Automation and Wage Inequality: Use of AI and Robotics in New York and their Effects on Wage Inequality


            The new age of technology and automation has significantly changed work patterns and employment. People in blue-collar and white-collar jobs are feeling the impact of automation on employment and wage inequality. In America, automation has contributed to a 50% to 70% reduction in wages (Acemoglu, 2021). The fourth wave of the industrial revolution has also paved the way for the integration of the latest technologies such as Artificial Intelligence, additive manufacturing, big data, and top-edge robotics (Venturini, 2022).

            Automation is the use of a wide range of technologies to reduce human intervention in the workplace or industry. Automation can be performed by using or introducing automatic equipment in a manufacturing plant or other facility (Ivančić, 2019). Automation was introduced into the working industry to improve the efficiency of the manufacturing process. The term automation was coined in 1946 in the automobile industry to describe the increased use of automatic devices and controls in mechanized production lines (Groover, 2018). The incorporation of technology and computer processors has revolutionized the use of automation in the workplace since 1946. Modern automated types of machinery such as cars, power monitoring devices, drones, robots, and mobile phones involve computers. Although primarily employed to reduce human interaction, these automated systems still required human intervention, until recently with the adoption of artificial intelligence (Janssen, 2019).

            There have been three phases of the industrial revolution. The first industrial revolution was mechanization, the use of machines powered by steam or water to increase production. The second industrial revolution began with the assembly line in manufacturing plants. People in this era used electricity as a means to mass-produce products. The third industrial revolution utilized electronics and information technology through modernized computer systems to perform tasks. The world is currently evolving from the third era as almost all industries have incorporated IT and automation into the production and daily running of their businesses. distribution  The fourth industrial revolution incorporates the use of current automated machines that will be incorporated with improved technology and intelligent computer systems (Janssen, 2019).

            The fourth revolution has already started to take shape with most companies integrating their AI systems into their products. Some examples of AI systems that are already being used are; Siri from apple, Alexa from amazon, google assistant, and Cortana from Microsoft. More integration is yet to be incorporated into our daily lives as AI systems such as self-driving cars and a fully automated financial investing system and the effects of the fourth industrial revolution are still being analyzed (Venturini, 2022).

            Wage inequality is income distribution disparity in a given population. it is characterized by either wage differences for people in the same job, hefty wage disparities between management and workers, or in the country, major differences in terms of wages between the rich and poor (Palomino, 2020). Wage inequality is caused by several reasons such as; automation and technological advancement, the level of unemployment, globalization, the decline of work unions, and inflation (Ee, et al, 2018). Automation and technological advancement is one of the major causes of wage inequality.

            The increased use of automation has increased panic amongst both the skilled and unskilled labor force. More unskilled workers are now finding it very difficult to get employed. Those who get employed have to deal with the minimum wage which has not changed in America even after inflation. For example, Foxconn, an iPhone manufacturer with over 1.2 million employees reported in 2016 that it would use robots and replace its workers in almost its entire company (Zhang, 2019). The incorporation of automation in industries is most likely to continue due to advancements in technology and the low costs of implementing automation in the workplace. Although human intervention is required, the incorporation of AI systems is slowly getting rid of human interaction which was deemed essential (Baek, 2022).

            This paper seeks to uncover the impact of automation on Wall Street in New York. Financial stock exchange institutions are rapidly integrating robotics and AI systems into their day-to-day running. There is little to no research on the effects of automation in financial investment companies and its effects on wage inequality in financial firms. This paper seeks to bridge this gap by uncovering how automation has affected investment brokers’ wages and whether automation has caused wage inequality in the sector.

Literature Review

            The literature review section will be used to analyze the relevant literature regarding automation and wage inequality. The section will also provide theoretical concepts that will be investigated by the researcher.

Automation and Wage distribution

            Automation is one of the causes of wage inequality. Unemployment is one of the major ways automation affects wage inequalities in America. The number of low-skilled workers in the United States has been shrinking for ages. The increased use of robots and automated machines in blue-collar jobs has caused more unskilled workers to lose their sources of livelihood (Lankisch, 2019). The threat of unemployment has now even spilled over to persons with white-collar jobs as the integration of AI with robotics will soon take over their livelihoods (Baek, 2022).

            Another way through which automation affects wage distribution in America is through disparities in income. Due to automation, most unskilled laborers have opted for minimum-wage income jobs as their source of livelihood (Smith, 2019). On the other hand, owners of companies that mainly use robotics and artificial intelligence continue to get richer due to the increase in production in their companies and the reduction of labor force costs (Palomino, 2020).

Incorporation of AI technology in the workplace

            The incorporation of AI systems in the workplace is becoming rampant. Although the company tends to benefit the most from the incorporation of AI systems in the workplace, employees may have different perceptions of AI systems. The risk of an employee losing their job due to the incorporation of AI systems is a constant fear amongst employees. This fear affects their work attitudes, career exploration behaviors, and employee productivity in the workplace (Presbitero, 2022). The incorporation of AI has also reshaped the social dynamics in the workplace with employees working in organizations that utilize AI providing that there are major issues such as privacy, health, and security implications of using AI technology (Cebulla, 2022).

            Financial institutions have increasingly become dependent on information technology and information systems, which makes incorporating the latest technology trends such as AI into the industry very important. Financial institutions need to use the aforementioned technologies to provide customized services to their customers as well as to monitor transactions (Sabharwal, 2014).  The use of AI will redefine how financial institutions function, institute ground-breaking products, and revamp the customer experience (Malali, 2020).

Use of Robotics in the Workplace

            Robotics have been incorporated across all industries by supporting workers in manufacturing, assembly, transportation, warehousing, logistical support, and surgery. Although there is a major concern that the incorporation of robotics may cost jobs, experience with collaborative robots has shown improvements in precision, productivity, and workplace satisfaction. This is due to the decrease in working hours and energy that would have been used without collaborating with robots (Lonner, 2019).

            Social humanoid robots are constantly being introduced in the workplace to occupy support staff roles such as receptionists and personal assistants. The robots significantly reduce the number of employees who perform support staff roles. The incorporation of these robotics however has brought up negative attitudes as most human employees do not recognize the robots as part of their team. Firms such as financial institutions are gradually incorporating social humanoid robots to perform support staff functions (Saari, 2022).

Effects of AI and Robotics on the Distribution of Work

            The use of AI is changing the role allocation for workers and machines in a firm (Dawid H., 2021). The use of AI technology in collaboration with skilled workers is seen to improve the production and profitability of a firm. AI technology on the other hand can cause negative effects on work distribution as AI can be used to replace workers (Aghion, 2019). The low-skilled labor force who occupied various support staff duties such as cleaners, receptionists, and personal assistants are slowly being removed from the workforce by AI-enabled machines. Automatic vacuums, Robotic Process Automation, and social humanoids are taking up work roles that would have been done by the low-skilled workforce (Saari, 2022).

            The use of robotics and AI has made it more difficult for people without computer knowledge to attract jobs. AI and Robotics have been integrated into the workplace to incorporate a collaborative workforce. That is employees, working together with robots and the AI system. Researchers argue that people in nonroutine jobs such as legal, writing track driving, and medicine have the lowest probability of losing their jobs. The incorporation of robotics and AI was not aimed at reducing low-skilled workers, but rather doing away with middle-skilled workers. Recent studies have shown that the incorporation of AI and robotics are going to threaten most skilled and nonroutine jobs (Aghion, 2019).

            Several theories on work are stratified by skills, level of education, and economic positioning of people. Although these theories are based on true historical occurrences, they do not take into account other factors such as the number of employees and the workflow dimensions of employees in terms of hours worked. Automation, on the other hand, is attributable to wage inequality (Mondolo, 2021).

Automation and Wage Inequality

            Automation has caused wage inequality in several industrial sectors including the mining sector. Mining is traditionally a low-skilled job venture. The use of automation in the mining industry has become rampant with most mining companies using labor substitution robots in a wide range of modern mining processes. The use of robotics in the mining sector was progressed by the covid-19 pandemic which propagated the reduction of human interaction in most critical operation procedures. Although the use of automation increases productivity, reduces risks and has positive economic impacts, it takes away job opportunities that were used to increase employment opportunities in mining communities. The reduction in employment opportunities creates a rift in the economic position of the miners and the mine owners as the miners are left jobless and poor and the mine owners have increased wealth opportunities (Paredes, 2021).

            The effects of automation on economic growth, education, and wage inequality are stratified according to the level of skills of an employee. That is, employees regarded as high-skilled workers are seen as complements of automation, and low-skilled workers as substitutes for automation. Due to this disparity, more people are enrolling in higher-level institutions. Although automation has increased interest in learning, income and wage inequality have increased and the labor share reduced (Prettner, 2020).

            The impacts of the fourth industrial revolution are most likely going to cause an increase in wage inequality and the distribution of income around the world. The acceleration of the wage inequality gap will be intensified due to one of two reasons. First, there will be increased polarization in the labor market such that employees at the top and bottom of the wage and skill distribution are expected to benefit extensively from the increase in technological advancements and inventions. The recent changes in work roles are dependent on the shifts in occupational roles rather than changes due to shifts in the distribution of employment among occupations. As such, wage inequality could also increase in organizational occupation statuses of employees depending on the employees’ willingness and ability to learn the new technologies or, in the hiring process where incumbent workers with a longer tenure have a large wage gap with the new employees. The second reason is, organizations are now more concerned about bridging the gender wage gap. The relationship between gender and technology in discerning whether the wage gap will affect the firm’s wage distribution rather than the economic gender wage distribution due to automation (Domini, Grazzi, Moschella, & Treibich, 2021).

The notion that automation takes job opportunities from low-skilled workers and increases opportunities for high-skilled workers may be an outdated outlook on the effects of automation on employment distribution. In the past couple of years, advancements in technology have allowed for more advanced tasks such as surgeries to be automated. This advancement poses a major threat to the job security of high-skilled workers (Montoya, 2020).

Suggested Policies to Overcome Wage Inequality

            Several policies have been suggested to mitigate the effects of wage inequality due to automation. One of the policies is investing in higher education. Education is a critical policy for responses to wage inequality and job loss. The age of automation will require employees to be well-versed in technological advancements and skills to use inventions from the advancements. Higher education facilities are some of the learning institutions that equip people with relevant skills and knowledge of automation. Also, people with a degree have a higher chance of attracting well-paying jobs than people without a degree. Investing in higher education will soften the negative effects of automation (Lankisch, 2019).

To achieve higher education learning, an education subsidy is required so that higher education does not only benefit high-income households but also low-income households. The education subsidy can be financed through robot tax and higher taxation rates for people with higher incomes (Prettner, 2020). Education policies will promote the formation of automation- complementing skills and reducing the need for costly labor markets and wealth redistributions in the future. Educating employees can reduce the wage inequality range between high-skilled employees and low-skilled employees by up to 3%. Education mitigates the worst-case expectation of automation’s marginal effect on wage inequality and unemployment in the workplace (Kattan, 2021).

            Implementation of a robot tax is another policy implementation strategy that can be adopted to cushion the effects of automation on wage inequality. The ideology behind the robot tax is that any company that replaces a human being with a robot should pay a robot tax. The tax would then provide a safety net for the person who is replaced by the robot. A robot tax may be implemented when a country wants to disincentivize firms from replacing human personnel with robots. The government could also impose a robot tax to encourage employers to maintain their human employees despite the many benefits that come from using robots. Imposing a robot tax will ensure that the effects of automation on wage inequality are mitigated (Ionescu, 2019).


            In this section, I will explain and use both Marxism theory and the new growth theory that have led to the increase in the use of automation which has affected wage inequality in the New York economy. By analyzing these theories, you will have a better idea of the economic driving forces that promote automation in the workplace. These theories will also help in explaining the wage inequality experienced between the rich (business owners/ bourgeoisie) and the working class (proletariat). The Marxism theory provides that the capitalist economy is comprised of two socioeconomic stratifications, the ruling class (bourgeoisie) and the working class (proletariat). The theory provides that the bourgeoisie control the means of production and strive to maximize production and offer minimum wages to their employees creating wage inequality (Zhang, 2021). Similar to Marxism theory, the new growth theory provides that human desire is the driving force for increased productivity. This desire for growth forces people to seek new and innovative ideas to maximize their potential to earn profits. An example of these ideas is the use of automation in the workplace (Lee, 2021).

            The empirical model to be employed in the research is the multifactor model. A multi-factor empirical model is a financial model that utilizes multiple factors in its calculations to explain a market phenomenon. The multifactor model is used to hypothesize cases with certain characteristics and in the process find their relationships. The model utilizes a regression analysis to find out relationships between two or more factors (Acemoglu, 2021). The multifactor empirical model is the best fit for the research as it will enable the researchers to analyze the effects of automation on wage inequality. The model, through regression, will enable the researcher to analyze the effects of automation on wage inequality, work distribution, and wage distribution in New York’s wall street.

Data Summary

            To understand the effects of automation on wage inequality, the researchers need to analyze the employment distribution, the payroll distribution, the analysis of the gross job gains and losses experienced by the finance industry, and the percentage increase and decrease in automation in the finance district in New York City’s financial industry. The researcher first analyzed the employment data from the Department of Labor website (Department of Labor, n.d.). The website provided secondary data on the employment distribution excluding nonfarm employment, hours, and earnings. The data provided is stratified according to the industry within New York City and dates back to 1990. The data from the Department of Labor website is pertinent to the research as the researchers will be able to analyze employment distribution in the finance sector from 1990 to date. The data provided will also enable the researcher to calculate the employment index from 1990 to 2022 and, in the process, analyze whether there has been an increase in employment in New York City or a decrease in employment in relation to the population of the city. The Department of Labor also provides employment data that is stratified according to industries within New York City (CES employment by Industry). The CES employment by industry enables a researcher to retrieve data for an industry based on geography, time period, and seasonally adjusted data. As the main focus of this research is the Wall Street financial district, the researchers will utilize the finance industry employment data in the analysis. The data will enable the researcher to analyze the change in employment in the financial industry from 1990 to 2022 (Department of Labor, n.d.). This analysis will help the researcher gain more understanding of the employment patterns of New York City’s financial industry and inevitably aid in the analysis of automation on work distribution.

            The second source of data will be the United States Bureau of Labor Statistics. Three types of data will be collected from this website; the average payroll for the New York City financial industry, the gross job gains and losses, and the average hours and earnings of all employees on private nonfarm payrolls (Bureau of Statistics, n.d.). The average payroll for the New York City financial industry will be analyzed to understand the disparities in wage allocation within the financial district. The payroll average will also enable the researcher to analyze the relationship between automation and wage distribution. The payroll disparities will also be matched with the employment levels to analyze whether there is an increase in payment or a reduction in the average pay by each employee. The gross job gains and losses describe the employment opportunities gained or lost within a specified period. The Bureau of Statistics provides the average data on the gross employment gains and losses of each industry in New York City. Employment gains are caused by the opening or expansion of private sector establishments. The website provides seasonal data from December 2020 to December 2021on the employment opportunities and losses experienced by every industry in New York City. This data is pertinent to the study as it will provide an analysis of the job creation opportunities and job losses experienced. As the data also provides reasons for employment gain or loss, such as; opening establishments, or expanding establishments for employment gains, and closing establishments, or downsizing establishments for job losses (Bureau of Statistics, n.d.).

            The research will also utilize data from the census to calculate the employment index (, n.d.). The Rockefeller Institute of Government provides data on the percentage integration of automation in various industries in New York City. The article provides an in-depth analysis of automation in the financial industry of New York City (Schultz, 2018). This data will be used as the dependent variable for the analysis to find out the effect of automation on wage inequality in New York.


This section is used to discuss the various methods of data collection and analysis that will be used to analyze the effect of automation on wage inequality on New York’s Wall Street. The research design, methods of data collection, measurements, the data collection process, and data analysis of the research.

Research Design

The research design is the overall strategy that is used to incorporate the different parts of the design in a logical manner, which enables the researcher to answer research questions effectively. It is the backbone for the collection, measurement, and analysis of data (Dannels, 2018). The research design is quantitative. Quantitative research is the process of collecting and analyzing empirical data. Quantitative research can be utilized to uncover patterns, make predictions, test causal relationships, and come up with generalized inferences about a wider population. Quantitative research was utilized as it is easy to replicate, a researcher can formulate direct comparisons of the results, it can be conducted on large populations, and conclusions from the research are precise (Bloomfield and Fisher, 2019). 

The research will utilize the causal quantitative research design. The research seeks to find out the effects of automation on wage inequality and therefore, a causal-comparative or quasi-experimental research design will be the best fit for the research. Causal research design is a research design that seeks to uncover the relationship between an independent variable with a dependent variable after an action has already occurred. The main goal of the causal research design is to uncover whether the independent variable, in this case, automation, affected the dependent variable, wage inequality, by comparing two or more groups in a population. The researcher in this case should not interfere with both the independent or dependent variables as causal research only analyses events that have already occurred (Asenahabi, 2019).

The type of causal-comparative research being analyzed is retrospective causal-comparative research. Retrospective causal-comparative research is a type of research where a researcher analyses the effects of a phenomenon after it has already occurred. The researcher, in this case, tries to uncover whether one variable directly affects another variable. Retrospective research is one of the most common types of causal research. Retrospective research analyses the history of variables analyzing whether the independent variable has impacted any effects on the dependent variable (Brooke, 2013). 

The research will also employ a case study approach where previous research on the topic will be analyzed to provide a correlational analysis between automation and wage inequality. A case study research is an in-depth analysis that is utilized to understand real-life situations.  A document review case study data collection approach will be employed in gathering the research (Crowe, et al., 2011).

            Data is a collection of facts, figures, objects, symbols, and or events that are gathered from different sources depending on the type of research being conducted. There are two types of data, primary data, and secondary data. Primary data is data collected from first-hand experiences or events. It is generated by the researcher. Primary data collection methods are normally tailored to answer research questions to the researcher’s specifications. The primary data is dependent on the research design, that is, quantitative design or qualitative design. Secondary data is data that has been used in the past. A researcher can obtain data from organizations being analyzed either internal sources or external sources using data sources (Taylor, 2020).

This research will utilize secondary data collection methods. Secondary data sources will be utilized to analyze the general effects of automation on wage inequality. Both internal data sources and external data sources will be analyzed to provide the causative effects of automation on wage inequality on Wall Street. Internal data sources that will be utilized are the company’s employment details and salary distribution in the past 10 years. This will enable the researchers to analyze whether the use of automation has reduced the number of workers on Wall Street. The internal data will also enable the researcher to uncover wage distribution patterns on wall street. External data sources to be utilized are the government statistical report on employment levels on wall street. The data will enable the researcher to analyze the wage distribution in wall street as compared to New York to gain a better understanding of whether automation has affected employment and wage inequality on wall street. Previous research on the topic will also be analyzed to gain a better idea of automation and its effects on wage inequality.

            In research methodology, measurement is referred to as the process of collecting and recording data. It is the process of assigning a numerical value to the data being collected especially in a questionnaire. Measurement is the foundation of scientific inquiry. The measurement applies to both quantitative and qualitative research. In quantitative research, measurement is used to compute height, weight, length, and income. In qualitative research, measurement can be used to compute attitude, opinion, personality, and preference. For measurement to occur at different levels, a scale must be used to determine the relationship among the assigned values of data. There are four different types of measurement scales; nominal scales, ordinal scales, interval scales, and ratio scales (Dalati, 2018).

            This research will utilize the ratio scale. Ratio scales are the most precise levels of measurement that is quantitative. The ratio scale has an absolute zero point of origin. The scale is used when measuring items that have an absolute zero such as weight, age, or income. For example, if person A earns $10,000 and person B earns $5,000, A earns twice as much as B. another characteristic of the ratio scale is that the scale has no negative number. This characteristic makes the scale least suitable for measurements such as temperature which does not have an absolute zero. The ratio scale also provides a unique characteristic for statistical analysis including descriptive statistics, chi-square tests, and regression. Lastly, the ratio scale units can be converted to other forms of measurement, for example, weight in kgs can be converted to tones (Dalati, 2018).


Acemoglu, D. (2021, June). Tasks, Automation, and the Rise in US Wage Inequality. The National Bureau of economic research. Retrieved from

Aghion, P. (2019). Artificial Intelligence, Growth and Employment: The Role of Policy. Journal of Economic Literature, 149-164.

Asenahabi, B. M. (2019). Basics of research design: A guide to selecting appropriate research design. International Journal of Contemporary Applied Researches, 6(5), 76-89.

Baek, S.-U. (2022). Association between Workers’ Anxiety over Technological Automation and Sleep Disturbance: Results from a Nationally Representative Survey. International Journal of Environmental Research and Public Health, 19(16).

Bureau of Statistics. (n.d.). Current employment statistics (State and Metro Area). Retrieved from

Ball, H. L. (2019). Conducting online Survey. Journal of Human Lactation, 35(3), 413-417.

Bloomfield, J., & Fisher, M. J. (2019). Quantitative research design. Journal of the Australasian Rehabilitation Nurses Association, 22(2), 27-30.  

Brooke, J. (2013). SUS: A Retrospective. Journal of Usability Studies, 8(2), 29-40.

Cebulla, A. (2022). Applying ethics to AI in the workplace: the design of a scorecard for Australian workplace health and safety. AI & Security. (n.d.). Quick Facts. Retrieved from,US#

Dalati, S. (2018). Measurement and measurement scales. In Modernizing the Academic Teaching and Research Environment (pp. 79–96). Springer, Cham.

Dannels, S. A. (2018). Research Design. The Reviewer’s Guide to Quantitative Methods in Social Sciences, 402-416.

Dawid H., N. (2021). Effects of Technological Change and Automation on Industry Structure and (Wage-)Inequality: Insights from a Dynamic Task-Based Model.

Department of Labor. (n.d.). Current Employment Statistics. Retrieved from

Domini, G., Grazzi, M., Moschella, D., & Treibich, T. (2021). For whom the bell tolls: The firm-level effects of automation on wage and gender inequality. Research Policy, 51(7).

Ee, M. S., Chao, C.-C., Wang, L. F., & Yu, E. S. (2018). Environmental corporate social responsibility, firm dynamics and wage inequality. International Review of Economics & Finance, 56, 63-74.

Gray, D. E. (2019). Doing research in the business world. Sage Publications Limited.

Groover, M. P. (2018). Automation. Brittanica, inc. Retrieved September 22, 2022, from

Ionescu, L. (2019). Should governments tax companies’ use of robots? Automated workers, technological unemployment, and wage inequality. Economics, Management, and Financial Markets, 14(2). doi:10.22381/emfm14220195

Ivančić, L. (2019). Robotic Process Automation: Systematic Literature Review. International Conference on Business Process Management (pp. 280–295).

Janssen, C. P. (2019). History and future of human-automation interaction. International Journal of Human-Computer Studies, 131, 99-107.

Kattan, R. B. (2021). The role of education in mitigating automation’s effect on wage inequality. Labour, 35(1), 79-104.

Lankisch, C. (2019). How can robots affect wage inequality? Economic Modelling, 81, 161-169.

Lee, Y.-S. (2021). New general theory of economic development: Innovative growth and distribution. Review of Development Economics, 24(2), 402-423.

Lonner, J. H. (2019). Emerging robotic technologies and innovations for hospital process improvement. Robotics in Knee and Hip Arthroplasty, 233–243.

Malali, A. B. (2020). Application of Artificial Intelligence and Its Powered Technologies in the Indian Banking and Financial Industry: An Overview. IOSR Journals.

Mondolo, J. (2021). The composite link between technological change and employment: A survey of the literature. Journal of Economic Surveys, 36(4), 1027-1068.

Montoya, C. (2020). Automation in a new age: The effects of technological advancement on labor market outcomes for low-and high-skill labor. Institutional Scholarship. Retrieved from

Palomino, J. C. (2020). Wage inequality and poverty effects of lockdown and social distancing in Europe. European Economic Review, 129.

Paredes, D. (2021). Automation and robotics in mining: Jobs, income and inequality implications. The Extractive Industries and Society, 8(1), 189-193.

Presbitero, A. (2022). Job attitudes and career behaviors relating to employees’ perceived incorporation of artificial intelligence in the workplace: a career self-management perspective. Personnel Review.

Prettner, K. (2020). Innovation, automation, and inequality: Policy challenges in the race against the machine. Journal of Monetary Economics, 116, 249-265.

Saari, U. A. (2022). Exploring factors influencing the acceptance of social robots among early adopters and mass market representatives. Robotics and Autonomous Systems, 151.

Sabharwal, M. (2014). The use of Artificial Intelligence (AI) based technological applications by Indian Banks. International Journal of Artificial Intelligence and Agent Technology, 2(1), 1-4.

Schultz, L. (2018, November 13). The Impact of Artificial Intelligence on the Labor Force in New York State. Retrieved from Rockefeller Institute of Government:

Smith, D. (2019). The robots are coming: Probing the impact of automation on construction and society. Construction Research and Innovation, 10(1), 2-6.

Taylor, S. (2020). Challenges to school-based physical activity data collection: Reflections from English primary and secondary schools. Health Education Journal, 80(1).

Venturini, F. (2022). Intelligent technologies and productivity spillovers: Evidence from the Fourth Industrial Revolution. Journal of Economic Behavior & Organization, 194, 220-243.

Zhang, P. (2019). Automation, wage inequality and implications of a robot tax. International Review of Economics & Finance, 59, 500-509.

Zhang, X. (2021). Exploring the sinicization of Marx’s social capital reproduction theory: review and reflection. China Political Economy, 4(2), 170-185.   

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