1. This is part of a larger, ongoing research by both authors on 1973 Argentine presidential elections (Cámpora and Perón). See J. R. Jorrat, “Algunas notas sobre la correlación negativa entre voto al Frejuli y clase obrera,” Desarrollo Económico 59, vol. 15 (1975); D. Canton, J. R. Jorrat, and E. Juarez, “Un intento de estimación de las celdas interiores de una tabla de contingencia (2×2) basado en el análisis de regresión: el caso de las elecciones presidenciales argentinas de 1946 y marzo de 1973,” Desarrollo Económico 63, vol. 16 (1976). For another approach to the same subject, see M. Mora y Araujo, “La estructura social del peronismo: un análisis electoral interprovincial,” Desarrollo Económico 56, vol. 14 (1975).

2. We worked with ecological correlations—based on the Spearman rank correlation coefficient—which only allow statements like the following: “In zones (polling places, electoral districts, etc.) with higher/lower presence of an occupational group, there is a higher/lower presence of votes for a specific party.” The limitations of this tool are well known but new and old empirical findings may allow us to speak of “electoral behavior.” It must be pointed out that we also made use of Pearson's coefficient, and that results were systematically consistent with Spearman's values. As Spearman's is an assumption-free coefficient, it will be used exclusively to present our results in this paper.

3. Zones were chosen because of their theoretical interest as well as for practical reasons. Buenos Aires, Rosario, and Córdoba are the most populated and industrialized cities in the country. La Matanza was selected not only because it is the most densely populated partido from the Buenos Aires suburban area (Gran Buenos Aires), but also on account of its very large working class concentration. Finally, Tucumán was included in preference to other equally important cities, like La Plata and Mendoza, because it had had—as Córdoba and Rosario—considerable social unrest in the years before the electoral contest.

4. We have had the opportunity, on another occasion, to address ourselves to the issue of the ecological fallacy (Canton, Jorrat, and Juarez, “Un intento”) by using a methodological alternative to estimate the percentage of workers voting for Peronism. Other results, obtained by the same method, are presented later (see note 16). Another attempt was to use multiple regression analysis in order to investigate electoral tendencies as a function of the presence (or absence) of different occupational groups, but inevitable methodological problems, such as that of “multicolineality”—high correlation between the independent variables—conspired against it. As a matter of fact we do not work with several independent variables, but with several categories of a single one: occupation.

5. “Illiterate” zones had a high, positive correlation with Peronism, and an equally high, negative one with UCR and APF.

6. We are greatly indebted to our colleague Miguel Murmis for the classification of occupations, to which he contributed a detailed knowledge of the 1960 and 1970 Censuses and of Argentina's social stratification.

7. See Rupert G. Miller, Jr., Simultaneous Statistical Inference (New York: McGraw Hill, 1966), p. 216.

8. Two different reasons might account for this, in part at least: (1) the peculiar situation of Peronism in Santa Fé, where two bitterly opposed factions fought, first, for their recognition as the official party's candidates in the district, and second, once the issue was decided, for the favor of the electorate amid all sorts of legal maneuverings and verbal attacks (see La Nación, 9 March 1973, p. 10); (2) discrepancies between official data and ours, which could contribute to the “lowering” of correlations otherwise uniformly high between workers and Peronism. It is interesting to note, however, that if we add people in agriculture to workers, the correlation coefficient rises to .41, and the individual correlation between people in agriculture and Peronism mounts to .78, a value closer to these found with workers in the rest of the zones.

9. Professionals and students were put together because we thought they belonged to the same occupational status; furthermore, when considered separately, no differences were found between their voting patterns. We even divided students by age groups (three), again no difference: the younger ones—presumably the “real” students—behaved like older ones—presumably professionals, whose occupational status had not been modified in the voters' registers by graduation time. Even though this finding is far from conclusive, it calls our attention to the generalized assumption that younger students were a relevant electoral force in the Peronist victory of March 1973.

10. Incidentally, the two Peronist factions present in Tucumán and Rosario showed different patterns of correlation with workers: FUP (Tucumán), considered a radical faction, .71; PJ (Rosario), a conservative sector, .03.

11. The reader may have noticed that according to their voting patterns, voters could easily be separated in two large polar groups: workers, to which people in agriculture seem to gravitate naturally, form one. Professionals and students, joined by technicians (not in Rosario), proprietors, and traders (not in Buenos Aires or Córdoba) form the other. In fact, from this observation we can surmise that the category of agricultores, which we have translated as people in agriculture, is formed by a majority of rural workers.

12. Calculations made in order to find out the minimum workers' support under the assumption that all nonworkers had voted Peronist in March, gave an average of 58.7 percent in 9 out of 14 mesas from La Matanza, and 42.0 percent in 12 out of 30 mesas from Tucumán.

13. It is worth pointing out that, in the case of Buenos Aires, the “truer” UCR vote is the one for senator, while in Córdoba the contrary holds good: it is in the vote for president that the “real” UCR can be found.

14. The early work by Gino Germani is the first example of these attempts, when he correlated census and electoral data (Estructura social de la Argentina [Buenos Aires: Editorial Raigal, 1946]). Pedro Huerta Palau, who worked along the same lines shown here, coincided with him (Análisis electoral de una ciudad en desarrollo: Córdoba, 1929–1957–1963 [Universidad Nacional de Córdoba, 1963]). See also Walter Little, “Electoral Aspects of Peronism, 1946–1954,” Journal of Inter-American Studies and World Affairs 15, no. 3 (August 1973). For a presentation showing that the association between workers and “populist” parties—UCR first, Peronism afterwards—is a feature that becomes stronger in Argentine electoral contests as we move from the Sáenz Peña Law (1912) on to the mid-century and after, see D. Canton, Elecciones y partidos políticos en la Argentina (Buenos Aires: Siglo XXI, 1973), especially chap. 6.

15. As an additional element we have estimated the proportion of workers who would have backed Peronism in March 1973, according to our mesas' data. Assumptions required by the method have not always been met, so that their value is merely indicative. The estimated proportion of workers voting Peronism in each zone were: Buenos Aires, 73 percent; La Matanza, 83 percent; Córdoba, 55 percent; Tucumán, 77 percent; Rosario, 77 percent. All zones, 79 percent. Córdoba, which has the lowest value, was where our data showed a poor fit when drawing a regression line between percent workers and percent Peronist vote from each mesa.

16. Calculations were made thus: we divide people in each mesa (or zone) according to the dichotomy workers/nonworkers; then those figures are proportionally adjusted to the percentage of absenteeism in each mesa (or zone); finally, we calculate the percentage of each category as part of the total number of votes received by the Peronist party. The reader must bear in mind the operational definition of workers used here, which gives us a figure we believe rather plays down its real numerical strength. Lines between workers and nonworkers drawn from different perspectives could lead to other conclusions.