Patch 1

matlab/section6/calibration/calcal_TD.m

@@ -177,7 +177,7 @@
 %%% input into the smodexpg.m solver
 
 % Update productivities and amenities
-[A06,B06,wage06] = calcal_adj_TD(obsvar06,tt06,nobs06,A06,B06);
+[A06,B06,wage06,CMA06] = calcal_adj_TD(obsvar06,tt06,nobs06,A06,B06);
 
 % *****************************;
 % **** TOTAL WORKER INCOME ****;

matlab/section6/calibration/calcal_adj_TD.m

@@ -24,7 +24,7 @@
 %%% rescaled wages, we amenities so that we match the city population in
 %%% the model (Phi = city population)
 
-function [Aout,Bout,wageout] = calcal_adj_TD(obsdata,distvar,noj,A,B)
+function [Aout,Bout,wageout,CMA] = calcal_adj_TD(obsdata,distvar,noj,A,B)
     % This program uses the following inputs
         % obsdata refers to an n by 4 object that contains a vector of four variables,
             % floor space prices, worplace employment, residence employment, and area        
@@ -85,6 +85,11 @@
 % Adjust amenities to match the right population
 EB=((HH./Ephi).^(1./epsilon)).*EB;                                          % If the ratio of the total population in the data over the population in the model is positive, we inflate amenities
                                                                             % From normalization of U_bar explained on page 18 in the supplement we have that H = Phi (the term in Brackets in the H equation). This equation reveals that we can move a multiplicative component Badjustment^epsilon in B_i out of the summation and use it to reach any population level H. 
+% Adjust CMA to conform to new wages;
+ECMA=d_ij_eps*(Ewage.^epsilon);                                             % We recreate residential commuter market access since we have normalized it before
+CMA=zeros(noj,1);                                                           % We create a column vector of length n
+CMA(Irsd)=ECMA;                                                             % Map commuter market access from the vector containing locations with postive residence employment into the vector containing all observations. Observations with zero residence employment receive a theory-consistent zero value. 
+
                                                                             % If we have H = "adjustment factor"^epsilon * Phi, the it follows that "adjustment factor" = (H/Phi)^(1/epsilon)
 % Compute recaled productivities and amenities for all blocks
 Aout=zeros(noj,1);
@@ -96,4 +101,5 @@
 display('>>>> Productivities and amenities updated <<<<');
 
 
-
+
+

matlab/section6/calibration/comegaoptC.m

@@ -66,7 +66,7 @@
 EHMT = HMT(Iwpl);                                                           % Generates a vector of workplace employment only containing observations with positive workplace emplyoment (it has nto observations)
 EHRT = HRT(Irsd);                                                           % Generates a vector of residence employment only containing observations with positive residence emplyoment (it has nfrom observations)
 Eomega = omega(Iwpl);                                                       % Generates a vector of guesses of transformed wages for observations with positive workplace employment (it has nto observations)
-Edistvar=distvar(Iwpl,Irsd);                                                % Generates a smaller travel time matrix that connects only observations with positive workplace employment to observations with residence emplyoment (it has nto by nfrom osbervations)
+Edistvar=distvar(Irsd,Iwpl);                                                % Generates a smaller travel time matrix that connects only observations with positive workplace employment to observations with residence emplyoment (it has nto by nfrom osbervations)
                                                                             % Notice that in this matrix, workplaces are rows and residences are columns 
 clear distvar                                                               % clear variable to save memory
 

matlab/section6/exogcftual/cdensityE.m

@@ -58,5 +58,6 @@
 
 % Total floor space demand;
 LD=LM+LR;                                                                   % Total floor space demand is simply the sum of commercial and residential floor space demand                                                              
-Vout=LD./(K.^0.75);                                                         % We solve for density of development varphi using Eq. S31
+Vout=LD./(K.^0.25);                                                         % We solve for density of development varphi using Eq. S31
 thetaout=LM./LD;                                                            % Share of commercial floor space theta is residential floor space
+

matlab/section6/exogcftual/smodexog.m

@@ -149,7 +149,7 @@
 % We compute residence and workplace employment via commuting probabilities using guesses of wages and floor space prices and fundamental amenity;
 % In the below matrices, we have living in i (rows) and working in j (cols);
 % Notice that _i subscripts may indicate guesses (not residence locations, e.g. for wages)
-EQQ=repmat(QT(Irsd),1,nwpl);                                                % We assign guesses of residence floor space prices to all bilaterals. We take the vector of floor space prices, generate a new vector only for observations with positive residence employment, and replicates it nwpl times to generate a nrsd x nwpl matrix
+EQQ=repmat(Q_i(Irsd),1,nwpl);                                                % We assign guesses of residence floor space prices to all bilaterals. We take the vector of floor space prices, generate a new vector only for observations with positive residence employment, and replicates it nwpl times to generate a nrsd x nwpl matrix
 EQQ=EQQ.^(-(1-beta).*epsilon);                                              % Generate the first component of bilateral commuting probabilities in Eq. 4: floor space prices to the power of (1-beta) x -epsilon 
 EBB=repmat(EB,1,nwpl);                                                      % We assign residence amenities to all bilaterals. Takes the vector of amenities for observations with positive residence emplyoment and replicates it to generate an nrds x nwpl matrix
 EBB=EBB.^epsilon;                                                           % Generates the next component of bilateral commuting probabilities: B to the power of epsilon 
@@ -217,7 +217,7 @@
 
 % Solve for theta for which the land market clears;
 theta_e=theta_i;                                                            % Completely specialized blocks never change since amenity or productivity are zero, so predicted theta correponds guesses (initial values). We only update mixed use shares in the next line
-theta_e(Iis)=((1-alpha).*Y(Iis))./(q_e(Iis).*LD(Iis));                      % We use the facotr input demand function derived from Eq. (10). Commercial floor space input LM is (1-alpha)*Y / q. Theta is LM/LD. Combining the two we can predict the commerical floor space share for imperfectly specialized blocks using predicted outputs and rents
+theta_e(Iis)=((1-alpha).*Y(Iis))./(q_i(Iis).*LD(Iis));                      % We use the facotr input demand function derived from Eq. (10). Commercial floor space input LM is (1-alpha)*Y / q. Theta is LM/LD. Combining the two we can predict the commerical floor space share for imperfectly specialized blocks using predicted outputs and rents
 
 %%% Now we have closed to loop from guesses to predicted values of the 
 %%% target variables wage, floor space price and floor space share;