416 APPENDIX A Methods Used for Analysis of Distributions ;
As mentioned earlier, the availability of data only at the concelho level was the major difficulty in the use of sophisticated techniques for a quantitative analysis of the regional variations of different Attributes. Data available for certain attributes for the sixty-nine sub-divisions was used for calculating the correlation coefficients for certain distributions as given in Table No.App-Ml Appendix -A. -H. However, this being possible for only a limited number of attributes, certain other techniques had to be used for this purpose. Two such techniques have been used in this work for the analysis of spatial variation using the data at the concelho level (and in some cases where the village level data were available the 3ame was plotted for smaller units consisting of groups of villages as per Appendix A.I. and Fig.App.A.1.)' Firstly, the Lorenz curve proved to be a simple yet useful technique for visualizing the regional variations and the extent of evenness or unevenness of the distribution over the area. The points showing the position of each concelho or region on the curve have been shown by Roman numbers, key for which is given at the end of this Appendix. This simple technique brings out clearly the variations in most cases
1. Villages were grouped into sixtynine utfits. These sub divisions were based on the available data for census units of i960 census (GGDD, 1966) and wherever necessary grouping of such jjnits with due regard to relief, concelho boundaries and the comparability of size. 417
though it fails to bring out precisely the relationship between the average for the area and the concentration in one or more concelhos. It is useful only for indicating the relative signific ance of areas of concentration. Comparison with the average for the whole area provides a better measure for concentrations and diffusion in different parts of the area.
A second method of mapping the location quotient for certain attributes, especially in case of attributes for which data were available for units at lower level, proved to be more fruitful for identifying the areas of concentration. Such an identifica tion was essential while searching for certain relationships related to physical or economic locational advantages.
The use of such simple techniques can be questioned on the grounds that they provide only a crude picture of the regional variations. However, it has to be borne in mind that in the absence of data at suitable level for the use of more sophisticated <*• 2 techniques this method can be considered as;fairly reliable one The problem is characteristic of underdeveloped areas (especially of economically backward areas). The fact that the area under study was under alien rule for a long time, and that too, under the rule of a power which itself does not belong to the economically advanced ones, cannot be overWWci in -tKis regard.
The largest concelho covers more than 20 per cent of the total area. Data at such level can be used only for broad generalizations. 413
The factors of production in the secondary type of activities, •!•»« industries, trade and transportation, have been exposed to efforts for development in the area under study in more recent times. Mining which is based on primary products (ores), is organized like industries; and hence, from this point, can be considered to belong to secondary type. All these secondary type of activities were not developed in the area to any consider able extent before fifties. A casual inspection of the regional incomes (^ig.App.B.l, Table App.J3.II) brings this out clearly.
Thus the data for such attributes as related to industrial development are not available for the uae of sophisticated techniques of analysis to bring out finer aspects of relationships. The officially published data are either at district or concelho levels (GGDD, 1963 and 1966). efforts made towards collection of data from official records, Government or private, were also not much successful mainly on account of the lack of such data and sometimes due to the reluctance of such sources to part with the same. The observations often mentioned in the text were made in field during the various, long as well as,short field trips undertaken at different times from 1966 to 1971. $his has to be noted here as the lack of quantitative data-in certain respects^ has been compensated by such observations though to a limited extent. Besides the Lorenz Curve, the location Quotients, regression analysis and field observations used for the analysis of the distribution of the various economic activities or the resource utilization, a different technique has been used in the analysis 4_i3 of the transportation network. This is similar to that used by Kansky (1963) for studying the evolution of road trans portation networks though in a slightly different manner. Again, the technique used by Kansky for the selection of initial points on the basis of economic data could not be employed directly. Thus, simple consideration of size and functions of a settlement were taken into account. The maps showing different stages in the simulation of the road network are based on such considerations (Figs. 10.3, 10.4, 10.5). Ports on the coast, the passes in the mountains (Western Ghats), the physiographic elements, major urban centres, administrative centres etc were taken into account while developing this model of the evolution of the road network.
Finally, as is the case with most of the studies of this nature, at many places the explanations provided are of the cause and effect type. Though this method for explanation provides a powerful model for analysis of geographical problems, its limitations have to be borne in mind while using it for such explanations . They have been often used to indicate the significance of the necessary condition for an occurence though it could not be tested whether it was always the sufficient condition to explain the relationships.
3. Kansky (1963) has used income statistics as the basis for choosing the vertices in the network. 4. As Harvey (1969, pp.406) has rightly pointed in his discussion on the cause and effect models. 423
I o co O 00 en rvic e tr z n o O (J 0-* =• 00 z <
po r T3 CI i/i i- -o c V o UJ T( >fl -c o Z b i_ i_ **•*-* <-> < h- h- O c < CO o ,
O - o O uoi||ioi sy co o rr o Unit Name of the Unit Villages included v.ith 1971 No. Census No. (COMCflLHO OF PjjKNjBM) Arambol (1-3-22), Paliem (1-3-23), i^uerim (1-3-24), ^iraool (1-3-25), 1 ^iraool Corgao (1-3-26). Paroem (1-3-17), Aagarvado (1-3-lS), 2 Aagarvado Chopdem (I-3-19), Morgiro (1-3-20), Mandrem (1-3-21 ). Darealim (1-3-Hj, Virnora (1-3-15), m 3 Pernera uem (1 -3-1t>), Pernem (1-3-27). 4 Ca snem Poroscodem (1-3-1), Casnem (1-3-2), Vnberem (1-3-3). Oguem. (1-3-4), ^amboxem (1 -3—5) > Torxem (1-3-6), Mopa (1-3-7), Varconda (1-3-12). 5 Ghandel Ghandel (1-3-3), Alorna (1-3-9), Ibrarnpur (1-3-10), Cansarvordem (1-3-11) OzoriiH (1-3-13). (CONOELHO OF BARDSZ) 6 3iolim /injuria (1-2-29), Assagao (1-2-33), iiarnn (1-2-34), Cxel (1-2-35), Siolim (1-2-36), Cunohelim (1-2-37), Camurlim (1-1-1). Calangute Oalangute (1-2-21;, Salisao (1-2-22), Parra (1-2-26), Nagoa (1-2-27), Arpora (1-2-28), Verla (I-2-30). iSfexu 1 Pilerne (1-2-18), Nerul (1-2-19), Candolim (1-2-20). 9 3erula .Jerule (1-2-17), Sangolda (1-2-23). 10 ilapusa Moira (1-2-9), Veassa5m (1-2-14), Punala (1-2-15), Paliem (1-2-16), Guirira (1-2-24), Bastora (1-2-25), Oauoa (1-2-31), Corlim (1-2-32), Map us a (1-2-42) 422 Unit Name of the Unit Tillages included with I97I No. Census No, (CONCSLHO OF BARDEZ - CON^D. ) 11 Colvale Colvale (1-2-2), Revore (1-2-3), Na^ore (1-2-4), Pirna (1-2-5), 12 ^iYim Assoaora (1-2-6), Sircalm (1-2-7), ^ivlu (1-2-3). 13 Aldona Nechinola (1-2-10), Aldona (1-2-11), Oiaulim (1-2-12), Poreburpa (1-2-13), Corjuem (1-2-39), Ponolem (1-2-40), Calvin (1-2-41). (CQNCSLHO OF GOA ILHAS) Chorao Chorao (1-1-1), Adbarim (1-1-2), H Ceraim (1-1-3), Cai>ao (1-1-4), Nevelim (1-1-5), Goltim (1-1-6), Malav (1-1-7), Naroa (1-1-8). 15 Santa Cruz Calapor (1-1-36), ^aleigao (1-1-37), Durgavado (1-1-3d). 16 Panaji Murda (1-1-32), „:orombi-';-Grande (heroes) (1-1-33), Rcnovadi (1-1-34), r.?orombi-C-Pequeno (Meroes) (1-1-35), Panaji (Urban) (1-1-39). 17 Chimbel Bain«uinim (1-1-15), Panelim (1-1-16), Chimbel (1-1-17), ^laulin (1-1-18), Go9llr«i-Moula (1-1-19). 18 Ba-ibolini Ganoid (1-1-21), MaAdur (1-1-22), Neura-O-Pequeno (1-1-23), . eur&.-0-Grande (1 -1 -24), Mercurim (1-1-25), Goa-Velha (1-1-26), Batiin (1-1-27;, Liridao (1-1-28), Ci-poe (1-1-29), Batfibolim (1-1-30), Cujira (1-1 -31 ,. 19 Azosslm Jua (J-1-9), Cumarjua (1-1-10), Gendaulim (1rl-1l), SUa (1-1-12), Cci'liui (1-1-13), (Carambolim (1-1-14), ^zoasim. (J-1-20), 423 Unit Name of the Unit Villages included vdth 1971 No. Census No. (C0NCELH0 OF BICHOLIM) 20 Sirigao dirigao (1-4-2), Naroa (1-4-24), Aturli (1-4-25), Vainguinim (1-4-26), Maem (1-4-27J. 21 Advalpale Advalpale (1-4-4), La tain bare em (1-4-5), Dumanoem (1-4-6), Meneurem (1-4-7), Salem (1-4-3). 22 Bioholim Bioholim Town ( 3d), Lamgao (1-4-1), Mulgao (1-4-3), Bordau (1-4-9), Sarvona (1-4-10). 23 Carapur Carapur (1-4-11), Cassabe-De-Sanguelim (1-4-12), Arvalem (1-4-13). Maulinguem-South (1-4-14)» Cudnem (1-4-15). Navelim (1-4-20), Ataone (1-4-21) Virdi (1-4-22), Piligao (1-4-23). 24 Usgao Velguem (1-4-16), Pale (Usgao) (1-4-17), Cotombi (1-4-13), Sutla (1-4-19). (CONCSLHO OF PONDA) 25 Pond a Curti (1-6-17). 26 Candepar Candepar (1-6-18), Yolvoi (1-6-13), Savoi-Verem (1-6-14), vagurbem (1-6-15), 27 Boma (1-6-7), Adoolna (1-6-3), Orgao *ivrem (1-6-9), Orgao (1-6-10), Candola (1-6-11), Betgui (1-6-12) Velinga (1-6-3), Priol (1-6-4), 2d Prlol Cuncoliem (1-6-5), Querim (1-6-16 )f 29 iviaroaim Maroaim (1-6-2), Cundaim (1-6-6). 30 Bandora Bandora (1-6-1), Vadi (1-6-23), Telaulim (1-o-29), Queula (1-6-31). 31 Borim Betora (1-6-22), Borim (1-6-27). 32 Codar Codar (1-0-21), Niranoal (1-6-23), Conxem (1-6-24). 4 4 Unit Narue of the Unit Villages included with 1971 No. Census No. 33 Si rod a Ponohavadi (1-6-25), Siroda (1-6-26). (CONCELHO OF SALC&^E) 34 Cur to rim Curtorim (1-10-13), Maoasana (1-10-19), Guirdolim (1-10-20), Chandor (1-10-21). 35 Poroda Cavorim (1-10-22), Poroda (1-10-23), Mule (1-10-24), Sarzora (1-10-25). 36 Cunoolim Talvorda (1-10-26), Varoda (1-10-27), Cuncolim (1-10-28). 37 Velin Velim (1-10-29), Ambelim (1-10-30}, Cavelossim (1-10-32), Carmona (1-10-35] Varca (I-I0-36), Orlim (1-10-37). 3d Narel im Assolna (1-10-31), Chinchlnim (1-10-32] Deussua (1-10-34), Sirlim (1-1O-38), T)ramapur (1-10-39), T)ioarpale (1-10-40 Davorlim (1-10-41), Aquera (1-10-42), ^alaulim (1-10-43;, Navelim (1-10-44). 39 Margao Margao (Urban). 40 Benaulim Sernabatim (1-10-1), Vanelira (1-10-2), Colva (1-10-3), Seraulim (1-10-4), Gaundavlim (1-10-5), Betalbatim (1-10-7), Calata (1-10-9), Consua (1-10-10), Majorda (1-10-11), Utorde <1 -10-12), Benaulim (1-10-45), Adsulim (1-10-46), Cana (1-10-47). 41 Verna nanoolira (1-10-6), Nuvem (1-10-3), Nagoa (1-10-13), Verna (1-10-14). 42 Loutulim Loutulim (1-10-15), Camurlim (1-10-16), Raia (1-10-17). (C0N3ELH0 OF MAB6&AOOA) 43 Cortalim Sanooale (1-11-4), Cortalim (1-11-5), Q,uelossim (1-11-6). 44 Velcao Cuelim (1-11-7), Arossim (1-11-8), Cansaulifd (1-11-9), Veloao (1-11-10), Pale (1-11-11). 425 Unit Name of the Unit Villages included with 1971 No. Census No. 45 M&rmagoa Vadem (1-11-1), Chioalim (1-11-2), Dabolim (1-11-3), Issoroim (1-11-12), Chicolna (1-11-13), Marraagoa (Urban). (CONCELHP OF SAFARI) 46 Que rim Querim (1-5-8), Mori em (1-5-9), ioriem (1-5-10), Podooem (1-5.11), Cuichirem (1-5-12), M&ullnguam (1-5-13), Ona (1-5-14), Revona (1-5-15)* Gonteli (1-5-16), Siruli (1-5-17), Anjuneru (1-5-18), Gululetn (1-5-19), Ponsuli (1-5-21), (tuftlaudem (1-5-22), 47 Codal Chorauudem (1-5-23), Ivrem-Buzreco (1-5-24), Jurem-Curdo (1-5-25), Golauli (1-5-26), Surla (1-5-27), aatraa (1-5-23', Rivata (1-5-29), Dongurli (1-5-30), Slgonem (1-5-31), Ccdal (1-5-32), Derodeia (1-5-33), Vainguinim (1-5-34), Nancrem (1-5-35), iiuloli (1-5-36), Xolopo-Buzreoo d-5-37). 48 Caranzol Ustem (1-5-40), Zarani (1-5-41), Codvol (1-5-42), Pendral (1-5-43), Cartuzol (1-5-44), Sonal (1-5-45), 49 Valpoi M&uzl (1-4-1), Dabem (1-5-5), Caapordem (1-5-6), Pale (1-5-20), Nacoli (1 - 5-38), Cararabolim-Brama (1-5-39), Daveni (I-5-46), Borabedam (1-5-47), Ambedem (1-5-48), Kogargao (1-5-49), Edorem (1-5-50), Satorem (1-5-51), Veluz (1-5-52), Cudeera (1-5-53), Sanvordera (1-5-54), Caraiiibolim-Buzruco (1 -5-55), Belguem (1-5-56), Cod qui (I-5-57), Birondem (1-5-71), Sanvoroea (1-5-72), Ansolea (1-5-73), Nanus (1-5-76), Massordea + Valpoi (1-5-77), Naguem (1-5-84). 50 Pissurlem Bhimpal (1-5-2), Onda (I-5-3), iiaieli (1-5-4), Zornien (1-5-7), Padeli 0-5-70), Vanteia (1-5-73), Advoi (1-5-74), Ponooem (1-5-78), 4\>6 Unit Name of the Unit Villages lnoluded with 1971 No. Census No. Vaguriem (1-5-79), Codiem (1-5-80), Vonvoliem (1-5-31), Pissurlem (1-5-32), Cumaroonda (1-5-83). 51 Gotoram Xelopo-Curdo (1-5-58), Slranguli (1-5-59)i Slrsodem (1-5-60), Assodem (1-5-61), Govanem (1-5-62), Mslpona (1-5-63), Arabeli (1-5-64), Melaull (1-5-65), Conquirem (1-5-66), Guleli (1-5-67), Daraodem (1-5-68), Cotcrem (I-5-69). (CONCilLHO 0? dAii&Jjfll) 52 Sanoordem Afidot.e (1-7-18), Surla (1-7-19), Sanoordem (1-7-20), 53 Darbandora Pill em (1-7-16), Darhandora (1-7-17), Sangod (1-7-22). 54 ^ulrlapale Rumbrem (1-7-10), Moissal (1-7-11), Bandoll (1-7-12), Cormonem (1-7-13), Camareonda (1-7-14), Codli (1-7-15). 55 Golem Molem (1-7-21), Golem (1-7-24), Caranzol (I-7-25), Sonaull (1-7-26), Boma (1-7-27). 5o Oalein Dudal (1-7-3), Calem (1-7-4), Santona (1-7-5), Sigao (1-7-23), Sigao (1-7-23), Oxel (1-7-28), Dongurll (1-7-29), Maulinguem (1-7-30), Patlem (1-7-31). 57 daaguem Mugull (1-7-1), Costi (1-7-2), Corsnginiru (1-7-6), Comptoi (1-7-7), Sanrofcdem (1-7-g), Antorles (1-7-9), Uguem (1-7-32^, Xelpem (1-7-51), Cotorll (1-7-52), tJangueni Town - ups. 5S Bati Tudou (1-7-33J, Potrem (1-7-34), Bati (1-7-35), Cumbari (1-7-36), Vlliena (1-7-37), Dongov (1-7-38), Sigonem (1-7-4' ). 59 Netorli Netorli (1-7-42), Verlem (1-7-43), Nundem (1-7-44), Viohundrem (1-7-45). 4:7 Unit Name of the Unit Villages included with 1971 No. Census No. 60 Curdi Naiquinim (1-7-39), Porte«m (1-7-40), Curpera (I- 7-46), Curdi (1-7-49), ^alauli (1-7-50). o1 RiTons Rivona (1-7-47), Colomba (1-7-43), (CONOttLHO OF wPliPJfifl) 62 Sirro Cacora (1-9-11), Sirvoi (1-9-12), Nagveui (1-9-13), Molcarnem (1-9-14), Zanodesi (1-9-15), Undorna (1-9-16), Molcopona (1-9-17). 63 ciuepem Araona (1-9-1), Xeldera (1-9-2), Chaifi (1-9-3), Avedeci (1-9-4), Cotoabi (1-9-5), Assolde (1-9-6), Xic-Xelvona (1-9-7), Xelrona (1-9-8), Odor (1-9-9), Curohorem (1-9-10), Q,uepern T'ovm - URQ. 64 Adnein Cavorem (1-9-13), Bendoretn (I-9-I9), Bali (1-9-37), Adnem (1-9-38), AmbaulLa (I-9-39). 65 Fatorpa ^uedam (1-9-31), iiorpila (1-9-32), Naauerita (1-9-33), Quitol (1-9-34), Fatorpa (1-9-35), *ili (1-9-36). oo Pirla Cordea (1-9-20), ivJaina (1-9-21), Pirla (1-9-22), Saloorna (1-9-23), Mangal (1-9-24), Cazur (1-9-25), Corla (1-9-26), ^uisoonda ^1-9-27), Gocoldem (1-9-28), Baro«a (1-9-29), Padi (1-9-30). (CONGELHO OF CANACONA 67 Cola (1-8-1), Agenda (1-8-2). b8 Chawri Nagorcem-Palolera (1-B-3), Canacona (Chawri) (1-8-4). 69 Cotigaon G&odougraa [1-8-5), Cotigao (1-8-6), Lollem (1-8-7), Poinguinim (1-8-8). 423 Simple Regression Analysis for Certain Variables To find the correlation between (1) percentage of workers in primary occupations, (ii) percentage of workers in agricul ture, (iii) percentage of workers in secondary occupations, (iv) density of population (1971), (v) density of population in 19011 - all these dependant variables - on one hand and (i) distance from the coast, (ii) percentage of land below 25 M. as indicator for relief and (iii) distance from the centre of tringle of urbanization i.e. point A - equidistant from Margao, Marmagoa and Panaji urban zones, as the independent variables on the other hand, equations for regression lines were derived and the values of r - the correlation coefficient were calculated. This was mainly to find out the relationships as indicated by the regional variations in the above variables. For this purpose the district was divided into s±«tynine smaller units for analysis. Each unit was a group of villages defined as per the availability of data for 1961 census and with due regard for the relief characteristics and the concelho boundaries. These are shown in Fig.App.A.1 with the key to numbers on the map given as Table App.A.I. The results of analysis are given in Table App.A.II. It will be evident from this table that the relationship is not significant in some respects and is not very strong in those respects in case of which it may be significant . This only indicates that a single variable is not a determinant of the regional variations in any one of the indicators of economic development. It only brings out the complex nature 429 of relationships and may yield to quantitative analysis if more sophisticated methods, such as factor analysis or multiple regression analysis, are used. 430 Table : Appendix A-i[ Correlation coefficients and regression line eouations for some variables. 1) Correlation between distance from the coast in Km (X) an<; percentage of total workers in Primary occupations (Y) a - 67.01 3 b - 0.7956 , Y - 67.01 + 0.7956 X ; r - 0.7790 r2 « 0.6068 significant at the 99 per cent level. 2) Correlation between distance from the coast in Kms... (X) and percentage of total workers in Primary occupation - Agriculture only (Y) a - 60.28 , b - 0.14 , Y - 60.28 + 0.14 X . r - 0.0756 r2- 0.0057 The relationship is not significant. 3) Correlation between distance from the coast.... (X) and percentage of total workers in secondary occupations (Y) a » 28.3 b « -0.94 , Y = 28.3 + (-0.94) X r - -0.7374 3 " 2 r = 0.5437 Significant at the 99 per cent level. 4) Correlation between distance from the coast in Kms..__(X) and population density 1971 ,..(Y) a - 711.1716 , b » 23.7104 , Y - 711.1716 + 23.7104 X . r - -0.4473 r2 - 0.2000 431 Significant at the 99 per cent level. ) Correlation between percentage of total land below 25 M .... (X) and percentage of total workers in Agriculture (Y) a - 67.83 , b = -0.1239, Y = 67.83 + (-0.1239) X; r - -0.1812 r2 - 0.3283 This relation is not significant. ) Correlation between percentage of total land below 25M...(X) and percentage of total workers in secondary occupations--(Y) a - 4.38 , b - 0.2446 , Y - 4.38 + 0.2446 X. r - 0.6022 r2 - 0.3626 Significant at the 99 per cent level. ) Correlation between percentage of total land below 25 M...(X) and Density of population 1961 - .... _ - (Y) a - 254.61 , b = 0.63 , Y - 254.61 + 0.63 X . r - 0.5368 r2 - 0.2881 Significant at the 99 per cent level. ) Correlation between percentage of total land below 25 M __(X) and Density of population 1971 .... (Y) a - 345.94 , b » 0.95 ? Y - 345.94 + 0.95 X . r - 0.5176 r2 - 0.2679 Significant at the 99 per cent level. 432 Correlation between Distance from the centre of the urbanization triangle i.e. from point A (in Pig. Appendix A.l)... (X) and percentage of total workers in primary occupations - c Agriculture'only .... (Y) a - 54.62 , b « 0.32, Y - 54.62 + 0.32 X . r - 0.1846 r2 - 0.3407 Relation is not significant. ) Correlation between Distance from the centre of the urbanization triangle i.e. from point A (X) and percentage of total workers in primary occupations.(Y) a » 52.73 , b-0.64^ Y » 52.73 + 0.64 X . r - 0.6994 r2 - 0.4891 Significant at the 99 per cent level. ) Correlation between Distance from the centre of the urbanization triangle i.e. from point A -(X) and percentage of total workers in secondary occupations(Y) a « 18.335 , b - -0.4463 , Y = 18.335 + (-0.4463) X . J r - -.6389 r2 - .4082 Significant at the 99 per cent level. 433 12) Correlation between Distance from the centre of the urbanization triangle i.e. from point A (X) and Density of population 1961 ... (Y) a - 654.16 b - -15.7 Y - 654.16 + (-15.7) X r - -.5160 r2 - 0.2662 Significant at the 99 per cent level. 13) Correlation between distance from the centre of the urbanization triangle i.e. from point A (X) and Density of Population 1971 (Y) a - 921.16 b - -22.52 Y - 921.16 + (-22.52)X r = -.4739 r2 - 0.2245 Significant at the 99 per cent level. 434- KEY FOR NUMBERS REPRESENTING CONCELHOS OR REGIONS IN THE l.ORENZ. CURVES IN CHAPTERS ii , .;!, IV & V. Nf). CONCELHOS Oa Unas Sa!; wte B a i d e z Marma^oa ! nr]a o Bl' iloJlfT! 7 PPr fI gin 8 Que pern Canacona S •• *-».f ; Sa $u i?fn N i ' REG!' )N Coa:> t.al ! owfands Nor I: tie!'?' 11 ansi tiona! Zone Southern iransitional Zone r: Eastern Upland 43o APPENDIX B Estimates of Regional Income Regional income estimates mentioned in the text are taken from two different sources. The estimates for 196O are taken from the NCABR (1964, pp.254-62) while those for 1967-68 and 1963-69 are taken from GOOD (Feb.1971, pp.14-16). The methods for calculation of the net income from each sector is based on deduction at the rate prescribed by Central Statistical Organisation from the gross value of product in each sector. The gross values were based on estimates of production and the current prices. The comparisons made between the estimates for different years have, thus, to be made with caution since the two different organizations have worked under different conditions. Moreover, the NCA5R (1964) Have relied on data available from earlier published sources while those for 1967-68 and 1968-69 have been based on data collected by the same organization that gives the estimates. The estimates for the different years are given in Table Appendix B.I. and the comparison of the estimated incomes in the different years is shown by iigs.App.B.1 and App.B.2. 436 Tqble App. B.I : Regional Incomes (Sectorwise) : i960, 1967-68 & '68-69 (Rupees in 00.000) Sr. Rs. Percent Rs. Percent Rs* Percent no. Industry 1960 age 1967-68 age 1968-69 age distribu distribu distribu tion te QR tion 1. Agriculture and Animal Husbandry 492.19 19.0 1624.09 35.1 1616.52 33.6 2. Forestry and Logging 8.31 0.3 16.97 0.4 19.75 0.4 3. Fishing 85.35 3.3 97.83 2.1 121.51 2.5 Sub-Total 5$5.*5 22^6 m$r$? 37.6 1757-7* 36.5 4. Mining and Quarrying 485.63 18.8 549.42 11.9 575.30 12.0 5. Large Scale Manufacturing 194.22 7.5 88.91 1.9 110.00 2.3 6. Shall Scale Manufacturing 133.23 2.9 134.57 2.8 7. Construction 36.27 1.4 292.03 6.3 335.49 7.0 8. Electricity,Gas, Water Supply 39.31 0.8 42.64 0.9 Sub Total •716.12 ruL 1102.90 23.8 119*.0Q 25.0 9. Transport and Communication 568.40 12.3 609.71 12.6 9.1 Railways 70.00 1.5 70.00 1.4 9.2 Communication 32.00 0.7 32.00 0.7 9.3 Transport by other means 466.40 10.1 507.71 10.5 10. Trade, storage, hotels and restaurant 290.97 6.3 296.97 6.2 Sub Total •1141.39 44.2 ISiti? 18.6 906.68 10.$ 11. Banking and Insurance 44.00 1.0 44.00 0.9 12* Real Sstate and ownership and dwellings 80.97 1.7 80.94 1.7 13. Public administra • tion and defence 293.73 6.4 271.20 5.6 14. Other services 278.88 10.8 505.75 10.9 551.67 11.5 Sub Total 339-H 12*1 924.45 2.Q-Q 947- $1. 19.7 Total; Net Domestic products 2782.50 107.60 4625.61 100.00 4810.27 100.00 Net Income from abroad 196.72 7.60 - - - - Total district income 2585.78 100.00 * Breakdown of these sub-total is not available i for 196C) . Source: (1) N C A S R 1964 pp.262 • (2) Govt.of Goa, 1971, pp.14 & 16 *a? 8 8 J O O o Or - MR /Vl *-/ Q. :- J saoiAjag jamo Si U aouajap >g 3 ,^<^\w O uoijBJjsiuiajpv 3!IQ^d s9ui||amp dii|Sj9umo • i ajnsj |Bay C7> I aouBjnsui $ ?ui>ju«^ cr 00 ^ o s;uBjnnsaJ y s|a}oq*»Pejoig' »pejj uouFDiunwwo;;) wsjss ^^ m ^ }jodsu*j^ h- O A|ddns jaiPM O z >- ss SSSS uoiaonjjsuoQ CD CD O ) yuunjoBpuBUJ .O a'leog liewg Q lLl.i I 1 i. 0^ SuuruDBjnimu oOc a. B a|poS afl-ie-; O ^^^^^»» ?uujpno 9 Sumiw (/) UJ O fe| 9u!Ms!d o UJ ?U|??0-| >$ AJJS9J0J a 10 ^^^^^^^^ ajrn|nou?vv. a UJ '•-•• o o •-• rri o O •