금융산업 빅데이터 투자의 일자리 창출 효과 (The Effects of Financial Industries’ Investments on Labor Supply)

서지정보

· 발행기관 : 한국생산성학회
· 수록지 정보 : 생산성연구: 국제융합학술지 / 34권 / 4호 / 149 ~ 175페이지
· 저자명 : 이해춘, 김남현

초록

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.

영어초록

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.

참고자료

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