How will demographic transformation and 4.0 industry innovation affect the labor market?

Nội dung text: How will demographic transformation and 4.0 industry innovation affect the labor market?

1. HOW WILL DEMOGRAPHIC TRANSFORMATION AND 4.0 INDUSTRY INNOVATION AFFECT THE LABOR MARKET? Phd. Ngo Quynh An annq@neu.edu.vn Faculty of Human Resource Economics and Management, National Economics University, Hanoi, Vietnam Abstract We look at the demographics, and automation, the major forces that will shape the 2020s-2040s labor market in Vietnam. The article explores the impact of aging populations and the end of plentiful labor. The baby boomer generation powered a long but temporary surge in labor force growth. Then this group is moving into retirement, and labor force growth is slowing. The occurrence of aging of the population carries a lot of negative consequences not only in the form of changes in the increase in the age structure of the population of people belonging to the oldest age categories. Since it influences aging of resources found in the labor market and also is not indifferent to pension schemes or educational system. The article also sees how will automation affect employment, labor skills and wages in the labor market. Keywords: ageing population, aging labor force baby boomer generation, 4.0 industry 1. Introduction The Vietnam workforce is aging rapidly. It is forecasted Vietnam labor force growth (population in the age of 15-64), will slow to 0.01% per year in the 2009-2049 (see Table 1). That major demographic shift is bringing an end to the abundance of labor since the 2010s. Thanks to longer and healthier lives, many people are working well into their 60s and beyond, but the trend toward later retirement is not likely to offset the negative effects of aging populations. As the total size of the labor force stagnates in the labor market, the momentum for economic growth should be affected. If it does, governments will face major challenges, including surging healthcare costs, old-age pensions, and high debt levels. On the advantage side, the wages of workers in the economy should benefit from the simple economics of greater demand and lesser supply. But demographics is not the only force in motion. 555
2. The phase of 4.0 industry innovation has begun, and it will accelerate in the years ahead. Faced with a rising scarcity of labor, companies, and investors are likely to draw increasingly on automation data exchange in technologies, which, in turn, would boost productivity. But to grow, economies need sufficient demand to match rising output. It is also showed automation 4.0 industry trends are likely to push output potential far ahead of demand potential. The rapid spread of automation and cyber-physical systems, the Internet of things, cloud computing and cognitive computing may eliminate as many as 47% of current jobs in the US within the over next 20 years, according to a recent Oxford study. These include blue and white collar jobs. So far, the loss has been restricted to the blue collar variety, particularly in manufacturing, and depress wage growth for many more workers. More than half of workers in five Southeast Asian countries are at high risk of losing their jobs to automation in the next two decades, an International Labour Organization study found, with those in the garments industry particularly vulnerable. Of the 9 million people working in the region’s textiles, clothing and footwear industry, 64 percent of Indonesian workers are at high risk of losing their jobs to automation, 86 percent in Vietnam, and 88 percent in Cambodia. Automation can help close a GDP growth gap resulting from declining growth rates of working-age populations. The benefits of automation will likely flow to new employment-primarily highly compensated, highly skilled one-as well as to the owners of capital. The growing scarcity of highly skilled workers may push their incomes even higher relative to lesser-skilled workers. As a result, automation has the potential to significantly increase income inequality. Adaptability of automation and aging trend requires agility and speed, as well as the strength to absorb sudden shocks and resilience to risk missteps and unforeseeable challenges along the way. The Leaders that should think now about shifting resources to build resilience will be better able to navigate off the coming transformation and face with increased volatility as the forces of demographics, and automation. This paper applies the method of literature review and labor economic theory analysis to assess the impact of the demographic transition and industrial revolution 4.0 on the labor market, particularly in Vietnam. The analysis of this paper relies on nationally representative secondary data, such as data from the Population and Housing Census and the Vietnam Household Living Standards Survey, as well as from related documents. Some key implications of results follow: Highly skilled labor will grow increasingly scarce: The pace at which displaced workers retrain and migrate toward higher-skilled jobs will likely be too 556
3. slow to alleviate shortages. The challenge for companies will be attracting, growing and retaining highly skilled talent and maximizing worker's productivity by rethinking how their businesses are structured. Baby boomer spending growth: Compared with previous generations, baby boomers will extend the period of high-income earning and spending by about 10 years (retire later). The sheer size of this generation means there are considerable market opportunities for most goods and services, including big-ticket items such as housing and transportation. But growth based on this demographic shift just more concentrated among the top of wealthy households. Intergenerational conflicts will potentially rise, drawing in businesses: As retirees and the working-age population competing for resources, businesses may become indirectly involved. The manager may feel hard as they grapple with existing pension obligations, the scarcity of highly skilled workers, social pressure to address job losses and declining incomes among mid- to low-skilled workers. The supply, demand, and costs of human labor affected which activities will be automated. As the nature of work changes with automation, millions of people may need to switch occupations and acquire new skills, they need to study longer, then labor supply reduces temporary. The rapid advance of information and communication technology may imply a drastic change in the workplace, business and job structure, which may alter the level impact of the aging workforce. What follows in the body of this paper is the extended narrative of the collision of demographics and automation that affect the supply-demand and wage in the labor market. 2. Method Realist review is used. This method are theory-driven interpretative reviews developed to inform, enhance, or supplement conventional systematic reviews by making sense of heterogeneous evidence about the complex effect in diverse contexts in a way that informs policy decision-making (Greenhalgh, Wong, Westhorp, & Pawson, 2011). The paper uses the theory of labor supply and demand to predict the possible effects of automation and demographic shift on the labor market. It is also overview of the actual situation of how this process takes place in developed countries and analyze trends in Vietnam using the source of national survey data. 557
4. 3. Results 3.1 The change in labor supply  For demographic driver The temporary trends converged to create the fastest labor force growth in 1980s-2000s are the coming of age of the baby boomer generation, and women's entry into the workforce as the lower fertility level. But shifting demographics, combined with technological and social changes, point to dramatically different labor market conditions in the coming decade. Vietnam today has abundant labor for granted. The economic growth has benefited from a rapidly expanding country labor pools—that will be ending in around 2040. Vietnam labor force growth slows to 0.01% per year in the 1990-2049, and reduce quickly since 2010 (Table 1, figure 1). Table 1: Projected population by age with constant fertility variant and by replacement fertility 0-14 15-64 65+ Rate in Rate in Rate in Annual Annual Annual Year P0-14 total P15-64 total P65+ total Increase Increase Increase (1000) population (1000) population (1000) population rate (%) rate (%) rate (%) DS (%) DS (%) DS (%) 2009 20,993 24.5 59,339 69.1 5,515 6.4 2014 21,613 23.8 0.15 63,762 70.1 0.36 5,603 6.2 0.079 2019 23,219 24.1 0.36 66,782 69.2 0.23 6,516 6.8 0.753 2024 24,439 24.0 0.26 68,946 67.7 0.16 8,452 8.3 1.293 2029 24,223 22.8 -0.04 71,206 66.9 0.16 10,949 10.3 1.287 2034 23,131 21.0 -0.23 73,530 66.8 0.16 13,494 12.2 1.041 2039 22,177 19.5 -0.21 75,310 66.3 0.12 16,112 14.2 0.884 2044 22,132 18.9 -0.01 76,232 65.2 0.06 18,614 15.9 0.720 2049 22,818 19.0 0.15 75.874 63,1 -0.02 21,545 17.9 0.730 Source: GSO 2011 Labor force growth, as well as labor productivity growth, form the two drivers that determine overall economic output growth. Labor productivity growth without the kicker from labor force growth can create an overall macroeconomic climate of stagnation. 558
5. Figuer 1: Annual Increase rate of Vietnam Labor force 1990-2018. Source: Derived using data from International Labour Organization, ILOSTAT database and World Bank population estimates. Labor data retrieved in September 2018. * Labor force comprises people ages 15 and older who supply labor for the production of goods and services during a specified period. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave  For 4.0 industry innovation Labor Economics theories have been interested in the effect of technological change on the labor supply in market by: (i) the retirement options of older workers (A. P. Bartel and N. Sicherman, 1993), (ii) the skill gaining of young workers (A. P. Bartel and N. Sicherman, 1998) (A. P. Bartel and N. Sicherman, 1999). (i) Technological Change and Retirement option Technological change can affect retirement decisions in two ways: 1) through the direct effect of technological change on the level of on-the-job training, and 2) through the indirect effect of technological change on the depreciation rate of the stock of human capital. Economic theory does not provide a unified prediction with regard to the effect of technological change on the optimal level of on-the-job training. This will depend 559
6. on the effects of technological change on the marginal return to training, and the complementarity and/or substitutability between schooling and training. Given a positive correlation between technological change and on-the-job training, human capital theory predicts that, ceteris paribus, workers in industries with higher rates of technological change will retire later (Y. Ben-Porath, 1967). However, in industries that have higher rates of technological change, human capital will depreciate at a faster rate, and higher rates of depreciation will lead to a lower optimal level of investment, causing earlier retirement. However, unexpected changes in the industry rate of technological change will produce an increase in the depreciation rate of the human capital stock, leading to a revised rate of investment in human capital. If older workers are unlikely to revise their planned investments in human capital, it can be shown that the higher depreciation rate will induce earlier retirement. And if the older worker decides earlier retirement, their labor supply will reduce and vice versa. (ii) Technological Change affect the Skill gaining of Young labor Technological change will influence the motivations of both employers and workers in training investments. One opinion is that technological change makes previously formal education and acquired skills obsolete. As a result, both workers and firms will find it optimal to invest in on-the-job training in order to match the specific requirements of each flow of innovation. The other view is that general education enables workers to adjust to and benefit from the technological change. Workers who expect to take a job with higher rates of technological change should invest more in formal schooling and rely less on acquiring specific training on the job. Hence it is impossible to predict a priori the sign of the relationship between technological change and investments in on the job training. Technological change is also likely to affect the relationship between education and training. In general, more educated workers receive more training, because human capital is input in the production of new human capital. The more educated worker will invest more in both schooling and training. Thus, if young workers are displaced by the automation of data collection and processing and predictable physical activities, they could move into lower paid occupations, increasing supply in those types of work. On the other way, they may invest more in formal education, they might take time to retrain into other high-skill positions, delaying their re- entry into the labor force, and temporarily reducing labor supply. 3.2. Change in labor demand Population aging has attracted impact on labor demand, mostly with respect to their high demand on health care and social assistance workers. Beside, technological advancement in information and communication technology may change 560
7. consumption technology of the general public, especially those who have been handicapped by physical problems as well as the old people. Thus information and communication technology may bring about a sizable change in the economy’s demand structure and therefore demand labor. For technology progress, the impact of automation investment on labor demand could be either positive or negative. According to neoclassical theory, investment would increase labor demand due to the complement of labor and capital. However, many economists tend to view automation investment as a substitute for labor rather than a complement, worrying that investment would decrease the demand for labor by increasing productivity of labor. These implications from the Solow growth model, where an increase in technological investment increases labor productivity. Ceteris paribus, firms would need fewer employees and would be incentivized to cut jobs. Nevertheless, with spillover effects on other industries, incomes, or aggregate demand (and thus output), the impact of automation investment is difficult to assess per traditional theory. A higher investment would increase production, leading to an increase in income and increase the demand for goods and services, overall employing more labor to produce these goods and services. Therefore, a decrease in employment resulting from increases in labor productivity would be offset by an increase in labor demanded to increase total output. Even if automation investment and labor were substitutes, there could be spillover effects (i.e. increases in demand for labor in related industries, impacts of increased income or aggregate demand, etc.) which could increase employment overall. In sum, we looked at different impacts that could significantly increase the demand for labor even of those activities that might be automated. They include the following: - Rising incomes or rising prosperity due to the increase in consumer. - Aging population: This drives the need for additional labor in healthcare. - The need to develop and deploy technologies. Digitization, automation, robotics, and artificial intelligence-that require highly skilled labor. - Investment in infrastructures, such as buildings, and road All that construction could drive the additional need for human demand. - Decreasing amounts of unpaid labor in the workforce due to automation. This may be domestic work that’s often done by women, that may be reduced housework due to the use of more high technological tools. More and more women could enter the labor market. 561
8. The negative relationship between technology investment and labor has been documented by different parts of the literature. Robots and automated systems have negatively impacted several occupations, almost entirely eliminating elevator operators, highway toll collectors, parking attendants, and others (Quereshi and Syed 2014). They could find themselves moving into lower paid occupations, or they might take time to retrain into other high-skill positions or become unemployment due to the lack of appropriate skill. Automation with advanced manufacturing refers to the use of innovative technology to improve processes and products. Human ingenuity and creativity become more important in this future production. One of the most important drivers of future readiness production is Human Capital. This driver assesses (WEF 2018b): Figure 2: WEF Readiness for Future of Production Report 2018, p 250 • The ability to respond to shifts in the labor market (supply and demand) that are triggered by the Fourth Industrial Revolution • Current labor force capabilities to adapt and use emerging technologies in production systems • The ability to cultivate the right skills and talent in the future workforce through education outcomes, talent attraction, and retention, and inclusion. 562
9. Figure 3: Assessment of ASEAN Readiness for Industry 4.0, 2018 However, the quality of Vietnamese human is low. The resources-human capital index of Vietnam is 62.19, rank 64 out of 130 countries in 2017, in which the indicator that indicates breadth and depth of specialized skills use at work is lowest (score 41,8; rank 120/130 countries) (WEFa). Moreover, Vietnamese laborers lack the necessary skills in foreign languages, information technology, teamwork, and communication, besides responsibility. According to the Readiness for the Future of Production Assessment 2018 report, Vietnam current level of readiness for the future of production, as well as corresponding opportunities and challenges is least and is classified as Nascent countries, that has the limited current base, at risk for the future or the group least ready for the future of production (Figure 2 and 3, table 2). Table 2: Readiness for the Future of Production Assessment 2018, Human Capital Driver Index Component Rank /100 Value Driver: Human Capital 0-10 (best) 70 4.5 Current Labor Force 0-10 (best) 70 5.4 3.01 Manufacturing employment % working population 28 14.4 3.02 Knowledge-intensive employment % working pop. 81 10.8 3.03 Female participation in labor force ratio 57 0.7 3.04 Mean years of schooling (Years ) 74 8.0 3.05 Availability of scientists and engineers 1-7 (best) 70 3.8 563
10. Index Component Rank /100 Value 3.06 Digital skills among population 1-7 (best) 66 4.0 Future Labor Force 0-10 (best) 62 3.5 3.07 Migration migrants/100,000 pop. 63 2.2 3.08 Country capacity to attract and retain talent 1-7 (best) 44 3.5 3.09 Quality of universities Count 75 0.0 3.10 Quality of math and science education 1-7 (best) 68 3.7 3.11 Quality of vocational training 1-7 (best) 80 3.6 3.12 School life expectancy Years 79 12.6 3.13 Pupil-to-teacher ratio in primary education Ratio 62 19.2 3.14 Critical thinking in teaching 1-7 (best) 63 3.2 3.15 Active labor policies 1-7 (best) 50 3.4 3.16 On-the-job training 1-7 (best) 74 3.8 3.17 Hiring and firing practices 1-7 (best) 39 4.0 Source: from World Economic Forum, 2018b, Readiness for the Future of Production Report 2018, p 250-251. These barriers of the Readiness for the Future of Production are mainly in the area of education and professional training as well as high education. If not quickly remove barriers, around 5 million workers in Vietnam are at high risk of losing their jobs by 2020 because of the boom in artificial intelligence, which may replace laborers with robots, according to a recent study by the International Labor Organization (ILO) . Technology change also has an effect on the interindustry wage structure, may lead to an inequality in labor income. There is evidence that skill-biased technological change is responsible for the dramatic increase in the earnings of more educated workers relative to less educated workers that took place. 4. Discussion and Conclusion The net result of these effect trends on supply and demand of labor include, older workers delaying retirement, younger workers delaying entry into the workforce and baby boom generation moving into retirement. It is projected from 2019, the population of those age 65 and older will grow faster than the working-age population in Vietnam. 564
11. The mid- and low-skilled workers that form the vast majority of the Vietnamese workforce face at least a several years of disruption as new automation technologies begin to transform many industries and the nature of work. Losing jobs and low income will make income inequality in the labor market increase. The analysis suggests the solution which government, companies and individuals could act to over the challenges: • Investing in human capital, particularly early childhood education, to develop high-order cognitive and socio-behavioral skills in addition to foundational skills for automation adaption. • Enhancing social protection. A solid guaranteed social minimum and strengthened social insurance, complemented by reforms in labor market rules. • Creating revenue for public financing of human capital development and social protection (through tax policies). Governments can optimize their taxation policy and improve tax administration to increase revenue without resorting to tax rate increases. • The immediate challenge for business over the next few years will be attracting and retaining workers, especially highly skilled ones who already are scarce • There is a complementary relationship between automation investment and growth in labor opportunities, rather than a substitution effect of workers moving from automation-intensive industries to non-automation intensive sectors. Thus, the public should embrace information and communication technology investment as a way in which to spur growth and expand labor market opportunities 5. References 1. A. P. Bartel and N. Sicherman, "Technological Change and Retirement Decisions of Older Workers," Journal of Labor Economics ,11 (January 1993), pp. 162-83. 2. A. P. Bartel and N. Sicherman, "Technological Change and the Skill Acquisition of Young Workers," Journal of Labor Economics, 16, (October 1998), pp. 718-55 3. A. P. Bartel and N. Sicherman, "Technological Change and Wages: An Interindustry Analysis," Journal of Political Economy, 107 (April 1999), pp. 285-325 4. Y. Ben-Porath, "The Production of Human Capital and the Life Cycle of Earnings," Journal of Political Economy, 75 (August 1967), pp. 352-65 5. Carl Benedikt Frey†and Michael A. Osborne, 2013, THE FUTURE OF EMPLOYMENT: HOW SUSCEPTIBLE ARE JOBS TO 565
12. COMPUTERISATION, Oxford University Engineering Sciences Department and the Oxford Martin Programme on the Impacts of Future Technology for hosting the “Machines and Employment” Workshop. 6. Greenhalgh T., Wong G., Westhorp G., Pawson R. Protocol–realist and meta- narrative evidence synthesis: evolving standards (RAMESES). BMC Medical Research Methodology. 2011;11:115. 7. ILO, 2016, The future of jobs at risk of automation in ASEAN. 8. Qureshi, Mohammed Owais, and Rumaiya Sajjad Syed. "The impact of robotics on employment and motivation of employees in the service sector, with special reference to health care." Safety and health at work 5.4 (2014): 198-202 9. World Economic Forum, 2018a, The Global Human Capital Report 2017. 10. World Economic Forum, 2018b, Readiness for the Future of Production Report 2018. 566