The Wealth of Nations in the Age of Artificial Intelligence:

An Opportunity for Latin America’s Development 

Ignacio Munyo, setiembre de 2026

Abstract 

The wealth of nations depends not on machines themselves, but on the institutions that determine how people use their time. As artificial intelligence frees up vast amounts of human time, Latin America faces a rare opportunity to break free from decades of productivity stagnation. Whether this technological revolution becomes a catalyst for development or yet another missed opportunity will depend on the region’s institutional capacity to redirect that time toward distinctively human activities—creativity, judgment, leadership, and innovation.

This paper argues that Latin America’s central challenge lies not in access to technology, but in the quality of its institutions. Without far-reaching structural reforms, artificial intelligence risks exacerbating existing inequalities rather than generating broad-based prosperity. Securing macroeconomic stability and the rule of law, promoting greater economic openness, modernizing labor regulations, transforming education systems, and redesigning social transfers to support workers’ adaptation and reinvention will be critical to turning the rise of artificial intelligence into a genuine engine of long-term development. 

Introduction

Although Adam Smith wrote before the Industrial Revolution had fully unfolded, he anticipated the basis that would later make that historical leap possible. Two and a half centuries later, these principles remain central to understanding the technological transformations of our time. 

He did not merely describe the relationship between specialization and productivity. He went further: he located the engine of progress in the organization of human action. In his view, the wealth of nations does not originate in machines themselves, but in individuals operating under rules, incentives, and markets that channel social cooperation. It was within that institutional framework that he situated the true core of economic development (Smith, 1776). 

Today humanity is experiencing another revolution, global in scope and profound in its implications: artificial intelligence. Its impact on the division of labor may prove even greater than that of the steam engine. This raises, with renewed force, a fundamental question: where does national wealth originate when technology assumes tasks previously performed by individuals?

The answer sheds an uncomfortable light on Latin America’s present. Progress does not arise from the machines themselves. It arises from how society reorganizes work in the face of those machines. Or, more precisely, from how people allocate and manage their time when the boundaries between the human and the artificial shift.

For Latin America, this discussion arrives at a moment when the region has accumulated decades of stagnation in productivity and development. With a challenging starting point, if it fails to improve the quality of its institutions, the artificial-intelligence revolution will be yet another missed opportunity.

The challenge for Latin America is not gaining access to new technology; it is reorganizing the productive structure to enhance human talent rather than replace it without generating new opportunities. Artificial intelligence substantially expands the frontier of available human time: they return hours to people previously trapped in mechanical processes. This advance translates into development when institutions make possible the reallocation of that time towards what is distinctively human: craftsmanship, creativity, judgment, leadership, co-ordination, persuasion, negotiation, empathy and the construction of meaning (Munyo, 2022).

This intuition rests on solid theoretical foundations. Gary Becker formulated it with precision when he incorporated the temporal constraint into the core of economic theory and emphasized that individuals maximize utility not only under income constraints but under an even more inexorable one: the 24 hours of the day (Becker, 1965). Therefore, since human time is the truly scarce resource, progress occurs when a society succeeds in expanding the opportunities for each individual to use that time in higher-value activities.

The celebrated example of the pin factory is remembered for its quantitative effect: specialized workers multiplying output. Yet its analytical power lies not in the number of pins but in the invisible structure behind it. The increase in productivity is possible because there exists an institutional framework that permits the co-ordination of dispersed efforts: capabilities, property rights, rules of the game, trust, capacities, enforcement mechanisms. The wealth of nations multiplies when its institutions succeed in transforming human time into positive personal interaction, learning, innovation and production with market value, rather than devoting it to repetitive tasks or bureaucratic controls.

Under this framework, artificial intelligence represents an extraordinary opportunity for Latin America. 

Artificial intelligence, human time, innovation and openness

If Smith showed that the wealth of nations arises from the division of labor and Becker formalized that the truly scarce resource is the hours of life that everyone can allocate among alternative uses, then the expansion of artificial intelligence can be understood as a historic opportunity for development.

Technology redefines the possible use of human time and expands the set of available alternatives. But progress is not automatic: it will depend on the capacity to reallocate that time towards activities of higher added value. The central challenge lies in consolidating a new division of labor in which algorithms coexist with people capable of capitalizing on their own comparative advantages.

Before the massive irruption of artificial intelligence, the recent literature on automation had already established a robust conclusion: technology does not eliminate human work, but transforms its composition, shifting it towards tasks of greater complexity, particularly those in which judgment, interpersonal interaction and creativity are determinant (Autor, Levy & Murnane, 2003; Acemoglu & Restrepo, 2020; Frey & Osborne, 2017).

Generative artificial intelligence deepens this process on an unprecedented scale by incorporating itself into more sophisticated tasks. Recent evidence shows a decline in employment in occupations affected by the new technologies available, with a particularly marked impact on profiles with less experience (Brynjolfsson, Chandar & Chen 2025; Hampole et al., 2025; Hosseini & Lichtinger, 2025).  

Artificial intelligence does not eliminate employment indiscriminately, but reconfigures the structure of labor demand, penalizing incipient trajectories and valuing accumulated human capital. In parallel, aggregate productivity gains are observed, together with expansion in firms that incorporate artificial intelligence as a tool for innovation. By lowering barriers to entry to innovation, artificial intelligence widens the margin for innovation for both large and small organizations, provided they possess the human capital capable of formulating relevant questions, diagnosing problems rigorously, experimenting with judgment and leading processes of change. 

This potential impact is especially relevant for Latin America, where empirical evidence shows a positive causal relationship between GDP growth and entrepreneurship developed within already established organizations (Munyo & Veiga, 2024). When measured correctly—extracting the impact of external variables such as international prices—, the region’s economic dynamism is not explained by the creation of new firms, but by the capacity of existing organizations to generate internal innovation and transform their own processes. With the rise of artificial intelligence, this finding acquires strategic relevance because the new technology acts as a multiplier of capabilities for those already operating and possessing the organizational capital needed to scale new initiatives. 

Evidence also indicates that countries’ openness to foreign trade acts as a mechanism of competitive pressure and learning. The data show a causal relationship between the degree of openness and innovation: greater insertion in international markets exposes firms to more demanding standards, intensifies competition and accelerates the adoption of new technologies (Dotta & Munyo, 2019). Consequently, the productive use of artificial intelligence as a tool for innovation is strengthened in open economies, where external competition not only disciplines but also expands access to knowledge, inputs and markets. 

The role of institutions

Artificial intelligence will drive development to the extent that the human time it liberates is reallocated towards uses of higher productivity. When that reallocation does not occur—because time is dissipated in informality, concealed unemployment or unproductive bureaucracy, the result is not more wealth, but an inefficient reallocation of resources.

The key, therefore, is not technological adoption, but the institutional capacity to facilitate that reallocation. Public policies must create the enabling environment: upholding the rule of law, promoting human capital, reducing friction in the labor market and supporting those in transition. 

This implies not only clear rules for those taking investment decisions, but an education system that forms transferable and updatable competencies throughout the life cycle; effective mechanisms of training for occupational reconversion; labor regulation that favors transition rather than rigidifying adjustments; and a social-protection network that accompanies change without discouraging reintegration. Without these components, artificial intelligence will hardly translate into sustained development. 

Recent international evidence confirms that institutional quality is the factor determining whether technology displaces or enhances employment. Where education, regulation and social protection are aligned with the new productive dynamics, the adoption of artificial intelligence raises productivity without reducing aggregate employment (Humlum & Vestergaard, 2025). In the absence of that institutional scaffolding which defines how human work is reallocated, technology does not act as an engine of development but as an accelerator of pre-existing inequalities.

Latin America’s underdevelopment 

To assess Latin America’s capacity to convert artificial intelligence into wealth and escape its structural underdevelopment, an informed diagnosis is essential.

History reveals a persistent regularity in the region: episodes of economic growth have been explained, to a large extent, by favorable external conditions—improvements in the terms of trade, lax international financial conditions or cycles of high commodity prices—rather than by internal transformations derived from structural reforms aimed at raising productivity (Munyo & Talvi, 2013; CERES, 2021a).

In particular, the last significant economic expansion in Latin America, 2003–2013, can be explained almost entirely by an exceptionally favorable international context that brought about high prices for its export products and low external financing costs. It was not a cycle driven by sustained increases in productivity, innovation or human-capital accumulation. When that external environment reversed, economic dynamism dissipated rapidly. Thus, the past decade has been marked by modest performance and recurrent episodes of stagnation. In the absence of reforms that strengthen internal sources of growth, the region remains exposed to external volatility and trapped in a low-dynamism trajectory.

Latin America’s relative development lag is not cyclical but structural. When one examines the evolution of the region’s GDP per capita in international comparison, average Latin American income remains persistently below a quarter of that observed in developed countries, with no clear signs of convergence over time. 

This gap is also reflected in broader indicators of well-being. According to the United Nations Human Development Index, which measures development in three basic dimensions: health, education and standard of living, the region consistently occupies the middle of the global ranking, a position that has barely changed over the past two decades, even during the period of favorable external conditions (2003-2013).

Underlying this stagnation is a central problem of productivity that the region has failed to reverse. Low productivity, which goes hand in hand with inefficient resource allocation and insufficient investment in technology and human capital, explains the absence of progress towards development. Latin America shows a sustained lag in total factor productivity compared with emerging economies in Asia and Europe that are advancing in their development (IMF, 2025).

Productivity in Latin America remains lagging not because of a lack of access to frontier technology but because of the inability to mobilize human labor towards more productive uses. The problem is not the availability of tools, but the institutional lag that prevents the creation of conditions for socially sustainable, generalized productivity gains. 

An examination of the governance indicators compiled by the World Bank reveals a persistent institutional deficit in Latin America. While the region, on average, sits in the middle of the global table across all these dimensions, countries that could be considered a “target group” to which the region should aspire to converge display institutional-quality scores that place them in the top 10% worldwide —a gap that has remained practically unchanged for the past two decades.  

This “target group”, defined as a demanding benchmark for evaluating the institutional performance of Latin American countries, consists of nations with high levels of human and democratic development and with population sizes or territorial extents comparable to the regional average (CERES, 2025).

The lag in the quality of public policies is not only persistent but broad: it is observed, in similar magnitudes, across sub indicators included in World Bank’s Worldwide Governance Indicators. First, it is reflected in the “government effectiveness” indicator, which captures the quality of public services, the technical capacity of the administration, its independence from political pressures and the credibility of the state in designing and implementing policies. It is also clearly manifested in the “regulatory quality” indicator, which evaluates the government’s capacity to formulate and implement regulations that promote private-sector development and the proper functioning of markets. Finally, the weakness is also evident in the “rule of law” indicator, which measures the extent to which citizens and firms trust and abide by the rules, including the quality of the judicial system, respect for property rights, contract enforcement and control of crime. 

The region’s institutional fragility manifests itself, among other things, in inadequate fiscal management that compromises sustainability and limits the quality of public spending (CERES, 2022a; CERES, 2022b). It also translates into deficiencies in critical areas such as education, where resources are neither allocated nor managed according to adequate criteria (CERES, 2021b).

The educational mismatch constitutes an evident constraint for Latin America. Training systems are not aligned with current labor-market demands nor with the skills required for productive interaction with technological advance. According to the OECD’s PISA tests, which assess key competencies associated with successful participation in the labor market, eight out of ten 15-year-old students in Latin America fail to reach the minimum proficiency level in at least one of the three areas assessed—reading, mathematics, and science. By comparison, the corresponding share in the “target group” countries averages about one-third. (CERES, 2025). This gap, which has remained of similar magnitude for two decades, reflects a deep structural weakness that limits the region’s capacity to take full advantage of the potential of artificial intelligence and other technologies as engines of development. 

In turn, training systems are usually not aligned with current labor-market demands and there is insufficient investment in the professional development of socio-emotional skills— communication, adaptability, teamwork, critical thinking—that are essential for fully deploying human potential alongside rapid technological advance. 

The functioning of the labor market has also failed to evolve at the pace of the technological transformations that are redefining it. According to the labor-market regulation sub-index of the Fraser Institute’s Economic Freedom of the World index, Latin America occupies a significantly more backward average position than the “target group”. While the region ranks around 86th out of 165—i.e., in the lower half of the global ranking—the “target group” sits much higher, in 34th place out of 165. This sub-index incorporates dimensions such as dismissal costs, contractual flexibility, degree of centralization of collective bargaining and rigidity in wage setting. The data clearly reflects a more rigid and costly labor environment in Latin America, with less capacity for adjustment to technological changes. In a context where technology demands dynamic labor markets, this rigidity constitutes a structural disadvantage for fully exploiting its development potential. 

Social transfers have also failed to adapt in Latin America and does not appear capable of fully meeting its objectives. Formal schemes designed for linear and formal career paths persist, which do not adequately accompany more unstable, intermittent or new forms of employment. The limitations of coverage, adequacy and sustainability of social-protection systems are aggravated by high informality. In this context, international organizations have pointed to the need to move towards broader and more universal protection schemes, including basic-income mechanisms or minimum-income guarantees, to ensure a floor of economic security independent of people’s employment situation (IDB, 2017; ILO, 2023; ECLAC, 2024). Although proposals vary, all seek alternatives to a complex structural problem in the region. 

To this picture is added, as an aggravating factor, the limited external competition faced by the region, associated with insufficient international insertion (CERES, 2023). The degree of openness—measured as exports plus imports as a percentage of GDP—averages 65% in Latin America, while in the “target group” countries it reaches 120% (CERES, 2025). The region has not consolidated a degree of openness that systematically exposes it to global standards of productivity and competition. Without that exposure, incentives to absorb technology, learn from best international practices and innovation are lower. In the absence of external competitive pressure, the productive adoption of technology loses urgency and innovation tends to dissipate.

The diagnosis is unequivocal: artificial intelligence constitutes a significant opportunity, but the region starts from a context of institutional dysfunction that hinders the conversion of technology into productivity. The problem is not the availability of advanced tools, but the capacity of public policies to ensure that their adoption generates sustained improvements in economic and social performance. If artificial intelligence liberates human time, but people lack the necessary competencies and operate in a regulatory environment that does not facilitate its reallocation towards higher-value activities, that time is not converted into wealth. On the contrary, it translates into bureaucracy, expansion of informality or criminal activities. Technology enhances preexisting capabilities; it does not create them when they are insufficient, nor does it compensate for institutional deficiencies. 

The data presented suggest that, without structural reforms, artificial intelligence is likely to act as an amplifier of pre-existing inequalities in the region. Persistent informality, accumulated deficits in human capital and regulatory frameworks poorly adapted to new productive dynamics shape an environment that conditions the impact of technology on the labor market, deepening gaps rather than correcting them.  

In this vein, a recent ILO and World Bank report concludes that, while artificial intelligence has the capacity to substantially raise the productivity of employment, shortcomings in labor competencies constitute a bottleneck that prevents capturing the potential benefits (Gmyrek, Berg & Winkler, 2024). Recent IDB studies also show that greater exposure to artificial intelligence in Latin America increases the potential for productivity but can intensify inequalities in a region characterized by high income concentration (Azuara Herrera, Ripani & Torres Ramírez, 2024; Ciaschi et al., 2025). 

In short, in a region institutions do not manage to function as they should, the challenge of seizing the technological potential is particularly demanding. 

An agenda for reform in Latin America

For Latin America to be able to take advantage of advances in artificial intelligence to boost productivity, it must succeed in processing the necessary institutional adjustments through appropriate public policies. 

Legal security and macroeconomic stability are prerequisites for investment in innovation. In the absence, incentives to invest in long-term projects erode and resource allocation becomes defensive. Innovative entrepreneurship within firms requires a horizon of predictability that allows future benefits to be discounted and calculated risks to be taken. This demands that the region maintain clear rules of the game, reduce political risk, improve fiscal and monetary responsibility to lower uncertainty and anchor expectations. 

Trade openness and integration into the global economy constitute a key condition for technological learning. In Latin America, firms often face weak incentives to adopt artificial intelligence and modernize their production processes—an outcome that greater trade openness could help reverse. Exposure to international competition tends to accelerate the incorporation of new technologies, as firms must adopt them in order to remain competitive. 

Regulatory readjustment is a central condition for processing an increase in productivity. In Latin America, laws, decrees, rules and procedures designed for productive contexts that no longer exist have accumulated. The problem is not only the quantity of norms, but their obsolescence, overlapping and lack of coherence, which raise the cost of economic activity. When states operate with persistent inefficiencies, it acts as a structural brake on the private sector. Advancing this agenda requires sustained political will and trained teams capable of dealing with diverse bureaucratic layers. 

The modernization of labor regulations is a key component if the region is to adapt to the demands of the current labor market, create formal employment and reallocate work towards more productive activities. Moving towards more dynamic schemes is determinant for increasing the capacity to absorb technological changes and reallocate resources efficiently. Hours banks that allow a flexible weekly distribution of daily working hours; reasonable dismissal costs that are not a brake on hiring; wages whose adjustment mechanisms weigh performance and attendance more heavily than seniority; and collective bargaining that prioritizes firm-level agreements over sectoral conventions can substantially improve the capacity for adaptation. 

Education constitutes a major constraint if artificial intelligence is to translate into development in Latin America. Without adequate human capital, there is no real possibility of productively reallocating the work liberated by the incorporation of technology. The region’s education systems must urgently implement deep reforms that accelerate the transition from a curriculum centered on the accumulation of content to one oriented towards the development of higher cognitive skills that foster critical thinking and personal interaction. Likewise, links between secondary and tertiary education and the world of formal work must be strengthened through dual training schemes that integrate learning and productive experience. 

In the current reality of Latin America, support for those left outside the market for lack of adequate human capital is also a central component of any technological transformation strategy. Because of the sustained failure of education systems over decades, a large proportion of the population lacks the minimum skills necessary to integrate into the high-productivity labor circuit. 

Within this framework, existing social-transfer programs should be reviewed and reoriented towards a subsidy focused on labor reinvention. This subsidy would be a form of essential income that is neither basic nor universal. It would not be basic because it would require, in exchange, an active process of labor reinvestment; and it would not be universal because it would be targeted at those people whose skills have suffered the greatest depreciation in the face of technological advance (Munyo, 2023). The subsidy is direct, targeted and without unnecessary state intermediation, granted for a determined period and conditional on training and active job search. This social-transfer mechanism would not be a permanent substitute for work, but a transitory support for those displaced by technological change. Its objective is to sustain people while they rebuild their capacity to convert time into productive work. In a society where machines assume ever more tasks, this instrument does not weaken the work ethic; it reinforces it, by preserving dignity, self-esteem and the effective possibility of reintegration into the labor market. 

The challenges Latin America faces in capitalizing on the opportunity offered by artificial intelligence are of great magnitude. Throughout the analysis it has been shown that technology itself does not guarantee convergence or sustained growth. For innovative entrepreneurship within firms to flourish, clear structural foundations are required: macroeconomic stability that provides predictable investment horizons and legal security that ensures clear rules and effective contract enforcement, combined with greater international insertion that exposes the region to global standards of competition. On those pillars, the region must abandon an institutional structure still anchored in past logics and advance towards a regulatory, educational and social-protection system consistent with current productive demands. Only then can artificial intelligence become a lever for development rather than an amplifier of structural lags.

Conclusion 

In The Wealth of Nations, Adam Smith maintains that the prosperity of a country is founded on its productive capacity, which expands through the division of labor, specialization and exchange in free markets. Two and a half centuries later, that logic not only retains its validity but continues to offer a fertile framework for understanding the sources of contemporary economic development. 

The expansion of artificial intelligence introduces a disruption with structural potential for Latin America. If articulated with the right incentives and institutions, it can become the engine of a development that has eluded the region for decades, under a new logic of specialization, scaling and complementarity between human capital and advanced technologies. The outcome will be determined by governments’ capacity to adapt institutions conceived for another historical context and to build normative frameworks that orient technological progress towards the creation of sustainable wealth and the effective expansion of opportunities for people. 

In the age of artificial intelligence, the role of the state in the Latin American context is not to hinder the technology or tax it as if it were a risky anomaly, but to implement the necessary reforms that allow its potential to be maximized. 

Technology expands the set of what is possible, but it is rules, incentives and organizational structures that determine whether that potential is converted into prosperity. The generation of wealth depends in large measure on how the human time liberated by technology is reallocated, especially within already existing organizations, within a framework of social coexistence. It is there that it is decided whether artificial intelligence will be an engine of development or another wasteful opportunity. 

Public policies in the age of artificial intelligence must focus on maximizing the social conversion of the time liberated by technology. This requires, in addition to guaranteeing the basic conditions of citizen coexistence, the design of adequate incentives to build more effective training systems, more dynamic labor markets and better-designed social-transfer schemes than those currently in force. 

Artificial intelligence, by itself, will not solve Latin America’s structural problems. But the region, if it can reform its institutions, can transform this technological revolution into an opportunity to redefine its destiny. 

References 

Acemoglu, D., & Restrepo, P. (2020). Automation and new tasks: How technology displaces and reinstates labor. Journal of Economic Perspectives, 34(1), 3–30. 

Autor, D. H., Levy, F., & Murnane, R. J. (2003). The skill content of recent technological change: An empirical exploration. The Quarterly Journal of Economics, 118(4), 1279–1333. 

Azuara Herrera, O., Ripani, L., & Torres Ramírez, E. (2024). AI and the increase of productivity and labor inequality in Latin America: Potential impact of large language models on Latin American workforce. Inter-American Development Bank. 

Becker, G. S. (1965). A theory of the allocation of time. The Economic Journal, 75(299), 493–517. 

BID. (2017). El futuro del trabajo: perspectivas regionales (capítulo América Latina y el Caribe). En The Future of Work: Regional Perspectives. Banco Interamericano de Desarrollo. 

Brynjolfsson, E., Chandar, B., & Chen, D. (2025). Canaries in the coal mine? Six facts about the recent employment effects of artificial intelligence (Stanford Digital Economy Lab Working Paper). 

CEPAL. (2024). The future of social protection in the midst of a protracted social crisis in Latin America: advancing towards universal, comprehensive, sustainable and resilient systems. Comisión Económica para América Latina y el Caribe. 

CERES. (2021a). Tailwinds in the short run coupled with long run challenges. Montevideo, Uruguay. 

CERES. (2021b). Towards a dual agenda of structural reforms. Montevideo, Uruguay. 

CERES. (2022a). Straw, wood and brick: Latin American economies in the context of global inflation. Montevideo, Uruguay. 

CERES. (2022a). Straw, wood and brick: Latin American economies in the context of global inflation. Montevideo, Uruguay. 

CERES. (2022b). Clouds on the horizon, opportunities beyond. Montevideo, Uruguay. 

CERES. (2023). Embracing economic openness and global integration to address slow growth and development. Montevideo, Uruguay. 

CERES. (2025). Monitor de Desarrollo de Uruguay 2025: De dónde venimos, dónde estamos y hacia dónde podemos ir. Montevideo, Uruguay. 

Ciaschi, M., Falcone, G., Garganta, S., Gasparini, L., Bertín, O., & Ramírez-Leira, L. (2025). The potential distributive impact of AI-driven labor changes in Latin America. Inter-American Development Bank. 

Dotta, S., & Munyo, I. (2019). Trade, openness and innovation. Innovation, 24(2). 

Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, 254–280. 

Gmyrek, P., Berg, J., & Winkler, H. (2024). Buffer or bottleneck? Employment exposure to generative AI and the digital divide in Latin America (ILO Working Paper No. 121). International Labour Organization & World Bank. 

Hampole, M., Papanikolaou, D., Schmidt, L. D. W., & Seegmiller, B. (2025). Artificial intelligence and the labor market (NBER Working Paper No. 33509). 

Hosseini, S. M., & Lichtinger, G. (2025). Generative AI as seniority-biased technological change: Evidence from U.S. résumé and job posting data (Harvard University Working paper). 

Humlum, A., & Vestergaard, E. (2025). Large language models, small labor market effects (NBER Working Paper). 

IMF. (2025). Regional economic outlook: Western Hemisphere, October 2025. International Monetary Fund. 

Munyo, I. (2020, October 31). Vulnerabilidad laboral. El País. Montevideo, Uruguay. 

Munyo, I. (2021). La revolución de los humanos: El futuro del trabajo. Penguin Random House. 

Munyo, I. (2023). La construcción de la esperanza: Reflexiones sobre las fortalezas y mejoras para el Uruguay del futuro. Penguin Random House. 

Munyo, I., & Talvi, E. (2013). Latin America macroeconomic outlook in the global context: Are the golden years for Latin America over? Brookings Institution. Washington DC, United States of America. 

Munyo, I., & Veiga, L. (2024). Entrepreneurship and economic growth. Journal of the Knowledge Economy, 15(1), 319–336. 

OIT. (2023). El futuro de la protección social en América Latina. Organización Internacional del Trabajo.

Smith, A. (1776). An inquiry into the nature and causes of the wealth of nations. W. Strahan and T. Cadell. 

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¹ Paper prepared for presentation at the 35th Economic Forum in Karpacz, Poland.