IELTS Reading: Tự động Hóa Và Bất Bình đẳng Thu Nhập - Đề Thi Mẫu Có đáp án Chi Tiết » IELTS.NET

Chủ đề về tự động hóa và tác động của nó đến bất bình đẳng thu nhập đang ngày càng trở nên phổ biến trong các kỳ thi IELTS Reading gần đây. Đây là một chủ đề thuộc lĩnh vực kinh tế-xã hội, thường xuất hiện dưới dạng các bài đọc học thuật về công nghệ, thị trường lao động và những thay đổi xã hội. Hiểu rõ chủ đề này không chỉ giúp bạn chuẩn bị tốt cho kỳ thi mà còn nâng cao hiểu biết về các xu hướng toàn cầu đang định hình tương lai việc làm.

Trong bài viết này, bạn sẽ được thực hành với một đề thi IELTS Reading hoàn chỉnh bao gồm 3 passages với độ khó tăng dần từ Easy đến Hard, tổng cộng 40 câu hỏi đa dạng giống thi thật. Mỗi passage đi kèm với đáp án chi tiết, giải thích cụ thể và bộ từ vựng quan trọng được phân tích kỹ lưỡng. Đề thi này phù hợp cho học viên từ band 5.0 trở lên, giúp bạn làm quen với format câu hỏi thực tế, rèn luyện kỹ năng skimming-scanning và nâng cao khả năng paraphrase – những kỹ năng then chốt để đạt band điểm cao trong IELTS Reading.

Hướng dẫn làm bài IELTS Reading

Tổng Quan Về IELTS Reading Test

IELTS Reading Test kéo dài 60 phút và bao gồm 3 passages với tổng cộng 40 câu hỏi. Mỗi câu trả lời đúng được tính là 1 điểm, không có điểm âm nếu trả lời sai.

Phân bổ thời gian khuyến nghị:

Passage 1: 15-17 phút (độ khó Easy, câu hỏi 1-13)
Passage 2: 18-20 phút (độ khó Medium, câu hỏi 14-26)
Passage 3: 23-25 phút (độ khó Hard, câu hỏi 27-40)

Lưu ý: Không có thời gian bổ sung để chép đáp án sang Answer Sheet, vì vậy bạn cần quản lý thời gian chặt chẽ và viết đáp án trực tiếp vào phiếu trả lời trong 60 phút.

Các Dạng Câu Hỏi Trong Đề Này

Đề thi mẫu này bao gồm các dạng câu hỏi phổ biến nhất trong IELTS Reading:

Multiple Choice – Trắc nghiệm nhiều lựa chọn
True/False/Not Given – Xác định thông tin đúng/sai/không được đề cập
Matching Headings – Nối tiêu đề với đoạn văn
Sentence Completion – Hoàn thành câu
Summary Completion – Hoàn thành đoạn tóm tắt
Matching Features – Nối thông tin với đối tượng
Short-answer Questions – Câu hỏi trả lời ngắn

IELTS Reading Practice Test

PASSAGE 1 – The Rise of Automation in Modern Workplaces

Độ khó: Easy (Band 5.0-6.5)

Thời gian đề xuất: 15-17 phút

The world of work is undergoing a dramatic transformation as automation technologies become increasingly sophisticated and widespread. From self-checkout machines in supermarkets to automated manufacturing lines in factories, machines are taking over tasks that were once performed exclusively by human workers. This shift towards automation has sparked considerable debate about its impact on employment and income distribution across different sectors of society.

Automation, in its simplest form, refers to the use of technology to perform tasks with minimal human intervention. While the concept is not new – mechanical looms revolutionized textile production in the 18th century – the pace and scope of modern automation are unprecedented. Today’s technologies, powered by artificial intelligence (AI) and machine learning, can handle not just repetitive physical tasks but also complex cognitive functions such as data analysis, customer service, and even medical diagnosis.

The adoption of automation varies significantly across industries. Manufacturing has been at the forefront of this trend, with robots now performing tasks ranging from welding car parts to packaging consumer goods. The automotive industry, for instance, employs thousands of robots that can work 24 hours a day without breaks, producing vehicles with remarkable precision and consistency. Similarly, the logistics sector has embraced automation through warehouse robots and autonomous delivery vehicles, dramatically improving efficiency and reducing operational costs.

However, the impact of automation extends far beyond blue-collar jobs. White-collar professions are also experiencing significant changes. Accounting software can now process financial transactions and generate reports that once required teams of bookkeepers. Legal research tools powered by AI can review thousands of documents in minutes, a task that would take human lawyers weeks to complete. Even creative fields are not immune – algorithms can compose music, write news articles, and design graphics.

The relationship between automation and income inequality is complex and multifaceted. On one hand, automation can lead to job displacement, particularly affecting workers in routine and repetitive roles. When companies replace human workers with machines, those displaced employees may struggle to find new employment, especially if they lack the skills required for the changing job market. This can result in prolonged unemployment or underemployment, pushing affected workers into lower-paying positions and widening the income gap between different social groups.

On the other hand, automation creates new opportunities and can potentially increase overall productivity and economic growth. As machines take over mundane tasks, human workers can focus on more creative, strategic, and interpersonal activities that require uniquely human capabilities such as emotional intelligence, critical thinking, and innovation. Companies that successfully implement automation often see increased profits, which could theoretically be distributed to benefit workers through higher wages or shorter working hours.

The key question is whether the benefits of automation are shared equitably across society. Research suggests that the gains from automation have been disproportionately captured by business owners, shareholders, and highly skilled workers who can work alongside advanced technologies, while low-skilled workers face declining wages and job security. This divergence in outcomes contributes to growing income inequality, with the wealthy becoming wealthier while the economic prospects of middle and lower-income groups stagnate or decline.

Educational attainment plays a crucial role in determining who benefits from automation and who is left behind. Workers with advanced degrees and specialized skills in fields like software engineering, data science, and robotics maintenance are in high demand and command premium salaries. Meanwhile, those with only basic education or training in routine manual tasks find their job prospects diminishing as machines become more capable and cost-effective alternatives to human labour.

Geographic factors also influence how automation affects income inequality. Urban areas with thriving technology sectors tend to offer more opportunities for high-skilled workers, while rural and post-industrial regions that relied heavily on manufacturing jobs face economic decline as factories automate or relocate. This spatial inequality can create a vicious cycle where talented young people leave struggling regions for better opportunities elsewhere, further concentrating wealth and economic activity in already prosperous areas.

Policymakers face the challenge of managing the transition to an increasingly automated economy while ensuring that the benefits are broadly shared. Proposals include strengthening social safety nets, investing in education and retraining programs, implementing progressive taxation on the profits generated by automation, and even exploring concepts like universal basic income. The goal is to help workers adapt to the changing labour market while preventing automation from exacerbating existing inequalities.

In conclusion, automation represents both an opportunity and a challenge for modern societies. While it has the potential to increase productivity and free humans from tedious work, it also risks widening income gaps if not managed carefully. The ultimate impact of automation on income inequality will depend largely on the policy choices made today and the ability of educational systems to prepare workers for the jobs of tomorrow.

Questions 1-13

Questions 1-5: Multiple Choice

Choose the correct letter, A, B, C, or D.

According to the passage, modern automation differs from historical automation because it:
- A. Only affects manufacturing jobs
- B. Can perform both physical and cognitive tasks
- C. Is cheaper to implement
- D. Requires more human supervision
The automotive industry is mentioned as an example of:
- A. An industry resistant to automation
- B. A sector experiencing job growth
- C. Early adoption of robotic technology
- D. The negative effects of automation
White-collar jobs affected by automation include:
- A. Only accounting positions
- B. Legal and financial professions
- C. Creative fields exclusively
- D. Customer service roles only
According to the passage, automation can potentially increase income inequality by:
- A. Reducing company profits
- B. Creating too many new jobs
- C. Displacing workers in routine roles
- D. Eliminating all manual labour
Educational attainment is important because it:
- A. Guarantees employment
- B. Determines who can benefit from automation
- C. Prevents job displacement
- D. Reduces the need for retraining

Questions 6-9: True/False/Not Given

Do the following statements agree with the information given in the passage?

Write:

TRUE if the statement agrees with the information
FALSE if the statement contradicts the information
NOT GIVEN if there is no information on this

Mechanical looms in the 18th century were the first form of automation in human history.
Robots in the automotive industry can work continuously without requiring breaks.
All creative professionals have been replaced by algorithms.
Income gains from automation have been evenly distributed across all social classes.

Questions 10-13: Sentence Completion

Complete the sentences below.

Choose NO MORE THAN TWO WORDS from the passage for each answer.

Workers who lose their jobs to automation may experience __ or move to lower-paying positions.
Human workers possess __ that machines cannot replicate, such as emotional intelligence.
Geographic inequality creates a __ where talented individuals leave struggling areas.
Policymakers have proposed concepts such as __ to address income inequality caused by automation.

PASSAGE 2 – Economic Theories on Automation and Wage Distribution

Độ khó: Medium (Band 6.0-7.5)

Thời gian đề xuất: 18-20 phút

The relationship between technological advancement and income distribution has been a subject of scholarly debate for centuries, but the current wave of automation presents unique challenges that warrant fresh examination. Economists have developed various theoretical frameworks to understand how automation affects wage structures and income inequality, each offering distinct perspectives on the mechanisms at play and the likely outcomes for different segments of the workforce.

Classical economic theory, rooted in the work of economists like David Ricardo and John Stuart Mill, suggests that technological progress, including automation, is generally benign for society in the long run. According to this view, while technological change may cause temporary disruptions in the labour market, it ultimately leads to increased productivity and higher living standards for all. The theory posits that workers displaced by new technologies will eventually find employment in emerging sectors, and that the overall economic expansion generated by productivity gains will create sufficient demand for labour to absorb displaced workers. This optimistic perspective dominated economic thinking for much of the 20th century.

However, more recent theoretical developments have challenged this sanguine view. The skill-biased technological change (SBTC) hypothesis, which gained prominence in the 1980s and 1990s, argues that modern technologies disproportionately benefit workers with higher levels of education and skills while reducing demand for less-skilled workers. Under this framework, automation acts as a complement to skilled labour but a substitute for unskilled labour. For instance, computer systems enhance the productivity of engineers and analysts but replace assembly line workers and data entry clerks. This asymmetric impact creates a bifurcated labour market where high-skilled workers see their wages rise while low-skilled workers face stagnant or declining compensation.

The SBTC hypothesis helps explain the widening wage gap between college-educated and high school-educated workers observed in many developed economies since the 1980s. Statistical evidence shows a strong correlation between the proliferation of computer technology and the growing earnings premium associated with higher education. As firms invested heavily in information technology, they increasingly sought workers who could effectively utilize these tools, driving up demand and wages for technically proficient employees while reducing opportunities for those without such capabilities.

Building on SBTC theory, the concept of task-based automation offers a more nuanced analysis of how specific job functions are affected by technology. Developed by economists Daron Acemoglu and David Autor, this framework distinguishes between routine tasks – those that can be codified and performed by machines – and non-routine tasks that require human judgment, flexibility, and interpersonal skills. Routine tasks exist across both blue-collar and white-collar occupations, from assembly line work to data processing. Automation tends to target these routine tasks, potentially affecting workers at various skill levels.

This task-based perspective reveals a more complex picture than simple high-skill versus low-skill divisions. Middle-skill jobs that involve primarily routine tasks – such as clerical work, manufacturing operations, and basic bookkeeping – are particularly vulnerable to automation. Meanwhile, both high-skill jobs requiring abstract reasoning and problem-solving (like engineering and management) and low-skill jobs requiring manual dexterity and adaptability in unpredictable environments (such as home health care or food service) are relatively more insulated from automation. This dynamic contributes to job polarization, where employment grows at the top and bottom of the wage distribution while middle-class jobs hollow out.

Another important theoretical perspective focuses on the distributional consequences of who owns the technology. As automation increases productivity, the economic gains must be divided between labour income (wages) and capital income (profits and returns on investment). If automation-driven productivity growth primarily accrues to capital owners rather than workers, income inequality will widen even if overall economic output increases. This concern is particularly acute given trends showing that labour’s share of national income has declined in many countries over recent decades, while returns to capital have increased.

The bargaining power of workers relative to employers also plays a crucial role in determining how automation affects income distribution. When labour markets are tight and workers have strong collective bargaining rights through unions or other mechanisms, they are better positioned to negotiate for their share of productivity gains. Conversely, in environments where workers have limited negotiating leverage – due to factors such as union decline, globalisation, or high unemployment – employers can capture a larger portion of the benefits from automation, exacerbating income inequality.

Network effects and winner-take-all dynamics in technology-driven markets further complicate the relationship between automation and income inequality. In many digital sectors, a small number of firms achieve dominant market positions, generating enormous profits for their owners and highly paid executives while employing relatively few workers. For example, technology giants like Amazon and Google have market valuations in the hundreds of billions of dollars but employ far fewer people per unit of revenue than traditional retailers or manufacturers. This concentration of economic value in a handful of firms and individuals can dramatically increase income inequality even as these companies provide useful services to millions of consumers.

Geographic clustering of automation-related industries also influences regional income inequality. Technology hubs like Silicon Valley, Seattle, and Austin attract highly skilled workers and generate substantial wealth, but these benefits are geographically concentrated. Workers in regions dependent on industries susceptible to automation – such as traditional manufacturing or routine clerical work – may face declining economic prospects without access to the opportunities available in thriving tech centres. This spatial dimension of inequality can perpetuate regional disparities and limit social mobility for those unable to relocate.

Recent empirical research has begun to test these theoretical predictions against real-world data, yielding mixed results. Some studies find evidence supporting the view that automation increases income inequality by displacing workers and concentrating gains among capital owners and highly skilled workers. Other research suggests that the negative impacts may be overstated, noting that many jobs adapt rather than disappear entirely, and that new occupations emerge to replace those automated away. The actual outcomes likely depend on specific contextual factors, including the pace of technological change, the responsiveness of educational systems, and the policy environment.

Understanding these various theoretical perspectives is essential for developing effective policy responses. If automation’s effects are primarily skill-biased, then investing in education and training may help workers adapt. If the issue is one of distribution between labour and capital, then tax policy and corporate governance reforms might be more appropriate. If bargaining power is the key factor, then strengthening workers’ rights and labour market institutions could help ensure more equitable outcomes. Most likely, a comprehensive approach addressing multiple dimensions of the problem will be necessary to ensure that automation benefits society broadly rather than exacerbating existing inequalities.

Questions 14-26

Questions 14-18: Yes/No/Not Given

Do the following statements agree with the views of the writer in the passage?

Write:

YES if the statement agrees with the views of the writer
NO if the statement contradicts the views of the writer
NOT GIVEN if it is impossible to say what the writer thinks about this

Classical economic theory accurately predicted all the effects of modern automation.
The skill-biased technological change hypothesis explains why college-educated workers have seen wage increases.
All middle-skill jobs are equally vulnerable to automation.
Technology companies employ fewer workers per unit of revenue than traditional businesses.
Educational investment alone is sufficient to address automation-related inequality.

Questions 19-22: Matching Headings

The passage has seven paragraphs (Paragraphs 3-9). Choose the correct heading for paragraphs 3, 5, 7, and 9 from the list of headings below.

List of Headings:

i. The role of worker bargaining power
ii. Classical economic optimism about technology
iii. The unequal distribution of automation benefits
iv. Regional disparities in automation’s effects
v. Routine versus non-routine tasks
vi. The decline of manufacturing jobs
vii. Evidence from statistical studies
viii. The growth of technology monopolies

Paragraph 3 (begins with “Classical economic theory…”)
Paragraph 5 (begins with “Building on SBTC theory…”)
Paragraph 7 (begins with “The bargaining power…”)
Paragraph 9 (begins with “Geographic clustering…”)

Questions 23-26: Summary Completion

Complete the summary below.

Choose NO MORE THAN TWO WORDS from the passage for each answer.

The skill-biased technological change hypothesis suggests that automation acts as a complement to skilled workers but a 23. __ for unskilled workers. This creates a 24. __ labour market where highly educated workers earn more while others struggle. The task-based automation framework reveals that middle-skill jobs involving 25. __ are particularly at risk, leading to job polarization. Meanwhile, the concentration of wealth in a few technology firms creates 26. __ dynamics that increase income inequality.

PASSAGE 3 – Empirical Evidence and Policy Implications of Automation-Driven Inequality

Độ khó: Hard (Band 7.0-9.0)

Thời gian đề xuất: 23-25 phút

The proliferation of automation technologies across diverse economic sectors has prompted a surge in empirical research aimed at quantifying their precise effects on income distribution and employment patterns. While theoretical frameworks provide valuable conceptual tools for understanding potential mechanisms, rigorous empirical analysis is essential to distinguish between speculative scenarios and observable realities. Recent studies employing sophisticated econometric techniques and leveraging granular data from multiple countries have begun to paint a more detailed, albeit still incomplete, picture of automation’s actual impact on income inequality.

Nghiên cứu thực nghiệm về tác động của tự động hóa đến bất bình đẳng thu nhập trong các ngành công nghiệp

A seminal study by Acemoglu and Restrepo (2020) examined the impact of industrial robots on local labour markets in the United States between 1990 and 2007. Employing a quasi-experimental design that exploited regional variation in robot adoption – largely driven by industry composition rather than local economic conditions – the researchers found that each additional robot per thousand workers reduced the employment-to-population ratio by approximately 0.2 percentage points and lowered wages by 0.37 percent. Moreover, the negative effects were disproportionately concentrated among workers without college degrees, particularly those in routine manual occupations. This spatial analysis revealed that commuting zones with greater exposure to automation experienced not only job losses but also significant declines in wage growth, suggesting that the impact extended beyond direct displacement to affect overall labour market conditions.

Extending this line of inquiry to a broader set of technologies, Autor and Salomons (2018) conducted a cross-country analysis examining the relationship between technological change and employment across 19 countries over four decades. Their findings challenged the dystopian narrative of widespread technological unemployment, demonstrating that industries experiencing rapid productivity growth due to automation actually showed employment increases at the aggregate level. However, this ostensibly positive result masked significant heterogeneity in outcomes. While some workers – particularly those with complementary skills – benefited from productivity-induced demand expansion, others faced displacement and wage stagnation. The study underscored the importance of spillover effects and general equilibrium considerations: automation in one sector could create jobs in others through increased consumer spending and demand for complementary services, but these new opportunities might not be accessible to displaced workers due to skill mismatches or geographic constraints.

Research focusing specifically on income inequality has yielded particularly compelling evidence of automation’s distributional consequences. Analyzing data from the Luxembourg Income Study, which provides harmonized household income data across multiple countries, economists have documented a strong correlation between the adoption of computer-controlled machinery and the growth of the Gini coefficient – a standard measure of income inequality. Countries that experienced more rapid automation saw larger increases in income concentration at the top of the distribution, with the top 1 percent capturing an outsized share of economic gains. This pattern persisted even after controlling for other factors known to influence inequality, such as globalisation, declining unionisation, and changes in tax progressivity.

The occupational structure of employment has undergone dramatic reconfiguration in response to automation, with profound implications for income distribution. Berg, Buffie, and Zanna (2018), working with International Monetary Fund data, documented that middle-skill occupations – including administrative support, production workers, and operators – have declined as a share of total employment in virtually all advanced economies. Simultaneously, both high-skill occupations requiring advanced education and low-skill service jobs have expanded, creating the polarized labour market predicted by task-based automation theories. This hollowing out of the middle class contributes directly to income inequality by reducing the number of jobs offering middle-class wages, forcing workers into either highly paid professional positions (accessible only to those with extensive education) or low-paid service roles.

Longitudinal studies tracking individual workers over time provide particularly valuable insights into the mechanisms through which automation affects economic outcomes. Research by Cortes, Jaimovich, and Siu (2020) followed workers displaced from routine occupations and found that they experienced substantial and persistent earnings losses averaging 10-15 percent even years after displacement. Many downwardly mobile workers transitioned to lower-skill service occupations with reduced wages and benefits, while others withdrew from the labour force entirely. The study revealed that earnings trajectories diverged significantly based on workers’ initial educational attainment and geographic location, with college-educated workers in dynamic urban markets recovering more quickly than those with limited education in economically stagnant regions.

The interaction between automation and other macroeconomic trends complicates efforts to isolate its independent effects on inequality. Globalisation and automation often work in tandem, as firms simultaneously adopt labour-saving technologies and offshore production to countries with lower labour costs. Decomposition analyses attempting to separate these effects suggest that automation and trade reinforced each other’s impacts on inequality, together accounting for a substantial portion of the decline in labour income share and the wage stagnation experienced by middle-income workers in developed countries. Furthermore, weakening labour market institutions – including declining union density and eroding minimum wage protections – may have amplified automation’s disequalizing effects by reducing workers’ ability to claim a share of productivity gains.

The COVID-19 pandemic has served as an unexpected natural experiment, accelerating automation adoption and revealing its potential long-term consequences for income inequality. Faced with public health restrictions and operational challenges, many firms rapidly deployed technologies previously considered too expensive or risky, from contactless payment systems to automated warehousing solutions. Research by Chernoff and Warman (2021) found that occupations amenable to telework – which disproportionately consist of high-paying professional jobs – weathered the pandemic relatively well, while workers in non-automatable service occupations requiring physical presence but lacking opportunities for remote work experienced severe employment and income losses. This bifurcated pandemic experience risks entrenching and exacerbating pre-existing inequalities.

Drawing on this empirical evidence, economists and policymakers have proposed various interventions to mitigate automation-driven inequality. One prominent recommendation involves substantial investment in human capital development, particularly in skills complementary to automation such as complex problem-solving, creativity, and interpersonal communication. However, critics note that educational upgrading alone cannot address inequality if the number of high-skill jobs created by automation is insufficient to employ all qualified workers, or if credential inflation merely ratchets up educational requirements without increasing productivity. Moreover, education-focused solutions offer little help to current workers with limited capacity to acquire new skills.

Alternative policy proposals focus on the distributional mechanisms through which automation’s benefits are shared. These include progressive taxation of capital income and corporate profits, which could fund expanded social insurance programs or even universal basic income to ensure that productivity gains benefit society broadly. Some economists advocate for worker ownership structures or profit-sharing arrangements that would give employees a direct stake in automation-driven productivity improvements. Others emphasize the importance of strengthening collective bargaining institutions to restore workers’ negotiating power vis-à-vis employers.

A particularly innovative proposal involves robot taxes or automation levies – charges on firms proportional to their use of labour-replacing technologies. Proponents argue that such taxes could slow the pace of automation to a more socially manageable rate while generating revenue to support displaced workers and fund retraining programs. Critics, however, warn that taxing automation could stifle innovation, reduce competitiveness, and prove difficult to implement given the challenges of defining and measuring automation precisely. The optimal policy response likely involves a multifaceted approach combining elements of education, redistribution, labour market reform, and perhaps judicious technology regulation, calibrated to each country’s specific circumstances and institutional capabilities.

The empirical evidence accumulated thus far suggests that automation has indeed contributed to rising income inequality, though the magnitude of its effect remains contested and appears to vary across contexts. While automation need not inevitably increase inequality – its impact depends critically on institutional choices and policy responses – the distributional outcomes observed to date in many countries have been decidedly inegalitarian. Ensuring that future technological progress benefits society broadly rather than accruing narrowly to economic elites represents one of the central challenges facing contemporary policymakers. Meeting this challenge will require not only technical economic expertise but also political will to implement potentially controversial reforms in the face of opposition from entrenched interests who benefit from the current distribution of automation’s gains.

Questions 27-40

Questions 27-31: Multiple Choice

Choose the correct letter, A, B, C, or D.

The Acemoglu and Restrepo (2020) study found that robot adoption:

A. Had no effect on employment
B. Increased wages for all workers
C. Particularly affected workers without college degrees
D. Only impacted manufacturing jobs

According to Autor and Salomons (2018), industries with rapid automation:

A. Always experienced employment declines
B. Showed aggregate employment increases
C. Had no change in employment levels
D. Only created low-skilled jobs

The Luxembourg Income Study revealed that:

A. Automation reduced income inequality
B. The Gini coefficient decreased with automation
C. The top 1 percent captured a large share of gains
D. All countries experienced the same inequality patterns

Workers displaced from routine occupations experienced:

A. Immediate recovery of earnings
B. Persistent earnings losses of 10-15 percent
C. Better job opportunities
D. No long-term effects

The COVID-19 pandemic’s effect on automation:

A. Slowed down technology adoption
B. Equally affected all occupations
C. Accelerated automation deployment
D. Reversed automation trends

Questions 32-36: Matching Features

Match each policy proposal (32-36) with the correct description (A-H).

Policy Proposals:
32. Human capital development
33. Robot taxes
34. Progressive taxation
35. Worker ownership structures
36. Universal basic income

Descriptions:

A. Charges on firms using labour-replacing technologies
B. Giving employees a direct stake in productivity improvements
C. Investment in skills complementary to automation
D. Reducing educational requirements for jobs
E. Providing regular payments to all citizens
F. Taxation of capital income and corporate profits
G. Eliminating all taxes on businesses
H. Preventing all forms of automation

Questions 37-40: Short-answer Questions

Answer the questions below.

Choose NO MORE THAN THREE WORDS from the passage for each answer.

What type of design did Acemoglu and Restrepo use to study the impact of robots?
What measure of income inequality is mentioned as correlating with automation adoption?
What type of labour market does automation create according to empirical evidence?
What two factors, working together, account for much of the decline in labour income share?

Answer Keys – Đáp Án

PASSAGE 1: Questions 1-13

B
C
B
C
B
NOT GIVEN
TRUE
FALSE
FALSE
prolonged unemployment
uniquely human capabilities
vicious cycle
universal basic income

PASSAGE 2: Questions 14-26

NO
YES
NOT GIVEN
YES
NO
ii
v
i
iv
substitute
bifurcated
routine tasks
winner-take-all

PASSAGE 3: Questions 27-40

C
B
C
B
C
C
A
F
B
E
quasi-experimental design
Gini coefficient
polarized labour market
automation and trade

Giải Thích Đáp Án Chi Tiết

Passage 1 – Giải Thích

Câu 1: B

Dạng câu hỏi: Multiple Choice
Từ khóa: modern automation, differs, historical automation
Vị trí trong bài: Đoạn 2, dòng 4-6
Giải thích: Bài đọc nói rõ “Today’s technologies, powered by artificial intelligence (AI) and machine learning, can handle not just repetitive physical tasks but also complex cognitive functions such as data analysis, customer service, and even medical diagnosis.” Điều này cho thấy tự động hóa hiện đại khác biệt vì có thể thực hiện cả nhiệm vụ vật lý lẫn nhận thức phức tạp.

Câu 2: C

Dạng câu hỏi: Multiple Choice
Từ khóa: automotive industry, example
Vị trí trong bài: Đoạn 3, dòng 3-5
Giải thích: Ngành ô tô được đề cập như một ví dụ về “Manufacturing has been at the forefront of this trend” – nghĩa là việc áp dụng công nghệ robot sớm và tích cực.

Câu 3: B

Dạng câu hỏi: Multiple Choice
Từ khóa: white-collar jobs, affected
Vị trí trong bài: Đoạn 4
Giải thích: Bài đọc liệt kê: “Accounting software…Legal research tools…algorithms can compose music, write news articles” – bao gồm cả ngành tài chính và pháp lý.

Câu 6: NOT GIVEN

Dạng câu hỏi: True/False/Not Given
Từ khóa: mechanical looms, first form of automation
Vị trí trong bài: Đoạn 2
Giải thích: Bài chỉ nói mechanical looms “revolutionized textile production in the 18th century” nhưng không khẳng định đây là hình thức tự động hóa đầu tiên trong lịch sử.

Câu 7: TRUE

Dạng câu hỏi: True/False/Not Given
Từ khóa: robots, automotive industry, work continuously
Vị trí trong bài: Đoạn 3
Giải thích: “robots now performing tasks…can work 24 hours a day without breaks” – khớp chính xác với câu hỏi.

Câu 10: prolonged unemployment

Dạng câu hỏi: Sentence Completion
Từ khóa: lose jobs, automation, experience
Vị trí trong bài: Đoạn 5
Giải thích: “This can result in prolonged unemployment or underemployment” – đáp án phải lấy không quá hai từ.

Câu 13: universal basic income

Dạng câu hỏi: Sentence Completion
Từ khóa: policymakers, proposed concepts
Vị trí trong bài: Đoạn 9
Giải thích: “exploring concepts like universal basic income” – chính xác ba từ như yêu cầu.

Passage 2 – Giải Thích

Câu 14: NO

Dạng câu hỏi: Yes/No/Not Given
Từ khóa: Classical economic theory, accurately predicted
Vị trí trong bài: Đoạn 2-3
Giải thích: Bài viết nói “more recent theoretical developments have challenged this sanguine view” – cho thấy lý thuyết cổ điển không dự đoán chính xác tất cả các tác động.

Câu 15: YES

Dạng câu hỏi: Yes/No/Not Given
Từ khóa: SBTC, explains, wage increases
Vị trí trong bài: Đoạn 4
Giải thích: “The SBTC hypothesis helps explain the widening wage gap between college-educated and high school-educated workers” – tác giả đồng ý với quan điểm này.

Câu 19: ii

Dạng câu hỏi: Matching Headings
Vị trí trong bài: Đoạn 3
Giải thích: Đoạn văn bắt đầu với “Classical economic theory” và thảo luận về quan điểm lạc quan của nó về công nghệ, khớp với heading “Classical economic optimism about technology”.

Câu 20: v

Dạng câu hỏi: Matching Headings
Vị trí trong bài: Đoạn 5
Giải thích: Đoạn văn tập trung vào “task-based automation” và phân biệt giữa “routine tasks” và “non-routine tasks”, khớp hoàn hảo với heading này.

Câu 23: substitute

Dạng câu hỏi: Summary Completion
Từ khóa: acts as, for unskilled workers
Vị trí trong bài: Đoạn 3
Giải thích: “automation acts as a complement to skilled labour but a substitute for unskilled labour” – đối lập với complement là substitute.

Câu 26: winner-take-all

Dạng câu hỏi: Summary Completion
Từ khóa: concentration of wealth, dynamics
Vị trí trong bài: Đoạn 8
Giải thích: “Network effects and winner-take-all dynamics in technology-driven markets” – cụm từ chính xác từ bài đọc.

Passage 3 – Giải Thích

Câu 27: C

Dạng câu hỏi: Multiple Choice
Từ khóa: Acemoglu and Restrepo, robot adoption
Vị trí trong bài: Đoạn 2
Giải thích: “the negative effects were disproportionately concentrated among workers without college degrees” – đáp án C phản ánh chính xác thông tin này.

Câu 28: B

Dạng câu hỏi: Multiple Choice
Từ khóa: Autor and Salomons, rapid automation
Vị trí trong bài: Đoạn 3
Giải thích: “industries experiencing rapid productivity growth due to automation actually showed employment increases at the aggregate level” – khớp với đáp án B.

Câu 30: B

Dạng câu hỏi: Multiple Choice
Từ khóa: displaced, routine occupations, experienced
Vị trí trong bài: Đoạn 6
Giải thích: “workers displaced from routine occupations…experienced substantial and persistent earnings losses averaging 10-15 percent” – đáp án chính xác.

Câu 32: C

Dạng câu hỏi: Matching Features
Từ khóa: human capital development
Vị trí trong bài: Đoạn 8
Giải thích: “substantial investment in human capital development, particularly in skills complementary to automation” – khớp với mô tả C.

Câu 37: quasi-experimental design

Dạng câu hỏi: Short-answer Questions
Từ khóa: Acemoglu and Restrepo, design
Vị trí trong bài: Đoạn 2
Giải thích: “Employing a quasi-experimental design that exploited regional variation” – đáp án chính xác ba từ.

Câu 38: Gini coefficient

Dạng câu hỏi: Short-answer Questions
Từ khóa: measure, income inequality
Vị trí trong bài: Đoạn 4
Giải thích: “the growth of the Gini coefficient – a standard measure of income inequality” – hai từ chính xác.

Câu 40: automation and trade

Dạng câu hỏi: Short-answer Questions
Từ khóa: two factors, decline, labour income share
Vị trí trong bài: Đoạn 7
Giải thích: “automation and trade reinforced each other’s impacts…together accounting for a substantial portion of the decline in labour income share” – ba từ như yêu cầu.

Từ Vựng Quan Trọng Theo Passage

Passage 1 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
sophisticated	adj	/səˈfɪstɪkeɪtɪd/	tinh vi, phức tạp	automation technologies become increasingly sophisticated	sophisticated technology/system
unprecedented	adj	/ʌnˈpresɪdentɪd/	chưa từng có	the pace and scope of modern automation are unprecedented	unprecedented change/growth
forefront	n	/ˈfɔːfrʌnt/	vị trí tiên phong	Manufacturing has been at the forefront of this trend	at the forefront of
displaced	adj	/dɪsˈpleɪst/	bị thay thế, mất việc	when companies replace human workers, those displaced employees	displaced workers/employees
multifaceted	adj	/ˌmʌltiˈfæsɪtɪd/	nhiều mặt, đa diện	The relationship is complex and multifaceted	multifaceted problem/issue
repetitive	adj	/rɪˈpetətɪv/	lặp đi lặp lại	particularly affecting workers in routine and repetitive roles	repetitive tasks/work
underemployment	n	/ˌʌndərɪmˈplɔɪmənt/	tình trạng làm việc dưới trình độ	prolonged unemployment or underemployment	chronic underemployment
divergence	n	/daɪˈvɜːdʒəns/	sự khác biệt, phân kỳ	This divergence in outcomes contributes to growing inequality	divergence in/between
premium	n/adj	/ˈpriːmiəm/	cao cấp, phí bảo hiểm	command premium salaries	premium price/quality
vicious cycle	n phrase	/ˈvɪʃəs ˈsaɪkl/	vòng luẩn quẩn xấu	can create a vicious cycle where talented young people leave	trapped in a vicious cycle
spatial inequality	n phrase	/ˈspeɪʃl ˌɪnɪˈkwɒləti/	bất bình đẳng về không gian	This spatial inequality can create problems	address spatial inequality
progressive taxation	n phrase	/prəˈɡresɪv tækˈseɪʃn/	thuế lũy tiến	implementing progressive taxation on profits	progressive taxation system

Passage 2 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
warrant	v	/ˈwɒrənt/	đòi hỏi, bảo đảm	the current wave of automation presents challenges that warrant fresh examination	warrant attention/consideration
benign	adj	/bɪˈnaɪn/	lành tính, tốt	technological progress is generally benign for society	benign effect/influence
absorb	v	/əbˈzɔːb/	hấp thụ	create sufficient demand for labour to absorb displaced workers	absorb workers/costs
disproportionately	adv	/ˌdɪsprəˈpɔːʃənətli/	không cân xứng	modern technologies disproportionately benefit workers	disproportionately affect/impact
asymmetric	adj	/ˌeɪsɪˈmetrɪk/	bất đối xứng	This asymmetric impact creates a bifurcated labour market	asymmetric information/impact
bifurcated	adj	/ˈbaɪfəkeɪtɪd/	chia đôi, phân hai	creates a bifurcated labour market	bifurcated system/structure
proliferation	n	/prəˌlɪfəˈreɪʃn/	sự gia tăng nhanh	the proliferation of computer technology	nuclear proliferation
nuanced	adj	/ˈnjuːɑːnst/	tinh tế, nhiều sắc thái	offers a more nuanced analysis	nuanced understanding/view
vulnerable	adj	/ˈvʌlnərəbl/	dễ bị tổn thương	are particularly vulnerable to automation	vulnerable to attack/criticism
insulated	adj	/ˈɪnsjuleɪtɪd/	được bảo vệ, cách ly	are relatively more insulated from automation	insulated from competition
bargaining power	n phrase	/ˈbɑːɡənɪŋ ˈpaʊə/	khả năng thương lượng	The bargaining power of workers	strong bargaining power
exacerbate	v	/ɪɡˈzæsəbeɪt/	làm trầm trọng thêm	employers can capture benefits, exacerbating income inequality	exacerbate problems/tensions
perpetuate	v	/pəˈpetʃueɪt/	duy trì, kéo dài	can perpetuate regional disparities	perpetuate stereotypes/myths
contextual factors	n phrase	/kənˈtekstʃuəl ˈfæktəz/	yếu tố bối cảnh	outcomes depend on specific contextual factors	consider contextual factors
comprehensive	adj	/ˌkɒmprɪˈhensɪv/	toàn diện	a comprehensive approach addressing multiple dimensions	comprehensive study/plan

Passage 3 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
proliferation	n	/prəˌlɪfəˈreɪʃn/	sự gia tăng nhanh	The proliferation of automation technologies	rapid proliferation
speculative	adj	/ˈspekjələtɪv/	mang tính suy đoán	distinguish between speculative scenarios and observable realities	speculative investment/theory
econometric	adj	/ɪˌkɒnəˈmetrɪk/	thuộc kinh tế lượng	employing sophisticated econometric techniques	econometric analysis/model
granular	adj	/ˈɡrænjələ/	chi tiết, cụ thể	leveraging granular data from multiple countries	granular data/information
quasi-experimental	adj	/ˌkweɪzaɪ ɪkˌsperɪˈmentl/	bán thực nghiệm	Employing a quasi-experimental design	quasi-experimental study
dystopian	adj	/dɪsˈtəʊpiən/	bi quan, đen tối	challenged the dystopian narrative	dystopian future/vision
heterogeneity	n	/ˌhetərəʊdʒəˈniːəti/	tính không đồng nhất	this result masked significant heterogeneity in outcomes	considerable heterogeneity
spillover effects	n phrase	/ˈspɪləʊvə ɪˈfekts/	hiệu ứng lan tỏa	the importance of spillover effects	positive spillover effects
harmonized	adj	/ˈhɑːmənaɪzd/	được hài hòa hóa	provides harmonized household income data	harmonized standards/data
reconfiguration	n	/ˌriːkənˌfɪɡjəˈreɪʃn/	sự cấu hình lại	has undergone dramatic reconfiguration	major reconfiguration
longitudinal	adj	/ˌlɒndʒɪˈtjuːdɪnl/	theo chiều dọc, dài hạn	Longitudinal studies tracking individual workers	longitudinal research/data
downwardly mobile	adj phrase	/ˈdaʊnwədli ˈməʊbaɪl/	di chuyển xuống tầng lớp thấp hơn	Many downwardly mobile workers transitioned	downwardly mobile workers
bifurcated	adj	/ˈbaɪfəkeɪtɪd/	chia đôi	This bifurcated pandemic experience	bifurcated system/approach
credential inflation	n phrase	/krɪˈdenʃl ɪnˈfleɪʃn/	lạm phát bằng cấp	if credential inflation merely ratchets up requirements	combat credential inflation
entrenched interests	n phrase	/ɪnˈtrentʃt ˈɪntrəsts/	lợi ích ăn sâu	opposition from entrenched interests	protect entrenched interests
inegalitarian	adj	/ˌɪnɪˌɡælɪˈteəriən/	bất bình đẳng	outcomes have been decidedly inegalitarian	inegalitarian society/system
calibrated	adj	/ˈkælɪbreɪtɪd/	được hiệu chỉnh	calibrated to each country’s specific circumstances	carefully calibrated response

Kết bài

Qua bài luyện tập IELTS Reading toàn diện này về chủ đề “How Does Automation Affect Income Inequality?”, bạn đã được tiếp cận với một đề thi hoàn chỉnh mô phỏng chính xác kỳ thi thực tế. Ba passages với độ khó tăng dần từ Easy đến Hard đã cung cấp cho bạn cái nhìn đa chiều về tác động của tự động hóa đến bất bình đẳng thu nhập – từ những khái niệm cơ bản, qua các lý thuyết kinh tế, cho đến những bằng chứng thực nghiệm phức tạp.

Đề thi này không chỉ giúp bạn làm quen với các dạng câu hỏi đa dạng trong IELTS Reading mà còn trang bị cho bạn vốn từ vựng phong phú về chủ đề kinh tế-công nghệ – một trong những chủ đề thường xuyên xuất hiện trong kỳ thi. Phần đáp án chi tiết kèm theo giải thích cụ thể về vị trí thông tin và cách paraphrase sẽ giúp bạn hiểu rõ chiến lược làm bài hiệu quả, từ đó tự tin hơn khi đối mặt với các bài đọc khó.

Hãy dành thời gian xem lại những câu trả lời sai của mình, phân tích lý do và học từ những sai lầm đó. Đặc biệt chú ý đến các từ vựng được làm đậm trong passages – đây là những từ academic quan trọng thường xuất hiện trong IELTS. Luyện tập thường xuyên với các đề thi chất lượng như thế này sẽ giúp bạn cải thiện đáng kể band điểm Reading và đạt được mục tiêu IELTS của mình. Chúc bạn học tập hiệu quả và thành công rực rỡ trong kỳ thi sắp tới!

What are You Looking For?

IELTS Reading: Tự động hóa và Bất bình đẳng Thu nhập – Đề thi mẫu có đáp án chi tiết