metadata: authors: 0: author: Maloney,William F.
1: author: Zambrano,Andrés
count: Latin America
docna: 0: docna: Learning to Learn : Experimentation,
            Entrepreneurial Capital, and Development
docty: Policy Research Working Paper
subtopic: Mining & Extractive Industry (Non-Energy),Primary Metals,International Trade and Trade Rules,Private Sector Economics,Private Sector Development Law,Marketing
teratopic: Industry,International Economics and Trade,Law and Development,Private Sector Development
abstracts: cdata!: This paper models an entrepreneur’s
            choice between investing in a safe activity or experimenting
            with a new risky one, and how much to invest in the
            “entrepreneurial capital” that would permit more effective
            use of the arriving information on the latter- how much to
            learn how to learn. Optimal investment in entrepreneurial
            capital depends the expected return on the risky activity.
            It can lead to three learning regimes, two of which can
            generate a development trap where firms and countries are
            unable to assess the potential of newly arriving
            technologies and hence grow more slowly. The first arises
            purely because it is too expensive to learn to learn, the
            second because the returns to the new activity are so high
            that they obviate the need to distinguish between activities
            and hence invest in entrepreneurial capital. The paper draws
            on historical evidence to show how the model offers insights
            into three understudied features of the industrialization
            process in the Western Hemisphere at the beginning of the
            20th century: the disproportionate influence of
            immigrant/foreign entrepreneurs in driving industrialization
            in Latin America; the emergence of selective exceptions to
            this pattern, as well as episodes of entrepreneurial
            retrogression; and the differing effects of similar economic
            structures across countries that suggest the possibility of
            a learning-displacing resource curse. The model can simulate
            the respective decline and boom in the Chilean and US copper
            industries at the turn of the century, arising either from
            initially high relative returns or low initial endowments of
            entrepreneurial capital in the latter.
repnme: repnme: Learning to Learn : Experimentation,
            Entrepreneurial Capital, and Development
display_title: Learning to Learn : Experimentation,
            Entrepreneurial Capital, and Development
listing_relative_url: /research/2021/12/33686674/learning-learn-experimentation-entrepreneurial-capital-development
topicv3: Private Sector Development m1326060 1921,Mining Services m1326410 1701,Trade m1326924 698,Mining Sector m1327113 554,Copper m1503516 2934
publishtoextweb_dt: 2021-12-20T00:00:00Z
dois: 10.1596/1813-9450-9890
historic_topic: Industry,International Economics and Trade,Law and Development,Private Sector Development
origu: Chief Economist (LCRCE)
no_of_pages: 55
author_keys: 0: author_key: 93000
model_extractions: Policy Research Working Paper 9890

# Learning to Learn

Experimentation, Entrepreneurial Capital, and Development _William F. Maloney Andrés Zambrano_ Latin America and the Caribbean Region Office of the Chief Economist December 2021
Policy Research Working Paper 9890

# **Abstract**

This paper models an entrepreneur’s choice between investing in a safe activity or experimenting with a new risky one, and how much to invest in the “entrepreneurial capital” that would permit more effective use of the arriving information on the latter- how much to learn how to learn. Optimal investment in entrepreneurial capital depends the expected return on the risky activity. It can lead to three learning regimes, two of which can generate a development trap where firms and countries are unable to assess the potential of newly arriving technologies and hence grow more slowly. The first arises purely because it is too expensive to learn to learn, the second because the returns to the new activity are so high that they obviate the need to distinguish between activities and hence invest in entrepreneurial capital. The paper draws on historical evidence to show how the model offers insights into three understudied features of the industrialization process in the Western Hemisphere at the beginning of the 20th century: the disproportionate influence of immigrant/foreign entrepreneurs in driving industrialization in Latin America; the emergence of selective exceptions to this pattern, as well as episodes of entrepreneurial retrogression; and the differing effects of similar economic structures across countries that suggest the possibility of a learning-displacing resource curse. The model can simulate the respective decline and boom in the Chilean and US copper industries at the turn of the century, arising either from initially high relative returns or low initial endowments of entrepreneurial capital in the latter.

This paper is a product of the Office of the Chief Economist, Latin America and the Caribbean Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at wmaloney@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Learning to Learn: Experimentation, Entrepreneurial Capital, and Development<sup>∗</sup>

William F. Maloney<sup>†</sup> Andr ́es Zambrano<sup>‡</sup> Keywords: Learning, Experimentation, Entrepreneurial capital, Development JEL Codes: O31, O32, O13, D81 ∗ Corresponding author Email: wmaloney@worldbank.org. This paper was partly supported by the regional studies budget of the Office of the Chief Economist for Latin America and the Knowledge for Change trust fund (KCP). We gratefully acknowledge comments from Nick Bloom, Serguey Braguinsky, Javier Mejia, Andres Solimano, Jose Tessada and participants at the Harvard-MIT-Stanford Empirical Management Conference, 4th RIE Conference and the 6th EH-Clio Lab Conference at Universidad Catolica de Chile. We also thank the valuable assistance of Julian Gutierrez and Daniel Gomez. All remaining errors are ours.

> †Chief Economist, Latin America and the Caribbean, The World Bank.

> ‡Universidad de los Andes, Bogot ́a, Colombia.
# **1 Introduction**

Entrepreneurship is experimentation in projects of uncertain payoff (Knight, 1921; Callander, 2011; Kerr et al., 2014; Manso, 2016; Braguinsky et al., 2020). More broadly, development can be thought of as a process of experimenting with a series of new projects ranging from adopting new technologies, to entering new industries, to experimenting with new and untried ways of organizing society. This paper models the entrepreneur’s problem of whether to engage in such projects as not only one of acting on signals about their profitability, but critically, of how much to invest in the ability to learn from these signals, what we term “entrepreneurial capital.”<sup>1</sup> The idea that firms learn from experimentation is well-established in the literature: Rosen (1972) and Ericson & Pakes (1995) specifically model firm learning by doing.<sup>2</sup> Callander (2011) models agents learning about new products of varying complexity through a trial and error process and observing previous agents’ experience. Most recently, Braguinsky et al. (2020), drawing on the case of Japan at the turn of the 20th century, see diversification as a risky process requiring new machines and technologies and hence firms first introduce innovative products on experimental basis. However, history suggests that some entrepreneurs are better at learning than others- as Rosen (1972) notes “...decision makers are ‘made’ as well as ‘born’ ” and we model this by allowing for costly investment not in the precision of arriving information, which we take as a central but now well-explored problem, but in how much the new information changes the beliefs on the relative payoffshow well agents learn.

Our model is in the tradition of the literature on optimal investment in R&D. Roberts & Weitzman (1981) is among the earliest papers that study R&D from a learning perspective in a sequential context. There, the agent experiments to learn about the value of a project and optimally decides when to stop experimentation. Bolton & Harris (1999) and Moscarini

> 1The term entrepreneurial capital is also used in a related context by Gonzalez-Uribe & Leatherbee (2018) to distinguish start up skills from the “managerial capital” found in established firms.

> 2This said, Ericson and Pakes elsewhere argue that that learning by doing, an automatic or “passive” form of learning, differs from active experimentation although, as Braguinsky suggests, they may be difficult to distinguish empirically. We are grateful to Serguey Braguinsky for this clarification.

1
& Smith (2001) subsequently approach the problem in a bandit (slot machine) context in continuous time and the first part of our model draws on the former. Here, each agent chooses the frequency of playing (pulling) a risky arm whose payoffs follow Brownian motion, and a safe arm, and thus endogenizes the precision of an “aggregate signal.” Pulling the risky arm helps learn the unknown drift of the risky arm but the cost of increasing the precision is the opportunity cost of not exploiting the safe arm.<sup>3</sup> Entrepreneurship poses a similar problem in that agents need to decide how much to invest to find out whether a given idea or industrial project will prove profitable- how many experts should be hired, how many technical missions should be undertaken abroad, how much time should be spent trying to adapt a technology to a local context, or sell a new product to a local market. However, we go further to explore how efficiently this arriving information is used. We capture the “efficiency of learning” through the precision of the prior distribution, which we treat as inversely related to the level of accumulated entrepreneurial capital. That is, two individuals, one in Boston and one in Bogot ́a may receive identical information on the increasing use of steam engines in Manchester, but the first may have the accumulated technical and business background to conclude that, while not without risk, the expected pay off of the steam engine in the local context justifies experimenting with it, while the second can neither comprehend the technical parameters, nor assess the risk-return profile of the likely stream of benefits of the technology and hence his prior is unaffected by new information. The needed positive signal on return to induce experimentation is insuperably high- the entrepreneur is effectively blind to new opportunities and hence, stays with the known and ‘safe’ technology. The stickiness in updating priors is observationally equivalent to being more “risk averse” and in that sense we are providing an explanation for differences in observed risk taking across countries that does not rely on postulating different core parameters in the utility function. The impact is the same, however: If agents forego the risky project, they will also forego the knowledge that engaging in that task would bring, and hence they may not see the value of investing in how to learn about the profitability of

> 3Keller et al. (2005) and Keller & Rady (2010) have explored the same issue but for exponential and Poisson bandits respectively.

2
new activities, with long term growth consequences.

We show the decision to invest in entrepreneurial capital is affected by the perceived benefit from investing in it, which is non-monotonic in the expected returns to the risky project, the cost of acquiring it, and the distance from ”frontier” levels of entrepreneurial capital. This raises the possibility of a new information-driven development trap: In a country which, for historical reasons comes late to modernity and begins with low entrepreneurial capital, and/or has very high costs of accumulating it, entrepreneurs cannot see the potential in the industrialization project because they cannot confidently assess it, do not experiment, and hence do not invest in the ability to interpret the signals associated with future new opportunities. Even worse, absent such investment, as modernization proceeds and new projects become more complex, the frontier-adjusted level of signal interpretation skills will fall, potentially leading entrepreneurs and countries to regress- abandoning even established industries as they become more sophisticated.<sup>4</sup> The model identifies three equilibrium ”learning regimes.” In the first, if it is very costly to accumulate entrepreneurial capital and/or the expected return to risky projects is low, it will not pay to invest in better ability to process information about them. This is also true at the other extreme, the second regime, where returns to the risky project are expected to be far above those to the safe project thus obviating the need to invest in the ability to more finely compare between them. As a example relevant to our historical discussion, Adam Smith argued in the _Wealth of Nations_ that investors had “absurd confidence” in exaggeratedly high returns to mining which, in our model, will discourage investment in entrepreneurial capital and pose a type of learning-displacing resource curse. Both regimes can lead to a development trap where entrepreneurs never gain the ability to assess the value of newly arriving technologies and products, and hence stay in low growth or even rent seeking sectors. The third regime occurs in the intermediate zone where the expected safe and risky returns are not too far apart, raising the benefit of being able to better use

> 4The same phenomenon captured in the models of Howitt (2000); Aghion et al. (2005); Howitt & MayerFoulkes (2005) where as the technological frontier shifts out, the skill level required to maintain the same level of absorptive capacity also increases, applies to management skills as well.

3
information on the latter, and leading to investment in entrepreneurial capital.

The model permits interpreting three central but heretofore understudied stylized facts surrounding entrepreneurship and industrialization in the Americas at the turn of the 20th century. This period comprises the Second Industrial Revolution and the initiation of the Great Divergence where the US surged ahead while most of Latin America fell behind.<sup>5</sup> First, despite facing the same institutional and business climate and similar access to finance, immigrants and foreigners were disproportionately the drivers of industrialization in Latin America, particularly in more complex sectors, suggesting the possession of a differentiating entrepreneurial capital beyond basic human capital which presumably local elites also had. Second, there appears a substantial degree of variability in indigenous entrepreneurial zeal across regions, despite a purportedly prevalent anti-entrepreneurial cultural inheritance across Latin America, with some regions, such as Antioquia, Colombia, gaining fame as entrepreneurial hot beds. Further, this zeal varies over historically short time periods sometimes appearing as what we term _entrepreneurial retrogression_ where once clearly dynamic regions or industries fall behind and then, in some cases, regain dynamism. Both are more consistent with differential learning occurring amid a rapidly shifting technological frontier than slower moving cultural factors. Third, very similar economic structures, for instance a heavy presence of mining, generated very different development outcomes across countries, consistent with vastly differing complements of entrepreneurial capital. In particular, historical accounts are consistent with the high returns to copper extraction in Chile (and arguably mining across all of Latin America) making investments in entrepreneurial capital unnecessary in the moment, a decision which subsequently impeded both modernizing the sector and entering new ones. By contrast, in the US and Japan, mining of the same homogenous good appeared to contribute a foundation for growth and diversification.

In the final section, we show that the model is able to simulate the respective decline

> 5Mokyr (1998) dates the second Industrial Revolution as taking place roughly 1870-1914 where there was an acceleration in innovation after a lull after the first phase, and an increased emphasis on more structured scientific inquiry such as laboratory based R&D.

4
and boom in the Chilean and US copper industries at the turn of the century, arising either from initially high relative returns or low initial endowments of entrepreneurial capital in the latter, either of which would discourage investment in entrepreneurial capital in Chile, and impede upgrading and diversification The paper speaks to the importance of the emerging literature documenting the lag in managerial capabilities in developing countries (Bloom & van Reenen, 2007), where Latin America is shown to continue to lag the advanced countries, and the potential for policy to engineer their improvement (Bloom et al., 2013; Bruhn et al., 2010; Giorcelli, 2019; Iacovone et al., 2021). It contributes to the discussion of organizational capital (Atkeson & Kehoe, 2005; Lustig et al., 2011; Eisfeldt & Papanikolaou, 2013) -a type of unmeasured and non-transferable capital distinct from physical, and human capital that affects the technology of production- which Atkeson & Kehoe (2005) allow to encompass endogenous firm learning by doing such as that of Ericson & Pakes (1995) and Rosen (1972). It also speaks to the literature stressing the choices entrepreneurs make between productive and unproductive (redistributive or rent seeking) activities. Baumol (1990, 2010) stresses the critical role of incentives in the emergence of a dynamic entrepreneurial class, including those embodied in social norms and culture and Murphy et al. (1991) model how the returns to entrepreneurial ability lead to sorting of talent into or out of entrepreneurship. In both cases, the overall business climate and institutional regime (see for example North, 1990; Robinson & Acemoglu, 2012) can alter the relative rewards and hence, the potential rate of growth. While acknowledging the importance of these factors, we argue for the quality of entrepreneurs as a necessary complement to the enabling environment, and that it is the endogenous outcome of cumulative entrepreneurial decisions. In a potential inversion of the logic above, below a certain level of entrepreneurial capital, entrepreneurs may not be able to identify or explore productive new opportunities and hence default to safer lower return activities, which might include creating rent seeking options. Finally, the paper shares a kinship, albeit with a different mechanism, with the Schumpeterian literature stressing how different technological endowments can lead to different abilities to absorb or invent new 5
technologies and hence to different growth convergence clubs (Howitt & Mayer-Foulkes, 2005), the empirical work on innovative capacity proxied by engineering as a determinant of growth (see, for example, Maloney & Valencia, 2022), as well as the broader literature on national learning (see, for example, Stiglitz & Greenwald, 2014).

# **2 Historical Evidence on the Importance of Entrepreneurial Capital**

In the absence of clear measures of entrepreneurial capital, we explore three features of entrepreneurship at the time of the second industrial revolution that are consistent with our framework.

## **2.1 The dominance of foreign entrepreneurs in the industrialization process**

The fact that immigrants and foreign entrepreneurs play a disproportionate role in the industrialization process in Latin America despite facing an identical institutional and business climate and without enjoying especially favorable access to credit suggests that they embodied a cultural or human capital that local entrepreneurs lacked. Table 1 draws on collected data on firm ownership from detailed census data for Argentina, Mexico and the US, as well as other industrial source data from Brazil (Bresser Pereira, 1994; Birchal, 1999), Chile (Ortega, 1990), Colombia (Rinc ́on et al., 2005; EAFIT, 2013; Becerra & Restrepo, 1987), and the US, at the turn of the 20th century (see Annex 1). Column 2 presents the share of firms owned or started by locals and immigrants for select countries and regions around 1900. Column 3 presents the share of immigrants in the population. Columns 4 and 5 standardize immigrant ownership by share in the population and can be interpreted as a measure of “comparative advantage in entrepreneurship” both relative to the total population and to the male population. Since in this period, most firms were started by men and most immigrants were men, the latter, while a lower bound for immigrant CA, is probably closer to the truth.

Looking first at the US, we find that roughly 30% of firm owners were immigrants which 6
does suggest the importance of immigrants to the US growth process. That said, roughly 27% of the male population was immigrant so that overall, immigrants do not appear more or less entrepreneurial than native born. Another indicator is that of fortune 500 firms started by immigrants and here immigrants are slightly underrepresented.<sup>6</sup> The picture from Latin America is dramatically different. In Argentina, the richest Latin country in our sample, 80% of owners/directors were immigrants (see Annex 1 for detail). However, it is also the case that 60% of the male population was comprised of immigrants so that overall they show perhaps 30% more propensity to start businesses than locals. The dominance of immigrants is supported by secondary sources that suggest that much of the dominant meat industry and the railroads, which were sponsored, financed, and constructed largely by nationals in Australia, Canada, and the United States, were undertaken by Europeans. The emblematic meat salting industry was started by two Englishman (Scobie, 1964), and more generally, the beef industry was spearheaded by recent arrivals who formed the _Sociedad Rural Argentina_ in 1866 which led the transformation of the pampas, improving the quality of livestock, pastures, and methods of animal husbandry (Fogarty et al., 1985).

In S ̃ao Paulo, Brazil 50% of large businesses were started by immigrants (70% if we include progeny), 50% more than would be expected given their share in the population. This was also the case in Rio de Janeiro and some parts of Minas Gerais. In the former, between 1878 and 1895, (Prado in Birchal (1999)) most textile mills were founded by foreigners, who owned 62% of wholesale textile trade in Rio, and foreigners soon dominated the manufacturing activities closely linked to their commercial activities. In both S ̃ao Paulo and Rio, the first electricity generating companies were founded by foreigners. Birchal (1999) documents that Juis de Fora, Minas Gerais, a major steel and manufacturing area from 1858-1912, immigrants were responsible for 66% of industries (Birchal, 1999, p.26).

In Chile, 70% of steam powered businesses were started by immigrants, roughly 12 times their share in the male population. Again, this conforms with other historical accounts. Silva Vargas (1977a) notes that “the lack of entrepreneurs and of qualified national workers

> 6We are very grateful to Richard Sutch for the census sample data and to he and Larry Neal for extremely helpful discussions.

7
gave to the incipient Chilean industry, a markedly foreign air.”(p 94)<sup>7</sup> Collier & Sater (1996) document that thirty-six of the forty-six dressmakers counted in 1854 were French, Americans installed the flour mills and Americans and British built the railroads. The port of Valparaiso became a major center of commerce dominated by foreigners. In 1860, of the 909 establishments surveyed by the government, 674 belonged to foreigners. For the country as a whole, the list of officers and members of the executive committee of SOFOFA, the principal organization of industrialists for the country, showed that that only three Spanish surnames accompanied those of the other 18 members of the directorate (Loveman, 1979, p. 193). Further, by 1920, as with mining more generally in Mexico, the vast copper enterprises were entirely foreign.

Two of Colombia’s subregions also show very strong immigrant dominance: In the dynamic port of Barranquilla 64% of establishments were foreign owned, 3 times what would be predicted by the male immigrant share, and tax records suggest that, as in Mexico, they were largest ones. Further, as in Chile, foreigners introduced steam technology for manufacturing and transport, revolutionizing navigation from the port to the interior of the country. As Becerra & Restrepo (1987) note, “the entrepreneurs of foreign origin occupied an indisputable place as pioneers across the 19th century, in the emergence of Barranquilla as the primary port of the country.”(p.35) High foreign participation is also notable in Santander where 50% of the most notable entrepreneurs were foreigners, roughly 8 times their representation in the population.

In Mexico, the industrial census shows that 50% of businesses were owned by immigrants or 26 times their share of the male population. As Annex 1 shows, the share of investment and production by foreigners is even greater, especially in mining, suggesting that our census data may be understating foreign influence in all countries. This is in line with Hansen (1971) who argues that industrialization was undertaken almost entirely by the resident

> 7 ... the lack of entrepreneurs and of qualified national workers gave to the incipient Chilean industry, a markedly foreign air. Anwandter and Meschner (Beer), Poppe (cotton), Tiffou (tanneries) Rudlof and Benedetti (shoes), Gleisner (furniture, tanning, soap), Kuppfer (smelting), Reiche (spinning), Sciaccaluga (canning), Goeckel (soap), Osthaus (rigging), are some of the names linked to the earliest manufacturing activities....p. 94.

8
foreigners. The French started the textile industries in Veracruz and Puebla, and foreigners also started Mexico’s first iron and steel plant in 1903, the Fundidora de Fierro y Acero de Monterrey, which would anchor what is now the premier industrial city in the country (Haber, 1997; Buffington & William, 1999). Though Mexican entrepreneurs emerged to play important roles, the census data, confirms the large foreign participation even deep into the 20th century.

This outcome does not appear a result of access to finance. Accounts from S ̃ao Paulo, Brazil (Bresser Pereira, 1994), Minas Gerais, Brazil (Birchal, 1999), Barranquilla, Colombia, Becerra & Restrepo (1987) and as discussed below, Chile, show that immigrant entrepreneurs were predominantly middle class or below and most of their ventures were self-financed.

Further, foreigners dominate across the product space, but especially in the more technologically advanced sectors. For Argentina, Annex Table A.1 selectively draws from a 200 sector disaggregation to show that in only a few sectors is there a high or dominant participation of locals and these are the traditional ones, such as sugar cane processing mills sugar refining, and wool fabric. Immigrants dominate in fields ranging from seemingly basic sectors (espadrilles, shoes, shirt-making, dressmaking carpentry, baking furniture making, tailoring and tanneries) as well as the most technically advanced sectors sectors (lumber mills, carriages and other vehicles, trolleys, iron works and mechanics shops). In Barranquilla, Colombia, foreigners again dominate all industries related to the new steam technologiessteam boats-the critical innovation for integration of the country via the Magdalena river-, steam sawmills, as well as insurance, telephones and trading houses. Colombians are most represented, although not always dominant, in brokerage, canned foods, retail, and hostelry.

In Mexican manufacturing, table A.2 does not suggest patterns as clear as those found in Argentina or Barranquilla. However, Table A.3 allows us to examine the extraction-related industries and reveals an overwhelming participation of foreigners in what was among the most technically advanced sectors of the age, mining as well as oil extraction, and refining, sectors in which Mexicans held under 2% of investment.

9
# **2.2 Learning to Learn**

A long literature would explain these patterns by cultural inheritance. An overwhelming consensus exists among historians of virtually all countries of Latin America of an attitude of disdain for productive labor, derived from the colonial masters, and mainlined into the emerging societies across the social strata (see Lipset, 1967; Safford, 1976; Stein & Stein, 1970, for an overview and Annex 2).

We treat such cultural baggage as one of many possible costs of accumulating entrepreneurial capital, but do not see it as deterministic or satisfying as an explanation in itself. On the one hand, Table 1 shows that the dynamic industrialization process in Antioquia, Colombia, and to a lesser extent, Minas Gerais, Brazil, despite being steeped in the same Roman/Hispanic tradition, is domestically driven with even less than proportionate influence of foreigners. We offer an account of how Antioquians learned how to learn and hence dominate local industrialization in the next section. Second, as discussed below for Chile and then simulated in section 3.4, there are periods of entrepreneurial retrocession- a loss of entrepreneurial energy and activity which seems more consistent with a depreciation of entrepreneurial capital adjusted for an advancing technological frontier, than as an unexplained relapse to traditional cultural norms.

In general, and counter to the cultural literature, there is evidence of substantial latent entrepreneurial energy in the region, but also of impediments to learning posed by the business and institutional climate of the colonial period. Aspiring creole merchants were severely constrained by both the legal requirement to trade primarily with Spain, a country that came exceptionally late to the industrial revolution, and even this trade was prohibited except through peninsular intermediaries. Hence, local entrepreneurs would never see even the relatively backward port of C ́adiz, let alone Manchester. However, the _demand_ for greater commercial interaction was keen. In the 1720s and 1730s, the merchant classes of Peru and Mexico city, among the most developed in the region, sought direct trade with Spain but were rebuffed, and a vigorous contraband trade prevailed throughout the Caribbean (McFarlane, 2002). In Brazil, until the end of the 18th century even the 10
establishment of industries was prohibited by the Portuguese colonial government, but they were latent, as in much of the region, emerging upon independence (Birchal, 1999). By contrast, the U.S. colonies were tightly integrated into England’s industrialization process, sharing extensive trade and travel, engaging in industrial espionage, and accumulating knowledge and entrepreneurial capital.<sup>8</sup> Latin America also started with a far lower level of technological literacy: Maloney & Valencia (2022) show that Latin American, as the colonial mother countries, had one-tenth the presence of engineers that the US North had in 1900. Stronger industrialization efforts arguably failed to appear less because of indolence, and more because the costs of accumulating entrepreneurial capital were dramatically higher in Latin America than in the US.

# **2.2.1 The construction of indigenous entrepreneurial capacity in Antioquia, Colombia**

The experience of Antioquia, Colombia shows a path where these disadvantageous initial conditions and high costs of investment in entrepreneurial capital were overcome by sustained exposure to foreign business practices and technologies and experience abroad that lowered the costs of accumulation and led a locally dominated industrialization process. As numerous studies have documented, the Antioquians developed a sui-generis spirit of capitalism with far reaching results. Rodriguez (1985) notes “...the Antioque ̃nos played the role of modernizer assigned, in other societies, to foreign entrepreneurs...They showed to other regions the path for financial business, the modernization of commerce, and the creation of a manufacturing industry (p 8).” The legendary entrepreneurs of the region seem so at odds with the Hispanic tradition that various cultural/genetic explanations have been invoked- a profusion of Basque immigrants, or the predominance of Jews fleeing the inquisition (Twinam, 1980).

However, a recent generation of scholars (Botero, 2007; Lopez, 2007) stresses instead the accumulation of business acumen and international experience through managing the entire

> 8As an example, the fact that the Quakers started both Barclays and Lloyds Banks in England and then established Philadelphia as the nation’s first financial hub may suggest a cultural link between the Friends’ religion and high finance, but in our view, it is more likely that they gained substantial entrepreneurial capital and brought it to the new world.

11
production chain of gold, from extraction to marketing in London after Independence, in collaboration with a modest number of foreigners. As Meisel Roca (2011) observes, gold mining was one of the sectors that most benefited from the arrival of foreign entrepreneurs and technicians. Early descriptions of regional mining note a wild-catter mentality, consistent with Adam Smith’s characterization, with a short horizon and a yen to be landed gentry. The French engineer Amour argued that Antioqu ̃no entrepreneurs “would want almost immediate reimbursement of capital employed and, if there was the smallest obstacle or other accident, they would abandon the exploitation.” This corresponds to our second learning regime where perceived high returns, and high cost of entrepreneurial capital deter investment in it, and thereby the possibility of adopting technologies that might offer a medium term path around these obstacles. Fischer (1996) notes that “While the Colombian firms understood better the economic and political limitations of the country, it was the foreign firms were typified by the love of risk in the moment of discovering new gold deposits and then the experimentation with new technologies to explore them.” Our alternative casting is that they were more able to _assess_ the risk and returns associated with this experimentation process necessary to bring a new project to fruition over the longer term.

But, critically, what emerges from the mining period is a dynamic interaction of local with foreign actors that sustained a process of learning to learn by the former. While in 1820 the region was backward and isolated even by Colombian standards (Lopez, 2007), in the next decade, Antioquia received an injection of technicians, mechanics and engineers that, while modest in number, was unlike in any other region of Colombia (Brew, 1977; Meisel Roca, 2011, and others).<sup>9</sup> The 1850s and 1860s were a period of modernization and Botero notes from 1860-1870 “As processes of extraction and smelting became more complex, [local entrepreneurs] turned to a more modern administration and direction. Directors of mines with ample experience were partners with foreign engineers and Antioque ̃nos with

> 9“Names like Moore, Boussingault, White, Johnson, Paschke, de Greiff, Eastman, Jones are still found in the region. They were crucial in raising the technical quality. This system of exploitation of the mines, and particular empresarial relations led mining to be ‘the seedbed of technical innovation.’” (Brew, 1977, p.126). Perhaps because of the small number of foreign engineers- on the order of 50 in the period- until the 1880s, the mining industry remained dominated by locals.

12
technical education.” Poveda argues that this experience raised “the inventive capacity of entrepreneurs”(Poveda Ramos (1993) p. 49), an essential component of which is the ability to assess new projects. At the same time, Antioque ̃nos dominated the national market for gold and became adept at managing financial transactions both locally and internationally. Travels to London and surroundings would have introduced them to industries and practices unknown to other regional elites. Finally, the Antioquia School of Mines, established by U.C. Berkeley graduates in 1895, is credited with radically modernizing and rationalizing the entrepreneurial outlook of generations of managers who dominated the ranks of Antioquian firms until the 1930s (Brew, 1977; Mayor Mora, 1984; Botero, 2007; Lopez, 2007).<sup>10</sup> Antioque ̃nos would eventually sell most mining claims to foreignersby regional standards, local mining was a difficult business and modestly profitable business- and, in line with the growth model laid out below, they used their accumulated entrepreneurial capital to enter new fields with higher returns, first coffee and then manufactures where they became one of the three celebrated industrialization poles of Latin America.

# **2.2.2 Entrepreneurial retrogression and recovery in Chile**

Chile provides an example of entrepreneurial retrogression where a combination of high returns to mining, low initial entrepreneurial capital, and initially high costs of accumulating it led to a loss of industries it originally dominated, and an initial abdication of the industrialization project to foreigners and immigrants. Consistent with cultural explanations, Encina (1911) in _Nuestra Inferioridad Economica_ (Our Economic Inferiority) and Pinto (1959) in his _Chile, Un Caso de Dessarollo Frustrado_ (A Case of Frustrated Development) are only the best read of a line of critics of aristocratic dandyism and indolence at the root of Chile’s stagnation and dependence on foreigners. Yet, there is strong evidence of a formidable indigenous entrepreneurial presence across the first half of the 19th century(Encina, 1911;

> 10Mayor Mora (1984) p. 21 argues: “If the School of Mines set out to endow the Antioque ̃no and national businessman with an economic rationality, namely, linking the entrepreneurial spirit with calculation, organization of the company to the expectations of a normal market, a rational responses of production techniques to forecasts, and, in the end, in the measurement of labor productivity, all this meant that the old practical rationality of Antioque ̃no , based on ingenious speculation, usury, or mere audacity, was no longer enough in the new era.”

13
Pinto Santa Cruz, 1959; Villalobos et al., 1990; Nazer, 2000). Pinto is also clear that the elimination of Spanish restrictions on trade caused Chilean exports to boom immediately after independence, although entrepreneurial practices importantly lagged those of the advanced countries in every dimension.<sup>11</sup> Chilean entrepreneurs were the second largest presence in Peruvian nitrate fields, ahead of the British, and the nitrate industry there and in the _norte grande_ elicited a strong response from Chilean entrepreneurs across the economy (Cariola & Sunkel, 1985). Locals pioneered and dominated copper mining during the period of Chilean global leadership from 1850-1880, agilely responding to a rise in price with a four-fold increase in production from 1844-1860, and seeding empires spanning railroads to banking under surnames that remain pre-eminent today.<sup>12</sup> In response to increased demand rising from the Gold rushes in California and Australia, Chilean wheat exports rose ten-fold in value from 1848-1850 (Conning, 2001) as hacendados borrowed heavily to clear lands on the southern frontier (Monte ́on, 1982). Up to 50,000 Chileans sailed to San Francisco to search for gold and brought new mining technologies to their Anglo counterparts (Monaghan, 1973).<sup>13</sup> Hence, what alarmed contemporary observers sought to explain was the marked _decline_ in entrepreneurial energy at the end of the 19th century as foreigners began to dominate all areas of the national economy, and much of the discussion revolved around entrepreneurial skills (Mac Iver, 1900; Encina, 1911; Pinto Santa Cruz, 1959). Despite continuing to be an “entrepreneurial people,” Encina highlights that Chileans had“qualities little appropriate for industrial activities”-like the early Antioquians, they had an ‘obsession for fortune at one blow, (ganada de un arretazo)’ and lacked the technical and managerial skills to enter

> 11Silva (1977b) notes “the surprising ignorance of established merchants techniques, accepted and in common usage in Europe for centuries, like letters of exchange, double entry bookkeeping, or banking operations” as well as the lack of “the basic theoretical knowledge of credit, simple and compound interest, amortization, capitalization, banks, etc. ” (Silva 1977 p. 50).

> 12In 1871, two entrepreneurs alone Tomas Urmeneta and Maximiano Errazuriz accounted for 58% of total national production. Also building empires were entrepreneurs whose names remain pre-eminant todayCousino, Edwards, Subercaseaux. Villalobos et al. (1990) asks “What would be Chile and what would it be now if there hadn’t been in the 19th century a dynamic group of copper and silver miners in Atacama, pioneers of railroads and steam navigation, risk taking industrialists and active bankers. These were the ones who put together capital, invested, explored the territory, brought technicians and machinery, and and gained their fortunes in audacious businesses. Without this, we would imagine a country of rural and backward tone”.

> 13The “Chili Mill” for breaking up rock displaced the Mexican _arrastre_ and is on display at Sutter’s Mill where gold was first discovered.

14
newly emerging sectors with longer horizons ”taking little care about the exactitude of data and the legitimacy of the calculations based upon them.”(Encina, 1911, p 195, 8082n). Unlike Antioquia, as (Nazer, 2000, p.63) notes, the high concentration (ley) of copper ore and easy access to veins meant that, with some exceptions, there was little need to mechanize and extraction remained “artisanal, pre-capitalistic.” This doomed the indigenous copper industry as it entered the second industrial revolution- (Nazer, 2000, p.66) cites a ”lack of entrepreneurial capacity to innovate...the introduction of new technologies, forms of exploitation, and administration.” Pinto Santa Cruz (1959) precisely sees this as a choice not to invest in entrepreneurial capital: The technological demands of the [earlier] period, in contrast to what is occurring today in some areas of mining or industry, were relatively modest and thus not too costly. What could and had to be done in the national mining companies and in agriculture was perfectly compatible with the resources accumulated in the long periods of bonanza. If the process had been initiated and maintained adequately, without doubt it would have created the means to confront more challenging tasks, such as those posed by copper mining when it was necessary to exploit less rich veins. However, faced with the technological revolution, the local mining companies did not have either sufficient accumulated resources or organizational and administrative capacity-both of which were indispensable. In these circumstances, there was no other option but the introduction of foreign capital and expertise.” Pinto Santa Cruz (1959)(p 103n (59))(see also Sutulov (1975).

As we simulate in the next section, this can explain why the Chilean owned segment of the copper industry was effectively dead by 1920.<sup>14</sup> Similarly, although President Balmeceda in 1889 blamed the indolence, timidity and negligence of Chilean entrepreneurs for deferring to the British interests who were developing the vast nitrate fields recently conquered from Peru at the end of the 19th century, while other anguished critics, more aligned with our model, others saw the same absence of entrepreneurial and administrative capabilities, and deficient technical education, that led them to live from the rents instead (Pinto Santa Cruz, 14This appears not to be a question of the arrival of information _per se_ since Chileans had a clear view of their own backyard over a long period: Sutulov (1975) asks “... if the foreigners have been able across 100 years to make this an excellent area of business, why cannot the Chileans do the same? It is here where a change of mentality and or processes or of both is urgently necessary.” (p. 59) 15
1959; Meller, 1996; Monte ́on, 1982). This decision to not play the Schumpeterian midwife and invest in entrepreneurial capital allowed foreigners to dominate every field of endeavor.

In neither sector, nor in industry in general, is the Chilean abdication attributed predominantly to lack of capital much as it was not in Brazil or Colombia. A large Chilean literature notes the presence of vast wealth among the elites directed at conspicuous consumption,<sup>15</sup> but raises the possibility that these resources were not productively directed because they were not able recognize or exploit opportunities.

It is important to note that the recent arrivals, frequently linked to important commercial houses, did not necessarily have a lot of capital, but they knew bookkeeping perfectly, and the possibilities of commerce and of investment over long periods...(Silva Vargas, 1977a, p. 45) Villalobos et al. (1990) further notes that the foreign entrepreneurs arriving in Valparaiso after independence precisely had the entrepreneurial capital that Chileans had not built: It is worth noting that the empresarial spirit united with the motivation to apply new techniques was almost always the result of initiatives on the part of foreigners who came to Chile and _saw opportunities_ (italics ours) to develop or find solutions to problems based on practical experience. They brought a greater tradition of information, spirit of action, attention to detail, and urgency to capitalize on the results or resources generated; these were not common traits of the average inhabitant of the country, whose nature of work was little developed beyond the artesanal level (italics ours. 99).

Viewed through our model, Chileans lived off the safe returns of artisan copper mining and then rents from British nitrate exploitation while and allowing their frontier adjusted entrepreneurial capital to depreciate, becoming technically and entrepreneurially dependent on foreigners who could identify and assess more complex new techniques and products as they appeared. (Encina, 1911, p. 292-293n) argues that this “manufacturing and commercial ineptitude”contributed to a “parasitism” of extensive and unproductive public employment

> 15Juan Jose Santa Cruz, in his _Reflections on the Economic State of Chile_ in 1791 saw the potential for displacing the British fishing and whaling activity off the Chilean coast with a small outlay. But he lamented the presence in the colony of “luxury, ostentation and expensive tastes” and saw no permanent improvement in the economic conditions of Chile as possible as long as the population remained improvident and susceptible to sumptuous living (Will, 1957, p. 57). The theme recurs in a speech by Marcial Gonzalez in 1874 entitled “Luxury our Enemy,” in which he argued that the cloths, jewels, coaches, and statues exceeded those found anywhere else in America (Monte ́on, 1982). Pinto Santa Cruz (1959) cites Encina: “If half of what we have wasted in the last forty years or invested in luxury we had applied to buying nitrate mining machinery or to setting up the copper industry, or to irrigating our fields, the position of Chile in America would today be different. The propensity to save and invest was not, then, the most striking virtue of our community.”

16
and related professions, in an inversion the distorted incentives story where talent is diverted away from productive opportunities. Chileans would gradually learn to learn from the immigrants, whose influx, while increasing competition, also dramatically dropped the cost of investing in the necessary entrepreneurial capital(Silva Vargas, 1977b; Villalobos et al., 1990). Chileans increased their share in the nitrates industry, and by the 1960s major Chilean industrial groups were headed by families who were of the traditional landed aristocracy (Zeitlin & Ratcliff, 1988) and who might have deployed their capital earlier if they knew how to learn about the opportunities.

# **2.3 Identical products, different outcomes: the resource curse as a learning problem**

Chile’s trajectory is emphatically not intrinsic to either copper or nitrates and this brings us to our third stylized fact: identical products can lead to radically different development outcomes depending on the learning regime in place. While the high expected returns to copper, lower initial endowment, and higher costs of investment in EC may have led to a low learning regime in Chile, offering a new genre of resource curse, this was not the case elsewhere. Not only does Wright (1999) argue that the US in the 19th century parlayed its [natural] resource based industrial prosperity into a well-educated labor force, an increasingly sophisticated science-based technology, and world leadership in scientific research itself” (p. 665), but he uses precisely the US copper industry as an example of national learning and of innovation as a network phenomenon. Japan also leveraged its position as a leading copper exporter for rapid and diversified national development.<sup>16</sup> Major zaibatsu, such as Furukawa and Sumitomo, began as copper mining companies (Yonekura & Shimizu, 2010), but then rapidly applied Western technologies and diversified. Furukawa gave birth to Fujitsu, the fourth largest information and computing firm in the world. The in-house unit that built motors for the Hitachi copper mine similarly developed into a global technology giant of the same name. To interpret Braguinsky et al. (2020) through our lens, firms that accumulated

> 16In the early 1800s, Japan was second only to England in copper exports before being displaced by Chile in the 1840s, a position it would regain behind the U.S. in the 1910s (Sutulov, 1975).

17
the entrepreneurial capital needed to identify new technology to upgrade vertically, were also able to use it to diversify into new areas. Relatedly, Braguinsky & Hounshell (2016); Agarwal et al. (2020) note that information on new technologies and processes was available to all firms in the emerging Meiji textile industry, but a few capable entrepreneurs, often with a strong engineering formation, and the skills their firms _acquired_ drove the sector to global prominence. As one suggestive source of higher initial capabilities across the economies, Table 1 suggests that Japan, might have had a group playing the role of Latin America’s immigrants. Odagiri & Goto (1996) note that nearly half of managers (those born before 1869) were lower class Shizoku (ex-samurais) who accounted for only 5 % of the population, partly because of the level and orientation of education they received during the pre-Restoration era.<sup>17</sup> But critical also was that the example of colonial domination of China made catching up and learning Western technologies imperative, dramatically raising the social return and spurring government efforts to lower the costs of learning how to learn.<sup>18</sup> All three cases were among the global leaders in copper exporting across the 19th century, yet different learning regimes dictated radically different development outcomes. Annex 3 documents that the same retrocession found in Chile was seen in both the iron and electrical industry in Minas Gerais, Brazil, again due to inability to manage new risky projects, and the Antioquian textile industry, too, would stumble due to weak productivity and quality driven by lagging managerial capabilities (Morawetz, 1980).

> 17Demobilized as warriors in the Tokugawa period, many Samurai studied and developed skills as administrators and in the mid 18th century _Bushi_ (samurai) education gradually shifted its emphasis from classics to practical subjects including medicine, arithmetic and Western studies and new schools specializing in Western studies emerged in the early 1830s where many Meiji leaders were trained (Yasuba, 1987). Arguably, Japan had a base to begin from.

> 18Odagiri (1998) notes precisely the emphasis put on developing managerial and technical schools as critical to catch up and as knowledge networks. Pre-WWI management was dominated by graduates from select schools with Hitachi being singled out as headed by engineering-managers from University of Tokyo.

18
# **3 An Information-Based Model of Entrepreneurial Choice**

## **3.1 One agent and one risky project**

We consider a risk-neutral agent who must decide between exploiting a new risky project whose expected return is unknown or continuing to exploit a safe project with known returns. As discussed in the literature on R&D, this is at once a decision to bet on a risky project as well as a decision to collect more information about it. There is learning by doing defined as refining the prior on the distribution of returns of the risky project by engaging in it and then observing its returns. The more the risky project is tried, the higher the number of observed returns, and the more precise the prior on the distribution of returns.

In addition, there is process of learning about how to learn about projects- becoming better about processing new information to form a correct prior on a new product. We relate this ability with the notion of entrepreneurial capital, which is inversely related to the precision of the prior belief. When the prior belief is too precise (low variance), the arrival of new information will not shift the prior significantly and learning the correct distribution of the risky project becomes more difficult. We can think of a conservative agent, resistant to “newfangled” products and technologies for whom no amount of new information will shift him away from the traditional safe product. At the other extreme, we may see the Silicon Valley venture capitalist willing to bet on a new web product with very slim evidence on its eventual profitability. We see this as the same problem for the introduction of new industries in an economy and, at a more abstract level, the whole process of modernization.

The expected return of the safe project is _μs_ , whereas the returns of the risky project _yr_ are drawn from a normal distribution with unknown mean _u_ and known precision (inverse of the variance) _t_ . The agent has a prior distribution over _u_ given by a normal distribution with mean _μr_ and precision _τ_ . If the agent decides to undertake the risky project, s/he will observe a realization of its distribution in the next period that will later be used to update the agent’s prior using Bayes rule. Therefore, a realization of profit _x_ , will lead to the posterior of the unknown mean _u_ to become a normal distribution with mean _μ_<sup>_′_</sup> _r_<sup>=</sup><sup>_τ_</sup><sup>_<u>μ</u>_</sup> _τ_ 0<sup>_<u>r</u>_</sup> +<sup><u>+</u></sup> _t_<sup>_tx_</sup> and precision _τ_ + _t_ .

19
We interpret _τ_ as a decreasing function of entrepreneurial human capital-some combination of managerial, technical, even linguistic skills- since a higher _τ_ implies that the prior belief has a higher weight in the updated belief when the agent decides to experiment. This implies that realizations of the risky project will have a relatively smaller weight in the updated distribution, thus lowering the incentive to engage in the new project. On the other hand, we interpret the precision of the signal _t_ as the number of signals that generated the aggregate average signal, as Bolton & Harris (1999) suggests. In our context it could be interpreted as the number of people (entrepreneurs) trying the new risky technology in the society. It is also linked to the number of migrants that had access to more advanced technologies in other societies or the strength of the ties with such societies, that make the realization of the risky signal more credible.

The agent maximizes the discounted sum of profits over an infinite lifetime and each period chooses which project to pursue. The problem for the agent can be stated recursively as: Note that choosing the safe project means that the agent will face the same problem the next period since no learning will have occurred and hence will continue to do so going forward as long as the process of the risky project does not change (Gittins, 1979). This strategy generates a lifetime utility of 1 _<u>μ−sβ</u>_<sup>given the normalized payoffs.Hence the problem simplifies</sup> to:

**Lemma 1.** _There exists a unique solution to v_ ( _μr, τ_ ) _. Moreover, it is increasing and convex_

20
_in μr and μs, increasing in t, decreasing in τ , and_ The expectation of _v_ ( _μr, τ_ ) is often called the value of experimentation. The greater the expected return to the risky project and its precision _t_ , the better off is the agent and the greater the willingness to experiment in the new project. Suppose any signal leads the agent to choose the safe project, then the value of experimentation would be equal to the discounted value of choosing the safe project forever. However, such case becomes uninteresting since there is no room for learning. Now, if for some signals the agent prefers the risky project, it means that the value of the risky project given that signal is greater than choosing the safe project forever. Therefore the value of experimentation (the ex-ante expectation before the signal is realized) is always greater than choosing the safe project since some signals will lead to a greater value whereas the others lead to the same value (the ones where the safe project is preferred to the risky one). Hence, we obtain _β_ E _μ′r_ � _v_ ( _μ_<sup>_′_</sup> _r_<sup>_, τ_+</sup><sup>_t_)</sup> � _> β_ 1<sup>_<u>μ</u>_</sup> _−_<sup>_<u>s</u>_</sup> _β_<sup>.Therefore, there</sup> exists a unique _μ_<sup>_∗_</sup> _< μs_ such that This in turn implies that the agent will experiment with the risky project for any _μr ≥ μ_<sup>_∗_</sup> - as long as the expected return is not “too much” lower than the safe return. It also implies that a bad realization _x_ sufficiently lower than _μ_<sup>_∗_</sup> will lead the entrepreneur to stop investing in the risky project since the new updated expected mean would become lower than _μ_<sup>_∗_</sup> .

The value of information is also increasing in entrepreneurial capital. In the limit, where _τ_ tends to infinity and there is no entrepreneurial capital, prior beliefs are too strong and there will be no learning. In that case the expected value function is simply the discounted value of the greatest expected return. As _τ_ decreases, the value of information becomes less convex since more weight is given to the information yet to be realized. Since the boundary 21
values remain equal, the lower convexity implies that the value of information increases with more entrepreneurial capital.

This optimal behavior is illustrated in Figure 1 for _τ_ 1 _< τ_ 2, where the blue line represents the first case and the red line the second case. Since E _μ′r_ � _v_ ( _μ_<sup>_′_</sup> _r_<sup>_, τ_1)</sup> � _>_ E _μ′r_ � _v_ ( _μ_<sup>_′_</sup> _r_<sup>_, τ_2)</sup> �, then the required threshold to experiment is lower in the first case; formally, _μ_<sup>_∗_</sup> is increasing in _τ_ . Thus the required positive signal is higher the lower the entrepreneurial human capital to keep exploiting the risky project and if not high enough, we may see abandonment of the project or “retrogression”. In other words, if two societies with different levels of entrepreneurship were to observe the same signal _x_ such that _x_<sup>_∗_</sup> _< x < x_<sup>_∗∗_</sup> , then the society with lower entrepreneurial capital will experience retrogression whereas the other will continue exploiting the risky project. We present a longer term mechanism for retrogression below.

# **3.2 New projects arising and growth**

To introduce growth, we assume that a new risky project emerges each period. Now, the entrepreneur decides each period whether to exploit the safe project or the risky one. If the agent chooses the risky project, the next period a signal will be observed, the entrepreneur will update the distribution of the risky project, and will choose definitely between the previous safe and risky project with the updated distribution. If the entrepreneur chooses the risky project, it will become the new safe project. At the same time a new risky project will arise and the entrepreneur will face the same problem.

Let _μ_<sup>_′_</sup> _s_<sup>bethesafereturnofthenextperiod,thatis</sup><sup>_μ′_</sup> _s_<sup>=max</sup> � _μs,_<sup>_τ_</sup><sup>_<u>μ</u>_</sup> _τ_<sup>_<u>r</u>_</sup> +<sup><u>+</u></sup> _t_<sup>_tx_</sup> �. The new risky project has an unknown mean whose prior is also a normal distribution with mean _μ_<sup>_′_</sup> _r_<sup>andinheritedprecision</sup><sup>_τ_.Theunconditionalmean</sup><sup>_μ′_</sup> _r_<sup>willberandomwithsomeknown</sup> distribution. For simplicity, we assume that this distribution does not depend on the rest of the parameters of the model. The problem can be expressed recursively as:
Using the results from the previous section we know that the value function will be increasing and convex in _μs_ and _μr_ , increasing in _t_ and decreasing in _τ_ . As before, an agent will experiment with the risky project, as long as its prior mean is greater than some threshold that is lower than the safe mean _μs_ . However, the observed signal from the risky project must be larger than before to continue with the risky project since there is only one stage of experimentation. It also implies that societies with lower entrepreneurial capital (higher _τ_ ) face lower incentives to experiment since they give more weight to their prior and less weight to their signals. Hence, we are more likely to observe retrogression in societies with less entrepreneurial capital, which also implies that they have less capacity to grow over time.

# **3.3 Endogenous Entrepreneurial Capital**

Introducing depreciation and investment in entrepreneurial capital yields richer growth dynamics. It allows us to model a moving technological frontier and to understand retrogression cases where having high initial returns lowers incentives to invest in entrepreneurial capital.

We now assume that the precision of the new risky project is given by _τ_ + _δ_ (1 _− e_ ). _δ_ is interpreted as the frontier-adjusted depreciation of entrepreneurial capital that occurs as technological progress increases the complexity of projects and hence the necessary entrepreneurial capital to process signals. The depreciation in a given period can be offset by investing in entrepreneurial capital _e ∈{_ 0 _,_ 1 _}_ , at a cost _c_ . If societies do not invest, depreciation makes experimentation more costly and entrepreneurs will give less weight to the arriving signals to evaluate them. We can think of the Second Industrial Revolution as introducing projects with new levels of complexity that countries that had progressively invested in entrepreneurial capital could evaluate and bet on, while others became progressively less able to evaluate these projects and hence did not take up industrialization.

23
<!-- Start of picture text --> { }<br>( 1<br>ft 1]<br>_<br>(<br>La)<br>()<br><!-- End of picture text -->
_c_ , otherwise it will be zero. Moreover, if _es_ = 1 and _vr ≥ vs_ it must be the case that _er_ = 1 since the value of information is greater when the risky project is chosen.

A central finding is the complementarity of betting on risky projects and investing in entrepreneurial capital. Having a lower precision of the prior increases the value of experimentation as discussed before. Therefore, the investment in entrepreneurial capital is more productive when the risky project is chosen rather when the safe project is preferred. This implies that the agent invests more in entrepreneurial capital when the risky project is preferred. A corollary is that countries with lower entrepreneurial capital, those less likely to bet on risky projects, will also invest less in additional capital. This dynamic generates a new type of growth trap- societies with low entrepreneurial capital are more likely to choose the safe project, which in turn also implies a lower investment in entrepreneurial capital, locking them into progressively less profitable projects.

The model also suggests that the investment in entrepreneurial capital is more productive when the risky mean prior is close to the safe return since that is the point where new information is more important given the close indifference between the projects. Therefore we show that societies will not invest in entrepreneurial capital when agents have pessimistic priors on the risky project or very optimistic. This implies that societies where the risky prior is high enough will experiment with the risky project but will not invest importantly in entrepreneurial capital. As new projects arise and its complexity increases, these societies will remain exploiting those old risky projects (now safe ones) and will not grow as more entrepreneurial societies. Note this low growth arises from an endogenous decision of not investing in offsetting the depreciation in entrepreneurial capital after observing a very profitable project.

The cost of investing in entrepreneurial capital is also critical to the set of choices. Optimal investment when the risky project is chosen is drawn in Figure 2 for _c_ 1 _< c_ 2. The black line is the case where agents do not invest in entrepreneurial capital (note the similarity with Figure 1), whereas the red and blue lines depict the cases where there is investment at a high and a low cost, respectively. Note that the black line is more convex than the other two, since 25
entrepreneurial capital is lower, whereas the other two lines have the same convexity. In the absence of a fixed cost it would be preferable to have higher entrepreneurial capital (lower _τ_ ) as shown in Figure 1. A positive cost implies that investing in entrepreneurial capital makes sense only for prior beliefs that see information as more valuable which is where the expected risky return is close to the safe return. Formally this happens in the interval [ _ai, bi_ ] for cost _ci_ , which is where the less convex (red and blue) lines are above the more convex (black) line. As expected this interval must be decreasing in _c_ , since the investment becomes more costly. Therefore, societies whose cost of investing in entrepreneurial capital is higher, will incur these costs only if the risky return is expected to be very similar to the safe return. However, if the cost is sufficiently high, they will never invest in entrepreneurial capital. Such costs can be associated to institutions that disincentivize entrepreneurship such as prohibitions on trade or restrictions to opening new businesses such as was the case in Latin America or, incentivize them, such as was the case with the Meiji national imperative to learn western technologies.

Another way to summarize the previous lemma is to examine the marginal return of investing in entrepreneurial capital. Figure 3 shows such marginal return for the calibration we described in the next subsection. Note the marginal return when the risky project is chosen is highest for intermediate values of the expected risky return. However, even though the expected return exceeds just staying in the safe product, the cost of the investment represented by the dotted horizontal line, makes investment prohibitive. Consistent with Figures 1 and 2 above, a higher initial level of entrepreneurial capital will also shift up the return to investment at every level of the expected return to the risky asset, leading to a wider range of expected returns to the risky asset and costs of investment where investment will occur.

# **3.4 Validation of the model: Chile and US experience in copper**

In this section, we show that plausible parameters can generate the distinct time paths of the Chilean and US copper industries across the 1880 to 1920, the period that Chile’s industry declined and the US rose. As a proxy for the entrepreneurial skills we model, we follow 26
Murphy et al. (1991) in using engineering density derived from Maloney & Valencia (2022) and census data. Figure 4 shows the evolution of the logarithm of copper revenue by Chilean nationals, and US nationals in the US, and Figure 5 shows the evolution of the number of domestic engineers for both countries. Note Chile has a higher initial revenues, but is overtaken by the US within the first decades. On the other hand, US has a higher initial entrepreneurial capital and keeps investing in it at a higher pace than Chile.

To replicate the observed data with our model we start, for each country, with an initial _τ_ 0 equal to the inverse of the number of engineers in the population in 1850, and an initial _μs_ equal to the logarithm of copper revenue in US in 1850. The initial _μr_ is also set equal to the initial _μs_ , the second _μr_ is set equal to the initial logarithm of copper revenue for each country, and _t_ is set to infinity, that is the signal reflects the true expected return of the risky project. The depreciation parameter _δ_ is set to 8 and the cost of investing in entrepreneurial capital _c_ is set to 0 _._ 5 for both countries. The process for the risky projects arising each period follows a normal distribution with mean equal to _μs_ and variance equal to the inverse of _τ_ given the perfect informativeness of the signals.

We use the same seed for the realized returns of the risky project for both countries. Figure 6 shows the generated evolution of copper revenue, while Figure 7 shows the implied evolution of entrepreneurial capital. Note the numerical exercise replicates the observed paths in Figures 4 and 5. Despite Chile having a higher initial return, US quickly surpasses it because of a higher initial entrepreneurial capital and a lower initial return.

To gain further clarity, we decompose the effect of each by assigning the U.S. initial value to each as a base and then varying them individually. Figures 8 and 9 show the evolution of the returns and the entrepreneurial capital, respectively, for the case where both countries have the same initial entrepreneurial capital, but a higher initial return for Chile reduces the incentives to invest further. Even when returns moderate, the resulting lag in entrepreneurial capital implies that it is no longer profitable to invest further and frontier adjusted stock continues to depreciate. The model implies a similar path as the previous figures, except that the US overtakes Chile later given the latter’s higher initial stock of entrepreneurial 27
capital.

Figures 10 and 11 show that relative trajectories could also be generated by having the exact same returns in both countries, but different initial levels of entrepreneurial capital. The greater initial facility in recognizing new opportunities in the US leads to investment in riskier projects, more investment in entrepreneurial capital, and a higher long run growth trajectory. In sum, both the higher initial return and low initial entrepreneurial capital plausibly contribute to the decision to invest in further entrepreneurial capital.

# **4 Conclusion**

The ability to productively experiment with new technologies and products, and learn from that process is essential to development. This paper argues that the divergence in growth dynamism within the Western Hemisphere can be seen as the result of differential investment in the entrepreneurial capital that permits more efficient use of arriving information about new technologies and products- more advanced countries have learned better how to learn. Our model shows that that investment in turn depends on the cost, the distance from the frontier and, non-monotonically, on the expected return to the risky project. Together these factors lead to three learning regimes which aid in interpreting key features of the industrialization process in Latin America: the disproportionate role of immigrants and foreigners in industrialization, the emergence of some poles of entrepreneurial dynamism, and entrepreneurial retrocession in others; and the differing development outcomes of identical industries. We show that, under reasonable parameters, the model is able to simulate the differential experiences with copper in Chile and the US.

These findings confirm the salience of the emerging literature that documents the gap in a range of managerial and technological practices between advanced and developing countries and provides an additional rationale for the substantial efforts of industry upgrading agencies across the advanced world.

28