze technical statistics (mean, standard deviation, frequency, percentage) and hierarchical linear model analysis using the HLM 8.1 for Windows program. In all analyses, the statistical significance level was set at 0.05. The findings of the study were as follows: First, organizational adjustment level of early-career workers graduated from meister high school was 4.13. Second, after controlling individual variables, 54.6% of total variance in organizational adjustment was organizational level variance, and 45.4% of total variance in that was individual level variance. Third, the amount of explanation for individual variables for organizational adjustement was 80.6%. Fourth, after controlling individual variables, the total amount of organizational variables was 39.8%. Fifth, person-job fit had a statistically significant effect on organizational adjustment through interaction with career growth opportunity(ฮฒ=0.211, p<0.05). Based on the findings of the study, the major conclusions of this study were as follows: First, early-career workers graduated from meister high school have a high level of organizational adjustment. Second, the level of organizational adjustment of early-career workers graduated from meister high school is more affected by organizational level than by individual level. Third, individual-level variables of early-career workers graduated from meister high school have a significant effect on organizational adjustment in the order of job self-efficacy, person-job fit and job autonomy. Fourth, career growth opportunity of early-career workers graduated from meister high school has a significant effect on organizational adjustment. Fifth, person-job fit of early-career workers graduated from meister high school has a effect on organizational adjustment through interaction with career growth opportunity.๊ตญ๋ฌธ์ดˆ๋ก I. ์„œ๋ก  1 1. ์—ฐ๊ตฌ์˜ ํ•„์š”์„ฑ 1 2. ์—ฐ๊ตฌ ๋ชฉ์  4 3. ์—ฐ๊ตฌ ๊ฐ€์„ค 4 4. ์šฉ์–ด์˜ ์ •์˜ 7 โ…ก. ์ด๋ก ์  ๋ฐฐ๊ฒฝ 10 1. ๋งˆ์ด์Šคํ„ฐ๊ณ ์กธ ์ดˆ๊ธฐ๊ฒฝ๋ ฅ์ž ์–‘์„ฑ 10 2. ์กฐ์ง์ ์‘์˜ ๊ฐœ๋…, ๊ตฌ์„ฑ์š”์ธ ๋ฐ ์ธก์ • 14 3. ์กฐ์ง์ ์‘๊ณผ ๊ฐœ์ธ ๋ณ€์ธ์˜ ๊ด€๊ณ„ 24 4. ์กฐ์ง์ ์‘๊ณผ ์กฐ์ง ๋ณ€์ธ์˜ ๊ด€๊ณ„ 39 5. ์กฐ์ง์ ์‘์— ๋Œ€ํ•œ ๊ฐœ์ธ ๋ฐ ์กฐ์ง ๋ณ€์ธ์˜ ์ƒํ˜ธ์ž‘์šฉ 49 โ…ข. ์—ฐ๊ตฌ ๋ฐฉ๋ฒ• 50 1. ์—ฐ๊ตฌ ๋ชจํ˜• 50 2. ์—ฐ๊ตฌ ๋Œ€์ƒ 54 3. ์กฐ์‚ฌ ๋„๊ตฌ 55 4. ์ž๋ฃŒ ์ˆ˜์ง‘ 85 5. ์ž๋ฃŒ ๋ถ„์„ 88 โ…ฅ. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ ๋ฐ ๋…ผ์˜ 95 1. ๋งˆ์ด์Šคํ„ฐ๊ณ ์กธ ์ดˆ๊ธฐ๊ฒฝ๋ ฅ์ž์˜ ์กฐ์ง์ ์‘ ๋ฐ ๊ด€๋ จ ๋ณ€์ธ ์ˆ˜์ค€ 95 2. ์กฐ์ง์ ์‘์˜ ๊ฐœ์ธ ๋ฐ ์ง‘๋‹จ ์ฐจ์› ๋ณ€๋Ÿ‰ 107 3. ๊ฐœ์ธ ๋ณ€์ธ์˜ ์กฐ์ง์ ์‘์— ๋Œ€ํ•œ ์ˆœ์ˆ˜ ํšจ๊ณผ 110 4. ์กฐ์ง ๋ณ€์ธ์˜ ์กฐ์ง์ ์‘์— ๋Œ€ํ•œ ์ˆœ์ˆ˜ ํšจ๊ณผ 115 5. ๊ฐœ์ธ ๋ฐ ์กฐ์ง ๋ณ€์ธ์˜ ์กฐ์ง์ ์‘์— ๋Œ€ํ•œ ์ƒํ˜ธ์ž‘์šฉ ํšจ๊ณผ 118 6. ์—ฐ๊ตฌ๊ฒฐ๊ณผ์— ๋Œ€ํ•œ ๋…ผ์˜ 120 โ…ค. ์š”์•ฝ, ๊ฒฐ๋ก  ๋ฐ ์ œ์–ธ 127 1. ์š”์•ฝ 127 2. ๊ฒฐ๋ก  129 3. ์ œ์–ธ 130 ์ฐธ๊ณ ๋ฌธํ—Œ 132 [๋ถ€ ๋ก] 154 Abstract 188๋ฐ•

    The Hierarchical Linear Relationship between Individual and Organizational Variables and Burnout of Teachers in Specialized Vocational High Schools

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๋†์—…์ƒ๋ช…๊ณผํ•™๋Œ€ํ•™ ๋†์‚ฐ์—…๊ต์œก๊ณผ, 2018. 2. ๋‚˜์Šน์ผ.์ด ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์€ ํŠน์„ฑํ™”๊ณ ๋“ฑํ•™๊ต ๊ต์‚ฌ์˜ ์ง๋ฌด์†Œ์ง„๊ณผ ๊ฐœ์ธ ๋ฐ ์กฐ์ง ๋ณ€์ธ์˜ ์œ„๊ณ„์  ๊ด€๊ณ„๋ฅผ ๊ตฌ๋ช…ํ•˜๋Š” ๋ฐ ์žˆ์—ˆ๋‹ค. ์ด์— ๋”ฐ๋ผ ๋‹ค์Œ๊ณผ ๊ฐ™์€ ์—ฐ๊ตฌ ๋ชฉํ‘œ๋ฅผ ์„ค์ •ํ•˜์˜€๋‹ค. ์ฒซ์งธ, ํŠน์„ฑํ™”๊ณ ๋“ฑํ•™๊ต ๊ต์‚ฌ์˜ ์ง๋ฌด์†Œ์ง„ ์ˆ˜์ค€ ๋ฐ ๊ฐœ์ธ ํŠน์„ฑ๊ณผ ์กฐ์ง ํŠน์„ฑ์„ ๊ตฌ๋ช…ํ•œ๋‹ค. ๋‘˜์งธ, ๊ต์‚ฌ์˜ ์ง๋ฌด์†Œ์ง„์— ๋Œ€ํ•œ ๊ต์‚ฌ ๋ฐ ํ•™๊ต์˜ ๋ณ€๋Ÿ‰์„ ๊ตฌ๋ช…ํ•œ๋‹ค. ์…‹์งธ, ๊ต์‚ฌ์˜ ์ง๋ฌด์†Œ์ง„์— ๊ฐœ์ธ ๋ณ€์ธ์ด ๋ฏธ์น˜๋Š” ํšจ๊ณผ๋ฅผ ๊ตฌ๋ช…ํ•œ๋‹ค. ๋„ท์งธ, ๊ต์‚ฌ์˜ ์ง๋ฌด์†Œ์ง„์— ์กฐ์ง ๋ณ€์ธ์ด ๋ฏธ์น˜๋Š” ํšจ๊ณผ๋ฅผ ๊ตฌ๋ช…ํ•œ๋‹ค. ๋‹ค์„ฏ์งธ, ๊ต์‚ฌ์˜ ์ง๋ฌด์†Œ์ง„์— ๋Œ€ํ•œ ๊ฐœ์ธ ๋ณ€์ธ๊ณผ ์กฐ์ง ๋ณ€์ธ์˜ ์ƒํ˜ธ์ž‘์šฉ ํšจ๊ณผ๋ฅผ ๊ตฌ๋ช…ํ•œ๋‹ค. ๋ชจ์ง‘๋‹จ์€ ์ „๊ตญ ํŠน์„ฑํ™”๊ณ ๋“ฑํ•™๊ต์—์„œ ๋ณดํ†ต ๊ต๊ณผ์™€ ์ „๋ฌธ ๊ต๊ณผ๋ฅผ ๊ฐ€๋ฅด์น˜๋Š” ๋ชจ๋“  ๊ต์‚ฌ๋“ค๋กœ 2016๋…„ ๊ธฐ์ค€ ์ด 22,738๋ช…์ด์—ˆ๋‹ค. ํ‘œ๋ณธ์€ ์ธตํ™”๋น„๋ก€ํ‘œ์ง‘ ๋ฐฉ๋ฒ•์œผ๋กœ ํ•™๊ต์˜ ์†Œ์žฌ์ง€ ๋ฐ ๊ณ„์—ด์— ๋”ฐ๋ผ 40๊ฐœ๊ต๋กœ๋ถ€ํ„ฐ 12๋ช…์”ฉ ์ด 480๋ช…์„ ์„ ์ •ํ•˜์˜€๋‹ค. ์กฐ์‚ฌ๋„๊ตฌ๋Š” ์˜จ๋ผ์ธ์ƒ์œผ๋กœ ์‘๋‹ต์ด ๊ฐ€๋Šฅํ•˜๋„๋ก ์ œ์ž‘๋œ ์„ค๋ฌธ์„ ํ™œ์šฉํ•˜์˜€๋‹ค. ์กฐ์‚ฌ๋„๊ตฌ๋Š” ํƒ€๋‹น๋„์™€ ์‹ ๋ขฐ๋„๊ฐ€ ๊ฒ€์ฆ๋œ ์ง๋ฌด์†Œ์ง„, ๋Šฅ๋™์  ๊ต์ง ์„ ํƒ ๋™๊ธฐ, ๊ต์‚ฌ ํšจ๋Šฅ๊ฐ, ์—…๋ฌด๋Ÿ‰, ์—ญํ•  ๊ฐˆ๋“ฑ, ์ง๋ฌด ์ŠคํŠธ๋ ˆ์Šค, ํšŒ๋ณต ํƒ„๋ ฅ์„ฑ, ์ง๋ฌด ๋งŒ์กฑ, ๊ต์žฅ์˜ ๊ฐ์„ฑ์  ๋ฆฌ๋”์‹ญ, ๋™๋ฃŒ๊ต์‚ฌ ์ง€์ง€ ๋ฐ ์นœํ™”์ ์ธ ์กฐ์ง๋ฌธํ™” ์ธก์ •๋„๊ตฌ๋“ค๊ณผ ์„ฑ๋ณ„, ๊ต์ง ๊ฒฝ๋ ฅ ๋“ฑ์˜ ์กฐ์‚ฌ๋ฌธํ•ญ์œผ๋กœ ๊ตฌ์„ฑ๋˜์—ˆ๋‹ค. ๋‹ค๋งŒ, ํŠน์„ฑํ™”๊ณ ๋“ฑํ•™๊ต์˜ ์ทจ์—…๋ฅ  ๋ณ€ํ™”์™€ ํ•™์—…์ค‘๋‹จ๋ฅ ์€ ํ•œ๊ตญ๊ต์œกํ•™์ˆ ์ •๋ณด์›์ด ํ•™๊ต์•Œ๋ฆฌ๋ฏธ ์‚ฌ์ดํŠธ๋ฅผ ํ†ตํ•ด ์ œ๊ณตํ•˜๋Š” ๊ณต์‹œ์ •๋ณด๊ฐ€ ์ด์šฉ๋˜์—ˆ๋‹ค. ์ž๋ฃŒ ์ˆ˜์ง‘์€ ์˜จ๋ผ์ธ ์„ค๋ฌธ(http://www.ksdcdb.kr/answer.jsp?a=15148)์„ ํ™œ์šฉํ•˜์—ฌ 2017๋…„ 11์›” 1์ผ๋ถ€ํ„ฐ 11์›” 20์ผ๊นŒ์ง€ ์ด๋ฃจ์–ด์กŒ๋‹ค. 40๊ฐœ๊ต๋กœ๋ถ€ํ„ฐ ์ด 454๋ช…์˜ ๊ต์‚ฌ๊ฐ€ ์‘๋‹ตํ•˜์˜€์œผ๋ฉฐ, ์ด ์ค‘ ๋ถˆ์„ฑ์‹ค ๋ฐ ์ค‘๋ณต ์‘๋‹ต, ๋ฏธ์‘๋‹ต, ์ด์ƒ์น˜ ์‘๋‹ต์„ ์ œ์™ธํ•œ 407๋ช…์˜ ๊ต์‚ฌ๊ฐ€ ์‘๋‹ตํ•œ ์ž๋ฃŒ๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. ์ž๋ฃŒ ๋ถ„์„์—๋Š” SPSS 23.0 for Windows ํ”„๋กœ๊ทธ๋žจ์„ ์ด์šฉํ•˜์—ฌ ๊ธฐ์ˆ ํ†ต๊ณ„์™€ ์ƒ๊ด€๋ถ„์„์„ ์‹ค์‹œํ•˜์˜€๊ณ , HLM 6.08 for Windows ํ”„๋กœ๊ทธ๋žจ์„ ์ด์šฉํ•˜์—ฌ 2์ˆ˜์ค€(๊ฐœ์ธ๊ณผ ์กฐ์ง) ์œ„๊ณ„์  ์„ ํ˜•๋ชจํ˜• ๋ถ„์„์ด ์ด๋ฃจ์–ด์กŒ๋‹ค. ์—ฐ๊ตฌ์˜ ๊ฒฐ๊ณผ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ํŠน์„ฑํ™”๊ณ ๋“ฑํ•™๊ต ๊ต์‚ฌ์˜ ์ง๋ฌด์†Œ์ง„ ์ˆ˜์ค€์€ ํ‰๊ท ์ ์œผ๋กœ ๋‚ฎ์€ ์ˆ˜์ค€์ด์—ˆ๊ณ (2.54), ํ•˜์œ„ ์š”์ธ์— ๋”ฐ๋ผ ์ •์„œ์  ๊ณ ๊ฐˆ(2.93), ๊ฐœ์ธ์  ์„ฑ์ทจ๊ฐ ๊ฐ์†Œ(2.36), ๋น„์ธ๊ฐ„ํ™”(2.14) ์ˆœ์œผ๋กœ ๋‚ฎ์•˜๋‹ค. ์ „์ฒด ๊ต์‚ฌ ์ค‘ ์•ฝ 60%๊ฐ€ ์ •์„œ์  ๊ณ ๊ฐˆ์„ ๊ฒฝํ—˜ํ•˜๊ณ , ์•ฝ 30~40%๊ฐ€ ๋น„์ธ๊ฐ„ํ™”์™€ ๊ฐœ์ธ์  ์„ฑ์ทจ๊ฐ ๊ฐ์†Œ๋ฅผ ๊ฒฝํ—˜ํ•˜๊ณ  ์žˆ์—ˆ๋‹ค. ๊ต์‚ฌ๋“ค์˜ ํšŒ๋ณต ํƒ„๋ ฅ์„ฑ ์ˆ˜์ค€์€ ๋†’์€ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๊ณ (3.85), ๋Œ€์ฒด๋กœ ๋Šฅ๋™์ ์ธ ๋™๊ธฐ๋กœ ์ธํ•ด ๊ต์ง์„ ์„ ํƒํ•˜์˜€์œผ๋ฉฐ(3.75), ์ž์‹ ์˜ ์—…๋ฌด์— ๋งŒ์กฑํ•˜๊ณ  ์žˆ์—ˆ๋‹ค(3.45). ๋˜ํ•œ, ๊ต์‚ฌ๋“ค์˜ ๊ต์‚ฌ ํšจ๋Šฅ๊ฐ ์ˆ˜์ค€์€ ํ‰๊ท ์ ์œผ๋กœ ๋ณดํ†ต ์ˆ˜์ค€์ด์—ˆ๊ณ (3.40), ์—…๋ฌด๋Ÿ‰์€ ๊ณผ๋‹คํ•˜์ง€ ์•Š์€ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ์œผ๋ฉฐ(3.21), ์—ญํ•  ๊ฐˆ๋“ฑ(2.88)๊ณผ ์ง๋ฌด ์ŠคํŠธ๋ ˆ์Šค(2.63) ์ˆ˜์ค€์€ ๋‚ฎ์€ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ํ•œํŽธ, ํŠน์„ฑํ™”๊ณ ๋“ฑํ•™๊ต์˜ ๋™๋ฃŒ๊ต์‚ฌ ๊ฐ„ ์ง€์ง€๋Š” ๋†’์€ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๊ณ (3.95), ๊ต์žฅ์€ ๋Œ€์ฒด๋กœ ๊ฐ์„ฑ์ ์ธ ๋ฆฌ๋”์‹ญ์„ ๋ฐœํœ˜ํ•˜๊ณ  ์žˆ์—ˆ์œผ๋ฉฐ(3.70), ์ „๋ฐ˜์ ์œผ๋กœ ์นœํ™”์ ์ธ ์กฐ์ง๋ฌธํ™”๋ฅผ ํ˜•์„ฑํ•˜๊ณ  ์žˆ์—ˆ๋‹ค(3.40). ์ทจ์—…๋ฅ ์€ ์ „๋…„๋„์— ๋น„ํ•ด ํ‰๊ท ์ ์œผ๋กœ 3.89% ์ฆ๊ฐ€ํ•˜์˜€๊ณ , ๊ณผ๋ฐ˜์ˆ˜์ด์ƒ์˜ ํŠน์„ฑํ™”๊ณ ๋“ฑํ•™๊ต๊ฐ€ ํŠน์„ฑํ™” ์ •์ฑ… ์‚ฌ์—…์„ ์ถ”์ง„ํ•˜๊ณ  ์žˆ์—ˆ๋‹ค. ๋‘˜์งธ, ํŠน์„ฑํ™”๊ณ ๋“ฑํ•™๊ต ๊ต์‚ฌ์˜ ์ง๋ฌด์†Œ์ง„ ๋ณ€๋Ÿ‰์€ ํ•™๊ต ๊ฐ„ ์ฐจ์ด๋กœ 11.5%, ๊ฐœ์ธ ๊ฐ„ ์ฐจ์ด๋กœ 88.5%๋ฅผ ๊ฐ๊ฐ ์„ค๋ช…ํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์…‹์งธ, ๊ฐœ์ธ ๋ณ€์ธ์ธ ๊ต์‚ฌ์˜ ์—…๋ฌด๋Ÿ‰(=.088, p<.001), ์—ญํ•  ๊ฐˆ๋“ฑ(=.103, p<.001), ์ง๋ฌด ์ŠคํŠธ๋ ˆ์Šค(=.213, p<.001), ํšŒ๋ณต ํƒ„๋ ฅ์„ฑ(=-.389, p<.001)์ด ์ง๋ฌด์†Œ์ง„์— ํšจ๊ณผ๊ฐ€ ์žˆ๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋„ท์งธ, ์กฐ์ง ๋ณ€์ธ์ธ ํ•™๊ต์˜ ์ทจ์—…๋ฅ  ๋ณ€ํ™”(=.005, p<.005), ํŠน์„ฑํ™” ์ถ”์ง„(=.179, p<.001), ํ•™๊ต ์„ค๋ฆฝ ์œ ํ˜•(=-.146, p<.005), ๋™๋ฃŒ๊ต์‚ฌ ์ง€์ง€(=-.238, p<.005)๊ฐ€ ๊ต์‚ฌ์˜ ์ง๋ฌด์†Œ์ง„์— ํšจ๊ณผ๊ฐ€ ์žˆ๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋‹ค์„ฏ์งธ, ์ง๋ฌด์†Œ์ง„์— ์—ญํ•  ๊ฐˆ๋“ฑ๊ณผ ์ทจ์—…๋ฅ  ๋ณ€ํ™”(=-.009, p<.001), ์—…๋ฌด๋Ÿ‰๊ณผ ์ทจ์—…๋ฅ  ๋ณ€ํ™”(=.010, p<.001), ์ง๋ฌด ์ŠคํŠธ๋ ˆ์Šค์™€ ๋™๋ฃŒ๊ต์‚ฌ ์ง€์ง€(=.369, p<.005) ๊ฐ„ ์ƒํ˜ธ์ž‘์šฉ ํšจ๊ณผ๊ฐ€ ์žˆ๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์—ฐ๊ตฌ์˜ ๊ฒฐ๋ก ์€ ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ํŠน์„ฑํ™”๊ณ ๋“ฑํ•™๊ต ๊ต์‚ฌ๋“ค์€ ๋Œ€์ฒด๋กœ ์ง๋ฌด์†Œ์ง„์„ ์ ๊ฒŒ ๊ฒฝํ—˜ํ•œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๊ณผ๋ฐ˜์ˆ˜์ด์ƒ์€ ์ •์„œ์ ์œผ๋กœ ๊ณ ๊ฐˆ ์ƒํƒœ์ด๊ฑฐ๋‚˜ ๋น„์ธ๊ฐ„ํ™” ๋ฐ ์„ฑ์ทจ๊ฐ ๊ฐ์†Œ๋ฅผ ๊ฒฝํ—˜ํ•œ๋‹ค๊ณ  ๋ณผ ์ˆ˜ ์žˆ๋‹ค. ์ด๋“ค์˜ ๊ฐœ์ธ ํŠน์„ฑ์€ ์ „๋ฐ˜์ ์œผ๋กœ ๋Šฅ๋™์ ์ด๊ณ , ํƒ„๋ ฅ์ ์ธ ์ž์„ธ๋กœ ๊ต์ง์„ ์ˆ˜ํ–‰ํ•˜๊ณ  ์žˆ์œผ๋ฉฐ, ๊ณผ๋‹คํ•˜์ง€ ์•Š์€ ์—…๋ฌด๋Ÿ‰๊ณผ ๋‚ฎ์€ ์ŠคํŠธ๋ ˆ์Šค๋กœ ๋Œ€์ฒด๋กœ ์ž์‹ ์˜ ์—…๋ฌด์— ๋งŒ์กฑํ•œ๋‹ค. ํ•œํŽธ, ์ด๋“ค์ด ๊ทผ๋ฌดํ•˜๋Š” ํŠน์„ฑํ™”๊ณ ๋“ฑํ•™๊ต์˜ ํŠน์„ฑ์€ ๊ต์‚ฌ๋“ค ๊ฐ„ ์กด์ค‘ํ•˜๋Š” ๋ถ„์œ„๊ธฐ์™€ ๋Œ€์ฒด๋กœ ํ™”๋ชฉํ•œ ์กฐ์ง๋ฌธํ™”๋ฅผ ํ˜•์„ฑํ•˜๊ณ  ์žˆ๊ณ , ๊ต์žฅ์€ ๊ต์‚ฌ์—๊ฒŒ ๊ณต๊ฐํ•˜๊ณ  ๋ฐฐ๋ คํ•˜๋Š” ๋ฆฌ๋”์‹ญ์„ ๊ฐ€์ง€๊ณ  ์žˆ๋‹ค. ํŠน์„ฑํ™”๊ณ ๋“ฑํ•™๊ต์˜ ๋Œ€ํ•™ ์ง„ํ•™ ๋น„์œจ์€ ์ ์ฐจ ๊ฐ์†Œํ•˜๊ณ , ์ทจ์—… ๋น„์œจ์ด ์ฆ๊ฐ€ํ•˜๊ณ  ์žˆ์œผ๋ฉฐ, ๋Œ€๋ถ€๋ถ„ ํ•™๊ต ํŠน์„ฑํ™”๋ฅผ ์ถ”์ง„ํ•˜๊ณ  ์žˆ๋‹ค. ๋‘˜์งธ, ๊ต์‚ฌ์˜ ์ง๋ฌด์†Œ์ง„ ์ฐจ์ด๋Š” ํ•™๊ต ๊ฐ„ ์ฐจ์ด์— ์˜ํ•ด ์„ค๋ช…๋˜๋Š” ์–‘๋ณด๋‹ค ๊ฐœ์ธ ๊ฐ„ ์ฐจ์ด์— ์˜ํ•ด ์„ค๋ช…๋˜๋Š” ์–‘์ด ํฌ๋‹ค๊ณ  ๋ณผ ์ˆ˜ ์žˆ๋‹ค. ์…‹์งธ, ๊ต์‚ฌ์˜ ์ง๋ฌด์†Œ์ง„์€ ์—…๋ฌด๋Ÿ‰์ด ๋งŽ์„์ˆ˜๋ก, ์ž์‹ ์—๊ฒŒ ์š”๊ตฌ๋˜๋Š” ์—ญํ• ์ด ์ƒ๋ฐ˜๋œ๋‹ค๊ณ  ๋Š๋ผ๋Š” ์ •๋„๊ฐ€ ํด์ˆ˜๋ก, ์ง๋ฌด๋ฅผ ์ˆ˜ํ–‰ํ•˜๋ฉด์„œ ๋Š๋ผ๋Š” ์ŠคํŠธ๋ ˆ์Šค ์ˆ˜์ค€์ด ๋†’์„์ˆ˜๋ก, ๋ณ€ํ™”ํ•˜๋Š” ํ™˜๊ฒฝ์— ๋Œ€ํ•ด ํƒ„๋ ฅ์ ์œผ๋กœ ๋Œ€์ฒ˜ํ•˜์ง€ ๋ชปํ• ์ˆ˜๋ก ๋†’์•„์ง„๋‹ค. ๋„ท์งธ, ๊ต์‚ฌ์˜ ์ง๋ฌด์†Œ์ง„์€ ์ „๋…„๋„ ๋Œ€๋น„ ์ทจ์—…๋ฅ ์ด ์ฆ๊ฐ€ํ•œ ํ•™๊ต๋‚˜ ์‚ฌ๋ฆฝ ํ•™๊ต, ํŠน์„ฑํ™”๋ฅผ ์ถ”์ง„ํ•˜๋Š” ํ•™๊ต, ๋™๋ฃŒ๊ต์‚ฌ์˜ ์ง€์ง€๋„๊ฐ€ ๋‚ฎ์€ ํ•™๊ต์— ์†Œ์†๋œ ๊ต์‚ฌ๊ฐ€ ๋†’๋‹ค. ๋‹ค์„ฏ์งธ, ์ทจ์—…๋ฅ  ๋ณ€ํ™”์— ๋”ฐ๋ผ ๊ต์‚ฌ๋“ค์˜ ์—ญํ•  ๊ฐˆ๋“ฑ์ด๋‚˜ ์—…๋ฌด๋Ÿ‰์ด ์ง๋ฌด์†Œ์ง„ ์ฆ๊ฐ์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๊ณ , ๋™๋ฃŒ๊ต์‚ฌ์˜ ์ง€์ง€ ์ˆ˜์ค€์˜ ๋ณ€ํ™”์— ๋”ฐ๋ผ ๊ต์‚ฌ๋“ค์˜ ์ง๋ฌด์ŠคํŠธ๋ ˆ์Šค๊ฐ€ ์ง๋ฌด์†Œ์ง„ ์ฆ๊ฐ์— ์˜ํ–ฅ์„ ๋ฏธ์นœ๋‹ค. ์—ฐ๊ตฌ์˜ ์ œ์–ธ์€ ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ์ •์„œ์  ๊ณ ๊ฐˆ์ด๋‚˜ ๋น„์ธ๊ฐ„ํ™” ๋ฐ ์„ฑ์ทจ๊ฐ ๊ฐ์†Œ๋ฅผ ๊ฒฝํ—˜ํ•˜๋Š” ๊ต์‚ฌ๋“ค์„ ๋Œ€์ƒ์œผ๋กœ ์ง๋ฌด์†Œ์ง„์„ ์™„ํ™”์‹œํ‚ค๋„๋ก ํ•ด์•ผ ํ•œ๋‹ค. ๋‘˜์งธ, ์ด ์—ฐ๊ตฌ์—์„œ ํŠน์„ฑํ™”๊ณ ๋“ฑํ•™๊ต ๊ต์‚ฌ์˜ ์ง๋ฌด์†Œ์ง„์— ์œ ์˜๋ฏธํ•˜์ง€ ์•Š๊ฒŒ ๋‚˜ํƒ€๋‚œ ๋ณ€์ธ๋“ค์— ๋Œ€ํ•œ ์ถ”๊ฐ€์ ์ธ ๋ถ„์„์ด ํ•„์š”ํ•˜๋‹ค. ์…‹์งธ, ํŠน์„ฑํ™”๊ณ ๋“ฑํ•™๊ต ๊ต์‚ฌ์˜ ์ง๋ฌด์†Œ์ง„์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์ž ์žฌ์ ์ธ ๊ฐœ์ธ ๋ฐ ์กฐ์ง ๋ณ€์ธ์— ๋Œ€ํ•œ ํƒ์ƒ‰์ด ํ•„์š”ํ•˜๋‹ค. ๋„ท์งธ, ์ •๋ถ€๊ฐ€ ํŠน์„ฑํ™”๊ณ ๋“ฑํ•™๊ต๋ฅผ ๋Œ€์ƒ์œผ๋กœ ํŠน์„ฑํ™” ์ •์ฑ… ์‚ฌ์—… ๋“ฑ์˜ ๋ณ€ํ™”๋ฅผ ์ถ”์ง„ํ•  ๋•Œ ๊ต์‚ฌ๋“ค์˜ ์ง๋ฌด์†Œ์ง„์„ ๊ณ ๋ คํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ๋‹ค์„ฏ์งธ, ์ง๋ฌด์†Œ์ง„ ๊ด€๋ จ ๋ณ€์ธ๋“ค ๊ฐ„์˜ ๋งฅ๋ฝ์ ์ธ ๊ด€๊ณ„์— ๋”ฐ๋ผ ํŠน์„ฑํ™”๊ณ ๋“ฑํ•™๊ต์˜ ๊ต์‚ฌ์˜ ์ง๋ฌด์†Œ์ง„์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์ด ๋‹ฌ๋ผ์ง์„ ์œ ์˜ํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค.The purpose of the study was to determine the hierarchical relationship between the individual and organizational variables with burnout of teachers in specialized vocational high schools. Specific objectives to accomplish the research goal were as follows: first, to investigate the level of the burnout and personal and organizational characteristics of teacherssecond, to assess whether there is any difference in the level of the burnout based on individual and organizational levelthird, to examine the effect of individual variables on the burnout of teachersfourth, to examine the effect of organizational variables on the burnout of teachersfifth, to examine the interaction effects of individual and organizational variables on the burnout of teachers. The sample population for this study includes all teachers who teach subjects at specialized vocational high schools. As of 2016, there are 22,738 teachers. The samples were sampled using proportional sampling by the location and affiliation of schools, and these amounts to a sample of 40 schools and 480 teachers. The research questionnaire was utilized online survey consisted of the burnout, individual variables(active motivation for Choosing the Teaching Profession, teacher efficacy, workload, role conflict, job stress, resilience, job satisfaction) and organizational variables(principals' emotional leadership, friendly organizational culture, social support). Moreover, the questionnaire also includes survey items on gender, career experience and more, in which validity and reliability have been verified. Employment rate and dropout rate included in organizational variables was obtained from data of school public notice information from KERIS, 2017. The survey was undertaken from November 1st to 20th 2017 with a target sample population of 480 teachers from 40 specialized vocational high schools. Data was collected from 454 students belonging to 40 different schools, 407 were used of analysis, whereby duplicate(11) and unresponsive(17) responses, as well as outlier(19) were excluded in the final analysis. Data analysis was performed using SPSS 23.0 for descriptive statistics and correlation analysisand HLM 6.08 was used for multi-level analysis of 2 level hierarchical linear modelling. The results of the study were summarized as follows. First, the burnout of teachers are lower than average(2.54) sub-factors with the lower level are emotional exhaustion(2.93), reduced personal accomplishment(2.36), depersonalization(2.14). About 60% of teachers experienced emotional exhaustion, about 30 to 40% teachers experienced depersonalization and reduced personal accomplishment. Resilience(3.85) was the highest, teachers usually chose the teaching profession with active motivation(3.75) and satisfied with their job(3.45). Teacher efficacy was moderate on average(3.40), workload was not excessive(3.21), role conflict(2.88) and job stress(2.63) was low. Meanwhile, supports among teachers was highest(3.95), principals' emotional leadership were quite high(3.70) and friendly organizational culture(3.40) was formed. Employment rate increased by 3.89% and most schools manages specialized projects(70.02%). Second, a significant between-group variance in the burnout was found, whereby about 11.5% of the variance of the burnout is found between different schools while the remainder 88.5% is attributed to individual variables. Third, in the random coefficient regression model, workload(=.088, p<.001), role conflict(=.103, p<.001), job stress(=.213, p<.001), resilience(=-.389, p<.001) had a significant positive effect on the burnout while the teaching career, active motivation for choosing the teaching profession, teacher efficacy, job satisfaction did not. Fourth, in the regression with means-as-outcomes, employment rate(=.005, p<.005), specialized projects(=.179, p<.001), types of foundation(=-.146, p<.005), social supports(=-.238, p<.005) had a significant positive effect on the burnout, while school size, principals' emotional leadership, friendly organizational culture did not. Fifth, the analysis of the interaction effect revealed that employment rate had a significant interaction effect on the relationship between the burnout and role conflict(=-.009, p<.005)the relationship between the burnout and workload(=.010, p<.005). In addition, social supports had a significant interaction effect on the relationship between the burnout and job stress( =.369, p<.005). The major conclusions drawn from the study were as follows. First, teachers in specialized vocational high schools experience burnout generally less. However, most of them are emotionally exhausted or experience depersonalization and reduced personal accomplishment. Teachers are generally flexible in dealing with situations and chose the teaching profession with active motivation. The level of satisfaction on the job, belief which could affect on students' achievement and workload is on average. On the other hand, the level of experience on confliction between expected roles and stress related to job is low. Meanwhile, the principal generally shows emotional leadership, the teachers are highly supported by fellow teachers and friendly organizational culture is normal. Employment rate increased generally, most schools operate the specialized projects and dropout rate were higher than the general high schools. Second, the significant in-group variance is bigger than the significant between-group variance in the burnout. Third, the burnout of teachers is increased when the workload is heavywhen the expected roles are conflictedwhen the job puts teachers under a lot of stresswhen the resilience which means be flexible in dealing with a situation is low. Fourth, teachers' burnout increased when the schools' employment rate increasedor when the school was established as private schoolor when the school operated or is operating specialized projectsor when the social supports from fellow teachers are low. Fifth, employment rate control the relationship between the burnout of teachers and role conflict or workload, social supports control the relationship between the burnout of teachers and job stress. Recommendations from the results are as follows: first, it is necessary to draw up measures which could lower the burnout of teachers who experienced emotional exhaustion, experience depersonalization or reduced personal accomplishment. Second, further research is needed on the variables which were not meaningful to the burnout of teachers in specialized vocational high schools in this study. Third, further research is needed on the potential individual and organizational variables that may influence the burnout of teachers in specialized vocational high schools. Fourth, it is necessary to consider the burnout of teachers in specialized vocational high schools when the government plan to make or change operating specialized projects. Fifth, it is necessary to note the contextual relationships between the variables affect to burnout of teachers in specialized vocational high schools differently.I. ์„œ๋ก  1 1. ์—ฐ๊ตฌ์˜ ํ•„์š”์„ฑ 1 2. ์—ฐ๊ตฌ์˜ ๋ชฉ์  2 3. ์—ฐ๊ตฌ ๋ฌธ์ œ 3 4. ์šฉ์–ด์˜ ์ •์˜ 4 โ…ก. ์ด๋ก ์  ๋ฐฐ๊ฒฝ 9 1. ํŠน์„ฑํ™”๊ณ ๋“ฑํ•™๊ต ๊ต์œก ๋ฐ ๊ต์‚ฌ ํŠน์„ฑ 9 2. ์ง๋ฌด์†Œ์ง„์˜ ๊ฐœ๋…๊ณผ ํ•˜์œ„ ์š”์ธ 23 3. ๊ต์‚ฌ์˜ ์ง๋ฌด์†Œ์ง„๊ณผ ๊ฐœ์ธ ๋ณ€์ธ์˜ ๊ด€๊ณ„ 29 4. ๊ต์‚ฌ์˜ ์ง๋ฌด์†Œ์ง„๊ณผ ์กฐ์ง ๋ณ€์ธ์˜ ๊ด€๊ณ„ 46 โ…ข. ์—ฐ๊ตฌ ๋ฐฉ๋ฒ• 59 1. ์—ฐ๊ตฌ ๋ชจํ˜• 59 2. ์—ฐ๊ตฌ ๋Œ€์ƒ 61 3. ์กฐ์‚ฌ ๋„๊ตฌ 64 4. ์ž๋ฃŒ ์ˆ˜์ง‘ 73 5. ์ž๋ฃŒ ๋ถ„์„ 75 โ…ฃ. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ ๋ฐ ๋…ผ์˜ 81 1. ์ง๋ฌด์†Œ์ง„ ์ˆ˜์ค€๊ณผ ๊ฐœ์ธ ๋ฐ ์กฐ์ง ํŠน์„ฑ 81 2. ์ง๋ฌด์†Œ์ง„์˜ ๊ต์‚ฌ ๋ฐ ํ•™๊ต์˜ ๋ณ€๋Ÿ‰ 85 3. ์ง๋ฌด์†Œ์ง„์— ๋Œ€ํ•œ ๊ฐœ์ธ ๋ณ€์ธ์˜ ํšจ๊ณผ 89 4. ์ง๋ฌด์†Œ์ง„์— ๋Œ€ํ•œ ์กฐ์ง ๋ณ€์ธ์˜ ํšจ๊ณผ 90 5. ์ง๋ฌด์†Œ์ง„์— ๋Œ€ํ•œ ๊ฐœ์ธ ๋ฐ ์กฐ์ง ๋ณ€์ธ ๊ฐ„์˜ ์ƒํ˜ธ์ž‘์šฉ ํšจ๊ณผ 92 6. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ์— ๋Œ€ํ•œ ๋…ผ์˜ 97 โ…ค. ์š”์•ฝ, ๊ฒฐ๋ก  ๋ฐ ์ œ์–ธ 109 1. ์š”์•ฝ 109 2. ๊ฒฐ๋ก  113 3. ์ œ์–ธ 114 ์ฐธ๊ณ ๋ฌธํ—Œ 117 [๋ถ€๋ก1] ์„ค๋ฌธ์ง€ 147 Abstract 165Maste

    Agricultural Literacy in the Context of Agricultural Education : a Multi-level Analysis of Urban Elementary School Students and Classrooms

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๋†์—…์ƒ๋ช…๊ณผํ•™๋Œ€ํ•™ ๋†์‚ฐ์—…๊ต์œก๊ณผ, 2018. 2. ์ตœ์ˆ˜์ •.์ด ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์€ ์ด ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์€ ์šฐ๋ฆฌ๋‚˜๋ผ ๋„์‹œ ์ง€์—ญ ์ดˆ๋“ฑํ•™์ƒ์˜ ๋†์—…๋ฌธํ•ด์™€ ํ•™์ƒ ๋ฐ ๊ต์‚ฌ ์ˆ˜์ค€์˜ ๋ณ€์ธ์˜ ๋‹ค์ธต ์ˆ˜์ค€์„ ๊ตฌ๋ช…ํ•˜๋Š” ๋ฐ ์žˆ์—ˆ๋‹ค. ์ด๋ฅผ ์œ„ํ•˜์—ฌ ๋„์‹œ์ง€์—ญ ์ดˆ๋“ฑํ•™๊ต ํ•™์ƒ๋“ค์˜ ๋†์—…๋ฌธํ•ด ์ˆ˜์ค€์„ ํ™•์ธํ•˜๋ฉฐ, ๊ฐœ์ธ์  ๋ฐ ๊ฐ€์ •์  ๋ณ€์ธ์— ๋”ฐ๋ฅธ ๋†์—…๋ฌธํ•ด ์ˆ˜์ค€์˜ ์ง‘๋‹จ ๊ฐ„ ์ฐจ์ด๋ฅผ ์ฒœ๊ตฌํ•˜๋ฉฐ, ๋†์—…๋ฌธํ•ด์— ์˜ํ–ฅ์„ ์ฃผ๋Š” ๊ฐœ์ธ ํŠน์„ฑ ๋ฐ ํ•™๊ธ‰ ์ˆ˜์ค€ ๋ณ€์ธ์˜ ์œ„๊ณ„์  ๊ด€๊ณ„๋ฅผ ๊ตฌ๋ช…ํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ๋ชจ์ง‘๋‹จ์€ ์ค‘์†Œ๋„์‹œ์™€ ๋Œ€๋„์‹œ์— ์œ„์น˜ํ•œ ์ดˆ๋“ฑํ•™๊ต 3,504๊ฐœ๊ต์— ์žฌํ•™ํ•˜๋Š” 5, 6ํ•™๋…„ ํ•™์ƒ 728,496๋ช…์ด๋ฉฐ, ํ‘œ๋ณธ์€ 12๊ฐœ์˜ ํ•™๊ต์— ์†Œ์†๋œ 48๊ฐœ ํ•™๊ธ‰์˜ ํ•™์ƒ 1,255๋ช…์ด์—ˆ๋‹ค. ์กฐ์‚ฌ๋„๊ตฌ๋Š” ํ•™์ƒ ์ˆ˜์ค€ ๋ณ€์ธ๊ณผ ํ•™๊ธ‰ ์ˆ˜์ค€ ๋ณ€์ธ์— ๋Œ€ํ•ด ์กฐ์‚ฌํ•˜๋Š” ์งˆ๋ฌธ์ง€๋ฅผ ํ™œ์šฉํ•˜์˜€์œผ๋ฉฐ, ์ „๋ฌธ๊ฐ€์˜ ๊ฒ€ํ† ๋ฅผ ๊ฑฐ์ณ ์ˆ˜์ •ํ•˜์—ฌ ์‚ฌ์šฉํ•˜์˜€๋‹ค. ์„ค๋ฌธ์กฐ์‚ฌ ๊ธฐ๊ฐ„์€ 2017๋…„ 9์›” 30์ผ๋ถ€ํ„ฐ 11์›” 2์ผ๊นŒ์ง€๋กœ, ์ž๋ฃŒ ์ˆ˜์ง‘์„ ์œ„ํ•˜์—ฌ ์ดˆ๋“ฑํ•™๊ต ๋‹ด์ž„๊ต์‚ฌ๊ฐ€ ์„ค๋ฌธ์ง€๋ฅผ ํ•™์ƒ๋“ค์—๊ฒŒ ๋ฐฐ๋ถ€ํ•˜์—ฌ ํšŒ์ˆ˜ํ•˜๊ฑฐ๋‚˜ ์—ฐ๊ตฌ์ž๊ฐ€ ์ง์ ‘ ํ•™๊ต์— ๋ฐฉ๋ฌธํ•˜์—ฌ ๊ต์‚ฌ ๋ฐ ํ•™์ƒ์—๊ฒŒ ์„ค๋ฌธ์ง€๋ฅผ ๋ฐฐ๋ถ€ํ•œ ํ›„ ํšŒ์ˆ˜ํ•˜๋Š” ๋ฐฉ์‹์œผ๋กœ ์ง„ํ–‰ํ•˜์˜€๋‹ค. ๋Œ€๋„์‹œ์™€ ์ค‘์†Œ๋„์‹œ์— ๋ฌด์„ ์œผ๋กœ ์„ ์ •๋œ 12๊ฐœ ํ•™๊ต์— 4ํ•™๊ธ‰์”ฉ ๋ฐฐ์ •ํ•˜์—ฌ 24ํ•™๊ธ‰์”ฉ ์ด 48ํ•™๊ธ‰์„ ์กฐ์‚ฌํ•˜์˜€์œผ๋ฉฐ, ํ•™์ƒ ๋Œ€์ƒ์œผ๋กœ 1,255๋ถ€์˜ ์„ค๋ฌธ์ง€๋ฅผ ๋ฐฐํฌ์˜€๋‹ค. ์ด 1,010๋ถ€๊ฐ€ ํšŒ์ˆ˜๋˜์—ˆ๊ณ , ํšŒ์ˆ˜์œจ์€ 80.4%๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ด ๊ฐ€์šด๋ฐ ์ผ๋ฐ˜์  ํŠน์„ฑ์— ํ•œ ๋ฌธํ•ญ ์ด์ƒ ์‘๋‹ตํ•˜์ง€ ์•Š๊ฑฐ๋‚˜ ๋ถˆ์„ฑ์‹คํ•œ ์‘๋‹ต๊ณผ ํ•จ๊ป˜ ์‚ฐํฌ๋„ ์ƒ์—์„œ ๊ฒฐ์ธก๊ฐ’์œผ๋กœ ํ‘œ์‹œ๋˜๋Š” 81๊ฐœ์˜ ์„ค๋ฌธ์„ ์ œ๊ฑฐํ•˜์—ฌ 929๋ถ€๋ฅผ ๋ถ„์„์— ์‚ฌ์šฉํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋„์‹œ์ง€์—ญ ์ดˆ๋“ฑํ•™์ƒ 5, 6ํ•™๋…„์˜ ๋†์—…๋ฌธํ•ด์˜ ์ˆ˜์ค€์„ ๊ตฌ๋ช…ํ•˜๊ณ  ๋†์—…๋ฌธํ•ด์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์š”์ธ์„ ํŒŒ์•…ํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ๋จผ์ €, ๋†์—…๋ฌธํ•ด ์ˆ˜์ค€์„ ๊ตฌ๋ช…ํ•˜๊ณ  ์ผ๋ฐ˜์  ํŠน์„ฑ์— ๋”ฐ๋ฅธ ํ‰๊ท ์˜ ์ฐจ์ด๋ฅผ ํ™•์ธํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ๋นˆ๋„๋ถ„์„, t๊ฒ€์ •์„ ํ™œ์šฉํ•˜์˜€๋‹ค. ๋‹ค์Œ์œผ๋กœ ๋„์‹œ์ง€์—ญ ์ดˆ๋“ฑํ•™์ƒ์˜ ๋†์—…๋ฌธํ•ด์— ์˜ํ–ฅ์„ ๋ผ์น˜๋Š” ํ•™๊ธ‰ ์ˆ˜์ค€ ๋ณ€์ธ ๋ฐ ํ•™๊ธ‰ ์ˆ˜์ค€ ๋ณ€์ธ์„ ๊ตฌ๋ช…ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ๋‹ค์ธต๋ถ„์„๋ฐฉ๋ฒ•์„ ํ™œ์šฉํ•˜์—ฌ 4๊ฐœ์˜ ๋ชจํ˜•์„ ์„ค๊ณ„ํ•˜์˜€๋‹ค. ๋จผ์ € ์šฐ์„  ๊ธฐ์ดˆ ๋ชจํ˜•์œผ๋กœ ํ•™์ƒ์˜ ๋†์—…๋ฌธํ•ด์— ๋Œ€ํ•œ ํ•™๊ธ‰ ๊ฐ„ ์ฐจ์ด๊ฐ€ ์กด์žฌํ•˜๋Š”์ง€๋ฅผ ๊ฒ€์ฆํ•˜์˜€๋‹ค. ์ดํ›„ ๋ฌด์„ ํšจ๊ณผ ํšŒ๊ท€๊ณ„์ˆ˜ ๋ฌด์กฐ๊ฑด ๋ชจํ˜•์œผ๋กœ ์ดˆ๋“ฑํ•™์ƒ์˜ ๋†์—…๋ฌธํ•ด์— ๋Œ€ํ•œ ๊ฐœ์ธ ํŠน์„ฑ์˜ ์˜ํ–ฅ์ด ํ•™๊ธ‰์— ๋”ฐ๋ผ ์ฒด๊ณ„์ ์ธ ์ฐจ์ด๊ฐ€ ์žˆ๋Š”์ง€๋ฅผ ๊ฒ€์ฆํ•˜์˜€๋‹ค. ๋‹ค์Œ์œผ๋กœ ๊ณต๋ณ€๋Ÿ‰ ๋ถ„์„ ๋ชจํ˜•์œผ๋กœ ํ•™๊ธ‰ ์ˆ˜์ค€ ๋ณ€์ธ์˜ ์˜ํ–ฅ์„ ์‚ดํŽด๋ณด์•˜๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ๋ฌด์„ ํšจ๊ณผ ํšŒ๊ท€๊ณ„์ˆ˜ ์กฐ๊ฑด ๋ชจํ˜•์„ ํ†ตํ•˜์—ฌ ํ•™๊ธ‰ ์ˆ˜์ค€ ๋ณ€์ธ๊ณผ ํ•™์ƒ ์ˆ˜์ค€ ๋ณ€์ธ์˜ ์ƒํ˜ธ์ž‘์šฉํšจ๊ณผ๊ฐ€ ๋†์—…๋ฌธํ•ด์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๋ถ„์„ํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์— ์ˆ˜์ง‘๋œ ์ž๋ฃŒ๋Š” SPSS 23.0 for Windows์™€ HLM 6.08์„ ์ด์šฉํ•˜์—ฌ ๋ถ„์„ํ•˜์˜€์œผ๋ฉฐ, ํ†ต๊ณ„์  ์œ ์˜์ˆ˜์ค€์€ 5%๋กœ ์„ค์ •ํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋ฅผ ์š”์•ฝํ•˜๋ฉด ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ๋„์‹œ์ง€์—ญ ์ดˆ๋“ฑํ•™์ƒ์˜ ๋†์—…๋ฌธํ•ด๋Š” ์‘๋‹ต๋ฒ”์œ„ ์ตœ๋Œ“๊ฐ’ 32์  ๊ฐ€์šด๋ฐ ํ‰๊ท  23.31์ , ํ‘œ์ค€ํŽธ์ฐจ 4.365์ ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ด ๊ฐ€์šด๋ฐ ๊ฐ€์žฅ ๋†’์€ ์˜์—ญ์€ ๋†์‚ฐ๋ฌผ ์ƒ์‚ฐ ๋ถ„์•ผ์ด๋ฉฐ, ๊ฐ€์žฅ ๋‚ฎ์€ ์˜์—ญ์€ ๋†์‹ํ’ˆ์˜ ๊ฐ€์น˜์™€ ์•ˆ์ „์„ฑ ์˜์—ญ์ด์—ˆ๋‹ค. ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ด ๊ฐ€์šด๋ฐ ๊ฐ€์žฅ ๋†’์€ ์˜์—ญ์€ ๋†์‚ฐ๋ฌผ ์ƒ์‚ฐ ๋ถ„์•ผ์ด๋ฉฐ, ๊ฐ€์žฅ ๋‚ฎ์€ ์˜์—ญ์€ ๋†์‹ํ’ˆ์˜ ๊ฐ€์น˜์™€ ์•ˆ์ „์„ฑ ์˜์—ญ์ด์—ˆ๋‹ค. ๋˜ํ•œ ํ•™๋…„(p<.001), ์นœ์ฒ™ ๋†์—…๊ด€๋ จ์ง ์ข…์‚ฌ ์—ฌ๋ถ€(p<.01), ๋†์—…๊ด€๋ จ์ง ์ข…์‚ฌ ํฌ๋ง ์—ฌ๋ถ€(p<.001)์— ๋”ฐ๋ผ์„œ ๋†์—…๋ฌธํ•ด์˜ ํ‰๊ท ์— ์ง‘๋‹จ ๊ฐ„ ์ฐจ์ด๊ฐ€ ์žˆ๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋‘˜์งธ, ๋†์—…๋ฌธํ•ด๋ฅผ ์˜ˆ์ธกํ•˜๋Š” ๋ณ€์ธ ๊ฐ€์šด๋ฐ ํ•™๊ธ‰๊ฐ„์˜ ์ฐจ์ด๋กœ ์ธํ•œ ์„ค๋ช…๋ ฅ์€ ์•ฝ 13.6%์ธ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์…‹์งธ, ๋„์‹œ์ง€์—ญ ์ดˆ๋“ฑํ•™์ƒ์˜ ๋†์—…๋ฌธํ•ด์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๊ฐœ์ธ๋ณ€์ธ์ด ์กด์žฌํ•˜์˜€์œผ๋ฉฐ, ์ด๋Š” ๋Œ€๋„์‹œ ๊ฑฐ์ฃผ ์—ฌ๋ถ€(๏ฝ‚=1.496, p<.01), ํ•™๊ต ์ˆ˜์—… ํƒœ๋„(๏ฝ‚=0.365, p<.05), ํ•™์—…์„ฑ์ทจ ์ˆ˜์ค€(๏ฝ‚=0.944, p<.05), ์‹๋ฌผ ๊ด€๋ จ ๊ฒฝํ—˜(๊ด€๋ง์  ์‹๋ฌผ ๊ฒฝํ—˜)(๏ฝ‚=0.683, p<.05)๊ณผ ์ž์—ฐ์ง€๋Šฅ(๏ฝ‚=0.539, p<.01)์ด์—ˆ๋‹ค. ํ•™๊ธ‰ ๊ฐ„ ์ฐจ์ด๋ฅผ ๋‚˜ํƒ€๋‚ด๋Š” ๋ณ€์ธ์€ ์‹๋ฌผ ๊ด€๋ จ ๊ฒฝํ—˜๊ฐ€์šด๋ฐ ๊ด€๋ง์  ์‹๋ฌผ ๊ฒฝํ—˜(chi=22.808, p<.05). ์ƒํ™œ ์‹๋ฌผ ๊ฒฝํ—˜(chi=22.666, p<.05). ์•ผ์™ธ ํ•™์Šต ๊ฒฝํ—˜(chi=24.540, p<.05)์ธ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋„ท์งธ, ๋„์‹œ์ง€์—ญ ์ดˆ๋“ฑํ•™์ƒ์˜ ๋†์—…๋ฌธํ•ด์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ํ•™๊ธ‰ ์ˆ˜์ค€ ๋ณ€์ธ์ด ์กด์žฌํ•˜์˜€์œผ๋ฉฐ, ๊ต์‚ฌ ์„ฑ๋ณ„(๏ฝ‚=-1.040, p<.05)๊ณผ ๊ต์‚ฌ ๋‹ด์ž„ ํ•™๋…„(๏ฝ‚=1.653, p<.01), ๊ต์‚ฌ ๋†์—… ๊ด€๋ จ ๊ฒฝํ—˜(๏ฝ‚=0.969, p<.05)์ด ์œ ์˜๋ฏธํ•œ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, ์ดˆ๋“ฑํ•™์ƒ์˜ ๋†์—…๋ฌธํ•ด์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ํ•™๊ธ‰ ๋ฐ ํ•™์ƒ ๋ณ€์ธ์˜ ์ƒํ˜ธ์ž‘์šฉ์„ ๋ถ„์„ํ•œ ๊ฒฐ๊ณผ, ์‹๋ฌผ ๊ด€๋ จ ๊ฒฝํ—˜(๊ด€๋ง์  ์‹๋ฌผ ๊ฒฝํ—˜)๊ณผ ํ•™์ƒ ์ค‘์‹ฌ ๊ต์ˆ˜ํ•™์Šต ๊ฐ„, ์‹๋ฌผ ๊ด€๋ จ ๊ฒฝํ—˜(์ƒํ™œ ์‹๋ฌผ ๊ฒฝํ—˜)๊ณผ ํ•™์ƒ ์ค‘์‹ฌ ๊ต์ˆ˜ํ•™์Šต ๊ฐ„, ์‹๋ฌผ ๊ด€๋ จ ๊ฒฝํ—˜(์•ผ์™ธ ํ•™์Šต ๊ฒฝํ—˜)๊ณผ ๊ต์‚ฌ ๋†์—… ๊ด€๋ จ ๊ฒฝํ—˜ ๋ณ€์ธ ๊ฐ„์˜ ์ƒํ˜ธ์ž‘์šฉ ํšจ๊ณผ๊ฐ€ ๋†์—…๋ฌธํ•ด์— ์œ ์˜๋ฏธํ•œ ์˜ํ–ฅ์„ ๋ฏธ์น˜๊ณ  ์žˆ์—ˆ๋‹ค. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ์— ๋”ฐ๋ผ ๋‹ค์Œ๊ณผ ๊ฐ™์€ ๊ฒฐ๋ก ์„ ๋„์ถœํ•˜์˜€๋‹ค. ์ฒซ์งธ, ์šฐ๋ฆฌ๋‚˜๋ผ ๋„์‹œ์ง€์—ญ ์ดˆ๋“ฑํ•™์ƒ์˜ ๋†์—…๋ฌธํ•ด ์ˆ˜์ค€์€ 32์  ๋งŒ์ ์— ํ‰๊ท  23.31์ ์ด๋ฉฐ, 100์  ํ™˜์‚ฐ ์‹œ 72.8์ ๊ณผ ๊ฐ™๋‹ค. ๋†์—…๋ฌธํ•ด ํ•˜์œ„ ์š”์ธ ๊ฐ€์šด๋ฐ ํ•™์ƒ๋“ค์€ ๋†์‚ฐ๋ฌผ ์ƒ์‚ฐ ์˜์—ญ์— ๋Œ€ํ•œ ์ง€์‹์ˆ˜์ค€์€ ๋†’์€ ๋ฐ˜๋ฉด ๋†์‹ํ’ˆ์˜ ๊ฐ€์น˜์™€ ์•ˆ์ „์„ฑ ์˜์—ญ์— ๋Œ€ํ•œ ์ง€์‹์ˆ˜์ค€์€ ๋‚ฎ์€ ํŽธ์ด๋‹ค. ๋‘˜์งธ, ๋†์—…๋ฌธํ•ด ์ˆ˜์ค€์€ ํ•™์ƒ ๋ฐ ํ•™๊ธ‰ ์ˆ˜์ค€์— ๋”ฐ๋ผ ์ฐจ์ด๋ฅผ ๋ณด์ด๋ฉฐ, ํ•™์ƒ ๊ฐœ์ธ ๋ฐ ํ•™๊ธ‰ ์ˆ˜์ค€ ๋ณ€์ธ ๋ชจ๋‘์— ์˜ํ–ฅ์„ ๋ฐ›๋Š”๋‹ค. ์…‹์งธ, ํ•™์ƒ ์ˆ˜์ค€ ๋ณ€์ธ์ธ ๋Œ€๋„์‹œ ๊ฑฐ์ฃผ ์—ฌ๋ถ€, ํ•™๊ต ์ˆ˜์—… ํƒœ๋„, ํ•™์—…์„ฑ์ทจ์ˆ˜์ค€, ์‹๋ฌผ ๊ด€๋ จ ๊ฒฝํ—˜(๊ด€๋ง์  ์‹๋ฌผ ๊ฒฝํ—˜), ์ž์—ฐ ์ง€๋Šฅ์€ ํ•™์ƒ์˜ ๋†์—…๋ฌธํ•ด์— ์ •์ ์œผ๋กœ ์œ ์˜ํ•œ ์˜ํ–ฅ์„ ๋ฏธ์นœ๋‹ค. ๋„ท์งธ, ํ•™๊ธ‰ ์ˆ˜์ค€ ๋ณ€์ธ์ธ ๊ต์‚ฌ ์„ฑ๋ณ„, ๊ต์‚ฌ ๋‹ด์ž„ ํ•™๋…„, ๊ต์‚ฌ ๋†์—… ๊ด€๋ จ ๊ฒฝํ—˜ ๋ณ€์ธ์€ ์ดˆ๋“ฑํ•™์ƒ์˜ ๋†์—…๋ฌธํ•ด์— ์œ ์˜ํ•œ ์˜ํ–ฅ์„ ๋ฏธ์นœ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, ๊ต์ˆ˜ํ•™์Šต ์ฃผ๋„ ํ˜•ํƒœ ๋ฐ ๊ต์‚ฌ ๊ด€๋ จ ๊ฒฝํ—˜ ๋ณ€์ธ์€ ํ•™์ƒ์˜ ์‹๋ฌผ ๊ด€๋ จ ๊ฒฝํ—˜์ด ํ•™์ƒ์˜ ๋†์—…๋ฌธํ•ด ์ˆ˜์ค€์„ ์˜ˆ์ธกํ•˜๋Š” ๋ฐ ์œ ์˜ํ•˜๊ฒŒ ์˜ํ–ฅ์„ ๋ฏธ์นœ๋‹ค. ์ด ์—ฐ๊ตฌ์˜ ๊ฒฐ๊ณผ ๋ฐ ๊ฒฐ๋ก ์„ ํ† ๋Œ€๋กœ ๋‹ค์Œ๊ณผ ๊ฐ™์€ ์ œ์–ธ์„ ํ•˜๊ณ ์ž ํ•œ๋‹ค. ์ฒซ์งธ, ์ดˆ๋“ฑํ•™๊ต ๊ต์–‘๋†์—…๊ต์œก ํ˜„์žฅ์—์„œ ํ•™์ƒ๋“ค์ด ์ง์ ‘ ๊ฒฝํ—˜ํ•  ์ˆ˜ ์žˆ๋Š” ๋†์—… ๊ต์œก์˜ ๊ธฐํšŒ๋ฅผ ๋งˆ๋ จํ•˜๊ณ  ๋ณด๋‹ค ํ†ตํ•ฉ์  ๊ด€์ ์—์„œ ๋†์—…๋ฌธํ•ด๋ฅผ ๊ฐ€๋ฅด์น˜๋Š” ์ˆ˜์—…์„ ์„ค๊ณ„ํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ๋‘˜์งธ, ์ดˆ๋“ฑํ•™๊ต ๊ต์‚ฌ๋ฅผ ๋Œ€์ƒ์œผ๋กœ ๋†์—…์˜ ๋‹ค์–‘ํ•œ ๊ธฐ๋Šฅ๊ณผ ๊ฐ€์น˜๋ฅผ ์ดํ•ดํ•˜๊ณ  ๊ฑด์ „ํ•œ ์˜์‹์„ ์ œ๊ณ ํ•  ์ˆ˜ ์žˆ๋Š” ๊ธฐํšŒ๊ฐ€ ๋งˆ๋ จ๋˜์–ด์•ผ ํ•œ๋‹ค. ์…‹์งธ, ์‹๋ฌผ๊ณผ ๊ด€๋ จํ•œ ๋‹ค์–‘ํ•œ ๊ฒฝํ—˜ ํ™œ๋™์„ ๋ฐ˜์˜ํ•˜์—ฌ ๋†์—… ์ˆ˜์—…์„ ์ง„ํ–‰ํ•  ๋•Œ ๊ฐ ํ™œ๋™์— ์ ํ•ฉํ•œ ๊ต์ˆ˜ํ•™์Šต๋ฐฉ๋ฒ•์„ ๊ณ ๋ คํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ๋„ท์งธ, ์ด ์—ฐ๊ตฌ์—์„œ ์„ ์ •ํ•œ ๋ณ€์ธ์œผ๋กœ ์„ค๋ช…๋˜์ง€ ์•Š์œผ๋‚˜ ๋†์—…๋ฌธํ•ด๋ฅผ ์„ค๋ช…ํ•˜๋Š”๋ฐ ํ•™๊ธ‰ ๊ฐ„ ์ฐจ์ด๋ฅผ ๋ถˆ๋Ÿฌ์ผ์œผํ‚ค๋Š” ๋‹ค๋ฅธ ๋ณ€์ธ์„ ํƒ์ƒ‰ํ•˜๋Š” ํ›„์† ์—ฐ๊ตฌ๊ฐ€ ํ•„์š”ํ•˜๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, ๋ณด๋‹ค ๋†์—…๋ฌธํ•ด๋ฅผ ์ฒด๊ณ„์ ์œผ๋กœ ์ธก์ •ํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ์‘๋‹ต ๋ฐฉ์‹์— ์žˆ์–ด ๊ณตํ†ต๋œ ๊ธฐ์ค€์„ ์ ์šฉํ•˜๊ณ  ๋ณ€ํ™”๋˜๋Š” ๋‚ด์šฉ ์˜์—ญ์„ ์ธก์ •๋„๊ตฌ์— ์ง€์†์ ์œผ๋กœ ๋ฐ˜์˜ํ•ด์•ผ ํ•  ๊ฒƒ์œผ๋กœ ๋ณด์ธ๋‹ค. ๋†์—… ๋ฐ ๋†์ดŒ์˜ ์—ญํ• ๊ณผ ๊ฐ€์น˜๋Š” ๋ฏผ์ฃผ์‚ฌํšŒ ๊ตฌ์„ฑ์›์ด๋ผ๋ฉด ๋ˆ„๊ตฌ๋‚˜ ๊ฐ–์ถ”์–ด์•ผ ํ•  ์ง€์‹์ด๋ฉฐ, ์ดˆ๋“ฑํ•™๊ต์—์„œ๋ถ€ํ„ฐ ํ•™์Šต๋  ๋•Œ ๋†์—…์— ๋Œ€ํ•œ ์˜ฌ๋ฐ”๋ฅธ ์ง€์‹์„ ํšจ๊ณผ์ ์œผ๋กœ ํ•จ์–‘ํ•  ์ˆ˜ ์žˆ๋‹ค. ํŠนํžˆ ๋†์—… ๋ฐ ๋†์ดŒ์— ์ ‘์ด‰ํ•˜๊ธฐ ์–ด๋ ค์šด ๋„์‹œ ์ง€์—ญ์˜ ์ดˆ๋“ฑํ•™์ƒ์—๊ฒŒ ํ•™๊ต์—์„œ ๋†์—…๋ฌธํ•ด๋ฅผ ๊ฐ€๋ฅด์น˜๋Š” ๊ฒƒ์€ ํ•™์ƒ์ด ๋†์—…์˜ ๋‹ค์›์ ์ธ ๊ธฐ๋Šฅ์„ ์ดํ•ดํ•˜๊ณ  ๋†์—…์— ๋Œ€ํ•œ ์˜ฌ๋ฐ”๋ฅธ ์ง€์‹์„ ๊ฐ–์ถ”๋Š”๋ฐ ๋„์›€์ด ๋  ์ˆ˜ ์žˆ๋‹ค. ํ•™์ƒ ํŠน์„ฑ ์ด์™ธ์—๋„ ๊ต์‚ฌ์˜ ๋†์—… ๊ด€๋ จ ๊ฒฝํ—˜ ๋ฐ ๊ต์ˆ˜ํ•™์Šต ๋ฐฉ๋ฒ• ๋“ฑ์€ ๊ฒฝํ—˜ํ•™์Šต์„ ํ†ตํ•˜์—ฌ ํ•™์ƒ์˜ ๋†์—…๋ฌธํ•ด๋ฅผ ํ˜•์„ฑํ•˜๋Š”๋ฐ ๊ธฐ์—ฌํ•˜๊ณ  ์žˆ๋‹ค. ์ด๋ฅผ ํ† ๋Œ€๋กœ ๊ต์‚ฌ๋Š” ๋‹ค์–‘ํ•œ ๊ฒฝํ—˜ ํ™œ๋™ ๋ฐ ์ˆ˜์—… ๋ฐฉ๋ฒ•์„ ๋ฐ˜์˜ํ•˜์—ฌ ์ˆ˜์—…์„ ์‹คํ–‰ํ•จ์œผ๋กœ์จ ๋ณด๋‹ค ํšจ๊ณผ์ ์œผ๋กœ ํ•™์ƒ์˜ ๋†์—…์— ๋Œ€ํ•œ ์ดํ•ด๋„๋ฅผ ๋†’์ผ ์ˆ˜ ์žˆ๋‹ค. ๋†์—…์— ๋Œ€ํ•œ ์˜ฌ๋ฐ”๋ฅธ ์ดํ•ด์™€ ์†Œ์–‘์„ ๊ฐ–์ถ˜ ํ•™์ƒ๋“ค์€ ํ–ฅํ›„ ์‚ฌํšŒ์˜ ์ฃผ์š” ๊ตฌ์„ฑ์›์œผ๋กœ ์„ฑ์žฅํ•˜์—ฌ ๋†์—… ๋ฐ ๋†์ดŒ์— ๋Œ€ํ•˜์—ฌ ํ•ฉ๋ฆฌ์ ์œผ๋กœ ์˜์‚ฌ๋ฅผ ๊ฒฐ์ •ํ•˜๊ณ  ์—ฌ๋ก ์„ ํ˜•์„ฑํ•˜๋Š”๋ฐ ๊ธฐ์—ฌํ•  ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€๋œ๋‹ค.I. ์„œ๋ก  1 1. ์—ฐ๊ตฌ์˜ ํ•„์š”์„ฑ 1 2. ์—ฐ๊ตฌ์˜ ๋ชฉ์  4 3. ์—ฐ๊ตฌ ๋ฌธ์ œ 5 4. ์šฉ์–ด์˜ ์ •์˜ 6 5. ์—ฐ๊ตฌ์˜ ์ œํ•œ 8 II. ์ด๋ก ์  ๋ฐฐ๊ฒฝ 9 1. ๋†์—…๋ฌธํ•ด 9 2. ์ดˆ๋“ฑํ•™๊ต์˜ ๊ต์–‘๋†์—…๊ต์œก 25 3. ์ดˆ๋“ฑํ•™์ƒ์˜ ๋†์—…๋ฌธํ•ด์™€ ํ•™์ƒ ์ˆ˜์ค€ ๋ณ€์ธ์˜ ๊ด€๊ณ„ 37 4. ์ดˆ๋“ฑํ•™์ƒ์˜ ๋†์—…๋ฌธํ•ด์™€ ํ•™๊ธ‰ ์ˆ˜์ค€ ๋ณ€์ธ์˜ ๊ด€๊ณ„ 46 III. ์—ฐ๊ตฌ ๋ฐฉ๋ฒ• 59 1. ์—ฐ๊ตฌ ๋ชจํ˜• 59 2. ์—ฐ๊ตฌ ๋Œ€์ƒ 62 3. ์กฐ์‚ฌ ๋„๊ตฌ 66 4. ์ž๋ฃŒ ์ˆ˜์ง‘ 79 5. ์ž๋ฃŒ ๋ถ„์„ 80 IV. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ 85 1. ์ž๋ฃŒ์˜ ์ผ๋ฐ˜์  ํŠน์„ฑ 85 2. ๋„์‹œ์ง€์—ญ ์ดˆ๋“ฑํ•™์ƒ์˜ ๋†์—…๋ฌธํ•ด ์ˆ˜์ค€ 90 3. ๋†์—…๋ฌธํ•ด์™€ ํ•™์ƒ ๋ฐ ํ•™๊ธ‰ ์ˆ˜์ค€ ๋ณ€์ธ์˜ ๊ด€๊ณ„ ๋ถ„์„ 97 4. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ์— ๋Œ€ํ•œ ๋…ผ์˜ 116 V. ์š”์•ฝ, ๊ฒฐ๋ก  ๋ฐ ์ œ์–ธ 129 1. ์š”์•ฝ 129 2. ๊ฒฐ๋ก  131 3. ์ œ์–ธ 135 ์ฐธ๊ณ ๋ฌธํ—Œ 139 ๋ถ€๋ก1. ํ•™์ƒ ๋Œ€์ƒ ์ง„๋‹จ ๋„๊ตฌ(์˜ˆ๋น„์กฐ์‚ฌ) 153 ๋ถ€๋ก2. ๊ต์‚ฌ ๋Œ€์ƒ ์ง„๋‹จ ๋„๊ตฌ(์˜ˆ๋น„์กฐ์‚ฌ) 160 ๋ถ€๋ก3. ํ•™์ƒ ๋Œ€์ƒ ์ง„๋‹จ ๋„๊ตฌ(๋ณธ ์กฐ์‚ฌ) 165 ๋ถ€๋ก4. ๊ต์‚ฌ ๋Œ€์ƒ ์ง„๋‹จ ๋„๊ตฌ(๋ณธ ์กฐ์‚ฌ) 172 Abstract 179Maste

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์‚ฌํšŒ๊ณผํ•™๋Œ€ํ•™ ์‚ฌํšŒ๋ณต์ง€ํ•™๊ณผ, 2019. 2. ๊ตฌ์ธํšŒ.์ธ๊ตฌ๊ณ ๋ นํ™”๊ฐ€ ์‹ฌ๊ฐํ•œ ์‚ฌํšŒ๋ฌธ์ œ๋กœ ๋ถ€๊ฐ๋˜๋ฉด์„œ ๋…ธ์ธ์˜ ๋…ธ๋™์ฐธ์—ฌ์— ๋Œ€ํ•œ ์‚ฌํšŒ์  ๊ด€์‹ฌ์ด ์ปค์ง€๊ณ  ์žˆ๋‹ค. ๋…ธ์ธ์˜ ๊ฒฝ์ œํ™œ๋™ ์ฐธ์—ฌ๋Š” ์ƒ์‚ฐ์ธ๊ตฌ์˜ ๊ฐ์†Œ์— ๋Œ€์‘ํ•˜๊ณ , ์ •๋ถ€์˜ ์žฌ์ • ๋ถ€๋‹ด์„ ์ค„์ด๋ฉฐ, ๊ฐœ์ธ์˜ ์‚ถ์˜ ์งˆ์„ ๋†’์ด๋Š” ๋ฐ ๊ธฐ์—ฌํ•  ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€๋ฅผ ๋ฐ›๊ณ  ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ํ•œ๊ตญ ์‚ฌํšŒ๋Š” OECD ํ‰๊ท ์˜ ๋‘ ๋ฐฐ๊ฐ€ ๋„˜๋Š” ๋…ธ์ธ ๊ณ ์šฉ๋ฅ ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ์—ฌ์ „ํžˆ ์ ˆ๋ฐ˜์— ๊ฐ€๊นŒ์šด ๋…ธ์ธ์ด ๋นˆ๊ณค์— ์ฒ˜ํ•ด ์žˆ๊ณ , ์ผํ•˜๋Š” ๋…ธ์ธ์˜ ์ƒ๋‹น์ˆ˜๋Š” ๊ณ ์šฉ๋ถˆ์•ˆ๊ณผ ์ €์ž„๊ธˆ์— ์‹œ๋‹ฌ๋ฆฌ๊ณ  ์žˆ๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” ๋…ธ์ธ์˜ ๋…ธ๋™์‹œ์žฅ ์ฐธ์—ฌ๋ฅผ ๋†’์ด๋ ค๋Š” ์‚ฌํšŒ์ •์ฑ…์  ๋…ธ๋ ฅ์— ์•ž์„œ, ํ•œ๊ตญ์˜ ๋…ธ์ธ ๊ณ ์šฉ๋ฅ ์ด ์ง€์†์ ์œผ๋กœ ๋†’์€ ์ˆ˜์ค€์„ ์œ ์ง€ํ•˜๋Š” ์ด์œ ๊ฐ€ ๋ฌด์—‡์ธ์ง€์— ๋Œ€ํ•œ ์ดํ•ด๊ฐ€ ์„ ํ–‰๋  ํ•„์š”๊ฐ€ ์žˆ๋‹ค๋Š” ์ธ์‹์—์„œ ์ถœ๋ฐœํ•œ๋‹ค. ์„œ๊ตฌ์˜ ๊ฒฝ์šฐ, 20์„ธ๊ธฐ ์ดํ›„๋ถ€ํ„ฐ 1980๋…„๋Œ€ ์ค‘๋ฐ˜๊นŒ์ง€ ๋…ธ์ธ์„ ๋น„๋กฏํ•œ ๊ณ ๋ น์ธต์˜ ๋…ธ๋™์‹œ์žฅ์ฐธ์—ฌ๊ฐ€ ์ง€์†์ ์œผ๋กœ ๊ฐ์†Œํ•˜๋Š” ์ถ”์ด๋ฅผ ๋ณด์—ฌ์™”์œผ๋ฉฐ, ์ดํ›„ 1990๋…„๋Œ€ ๋“ค์–ด์„œ ๋น„๊ต์  ์•ˆ์ •์ ์ธ ์ˆ˜์ค€์„ ์œ ์ง€ํ•˜๋‹ค๊ฐ€, 1990๋…„๋Œ€ ํ›„๋ฐ˜๋ถ€ํ„ฐ๋Š” ์ด์ „๊ณผ ๋‹ฌ๋ฆฌ ์ฆ๊ฐ€ํ•˜๊ธฐ ์‹œ์ž‘ํ•˜์˜€๋‹ค. ๋…ธ์ธ ๋…ธ๋™์ด ๊ฐ์†Œํ•˜๋˜ ์‹œ๊ธฐ์—๋Š” ๋†์—… ๋น„์ค‘์˜ ๊ฐ์†Œ์™€ ๊ฐ™์€ ์‚ฐ์—…๊ตฌ์กฐ์˜ ๋ณ€ํ™”, ๋…ธ์ธ์„ ๋Œ€์ƒ์œผ๋กœ ํ•œ ์†Œ๋“๋ณด์žฅ์ œ๋„์˜ ํ™•๋Œ€๊ฐ€ ์ฃผ๋„์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์ณค๋‹ค. ๋ฐ˜๋ฉด, ๋…ธ์ธ์˜ ๊ณ ์šฉ๋ฅ ์ด ๋ฐ˜๋“ฑํ•œ ์‹œ๊ธฐ์—๋Š” ๋…ธ์ธ์˜ ๋…ธ๋™์ƒ์‚ฐ์„ฑ ํ–ฅ์ƒ์ด๋‚˜, ์—ฌ์„ฑ์˜ ๋…ธ๋™์‹œ์žฅ ์ฐธ์—ฌ๊ฐ€ ํ™œ๋ฐœํ•ด์ง„ ๋ณ€ํ™” ์™ธ์—๋„, ๊ณต์ ์—ฐ๊ธˆ์ œ๋„๋ฅผ ๋น„๋กฏํ•œ ๋…ธํ›„์†Œ๋“๋ณด์žฅ์ œ๋„๊ฐ€ ์ถ•์†Œ๋˜๋Š” ๋“ฑ ์ด์ „ ์‹œ๊ธฐ์™€๋Š” ๋‹ค๋ฅธ ๋ฐฉํ–ฅ์˜ ์‚ฌํšŒ์ œ๋„์  ๋ณ€ํ™”๊ฐ€ ์ฃผ๋œ ์˜ํ–ฅ์„ ๋ฏธ์นœ ๊ฒƒ์œผ๋กœ ํ‰๊ฐ€๋œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์„œ๊ตฌ์˜ ๊ฒฝํ—˜์— ๊ธฐ์ดˆํ•œ ์ด๋Ÿฌํ•œ ์„ค๋ช…๋“ค๋กœ ํ•œ๊ตญ ๋…ธ์ธ์˜ ๊ณ ์šฉ๋ฅ  ๋ณ€ํ™” ์ถ”์ด๋ฅผ ์ดํ•ดํ•˜๊ธฐ์—๋Š” ๋ฌด๋ฆฌ๊ฐ€ ์žˆ์–ด ๋ณด์ธ๋‹ค. 1960๋…„๋Œ€ ์ดํ›„ ์‚ฐ์—…๊ตฌ์กฐ์˜ ๋ณ€ํ™”๊ฐ€ ๊ฐ€์žฅ ๋น ๋ฅด๊ฒŒ ๋‚˜ํƒ€๋‚œ ์‹œ๊ธฐ์— ์„œ๊ตฌ์™€๋Š” ๋ฐ˜๋Œ€๋กœ ๋…ธ์ธ์˜ ๋…ธ๋™์ฐธ์—ฌ๊ฐ€ ๋†’์•„์กŒ๊ณ , 2000๋…„๋Œ€ ์ดํ›„ ๋…ธํ›„์†Œ๋“๋ณด์žฅ์ œ๋„๊ฐ€ ์ง€์†์ ์œผ๋กœ ํ™•๋Œ€๋œ ์‹œ๊ธฐ์—๋„ ๋…ธ์ธ์˜ ๋…ธ๋™์ฐธ์—ฌ๋Š” ๊ฐ์†Œํ•˜์ง€ ์•Š์•˜๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ์„œ๊ตฌ์—์„œ ๋…ธ์ธ์˜ ๋…ธ๋™์ฐธ์—ฌ๊ฐ€ ๊ฐ์†Œํ•œ ์‹œ๊ธฐ์™€ ๋น„์Šทํ•œ ๋ฐฉํ–ฅ์˜ ๋ณ€ํ™” ์†์—์„œ๋„, ํ•œ๊ตญ ๋…ธ์ธ์˜ ๋…ธ๋™์ฐธ์—ฌ๊ฐ€ ์„œ๊ตฌ์™€๋Š” ๋‹ค๋ฅธ ๋ณ€ํ™” ์ถ”์ด๋ฅผ ๋ณด์ด๋Š” ์ด์œ ๊ฐ€ ๋ฌด์—‡์ผ๊นŒ? ์ด ์—ฐ๊ตฌ๋Š” ๋…ธํ›„์†Œ๋“๋ณด์žฅ์ œ๋„๊ฐ€ ํ™•๋Œ€๋œ 2000๋…„๋Œ€ ์ค‘๋ฐ˜ ์ดํ›„ ์‹œ๊ธฐ๋ฅผ ์ค‘์‹ฌ์œผ๋กœ ํ•œ๊ตญ์˜ ๋…ธ์ธ ๊ณ ์šฉ๋ฅ  ๋ณ€ํ™” ์ถ”์ด๋ฅผ ํ™•์ธํ•˜๊ณ , ์–ด๋– ํ•œ ์š”์ธ๋“ค์ด ๊ทธ๋Ÿฌํ•œ ๋ณ€ํ™”์— ์˜ํ–ฅ์„ ๋ฏธ์ณค๋Š”์ง€๋ฅผ ๋ถ„์„ํ•œ๋‹ค. ๋ถ„์„์— ์‚ฌ์šฉํ•˜๋Š” ์ž๋ฃŒ๋Š” ๊ณ ๋ นํ™”์—ฐ๊ตฌํŒจ๋„์กฐ์‚ฌ์˜ 2008๋…„, 2016๋…„์˜ ๊ธฐ๋ณธ์กฐ์‚ฌ ์ž๋ฃŒ์™€ 2007๋…„์— ์กฐ์‚ฌ๋œ ์ง์—…๋ ฅ ์ž๋ฃŒ์ด๋ฉฐ, ๊ตญ๋ฏผ์—ฐ๊ธˆ์„ ์ˆ˜๊ธ‰ํ•˜๋Š” ์—ฐ๋ น๋Œ€๋ฅผ ๊ณ ๋ คํ•˜์—ฌ 60-84์„ธ ์—ฐ๋ น์ง‘๋‹จ์„ ๋ถ„์„๋Œ€์ƒ์œผ๋กœ ํ•œ๋‹ค. ๋ถ„์„๋ฐฉ๋ฒ•์œผ๋กœ๋Š” ๋…ธ๋™๊ฒฝ์ œํ•™์—์„œ ์‹œ์  ๊ฐ„(ํ˜น์€ ์ง‘๋‹จ ๊ฐ„) ๊ฒฐ๊ณผ๋ณ€์ˆ˜์˜ ์ฐจ์ด๋ฅผ ๋ถ„ํ•ดํ•˜๋Š” ๋ฐ ๋งŽ์ด ์‚ฌ์šฉ๋˜์–ด ์˜จ ๋ถ„ํ•ด๋ฐฉ๋ฒ•(decomposition methods) ์ค‘, ๋น„๋ชจ์ˆ˜์  ๋ฐฉ๋ฒ•์— ๊ธฐ์ดˆํ•œ ์žฌ๊ฐ€์ค‘(reweighting) ๋ถ„ํ•ด๋ฐฉ๋ฒ•์„ ์ ์šฉํ•œ๋‹ค. ์ด ๋ฐฉ๋ฒ•์€ ๊ด€์ธก๋œ ๋‘ ์‹œ์  ์ค‘ ํ•œ ์‹œ์ ์— ์žฌ๊ฐ€์ค‘์น˜๋ฅผ ๋ถ€์—ฌํ•˜์—ฌ ๋…ธ์ธ์˜ ์ทจ์—…์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์š”์ธ๋“ค์˜ ๋ถ„ํฌ๊ฐ€ ๋‹ค๋ฅธ ์‹œ์ ๊ณผ ๋™์ผํ•ด์ง€๋„๋ก ์กฐ์ •ํ•œ ์žฌ๊ฐ€์ค‘ ํ‘œ๋ณธ์„ ๊ตฌ์„ฑํ•œ ํ›„, ๋‘ ์‹œ์  ๊ฐ„ ๊ฒฐ๊ณผ๋ณ€์ˆ˜์˜ ์ฐจ์ด๋ฅผ ์„ค๋ช…๋ณ€์ˆ˜์˜ ๋ถ„ํฌ ๋ณ€ํ™”๋กœ ์ธํ•œ ์ฐจ์ด์™€ ๊ธฐํƒ€ ๋‹ค๋ฅธ ์š”์ธ์˜ ๋ณ€ํ™”๋กœ ์ธํ•œ ์ฐจ์ด๋กœ ๊ตฌ๋ถ„ํ•œ๋‹ค(์ง‘๊ณ„๋ถ„ํ•ด). ๋˜ํ•œ ์„ค๋ช…๋ณ€์ˆ˜์˜ ๋ถ„ํฌ ๋ณ€ํ™”๋กœ ์ธํ•œ ๊ฒฐ๊ณผ๋ณ€์ˆ˜์˜ ๋ณ€ํ™”๋ฅผ ๋‹ค์‹œ ๊ฐœ๋ณ„ ์„ค๋ช…๋ณ€์ˆ˜์˜ ๋ณ€ํ™”๋กœ ์ธํ•œ ๊ธฐ์—ฌ ์ •๋„๋กœ ๊ตฌ๋ถ„ํ•˜๋Š” ์„ธ๋ถ€๋ถ„ํ•ด๋„ ๊ฐ€๋Šฅํ•˜์—ฌ, ์—ฌ๋Ÿฌ ์š”์ธ๋“ค์˜ ์ƒ๋Œ€์ ์ธ ์˜ํ–ฅ ์ •๋„๋ฅผ ๋น„๊ตํ•˜๋Š” ๋ณธ ์—ฐ๊ตฌ์— ์ ํ•ฉํ•œ ๋ฐฉ๋ฒ•์ด๋ผ ํŒ๋‹จํ•˜์˜€๋‹ค. ๋…ธ์ธ์˜ ๋…ธ๋™์ฐธ์—ฌ์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์š”์ธ์€ ๋…ธ๋™๊ฒฝ์ œํ•™์˜ ํ•ฉ๋ฆฌ์  ์„ ํƒ ์ด๋ก (rational choice theory)๊ณผ ์‚ฌํšŒํ•™์˜ ์ƒ์• ๊ณผ์ • ๊ด€์ (life-course perspective)์— ๊ธฐ์ดˆํ•˜์—ฌ ์„ ์ •ํ•˜์˜€๋‹ค. ๊ตฌ์ฒด์ ์œผ๋กœ๋Š”, ์ฃผ๋กœ ๋…ธ๋™๊ณต๊ธ‰ ์ธก๋ฉด๊ณผ ๊ด€๋ จ๋œ ์„ฑ, ์—ฐ๋ น, ๊ต์œก์ˆ˜์ค€, ๊ฑด๊ฐ•, ๋ฐฐ์šฐ์ž ์ง€์œ„์™€ ๊ฐ™์€ ๋ฏธ์‹œ์  ์š”์ธ๋“ค๊ณผ ๋…ธ์ธ์ด ๊ฑฐ์ฃผํ•˜๋Š” ์ง€์—ญ์˜ ๋…ธ๋™์‹œ์žฅ ํŠน์„ฑ๋“ค์„ ํฌํ•จํ•˜๋Š” ๊ฑฐ์‹œ์  ์š”์ธ์„ ๋น„๋กฏํ•˜์—ฌ, ๊ฐœ๋ณ„ ๋…ธ์ธ์ด ํ•ต์‹ฌ ๋…ธ๋™์—ฐ๋ น๋Œ€์— ์ฃผ๋กœ ๊ฒฝํ—˜ํ•œ ์ข…์‚ฌ์ƒ ์ง€์œ„์™€ ์ฃผ๋œ ์‚ฐ์—…, ๊ฒฝ๋ ฅ ๊ธฐ๊ฐ„๊ณผ ๊ฐ™์€ ์ƒ์• ๊ณผ์ • ์š”์ธ๋“ค๋„ ํฌํ•จํ•˜์˜€๋‹ค. ๋ฏธ์‹œ์  ์š”์ธ ์ค‘, ์ด ๋…ผ๋ฌธ์—์„œ ์ฃผ๋กœ ๊ด€์‹ฌ์„ ๊ฐ€์ง€๋Š” ๋…ธ์ธ์˜ ๊ฒฝ์ œ์  ๋ถ€์–‘๊ณผ ๊ด€๋ จ๋œ ๊ณต์ ๋ถ€์–‘ ๋ฐ ์‚ฌ์ ๋ถ€์–‘ ๊ด€๋ จ ์š”์ธ๋“ค์€ ๋ณ„๋„์˜ ๋ฒ”์ฃผ๋กœ ๊ตฌ๋ถ„ํ•˜์—ฌ ์‚ดํŽด๋ดค์œผ๋ฉฐ, ๊ฒฐ๊ณผ๋ณ€์ˆ˜์ธ ๋…ธ๋™์ฐธ์—ฌ์™€์˜ ์—ญ์ธ๊ณผ๊ด€๊ณ„(reverse causality)๋ฅผ ์ค„์ด๊ธฐ ์œ„ํ•ด์„œ, ์ธก์ • ๋ฐฉ๋ฒ•์„ ์กฐ์ •ํ•œ ๋ณ€์ˆ˜๋ฅผ ์‚ฌ์šฉํ•˜์˜€๋‹ค. ๋…ธ์ธ ๋‚ด ์กด์žฌํ•˜๋Š” ๋‹ค์–‘ํ•œ ์ด์งˆ์„ฑ์„ ๊ณ ๋ คํ•˜์—ฌ, ์‘๋‹ต์ž์˜ ์„ฑ๊ณผ ์ฃผ๋œ ์ข…์‚ฌ์ƒ ์ง€์œ„๋ฅผ ๊ธฐ์ค€์œผ๋กœ ํ•˜์œ„์ง‘๋‹จ์„ ๊ตฌ์„ฑํ•˜์˜€๊ณ , ๋ชจ๋“  ๋ถ„์„์—์„œ ํ•˜์œ„์ง‘๋‹จ๋ณ„ ๋ถ„์„๊ฒฐ๊ณผ๋ฅผ ๊ฐ™์ด ์ œ์‹œํ•˜์˜€๋‹ค. ์ฃผ์š” ๋ถ„์„๊ฒฐ๊ณผ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, 2008-2016๋…„์˜ ๋‘ ์‹œ์  ์‚ฌ์ด์— 60-84์„ธ ์—ฐ๋ น์ง‘๋‹จ์˜ ๊ณ ์šฉ๋ฅ ์€ 29.8%์—์„œ 38.7%๋กœ 8.9%p ์ƒ์Šนํ•˜์˜€์œผ๋ฉฐ, ์ง‘๊ณ„๋ถ„ํ•ด ๋ถ„์„๊ฒฐ๊ณผ, ๋ถ„์„์— ํฌํ•จ๋œ ์„ค๋ช…๋ณ€์ˆ˜์˜ ๋ถ„ํฌ ๋ณ€ํ™”๋Š” ์ „์ฒด ๋ณ€ํ™” ์ค‘ 3.6%p๋ฅผ ์„ค๋ช…ํ•˜์˜€๊ณ , ๋‚˜๋จธ์ง€ 5.3%p๋Š” ๋‘ ์‹œ์  ๊ฐ„ ์„ค๋ช…๋ณ€์ˆ˜์™€ ๊ฒฐ๊ณผ๋ณ€์ˆ˜์˜ ํšก๋‹จ์  ๊ด€๊ณ„ ๋ณ€ํ™” ๋ฐ ๋ถ„์„์— ํฌํ•จ๋˜์ง€ ์•Š์€ ์š”์ธ์˜ ๋ณ€ํ™”๋กœ ์ธํ•œ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ด๋Ÿฌํ•œ ๊ฒฐ๊ณผ๋Š” 2000๋…„๋Œ€ ์ค‘๋ฐ˜ ์ดํ›„ ๋…ธ์ธ ๊ณ ์šฉ๋ฅ ์ด ๋†’์•„์ง„ ๊ฒƒ์€ ๋…ธ์ธ์˜ ์ทจ์—…์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์ฃผ์š” ํŠน์„ฑ์˜ ๋ณ€ํ™”๋ณด๋‹ค ์‚ฌํšŒ๊ตฌ์กฐ์ ์ธ ๋ณ€ํ™”๋กœ ์ธํ•œ ์„ค๋ช…๋ณ€์ˆ˜์™€ ๊ฒฐ๊ณผ๋ณ€์ˆ˜์˜ ๊ด€๊ณ„ ๋ณ€ํ™”๊ฐ€ ๋” ํฐ ์˜ํ–ฅ์„ ๋ฏธ์ณค์Œ์„ ์˜๋ฏธํ•œ๋‹ค. ๋‘˜์งธ, ์ „์ฒด ๋ถ„์„๋Œ€์ƒ์˜ ๊ตฌ์„ฑํšจ๊ณผ์— ๋Œ€ํ•œ ์„ธ๋ถ€๋ถ„ํ•ด ๊ฒฐ๊ณผ, ๋…ธ์ธ์˜ ์ทจ์—…์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์ฃผ์š” ํŠน์„ฑ์˜ ๋ณ€ํ™”๊ฐ€ ๋…ธ์ธ ๊ณ ์šฉ๋ฅ ์˜ ๋ณ€ํ™”์— ๋ฏธ์นœ ์˜ํ–ฅ์€ ์ •(+)์ ์ธ ํšจ๊ณผ์™€ ๋ถ€(-)์ ์ธ ํšจ๊ณผ๊ฐ€ ๋ชจ๋‘ ๊ด€์ฐฐ๋˜์—ˆ๊ณ , ์„œ๋กœ์˜ ์˜ํ–ฅ์„ ์ƒ๋‹น ๋ถ€๋ถ„ ์ƒ์‡„ํ•˜์˜€๋‹ค. ์„ฑ, ๊ฒฝ๋ ฅ, ๋ฐฐ์šฐ์ž ์ง€์œ„, ๊ฑด๊ฐ•๊ณผ ๊ฐ™์€ ํŠน์„ฑ์˜ ๋ณ€ํ™”๋Š” ๋…ธ์ธ์˜ ๊ณ ์šฉ๋ฅ ์„ ๋†’์ด๋Š” ๋ฐฉํ–ฅ์œผ๋กœ ์˜ํ–ฅ์„ ๋ฏธ์นœ ๋ฐ˜๋ฉด, ๊ต์œก์ˆ˜์ค€์ด๋‚˜ ์ฃผ๋œ ์ข…์‚ฌ์ƒ ์ง€์œ„, ์ฃผ๋œ ์‚ฐ์—…๊ณผ ๊ฐ™์€ ํŠน์„ฑ์˜ ๋ณ€ํ™”๋Š” ๋…ธ์ธ์˜ ๊ณ ์šฉ๋ฅ ์„ ๊ฐ์†Œ์‹œํ‚ค๋Š” ๋ฐฉํ–ฅ์œผ๋กœ ์˜ํ–ฅ์„ ๋ฏธ์ณค๋‹ค. ์ด์ฒ˜๋Ÿผ ์„ค๋ช…๋ณ€์ˆ˜๋“ค ๊ฐ„ ์—‡๊ฐˆ๋ฆฌ๋Š” ํšจ๊ณผ๋Š” ๋ถ„ํ•ด๋ถ„์„์— ์žˆ์–ด์„œ ์„ธ๋ถ€๋ถ„ํ•ด๊ฐ€ ๊ฐ€์ง€๋Š” ์ค‘์š”์„ฑ์„ ๋ณด์—ฌ์ค€๋‹ค. ์…‹์งธ, ๋…ธ์ธ์„ ๋Œ€์ƒ์œผ๋กœ ํ•œ ๊ณต์ ๋ถ€์–‘ ์ œ๋„์˜ ํ™•๋Œ€๋Š” ์„œ๊ตฌ์˜ ์—ฐ๊ตฌ๋“ค๊ณผ๋Š” ์ƒ์ดํ•œ ๊ฒฐ๊ณผ๋ฅผ ๋ณด์˜€๋‹ค. ๊ณต์ ์—ฐ๊ธˆ ์†Œ๋“์˜ ๋ถ„ํฌ ๋ณ€ํ™”๋Š” ์˜คํžˆ๋ ค ๋…ธ์ธ์˜ ๊ณ ์šฉ๋ฅ  ์ฆ๊ฐ€์™€ ๊ด€๋ จ์ด ์žˆ์—ˆ๊ณ , ๊ธฐ์ดˆ(๋…ธ๋ น)์—ฐ๊ธˆ์˜ ์ˆ˜๊ธ‰๊ทœ๋ชจ ํ™•๋Œ€๋Š” ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜ํ•œ ์ˆ˜์ค€์˜ ์˜ํ–ฅ์„ ๋ฏธ์น˜์ง€ ์•Š์€ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๊ณต์ ์—ฐ๊ธˆ ํ™•๋Œ€์˜ ๊ฒฝ์šฐ, ๋น„์ˆ˜๊ธ‰์ง‘๋‹จ๋ณด๋‹ค ์ทจ์—…ํ™•๋ฅ ์ด ๋†’์€ ์ €์—ฐ๊ธˆ ์ˆ˜๊ธ‰์ง‘๋‹จ์˜ ๋น„์ค‘์ด ์ฆ๊ฐ€ํ•˜์—ฌ ๋…ธ์ธ ๊ณ ์šฉ๋ฅ ์„ ๋†’์ด๋Š” ๋ฐฉํ–ฅ์œผ๋กœ ์ž‘์šฉํ•˜์˜€๋‹ค. ๊ธฐ์ดˆ์—ฐ๊ธˆ์€ ๊ธฐ์ค€์‹œ์ ์ธ 2016๋…„์˜ ๊ฒฝ์šฐ ์ˆ˜๊ธ‰์ง‘๋‹จ๊ณผ ๋น„์ˆ˜๊ธ‰์ง‘๋‹จ์˜ ์กฐ๊ฑด๋ถ€ ๊ณ ์šฉ๋ฅ  ์ฐจ์ด๊ฐ€ ํฌ๊ฒŒ ๊ฐ์†Œํ•˜์—ฌ, ๊ธ‰์—ฌ์ˆ˜์ค€ ๋ฐ ์ˆ˜๊ธ‰์ง‘๋‹จ์˜ ๊ทœ๋ชจ ํ™•๋Œ€๊ฐ€ ๋…ธ์ธ์˜ ๊ณ ์šฉ๋ฅ  ๋ณ€ํ™”์— ์œ ์˜ํ•œ ์ˆ˜์ค€์˜ ์˜ํ–ฅ์„ ๋ฏธ์น˜์ง€ ๋ชปํ•˜์˜€๋‹ค. ๋„ท์งธ, ๋…ธ์ธ์ด ์ž๋…€์—๊ฒŒ์„œ ๋ฐ›๋Š” ์‚ฌ์ ๋ถ€์–‘์—์„œ๋Š” ๋™๊ฑฐ์™€ ์‚ฌ์ ์ด์ „์˜ ์˜ํ–ฅ์ด ์ฐจ์ด๋ฅผ ๋ณด์˜€๋‹ค. 2008-2016๋…„ ์‚ฌ์ด ๊ธฐํ˜ผ์ž๋…€์™€ ๋™๊ฑฐํ•˜๋Š” ๋…ธ์ธ์˜ ๋น„์ค‘์ด ๊ฐ์†Œํ•˜์˜€๊ณ , ๋น„๋™๊ฑฐ ์ž๋…€๋กœ๋ถ€ํ„ฐ ์‚ฌ์ ์ด์ „์„ ๋ฐ›๋Š” ๋น„์ค‘๊ณผ ์‚ฌ์ ์ด์ „ ์†Œ๋“์˜ ๊ทœ๋ชจ๋„ ๊ฐ์†Œํ•˜์˜€๋‹ค. ๋‘ ์š”์ธ์—์„œ ๋ชจ๋‘ ์‚ฌ์ ๋ถ€์–‘์˜ ๊ฐ์†Œ ์ถ”์ด๊ฐ€ ํ™•์ธ๋˜์—ˆ์ง€๋งŒ, ๋™๊ฑฐ์˜ ๊ฐ์†Œ๋Š” ์ „์ฒด ๋…ธ์ธ์˜ ๊ณ ์šฉ๋ฅ ์— ์œ ์˜ํ•œ ์˜ํ–ฅ์„ ๋ฏธ์น˜์ง€ ๋ชปํ•œ ๋ฐ˜๋ฉด, ์‚ฌ์ ์ด์ „์˜ ๊ฐ์†Œ๋Š” ๋…ธ์ธ์˜ ๊ณ ์šฉ๋ฅ ์„ ๋†’์ด๋Š” ๋ฐฉํ–ฅ์œผ๋กœ ์˜ํ–ฅ์„ ๋ฏธ์ณค๋‹ค. ๋˜ํ•œ ์‚ฌ์ ์ด์ „์˜ ๊ฐ์†Œ๋Š” ๋ถ„์„์— ํฌํ•จ๋œ ์˜ํ–ฅ์š”์ธ๋“ค ์ค‘์—์„œ ๊ณ ์šฉ๋ฅ  ๋ณ€ํ™”์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์˜ ์ •๋„๊ฐ€ ๊ฐ€์žฅ ํฐ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋Š”๋ฐ, ์ด๋Š” 2000๋…„๋Œ€ ์ค‘๋ฐ˜ ์ดํ›„ ๋…ธ์ธ ๊ณ ์šฉ๋ฅ  ์ฆ๊ฐ€์˜ ์ƒ๋‹น ๋ถ€๋ถ„์ด ๊ฒฝ์ œ์ ์ธ ๋ชฉ์ ์œผ๋กœ ๋…ธ๋™์‹œ์žฅ์— ์ฐธ์—ฌํ•˜๋Š” ์ƒ๊ณ„ํ˜• ๋…ธ๋™์˜ ์ฆ๊ฐ€์— ๊ธฐ์ธํ•œ ๊ฒƒ์ž„์„ ๋ณด์—ฌ์ค€๋‹ค. ๋‹ค์„ฏ์งธ, ํ•˜์œ„์ง‘๋‹จ๋ณ„ ์„ธ๋ถ€๋ถ„ํ•ด ๊ฒฐ๊ณผ๋Š” ์ง‘๊ณ„๋ถ„ํ•ด๋ณด๋‹ค ๋” ํฐ ์ฐจ์ด๋ฅผ ๋ณด์˜€๋‹ค. ์„ฑ๋ณ„ ํ•˜์œ„์ง‘๋‹จ์˜ ์„ธ๋ถ€๋ถ„ํ•ด ๊ฒฐ๊ณผ๋ฅผ ๋ณด๋ฉด, ๊ฐ ์ง‘๋‹จ์˜ ๊ณ ์šฉ๋ฅ  ๋ณ€ํ™”์— ์˜ํ–ฅ์„ ๋ฏธ์นœ ์„ค๋ช…๋ณ€์ˆ˜์˜ ๋ณ€ํ™”๊ฐ€ ํ™•์—ฐํ•˜๊ฒŒ ๊ตฌ๋ถ„๋˜์—ˆ๋‹ค. ๋‚จ์„ฑ ๋…ธ์ธ์˜ ๊ณ ์šฉ๋ฅ  ๋ณ€ํ™”์—๋Š” ์ฃผ๋œ ์ข…์‚ฌ์ƒ ์ง€์œ„, ๋ฐฐ์šฐ์ž ์ง€์œ„, ๊ธฐํƒ€ ์‚ฌํšŒ๋ณด์žฅ๊ธ‰์—ฌ์˜ ์ˆ˜๊ธ‰ ๋ณ€ํ™”๊ฐ€ ์˜ํ–ฅ์„ ๋ฏธ์นœ ๋ฐ˜๋ฉด, ์—ฌ์„ฑ ๋…ธ์ธ์˜ ๊ณ ์šฉ๋ฅ  ๋ณ€ํ™”์—๋Š” ๊ต์œก์ˆ˜์ค€, ์ฃผ๋œ ์‚ฐ์—…, ๊ฒฝ๋ ฅ, ๊ฑฐ์ฃผ์ง€์—ญ, ๊ฑด๊ฐ• ์ˆ˜์ค€์˜ ๋ณ€ํ™”๊ฐ€ ์˜ํ–ฅ์„ ๋ฏธ์นœ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ํ•œํŽธ, ์ฃผ๋œ ์ข…์‚ฌ์ƒ ์ง€์œ„๋ณ„๋กœ ๊ตฌ๋ถ„ํ•œ ํ•˜์œ„์ง‘๋‹จ์—์„œ๋„ ์„ธ ์ง‘๋‹จ ๊ฐ„ ์™„์ „ํžˆ ๋‹ค๋ฅธ ์–‘์ƒ์ด ๋ฐœ๊ฒฌ๋˜์—ˆ๋‹ค. ์ž„๊ธˆ๋…ธ๋™ ์ง‘๋‹จ์€ ๊ณต์ ์—ฐ๊ธˆ์ด๋‚˜ ๋™๊ฑฐ, ์‚ฌ์ ์ด์ „๊ณผ ๊ฐ™์€ ๊ฒฝ์ œ์  ๋ถ€์–‘ ๊ด€๋ จ ์š”์ธ์˜ ๋ณ€ํ™”๊ฐ€ ๊ณ ์šฉ๋ฅ  ๋ณ€ํ™”์— ์ฃผ๋กœ ์˜ํ–ฅ์„ ๋ฏธ์นœ ๋ฐ˜๋ฉด, ์ž์˜์—… ์ง‘๋‹จ์—์„œ๋Š” ์ƒ์•  ์ดˆ๊ธฐ์™€ ํ›„๊ธฐ์˜ ๋ฏธ์‹œ์  ์š”์ธ๋“ค๊ณผ ๊ธฐํƒ€ ์‚ฌํšŒ๋ณด์žฅ๊ธ‰์—ฌ์˜ ๋ณ€ํ™”๋งŒ์ด ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜ํ•œ ๊ด€๊ณ„๋ฅผ ๋ณด์˜€๊ณ , ๊ธฐํƒ€ ๋…ธ๋™์ง€์œ„ ์ง‘๋‹จ์—์„œ๋Š” ์ƒ์•  ์ดˆ๊ธฐ์™€ ์ค‘๊ธฐ์˜ ๋ฏธ์‹œ์  ์š”์ธ ๋ณ€ํ™”๊ฐ€ ๊ณ ์šฉ๋ฅ  ๋ณ€ํ™”์— ์œ ์˜ํ•œ ์ˆ˜์ค€์˜ ์˜ํ–ฅ์„ ๋ฏธ์ณค๋‹ค. ์ด๋Ÿฌํ•œ ๊ฒฐ๊ณผ๋Š” ํ•˜์œ„์ง‘๋‹จ๋ณ„๋กœ ๋…ธ๋™๊ณต๊ธ‰์ด ๋‹ค๋ฅธ ์ง‘๋‹จ๊ณผ ๊ตฌ๋ณ„๋˜๋Š” ๊ณ ์œ ์˜ ๋ฐฉ์‹์œผ๋กœ ์ด๋ค„์ง€๊ณ  ์žˆ์„ ๊ฐ€๋Šฅ์„ฑ์„ ์‹œ์‚ฌํ•œ๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” ์ด๋ก ์  ์ธก๋ฉด์—์„œ, ์„œ๊ตฌ์˜ ๊ฒฝํ—˜์— ๊ธฐ์ดˆํ•˜์—ฌ ํ˜•์„ฑ๋œ ๋…ธ์ธ ๋…ธ๋™์˜ ๋ณ€ํ™”์— ๋Œ€ํ•œ ์ด๋ก ์„ ํ•œ๊ตญ ์‚ฌํšŒ์— ๋น„ํŒ์ ์œผ๋กœ ์ ์šฉํ•ด ๋ณธ๋‹ค๋Š” ์˜๋ฏธ๋ฅผ ์ง€๋‹Œ๋‹ค. ์„œ๊ตฌ์—์„œ ๋…ธ์ธ์˜ ๋…ธ๋™์ฐธ์—ฌ๊ฐ€ ๊ฐ์†Œํ•˜๋˜ ์‹œ๊ธฐ์™€ ๋น„์Šทํ•œ ๋ฐฉํ–ฅ์œผ๋กœ์˜ ์‚ฌํšŒ์  ๋ณ€ํ™”์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ , ํ•œ๊ตญ์—์„œ ๋…ธ์ธ์˜ ๋…ธ๋™์ฐธ์—ฌ๊ฐ€ ์ฆ๊ฐ€ํ•œ ์›์ธ์œผ๋กœ๋Š” ๊ณต์ ๋ถ€์–‘์˜ ํ™•๋Œ€๊ฐ€ ๋…ธ์ธ์˜ ๊ณ ์šฉ๋ฅ ์„ ๊ฐ์†Œ์‹œํ‚ค์ง€ ์•Š์€ ์ƒํ™ฉ์—์„œ ์‚ฌ์ ๋ถ€์–‘์˜ ๊ฐ์†Œ๊ฐ€ ๋…ธ์ธ์˜ ๊ณ ์šฉ๋ฅ ์„ ๋†’์ด๋Š” ํšจ๊ณผ๋ฅผ ๋ณด์˜€๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ๋˜ํ•œ, ๊ต์œก์ˆ˜์ค€๊ณผ ์—ฐ๋ น์˜ ๋ณ€ํ™”๊ฐ€ ๋ฏธ์นœ ์˜ํ–ฅ๋„ ์„œ๊ตฌ์™€๋Š” ์ฐจ์ด๋ฅผ ๋ณด์˜€๋‹ค. ์ •์ฑ…์  ์ธก๋ฉด์—์„œ ๋ณธ ์—ฐ๊ตฌ์˜ ๋ถ„์„๊ฒฐ๊ณผ๋Š” 2000๋…„๋Œ€ ์ค‘๋ฐ˜ ์ดํ›„ ๋…ธ์ธ ๊ณ ์šฉ๋ฅ  ์ฆ๊ฐ€์˜ ์ƒ๋‹น ๋ถ€๋ถ„์ด ๊ฒฝ์ œ์ ์ธ ํ•„์š”๋กœ ์ธํ•ด ๋…ธ๋™์‹œ์žฅ์— ์ฐธ์—ฌํ•˜๋Š” ๋…ธ์ธ์˜ ์ฆ๊ฐ€์— ๊ธฐ์ธํ•  ๊ฐ€๋Šฅ์„ฑ์„ ์‹œ์‚ฌํ•œ๋‹ค. ์‚ฌ์ ๋ถ€์–‘์˜ ๊ฐ์†Œ ์ถ”์ด๊ฐ€ ๋‹น๋ถ„๊ฐ„ ์ง€์†๋  ๊ฒƒ์œผ๋กœ ์˜ˆ์ƒ๋˜๋Š” ์ƒํ™ฉ์—์„œ, ์ด๋Ÿฌํ•œ ์ถ”์ด๋ฅผ ๋ฐ˜๋“ฑ์‹œํ‚ค๊ธฐ ์œ„ํ•ด์„œ๋Š” ์‚ฌ์ ๋ถ€์–‘๊ณผ ๋…ธ์ธ ๋…ธ๋™์ฐธ์—ฌ์˜ ํšก๋‹จ์  ๊ด€๊ณ„๋ฅผ ๋ณ€ํ™”์‹œํ‚ค๊ธฐ ์œ„ํ•œ ๋…ธ๋ ฅ์ด ํ•„์š”ํ•˜๋‹ค. ์ด๋ฅผ ์œ„ํ•ด์„œ๋Š” ๊ณต์ ์—ฐ๊ธˆ์„ ์ค‘์‹ฌ์œผ๋กœ ํ•œ ๊ณต์ ๋ถ€์–‘์„ ์ง€์†์ ์œผ๋กœ ํ™•๋Œ€ํ•˜๊ณ , ๊ฐœ์ธ์—ฐ๊ธˆ์ด๋‚˜ ์ž์‚ฐ ์ถ•์ ์„ ํ†ตํ•ด ๋‹ค์ธต์ ์ธ ๋…ธํ›„ ์ค€๋น„๋ฅผ ์œ ๋„ํ•˜๋ฉฐ, ๊ธฐ์ดˆ์—ฐ๊ธˆ์„ ํ†ตํ•ด์„œ ์ตœ์†Œํ•œ์˜ ์ƒํ™œ์ˆ˜์ค€์„ ๋ณด์žฅํ•˜๋Š” ๋“ฑ์˜ ๋ฐฉ๋ฒ•์„ ํ†ตํ•ด์„œ, ์ž๋…€์˜ ์‚ฌ์ ๋ถ€์–‘์— ์˜์กดํ•˜์—ฌ ์ƒ๊ณ„๋ฅผ ์œ ์ง€ํ•˜๋Š” ๋…ธ์ธ์˜ ๋น„์ค‘์„ ์ค„์—ฌ๋‚˜๊ฐ€๊ธฐ ์œ„ํ•œ ์ •์ฑ…์  ๋…ธ๋ ฅ์ด ์š”๊ตฌ๋œ๋‹ค. ๋˜ํ•œ, ์žฅ๊ธฐ์ ์ธ ๊ด€์ ์—์„œ๋Š” ๋…ธ๋™์‹œ์žฅ ๋‚ด์— ์—ฐ๋ น์œผ๋กœ ์ธํ•œ ์ฐจ๋ณ„์„ ํ•ด์†Œํ•˜๊ณ , ๋…ธ์ธ ์นœํ™”์ ์ธ ๊ณ ์šฉํ™˜๊ฒฝ์„ ์กฐ์„ฑํ•˜๋ฉฐ, ๋…ธ์ธ์ด ์ทจ์—…ํ•  ์ˆ˜ ์žˆ๋Š” ์ง์ข…์„ ๋‹ค์–‘ํ•œ ๋ฒ”์œ„๋กœ ํ™•๋Œ€ํ•˜๋Š” ๋“ฑ์˜ ๋…ธ๋ ฅ์ด ํ•„์š”ํ•˜๋‹ค.This study aims to investigate why the old people in Korea have participated in the labor market after the expansion of social security institutions for the aged since 2000. In Western countries, the change of the employment rate has connected to the social security system. Specifically, a decrease of agriculture and an expansion of the social security system for the old had led the declining of the employment rates of older people from the 20th century until the mid-1980s. As public pensions shrunk due to the welfare state reorganization after the 1990s, the old people's employment rate started to increase. However, in South Korea, many old people still participated in the labor market, even though Korea also implemented old-age pension system and had expanded the coverage of social security as well as also experienced industrial structure change from primary industry to secondary and tertiary industry. The employment rate of the old Koreans is more than twice the OECD average. Why do many old people in Korea have paid job? This study analyzed which factors, selected based on the rational choice theory of labor economics and the life-course perspective of sociology, have significant impacts on the employment of the old people. For analyzing the trend and determinants of the change of the employment of the old people aged 60 to 84 who covered by the National Pension Scheme (NPS), this study used Korean Longitudinal Study of Ageing (KLoSA). With considering various heterogeneities in older people, this study divided into subgroups based on their gender and main job career. Besides, this study adopted the reweighting decomposition method based on the non-parametric process which has widely used to decompose the difference between groups in labor economics. The main results are as follows. First, between 2008-2016, the employment rate of the increased by 8.9%p from 29.8% to 38.7%. The change of covariates distribution, which included in the analysis model, explained 3.6%p of the total change. That is, the increase in the employment rate of older adults since the mid-2000s is due to the social structural change rather than the change of the major influencing factors. Second, in the detailed decomposition results, both positive and negative effects on the change of the old employment rate were observed, and these factors largely offset each other's influence. For instances, Sex, and the change of career, spouse status, and health status have positive impacts on the employment rate of the old. On the other hand, the level of education, and the change in job position and industry reduced the employment rate of the old. The opposite effects among influencing factors show the importance of detailed decomposition analysis. Third, the extension of the public support for the aged showed different outcomes according to the scheme. The expansion of the earned-related pension encouraged the employment rate of the old by increasing the proportion of low-pension entrants with a higher probability of employment than non-pensioners while the expansion of the coverage of the Basic Pension Scheme (BPS) had no significant effect on the employment. Because the difference in the conditional employment rates between the beneficiary and non-beneficiary groups drastically decreased in 2016. Fourth, during the analysis period, the decline in private transfers is noticeable. This reduction in private transfers has a stronger positive impact on employment for the old than any other factor. Since the mid-2000s, many old people have participated in the labor market for economic reasons. Fifth, the effects of individual factors on the employment of the old differ according to gender and job status. For examples, changes in other social security benefit levels that affect the employment of the old male people have no significant impact on the female. Change in public pension affects employment of wage workers, but they are ineffective for self-employed. This result suggests that the labor supply in each subgroup may have a unique way of distinguishing it from other groups. This study showed the unique feature of the employment of old Korean people. The employment of old Korean people mainly depends on the decrease in private transfer not the increase of public support. Further, the old people work to earn a living since the mid-2000s. The problems are, most of them have faced job insecurity, low wages as well as poverty. For promoting the economic stability of the old, it is necessary to expand the public support for the aged and to guarantee the minimum standard of living through the basic pension. Besides, it is also useful to prepare for retirement through personal pension and asset accumulation. Moreover, it is essential to create an elderly-friendly working environment, such as eliminating age discrimination and expanding the elderly-friendly occupations.์ œ1์žฅ. ์„œ๋ก  1 ์ œ2์žฅ. ๋ฌธํ—Œ ๊ฒ€ํ†  7 ์ œ1์ ˆ. ์ด๋ก ์  ๋…ผ์˜ ๋ฐ ์„ ํ–‰์—ฐ๊ตฌ ๊ฒ€ํ†  7 1. ๋…ธ์ธ ๋…ธ๋™์ฐธ์—ฌ์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์š”์ธ 7 2. ๋…ธ์ธ ๋…ธ๋™์ฐธ์—ฌ์˜ ๋ณ€ํ™”๋ฅผ ๋ถ„์„ํ•œ ์„ ํ–‰์—ฐ๊ตฌ ๊ฒ€ํ†  17 ์ œ2์ ˆ. ๋…ธ์ธ ๋…ธ๋™์ฐธ์—ฌ ๋ฐ ์˜ํ–ฅ์š”์ธ์˜ ๋ณ€ํ™” ์ถ”์ด ๊ฒ€ํ†  22 1. ๊ณ ์šฉ๋ฅ  ๋ณ€ํ™” ์ถ”์ด 22 2. ์˜ํ–ฅ์š”์ธ๋ณ„ ๋ณ€ํ™” ์ถ”์ด 25 ์ œ3์ ˆ. ์—ฐ๊ตฌ๋ฌธ์ œ 42 ์ œ3์žฅ. ์—ฐ๊ตฌ ๋ฐฉ๋ฒ• 44 ์ œ1์ ˆ. ๋ถ„์„ ์ž๋ฃŒ 44 ์ œ2์ ˆ. ๋ณ€์ˆ˜์˜ ์ธก์ • 51 1. ๊ฒฐ๊ณผ๋ณ€์ˆ˜ 51 2. ์„ค๋ช…๋ณ€์ˆ˜ 52 ์ œ3์ ˆ. ๋ถ„์„๋ฐฉ๋ฒ• 60 1. ๋ถ„ํ•ด๋ฐฉ๋ฒ• ๊ด€๋ จ ์„ ํ–‰์—ฐ๊ตฌ ๊ฒ€ํ†  60 2. ์žฌ๊ฐ€์ค‘ ๋ถ„ํ•ด๋ฐฉ๋ฒ• 62 3. ์„ ํ–‰ ๋ถ„์„ ๋ฐ ํ•˜์œ„์ง‘๋‹จ์˜ ๊ตฌ๋ถ„ 65 4. ๊ฒฝ์ œ์  ๋ถ€์–‘ ์š”์ธ์˜ ์—ญ์ธ๊ณผ๊ด€๊ณ„ 66 ์ œ4์ ˆ. ์—ฐ๊ตฌ๋ชจํ˜• 74 ์ œ4์žฅ. ๋ถ„์„ ๊ฒฐ๊ณผ 77 ์ œ1์ ˆ. ๊ณ ์šฉ๋ฅ ๊ณผ ํŠน์„ฑ์˜ ๋ณ€ํ™” 77 1. ๊ณ ์šฉ๋ฅ  ๋ณ€ํ™” ์ถ”์ด 77 2. ์˜ํ–ฅ์š”์ธ๋ณ„ ๋ณ€ํ™” ์ถ”์ด 80 ์ œ2์ ˆ. ์„ค๋ช…๋ณ€์ˆ˜์™€ ๊ฒฐ๊ณผ๋ณ€์ˆ˜์˜ ํšก๋‹จ์  ๊ด€๊ณ„ ๋ถ„์„ 98 1. ์ „์ฒด ๋ถ„์„๋Œ€์ƒ์—์„œ์˜ ํšก๋‹จ์  ๊ด€๊ณ„ 99 2. ํ•˜์œ„์ง‘๋‹จ๋ณ„ ํšก๋‹จ์  ๊ด€๊ณ„ 108 ์ œ3์ ˆ. ์žฌ๊ฐ€์ค‘ ๋ถ„ํ•ด ๋ถ„์„๊ฒฐ๊ณผ 115 1. ์žฌ๊ฐ€์ค‘ ํ‘œ๋ณธ์˜ ํŠน์„ฑ 117 2. ์„ค๋ช…๋ณ€์ˆ˜ ๋ณ€ํ™”์™€ ๋…ธ์ธ ๊ณ ์šฉ๋ฅ  ๋ณ€ํ™”์˜ ๊ด€๊ณ„ ๋ถ„ํ•ด 120 3. ์ถ”๊ฐ€๋ถ„์„ 147 ์ œ5์žฅ. ๊ฒฐ๋ก  154 ์ œ1์ ˆ. ๋ถ„์„๊ฒฐ๊ณผ ์š”์•ฝ 154 ์ œ2์ ˆ. ์ด๋ก ์  ํ•จ์˜ 158 ์ œ3์ ˆ. ์ •์ฑ…์  ํ•จ์˜ 161 ์ œ4์ ˆ. ์—ฐ๊ตฌ์˜ ํ•œ๊ณ„ 165 ์ฐธ๊ณ  ๋ฌธํ—Œ 167 ๋ถ€ํ‘œ 182 Abstract 191Docto
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