Earticle

초록

영어

Recently, big data has become an important foundation in the flow of the Fourth Industrial Revolution. In particular, in the financial sector, such as credit card companies, financial companies, and FinTech, high quality data are accumulated, so it is highly utilized, and job creation with related industries is expected. Against this background, the study aims to analyze how the readjustment of laws and systems for the construction and utilization of big data in the financial sector affects employment. The analysis uses annual big data-related costs and financial information for the financial insurance sector from 2013 to 2018. The Pooled OLS estimates of the employment function, which consists of investments related to financial big data, utilization of big data and sales, shows that investment and sales related to financial big data increase the number of employees. In addition, it is analyzed that companies that utilize financial big data have higher employment effects. As a result of including interaction terms after classifying each enterprise into three sectors: financial, insurance and pension industries, and financial and insurance-related services, the employment effects of big data-related investments are significant in financial and insurance-related services. Based on the results of estimating the expected employment growth rate of companies through CVM(Contingent Valuation Method) and DBDC(Double-Bound Dichotomous Choice) using the survey data, the more cumulative the investment involved, the more willing the investment in big data, and the more employees expected to increase the number of employees when big data use is activated, the more positive the employment growth is caused by the revision of the Financial Big Data Act. Using this estimate results, the average expected employment growth rate of a representative company is identified as 2.09%. Also, through CVM(Contingent Valuation Method) and DBDC(Double-Bound Dichotomous Choice), the growth rate of employment under the scenario of financial big data investments and sales growth is 2.09-3.23%. If this is applied to 769,000 employees as of 2018, the increase in employment can be seen to be 16,072~24,839 employees. There are limits to the fact that this study estimated employment effects through counterfactual approaches before the implementation of the Big Data Act. Since the employment effects of real big data-related companies may be different from the results, it is necessary to estimate the employment effects by utilizing more samples and variables. Currently, the employment effects may not be accurate due to the influence of COVID-19, but it is expected that more precise analysis will be possible after the data accumulates in the next year or two. In addition, a survey of 130 companies in the financial sector is not enough to be used as a population. If we analyze the employment effects of the revision of the Big Data Act on more companies in the next year or two, we think we can produce practical effects on the revision. Nevertheless, we think it is meaningful that this study has verified the preliminary feasibility of the amendment by analyzing its effects in advance before the actual bill is passed.

목차

Ⅰ. 서론
Ⅱ. 분석방법과 자료
Ⅲ. 추정결과
1. 금융산업의 고용함수
2. 진술선호를 이용한 고용효과 추정
3. 시나리오 하에서의 고용효과 계산
Ⅳ. 결론
1. 연구 결과 및 이론적 시사점
2. 생산성 함의에 대한 실무적 시사점
3. 향후 연구과제
참고문헌
Abstract

키워드

저자

• 이해춘 [ Hae-Chun Rhee | 성균관대학교 초빙교수 ] 주저자
• 김남현 [ Nam-Hyun Kim | 예금보험공사 예금보험연구센터 부연구위원 ] 교신저자

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  생산성연구: 국제융합학술지 [Productivity Research: An International Interdisciplinary Journal]

• 간기

  격월간

• pISSN

  1225-3553

• 수록기간

  1987~2025

• 등재여부

  KCI 등재

• 십진분류

  KDC 325 DDC 330