[논문]Economic Growth and Employment in the Korean Agri-Food Industry: Examining the Buffering Effect and Sensitivity of Temporary Employment

Byung Min SOON

doi:10.13106/ajbe.2024.vol14.no2.19

[국내논문] Economic Growth and Employment in the Korean Agri-Food Industry: Examining the Buffering Effect and Sensitivity of Temporary Employment

Asian journal of business environment, v.14 no.2, 2024년, pp.19 - 30

Byung Min SOON (Department of Agricultural Economics, Chungnam National University)

Abstract ▼ AI-Helper

Purpose: This research article investigates the intricate relationship between economic growth and employment in the Korean agri-food industry. Research design, data and methodology: Drawing on Okun's law, which proposes a negative correlation between economic growth and unemployment, the study explores the applicability of this law to different sectors. By focusing on the agri-food industry, the study examines the impact of economic growth on both full-time and temporary employment. Results: The findings highlight the industry's role as a buffer, absorbing workers from other sectors, particularly manufacturing. Moreover, the study reveals that temporary employment is more sensitive to economic growth fluctuations compared to full-time employment. Conclusions: The research emphasizes the importance of implementing employment programs that support transitioning workers in the agri-food industry, facilitating knowledge and skill transfer to ensure sustained employment. Furthermore, it recommends government and company support for temporary employment during buffering periods to ensure safe job transitions. This study provides valuable evidence to understand the nuanced relationship between economic growth and employment in the Korean agri-food industry.

주제어

표/그림 (14)

그림 Figure 1: Share of GDP
표 Table 1: Number of Workers
표 Table 2: Selection of Model Variables
표 Table 3: Unit Root Test Results
표 Table 4: Statistic Description
표 Table 5: Statistic description
그림 Figure 2: Food manufacturing employment impulse response function results for real GDP
그림 Figure 3: Restaurant Employment Impact Response Function Results for Real GDP
그림 Figure 5: Restaurant Employment Response to GDP Shock by Industry
그림 Figure 4: Food manufacturing employment impulse response function results for GDP by industry
표 Table 6: Long-Run Equilibrium Relationship between the Economy and Employment
표 Table 7: Short-run Equilibrium Relationship between Economy and Employment in Food Manufacturing Industry
표 Table 8: Short Term Equilibrium Relationship between Economy by Industry and Employment in the Food Manufacturing Industry
그림 Figure 6: CUSUM test results

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제안 방법

The number of temporary and daily workers in the food manufacturing and restaurant industries is smaller than that of regular workers, and the proportion of temporary and daily workers in the restaurant industry is greater than that of the food manufacturing and manufacturing industries. The definition of temporary and daily workers differs depending on the working period, but in this study, it was recalculated to enable comparison with regular workers by representing the sum of temporary workers and daily workers. The number of temporary daily workers in the restaurant industry was almost the same as the number of regular workers.

대상 데이터

The data collected for analysis included the real GDP and the number of workers (total, regular, and daily temporary) in the manufacturing industry (excluding food manufacturing), food manufacturing, and restaurant industries. Real GDP data by economic activity was obtained from the Bank of Korea's economic statistics system.

이론/모형

The stability of the estimation coefficient was tested using the Cumulative Sum of Recursive Residuals(CUSUM) test. This test is used to check the stability of estimated coefficients by continuously changing the variables used, and has the advantage of not requiring prior representation of structural changes.
To analyze this relationship, Pesaran et al.'s cointegration method was used, which allows for analysis regardless of the unit root test results

성능/효과

The employment of temporary daily workers in the food manufacturing industry responds most sensitively to the food service industry, followed by the food manufacturing industry. Although it responds most sensitively to the restaurant business, the estimation coefficient was not statistically significant. Changes in the food manufacturing industry respond more sensitively to the labor of temporary daily workers in the food manufacturing industry than to fluctuations in the manufacturing industry.
An increase in food manufacturing GDP leads to an increase in the number of full-time workers in the food manufacturing industry but a decrease in the number of temporary daily workers. This is different from the overall GDP shock response, which showed that an increase in overall GDP reduced the number of full-time workers in the food manufacturing industry.
An increase in restaurant industry GDP reduces the number of regular workers in the restaurant industry while increasing the number of daily workers. When the restaurant business improves, the number of regular workers in the industry decreases, and this is interpreted as the booming business in the restaurant industry expanding daily employment rather than regular employment.
If the CUSUM falls within the critical line of the 5% significance level, the estimation coefficient of the corresponding function can be judged to be stable and therefore reliable. As a result of the CUSUM test, it was confirmed that all ARDL models are stable, indicating that the estimated ARDL models are reliable.
To complement the results of the ADF test, we conducted the Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test, which tests the null hypothesis that there is no unit root. As a result of the KPSS analysis, we found that the unit root exists in the number of workers in the food industry and real GDP by industry, as the null hypothesis that the unit root does not exist in all numbers of workers and real GDP was rejected.
(2013) pointed out and analyzed the widening gap between the economy and employment, where economic growth slowed down after the global financial crisis, while the number of employed people continued to increase. As a result of the analysis, it was found that there was a buffering effect through job creation in the service industry and the public sector as the economy worsened.
The number of workers by economic activity was collected from the Survey on Labor Status at Enterprises (1976-1997) and Current Status of Labor at Enterprises (1998-2019) provided by the Ministry of Employment and Labor. As the workplace labor status survey investigated the number of businesses with 5 or more employees, the number of workers after 1998 was collected as the number of workers corresponding to the number of businesses with 5 or more employees for consistency of data. The data for the GDP and the number of workers in the manufacturing and food manufacturing industries were collected from 1976 to 2019, while the data for the number of workers in the food service industry was collected from 1998 to 2019.
Based on the analysis, a positive shock to total real GDP results in a decrease in the number of regular and temporary daily workers in the food manufacturing industry. It appears that when the economy improves, the number of full-time workers in the food manufacturing industry decreases, and this trend continues in the aftermath of the shock.
We investigated the presence of unit roots in the number of workers and GDP by industry. Based on the results of the Augmented Dickey-Fuller (ADF) test, we could not reject the null hypothesis that both real GDP and the number of workers have a unit root, indicating that all time series variables are unstable. We further analyzed the presence or absence of unit roots for the first difference and found that there is no unit root in the following variables: total number of workers in the food industry, regular workers in the food industry, temporary daily workers in the food industry, all manufacturing workers, total GDP, GDP in the food industry, and GDP in the manufacturing industry.
Although it responds most sensitively to the restaurant business, the estimation coefficient was not statistically significant. Changes in the food manufacturing industry respond more sensitively to the labor of temporary daily workers in the food manufacturing industry than to fluctuations in the manufacturing industry. The boom in food manufacturing and the manufacturing industry reduces the number of temporary and daily workers in the food manufacturing industry.
Therefore, as this study utilized data up until 2019, it is crucial to analyze employment changes in the food industry considering the effects of COVID-19 by incorporating employment indicators up to 2021 for a more accurate assessment. Despite this limitation, this study remains valuable as it demonstrates the buffering role of the food industry in response to broader economic conditions.
Finally, an increase in the food service industry GDP reduces the number of regular and daily workers in the food manufacturing industry. This means that when the restaurant industry improves, the number of regular workers in the food manufacturing industry decreases, and this shock effect continues for a significant period of time.
For a positive shock in total real GDP, the number of full-time workers in the restaurant industry decreases while the number of temporary daily workers increases. Following the economic improvement, there is a decrease in the number of full-time workers for about two years, followed by an increase. This indicates a shift in employment towards other industries, similar to what was observed in the food manufacturing industry.
For a positive shock in total real GDP, the number of full-time workers in the restaurant industry decreases while the number of temporary daily workers increases. Following the economic improvement, there is a decrease in the number of full-time workers for about two years, followed by an increase.
This implies that the improvement in the overall economy of the country leads to a shift in employment away from the food manufacturing industry, with regular workers in this industry moving to other industries. Furthermore, the number of temporary daily workers in the food manufacturing industry experiences a significant decline due to the economic shock, but it recovers after 5 years. This indicates that when the economy improves, employment shifts to other industries, similar to regular workers in the food manufacturing industry, and the impact is more significant.
This test is used to check the stability of estimated coefficients by continuously changing the variables used, and has the advantage of not requiring prior representation of structural changes. If the CUSUM falls within the critical line of the 5% significance level, the estimation coefficient of the corresponding function can be judged to be stable and therefore reliable. As a result of the CUSUM test, it was confirmed that all ARDL models are stable, indicating that the estimated ARDL models are reliable.
The rotation of temporary and daily workers has a larger range of fluctuation than that of full-time workers, and while the circulation of temporary and daily workers precedes real GDP, the rotation of permanent workers lags behind real GDP. In addition, temporary and daily job rotation was time-reversible, while full-time job rotation was time-irreversible. Jung (2015) revealed that there is a cross-correlation between the business cycle and employment through the manufacturing job market.
Lee and Lee (2020) used the ARDL cointegration model to examine whether jobs respond sensitively to growth through employment elasticity. In particular, the sensitivity of jobs in each industry to growth was shown through short- and long-term employment elasticity by classifying them into service and manufacturing industries. Park et al.
In the study, it was observed that the number of regular and temporary daily workers in the food manufacturing industry responds more sensitively to other industries than to the same industry. Moreover, while the number of full-time workers in the restaurant industry responds equally to the economy of each industry, the number of temporary and daily workers responds more sensitively to the economy of the same industry.
Dividing the total number of workers into full-time workers and temporary daily workers shows different trends. Looking at the ratio of full-time workers to the total number of workers, the manufacturing and food manufacturing industries show a high rate, while the restaurant industry has a low rate. The number of full-time workers in the food manufacturing industry increased in the 2000s, and the number of full-time workers in the restaurant industry accounted for half of the total number of workers in the restaurant industry.
53 million in 2019. Looking at the share of the total number of workers, the share of the food manufacturing industry continued to decline, while the portion of the restaurant industry maintained 10% and recently rose to 11%.
Specifically, we explore whether the food industry, encompassing food manufacturing and the restaurant sector, functions as a buffer, absorbing employment shocks during economic downturns. Moreover, we analyze whether temporary and daily workers in the food industry display greater sensitivity to economic conditions compared to regular workers. To achieve this, we employ the impulse response function and the autoregressive distributed lag (ARDL) model as analytical tools.
In the study, it was observed that the number of regular and temporary daily workers in the food manufacturing industry responds more sensitively to other industries than to the same industry. Moreover, while the number of full-time workers in the restaurant industry responds equally to the economy of each industry, the number of temporary and daily workers responds more sensitively to the economy of the same industry. These findings suggest that the food manufacturing industry absorbs employment by responding sensitively to job instability caused by economic recession in other industries.
Note: The number of manufacturing workers is the number of workers excluding the number of food manufacturing workers. Source: Ministry of Employment and Labor.
On the other hand, an increase in manufacturing GDP results in a slight decrease in the number of full-time workers in the food manufacturing industry and a relatively larger decrease in the number of daily workers. This implies that when the manufacturing economy deteriorates, the number of workers in the food manufacturing industry may increase, which can act as a buffering effect to some extent during a manufacturing industry recession.
In this study, an error correction model (ECM) using the ARDL model is constructed. The ECM applied to the ARDL model differs from the existing ECM in that it uses time lag for the error correction term instead of the difference between the actual and estimated values.
The cointegration analysis did not yield significant results, except for daily workers with temporary employment in the restaurant industry in the expected long-run equilibrium relationship between industry-wise GDP and the number of workers. Since this study used annual data, it is suggested that examining the response of the number of workers per year to GDP through the short-term equilibrium relationship, instead of the long-run equilibrium relationship between GDP and the number of workers, would be more appropriate.
If the economy deteriorates, the food industry can create a buffer that absorbs employment. The decline in real GDP increased the number of regular and temporary daily workers in the food manufacturing industry, and the number of regular workers in the restaurant industry also increased. It can be seen that the employment buffering effect of the food industry alleviated the slump in employment in other industries, such as the service industry and the public sector, and provided jobs.
This is different from the overall GDP shock response, which showed that an increase in overall GDP reduced the number of full-time workers in the food manufacturing industry. The increase in regular workers in the food manufacturing industry occurs only when the food manufacturing economy improves, while the number of daily workers decreases in the short term.
The number of temporary and daily workers in the food manufacturing and restaurant industries is smaller than that of regular workers, and the proportion of temporary and daily workers in the restaurant industry is greater than that of the food manufacturing and manufacturing industries. The definition of temporary and daily workers differs depending on the working period, but in this study, it was recalculated to enable comparison with regular workers by representing the sum of temporary workers and daily workers.
The ratio of full-time workers to total workers was in the order of manufacturing, restaurant, and food manufacturing, but the ratio of temporary and daily workers was in the order of restaurant, manufacturing, and food manufacturing. In other words, the food service industry has more temporary daily workers than regular workers, and the share of temporary daily workers in the manufacturing and food manufacturing industries is on the decline.
The real GDP of the food industry has shown a steady increase, but its share in the total GDP has been declining. The food manufacturing industry and the restaurant industry both have a relatively low share of real GDP, with the restaurant industry having a larger share than the food manufacturing industry.
(2001). The results indicated that there is a long-term equilibrium relationship between the number of full-time workers in the restaurant industry and most of the economic and employment variables, except for the economy.
Hwang (2009) conducted an empirical analysis of the cyclical behavior of full-time and temporary daily workers in Korea. The rotation of temporary and daily workers has a larger range of fluctuation than that of full-time workers, and while the circulation of temporary and daily workers precedes real GDP, the rotation of permanent workers lags behind real GDP. In addition, temporary and daily job rotation was time-reversible, while full-time job rotation was time-irreversible.
Jung (2015) revealed that there is a cross-correlation between the business cycle and employment through the manufacturing job market. The variability of manufacturing employment by occupational status was found to be greater for temporary and daily workers than for full-time workers, suggesting that this heterogeneity can respond more sensitively depending on personal characteristics. These findings suggest that we need to look at the relationship between employment and the economy on a case-by-case basis.
Therefore, labor policies for the food industry that can resolve the instability of the food industry labor market through temporary employment absorption by the food industry's buffering action are needed.
Furthermore, the number of temporary daily workers in the food manufacturing industry experiences a significant decline due to the economic shock, but it recovers after 5 years. This indicates that when the economy improves, employment shifts to other industries, similar to regular workers in the food manufacturing industry, and the impact is more significant. However, over time, the number of temporary daily workers in the food manufacturing industry seems to increase again.
An increase in food manufacturing GDP leads to an increase in the number of full-time workers in the food manufacturing industry but a decrease in the number of temporary daily workers. This is different from the overall GDP shock response, which showed that an increase in overall GDP reduced the number of full-time workers in the food manufacturing industry. The increase in regular workers in the food manufacturing industry occurs only when the food manufacturing economy improves, while the number of daily workers decreases in the short term.
The stability of the estimation coefficient was tested using the Cumulative Sum of Recursive Residuals(CUSUM) test. This test is used to check the stability of estimated coefficients by continuously changing the variables used, and has the advantage of not requiring prior representation of structural changes. If the CUSUM falls within the critical line of the 5% significance level, the estimation coefficient of the corresponding function can be judged to be stable and therefore reliable.
This study is subject to certain limitations that should be acknowledged. Unlike previous crises such as foreign exchange or financial crises, the COVID-19 pandemic had a minimal direct impact on the manufacturing industry. However, due to the implementation of measures such as social distancing, the contraction of the real economy led to a significant shock in the demand for the food industry.
It has a negative relationship with the food manufacturing industry and a positive relationship with the manufacturing industry. When the food manufacturing industry improves, the number of regular workers in the same industry decreases, and when the manufacturing industry improves, the number of regular workers in the food manufacturing industry increases. This suggests that workers in the food manufacturing industry tend to move to the manufacturing industry when the food manufacturing and manufacturing industries improve, and they respond more sensitively than workers in the restaurant industry.

후속연구

These studies have consistently revealed that the service industry exhibits a higher degree of sensitivity to economic fluctuations compared to the manufacturing industry. Consequently, it becomes imperative to propose a comprehensive plan aimed at mitigating employment market imbalances within the food industry. Such a plan should center around analyzing the intricate relationship between the economy and employment, with a particular emphasis on the food industry encompassing food manufacturing and restaurant sectors .
In order to alleviate the cycle in which the food industry absorbs employment due to economic downturn and then loses employment when the economy improves, a stable employment guarantee system, such as guaranteeing full-time jobs and improving job conversion programs, is needed. In addition, through government support for employment in the food industry, employment programs centered on the food industry, such as support for small businesses to maintain employment and free employment agencies, are needed so that the buffering effect of employment can work well in times of economic downturn.
Overall, this research seeks to shed light on the dynamic interplay between the food industry's economy and employment, with a particular focus on the buffering role of the industry and the differential responsiveness of workers to economic fluctuations.
The cointegration analysis did not yield significant results, except for daily workers with temporary employment in the restaurant industry in the expected long-run equilibrium relationship between industry-wise GDP and the number of workers. Since this study used annual data, it is suggested that examining the response of the number of workers per year to GDP through the short-term equilibrium relationship, instead of the long-run equilibrium relationship between GDP and the number of workers, would be more appropriate.
The Ministry of Agriculture, Food and Rural Affairs (2020) has also highlighted the substantial shock experienced by the food service industry during the pandemic, while the food manufacturing industry faced a lower level of impact. Therefore, as this study utilized data up until 2019, it is crucial to analyze employment changes in the food industry considering the effects of COVID-19 by incorporating employment indicators up to 2021 for a more accurate assessment. Despite this limitation, this study remains valuable as it demonstrates the buffering role of the food industry in response to broader economic conditions.
Therefore, it becomes crucial to examine whether the food industry can be categorized as a peripheral industry within the core-periphery strategy framework and analyze the correlation between the food industry's economy and employment dynamics.
Both the food manufacturing and restaurant industries, which represent the food industry, respond sensitively to domestic economic conditions as well as other industries. Therefore, it is necessary to continuously monitor whether a buffering effect in the food industry can occur due to economic deterioration in not only the food industry but also other industries. A plan for rapid employment absorption needs to be devised when a buffer effect occurs, as the transition to temporary and daily workers can occur quickly.
This study aims to empirically investigate the correlation between the food industry's economy and employment dynamics
However, it is also important to examine how employment changes in response to changes in GDP by industry. To do this, we need to analyze the shock response of each industry (including the number of regular and temporary daily workers) to changes in real GDP by industry (specifically, food manufacturing, manufacturing, and restaurant).

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