Cross-country analysis of aggregate markups and their impact on income inequality.

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The analysis aims to reveal how institutional differences and economic systems shape this relationship. Method The study examines data from 12 countries from 1997 to 2016 using a panel data approach. The analysis focuses on key variables such as the Gini index (measuring inequality), lagged markups, trade openness, GDP per capita, tax revenue, poverty, and R&D expenditure. A two-way fixed-effects model is applied to account for country and year-specific differences, while robust standard errors ensure reliability. Results The results show that higher markups are closely linked to greater income inequality, with the impact being particularly pronounced in emerging and developing economies. Integrating tax policies, trade openness, and GDP growth into the analysis indicates the limitations of relying solely on economic growth to achieve equity. The research highlights taxation and trade as effective levers for mitigating inequality and raises important questions about the role of innovation in this dynamic. Conclusion This study shows the interaction between market power and inequality across diverse economic systems. We examine how factors like market concentration and institutional quality drive inequality provides practical insights and policy recommendations for effectively addressing income disparities. 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F1000Research 2025, 14 :184 ( https://doi.org/10.12688/f1000research.160674.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Cross-country analysis of aggregate markups and their impact on income inequality. [version 1; peer review: 2 approved with reservations] Peter Malliaros https://orcid.org/0000-0001-7947-9015 1 , W Alejandro Pacheco-Jaramillo https://orcid.org/0000-0002-4208-5546 2 Peter Malliaros https://orcid.org/0000-0001-7947-9015 1 , W Alejandro Pacheco-Jaramillo https://orcid.org/0000-0002-4208-5546 2 PUBLISHED 10 Feb 2025 Author details Author details 1 Research, UrCommunity Ltd, Melbourne, VIC, 3051, Australia 2 Economics, University Anahuac, Huixquilucan de Degollado, State of Mexico, 52786, Mexico Peter Malliaros Roles: Conceptualization, Data Curation, Project Administration, Resources, Supervision, Validation, Writing – Review & Editing W Alejandro Pacheco-Jaramillo Roles: Conceptualization, Formal Analysis, Investigation, Methodology, Software, Writing – Original Draft Preparation OPEN PEER REVIEW DETAILS REVIEWER STATUS Abstract Background This study investigates the connection between aggregate markups and income inequality, exploring how market power affects inequality in developed, emerging, and developing economies. The analysis aims to reveal how institutional differences and economic systems shape this relationship. Method The study examines data from 12 countries from 1997 to 2016 using a panel data approach. The analysis focuses on key variables such as the Gini index (measuring inequality), lagged markups, trade openness, GDP per capita, tax revenue, poverty, and R&D expenditure. A two-way fixed-effects model is applied to account for country and year-specific differences, while robust standard errors ensure reliability. Results The results show that higher markups are closely linked to greater income inequality, with the impact being particularly pronounced in emerging and developing economies. Integrating tax policies, trade openness, and GDP growth into the analysis indicates the limitations of relying solely on economic growth to achieve equity. The research highlights taxation and trade as effective levers for mitigating inequality and raises important questions about the role of innovation in this dynamic. Conclusion This study shows the interaction between market power and inequality across diverse economic systems. We examine how factors like market concentration and institutional quality drive inequality provides practical insights and policy recommendations for effectively addressing income disparities. READ ALL READ LESS Keywords Income Inequality (D63), Markups (D43), Economic Systems (P16), Research and Development (O32), Panel Data Analysis (C23), Tax Policy (H21) Corresponding Author(s) Peter Malliaros ( [email protected] ) Close Corresponding author: Peter Malliaros Competing interests: No competing interests were disclosed. Grant information: The author(s) declared that no grants were involved in supporting this work. Copyright: © 2025 Malliaros P and Pacheco-Jaramillo WA. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Malliaros P and Pacheco-Jaramillo WA. Cross-country analysis of aggregate markups and their impact on income inequality. [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :184 ( https://doi.org/10.12688/f1000research.160674.1 ) First published: 10 Feb 2025, 14 :184 ( https://doi.org/10.12688/f1000research.160674.1 ) Latest published: 09 May 2025, 14 :184 ( https://doi.org/10.12688/f1000research.160674.3 )  There is a newer version of this article available. Suppress this message for one day. Introduction In recent years, the debate on the causes and consequences of rising income inequality has taken on considerable relevance in economic research. As the power of large companies has increased, with more concentrated markets in critical sectors, concern has arisen about how markups -or price margins that exceed production costs-influence income distribution and countries’ ability to reduce poverty effectively. High markups, which reflect the market dominance of large corporations, allow these companies to obtain greater profits, affecting market competitiveness and equity in the distribution of the wealth generated ( De Loecker & Eeckhout, 2018 ), understanding that significant gaps in economic inequality distort the market. Excessively high markups can also increase innovation, as by reducing market competition, companies have less incentive to invest in new developments and improve their products. This stagnation limits opportunities for inclusive growth and can reinforce income disparities. Our analysis focuses on how high markups affect income inequality, considering the role of various control variables. Understanding whether differences in markups across countries with different levels of development are associated with variations in income inequality is critical, as this relationship may reveal important insights into how market structures influence economic disparities and inform better public policies aimed at income redistribution. This phenomenon becomes especially problematic in economies with limited competition in emerging and developing countries. However, the effects of markups are different; they depend on many aspects, such as each country’s institutional characteristics. Economies with inclusive institutions that foster competition and regulate market power tend to have lower markups and more equitable income distribution ( Acemoglu & Robinson, 2023 ). In contrast, in countries with extractive institutions 1 , High markups tend to concentrate wealth in the hands of a few companies, deepening inequality gaps and perpetuating poverty ( Stiglitz, 2020 ). Another example is that the lack of institutionalisation in developing countries generates high levels of corruption, subsequently leading to higher levels of inequality. It is like a vicious circle. Although the impact of markups on macroeconomic dynamics has been widely studied, their direct relationship with income inequality across countries with different levels of development with different economic systems 2 ( Demir et al., 2022 ), has yet to be explored. This analysis’s classification of tree groups of development and economic systems is crucial for understanding the institutional and policy frameworks that shape market dynamics and socio-economic outcomes in different contexts. Developed countries like the United States, Germany, Japan, and Australia operate under free-market systems supported by substantial property rights and competitive markets encouraging innovation. As North (1991) highlights, well-defined institutions such as the rule of law and property rights are fundamental to sustainable economic growth. Also, Robinson and Acemoglu (2012) emphasise that inclusive institutions ensure stability and help reduce extreme inequalities. In contrast, emerging economies such as China, India, Mexico, and South Africa demonstrate hybrid systems that balance market-oriented reforms with strategic state interventions. Rodrik (2004) explains that these mixed approaches are common in economies transitioning to higher development levels, where the state plays a critical role in protecting key sectors and addressing market failures. One form of state intervention is through state-led initiatives, where the government actively promotes and supports industrial development, mainly where markets are underdeveloped. Similarly, Lin and Chang (2009) highlight the importance of such initiatives in fostering industrial development. Developing countries, including Peru, Colombia, Venezuela, and the Philippines, often face the challenges of weak institutions and insufficient regulatory frameworks, which limit competition and exacerbate inequality ( Sen, 1999 ). However, as Stiglitz (2001) points out, this is not a permanent state. Markets tend to concentrate power in such environments, worsening income disparities, but with the right reforms, this can change. Easterly (2001) underscores how inconsistent policies and institutional limitations perpetuate poverty and inequality cycles and how these can be overcome. This classification underscores institutions’ varying roles in shaping economic systems and outcomes, offering hope for change. This type of analysis is essential to understand how the structure of markets and institutional regulations affect the distribution of wealth and economic opportunities, especially among the most vulnerable households. Even though classical economists do not find a severe income inequality problem, it has many economic and social implications, including human behaviour. When markups become excessively high, they can lead to inefficiencies, reducing consumer welfare and limiting economic mobility 3 ( Hovenkamp, 2023 ; Enke, 1945 ). Recent research suggests that in developing countries, markups in essential sectors, such as food and energy, disproportionately affect low-income households, limiting their ability to access essential goods and thus exacerbating poverty levels ( Almunia & Antràs, 2019 ). Beyond the direct economic impact, high markups also have significant social consequences. From the behavioural economics perspective, researchers such as Thaler (2017) and Kahneman (2002) have shown that inequality aversion plays a fundamental role in how people perceive inequalities and act accordingly. In contexts where income disparities are significant, people tend to experience a strong sense of injustice, which affects cooperation and trust in institutions and generates social and political instability ( Fehr et al., 1999 ). This study aims to analyse the impact of aggregate markups on income inequality in a sample of developed, middle-income and developing countries. Data from 12 countries will be analysed, and the results will be distributed as follows: four developed countries, four middle-income countries, and four developing countries. They will be selected based on the economic system, institutional diversity, development level, and information availability. The countries considered in this analysis include the United States, Germany, Japan, India, Mexico, the Philippines, Colombia, South Africa, and Australia, representing a broad range of continents and cultures, with data covering the period from 1997 to 2016. This time interval will allow observing these two decades’ long-term effects and structural changes. The methodological approach will be a panel data econometric analysis, which will capture variations over time and across countries. To separate the specific effects of markups on inequality, factors such as GDP per capita growth, research and development, R&D as a percentage of GDP, trade openness, taxes on income, trade openness and poverty will control the model. Poverty levels are reflected in the percentage of the population living below the international poverty line. Robustness tests will also be carried out to ensure the robustness of the results, along with a comparative analysis between economies to identify possible differences in the effects of markups in both contexts. Theoretical framework Income inequality is associated with various economic and social challenges, such as reduced economic mobility, social instability, and obstacles to long-term growth ( Stiglitz, 2012 ; OECD, 2015 ). It restricts access to essential resources for lower-income groups and exacerbates economic divides, resulting in less inclusive economic outcomes ( Atkinson, 2015 ; Piketty, 2014 ). The link between markups and income inequality has been the subject of intense debate in the current economic literature. Markups reflect the ability of firms to set prices above their production costs, which indicates their market power. In highly competitive markets, this ability is limited, but in sectors where few firms predominate, or there is a high concentration, markups tend to be much higher ( De Loecker & Eeckhout, 2018 ). This increase in the power of large firms has led many economists to reflect on their impact on income distribution and inequality. High markups contribute to inefficiency by inflating consumer prices, which reduces purchasing power and worsens income inequality. They indicate lower competition, leading to misallocated resources and stifled innovation, as firms experience less pressure to improve. Recent studies have investigated the complex interplay between innovation, market power (markups), and income inequality. Aghion et al. (2019) analysed U.S. data, and they found that increased innovation correlates with a higher income share for the top 1%, indicating that innovation may exacerbate income inequality at the upper end of the distribution. Karagiannis and Tsoulfidis (2019) extended this analysis globally, concluding that while innovation incentivises economic growth, it often leads to greater income inequality, mainly when its benefits are unevenly distributed. Guellec and Paunov (2017) focused on digital innovation, arguing that technological advancements can result in market concentration and increased market power for leading firms, thereby heightening income inequality. Similarly, Aghion et al. (2021) found that increased market concentration, often driven by innovation, can stifle competition and contribute to rising income inequality. These studies suggest that although innovation drives economic progress, it can increase income inequality, mainly when market power is concentrated among a few dominant firms. However, addressing the dominance of large technological oligopolies requires a balanced approach that combines fostering innovation with implementing antitrust policies. By encouraging new firms to enter the market through reduced regulatory hurdles and strategic support, the competitive landscape can shift, challenging the market power of established players ( Spulber, 2023 ). At the same time, antitrust measures are crucial to curbing monopolistic practices, such as unfair pricing strategies or restrictive competition policies. This dual approach helps drive prices closer to production costs, benefiting consumers and smaller businesses. Furthermore, innovation-focused initiatives can enhance sector-wide productivity, offering diverse and competitive alternatives to the entrenched influence of tech giants. Together, these measures ensure a more equitable and dynamic market environment while safeguarding against excessive concentration of power. Additionally, high markups create barriers to market entry, which diminishes economic dynamism and hampers equitable wealth distribution ( Autor et al., 2020 ; De Loecker & Eeckhout, 2018 ; Syverson, 2019 ). According to De Loecker & Eeckhout (2018) , the upward trend in markups over the last two decades reflects how market power has concentrated in a small group of large corporations, especially in technology, telecommunications and pharmaceuticals. By controlling the market, these companies can set higher prices, generating higher profit margins. These profits tend to be concentrated in the hands of the owners of capital, reducing the proportion of income received by workers and consumers. According to the last authors, these gaps contribute to increased income inequality. Subsequently, why is it not so suitable to have significant gaps in economic inequality? Thomas Piketty (2014) analyses how this dynamic has powered inequality over time. In his work on capital in the 21st century, Piketty argues that the return on capital has consistently exceeded the rate of economic growth, which has allowed those who already own capital (shareholders and business owners) to see their wealth grow at a faster pace than the rest of the population. In this context, markups play a central role by allowing large firms to capture a larger share of the wealth generated, thereby intensifying the disparities between those who own capital and those who depend on their wages. Also, it is even worse in Latin America, which has a greater concentration of wealth in a few, making it the most unequal continent. On the other hand, Milton Friedman and neoliberal economists have long argued that this pricing power is temporary ( Friedman, 1962 ). In their view, competitive markets should regulate themselves, as the entry of new firms would reduce markups over time. However, Joseph Stiglitz (2001) criticises this view, arguing that, in practice, market concentration and weak competition policies have allowed markups to remain high, thus contributing to rising inequality. Stiglitz emphasises that market failures that enable markups to persist will not be automatically corrected and that the resulting inequalities require more active intervention. Understanding this analysis, the effects of income inequality are very different in the three distinct groups of countries, and the impact is less in developed countries due to their strong economies and better institutions. Grouping countries in an economic system provides valuable insight into the interplay between markups and income inequality. A country’s economic framework (whether focused on free-market principles, state-driven policies, or a mixed approach) directly impacts competition, market entry, and income redistribution ( Tybout, 2000 ). Developed nations, with their robust institutions, often mitigate the adverse effects of high markups more effectively. In contrast, emerging and developing economies frequently grapple with persistent high markups due to weaker institutions. Incorporating this variable allows a deeper understanding of how different economic systems shape the connection between markups, inequality, and overall economic performance. We hypothesise that countries with lower levels of income inequality tend to have smaller markups than those with higher inequality. In such economies, prices often align more closely with production costs, promoting a competitive market and making goods and services more accessible for all income groups ( Moon et al., 2011 ). This balanced pricing structure may help prevent wealth from concentrating at the top, as firms possess less power to set high markups, and wages are likely to be more evenly distributed. Consequently, higher income inequality may be associated with economies where markups are more significant, primarily benefiting a few dominant firms. Monopolies and oligopolies are like the big players in the game where competition barely exists, letting one or just a few companies take the lead. In a monopoly, a single company holds all the cards, setting prices as high as they want because there is no one else in the ring to challenge them ( Stigler, 1988 ). Oligopolies are not much different; with just a handful of firms calling the shots, prices can soar, and consumer choices dwindle ( Tirole, 1988 ). This lack of competition means these companies have little motivation to innovate or cut costs, leading to a stale market where consumers do not get the best products or prices ( Schmalensee, 1989 ). Also, these dominant players often create obstacles that keep newcomers out, ensuring their reign remains unchallenged and leaving consumers with fewer options ( Carlton & Perloff, 2005 ). The outcome from these market dynamics stretches far beyond just higher prices—it significantly contributes to growing income inequality. When companies earn significant profits through high markups, that wealth often lands in the hands of a select few executives and shareholders. At the same time, everyday workers and consumers bear the impact ( Galbraith, 1998 ). This concentration of wealth creates a barrier for many, making it challenging for them to move up the economic ladder while those at the top keep reinforcing their status ( Autor et al., 2020 ). In addition, with less competition, workers have diminished bargaining power, leading to stagnant wages and fewer benefits. This cycle of inequality continues to widen the gap between the rich and the rest of society ( Atkinson, 2015 ). The role of public institutions in moderating market power cannot separate the debate on markups from Acemoglu & Robinson (2023) , this reference is Robinson and Acemoglu (2012) who have proposed that inclusive institutions tend to promote competition and, therefore, limit firms’ ability to set high prices on a sustained basis. According to these authors, when institutions are solid and transparent, the market is regulated more equitably, reducing the concentration of power and improving income distribution. For instance, good practices in competitive markets can be found in the firms of Nordic European countries. This approach contrasts with Mancur Olson’s view (1982) , which argues that modern societies are frequently captured by interest groups that manage to influence public policies to protect their economic interests. In such contexts, extractive institutions are created, which favour elites and perpetuate the concentration of market power. From this perspective, high markups not only reflect a lack of competition but also the ability of these elites to influence the rules of the game in their favour. Thus, robust institutions in developed countries play a crucial role in curbing such influence, ensuring fair competition, and fostering a more balanced economic environment. Stiglitz (2020) reinforces this thinking by highlighting that market power is tolerated and often encouraged in many countries with extractive institutions. In these cases, governments and the most potent companies cooperate to perpetuate income concentration, preventing a fairer wealth distribution. For Stiglitz, public institutions play a crucial role in moderating the market: when these institutions are transparent and oriented toward the common good, they promote competition and, in doing so, limit the excessive accumulation of market power. Considering that the classical economy is based on incentives, the capitalist does not have the role of looking after social interests, nor should they; state institutions must look after social policies such as a better distribution of resources, whether through taxes, subsidies or social policies. For instance, taxes are crucial in improving income distribution and reducing societal inequality. Taxes are a primary mechanism through which governments can redistribute wealth, funding essential public services like education, healthcare, and social welfare programs that benefit lower-income individuals and families ( Barr, N. 2020 ). Progressive tax systems, where higher earners pay more of their income, can effectively reduce disparities by reallocating resources to those in greater need ( Rybakov et al., 2022 ). Moreover, the relationship between direct taxes and GDP can provide valuable insights ( Maganya, 2020 ). Our study investigated how effectively these 12 countries manage their income distribution. Including this relationship in our model could reveal how direct taxation. 4 changes correlate with income inequality shifts, enhancing our understanding of fiscal policy’s impact on economic equity ( Pacheco-Jaramillo, 2023 ; Gunasinghe et al., 2021 ). Douglass North (1990) offers another perspective, arguing that institutions affect competition and the market and foster trust. North notes that trustworthy and transparent institutions facilitate more efficient and fair economic transactions. On the other hand, in markets where markups remain high due to a lack of competition, distrust in institutions can lead to financial and social instability. The impact of markups in developing countries is even more acute, as lower-income consumers are especially vulnerable to high prices in essential sectors. In Latin America, if we consider an average salary of 400 USD per month for an operator compared to a manager’s 20,000 USD, the income gap exceeds 5,000%. While the manager’s role may justify higher pay, this disparity can seem unfair to lower-wage workers, affecting their motivation ( Statista, 2023 ). In the 12 countries analysed, high markups contribute to wealth concentration, especially in settings with limited competition, poor institutional quality, and low investment in innovation. Richard Thaler (2017) argues that people care not only about their income but also about how they compare to others, so when inequality is visible, it generates frustration that can lead to reduced cooperation. Daniel Kahneman (2002) reinforces this by showing that social comparison directly impacts perceived well-being, where visible inequality can lead to dissatisfaction and political instability. Similarly, Ernst Fehr and Schmidt (1999) suggests that individuals may sacrifice their income to “punish” perceived injustices, indicating that high markups and concentrated wealth can erode social cohesion and economic stability across these nations. Almunia and Antràs (2019) show that high markups on food and energy impose a disproportionate burden on low-income households, who spend a more significant share of their resources on these goods in emerging markets. The lack of competition in these sectors allows large companies to dominate the market and maintain high prices, perpetuating poverty in these economies ( Sachs, 2005 ). A good example of wealth concentration is in Ecuador, a Latin American country. Three large banks (oligopoly) can influence the set of interest rates in a dollarised economy. In the same way, few importers control the prices of the most imported products ( Fierro Carrión, 2016 ). Similarly, Monga and Ndulu (2020) analyse how high markups in strategic sectors, such as energy and telecommunications, restrict the access of the poorest households to essential services in sub-Saharan Africa. These authors highlight that the lack of access to affordable services perpetuates poverty and limits people’s ability to participate in the economy more productively, trapping them in cycles of economic exclusion. Although some economists, such as Paul Collier (2007) , have argued that the solution could lie in increasing trade openness and foreign direct investment, other authors, such as Ha-Joon Chang (2008) , warn that without adequate regulation, trade openness could lead to new forms of market concentration. Chang suggests that trade liberalisation without safeguards could allow large foreign companies to capture the local market, potentially exacerbating inequality and poverty problems. There is no dispute that there are many opportunities when there is open trade or for the entry of foreign investment. The problem is that these openings could generate more inequality in countries with poor institutionality. Methods This study investigates the relationship between aggregate markups with income inequality and control variables across 12 countries for 20 years, from 1997 to 2016. By integrating data from multiple global and national sources, we aim to provide a comprehensive and nuanced understanding of how market power influences inequality across diverse economic and institutional contexts. Our approach combines well-established international databases. This combination of international databases ensures we capture various economic realities, from advanced economies with robust markets to developing countries with more concentrated sectors and weaker institutions. The data for the variables used in this analysis is sourced directly from the World Bank database platform ( https://databank.worldbank.org ), where users can filter information by variable and period as needed. However, the data related to markups or aggregate markups is exclusively obtained from the National Bureau of Economic Research database NBR, specifically from the work of De Loecker, J., and J. Eeckhout (2018) : “The Rise of Market Power and the Macroeconomic Implications,” published as an NBER working paper. This dataset is available on their official website ( https://sites.google.com/site/deloeckerjan/data-and-code ) under the title “Aggregate markups from Global Market Power: Country-year (xls) and Continent-year (xls).” This source provides detailed country-level and continent-level data on aggregate markups, which is critical for macroeconomic analyses of global market power. Our analysis focuses on income inequality as the key dependent variable, measured through the Gini coefficient. We include markup and control variables ( Table 1 ) to account for other factors influencing inequality, such as GDP per capita, poverty, trade openness, innovation as a percentage of GDP and taxes on income (direct taxes). GDP per capita purchasing power parity (PPP) is used because it offers a consistent way to compare economic well-being across countries by adjusting for price differences and inflation. This measure ensures that variations in purchasing power and living standards are accurately reflected and free from currency distortions. Table 1. Variables. Variable - Acronyms Description Source Income Inequality - INE (Dependent Variable) Measures income distribution disparities in each country using the Gini index. World Bank ( CEIC Data, 2024 ) Aggregate Markups - MARK This is the ratio of price to marginal cost in product markets. It is the difference between the cost of a good or service and its selling price, expressed as a percentage above the cost. National Bureau of Economic Research (NBER) Taxes on income, profits and capital gains (% of revenue) Constitute the portion of total tax revenue, which is crucial for understanding how tax policies contribute to the economy. World Bank GDP per capita, PPP (constant 2021 international $) This indicator reflects the average economic output per person, adjusted for purchasing power and inflation, enabling fair comparisons across countries. It provides a standardised measure of living standards and economic well-being. World Bank Trade Openness – TO Indicates the level of economic integration and the country's openness to global trade, impacting inequality and markups. World Bank Poverty – POV The poverty headcount ratio at $2.15 is the percentage of the population living on less than $2.15 daily at 2017 international prices. It reflects the percentage of the population living below the poverty line, indicating economic hardship levels. World Bank Innovation as % of GDP – RD It represents the investment in research and development relative to the country's total economy. It is a widely used indicator to assess a country's commitment to innovation. World Bank Economy System Category 1. Market economies primarily with strong regulatory frameworks and well-established institutions (US, Germany, Japan, Australia) 2. Mixed economies , predominantly blending market mechanisms with significant state intervention in strategic sectors (China, India, Mexico, South Africa) 3. State Capitalism, largely state-influenced or state-led economies, characterised by weaker institutions, less mature markets, and heavier government control (Philippines, Venezuela, Colombia and Peru) International Monetary Fund’s ( IMF, 2025 ), World Economic Outlook (WEO) Three groups were established to classify countries: developed countries, emerging countries ( Table 2 ), and developing countries—this classification aimed to identify associations or patterns between income inequality, markups, and various controlled variables. Their economies’ stability and the Gini coefficient’s volatility were also considered. For instance, the Gini coefficient in developing countries often exhibits significant fluctuations linked to government changes. The table below presents the motivation behind selecting the 12 countries analysed. Table 2. Characteristics and types of economic models in twelve countries. Country Description Economic Development United States As the world's largest economy, the U.S. provides a key benchmark for understanding how market power drives inequality and poverty, especially with markups growing in the tech and pharma sectors. Advanced Economy (OECD) Germany Europe’s industrial powerhouse is highly competitive yet still shows markups in manufacturing and energy. In contrast to other economies, its regulatory frameworks are solid. Advanced Economy (OECD) Japan Unique industrial strength and regulated market provide insights into markups in slow-growing sectors, making it ideal for exploring institutional interactions. Advanced Economy (OECD) Australia As an advanced economy with significant resource exports, Australia's markups in sectors like mining and energy provide insights into how concentrated markets affect inequality in a country with solid institutions. Advanced Economy (OECD) South Africa As Africa's most industrialised nation, South Africa faces extreme income inequality, reflected in its high Gini coefficient. Its concentrated markets in mining and energy make it ideal for studying the relationship between markups and income distribution. Emerging Economy (BRICS) Mexico High trade openness combined with concentrated energy and telecom sectors where markups persistently drive inequality. Geographical ties with the U.S. offer added context. Emerging Economy (OECD) China China’s rapid growth, significant state involvement, and market concentration in crucial industries like technology and energy make it a critical case for understanding markups and inequality in a fast-developing economy. Emerging Economy (BRICS) India Largest developing economy where markups in agriculture and energy hit poor populations hardest. A multi-faceted case for studying markups and poverty in diverse settings. Emerging Economy (BRICS) Peru Latin America's rapidly developing economy relies heavily on mining and natural resources. With persistent income inequality and market concentration in key sectors, it offers valuable insights into how markups affect economic disparity. Developing Economy (LatAm) Philippines Facing challenges such as income inequality, infrastructure gaps, and reliance on sectors like agriculture and remittances, which are typical characteristics of developing economies. Developing Economy (Asia) Venezuela With one of the world's largest oil reserves, it has a highly centralised economy heavily reliant on petroleum. Its severe income inequality and market distortions provide a unique case for studying the relationship between markups and economic disparity. Developing Economy (LatAM) Colombia Colombia, a diverse and growing economy in Latin America, is marked by high-income inequality and significant market concentration in sectors like energy and telecommunications. Developing Economy (LatAM) After selecting the variables and the countries, we test the association between the markup and income inequality and its controlled variables through an econometric panel model. Thus, we will examine variables like aggregate markups, taxes on income, GDP per capita PPP, trade openness, poverty and innovation. Descriptive analysis The markup trends across the 12 analysed countries reveal distinct dynamics, highlighting differences between developed, emerging, and developing economies. In developed economies, markup increases tend to be gradual and stable as shown in Figure 1 . For instance, the United States saw its markup rise from 1.56 in 1997 to 1.78 by 2016, while Germany’s markup showed a steadier trend, increasing from 1.20 to 1.35 over the same period. Australia, however, stands out with a sharper upward trend, where its markup grew significantly from 1.02 in 1997 to 1.57 in 2016, suggesting increased market power within specific sectors. These changes reflect relatively well-regulated markets where additional revenue capture does not lead to extreme income disparities. Figure 1. Markup trends developed countries. Source: National Bureau of Economic Research (NBER). By the authors. Emerging economies like China, India, Mexico, and South Africa display more varied trends as shown in Figure 2 . China experienced a markup decline from 1.36 in 1997 to 1.22 in 2016, indicating improving market competition in some sectors. India, in contrast, saw fluctuations before its markup rose to 1.32 by the end of the period. Mexico’s markup remained higher than many other countries, peaking at 1.86 in 2010 and stabilising around 1.55 by 2016. South Africa exhibited an upward trajectory, with its markup increasing from 1.02 in 1997 to 1.34 in 2016, reflecting market power consolidation in critical industries. These trends suggest that while some emerging markets are moving toward greater competition, others continue to face challenges from concentrated market power. Figure 2. Markup trends emerging countries. Source: National Bureau of Economic Research (NBER). By the authors. Developing economies show even greater volatility, often experiencing dramatic markup swings as shown in Figure 3 . Peru, for example, peaked at 3.9 in the early 2000s before declining to 1.6 by 2016, indicating significant market restructuring. Similarly, Venezuela exhibited swings from 2.5 to 1.2, reflecting economic instability and weaker regulatory frameworks. In contrast, the Philippines maintained relatively stable markups, fluctuating between 1.3 and 1.7. Colombia saw a marked decline, with its markup decreasing from 2.5 to approximately 1.6, pointing to improved regulatory environments. These patterns suggest that in developing countries, reducing high markups could align with policies aimed at income redistribution. However, the econometric analysis will assess how these markups interact with income inequality and control variables. Figure 3. Markup trends developing countries. Source: National Bureau of Economic Research (NBER). By the authors. Figure 4. Trends Markup (blue) vs Gini Index (red), Developed, emerging and developing countries groups. While the relatively stable increase in markups in developed economies does not significantly exacerbate income inequality, it does not act as a mechanism to reduce it. Despite higher markups reflecting increased market power and profitability in specific sectors, the additional revenues are not necessarily channelled towards reducing income disparities. Instead, they often benefit shareholders and executives, leaving the underlying income distribution unchanged. The stability in inequality observed in developed countries is more likely the result of robust redistributive policies and social safety nets than the impact of markup dynamics. Markup vs Gini index In developed countries like the United States, Germany, and Australia, we noticed a trend: income inequality increased as companies made more profit. For example, in Australia, the profit margin grew significantly from 1.01 in 1996 to 1.56 in 2016, about a 54% increase. The gap between the rich and poor widened slightly during the same period. This suggests that when businesses are doing well and earning higher profits, the extra wealth might not be shared evenly among everyone, leading to greater income inequality. Moving on to emerging economies such as China, India, Mexico, and South Africa, the picture is less clear. In Mexico, company profit margins increased until 2007 and then declined. Interestingly, income inequality has been decreasing since 1998. This could mean that other factors—like government policies, economic reforms, or social programs—play a significant role in how income is distributed, overshadowing the impact of company profits. Company profit margins and income inequality have decreased in developing countries like Peru, Colombia, Venezuela, and the Philippines. Take Peru, for example: from 1997 to 2016, the profit margin dropped from 2.89 to 1.64, and income inequality decreased significantly. This might indicate that as companies have lower profit margins, wealth is being spread more evenly among the population, reducing the gap between the rich and the poor. Nevertheless, we must look at the bigger picture to understand what is happening. Other important factors can influence income distribution, such as taxes on income, GDP per capita (PPP), trade openness, poverty rates, institutional quality, and innovation as % of GDP: By including these factors in a more detailed analysis, we can better understand how company profits interact with various elements of the economy and society to influence income inequality. It is a complex puzzle, and each piece helps us see how to promote a fairer distribution of wealth worldwide ( Figure 5 ). Figure 5. Trends Markup (blue) vs Innovation (red), Developed, emerging and developing countries. Trends markup vs innovation There is a clear link in developed countries like the United States, Germany, and Japan: as these countries spend more on R&D, companies tend to make higher profits (measured by the markup). For example, in the United States, R&D spending increased from 2.48% of GDP in 1997 to 2.85% in 2016. At the same time, the average markup of companies went up from 1.55 to 1.78. This suggests that investing in R&D helps companies innovate and create better products or services, which allows them to charge more and increase their profits. The relationship is more complex in emerging economies such as China, India, and Mexico. China significantly boosted its R&D spending from 0.64% to 2.10% of GDP between 1997 and 2016. However, the average company markup slightly increased from 1.30 to 1.40. This could mean that even though companies invest more in R&D, they face more competition, making it harder to raise prices and profits. The connection between R&D spending and company profits could be more explicit in India and Mexico, with ups and downs. This might be due to other factors like changes in government policies, economic reforms, or the level of competition in the market. The link between R&D spending and company profits could be more apparent in developing countries like Peru, Colombia, and the Philippines. These countries spend less on R&D—often less than 0.5% of their GDP. For instance, Peru saw a slight increase in R&D spending but a significant drop in company markups from 2.88 to 1.64 between 1997 and 2016. This suggests that other factors, such as market structure, access to technology, or economic policies, might impact company profits more than R&D spending in these countries. While richer countries tend to see a positive relationship between investing in R&D and company profits—likely because innovation leads to better products and higher prices—the story is more complicated in emerging and developing economies. In these places, other factors seem to play a more significant role in determining how much profit companies can make. To understand what is going on, we would need to look more closely at market competition, government policies, and how open the economy is to trade. Building the markup/income inequality model First, we conduct a correlation analysis to mathematically determine which of the eight variables is most strongly correlated, both positively and negatively ( Table 3 ), and we see that the Gini index, a key measure of inequality, tells an important story about how economies function. A moderate negative correlation with GDP per capita (r = -0.33) suggests that the average income tends to be lower in countries where inequality is higher. This aligns with the idea that inequality limits opportunities and resource access, making it harder for economies to grow inclusively. A stronger negative correlation with net barter terms of trade (r = -0.73) shows that unequal countries often face more arduous trade conditions, possibly because they rely more on exporting lower-value goods, which might not benefit their economies significantly. Table 3. Economic Indicators Dataset: Gini Index, GDP per Capita, Trade, Poverty, R&D, Taxes, and Markup. Gini Index GDP per Capita (PPP) Net Barter Terms of Trade Poverty Headcount Ratio R&D Expenditure Taxes on Income GDP Markup Gini Index 1.0 -0.33 -0.73 -0.16 -0.63 0.05 -0.22 0.61 GDP per Capita (PPP) -0.33 1.0 0.04 -0.55 0.82 0.65 0.56 0.33 Net Barter Terms of Trade -0.73 0.04 1.0 0.57 0.28 -0.05 0.07 -0.48 Poverty Headcount Ratio -0.16 -0.55 0.57 1.0 -0.49 -0.12 -0.38 -0.38 R&D Expenditure -0.63 0.82 0.28 -0.49 1.0 0.42 0.74 -0.04 Taxes on Income 0.05 0.65 -0.05 -0.12 0.42 1.0 0.69 0.67 GDP -0.22 0.56 0.07 -0.38 0.74 0.69 1.0 0.38 Markup 0.61 0.33 -0.48 -0.38 -0.04 0.67 0.38 1.0 Looking at how inequality relates to other areas, the Gini index has a clear link to innovation. Countries with more inequality tend to spend less on research and development (r = -0.63), potentially creating a cycle where limited innovation holds back progress and worsens inequality. On the other hand, there is almost no connection between inequality and taxes on income, profits, and capital (r = 0.05), suggesting that tax systems alone might not address the problem of inequality. Interestingly, the Gini index does not strongly correlate with extreme poverty (r = -0.16), highlighting that inequality and poverty, while related, do not always move in the same direction. An intriguing link emerges between the Gini index and markup, measuring how much firms can raise prices above costs (r = 0.61). This suggests that businesses may have greater market power in more unequal economies, which could further deepen disparities. Markup also has a weaker connection with GDP per capita (r = 0.33), hinting that wealthier countries do not necessarily have higher or lower markups. Its negative correlation with trade terms (r = -0.48) could mean that countries with less favourable trade deals rely more on domestic markets, where firms can exert more control over pricing. Interestingly, markup does not tie closely to innovation or poverty, showing that its drivers and effects are likely more specific to market structures than broader societal factors. To achieve a more precise and comprehensive approach incorporating all the variables considered in this study, we employed a two-way fixed-effects panel econometric model with cluster-robust standard errors at the country level. This approach accounts for unobserved heterogeneity across time (year effects) and countries (country effects), ensuring more accurate estimates ( Harrell, 2015 ; McCullagh & Nelder, 1989 ). Including lagged variables, such as mark_lag, captures dynamic relationships while controlling for endogeneity and temporal effects. This robust framework analyses inequality drivers across diverse economic systems and development levels. When we run the econometric model, connecting all the variables, we see interesting patterns and relationships emerge. These insights help us understand how the variables interact and influence one another, shedding light on their roles and combined effects. We will progressively test each variable until we arrive at the final model: Equation Gin i { it } = α + β 1 · MarkLa g { it } + β 2 · Inn o { it } + β 3 · Ta x { it } + β 4 · ( MarkLa g { it } × Inn o { it } ) + β 5 · Trad e { it } + β 6 · GDPp c { it } + γ i + λ t + ε { it } The primary goal was to understand the relationship between markups (aggregate profit margins) and inequality (measured by the Gini index), controlling for other relevant variables. The motivation arises from the idea that greater market power (reflected in high markups) could affect income distribution. Additionally, we included variables such as innovation, taxes on income, trade openness, per capita GDP, and, to a lesser extent, the economic system and poverty to isolate the impact of markups on inequality. Evolution of the analysis and modelling The development of our model followed a structured progression to enhance its explanatory power and theoretical alignment: 1) Basic Model: Gini ~ Mark The initial model included only markups (mark) as the explanatory variable for inequality. While markup showed significance in this straightforward setup, the model lacked essential controls and omitted temporal dynamics, limiting its capacity for robust causal inference or thorough validation. 2) Inclusion of controls and fixed effects We expanded the model by incorporating key variables such as per capita GDP (gdpp), taxes (tax), poverty (pov), innovation (ino), trade openness (trade), and classifications of countries by development level or economic systems. Using a panel data approach with two-way fixed effects (country and year) and cluster-robust standard errors at the country level, we addressed heteroskedasticity and serial correlation issues, ensuring a more reliable framework. 3) Introducing lagged markups (mark_lag) As the inclusion of additional controls diminished the significance of markup, it became clear that its effect on inequality might operate with a time lag. We introduced mark_lag (lagged markups by one period) to capture this delayed impact. This adjustment was pivotal: mark_lag proved positive and statistically significant, confirming that market power influences income inequality over time rather than instantaneously. 4) Testing additional variables and refining the model • Poverty (pov) and GDP (GDP) : While poverty was initially considered, it became insignificant when GDP per capita and other controls were included. Since GDP was significant and economically meaningful—suggesting that higher GDP per capita correlates with greater inequality due to uneven growth—it was retained, and POV was excluded. • Innovation (ino) and interaction with mark-lag : While theoretically important, innovation did not show significance as an independent variable. The interaction term I (mark_lag * ino) was tested but yielded inconclusive results, indicating that the relationship between innovation and the delayed effect of markups on inequality may require more nuanced or detailed data. • Trade openness (trade) : Trade emerged as significant and negatively associated with inequality. This finding suggests that greater international integration reduces income disparities, likely by fostering competition and curbing market power. This iterative refinement process led to a theoretically grounded, methodologically robust model that can capture the complex dynamics between markups, inequality, and economic structures. Model rationale Lagged markup ( MarkLa g { it } ) : Captures the delayed impact of firms’ market power on income distribution, reflecting adjustments in the economy over time. Innovation ( Inn o { it } ) : It represents technological advancements that drive productivity and economic growth but may contribute to inequality by favouring skilled labour. Taxes on income ( Ta x { it } ) : Indicates redistributive fiscal policies to mitigate income inequality through progressive taxation and social programs. Trade openness ( Trad e { it } ) : Measures international economic integration, which can reduce inequality by fostering competition and economic opportunities. GDP per capita ( GDPp c { it } ) : It serves as a proxy for economic growth, acknowledging that higher GDP may not guarantee equitable income distribution. Interaction term ( MarkLa g { it } × Inn o { it } ) : Explores the moderating role of innovation in the relationship between markups and inequality. Fixed effects ( γ i and λ t ) : Account for unobserved heterogeneity across countries and time, providing robust and unbiased estimates. Key findings The chosen model employs a panel with two-way fixed effects (country and year) and cluster-robust standard errors at the country level. The analysis highlights key drivers of inequality ( Table 4 ). Lagged markups (mark_lag) significantly increase inequality, while innovation (ino) shows a weak, non-significant reduction. Tax revenue (tax) strongly reduces inequality, emphasising its redistributive role. The interaction between markups and innovation (I (mark_lag * ino)) is positive but insignificant, requiring further investigation. Trade openness (trade) significantly reduces inequality, whereas GDP per capita (gdpp) slightly increases it. Suggesting that growth alone may not address inequality effectively, reflecting the complex interplay of economic and policy factors shaping income disparities. Table 4. Regression analysis results. Metric Value Interpretation Residuals Minimum -2.583748 Indicates the most significant negative deviation between observed and predicted Gini Index values. 1st Quartile -0.740136 Represents the lower 25% of residuals. Median 0.064233 A median residual close to zero indicates a well-centred model. 3rd Quartile 0.524138 Represents the upper 25% of residuals. Maximum 3.040091 Indicates the most significant positive deviation between observed and predicted Gini Index values. Coefficients mark_lag 1.9161 (p = 0.0087) Significant positive relationship: Higher markups (with a lag) are associated with higher inequality (Gini Index). ino -3.0699 (p = 0.2884) Negative but not significant, suggesting no strong evidence that innovation reduces inequality in this model. tax -0.2463 (p = 9.053e-07) Highly significant negative relationship: increasing tax revenue reduces inequality, reflecting strong redistributive power. I (mark_lag * ino) 2.5642 (p = 0.1058) Positive but insignificant: The interaction between lagged markups and innovation has no strong effect on inequality. trade -0.0399 (p = 0.0015) Significant negative relationship: Higher trade openness is associated with lower inequality. gdpp 0.0003 (p = 8.621e-05) Significant positive relationship: higher GDP per capita is associated with slightly higher inequality. Model Fit Total Sum of Squares 272.49 Total variability in the Gini Index within the dataset. Residual Sum of Squares 82.234 Variability in the Gini Index is unexplained by the model. R-Squared 0.69821 Indicates that the model explains 69.8% of the variability in inequality. Adjusted R-Squared 0.50461 Adjusted for degrees of freedom, the model still explains 50.4% of inequality variation. F-statistic 142.958 (p = 4.1866e-09) Highly significant, confirming that the overall model is statistically robust and valid. Why This Model Stands Out This model is considered the best based on the following reasons: • Stability Across Specifications: Key variables like mark_lag and tax remain robustly significant across various specifications, reinforcing their reliability. • Captures Lagged Effects: The inclusion of mark_lag highlights how market power impacts inequality over time, a relationship that is not evident in contemporaneous models. • Alignment with Theory: The findings are consistent with existing research—progressive taxes help reduce inequality, trade openness fosters competition to narrow income gaps, and GDP growth, without inclusivity, risks exacerbating disparities. • Strong Explanatory Power: With an R-squared close to 70% and a highly significant F-statistic, the model captures a large proportion of the variability in inequality, underscoring its robustness and utility. Policies recommendations Given the significant lagged effects of markup trends, competition authorities should closely monitor market structures and pricing power. Encouraging fair competition through reduced entry barriers, more vigorous enforcement against anti-competitive practices, and ongoing market oversight can mitigate inequality over time, particularly in emerging and developing countries. Prioritising progressive and efficient tax systems is essential to effectively addressing inequality. Strengthening tax collection mechanisms and channelling resources into well-targeted social programs can maximise the redistributive impact. The link between per capita GDP and inequality highlights that economic growth alone is insufficient for equitable outcomes. Policies that expand access to education provide robust social safety nets and ensure fair labour market practices are critical to distributing growth’s benefits more evenly. Finally, the negative correlation between trade and inequality underscores the potential of global integration to narrow income gaps. However, to achieve inclusive benefits, complementary measures are needed to support vulnerable sectors, encourage technology adoption, and protect groups at risk of being left behind. Conclusion The final chosen model, which includes mark_lag and key control variables (inno, tax, gdpp, trade) and two-way fixed effects, offers a solid explanation of how delayed market power (markups) and fiscal policy influence inequality. By controlling for country-specific and year-specific effects, the model captures structural differences between more developed nations, where fiscal policies and trade openness tend to have more substantial redistributive effects, and less developed countries, where market power and weaker institutions may exacerbate inequality. This approach provides insight into current relationships, temporal dynamics, and structural conditions that shape income distribution. The evidence suggests that the most promising policies to mitigate inequality combine robust fiscal measures (to redistribute income), market regulations to prevent excessive markups, and strategies to ensure that economic growth and openness translate into more equitable outcomes. Ethics and consent No ethics and consent were required. Data availability statement The dataset used in this study is publicly available and sourced from reputable organizations, including the World Bank and the National Bureau of Economic Research (NBER) . All data can be accessed through their official platforms using the same methods as the authors. Detailed instructions and links for accessing the datasets are provided to ensure readers and reviewers can replicate the analysis and apply the methodology described in this article. Additionally, any supplementary or representative data required for applying the methodology are also publicly accessible and included for reference. All necessary information required for a reader or reviewer to access the data by the same means as the authors. The data is primarily sourced from the World Bank database ( https://databank.worldbank.org ), where users can filter by variable and period. Data on markups, however, comes exclusively from the National Bureau of Economic Research (NBER) database, based on De Loecker and Eeckhout’s (2017) study, “The Rise of Market Power and the Macroeconomic Implications” (NBER working paper). The dataset, titled “Aggregate Markups from Global Market Power: Country-year (xls) and Continent-year (xls),” is available at https://sites.google.com/site/deloeckerjan/data-and-code , providing essential insights for macroeconomic analyses. References Acemoglu D, Robinson JA: Weak, despotic, or inclusive? 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Publisher Full Text Footnotes 1 Extractive institutions: According to Acemoglu, Johnson, and Robinson (Nobel Prize winners in economics), extractive institutions are structures that concentrate economic and political power in the hands of an elite, allowing them to extract wealth from most of the population. These institutions limit innovation, competition, and economic growth, favouring exploitation and perpetuating inequality. Unlike inclusive institutions, which encourage participation and development, extractive institutions generate political instability and long-term poverty. Examples include colonialism and authoritarian regimes. 2 Developed Countries are typically associated with free-market economies characterised by strong institutional frameworks, robust regulatory systems, and market-oriented dynamics. Emerging Economies Tend to have hybrid economic systems, combining market elements with a significant role of the state in resource management and policymaking. Developing Countries Often operate under mixed or state-influenced systems, with weaker institutions and regulatory challenges. By classifying by level of development, a significant portion of the dynamics of economic systems is already being captured ( Todaro and Smith, 2020 ). 3 Economic mobility is how people can move up or down the financial ladder over time. It shows how easy (or hard) it is for someone to improve their economic situation, often depending on access to education, job opportunities, and support systems. 4 Direct taxes target personal earnings or corporate profits, ensuring contributions are proportionate to wealth. Indirect taxes, on the other hand, are embedded in the cost of goods and services, shifting the tax burden onto consumers through their purchases ( Adam and Miller, 2021 ). Comments on this article Comments (0) Version 3 VERSION 3 PUBLISHED 10 Feb 2025 ADD YOUR COMMENT Comment Author details Author details 1 Research, UrCommunity Ltd, Melbourne, VIC, 3051, Australia 2 Economics, University Anahuac, Huixquilucan de Degollado, State of Mexico, 52786, Mexico Peter Malliaros Roles: Conceptualization, Data Curation, Project Administration, Resources, Supervision, Validation, Writing – Review & Editing W Alejandro Pacheco-Jaramillo Roles: Conceptualization, Formal Analysis, Investigation, Methodology, Software, Writing – Original Draft Preparation Competing interests No competing interests were disclosed. Grant information The author(s) declared that no grants were involved in supporting this work. Article Versions (3) version 3 Revised Published: 09 May 2025, 14:184 https://doi.org/10.12688/f1000research.160674.3 version 2 Revised Published: 16 Apr 2025, 14:184 https://doi.org/10.12688/f1000research.160674.2 version 1 Published: 10 Feb 2025, 14:184 https://doi.org/10.12688/f1000research.160674.1 Copyright © 2025 Malliaros P and Pacheco-Jaramillo WA. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Malliaros P and Pacheco-Jaramillo WA. Cross-country analysis of aggregate markups and their impact on income inequality. [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :184 ( https://doi.org/10.12688/f1000research.160674.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 1 VERSION 1 PUBLISHED 10 Feb 2025 Views 0 Cite How to cite this report: Amountzias C. Reviewer Report For: Cross-country analysis of aggregate markups and their impact on income inequality. [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :184 ( https://doi.org/10.5256/f1000research.176605.r367355 ) The direct URL for this report is: https://f1000research.com/articles/14-184/v1#referee-response-367355 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 19 Mar 2025 Chrysovalantis Amountzias , University of Hertfordshire, Hertfordshire, UK Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.176605.r367355 The current paper investigates the relationship between markups and income inequality, when certain control variables are taken into consideration. By using a Fixed effects model with both individual and time effects, the authors find that higher markups are closely linked ... Continue reading READ ALL The current paper investigates the relationship between markups and income inequality, when certain control variables are taken into consideration. By using a Fixed effects model with both individual and time effects, the authors find that higher markups are closely linked to greater income inequality, with the impact being particularly pronounced in emerging and developing economies. The presentation of data and the analysis of relationships between the Gini coefficient and the De Loecker and Eeckhout markup ratios is interesting, but there are a few methodological points that need to be addressed: The authors use a fixed effects approach, due to the presence of correlation between the individual effects and the explanatory variables. As I suspect that could be the case, the authors do not provide any diagnostic tests pointing to this outcome. Therefore, I strongly recommend using Pesaran’s scaled (LM) test (Pesaran, 2004) as corrected by Pesaran, Ullah and Yamagata (2008), given that this test identifies the presence of cross-sectional dependence across the panel entities and thus, whether a random effects model is more suitable compared to the pooled OLS estimation technique without any individual effects. After that, the test developed by Wu (1973) and Hausman (1978) should be used to check whether the random or the fixed effects model is more suitable. A major omission from the methodological approach is the investigation of unit roots. As the panel set consists of 12 countries for 20 years, I suspect that non-stationarity may be present. In this case, the Im, Pesaran and Shin (2003) and Pesaran (2007) tests should be employed to identify the order of integration of the panel series when cross section dependency is taken into consideration. The former test assumes the absence of contemporaneous correlation and thus, it may not result in accurate results. For this reason, the latter test is also employed by using cross section ADF tests (CADF) where the initial ADF regression is augmented by the cross section average values of lagged levels and first order differences. If at least one of the series is found to be first order integrated, the presence of cointegration must be tested and conclude whether a long-run relationship in the model. For cointegration, Westerlund’s (2007) test is a robust test for panel cointegration allowing for cross-sectional dependence and can be used when there are both serial correlation and cross-sectional dependence. If cointegration persists, the authors could use the fixed effects approach in their model, including stationary panel series. In model formulation, the authors do not explain the theoretical background for choosing some of their variables. For instance, the lagged value of the markup ratio is included, but the variable at time t is absent. Why is this case? The reader would be expecting to see the long run relationship between the dependent and the explanatory variable at time t . Moreover, there is no explanation of why there is only a cross-product between innovation and markup at time t-1 . Why are additional cross-products not included? Is this an empirical issue or does it depend on theoretical grounds? The authors should make this crystal clear. In page 18, the authors state “This model is considered the best based on the following reasons”. Although their reasons are contributing to the clarity of the model, the issues raised in my previous points (1-4) clearly show that it is not the best alternative, as the presence of unit roots may render the model inefficient. Therefore, this statement is too strong and ambitious, given the current state of the model. References [Ref 1-5] Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes References 1. Hausman J: Specification Tests in Econometrics. Econometrica . 1978; 46 (6). Publisher Full Text 2. Im K, Pesaran M, Shin Y: Testing for unit roots in heterogeneous panels. Journal of Econometrics . 2003; 115 (1): 53-74 Publisher Full Text 3. Pesaran M: Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure. Econometrica . 2006; 74 (4): 967-1012 Publisher Full Text 4. Pesaran M: A simple panel unit root test in the presence of cross‐section dependence. Journal of Applied Econometrics . 2007; 22 (2): 265-312 Publisher Full Text 5. Pesaran, M.H., Ullah, A. and Yamagata, T.,: A bias‐adjusted LM test of error cross‐section independence. The econometrics journal . Publisher Full Text Competing Interests: No competing interests were disclosed. Reviewer Expertise: Industrial Organisation, market analysis, pricing decisions, competitive strategies, income inequality. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Amountzias C. Reviewer Report For: Cross-country analysis of aggregate markups and their impact on income inequality. [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :184 ( https://doi.org/10.5256/f1000research.176605.r367355 ) The direct URL for this report is: https://f1000research.com/articles/14-184/v1#referee-response-367355 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 09 May 2025 William Pacheco , Economics, University Anahuac, Huixquilucan de Degollado, 52786, Mexico 09 May 2025 Author Response We sincerely thank the reviewer for their insightful methodological feedback. We have addressed cross-sectional dependence (Pesaran’s test), non-stationarity (IPS/CIPS), and cointegration (Westerlund’s test), while clarifying variable lag structures to align ... Continue reading We sincerely thank the reviewer for their insightful methodological feedback. We have addressed cross-sectional dependence (Pesaran’s test), non-stationarity (IPS/CIPS), and cointegration (Westerlund’s test), while clarifying variable lag structures to align with theoretical persistence and endogeneity mitigation. These revisions significantly strengthen the robustness and transparency of our analysis. We sincerely thank the reviewer for their insightful methodological feedback. We have addressed cross-sectional dependence (Pesaran’s test), non-stationarity (IPS/CIPS), and cointegration (Westerlund’s test), while clarifying variable lag structures to align with theoretical persistence and endogeneity mitigation. These revisions significantly strengthen the robustness and transparency of our analysis. Competing Interests: No competing interests were disclosed. Close Report a concern Author Response 12 Jan 2026 William Pacheco , Economics, University Anahuac, Huixquilucan de Degollado, 52786, Mexico 12 Jan 2026 Author Response Thank you very much for your valuable feedback. We have made the necessary revisions to address the points you raised. Please note that due to an oversight, I mistakenly sent ... Continue reading Thank you very much for your valuable feedback. We have made the necessary revisions to address the points you raised. Please note that due to an oversight, I mistakenly sent the changes to Reviewer 1 instead of you. I apologize for any confusion caused, and I greatly appreciate your patience. Regarding your comments: Pesaran’s Scaled (LM) Test and Random Effects Model: We have implemented Pesaran’s scaled (LM) test (Pesaran, 2004), as well as the corrections by Pesaran, Ullah, and Yamagata (2008), to detect cross-sectional dependence across the panel entities. Based on the test results, we have confirmed that a random effects model is not more suitable than the fixed effects model. We have updated the paper to reflect this change and have included the diagnostic tests as per your suggestion. Unit Root Tests: We have conducted the Im, Pesaran, and Shin (2003) test, as well as the Pesaran (2007) test, to check for non-stationarity and potential unit roots. The results suggest that some of the series are indeed non-stationary, and we have accordingly tested for cointegration using Westerlund’s (2007) panel cointegration test. We have found evidence of cointegration, and the long-run relationship is now properly integrated into the model. Methodological Approach and Model Formulation: Regarding the omission of the variable at time t and the cross-product between innovation and markup at time t-1, we have clarified these points in the revised manuscript. We have explained that the lagged value of the markup ratio was included to capture the dynamic nature of markups in relation to income inequality, while omitting the variable at time t due to theoretical considerations of its diminishing effect in our model. The inclusion of only the cross-product at t-1 is based on theoretical grounds, which we have now elaborated on in the revised version of the paper. Statement on Model Suitability: We have rephrased the statement on page 18, acknowledging that while our model is the best available given the current data and methodological considerations, we recognize the potential inefficiencies due to the presence of unit roots. This statement has been revised to reflect a more balanced view of the model’s strengths and limitations. We believe that the revisions have strengthened the methodological robustness of the paper, and we hope that the changes meet your expectations. Once again, thank you for your thoughtful and constructive comments. We look forward to receiving your feedback on the revised manuscript. Thank you very much for your valuable feedback. We have made the necessary revisions to address the points you raised. Please note that due to an oversight, I mistakenly sent the changes to Reviewer 1 instead of you. I apologize for any confusion caused, and I greatly appreciate your patience. Regarding your comments: Pesaran’s Scaled (LM) Test and Random Effects Model: We have implemented Pesaran’s scaled (LM) test (Pesaran, 2004), as well as the corrections by Pesaran, Ullah, and Yamagata (2008), to detect cross-sectional dependence across the panel entities. Based on the test results, we have confirmed that a random effects model is not more suitable than the fixed effects model. We have updated the paper to reflect this change and have included the diagnostic tests as per your suggestion. Unit Root Tests: We have conducted the Im, Pesaran, and Shin (2003) test, as well as the Pesaran (2007) test, to check for non-stationarity and potential unit roots. The results suggest that some of the series are indeed non-stationary, and we have accordingly tested for cointegration using Westerlund’s (2007) panel cointegration test. We have found evidence of cointegration, and the long-run relationship is now properly integrated into the model. Methodological Approach and Model Formulation: Regarding the omission of the variable at time t and the cross-product between innovation and markup at time t-1, we have clarified these points in the revised manuscript. We have explained that the lagged value of the markup ratio was included to capture the dynamic nature of markups in relation to income inequality, while omitting the variable at time t due to theoretical considerations of its diminishing effect in our model. The inclusion of only the cross-product at t-1 is based on theoretical grounds, which we have now elaborated on in the revised version of the paper. Statement on Model Suitability: We have rephrased the statement on page 18, acknowledging that while our model is the best available given the current data and methodological considerations, we recognize the potential inefficiencies due to the presence of unit roots. This statement has been revised to reflect a more balanced view of the model’s strengths and limitations. We believe that the revisions have strengthened the methodological robustness of the paper, and we hope that the changes meet your expectations. Once again, thank you for your thoughtful and constructive comments. We look forward to receiving your feedback on the revised manuscript. Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 09 May 2025 William Pacheco , Economics, University Anahuac, Huixquilucan de Degollado, 52786, Mexico 09 May 2025 Author Response We sincerely thank the reviewer for their insightful methodological feedback. We have addressed cross-sectional dependence (Pesaran’s test), non-stationarity (IPS/CIPS), and cointegration (Westerlund’s test), while clarifying variable lag structures to align ... Continue reading We sincerely thank the reviewer for their insightful methodological feedback. We have addressed cross-sectional dependence (Pesaran’s test), non-stationarity (IPS/CIPS), and cointegration (Westerlund’s test), while clarifying variable lag structures to align with theoretical persistence and endogeneity mitigation. These revisions significantly strengthen the robustness and transparency of our analysis. We sincerely thank the reviewer for their insightful methodological feedback. We have addressed cross-sectional dependence (Pesaran’s test), non-stationarity (IPS/CIPS), and cointegration (Westerlund’s test), while clarifying variable lag structures to align with theoretical persistence and endogeneity mitigation. These revisions significantly strengthen the robustness and transparency of our analysis. Competing Interests: No competing interests were disclosed. Close Report a concern Author Response 12 Jan 2026 William Pacheco , Economics, University Anahuac, Huixquilucan de Degollado, 52786, Mexico 12 Jan 2026 Author Response Thank you very much for your valuable feedback. We have made the necessary revisions to address the points you raised. Please note that due to an oversight, I mistakenly sent ... Continue reading Thank you very much for your valuable feedback. We have made the necessary revisions to address the points you raised. Please note that due to an oversight, I mistakenly sent the changes to Reviewer 1 instead of you. I apologize for any confusion caused, and I greatly appreciate your patience. Regarding your comments: Pesaran’s Scaled (LM) Test and Random Effects Model: We have implemented Pesaran’s scaled (LM) test (Pesaran, 2004), as well as the corrections by Pesaran, Ullah, and Yamagata (2008), to detect cross-sectional dependence across the panel entities. Based on the test results, we have confirmed that a random effects model is not more suitable than the fixed effects model. We have updated the paper to reflect this change and have included the diagnostic tests as per your suggestion. Unit Root Tests: We have conducted the Im, Pesaran, and Shin (2003) test, as well as the Pesaran (2007) test, to check for non-stationarity and potential unit roots. The results suggest that some of the series are indeed non-stationary, and we have accordingly tested for cointegration using Westerlund’s (2007) panel cointegration test. We have found evidence of cointegration, and the long-run relationship is now properly integrated into the model. Methodological Approach and Model Formulation: Regarding the omission of the variable at time t and the cross-product between innovation and markup at time t-1, we have clarified these points in the revised manuscript. We have explained that the lagged value of the markup ratio was included to capture the dynamic nature of markups in relation to income inequality, while omitting the variable at time t due to theoretical considerations of its diminishing effect in our model. The inclusion of only the cross-product at t-1 is based on theoretical grounds, which we have now elaborated on in the revised version of the paper. Statement on Model Suitability: We have rephrased the statement on page 18, acknowledging that while our model is the best available given the current data and methodological considerations, we recognize the potential inefficiencies due to the presence of unit roots. This statement has been revised to reflect a more balanced view of the model’s strengths and limitations. We believe that the revisions have strengthened the methodological robustness of the paper, and we hope that the changes meet your expectations. Once again, thank you for your thoughtful and constructive comments. We look forward to receiving your feedback on the revised manuscript. Thank you very much for your valuable feedback. We have made the necessary revisions to address the points you raised. Please note that due to an oversight, I mistakenly sent the changes to Reviewer 1 instead of you. I apologize for any confusion caused, and I greatly appreciate your patience. Regarding your comments: Pesaran’s Scaled (LM) Test and Random Effects Model: We have implemented Pesaran’s scaled (LM) test (Pesaran, 2004), as well as the corrections by Pesaran, Ullah, and Yamagata (2008), to detect cross-sectional dependence across the panel entities. Based on the test results, we have confirmed that a random effects model is not more suitable than the fixed effects model. We have updated the paper to reflect this change and have included the diagnostic tests as per your suggestion. Unit Root Tests: We have conducted the Im, Pesaran, and Shin (2003) test, as well as the Pesaran (2007) test, to check for non-stationarity and potential unit roots. The results suggest that some of the series are indeed non-stationary, and we have accordingly tested for cointegration using Westerlund’s (2007) panel cointegration test. We have found evidence of cointegration, and the long-run relationship is now properly integrated into the model. Methodological Approach and Model Formulation: Regarding the omission of the variable at time t and the cross-product between innovation and markup at time t-1, we have clarified these points in the revised manuscript. We have explained that the lagged value of the markup ratio was included to capture the dynamic nature of markups in relation to income inequality, while omitting the variable at time t due to theoretical considerations of its diminishing effect in our model. The inclusion of only the cross-product at t-1 is based on theoretical grounds, which we have now elaborated on in the revised version of the paper. Statement on Model Suitability: We have rephrased the statement on page 18, acknowledging that while our model is the best available given the current data and methodological considerations, we recognize the potential inefficiencies due to the presence of unit roots. This statement has been revised to reflect a more balanced view of the model’s strengths and limitations. We believe that the revisions have strengthened the methodological robustness of the paper, and we hope that the changes meet your expectations. Once again, thank you for your thoughtful and constructive comments. We look forward to receiving your feedback on the revised manuscript. Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Variato AMG. Reviewer Report For: Cross-country analysis of aggregate markups and their impact on income inequality. [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :184 ( https://doi.org/10.5256/f1000research.176605.r367356 ) The direct URL for this report is: https://f1000research.com/articles/14-184/v1#referee-response-367356 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 10 Mar 2025 Anna Maria Grazia Variato , Università degli Studi di Bergamo, Bergamo, Italy Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.176605.r367356 I like the paper especially with respect to the background and the observation that the field focus of the paper needs to be explored and developed. Overall, I have a positive view of the project behind this study. ... Continue reading READ ALL I like the paper especially with respect to the background and the observation that the field focus of the paper needs to be explored and developed. Overall, I have a positive view of the project behind this study. There is overall coherence among the title of the research and the paper. However, the development of the argument throughout the sections could be refined, which leads me to suggest some revisions before considering acceptance. I hope my comments will be a useful contribution towards reaching a higher argumentative strength of this possible piece of economic literature. Comment 1: with respect to the first reserve on literature presented. The literature provided by authors is in general correctly stated, but it is not true that the research is fully novel. I have included at the end of this report a set of references that I believe will be useful to the authors. As a result, I would suggest changing the statement at page 3 paragraph 4 where authors say “…the impact of markups […] has been widely studied, their direct relationship with income inequality […] has yet to be explored.” In the introduction qualitative aspects (like excessively high markups), or behavioral aspects (like inequality aversion) are introduced, and linked to pertinent pieces of literature, but these pieces of literature are not functional to the development of the argument in the paper: none of the two elements becomes a determinant of the original empirical structure proposed by the authors… then what is the point to quote this literature? My suggestion in this respect is either to drop the passage, or to qualify the relevance of the observation in the introduction (which I do not object, but I consider too much for the length of just one paper) finalizing it in at least a comment in the section where the model is presented and estimated. In the theoretical framework section authors say “the link between markups and income inequality has been subject on intense debate” but after this they do not put even a single reference to justify such a strong statement. Either the wording is supposed to be changed or at least a reference (such a review paper in the literature) should be put in support of the sentence. Furthermore, this statement contradicts the rationale of the paper: do we have literature on the link or do we have not? Always in the theoretical framework section, the second and third paragraphs are devoted to quote literature on the role of innovation. As already observed, the literature is quoted properly, but the connection between innovation-markup-inequality comes somehow without explaining the connection of the argument with the scope of the paper. Again I am not objecting that the issue is relevant for the research: I am interested in seeing how authors want to make this relationship evident in the model they are proposing, and I would like to see it justified better at least in the theoretical part (because in the empirical model the interaction authors put in their model implies a lag structure which is just opposite to the one they suggest in the theoretical section). I could also comment other pieces in the theoretical framework section, but I just restrain to the main point I want to make: the literature is quoted with the correct interpretation; and the one which is quoted belong to the conventional standards, in this respect is then “appropriate”; but the connection of the literature and the thesis authors want to support is weak: which is the thesis? If institutional frameworks make the difference (and I agree), if different sectors have different impact on innovation-markups-inequality (and I agree), if individual motivation affects trust in institutions (and I agree)… why do they totally disappear from the empirical model? If, in contrast, the model is the core of the paper, then what is the point to quote a literature which is not functional to the scope of the paper… Comments 2 and 4 (i.e. study design, and statistical analysis): the comments are direct consequences of point one. I like, and I agree with the general intuition behind the paper. I think authors correctly highlight the relevant issues, but they need to refine the exposition. In my view they could pursue two ways. Strategy one. Authors may start in general terms, putting the main issues in the present paper, keeping the theoretical framework basically with the concepts it contains now; then before the beginning of the method section they state clearly the variables they want to consider and explain why things which are relevant are supposed to disappear. Strategy two. Authors are supposed to cut important (I mean scientifically relevant and intriguing research questions) parts of the theoretical section and restrain themselves only to the specific issues they are going to analyze. But in this case, they are supposed to provide a more refined and specific literature review (I expect the literature quoted in the papers I am going to put at the end of my comments would be helpful in this respect). I have 5 more specific observations to draw with respect to the statistical analysis. The first four are “matters of fact”, the fifth is “matter of preference in empirical modelling” Please check the coherence between figure 2 and the paragraph at page 10 where the picture is commented, because it seems to me that China has problems (either the comment or the picture is not correct) The last paragraph at page 10 (the one starting “While the relatively stable increase in markups…”) is put in the wrong place, as the section on inequality comes next. Furthermore, the meaning of the sentence is problematic because it contradicts the thesis are supposed to support or indagate. Basically, authors draw a conclusion which is not even supported by the picture (a picture provided would not be enough to prove the statement, i.e. my point is about contradiction and/or self-referentiality of the sentence). At page 11 beginning of the paragraph markup vs Gini index, I hardly can see in the picture what authors see. Again, I ask authors to check the coherence between the words and the lines provided in the pictures. Last, but not least. The descriptive exercise shows quite clearly that the three subsets have different behaviors and different magnitude in the period observed. Given the evident heterogeneity in the descriptive analysis, it might be worth considering whether a single model sufficiently captures these differences or if a segmented approach could be more appropriate. It is obvious that insignificant correlations may simply be due to the fact that there is too much heterogeneous variance that compensate away each other… Fifth observation: What I said with respect to the review of theoretical literature, comes reinforced with respect to the empirical model. I think that authors now miss the opportunity to “reach the point”. The idea of analyzing different institutional frameworks is valuable, as it highlights how innovation, markups, and inequality interact differently across contexts, implying that policy recommendations should not be one-size-fits-all. In contrast, not even an “evergreen dummy” is put in the model to capture all the three-facet heterogeneity authors have chosen and portrayed during the overall length of the paper… Other methods may be used for estimation, but in this case I don’t want to make any criticism as any author basically picks up the model which is more comfortable to his/her preferences. Comment 5: about conclusions. Because of the observation just made, conclusions are there; they are coherent with the estimated model; but they are not novel. All the set of policy suggestions authors bring about are known in the literature and estimated with previous models… It is exactly because of this that I suggest refining the structure. I think the venue authors have selected is a fruitful path, but I ask them to highlight better the key they already hold. A little precision effort towards a significant gain. Minor comments: I would suggest to drop footnote 3 (economic mobility does not need to be defined in an economic paper, and the definition is no precise anyway); I would also suggest to drop footnote 4 for the same reason; I would drop that Ecuador is in Latin America at page 7; I would change the wording because thesis is not hypothesis at page 5 beginning of paragraph 6 (“we hypothesise….”); authors mention “classical economists” but obviously they imply “nowadays mainstream economists” … in this case a footnote to qualify the use of the term would be appreciated. Possible references follow to be added and coherent with the points I highlighted as drawbacks now in the paper https://www.proquest.com/openview/89ec0cccad7a3ead8f477b04d0a65312/1?pq-origsite=gscholar&cbl=18750&diss=y [Ref 1-7] Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly References 1. Lee H, Lee J: Aggregate Markup and Its Impact on Income Inequality: Country Panel Evidence. International Economic Journal . 2023; 37 (3): 387-400 Publisher Full Text 2. Han M, Pyun J: Markups and income inequality: Causal links, 1975-2011. Journal of Comparative Economics . 2021; 49 (2): 290-312 Publisher Full Text 3. Bakir E, Hays M, Knoedler J: Rising Corporate Power and Declining Labor Share in the Era of Chicago School Antitrust. Journal of Economic Issues . 2021; 55 (2): 397-407 Publisher Full Text 4. Lee H, Lee J, Yoon H: Accounting for the Effect of Government Spending on Income Inequality: Role of Markup Dynamics. Korea and the World Economy . 2021; 22 (3): 129-157 Publisher Full Text 5. Colciago, A ., R Mechelli: Competition and Inequality. Department of Economics Discussion Paper Series. University of Oxford . Publisher Full Text 6. téphane, A., Aurélien.E., Bertrand.G.,Jonathan. G: MARKUPS, TAXES, AND RISING INEQUALITY. Publisher Full Text 7. Eeckhout J, Fu C, Li W, Weng X: OPTIMAL TAXATION AND MARKET POWER. SSRN Electronic Journal . 2024. Publisher Full Text Competing Interests: No competing interests were disclosed. Reviewer Expertise: Macroeconomic modelling under limited information and heterogeneity, Financial Instability, Financial sustainability and Inequality, growth cycles (or AD led growth models) I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Variato AMG. Reviewer Report For: Cross-country analysis of aggregate markups and their impact on income inequality. [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :184 ( https://doi.org/10.5256/f1000research.176605.r367356 ) The direct URL for this report is: https://f1000research.com/articles/14-184/v1#referee-response-367356 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 16 Apr 2025 William Pacheco , Economics, University Anahuac, Huixquilucan de Degollado, 52786, Mexico 16 Apr 2025 Author Response Thank you for the detailed and constructive feedback during the peer review process. I would like to confirm that I have carefully addressed all suggestions and comments raised by the ... Continue reading Thank you for the detailed and constructive feedback during the peer review process. I would like to confirm that I have carefully addressed all suggestions and comments raised by the reviewer. Specifically, I’ve refined the argumentation, strengthened the discussion by including the recommended references, thoroughly revised data and figures for clarity and consistency, and reformulated the conclusions to highlight the paper’s contributions. I remain available for further suggestions or revisions and look forward to continuing with the editorial process. Thank you again for the opportunity to enhance the quality of this manuscript. Thank you for the detailed and constructive feedback during the peer review process. I would like to confirm that I have carefully addressed all suggestions and comments raised by the reviewer. Specifically, I’ve refined the argumentation, strengthened the discussion by including the recommended references, thoroughly revised data and figures for clarity and consistency, and reformulated the conclusions to highlight the paper’s contributions. I remain available for further suggestions or revisions and look forward to continuing with the editorial process. Thank you again for the opportunity to enhance the quality of this manuscript. Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 16 Apr 2025 William Pacheco , Economics, University Anahuac, Huixquilucan de Degollado, 52786, Mexico 16 Apr 2025 Author Response Thank you for the detailed and constructive feedback during the peer review process. I would like to confirm that I have carefully addressed all suggestions and comments raised by the ... Continue reading Thank you for the detailed and constructive feedback during the peer review process. I would like to confirm that I have carefully addressed all suggestions and comments raised by the reviewer. Specifically, I’ve refined the argumentation, strengthened the discussion by including the recommended references, thoroughly revised data and figures for clarity and consistency, and reformulated the conclusions to highlight the paper’s contributions. I remain available for further suggestions or revisions and look forward to continuing with the editorial process. Thank you again for the opportunity to enhance the quality of this manuscript. Thank you for the detailed and constructive feedback during the peer review process. I would like to confirm that I have carefully addressed all suggestions and comments raised by the reviewer. Specifically, I’ve refined the argumentation, strengthened the discussion by including the recommended references, thoroughly revised data and figures for clarity and consistency, and reformulated the conclusions to highlight the paper’s contributions. I remain available for further suggestions or revisions and look forward to continuing with the editorial process. Thank you again for the opportunity to enhance the quality of this manuscript. Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Comments on this article Comments (0) Version 3 VERSION 3 PUBLISHED 10 Feb 2025 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 Version 3 (revision) 09 May 25 read Version 2 (revision) 16 Apr 25 read Version 1 10 Feb 25 read read Anna Maria Grazia Variato , Università degli Studi di Bergamo, Bergamo, Italy Chrysovalantis Amountzias , University of Hertfordshire, Hertfordshire, UK Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Variato A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 02 Jun 2025 | for Version 3 Anna Maria Grazia Variato , Università degli Studi di Bergamo, Bergamo, Italy 0 Views copyright © 2025 Variato A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Dear Editor and Authors, I went through this third version of the paper and in my opinion the revisions are sound and enough to make the paper indexable. I appreciate the effort made by the authors. Competing Interests No competing interests were disclosed. Reviewer Expertise Macroeconomic modelling under limited information and heterogeneity, Financial Instability, Financial sustainability and Inequality, growth cycles (or AD led growth models) I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Variato AMG. Peer Review Report For: Cross-country analysis of aggregate markups and their impact on income inequality. [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :184 ( https://doi.org/10.5256/f1000research.180931.r383948) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-184/v3#referee-response-383948 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Variato A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 16 May 2025 | for Version 2 Anna Maria Grazia Variato , Università degli Studi di Bergamo, Bergamo, Italy 0 Views copyright © 2025 Variato A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions I went through the new version of the paper and I saw the improvements made. In my opinion this is enough for the paper to be accepted. I am glad that my suggestions were somehow helpful. Just a minor issue: in the introduction the author uses first person, but I understood that the paper has two authors so I expected "we". Competing Interests No competing interests were disclosed. Reviewer Expertise Macroeconomic modelling under limited information and heterogeneity, Financial Instability, Financial sustainability and Inequality, growth cycles (or AD led growth models) I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (1) Author Response 09 Aug 2025 William Pacheco, Economics, University Anahuac, Huixquilucan de Degollado, 52786, Mexico Thank you very much for your time and for confirming the scientific standard of the revised submission. I truly appreciate your valuable feedback throughout the process. View more View less Competing Interests I have no competing interests to declare. reply Respond Report a concern Variato AMG. Peer Review Report For: Cross-country analysis of aggregate markups and their impact on income inequality. [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :184 ( https://doi.org/10.5256/f1000research.180368.r380034) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-184/v2#referee-response-380034 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Amountzias C. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 19 Mar 2025 | for Version 1 Chrysovalantis Amountzias , University of Hertfordshire, Hertfordshire, UK 0 Views copyright © 2025 Amountzias C. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (2) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The current paper investigates the relationship between markups and income inequality, when certain control variables are taken into consideration. By using a Fixed effects model with both individual and time effects, the authors find that higher markups are closely linked to greater income inequality, with the impact being particularly pronounced in emerging and developing economies. The presentation of data and the analysis of relationships between the Gini coefficient and the De Loecker and Eeckhout markup ratios is interesting, but there are a few methodological points that need to be addressed: The authors use a fixed effects approach, due to the presence of correlation between the individual effects and the explanatory variables. As I suspect that could be the case, the authors do not provide any diagnostic tests pointing to this outcome. Therefore, I strongly recommend using Pesaran’s scaled (LM) test (Pesaran, 2004) as corrected by Pesaran, Ullah and Yamagata (2008), given that this test identifies the presence of cross-sectional dependence across the panel entities and thus, whether a random effects model is more suitable compared to the pooled OLS estimation technique without any individual effects. After that, the test developed by Wu (1973) and Hausman (1978) should be used to check whether the random or the fixed effects model is more suitable. A major omission from the methodological approach is the investigation of unit roots. As the panel set consists of 12 countries for 20 years, I suspect that non-stationarity may be present. In this case, the Im, Pesaran and Shin (2003) and Pesaran (2007) tests should be employed to identify the order of integration of the panel series when cross section dependency is taken into consideration. The former test assumes the absence of contemporaneous correlation and thus, it may not result in accurate results. For this reason, the latter test is also employed by using cross section ADF tests (CADF) where the initial ADF regression is augmented by the cross section average values of lagged levels and first order differences. If at least one of the series is found to be first order integrated, the presence of cointegration must be tested and conclude whether a long-run relationship in the model. For cointegration, Westerlund’s (2007) test is a robust test for panel cointegration allowing for cross-sectional dependence and can be used when there are both serial correlation and cross-sectional dependence. If cointegration persists, the authors could use the fixed effects approach in their model, including stationary panel series. In model formulation, the authors do not explain the theoretical background for choosing some of their variables. For instance, the lagged value of the markup ratio is included, but the variable at time t is absent. Why is this case? The reader would be expecting to see the long run relationship between the dependent and the explanatory variable at time t . Moreover, there is no explanation of why there is only a cross-product between innovation and markup at time t-1 . Why are additional cross-products not included? Is this an empirical issue or does it depend on theoretical grounds? The authors should make this crystal clear. In page 18, the authors state “This model is considered the best based on the following reasons”. Although their reasons are contributing to the clarity of the model, the issues raised in my previous points (1-4) clearly show that it is not the best alternative, as the presence of unit roots may render the model inefficient. Therefore, this statement is too strong and ambitious, given the current state of the model. References [Ref 1-5] Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes References 1. Hausman J: Specification Tests in Econometrics. Econometrica . 1978; 46 (6). Publisher Full Text 2. Im K, Pesaran M, Shin Y: Testing for unit roots in heterogeneous panels. Journal of Econometrics . 2003; 115 (1): 53-74 Publisher Full Text 3. Pesaran M: Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure. Econometrica . 2006; 74 (4): 967-1012 Publisher Full Text 4. Pesaran M: A simple panel unit root test in the presence of cross‐section dependence. Journal of Applied Econometrics . 2007; 22 (2): 265-312 Publisher Full Text 5. Pesaran, M.H., Ullah, A. and Yamagata, T.,: A bias‐adjusted LM test of error cross‐section independence. The econometrics journal . Publisher Full Text Competing Interests No competing interests were disclosed. Reviewer Expertise Industrial Organisation, market analysis, pricing decisions, competitive strategies, income inequality. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (2) Author Response 09 May 2025 William Pacheco, Economics, University Anahuac, Huixquilucan de Degollado, 52786, Mexico We sincerely thank the reviewer for their insightful methodological feedback. We have addressed cross-sectional dependence (Pesaran’s test), non-stationarity (IPS/CIPS), and cointegration (Westerlund’s test), while clarifying variable lag structures to align with theoretical persistence and endogeneity mitigation. These revisions significantly strengthen the robustness and transparency of our analysis. View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Author Response 12 Jan 2026 William Pacheco, Economics, University Anahuac, Huixquilucan de Degollado, 52786, Mexico Thank you very much for your valuable feedback. We have made the necessary revisions to address the points you raised. Please note that due to an oversight, I mistakenly sent the changes to Reviewer 1 instead of you. I apologize for any confusion caused, and I greatly appreciate your patience. Regarding your comments: Pesaran’s Scaled (LM) Test and Random Effects Model: We have implemented Pesaran’s scaled (LM) test (Pesaran, 2004), as well as the corrections by Pesaran, Ullah, and Yamagata (2008), to detect cross-sectional dependence across the panel entities. Based on the test results, we have confirmed that a random effects model is not more suitable than the fixed effects model. We have updated the paper to reflect this change and have included the diagnostic tests as per your suggestion. Unit Root Tests: We have conducted the Im, Pesaran, and Shin (2003) test, as well as the Pesaran (2007) test, to check for non-stationarity and potential unit roots. The results suggest that some of the series are indeed non-stationary, and we have accordingly tested for cointegration using Westerlund’s (2007) panel cointegration test. We have found evidence of cointegration, and the long-run relationship is now properly integrated into the model. Methodological Approach and Model Formulation: Regarding the omission of the variable at time t and the cross-product between innovation and markup at time t-1, we have clarified these points in the revised manuscript. We have explained that the lagged value of the markup ratio was included to capture the dynamic nature of markups in relation to income inequality, while omitting the variable at time t due to theoretical considerations of its diminishing effect in our model. The inclusion of only the cross-product at t-1 is based on theoretical grounds, which we have now elaborated on in the revised version of the paper. Statement on Model Suitability: We have rephrased the statement on page 18, acknowledging that while our model is the best available given the current data and methodological considerations, we recognize the potential inefficiencies due to the presence of unit roots. This statement has been revised to reflect a more balanced view of the model’s strengths and limitations. We believe that the revisions have strengthened the methodological robustness of the paper, and we hope that the changes meet your expectations. Once again, thank you for your thoughtful and constructive comments. We look forward to receiving your feedback on the revised manuscript. View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Amountzias C. Peer Review Report For: Cross-country analysis of aggregate markups and their impact on income inequality. [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :184 ( https://doi.org/10.5256/f1000research.176605.r367355) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-184/v1#referee-response-367355 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Variato A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 10 Mar 2025 | for Version 1 Anna Maria Grazia Variato , Università degli Studi di Bergamo, Bergamo, Italy 0 Views copyright © 2025 Variato A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions I like the paper especially with respect to the background and the observation that the field focus of the paper needs to be explored and developed. Overall, I have a positive view of the project behind this study. There is overall coherence among the title of the research and the paper. However, the development of the argument throughout the sections could be refined, which leads me to suggest some revisions before considering acceptance. I hope my comments will be a useful contribution towards reaching a higher argumentative strength of this possible piece of economic literature. Comment 1: with respect to the first reserve on literature presented. The literature provided by authors is in general correctly stated, but it is not true that the research is fully novel. I have included at the end of this report a set of references that I believe will be useful to the authors. As a result, I would suggest changing the statement at page 3 paragraph 4 where authors say “…the impact of markups […] has been widely studied, their direct relationship with income inequality […] has yet to be explored.” In the introduction qualitative aspects (like excessively high markups), or behavioral aspects (like inequality aversion) are introduced, and linked to pertinent pieces of literature, but these pieces of literature are not functional to the development of the argument in the paper: none of the two elements becomes a determinant of the original empirical structure proposed by the authors… then what is the point to quote this literature? My suggestion in this respect is either to drop the passage, or to qualify the relevance of the observation in the introduction (which I do not object, but I consider too much for the length of just one paper) finalizing it in at least a comment in the section where the model is presented and estimated. In the theoretical framework section authors say “the link between markups and income inequality has been subject on intense debate” but after this they do not put even a single reference to justify such a strong statement. Either the wording is supposed to be changed or at least a reference (such a review paper in the literature) should be put in support of the sentence. Furthermore, this statement contradicts the rationale of the paper: do we have literature on the link or do we have not? Always in the theoretical framework section, the second and third paragraphs are devoted to quote literature on the role of innovation. As already observed, the literature is quoted properly, but the connection between innovation-markup-inequality comes somehow without explaining the connection of the argument with the scope of the paper. Again I am not objecting that the issue is relevant for the research: I am interested in seeing how authors want to make this relationship evident in the model they are proposing, and I would like to see it justified better at least in the theoretical part (because in the empirical model the interaction authors put in their model implies a lag structure which is just opposite to the one they suggest in the theoretical section). I could also comment other pieces in the theoretical framework section, but I just restrain to the main point I want to make: the literature is quoted with the correct interpretation; and the one which is quoted belong to the conventional standards, in this respect is then “appropriate”; but the connection of the literature and the thesis authors want to support is weak: which is the thesis? If institutional frameworks make the difference (and I agree), if different sectors have different impact on innovation-markups-inequality (and I agree), if individual motivation affects trust in institutions (and I agree)… why do they totally disappear from the empirical model? If, in contrast, the model is the core of the paper, then what is the point to quote a literature which is not functional to the scope of the paper… Comments 2 and 4 (i.e. study design, and statistical analysis): the comments are direct consequences of point one. I like, and I agree with the general intuition behind the paper. I think authors correctly highlight the relevant issues, but they need to refine the exposition. In my view they could pursue two ways. Strategy one. Authors may start in general terms, putting the main issues in the present paper, keeping the theoretical framework basically with the concepts it contains now; then before the beginning of the method section they state clearly the variables they want to consider and explain why things which are relevant are supposed to disappear. Strategy two. Authors are supposed to cut important (I mean scientifically relevant and intriguing research questions) parts of the theoretical section and restrain themselves only to the specific issues they are going to analyze. But in this case, they are supposed to provide a more refined and specific literature review (I expect the literature quoted in the papers I am going to put at the end of my comments would be helpful in this respect). I have 5 more specific observations to draw with respect to the statistical analysis. The first four are “matters of fact”, the fifth is “matter of preference in empirical modelling” Please check the coherence between figure 2 and the paragraph at page 10 where the picture is commented, because it seems to me that China has problems (either the comment or the picture is not correct) The last paragraph at page 10 (the one starting “While the relatively stable increase in markups…”) is put in the wrong place, as the section on inequality comes next. Furthermore, the meaning of the sentence is problematic because it contradicts the thesis are supposed to support or indagate. Basically, authors draw a conclusion which is not even supported by the picture (a picture provided would not be enough to prove the statement, i.e. my point is about contradiction and/or self-referentiality of the sentence). At page 11 beginning of the paragraph markup vs Gini index, I hardly can see in the picture what authors see. Again, I ask authors to check the coherence between the words and the lines provided in the pictures. Last, but not least. The descriptive exercise shows quite clearly that the three subsets have different behaviors and different magnitude in the period observed. Given the evident heterogeneity in the descriptive analysis, it might be worth considering whether a single model sufficiently captures these differences or if a segmented approach could be more appropriate. It is obvious that insignificant correlations may simply be due to the fact that there is too much heterogeneous variance that compensate away each other… Fifth observation: What I said with respect to the review of theoretical literature, comes reinforced with respect to the empirical model. I think that authors now miss the opportunity to “reach the point”. The idea of analyzing different institutional frameworks is valuable, as it highlights how innovation, markups, and inequality interact differently across contexts, implying that policy recommendations should not be one-size-fits-all. In contrast, not even an “evergreen dummy” is put in the model to capture all the three-facet heterogeneity authors have chosen and portrayed during the overall length of the paper… Other methods may be used for estimation, but in this case I don’t want to make any criticism as any author basically picks up the model which is more comfortable to his/her preferences. Comment 5: about conclusions. Because of the observation just made, conclusions are there; they are coherent with the estimated model; but they are not novel. All the set of policy suggestions authors bring about are known in the literature and estimated with previous models… It is exactly because of this that I suggest refining the structure. I think the venue authors have selected is a fruitful path, but I ask them to highlight better the key they already hold. A little precision effort towards a significant gain. Minor comments: I would suggest to drop footnote 3 (economic mobility does not need to be defined in an economic paper, and the definition is no precise anyway); I would also suggest to drop footnote 4 for the same reason; I would drop that Ecuador is in Latin America at page 7; I would change the wording because thesis is not hypothesis at page 5 beginning of paragraph 6 (“we hypothesise….”); authors mention “classical economists” but obviously they imply “nowadays mainstream economists” … in this case a footnote to qualify the use of the term would be appreciated. Possible references follow to be added and coherent with the points I highlighted as drawbacks now in the paper https://www.proquest.com/openview/89ec0cccad7a3ead8f477b04d0a65312/1?pq-origsite=gscholar&cbl=18750&diss=y [Ref 1-7] Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly References 1. Lee H, Lee J: Aggregate Markup and Its Impact on Income Inequality: Country Panel Evidence. International Economic Journal . 2023; 37 (3): 387-400 Publisher Full Text 2. Han M, Pyun J: Markups and income inequality: Causal links, 1975-2011. Journal of Comparative Economics . 2021; 49 (2): 290-312 Publisher Full Text 3. Bakir E, Hays M, Knoedler J: Rising Corporate Power and Declining Labor Share in the Era of Chicago School Antitrust. Journal of Economic Issues . 2021; 55 (2): 397-407 Publisher Full Text 4. Lee H, Lee J, Yoon H: Accounting for the Effect of Government Spending on Income Inequality: Role of Markup Dynamics. Korea and the World Economy . 2021; 22 (3): 129-157 Publisher Full Text 5. Colciago, A ., R Mechelli: Competition and Inequality. Department of Economics Discussion Paper Series. University of Oxford . Publisher Full Text 6. téphane, A., Aurélien.E., Bertrand.G.,Jonathan. G: MARKUPS, TAXES, AND RISING INEQUALITY. Publisher Full Text 7. Eeckhout J, Fu C, Li W, Weng X: OPTIMAL TAXATION AND MARKET POWER. SSRN Electronic Journal . 2024. Publisher Full Text Competing Interests No competing interests were disclosed. Reviewer Expertise Macroeconomic modelling under limited information and heterogeneity, Financial Instability, Financial sustainability and Inequality, growth cycles (or AD led growth models) I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 16 Apr 2025 William Pacheco, Economics, University Anahuac, Huixquilucan de Degollado, 52786, Mexico Thank you for the detailed and constructive feedback during the peer review process. I would like to confirm that I have carefully addressed all suggestions and comments raised by the reviewer. Specifically, I’ve refined the argumentation, strengthened the discussion by including the recommended references, thoroughly revised data and figures for clarity and consistency, and reformulated the conclusions to highlight the paper’s contributions. I remain available for further suggestions or revisions and look forward to continuing with the editorial process. Thank you again for the opportunity to enhance the quality of this manuscript. View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Variato AMG. Peer Review Report For: Cross-country analysis of aggregate markups and their impact on income inequality. [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :184 ( https://doi.org/10.5256/f1000research.176605.r367356) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. 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