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Update Regional Tradeflows
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@Alek327
Alek327 authored Oct 8, 2024
1 parent 9513142 commit 045c887
Showing 1 changed file with 16 additions and 16 deletions.
32 changes: 16 additions & 16 deletions Regional Tradeflows
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Original file line number Diff line number Diff line change
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# ------------------------ Part 1: Initial Setup and Data Preparation ------------------------
# Part 1: Initial Setup and Data Preparation

# Load necessary libraries (if needed)
# library(...) # Uncomment if you need to load specific libraries
Expand All @@ -9,7 +9,7 @@ load("~/MRIO (ALL).RData")
# Define the identity matrix (L) with dimensions matching the number of sectors (43 sectors)
L <- diag(43) # Identity matrix of size 43x43

# ------------------------ Part 2: Total Resources and Imports Calculation ------------------------
# Part 2: Total Resources and Imports Calculation

# Step 1: Calculate total resources for each region (restricted to the first 43 sectors)
Total_resources_NW <- rowSums(IOT_NW[1:43, 1:43]) # Northwest Italy
Expand All @@ -19,7 +19,7 @@ Total_resources_Centro <- rowSums(IOT_Centro[1:43, 1:43]) # Centro
Total_resources_Sud <- rowSums(IOT_Sud[1:43, 1:43]) # Sud
Total_resources_Ext <- rowSums(IOT_Ext[1:43, 1:43]) # Ext

# ------------------------ Part 3: Total Intermediate Purchases Calculation ------------------------
# Part 3: Total Intermediate Purchases Calculation

# Calculate total intermediate purchases as shares of total resources for each region
totalIntermediatePurchases_NW <- IOT_NW[1:43, 1:43] / Total_resources_NW[1:43]
Expand All @@ -29,7 +29,7 @@ totalIntermediatePurchases_Centro <- IOT_Centro[1:43, 1:43] / Total_resources_Ce
totalIntermediatePurchases_Sud <- IOT_Sud[1:43, 1:43] / Total_resources_Sud[1:43]
totalIntermediatePurchases_Ext <- IOT_Ext[1:43, 1:43] / Total_resources_Ext[1:43]

# ------------------------ Part 4: Intermediate Imports Calculation ------------------------
# Part 4: Intermediate Imports Calculation

# Step 1: Extract intermediate imports for each region
Imp_intermediate_NW <- IOT_NW[46, 1:43] + IOT_NW[48, 1:43]
Expand All @@ -55,7 +55,7 @@ Imp_final_Centro <- IOT_Centro[47, 1:43] + IOT_Centro[49, 1:43]
Imp_final_Sud <- IOT_Sud[47, 1:43] + IOT_Sud[49, 1:43]
Imp_final_Ext <- IOT_Ext[47, 1:43] + IOT_Ext[49, 1:43]

# ------------------------ Part 5: Define and Calculate Internal Production Shares (IntShares_*) ------------------------
# Part 5: Define and Calculate Internal Production Shares (IntShares)

# Define the internal production shares for each region as the complement of intermediate import shares
IntShares_NW <- 1 - (Imp_intermediate_NW / Total_resources_NW[1:43])
Expand All @@ -65,7 +65,7 @@ IntShares_Centro <- 1 - (Imp_intermediate_Centro / Total_resources_Centro[1:43])
IntShares_Sud <- 1 - (Imp_intermediate_Sud / Total_resources_Sud[1:43])
IntShares_Ext <- 1 - (Imp_intermediate_Ext / Total_resources_Ext[1:43])

# ------------------------ Part 6: Define and Calculate Interregional Final Exports (exportsInterregionalfinal_*) ------------------------
# Part 6: Define and Calculate Interregional Final Exports (exportsInterregionalfinal)

# Define interregional final exports for each region (extracted from trade_finale_matrix)
# For Toscana region
Expand Down Expand Up @@ -132,7 +132,7 @@ int_trade_int_Ext5 <- trade_finale_matrix[216:258, 6]

exportsInterregionalfinal_Ext <- int_trade_int_Ext1 + int_trade_int_Ext2 + int_trade_int_Ext3 + int_trade_int_Ext4 + int_trade_int_Ext5

# ------------------------ Part 7: Final Consumption Shares (FinShares_*) Calculation ------------------------
# Part 7: Final Consumption Shares (FinShares) Calculation

# Step 1: Calculate total final consumption for each region
total_final_consumption_NW <- rowSums(IOT_NW[1:43, 44:49])
Expand All @@ -150,17 +150,17 @@ FinShares_Centro <- 1 - (Imp_final_Centro / total_final_consumption_Centro)
FinShares_Sud <- 1 - (Imp_final_Sud / total_final_consumption_Sud)
FinShares_Ext <- 1 - (Imp_final_Ext / total_final_consumption_Ext)

# ------------------------ Part 8: Internal Production Calculation ------------------------
# Part 8: Internal Production Calculation

# Ensure the correct matrix multiplication and preserve 43x43 structure for internalProduction
# Ensure the correct matrix multiplication and preserve 43x43 structure for internal production
internalProduction_NW <- IOT_NW[1:43, 1:43] %*% diag(IntShares_NW) # Now 43x43
internalProduction_NE <- IOT_NE[1:43, 1:43] %*% diag(IntShares_NE)
internalProduction_Toscana <- IOT_Toscana[1:43, 1:43] %*% diag(IntShares_Toscana)
internalProduction_Centro <- IOT_Centro[1:43, 1:43] %*% diag(IntShares_Centro)
internalProduction_Sud <- IOT_Sud[1:43, 1:43] %*% diag(IntShares_Sud)
internalProduction_Ext <- IOT_Ext[1:43, 1:43] %*% diag(IntShares_Ext)

# ------------------------ Part 9: Final Consumption Internal Production Calculation ------------------------
# Part 9: Final Consumption Internal Production Calculation -

# Final consumption internal production for each region (element-wise multiplication)
finalConsumptionInternal_NW <- total_final_consumption_NW * FinShares_NW
Expand All @@ -170,7 +170,7 @@ finalConsumptionInternal_Centro <- total_final_consumption_Centro * FinShares_Ce
finalConsumptionInternal_Sud <- total_final_consumption_Sud * FinShares_Sud
finalConsumptionInternal_Ext <- total_final_consumption_Ext * FinShares_Ext

# ------------------------ Part 10: Define and Calculate Interregional Final Imports (importsInterregionalfinal_*) ------------------------
# Part 10: Define and Calculate Interregional Final Imports (importsInterregionalfinal)

# For NW region
int_trade_df_NW2 <- matrix(trade_finale_matrix[44:86, 1], ncol = 1) # Imports from NE to NW
Expand Down Expand Up @@ -267,7 +267,7 @@ importsInterregionalfinal_Ext5 <- diag(as.vector(int_trade_df_Ext5)) %*% totalIn

importsInterregionalfinal_Ext <- importsInterregionalfinal_Ext1 + importsInterregionalfinal_Ext2 + importsInterregionalfinal_Ext3 + importsInterregionalfinal_Ext4 + importsInterregionalfinal_Ext5

# ------------------------ Part 11: Consistency Checks ------------------------
# Part 11: Consistency Checks

# Consistency checks for Total_columns and Total_rows for each region

Expand Down Expand Up @@ -316,14 +316,14 @@ importsInterregional_Ext_vector <- rowSums(importsInterregionalfinal_Ext)
Total_columns_Ext <- internalProduction_Ext_vector + importsInterregional_Ext_vector + Foreign_imp_int_Ext
Total_rows_Ext <- internalProduction_Ext_vector + exportsInterregionalfinal_Ext + finalConsumptionInternal_Ext + rowSums(importsInterregionalfinal_Ext)

# ------------------------ Part 12: Final Consistency Check ------------------------
# Part 12: Final Consistency Check

# Check if row sums and column sums match expected results for NW
# Check if row sums and column sums match the expected results for NW
expected_totals_NW <- c(12523.4, 122.5, 2048.7, 45288.7, 22921.8, 4769.9, 6984.4, 4407.5, 10967.8, 26809.9, 10574.6, 22284.3, 7512.3, 30433.3, 40536.9, 9335.2, 15263.8, 52303.1, 31150.6, 11316.9, 12813.6, 7439.7, 27163.1, 3105.1, 9496.2, 65240.8, 122628.5, 73290.3, 32396.5, 12259.1, 13380.0, 29280.7, 54689.9, 78928.5, 52360.8, 8017.5, 25312.2, 42487.8, 28971.0, 20198.9, 48134.3, 13728.1, 18487.8) # Use provided totals for NW region
row_check_NW <- all.equal(Total_rows_NW, expected_totals_NW)
column_check_NW <- all.equal(Total_columns_NW, expected_totals_NW)

# Check if row sums and column sums match expected results for NE
# Check if row sums and column sums match the expected results for NE
expected_totals_NE <- c(16291.8, 335.3, 617.6, 52573.4, 23405.5, 5835.7, 7093.6, 3527.3, 1483.9, 13509.0, 3946.2, 12387.0, 13947.4, 14059.7, 31680.5, 6998.6, 14771.8, 57656.2, 12137.5, 6483.3, 18917.8, 5815.9, 17052.9, 2662.5, 7435.1, 51107.9, 82281.6, 46193.9, 32523.4, 2044.7, 4343.0, 12738.3, 32646.7, 54566.3, 20415.7, 4455.5, 6959.7, 23192.2, 26451.7, 15688.4, 34958.2, 10441.2, 13554.7)
row_check_NE <- all.equal(Total_rows_NE, expected_totals_NE)
column_check_NE <- all.equal(Total_columns_NE, expected_totals_NE)
Expand All @@ -333,7 +333,7 @@ expected_totals_Toscana <- c(3547.1, 64.5, 397.7, 5750.2, 20967.7, 744.0, 4163.7
row_check_Toscana <- all.equal(Total_rows_Toscana, expected_totals_Toscana)
column_check_Toscana <- all.equal(Total_columns_Toscana, expected_totals_Toscana)

# Check if row sums and column sums match expected results for Centro
# Check if row sums and column sums match the expected results for Centro
expected_totals_Centro <- c(5710.6, 193.8, 971.6, 10228.0, 10216.7, 1203.2, 2714.2, 1671.3, 2349.1, 2680.8, 6882.3, 3416.0, 2921.1, 4098.0, 6706.7, 2119.4, 4202.5, 6305.9, 1902.6, 3719.9, 4904.3, 2376.3, 9954.4, 1724.9, 4220.9, 25971.0, 54791.9, 30043.4, 17401.2, 7810.6, 10649.8, 13254.7, 20989.9, 36937.8, 19973.4, 2948.4, 5816.9, 24497.8, 31301.9, 11450.3, 23924.4, 8280.3, 10048.9)
row_check_Centro <- all.equal(Total_rows_Centro, expected_totals_Centro)
column_check_Centro <- all.equal(Total_columns_Centro, expected_totals_Centro)
Expand Down

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