the discrepancies caused by differences in estimates, either in size or in extremes of income of the intervals for the first three years of the distribution series, when regressions were performed, were either offset or reduced, without ruining the results.A final methodological comment on the estimates of inequality measures used, Gini, Theil and P. Our estimates usually have different magnitudes from those of FGV/IPEA, included below for comparison of trends. It is possible and quite likely that this is due to the form of the information used. This research used the information published by IBGE on its website.On the other hand, FGV and IPEA use a database with more information, including individual information. Thus, the calculations of the measures may differ due to the provision of information by size and amount of income intervals. The source of the information also differs, either from samples or the entire survey.Data for income brackets and respective average incomes, as published by the IBGE, were used in this work. Thus, the respective calculations of theGini coefficient, calculated and published, were made also taking into account the fractions of inequality within each income bracket, which this study, or any other that uses only data published by the censuses, does not have access to. For this reason, estimates of income inequality made here differ from those made by IBGE.There were also methodological adjustments to the IBGE surveys. One of the striking differences was the change in reference periods used. Until 1991, the period used to capture occupied people was 12 months (from September 1 of the year preceding the Census until August 31 of the Census year). In 2000 and 2010, the period to capture occupied person was changed to the last full week of July. This change, in itself, is reflected in the captured work income.For income distributions, however, which are calculated for the entire working population, these changes should be diluted due to the huge amount of information, according to the Law of Large Numbers, and will have little influence on any discrepancies caused. Thus, the larger the amount of aggregated information, the lower the possibility of significant differences between magnitudes, values and data estimates.In the case of P, however, the magnitudes of its results matters less than the trends shown by them over time, which relate to variations in movements that occur on the income scales and occupational structures. Not that the magnitudes have no validity, after all they are part of the measurement, but, given the methodological considerations made above, the evolution and swaying of the magnitudes end up having greater analytical relevance here.Table 1Income distributions of the economically paid active populationBrazil: 1960, 1970, 1980, 2000 e 201019601970198020002010Income*Freq.**Income*Freq.**Income*Freq.**Income*Freq.**Income*Freq.**1,1852,6653,8725,2177,83314,3832,34114,545422891222428683153173859212156,5123,9174,4224,6309,5613,214165470933937704351131831552856660296221651237212102261620329709592952250711429578574118482341992532107943947,23132,0242,2385576,91064224367663593117311670882688377727430371629150,2464,7778,513152030366773612216275682044629206994978595843220010896,58 a18129 b284,8 a25745 b11616 a39342 b649,2 a60617 b1475 a84160 bSource: IBGE; *Current values **Thousands; a average incomes, nominal values; b- total frequencies.Table 2P index and income, frequency and interaction effects and Gini CoefficientBrazil: 1960, 1970, 1980, 2000 e 2010Measures19601970198020002010Income effect0,1530,1610,1440,1810,152Frequency effect0,1690,2120,2140,1720,216Interaction effect0,2250,2070,1910,2080,251P0,300,450,470,410,32Gini a0,410,490,490,530,43Ginib0,530,580,580,590,53Theil a0,580,820,840,750,70Source: previous table; a own estimates; b- FGV (Fundacao Getulio Vargas) estimates using IPEA data.The results in Table 2 immediately indicate that the