自動化對宏都拉斯製造業的就業影響 - 政大學術集成

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(1)國立政治大學商學院國際經營管理英語碩士學位學程 International MBA Program College of Commerce National Chengchi University. 政治大. 學. ‧ 國. 立碩士論文. Master’s Thesis. ‧. sit. y. Nat. 自動化對宏都拉斯製造業的就業影響 er. io. Automation and its Effect on Employment in Honduras. a. n. v. l C Manufacturing Industry ni. hengchi U. Student: Jose Alfredo Sosa Advisor: Jack Wu. 中華民國一〇八年六月 June 2019 DOI:10.6814/THE.NCCU.IMBA.012.2019.F08.

(2) 自動化對宏都拉斯製造業的就業影響 Automation and its Effect on Employment in Honduras Manufacturing Industry. 研究生：蘇嘉勛. Student: Jose Alfredo Sosa. 指導教授：吳文傑. 立. Advisor: Jack Wu. 政治大國立政治大學. ‧ 國. 學. 商學院國際經營管理英語碩士學位學程碩士論文. ‧. Nat. sit. y. A Thesis. er. io. Submitted to International MBA Program. n. National Chengchi Universityv a. i l C n in partial fulfillment U h e n gofctheh iRequirements for the degree of Master in Business Administration. 中華民國一〇八年六月 June 2019. DOI:10.6814/THE.NCCU.IMBA.012.2019.F08.

(3) Acknowledgements I would first like to thank my thesis advisor Professor Jack Wu. The door to Prof. Wu’s office was always open whenever I needed advice or had questions about my writing. He consistently allowed this paper to be my own work but pointed at the right directions whenever I needed guidance. I would also like to express my sincere gratitude to the IMBA office team for their outstanding work. Li-chi, Jasmine and Emily, thank you for the passionate and selfless care you provide for all students in the program.. 立. 政治大. Finally, I must express my profound gratitude towards Belle who provided constant support. ‧ 國. 學. and encouragement throughout the process of writing this thesis and far beyond that. To my SOB friends Erick, Mert, Alan & Noel for providing an unfailing source of friendship and. ‧. laughs during our IMBA time. To my family, who even though are hundreds of miles away, have always been present through tough and good times and have given me unwavering support. y. Nat. sit. and encouragement to follow my dreams. This accomplishment would not have been possible. n. al. er. io. without them. Thank you. Jose Alfredo Sosa. Ch. engchi. i n U. v. i. DOI:10.6814/THE.NCCU.IMBA.012.2019.F08.

(4) Abstract Automation and its Effect on Employment in Honduras Manufacturing Industry By Jose Alfredo Sosa The impact that automation and the introduction of new technologies have on employment has been a subject of study for decades. Technological advances throughout history have deeply altered the employment structure of economies in all parts of the world. Since the first industrial. 政治大 technologies that would automate tasks and even eliminate entire jobs. 立. revolution in 1760, workers began to question and somewhat fear the introduction of. 學. ‧ 國. The focus of this research lies in evaluating the effects automation has had on the manufacturing industry of Honduras. In order to achieve this, a number of quantitative and qualitative sources. ‧. of data were evaluated. These include studies and researches on the topics of automation, employment and reshaping of skills, reports and statistics on Honduras macroeconomic data as. y. Nat. sit. well as employment trends and impact of foreign investment. Finally, an interview was. er. io. conducted with an expert in technology implementations to gain a first-hand perspective in the subject of automation in Honduras.. n. al. Ch. engchi. i n U. v. After the evaluation of this data, the following key results were obtained: A gradual transition into more deeply automated manufacturing plants in Honduras is expected in the long run. For now, the country’s low labor costs act as a cost-effective way for international companies to continue manufacturing their products. Developed countries with higher levels of education will not be severely affected by automation. These countries have a larger number of workers who possess higher skill levels and whose jobs are not easily automatable. Underdeveloped economies like Honduras should stress the implementation of policies that will enhance their population’s skills and education levels, these policies should serve to enhance the quality of life at all levels of society. Keywords: Automation, Employment, Manufacturing Industry, Honduras. ii. DOI:10.6814/THE.NCCU.IMBA.012.2019.F08.

(5) TABLE OF CONTENTS 1. Introduction. 1. 1.1. Objectives. 2. 1.1.1.. General. 2. 1.1.2.. Specific. 2. 2. Literature Review. 3. 2.1. What is Automation?. 3. 政治大 Keynes & Technological Unemployment - 1930 立. 2.2. Automation & Employment in History - Theories and Facts. 2.2.2.. 4. Sector Economic Model by Fisher, Clark & Fourastiè - 1939. 學. ‧ 國. 2.2.1.. 2.2.4.. Shaiken & the Evaluation of the American Industry - 1984. 2.2.5.. Zuboff & the Age of the Smart Machine - 1989. 2.2.6.. Rifkin & the Decline of the Global Labor Force - 1995. 2.2.7.. Levy, Murnane & the New Division of Labor - 2004. 2.2.8.. Cowen & the Great Stagnation - 2011. 2.2.9.. McAfee, Brynjolfsson & the Race against the Machine - 2011. er. io. sit. y. ‧. Schumpeter & Creative Destruction - 1950. Nat. 2.2.3.. al. 4. 5 6 7 7 8 9 10 10. 2.2.12. McKinsey’s Views on Automation, Employment & Productivity - 2017. 17. 2.2.13. PricewaterhouseCoopers & the Impact of Automation - 2018. 20. 2.2.10. 2.2.11.. n. v i n CFuture Frey, Osborne & the - 2013 h e nofgEmployment hi U c Automation in OECD Countries by Arntz, Gregory and Zierahn - 2016. 3. Research Question. 12 14. 25. 3.1. Hypothesis. 25. 4. Country Overview: Honduras. 26. 4.1. Economic Landscape. 27. 4.1.1.. Exports. 27. 4.1.2.. 4.1.2. Imports. 29 iii. DOI:10.6814/THE.NCCU.IMBA.012.2019.F08.

(6) 4.2. The Manufacturing Industry. 30. 4.2.1.. National Perspective & Background. 30. 4.2.2.. Gross Output (GO). 31. 4.2.3.. Gross Value Added (GVA). 32. 4.2.4.. Contribution to Gross Domestic Product (GDP). 32. 4.2.5.. Commercial Balance. 34. 4.3. Employment Trend 4.3.1.. 36. Industry Participants. 38. 4.4. Projections. ‧ 國. 5. Methodology. 學. 4.5. Risks & Rewards. 41 44 45. ‧. 45. y. 4.4.2.. 40. 45. sit. 4.4.1.. 40. 治 Plan Honduras 20/20 政大 Direct Investment 立. 46. 5.1. Research Method Pragmatism. 5.1.2.. Positivism. 5.1.3.. Realism. 5.1.4.. Interpretivism. 5.1.5.. Selected Research Ideology. io. n. al. er. Nat. 5.1.1.. Ch. engchi. i n U. 5.2. Data Collection. v. 46 46 47 47. 5.2.1.. Primary Data. 47. 5.2.2.. Secondary Data. 48. 5.3. Data Analysis. 49. 6. Results. 50. 6.1. Impact of New Technologies on the Labor Market. 50. 6.2. Winners and Losers in Industry 4.0. 51. 6.3. Costs and Benefits of Automation. 52 iv. DOI:10.6814/THE.NCCU.IMBA.012.2019.F08.

(7) 6.4. What does the Future Hold?. 53. 7. Conclusions & Limitations. 54. 8. References. 57. 9. Appendix. 60. 立. 政治大. ‧. ‧ 國. 學. n. er. io. sit. y. Nat. al. Ch. engchi. i n U. v. v. DOI:10.6814/THE.NCCU.IMBA.012.2019.F08.

(8) List of figures and tables Figure 1: Clark's Sector Economic Model. 5. Figure 2: Labor Productivity & Employment in the USA. 11. Figure 3: Changes in Wages for Male USA Workers Depending on Academic Levels. 12. Figure 4: US Employment by Risk Category. 14. Figure 5: Share of Workers with High Automation Risk by OECD Countries. 15. Figure 6: Share of Workers with High Automation Risk by Education Levels. 16. Figure 7: Automation Potential Based on Demonstrated Technology in the United States. 18. Figure 8: Employee Overall % of Activities that can be Automated. 19. Figure 9: Potential Job Automation Rates by Country across Waves. 21. 政治大 Figure 10: Potential Job Automation Rate over Time across Industries 立 Figure 11: Task Automation across the Three Waves. ‧ 國. 學. Figure 12: Honduras GDP Composition 2018 Figure 13: Honduras Total FOB Exports 2015-2018. 22 24 26 28. ‧. Figure 14: Honduras Total CIF Imports 2015-2018. y. Nat. Figure 15: Manufacturing Industry Contribution to GDP 2015-2018. sit. Figure 16: Honduras Finished Goods Exports Distribution. er. io. Figure 17: Direct Employment by Industrial Parks in Honduras 2009-2018. al. 29 33 35 36 38. Table 1: Occupations with the Highest Probability of Computerization in the US. 13. Table 2: Key Impacts in the Three Waves of Automation. 23. Table 3: Honduras Manufacturing Industry Commercial Balance 2016-2018. 34. Table 4: Economically Active Population & the Manufacturing Industry 2016-2018. 37. Table 5: Average No. of Employees per Manufacturing Plant in Honduras 2009-2018. 39. Table 6: Foreign Direct Investment by Economic Activity (In Millions US$). 43. Table 7: Advantages and Disadvantages of Interviews. 48. Table 8: Advantages and Disadvantages of Secondary Data. 49. Table 9: Labor Cost Balance Example - Honduras & Germany. 52. n. v i n C hin Honduras 2009-2018 Figure 19: Foreign Direct Investment engchi U. Figure 18: No. of Manufacturing Plants in Honduras Industrial Parks 2009-2017. 42. vi. DOI:10.6814/THE.NCCU.IMBA.012.2019.F08.

(9) 1. Introduction Over the past years, there is an increasing fear of new automated technologies stripping us from our jobs. The leaping advances in robotics, artificial intelligence and machine learning pave the road towards a new era in which humanity will need to reinvent the employment structure and adapt to new technologies with the capacity to equal or surpass our very own capabilities. However, this is not a new dilemma. Throughout human history, industrialization and the emergence of new technologies have displaced jobs but have also created new sources of. 治政 and hydraulic power machinery, to the invention of electricity, 大 mass production and assembly 立 in history have deeply affected the nature and structure of lines, all of these critical points employment. Going back in time to the first industrial revolution in 1760 with the new steam. ‧ 國. 學. employment and productivity.. Nowadays in the face of recent innovations, the impacts and consequences that new. ‧. technologies will generate are under the magnifying glass once again. Recent decades have seen. y. Nat. remarkable breakthroughs in creating new and evolving artificial intelligence, robotics and. n. al. er. io. jobs have the highest risks of being automated?. sit. other technologies that will directly affect all levels of labor. But which industries and specific. i n U. v. According to a study presented by the international services firm PricewaterhouseCoopers, the. Ch. engchi. greatest impact of jobs that could be automated might be felt in the manufacturing sector which has an estimated automatability of 45% and possesses a median employment share across countries of 14% (PricewaterhouseCoopers, 2018). In terms of my home country, Honduras, the manufacturing sector employs over 458,000 people as of EOY 2018. Over 35% of these employees work across various manufacturing plants known as “industrial parks”. The majority of the companies within these industrial parks serve the textile industry with a representation of almost 40% of the total. The remaining companies focus on automobile parts assembly and complementary products. This leads me to the purpose of this research in discovering how these jobs in a naturally high automatability risk and low employee skill level have been affected so far by new technologies and how will they be further disrupted and displaced in the future.. 1. DOI:10.6814/THE.NCCU.IMBA.012.2019.F08.

(10) Whereas there is a considerable number of researches on the topics of automation and employment, the vast majority of these analyses uses data collected from developed economies like the United States of America and European countries. Many of these countries possess high levels of education and higher skilled employees not performing routine tasks, therefore the automation risk for countries with these characteristics lowers considerably. This research adds value to the historical analysis on the impact of automation on employment by analyzing data from an underdeveloped economy which is heavily reliant on low skilled level employment provided in a large portion by foreign companies. This paper is divided into seven major chapters: Chapter 1 contains the introduction of the. 政治大 literature analyzed on the concerning topics. Chapter 3 presents the research question and 立. research and objectives. Chapter 2 presents a chronological sequence of the different works of. hypothesis. Chapter 4 introduces the subject country Honduras and various data and statistics. ‧ 國. 學. crucial for the thesis results. Chapter 5 details the research methodology used. Chapter 6 presents the results from the extensive analysis of the primary and secondary data presented.. ‧. Finally, Chapter 7 contains a set of conclusions and limitations on the research.. y. sit. al. er. io. 1.1.1. General. Nat. 1.1. Objectives. n. The general purpose of this research is to evaluate the impact automation and. Ch. i n U. v. technological advances have had on the manufacturing industry of Honduras as well as to. engchi. analyze how these advances have shifted jobs and skills in the latest years.. 1.1.2. Specific . Analyze the causes of the accelerated advances in technology in recent years that have led to an increased fear of automation and robots to render certain jobs and skills as useless.. . How extensively (or not extensively) automated companies in the manufacturing industry of Honduras currently are, what downfalls and opportunities this brings to the workforce, and what can employees in this sector expect in the future.. . Which skills and abilities will be in higher demand with industry 4.0 at our doorstep?. 2. DOI:10.6814/THE.NCCU.IMBA.012.2019.F08.

(11) 2. Literature Review In the literature review, I will present a chronological detail of major contributions and studies made on the topics of automation and its costs and benefits, displacement and creation of jobs by technology, where we stand now and what we can expect in the future.. 2.1. What is Automation? Automation is defined as the technology by which a process or procedure is performed with minimal human assistance (Groover, 2006). This new era in which robotics, artificial intelligence and machine learning are in a rapid rise poses a threat to jobs traditionally. 治政 the question is to what extent automation will displace大 jobs and what new opportunities will 立 come alongside this phenomenon.. performed by human labor. As machines can match and even outperform human capabilities,. ‧ 國. 學. Automation of activities allows companies to improve efficiency by drastically reducing the human error, improving quality, speed and achieve standardization impossible to be matched. ‧. by the traditional workforce. It encompasses many vital elements, systems and job functions. y. Nat. and can provide benefits to most sectors of the industry. Automation crosses all functions within. sit. an industry from installation, integration, and maintenance to design, procurement and. er. io. management. It can even reach into the marketing and sales functions of these industries. The. al. n. v i n C has robotics, expert U workplace, however elements such e n g c h i systems, telemetry & communications, electro-optics, cybersecurity, process measurement, sensors, wireless applications, systems. automation process varies depending on to what extent technologies are implemented in a. integration, test measurements and more can commonly be seen when implementing automating technologies (International Society of Automation, 2018). Automation does not only present a threat to employment, some argue that automation technology will ultimately subjugate rather than serve humankind. The risks include loss of privacy due to vast computer data networks, human error in technology management that could somehow endanger civilization and the increased dependency on automation for economic well-being (Encyclopaedia Britannica, 2019). This aside, automation poses an opportunity to relieve human labor from repetitive, hazardous or unpleasant tasks and at the same time provides a platform for a better social and economic environment. 3. DOI:10.6814/THE.NCCU.IMBA.012.2019.F08.

(12) 2.2. Automation & Employment in History - Theories and Facts Throughout history, humankind has been confronted with technological developments that seem to threaten their livelihoods and alter the status quo. The first Industrial Revolution taking place in Europe and the United States of America during the period of 1760 to sometime between 1820 and 1840, was a period during which predominant agrarian and rural societies became industrial and urban. Prior to this era, manufacturing was often done in people’s homes using hand tools or basic machinery. Industrialization marked a shift to powered, specialpurpose machinery, factories and mass production. The Iron and Textile industries, along with the development of the steam engine, played central roles in the industrial revolution. While. 政治大 improved standard of living for some, it also resulted in grim employment and poor living 立 conditions for the lower working class (History.com Editors, 2019). This era also portrays the industrialization brought about an increased volume and variety of manufactured goods and an. ‧ 國. 學. earliest example of laborers resisting the tide of technological progress. In the early 19th century, groups of self-named “Luddites” spanned across England, raiding factories and destroying. ‧. equipment and machinery of which they feared would take their jobs. This movement was not successful in removing technology from workplaces and workers could only consent to their. y. Nat. sit. new robotic coworkers and abandon some physically demanding and repetitive tasks. The term. al. er. io. “Luddite”, based on Ned Ludd - the supposed leader of the movement, persisted in history as a. v. n. reference to people who are opposed to technological change (Carr, 2014).. C. i n U. 2.2.1. Keynes & Technological h Unemployment e n h i - 1930. gc. In his 1930 published paper, “Economic Possibilities for our Grandchildren”, John. Maynard Keynes introduced the term “Technological Unemployment” which refers to the loss of jobs caused by technological change. In his paper, the notorious economist wrote: “We are being afflicted with a new disease of which some readers may not have heard the name, but of which they will hear a great deal in the years to come – namely, technological unemployment.” (Keynes, 1930) Keynes predicted that the employment crisis caused by the introduction of new technologies was only temporary and what we now call automation would lead to a future of affluence and leisure for all. Keynes made a large number of predictions that came to reality in his paper, like the exponential growth of wealth to the end of the struggle for subsistence in the rich world. He 4. DOI:10.6814/THE.NCCU.IMBA.012.2019.F08.

(13) also predicted that by the year 2030, work would be rarer to get and that people would need to share the amount of work left until we arrived at a 15-hour workweek. Although the workweek has shortened in many countries (especially first world economies), a 15-hour workweek is nowhere close to be achieved in the near future so this prediction is unlikely to become a reality.. 2.2.2. Sector Economic Model by Fisher, Clark & Fourastiè - 1939 A few years later, in 1939, Allan Fisher, Colin Clark and Jean Fourastiè developed the threesector model economy. This model divided economies into three sectors of activity: . Primary Sector: Extraction of raw materials. . Secondary Sector: Manufacturing. . Tertiary Sector: Services. 立. 政治大. According to the model, the main focus of an economy’s activity shifts from the primary, the. ‧ 國. 學. secondary and finally the tertiary sector. Countries with a low per capita income are in an early stage of development, the main part of their national income is achieved through production in. ‧. the primary sector. Countries in a more advanced stage of development, with a medium national. y. Nat. income, generate their income mostly in the secondary sector. In highly developed countries,. n. al. er. io. sit. the tertiary sector dominates the total output of the economy (Fisher, Clark, & Fourastiè, 1939).. Ch. engchi. i n U. v. Figure 1: Clark's Sector Economic Model. 5. DOI:10.6814/THE.NCCU.IMBA.012.2019.F08.

(14) Later on, Clark suggested a fourth sector (quaternary), which compromises intellectual and knowledge-based activities such as scientific research, education, consulting, information management and financial planning aimed at future growth and development.. 2.2.3. Schumpeter & Creative Destruction - 1950 Creative destruction (sometimes known as Schumpeter’s gale) is a concept popularized and mostly associated with Austrian economist Joseph Schumpeter, who himself derived it from previous works by Karl Marx and transformed into an economic innovation and business cycle theory. According to Schumpeter, the process of industrial mutation that constantly revolutionizes the economic structure from within destroys the old one to make place for the. 政治大 entrepreneurs was the disruptive 立 force that promoted economic growth, even as it diminished. new economic order in a perpetual cycle. In his vision of capitalism, the innovative entry by. ‧ 國. 學. the value of established companies and conglomerates that enjoyed some degree of monopoly power derived from previous technological, organizational, regulatory and economic paradigms.. ‧. This innovative entry can also be related to industry with the introduction of technology and automated processes that render lower skilled labor as useless in a perpetual cycle of new. Nat. sit. y. technology displacing labor, new employment for the displaced being created in other industries,. io. er. newer technologies rendering older technology as useless and so on. This cycle makes automation a driver of the creation of new employment while at the same time being the driver. n. al. Ch. for the destruction of routine labor tasks.. engchi. i n U. v. On his book “Capitalism, Socialism and Democracy” published in 1942, Schumpeter initially addressed his beliefs on industrial innovation’s perpetual cycle: “The opening up of new markets and the organizational development from the craft shop and factory to such concerns as US Steel illustrate the process of industrial mutation that incessantly revolutionizes the economic structure from within, incessantly destroying the old one, incessantly creating a new one. The process must be seen in its role in the perennial gale of creative destruction; it cannot be understood on the hypothesis that there is a perennial lull.” (Schumpeter, 1942). 6. DOI:10.6814/THE.NCCU.IMBA.012.2019.F08.

(15) 2.2.4. Shaiken & the Evaluation of the American Industry - 1984 In 1984, Harley Shaiken reviewed the results of a generation of automation in the American industry. He contributed an analysis of how automation leads to a gradual “deskilling” of the workforce. In the journal, Shaiken explains how new technology had caused a redesign of the floor machinists at a General Motors plant. He also describes how automation in computer algorithms helped break the US traffic controllers strike of 1981 and eventually made 12,000 jobs redundant. Shaiken’s work is important to emphasize that it is not always just the low skilled jobs that are directly threatened by automation as is commonly believed and his criticism on how technologies are implemented in workplaces is evident from the following excerpt:. 政治大 workplace. They could just as 立easily be deployed to make jobs more creative and increase shop “It is ironic that computers and microelectronics should be used to create a more authoritarian. ‧ 國. 學. floor decision-making. Rather than pace workers, systems could be designed to provide them with more information about the production operation in general and their own jobs in particular. The technology could be used to bring the work under the more complete control of. ‧. the people who do it rather than the other way around.” (Shaiken, 1984). Nat. sit. y. 2.2.5. Zuboff & the Age of the Smart Machine - 1989. er. io. In the book “In the Age of the Smart Machine”, published in 1989, Shoshanna Zuboff introduces the argument that computers make possible two distinct transformations of the. n. al. Ch. i n U. v. workplace: On one hand, they can be used to automate production, relieving human beings of. engchi. physical effort and replacing skilled with unskilled labor. On the other hand, they can be used to “informate” – Zuboff’s term referring to the integration of workers and machines in a reskilled labor process. Informing is not exactly an alternative to automation in the usual sense, but a better way of automating that realizes the human potentialities of the workforce as well as the technical potentialities of the computer (Zuboff, 1989). Zuboff attempts to pinpoint the unique properties of computers and their support in the demand for increased skill in labor. According to the author, a process of automation or implementation of technologies that emphasizes the replacement of human employees by machines rests on the actual capabilities of the information technologies of their time and could lead to a continuous sub utilization of the technology’s potential.. 7. DOI:10.6814/THE.NCCU.IMBA.012.2019.F08.

(16) 2.2.6. Rifkin & the Decline of the Global Labor Force - 1995 The American economist Jeremy Rifkin is one of the first authors to urgently stress the seriousness of technological unemployment. In his book “The End of Work: The Decline of the Global Labor Force and the Dawn of the Post-market Era”, the author argues that we are entering a new phase in history, one that is characterized by the gradual and inevitable decline of jobs. Rifkin argues that the world is polarizing into two forces: On one side, an information elite that controls the high technology global economy, these corporate managers and knowledge workers would reap the benefits of the high-tech world. On the other side, an exponentially increasing number of displaced workers (mostly blue-collar, retail and wholesale. 政治大 their impossibility to keep up with the increasingly automated world they live in. These factors 立 could lead to the end of civilization as we know it or signal the beginning of a great social employees) who have fewer prospects for meaningful employment due to their lower skills and. ‧ 國. 學. transformation (Rifkin, 1995).. At this point in time, global unemployment was at its highest point since the great depression. ‧. and Rifkin contended that worldwide unemployment would continue to rise as information. y. Nat. technologies eliminated tens of millions of jobs in the manufacturing, agricultural and service. io. sit. sectors. He predicted the American middle class would continue to shrink and contrary to. er. Keynes predictions in 1930, Rifkin stated the workplace would become ever more stressful.. al. n. v i n C h retail and transportation threatened to disrupt the manufacturing, industries. However, the engchi U The emergence of faster and more advanced computers and telecommunication technologies. author did not consider the effects of “creative destruction”, which states that technology does not create permanent unemployment, but instead creates jobs in new industries and transfers employment from agriculture and manufacturing to service and innovation sectors. Many authors criticized Rifkin’s work on the accounts that it was flawed due to the oversight of the dynamics of employment and technological changes in the capitalist era. The polarizing phenomenon Rifkin introduces reflects what is happening in the world now, with technological advances moving at such a rapid pace, societies and world economies face a fundamental shift in defining the role of people vs machines.. 8. DOI:10.6814/THE.NCCU.IMBA.012.2019.F08.

(17) 2.2.7. Levy, Murnane & the New Division of Labor - 2004 In their book “The New Division of Labor: How Computers are Creating the Next Job Market”, Frank Levy and Richard Murnane discuss how computers are changing the employment landscape and how employees possessing the right kind of education will easily transition to the newly transformed job market. The authors merge personal stories from employees in diverse industry sectors and combine their stories with cognitive science, computer science and economics to show just how computers are enhancing productivity even as they eliminate the need for other jobs. They explain, similarly to previous authors introduced in this literature review, that the greater risks lie with jobs that can be expressed in programmable rules (blue. 政治大 jobs would create a gaping division between those who can and those who cannot earn a good 立 living in a computerized economy (Levy & Murnane, 2004). collar, clerical, manufacture) and whose activities require moderate skills. The loss of these. ‧ 國. 學. Murnane and Levy thoroughly discuss two types of jobs susceptible to automation: Fully automated jobs: These type of jobs will render human input useless, as they will. ‧. . not be able to compete with the capabilities of computers and advances in technology.. Nat. sit. er. Hybrid jobs: These jobs relate to those who will be enhanced by technology and promote. io. . y. These type of jobs promote the deskilling of labor.. increased productivity with new tools required to be learned by the employees. This. n. al. Ch. type of jobs promotes the upskilling of labor.. engchi. i n U. v. The two types of jobs discussed above would create a “digital divide” within the workforce that can only be addressed by public policy and educational reforms. Due to the limitations of their time, the authors did not consider the extent to which computers and other technologies could advance; this issue became one of the major critiques of their work. They claimed that for a computer to carry out an action, it needed to be broken down into smaller tasks assuming that they would not be able to carry out higher complexity operations. Now a day we see computers and programs performing highly complex tasks through millions of lines of coding.. 9. DOI:10.6814/THE.NCCU.IMBA.012.2019.F08.

(18) 2.2.8. Cowen & the Great Stagnation - 2011 Professor of economics at George Mason University, Tyler Cowen, argues that the American economy has reached a sort of technological plateau in which the factors that drove the country’s economic growth were mostly exhausted. His book “The Great Stagnation” introduces the hypothesis that economic growth in the United States of America and in other advanced world economies has slowed down due to the falling rates of innovation. On the other hand, less developed countries would be subject to a sort of catch up mechanic by adapting already invented technologies imported from advanced countries (Cowen, 2011). According to the author, the largest setback for advanced economies is the exhaustion of extra. 政治大 and bioengineering). For example, 立 people in the 1930’s experienced drastic changes to their gains led by major innovations in the past (Such as industry, electricity, chemistry, computing. ‧ 國. 學. lifestyle as technology within the households changed completely, similar to later generations who saw the introduction of appliances such as refrigerators, dishwashers, laundry machines, air conditioning, etc. Newer generations would have lives less impacted by disrupting. ‧. technological changes as it has evolved so much, it has reached a point in which advances are. y. sit. Nat. much rarer and technological progress seems less impactful.. io. er. 2.2.9. McAfee, Brynjolfsson & the Race against the Machine - 2011 Taking an opposite stance to Tyler Cowen’s “Great Stagnation”, Erik Brynjolfsson and. n. al. Ch. i n U. v. Andrew McAfee published “The Race Against the Machine”. In their book, the authors present. engchi. a very different explanation to the American economy’s low growth and do not see a stagnation in technological advances as a cause of it. Using their own research at MIT’s Center for Digital Business, they prove there had been no stagnation in technology; in fact, the digital revolution was accelerating. They argue technological progress has been compounding throughout the years, and they point out compounding growth does not seem too overwhelming before it suddenly grows out of control. The implications of such an exponential growth are that humanity would be on the verge of a large disruptive technological change, as a result society could enter a future of chronic unemployment. (Brynjolfsson & McAfee, 2011) Digital innovation has changed how value is created and the ways in which the economy distributes that value. After the financial crisis of 2008, many measures of economic health such as GDP, corporate profits and direct investments recovered rather quickly. However, one 10. DOI:10.6814/THE.NCCU.IMBA.012.2019.F08.

(19) measure was left behind: Unemployment. This observation led the authors to conclude that an important shift in the means of production was taking place and they argued that the leading factor in this shift was technology by simultaneously boosting the productivity of firms and eliminating the need for many hours of human labor.. 立. 政治大. ‧. ‧ 國. 學 er. io. sit. y. Nat. Figure 2: Labor Productivity & Employment in the USA (Brynjolfsson & McAfee, 2011). n. al. Ch. engchi. i n U. v. One of the books supporting point is the rapid pace of automation in companies recorded in recent years. This was due to a mix of technologies such as robotics, numerically controlled machines, computerized inventory management software, speech recognition, language translation, self-driving vehicles and online commerce increasingly substituting human labor in order to increase productivity and quality while at the same time lowering costs. McAfee and Brynjolfsson recommend education reforms and increased educational investments as well as focusing on organizational innovation and making it easier for entrepreneurs to establish new ventures and succeed, as they are needed to develop the new possibilities that technological advances create.. 11. DOI:10.6814/THE.NCCU.IMBA.012.2019.F08.

(20) 立. 政治大. ‧. ‧ 國. 學 sit. y. Nat. Figure 3: Changes in Wages for Male USA Workers Depending on Academic Levels (Brynjolfsson & McAfee, 2011). al. er. io. 2.2.10.Frey, Osborne & the Future of Employment - 2013. n. Professors Carl Benedikt Frey and Michael J. Osborne discuss the susceptibility of jobs. Ch. i n U. v. to computerization in their paper "The Future of Employment” published in 2013. The authors. engchi. introduced a novel methodology to calculate the probabilities of 702 detailed occupations of becoming fully automated. They used a Gaussian process classifier to examine the impacts of future technification on the US labor market with the objective of analyzing the number of jobs at risk and the relationship between an occupation’s probability of automation, wages and educational levels (Frey & Osborne, 2013). Frey and Osborne utilized the O*Net Content Model as a base for their work, this database provides a framework that identifies and organized important information about work. It also covers work performed in the US economy and defines the set of occupations for which data is collected (Occupational Information Network (O*NET), 2019). They divided each job task into three major categories: Social intelligence, creativity and perception & manipulation. Then with the use of O*Net’s database went on and like these categories to the tasks, skills and abilities 12. DOI:10.6814/THE.NCCU.IMBA.012.2019.F08.

(21) registered by the database. The authors evaluated the connection between each job and its connection to those tasks, skills and abilities in order to calculate a “risk of automation” score. The paper’s conclusion was that 47% of total US employment was at risk of being automated and that wages and educational attainment exhibited a strong negative relationship with the occupations probability of computerization. The study by Frey and Osborne supports the stance that sophisticated algorithms can easily perform occupations mainly consisting of tasks following well-defined procedures (routine tasks) and that the decline of employment in routine intensive occupations like manufacturing will continue its rise as newer technologies reduce the necessity of human input. According to the study, the 12 occupations with the highest. 政治大. probability of being computerized are:. 立. New Accounts Clerk. 0.99. Processing Machine Operators. 0.99. y. 51-9151. Tax Preparers. 0.99. Data Entry Keyers. Nat. io. 0.99. al. Cargo & Freight Agents. er. 25-5031. sit. ‧ 國. Library Technicians. 0.99 0.99. ‧. 學. Table 1: Occupations with the Highest Probability of Computerization in the US Computerization Occupation SOC Code Probability. n. v i 0.99 n Watch Repairers Ch U i e h n 0.99 c g Insurance Underwriters. 43-9021. 43-4141. 13-2082 43-5011 49-9064 13-2053. Mathematical Technicians. 0.99. 15-2091. Hand Sewers. 0.99. 51-6051. Title Examiners. 0.99. 23-2093. Telemarketers. 0.99. 41-9041 (Frey & Osborne, 2013). One of the major critiques of this paper is the consideration of the automation of occupations as a yes and no answer and not taking into account the relative impact certain degrees of automation on specific tasks could have on occupations whether it be positive or negative.. 13. DOI:10.6814/THE.NCCU.IMBA.012.2019.F08.

(22) 立. 政治大. ‧. ‧ 國. 學 sit. y. Nat. n. a. er. io. Figure 4: US Employment by Risk Category (Frey & Osborne, 2013). v. l Countries by Arntz, Gregory 2.2.11.Automation in OECD and Zierahn - 2016 ni. Ch. engchi U. Due to the increasing debate regarding automation and digitalization’s effect in the labor market worldwide, Melanie Arntz, Terry Gregory and Ulrich Zierahn form the University of Mannheim published the paper “The Risk of Automation for Jobs in OECD Countries: A Comparative Analysis” in 2016. Their research was backed by the Organization for Economic Co-operation and Development (OECD), which is an international organization that works with governments, policymakers and citizens on the grounds of establishing international norms, finding evidencebased solutions to a range of social, economic and environmental challenges. The OECD seeks to improve economic performances and create jobs to foster education and wellbeing for their 48 member nations citizens (Organization for Economic Co-operation and Development (OECD), 2019).. 14. DOI:10.6814/THE.NCCU.IMBA.012.2019.F08.

(23) The authors claim previous studies made on the subject, like the risk categories and computerization probabilities proposed by Frey and Osborne in 2013, followed an occupational-based approach and these studies assume that entire occupations rather than single job-tasks are at risk of being automated by technology implementations. They argued this approach might lead to an overestimation of job automation, as many of the higher risk labeled occupations still contained a considerable number of tasks within that were hard to automate. Alternatively, they propose an estimation of the job risk of automation for 21 OECD countries based on a task approach, taking into account the heterogeneity of workers tasks with occupations (Arntz, Gregory, & Zierahn, 2016).. 政治大 Gregory and Zierahn utilized 立 the Programme for the International Assessment of Adult Based on their approach of task displacement rather than entire occupation displacement, Arntz,. Competencies (PIACC), which surveys task structures across OECD countries, as the backbone. ‧ 國. 學. of the research. The results of their studies concluded that a task-based approach results in a much lower risk of automation, varying by country, compared to the occupation-based approach. ‧. as seen below:. n. er. io. sit. y. Nat. al. Ch. engchi. i n U. v. Figure 5: Share of Workers with High Automation Risk by OECD Countries (Arntz, Gregory, & Zierahn, 2016). 15. DOI:10.6814/THE.NCCU.IMBA.012.2019.F08.

(24) While Frey and Osborne concluded that 47% of US jobs are automatable, the OECD based research averaged a 9% risk of automation in jobs across its member countries. The study shows differences in automation risks among countries that reflect general differences in workplace organization, differences in investments on technification processes as well as the difference in the education of the workforce across countries. Countries with a strong focus on high-qualified workers typically have lower shares of workers at high risk, since these workers perform fewer automatable tasks than low qualified workers do. One of the studies major findings is that automation risk strongly decreases as countries possess higher levels of educated workers and higher levels of workers with better income, making low. 政治大. skilled and low-income individuals the highest at risk with the introduction of new technologies.. 立. ‧. ‧ 國. 學. n. er. io. sit. y. Nat. al. Ch. engchi. i n U. v. Figure 6: Share of Workers with High Automation Risk by Education Levels (Arntz, Gregory, & Zierahn, 2016) The study views the threat from technological advances much less alarming than previous studies on the matter. Instead, it demonstrates the necessity to view technological advances as a substitute or complement to certain routine tasks which will have an overall less harmful effect on employees around the world and would actually aid them in the enhancement of skills, productivity and output. 16. DOI:10.6814/THE.NCCU.IMBA.012.2019.F08.

(25) 2.2.12.McKinsey’s Views on Automation, Employment & Productivity - 2017 The McKinsey Global Institute (MGI) since its founding in 1990 has established an objective to create a deep understanding of the evolving global economy. As a business and economic research arm for the McKinsey & Company, MGI’s goal is to provide leaders in the commercial, public and social sectors with diverse facts and insights, which aid in strategic decision-making and the development of policies. In the year 2017, MGI published the report “A Future That Works: Automation, Employment and Productivity” in an attempt to explain how advances in robotics, artificial intelligence and machine learning are introducing a new age of automation. The report analyzes the automation. 政治大 of the adoption and acceptance 立in workplaces. Their research is similar to that of Arntz, Gregory. potential of the global economy as well as the factors that will determine its pace and the extent. ‧ 國. 學. and Zierahn on OECD economies potential for automation (introduced in the previous subchapter), both of them base their approach on specific task displacement since they consider them more relevant due to occupations being made up of a range of activities with different. ‧. potential for automation. MGI’s research stresses the fact that automation can enable businesses. y. Nat. to improve their performance, by reducing the human error, standardizing quality and. io. sit. increasing speed. In many cases achieving outcomes that go far beyond human capabilities due. n. al. er. to our own limitations. The scenario modeling estimates automation could raise productivity. i n U. v. growth globally by 0.8% to 1.4% annually (McKinsey Global Institute, 2017).. Ch. engchi. Their study uses the state of technology with respect to 18 performance capabilities grouped in five major categories: 1. Sensory Perception: Sensory abilities 2. Cognitive Capabilities: Retrieving information, recognizing patterns, generating patterns, logical reasoning, optimizing & planning, creativity, articulating and coordinating 3. Natural Language Processing: Language generation and understanding 4. Social & Emotional Capabilities: Social and emotional sensing & reasoning, Social and emotional output 5. Physical Capabilities: Fine and gross motor skills, navigation, mobility. 17. DOI:10.6814/THE.NCCU.IMBA.012.2019.F08.

(26) These performance capabilities span across 2,000 work activities/tasks and 800 occupations in order to calculate their potential to be automated using currently demonstrated technologies. Their results show that less than 5% of all occupations can be fully automated (much less than the 47% of automation risk proposed by Frey & Osborne in 2013) and that about 60% of all occupations have at least 30% of tasks that could be automated. This constitutes more than half of the activities people are paid to do in the world’s workforce that will transform and evolve due to technology rather than be automated away.. 立. 政治大. ‧. ‧ 國. 學. n. er. io. sit. y. Nat. al. Ch. engchi. i n U. v. Figure 7: Automation Potential Based on Demonstrated Technology in the United States (McKinsey Global Institute, 2017) The activities with the highest susceptibilities to automation involve physical activities in highly structured and predictable environments and those involving the collection and processing of data. These jobs are highly prone to become fully automated due to their predictable routine tasks and lower skill entry. Together they make up 51% of the activities in the economy accounting for almost US$ 2.7 Trillion in yearly wages. These jobs are more prevalent in industries like manufacturing, accommodation and food service, retail trade and some middle-skill jobs.. 18. DOI:10.6814/THE.NCCU.IMBA.012.2019.F08.

(27) The study points out that the pace an extent of automation will vary from country to country and it will depend on technical, economic and social factors. Technical progress affecting the performance capabilities will give ground for more tasks to be automated. The cost of technology, supply and demand dynamics, performance analyses and regulatory policies will also affect the pace and scope of automation. Humans will need to adapt and work alongside machines in order to contribute to growth in per capita GDP. This implies an inevitable change in the nature of work, in which processes are transformed by the automation of certain tasks causing humans to perform complementary activities to the work done by machines (and vice versa). For policymakers, it is recommended to embrace the opportunity for economic. 治政大 of shifts in work activities is not ensure continued progress and innovation. The magnitude 立 unprecedented. In the United States of America, the share of farm employment fell from 40% development by creating policies to encourage investment and market incentives that will. ‧ 國. 學. in 1900 to 2% in 2000, while the share of manufacturing employment fell from 25% in 1950 to less than 10% in 2010. In both cases, new previously inexistent jobs were created across various. ‧. industries once again demonstrating the effects of the “creative destruction”.. n. er. io. sit. y. Nat. al. Ch. engchi. i n U. v. Figure 8: Employee Overall % of Activities that can be Automated (McKinsey Global Institute, 2017). 19. DOI:10.6814/THE.NCCU.IMBA.012.2019.F08.

(28) 2.2.13.PricewaterhouseCoopers & the Impact of Automation - 2018 PricewaterhouseCoopers (PwC) is a multinational professional services network; it is currently ranked as the largest professional services firm in the world. PwC’s network extends across 158 countries in the world and they provide services to 420 out of 500 Fortune 500 companies (PricewaterhouseCoopers, 2018). Due to its vast network, the firm is able to contribute data analysis to a wide range of areas and industries, including to one in particular which is important to this research: Automation and its effect on employment. PwC released their report “Will robots really steal our jobs? An international analysis of the potential long term impact of automation” in February 2018, in this comprehensive report the. 政治大 Intelligence, robotics and other 立forms of “smart automation” have on production levels, creation firm attempts to demonstrate the actual impact in economies that technologies such as Artificial. ‧ 國. 學. of better products and services and contributions to economic measures such as GDP. The research was primarily built around two previous studies on the subject: 1) Frey and Osborne’s research regarding the future of employment published in 2013, which utilized the Occupational. ‧. Information Network (O*Net) Content Model. 2) Arntz, Gregory and Zierahn’s 2016 research. y. Nat. focusing on the impact of automation on OECD member countries utilizing the Programme for. io. sit. the International Assessment of Adult Competencies (PIACC). Both of these studies were. n. al. er. previously covered in this literature review.. Ch. i n U. v. The final report estimated the proportion of existing jobs that could be at high risk of automation. engchi. by the year 2030 for 29 member countries of the OECD, different industry sectors, occupations within industries and workers of different gender, ages and educational levels. PwC also elaborated a timeline on how this process might unfold in the time between 2018 and 2030 through three overlapping waves: . Algorithm Wave: Focus on automation of simple computational tasks. This wave is already present at the time of this study. . Augmentation Wave: Focus on automation of repeatable tasks and the communication and exchange of information through dynamic technological support. Also includes the analysis of unstructured data in semi-controlled environments. This wave is also underway at the time of this study but is expected to reach full maturity in the 2020’s.. 20. DOI:10.6814/THE.NCCU.IMBA.012.2019.F08.

(29) . Autonomy Wave: Focus on automation of physical labor, manual tasks and problemsolving in real-world situations that require responsive actions in industries such as manufacturing and transportation. The technologies enabling this wave are under current development but will become fully mature on a worldwide scale in the 2030’s.. Their research shows the proportion of jobs at high risk of automation by 2030 varies significantly by country. Some of the highest automation risk levels (> 40%) are observed in countries like Slovakia and Slovenia whose economies are more reliant on industrial production which tends to be easier to automate due to the routine tasks and programmable features. Lower automation risk levels (< 25%) are observed in countries like Finland and Korea which. 政治大 UK and USA lie in the middle-risk 立 tier (Between 30% and 40%) due to their vast amount of relatively higher education levels across their population. Service focused economies like the. lower-skilled workers that could see intermediate levels of automation in the long run. ‧ 國. 學. (PricewaterhouseCoopers, 2018).. ‧. n. er. io. sit. y. Nat. al. Ch. engchi. i n U. v. Figure 9: Potential Job Automation Rates by Country across Waves (PricewaterhouseCoopers, 2018) 21. DOI:10.6814/THE.NCCU.IMBA.012.2019.F08.

(30) Results show that countries with similar labor market performances and economic structures have broadly similar levels of potential automation. The figure above also details the levels of automation each of the countries may incur in throughout the firms proposed waves, it provides a short term and long-term view depending on the actual economic conditions in each country and the different technologies already in place. According to the study, the relative automatability of jobs across countries depends on a range of factors such as level of training, education, and skill enhancement. Therefore, countries with higher levels of education are estimated to have lower potential automation rates. Different industries also see variations in automation risks across different waves. In the short. 政治大 outperforming human capability 立 in a wide range of tasks involving pure data analysis. On the. term, financial services and other programmable sectors are more exposed due to algorithms. long run, sectors such as transportation are highly exposed as the worldwide scale introduction. ‧ 國. 學. of driverless vehicles takes place. An industry’s task composition and educational requirements are the primary drivers behind its automatability. Industries where large number of workers are. ‧. involved in routine tasks are likely to see more automation. Less automatable sectors have a. y. Nat. larger portion of time spent on social and literacy-based tasks, these sectors generally possess. n. er. io. al. sit. higher educational requirements.. Ch. engchi. i n U. v. Figure 10: Potential Job Automation Rate over Time across Industries (PricewaterhouseCoopers, 2018) 22. DOI:10.6814/THE.NCCU.IMBA.012.2019.F08.

(31) PwC’s research demonstrates how AI, robotics and automation technologies can aid in the potential boost of productivity and the creation of better products and services. The rate at which these technologies will affect businesses will vary across industries and countries. Nonetheless, companies should start investing now to reap the benefits of new trends. The direct impact of the implementation of new technologies on different levels of business will also vary depending on the diversity of tasks performed: Table 2: Key Impacts in the Three Waves of Automation Phase. Description. mathematical calculations, basic software packages and internet searches. Fundamental computational job tasks will be higher in this wave.. Automation of simple computational tasks and analysis of structured data.. ‧. sit. io. Dynamic interaction with technology for support and decision-making. Robotic tasks in semi-controlled environments.. al. n Autonomy Wave. Data driven sectors like financial & insurance, information & communication and professional, scientific and technical services.. y. Nat Augmentation Wave. Industries Impacted. Ch. The financial and insurance sector will Routine tasks such be highly impacted. as filling forms or Other sectors with exchanging high proportions of information. Vast clerical support like usage of learning public algorithms. administration, manufacturing and storage. Sectors like AI and robotics will construction, sewage increasingly & waste automate routine management, tasks focusing on transportation & those involving storage with the physical labor or introduction of manual dexterity. autonomous vehicles and robots. (PricewaterhouseCoopers, 2018). engchi. Automation of physical labor. More problem-solving in real situations requiring responsive actions such as transport and manufacturing.. er. Algorithm Wave. 政治Manually 大conducted. 學. ‧ 國. 立. Tasks Impacted. i n U. v. 23. DOI:10.6814/THE.NCCU.IMBA.012.2019.F08.

(32) 立. 政治大. ‧ 國. 學. Figure 11: Task Automation across the Three Waves (PricewaterhouseCoopers, 2018). ‧. The introduction of new technologies will undoubtedly disrupt labor markets across the world, the extent to which automation will affect employment will vary due to a series of factors. Some. Nat. sit. y. of these factors relate to industries and categories of workers; however, countries will also need. io. er. to consider the public policies implications and requirements. This means that the extent of automation in different economies may be more or less due to economic, legal, regulatory and. al. n. organizational constraints.. Ch. engchi. i n U. v. PwC’s research suggests that job losses due to automation are more likely to be offset in the long run by new jobs made possible by these new technologies. The process of creative destruction observed through periods of major technological changes since the industrial revolution will undoubtedly repeat itself in the era of industry 4.0.. 24. DOI:10.6814/THE.NCCU.IMBA.012.2019.F08.

(33) 3. Research Question Taking into account the ever-changing world we live in with unparalleled technological advances and the scale and scope of the major researches and theories regarding the topic of automation and employment introduced in the literature review. Considering the lack of research and in-depth studies on the effects of this phenomenon in Honduras and the rapidly growing manufacturing industry, which has a great impact on the country’s exports, employment and GDP. This research will attempt to answer the following main question: “How has automation affected employment in the manufacturing industry of Honduras?”. 政治大 attempting to answer the following sub-questions: 立. In addition to my main research question, I will dive further into the topic by analyzing and. ‧ 國. 學. 1. How will automation continue to shape the skills and abilities required in the manufacturing industry of Honduras?. 2. What costs, benefits and opportunities do automation present to a developing economy. ‧. such as Honduras?. sit. y. Nat. 3.1. Hypothesis. er. io. The research questions above lead to the formulation of the following hypothesis. al. regarding the impact of automation in the manufacturing industry of Honduras:. n. v i n “If automation has significantlyCaffected within the manufacturing industry h e n employment gchi U. of Honduras, the lower skilled employees will have been more extensively impacted than higher skilled employees as a direct result of the introduction of technologies that automatize tasks and processes.”. 25. DOI:10.6814/THE.NCCU.IMBA.012.2019.F08.

(34) 4. Country Overview: Honduras Honduras is strategically located in the middle of Central America and is bordered by Guatemala to the west, El Salvador to the southwest, Nicaragua to the southeast, the Caribbean Sea to the north and the Pacific Ocean to the south. The country is divided into 18 departments, its major cities are Tegucigalpa (Political Capital) and San Pedro Sula (Industrial Capital – Which we will consider especially for this research as it provides a major contribution to the country’s economy as well as serves as the largest Hub for the manufacturing industry housing the majority of the industrial parks in the country). Honduras official language is Spanish and the currency is the Lempira (HNL).. 政治大. The country’s location has allowed it to become a distribution center, with Puerto Cortes. 立. serving as the main seaport in Central America. The economy relies heavily on a range of. ‧ 國. 學. exports, notably clothing apparel, bananas and coffee. It has rapidly developing sectors in agriculture, textile, light manufacturing, sustainable tourism and business services.. ‧. (PricewaterhouseCoopers, 2019). The 2018 Gross Domestic Product (GDP) composition by sector is:. sit. y. Nat. Agriculture 14%. n. al. er. io. HONDURAS GDP COMPOSITION BY SECTOR - 2018. Ch. engchi. i n U. v. Industry 28%. Service 58%. Figure 12: Honduras GDP Composition 2018 26. DOI:10.6814/THE.NCCU.IMBA.012.2019.F08.

(35) Honduras spans about 112,492 km2 and has a population exceeding 9 million habitants. It is a low middle-income country where more than 60% of the population lives in poverty. In rural areas, this fact is even more aggravating with one out of five Hondurans living in extreme poverty (less than US$ 1.90 per day). (World Bank, 2019). 4.1. Economic Landscape Since the 2008 global economic crisis, Honduras has experienced a moderate recovery, driven by public investments, exports and higher remittances mainly from the USA. In 2017 the country’s economy grew by 4.8%, in 2018 by 3.5% and the projected growth for 2019 is 3.6% (World Bank, 2019). The principal commercial activities contributing to this growth are. 政治大. financial intermediation, manufacturing industry, agriculture, livestock and fishing industry.. 立. By the end of the year 2018, the current account deficit reached -US$ 1,003.5 Million,. ‧ 國. 學. representing 4.2% of the Growth Domestic Product (GDP) and more than doubled that of 2017 (-US$ 408.9). Imports increased 7.7% compared to 2017 while exports only increased by 0.3%. ‧. leading to a trade deficit of -US$ 3,530.6 (US$ 853.6 higher than recorded in 2017). The Central bank report attributes this higher deficit to a reduction in exports of commodities like coffee. Nat. er. io. manufacturing plants (Honduras Central Bank, 2018).. sit. y. and palm tree oil as well as a rise in imports of raw material for the transformation of goods in. al. n. v i n C h total FOB (Freight At the close of the year 2018 Honduras e n g c h i U on Board) exports amounted US$ 4.1.1. Exports. 8,669.3 Million divided into the following three major categories: . General Goods: US$ 4,285.1 Million. . Finished Goods (This category includes all the products elaborated within the industrial parks across the country): US$ 4,263.1 Million. . Other Goods: US$ 121.1 Million. The Central Bank details the general goods exports being 3.4% less than in 2017 due to lower demand for products like palm tree oil and a decline in the price of coffee around the world. Regarding the finished goods category exports, these increased by 4.5% compared to 2017 due to an influx of foreign investment for the establishment of new manufacturing plants serving. 27. DOI:10.6814/THE.NCCU.IMBA.012.2019.F08.

(36) the textile and automotive industry during the year 2018. Finally, the other goods category reported an 8.1% decrease in FOB Exports in 2018.. Honduras Total FOB Exports 2015-2018 $8,800.00. $8,646.50. $8,669.30. $8,600.00 $8,400.00 $8,226.00 $8,200.00. 立 2015. 學. ‧ 國. $7,800.00 $7,600.00. 政治大 $7,959.50. $8,000.00. 2016. 2017. 2018. FOB Exports (Millions US$). ‧. sit. y. Nat. Figure 13: Honduras Total FOB Exports 2015-2018. al. er. io. Honduras main trading partner continues to be the United States of America (USA), which. n. received US$ 1,543.5 Million of the country’s general goods exports in 2018 (35%). The rest. Ch. i n U. v. of the general goods exports during 2018 were divided as follows: Europe received US$ 1,292.0. engchi. Million (30%), Central America US$ 879.4 Million (20%) and the Rest of the World amounted for US$ 658.3 Million (15%). Notable partners in the Rest of the World include Mexico with US$ 76.1 Million and Taiwan with US$ 60.1 Million of Honduras general goods received during the year 2018. Regarding the finished goods sector, 82.5% of the total finished goods exported by Honduras correspond to textile products manufactured in industrial parks (US$ 3,517.5 Million). 16% corresponds to automotive industry parts (US$ 680.4 Million) and the rest to other finished products. Once again, the USA stands as Honduras main trading partner by receiving a whopping 72% of all finished products elaborated in the country (US$ 3,067.9 Million) and Central America following with a 19% (US$ 829.9 Million).. 28. DOI:10.6814/THE.NCCU.IMBA.012.2019.F08.

(37) These figures stress the importance of the manufacturing industry in Honduras, not just from an employment source point of view, but also from a macroeconomic standpoint. It is clear to the eye how reliant the country’s economy is on the exports of products elaborated within the industrial parks and how incredibly impactful to the commercial balance our trading relationship with the USA actually is.. 4.1.2. 4.1.2. Imports At the close of the year 2018, Honduras total CIF (Cost, Insurance and Freight) imports amounted US$ 12,200.00 Million divided as follows:  . 政治大 Goods for Transformation 立 (Mostly raw material like threads and clothes destined for General Goods: US$ 9,482.8 Million. ‧ 國. . 學. use in industrial parks): US$ 2,708.0 Million Other Goods: US$ 9.2 Million. ‧. Honduras Total CIF Imports 2015-2018. y. sit. io. al. $11,175.20. $11,000.00. v. $11,323.50. n. $11,500.00. $12,200.00. er. $12,000.00. Nat. $12,500.00. Ch. e$10,558.90 ngchi. i n U. $10,500.00 $10,000.00 $9,500.00 2015. 2016. 2017. 2018. CIF Imports (Millions US$). Figure 14: Honduras Total CIF Imports 2015-2018 The country’s main commercial partner for import of general goods is the USA with a representation of 46.7% of the total general goods imported (US$ 4,430.2 Million). The main 29. DOI:10.6814/THE.NCCU.IMBA.012.2019.F08.

(38) products in this category include fuels, medical and surgical equipment, mobile devices and automobiles. The second largest partner in the imports of general goods is Central America with 20.2% of the total (US$ 1,912.1 Million). Notably for this research, during 2018 a total of US$ 704.8 Million worth of imports were received from Europe consisting of machinery to be used in the textile industry. The goods for transformation were primarily sourced from the USA, with a total of US$ 1,879.1 Million worth of raw materials to be worked within the country’s manufacturing industry (69.4% of total).. 治政大are related to the processing and Manufacturing industry refers to those industries which 立items can have two natures: new commodities or value-added manufacturing of items, these 4.2. The Manufacturing Industry. ‧ 國. 學. commodities. Manufacturing industries came into being with the occurrence of technological and socio-economic transformations in the western countries in the 18th-19th century. This was. ‧. widely known as the industrial revolution starting in Britain with a disruptive force replacing the labor-intensive textile production in the country with mechanization and use of fuels. Nat. sit. y. (Economy Watch, 2010).. er. io. Manufacturing industries are important for an economy as they employ a big share of the labor. al. n. v i n the previous chapter made it clearConhhow important this e n g c h i Usector is for the country’s economy in force and produce goods required by sectors of strategic importance. In the case of Honduras,. terms of employment and finished goods exports.. 4.2.1. National Perspective & Background The manufacturing industry represents one of the most productive and economically impactful sectors of Honduras; throughout the years, it has bolstered employment as well as harnessed the consolidation and development of companies dedicated to the transformation of raw material into finished goods. The manufacturing industries in Honduras really took off in the 1980’s, when the decree for the creation of the “Industrial Zones for Manufacturing of Export Goods” was approved. This decree awarded immense facilities and preferable treatment regarding import/export taxes, property taxes, municipal taxes, company establishment tariffs, government procedures, etc for 30. DOI:10.6814/THE.NCCU.IMBA.012.2019.F08.

(39) companies that established themselves within these designated areas. This led to the origin of the first industrial parks known as INHDELVA and ZIP CHOLOMA located in the northern part of the country in the department of Cortes, which would later hold the highest concentration of manufacturing plants in Honduras. Later in the 1990’s these zones became nationwide and not tied to specific geographic locations and became known as “Free Zones”, as long as the company passed all requirements they could apply for the preferential treatment. This behavior can be observed throughout many Latin-American countries during this decade; the belief was that by awarding more facilities and benefits to international investors they would be more motivated to invest in the local country and further improve the national competencies. This. 治政大 industry as a vital contributor since the 1990’s and since then has positioned the manufacturing 立 to the country’s economy. proved to be the case for Honduras, where foreign investment in the free zones skyrocketed. ‧ 國. 學. The favorable evolution of external and internal demand and increase of private and public investment in the country has resulted in economic growth of 4.8% and 3.1% in 2017 and 2018. ‧. respectively (Honduras Central Bank, 2017). The positive trends are expected to extend into. sit. y. Nat. the year 2019 as new government projects and foreign capital investments take place.. io. er. 4.2.2. Gross Output (GO). Gross Output (GO) is defined as a measure of an industry’s sales or receipts, which can include. n. al. Ch. i n U. v. sales to final users in the economy (GDP) or sales to other industries (intermediate inputs).. engchi. Gross output can also be measured as the sum of an industry’s value added and intermediate inputs (Bureau of Economic Analysis, 2018). During the year 2017, the gross output of. Honduras manufacturing plants reached L.. 150,583.00 Million (Approximately US$ 6,275.00 Million), this represented a 3.9% increase compared to the previous year. The vast majority of the goods accounted for the GO correspond to textiles and apparel with a 75.9% representation of the total with a 3.0% yearly increase. The automotive parts reflected a similar 3.0% increase compared to the previous year due to higher demand from the USA (Honduras Central Bank, 2017). 31. DOI:10.6814/THE.NCCU.IMBA.012.2019.F08.

(40) 4.2.3. Gross Value Added (GVA) Gross Value Added (GVA) is an economic productivity metric that measures the contribution of a corporate subsidiary, company or municipality to an economy, producer, sector or region. It basically provides a dollar value for the number of goods and services that have been produced in a country, minus the cost of all inputs and raw materials that are directly attributable to that production (Kenton, 2019) In order to calculate the Gross Value Added (GVA) of the manufacturing plants in Honduras, another vital piece of information is the total inputs that were used for the production output. The value of these inputs in the year 2017 reached L. 117,330.8 Million (Approximately US$. 政治大. 4,888.00 Million) reflecting a 4.1% increase compared to the previous year.. 立. The result of subtracting the total inputs from the gross output will result in the country’s gross. ‧ 國. 學. value added for the year 2017: L. 150,583.00 M – L. 117,330.80 M = L. 33,252.20 (Approximately US$ 1,385 Million). This figure presented a 2.9% increase to that of 2016. The. ‧. major components of the GVA were: Salaries 65.8%, Social Contributions 8.3%, Tax on Production 0.9% Gross Operating Surplus 25.0%. The GVA represents an important figure. Nat. sit. y. since it can be used to observe how much value is added (or lost) from a particular region or. io. the next section.. er. state as well as serving for the calculation of the gross domestic product (GDP) as described in. al. n. v i n 4.2.4. Contribution to GrossC Domestic (GDP) U h e n gProduct i h c The manufacturing industry represents one of the fastest growing and productive industries of Honduras. Throughout the years, they have achieved this in part due to the creation of an effective vertical integration of the production processes in which the final goods are manufactured under a single roof. This promotes the reduction of costs for the companies and optimizes the transformation process by reducing the time to process while at the same time standardization and quality is increased. Another phenomenon arising from the success of these companies in the country is the birth of economic groups, which are compromised by the companies who import the raw material and other which provide freight service, equipment maintenance, quality assurance, job placements, miscellaneous items providers, etc.. 32. DOI:10.6814/THE.NCCU.IMBA.012.2019.F08.

(41) In order to measure the true growth of the series, the following chart presents the manufacturing’s industry contribution to Honduras GDP in constant prices:. Manufacturing Ind. Contribution to GDP 2015-2018 $1,750.00. $1,726.08. $1,700.00 $1,662.25 $1,650.00 $1,597.50. $1,600.00 $1,551.23 $1,550.00. 立 2015. 學. $1,450.00. ‧ 國. $1,500.00. 政治大 2016. 2017. 2018. Contribution to GDP (Millions US$). ‧. sit. y. Nat. Figure 15: Manufacturing Industry Contribution to GDP 2015-2018. er. io. In the year 2018, the manufacturing industry was officially the largest contributor to Honduras. al. n. v i n Ch stood at L. 214,705.00 Million (Approximately U Million) in constant prices. Other e n g cUS$ h i 8,946. Gross Domestic Product by having a 19.3% participation of the country’s total GDP, which. major contributors include the agriculture sector with a participation of 14.5%, financial. intermediation services with a participation of 18.7% and hotels & restaurants with a participation of 11.6%. The increasing trend on the contribution of the manufacturing industry is clear to the eye and is expected to continue in that same way for the near future as new investment projects take off. On the other hand, the yearly variation on what percentage the manufacturing industry represents of the GDP is not drastic with it being between 19% and 20% of the total GDP since the year 2015 (2015 – 19.6%, 2016 – 19.4%, 2017 – 19.3%, 2018 – 19.3%). (Honduras Central Bank, 2018). 33. DOI:10.6814/THE.NCCU.IMBA.012.2019.F08.

(42) 4.2.5. Commercial Balance Unlike the overall trade deficit the country’s commercial balance experiences, the manufacturing industry’s commercial balance has constantly been a surplus. This is due to the value that is added to the imported raw materials in their transformation process into finished goods for exports. Table 3: Honduras Manufacturing Industry Commercial Balance 2016-2018 Concept 2016 2017 2018 Finished Goods FOB Exports. US$ 4,018.9. US$ 4,079.1. US$ 4,263.1. Goods for Transform. CIF Imports. US$ 2,567.5. US$ 2,723.8. US$ 2,708.0. Commercial Balance. 立. 治 US$ 1,355.3 US$ 1,555.1 政US$ 1,451.4 大 (Honduras Central Bank, 2018). ‧ 國. 學. Within the finished goods exports, there are two major types of goods which have come to. ‧. dominate the whole manufacturing industry’s total output by a landslide. The two major goods are Textile Products and Automobile Equipment & Parts which together accounted for 98.5%. y. Nat. sit. of the total finished goods exported by the country in 2018. Out of total market share, Textile. al. er. io. Products position themselves as the largest contributor to finished goods exports in the country. n. (82.4% in 2016, 82.2% in 2017 and 82.5% in 2018) this trend is expected to continue, as. Ch. i n U. v. Honduras is an increasingly more attractive destination for major international textile firms to. engchi. manufacture their products. The same can be said for the Automobile Equipment & Parts, however, this sector is at a lower developing stage compared to the textile industry. In the past few years, more and more international firms have started investing in the country motivated by the lower costs for the fabrication of specific automobile equipment and parts. These two major goods have accounted for more than 98% of Honduras finished goods exports since the early 2000’s, establishing themselves as a pillar for the economy and a vital source of employment.. 34. DOI:10.6814/THE.NCCU.IMBA.012.2019.F08.