105
esan-cuadernos de difusión 106
107 Value and quality creation Annex 1
GRAHAM´S MODEL1
In an initial situation of autarchy (case 1), Country A produces and consumes 800 units of wheat and 800 units of watches. Average productivity of both activities is 4 units per worker-day and the labor used in each activity is 200 worker-days.
Country B produces and consumes 800 units of wheat and 600 units of watches. Wheat productivity is 4 units per worker-day and that of watches is 3 units per worker-day. Each activity employs 200 worker-days.
Based on these assumptions, the world’s wheat production would be 1.600 units and 1.400 watches or a total production of 3.200 units in wheat terms, given the relative price of the two products.
Country A’s product is 54% of the world’s production (or 1.714 wheat units) while Country B accounts for 46% of the total (or 1.486 wheat units).
When opening trade (case 2), and given their respective comparative advantages, Country A specializes partially in watch making while Country B turns to wheat production. Presumably, Country A will transfer 100 worker-days from wheat cropping to watch making, while country B transfers labor in the opposite direction, i.e. from watches to wheat.
Since both activities show constant returns, world’s watch output should increase by 100 units while wheat’s remains stable. Consequently, global world production in wheat terms grows by 114 units, or 3,6%.
1 Based on Reinert, 1996. p. 133.
Country A Country B World Man-days Output-man Product Man-days Output-man Product Product
Wheat 200 4 800 200 4 800 1.600
Watches 200 4 800 200 3 600 1.400
Total 400 400
Price: 1 wheat = 0,875 watches.
Product** (%)
Country A: 1.714 54%
Country B: 1.486 46%
World: 3.200 100%
** In wheat terms.
Case 1: Without trade
107
esan-cuadernos de difusión 108
Increased world production (equivalent to the added balance of commerce between the two countries) is distributed equally, thus demonstrating that the specialization through trade, given each nation’s comparative advantages, is beneficial for both countries, to the extent both activities show constant returns.
Case 2: With trade
+ Specialization by comparative advantage.
Country A transfers workers from wheat to watches.
Country B transfers workers from watches to wheat.
+ Constant returns in both activities.
However, if wheat has diminishing returns and watches, increasing returns (case 3), international trade, based on comparative advantages, will be beneficial only for Country A, i.e. product will grow by 16,3% when compared to the autarchic situation, while B will hurt from a 13,9% production fall.
In this example, the world’s wheat production drops by 100 units, and that of watches grows by 150 units. Consequently, product and world trade, in wheat terms, will grow in 2,2% (or 71 units).
Graham’s model predicts that the country specializing in production of goods with diminishing returns will experience both, a drop in its GDP and a trade gap. Just the opposite will happen to the country specializing in goods with increasing returns.
Wheat 800 400 (400) 800 1.200 400 0
Watches 800 1.200 400 600 300 (300) 100
Total** 1.714 1.771 57 1.486 1.543 57 114
* Production before trade.
** In wheat terms.
Country A Country B World
Man-days Output-man Product Man-days Output-man Product Product
Country A Country B World Domestic Product Trade Domestic Product Trade Trade demand* balance demand* balance balance
Wheat 100 4 400 300 4 1.200 1.600
Watches 300 4 1.200 100 3 300 1.500
Total 400 400
Price: 1 wheat = 0,875 watches
Product** (%) (+/–) (+/–)%var
Country A: 1.771 53% 57 3,3%
Country B: 1.543 47% 57 3,8%
World: 3.314 100% 114 3,6%
109 Value and quality creation
Case 3: With trade
+ Specialization by comparative advantage.
Country A transfers workers from wheat to watches.
Country B transfers workers from watches to wheat.
+ Increasing returns in watches and diminishing returns in wheat.
That is, if one of the activities shows either increasing or diminishing returns, trade will hurt one of the two countries. Otherwise said, trade will not be equal if there are differences in returns.
Should wheat have diminishing returns and watches constant returns (case 4), trade specialization reduces Country B’s GDP leading to a trade gap. To wit, trade will adversely impact the nation with comparative advantages in the good with diminishing returns.
The world’s production would grow very slightly, and so would commercial exchanges, and, in general, the gain in Country A almost equals Country B’s loss.
Wheat 800 450 (350) 800 1.050 250 (100)
Watches 800 1.350 550 600 200 (400) 150
Total** 1.714 1.993 279 1.486 1.279 (207) 71
* Production before trade.
** In wheat terms.
Country A Country B World Domestic Product Trade Domestic Product Trade Trade demand* balance demand* balance balance Product** (%) (+/–) (+/–) % var
Country A: 1.993 61% 279 16,3%
Country B: 1.279 39% (207) –13,9%
World: 3.271 100% 71 2,2%
Price: 1 wheat = 0,875 watches
Wheat 100 4,5 450 300 3,5 1.050 1.500
Watches 300 4,5 1.350 100 2 200 1.550
Total** 400 400
Country A Country B World Man-days Output-man Product Man-days Output-man Product Product
esan-cuadernos de difusión 110
Case 4: With trade
+ Specialization by comparative advantage.
Country a transfers workers from wheat to watches.
Country B transfers workers from watches to wheat.
+ Constant returns in watches and diminishing returns wheat.
If watch production shows increasing returns and that of wheat, constant returns (case 5), the final solution would be quite similar to the previous example, since the production in Country B would fall and in Country A it would grow. A’s trade balance would show a surplus while B’s would be negative.
In spite of this similarity, the world’s product and commercial trade would grow more than in the previous case.
Case 5: With trade
+ Specialization by comparative advantage.
Country A transfers workers from wheat to watches.
Country B transfers workers from watches to wheat.
+ Increasing returns in watches and Constant returns in wheat.
Wheat 100 4,5 450 300 3,5 1.050 1.500
Watches 300 4 1.200 100 3 300 1.500
Total 400 400
Country A Country B World Man-days Output-man Product Man-days Output-man Product
Product** (%) (+/–) (+/–) %var
Country A: 1.821 57% 107 6,3%
Country B: 1.393 43% (93) –6,2%
World: 3.214 100% 14 0,4%
Price: 1 wheat = 0,875 watches.
Country A Country B World Domestic Product Trade Domestic Product Trade Trade demand* balance demand* balance balance
Wheat 800 450 (350) 800 1.050 250 (100)
Watches 800 1.200 400 600 300 (300) 100
Total** 1.714 1.821 107 1.486 1.393 (93) 14
* Production before trade.
** In wheat terms.
111 Value and quality creation
Wheat 100 4 400 300 4 1.200 1.600
Watches 300 4,5 1.350 100 2 200 1.550
Total 400 400
Price: 1 wheat = 0,875 watches.
Product** (%) (+/–) (+/–)% var
Country A: 1.943 58% 229 13,3%
Country B: 1.429 42% (57) –3,8%
World: 3.371 100% 171 5,4%
Wheat 800 400 (400) 800 1.200 400 0
Watches 800 1.350 550 600 200 (400) 150
Total** 1.714 1.943 229 1.486 1.429 (57) 171
* Production before trade.
** In wheat terms.
Country A Country B World
Man-day s Output-man Product Man-days Output-man Product Product
Country A Country B World
Domestic Product Trade Domestic Product Trade Trade demand* balance demand* balance balance
esan-cuadernos de difusión
112 Annex 2
IMPACT OF PRODUCTIVE SPECIALIZATION ON STANDARDS OF LIVING
Primary sector
LS // Dependent Variable is SER37VP Date: 11/20/98 Time: 19:31
Sample (adjusted): 1952 1997
Included observations: 46 after adjusting endpoints Convergence achieved after 6 iterations
Variable Coefficient Std. Error t-Statistic Prob.
C 0,463026 0,883386 0,524149 0,6029
D(SER33) –2,580097 0,619951 –4,161778 0,0001
AR(1) 0,293809 0,148946 1,972585 0,0550
R-squared 0,414515 Mean dependent var 0,928309
Adjusted R-squared 0,387283 S.D. dependent var 5,370544 S.E. of regression 4,203861 Akaike info criterion 2,935000 Sum squared resid 759,9152 Schwarz criterion 3,054259
Log likelihood –129,7762 F-statistic 15,22167
Durbin-Watson stat 1,854775 Prob (F-statistic) 0,000010 Inverted AR Roots 0,29
Manufacture
LS // Dependent Variable is SER37VP Date: 12/01/98 Time: 11:05
Sample (adjusted): 1952 1997
Included observations: 46 after adjusting endpoints Convergence achieved after 7 iterations
Variable Coefficient Std. Error t-Statistic Prob.
C 0,664635 0,986129 0,673984 0,5039
D(SER41) 4,221452 0,975329 4,328234 0,0001
AR(1) 0,378628 0,139948 2,705492 0,0097
R-squared 0,429092 Mean dependent var 0,928309
Adjusted R-squared 0,402538 S.D. dependent var 5,370544 S.E. of regression 4,151198 Akaike info criterion 2,909787 Sum squared resid 740,9950 Schwarz criterion 3,029046
Log likelihood –129,1963 F-statistic 16,15931
Durbin-Watson stat 1,825283 Prob(F-statistic) 0,000006 Inverted AR Roots 0,38
IMPACT ON CONSUMPTION PER CAPITA
112
113 Value and quality creation
113 Construction
LS // Dependent Variable is SER37VP Date: 12/01/98 Time: 11:06
Sample (adjusted): 1952 1997
Included observations: 46 after adjusting endpoints Convergence achieved after 4 iterations
Variable Coefficient Std. Error t-Statistic Prob.
C 0,680856 1,194115 0,570177 0,5715
D(SER42A) 3,122543 1,277982 2,443339 0,0187
AR(1) 0,423645 0,137006 3,092160 0,0035
R-squared 0,282018 Mean dependent var 0,928309
Adjusted R-squared 0,248623 S.D. dependent var 5,370544 S.E. of regression 4,655295 Akaike info criterion 3,139004 Sum squared resid 931,8863 Schwarz criterion 3,258263
Log likelihood –134,4683 F-statistic 8,445025
Durbin-Watson stat 1,654441 Prob(F-statistic) 0,000806 Inverted AR Roots 0,42
Services
LS // Dependent Variable is SER37VP Date: 12/01/98 Time: 11:07
Sample (adjusted): 1952 1997
Included observations: 46 after adjusting endpoints Convergence achieved after 5 iterations
Variable Coefficient Std. Error t-Statistic Prob.
C 0,859997 1,265832 0,679393 0,5005
D(SER44) 0,048174 0,832208 0,057887 0,9541
AR(1) 0,420857 0,140083 3,004335 0,0044
R-squared 0,182393 Mean dependent var 0,928309
Adjusted R-squared 0,144364 S.D. dependent var 5,370544 S.E. of regression 4,967784 Akaike info criterion 3,268941 Sum squared resid 1061,192 Schwarz criterion 3,388201
Log likelihood –137,4568 F-statistic 4,796240
Durbin-Watson stat 1,644385 Prob(F-statistic) 0,013174 Inverted AR Roots 0,42
Variables
SER37VP Private consumption per capita (∆%) SER33 Primary sector (% of GDP)
SER41 Manufacture sector (% of GDP) SER42A Construction sector (% of GDP) SER44 Services sector (% of GDP)
D(X) X(t)–X(t–1)
AR(1) Correction of autocorrelation
esan-cuadernos de difusión 114
IMPACT ON WHITE COLLAR WAGES
Primary sector
LS // Dependent Variable is SER6VP Date: 11/20/98 Time: 19:43 Sample (adjusted): 1962 1997
Included observations: 36 after adjusting endpoints Convergence achieved after 5 iterations
Variable Coefficient Std. Error t-Statistic Prob.
C –3,161690 2,704991 –1,168836 0,2508
D(SER33) –5,413849 1,736494 –3,117690 0,0038
AR(1) 0,358559 0,167481 2,140894 0,0398
R-squared 0,393680 Mean dependent var –2,280831
Adjusted R-squared 0,356934 S.D. dependent var 12,91490 S.E. of regression 10,35664 Akaike info criterion 4,754912 Sum squared resid 3539,583 Schwarz criterion 4,886872
Log likelihood –133,6702 F-statistic 10,71337
Durbin-Watson stat 1,893578 Prob(F-statistic) 0,000260 Inverted AR Roots 0,36
Manufacture
LS // Dependent Variable is SER6VP Date: 12/01/98 Time: 11:09 Sample (adjusted): 1962 1997
Included observations: 36 after adjusting endpoints Convergence achieved after 6 iterations
Variable Coefficient Std. Error t-Statistic Prob.
C –1,663838 3,293716 –0,505155 0,6168
D(SER41) 10,59519 2,649683 3,998662 0,0003
AR(1) 0,509155 0,150606 3,380714 0,0019
R-squared 0,469500 Mean dependent var –2,280831
Adjusted R-squared 0,437349 S.D. dependent var 12,91490 S.E. of regression 9,687482 Akaike info criterion 4,621324 Sum squared resid 3096,961 Schwarz criterion 4,753284
Log likelihood –131,2656 F-statistic 14,60274
Durbin-Watson stat 1,782727 Prob(F-statistic) 0,000029 Inverted AR Roots 0,51
115 Value and quality creationConstruction
LS // Dependent Variable is SER6VP Date: 12/01/98 Time: 11:11 Sample (adjusted): 1962 1997
Included observations: 36 after adjusting endpoints Convergence achieved after 4 iterations
Variable CoefficientStd. Error t-Statistic Prob.
C –2,707635 3,313459 –0,817163 0,4197
D(SER42A) 5,788838 3,949948 1,465548 0,1522
AR(1) 0,424074 0,163881 2,587691 0,0143
R-squared 0,264485 Mean dependent var –2,280831
Adjusted R-squared 0,219909 S.D. dependent var 12,91490 S.E. of regression 11,40680 Akaike info criterion 4,948075 Sum squared resid 4293,799 Schwarz criterion 5,080035
Log likelihood –137,1471 F-statistic 5,933271
Durbin-Watson stat 1,779658 Prob(F-statistic) 0,006292 Inverted AR Roots 0,42
Services
LS // Dependent Variable is SER6VP Date: 12/01/98 Time: 11:11 Sample (adjusted): 1962 1997
Included observations: 36 after adjusting endpoints Convergence achieved after 6 iterations
Variable Coefficient Std. Error t-Statistic Prob.
C –2,327721 3,644633 –0,638671 0,5274
D(SER44) 0,280807 2,195485 0,127902 0,8990
AR(1) 0,458819 0,156451 2,932663 0,0061
R-squared 0,214649 Mean dependent var –2,280831
Adjusted R-squared 0,167052 S.D. dependent var 12,91490 S.E. of regression 11,78692 Akaike info criterion 5,013635 Sum squared resid 4584,735 Schwarz criterion 5,145595
Log likelihood –138,3272 F-statistic 4,509711
Durbin-Watson stat 1,705693 Prob(F-statistic) 0,018559 Inverted AR Roots 0,46
Variables
SER6VP White-collar wages (∆%) SER33 Primary sector (% of GDP) SER41 Manufacture sector (% of GDP) SER42A Construction sector (% of GDP) SER44 Services sector (% of GDP)
D(X) X(t)–X(t–1)
AR(1) Correction of autocorrelation 115
esan-cuadernos de difusión 116
IMPACT ON BLUE COLLAR SALARIES
Primary
LS // Dependent Variable is SER7VP Date: 11/20/98 Time: 19:46 Sample (adjusted): 1961 1997
Included observations: 37 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
C –3,220570 1,790782 –1,798416 0,0807
D(SER33) –7,359915 1,677699 –4,386911 0,0001
R-squared 0,354779 Mean dependent var –2,059666
Adjusted R-squared 0,336344 S.D. dependent var 13,22445 S.E. of regression 10,77331 Akaike info criterion 4,806682 Sum squared resid 4062,248 Schwarz criterion 4,893759
Log likelihood –139,4243 F-statistic 19,24498
Durbin-Watson stat 2,127444 Prob(F-statistic) 0,000101
Manufacture
LS // Dependent Variable is SER7VP Date: 12/01/98 Time: 11:12 Sample (adjusted): 1962 1997
Included observations: 36 after adjusting endpoints Convergence achieved after 5 iterations
Variable Coefficient Std. Error t-Statistic Prob.
C –1,435903 2,114486 –0,679079 0,5018
D(SER41) 15,51529 2,841962 5,459360 0,0000
AR(1) 0,229994 0,170252 1,350901 0,1859
R-squared 0,499829 Mean dependent var –2,207953
Adjusted R-squared 0,469516 S.D. dependent var 13,38081 S.E. of regression 9,745828 Akaike info criterion 4,633334 Sum squared resid 3134,378 Schwarz criterion 4,765294
Log likelihood –131,4818 F-statistic 16,48871
Durbin-Watson stat 1,971463 Prob(F-statistic) 0,000011 Inverted AR Roots 0,23
117 Value and quality creation
Construction
LS // Dependent Variable is SER7VP Date: 12/01/98 Time: 11:13 Sample (adjusted): 1962 1997
Included observations: 36 after adjusting endpoints Convergence achieved after 5 iterations
Variable Coefficient Std. Error t-Statistic Prob.
C –2,731320 2,636522 –1,035956 0,3078
D(SER42A) 6,966229 4,584695 1,519453 0,1382
AR(1) 0,171557 0,176170 0,973812 0,3372
R-squared 0,109188 Mean dependent var –2,207953
Adjusted R-squared 0,055199 S.D. dependent var 13,38081 S.E. of regression 13,00626 Akaike info criterion 5,210517 Sum squared resid 5582,375 Schwarz criterion 5,342477
Log likelihood –141,8711 F-statistic 2,022421
Durbin-Watson stat 1,920602 Prob(F-statistic) 0,148413 Inverted AR Roots 0,17
Services
LS // Dependent Variable is SER7VP Date: 12/01/98 Time: 11:13 Sample (adjusted): 1962 1997
Included observations: 36 after adjusting endpoints Convergence achieved after 6 iterations
Variable Coefficient Std. Error t-Statistic Prob.
C –2,309896 2,824288 –0,817868 0,4193
D(SER44) 0,537742 2,953857 0,182048 0,8567
AR(1) 0,197867 0,184338 1,073392 0,2909
R-squared 0,046205 Mean dependent var –2,207953
Adjusted R-squared –0,011600 S.D. dependent var 13,38081 S.E. of regression 13,45820 Akaike info criterion 5,278832 Sum squared resid 5977,060 Schwarz criterion 5,410792
Log likelihood –143,1008 F-statistic 0,799324
Durbin-Watson stat 1,895026 Prob(F-statistic) 0,458147 Inverted AR Roots 0,20
Variables
SER7VP Blue-collar salaries (∆%) SER33 Primary ector (% of GDP) SER41 Manufacture sector (% of GDP) SER42A Construction sector (% of GDP) SER44 Services sector (% of GDP)
D(X) X(t)–X(t–1)
AR(1) Correction of autocorrelation 117
esan-cuadernos de difusión 118
GDP BY ACTIVITIES* 1950865,58205,552,1065,56164,74 2,9050,92 35,39 51,09287,34 1951936,45211,722,1770,73181,173,0059,8236,0253,48318,34 1952994,86217,862,3573,07190,903,1273,8936,7855,96340,94 19531.047,95222,872,0468,83215,623,9377,8837,6658,54360,58 19541.115,56227,322,6891,96236,004,0087,4639,0261,21365,90 19551.168,84225,513,1292,60253,744,3692,6240,4264,18392,30 19561.218,97214,683,90102,42261,514,65103,7441,9667,33418,79 19571.301,27215,544,36112,25288,265,24106,5443,8170,67454,60 19581.293,88230,416,53101,70280,545,9396,7445,6574,30452,10 19591.341,45241,7010,51103,93306,775,9785,2347,3478,02461,98 19601.504,74257,1714,31155,38355,796,8681,8249,2382,03502,16 19611.615,81264,8920,16169,83383,478,52100,5550,7692,70524,95 19621.750,84271,2426,00161,51414,998,87109,7052,3399,31606,88 19631.815,55275,3127,09171,85438,399,4394,7853,95106,67638,08 19641.935,37288,8034,90180,44469,1710,08104,8355,62114,57676,95 19652.030,90294,5828,86183,14500,0410,91118,4657,35120,95716,61 19662.201,56310,4934,05201,09538,7411,89129,0059,12128,32788,85 19672.284,92322,6038,89203,51559,9713,00128,2360,96132,68825,09 19682.293,03311,6339,59216,12570,6113,74109,7662,85135,73832,99 19692.379,35332,1935,55214,61577,0614,43117,1264,80138,45885,15 19702.518,57358,1047,25229,85626,7415,09133,0464,93144,41899,16 19712.623,78365,2732,60215,89662,4016,74145,3167,38151,16967,04 19722.699,22356,5017,34229,41676,1318,23160,9370,16162,051.008,47 19732.844,33357,9312,55238,09720,1420,31176,2973,44169,621.075,96 19743.107,39371,1717,54251,34780,8222,22211,9377,11177,761.197,50
Global Agriculture Fisheries Mining Manufacture Electricity, Cons- Housing Government Other GDP Gas & Water truction Continue...
118
Annex 3: TABLES
119 Value and quality creation
Global Agriculture Fisheries Mining Manufacture Electricity, Cons- Housing Government Other GDP Gas & Water truction
Continue...
119
19753.213,06371,0515,56231,85805,2023,92216,43 79,48 191,111.278,46 19763.276,07376,6118,33245,68834,3527,18216,5981,10204,451.271,78 19773.289,33376,2316,00296,96820,2630,65192,2482,18213,291.261,52 19783.298,58370,5920,20389,94787,1531,95172,4483,03213,251.230,04 19793.490,13385,0422,67459,11819,7934,26181,4484,15214,511.289,17 19803.646,73362,6318,88468,79866,7639,02202,2988,58233,491.366,29 19813.807,70395,4220,60454,28872,6141,87224,9990,61239,791.467,54 19823.815,76404,1624,19459,87862,3645,29229,5092,18241,731.456,48 19833.334,32365,2317,00414,73705,8938,04181,6993,18257,941.260,61 19843.494,78402,6024,86434,54746,3338,15183,1594,50277,621.293,03 19853.573,92414,3329,10453,22779,9040,50163,9695,31279,041.318,57 19863.904,23432,2938,45432,92901,5447,64199,0498,11302,461.451,78 19874.234,70460,7733,88420,011.017,0851,32234,33101,51316,851.598,95 19883.881,52493,3940,13357,05903,0851,59218,49101,60288,451.427,74 19893.428,62465,7642,24339,67761,6150,91186,40102,58247,481.231,97 19903.243,58433,4441,96326,84717,4451,10192,29103,59217,431.159,50 19913.334,52447,1337,48316,76761,7253,12195,58104,30202,651.215,78 19923.287,20412,7142,31308,53743,6754,27204,14104,52205,101.211,95 19933.497,23450,9651,20333,65779,4360,14233,42105,47211,171.271,79 19943.953,93513,0666,02348,42902,1965,59308,44106,54217,611.426,07 19954.240,31554,1553,43356,15943,0969,46361,50107,43222,941.572,16 1996**4.350,84584,9053,10365,43966,4370,64344,87109,21218,381.637,88 1997**4.664,10613,5647,90386,621.024,4282,01410,05117,07214,551.767,92 * Nuevos soles of 1979. ** Estimate. Source: INEI.
esan-cuadernos de difusión 120
1950865,58608,0353,02158,10168,08121,65 1951936,45664,0854,56217,45164,72164,35 1952994,86677,0260,70250,15189,26182,27 19531.047,95705,3461,94271,20207,24197,76 19541.115,56759,8673,45234,55221,33173,64 19551.168,84813,8173,41271,60232,27222,25 19561.218,97828,2577,71322,62251,39261,00 19571.301,27874,0289,94365,52258,68286,89 19581.293,88876,9589,86319,81260,46253,21 19591.341,45885,58100,69272,47294,58211,87 19601.504,74926,61118,34334,58378,60253,39 19611.615,81989,40136,74351,07446,75308,14 19621.750,841.092,34143,66385,54476,87347,58 19631.815,551.200,98152,05378,62468,29384,38 19641.935,371.274,22171,10400,00500,60410,56 19652.030,901.371,07182,66442,00515,12479,94 19662.201,561.490,97182,90532,46539,21543,97 19672.284,921.609,15189,24518,78570,95603,19 19682.293,031.625,76201,32387,01627,29548,36 19692.379,351.683,05212,44412,59616,22544,95 19702.518,601.785,39222,98441,01651,43582,23 19712.623,881.848,78238,37512,93632,35608,55 19722.699,221,908,83254,13452,00691,14606,88 19732.844,351.987,14269,44706,16563,18681,57 19743.107,392.122,12285,60973,85592,48866,66 Global Consumption Government Investment Exports Imports Expenditure
GDP BY TYPE OF EXPENDITURE* Continue...
121 Value and quality creation
19753.213,042.209,67317,25921,92606,11841,91 19763.276,072.249,32332,49803,25626,88735,87 19773.289,342.254,32380,43684,95708,08738,45 19783.298,592.081,67332,20643,50799,69558,46 19793.490,142.130,72300,68756,81968,16666,22 19803.646,642.236,39367,831.030,81879,34867,72 19813.807,722.355,77362,031.242,24853,501.005,82 19823.815,752.376,39410,181.151,48905,741.028,04 19833.334,222.167,11374,20704,19812,09723,37 19843.494,782.209,01357,09634,37886,13591,83 19853.573,932.255,69369,70564,06925,08540,60 19863.904,222.592,61382,28748,97831,08650,72 19874.234,712.847,16404,79959,42770,70747,35 19883.881,282.636,46340,70869,54714,97680,38 19893.428,612.185,85315,74586,57848,82508,36 19903.243,762.132,61285,75672,08722,04568,72 19913.334,502.173,17291,18747,87805,09682,80 19923.287,202.212,50299,62739,73811,50776,15 19933.497,232.312,50309,03821,29840,42786,01 19943.953,932.542,60335,601.052,34997,57974,18 19954.240,312.767,17368,661.247,501.074,211.217,22 1996**4.350,842.837,66361,661.183,171.195,891.227,53 1997**4.664,102.951,17378,291.346.651.351.351.366,24
Global Consumption Government Investment Exports Imports Expenditure *Nuevos soles of 1979. ** Estimate. Source: INEI.
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esan-cuadernos de difusión 122
Population White C. Blue C. Traditional Non Non Financ. Imports Non Banking Terms of Real 1/ Wages Salaries Exports 3/ Traditional Services 3/ Financ. System Nir Trade Exchange 2/ 2/ Exports 3/ Exports 3/ Services 3/ 4/ Rate 5/ Imports 3/ 4/ Continue...
OTHER INDICATORS
122
19507.632,51692917513933,3103,8 19517.826,32144526214636,0100,5 19528.025,72083828814435,597,4 19538.232,21913729313836,697,8 19548.447,02094624914541,1109,7 19558.671,52394230014152,6101,8 19568.904,92714938415657,898,0 19579.146,12815045012260,493,4 19589.396,72484438410953,0108,9 19599.657,827944317126101,2111,4 19609.931,0473,0338,64034137315698,2106,4 196110.217,5480,7349,645654467190107,8100,0 196210.516,5476,0356,749660542222109,194,6 196310.825,8482,2369,85035257925799,990,6 196411.143,5495,5379,662956587300132,083,3 196511.467,3453,5362,864045718308125,772,9 196611.796,4430,4367,874543818264146,869,0 196712.132,2426,6356,471725819107154,472,8 196812.476,0431,9379,281030631133203,181,4 196912.829,1447,0355,084634601166186,180,7 197013.192,8465,0374,21.00034178700260423188,081,3 197113.568,3495,8409,285831179730286347179,179,4 197213.954,7534,5442,889550208812291397218,076,7 197314.350,3544,3486,19981142351.033367411112,174,3 197414.753,1521,2479,41.3521513251.908553693117,370,5
123 Value and quality creation
Population White C. Blue C. Traditional Non Non Financ. Imports Non Banking Terms of Real 1/ Wages Salaries Exports 3/ Traditional Services 3/ Financ. System Nir Trade Exchange 2/ 2/ Exports 3/ Exports 3/ Services 3/ 4/ Rate 5/ Imports 3/ 4/ 1/Thousands 2/ Real, base 1990 = 100 3/ US$ millions 4/ Base 1979 = 100 5/ Include other exports of good * Estimate. Source: INEI, BCRP.
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123
197515.161,2504,4431,31.234964122.42764311680,965,1 197615.577,7433,7440,81.2041373752.016479(752)91,271,2 197716.003,5377,2372,81.5022243772.148403(1.101)93,383,0 197816.434,6323,9335,91.6193534341.668386(1.025)75,8105,1 197916.866,6295,8336,22,8668105221.954512554100.0100,0 198017.324,1317,7355,73.0718457143.0908801.276110,291,8 198117.758,9323,9348,12.5487017703.8021.08877190,384,4 198218.195,4348,5351,92.5317627843.7221.09889674,489,9 198318.631,4299,0291,52.4605557112.72296485679,9102,7 198419.064,5275,4248,12.4217266702.1408911.10372,9108,5 198519.492,4253,7214,22.2647148141.8069841.38365,5135,0 198619.915.5316,6287,71.8866458362.5961.01386648,098,2 198720.335,2332,0311,31.8897167933.1821.1648148,366,2 198820.751,2229,8195,31.9447478312.7901.164(317)52,369,2 198921.162,7145,2136,82.5099798362.2911.14354650,542,9 199021.569,3100,0100,02.2659667992.8851.16468245,642,1 199121.966,4105,9115,22.3789518263.4951.2391.93343,435,3 199222.354.4109,0111,12.4721.0138364.0511.4112.42542,634,0 199322.740,2127,3110,12.3341.1898374.1231.4122.91039,137,5 199423.130,3153,0127,43.1531.4201.0645.5951.5656.02543,234,4 199523.531,7146,1116,73.9811.5951.1317.7611.9046.69346,032,6 199623.931,7147,5111,24.2131.6851.4147.8662.0998.86243,632,6 1997*24.338,5149,6110,34.6922.1221.5408.5522.2887.98245,932,2