Regional disparities in income per capita in Argentina in 1914 *

Transcription

1 Regional disparities in income per capita in Argentina in 1914 * María Florencia Aráoz CIEDH - Universidad del Norte Santo Tomás de Aquino Esteban A. Nicolini + CIEDH - Universidad del Norte Santo Tomás de Aquino INVECO Universidad Nacional de Tucumán María Florencia Tenuta Ciale CIEDH - Universidad del Norte Santo Tomás de Aquino VERY PRELIMINARY VERSION: Do not cite without authors permission!! Abstract The economic performance of Argentina between the end of the 19 th and the beginning of the 20 th century has been a field of intensive research in the last 40 years. Two crucial results of this research are that the growth rates of income per capita had been very high during the period and that the income per capita of Argentina in 1914 was among the highest in the world. However, our understanding of the regional dimension of this process of economic growth is rather limited. We know that some regions of the country specialized in the production of cereals and livestock, for which they have very large international comparative advantages based in the abundance of high-quality land, while other regions specialized in the production of some goods oriented to the internal market (and eventually benefitted by high tariffs). The production of wine in Mendoza and sugar in Tucumán are the two most obvious examples of the latter. Did these differences in specialization generated systematic differences across regions of economic structure in terms sector shares of labor, capital per capita, cultivated land per capita, etc? Were all the regions in Argentina equally benefitted by the process of economic growth in this period, characterized by a strong integration in the world market? In this paper we reconstruct a set of key macroeconomic variables from two provinces (Buenos Aires one of the most efficient producers of grain and meat in the period- and Tucumán -specialized in sugar production-) in 1914: in particular we offer the first estimates of their provincial GDPs. JEL Classification Numbers: E01, R11, R12. Keywords: regional development, inequality, Argentina, Buenos Aires, Tucumán. * The authors thank comments and previous discussions on the topic with Beatriz Álvarez, María Florencia Correa-Deza and Andrés Michel Rivero. Esteban Nicolini acknowledges financial support by Spanish Ministry of Science and Innovation through Project ECO and CIUNT through subsidy 26/F410. María Florencia Aráoz acknowledges financial support by Spanish Ministry of Economy through project ECO C The three authors thanks continuous financial support by the Universidad del Norte Santo Tomás de Aquino + for correspondence: esteban.nicolini@gmail.com 1

2 1. Introduction Most of the estimations coincide that economic growth in Argentina between 1880 and 1914 was impressive. Cortés Conde (2005) emphasizes that this period witnessed the longest and the highest spell of growth in the Argentine history with an average growth rate of aggregate GDP of 6%. According with this author, the outstanding economic growth of that period was based on the incorporation of productive factors (land, capital and labor) and, less importantly, some growth of total factor productivity. This process of economic development, with growth rates higher than the USA, transformed the country and located Argentina among the 10 richest countries in the world. In the period between 1900 and 1913, Argentina per capita GDP grew at an annual rate of 2,5%, while the United States did at 2.0% (Hoffman 2001). In fact, in 1900, GDP was $4,091 per capita 1 while in Argentina was $2.756, meaning it was 1,48 times greater. In 2008, the U.S. GDP was $ per capita while in Argentina it is only $10.995, i.e. 2,8 times greater 2. The relative economic performance of the provinces within the country was not thoroughly analyzed so far. There are provinces and geographic regions that in Argentina have been regarded consistently poorer than the average and underdeveloped but there is no formal quantitative basis for these hypotheses. This lack of information is particularly regrettable for provinces that seem to have had some periods of economic expansion and processes of decline or stagnation in relative terms. This is the case of Tucumán that in the middle of the 19 th century has been considered a relatively prosperous area, at the end of the 19 th century experienced a period of rapid industrial growth based on the sugar production but in the second half of the 20 th century is considered a backward province with levels of income per capita and welfare well below the national average (Campi 2004, Eliás 1996). The first set of consistent estimations to compare the level of economic activity across the provinces is provided by the Federal Council of Investment (CFI in the Spanish acronym) and collected by Elías (1996) which published the series of Producto Bruto Geográfico (PBG) 3 for the Argentine provinces in 1884 (see below), 1953 and between 1959 and In 1953 Tucumán had a per capita PBG (2860) which was clearly lower than the national average (3421) and also smaller than those of Buenos Aires (3910) and Capital Federal (5129). In that year, the level of per capita GDP in Tucumán was 65,76% of the weighted average of the PBG of Buenos Aires and Capital Federal. We have almost nothing for any period before the middle of 20 th century. The journalists, publishers and social observers Michael and Edward Mulhall provide a guesstimation of wealth and income of the different provinces in 1891 (Mulhall and Mulhall 1892). Their figures are presented in Table 1: 1 Measured in constant Geary-Khamis dollars of Own calculations based on data from Maddison, A. (2008) and International Monetary Fund. 3 The denomination for the equivalent to the GDP for provinces in Argentina is called Producto Bruto Geográfico. The main difference between the notion of GDP and PBG is the unit of observation for measuring the economic activity. When GDP is estimated for countries and a firm has productive units in more than one country, differentiation between National product and Domestic product is used and the respective flows are incorporated in the balance of payments. When GDP is estimated for a geographic unit smaller than a country (for instance a province), the added value is directly assigned to the productive unit according its geographic location. In this article we will use the English acronym GDP following notation in Tirado, Rosés y Martinez Galarraga (2008) for Spanish regions and Crafts (2005) for regional GDP in Britain. 2

3 Province Table 1: Earnings per province. Year Earnings Earnings p/c In relation to national average income Buenos Aires y Capital ,42 Entre Ríos ,35 Santa Fe ,59 Córdoba ,87 Corrientes ,63 Tucumán ,73 Mendoza ,77 Salta ,58 Santiago ,67 Catamarca ,65 San Luis ,67 San Juan ,72 La Rioja ,58 Jujuy ,53 Argentina ,00 Source: Mulhall, M. G. and E. T. (1892, p. 21). Buenos Aires, Santa Fe and Entre Ríos are the only provinces above the national average 4 and Tucumán is among the poorest 4 together with Salta, La Rioja and Jujuy. The position of Tucumán is unexpected given that while several references of contemporaneous observers and more recent research describe Tucumán as a land of relatively high prosperity, in the quantitative account of the Mulhall brothers, it appears below other provinces like San Luis, San Juan, Santiago del Estero and Catamarca which have been usually described as clearly poorer and underdeveloped. The income of Tucumán is only 29,19% of the combined income of Buenos Aires and Capital Federal. In the book by Elias (1996), in addition to numbers of per capita PBG of the Argentine provinces between 1953 and 1992, the PBG for the year 1884 is added in the tables. Of course, the figures for 1884 have not been produced with the same methodology and sources used by CFI for the second half of the 20 th century but the citation of the sources does not provide details on this particular item. 5 Some characteristics of the picture arising from these numbers are similar to the ones coming from the guesstimations of Mulhall and Mulhall for 1892 like the higher than average income of Santa Fe and Buenos Aires, the position in the ranking of Tucumán (5 th and 3 rd respectively) and the similar level of income of Tucumán and Mendoza. However, the per capita PBGs of Tucumán and Mendoza are higher than the national average in the estimation of 1884 but considerably smaller in the estimation of It is important to remember that Buenos Aires and Capital Federal had 40 % of the total population in 1895 and 46 % in It is reasonable to suspect that the numbers come from the Mulhall and Mulhall (1885) but we have not been able to confirm this hypothesis so far. 3

4 Table 2: GDP per capita per Province. Year Province GDP per capita In relation to national average Buenos Aires * ,32 Entre Ríos ,95 Santa Fe ,34 Córdoba 990 0,66 Corrientes ,74 Tucumán ,05 Mendoza ,04 Salta ,87 Santiago 904 0,60 Catamarca ,74 San Luis 946 0,63 San Juan ,98 La Rioja ,85 Jujuy ,92 Argentina ,00 Note: the value for Buenos Aires is a weighted average of Buenos Aires and the Capital Federal. GDP per capita in pesos of June Source: Elías (1996, p. 97). In this paper we present a quantitative description of the economic structure of two different areas of Argentina: on the one hand the unit composed by the province of Buenos Aires and the city of Capital Federal (hereafter we will use generically Buenos Aires or BACF referring to the area composed by the province of Buenos Aires and Capital Federal) 6 ; on the other hand, the province of Tucumán, in the north-west of the country. For both areas we analyze quantities of capital, land and labor, employment structure, capital per worker, land per worker, main cultivations, etc. The combination of these results with several assumptions, mainly about some prices and rates of return, leads to the calculation of the estimates of regional GDP for the province of Buenos Aires and Capital Federal and the province of Tucumán. The findings shed some light on the debates about regional differentiation in economic performances in a country highly incorporated to the global economy and about regional inequality. The rest of the paper is organized as follows: in section 2 we briefly introduce the available estimations of the national GDP and provincial GDPs and present our methodology and data. In section 3 we present the results of our estimations in terms of labor force, economic structure and economic assets. In Section 4 present and compare the estimations of provincial GDP for the two geographic units under study. Section 5 concludes with the implications of our results, an acknowledgement of some limitations of our approach and some lines for further research. The appendices at the end provide ancillary information on some topics discussed in the paper. 6 Even though it is possible to explore the Capital Federal separately from the province of Buenos Aires, we preferred to analyze them as a single unit because they were complementary economies with very close lings and interconnections. 4

5 2. Previous Estimations, Methodology and Data The best known series of national GDP in Argentina comes from the estimation of Cortés Conde (1994). In order to calculate the levels of GDP between 1875 and 1935, the author estimates the GDP in 1914 taking the difference between the gross value of production and the cost of the intermediate goods in each sector in that year. The second step, the growth rates of the added value in each sector was approximated by the growth rates of the physical volume of production in that sector. Finally, the growth rates of GDP are calculated as a weighted average of the sectoral growth rates. There are other estimations of national GDP like CEPAL (1958), Ferreres (2005), Della Paolera (2003) and IEERAL(1986). There are some reconstructions of macro magnitudes for Argentine provinces in the 19 th century but they are not strictly comparable because the methodology applied in each case was different. Coria (2004) estimates the PBG of Mendoza in 1914 by calculating the added value for each branch of activity taking the difference between the production and the value of intermediate products; the methodology and sources to calculate gross value of production and the cost of intermediate goods differ across activities and several approximations and assumptions are made in each step of the estimation. For Salta, another province in the north of Argentina, Antonelli et al. (2011) estimate the provincial GDP at prices of 2000 combining techniques of added value per branch of activity and techniques based on the expenditures approach. As we will see in the next sections, the investigation of Coria (2004) provides an interesting reference to compare our results for Tucumán and Buenos Aires but the estimation of Antonelli does not because, even though the trends are more or less reliable, the levels are very probably a gross underestimation. 7 In this paper, we will calculate several macro-magnitudes including the provincial GDP for Buenos Aires and Capital Federal (BACF) and Tucumán and our methodology will be based on the identity between the GDP and the sum of the retributions to productive factors (labor, land and capital). The main source in which we base our research is the Tercer Censo Nacional de la República Argentina, collected on June, the 1st, 1914, during the administration of Dr. Roque Saenz Peña. From data included in Volume 2, Población, published in 1916, we produced the figures of total population and occupational categories. From Volume 5: Explotaciones Agropecuarias, published in 1919, we obtained the information on land used for agriculture and for cattle production. From Volume 6: Censo Ganadero, published in 1917, we obtained information on livestock and the value of land used for agriculture and cattle production. All the information related to Industry and Trade and Services was obtained from Volume 7, Censo de las Industrias and Volume 8, Censo del Comercio. Fortuna Nacional. Diversas Estadísticas, both published in Another important source of information is the book Riqueza y renta de la Argentina. Su distribución y su capacidad contributiva, published by Alejandro Bunge in 1917; it was crucial to estimate the added value in agriculture and cattle production. More details about the way we used the information from Bunge in section 2.b, Land and capital in primary sector. Wages in Buenos Aires come from the Anuario Estadístico del Trabajo. Año This publication collects information on more than observations of wages coming from 7 The authors say los resultados [obtenidos] no hacen justicia al verdadero tamaño del PBG de Salta, porque, por carencia de información se han omitido numerosas actividades, a la vez que se ignora la dimensión de otras probablemente muy importantes. Antonelli et al. (2011, p. 30). 5

6 inspections of the police in industrial and commercial establishments. The inspections are motivated by some labor accident but the public servant recorded the all the wages in the establishment (INDEC 1916) In order to calculate the added value in each province we will use the income approach and use the identity between the sum of the added values and the sum of the retributions to the productive factors. In particular we will assume that the provincial GDP (Y) will be N = i=1 [ r K + r K + r K + r K ] + [ q T + q T ] s L Yt Li wi + A A L L I I S S A A L L + The first term is the remuneration to labor that is equal to the sum of all the wages paid to the workers across the N different occupations. The second term in brackets encompasses the rents (r) paid to physical capital (K) in agriculture (A), livestock production (L), and in establishments in industry (I) and services (S). The third term, also in brackets is the rent (q) paid to land (T) in agriculture and in livestock production and the last term is the rent (s) produced by capital held in cattle (L). L 2-a. Labor remuneration Regarding the calculus of labor remuneration, the census of 1914 classifies all the workers of each province in 436 occupations (so N=436) and gives information on the total number of male and female and argentine and foreigner workers in each category. One of the Occupations mentioned by the census is the Varias y sin especificar with individuals (35,24% of the total population over 14) in Buenos Aires and in Tucumán. Although this broad and loosely defined category can represent many things, we will assume that it is mainly capturing the non-active population. We have some other categories associated to non-active population (students, for instance) for which we assume income zero. Information about wages in Buenos Aires comes from the Encuesta de salarios From this source we have been able to assign a wage to 124 categories which represent 62,55% of the total active population 8 in PBACF. To assign an income to the remaining categories we have classified the categories according to the level of human capital required for the job: those with low qualification (like aprendices, mucamos) will be assigned a wage similar to the one of the jornaleros ($74). Those with medium-low qualification (mayordomos, tamberos, bordadoras, tejedores, oficios diversos, conductores, empleados de ferrocarril, telegrafistas, niñeras and empleados) will be assumed the qualification of torneros ($120); for those with medium qualification (empleados de gobierno, tenedores de libros, joyeros, enfermeros, profesores de música) we will assume an income which is an average between linotipistas 9 and capataces. There is also a group of workers with high qualification (lawyers and accountants) with earnings assumed to be double than the ones with medium-high according to the ratio observed in Tucumán (Álvarez 2010) Wages in Tucumán come mainly from the Statistical Bulletin of Tucumán (Boletín de Estadísticas de Tucumán) and other sources mentioned in Alvarez (2010). 10 Many of the quotations of wages both in Buenos Aires and in Tucumán are on a daily basis. In these cases we assumed that there are 25 days of work per month. In some cases in Tucumán, 8 Active population is total population over 14 less Varias y sin especificar and Estudiantes 9 Linotypists were operators of a linotype machine in the printing industry. 10 We thank Beatriz Alvarez for providing us with her data set on wages in Tucumán in

7 the source mentions that in addition to the monetary wage, the worker receive food or shelter or both. In these occasions, the monetary value of the food or shelter is added to the monetary wage in order to get the full wage. 2-b. Land and capital in primary sector q A A + L L, the information comes mainly from two sources: the Third National Census Volumes 5-6 and Bunge (1917). This author estimates the value of land under exploitation (VTE) in such a way that: Regarding income in agriculture and pastoral activities, [ T q T ] VTE = P T. h T = T A + T L = P A. h A + P L. h L Where P T is the average price of land effectively used either for agriculture or for cattle rising; 11 h T is the total land uses either for agriculture or cattle rising; P A y P L are the price per hectare of agricultural land and the price per hectare of land used for cattle rising respectively; h A and h L are the area under effective economic exploitation in agriculture and cattle rising respectively. Bunge (1917) estimates values for P T and P L and the Census provides information on h A and h L so P A can be easily obtained. The value of land for agriculture and cattle rising is presented in Table Table 3: Value of land in agrictulture and livestock production. Tucumán and Buenos Aires. Año Total Value in use VTE Value in Agriculture - T A Value in livestock production - T L Buenos Aires Tucumán Source: Own calculations based on data from the Third National Census, V. 5 (1919, p ) 2 and V. 6 (1917, ps ) and Bunge (1917). Regarding the income generated by land (q A and q B ), Bunge (1917, p.73) says that es muy posible que el suelo agrícola exclusivo produzca a su dueño, en nuestro país, un rendimiento neto superior al 3%, 11 Bunge (1917, p ) distinguishes the land under effective exploitation which is the one that is under cultivation or occupied by livestock. Regarding the latter, he assumes the quantity of land required for each unit of cattle and using the number of units he calculates the area needed by that economic activity. In this way he gets that the total area required for livestock is hectares for Tucumán and hectares for Buenos Aires. The number for Buenos Aires is curious because total land for livestock in that province according to the census (V. 5 ps and V. 6 ps ) was hectares. Given that higher density of livestock in Buenos Aires is plausible, we will assume that all the area mentioned in the census is effectively used for livestock production. In Tucumán, the area effectively used for livestock production turns to be only 46 % of the area mentioned in the Census. 12 To calculate the value of land we use prices quoted in Bunge (1917, p. 60 and 74). According this author the average value of land effectively used for both in agriculture and cattle rising was $ 70 per hectare while the average price of land for cattle rising was $ 45,20 per hectare. Using the shares of land devoted to each activity, the price of land for agriculture can be deduced. In this case we are probably underestitmating the value of land in Buenos Aires (with land of higher quality than the average) and perhaps overestimating the value of land in Tucumán. 7

8 generalmente supuesto en otros. La explicación estaría en que el propietario percibe, además del producto neto de su tierra, parte de lo que, en una distribución más equitativa, correspondería al trabajo del agricultor. Accordingly, and making a conservative estimation we will assume that the rate of return of land used for cultivation and for cattle rising would be 4 % per annum. The income generated by livestock s L L was calculated as the product between the monetary value of livestock (L) and the net rate of return ( s L ). Bunge (1917, p. 74) estimates that the average rate of return for Argentina is 6 %. Following the methodology proposed by this author we find a rate of return of 8 % for Buenos Aires and 3 % for Tucumán. The value of livestock is calculated using the following information from the Census: quantity of livestock taking into account species and breed and the price of each one in each region. The value of livestock in Tucumán in 1914 was $ while in Buenos Aires it was $ The values of capital used in agriculture and pastoral production, K A y K G respectively, come directly from the Census (Volume 6, p. 46. XLVI). 2.c. Capital used in industry and services Secondary and tertiary sectors are usually identified with industry and services respectively. We include in the secondary sector (also called Industrial Sector) all the activities associated to the transformation of food and raw materials through industrial processes and other activities like building, energy, siderurgy, textiles, etc. The tertiary sector includes commercial activities, transportation, financial services, insurances, education, etc. It is also called Services Sector (Rouco Yáñez and Martínez Teruel, 1997). The capital stock for each of the sectors mentioned in the previous paragraph was obtained directly from the Census, volumes 7 and 8, corresponding to Industry and Commerce respectively. The capital stock used in Industry encompasses both fixed capital (building, land, machinery and implements) and working capital (all the material used in the process of production) (Third Census, Volume 7, p. 48). The branches of activity under consideration are General Industries, Mills, Saladeros, Wine factories, Beer factories, Sugar Mills, Distilleries, Gas Factories, and Electric light plants. After the compilation of all the information, the editor of the census mentioned that: Lo que puede afirmarse es, que bajo ningún concepto, podrían reputarse como exagerados los capitales que aparecen como aplicados a la producción industrial; habría que considerarlos, por el contrario, inferiores un 30 por ciento 13. Taking into consideration this statement we will expand total capital in the secondary sector by a 30%. Some branches included in the Services Sector are Food, Clothes and Toiletries, Teaching, Building 14, Transports. 15 It is important to mention that, even though the Census label this sector as Commerce, it includes several activities that are not specifically trade but are related with the provision of general services. In this case the underestimation of the capital stock seems to be even larger than in the case of Industry and the number of the census should be increased by a 50%. The commentator of the census, Alberto B. Martínez, said: los mismos inconvenientes que observó el censo de 1895, consistentes en una manifestación incompleta de los capitales declarados, que aquel censo calculó en un 50%, deben haber existido en 1914, porque siempre, y en 13 Third National Census (1917, Volume 7, p. 48). 14 It is not building activity in itself but the selling of inputs and raw materials for the building process. 15 A complete list of the categories is in Table 15. 8

9 todas partes, aún en los países más habituados a este género de investigación, los capitales declarados, tanto en el comercio cuanto en la industria, son inferiores a los verdaderos 16. Taking into consideration this statement we will expand total capital in the secondary sector by a 50%. To calculate r I K I, + r S K S, which is the contribution of secondary and tertiary sectors to total output, we have used a rate of return to capital of 8%. In the General Considerations published in the Census we can read El interés del dinero, estimado por el ilustre patricio [Nicolás Avellaneda] en un 12% (unas décadas antes, bajo Rodríguez y Rivadavia, era 25 y 30%), es actualmente del 8% Results The economies of Buenos Aires and Tucumán differ in many dimensions. Buenos Aires is a kind of archetypical example of comparative advantages based on land abundance, productive specialization and a high integration on transatlantic international trade. It is important to mention that BACF contains the Capital Federal which is the largest city in the country and a very important commercial and administrative center. On the other hand, Tucumán was characterized by a large industrial sector based on the sugar production and the associated sugar-mills and sugar-cane cultivation. Given that this activity was not competitive in the international market, it enjoyed some degree of protection from the international competition and some support from the national and provincial political authorities and most of the production was directed to the internal market. (Cortés Conde 1979, Campi 2004). The two provinces have very different economic importance and demographic magnitude in the national context. Buenos Aires had inhabitants which is the 46,08% of the total national while Tucumán had which is only a 4,21%. The share of population older than 14 was 67,06% in Buenos Aires and 62,36% in Tucumán. The proportion of foreigners was very different: it was only 9,80% in Tucumán while it reached 40,68% in Buenos Aires. 3.a. Labor After combining the information of the size of the occupational categories (L i ) and the wages of each of them (w i ) it is possible to calculate the total labor income of each category and the total labor income in each province. The census includes all the occupational categories into seventeen different economic activities. We group these economic activities in the three traditional economic sectors (Primary, secondary and tertiary) according to the criteria presented in Appendix 2. Two of the most important occupational categories defined by the census are Jornaleros and Peones. These categories include the typical unskilled worker and it is not possible to distinguish how many of them were working in the primary, secondary or tertiary sector. In fact, it is possible that most of them had very flexible attachment to their jobs and indeed they were probably moving from one sector to another. 18 We will group these two categories under the general name Jornaleros. 16 Third National Census (1917, Volume 8, p. 133.) 17 Third National Census (1917, Volume 6, p. 42). 18 See for instance Cotrés Conde (1979, p. 200; 1994, p.8). 9

10 Table 4: sectoral shares of the workforce. Tucumán and Buenos Aires. Year 1914 Tucumán Buenos Aires Primary % % Secondary % % Tertiary % % Jornaleros % % Total % % Source: Own calculations. See text. The structure of the labor force shows the relevance of the secondary sector in Tucumán (with at least 31% of the total labor and (if we assume that the jornaleros were engaged in the industrial sector) an upper bound of 60%. The primary sector had at least 19% and at most 48% of the labor force. In BACF the share of labor force in the tertiary sector is very large with at least 41% of the labor force. It is surprising the rather small relative size of the primary sector with a minimum of 9% of the labor force and a maximum of 32%. Wages in Buenos Aires seem to be consistently higher than in Tucumán, at least when the most common categories are compared. Jornaleros earned $51,42 in Tucumán while they earned $74 in Buenos Aires. Carpenters earned $115 in BACF and $100 in Tucumán and building workers earned $105,75 in Tucumán and $118 in Buenos Aires. 19 In order to include in our estimation the income generated by all the individuals engaged in the productive process, it is necessary to deal with some occupational categories whose names suggest that income does not come from labor but it is mainly associated to entrepreneurial profits linked to administration of capital or land (for instance comerciantes, industriales, rentistas, haecndados, agricultores, etc.). In order to incorporate the income of these individuals (entrepreneurs or self-employed) to our calculation we have considered two strategies: 1) To assume that they receive some retribution similar to the one they would have received if they worked for other, and conditional on their human capital. This would imply to assign them a kind of imputed labor income. Eventually this can be considered a kind of profit that these individuals receive in their role of entrepreneurs. 20 The results with this methodology will be called Estimation 1 2) To assume that these people receive no labor income at all but they receive some profit proportional to the capital they have and therefore in order to calculate the total income of the economy we have to increase the rate of return of land, capital and livestock in order to get a modified rate of return that includes profits. Given that we don t know anything about the profits rate, the choice of the increase is arbitrary. We will assume that profits rates is equal to the specific rate of return of each asset and therefore the modified rate of return will be equal to two times the specific rate of return (for instance if the rate of return of physical capital in industry was assumed to be 8% in Estimation 1, the modified rate of return of physical capital in industry will be assumed to be 16%). The results with this methodology will be called Estimation 2 19 It is probable that these differences were larger in real terms because prices in the provinces in the north were usually higher than in Buenos Aires. See Correa Deza and Nicolini (2012). 20 In most of the estimations of GDP this would be included as Ingresos mixtos or Excedente Bruto de Explotación. (INDEC 2008). 10

11 Given these assumptions, Estimation 1 will tend to (relatively) overestimate the income of the areas where there are many individuals included in the categories that we call entrepreneurs while Estimation 2 will tend to (relatively) overestimate the income of the areas with more assets for worker. The distribution across economic sectors of aggregated labor income of workers when the categories of entrepreneurs and self employed are excluded is similar in estimations 1 and 2 and is presented in table 5 Table 5: Labor earnings of workers by economic sector in $. Buenos Aires and Tucumán. Year 1914 Economic Sector Tucumán Buenos Aires Primary % % Secondary % % Terciary % % Jornaleros % % % % Source: Own calculations. See text. The share of labor earnings going to workers in the primary sector is very small both in Tucumán and Buenos Aires; taking into account that many Jornaleros worked in the primary sector the numbers seem to be reasonable and show the importance of industry in Tucumán and the importance of the tertiary sector in BACF probably influenced by the large share of population in the city of Buenos Aires. In Estimation 1 we can incorporate the earnings of these entrepreneurs and self-employed to the total earnings of workers. As we have advanced in above in this section, the relative size of primary sector would increase in Tucumán because of the large quantities of Labradores and Agricultores ; the relative size of tertiary sector will increase in Buenos Aires because the large quantity of Comerciantes. 21 These figures are presented in Table 6 Table 6: Labor earnings of workers and entrepreneurs by economic sector in $. Buenos Aires and Tucumán. Year 1914 Economic Sector Tucumán Buenos Aires Primary % % Secondary % % Tertiary % % Jornaleros % % % % Source: Own calculations. See text. 3.b. Land According to the data from the Census, a 49 % of the total area of Argentina was occupied by some kind of farms whose main activity was cattle-rising and 6 % was occupied by some cultivation. In Buenos Aires 66 % of the area was occupied by stockbreeding farms while 20 % of the land was used for agriculture. In Tucumán these shares were 75 % and 16 % respectively. 21 In the Appendix 1 we present a list of the main categories included as entrepreneurs in each economic sector and in each province. 11

12 Table 7: Area occupied by agriculture and livestock production. Argentina, Buenos Aires and Tucumán. Year Argentina Buenos Aires Tucumán Has. % Has. % Has. % Livestock % % % Agricultural % % % Rest % % % Total Area % % % Source: Third National Census, V.5 (1919, ps ) and V.6 (1917, ps ). Neither the whole area for agriculture was effectively under cultivation nor the whole area for livestock was effectively used for cattle production. In Buenos Aires, from the hectares occupied by agricultural farms, only 89 % were effectively under cultivation and these were devoted mainly to wheat (41 %), maize (30 %) and oats (19 %). In Tucumán, from the hectares occupied by agricultural farms, only 36 % was under effective cultivation and sugar cane (69 %) and maize (26 %) were the most important crops. In order to approximate the quantity of land effectively used for livestock production, Bunge (1917, p ) suggests a mechanism based on the assumption that each unit of each species of cattle requires a fixed amount of land. 22 Once the number of each species of cattle is obtained from the census, and the coefficient of hectares for unit is defined, it is possible to compute the total area used for cattle rising. Table 8 presents the details. Table 8: Derivation of the quantity of land effectively used for livestock production Buenos Aires and Tucumán. Livestock Required Area for Livestock Unit Livestock Buenos Aires Total required area (has) Livestock Tucumán Total required area (has) Bovine 1, , ,60 Horses 1, , ,00 Mules 1, , ,50 Asses 1, , ,00 Ship 0, , ,50 Goats 0, , ,50 Porcine 0, , ,60 TOTAL , ,70 Source: Own calculations based on Bunge (1917, p. 59). See text. Applying this mechanism to the case of Tucumán, it turns out that from the has that the Census assigns to cattle rising only hectares (46 %) were effectively been used. If we use the same procedure for Buenos Aires the total area under effective use is larger than the area mentioned by the Census. This is not surprising given that the coefficient used by 22 In Spanish, the quotation is la superficie requerida por nuestro sotck ganadero, según su especie. 12

13 Bunge is a national average and probably cattle rising activities in Buenos Aires used land in a more intensive way than the national average. 23 Given this result, we will assume that 100 % of the land quoted by the census as being employed for cattle rising ( has) was effectively been used. 24 In Table 9 the value of land and livestock per capita is summarized for both BACF and Tucumán. In per capita terms the value of land under cultivation in Buenos Aires is almost three times larger than in Tucumán and the value of land used for pastoral activities is two and a half larger. Value of assets Table 9: Value of land and livestock and generated income. Tucumán and Buenos Aires. Year 1914 Tucumán Income Income per capita Income per productive unit Value of assets Buenos Aires Income Income per capita Income per productive unit LA LL LV Note: LA is land in Agriculture; LL is land used for livestock; LV is livestock Source: Own calculations. See text. The flow of income coming from land is considerable larger in Buenos Aires than in Tucumán. In per capita terms, land in Buenos Aires produced a rent of $ 20 per year while in Tucumán it produced $ 7. Nonetheless, in both cases the income per capita generated by land is very low (remember that a Jornalero earned $ 74 per month in Buenos Aires)Proportionally, there were more proprietors in Tucumán than in BACF (See Table 10) and the average income per productive unit was much higher in BACF than in Tucumán. The average income generated by a cattle-rising establishment was higher than the income of a worker with the highest qualification like a lawyer. Table 10: Productive units by type of economic activity. Tucumán and Buenos Aires. Year Number of productive units by type of economic activity % de productive units per capita agropecuarias per cápita by type of economic activity Tucumán Buenos Aires Tucumán Buenos Aires Livestock ,012 0,006 Agriculture ,025 0,013 Source: Third National Census, Volume 5 p This point was already mentioned in note We are assuming that the quantity of livestock per unit of land in Tucumán is equal to the national average. With the information we have so far it is not clear the kind of bias arising from this assumption. 13

14 3.c. Capital in Industry Capital in the industrial sector was clearly larger in Buenos Aires which had 54,98% of the total national than in Tucumán which had only the 7,41% of the total. However, when measured in per capita terms, the industrial profile of Tucumán is quite clear, even though the capital per capita in Buenos Aires is larger than the national average, in Tucumán was 47,49% higher than in Buenos Aires. Table 11: Capital Stock in Secondary Sector. Buenos Aires, Tucumán and Argentina. Year Buenos Aires Tucumán Argentina En $m/n % En $m/n % En $m/n % Food Processing , , ,72 Clothing and toiletries , , ,41 Building , , ,68 Furniture , , ,38 Arts and Ornaments , , ,79 Metals , , ,81 Chemicals , , ,05 Graphic arts , , ,78 Fibers, yarns, fabrics , , ,86 Varoius , , ,54 Sugar Mills and Refineries , , ,25 Grain Mills , , ,71 Refrigerating and Salting Plants , , ,02 Stock de capital , , ,00 Población Stock per capita 363,42 535,87 304,53 Notas: The refinery in Capital Federal was under construction in Food Processing does not include Sugar Mills, Mills and Refrigerating Plants. They are separately quoted. Source: Third National Census, V. 7 (1917, p , , , 504,547). In Buenos Aires the largest branches are Food Processing and Various Industries. Within Food the most important is milk production with a 34,97 % of the total of the branch. 14

15 Table 12: Main sub-branches in Food Processing branch. Buenos Aires. Year Main sub-branches in Food Processing Capital in $m/n Percetange of the branch Milk plants ,97% Breweries ,01% Bakeries ,85% Liquors and non alcoholic beverages ,06% Soda and ice factories ,27% Candies, chocolates factories ,44% Total main sub-branches ,59% Rest ,41% Total branch Food Processing ,00% Source: Third National Census, V. 7 (1917, p. 115, 121). Within Various, the most important are Electric Lighting, Alcohol and Gas with 69,33% of the branch. Table 13: Main sub-branches in the Various branch. Buenos Aires. Year Main sub-branches in Various Industries Capital in $m/n Percetange of the branch Electric lighting, alcohol, and gas factories ,33% Cigarettes ,74% Cotton jute bags factory ,15% Grain deposits and elevators ,48% Total main sub-branchs ,70% Rest ,30% Total branch Various Industries ,00% Source: Third National Census, V. 7 (1917, p ). In Tucumán, the relevance of sugar industry is very clear: the 82,87% of total industrial capital in the province is in the category Sugar Mills and Refineries. In 1914, Tucumán had 72% of sugar mills in the country (30 of 42) and produced 83% of the sugar ( tons of tons). Most of the sugar mills were located in the area of Cruz Alta (12) and 47% of the total capital of the sugar industry was in that department. The largest branch, after sugar production is Various Industries with a share of 8,85%. Within this branch the main sub-branches are Lighting and Gas with almost 80% and Tanneries with 11%. 15

16 Table 14: Main sub-branches in the Branch Various. Tucumán. Year Capital in $m/n Percetange of the branch Electric lighting and gas factories ,29% Tanneries ,27% Rest ,44% ,00% Source: Third National Census, V. 8 (1917, p , , ). 3.d Capital in the Tertiary Sector In the tertiary sector in Buenos Aires the largest branches are Food and Various 25 with slightly more than 23 % each. In Tucumán, the largest branches is Food with 44 %, followed by Clothing and Toiletries with almost 18 %. Table 15: Capital Stock in the tertiary sector. Buenos Aires, Tucumán and Argentina. Year Buenos Aires Tucumán Argentina In $m/n % In $m/n % In $m/n % Food , , ,24 Clothing and Toiletries , , ,54 Building , , ,31 Furniture , , ,09 Teaching , , ,16 Art and Decoration , , ,75 Transports , , ,29 Medicine and Hygiene , , ,08 Recreation and Sports , , ,58 Advertising ,33 0 0, ,22 Banks and Insurance , , ,06 Change and Lottery , , ,22 Appropiations and autcion fees , , ,68 Various , , ,77 Capital Stock , , ,00 Population Per Capita Capital Stock 563,22 141,37 401,28 Source: Third National Census, V. 8 (1917, p , , ). The participation of BACF in the total national is 64,67% while in Tucumán it is only 1,48%. The capital per in the tertiary sector divided by the population is $563,22 in BACF and $141,37. While in the industrial sector, Tucumán was more capital-intensive, in the tertiary sector BACF has more capital per capita. Adding up the capital stocks of the two sectors, Buenos Aires ends up with 36,82% more capital per capita than Tucumán. 25 Incluye: Armerías, barracas, venta de barriles, botellas, bolsas etc., cigarrerías, venta de carbón y leña, maquinas y artículos para la industria y rurales, entre otros. 16

17 4. The aggregate production Using the formula in page 6 and the information presented in the previous sections, we are able to make a first approximation of the value added in the economy of Buenos Aires and Tucumán. As we have said in the previous section we have two possible estimations. According to Estimation 1 total GDP in Buenos Aires is $ while in Tucumán is $ GDP per capita are $ 706 and $ 434 respectively. GDP per capita according to Estimation 2 are $ 393 in Tucumán and $ 728 in Buenos Aires. Differences between the estimations are not negligible and more research is needed in order to reduce the range of variation. However some implications of the results are interesting: Tucumán is clearly poorer than Buenos Aires and it is consistently below the national average that according to Cortés Conde (1994) is $ 572 and according to IEERAL (1984) is $ 533. It is also interesting to remark that the levels of per capita GDP in Tucumán in both estimations are in line with the GDP per capita obtained by Coria (2004) for Mendoza ($ 465) which in many traditional interpretations has been suggested to be similar in many respects to Tucumán. Table 16: Added value by sector. Tucumán and Buenos Aires. Year Estimation 1 Tucumán Buenos Aires Stock Income Stock Income Capital in Agriculture Capital in Cattle Rising Capital in Industry Capital in Services Land for Agriculture Land for Cattle Rising Livestock Totals Population Per capita Income of Workers % % Income of entrepreneurs % % Total % % Aggregated GDP % Per capita GDP Source: Own calculations. See text. Our approach opens the possibility to analyze the share of each sector (primary, secondary and tertiary) in total GDP. Estimation 1 assumes that profits go to entrepreneurs and self employed and therefore income will go to the economic sector at which they belong. In Tucumán entrepreneurs are mainly in the primary sector (the largest occupational categories are agricultores and labradores); in Buenos Aires, even though there are some important categories in the primary sector (agricultores) they largest are in the tertiary sector (comerciantes and rentistas) and therefore, in this estimation these sectors will be relatively larger. 17

18 Estimation 2 assumes that profits are proportional to the stock of land and capital and therefore they will be assigned to the sectors according to the relative importance of their assets. Table 17: Added value by sector. Tucumán and Buenos Aires. Year Estimation 2 Tucumán Buenos Aires Stock Income Stock Income Capital in Agriculture Capital in Cattle Rising Capital in Industry Capital in Services Land for Agriculture , Land for Cattle Rising Livestock TOTALES Population Per capita Income of workers % % Aggregated GDP Per capita GDP Source: Own calculations. See text. In addition to the problem mentioned in the previous paragraph, it is important to recall that the category Jornaleros does not belong to any specific sector. So in the tables below we report the income generated by these two categories in a separate row. Table 18: Sectoral shares of GDP according to Estimation 1 Total Tucumán Buenos Aires Primary % % Secondary % % Tertiary % % Jornaleros % % % % Source: Own calculations. See text. Sectoral distribution emerging from Estimation 1 shows a relatively large primary and secondary sector in Tucumán while the tertiary sector is comparatively larger in Buenos Aires. Sectoral distribution emerging from Estimation 2 presents a relatively very large industrial sector in Tucumán and a reduction of the primary sector to shares close to the share in Buenos Aires. Agricultural sectors are rather small in both areas: assuming all the Jornaleros working in 18

19 the primary sector, the largest possible share for BACF is 31 % (with Estimation 2); for Tucumán it is 35 % (with Estimation 1). 26 Table 19: Sectoral shares of GDP according to Estimation 2 Total Tucumán Buenos Aires Primario % % Secundario % % Terciario % % Jornaleros % % % % Source: Own calculations. See text. It is necessary to mention that we are not adjusting for price levels and therefore in every cross-province comparison we are making the implicit assumption that BACF and Tucumán have the same price level. There are no estimations of regional GDP deflators but we have some information of the relative prices of food and they suggest that BACF was cheaper than Tucumán (Correa Deza and Nicolini 2012) which would imply that the differences between the provinces in real terms were larger than what we suggest in our estimation. 5. Conclusions, caveats and further research This paper presents a first approximation to a systematic estimation of the economic structure of some provinces or Argentina in 1914 using the information provided mainly by the census of that year and complemented by other sources for specific sectors and provinces. A large share of the data we used to analyze the macroeconomic characteristics of the province of Buenos Aires, Capital Federal and Tucumán is available for most of the other provinces suggesting that the analysis can be extended to cover the whole country. 27 In this version, the main purpose of the paper is to present the methodology, discuss the main and more debatable assumptions and suggest some implications of the preliminary outcomes. The results of the paper shed some light on the relative positions and economic structures of two geographic areas with different development strategies. One of them, Buenos Aires and Capital Federal, specialized in primary production and commercial activities tightly linked to the international market; the other, Tucumán, was claimed to be an example of development based on a specific industrial sector (sugar) whose production was sold in the internal market. Most of the structural characteristics of the two economies arising from our analysis are consistent with that description (large tertiary sector in BACF and large secondary sector in Tucumán) but some results are suggestive and open questions for future research: one of them is the rather small share of the agricultural sector in the total income of the economies. Another interesting point to understand the patterns of relative development is that Tucumán had smaller capital per capita than the national average when secondary and tertiary sectors are considered together. 26 The share of the primary sector in the national GDP is 33,72 % according to Cortés Conde (1994) and 35,43 % according to IEERAL (1986). 27 Probably the main constraint for this extension is the availability of the provincially specific wage structure. 19

20 Possibly the most promising result of the paper is the one associated to the relative levels of GDP per capita. Even though this paper presents only a first approximation, it seems that the differences between the rich areas of Buenos Aires and many other areas of the country were considerable. If it is true that Tucumán was a rather affluent society compared with other provinces of the north of Argentina, the benefits of the process of economic growth during the period have been concentrated in a specific part of the country. Consequently, regional inequality was probably very large. Many steps in our process of combining data and what we think are plausible assumptions can be improved: one of them is probably the set of prices of land used for agriculture and livestock production. At this stage of the research we had to use the informed guesstimations by Bunge with almost no discrimination across provinces whatsoever; we are already in the process of collecting direct information from official sources about agrarian statistics. The rate of return of the assets in each sector is based in specific information from the census and some quotations from alternative sources but, given the crucial role they play in our results, more detailed research about these variables is one of our priorities. The calculation of the income of workers is based in quite detailed and robust data sets but several points deserve a closer analysis and more research: the first one is the definition and calculation of income of the categories that are probably including not only workers but also entrepreneurs and self-employed (like agricultores or comerciantes). The second one is the income of the occupational categories with high levels of human capital and probably high incomes (like lawyers and accountants) for which we have just a few of quotations in the sources. The third one is the characteristics of work of jornaleros in particular the economic sector in which they do their job. Another important point related with the income of workers is the percentage (of workers and/or time) of unemployment. Despite all the limitations of our estimations and the caveats associated to them, we are convinced that the agenda of assessing the relative economic performance of the Argentine provinces is crucial for understanding the deep implications of the process of economic growth taking place between 1870 and 1914 in Argentina based in the integration in the world market of goods, services and factors of production. We hope that this paper is a step forward in that agenda. 6. References Álvarez, Beatriz (2010), La evolución de la desigualdad de ingresos en Tucumán ( ), mimeo UNT, Argentina. Antonelli, Eduardo, Carrazán Mena, Gastón y Romero, Fernando (2011), La Economía de Salta. Entre finales del Siglo XIX y comienzos del Siglo XX, 1º Ed, Editorial Enfoques Alternativos, Salta, Argentina. Bunge, Alejandro E. (1917), Riqueza y Renta de la Argentina. Su distribución y su capacidad contributiva, Buenos Aires, Argentina. Campi, Daniel (2004). La evolución del salario real del peón azucarero en Tucumán (Argentina) en un contexto de coacción y salario arcaico. América Latina en la Historia Económica p. 22, CEPAL (1958), El desarrollo económico de la Argentina, Santiago de Chile, 30 de Junio. 20