GDPMPC1872 14/4/2009 11:55 Preliminary version Do not quote

GDPMPC1872
14/4/2009 11:55
Preliminary version
Do not quote
Income per capita of Brazilian municipalities in the 1870´s∗
Eustáquio J. Reis
[email protected]
This paper estimates income per capita of Brazilian municipalities in the 1870´s.
Estimations use data on wages of municipal civil servants in 1876 published by the
Statistical Report of 1878 (Brasil 1878); demographic data from the Census of 1872
(Brasil 1876); and geographic data obtained from various sources. The use of data from
different years justifies the loose reference to the period of estimation.
The ostensible lack of reliable statistics for sub-national levels of the Brazilian economy
in the XIX c. validates the estimation exercise. The few estimates available are based
upon scanty data using methodologies which combine simple macroeconomic
hypothesis about growth rates for the country and its main regions with microeconomic
data for specific localities (municípios or freguesias) and time periods lacking a
satisfactory statistical coverage (Contador and Haddad 1980; Neuhaus 1980; Santos
1980; Goldsmith 1986; Maddison 2003; Bértola, Willebald et al. 2006; Monasterio and
Zell s.d.).
The models used in the estimation assume that wages of municipal civil servants were
set autonomously by local authorities and reflected the labor productivity which,
according to a production function, was determined by the demographic conditions
(distribution of population according to sex, age, occupation and the free or slave
juridical status) and the geographic characteristics (distance to sea, altitude, climate
and soil attributes) of each municipality. The basic idea is thus to filter the data on
average wages of municipal civil servants by the available information on the
∗
Paper to be presentado at the UCLA Von Gremp Wrokshop in Economic History, April 22, 2009.
Project undertaken by Research Network on Spatial Models (www.nemesis.gov.br) based at IPEA, Rio
de Janeiro, under the financial support of Faperj/CNpq (Proc. E52 168.171/2006 /Pronex). Grateful
acknowledgements to both institutions, as well as to Márcia Pimentel for the computer assistance and
Bárbara Bravo for the data compilation. With the usual disclaims, the paper benefited from comments of
Cláudio Ferraz, Ajax Moreira, Felipe Fernandes and the participants of the seminars at IPEA/RJ, and at
the XVII Seminario de Economia Mineira held in Diamantina, November 2008, where a slightly
modified version of the paper was presented.
2
productive structure of the municipality in an attempt to clear them from eccentric
values.
The first section of the paper makes some analytical considerations on the specification
used for estimation. The second section describes data and variables constructed for the
analysis. The third presents estimation results. The last section makes an assessment of
the estimates of municipal and provincial income in 1872.
Analytical considerations
The basic hypothesis is that municipal civil servants remuneration were set
autonomously by local authorities. The main motivation for this assumption is the
empirical finding that average wages of municipal civil servants in the Statistical
Report of 1878 display significant variance both within and between provinces. The
presumption is therefore that there were no strict provincial rules in the determination of
municipal civil servant wages.
Historical evidence on the decision processes about municipal budgets and civil
servants hiring are sparse and ambiguous. Among contemporary opinions it is possible
to find non compelling support to the hypothesis that the municipal economic regimen
was under the discretionary decision of municipal assemblies.1
A second hypothesis is that average wages of municipal civil servants reflected the
average income of the labor force in the municipality. The rationale for this assumption
are, on the one hand, that competition in the labor market would preclude local
authorities from paying wages to civil servant far below the median income of free
workers in the municipality.2 On the other hand, the perception of injustice and
political opposition would preclude wages of municipal civil servants far above the
median income of free workers in the municipality, in particular if wage payments
relied on local taxation.
1
Thus, authors like (Carreira 1980; Bessa 1981; Tavares Bastos 1997) made no reference at all to the
ways and means of municipal budgets or to the determination of municipal public servants pay.
(Rebouças 1875):136 mentions that the economic and municipal regimens were prerrogatives of
municipal assemblies. “Estas corporações tem receitas próprias para acudir ás competentes despezas... e,
têm, pelo Acto Addicional, os meios para occorrer (sic) às despezas de seus municípios.” However, he
contradicts himself in the next page by describing as atributes of Provincial Assemblies “ [f]ixar as
despezas provinciais e municipais, as primeiras sobre o orçamento do presidente da província, as
segundas sobre orçamento das respectivas câmaras; decretar os impostos e meios para a receita provincial
e municipal ...; crear e supprimir empregos provinciais e municipais, decretar as obras públicas da mesma
natureza (p. 135).
2
The Provincial Report of Rio de Janeiro in 195? present some evidence on municipalities which were
not able to hire municipal civil servants because wages were too low.
3
The third hypothesis specifies the determinants of production and distribution of an
agrarian society. The average income of the labor force was mainly determined by the
productivity of labor in agriculture which depended on the demographic and
geographic factors describing, respectively, the quantity and quality of the labor force
and land availability in each municipality. Slavery played a very important role
affecting both the productivity of labor and the rules conditioning the distribution of
output.3
The main demographic variables used are the number of (free) households, size of
population, as well as its distribution according to age, gender, litteracy, professional
occupation, and juridical status (free or slave). Unfortunately, the distribution of slave
property which was an important aspect in the determination of both output and
distribution of income was not compiled in Census of 1872.
The main geographic characteristics of the municipality are area; altitude; soil
attributes, erosion susceptibility, and agricultural suitability; temperature and
precipitation; among other. The distance to the sea is another important geographic
attribute in the determination of the average income of a municipality to the extent that
it affects the transport costs to both major domestic and international markets.
Additionally, it is legitimate to assume that administrative and political factors played
an important role in the determination of the number and wages of municipal civil
servants. In this way, it is reasonable to assume that provincial differences in terms of
tax base and fiscal institutions would be reflected in the number and wages of municipal
civil servants. This is particularly true in the case of the Imperial Capital (Rio de
Janeiro) and provincial capitals which, for that reason, were entitled to larger shares of
the transferences of imperial and provincial taxation revenues and provided a bigger and
more diversified bundle of public goods. Other important political factors were
perhaps the concentration of power as reflected in available indicators of political
participation of the population which probably affected both the number and the
remuneration of civil servants.
Finally, it should be kept in mind that wages of municipal civil servants are used to
estimate the average income of the (free and slave) labor force of the municipalities. To
3
If y is called output per labor force population, p, f, s, the share of proprietors, free workers, and slaves
in the labor force, and αf and αs, the labor productivity of free worker and slaves, respectively; assuming
that proprietors do no work, it is possible to show that
y = (1/h).(f. αf + s. αs) where h + f + s = 1
4
obtain the income per capita of the municipalities we have to multiply it by the size of
labor force and to divide the product by the size of total population of the
municipalities.
Sources and data
The Statistical Report of 1878 (Brasil 1878):105-116) reports the number (NFPM76) of
and annual expenditures (STFPM76) with municipal civil servants which had fixed
wage contracts for 667 Brazilian municipalities in 1876. The total number of municipal
civil servants was 4,449, out of which 3,504 had fixed wage contracts and the remaining
945 perceived a percentage of what they collected of municipal taxes. On average,
therefore, municipalities had 6.7 civil servants, out of which 5.2 had fixed wage
contracts and 1.5 received a percentage of municipal taxes.4
The annual expenditure with payment of wages fixed by contract in 1876 was 1,032,661
mil-réis, thus implying a national average wage of 295 mil-réis.
The amount paid as
percentage of tax revenues was not published for any municipality with the excuse that
there was no way of calculating it. If we assume, as seems legitimate, that there was no
intersection between the set of municipal civil servants under fixed wage contracts and
those which received a percentage of taxes, then the average fixed wages are not
distorted on this account.
The other main source of data is the Census of 1872 (Brasil 1876) which was actually
undertaken from 1872 to 1874 depending on the province in case (Puntoni ; Puntoni
(coord.) 2003). The cross identification of municipalities in the Census and the Report
of 1878 resulted in a sample of 615 observations. For that sample, the number of
municipal civil servants with fixed contract was 3,664 and the national average wage
was 212,8 mil-réis.
From the Census of 1872 the following demographic variables were obtained: the
number of “fogos” or households headed by free men (FOG72); the size of total
population (POP72) categorized by free men (HLTi,e, o), slave men (HETi,e,o), free
women (MLTi,e), slave women (METi,e,o) according to classes of age (i = 11 a 15, 16 a
4
In the summary tables of the 1978 Report reference is made to 4,682 civil servants, out of which 3,718
paid by fixed wage contracts and 964 paid by a contract of tax revenues collected but municipal data are
not available for all municipalities: for some municipalities of the provinces of Amazônia and Pará fiscal
workers from suburban districts were not included. In the administrative division of Brazil of the report
(Brasil, 1878:173), the total number of municipalities referred is 738. Probably, some of the
municipalities were installed yet or they did not report the number and the expenditure with wages of
municipal civil servants. In the 1872 Census, the number of municipalities is 643.
5
40 e 41 a 60 anos), and litteracy (e = literate or illiterate); the distribution of masculine
population according to 10 classes of professional occupation (HTPROFo) ; and,
finally, the population of non-African foreigners (ESTRL72).
The geographic variables available at municipal level were restricted to latitude
(LAT_GMS) and longitude (LONG_GMS), altitude (ALT_M) and the distance to the
sea (DSHOR) of the seat of municipalities. To include other geographic variables,
municipios have to be aggregated in minimum comparable areas between the Census of
1872 and 2000 (AMC 1872-2000) because georeferenced information at municipal
level were not available before the Census of 1991.5 Thus, only for the Census of 1991
and 2000 it became possible to superimpose geographical attributes on the maps of
municipal networks to obtain measures like the geographic area of minimum
comparable area (AMC1872-2000); the proportion of this area in different classes of
altitude in meter (PALTx), soil declivity (in degrees) or susceptibility to erosion
(PEROx), soil agricultural aptitude (PPTNCx); geo-morphological classes of soil
(PSOLOx); the average precipitation (PRE30) and the average temperature (TMP30)
of municipalities in the different seasons of the year, namely, summer (December to
February), autumn (March to May), winter (June to August), and spring (September to
November)
Soil attributes were obtained from georeferenced interpretations of satellite images
from recent decades (IBGE 2003; Anderson and Reis 2007) . Climate variables refer to
seasonal averages for the period 1961-90 obtained from interpolation of observations
from Brazilian meteorological stations (Anderson and Reis 2007). Since the figures for
climate variables are average values for a thirty year period, it is fair to assume, that
they are time invariant, as in the case of other geographic variables.
Other sources of data are the electoral lists published in the Statistical Reports of 1873,
1874 and 1875 ((Brasil 1873; Brasil 1874)) providing for each parish the number of
citizens qualified as voters (VOTANTE74) and elegibles (ELEGIVEIS74) to the
electoral colleges which, in a second turn, nominated representatives to the Provincial
and Imperial Assemblies, and to the Senate. The number of voters and elegibles in each
parish were aggregated to municipal level thus making possible to construct measures
5
In constructing AMC 1872-2000, it should be noted that the State of Acre was not part of Brazil in
1872. In addition, the states of Mato Grosso and Rondônia are joined with Amazonas and the North
Region; and the state of Tocantin is included in Region Center-West (Reis, Pimentel et al. 2007)
6
of political participation or political power concentration using the proportion of adult
population which is able to express themselves in the political system or to access its
decision echelons.
In addition to civic status criteria – like to be married or be older than 25 and
autonomous, or else to be a liberal professional, priest, civil servant, or military -- the
electoral legislation imposed annual income requirements of 200,000 mil-réis for a
voter and 400,000 mil-réis for an elegible. Thus, in addition to political participation,
the proportion of voters and elegibles give at least a rough idea of income distribution
in each municipality.
Finally, variable dummies were used to capture the differential effects of provinces
(DUFxx where xx is the IBGE state code), provincial capitals (UFCAPITAL) and the
Imperial Capital (or Municipio Neutro located in the city of Rio de Janeiro, RJ) on the
wages paid to municipal civil servants.
Tables A.1 and A.2. in the Appendix present, respectively, the list of variables and the
basic statistics for them in the most comprehensive sample of municipalities with data.
Table 1 present the distribution of the number of municipalities and the civil servants
with fixed wage contracts according to the classes of value of average wages. Figure 1
maps the geographical distribution of these classes for the minimum comparable in the
period 1872-2000 showing that, in 1876, more than 63 percent of the municipalities
paid annual average wages below 200 mil-réis, 25 percent between 200 and 400 milréis, and 12 percent higher than 400 mil-réis. In terms of number of municipal civil
servants, analogous figures were 58, 26 and 26 percent, respectively. Geographically,
municipalities which paid higher salaries were concentrated in the South and CenterSouth regions of the country terms, and in provincial capitals which paid annual average
wages above 800 mil-réis.
Figures in Table A.2. show that, in 1872, Brazilian population was 9.9 million persons
living in 1.33 million free headed households dispersed in 636 municipalities which
were located, on average, 204 km from the sea at an altitude of 336 meter. The labor
force of the country was composed of 1.1 million male and female slaves (older than 10
7
and younger than 60 years old) and 2.48 million free men (older than 15 and younger
than 60 years old).6 The non-African foreigners represented only 2% of the population.
Model estimation results
Figures of Table A.2. also show that the number of municipal civil servants represented
a negligible 0.18% of free labor force in the country. Thus it is legitimate to assume
they do not introduce problems of endogeneity in the estimation since their wages were
unlikely to affect the wages or any other attribute of the occupational distribution of the
labor force of the municipality. However, since the number of municipal civil servants
show wide variance, estimations were made using a weighted least square method using
the number of municipal civil servants with wages fixed by contracts as weights.
The first estimation (Model I) uses data at the municipal level and, therefore, can not
include geographic factors, except for latitude, longitude, distance to the sea and the
dummies for capitals. Table A.3. presents the estimation results showing that the model
is able to explain, approximately, 79% of the total variance of average wages fixed by
contracts. Since the main purpose is to estimate labor productivity based upon
demographic and geographic factors the goodness of fit is encouraging.
Though there is no particular interest in effects of specific variables, it is interesting to
observe that the following parameters were significant at the 5% level of significance
(the signal of the parameter is reported in the parenthesis): dummy for imperial capital
(-), dummies for provincial (+) capital, the distance to the sea (-), the altitude (-); the
size of population (+); the longitude (-); the percent of non-African foreigners in
population (+); percent of population in industrial and commercial professions (+);
percent of population in salaried positions (-). In addition, given the likelihood of
multicolinearity among explanatory variables, tests of significance were made for the
joint effects of some subsets of variables. Results were significant for the joint
distribution of the classes of occupational professions; the age classes for male slaves;
and provincial dummies, but not significant for the age classes of free men and women
as well as for slave women.
A suggested interpretation of the results is that major explanatory variables are proxies
of "urban" activities and locations near sea ports. The exceptions are given by
6
Since women were mainly dedicated to household activities, they were excluded from the labor force to
make the estimation of output consistent with figures from later Census years.
8
negative sign of the Imperial Capital which is probably related to the large number and
low wages paid to municipal civil servants, and the percent of population in salaried
positions which lacks an obvious explanation. On the other hand, the age distribution of
male slaves was more likely to be a proxy of rural activities, in particular related to
coffee production in the South-Center regions.
Figure A.1 presents the scatter diagram of the estimated and observed values of the
logarithm of average wages of municipal servants in 1876. The diagram shows strong
heteroskedasticity with underestimation in the case of high wages municipalities and
overestimation in the case of low wage municipalities.
The second estimation (Model II) uses data at the level of minimum comparable areas in
the period 1872 to 2000 (AMC 1872-2000) thus making possible to include the all the
available geographic variables in the specification. The results presented in Table A.4.
show significant improvements compared to the previous estimation. In terms of
goodness of fit, corrected R2 is now close to 0.83. Keeping the same urban flavor of the
previous model, the significant variables are the dummies for imperial (-) and
provincial capitals (+) the size of population (+); the number of households (+); the
number of municipal civil servants (-); the percent of non-African foreigners in the
population (+) probably related to ports; the percent of slaves older than 10 and younger
than 16 years old (+); free women older that 40 and younger than 60 (-) which are out of
the labor force; percent of population in industrial and commercial occupations (+);
percent of population in agricultural occupations (-); and 7 out of 20 provincial
dummies; latitude (+); precipitation in spring (September to November) (+), highest
class of altitude (-).
Tests of the significance for joint distribution of some subset of variables show that the
distribution of slave men and free women according to age classes; professional
occupations; seasonal temperature and precipitation; agricultural aptitude of soil; and
provincial dummies are significant factors. On the other hand, the distribution of free
men (probably captures by professions) and slave women according to age classes,
classes of soil suitability to agriculture and susceptibility to erosion did not come out
as significant factors in the determination of wages.
Figure A.2 shows that the introduction of geographic variables brings a significant
reduction in the heteroskedasticity problem observed in the previous estimation. The
map in Figure 2 show estimated values of the average wage of municipal civil servants
9
which, by assumption, is made equal the income per worker of Brazilian municipalities
in 1872. Figure 2.A presents the spatial distributions of the residuals of the regression.
Finally, estimation of a third model (Model III) including the degree of political
participation and concentration of political power in 1874 as explanatory variables did
not show significant improvements when compared to Model II. In addition, the
introduction of these variables implied a significant reduction in the sample of
municipalities and brought a possible endogeneity problem. Thus, results are not
reported and the decision is to keep the results of Model II in the remaining analysis.
Municipal income estimates
Income estimates are assumed to refer to 1872, despite the fact that wages of municipal
civil servants are from 1876. The use of the demographic structure of the Census of
1872 justifies the assumption. A supplementary assumption required is that changes
in wages and prices between the two reference years were negligible. Giving support to
this assumption, according to (Catão 1992) wholesale price index, inflation from 1872
to 1876 was merely 0.65%.7
Another main assumption for GDP estimation is the definition of labor force as the sum
of free male population between 16 and 60 years old with male and female slave
population between 11 and 60 years old. For the whole country, this hypothesis
implies a labor force equivalent of 34% of total population.
The age classes selected for the definition of the labor force as well as the exclusion of
free women are quite arbitrary assumptions. A possible alternative candidate would be
to define the labor force based upon the population with declared professions in the
Census of 1872. That implies excluding from total population all the persons under the
heading “Without profession” which includes both persons in the labor force who had
not declared a profession and the persons in the age classes outside the labor force, that
is, children and old age persons. In this case, the labor force would be equivalent to 60%
of total population. The differences between the two definitions are thus quite
significant and the first definition is preferred because it sounds more reasonable when
compared to other Censuses years.
7
However, (Lobo, Canavarros et al. 1971; Lobo, Canavarros et al. 1973) report disparate CPI inflation
rates in Ro de Janeiro ranging from 24% to 2.5% depending on the weights used in the calculations (the
larger values refer to the slave food budget); (Graça Filho 2002) report inflation rates in São joão Del
Reis of 10%, approximately, based upon a simple mean of 14 food items; analogously, (Queiros Mattoso
1986) reports 14% for Salvador, Bahia, based upon a sample of 16 food items.
10
The maps in Figures 2, 3, 4, and 5 show, respectively, the geographic distribution of
the levels of municipal income per worker (RPL), income per capita (RPC), aggregate
municipal income (RM), and the geographic density (mil-réis/km2) of municipal
income (RMD). Table 2 presents data on population, labor force and aggregate income
for provinces (UF) and major regions of the country. Figure 6 shows the provincial
distribution of income per capita and income per worker.
For the Brazilian economy estimates of national income in 1872 are 1.075 billions of
mil-réis thus implying levels of income per capita (RPC) of 108 mil-réis and income
per worker (RPL) of 299 mil-réis. Compared with the available estimate of GDP in
1900 (www.ipeadata.gov.br), the average nominal growth rate of national income from
1872 to 1900 was 4.7% p.a., approximately. Using the wholesale price index (Catão
1992) as deflator, in real terms, the rate of growth was 2.6% p.a.; and, in real per
capita terms, 0.5% p.a.
The small growth rates estimated seem hard to reconcile with the impressionistic
evidence on Brazilian development in the last quarter of the 19th c. Indeed, this period
was one of steady growth of exports, particularly of rubber, large government
investments in railroads, massive international migration flows, and the rise of
industrialization. It is true, however, that the last decade of the 19th c. was characterized
by recession and inflation. In addition, the upsurge of international immigration
during the decade would take some time to show up in the GDP figures. Finally, a
substantial part of the problem might come from the underestimation of Brazilian GDP
in the beginning of the 20th c. (Reis 1980; Reis, Blanco et al. 2004)
Among the alternative attempts to estimate the Brazilian income per capita circa 1872,
Bertola et al. (2007) is perhaps the most systematic and reliable effort. Their
methodology is based upon the cross product of the distribution of population according
to occupational categories (disaggregated by sex, age, juridical conditions and
productive sectors) in the Census of 1872 with the distribution of income according to
occupations (disaggregated by the same attributes whenever possible) obtained from
diverse sources and for different years of the 1870’s. Their income per capita figure is
113 mil-réis which is surprisingly close of the estimate presented in this paper,
especially taking account of the differences in methodology and sources of data.
Another often quoted reference is Goldsmith (1986:23) which presents figures of 122
mil-réis for Brazilian GDP per capita in 1872. Though his methodology is not clearly
11
exposed, it seems to be based upon "reasonable" conjectures about GDP growth rates
for the Brazilian economy in specific historical periods.8
Looking at the estimates of provincial income, attention should be paid to the
concentration of income as well as the relatively high levels of income per capita in the
Province of Rio de Janeiro. The provinces of Rio Grande do South and of the North
Region follow next in terms of income per capita.9 The provinces of the Center-South,
together with Bahia, Pernambuco e Paraná take an intermediate position in the
ranking, while the remaining provinces of the Northeast and the Center-West Region
display relatively low levels of income per capita.
The position of Rio de Janeiro is explained by her importance as the leading coffee
producer at the time and also by the commercial, industrial and service privileges
derived from the location of the Imperial Capital as well as from other geographic
advantages. The prominence of Rio Grande do South is mainly related to the good
quality of her soils which gave rise to both the cattle raising activities and the salted
meat industry. The North Region provinces during this period profited from the rubber
boom.
The differences between income per capita and income per worker are obviously related
to the diversity of demographic structure among the different provinces, in particular
concerning the number of slaves and free women in population. It should be observed,
however, that in almost all of the provinces the levels of income per capita are
approximately 1/3 of the levels of income per worker. The noteworthy exceptions are
perhaps Rio de Janeiro, and to a lesser degree, Minas Gerais.
Comparison with the estimates of provincial income of Goldsmith (1986), based upon
Buescu,1979) show some significant discrepancies. In particular, his figures for GDP
per capita are 198 mil-réis in the Center-South, 62 mil-réis in the Northeast, 49 milréis in South and North regions (Goldsmith, 1986:13) show much wider regional
8
Two other reference are Buescu (1978) and Santos (1980). Assuming that Brazilian exports were
equivalent to ¼ of GDP, Buescu (1979:28) estimates in 77 mil-réis the value of GDP per capita in 1872.
His figures seem exceedingly low and his model too naive to be defensible. Even less defensible are the
hypothesis made to disaggregate the GDP by main regions:
y = X.(x. + (p*m/k)) where y, x, m, denotates regional shares in income, exports, and imports
respectively, X, value of national exports, and k, the national export coefficient. Santos (1980) estimates
Amazonia regional income as 37 million mil-réis in 1875 which is also far below the estimates presented
in this paper.
9
It should be observed, however, that the sample of municipalities (AMC1872-2000) in the North region
is not statistically representative despite the large size of the geographic area.
12
differences than the results presented in Table 2. At provincial level, he estimates a
share of 68% in Rio de Janeiro and Minas Gerais together, thus much higher than the
49% presented in Table 2.
Indirect evidence on the regional distribution of GDP are provided by the share of
provinces in foreign trade in the period 1872-77 presented in Table 3 and Figure 7.
The table suggests that the regional figures of Goldsmith/Buescu (1986) tend to
overestimate the share of the Center-South and to underestimate the remaining regions.
The graph on provincial foreign trade in Figure 7, on its turn, corroborates the estimates
of provincial income presented in Table 2.
Another interesting aspect of the data on trade flows are the import (M/GDP) and export
coefficient (X/GDP) of Brazilian provinces as displayed in Figure 8. At national level,
these coefficients were 15% and 19%, respectively.10 The degree of openness of the
economy – (X+M)/GDP – may seem low for a primary export economy, but it is still
larger than the values of observed during the whole 20th c. except for the first five
years.
At provincial level, São Paulo was an outlier with the largest export coefficient (42% of
GDP) and one of the smallest import coefficients (9% of GDP). The extreme values
make it hard to appraise whether the provincial income level was over or
underestimated. A plausible speculation would be that the province was in a phase of
booming coffee export and very high domestic absorption associated with the
expansion of the agricultural frontier (e.g. imports of slaves from other provinces). In
addition, the fact that several other provinces also present disparate values of imports
and exports (PB, RN, AL, SE, ES, MT+GO+TO) suggests that imports were to some
extent overvalued or were made by large and/or traditional ports (Recife, Salvador, Rio
de Janeiro, Belém, etc.). In the case of São Paulo, imports flows destined to the Paraíba
Valey and the North Region of the province were likely to come from the ports of Rio
de Janeiro (Corte, Angra, etc.). Taxation of exports could be the explanation for the
absence of the same effect in the case of exports.
10
Buescu (1979) uses 25% reason why his estimates of income per capita are lower.
13
Secular convergence of labor productivity and income per capita in Brazil, 18722000
To analyze patterns of spatial convergence of income per capita and labor productivity
in Brazil from 1872 to 2000, the income estimates for 1872 are complemented with data
on GDP, income, population, and labor force obtained from the Demographic and
Economic Censuses of 1920, 1950, 1980 e 2000 (www.ipeadata.gov.br). For the
Census of 1920 and 1950 there was no income estimates but only GDP which, to be
precise, refer to 1919 and 1949.
The model specified is extremely simple. Specifications were restricted to nonconditional models of spatial convergence. The specification of a conditional model
with the period of estimation starting in 1872 would pose problems of endogeneity since
the income per capita in 1872 was estimated from a model using the set of conditional
variables. Only from 1920 on it is possible to specify a conditional model including
variables like infrastructure, geographical attributes, human capital, among other. The
specification and estimation of these models are postponed for a future version of the
paper.
In the non-conditional growth equation specified, for each variable -- income per
capita or labor productivity -- the growth rate is a simple function of the level of the
variable in the initial period. The basic specification of the convergence model is thus:
(1)
log (yi, t/yi, t-n) 1/n = a + b. log(yi ,t-n)
where
yi,t= (Yi,t / Popi) is GDP per capita in municipality i, Census year t
Yi,t is GDP per capita in municipality i, Census year t
Popi,t is population in municipality i, Census year t
Estimation was made for the sample of minimum comparable area of Brazilian
municipalities in the period 1872 to 2000 (AMC 1872-2000) and for the sub-periods
1872-1919, 1919-1949, 1949-1980 e 1980-2000. Though primarily determined by the
availability of Census data, the choice of sub-periods allows a fairly broad
characterization of the main phases of Brazilian development.
Up to the 1920, growth was driven by the export of primary commodities including
coffee and rubber, and to a lesser extent, cocoa and sugar cane. From 1920 to 1950, the
14
country experienced the first phase of the import substitution industrialization which
was mainly based upon light consumer goods industry. From 1950 to 1980, the import
substitution industrialization deepened going into basic raw material and capital goods
industries. By the end of this phase the economy was pretty much closed to trade with
an import coefficient of 5%, approximately. After 1980, the economy experiences the
debt crisis, hyperinflation, stagnation followed by fiscal adjustment and trade
liberalization policies. It is reasonable to assume that patterns of spatial convergence of
income per capital and labor productivity were significantly different between these
various development phases (Reis et al. 2003).
For the whole period 1872-2000, estimations were disaggregated by main regions. –
North (NO), Northeast (NE), Center-South (CS), South (SU) e Center-West (CO) – to
get a more detailed picture of patterns of spatial convergence of income per capita and
labor productivity.
The results of estimates by Ordinary Least Square (OLS) are presented in Tables 4 to 7.
Table 4 show a quite good adjustment – corrected R2 equal to 0,22 – for the growth of
income per capita for the period from 1872 to 2000, The estimate of β is -0,0037,
negative and highly significant, thus implying convergence in the distribution of income
per capita of Brazilian municipalities from 1872 to 2000. Thus, each 1% more in the
level of income per capital in 1872, implies a reduction of 0.37 percentage points in the
annual average growth rates in the period 1872-2000. Table 5 presents analogous results
for the productivity of labor (income per worker) of Brazilian municipalities from 1872
to 2000.
Estimations of the model for the selected sub-periods show that in all of them there was
convergence of income per capita among Brazilian municipalities. The values of β were
significantly negative in all sub-periods. The absolute magnitude of the parameters
show that the speed of convergence was significantly bigger in the periods 1872-1920
and 1980-2000 when [β] > 0,01. In the other two sub-periods, the speed of convergence
was smaller, particularly in the period 1919-49 when [β] was ~ 0,0057.
The suggested interpretation is that import substitution phases were characterized by
the exploitation of economies of scale, urban concentration, and relatively slow
decrease in the spatial inequality in both labor productivity and income per capita. On
the other hand, export led growth phases were characterized by intense use of natural
15
resources and the spatial dispersion of economic activities thus implying a much faster
convergence of the spatial distribution of productivity and income per capita.
A complementary observation is that the value of β in all sub-periods is significantly
larger than the one estimated for the 1872-2000 period as a whole. Thus, the processes
of spatial convergence of income per capita of municipalities in the different subperiods are not reinforcing but reversing themselves. Finally, it should be observed,
that average growth rates were much higher in the import substitution phases.
Compared to other countries, the historical process of spatial convergence of income per
capita in the Brazilian economy seems quite slow. Indeed, estimates β are close to –
0,02, both in the case of personal income in the US states in the period 1950-80 and of
income per capita of Japanese in the period 1955-87 (Barro and Sala-i-Martin 1995).
Equivalent estimates for income per capita of municipalities in Japan are –0,025 for the
period 1951-70, and –0,003 for the period 1970--2000 (Arbbia et al.). Despite all the
differences in variables, units of observation, and methods of estimation, the estimates
(except for Italy in recent decades) are twice the magnitude of those estimated for Brazil
in the periods 1950-80 and 1980-2000.
Table 6 and 7 present the regional decomposition of the analysis of spatial convergence
of income per capita for the whole period 1872-2000. Though the samples in the case of
the North and North regions are relatively small, including 14 and 18 observations,
respectively, estimates of β are negative and significant for all regions (at 8% for the
Center-West, however).
Comparing the magnitudes β in Table 6, the speed of convergences was significantly
faster in the South region where β is equal to -0,0086, compared to -0,0069 in the
North Region and even lower in the remaining three where β is very similar ranging
from -0,0053 and -0,0055.
In all the regions, however, the speed of convergence was faster (β were larger) than in
Brazil as a whole. That implies a process of regional divergence which reduces
counteracts the regional convergence observe inside each region. The concentration of
import substitution industrialization in the Center-South region of the country was,
undoubtedly, a major factor in the process of regional divergence.
Figures 9 and 10 show the results of the process of spatial convergence both in terms
of income per capita and productivity of labor in the various sub-periods.
16
Conclusions and extensions
The paper uses data of wages of municipal civil servants in 1876 to estimate the
productivity of labor and income per capita in Brazilian municipalities in 1872. The
estimation presents a satisfactory adjustment, in particular when geographic factors are
incorporated into the model. Aggregation at provincial and national levels make the
results more reliable.
For the Brazilian economy, estimates of income per capita in 1872 are 108 mil-réis,
approximately. This figure implies very small growth rates of per capita income (0.5%
p.a.) in the last quarter of the 19th c. despite the strong expansion of primary exports in
the period. Part of the problem is the massive international immigration and its impact
on the demographic growth of the period.
Comparing the income estimates with foreign trade data, Brazil appears as a relatively
closed economy in the face of the stylized facts associated with primary export
economies.
From a spatial perspective, estimation results show wide disparities of labor
productivity which ranges from 28 to 1,280 mil-réis. The rank of the province of São
Paulo with income per capita of 64 mil réis is worth mentioning.
Estimations of a non-conditional model show the slow convergence of municipal
income per capita in the period 1872-2000. The decompositon of the analysis for the
main phases of Brazilian development show that spatial patterns of convergence were
relatively more accelerated in the export led growth periods, though the average rates
of growth were higher in the import substitution periods.
Suggested extensions of research would be a meta-analysis comparing the municipal
estimates with those obtained from alternative methodologies applied to specific
municipalities or localities ((Buescu 1979; Buescu 1981; Klein 1995; Silva Jr. 2002;
Nunes 2003; Leite 2006; Monasterio and Zell s.d.).
17
Referencias bibliográficas
Anderson, K. and E. Reis (2007). The Effects of Climate Change on Profitability and
Land Uses in Brazilian Agriculture: A Municipal Cross-Section Analysis. Rio de
Janeiro, Instituto de Pesquisa Econômica Aplicada - IPEA.
Barro, R. and X. Sala-i-Martin (1995). Economic Growth. New York, Mc Graw Hill.
Bértola, L., H. Willebald, et al. (2006). An exploration of the distribution of income in
Brazil, 1839-1939. XIV International Economic History Congress, Helsinki,
Finland.
Bessa, A. L. d. (1981). História financeira de Minas Gerais em 70 anos de República.
Belo Horizonte, Secretaria do Estado da Fazenda.
Brasil, D. G. d. E. (1873). Relatório e trabalhos estatísticos apresentados ao Illm. e
Exm. Sr. Conselheiro Dr. João Alfredo Corrêa de Oliveira pelo Director Geral
Interino, Dr. José Maria do Couto. Typographia de Hyppolito José Pinto, Rio de
Janeiro.
Brasil, D. G. d. E. (1874). Relatório e trabalhos estatísticos apresentados ao Illm. e
Exm. Sr. Conselheiro Dr. João Alfredo Corrêa de Oliveira pelo Director Geral,
Conselheiro Manoel Francisco Correia. Typographia Franco-Americana, Rio de
Janeiro.
Brasil, D. G. d. E. (1876). Recenseamento Geral do Império de 1872. Rio de Janeiro,
Typ. Leuzinger / Tip. Commercial.
Brasil, D. G. d. E. (1878). Relatório e trabalhos estatísticos apresentados ao Illm. e
Exm. Sr. Conselheiro Dr. Carlos Leoncio de Carvalho Ministro e Secretario de
Estados dos Negócios do Império pelo Director Geral Conselheiro Manoel
Francisco Correia em 20 de Novembro de 1878. Typographia Nacional, Rio de
Janeiro.
Buescu, M. (1979). Brasil: disparidades de renda no passado: subsídios para o estudo
dos problemas brasileiros. Rio de Janeiro, APEC.
Buescu, M. (1981). "No Centenário da Lei Saraiva." Revista do Instituto Histórico
Geográfico Brasileiro(330): 176
Carreira, L. d. C. (1980). História Financeira e Orçamentária do Império no Brasil.
Brasília, Senado Federal/Fundação Casa de Rui Barbosa.
Catão, L. A. V. (1992). "A new wholesale price index for Brazil during the period 18701913." Revista Brasileira de Economia 46(4): 519.
Contador, C. and C. Haddad (1980). Economia Brasileira: Uma Visão Histórica. P.
Neuhaus. Rio de Janeiro, Editora Campus.
Goldsmith, R. (1986). História Financeira do Brasil. Rio de Janeiro.
IBGE (2003). Base Cartográfica Integrada Digital do Brasil ao Milionésimo Versão 1.0
para ArcGis Desktop/ArcView. Rio de Janeiro, Instituto Brasileiro de Geografia
e Estatística - IBGE, Diretoria de Geociências Coordenação de Cartografia.
Klein, H. (1995). "A participação política no Brasil do Século XIX: os votantes em São
Paulo em 1880." Dados 38(3): 527.
Leite, R. M. (2006). Desigualdades regionais brasileiras: comércio marítimo e posse de
cativos na década de 1870. Faculdade de Economia e Administração de Ribeirão
Preto. Ribeirão Preto, SP, USP.
Maddison, A. (2003). The World Economy: Historical Statistics. Paris - France, OECD.
Monasterio, L. M. and D. C. Zell (s.d.). "Uma estimativa de renda per capita municipal
na Província de São Pedro do Rio Grande do Southem 1872."
Neuhaus, P., Ed. (1980). Economia Brasileira: Uma Visão Histórica. Rio de Janeiro,
Editora Campus.
18
Nunes, N. F. M. (2003). ". A experiência em Campos dos Goytacazes (1870-1889):
Freqüência eleitoral e perfil da população votante." Dados 46(2): 329.
Puntoni (coord.), P. (2003). Os recenseamentos brasileiros no Século XIX: 1872 e 1890,
Cebrap,. São Paulo, CEBRAP.
Puntoni, P. "Os recenseamentos do Século XIX: um estudo crítico."
Rebouças, A. (1875). Provincia do Paraná dados estatisticos e esclarecimentos para os
emigrantes, publicado por ordem do Ministerio dos Negocios da Agricultura do
Commercio e das Obras Publicas. Rio de Janeiro, G. Leuzinger & Filhos.
Reis, E. (1980). "Resenha bibliográfica de Paulo Neuhaus (ed.). Economia brasileira:
uma visáo histórica. Editora Campus. Rio de Janeiro. 1980." Pesquisa e
Planejamento Econômico 10(3).
Reis, E., F. Blanco, et al. (2004). O Século XX nas Contas Nacionais. Estatísticas do
Século XX. Rio de Janeiro, IBGE.
Reis, E., M. Pimentel, et al. (2007). Áreas mínimas comparáveis para os períodos
intercensitários de 1872 a 2000. Rio de Janeiro, IPEA.
Santos, R. (1980). História Econômica da Amazônia. São Paulo, TAQ.
Silva Jr., A. L. d. M., Paulo Roberto Staudt (2002). Nota preliminar sobre os votantes da
paróquia do Rosário (Porto Alegre, 1880). Porto Alegre.
Tavares Bastos, A. C. (1997). A Província: estudo sobre a descentralização no Brasil.
Rio de Janeiro, Academia Brasileira de Letras.
19
Tablel
Distribution of the number of municipalities (AMC), civil servants, wage bill and average
wages according to average wages in 1876
Average
AMC
Civil servants
Total wage bill
wage
Classes of
average
wage in
mil-réis
Número
%
Número
%
Mil-réis
%
Mil-réis/ano
Sem valor
20
4,6
9
0,3
0 |--- 50
17
3,9
92
2,6
3.484,2
0,3
37
50 |-- 100
90 20,8
538
15,4
39.567,0
3,8
74
100 |-- 200
147 34,0
1033
29,6
146.589,0
14,3
139
200 |-- 400
104 24,1
915
26,2
265.511,2
25,8
273
400 |-- 800
49 11,3
718
20,6
388.098,2
37,7
496
Above 800
5
1,2
183
5,2
185.312,5
18,0
1018
Total
432 100,0
3488
100,0 1.028.562,1 100,0
208
20
Figure 1
Average Wages of Municipal Civil Servants in 1876 (in mil-réis)
21
Figure 2
Municipal income per worker in 1872 (in mil-réis)
Figure 2.A
Residual of the estimation
22
Table 2 – Brazil: Estimates of income, population, labor force in main regions and provinces (UF) in 1872 (values in mil-réis)
Wages of
Average
Income
% Mun.
Municipal Municipal
wages
Income
% Labor
%
Population
Labor
Civil
Income
of civil
per
civil
Civil
per
capita
% Pop.
force
servants
Income
Region/
in
force
Servants. Servants
in 1872
servants worker in
1872
1872
1872
1872
1872
1872
1876
Porvince
Code
1876
1876
1876
1872
479
North
1
393264
133,244
239
117720
63,774,528
493
151
4,0%
3,7%
9,6%
5,9%
219
Northeast
2
4631335 1,476,810
1648
403836
322,852,553
245
70
46,7%
41,1%
33,0%
30,0%
346
Centro-Sul
3
4016922 1,695,315
1299
538580
586,297,344
415
146
40,5%
47,2%
44,0%
54,6%
398
South
4
721337
247,679
396
158688
98,577,636
401
137
7,3%
6,9%
13,0%
9,2%
82
Center-West
5
163456
38,831
85
6716
3,189,936
79
24
1,6%
1,1%
0,5%
0,3%
1,074,691,997
299
108
Brasil
9923253 3,591,879
3664
1225159
334
100,0%
100,0%
100,0%
100,0%
20,744,465
185
1.13%
1.06%
2.37%
1.93%
AM+MT
513
112202
79848
87
46815
538
543
109,863
19
0.06%
0.05%
0.11%
0.01%
RR
14
5825
2101
4
235
59
59
42,090,405
155
2.74%
2.31%
3.90%
3.92%
PA
15
272089
134503
143
69590
487
507
302,079
19
0.03%
0.03%
0.14%
0.03%
AP
16
3148
2000
5
1080
216
64
21,508,636
60
3.62%
3.41%
4.97%
2.00%
MA
21
359040
106986
182
35523
195
176
9,525,440
53
1.82%
1.95%
2.59%
0.89%
PI
22
181089
64968
95
12548
132
136
32,071,989
43
7.49%
5.84%
9.83%
2.98%
CE
23
742819 256744,4
360
69366
193
153
12,668,546
54
2.36%
2.14%
2.73%
1.18%
RN
24
233979
83599
100
15798
158
165
18,996,492
50
3.79%
2.84%
2.73%
1.77%
PB
25
376226
110198
100
22435
224
186
86,331,049
103
8.41%
7.41%
7.53%
8.03%
PE
26
834314 331248,4
276
127073
460
324
19,677,033
57
3.51%
2.91%
2.59%
1.83%
AL
27
348009
95214
95
16599
175
188
7,811,698
44
1.78%
1.62%
2.81%
0.73%
SE
28
176243
60674
103
18535
180
134
114,261,671
83
13.90%
13.00%
9.20%
10.63%
BA
29
1379616
434905
337
85958
255
245
191,804,172
94
20.56%
24.06%
9.36%
17.85%
MG
31
2039735
650293
343
79759
233
222
8,071,207
98
0.83%
0.91%
1.72%
0.75%
ES
32
82137
30862
63
14335
228
246
327,972,342
310
10.66%
13.87%
13.54%
30.52%
RJ
33
1057696 588054,3
496
343995
694
658
58,449,623
70
8.44%
8.35%
10.84%
5.44%
SP
35
837354 288084,2
397
100490
253
195
9,663,682
76
1.28%
1.18%
2.16%
0.90%
PR
41
126722
54165
79
19284
244
227
15,536,356
97
1.61%
1.27%
1.23%
1.45%
SC
42
159802
52742
45
17114
380
340
73,377,598
169
4.38%
4.44%
7.42%
6.83%
RS
43
434813 232846,7
272
122290
450
460
639,563
19
0.33%
0.28%
0.41%
0.06%
TO
17
33111
9551
15
909
61
64
3,078,089
24
1.28%
1.06%
1.83%
0.29%
GO
52
127284
37149
67
5427
81
81
Source: Author estimates Obs.: Acre was not part of Brazil. Mato Grosso and Rondônia included in Amazonas and in North Region. Tocantins included in Region Center-West. .
23
Figura 3
Gráfico II.2:
Renda per capita e por trabalhador por UF circa 1876 (mil-réis)
Renda por PEA 1876
Renda per capita 1876
700
640
600
509
499
400
369
297
301
282
300
267
235
199
200
141
265
220
226
194
156
155
148
142
158
118
45
45
43
PB
49
RN
50
57
CE
90
66
183
179
160
138
107
100
90
71
93
90
89
105
79
64
39
24
TO
RS
SC
PR
SP
RJ
ES
MG
BA
SE
AL
PE
PI
MA
AP
PA
RR
AM+MT
0
Brasil
Mil-réis
500
24
Figure 4
Income per capita of Brazilian municipalities in 1872 (mil-réis)
25
Figure 5
Aggregate income of Brazilian municipalities in 1872 (mil-réis)
26
Figure 6
Geographic density of income in Brazilian municipalities (mil-réis/km2)
27
Tabela 3:
Regional shares of exports, imports , and the volume of trade in the period 1872/73-76/77
(%)
Imp
Exp
Imp+Exp
4,6%
6,4%
5,6%
North
30,1%
25,8%
27,8%
Northeast
58,7%
61,3%
60,1%
Centro-Sul
5,8%
6,4%
6,1%
South
0,9%
0,1%
0,4%
Center-West
Source: Brasil. Directoria Geral de Estatistica. Boletim Comemorativo da Exposição Nacional de
1908. Rio de Janeiro. Typographia da Estatistica 1908, p. 108.
28
Figure 7
Share of income in 1872 (%Y) versus the share of the volume of trade (% X+M) in Brazilian provinces
(log scale)
Percentual da renda 1876 (% Y) versus percentual do volume de comércio (% X+M) nas provincias do Império do Brasil (escala logarítimica)
MT+GO+TO
RS
SC
PR
ES
SP
RJ+MG
BA
%Y
SE
AL
PE
RN
PB
CE
PI
MA
PA
AM
% X+M
100,0%
52%48%
10,0%
12%
10%
11%
7%
6%
2%
1,0%
4%
3%
2%
2%
8%
5%
5%
2%
2%
1%
8%
2%
2%
1%
1%
1%
1%
1%
1% 1%
0%
0,1%
0%
0%
0,0%
0%
1%
0%
0%
1%
29
Figure 8
Export (X/Y) and import (M/Y) coefficients for the Brazilian provinces, circa 1872
Coeficientes de importação (M/Y) e de exportação (X/Y) para as províncias do Império do Brasil
circa 1876 (%)
M/Y
X/Y
45%
42%
40%
33%
35%
31%
30%
30%
30%
24%
25%
20%
20%
17%
15%
14%
15%
3%
2%
1%1%
15%
14%
12%
11%
9%
19%
18%
16%
13%
10%
5%
19%
18%
17%
13%
11%
9%
0%
1%
1%
1%
0%
1%
4%
2%
3%
BR
MT+GO+TO
RS
SC
PR
ES
SP
RJ+MG
BA
SE
AL
PE
RN
PB
CE
PI
MA
PA
AM
0%
30
Table 4
Brazil: Convergence of the municipal distribution (AMC
1872-2000) of labor productivity (GDP/Labor force)
for selected sub-periods from 1872 to 2000
Dependente
Log
(PIB/
PEA)
Log
(PIB/
PEA)
Log
(PIB/
PEA)
Log
(PIB/
PEA)
Log
(PIB/
PEA)
Região
Brasil
Brasil
Brasil
Brasil
Brasil
Período
18722000
18721919
19191980
19191949
19802000
N,obs,
380
380
427
n.d.
431
R2 corr,
0,15
0,20
0,09
n.d.
0,18
Beta
-0,0034
-0,011
-0,0042
n.d.
-0,014
Erro padrão
-0,0005
0,001
-0,0006
n.d.
-0,0014
31
Table 5
Brazil and regions : Convergence of the municipal distribution (AMC 18722000) of labor productivity (GDP/Labor force)
In the period 1872-2000
Dependente
Log
(PIB/
PEA)
Log
(PIB/
PEA)
Log
(PIB/
PEA)
Log
(PIB/
PEA)
Log
(PIB/
PEA)
Log
(PIB/
PEA)
Região
Brasil
North
Northeast
Centro-Sul
Sul
Centro Oeste
18722000
18722000
18722000
18722000
18722000
18722000
Período
N.obs.
380
14
190
134
20
18
R2 corr.
0,15
0,59
0,23
0,27
0,74
0,13
Beta
-0,0034
-0,007
-0,004
-0,005
-0,008
-0,005
Erro padrão
-0,0005 -0,0017
-0,0006
-0,0007 -0,0001
-0,0027
32
Table 6
Brazil: Convergence of the municipal distribution (AMC
1872-2000) income per capita (GDP/POP)
for selected sub-periods from 1872 to 2000
Dependente
Log
(RPC)
Log
(RPC)
Log
(RPC)
Log
(RPC)
Log
(RPC)
Região
Brasil
Brasil
Brasil
Brasil
Brasil
Período
18722000
18721919
19191949
19491980
19802000
N,obs,
380
380
427
430
431
R2 corr,
0,43
0,36
0,038
0,45
0,01
Beta
-0,0046
-0,012
-0,0058
-0,013
-0,002
Erro padrão
-0,0003
-0,0008
-0,0014
-0,0007
-0,0008
33
Table 7
Brazil and regions : Convergence of the municipal distribution (AMC
1872-2000) of income per capita (GDP/POP)
in the period 1872-2000
Dependente
Log
(RPC)
Log
(RPC)
Log
(RPC)
Log
(RPC)
Log
(RPC)
Log
(RPC)
Região
Brasil
NO
NE
CS
SU
CO
Período
18722000
18722000
18722000
18722000
18722000
18722000
N,obs,
380
14
190
134
20
18
R2 corr,
0,43
0,59
0,73
0,77
0,83
0,25
Beta
-0,0046
-0,007
-0,006
-0,007
-0,006
-0,006
Erro padrão
-0,0003
-0,0015
-0,0003
-0,0003
-0,0006
-0,0021
34
Figure 9
Brazil: Geographic distribution (AMC 1872-2000) of labor productivity (PIB/PEA)
in 1872, 1919, 1980 e 2000 (units and scales variable)
35
Figure 10
Brasil: Geographic distribution (AMC 1872-2000) of income per capita (PIB/POP)
in 1872, 1919, 1949, 1980 and 2000 (units and escales variables).
36
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
Apêndice
Tabela A.1
Lista de variáveis
POP72 – População total em 1872
FOG72 -- Fogos - Homens livres (domicílios de homens livres) em 1872
HTT72 -- População masculina (livre e escrava) em 1872
HET111572 idade - 11 a 15 anos - Homens escravos (pop. presente) em 1872
MET111572 idade - 11 a 15 anos - Mulheres escravas (pop. presente) em 1872
HLT164072 – População masculina livre entre 16 e 40 anos em 1872
HET164072– População masculina escrava entre 16 e 40 anos em 1872
MET164072– População feminina escrava entre 16 e 40 anos em 1872
HLT416072 idade - 41 a 60 anos - Homens livres (pop. presente) em 1872
MLT416072 idade - 41 a 60 anos - Mulheres livres (pop. presente) em 1872
HET416072 idade - 41 a 60 anos - Homens escravos (pop. presente) em 1872
MET416072 idade - 41 a 60 anos - Mulheres escravas (pop. presente) 3m 1872
ESTRL72 -- Estrangeiros livres, não-africanos em 1872
HTALF72 – Homens livres alfabetizados em 1872
MTALF72 – Mulheres livre alfabetizadas em 1872
HTPROFCAP72 – Capitalistas e proprietários em 1872
HTPROFSAL72 -- Assalariados - criados e jornaleiros - em 1872
HTPROFDOM72 -- Serviço doméstico em 1872
HTPROFLIB72 – Profissionais liberais em 1872
HTPROFMIL72 – Militares em 1872
HTPROFINC72 – Profissões industriais e comerciais em 1872
HTPROFMM72 – Profissões mecânicas e manuais em 1872
HTPROFAGR72 – Profissões agropecuárias em 1872
PROF0105_som_ger_1872,PROF0106_som_ger_1872,PROF0107_1872,
PROF0109_som_ger_1872,PROF0110_som_ger_1872,PROF0111
SUM(PROF0101_som_ger_1872,PROF0102_som_ger_1872,PROF0103_1872,
ELEGÍVEL75 – Número de cidadãos qualificados como elegíveis na lista eleitoral
de 1875
VOTANTE75 – Número de cidadãos qualificados como votantes na lista eleitoral
de 1875
LAT_GMS – Latitude da localidade sede a AMC1872-2000
LONG_GMS – Latitude da localidade sede a AMC1872-2000
PALT1 -- Proporção da área do município com altitude de 0 a 99 metros
PALT2 -- Proporção da área do município com altitude de 100 a 199 metros
PALT3 -- Proporção da área do município com altitude de 200 a 499 metros
PALT4 -- Proporção da área do município com altitude de 500 a 799 metros
PALT5 Proporção da área do município com altitude de 800 a 1199 metros
PALT6 Proporção da área do município com altitude de 1200 a 1799 metros
PALT7 Proporção da área do município com altitude de 1800 a 3000 metros
PERO1 Proporção da area do município com limitação moderada de erosão =
proporção nas classes 2 e 3 (7.5 e 15% inclinação)
PERO2 proporção da area do município com limitação acentuado de erosão =
proporção nas classes 4 e 5 (30 e 45% inclinação)
PPTNC1 Proporção da área de solo com potencialidade agrícola na classe 1 - alta
37
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
PPTNC2 Proporção da área de solo com potencialidade agrícola na classe 2 média a alta
PPTNC3 Proporção da área de solo com potencialidade agrícola na classe 3 média a baixa
PPTNC4 Proporção da área de solo com potencialidade agrícola na classe 4 - baixa
PPTNC5 Proporção da área de solo com potencialidade agrícola na classe 5 desaconselhável ao uso agrícola
PSOLO1 Proporção da área de solo na classe 1
PSOLO10 Proporção da área de solo na classe 10
PSOLO11 Proporção da área de solo na classe 11
PSOLO12 Proporção da área de solo na classe 12
PSOLO13 Proporção da área de solo na classe 13
PSOLO2 Proporção da área de solo na classe 2
PSOLO3 Proporção da área de solo na classe 3
PSOLO4 Proporção da área de solo na classe 4
PSOLO5 Proporção da área de solo na classe 5
PSOLO6 Proporção da área de solo na classe 6
PSOLO7 Proporção da área de solo na classe 7
PSOLO8 Proporção da área de solo na classe 8
PSOLO9 Proporção da área de solo na classe 9
PRE30DJF – Precipitação média nos meses de verão (dezembro a fevereiro), 196190
PRE30JJA Precipitação média nos meses de inverno (julho a agosto), 1961-90
PRE30MAM
Precipitação média nos meses de outono (março a maio), 1961-90
PRE30SON
Precipitação média nos meses de primavera (setembro a
novembro), 1961-90
TMP30DJF
Temperatura média nos meses de verão (dezembro a fevereiro),
1961-90
TMP30JJA
Temperatura média nos meses de inverno (julho a agosto), 196190
TMP30MAM
Temperatura média nos meses de outono (março a maio), 1961-90
TMP30SON
Temperatura média nos meses de primavera (setembro a
novembro), 1961-90
38
Tabela A.2.:
Basic statistics of variables in the sample of municipalities of the Statistical Report of 1978
identified in the Census of 1872
Variável
Soma
Média
Desvio
Mediana Máximo
Mínimo
N. obs.
213.162,0
336,2
329,1
210,0
1.179,0
1,4
634,0
ALT_M
129.176,9
203,7
259,6
107,9
1.620,3
0,1
634,0
DSHOR
205.412,0
323,0
2.704,7
24,0
66.213,0
0,0
636,0
ESTRL72
1.335.467,0
2.099,8
2.588,9
1.489,5
43.911,0
0,0
636,0
FOG72
74.338,0
116,9
167,6
63,0
1.935,0
0,0
636,0
HET111572
378.822,0
595,6
963,7
262,0
11.394,0
0,0
636,0
HET164072
140.516,0
220,9
457,8
79,0
6.623,0
0,0
636,0
HET416072
1.888.331,0
2.969,1
3.784,8
2.023,5
69.393,0
11,0
636,0
HLT164072
593.246,0
932,8
1.205,3
593,5
20.690,0
0,0
636,0
HLT416072
1.013.587,0
1.593,7
3.151,1
999,0
65.384,0
48,0
636,0
HTALF72
3.239,0
36.421,0
0,0
636,0
HTPROFAGR72 2.948.370,0 4.635,8 4.180,1
37.491,0
58,9
168,8
14,0
2.007,0
0,0
636,0
HTPROFCAP72
1.262,0
55.012,0
0,0
636,0
HTPROFDOM72 1.347.761,0 2.119,1 3.114,0
160.121,0
251,8
1.057,7
104,5
24.303,0
0,0
636,0
HTPROFINC72
82.619,0
129,9
626,2
55,5
14.425,0
0,0
636,0
HTPROFLIB72
50.898,0
80,0
584,9
6,0
13.513,0
0,0
636,0
HTPROFMIL72
861.343,0
1.354,3
1.985,7
760,0
30.909,0
0,0
636,0
HTPROFMM72
576.905,0
907,1
1.761,8
301,5
25.686,0
0,0
636,0
HTPROFSAL72
5.120.221,0
8.050,7
9.328,2
5.658,5
158.766,0
279,0
636,0
HTT72
-9.062,6
-14,3
8,4
-13,0
0,0
-32,6
634,0
LAT_GRAUS
-27.567,8
-43,5
5,5
-43,4
-32,4
-64,7
634,0
LONG_GRAUS
65.526,0
103,0
152,2
58,0
2.209,0
0,0
636,0
MET111572
336.309,0
528,8
836,4
248,0
12.282,0
0,0
636,0
MET164072
114.791,0
180,5
328,7
69,5
4.704,0
0,0
636,0
MET416072
1.837.713,0
2.889,5
3.031,9
2.038,0
42.681,0
6,0
636,0
MLT164072
541.237,0
851,0
1.000,9
562,5
14.103,0
1,0
636,0
MLT416072
551.207,0
866,7
1.823,0
484,5
34.101,0
8,0
636,0
MTALF72
4.605,0
7,2
9,3
5,0
187,0
0,0
642,0
NUMFPM78
3.664,0
6,0
8,8
4,0
177,0
1,0
615,0
N_EMPR_VENC
FIXO_1878
POP72
SALMFPM78
UF
9.923.253,0 15.602,6 17.103,3
130.653,5
212,8
202,2
19.105,0
29,7
8,2
Fonte: Brasil ( 1876 and 1878). Elaboração do autor
11.352,5 274.972,0
153,3
2.606,4
29,0
52,0
535,0
0,0
13,0
636,0
614,0
643,0
39
Tabela A.3.
Estimates of average wages of municipal civil servants in 1867
Weighted least squared by the number of civil servants with fixed wage contract
Municipalities in 1872
# Observations
Degrees of freedom
R2 corrected
F-value Root MSE
552
498
0,79
38.5
1.07
Variable
Parameter estimate
Standard error t value
PR > |t|
Intercept
1.35
1.37
0.99
0.32
LOG_NUMFPM78
-0.09
0.05
-1.92
0.06
LOG_POP72
0.35
0.06
5.44
<.0001
LOG_FOG72
0.09
0.06
1.52
0.13
P_HTT72
-3.40
1.69
-2.02
0.04
P_ESTRL72
1.49
0.69
2.16
0.03
P_HET111572
16.85
8.69
1.94
0.05
P_HLT164072
0.96
1.29
0.74
0.46
P_HET164072
-0.68
2.66
-0.26
0.80
P_HLT416072
0.75
1.97
0.38
0.71
P_HET416072
7.38
4.01
1.84
0.07
P_HTALF72
0.75
0.89
0.84
0.40
P_MET111572
-13.11
9.27
-1.41
0.16
P_MLT164072
-0.89
1.27
-0.70
0.48
P_MET164072
1.84
3.01
0.61
0.54
P_MLT416072
-2.71
1.96
-1.38
0.17
P_MET416072
0.20
4.49
0.04
0.96
P_MTALF72
-1.06
1.08
-0.98
0.33
P_HTPROFLIB72
4.32
3.81
1.13
0.26
P_HTPROFMIL72
4.22
2.56
1.65
0.10
P_HTPROFCAP72
0.13
3.23
0.04
0.97
P_HTPROFINC72
5.59
2.51
2.23
0.03
P_HTPROFMM72
0.84
0.55
1.54
0.13
P_HTPROFAGR72
-0.41
0.21
-1.98
0.05
P_HTPROFSAL72
-1.24
0.50
-2.49
0.01
P_HTPROFDOM72
0.32
0.34
0.96
0.34
DSHOR
0.00
0.00
-3.33
0.00
ALT_M
0.00
0.00
-3.27
0.00
LAT_GRAUS
-0.01
0.02
-0.53
0.59
LONG_GRAUS
-0.06
0.02
-2.76
0.01
LOG_AREAMUN
-0.05
0.03
-1.63
0.10
DUMMY_MUN_NEUTRO
-1.70
0.21
-8.13
<.0001
DUMMY_CAPITAL
0.64
0.11
5.66
<.0001
DUF13
0.47
0.50
0.93
0.35
DUF15
-0.08
0.43
-0.17
0.86
DUF16
0.10
0.69
0.15
0.88
DUF17
-0.53
0.36
-1.45
0.15
DUF21
-0.80
0.36
-2.20
0.03
DUF22
-0.47
0.34
-1.36
0.17
DUF23
-0.36
0.33
-1.08
0.28
DUF24
0.01
0.34
0.02
0.98
DUF25
0.14
0.33
0.41
0.68
DUF26
-0.03
0.30
-0.09
0.93
DUF27
-0.26
0.29
-0.89
0.37
DUF28
-0.29
0.28
-1.02
0.31
DUF29
-0.66
0.22
-3.04
0.00
DUF31
-0.41
0.13
-3.10
0.00
DUF32
-0.28
0.19
-1.50
0.13
40
DUF35
DUF41
DUF42
DUF43
DUF50
DUF51
DUF52
-0.30
-0.37
-0.74
-0.48
0.00
0.12
-0.57
0.15
0.22
0.27
0.25
0.53
0.38
0.25
Obs.: P_ refer to the percent of population for the variable in case.
-2.00
-1.67
-2.70
-1.90
0.01
0.31
-2.31
0.05
0.10
0.01
0.06
0.99
0.75
0.02
41
Figure A.1.
Observed and predicted log values of wages of municipal civil servants in 1876
(Reg04W3)
8
7
Predicted
6
5
4
3
2
2
3
4
Obse5rved
6
7
8
42
Tabela A.4.
Estimates of average wages of municipal civil servants in 1867
Weighted least squared by the number of civil servants with fixed wage contract
AMC 1872-2000
Number of observations
Degrees of freedom
R2 corrected Root MSE
454
379
0,83
1.19
Variable
Paremeter
Standard t Value
Pr > |t|
Estimate
Error
Intercept
-4.56
5.63
-0.81
0.42
LOG_NUMFPM78
-0.23
0.06
-3.91
0.00
LOG_POP72
0.38
0.08
4.79
<.0001
LOG_FOG72
0.14
0.07
2.14
0.03
P_HTT72
-4.27
2.34
-1.82
0.07
P_ESTRL72
2.06
0.90
2.28
0.02
P_HET111572
24.36
12.09
2.01
0.04
P_HLT164072
3.16
1.78
1.78
0.08
P_HET164072
2.74
3.41
0.80
0.42
P_HLT416072
2.11
2.74
0.77
0.44
P_HET416072
5.88
5.08
1.16
0.25
P_HTALF72
1.92
1.25
1.54
0.12
P_MET111572
0.07
12.56
0.01
1.00
P_MLT164072
-2.27
1.72
-1.32
0.19
P_MET164072
-6.00
4.34
-1.38
0.17
P_MLT416072
-7.43
2.87
-2.59
0.01
P_MET416072
7.25
6.16
1.18
0.24
P_MTALF72
-2.49
1.60
-1.55
0.12
P_HTPROFLIB72
2.63
5.51
0.48
0.63
P_HTPROFMIL72
9.77
5.10
1.92
0.06
P_HTPROFCAP72
-1.34
5.94
-0.23
0.82
P_HTPROFINC72
12.66
3.61
3.51
0.00
P_HTPROFMM72
0.56
0.69
0.81
0.42
P_HTPROFAGR72
-0.55
0.28
-1.95
0.05
P_HTPROFSAL72
-0.90
0.67
-1.34
0.18
P_HTPROFDOM72
-0.36
0.46
-0.79
0.43
DUMMY_MUN_NEUTRO
-2.30
0.31
-7.34
<.0001
DUMMY_CAPITAL
0.52
0.10
5.10
<.0001
DUF13
-1.37
0.72
-1.89
0.06
DUF14
-2.83
1.03
-2.75
0.01
DUF15
-0.31
0.60
-0.53
0.60
DUF16
0.49
0.84
0.58
0.56
DUF17
-0.79
0.47
-1.67
0.10
DUF21
-0.97
0.46
-2.11
0.04
DUF22
-1.07
0.46
-2.35
0.02
DUF23
-0.65
0.43
-1.49
0.14
DUF24
-0.25
0.42
-0.60
0.55
DUF25
-0.18
0.43
-0.41
0.68
DUF26
-0.37
0.40
-0.91
0.36
DUF27
-0.79
0.38
-2.05
0.04
DUF28
-0.53
0.37
-1.42
0.16
DUF29
-0.94
0.29
-3.21
0.00
DUF31
DUF32
DUF35
DUF41
DUF42
-0.28
-0.31
-0.12
-0.66
-0.57
0.19
0.23
0.20
0.30
0.37
-1.51
-1.33
-0.59
-2.18
-1.56
0.13
0.18
0.55
0.03
0.12
43
Tabela A.4.
Estimates of average wages of municipal civil servants in 1867
Weighted least squared by the number of civil servants with fixed wage contract
AMC 1872-2000
Number of observations
Degrees of freedom
R2 corrected Root MSE
454
379
0,83
1.19
Variable
Paremeter
Standard t Value
Pr > |t|
Estimate
Error
DUF43
-0.70
0.53
-1.33
0.19
DUF51
-0.04
0.83
-0.04
0.96
DUF52
-0.75
0.33
-2.26
0.02
ALT_M
0.00
0.00
0.38
0.70
LAT_GRAUS
0.08
0.03
2.54
0.01
LONG_GRAUS
-0.06
0.03
-2.32
0.02
LOG_AREAMUN
-0.15
0.04
-4.22
<.0001
TMP30DJF
0.14
0.17
0.83
0.41
PRE30DJF
0.00
0.00
-1.56
0.12
TMP30MAM
0.01
0.17
0.06
0.95
PRE30MAM
0.00
0.00
0.24
0.81
TMP30JJA
-0.20
0.13
-1.61
0.11
PRE30JJA
0.00
0.00
-1.06
0.29
TMP30SON
0.13
0.14
0.88
0.38
PRE30SON
0.01
0.00
4.24
<.0001
PERO1
-0.18
0.36
-0.50
0.62
PERO2
-0.16
0.36
-0.45
0.65
PALT1
0.04
0.19
0.19
0.85
PALT3
-0.23
0.20
-1.11
0.27
PALT4
-0.07
0.25
-0.28
0.78
PALT5
-0.18
0.39
-0.46
0.65
PALT6
0.86
1.10
0.78
0.43
PALT7
-21.36
10.84
-1.97
0.05
PSOLO1
5.63
5.29
1.06
0.29
PSOLO2
5.83
5.28
1.10
0.27
PSOLO3
5.99
5.36
1.12
0.26
PSOLO4
5.31
5.28
1.01
0.32
PSOLO5
5.49
5.22
1.05
0.29
PSOLO6
5.41
5.28
1.02
0.31
PSOLO7
5.94
5.31
1.12
0.26
PSOLO8
5.89
5.31
1.11
0.27
PSOLO9
7.50
5.35
1.40
0.16
PSOLO10
5.68
5.30
1.07
0.28
PSOLO11
5.94
5.21
1.14
0.26
PSOLO12
4.39
21.09
0.21
0.84
DSHOR
0.00
0.00
-1.86
0.06
PPTNC1
-0.76
0.40
-1.92
0.06
PPTNC2
0.17
0.17
0.98
0.33
PPTNC3
-0.83
0.91
-0.91
0.36
PPTNC4
0.34
0.14
2.46
0.01
Intercept
-4.55579
5.63219
-0.81
0.4192
44
Figura A.2.
Observed and predicted log values of wages of municipal civil servants in 1876
(Reg04AMCW_3L)
Fig.A.2.
8
7
Predicted
6
5
4
3
3
4
5
6
Observed
7
8