metadata: authors: 0: author: Valencia Caicedo,Felipe
1: author: Maloney,William F.
count: World,Canada
docna: 0: docna: Engineers, Innovative Capacity and
            Development in the Americas
docty: Policy Research Working Paper
theme: Improving labor markets,Social Safety Nets/Social Assistance &
            Social Care Services
majtheme: Social protection and risk management
subtopic: Mining & Extractive Industry (Non-Energy),Educational Sciences,Employment and Unemployment,Energy and Mining
teratopic: Science and Technology Development,Information and Communication Technologies,Education,Macroeconomics and Economic Growth,Industry
abstracts: cdata!: This paper offers the first evidence on
            the prevalence of a central actor in modern growth
            theory—the engineer. Using newly collected sub-national, and
            international data as well as historical case studies, it
            then argues that differences in innovative capacity,
            captured by the density of engineers and patents at the dawn
            of the Second Industrial Revolution, in fact, are important
            to explaining present income differences across US counties,
            states within countries, and between the US and Latin
            America. This remains the case after controlling for
            literacy, other higher order human capital, and demand side
            elements that might be confounded with engineering or
            patenting. Instrumenting engineering using the Land Grant
            Colleges program further limits remaining endogeneity. A 1
            SD increase in engineers at the turn of the 20th century
            accounts for a 16 percent increase in US county income
            today, and patenting capacity contributes another 10
            percent. This can partly explain why countries with similar
            levels of income in 1900, but ten fold differences in
            engineering density diverged in their growth trajectories
            over the next century.
repnme: repnme: Engineers, Innovative Capacity and
            Development in the Americas
display_title: Engineers, Innovative Capacity and
            Development in the Americas
listing_relative_url: /research/2014/03/19292695/engineers-innovative-capacity-development-americas
topicv3: Mining Services m1326410 1701,Education m1327048 470,Mining Sector m1327113 554,Iron m1503514 2931,Census Data m1504073 2763
publishtoextweb_dt: 2014-03-01T00:00:00Z
historic_topic: Science and Technology Development,Information and Communication Technologies,Education,Macroeconomics and Economic Growth,Industry
no_of_pages: 72
author_keys: 0: author_key: 1001892
1: author_key: 93000
model_extractions: WPS6814 Policy Research Working Paper 6814 Engineers, Innovative Capacity and Development in the Americas _William F. Maloney Felipe Valencia Caicedo_ The World Bank Development Research Group Macroeconomics and Growth Team March 2014
Policy Research Working Paper 6814

# **Abstract**

This paper offers the first evidence on the prevalence of a central actor in modern growth theory—the engineer. Using newly collected sub-national, and international data as well as historical case studies, it then argues that differences in innovative capacity, captured by the density of engineers and patents at the dawn of the Second Industrial Revolution, in fact, are important to explaining present income differences across US counties, states within countries, and between the US and Latin America. This remains the case after controlling for literacy, other higher order human capital, and demand side elements that might be confounded with engineering or patenting. Instrumenting engineering using the Land Grant Colleges program further limits remaining endogeneity. A 1 SD increase in engineers at the turn of the 20th century accounts for a 16 percent increase in US county income today, and patenting capacity contributes another 10 percent. This can partly explain why countries with similar levels of income in 1900, but ten fold differences in engineering density diverged in their growth trajectories over the next century.

This paper is a product of the Macroeconomics and Growth Team, Development Research Group. 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://econ.worldbank.org. 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
# Engineers, Innovative Capacity and Development in the Americas<sup>_∗_</sup>

William F. Maloney<sup>_†_</sup> Felipe Valencia Caicedo<sup>_‡_</sup> Keywords: Innovative Capacity, Human Capital, Engineers, Technology Diffusion, Patents, Growth, Development, History.

JEL: O1, O31, O33, O4, N1

> _∗_ Corresponding author Email: wmaloney@worldbank.org. We thank Ufuk Akcigit, Leo Feler, Claudio Ferraz, Christian Fons-Rosen, Oded Galor, Steve Haber, Lakshmi Iyer, David Mayer-Foulkes, Stelios Michalopoulos, Guy Michaels, Giacomo Ponzetto, Luis Serven, Andrei Shleifer, Enrico Spolaore, Uwe Sunde, Nico Voigtl ̈ander, Hans-Joachim Voth, Fabian Waldinger, David Weil and Gavin Wright for helpful discussions. We are grateful to William Kerr for making available the county level patent data. Our thanks to Carlos Andres Molina, Melissa Rubio, and Mauricio Sarrias for meticulous research assistance. This paper was partly supported by the regional studies budget of the Office of the Chief Economist for Latin America and Knowledge for Change (KCP) trustfund.

> _†_ Development Economics Research Group, The World Bank.

> _‡_ Department of Economics and Institute of Macroeconomics and Econometrics, University of Bonn.
“You have all the elements, but you cannot make steel” —Andrew Carnegie<sup>1</sup>

# **1 Introduction**

Carnegie’s taunt to the owners of the Birmingham Steel Company in Alabama highlights the complexity of technological transfer to lagging regions. As Wright (1986) documents, shortfalls in management, learning by doing, and the R&D capacity necessary to adapt technologies to local conditions all impeded the establishment of a dynamic steel (as well as textile and lumber) industry in the American South, and hence slowed catch-up to the North. The issue of differential technological transfer remains central to the more general convergence debate.<sup>2</sup> Most recently Comin et al. (2008); Comin & Ferrer (2013) argue that the diverging measured intensity of use of new technologies (as a share of economic activity) plausibly explains observed TFP differentials and can drive simulations that closely track the magnitudes of the Great Divergence of the last two centuries (e.g. Pritchett, 1997; Galor & Weil, 2000; Galor, 2011).<sup>3</sup> That human capital is a critical ingredient in technology adoption also enjoys a substantial supporting literature.<sup>4</sup> The nature of the relevant human capital has been less clear. Literacy, and accumulated years of schooling or enrollment have received the most attention, although other dimensions figure importantly in the literature as well: Lucas (1993); Young (1993) and Foster & Rosenzweig (1995), among others, stress the importance of accumulated “learning by doing”;

> 1Cited in Wright (1986) p.171.

> 2See also, Parente & Prescott (1994); Eaton & Kortum (1999, 2001); Caselli (2001); Comin & Hobijn (2004); Keller (2004); Klenow & Rodriguez-Clare (2005); Comin et al. (2008, 2010a); Comin & Hobijn (2010); Comin et al. (2012).

> 3Though not clear on the underlying determinants, recently Acemoglu et al. (2013) argue for the importance of a the shift in industrial composition from those with low to high innovative capacity as critical to growth.

> 4See, for example, Nelson & Phelps (1966); Foster & Rosenzweig (1996); Cohen & Levinthal (1989); Benhabib & Spiegel (1994); Basu & Weil (1998); Temple & Voth (1998); Howitt (2000); Acemoglu & Zilibotti (2001); Caselli (2001); Comin & Hobijn (2004); Benhabib & Spiegel (2005); Aghion et al. (2005); Howitt & Mayer-Foulkes (2005); Ciccone & Papaioannou (2009); Goldin & Katz (2009).

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Baumol (1990) and Murphy et al. (1991), entrepreneurial skills and orientation; in his classic article on endogenous growth; Romer (1990) highlights the research engineer; Mokyr (2005), the minority of “trained engineers, capable mechanics and dexterous craftsmen on whose shoulders the inventors could stand” (pg.16); Rosenberg (2000) and Nelson (2005), the accumulated ability and scientific institutions to manage new ideas for innovation and invention; Cohen & Levinthal (1989); Griffith et al. (2004), the capacity for research and development needed for technological transfer. Yuchtman (2014) shows that in early 20th century China, engineers enjoyed massive wage premia over all other classes of higher education and that modern Western education and engineers in particular were critical to China’s ability to adopt Western technologies.

Several authors including Mokyr (1998), and Howitt & Mayer-Foulkes (2005) stress that higher-order human capital and the institutions that generated and housed it may have had an even more determinant role at the dawn of the Second Industrial Revolution (circa 1870-1914), which saw an increased emphasis on more structured scientific inquiry such as laboratory-based R&D.<sup>5</sup> This scientifically oriented human capital, and a technologically savvy entrepreneurial class<sup>6</sup> were necessary to tap into the expanding and increasingly sophisticated global stock of knowledge and convert it into local growth. The technological leap forward also meant an erosion in the efficacy of existing levels of human capital and innovative capacity relative to that needed to continue to adopt (see Howitt (2000); Aghion et al. (2005)). Building on this insight, Howitt & Mayer-Foulkes (2005) argue for multiple equilibria in innovation where countries whose human capital evolved with the frontier at the time of the technological leap forward could innovate or adopt, but those whose frontier adjusted human capital did not keep up slipped to an equilibrium where even the adoption

> 5Rosenberg (2000) and Nelson (2005) stress the incremental and cumulative nature of technological progress and related institutions more generally as a central dynamic of industrialization.

> 6As numerous authors have stressed from Schumpeter (1934) to the present, technological progress without entrepreneurs to take it to market does not lead to growth (see Aghion & Howitt, 1992; Baumol, 1990, 2010; Glaeser, 2007; Glaeser et al., 2009, 2010; Braunerhjelm et al., 2010; Iyigun & Owen, 1998, 1999). Over the longer term this reflects the accumulation of a specific kind of human capital, at the very least, suited to the evaluation and management of risk, but extending to skills for managing people, credit, and technologies which need to be learned.

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of technologies was difficult, and stagnation followed.<sup>7</sup> Iyigun & Owen (1998, 1999) relatedly argue that if the the initial stock of both professional (scientific) and entrepreneurial human capital is too low, the return to accumulating human capital will be low and economies can find themselves in a development trap.

Empirically, documenting the impact of even very basic measures of human capital on growth or relative incomes has proved surprisingly complex since the seminal studies of Barro (1991) and Mankiw et al. (1992).<sup>8</sup> Most recently, and in a similar spirit to the present work, Hanushek et al. (2015) find that differences in human capital account for 20-35 percent of the current variation in per-capita GDP among US states. Further, there have been relatively few efforts to systematically capture what kind of human capital matters, or even to document the stocks of different types of capital. Goldin & Katz (2011); Goldin (1999); Goldin & Katz (1999) have documented the evolution of secondary and tertiary education in the context of US growth. Judson (1998), Wolff & Gittleman (1993), Self & Grabowski (2004), Castell ́o-Climent & Mukhopadhyay (2013) attempt to document whether tertiary education matters more or less than primary education. Murphy et al. (1991) document that countries with a higher proportion of engineers grow faster relative to those with a higher proportion of law concentrators.

The problem is exacerbated when a longer historical perspective is taken. Campante & Glaeser (2009) argue that the “lion’s share” of the differences in long run income level between the cities of Buenos Aires and Chicago is human capital, but they lament that literacy remains the primary, albeit coarse, measure (see, for example, Mariscal & Sokoloff (2000)). Voigtl ̈ander & Squicciarini (2014) show that mid-18th century French cities with

> 7See also Gancia et al. (2008) for related discussion of convergence clubs resulting from educationtechnology complementarities.

> 8For overviews see Krueger & Lindahl (2001); Sianesi & VanReenen (2003); Stevens & Weale (2004) and Acemoglu & Zilibotti (2001); Pritchett (2001); Easterly (2002); Benhabib & Spiegel (1994). A recent literature has sought to explain the often small measured impact by incorporating measures of the quality of human capital (see, for example Hanushek & Woessmann, 2007; Behrman & Birdsall, 1983; Hanushek & Woessmann, 2012).

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more subscriptions to the _Encyclop ́edie_ , capturing higher order human capital, grew faster after the dawn of the industrial revolution. Waldinger (2012) shows that the dismissal of scientists in Nazi Germany led to a permanent decrease in growth.

To date, however, neither the historical prevalence of Romer’s research engineers or Mokyr’s engineers and mechanics, nor Rosenberg’s and Nelson’s systems of innovation have been quantified in a globally comparable form. That is, the profession has no systematic historical evidence on allegedly _the_ central actor in modern economic growth.<sup>9</sup> In this paper we, first, make this very basic contribution. We establish the stylized facts surrounding the relative density of engineers at the end of the 19th century at the national level for the Western Hemisphere and representative benchmark countries of Europe, and the same at the sub-national level for six countries in the Americas. This is done in a systematic way drawing on graduation records, membership in professional societies, and census data. We see these estimates, in the first instance, as a measure of what they are: the stock of higher level scientifically oriented human capital directly linked to productive activity available at the beginning of the Second Industrial Revolution. Second, because our national measures are based on graduates of domestic engineering schools, they also proxy for the universities and institutions that support them, and for which data are more elusive (see Nelson, 2005). Further, we are sympathetic to the use in Murphy et al. (1991) of engineering density as a proxy for “good entrepreneurship,” perhaps more specifically the advancement of the long-run cumulative process of developing entrepreneurial technological depth and accompanying cultural factors discussed by Phelps (2013). In sum, we see it as a broad proxy for innovative capacity. We document an extraordinary variance in engineering density across countries of very similar income levels in 1900-for instance Argentina, Chile, Denmark, Sweden and the Southern US- that correlates to their divergent income positions today.

> 9Collected articles in Fox & Guagnini (1993) have examined the evolution of engineering capacity in several advanced countries although the comparability of the measures across countries is not always clear, and there is no attempt to establish a link to economic performance.

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To more confidently establish the importance of innovative capacity as a determinant of income, we then develop a rich data set for the United States at the _county_ level. This vastly expands the degrees of freedom and permits controlling for an extensive set of geographical, growth related, human capital variables and instrumenting for possible endogeneity of engineering using the Land Grant Colleges program. Further, we incorporate patenting density as collected by Acemoglu et al. (2013). Patenting appears only weakly explained by county engineering density and hence, we treat it as driven by human capital more dedicated to invention (Griliches (1990); Sokoloff (1988)) while engineering may arguably be capturing more adoptive activities. Both variables show an important and robust effect: a one standard deviation in engineering density in 1900 accounts for a 16% rise in income today, and patenting capacity another 10%. For a broader sample of countries in the hemisphere, we then demonstrate the continued relevance of engineering at the “state” level. Applying the well-estimated US coefficient to the national level data, a 1 standard deviation in engineering density in 1900 accounts for a 36% difference in income today. We then use historical evidence to validate our ranking of innovative capacity and through case studies document the divergent development outcomes arising within industries and even identical goods in countries with the differing innovative capacities we quantify. Finally, we present some suggestive findings on the determinants of innovative capacity.

# **2 Data**

## **2.1 Measuring engineering/innovative Capacity**

Our primary measure of innovative capacity at the national level across all countries is the number of engineers with domestically emitted university degrees per 100,000 male workers. Unlike literacy data, which countries sometimes collect as part of the census, countries do not tabulate such information in a uniform fashion. Hence, we construct these series using three sources of data.

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_Engineering Graduates_ : To the degree possible, calculations are done with actual graduates of engineering colleges and universities within the country. Clearly, many engineers even in the US acquired valuable training on the ground, or may have had partial degrees from some type of technical program. However, such skills are difficult to capture with any degree of commonality across geographical units. As a consistent metric across countries, we take the number of degrees awarded.<sup>10</sup> Though most countries also employed foreign engineers, we are interested in indigenous technical capacity and the institutional structure to generate it, so we focus on domestically trained engineers. Some nationals studied abroad, however the historical evidence from Argentina, Chile, Colombia and Mexico suggests the numbers are small and difficult to document. Since the working life of an engineer is roughly 40 years, we begin accumulating the stock in 1860, discounting the stock in each period by .983 as the rate of death/attrition in each year. In some cases, we have a long series of graduation records which make this procedure straightforward. In Bolivia, Denmark, Mexico, New York, Per ́u, Spain, Sweden, Venezuela, or Mexico, the flow of graduated engineers is available for the 1860-1900 period. We refer to Ahlstr ̈om (1982)’s estimates for Germany and France, the frontier countries, which are allegedly generated in a virtually identical manner, yet we do not have access to his data and these are not our calculations. In general, the larger and more advanced the country, the greater the number and types of institutions and hence the more difficult guaranteeing comparability and this also influenced our decision to limit our geographical focus.<sup>11</sup> In other cases, for instance, Argentina, Brazil, Chile, Colombia, Ecuador, and the US as a whole, the information is less complete and we bring other sources of data to bear to fill in the gaps in the series.

> _Membership in Engineering Societies_ : Data on membership in Engineering societies

> 10Fox & Guagnini (1993) tabulate for several advanced countries the number of students enrolled. However, we find often that the difference between students enrolled and eventual graduates can differ greatly so enrollment rates are not as reliable.

> 11In fact, he calculates the stock for France, Germany as well as Sweden for which we do have the raw underlying data and hence can verify that we are doing the calculations in a virtually identical fashion.

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or official registries validate broad orders of magnitude of our generated stocks. In some cases, such as Brazil, registry with the government was required to be a practicing engineer. What is considered an engineer, however, is less clear and hence these measures are less definitionally tight. In other countries, such as Colombia or Argentina, membership in Engineering associations was not required so registration likely underestimates.

_Census Data_ : Census data are also available in several countries. Census data have the advantage of being collected over time by several countries and across sub-national units. However, here, also, it is the individual respondent who is deciding whether he is an engineer or not with limited institutional confirmation, or detail on the actual level of education. This is fine for within country analysis but less reliable for cross country comparisons.<sup>12</sup> Sub-national data derive from the Argentine census of 1895, Mexican National Census of 1895, Chilean Census of 1907, Colombian census of 1912, Venezuelan Census of 1926, and US Census of 1900. For the US county level data, we use the census of 1880 to generate measures of literacy, and density of engineers, lawyers and medical doctors.

Annex I discusses how these three sources of data were employed for each country in detail.

_Patenting_ For the US, we also construct a measure of average patenting activity from 1890-1910 drawing on patents granted by the United States Patent and Trademark Office (USPTO) again, at the county level, provided by Acemoglu et al. (2013). This is likely to capture Mokyr’s more structured scientific inquiry at the frontier that began to assume a central role in the Second Industrial revolution in addition to its direct interpretation as new ideas. It does not appear driven by engineers or the other human capital variables and hence interpret it as captures a distinct type of human capital (see Sokoloff (1988). Comparable patent data is not available for the Latin American countries and hence the variable only

> 12As numerous local historians have noted (Serrano (1993) in Chile, Bazant de Salda ̃na (1993) in Mexico) and in the case for which we have the best information, the US, censuses are often substantially higher than the actual graduates of engineering programs.

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enters in the county-level US analysis.

# **2.2 Sub-national Income per Capita**

Income in 2005 PPP US Dollars is drawn from a highly disaggregated spatial data set on population, income and poverty constructed on the basis of national census data by the World Bank (2009) for the World Development Report on _Reshaping Economic Geography_ .<sup>13</sup> Modern county level US income in dollars is mean household income taken from the 2000 US Census.

# **2.3 Controls**

As controls in our regressions, we also employ data on: _Literacy_ : Aggregate literacy rates we take from (Mariscal & Sokoloff, 2000; N ́u ̃nez, 2005). Sub-national literacy rates for Argentina, Chile, Mexico, Venezuela, and the US taken from same census data as above. County level from 1880 census.

_Secondary Schooling_ : Similar to Goldin & Katz (2011); Goldin (1999) for the US we construct a proxy for secondary schooling by the share of 14-17 years olds who report that they are attending school (1880 US census).

_Higher Level Non-Engineering Human Capital_ : For the US, this is measured as number of lawyers and medical doctors per 100,000 inhabitants (US Census 1880). We also aggregate across occupational categories the EDSCOR50 measure that indicates the percentage of people in the respondent’s occupational category who had completed one or more years of college. Together these allow us to ensure that our engineering measure is not proxying for the availability of higher order human capital in general.

> 13For further detail see Maloney & Valencia (2015)

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_Railroads_ : At the national level, we employ the density of railroads measured as kilometers of track per 1000 square kilometers in 1900 (Pach ́on & Ram ́ırez, 2006; Thorp, 1998). At the sub-national level, we employ the Interstate Commerce Commission’s data on miles of track per 100 square miles converted to the same units above for consistency in 1899 (ICC, 1899). Individual country level data is not available for Latin America.

_Manufacturing output per capita_ : The value of manufactured products and labor in manufacturing in 1870 taken from NHGIS to compute the per capita and per unit of labor labor manufacturing product.<sup>14</sup> _Population Density in 1900_ : These are collected from census data from the individual countries. Argentina (1895), Brazil (1900), Chile (1907), Colombia (1912), Mexico (1895), Peru (1876), Venezuela (1926), and the US (1900).

_Pre-colonial Population Density_ : This measures the estimated number of indigenous people per square kilometer just before colonization. See Maloney & Valencia (2015) for more detail.

_Slavery_ : As a measure of institutions that is available for the United States sample, we used the 1860 Census as well as the data compiled in Nunn (2008).

_Geographical Controls_ : in addition to the set of sub-national geographical variables collected by Bruhn & Gallego (2011) including temperature, altitude, and annual rainfall, we add a measure of agricultural suitability and river density as developed in Maloney & Valencia (2015). Clearly, populations could also be sustained by marine-based economies where farmland and rivers were of less importance. Hence proximity to the coast for 14https://www.nhgis.org/documentation/gis-data 9
saltwater trade, transport, fishing potential and amenities potentially persists in importance, much as it was subsequently for European settlement, and to capture this we employ a measure of distance to the coast as calculated by Gennaioli et al. (2013). We further include a measured of ruggedness of terrain from Nunn & Puga (2012).

Table 1 presents the summary statistics at the state level and table 2 at the US county level.

# **3 Historical Innovative Capacity as a Potential Driver of Present Income National Data**

Figure 1 plots our measure of national innovative capacity against GDP per capita, both in 1900.<sup>15</sup> The availability of data means that our effective sample going forward is restricted to the relatively larger countries depicted here. Several facts merit note.

First, there is substantial variance in the stock of engineers that is weakly related to income in 1900. The Northern United States with a density of 160 is the highest in our sample, roughly double the average for the country as a whole, 84, while the US South shows over a third of the engineering density of the North at 60. Lagging as it is, the American South is miles ahead of the Latin American countries who average under 20. What is most striking is that countries that we tend to associate with declining relative position across the previous century, especially Argentina and to a lesser degree Chile and Mexico, show densities below countries of very similar levels of income: Argentina and Chile had roughly the same level of income as the American South, Sweden and Denmark yet had roughly a third of its engineering capacity of the South, and a fifth of the Scandinavian

> 15Maddison does not tabulate a separate series for the American South, but Mitchener & McLean (2003) estimates place the US South roughly 50% below the national average and New England 50% above. Imposing these differentials on Maddison’s data, places the South roughly 15% higher than Spain and the North roughly triple. Clearly, issues can be taken with even Maddison’s Herculean effort, however, the available alternatives do not suggest that the picture would change much. Prados de la Escosura (2000) PPP based estimates with the OECD correlate .89 with those of Maddison, and do not significantly change the level of Spain relative to the US, although they move Portugal up perhaps 40%, now above Mexico but still 20% below Spain.

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countries (100). Even if the number of engineers were underestimated by a factor of two, the lag with the US and Scandinavia would still be dramatic. We argue that that natural resource rents, while elevating income, were not being deployed as they were in the US or Scandinavia to the development of innovative capacity that would prepare them for the next phase of industrialization. In Howitt & Mayer-Foulkes (2005)’s framework, we have countries with similar levels of Schumpeterian backwardness, but with radically different levels of absorptive capacity.

Second, the dominance of the US in the Western Hemisphere is clearly not being driven by some idiosyncratic US data issue that would exaggerate its density. The US average is broadly in the same league as Denmark and Sweden, and even the North is below the calculations by Ahlstr ̈om (1982) for France (200) and Germany (250),the frontier countries of the era. Nor is some sort of idiosyncratic data issue driving the consistently low scores of Latin America. All cluster very near each other and the colonial mother countries.

# **3.1 Consistency with historical evidence**

Our engineering estimates are consistent with historical evidence. France and Germany were acknowledged leaders in the sciences and engineering. The relative positions of the two peripheral areas- Scandinavia vs. the Iberian peninsula correspond closely to Landes (1998)’ characterization of their attitudes towards science and the enlightenment. Both Sweden and Denmark’s institutions of higher technical learning date from the 1700s. Sweden’s high density is consistent with the characterization by Sandberg (1979) of the country as the “Impoverished Sophisticate.” The overproduction of engineers led many to emigrate to the US and 19<sup>_th_</sup> century Swedish engineers are credited with inventing the blowtorch, ball bearings, ship propellers, the safety match, the revolver, the machine gun, dynamite, and contributing to the development of bicycles, steam turbines, early calculators, telephony (Ericsson) among others.

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The US started relatively early and energetically in the training of engineers. The first institution of engineering education emerged from the revolutionary war at West Point, established in 1802, which trained engineers for both military and civilian purposes. Subsequently the American Literary, Scientific and Military Academy at Norwich, Vermont awarded its first Civil Engineering degrees in 1837, and Rensselaer School in New York, in 1835. By 1862 there were roughly a dozen engineering schools in the East, but also as far west as Michigan and south as Maryland. The Polytechnic College of the State of Pennsylvania, founded in 1853 granted degrees in mechanical engineering in 1854, and mining engineering in 1857.

The passage of the Morrill Land Grant Act in 1862 led to an acceleration in the establishment of engineering programs, roughly sextupling the number in the decade after passage. The Act led to the establishment of the Columbia School of Mines in 1864, Worcester Polytechnic in 1868, Thayer School of Civil Engineering at Dartmouth College in 1867, Cornell University as well as new Universities in Iowa, Nebraska, Ohio, and Indiana. It also gave impetus to the foundation and consolidation of engineering schools in the South. As early as 1838 the University of Tennessee was teaching courses in Civil Engineering, but in 1879 it began awarding doctorate degrees in Civil and Mining Engineering. Texas AM awarded its first degree in Civil Engineering in 1880, Virginia Tech in 1885 in Mining Engineering, and the University of Kentucky, although having an engineering program dating from 1869, graduated their first civil engineer in 1890. Auburn University in Alabama began its engineering program in 1872, and North Carolina State in 1887. In sum, the post-Civil War period saw the expansion of engineering education throughout the country. We further explore the use of the Morrill Land Grant as an instrument in subsequent sections.

It also saw a deepening, with the profession in the U.S. diversified further into subbranches. For example, the University of Missouri established both Civil and Military Engineering departments in 1868, and the first department of Electrical Engineering in 12
1886. The establishment of professional societies in Civil Engineering (1852), Mining (1871), Mechanical (1880) and Electrical (1884), testifies the the consolidation of a process of specialization and diversification. By 1890, a modern and world class engineering profession was firmly established in the US.

Canada’s degree of sophistication is probably higher than that suggested by the density numbers. Although the first graduates were in the 1870s, substantial engineering courses were in place by the 1850s. Further, the articulation of the different fields of engineering occurred later than in the US but not much.<sup>16</sup> It is also the case that the four principal Canadian Universities emitting graduates- McGill, University of Toronto, _Ecole Polytechnique_ in Montreal, and Queen’s University in Kingston Ontario- lay within a circle of 350 mile radius with Cornell University at its center, and that includes many of the principal US departments of the time. Hence, Canada was likely part of the greater New England scientific community.

The data for Latin America are consistent with the observation by Safford (1976) in his classic _Ideal of the Practical_ that “Latin American societies in general, and the upper classes in particular, have been considered weak in those pursuits that North Americans consider practical, such as the assimilation, creation, and manipulation of technology and business enterprise in general”(p 3). The national scientific establishments and professional training of civil engineers appeared much later and on a smaller scale. As an example, perhaps the richest country in Latin America at the time, Argentina, began graduating engineers only in 1870, and Peru, one of the premier mining centers of the hemisphere, in 1880, roughly on the same time line as Alabama. Further, in countries like Colombia and Mexico, political instability undermined programs begun relatively early leading to very low levels of graduation. In addition, the process of diversification and specialization was not as advanced as was the case, for example, in Missouri. General engineering associations were

> 16The development of, for instance, mechanical engineering as a separate course occurred about 20 years after in the US, and Electrical Engineering 10-15 years after.

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set up in many countries around the same time that, in the US, associations in individual were established.

Graham (1981) argues that Brazil, consistent with our estimates, lagged far behind the American South in every aspect of industrialization, transportation and agricultural technology (p. 634). In agriculture, there was little use of plows, scrapers, cultivators or mechanical seeders until the 20th century, partly because the low level of literacy rendered pointless the agricultural journals found commonly in the American South. A long literature has focused on Argentina’s weakness in innovation effort in comparison to countries such as Australia and Canada seen as similarly endowed. (see, for example Diaz Alejandro, 1985; Duncan & Fogarty, 1986; Campante & Glaeser, 2009). The determined efforts in both these countries to achieve widespread literacy in the prairies had no analogue in Latin America, nor did the extensive expansion in the form of experiment stations, seed testing services, and technical assistance.

# **3.2 Aggregate correlations**

In sum, the historical evidence confirms that lagged as the American South was relative to the North, as Carnegie suggests above, Latin America lagged even more. As a quick check, basic correlations suggest that these differences in engineering densities for 11 countries are indeed correlated with present income. Since we have population density and income variables at the subnational level, we run a simple regression as a panel and bootstrap the clustered SEs at the country level (the results also hold with simple OLS): where the variables are defined as above for country i and sub-national unit j: the dependent variable is income per capita today; the explanatory variables are Literacy, Engineering 14
density in 1900, Railroad density in 1900, Population density in 1900, and a set of geographical controls. Table 3 shows that engineering in 1900 appears significantly in explaining today’s income per capita despite controlling sequentially and then together for population density and railroad density. Were we to include Denmark and Sweden, whose density we have confidence in, not to mention France and Germany, the results would be even stronger. Columns 4 suggests that with few observations, it is impossible to separate the impact of engineering and literacy although in subsequent exercises we can.

# **4 Establishing the Importance of Engineering and Patenting Capacity: US County Level Data**

We construct engineering and patenting density data for 2380 US counties.<sup>17</sup> Table 4 presents the OLS estimates including the core controls for geography, economic activity and education which reduce the number of observations to 1905. All specifications have robust errors and are clustered at the state level and include both OLS and fixed effect estimators. We estimate: _′ ′ ′ Y_ 2000 _,i_ = _α_ + _γEEng_ 1900 _,i_ + _γP Pat_ 1900 _,i_ + **Geoi** _γ_ **Ge** + **Y1900** _,_ **i** _γ_ **Gr** + **HC1900** _,_ **i** _γ_ **H** + _μstate_ + _εi_ (2) To be consistent with the other samples, we begin with an OLS specification of engineering density along with a vector of geographical controls. Errors are robust and clustered by state. In column 1, engineering emerges strongly significantly and of expected sign in the OLS specification. Temperature and distance from the coast both enter negatively and significantly. Column 2 replicates the specification but with the smaller sample lim-

> 17A small number of counties change their borders in the last century and we drop them. 15
ited by slavery and education and results in only minor change in significance and magnitude.

Column 3 then includes the set of income related controls that together work to reduce the correlation that may occur simply because denser, richer areas with more infrastructure and in particular manufacturing and railroads, may be correlated both with engineers in 1900 and also with income today through non-innovation related channels. We further include a measure of slavery as an institutional variable that may be correlated with low human capital but also provides other institutional channels to present income. Population density, our measure of lagged economic activity, and railroads enter positively and significantly and will remain so throughout the analysis. Slavery, which enters significantly and negatively with just geographical controls (not shown) loses significance.

The next 3 columns seek to ensure that the engineering measure is not simply a proxy for human capital more generally. Column 4 includes literacy which enters positively and significantly and reduces the engineering coefficient by just over 10%. Column 5 then includes a proxy for secondary education whose importance across the next century Goldin & Katz (2011) and Goldin (1999) have stressed and which enters significantly and positively. Goldin & Katz (1999) also discuss the importance of the expansion in higher education across the earlier 1890-1940 period and in Column 6 we include several variables to ensure that engineers is not picking up higher order human capital more generally: the share of individuals who report attending at least one year of college, the density of lawyers (see Murphy et al., 1991), and density of physicians. The first two enter significantly and positively. Finally, column 7 includes all the human capital variables together. Literacy and lawyers remains significant although now neither secondary education, the college proxy, nor physicians do. Either literacy or college attendance can eliminate secondary education suggesting that, at this point in history, it is broadly proxying for literacy, or perhaps college readiness. The same remains true in column 8 which controls for state fixed effects. Slavery now enters positively and significantly. This may imply that it was picking up a negative 16
“Southern” effect which fixed effects now eliminate, but also that its negative impact works importantly through that channel.

In all cases, engineering retains its significance at the 1% level although losing perhaps 50% of its magnitude from the initial specification. The relative robustness of engineering vs. secondary education suggests that a small well-educated technological class was, at this time, more important than broad based post-literacy skills. Perhaps counter intuitively, the consolidation of higher education in engineering occurred before the major expansion of secondary education which Goldin & Katz (2011); Goldin (1999) document rose from only 18% in 1910 to 71% in 1940. Across our period, the corresponding average for the country is 7% with a maximum of 26%.

Table 5 next explores the interaction of the engineering term with patents. Annex Table A1 suggests that engineering density is significantly correlated with patenting, but it explains perhaps 5% of the variance across counties and is not robust to instrumenting. Further, even with a complete specification including all measures of formal human capital, we explain 50% of the variance. This suggests that, as in Sokoloff (1988) there may be an particular entrepreneurial or inventive human capital not captured by our engineering term. Columns 1 and 2 of Table 5 repeat the last two full specifications but replace engineering density with patenting density as the innovation capacity measure. Patenting enters significantly at 1% again with and without fixed effects. The human capital variables enter similarly to before. Columns 3 and 4 combine engineering and patenting in the full specification and both remain significant at at least the 5% level with and without fixed effects. Both measures decline 15-20% in magnitude suggesting that one channel through which engineering density operates is through patenting (as suggested by table A1) and that some of patenting’s effect is as a proxy for engineering density perhaps working through non-patenting channels. However, that each continues to enter very significantly and independently suggests that they are capturing different types of human capital: perhaps more technological adoption in en17
gineering and inventive activity in patenting. We fully accept the argument of Moser (2013) that much innovation including patentable invention is done outside the patent system and hence such distinctions cannot be drawn too sharply. However, our results suggest that there is a particular type of human capital captured by patents not captured by our other measures.

# **4.1 Instrumenting engineering**

Though we have attempted to control for other channels through which our engineering measure may be correlated with present income beside the capacity to manage and generate new technologies, we attempt to control for residual upward bias by instrumenting engineers. There may be additional bias in the opposite direction since the large share of counties reporting zero engineers is more likely to reflect measurement error, rather than that there was absolutely no capacity of any kind to adopt technologies.<sup>18</sup> In the spirit of Moretti (2004), we also attempt to instrument engineering density using the log distance to the nearest Morrill Land Grant colleges and universities. As discussed earlier, the Morrill program was introduced in 1868 precisely to remedy the perceived shortfalls in regional technical assistance in agricultural and mechanical innovation. It was to an important degree supply driven. Its motivation was driven by both democratic ideals of educating the working man, and by observation that the US had none of the agricultural and technical schools found in Europe (Nevins et al., 1962; Nienkamp, 2010). Early years saw struggles over definition of what form that education should take, and precarious finance suggesting little initial impulse from the private sector. Further, prior to the Civil War, the South had actively opposed the bill, fearing greater interference in matters such as universal primary education and only the withdrawal of the Confederate States from the US Congress

> 1850% of the countries report zeros. The vast majority are found in small counties which, in 1900, may not have had the scale to generate an engineer. The maximum county size is 1,240,403 with the 99th percentile at 140,000. 88.9% of zeros are found in counties below 20,000 of of counties with 5,000 inhabitants, 75% are zeros. The zeros also pose an issue in comparability of the OLS and IV estimates since IVs offer a local estimate over the support where there is variance in both the instrument and the variable to be instrumented. In the present case, this may also push up the IV estimates.

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allowed the bill to be passed. However, during Reconstruction, recognizing its technological lag, the South started privately institutes such as Georgia Tech, and actively embraced the Morrill Program. Hence, while public functionaries may have broadly felt that the structure of their economies would reward such investment (although the long absence of agricultural institutes suggest this is far from obvious), there is little evidence of organized industry lobbying, for example, that would suggest causality from industry demand for engineers to the establishment of these universities.

Though the promoters of the Morrill program did not envisage it as fomenting engineering per se, the program would eventually finance the first engineering departments in the West, Midwest and Especially the South and was more fundamentally a central driver in developing the higher order scientific and Engineering capacity of the country. Nienkamp (2010) in _Land-Grant Colleges and American Engineers_ argues that, especially the Mid-Western schools “provided the foundation, both in training and number, for twentieth-century American professional engineering” and were critical in defining their identity and ushering in the modern scientific, laboratory-based approach to technical education. Nevins et al. (1962) argues that the Morrill Program “promoted the emergence of the most effective engineering schools on the globe.” Goldin & Katz (1999) notes that while the majority of new universities of the time were privately started and Morrill cannot explain the expansion of higher education, as of 1908 they also note 60% of the nation’s engineering students were found in public universities and the “geographical dispersion of engineering students came mainly from those enrolled in public schools” the rise of which they attribute to the Morrill program (Goldin, 1999). Hence, the instrument is conceptually closely linked to engineering density, while the its amorphous early motivation and supply driven nature can explain, for instance, the lack of a significant positive correlation with manufacturing activity, at the county level.

The first stage of a basic 2SLS reveals a strong positive correlation between engineering 19
density and the Morrill proxy with very high Cragg-Donald F-tests (1% level) and acceptable F-tests suggesting a strong instrument. Column 5 presents the second stage results for the complete specification with fixed effects and engineers enter again at the 1% level with a value an order of magnitude higher than the OLS estimates. We do not present all previous specifications simply because, as with the OLS specifications, both engineering and patenting retain their significance and broad magnitudes throughout.

Column 6 presents the results of a Buchinsky quantile regression model with selection into being a zero on the instrument stage. This specification is also desirable because it relaxes the assumption of joint normality implicit in the standard Heckman model. The first stage again suggests a strong instrument at the 1% level. The coefficient on engineering falls to half the 2SLS, 3.51, and maintains its strong significance. As before, the first stage is strongly significant.

In the spirit of the next section that works at the “state” level, we replicate the exercise for the 1900 5% subsample (again, which does not permit us to work at the county level), with the number of covariates reflecting the reduced degrees. This higher level of aggregation allows for more mobility of labor among unit of analysis, although, clearly, this is not preventing the emergence of very significant coefficients. Second, it allows another 20 years of impact of the Morrill program and the full consolidation of the engineering infrastructure. Third it allows introducing a measure of mining activity which is available at the state, but not country level. Finally, the aggregation also offers an alternative approach to the zeros. The OLS and instrument estimates are 1.2 and 2.1 respectively and significant, even with 48 observations (Annex Tables A2 and A3).

Taken together, the US data suggests that innovative capacity, spanned by engineering and patenting density, has a strong and independent effect on future income levels. More specifically, running specification 6 in standard deviations suggests that a one standard 20
deviation rise in engineering density leads to a 16% increase in income. Though not instrumented, patenting capacity leads to another 9.7%. Hence higher level human capital plausibly accounts for large differences in state level income per capita. This does not imply the unimportance of lower level capital. A 1 SD increase in literacy accounts for 51%. A comparable increase in secondary schooling leads to only a 4.5% increase although, again, the high school movement began substantially later and the variance in 1880 is very small. As a final back-of-the-envelope exercise, taking the coefficient from the well saturated US regressions and applying it to the international data in Figure 1, a one standard deviation rise in engineers in 1900 leads to an difference of 36% in 2000 income. Clearly, we cannot take this result too literally, but again, it suggests that differing endowments of higher level scientific capital potentially contribute importantly to income differences.

# **5 International sub-national engineering data**

To see whether innovative capacity retains its predictive power beyond the US, we collect engineering data at the sub-national (state) level for Argentina, Chile, Colombia, Mexico, Venezuela and the US for which census data are available. To give a feel for the disparities, Figure 2 maps this data for the US and Mexico by decile of engineering density and strikingly confirms that the border divided worlds apart. In fact, the data likely understate the true difference since our calculated stocks in Figure 1 suggest substantial overstatement in the Mexican census data. Perhaps predictably, the advanced New England states and the heavily mining dependent and generally less populated Western states show the highest density while the emerging industrial centers of the Midwest are close behind. The South is concentrated in the lower ranks with South Carolina, Georgia, Arkansas and Alabama in the bottom deciles- the lowest density in the US. What is striking, however, is that the country that was the principal mining center of the Spanish empire is almost entirely concentrated in the first and second quintiles with Sonora and the two Baja Californias appearing in the third and fourth deciles. Taking out Mexico City and the border states, Mexico is almost 21
uniformly below even the American South in density of engineers. As we discuss next, despite four centuries of mining, it had not acquired a corpus of trained professionals in the field compared to relative newcomer, the American West. The other countries show similar patterns.

Table 6 employs the sub-national data and comes to similar conclusions to those previous. We estimate: Engineering density is persistently significant and broadly of the magnitude of the OLS estimates from the US. The finding of continued significance even after controlling for a measure of population density (column 2) is consistent with Comin et al. (2010a). In column 3, Literacy again enters positively and significantly and reduces the coefficient on engineering by 40%, again, broadly consistent with the US results. Though we cannot control for a more complete set of human capital measures, the county level exercises above suggest that, in fact, engineering is capturing innovation related human capital and not human capital accumulation more generally. We also run the regressions with a complete set of geographical controls but these absorb substantial degrees of freedom and do not enter significantly so we omit them.

In sum, the various estimations suggest that our county level US findings resonate internationally: both literacy and higher order human capital related to engineering and science and technology are important determinants of long run income.

# **5.1 Mechanisms of influence today**

Table 7 offers some suggestive mechanisms through which these engineering densities in 1900 could continue to affect output today. We collect five indicators at the country level that 22
broadly capture modern day innovation-related inputs and outputs and calculate the simple correlation. The first is the dynamism of the system of research and development measured as total R&D expenditures as a share of GDP. The second asks about firm capacity for innovation ranging from pure licensing to pioneering their own new products and processes.<sup>19</sup> These two, arguably, correspond most closely to Howitt & Mayer-Foulkes (2005)’s R&D model. The third draws from a globally consistent measure of management quality from Bloom & Van Reenen (2010) and in particular, the sum of the scores on the two questions dealing with how firms identify new production processes to adopt. On the output side, we have Comin & Hobijn (2010); Comin & Ferrer (2013); Comin et al. (2008)’s measure of technological adoption at the extensive margin, averaging their industrial and sectoral scores,<sup>20</sup> and finally patent applications filed under the Patent Cooperation Treaty (PCT) per million population as tabulated by the World Economic Forum (World Economic Forum et al., 2012). Together, these give an impression of measure of national absorptive and inventive capacity in the present. In virtually every case, the correlation between our engineering numbers in 1900 and these indicators today is above .9.

# **6 Support from History: Case Studies**

This section offers historical evidence that confirms that innovative capacity was a critical barrier to taking advantage of the advances of the Industrial Revolution. The interaction of lost learning by doing, weak higher level human capital, and underdeveloped technical institutions emerges in explanations of the lag of the US South as well as Latin America. However, it also offers support for more complex view of how innovative capacity affects steady state growth. Numerous models exist for modeling the micro economics of adoption. Comin et al. (2010b); Comin & Hobijn (2010); Comin et al. (2010a) for instance are closely aligned with the opening stylized facts (theirs) about divergence at the intensive margin.

> 19“In your country, how do companies obtain technology? [1 = exclusively from licensing or imitating foreign companies; 7 = by conducting formal research and pioneering their own new products and processes].

> 20Their data on the arguably more relevant intensive margin is not yet available 23
Human capital shortfalls are embedded in a scalar that reflects barriers to adoption for the agent that adapts the technology to the idiosyncrasies of the country or for individual producers that find a profitable use for the technology. The models of Howitt (2000); Aghion et al. (2005); Howitt & Mayer-Foulkes (2005) add a feature central to this paperthat as the technological frontier shifts out, the skill level required to maintain the same level of absorptive capacity increases as well. Howitt & Mayer-Foulkes (2005) further add that 1. the efficacy of education is a function of the level of technological advance and 2. the introduction of a new method of technological change, loosely termed “modern R&D” such as culminated in the late 19th century with the modern R&D laboratory (the rise of institution such as government research agencies, scientific academies, universities with close ties to industry etc.) gives rise to the possibility of an important and discrete shift in the productivity of innovation technology. This gives rise to three equilibria: countries with with a threshold level of skill could undertake this “modern R&D”’ and innovate; countries with less may still adopt and grow but with a persistent income gap relative to the innovators; countries with insufficient absorptive capacity cannot even adopt and stagnate (see also Howitt (2000) for a model of complete stagnation). Further, advances in the frontier not accompanied by a rise in local human capital can cause a country to lose its absorptive capacity and slide to a worse equilibrium.

Our data do not permit testing explicitly for convergence clubs and we must be satisfied with a correlation with present income. However, the historical case studies do suggest the importance of these dynamics and these ideas help organize what happened around 1900 in the US South and Latin America vs. the US North. The initial conditions in terms of skills broadly defined allowed the latter to fully adopt modern R&D technologies, while in the former, they did not. In the South they were unable in some cases to undertake the necessary R&D to adapt some new technologies to local conditions, for instance, in steel. In Latin America, the erosion of their frontier adjusted human capital was so severe when faced with new technologies in metallurgy and chemistry that they were forced to abandon 24
critical industries, in our case mining, altogether and could not, in many cases, mount a manufacturing effort.

# **6.1 The American South**

An extensive literature deals with Southern development and we present only a brief summary to draw the parallel with other cases. Wright (1986) casts much of his work explaining the US South’s persistent lag exactly in terms of an innovative capacity framework. “The fundamental reason for [protracted lack of uptake of technologies] is that early industrialization is a matter of learning in the broadest sense of that term: in management, in technology, in marketing and certainly-though this is often underestimated- in learning on the part on the part of the labor force”(pp. 124-125). The South came relatively late to industrialization and lacked the indigenous technical capacity necessary for rapid catch up.<sup>21</sup> The emblematic failure was of the Birmingham Steel industry, which Carnegie referred to. The problems were manifold-high labor costs, product quality and marketing- all of which reflect the low level of collective, accumulated learning by doing. But also central was that the low iron, high phosphorous nature of Alabama red hematite required substantial adaptations of technology to the Southern context which the local innovative capacity was not able to engineer. Nor was it able to develop Southern versions of new inventions in the paper and textile industries. By way of contrast, Japan also initially imported technology and processes, but over time it generated distinctive technologies and, by the 1920s, was making its own textile machinery. In lumbering and iron making, as well, Southern producers of the 1920s were not only not innovative, they were using methods phased out decades earlier elsewhere. Arguably, the big push by the federal government, ranging from the Land Grant colleges to selective location of advanced industries, and migration of higher order human capital, raised innovative capacity toward the frontier and permitted catch up.

> 21Wright (1986) argues that “Having missed the formative phases of the ‘American System’, the South was lacking a machine-tools and capital-goods sector almost entirely and therefore was bypassed by the kind of adaptive, dynamic, path-breaking series of technological breakthroughs that made ‘the American system’ distinctive.”(p. 124-125).

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# **6.2 Multiple equilibria in mining: Latin America vs. the US**

The potentially catastrophic impact of a dearth of innovative capacity is nowhere more in evidence than in the industry in which Latin America for centuries had a true comparative advantage, yet by the turn of the 20th century had completely stagnated: mining. Close observation by numerous historians offers a particularly compelling window on how a similar inability to adapt new technologies in the face of declining ore quality nearly destroyed the Chilean mining industry as well. As Figure 3 illustrates, Chile saw its world market share of copper fall from 40% to under 4 percent by 1911, and even as early as 1884 the _Sociedad de Miner ́ıa_ (Mining Association) wondered openly whether Chile’s copper mines would survive at all (Collier & Sater, 1996). Chilean historians date this technological slippage to the beginning of the nineteenth century, when “the work of mining was not very systematic” and the “receipt of industrial innovations [from abroad] was slow and without visible influence”(Villalobos et al., 1990, p. 95-96).<sup>22</sup> One of Chile’s most venerated historians, Francisco Encina noted that “from the point of view of capital and of technical and administrative aptitude, the copper industry is as demanding as the most complicated manufacturing industry” (Encina, 1972, p. 62). However, his studies revealed “an extraordinary economic ineptitude in the national population consequence of an education completely inadequate to meet the demands of contemporary life.”(p. 17). Another prominent historian, Pinto Santa Cruz (1959) argued that Chileans failed to take advantage of opportunities for learning by doing and to evolve the innovative capacity required to confront the technological revolution in mining and hence became dependent on foreign firms.<sup>23</sup>

> 22Charles Lambert, a representative of a British mining company in La Serena who was trained in the _ ́Ecole Polytechnique in Paris_ , noted in 1819 the primitive mining practice, scarce knowledge of minerals, and inefficient smelting, all of which represented poor technique relative to that employed in Europe. See also Maloney (2002).

> 23“The technological demands of the 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

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Figure 3 captures this importing of technical ability, first in the 1870s and 80s as Britain expanded in the nitrate and coal industry, and then after 1905 when major foreign copper companies took over the copper industry. The census data shows the density of foreign entrepreneurs increased from almost zero in 1870 to 80 in 1920, the last census which permitted disaggregating by nationality/profession, or roughly 4 times Chile’s domestic density in 1900. Local capacity was increasing as well, however, the share of foreign engineers in the country total increased sharply from 1% of total engineers in the country to around 40% across this period. These movements emerged concomitant with Chilean production reaching new highs and market share of an expanding global supply staging and important recovery. As in Mexico, below, and consistent with Howitt & Mayer-Foulkes (2005)’s education externality, both the quantity and the quality of the engineering graduates produced by local universities were thought inferior to the talent imported from Europe and the US (Serrano, 1993; Bazant de Salda ̃na, 1993). Increasing frustrated by the creeping influence of foreigners across the major industries, it is perhaps not surprising that Chileans developed a self-perception that they were perhaps “unfit for the modern era”(Monte ́on, 1982, p. 62 and 35).<sup>24</sup> By 1918, American interests controlled 87% of Chilean copper output (O’Brien, 1989).

Similar stories of an inability to exploit new technologies leading to decline in the mining industry can be found throughout the region. The engineering data in Figure 1 supports the historical evidence that in Mexico local entrepreneurs lost share in the industry they had dominated for centuries precisely due to lacking the capacity to master emerging 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 capacityboth 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 71)

> 24Prominent intellectual Tancredo Pinochet Le-Brun(1909), granting that Chileans were inferior to Europeans, still wondered, “Don’t we have minds in this country that can go to Europe to learn what professors, whom we have imported and continue importing, have studied? Are we truly incapable of steering our own ship?” _La Conquista de Chile en el Siglo XX_ , Santiago, La Ilustracion, page 81 cited in Monte ́on (1982)).

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technologies (Ruiz Larraguivel, 2004; Brading, 1971; Marichal, 1997). Even in Zacatecas, San Luis de Potos ́ı, and Guanajuato, long centers of mining, engineering density was at low levels compared to the newcomers in the US West. Around 1900, abandoned, underexploited and newly discovered mines fell to foreign hands that could bring new global technologies to bear. As an example, the Guggenheim interests opened smelters in Monterrey (1892) and Aguascalientes (1894) purchased the largest Mexican Smelting and Refining company in 1906, introduced modern methods of extracting and refining silver ores and in addition, started the production of lead and zinc mining (Bernstein, 1964; O’Brien, 1989). By the early 20th century, the Americans absolutely dominated the industry.<sup>25</sup> In Upper Peru (now Bolivia), the decline of silver production in some of the most famous mines, like those at Potos ́ı, arose from the “failure to apply new mining techniques, heavy mortality among Indian laborers and the exhausting of previous rich veins” (Scobie, 1964, p.59). In Ecuador, Hurtado (2007) argues that the discovery of new mineral deposits was hindered by a resistance to methods.<sup>26</sup> The US eventual dominance of the Chilean copper and Mexican mining industries strikingly illustrates the road that could have been taken with the same homogenous product. Not only does Wright (1999) argue that 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” (Wright, 1987, p. 665), but he uses precisely the US copper industry as an example of national learning and of innovation as a network phenomenon. Wright stresses that in

> 25See Maloney (2015) for data on nationality of owners.

> 26More generally, Di Tella (1985) argues that Argentina proved unable to move beyond a state of exploiting the pure rents of a frontier or extraction of mineral riches, and beyond the “collusive rents” offered by statesanctioned or otherwise imposed monopolies to tap the “unlimited source of growth” found in exploiting the quasi-rents of innovation, as the US, Canada and Australia were able to do. They remained in an adoption or, perhaps, stagnation equilibrium. The same phenomenon is seen in agriculture where the absence of innovative infrastructure was recognized by contemporary Argentines as key to explaining the lagging performance of the wheat industry compared to that in Canada and Australia. Fogarty et al. (1985) attributes to weak innovative capacity the outcome of a quasi-experiment whereby the Spanish Merino sheep were introduced into New South Wales, Australia, and Argentina’s River Plate region in the same year and had the same access to European capital, but by 1885 showed yields of only half the wool per sheep in Argentina.

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the post civil war period, the US became the foremost location for education in mining engineering and metallurgy. It was the revolution in metallurgy (e.g. the Bessemer process and the introduction of electrolysis on a commercial scale for the refining of copper) overwhelmingly an American achievement, that propelled the copper industry during the last decades of the 19th century. The transference of these technologies by US firms to their mines and smelters in Chile and Mexico revolutionized the antiquated industries in both countries, dramatically increased production, and left them dominant in both.<sup>27</sup> The US reaped high growth dividends from inventing new technologies in mining, and its engineering and scientific capital became the best in the world. At the same time, and with exactly the same goods, Latin America’s declining human capital relative to the expanding technological frontier left it unable even to adapt new technologies, and the domestic industries stagnated. The success of the Chilean State Copper Company, established in 1976, testifies that this need not be permanent and at least an adoption equilibrium was reachable by nationals with the accumulation of the necessary human and knowledge capital.

# **6.3 Retrogression in industry in Brazil: Minas Gerais**

In iron manufacturing, Baer (1969); Rogers (1962); Birchal (1999) argue that despite a tradition of iron smelting dating from the mid-sixteenth century, the techniques used at the end of the nineteenth century were primitive. While particularly the northern US colonies engaged in a sustained process of learning by doing and innovation in both iron and steel (Swank, 1965) from the early 18th century on, from 1830 to 1880 Brazil actually experienced a “retrogression in technique”(Rogers, 1962, p. 183). Unable to innovate, Brazilian firms

> 27The Guggenheim’s El Teniente mine was the first in the world to apply the flotation process in concentrating low-grade ores. Mechanizing digging made Chuquicamata in the north the largest open pit mine and, again, a new concentration process was introduced using sulfuric acid and electrolytic precipitation to treat the mine’s ore. From 1912-1926, copper production in Chile quintupled as a result, reversing a 25 year period of stagnation (O’Brien, 1989).

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instead lobbied for protection from cheaper iron imports.<sup>28</sup> The critical innovation for the development of the native steel industry was the foundation in 1879 of the _Escola de Minas_ (Mining School) at Ouro Preto, Minas Gerais, which, as the _Escuela de Minas de Antioquia_ was a major diffuser of new practices and led to the establishment of the first new blast furnace since the failures at the beginning of the century. On the other hand, the textile industry for which we have entrepreneurship data above was less fortunate. As (Birchal, 1999, p. 183) notes “the existing informal and spontaneous technological innovative system was not developed enough to take the process of technological assimilation farther in the direction of a profound modification of existing foreign technologies, or to create a more complex indigenous technological alternative” again, much as Wright argues for the American South.

This raises the larger question of, given the sheer disparity of our innovative capacity numbers, if it was so hard for the American South to start competitive industries with its innovative capacity captured by 60 engineers per 100,000 individuals, how did Latin America’s industry expand so quickly at the turn of the 20th century with an average of 10? Much also may lie in the Potemkin nature of much of Latin American industrialization that, as in the American South, was dependent on imported capital goods and innovated little. Across the region, blocks in the input-output table were rapidly filled in but, as Haber (2005, 2006) notes, these industries were not modern in the sense of being at the technological frontier or being able to export to other countries. Much as Wright notes in the American South, Latin America relied heavily on foreign technology imports, and was sluggish in developing an indigenous local machine and capital goods industry, a result attributed to low innovative capacity.<sup>29</sup> The fact that Latin America evolved the highest levels of tariffs in

> 28Of the thirty ironworks in the headwater region of the Rio Doce in 1879, only seven used Italian forging methods, while the rest used the old African cadinho (crucible) technique. Graduates of the _Escola de Engenharia do Exercito_ (Military Engineering School) established in 1930 led the steel industry as it developed through the 1960s.

> 29“Since... capital goods industries now required well-developed scientific and engineering capabilities, Mexico had little choice but to import its capital equipment. The blast furnaces and rolling mills came from the United States, the high-speed cigarette machinery from France, the paper-making machinery from

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the world prior to World War I –on average five times the rates in Europe– is arguably the result of the need to protect an industry erected on weak foundations of entrepreneurship, accumulated learning by doing, and higher level human scientific capital.<sup>30</sup> This supports Haber’s theory that the standard view of protectionism stimulating industrialization is potentially backwards. An emerging, but technologically backward and uncompetitive industry demanded protection. In turn, lack of exposure to foreign competition blunted the need to upgrade quality and technical capacity.

# **7 Drivers of innovative capacity**

## **7.1 The US**

Though a detailed examination of the factors determining innovative capacity at the turn of the century are beyond the scope of the paper, we briefly explore select potential drivers. Table 8 presents, first, the results for the US county patents for both engineers and patents. As a predetermined measure of agglomeration or economic activity, we introduce population density prior to colonization. We further introduce slavery as well as a southern dummy (at the state level), plus geographical controls. Because of the zeros in engineers, we present both OLS (Column 1) as well as quantile estimates with selection to report a zero (Column 2). Column 3 reports OLS for patents and Column 4 the corresponding quantile regressions.

Somewhat strikingly, pre-colonial population enters strongly significantly and positively in all specifications with both engineers and patents. This offers a channel through which agglomerations and arguably economic activity persist over time (see Maloney & Valencia (2015). Slavery enters negatively and very significantly in all specifications confirming that reduced investment in higher level human capital is a channel through which slavery had

> Switzerland, the textile looms, spindles, and other equipment from England, Belgium, the United States, or Germany.” Haber (1997), p. 18

> 30Coatsworth and Williamson and Clemson and Williamson, cited in Haber (2005).

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a depressing impact on future incomes. South continues to enter significantly in all except the engineers selection model suggesting an impact beyond the set of factors associated with slavery. The geographical controls tell a mixed story. For engineers, only ruggedness enters positively while for patenting it enters negatively, perhaps reflecting the importance of engineers to mining, but not necessarily the likelihood that mining would generate patents. Temperature enters positively and significantly, and rainfall negatively in almost all specifications. For patents, altitude enters positively and significantly, and coastal distance negatively.

# **7.2 The role of colonial heritage?**

The degrees of freedom available in the international data are insufficient to generate comparable results with any degree of confidence. However, the remarkable similarity between the engineering density in Spain and Portugal and their colonial dependencies is consonant with the inheritance of values and institutions related to educational and entrepreneurial capital formation from the Peninsular powers to their colonies, and in striking parallels in development outcomes among them. Spain and Portugal, confronted with the Industrial Revolution in England, failed to develop a scientific vocation nor the complementary innovation promoting institutions essential to industrialization. In fact, strikingly Spain’s own development path shows the exact same issues of inability to manage new technologies and external dominance characterizing Latin America (Tortella Casares, 2000) suggesting a high degree of commonality in underlying factors. For example, the evolution of the Spanish mining industry precisely foreshadows the subsequent experience in Chile and elsewhere discussed earlier. Though Spanish mines were rich, and mercury had been worked for a thousand years, the lack of technical capacity and capital, and the slow growth of domestic metallurgical know how led Spanish entrepreneurs to work close to the surface and then sell out to foreigners once easy veins had been exhausted. In 1873, a UK and German led conglomerate purchased the mines on the Rio Tinto river in Andalucia, introduced new technologies, and from 1877-1891, became the world’s largest producer of copper, contributing the to fall in Chile’s global share in Figure 3. As (Tortella Casares, 32
2000, pp. 96, 213-215)) summarizes: “extraction and processing constitute a classic example of the failure of Spanish entrepreneurs to confront the problems of developing an industrial sector with complex technology, intensive use of capital, [and] a fast-expanding horizon.” The colonies, legally cut off from direct commerce and interaction with Northern Europeeven trade with Cadiz was through peninsular intermediaries- were unlikely to absorb an alternate set of institutions and values from the industrializing center or evolve indigenous analogues, as the US did. Mass education was not prioritized on either side of the Atlantic: in both mother countries literacy lagged even the black (often freed slave) population in the American South, and is of similar magnitude to that of the Latin American colonies.<sup>31</sup> The Peninsular tradition of higher education was largely religiously based, focused on law, philosophy, and theology, proved resistant to attempts to introduce enlightenment-informed technical curricula,<sup>32</sup> and was exported to the colonies. As Safford continues (p. 3)“The Latin American upper classes have been noted for their devotion to the study of law, the humanities, and the arts and their lack of interest in the natural sciences and technology. In the hands of the upper classes, Latin America’s educational systems, at least in times past, have been dedicated to forming and maintaining the political elite and have been only mildly effective in furthering such economically practical aims as the broad diffusion of literacy and technical capacities.”<sup>33</sup> In sum, in the patterns of human capital accumulation and

> 31Tortella (1994) notes that in 1900 Spain had a literacy rate of 44%. This is slightly below that of Argentina (48 %) and blacks in the American South (49%) (Collins & Margo, 2006)), and not dramatically ahead of Peru (38%), Chile (37%), Colombia (32 %), or Mexico (22%). Portugal’s low level of literacy, 22%, is essentially identical to that of its colony Brazil (20%).

> 32In Portugal, the flagship university at Coimbra briefly established a more technical curriculum in 1759 under the Marquis of Pombal in the context of radical political, education and ecclesiastical reform to modernize the state and energize the economy. However, in 1777 there was a reaction against the reforms and the emphasis on natural science was abandoned and civil law regained its ancient prestige. The Spanish enlightenment in the same period saw the establishment of groups of autonomous sociedades econ ́omicas (economic societies) that sought to diffuse technology from abroad and establish libraries throughout the country, as well as some Royal Societies emphasizing applied science. But Spain began training engineers seriously only in the 1850s, and by 1867 the country had only one functioning School of Industrial Engineers, located in Barcelona (Riera i Tu ́ebols, 1993).

> 33Spanish America saw universities established from the moment of conquest, yet they were largely committed to the training of ministers to convert Indians, and lawyers to staff the empire (Benjamin, 1965). As (Will, 1957, p.17) documents for Chile, although it applies with greater generality “With the exception of the inadequate facilities provided by a few religious organizations, there did not exist before the middle of

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of development trajectories, the similarities are so close as to suggest a dominant role for inherited attitudes and institutions.

# **8 Conclusion**

Much of the growth literature stresses differences in the capacity to generate, import, and apply new technologies as central to explaining relative growth performance. In particular, Aghion et al. (2005) document that different frontier adjusted endowments of human capital broadly construed dictate very distinct growth trajectories and Howitt & Mayer-Foulkes (2005) argue that at the time of a radical shift in technological progress, such differences lead to convergence clubs of high growth and stagnant economies. To date, however, there has been little data generated on the higher level human capital and institutions required to make this possible, especially in a historical context, and none to attempt to verify its impact on income differentials.

This paper first generates national level engineering data for the Americas in 1900 that suggests that differences in innovative capacity plausibly can explain the disparate development trajectories of countries which, at the time, had very similar levels of income. This measure, compiled using graduates of local engineering universities, professional associations and censuses, is arguably the first to offer international comparability for this key period around the Second Industrial Revolution. We see it as a measure of higher level scientifically oriented human capital per se and supporting institutions, as well as arguably a more general measure of technological sophistication of the entrepreneurial class.

To more conclusively establish the link, we employ county level data from the US and the eighteenth century an institution capable of furnishing the youth of the colony with the barest essentials of a secular education.” The majority of the relatively few university educated members of the Brazilian elite at independence trained at Coimbra in the reinstated legal theological tradition (Carvalho, 1982). In Ecuador, (Hurtado, 2007, p. 115) argues that education quality even among the elite was poor and unfocused on practical elements. Again, he cites the American ambassador to Quito in the 1850s who found “convents instead of presses, barracks in place of schools.” See comparable accounts in Will (1957) for Chile; Safford (1976) for Colombia; and Lopez Soria (2012) for Peru.

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establish the robust explanatory power of engineering density and patenting, controlling for a variety of economic and geographic variables; measures of secondary and tertiary education that may be correlated, as well as attempting to control directly for endogeneity. Finally, we show the persistence of the impact of engineering density in a multi-country panel working at the “state” level.

We then provide historical evidence that broadly confirms the ranking of countries emerging from our engineering data and which suggests that precisely an inability to manage the new technologies emerging from the Second Industrial Revolution slowed growth in the Southern US, and led to a loss of competitiveness in a sector long an area of comparative advantage in Latin America– mining. The fact that the US was able to leverage this sector into a well-educated workforce, and world leadership in scientific research while Latin America lost the industry confirms the importance of innovative capacity, and the persistent effect of early human capital investments. It also lends support to the idea of multiple equilibria arising from differences in innovative capacity.

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