From 07ad677d1685c091e8747e00d41d5ac61730ed23 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Erik=20Igelstr=C3=B6m?= <eigelstrom@gmail.com>
Date: Thu, 8 Jan 2026 17:11:30 +0000
Subject: [PATCH 1/7] WIP: duplicate reg_health_mental do file for financial
 distress

---
 .../reg_financial_distress.do                 | 954 ++++++++++++++++++
 1 file changed, 954 insertions(+)
 create mode 100644 input/InitialPopulations/compile/RegressionEstimates/reg_financial_distress.do

diff --git a/input/InitialPopulations/compile/RegressionEstimates/reg_financial_distress.do b/input/InitialPopulations/compile/RegressionEstimates/reg_financial_distress.do
new file mode 100644
index 000000000..55688b0e8
--- /dev/null
+++ b/input/InitialPopulations/compile/RegressionEstimates/reg_financial_distress.do
@@ -0,0 +1,954 @@
+********************************************************************************
+* PROJECT:  		UC and mental health
+* SECTION:		Health and wellbeing
+* OBJECT: 		Health status and Disability
+* AUTHORS:		Andy Baxter
+* LAST UPDATE:		04 Dec 2025  
+* COUNTRY: 		UK 
+*
+* NOTES:		
+*   - This file updates GHQ12 Level (0-36) and Caseness (0-12) variables
+********************************************************************************
+clear all
+set more off
+set mem 200m
+set maxvar 30000
+
+
+*******************************************************************
+cap log close 
+log using "${dir_log}/reg_health_mental.log", replace
+*******************************************************************
+
+use "$dir_ukhls_data/ukhls_pooled_all_obs_09.dta", clear
+do "$dir_do/variable_update"
+
+
+
+* Sample selection 
+drop if dag < 16
+
+
+xtset idperson swv
+
+
+**********************************************************************
+* HM1_L: GHQ12 score 0-36 of all working-age adults - baseline effects *
+**********************************************************************
+
+reg dhm ///
+L.i.dhh_owned L.i.dcpst L.dnc L.dhe_pcs L.ib8.drgn L.i.ydses_c5 L.dlltsd L.dhm ///
+L.dag L.dagsq i.deh_c3 i.dot i.dgn stm ///
+[pweight=dimxwt]  ///
+, vce(cluster idperson)
+
+   * save raw results 
+matrix results = r(table)
+matrix results = results[1..6,1...]'
+putexcel set "$dir_raw_results/health_mental/health_mental", sheet("HM1_L") replace
+putexcel A3 = matrix(results), names nformat(number_d2) 
+putexcel J4 = matrix(e(V))
+
+gen in_sample = e(sample)	
+
+predict p
+
+save "$dir_validation_data/HM1_L_sample", replace
+
+
+scalar r2_p = e(r2_p) 
+scalar N = e(N)
+scalar rmse = e(rmse)	
+scalar chi2 = e(chi2)
+scalar ll = e(ll)	
+
+
+* Results 
+
+* Note: Zeros values are eliminated 
+	
+matrix b = e(b)	
+matrix V = e(V)
+
+
+* Store variance-covariance matrix 
+
+preserve
+
+putexcel set "$dir_raw_results/health_mental/var_cov", sheet("var_cov") replace
+putexcel A1 = matrix(V)
+
+import excel "$dir_raw_results/health_mental/var_cov", sheet("var_cov") clear
+
+describe
+local no_vars = `r(k)'	
+	
+forvalues i = 1/2 {
+	egen row_sum = rowtotal(*)
+	drop if row_sum == 0 
+	drop row_sum
+	xpose, clear	
+}	
+	
+mkmat v*, matrix(var)	
+putexcel set "$dir_results/reg_health_mental", sheet("UK_HM1_L") modify
+putexcel C2 = matrix(var)
+		
+restore	
+
+
+* Store estimated coefficients 
+
+// Initialize a counter for non-zero coefficients
+local non_zero_count = 0
+//local names : colnames b
+
+// Loop through each element in `b` to count non-zero coefficients
+forvalues i = 1/`no_vars' {
+    if (b[1, `i'] != 0) {
+        local non_zero_count = `non_zero_count' + 1
+    }
+}
+
+// Create a new row vector to hold only non-zero coefficients
+matrix nonzero_b = J(1, `non_zero_count', .)
+
+// Populate nonzero_b with non-zero coefficients from b
+local index = 1
+forvalues i = 1/`no_vars' {
+    if (b[1, `i'] != 0) {
+        matrix nonzero_b[1, `index'] = b[1, `i']
+        local index = `index' + 1
+    }
+}
+
+putexcel set "$dir_results/reg_health_mental", sheet("UK_HM1_L") modify
+putexcel A1 = matrix(nonzero_b'), names nformat(number_d2) 
+
+
+* Labelling 
+
+putexcel A1 = "REGRESSOR"
+putexcel A2 = "D_Home_owner_L1"
+putexcel A3 = "Dcpst_Single_L1"
+putexcel A4 = "Dcpst_PreviouslyPartnered_L1"
+putexcel A5 = "Dnc_L1"
+putexcel A6 = "Dhe_pcs_L1"
+putexcel A7 = "UKC"
+putexcel A8 = "UKD"
+putexcel A9 = "UKE"
+putexcel A10 = "UKF"
+putexcel A11 = "UKG"
+putexcel A12 = "UKH"
+putexcel A13 = "UKJ"
+putexcel A14 = "UKK"
+putexcel A15 = "UKL"
+putexcel A16 = "UKM"
+putexcel A17 = "UKN"
+putexcel A18 = "Ydses_c5_Q2_L1"
+putexcel A19 = "Ydses_c5_Q3_L1"
+putexcel A20 = "Ydses_c5_Q4_L1"
+putexcel A21 = "Ydses_c5_Q5_L1"
+putexcel A22 = "Dlltsd_L1"
+putexcel A23 = "Dhm_L1"
+putexcel A24 = "Dag_L1"
+putexcel A25 = "Dag_sq_L1"
+putexcel A26 = "Deh_c3_Medium"
+putexcel A27 = "Deh_c3_Low"
+putexcel A28 = "EthnicityAsian"
+putexcel A29 = "EthnicityBlack"
+putexcel A30 = "EthnicityOther"
+putexcel A31 = "Dgn"
+putexcel A32 = "Year_transformed"
+putexcel A33 = "Constant"
+
+putexcel B1 = "COEFFICIENT"
+putexcel C1 = "D_Home_owner_L1"
+putexcel D1 = "Dcpst_Single_L1"
+putexcel E1 = "Dcpst_PreviouslyPartnered_L1"
+putexcel F1 = "Dnc_L1"
+putexcel G1 = "Dhe_pcs_L1"
+putexcel H1 = "UKC"
+putexcel I1 = "UKD"
+putexcel J1 = "UKE"
+putexcel K1 = "UKF"
+putexcel L1 = "UKG"
+putexcel M1 = "UKH"
+putexcel N1 = "UKJ"
+putexcel O1 = "UKK"
+putexcel P1 = "UKL"
+putexcel Q1 = "UKM"
+putexcel R1 = "UKN"
+putexcel S1 = "Ydses_c5_Q2_L1"
+putexcel T1 = "Ydses_c5_Q3_L1"
+putexcel U1 = "Ydses_c5_Q4_L1"
+putexcel V1 = "Ydses_c5_Q5_L1"
+putexcel W1 = "Dlltsd_L1"
+putexcel X1 = "Dhm_L1"
+putexcel Y1 = "Dag_L1"
+putexcel Z1 = "Dag_sq_L1"
+putexcel AA1 = "Deh_c3_Medium"
+putexcel AB1 = "Deh_c3_Low"
+putexcel AC1 = "EthnicityAsian"
+putexcel AD1 = "EthnicityBlack"
+putexcel AE1 = "EthnicityOther"
+putexcel AF1 = "Dgn"
+putexcel AG1 = "Year_transformed"
+putexcel AH1 = "Constant"
+		
+* save RMSE
+putexcel set "$dir_results/reg_RMSE.xlsx", sheet("UK") modify
+putexcel A10 = ("HM1_L") B10 = rmse 
+
+drop in_sample p
+
+scalar drop r2_p N chi2 ll	
+***************************************************************
+* HM2_Females_L: GHQ12 Score 0-36 - causal employment effects *
+***************************************************************
+
+
+*Stage 2
+*Female
+reghdfe dhm ///
+ib11.exp_emp i.exp_poverty i.exp_incchange D.log_income financial_distress ///
+y2020 y2021 ///
+L.i.dhh_owned L.i.dcpst L.dnc L.dhe_pcs L.ib8.drgn L.i.ydses_c5 L.dlltsd L.dhm ///
+L.dag L.dagsq i.deh_c3 stm ///
+if dag>=25 & dag<=64 & dgn==0 ///
+[pweight=dimxwt]  ///
+, absorb(idperson) vce(cluster idperson)
+
+
+   * save raw results 
+matrix results = r(table)
+matrix results = results[1..6,1..10]'
+putexcel set "$dir_raw_results/health_mental/health_mental", sheet("HM2_Females_L") replace
+putexcel A3 = matrix(results), names nformat(number_d2) 
+putexcel J4 = matrix(e(V))
+
+gen in_sample = e(sample)	
+
+predict p
+
+save "$dir_validation_data/HM2_Females_L_sample", replace
+
+
+scalar r2_p = e(r2_p) 
+scalar N = e(N)
+scalar rmse = e(rmse)	
+scalar chi2 = e(chi2)
+scalar ll = e(ll)	
+
+
+* Results 
+
+* Note: Zeros values are eliminated 
+	
+matrix b = e(b)	
+matrix V = e(V)
+matrix V = V[1..14,1..14]
+
+forvalues i = 1/14 {
+    forvalues j = 1/14 {
+        if `i' == `j' {
+          continue
+        }
+        matrix V[`i',`j'] = 0
+    }
+}
+
+* Store variance-covariance matrix 
+
+preserve
+
+putexcel set "$dir_raw_results/health_mental/var_cov", sheet("var_cov") replace
+putexcel A1 = matrix(V)
+
+import excel "$dir_raw_results/health_mental/var_cov", sheet("var_cov") clear
+
+describe
+local no_vars = `r(k)'	
+	
+forvalues i = 1/2 {
+	egen row_sum = rowtotal(*)
+	drop if row_sum == 0 
+	drop row_sum
+	xpose, clear	
+}	
+	
+mkmat v*, matrix(var)	
+putexcel set "$dir_results/reg_health_mental", sheet("UK_HM2_Females_L") modify
+putexcel C2 = matrix(var)
+		
+restore	
+
+
+* Store estimated coefficients 
+
+// Initialize a counter for non-zero coefficients
+local non_zero_count = 0
+//local names : colnames b
+
+// Loop through each element in `b` to count non-zero coefficients
+forvalues i = 1/`no_vars' {
+    if (b[1, `i'] != 0) {
+        local non_zero_count = `non_zero_count' + 1
+    }
+}
+
+// Create a new row vector to hold only non-zero coefficients
+matrix nonzero_b = J(1, `non_zero_count', .)
+
+// Populate nonzero_b with non-zero coefficients from b
+local index = 1
+forvalues i = 1/`no_vars' {
+    if (b[1, `i'] != 0) {
+        matrix nonzero_b[1, `index'] = b[1, `i']
+        local index = `index' + 1
+    }
+}
+
+putexcel set "$dir_results/reg_health_mental", sheet("UK_HM2_Females_L") modify
+putexcel A1 = matrix(nonzero_b'), names nformat(number_d2) 
+
+* Labelling 
+
+putexcel A1 = "REGRESSOR"
+putexcel A2 = "EmployedToUnemployed"
+putexcel A3 = "UnemployedToEmployed"
+putexcel A4 = "PersistentUnemployed"
+putexcel A5 = "NonPovertyToPoverty"
+putexcel A6 = "PovertyToNonPoverty"
+putexcel A7 = "PersistentPoverty"
+putexcel A8 = "RealIncomeChange"
+putexcel A9 = "RealIncomeDecrease_D"
+putexcel A10 = "FinancialDistress"
+putexcel A11 = "Covid_2020_D"
+putexcel A12 = "Covid_2021_D"
+
+
+putexcel B1 = "COEFFICIENT"
+putexcel C1 = "EmployedToUnemployed"
+putexcel D1 = "UnemployedToEmployed"
+putexcel E1 = "PersistentUnemployed"
+putexcel F1 = "NonPovertyToPoverty"
+putexcel G1 = "PovertyToNonPoverty"
+putexcel H1 = "PersistentPoverty"
+putexcel I1 = "RealIncomeChange"
+putexcel J1 = "RealIncomeDecrease_D"
+putexcel K1 = "FinancialDistress"
+putexcel L1 = "Covid_2020_D"
+putexcel M1 = "Covid_2021_D"
+		
+* save RMSE
+putexcel set "$dir_results/reg_RMSE.xlsx", sheet("UK") modify
+putexcel A11 = ("HM2_Females_L") B11 = rmse 
+
+drop in_sample p
+scalar drop r2_p N chi2 ll	
+
+***************************************************************
+* HM2_Males_L: GHQ12 Score 0-36 - causal employment effects *
+***************************************************************
+
+
+*Stage 2
+*Male
+reghdfe dhm ///
+ib11.exp_emp i.exp_poverty i.exp_incchange D.log_income financial_distress ///
+y2020 y2021 ///
+L.i.dhh_owned L.i.dcpst L.dnc L.dhe_pcs L.ib8.drgn L.i.ydses_c5 L.dlltsd L.dhm ///
+L.dag L.dagsq i.deh_c3 stm ///
+if dag>=25 & dag<=64 & dgn==1 ///
+[pweight=dimxwt]  ///
+, absorb(idperson) vce(cluster idperson)
+
+
+   * save raw results 
+matrix results = r(table)
+matrix results = results[1..6,1..10]'
+putexcel set "$dir_raw_results/health_mental/health_mental", sheet("HM2_Males_L") replace
+putexcel A3 = matrix(results), names nformat(number_d2) 
+putexcel J4 = matrix(e(V))
+
+gen in_sample = e(sample)	
+
+predict p
+
+save "$dir_validation_data/HM2_Males_L_sample", replace
+
+
+scalar r2_p = e(r2_p) 
+scalar N = e(N)
+scalar rmse = e(rmse)	
+scalar chi2 = e(chi2)
+scalar ll = e(ll)	
+
+
+* Results 
+
+* Note: Zeros values are eliminated 
+	
+matrix b = e(b)	
+matrix V = e(V)
+matrix V = V[1..14,1..14]
+
+forvalues i = 1/14 {
+    forvalues j = 1/14 {
+        if `i' == `j' {
+          continue
+        }
+        matrix V[`i',`j'] = 0
+    }
+}
+
+* Store variance-covariance matrix 
+
+preserve
+
+putexcel set "$dir_raw_results/health_mental/var_cov", sheet("var_cov") replace
+putexcel A1 = matrix(V)
+
+import excel "$dir_raw_results/health_mental/var_cov", sheet("var_cov") clear
+
+describe
+local no_vars = `r(k)'	
+	
+forvalues i = 1/2 {
+	egen row_sum = rowtotal(*)
+	drop if row_sum == 0 
+	drop row_sum
+	xpose, clear	
+}	
+	
+mkmat v*, matrix(var)	
+putexcel set "$dir_results/reg_health_mental", sheet("UK_HM2_Males_L") modify
+putexcel C2 = matrix(var)
+		
+restore	
+
+
+* Store estimated coefficients 
+
+// Initialize a counter for non-zero coefficients
+local non_zero_count = 0
+//local names : colnames b
+
+// Loop through each element in `b` to count non-zero coefficients
+forvalues i = 1/`no_vars' {
+    if (b[1, `i'] != 0) {
+        local non_zero_count = `non_zero_count' + 1
+    }
+}
+
+// Create a new row vector to hold only non-zero coefficients
+matrix nonzero_b = J(1, `non_zero_count', .)
+
+// Populate nonzero_b with non-zero coefficients from b
+local index = 1
+forvalues i = 1/`no_vars' {
+    if (b[1, `i'] != 0) {
+        matrix nonzero_b[1, `index'] = b[1, `i']
+        local index = `index' + 1
+    }
+}
+
+putexcel set "$dir_results/reg_health_mental", sheet("UK_HM2_Males_L") modify
+putexcel A1 = matrix(nonzero_b'), names nformat(number_d2) 
+
+* Labelling 
+
+putexcel A1 = "REGRESSOR"
+putexcel A2 = "EmployedToUnemployed"
+putexcel A3 = "UnemployedToEmployed"
+putexcel A4 = "PersistentUnemployed"
+putexcel A5 = "NonPovertyToPoverty"
+putexcel A6 = "PovertyToNonPoverty"
+putexcel A7 = "PersistentPoverty"
+putexcel A8 = "RealIncomeChange"
+putexcel A9 = "RealIncomeDecrease_D"
+putexcel A10 = "FinancialDistress"
+putexcel A11 = "Covid_2020_D"
+putexcel A12 = "Covid_2021_D"
+
+
+putexcel B1 = "COEFFICIENT"
+putexcel C1 = "EmployedToUnemployed"
+putexcel D1 = "UnemployedToEmployed"
+putexcel E1 = "PersistentUnemployed"
+putexcel F1 = "NonPovertyToPoverty"
+putexcel G1 = "PovertyToNonPoverty"
+putexcel H1 = "PersistentPoverty"
+putexcel I1 = "RealIncomeChange"
+putexcel J1 = "RealIncomeDecrease_D"
+putexcel K1 = "FinancialDistress"
+putexcel L1 = "Covid_2020_D"
+putexcel M1 = "Covid_2021_D"
+		
+* save RMSE
+putexcel set "$dir_results/reg_RMSE.xlsx", sheet("UK") modify
+putexcel A12 = ("HM2_Males_L") B12 = rmse 
+
+drop in_sample p
+scalar drop r2_p N chi2 ll	
+
+
+**********************************************************************
+* HM1_C: GHQ12 score 0-12 of all working-age adults - baseline effects *
+**********************************************************************
+
+reg scghq2_dv ///
+L.i.dhh_owned L.i.dcpst L.dnc L.dhe_pcs L.ib8.drgn L.i.ydses_c5 L.dlltsd L.scghq2_dv ///
+L.dag L.dagsq i.deh_c3 i.dot i.dgn stm ///
+[pweight=dimxwt]  ///
+, vce(cluster idperson)
+
+   * save raw results 
+matrix results = r(table)
+matrix results = results[1..6,1...]'
+putexcel set "$dir_raw_results/health_mental/health_mental", sheet("HM1_C") replace
+putexcel A3 = matrix(results), names nformat(number_d2) 
+putexcel J4 = matrix(e(V))
+
+gen in_sample = e(sample)	
+
+predict p
+
+save "$dir_validation_data/HM1_C_sample", replace
+
+
+scalar r2_p = e(r2_p) 
+scalar N = e(N)
+scalar rmse = e(rmse)	
+scalar chi2 = e(chi2)
+scalar ll = e(ll)	
+
+
+* Results 
+
+* Note: Zeros values are eliminated 
+	
+matrix b = e(b)	
+matrix V = e(V)
+
+
+* Store variance-covariance matrix 
+
+preserve
+
+putexcel set "$dir_raw_results/health_mental/var_cov", sheet("var_cov") replace
+putexcel A1 = matrix(V)
+
+import excel "$dir_raw_results/health_mental/var_cov", sheet("var_cov") clear
+
+describe
+local no_vars = `r(k)'	
+	
+forvalues i = 1/2 {
+	egen row_sum = rowtotal(*)
+	drop if row_sum == 0 
+	drop row_sum
+	xpose, clear	
+}	
+	
+mkmat v*, matrix(var)	
+putexcel set "$dir_results/reg_health_mental", sheet("UK_HM1_C") modify
+putexcel C2 = matrix(var)
+		
+restore	
+
+
+* Store estimated coefficients 
+
+// Initialize a counter for non-zero coefficients
+local non_zero_count = 0
+//local names : colnames b
+
+// Loop through each element in `b` to count non-zero coefficients
+forvalues i = 1/`no_vars' {
+    if (b[1, `i'] != 0) {
+        local non_zero_count = `non_zero_count' + 1
+    }
+}
+
+// Create a new row vector to hold only non-zero coefficients
+matrix nonzero_b = J(1, `non_zero_count', .)
+
+// Populate nonzero_b with non-zero coefficients from b
+local index = 1
+forvalues i = 1/`no_vars' {
+    if (b[1, `i'] != 0) {
+        matrix nonzero_b[1, `index'] = b[1, `i']
+        local index = `index' + 1
+    }
+}
+
+putexcel set "$dir_results/reg_health_mental", sheet("UK_HM1_C") modify
+putexcel A1 = matrix(nonzero_b'), names nformat(number_d2) 
+
+
+* Labelling 
+
+putexcel A1 = "REGRESSOR"
+putexcel A2 = "D_Home_owner_L1"
+putexcel A3 = "Dcpst_Single_L1"
+putexcel A4 = "Dcpst_PreviouslyPartnered_L1"
+putexcel A5 = "Dnc_L1"
+putexcel A6 = "Dhe_pcs_L1"
+putexcel A7 = "UKC"
+putexcel A8 = "UKD"
+putexcel A9 = "UKE"
+putexcel A10 = "UKF"
+putexcel A11 = "UKG"
+putexcel A12 = "UKH"
+putexcel A13 = "UKJ"
+putexcel A14 = "UKK"
+putexcel A15 = "UKL"
+putexcel A16 = "UKM"
+putexcel A17 = "UKN"
+putexcel A18 = "Ydses_c5_Q2_L1"
+putexcel A19 = "Ydses_c5_Q3_L1"
+putexcel A20 = "Ydses_c5_Q4_L1"
+putexcel A21 = "Ydses_c5_Q5_L1"
+putexcel A22 = "Dlltsd_L1"
+putexcel A23 = "Dhm_L1"
+putexcel A24 = "Dag_L1"
+putexcel A25 = "Dag_sq_L1"
+putexcel A26 = "Deh_c3_Medium"
+putexcel A27 = "Deh_c3_Low"
+putexcel A28 = "EthnicityAsian"
+putexcel A29 = "EthnicityBlack"
+putexcel A30 = "EthnicityOther"
+putexcel A31 = "Dgn"
+putexcel A32 = "Year_transformed"
+putexcel A33 = "Constant"
+
+putexcel B1 = "COEFFICIENT"
+putexcel C1 = "D_Home_owner_L1"
+putexcel D1 = "Dcpst_Single_L1"
+putexcel E1 = "Dcpst_PreviouslyPartnered_L1"
+putexcel F1 = "Dnc_L1"
+putexcel G1 = "Dhe_pcs_L1"
+putexcel H1 = "UKC"
+putexcel I1 = "UKD"
+putexcel J1 = "UKE"
+putexcel K1 = "UKF"
+putexcel L1 = "UKG"
+putexcel M1 = "UKH"
+putexcel N1 = "UKJ"
+putexcel O1 = "UKK"
+putexcel P1 = "UKL"
+putexcel Q1 = "UKM"
+putexcel R1 = "UKN"
+putexcel S1 = "Ydses_c5_Q2_L1"
+putexcel T1 = "Ydses_c5_Q3_L1"
+putexcel U1 = "Ydses_c5_Q4_L1"
+putexcel V1 = "Ydses_c5_Q5_L1"
+putexcel W1 = "Dlltsd_L1"
+putexcel X1 = "Dhm_L1"
+putexcel Y1 = "Dag_L1"
+putexcel Z1 = "Dag_sq_L1"
+putexcel AA1 = "Deh_c3_Medium"
+putexcel AB1 = "Deh_c3_Low"
+putexcel AC1 = "EthnicityAsian"
+putexcel AD1 = "EthnicityBlack"
+putexcel AE1 = "EthnicityOther"
+putexcel AF1 = "Dgn"
+putexcel AG1 = "Year_transformed"
+putexcel AH1 = "Constant"
+		
+* save RMSE
+putexcel set "$dir_results/reg_RMSE.xlsx", sheet("UK") modify
+putexcel A13 = ("HM1_C") B13 = rmse 
+
+drop in_sample p
+scalar drop r2_p N chi2 ll	
+***************************************************************
+* HM2_Females_C: GHQ12 Score 0-12 - causal employment effects *
+***************************************************************
+
+gen RealIncomeDecrease_D = log_income - L.log_income
+gen scghq2_dv_L1 = L.scghq2_dv
+
+*Stage 2
+*Female
+reghdfe scghq2_dv ///
+ib11.exp_emp i.exp_poverty i.exp_incchange RealIncomeDecrease_D financial_distress ///
+y2020 y2021 ///
+i.dhh_owned i.dcpst dnc dhe_pcs ib8.drgn i.ydses_c5 dlltsd ///
+dag dagsq i.deh_c3 stm ///
+if dag>=25 & dag<=64 & dgn==0 ///
+, absorb(idperson) vce(cluster idperson)
+
+
+   * save raw results 
+matrix results = r(table)
+matrix results = results[1..6,1..10]'
+putexcel set "$dir_raw_results/health_mental/health_mental", sheet("HM2_Females_C") replace
+putexcel A3 = matrix(results), names nformat(number_d2) 
+putexcel J4 = matrix(e(V))
+
+gen in_sample = e(sample)	
+
+predict p
+
+save "$dir_validation_data/HM2_Females_C_sample", replace
+
+
+scalar r2_p = e(r2_p) 
+scalar N = e(N)
+scalar rmse = e(rmse)	
+scalar chi2 = e(chi2)
+scalar ll = e(ll)	
+
+
+* Results 
+
+* Note: Zeros values are eliminated 
+	
+matrix b = e(b)	
+matrix V = e(V)
+matrix V = V[1..14,1..14]
+
+forvalues i = 1/14 {
+    forvalues j = 1/14 {
+        if `i' == `j' {
+          continue
+        }
+        matrix V[`i',`j'] = 0
+    }
+}
+
+* Store variance-covariance matrix 
+
+preserve
+
+putexcel set "$dir_raw_results/health_mental/var_cov", sheet("var_cov") replace
+putexcel A1 = matrix(V)
+
+import excel "$dir_raw_results/health_mental/var_cov", sheet("var_cov") clear
+
+describe
+local no_vars = `r(k)'	
+	
+forvalues i = 1/2 {
+	egen row_sum = rowtotal(*)
+	drop if row_sum == 0 
+	drop row_sum
+	xpose, clear	
+}	
+	
+mkmat v*, matrix(var)	
+putexcel set "$dir_results/reg_health_mental", sheet("UK_HM2_Females_C") modify
+putexcel C2 = matrix(var)
+		
+restore	
+
+
+* Store estimated coefficients 
+
+// Initialize a counter for non-zero coefficients
+local non_zero_count = 0
+//local names : colnames b
+
+// Loop through each element in `b` to count non-zero coefficients
+forvalues i = 1/`no_vars' {
+    if (b[1, `i'] != 0) {
+        local non_zero_count = `non_zero_count' + 1
+    }
+}
+
+// Create a new row vector to hold only non-zero coefficients
+matrix nonzero_b = J(1, `non_zero_count', .)
+
+// Populate nonzero_b with non-zero coefficients from b
+local index = 1
+forvalues i = 1/`no_vars' {
+    if (b[1, `i'] != 0) {
+        matrix nonzero_b[1, `index'] = b[1, `i']
+        local index = `index' + 1
+    }
+}
+
+putexcel set "$dir_results/reg_health_mental", sheet("UK_HM2_Females_C") modify
+putexcel A1 = matrix(nonzero_b'), names nformat(number_d2) 
+
+* Labelling 
+
+putexcel A1 = "REGRESSOR"
+putexcel A2 = "EmployedToUnemployed"
+putexcel A3 = "UnemployedToEmployed"
+putexcel A4 = "PersistentUnemployed"
+putexcel A5 = "NonPovertyToPoverty"
+putexcel A6 = "PovertyToNonPoverty"
+putexcel A7 = "PersistentPoverty"
+putexcel A8 = "RealIncomeChange"
+putexcel A9 = "RealIncomeDecrease_D"
+putexcel A10 = "FinancialDistress"
+putexcel A11 = "Covid_2020_D"
+putexcel A12 = "Covid_2021_D"
+
+
+putexcel B1 = "COEFFICIENT"
+putexcel C1 = "EmployedToUnemployed"
+putexcel D1 = "UnemployedToEmployed"
+putexcel E1 = "PersistentUnemployed"
+putexcel F1 = "NonPovertyToPoverty"
+putexcel G1 = "PovertyToNonPoverty"
+putexcel H1 = "PersistentPoverty"
+putexcel I1 = "RealIncomeChange"
+putexcel J1 = "RealIncomeDecrease_D"
+putexcel K1 = "FinancialDistress"
+putexcel L1 = "Covid_2020_D"
+putexcel M1 = "Covid_2021_D"
+		
+* save RMSE
+putexcel set "$dir_results/reg_RMSE.xlsx", sheet("UK") modify
+putexcel A14 = ("HM2_Females_C") B14 = rmse 
+
+drop in_sample p
+scalar drop r2_p N chi2 ll	
+
+***************************************************************
+* HM2_Males_C: GHQ12 Score 0-12 - causal employment effects *
+***************************************************************
+
+
+*Stage 2
+*Male
+reghdfe scghq2_dv ///
+ib11.exp_emp i.exp_poverty i.exp_incchange RealIncomeDecrease_D financial_distress ///
+y2020 y2021 ///
+i.dhh_owned i.dcpst dnc dhe_pcs ib8.drgn i.ydses_c5 dlltsd ///
+dag dagsq i.deh_c3 stm ///
+if dag>=25 & dag<=64 & dgn==1 ///
+, absorb(idperson) vce(cluster idperson)
+
+   * save raw results 
+matrix results = r(table)
+matrix results = results[1..6,1..10]'
+putexcel set "$dir_raw_results/health_mental/health_mental", sheet("HM2_Males_C") replace
+putexcel A3 = matrix(results), names nformat(number_d2) 
+putexcel J4 = matrix(e(V))
+
+gen in_sample = e(sample)	
+
+predict p
+
+save "$dir_validation_data/HM2_Males_C_sample", replace
+
+
+scalar r2_p = e(r2_p) 
+scalar N = e(N)
+scalar rmse = e(rmse)	
+scalar chi2 = e(chi2)
+scalar ll = e(ll)	
+
+
+* Results 
+
+* Note: Zeros values are eliminated 
+	
+matrix b = e(b)	
+matrix V = e(V)
+matrix V = V[1..14,1..14]
+
+forvalues i = 1/14 {
+    forvalues j = 1/14 {
+        if `i' == `j' {
+          continue
+        }
+        matrix V[`i',`j'] = 0
+    }
+}
+
+* Store variance-covariance matrix 
+
+preserve
+
+putexcel set "$dir_raw_results/health_mental/var_cov", sheet("var_cov") replace
+putexcel A1 = matrix(V)
+
+import excel "$dir_raw_results/health_mental/var_cov", sheet("var_cov") clear
+
+describe
+local no_vars = `r(k)'	
+	
+forvalues i = 1/2 {
+	egen row_sum = rowtotal(*)
+	drop if row_sum == 0 
+	drop row_sum
+	xpose, clear	
+}	
+	
+mkmat v*, matrix(var)	
+putexcel set "$dir_results/reg_health_mental", sheet("UK_HM2_Males_C") modify
+putexcel C2 = matrix(var)
+		
+restore	
+
+
+* Store estimated coefficients 
+
+// Initialize a counter for non-zero coefficients
+local non_zero_count = 0
+//local names : colnames b
+
+// Loop through each element in `b` to count non-zero coefficients
+forvalues i = 1/`no_vars' {
+    if (b[1, `i'] != 0) {
+        local non_zero_count = `non_zero_count' + 1
+    }
+}
+
+// Create a new row vector to hold only non-zero coefficients
+matrix nonzero_b = J(1, `non_zero_count', .)
+
+// Populate nonzero_b with non-zero coefficients from b
+local index = 1
+forvalues i = 1/`no_vars' {
+    if (b[1, `i'] != 0) {
+        matrix nonzero_b[1, `index'] = b[1, `i']
+        local index = `index' + 1
+    }
+}
+
+putexcel set "$dir_results/reg_health_mental", sheet("UK_HM2_Males_C") modify
+putexcel A1 = matrix(nonzero_b'), names nformat(number_d2) 
+
+* Labelling 
+
+putexcel A1 = "REGRESSOR"
+putexcel A2 = "EmployedToUnemployed"
+putexcel A3 = "UnemployedToEmployed"
+putexcel A4 = "PersistentUnemployed"
+putexcel A5 = "NonPovertyToPoverty"
+putexcel A6 = "PovertyToNonPoverty"
+putexcel A7 = "PersistentPoverty"
+putexcel A8 = "RealIncomeChange"
+putexcel A9 = "RealIncomeDecrease_D"
+putexcel A10 = "FinancialDistress"
+putexcel A11 = "Covid_2020_D"
+putexcel A12 = "Covid_2021_D"
+
+
+putexcel B1 = "COEFFICIENT"
+putexcel C1 = "EmployedToUnemployed"
+putexcel D1 = "UnemployedToEmployed"
+putexcel E1 = "PersistentUnemployed"
+putexcel F1 = "NonPovertyToPoverty"
+putexcel G1 = "PovertyToNonPoverty"
+putexcel H1 = "PersistentPoverty"
+putexcel I1 = "RealIncomeChange"
+putexcel J1 = "RealIncomeDecrease_D"
+putexcel K1 = "FinancialDistress"
+putexcel L1 = "Covid_2020_D"
+putexcel M1 = "Covid_2021_D"
+		
+* save RMSE
+putexcel set "$dir_results/reg_RMSE.xlsx", sheet("UK") modify
+putexcel A15 = ("HM2_Males_C") B15 = rmse 
+
+drop in_sample p
+scalar drop r2_p N chi2 ll	

From 970a01f230191c6ac55b8efa8f12763d254111ea Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Erik=20Igelstr=C3=B6m?= <eigelstrom@gmail.com>
Date: Thu, 8 Jan 2026 17:15:11 +0000
Subject: [PATCH 2/7] WIP: update reg_financial_distress.do

---
 .../reg_financial_distress.do                 | 784 +-----------------
 1 file changed, 18 insertions(+), 766 deletions(-)

diff --git a/input/InitialPopulations/compile/RegressionEstimates/reg_financial_distress.do b/input/InitialPopulations/compile/RegressionEstimates/reg_financial_distress.do
index 55688b0e8..dfe299bc0 100644
--- a/input/InitialPopulations/compile/RegressionEstimates/reg_financial_distress.do
+++ b/input/InitialPopulations/compile/RegressionEstimates/reg_financial_distress.do
@@ -1,13 +1,12 @@
 ********************************************************************************
-* PROJECT:  		UC and mental health
+* PROJECT:		UC and mental health
 * SECTION:		Health and wellbeing
-* OBJECT: 		Health status and Disability
-* AUTHORS:		Andy Baxter
-* LAST UPDATE:		04 Dec 2025  
-* COUNTRY: 		UK 
+* OBJECT: 		Financial distress
+* AUTHORS:		Andy Baxter, Erik Igelström
+* LAST UPDATE:		08 Jan 2026  
+* COUNTRY:		UK 
 *
 * NOTES:		
-*   - This file updates GHQ12 Level (0-36) and Caseness (0-12) variables
 ********************************************************************************
 clear all
 set more off
@@ -17,7 +16,7 @@ set maxvar 30000
 
 *******************************************************************
 cap log close 
-log using "${dir_log}/reg_health_mental.log", replace
+log using "${dir_log}/reg_financial_distress.log", replace
 *******************************************************************
 
 use "$dir_ukhls_data/ukhls_pooled_all_obs_09.dta", clear
@@ -36,16 +35,17 @@ xtset idperson swv
 * HM1_L: GHQ12 score 0-36 of all working-age adults - baseline effects *
 **********************************************************************
 
-reg dhm ///
-L.i.dhh_owned L.i.dcpst L.dnc L.dhe_pcs L.ib8.drgn L.i.ydses_c5 L.dlltsd L.dhm ///
-L.dag L.dagsq i.deh_c3 i.dot i.dgn stm ///
+logit econ_dist ///
+ib11.exp_emp  i.lhw_c5 D.log_income i.exp_incchange ib0.exp_poverty L.ypncp L.ypnoab ///
+L.i.econ_benefits L.i.dhh_owned L.i.dcpst L.dnc L.dhe_pcs L.dhe_mcs L.ib8.drgn L.i.ydses_c5 L.dlltsd  L.econ_dist ///
+i.dgn L.dag L.dagsq i.deh_c3 i.dot stm ///
 [pweight=dimxwt]  ///
 , vce(cluster idperson)
 
    * save raw results 
 matrix results = r(table)
 matrix results = results[1..6,1...]'
-putexcel set "$dir_raw_results/health_mental/health_mental", sheet("HM1_L") replace
+putexcel set "$dir_raw_results/financial_distress/financial_distress", sheet("UK") replace
 putexcel A3 = matrix(results), names nformat(number_d2) 
 putexcel J4 = matrix(e(V))
 
@@ -53,7 +53,7 @@ gen in_sample = e(sample)
 
 predict p
 
-save "$dir_validation_data/HM1_L_sample", replace
+save "$dir_validation_data/financial_distress", replace
 
 
 scalar r2_p = e(r2_p) 
@@ -75,10 +75,10 @@ matrix V = e(V)
 
 preserve
 
-putexcel set "$dir_raw_results/health_mental/var_cov", sheet("var_cov") replace
+putexcel set "$dir_raw_results/financial_distress/var_cov", sheet("var_cov") replace
 putexcel A1 = matrix(V)
 
-import excel "$dir_raw_results/health_mental/var_cov", sheet("var_cov") clear
+import excel "$dir_raw_results/financial_distress/var_cov", sheet("var_cov") clear
 
 describe
 local no_vars = `r(k)'	
@@ -91,7 +91,7 @@ forvalues i = 1/2 {
 }	
 	
 mkmat v*, matrix(var)	
-putexcel set "$dir_results/reg_health_mental", sheet("UK_HM1_L") modify
+putexcel set "$dir_results/reg_financial_distress", sheet("UK") modify
 putexcel C2 = matrix(var)
 		
 restore	
@@ -122,473 +122,13 @@ forvalues i = 1/`no_vars' {
     }
 }
 
-putexcel set "$dir_results/reg_health_mental", sheet("UK_HM1_L") modify
+putexcel set "$dir_results/reg_financial_distress", sheet("UK") modify
 putexcel A1 = matrix(nonzero_b'), names nformat(number_d2) 
 
 
 * Labelling 
 
-putexcel A1 = "REGRESSOR"
-putexcel A2 = "D_Home_owner_L1"
-putexcel A3 = "Dcpst_Single_L1"
-putexcel A4 = "Dcpst_PreviouslyPartnered_L1"
-putexcel A5 = "Dnc_L1"
-putexcel A6 = "Dhe_pcs_L1"
-putexcel A7 = "UKC"
-putexcel A8 = "UKD"
-putexcel A9 = "UKE"
-putexcel A10 = "UKF"
-putexcel A11 = "UKG"
-putexcel A12 = "UKH"
-putexcel A13 = "UKJ"
-putexcel A14 = "UKK"
-putexcel A15 = "UKL"
-putexcel A16 = "UKM"
-putexcel A17 = "UKN"
-putexcel A18 = "Ydses_c5_Q2_L1"
-putexcel A19 = "Ydses_c5_Q3_L1"
-putexcel A20 = "Ydses_c5_Q4_L1"
-putexcel A21 = "Ydses_c5_Q5_L1"
-putexcel A22 = "Dlltsd_L1"
-putexcel A23 = "Dhm_L1"
-putexcel A24 = "Dag_L1"
-putexcel A25 = "Dag_sq_L1"
-putexcel A26 = "Deh_c3_Medium"
-putexcel A27 = "Deh_c3_Low"
-putexcel A28 = "EthnicityAsian"
-putexcel A29 = "EthnicityBlack"
-putexcel A30 = "EthnicityOther"
-putexcel A31 = "Dgn"
-putexcel A32 = "Year_transformed"
-putexcel A33 = "Constant"
-
-putexcel B1 = "COEFFICIENT"
-putexcel C1 = "D_Home_owner_L1"
-putexcel D1 = "Dcpst_Single_L1"
-putexcel E1 = "Dcpst_PreviouslyPartnered_L1"
-putexcel F1 = "Dnc_L1"
-putexcel G1 = "Dhe_pcs_L1"
-putexcel H1 = "UKC"
-putexcel I1 = "UKD"
-putexcel J1 = "UKE"
-putexcel K1 = "UKF"
-putexcel L1 = "UKG"
-putexcel M1 = "UKH"
-putexcel N1 = "UKJ"
-putexcel O1 = "UKK"
-putexcel P1 = "UKL"
-putexcel Q1 = "UKM"
-putexcel R1 = "UKN"
-putexcel S1 = "Ydses_c5_Q2_L1"
-putexcel T1 = "Ydses_c5_Q3_L1"
-putexcel U1 = "Ydses_c5_Q4_L1"
-putexcel V1 = "Ydses_c5_Q5_L1"
-putexcel W1 = "Dlltsd_L1"
-putexcel X1 = "Dhm_L1"
-putexcel Y1 = "Dag_L1"
-putexcel Z1 = "Dag_sq_L1"
-putexcel AA1 = "Deh_c3_Medium"
-putexcel AB1 = "Deh_c3_Low"
-putexcel AC1 = "EthnicityAsian"
-putexcel AD1 = "EthnicityBlack"
-putexcel AE1 = "EthnicityOther"
-putexcel AF1 = "Dgn"
-putexcel AG1 = "Year_transformed"
-putexcel AH1 = "Constant"
-		
-* save RMSE
-putexcel set "$dir_results/reg_RMSE.xlsx", sheet("UK") modify
-putexcel A10 = ("HM1_L") B10 = rmse 
-
-drop in_sample p
-
-scalar drop r2_p N chi2 ll	
-***************************************************************
-* HM2_Females_L: GHQ12 Score 0-36 - causal employment effects *
-***************************************************************
-
-
-*Stage 2
-*Female
-reghdfe dhm ///
-ib11.exp_emp i.exp_poverty i.exp_incchange D.log_income financial_distress ///
-y2020 y2021 ///
-L.i.dhh_owned L.i.dcpst L.dnc L.dhe_pcs L.ib8.drgn L.i.ydses_c5 L.dlltsd L.dhm ///
-L.dag L.dagsq i.deh_c3 stm ///
-if dag>=25 & dag<=64 & dgn==0 ///
-[pweight=dimxwt]  ///
-, absorb(idperson) vce(cluster idperson)
-
-
-   * save raw results 
-matrix results = r(table)
-matrix results = results[1..6,1..10]'
-putexcel set "$dir_raw_results/health_mental/health_mental", sheet("HM2_Females_L") replace
-putexcel A3 = matrix(results), names nformat(number_d2) 
-putexcel J4 = matrix(e(V))
-
-gen in_sample = e(sample)	
-
-predict p
-
-save "$dir_validation_data/HM2_Females_L_sample", replace
-
-
-scalar r2_p = e(r2_p) 
-scalar N = e(N)
-scalar rmse = e(rmse)	
-scalar chi2 = e(chi2)
-scalar ll = e(ll)	
-
-
-* Results 
-
-* Note: Zeros values are eliminated 
-	
-matrix b = e(b)	
-matrix V = e(V)
-matrix V = V[1..14,1..14]
-
-forvalues i = 1/14 {
-    forvalues j = 1/14 {
-        if `i' == `j' {
-          continue
-        }
-        matrix V[`i',`j'] = 0
-    }
-}
-
-* Store variance-covariance matrix 
-
-preserve
-
-putexcel set "$dir_raw_results/health_mental/var_cov", sheet("var_cov") replace
-putexcel A1 = matrix(V)
-
-import excel "$dir_raw_results/health_mental/var_cov", sheet("var_cov") clear
-
-describe
-local no_vars = `r(k)'	
-	
-forvalues i = 1/2 {
-	egen row_sum = rowtotal(*)
-	drop if row_sum == 0 
-	drop row_sum
-	xpose, clear	
-}	
-	
-mkmat v*, matrix(var)	
-putexcel set "$dir_results/reg_health_mental", sheet("UK_HM2_Females_L") modify
-putexcel C2 = matrix(var)
-		
-restore	
-
-
-* Store estimated coefficients 
-
-// Initialize a counter for non-zero coefficients
-local non_zero_count = 0
-//local names : colnames b
-
-// Loop through each element in `b` to count non-zero coefficients
-forvalues i = 1/`no_vars' {
-    if (b[1, `i'] != 0) {
-        local non_zero_count = `non_zero_count' + 1
-    }
-}
-
-// Create a new row vector to hold only non-zero coefficients
-matrix nonzero_b = J(1, `non_zero_count', .)
-
-// Populate nonzero_b with non-zero coefficients from b
-local index = 1
-forvalues i = 1/`no_vars' {
-    if (b[1, `i'] != 0) {
-        matrix nonzero_b[1, `index'] = b[1, `i']
-        local index = `index' + 1
-    }
-}
-
-putexcel set "$dir_results/reg_health_mental", sheet("UK_HM2_Females_L") modify
-putexcel A1 = matrix(nonzero_b'), names nformat(number_d2) 
-
-* Labelling 
-
-putexcel A1 = "REGRESSOR"
-putexcel A2 = "EmployedToUnemployed"
-putexcel A3 = "UnemployedToEmployed"
-putexcel A4 = "PersistentUnemployed"
-putexcel A5 = "NonPovertyToPoverty"
-putexcel A6 = "PovertyToNonPoverty"
-putexcel A7 = "PersistentPoverty"
-putexcel A8 = "RealIncomeChange"
-putexcel A9 = "RealIncomeDecrease_D"
-putexcel A10 = "FinancialDistress"
-putexcel A11 = "Covid_2020_D"
-putexcel A12 = "Covid_2021_D"
-
-
-putexcel B1 = "COEFFICIENT"
-putexcel C1 = "EmployedToUnemployed"
-putexcel D1 = "UnemployedToEmployed"
-putexcel E1 = "PersistentUnemployed"
-putexcel F1 = "NonPovertyToPoverty"
-putexcel G1 = "PovertyToNonPoverty"
-putexcel H1 = "PersistentPoverty"
-putexcel I1 = "RealIncomeChange"
-putexcel J1 = "RealIncomeDecrease_D"
-putexcel K1 = "FinancialDistress"
-putexcel L1 = "Covid_2020_D"
-putexcel M1 = "Covid_2021_D"
-		
-* save RMSE
-putexcel set "$dir_results/reg_RMSE.xlsx", sheet("UK") modify
-putexcel A11 = ("HM2_Females_L") B11 = rmse 
-
-drop in_sample p
-scalar drop r2_p N chi2 ll	
-
-***************************************************************
-* HM2_Males_L: GHQ12 Score 0-36 - causal employment effects *
-***************************************************************
-
-
-*Stage 2
-*Male
-reghdfe dhm ///
-ib11.exp_emp i.exp_poverty i.exp_incchange D.log_income financial_distress ///
-y2020 y2021 ///
-L.i.dhh_owned L.i.dcpst L.dnc L.dhe_pcs L.ib8.drgn L.i.ydses_c5 L.dlltsd L.dhm ///
-L.dag L.dagsq i.deh_c3 stm ///
-if dag>=25 & dag<=64 & dgn==1 ///
-[pweight=dimxwt]  ///
-, absorb(idperson) vce(cluster idperson)
-
-
-   * save raw results 
-matrix results = r(table)
-matrix results = results[1..6,1..10]'
-putexcel set "$dir_raw_results/health_mental/health_mental", sheet("HM2_Males_L") replace
-putexcel A3 = matrix(results), names nformat(number_d2) 
-putexcel J4 = matrix(e(V))
-
-gen in_sample = e(sample)	
-
-predict p
-
-save "$dir_validation_data/HM2_Males_L_sample", replace
-
-
-scalar r2_p = e(r2_p) 
-scalar N = e(N)
-scalar rmse = e(rmse)	
-scalar chi2 = e(chi2)
-scalar ll = e(ll)	
-
-
-* Results 
-
-* Note: Zeros values are eliminated 
-	
-matrix b = e(b)	
-matrix V = e(V)
-matrix V = V[1..14,1..14]
-
-forvalues i = 1/14 {
-    forvalues j = 1/14 {
-        if `i' == `j' {
-          continue
-        }
-        matrix V[`i',`j'] = 0
-    }
-}
-
-* Store variance-covariance matrix 
-
-preserve
-
-putexcel set "$dir_raw_results/health_mental/var_cov", sheet("var_cov") replace
-putexcel A1 = matrix(V)
-
-import excel "$dir_raw_results/health_mental/var_cov", sheet("var_cov") clear
-
-describe
-local no_vars = `r(k)'	
-	
-forvalues i = 1/2 {
-	egen row_sum = rowtotal(*)
-	drop if row_sum == 0 
-	drop row_sum
-	xpose, clear	
-}	
-	
-mkmat v*, matrix(var)	
-putexcel set "$dir_results/reg_health_mental", sheet("UK_HM2_Males_L") modify
-putexcel C2 = matrix(var)
-		
-restore	
-
-
-* Store estimated coefficients 
-
-// Initialize a counter for non-zero coefficients
-local non_zero_count = 0
-//local names : colnames b
-
-// Loop through each element in `b` to count non-zero coefficients
-forvalues i = 1/`no_vars' {
-    if (b[1, `i'] != 0) {
-        local non_zero_count = `non_zero_count' + 1
-    }
-}
-
-// Create a new row vector to hold only non-zero coefficients
-matrix nonzero_b = J(1, `non_zero_count', .)
-
-// Populate nonzero_b with non-zero coefficients from b
-local index = 1
-forvalues i = 1/`no_vars' {
-    if (b[1, `i'] != 0) {
-        matrix nonzero_b[1, `index'] = b[1, `i']
-        local index = `index' + 1
-    }
-}
-
-putexcel set "$dir_results/reg_health_mental", sheet("UK_HM2_Males_L") modify
-putexcel A1 = matrix(nonzero_b'), names nformat(number_d2) 
-
-* Labelling 
-
-putexcel A1 = "REGRESSOR"
-putexcel A2 = "EmployedToUnemployed"
-putexcel A3 = "UnemployedToEmployed"
-putexcel A4 = "PersistentUnemployed"
-putexcel A5 = "NonPovertyToPoverty"
-putexcel A6 = "PovertyToNonPoverty"
-putexcel A7 = "PersistentPoverty"
-putexcel A8 = "RealIncomeChange"
-putexcel A9 = "RealIncomeDecrease_D"
-putexcel A10 = "FinancialDistress"
-putexcel A11 = "Covid_2020_D"
-putexcel A12 = "Covid_2021_D"
-
-
-putexcel B1 = "COEFFICIENT"
-putexcel C1 = "EmployedToUnemployed"
-putexcel D1 = "UnemployedToEmployed"
-putexcel E1 = "PersistentUnemployed"
-putexcel F1 = "NonPovertyToPoverty"
-putexcel G1 = "PovertyToNonPoverty"
-putexcel H1 = "PersistentPoverty"
-putexcel I1 = "RealIncomeChange"
-putexcel J1 = "RealIncomeDecrease_D"
-putexcel K1 = "FinancialDistress"
-putexcel L1 = "Covid_2020_D"
-putexcel M1 = "Covid_2021_D"
-		
-* save RMSE
-putexcel set "$dir_results/reg_RMSE.xlsx", sheet("UK") modify
-putexcel A12 = ("HM2_Males_L") B12 = rmse 
-
-drop in_sample p
-scalar drop r2_p N chi2 ll	
-
-
-**********************************************************************
-* HM1_C: GHQ12 score 0-12 of all working-age adults - baseline effects *
-**********************************************************************
-
-reg scghq2_dv ///
-L.i.dhh_owned L.i.dcpst L.dnc L.dhe_pcs L.ib8.drgn L.i.ydses_c5 L.dlltsd L.scghq2_dv ///
-L.dag L.dagsq i.deh_c3 i.dot i.dgn stm ///
-[pweight=dimxwt]  ///
-, vce(cluster idperson)
-
-   * save raw results 
-matrix results = r(table)
-matrix results = results[1..6,1...]'
-putexcel set "$dir_raw_results/health_mental/health_mental", sheet("HM1_C") replace
-putexcel A3 = matrix(results), names nformat(number_d2) 
-putexcel J4 = matrix(e(V))
-
-gen in_sample = e(sample)	
-
-predict p
-
-save "$dir_validation_data/HM1_C_sample", replace
-
-
-scalar r2_p = e(r2_p) 
-scalar N = e(N)
-scalar rmse = e(rmse)	
-scalar chi2 = e(chi2)
-scalar ll = e(ll)	
-
-
-* Results 
-
-* Note: Zeros values are eliminated 
-	
-matrix b = e(b)	
-matrix V = e(V)
-
-
-* Store variance-covariance matrix 
-
-preserve
-
-putexcel set "$dir_raw_results/health_mental/var_cov", sheet("var_cov") replace
-putexcel A1 = matrix(V)
-
-import excel "$dir_raw_results/health_mental/var_cov", sheet("var_cov") clear
-
-describe
-local no_vars = `r(k)'	
-	
-forvalues i = 1/2 {
-	egen row_sum = rowtotal(*)
-	drop if row_sum == 0 
-	drop row_sum
-	xpose, clear	
-}	
-	
-mkmat v*, matrix(var)	
-putexcel set "$dir_results/reg_health_mental", sheet("UK_HM1_C") modify
-putexcel C2 = matrix(var)
-		
-restore	
-
-
-* Store estimated coefficients 
-
-// Initialize a counter for non-zero coefficients
-local non_zero_count = 0
-//local names : colnames b
-
-// Loop through each element in `b` to count non-zero coefficients
-forvalues i = 1/`no_vars' {
-    if (b[1, `i'] != 0) {
-        local non_zero_count = `non_zero_count' + 1
-    }
-}
-
-// Create a new row vector to hold only non-zero coefficients
-matrix nonzero_b = J(1, `non_zero_count', .)
-
-// Populate nonzero_b with non-zero coefficients from b
-local index = 1
-forvalues i = 1/`no_vars' {
-    if (b[1, `i'] != 0) {
-        matrix nonzero_b[1, `index'] = b[1, `i']
-        local index = `index' + 1
-    }
-}
-
-putexcel set "$dir_results/reg_health_mental", sheet("UK_HM1_C") modify
-putexcel A1 = matrix(nonzero_b'), names nformat(number_d2) 
-
-
-* Labelling 
+// TODO: update labels
 
 putexcel A1 = "REGRESSOR"
 putexcel A2 = "D_Home_owner_L1"
@@ -660,295 +200,7 @@ putexcel AH1 = "Constant"
 		
 * save RMSE
 putexcel set "$dir_results/reg_RMSE.xlsx", sheet("UK") modify
-putexcel A13 = ("HM1_C") B13 = rmse 
-
-drop in_sample p
-scalar drop r2_p N chi2 ll	
-***************************************************************
-* HM2_Females_C: GHQ12 Score 0-12 - causal employment effects *
-***************************************************************
-
-gen RealIncomeDecrease_D = log_income - L.log_income
-gen scghq2_dv_L1 = L.scghq2_dv
-
-*Stage 2
-*Female
-reghdfe scghq2_dv ///
-ib11.exp_emp i.exp_poverty i.exp_incchange RealIncomeDecrease_D financial_distress ///
-y2020 y2021 ///
-i.dhh_owned i.dcpst dnc dhe_pcs ib8.drgn i.ydses_c5 dlltsd ///
-dag dagsq i.deh_c3 stm ///
-if dag>=25 & dag<=64 & dgn==0 ///
-, absorb(idperson) vce(cluster idperson)
-
-
-   * save raw results 
-matrix results = r(table)
-matrix results = results[1..6,1..10]'
-putexcel set "$dir_raw_results/health_mental/health_mental", sheet("HM2_Females_C") replace
-putexcel A3 = matrix(results), names nformat(number_d2) 
-putexcel J4 = matrix(e(V))
-
-gen in_sample = e(sample)	
-
-predict p
-
-save "$dir_validation_data/HM2_Females_C_sample", replace
-
-
-scalar r2_p = e(r2_p) 
-scalar N = e(N)
-scalar rmse = e(rmse)	
-scalar chi2 = e(chi2)
-scalar ll = e(ll)	
-
-
-* Results 
-
-* Note: Zeros values are eliminated 
-	
-matrix b = e(b)	
-matrix V = e(V)
-matrix V = V[1..14,1..14]
-
-forvalues i = 1/14 {
-    forvalues j = 1/14 {
-        if `i' == `j' {
-          continue
-        }
-        matrix V[`i',`j'] = 0
-    }
-}
-
-* Store variance-covariance matrix 
-
-preserve
-
-putexcel set "$dir_raw_results/health_mental/var_cov", sheet("var_cov") replace
-putexcel A1 = matrix(V)
-
-import excel "$dir_raw_results/health_mental/var_cov", sheet("var_cov") clear
-
-describe
-local no_vars = `r(k)'	
-	
-forvalues i = 1/2 {
-	egen row_sum = rowtotal(*)
-	drop if row_sum == 0 
-	drop row_sum
-	xpose, clear	
-}	
-	
-mkmat v*, matrix(var)	
-putexcel set "$dir_results/reg_health_mental", sheet("UK_HM2_Females_C") modify
-putexcel C2 = matrix(var)
-		
-restore	
-
-
-* Store estimated coefficients 
-
-// Initialize a counter for non-zero coefficients
-local non_zero_count = 0
-//local names : colnames b
-
-// Loop through each element in `b` to count non-zero coefficients
-forvalues i = 1/`no_vars' {
-    if (b[1, `i'] != 0) {
-        local non_zero_count = `non_zero_count' + 1
-    }
-}
-
-// Create a new row vector to hold only non-zero coefficients
-matrix nonzero_b = J(1, `non_zero_count', .)
-
-// Populate nonzero_b with non-zero coefficients from b
-local index = 1
-forvalues i = 1/`no_vars' {
-    if (b[1, `i'] != 0) {
-        matrix nonzero_b[1, `index'] = b[1, `i']
-        local index = `index' + 1
-    }
-}
-
-putexcel set "$dir_results/reg_health_mental", sheet("UK_HM2_Females_C") modify
-putexcel A1 = matrix(nonzero_b'), names nformat(number_d2) 
-
-* Labelling 
-
-putexcel A1 = "REGRESSOR"
-putexcel A2 = "EmployedToUnemployed"
-putexcel A3 = "UnemployedToEmployed"
-putexcel A4 = "PersistentUnemployed"
-putexcel A5 = "NonPovertyToPoverty"
-putexcel A6 = "PovertyToNonPoverty"
-putexcel A7 = "PersistentPoverty"
-putexcel A8 = "RealIncomeChange"
-putexcel A9 = "RealIncomeDecrease_D"
-putexcel A10 = "FinancialDistress"
-putexcel A11 = "Covid_2020_D"
-putexcel A12 = "Covid_2021_D"
-
-
-putexcel B1 = "COEFFICIENT"
-putexcel C1 = "EmployedToUnemployed"
-putexcel D1 = "UnemployedToEmployed"
-putexcel E1 = "PersistentUnemployed"
-putexcel F1 = "NonPovertyToPoverty"
-putexcel G1 = "PovertyToNonPoverty"
-putexcel H1 = "PersistentPoverty"
-putexcel I1 = "RealIncomeChange"
-putexcel J1 = "RealIncomeDecrease_D"
-putexcel K1 = "FinancialDistress"
-putexcel L1 = "Covid_2020_D"
-putexcel M1 = "Covid_2021_D"
-		
-* save RMSE
-putexcel set "$dir_results/reg_RMSE.xlsx", sheet("UK") modify
-putexcel A14 = ("HM2_Females_C") B14 = rmse 
-
-drop in_sample p
-scalar drop r2_p N chi2 ll	
-
-***************************************************************
-* HM2_Males_C: GHQ12 Score 0-12 - causal employment effects *
-***************************************************************
-
-
-*Stage 2
-*Male
-reghdfe scghq2_dv ///
-ib11.exp_emp i.exp_poverty i.exp_incchange RealIncomeDecrease_D financial_distress ///
-y2020 y2021 ///
-i.dhh_owned i.dcpst dnc dhe_pcs ib8.drgn i.ydses_c5 dlltsd ///
-dag dagsq i.deh_c3 stm ///
-if dag>=25 & dag<=64 & dgn==1 ///
-, absorb(idperson) vce(cluster idperson)
-
-   * save raw results 
-matrix results = r(table)
-matrix results = results[1..6,1..10]'
-putexcel set "$dir_raw_results/health_mental/health_mental", sheet("HM2_Males_C") replace
-putexcel A3 = matrix(results), names nformat(number_d2) 
-putexcel J4 = matrix(e(V))
-
-gen in_sample = e(sample)	
-
-predict p
-
-save "$dir_validation_data/HM2_Males_C_sample", replace
-
-
-scalar r2_p = e(r2_p) 
-scalar N = e(N)
-scalar rmse = e(rmse)	
-scalar chi2 = e(chi2)
-scalar ll = e(ll)	
-
-
-* Results 
-
-* Note: Zeros values are eliminated 
-	
-matrix b = e(b)	
-matrix V = e(V)
-matrix V = V[1..14,1..14]
-
-forvalues i = 1/14 {
-    forvalues j = 1/14 {
-        if `i' == `j' {
-          continue
-        }
-        matrix V[`i',`j'] = 0
-    }
-}
-
-* Store variance-covariance matrix 
-
-preserve
-
-putexcel set "$dir_raw_results/health_mental/var_cov", sheet("var_cov") replace
-putexcel A1 = matrix(V)
-
-import excel "$dir_raw_results/health_mental/var_cov", sheet("var_cov") clear
-
-describe
-local no_vars = `r(k)'	
-	
-forvalues i = 1/2 {
-	egen row_sum = rowtotal(*)
-	drop if row_sum == 0 
-	drop row_sum
-	xpose, clear	
-}	
-	
-mkmat v*, matrix(var)	
-putexcel set "$dir_results/reg_health_mental", sheet("UK_HM2_Males_C") modify
-putexcel C2 = matrix(var)
-		
-restore	
-
-
-* Store estimated coefficients 
-
-// Initialize a counter for non-zero coefficients
-local non_zero_count = 0
-//local names : colnames b
-
-// Loop through each element in `b` to count non-zero coefficients
-forvalues i = 1/`no_vars' {
-    if (b[1, `i'] != 0) {
-        local non_zero_count = `non_zero_count' + 1
-    }
-}
-
-// Create a new row vector to hold only non-zero coefficients
-matrix nonzero_b = J(1, `non_zero_count', .)
-
-// Populate nonzero_b with non-zero coefficients from b
-local index = 1
-forvalues i = 1/`no_vars' {
-    if (b[1, `i'] != 0) {
-        matrix nonzero_b[1, `index'] = b[1, `i']
-        local index = `index' + 1
-    }
-}
-
-putexcel set "$dir_results/reg_health_mental", sheet("UK_HM2_Males_C") modify
-putexcel A1 = matrix(nonzero_b'), names nformat(number_d2) 
-
-* Labelling 
-
-putexcel A1 = "REGRESSOR"
-putexcel A2 = "EmployedToUnemployed"
-putexcel A3 = "UnemployedToEmployed"
-putexcel A4 = "PersistentUnemployed"
-putexcel A5 = "NonPovertyToPoverty"
-putexcel A6 = "PovertyToNonPoverty"
-putexcel A7 = "PersistentPoverty"
-putexcel A8 = "RealIncomeChange"
-putexcel A9 = "RealIncomeDecrease_D"
-putexcel A10 = "FinancialDistress"
-putexcel A11 = "Covid_2020_D"
-putexcel A12 = "Covid_2021_D"
-
-
-putexcel B1 = "COEFFICIENT"
-putexcel C1 = "EmployedToUnemployed"
-putexcel D1 = "UnemployedToEmployed"
-putexcel E1 = "PersistentUnemployed"
-putexcel F1 = "NonPovertyToPoverty"
-putexcel G1 = "PovertyToNonPoverty"
-putexcel H1 = "PersistentPoverty"
-putexcel I1 = "RealIncomeChange"
-putexcel J1 = "RealIncomeDecrease_D"
-putexcel K1 = "FinancialDistress"
-putexcel L1 = "Covid_2020_D"
-putexcel M1 = "Covid_2021_D"
-		
-* save RMSE
-putexcel set "$dir_results/reg_RMSE.xlsx", sheet("UK") modify
-putexcel A15 = ("HM2_Males_C") B15 = rmse 
+/* putexcel A10 = ("HM1_L") B10 = rmse  */ // TODO: make sure this doesn't overwrite existing stuff
 
 drop in_sample p
 scalar drop r2_p N chi2 ll	

From e1de46851e28d36b596389d9c5424340637c2674 Mon Sep 17 00:00:00 2001
From: Andy Baxter <andrewbaxter439@gmail.com>
Date: Mon, 12 Jan 2026 14:17:17 +0000
Subject: [PATCH 3/7] re-introducing missing variables for calculating
 regressions

---
 .../compile/01_prepare_UKHLS_pooled_data.do               | 8 ++++----
 .../compile/02_create_UKHLS_variables.do                  | 2 ++
 2 files changed, 6 insertions(+), 4 deletions(-)

diff --git a/input/InitialPopulations/compile/01_prepare_UKHLS_pooled_data.do b/input/InitialPopulations/compile/01_prepare_UKHLS_pooled_data.do
index bff821225..6f6e80dea 100644
--- a/input/InitialPopulations/compile/01_prepare_UKHLS_pooled_data.do
+++ b/input/InitialPopulations/compile/01_prepare_UKHLS_pooled_data.do
@@ -117,16 +117,16 @@ foreach w of global UKHLSwaves {
 	local waveno=strpos("abcdefghijklmnopqrstuvwxyz","`w'")
 
 	if (`waveno'==1) {
-		use `w'_hidp `w'_fihhmnnet1_dv `w'_fihhmngrs1_dv `w'_fihhmnsben_dv `w'_nch02_dv /*`w'_hhdenub_xw `w'_hhdenui_xw*/ `w'_hsownd using `w'_hhresp.dta, clear
+		use `w'_hidp `w'_fihhmnnet1_dv `w'_ieqmoecd_dv `w'_fihhmngrs1_dv `w'_fihhmnsben_dv `w'_nch02_dv /*`w'_hhdenub_xw `w'_hhdenui_xw*/ `w'_hsownd using `w'_hhresp.dta, clear
 	}
 	else if (`waveno'<6) {
-		use `w'_hidp `w'_fihhmnnet1_dv `w'_fihhmngrs1_dv `w'_fihhmnsben_dv `w'_nch02_dv `w'_hhdenub_xw /*`w'_hhdenui_xw*/ `w'_hsownd using `w'_hhresp.dta, clear
+		use `w'_hidp `w'_fihhmnnet1_dv `w'_ieqmoecd_dv `w'_fihhmngrs1_dv `w'_fihhmnsben_dv `w'_nch02_dv `w'_hhdenub_xw /*`w'_hhdenui_xw*/ `w'_hsownd using `w'_hhresp.dta, clear
 	}
 	else if (`waveno'<14) {
-		use `w'_hidp `w'_fihhmnnet1_dv `w'_fihhmngrs1_dv `w'_fihhmnsben_dv `w'_nch02_dv /*`w'_hhdenub_xw*/ `w'_hhdenui_xw `w'_hsownd using `w'_hhresp.dta, clear
+		use `w'_hidp `w'_fihhmnnet1_dv `w'_ieqmoecd_dv `w'_fihhmngrs1_dv `w'_fihhmnsben_dv `w'_nch02_dv /*`w'_hhdenub_xw*/ `w'_hhdenui_xw `w'_hsownd using `w'_hhresp.dta, clear
 	} 
 	else if (`waveno'==14) {
-		use `w'_hidp `w'_fihhmnnet1_dv `w'_fihhmngrs1_dv `w'_fihhmnsben_dv `w'_nch02_dv /*`w'_hhdenub_xw `w'_hhdenui_xw*/ `w'_hhdeng2_xw `w'_hsownd using `w'_hhresp.dta, clear
+		use `w'_hidp `w'_fihhmnnet1_dv `w'_ieqmoecd_dv `w'_fihhmngrs1_dv `w'_fihhmnsben_dv `w'_nch02_dv /*`w'_hhdenub_xw `w'_hhdenui_xw*/ `w'_hhdeng2_xw `w'_hsownd using `w'_hhresp.dta, clear
 	}
 	
 	gen swv = `waveno'
diff --git a/input/InitialPopulations/compile/02_create_UKHLS_variables.do b/input/InitialPopulations/compile/02_create_UKHLS_variables.do
index c56156783..e4e8b856e 100644
--- a/input/InitialPopulations/compile/02_create_UKHLS_variables.do
+++ b/input/InitialPopulations/compile/02_create_UKHLS_variables.do
@@ -1852,6 +1852,7 @@ keep ivfio idhh idperson idpartner idfather idmother dct drgn1 dwt dnc02 dnc dgn
 	dimxwt dhhwt jbhrs jshrs j2hrs jbstat les_c3 les_c4 lessp_c3 lessp_c4 lesdf_c4 ydses_c5 month scghq2_dv ydisp ///
 	ypnbihs_dv yptciihs_dv yplgrs_dv ynbcpdf_dv ypncp ypnoab swv sedex ssscp sprfm sedag stm dagsp lhw l1_lhw pno ppno hgbioad1 hgbioad2 der adultchildflag ///
         econ_benefits econ_benefits_nonuc econ_benefits_uc ///
+	fihhmnnet1_dv ieqmoecd_dv ///
 	sedcsmpl sedrsmpl scedsmpl dhh_owned dukfr dchpd dagpns dagpns_sp CPI lesnr_c2 dlltsd_sp dlltsd01_sp ypnoab_lvl *_flag  Int_Date dhe_mcs dhe_pcs dhe_mcssp dhe_pcssp dls dot dot01 unemp financial_distress
 
 sort swv idhh idperson 
@@ -1863,6 +1864,7 @@ foreach var in idhh idperson idpartner idfather idmother dct drgn1 dwt dnc02 dnc
 	jbhrs jshrs j2hrs jbstat les_c3 les_c4 lessp_c3 lessp_c4 lesdf_c4 ydses_c5 scghq2_dv ///
 	ypnbihs_dv yptciihs_dv yplgrs_dv swv sedex ssscp sprfm sedag stm dagsp lhw l1_lhw pno ppno hgbioad1 hgbioad2 der dhh_owned ///
         econ_benefits econ_benefits_nonuc econ_benefits_uc ///
+	fihhmnnet1_dv ieqmoecd_dv ///
 	scghq2_dv_miss_flag dchpd dagpns dagpns_sp CPI lesnr_c2 dlltsd_sp dlltsd01_sp ypnoab_lvl *_flag dhe_mcs dhe_pcs dhe_mcssp dhe_pcssp dls dot dot01 unemp {
 		qui recode `var' (-9/-1=-9) (.=-9) 
 }

From 8b7d281b8ada7ad99ae456fef46e750f66a669a4 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Erik=20Igelstr=C3=B6m?= <eigelstrom@gmail.com>
Date: Tue, 20 Jan 2026 11:12:47 +0000
Subject: [PATCH 4/7] Update labels

---
 .../compile/RegressionEstimates/master.do     |   2 +
 .../reg_financial_distress.do                 | 174 ++++++++++--------
 2 files changed, 101 insertions(+), 75 deletions(-)

diff --git a/input/InitialPopulations/compile/RegressionEstimates/master.do b/input/InitialPopulations/compile/RegressionEstimates/master.do
index 86930a566..61b2a8cd2 100644
--- a/input/InitialPopulations/compile/RegressionEstimates/master.do
+++ b/input/InitialPopulations/compile/RegressionEstimates/master.do
@@ -111,6 +111,8 @@ do "${dir_do}/reg_health_mental.do"
 
 do "${dir_do}/reg_health_wellbeing.do"
 
+do "${dir_do}/reg_financial_distress.do"
+
 *******************************************************************************
 * INTERNAL VALIDATION FILES
 ******************************************************************************
diff --git a/input/InitialPopulations/compile/RegressionEstimates/reg_financial_distress.do b/input/InitialPopulations/compile/RegressionEstimates/reg_financial_distress.do
index dfe299bc0..990896ea1 100644
--- a/input/InitialPopulations/compile/RegressionEstimates/reg_financial_distress.do
+++ b/input/InitialPopulations/compile/RegressionEstimates/reg_financial_distress.do
@@ -35,9 +35,9 @@ xtset idperson swv
 * HM1_L: GHQ12 score 0-36 of all working-age adults - baseline effects *
 **********************************************************************
 
-logit econ_dist ///
+logit financial_distress ///
 ib11.exp_emp  i.lhw_c5 D.log_income i.exp_incchange ib0.exp_poverty L.ypncp L.ypnoab ///
-L.i.econ_benefits L.i.dhh_owned L.i.dcpst L.dnc L.dhe_pcs L.dhe_mcs L.ib8.drgn L.i.ydses_c5 L.dlltsd  L.econ_dist ///
+L.i.econ_benefits L.i.dhh_owned L.i.dcpst L.dnc L.dhe_pcs L.dhe_mcs L.ib8.drgn L.i.ydses_c5 L.dlltsd  L.financial_distress ///
 i.dgn L.dag L.dagsq i.deh_c3 i.dot stm ///
 [pweight=dimxwt]  ///
 , vce(cluster idperson)
@@ -128,79 +128,103 @@ putexcel A1 = matrix(nonzero_b'), names nformat(number_d2)
 
 * Labelling 
 
-// TODO: update labels
-
-putexcel A1 = "REGRESSOR"
-putexcel A2 = "D_Home_owner_L1"
-putexcel A3 = "Dcpst_Single_L1"
-putexcel A4 = "Dcpst_PreviouslyPartnered_L1"
-putexcel A5 = "Dnc_L1"
-putexcel A6 = "Dhe_pcs_L1"
-putexcel A7 = "UKC"
-putexcel A8 = "UKD"
-putexcel A9 = "UKE"
-putexcel A10 = "UKF"
-putexcel A11 = "UKG"
-putexcel A12 = "UKH"
-putexcel A13 = "UKJ"
-putexcel A14 = "UKK"
-putexcel A15 = "UKL"
-putexcel A16 = "UKM"
-putexcel A17 = "UKN"
-putexcel A18 = "Ydses_c5_Q2_L1"
-putexcel A19 = "Ydses_c5_Q3_L1"
-putexcel A20 = "Ydses_c5_Q4_L1"
-putexcel A21 = "Ydses_c5_Q5_L1"
-putexcel A22 = "Dlltsd_L1"
-putexcel A23 = "Dhm_L1"
-putexcel A24 = "Dag_L1"
-putexcel A25 = "Dag_sq_L1"
-putexcel A26 = "Deh_c3_Medium"
-putexcel A27 = "Deh_c3_Low"
-putexcel A28 = "EthnicityAsian"
-putexcel A29 = "EthnicityBlack"
-putexcel A30 = "EthnicityOther"
-putexcel A31 = "Dgn"
-putexcel A32 = "Year_transformed"
-putexcel A33 = "Constant"
-
-putexcel B1 = "COEFFICIENT"
-putexcel C1 = "D_Home_owner_L1"
-putexcel D1 = "Dcpst_Single_L1"
-putexcel E1 = "Dcpst_PreviouslyPartnered_L1"
-putexcel F1 = "Dnc_L1"
-putexcel G1 = "Dhe_pcs_L1"
-putexcel H1 = "UKC"
-putexcel I1 = "UKD"
-putexcel J1 = "UKE"
-putexcel K1 = "UKF"
-putexcel L1 = "UKG"
-putexcel M1 = "UKH"
-putexcel N1 = "UKJ"
-putexcel O1 = "UKK"
-putexcel P1 = "UKL"
-putexcel Q1 = "UKM"
-putexcel R1 = "UKN"
-putexcel S1 = "Ydses_c5_Q2_L1"
-putexcel T1 = "Ydses_c5_Q3_L1"
-putexcel U1 = "Ydses_c5_Q4_L1"
-putexcel V1 = "Ydses_c5_Q5_L1"
-putexcel W1 = "Dlltsd_L1"
-putexcel X1 = "Dhm_L1"
-putexcel Y1 = "Dag_L1"
-putexcel Z1 = "Dag_sq_L1"
-putexcel AA1 = "Deh_c3_Medium"
-putexcel AB1 = "Deh_c3_Low"
-putexcel AC1 = "EthnicityAsian"
-putexcel AD1 = "EthnicityBlack"
-putexcel AE1 = "EthnicityOther"
-putexcel AF1 = "Dgn"
-putexcel AG1 = "Year_transformed"
-putexcel AH1 = "Constant"
+putexcel A1  = "REGRESSOR"
+putexcel A2  = "EmployedToUnemployed"       // 13.exp_emp
+putexcel A3  = "UnemployedToEmployed"       // 31.exp_emp
+putexcel A4  = "PersistentUnemployed"       // 33.exp_emp
+putexcel A5  = "Lhw_10"                     // 10.lhw_c5
+putexcel A6  = "Lhw_20"                     // 20.lhw_c5
+putexcel A7  = "Lhw_30"                     // 30.lhw_c5
+putexcel A8  = "Lhw_40"                     // 40.lhw_c5
+putexcel A9  = "RealIncomeChange"           // D.log_income
+putexcel A10 = "RealIncomeDecrease_D"       // 1.exp_incchange
+putexcel A11 = "NonPovertyToPoverty"        // 1.exp_poverty
+putexcel A12 = "PovertyToNonPoverty"        // 2.exp_poverty
+putexcel A13 = "PersistentPoverty"          // 3.exp_poverty
+putexcel A14 = "Ypncp_L1"                   // L.ypncp
+putexcel A15 = "Ypnoab_L1"                  // L.ypnoab
+putexcel A16 = "D_Econ_benefits"            // 1L.econ_benefits
+putexcel A17 = "D_Home_owner_L1"            // 1L.dhh_owned
+putexcel A18 = "Dcpst_Single_L1"            // 2L.dcpst
+putexcel A19 = "Dnc_L1"                     // L.dnc
+putexcel A20 = "Dhe_pcs_L1"                 // L.dhe_pcs
+putexcel A21 = "Dhe_mcs_L1"                 // L.dhe_mcs
+putexcel A22 = "UKC"                        // 1L.drgn1
+putexcel A23 = "UKD"                        // 2L.drgn1
+putexcel A24 = "UKE"                        // 4L.drgn1
+putexcel A25 = "UKF"                        // 5L.drgn1
+putexcel A26 = "UKG"                        // 6L.drgn1
+putexcel A27 = "UKH"                        // 7L.drgn1
+putexcel A28 = "UKJ"                        // 9L.drgn1
+putexcel A29 = "UKK"                        // 10L.drgn1
+putexcel A30 = "UKL"                        // 11L.drgn1
+putexcel A31 = "UKM"                        // 12L.drgn1
+putexcel A32 = "UKN"                        // 13L.drgn1
+putexcel A33 = "Ydses_c5_Q2_L1"             // 2L.ydses_c5
+putexcel A34 = "Ydses_c5_Q3_L1"             // 3L.ydses_c5
+putexcel A35 = "Ydses_c5_Q4_L1"             // 4L.ydses_c5
+putexcel A36 = "Ydses_c5_Q5_L1"             // 5L.ydses_c5
+putexcel A37 = "Dlltsd_L1"                  // L.dlltsd
+putexcel A38 = "FinancialDistress"          // L.financial_distress
+putexcel A39 = "Dgn"                        // 1.dgn
+putexcel A40 = "Dag_L1"                     // L.dag
+putexcel A41 = "Dag_sq_L1"                  // L.dagsq
+putexcel A42 = "Deh_c3_Medium"              // 2.deh_c3
+putexcel A43 = "Deh_c3_Low"                 // 3.deh_c3
+putexcel A44 = "EthnicityAsian"             // 2.dot
+putexcel A45 = "EthnicityBlack"             // 3.dot
+putexcel A46 = "EthnicityOther"             // 4.dot
+putexcel A47 = "Year_transformed"           // stm
+putexcel A48 = "Constant"                   // _cons
+
+putexcel B1 =  "COEFFICIENT"
+putexcel C1  = "EmployedToUnemployed"       // 13.exp_emp
+putexcel D1  = "UnemployedToEmployed"       // 31.exp_emp
+putexcel E1  = "PersistentUnemployed"       // 33.exp_emp
+putexcel F1  = "Lhw_10"                     // 10.lhw_c5
+putexcel G1  = "Lhw_20"                     // 20.lhw_c5
+putexcel H1  = "Lhw_30"                     // 30.lhw_c5
+putexcel I1  = "Lhw_40"                     // 40.lhw_c5
+putexcel J1  = "RealIncomeChange"           // D.log_income
+putexcel K1  = "RealIncomeDecrease_D"       // 1.exp_incchange
+putexcel L1  = "NonPovertyToPoverty"        // 1.exp_poverty
+putexcel M1  = "PovertyToNonPoverty"        // 2.exp_poverty
+putexcel N1  = "PersistentPoverty"          // 3.exp_poverty
+putexcel O1  = "Ypncp_L1"                   // L.ypncp
+putexcel P1  = "Ypnoab_L1"                  // L.ypnoab
+putexcel Q1  = "D_Econ_benefits"            // 1L.econ_benefits
+putexcel R1  = "D_Home_owner_L1"            // 1L.dhh_owned
+putexcel S1  = "Dcpst_Single_L1"            // 2L.dcpst
+putexcel T1  = "Dnc_L1"                     // L.dnc
+putexcel U1  = "Dhe_pcs_L1"                 // L.dhe_pcs
+putexcel V1  = "Dhe_mcs_L1"                 // L.dhe_mcs
+putexcel W1  = "UKC"                        // 1L.drgn1
+putexcel X1  = "UKD"                        // 2L.drgn1
+putexcel Y1  = "UKE"                        // 4L.drgn1
+putexcel Z1  = "UKF"                        // 5L.drgn1
+putexcel AA1 = "UKG"                        // 6L.drgn1
+putexcel AB1 = "UKH"                        // 7L.drgn1
+putexcel AC1 = "UKJ"                        // 9L.drgn1
+putexcel AD1 = "UKK"                        // 10L.drgn1
+putexcel AE1 = "UKL"                        // 11L.drgn1
+putexcel AF1 = "UKM"                        // 12L.drgn1
+putexcel AG1 = "UKN"                        // 13L.drgn1
+putexcel AH1 = "Ydses_c5_Q2_L1"             // 2L.ydses_c5
+putexcel AI1 = "Ydses_c5_Q3_L1"             // 3L.ydses_c5
+putexcel AJ1 = "Ydses_c5_Q4_L1"             // 4L.ydses_c5
+putexcel AK1 = "Ydses_c5_Q5_L1"             // 5L.ydses_c5
+putexcel AL1 = "Dlltsd_L1"                  // L.dlltsd
+putexcel AM1 = "FinancialDistress"          // L.financial_distress
+putexcel AN1 = "Dgn"                        // 1.dgn
+putexcel AO1 = "Dag_L1"                     // L.dag
+putexcel AP1 = "Dag_sq_L1"                  // L.dagsq
+putexcel AQ1 = "Deh_c3_Medium"              // 2.deh_c3
+putexcel AR1 = "Deh_c3_Low"                 // 3.deh_c3
+putexcel AS1 = "EthnicityAsian"             // 2.dot
+putexcel AT1 = "EthnicityBlack"             // 3.dot
+putexcel AU1 = "EthnicityOther"             // 4.dot
+putexcel AV1 = "Year_transformed"           // stm
+putexcel AW1 = "Constant"                   // _cons
 		
-* save RMSE
-putexcel set "$dir_results/reg_RMSE.xlsx", sheet("UK") modify
-/* putexcel A10 = ("HM1_L") B10 = rmse  */ // TODO: make sure this doesn't overwrite existing stuff
-
 drop in_sample p
 scalar drop r2_p N chi2 ll	

From 7c4b83a7b7321453595a14f1e497b482fdabae44 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Erik=20Igelstr=C3=B6m?= <eigelstrom@gmail.com>
Date: Tue, 20 Jan 2026 11:16:16 +0000
Subject: [PATCH 5/7] Update financial distress coefficients

---
 input/reg_financial_distress.xlsx | Bin 43008 -> 32805 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/input/reg_financial_distress.xlsx b/input/reg_financial_distress.xlsx
index a5c4528be3293b32313f4cdb73ceb6e275b0eeb4..63abf8a3a05af92a67fdf9e2ab816a0fd39aadc1 100644
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R05IS$U--?l%=>lre*gsHh0y>2


From 3f87c206214f2556eb33f66791658e55b21dbd17 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Erik=20Igelstr=C3=B6m?= <eigelstrom@gmail.com>
Date: Tue, 20 Jan 2026 12:44:21 +0000
Subject: [PATCH 6/7] Update integration test output

---
 .../expected/HealthStatistics1.csv            | 36 +++++++++----------
 .../integrationtest/expected/Statistics1.csv  |  8 ++---
 .../integrationtest/expected/Statistics21.csv |  8 ++---
 .../integrationtest/expected/Statistics31.csv |  4 +--
 4 files changed, 28 insertions(+), 28 deletions(-)

diff --git a/src/test/java/simpaths/integrationtest/expected/HealthStatistics1.csv b/src/test/java/simpaths/integrationtest/expected/HealthStatistics1.csv
index 899cea314..91ce402e2 100644
--- a/src/test/java/simpaths/integrationtest/expected/HealthStatistics1.csv
+++ b/src/test/java/simpaths/integrationtest/expected/HealthStatistics1.csv
@@ -2,24 +2,24 @@ run,time,id_HealthStatistics1,N,dhe_mcs_mean,dhe_mcs_median,dhe_mcs_p_10,dhe_mcs
 1,2019.0,1,11824,47.53145382273326,49.52,33.49,41.48,54.94,57.92,51.620701962111106,54.8,37.394999999999996,48.2325,57.76,60.16,11.339648173207037,10.0,6.0,8.0,13.0,19.0,5.081613667117726,6.0,3.0,4.0,6.0,6.0,Total,9863.580224193543,94419.28571428571
 1,2019.0,1,5816,48.750213204951876,51.0,35.317,43.54,55.61,58.209,52.15159559834913,54.81,39.49,49.66,57.76,59.89,10.696011004126547,10.0,6.0,7.0,12.0,17.0,5.1170907840440165,6.0,3.0,4.0,6.0,6.0,Male,4960.864357969549,46767.28571428571
 1,2019.0,1,6008,46.35164280958701,48.305,31.955,40.2,54.2,57.33,51.10677430093201,54.63,35.159,47.2225,57.76,60.31200000000001,11.96271637816245,11.0,6.0,8.0,14.0,20.0,5.047270306258322,6.0,3.0,4.0,6.0,6.0,Female,4902.715866224087,47652.0
-1,2020.0,1,11807,46.034562269734955,47.39041082676373,36.96429384017951,42.64143954684264,50.69094870948495,52.87247667267918,52.11492680554965,54.52939284665922,41.59887901660096,49.835379513313875,57.01227415279354,58.542536560911564,12.372032050780684,12.043212008996605,5.481542834596421,8.56088029700915,15.830568322826984,19.53961509012327,5.091640552214788,5.0,4.0,5.0,6.0,6.0,Total,9844.67221411682,94469.57142857142
-1,2020.0,1,5812,47.478143173904044,48.906894684297285,39.20472182979338,44.53886470566854,51.61337215922299,53.700661981168416,52.5034239251472,54.609192235712555,43.40371980276627,50.607243842341084,56.913141196847704,58.37288250353405,11.567239028052649,11.284105627175595,4.960830552309273,7.863555479704567,14.93228875050982,18.281570431083953,5.105987611837578,5.0,4.0,5.0,6.0,6.0,Male,4954.346690900281,46633.71428571428
-1,2020.0,1,5995,44.635047304759105,45.964608641410514,35.29951581462408,41.031993446986476,49.520151923486694,51.77359696448515,51.7382887306364,54.43688563303148,40.169945354830226,49.096369923658585,57.16607263147161,58.66988141039014,13.15225841409924,12.837626146203636,6.0571346138647035,9.354594840306047,16.717342373761,20.420791127961824,5.077731442869058,5.0,4.0,5.0,6.0,6.0,Female,4890.3255232164865,47835.857142857145
-1,2021.0,1,11797,45.45469777519979,46.34092269029304,39.016596969143805,43.03553328052061,48.83202441643715,50.65545140377125,51.92702134455362,53.739637012556464,43.768349619474726,50.04356703328603,56.148462055314624,57.624616490090446,12.80326011965059,12.666220170736105,5.846926364730157,9.042778900663766,16.35802518703874,19.97156480143891,5.03255064846995,5.0,4.0,5.0,5.0,6.0,Total,9757.440196656195,93294.14285714286
-1,2021.0,1,5815,46.864144575480985,47.72125598247262,41.24285737550787,44.90257955484807,49.913328950366534,51.469125927540766,52.23880314968087,53.83678764670283,45.128327408143335,50.52010997656093,56.1188125599783,57.522374615661604,11.982039979021282,11.845550139103917,5.151192887463544,8.270343253399792,15.440184162421161,19.012121074123385,5.046431642304385,5.0,4.0,5.0,5.0,6.0,Male,4910.946238883454,46113.57142857143
-1,2021.0,1,5982,44.08459862046333,44.97993784981293,37.53606841943058,41.591949161076684,47.468196606766284,49.30176906616391,51.62394357845263,53.62130353479878,42.20325059725796,49.506274873792535,56.191083834064614,57.72397065557655,13.601554188149322,13.397720547784848,6.666595861506849,9.862275851263101,17.200756227515416,20.801124851541406,5.019057171514544,5.0,4.0,5.0,5.0,6.0,Female,4846.493957772649,47180.57142857143
-1,2022.0,1,11789,45.18653962131347,45.86587640174769,39.87497774223522,43.23685097673018,48.0243885377321,49.72816217930098,51.35989722602302,52.88747620706973,44.120741120463855,49.724220252556826,55.21417296408043,56.632010504274234,12.847291941244796,12.721865752207691,6.008693845265697,9.203122346636958,16.33822784368125,19.887170761294712,4.98023581304606,5.0,4.0,5.0,5.0,6.0,Total,9630.585894394524,92261.71428571429
-1,2022.0,1,5814,46.47737074202606,47.033024752682635,41.91297340653034,44.85652431574681,49.08019027000923,50.445250729821964,51.760728157154055,53.14218659037424,45.61272706269639,50.24753889642045,55.343414276381466,56.724437040994644,12.116957762647914,11.975356004005489,5.527310777743885,8.531464046043062,15.620684676666675,18.84699550849929,4.993292053663571,5.0,4.0,5.0,5.0,6.0,Male,4852.9500962761595,45620.142857142855
-1,2022.0,1,5975,43.930490728288426,44.5697202382349,38.5821149116678,42.05243400744741,46.72617621901312,48.35831138991783,50.9698669275132,52.620903898312626,42.922569239446055,49.16215848327021,55.06632074218822,56.55317167436461,13.557946822309612,13.493136712004452,6.640125935320798,9.877652656510042,17.06148775061896,20.686827381941356,4.9675313807531385,5.0,4.0,5.0,5.0,6.0,Female,4777.635798118348,46641.57142857143
+1,2020.0,1,11807,46.06452392064124,47.42457118456709,37.00436722911305,42.68753020494616,50.71321173631065,52.888522700233494,52.117268234957876,54.532698904158494,41.60109899929764,49.83656965082806,57.01678127652667,58.542536560911564,12.350605719699496,12.030629225228099,5.473988552625796,8.549224431825174,15.811213484676367,19.488677503905187,5.097230456508851,5.0,4.0,5.0,6.0,6.0,Total,9848.736214841081,94573.28571428571
+1,2020.0,1,5812,47.50970066961485,48.93872357881739,39.24528831862553,44.547487275511415,51.64064351293031,53.71029773895296,52.507265469420545,54.6180363709514,43.40371980276627,50.607243842341084,56.916013034813545,58.37288250353405,11.54536897977525,11.265818043698253,4.948320456465493,7.843699905202383,14.894714768026123,18.251162122741714,5.111321403991742,5.0,4.0,5.0,6.0,6.0,Male,4956.521642639498,46682.42857142857
+1,2020.0,1,5995,44.663461824722255,45.99204701859836,35.40150234561958,41.08143774568398,49.5373645139119,51.79589998867753,51.73917583684257,54.43688563303148,40.169945354830226,49.096369923658585,57.16607263147161,58.67261917612982,13.131262255552564,12.823479958133209,6.0535437752439,9.340399877170062,16.685597918706733,20.38436941253535,5.083569641367807,5.0,4.0,5.0,6.0,6.0,Female,4892.214572201529,47890.857142857145
+1,2021.0,1,11797,45.52418695210397,46.39303127625742,39.211535209570556,43.12924702349663,48.86968030078427,50.67079413540152,51.93549694533086,53.75440064861357,43.768349619474726,50.04901315930336,56.15952949979507,57.62982961529141,12.754359671223236,12.62014801978989,5.8181816651591785,9.013415783180301,16.30683817314216,19.917702302705937,5.046452487920658,5.0,4.0,5.0,5.0,6.0,Total,9767.340951781696,93551.85714285714
+1,2021.0,1,5815,46.92790038707339,47.7907337868544,41.33611965341434,44.966761149895746,49.93338711736884,51.49143875285767,52.25030975700529,53.849473326960016,45.135054436948295,50.536531551124206,56.12675220181849,57.52607286845697,11.941424997428783,11.819242395509459,5.1095585883207075,8.22985235490628,15.385775623526936,18.963552112474844,5.057609630266552,5.0,4.0,5.0,5.0,6.0,Male,4915.593133309699,46215.71428571428
+1,2021.0,1,5982,44.15966110383496,45.0489291031741,37.72256998992805,41.702777397000276,47.52129084122596,49.320047260879676,51.62947278954885,53.6234060047726,42.20325059725796,49.51057337941535,56.19628591085163,57.72611486504198,13.544599578965588,13.339721812432707,6.606150865307401,9.816786572847976,17.136965139181314,20.768238328279097,5.035606820461384,5.0,4.0,5.0,5.0,6.0,Female,4851.747818471892,47336.142857142855
+1,2022.0,1,11789,45.28642689946913,45.946410084143196,40.046600881660964,43.35436655963382,48.07229692992574,49.76263547636237,51.37702955426053,52.911141633809734,44.161183121981686,49.74954413025653,55.2305983915341,56.639299951987965,12.776268023682508,12.660015493646874,5.970847099398739,9.135894295245224,16.2584081286192,19.763360441473363,4.997455254898634,5.0,4.0,5.0,5.0,6.0,Total,9645.726722158655,92580.71428571429
+1,2022.0,1,5814,46.568524807108645,47.10968713127697,42.10093768355638,44.964487865723314,49.13020241763307,50.475952179483585,51.78149641882255,53.17307476232695,45.676306373364405,50.28200336264757,55.35904125618599,56.727092556887115,12.057692930541238,11.8980396663586,5.465071828378473,8.49028768372782,15.563190085808666,18.790828734290223,5.008083935328518,5.0,4.0,5.0,5.0,6.0,Male,4860.050324564345,45755.28571428571
+1,2022.0,1,5975,44.03887589779249,44.66692029375793,38.749872689900464,42.2276639427635,46.78467281745357,48.38171913876602,50.983461294751905,52.62871367981865,42.97317131815861,49.164399095211024,55.087343057512925,56.55836585067512,13.475480674983483,13.416200742619502,6.580340088186091,9.786769322240765,16.96355476868069,20.578019813174805,4.987112970711297,5.0,4.0,5.0,5.0,6.0,Female,4785.676397594291,46825.42857142857
 2,2019.0,1,11824,47.53145382273326,49.52,33.49,41.48,54.94,57.92,51.620701962111106,54.8,37.394999999999996,48.2325,57.76,60.16,11.339648173207037,10.0,6.0,8.0,13.0,19.0,5.081613667117726,6.0,3.0,4.0,6.0,6.0,Total,9863.580224193543,94419.28571428571
 2,2019.0,1,5816,48.750213204951876,51.0,35.317,43.54,55.61,58.209,52.15159559834913,54.81,39.49,49.66,57.76,59.89,10.696011004126547,10.0,6.0,7.0,12.0,17.0,5.1170907840440165,6.0,3.0,4.0,6.0,6.0,Male,4960.864357969549,46767.28571428571
 2,2019.0,1,6008,46.35164280958701,48.305,31.955,40.2,54.2,57.33,51.10677430093201,54.63,35.159,47.2225,57.76,60.31200000000001,11.96271637816245,11.0,6.0,8.0,14.0,20.0,5.047270306258322,6.0,3.0,4.0,6.0,6.0,Female,4902.715866224087,47652.0
-2,2020.0,1,11807,46.034562269734955,47.39041082676373,36.96429384017951,42.64143954684264,50.69094870948495,52.87247667267918,52.11492680554965,54.52939284665922,41.59887901660096,49.835379513313875,57.01227415279354,58.542536560911564,12.372032050780684,12.043212008996605,5.481542834596421,8.56088029700915,15.830568322826984,19.53961509012327,5.091640552214788,5.0,4.0,5.0,6.0,6.0,Total,9844.67221411682,94469.57142857142
-2,2020.0,1,5812,47.478143173904044,48.906894684297285,39.20472182979338,44.53886470566854,51.61337215922299,53.700661981168416,52.5034239251472,54.609192235712555,43.40371980276627,50.607243842341084,56.913141196847704,58.37288250353405,11.567239028052649,11.284105627175595,4.960830552309273,7.863555479704567,14.93228875050982,18.281570431083953,5.105987611837578,5.0,4.0,5.0,6.0,6.0,Male,4954.346690900281,46633.71428571428
-2,2020.0,1,5995,44.635047304759105,45.964608641410514,35.29951581462408,41.031993446986476,49.520151923486694,51.77359696448515,51.7382887306364,54.43688563303148,40.169945354830226,49.096369923658585,57.16607263147161,58.66988141039014,13.15225841409924,12.837626146203636,6.0571346138647035,9.354594840306047,16.717342373761,20.420791127961824,5.077731442869058,5.0,4.0,5.0,6.0,6.0,Female,4890.3255232164865,47835.857142857145
-2,2021.0,1,11797,45.45469777519979,46.34092269029304,39.016596969143805,43.03553328052061,48.83202441643715,50.65545140377125,51.92702134455362,53.739637012556464,43.768349619474726,50.04356703328603,56.148462055314624,57.624616490090446,12.80326011965059,12.666220170736105,5.846926364730157,9.042778900663766,16.35802518703874,19.97156480143891,5.03255064846995,5.0,4.0,5.0,5.0,6.0,Total,9757.440196656195,93294.14285714286
-2,2021.0,1,5815,46.864144575480985,47.72125598247262,41.24285737550787,44.90257955484807,49.913328950366534,51.469125927540766,52.23880314968087,53.83678764670283,45.128327408143335,50.52010997656093,56.1188125599783,57.522374615661604,11.982039979021282,11.845550139103917,5.151192887463544,8.270343253399792,15.440184162421161,19.012121074123385,5.046431642304385,5.0,4.0,5.0,5.0,6.0,Male,4910.946238883454,46113.57142857143
-2,2021.0,1,5982,44.08459862046333,44.97993784981293,37.53606841943058,41.591949161076684,47.468196606766284,49.30176906616391,51.62394357845263,53.62130353479878,42.20325059725796,49.506274873792535,56.191083834064614,57.72397065557655,13.601554188149322,13.397720547784848,6.666595861506849,9.862275851263101,17.200756227515416,20.801124851541406,5.019057171514544,5.0,4.0,5.0,5.0,6.0,Female,4846.493957772649,47180.57142857143
-2,2022.0,1,11789,45.18653962131347,45.86587640174769,39.87497774223522,43.23685097673018,48.0243885377321,49.72816217930098,51.35989722602302,52.88747620706973,44.120741120463855,49.724220252556826,55.21417296408043,56.632010504274234,12.847291941244796,12.721865752207691,6.008693845265697,9.203122346636958,16.33822784368125,19.887170761294712,4.98023581304606,5.0,4.0,5.0,5.0,6.0,Total,9630.585894394524,92261.71428571429
-2,2022.0,1,5814,46.47737074202606,47.033024752682635,41.91297340653034,44.85652431574681,49.08019027000923,50.445250729821964,51.760728157154055,53.14218659037424,45.61272706269639,50.24753889642045,55.343414276381466,56.724437040994644,12.116957762647914,11.975356004005489,5.527310777743885,8.531464046043062,15.620684676666675,18.84699550849929,4.993292053663571,5.0,4.0,5.0,5.0,6.0,Male,4852.9500962761595,45620.142857142855
-2,2022.0,1,5975,43.930490728288426,44.5697202382349,38.5821149116678,42.05243400744741,46.72617621901312,48.35831138991783,50.9698669275132,52.620903898312626,42.922569239446055,49.16215848327021,55.06632074218822,56.55317167436461,13.557946822309612,13.493136712004452,6.640125935320798,9.877652656510042,17.06148775061896,20.686827381941356,4.9675313807531385,5.0,4.0,5.0,5.0,6.0,Female,4777.635798118348,46641.57142857143
\ No newline at end of file
+2,2020.0,1,11807,46.06452392064124,47.42457118456709,37.00436722911305,42.68753020494616,50.71321173631065,52.888522700233494,52.117268234957876,54.532698904158494,41.60109899929764,49.83656965082806,57.01678127652667,58.542536560911564,12.350605719699496,12.030629225228099,5.473988552625796,8.549224431825174,15.811213484676367,19.488677503905187,5.097230456508851,5.0,4.0,5.0,6.0,6.0,Total,9848.736214841081,94573.28571428571
+2,2020.0,1,5812,47.50970066961485,48.93872357881739,39.24528831862553,44.547487275511415,51.64064351293031,53.71029773895296,52.507265469420545,54.6180363709514,43.40371980276627,50.607243842341084,56.916013034813545,58.37288250353405,11.54536897977525,11.265818043698253,4.948320456465493,7.843699905202383,14.894714768026123,18.251162122741714,5.111321403991742,5.0,4.0,5.0,6.0,6.0,Male,4956.521642639498,46682.42857142857
+2,2020.0,1,5995,44.663461824722255,45.99204701859836,35.40150234561958,41.08143774568398,49.5373645139119,51.79589998867753,51.73917583684257,54.43688563303148,40.169945354830226,49.096369923658585,57.16607263147161,58.67261917612982,13.131262255552564,12.823479958133209,6.0535437752439,9.340399877170062,16.685597918706733,20.38436941253535,5.083569641367807,5.0,4.0,5.0,6.0,6.0,Female,4892.214572201529,47890.857142857145
+2,2021.0,1,11797,45.52418695210397,46.39303127625742,39.211535209570556,43.12924702349663,48.86968030078427,50.67079413540152,51.93549694533086,53.75440064861357,43.768349619474726,50.04901315930336,56.15952949979507,57.62982961529141,12.754359671223236,12.62014801978989,5.8181816651591785,9.013415783180301,16.30683817314216,19.917702302705937,5.046452487920658,5.0,4.0,5.0,5.0,6.0,Total,9767.340951781696,93551.85714285714
+2,2021.0,1,5815,46.92790038707339,47.7907337868544,41.33611965341434,44.966761149895746,49.93338711736884,51.49143875285767,52.25030975700529,53.849473326960016,45.135054436948295,50.536531551124206,56.12675220181849,57.52607286845697,11.941424997428783,11.819242395509459,5.1095585883207075,8.22985235490628,15.385775623526936,18.963552112474844,5.057609630266552,5.0,4.0,5.0,5.0,6.0,Male,4915.593133309699,46215.71428571428
+2,2021.0,1,5982,44.15966110383496,45.0489291031741,37.72256998992805,41.702777397000276,47.52129084122596,49.320047260879676,51.62947278954885,53.6234060047726,42.20325059725796,49.51057337941535,56.19628591085163,57.72611486504198,13.544599578965588,13.339721812432707,6.606150865307401,9.816786572847976,17.136965139181314,20.768238328279097,5.035606820461384,5.0,4.0,5.0,5.0,6.0,Female,4851.747818471892,47336.142857142855
+2,2022.0,1,11789,45.28642689946913,45.946410084143196,40.046600881660964,43.35436655963382,48.07229692992574,49.76263547636237,51.37702955426053,52.911141633809734,44.161183121981686,49.74954413025653,55.2305983915341,56.639299951987965,12.776268023682508,12.660015493646874,5.970847099398739,9.135894295245224,16.2584081286192,19.763360441473363,4.997455254898634,5.0,4.0,5.0,5.0,6.0,Total,9645.726722158655,92580.71428571429
+2,2022.0,1,5814,46.568524807108645,47.10968713127697,42.10093768355638,44.964487865723314,49.13020241763307,50.475952179483585,51.78149641882255,53.17307476232695,45.676306373364405,50.28200336264757,55.35904125618599,56.727092556887115,12.057692930541238,11.8980396663586,5.465071828378473,8.49028768372782,15.563190085808666,18.790828734290223,5.008083935328518,5.0,4.0,5.0,5.0,6.0,Male,4860.050324564345,45755.28571428571
+2,2022.0,1,5975,44.03887589779249,44.66692029375793,38.749872689900464,42.2276639427635,46.78467281745357,48.38171913876602,50.983461294751905,52.62871367981865,42.97317131815861,49.164399095211024,55.087343057512925,56.55836585067512,13.475480674983483,13.416200742619502,6.580340088186091,9.786769322240765,16.96355476868069,20.578019813174805,4.987112970711297,5.0,4.0,5.0,5.0,6.0,Female,4785.676397594291,46825.42857142857
\ No newline at end of file
diff --git a/src/test/java/simpaths/integrationtest/expected/Statistics1.csv b/src/test/java/simpaths/integrationtest/expected/Statistics1.csv
index a2fe3f70e..da32b4fc5 100644
--- a/src/test/java/simpaths/integrationtest/expected/Statistics1.csv
+++ b/src/test/java/simpaths/integrationtest/expected/Statistics1.csv
@@ -1,9 +1,9 @@
 run,time,id_Statistics1,edi_p50,giniEquivalisedHouseholdDisposableIncomeNational,giniMarketIncomeNational,grossLabourIncome_p20,grossLabourIncome_p40,grossLabourIncome_p60,grossLabourIncome_p80,medianEquivalisedHouseholdDisposableIncome,sIndex_p50,ydses_p20,ydses_p40,ydses_p60,ydses_p80
 1,2019.0,1,13095.307851314308,0.0,0.0,973.4096725036837,1525.932657685651,2158.2741367617946,3228.276924267956,13095.307851314308,NaN,0.0,4.858943553994427,7.849675860587956,8.375850062998584
 1,2020.0,1,15575.722944710858,0.0,0.0,1181.5608933150663,1747.6557506385611,2421.8789326058222,3469.277972897026,15564.82710517697,NaN,0.0,7.074433470218817,8.020759494695893,8.504765831925205
-1,2021.0,1,16065.533102583606,0.0,0.0,1272.3796963616005,1866.0562548962898,2518.1250379524363,3609.2257065397416,16062.088404354154,NaN,0.0,7.269555952812647,8.092402072381123,8.558265935260582
-1,2022.0,1,15996.959092080815,0.0,0.0,1279.8221681272157,1867.0081254382312,2531.450237689121,3631.6281423250266,15996.617252727145,NaN,0.0,7.241312772323086,8.086599019720326,8.576648786102183
+1,2021.0,1,16065.533102583606,0.0,0.0,1272.3796963616005,1866.9157912066653,2518.1250379524363,3609.2257065397416,16062.088404354154,NaN,0.0,7.269555952812647,8.092402072381123,8.558265935260582
+1,2022.0,1,15996.617252727145,0.0,0.0,1279.8221681272157,1867.0081254382312,2532.1567844667966,3631.6281423250266,15996.617252727145,NaN,0.0,7.241591812000429,8.086718105269671,8.576811152353452
 2,2019.0,1,13095.307851314308,0.0,0.0,973.4096725036837,1525.932657685651,2158.2741367617946,3228.276924267956,13095.307851314308,NaN,0.0,4.858943553994427,7.849675860587956,8.375850062998584
 2,2020.0,1,15575.722944710858,0.0,0.0,1181.5608933150663,1747.6557506385611,2421.8789326058222,3469.277972897026,15564.82710517697,NaN,0.0,7.074433470218817,8.020759494695893,8.504765831925205
-2,2021.0,1,16065.533102583606,0.0,0.0,1272.3796963616005,1866.0562548962898,2518.1250379524363,3609.2257065397416,16062.088404354154,NaN,0.0,7.269555952812647,8.092402072381123,8.558265935260582
-2,2022.0,1,15996.959092080815,0.0,0.0,1279.8221681272157,1867.0081254382312,2531.450237689121,3631.6281423250266,15996.617252727145,NaN,0.0,7.241312772323086,8.086599019720326,8.576648786102183
\ No newline at end of file
+2,2021.0,1,16065.533102583606,0.0,0.0,1272.3796963616005,1866.9157912066653,2518.1250379524363,3609.2257065397416,16062.088404354154,NaN,0.0,7.269555952812647,8.092402072381123,8.558265935260582
+2,2022.0,1,15996.617252727145,0.0,0.0,1279.8221681272157,1867.0081254382312,2532.1567844667966,3631.6281423250266,15996.617252727145,NaN,0.0,7.241591812000429,8.086718105269671,8.576811152353452
\ No newline at end of file
diff --git a/src/test/java/simpaths/integrationtest/expected/Statistics21.csv b/src/test/java/simpaths/integrationtest/expected/Statistics21.csv
index e5794f9b4..32f1576e1 100644
--- a/src/test/java/simpaths/integrationtest/expected/Statistics21.csv
+++ b/src/test/java/simpaths/integrationtest/expected/Statistics21.csv
@@ -1,9 +1,9 @@
 run,time,id_Statistics21,aaconsToLeisRatio,aadisposableIncome18to29,aadisposableIncome30to54,aadisposableIncome55to74,aaexpenditure18to29,aaexpenditure18to54,aaexpenditure30to54,aaexpenditure55to74,aaworkNone18to29,aaworkNone18to74,aaworkNone30to54,aaworkNone55to74,avkids18to29,avkids30to54,avkids55to74,aworkFulltime18to29,aworkFulltime30to54,aworkFulltime55to74,aworkParttime18to29,aworkParttime30to54,aworkParttime55to74,dispIncomeGrossOfLosses18to29,dispIncomeGrossOfLosses30to54,dispIncomeGrossOfLosses55to74,health18to29,health30to54,health55to74,investmentIncome18to29,investmentIncome30to54,investmentIncome55to74,investmentLosses18to29,investmentLosses30to54,investmentLosses55to74,labourIncome18to29,labourIncome30to54,labourIncome55to74,pensionIncome18to29,pensionIncome30to54,pensionIncome55to74,population18to29,population30to54,population55to74,prDisabled18to29,prDisabled30to54,prDisabled55to74,prMarried18to29,prMarried30to54,prMarried55to74,wealth18to29,wealth30to54,wealth55to74
 1,2019.0,1,-0.5567879131517617,-8.738097132978965,-238.2445723348635,-787.0615032827395,-720.0935764715266,-1107.7726152285552,-1342.8547994172,-1403.8364714559557,-0.017425895180722906,-0.03910275596534102,-0.05625761088690756,0.04594060142687284,0.295697074010327,0.9408728296574378,0.07491082045184304,0.5717728055077452,0.7864852182074143,0.32294887039239,0.10292598967297763,0.0652275926794932,0.02996432818073722,1277.4741242955922,1861.2763464151365,1291.7840913601176,3.719449225473322,3.44705146253715,3.125564803804994,0.0,0.0,0.0,0.0,0.0,0.0,415.5678951414117,592.5646718222849,573.6180048470663,0.0,0.0,0.0,2905.0,6393.0,4205.0,0.05783132530120482,0.09682465196308462,0.1464922711058264,0.2571428571428571,0.664476771468794,0.6275862068965518,0.0,0.0,0.0
 1,2020.0,1,1.851837766753299,201.84420631350986,-94.00133576494113,-680.4316125125422,-720.1271395625273,-1107.7503947924654,-1342.8000699318272,-1403.8432828573027,-0.07575559103060148,-0.07530048858433958,-0.09174279184185755,0.01678934413825317,0.2768202602884277,0.9002196422968308,0.0751985053713218,0.6535349982412945,0.8611546909319109,0.37085474077533864,0.07949349278930706,0.026043300909946658,0.011209715086408221,1488.056427742081,2005.5195829850588,1398.413982130315,3.6148434752022514,3.4033573893944147,3.0425035030359644,0.0,0.0,0.0,0.0,0.0,0.0,455.7146706047116,616.2792203258241,600.4531203718612,0.0,0.0,0.0,2843.0,6374.0,4282.0,0.057333802321491385,0.08362096015061186,0.16113965436711816,0.20612029546253957,0.6322560401631628,0.585007006071929,0.0,0.0,0.0
-1,2021.0,1,1.4934951450976033,265.0790446169656,-53.316329953279364,-633.833440536471,-720.1475014734575,-1107.800225291798,-1342.87225502104,-1403.8462154806814,-0.08438479644779334,-0.08619879087136928,-0.0986032721785602,-0.007173345157579947,0.2515249372084679,0.8663942156554543,0.07706464228203358,0.6663078579117331,0.8797547940899089,0.40073613986657464,0.07534983853606028,0.014303678088651368,0.005291005291005291,1551.2912660455368,2046.2045887967206,1445.012154106386,3.5166846071044136,3.3392015089594467,2.955831608005521,0.0,0.0,0.0,0.0,0.0,0.0,481.83100879778164,635.2503020473121,614.2336645553173,0.0,0.0,0.0,2787.0,6362.0,4347.0,0.05776820954431288,0.07843445457403332,0.17092247527030136,0.16361679224973089,0.6079849104055328,0.5500345065562456,0.0,0.0,0.0
-1,2022.0,1,4.2008915875651525,251.1337307951535,-43.12108976122818,-623.3994073976262,-720.1926982899406,-1107.8637486547523,-1342.9489340906748,-1403.8637477566897,-0.09236213649635039,-0.09155577022067019,-0.09833616463914277,-0.021909977726960905,0.2200729927007299,0.8328080680743776,0.0750057168991539,0.6704379562043795,0.883075953356445,0.4180196661330894,0.0791970802919708,0.010715411282697762,0.002744111593871484,1537.3459522237247,2056.399828988772,1455.446187245231,3.421167883211679,3.244405924992121,2.8433569631831697,0.0,0.0,0.0,0.0,0.0,0.0,479.3879405071666,641.0757403418188,609.3736283337812,0.0,0.0,0.0,2740.0,6346.0,4373.0,0.05583941605839416,0.0797352663094863,0.1781385776354905,0.12883211678832115,0.5836747557516546,0.5270981019894809,0.0,0.0,0.0
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+1,2022.0,1,4.2008915875651525,251.30086845038204,-43.09792569649835,-623.3441521979,-720.1926982899406,-1107.8637468673705,-1342.9489312044848,-1403.8637477566897,-0.09236213649635039,-0.09155577022067019,-0.09833616463914277,-0.021909977726960905,0.2200729927007299,0.8328080680743776,0.0750057168991539,0.6704379562043795,0.883075953356445,0.4180196661330894,0.0791970802919708,0.010715411282697762,0.002744111593871484,1537.5130898789532,2056.4229930535016,1455.501442444957,3.4262773722627737,3.2477150961235424,2.844729018980105,0.0,0.0,0.0,0.0,0.0,0.0,479.43287964555896,641.238570820656,609.4134967285311,0.0,0.0,0.0,2740.0,6346.0,4373.0,0.05583941605839416,0.0797352663094863,0.1781385776354905,0.12883211678832115,0.5836747557516546,0.5275554539217928,0.0,0.0,0.0
 2,2019.0,1,-0.5567879131517617,-8.738097132978965,-238.2445723348635,-787.0615032827395,-720.0935764715266,-1107.7726152285552,-1342.8547994172,-1403.8364714559557,-0.017425895180722906,-0.03910275596534102,-0.05625761088690756,0.04594060142687284,0.295697074010327,0.9408728296574378,0.07491082045184304,0.5717728055077452,0.7864852182074143,0.32294887039239,0.10292598967297763,0.0652275926794932,0.02996432818073722,1277.4741242955922,1861.2763464151365,1291.7840913601176,3.719449225473322,3.44705146253715,3.125564803804994,0.0,0.0,0.0,0.0,0.0,0.0,415.5678951414117,592.5646718222849,573.6180048470663,0.0,0.0,0.0,2905.0,6393.0,4205.0,0.05783132530120482,0.09682465196308462,0.1464922711058264,0.2571428571428571,0.664476771468794,0.6275862068965518,0.0,0.0,0.0
 2,2020.0,1,1.851837766753299,201.84420631350986,-94.00133576494113,-680.4316125125422,-720.1271395625273,-1107.7503947924654,-1342.8000699318272,-1403.8432828573027,-0.07575559103060148,-0.07530048858433958,-0.09174279184185755,0.01678934413825317,0.2768202602884277,0.9002196422968308,0.0751985053713218,0.6535349982412945,0.8611546909319109,0.37085474077533864,0.07949349278930706,0.026043300909946658,0.011209715086408221,1488.056427742081,2005.5195829850588,1398.413982130315,3.6148434752022514,3.4033573893944147,3.0425035030359644,0.0,0.0,0.0,0.0,0.0,0.0,455.7146706047116,616.2792203258241,600.4531203718612,0.0,0.0,0.0,2843.0,6374.0,4282.0,0.057333802321491385,0.08362096015061186,0.16113965436711816,0.20612029546253957,0.6322560401631628,0.585007006071929,0.0,0.0,0.0
-2,2021.0,1,1.4934951450976033,265.0790446169656,-53.316329953279364,-633.833440536471,-720.1475014734575,-1107.800225291798,-1342.87225502104,-1403.8462154806814,-0.08438479644779334,-0.08619879087136928,-0.0986032721785602,-0.007173345157579947,0.2515249372084679,0.8663942156554543,0.07706464228203358,0.6663078579117331,0.8797547940899089,0.40073613986657464,0.07534983853606028,0.014303678088651368,0.005291005291005291,1551.2912660455368,2046.2045887967206,1445.012154106386,3.5166846071044136,3.3392015089594467,2.955831608005521,0.0,0.0,0.0,0.0,0.0,0.0,481.83100879778164,635.2503020473121,614.2336645553173,0.0,0.0,0.0,2787.0,6362.0,4347.0,0.05776820954431288,0.07843445457403332,0.17092247527030136,0.16361679224973089,0.6079849104055328,0.5500345065562456,0.0,0.0,0.0
-2,2022.0,1,4.2008915875651525,251.1337307951535,-43.12108976122818,-623.3994073976262,-720.1926982899406,-1107.8637486547523,-1342.9489340906748,-1403.8637477566897,-0.09236213649635039,-0.09155577022067019,-0.09833616463914277,-0.021909977726960905,0.2200729927007299,0.8328080680743776,0.0750057168991539,0.6704379562043795,0.883075953356445,0.4180196661330894,0.0791970802919708,0.010715411282697762,0.002744111593871484,1537.3459522237247,2056.399828988772,1455.446187245231,3.421167883211679,3.244405924992121,2.8433569631831697,0.0,0.0,0.0,0.0,0.0,0.0,479.3879405071666,641.0757403418188,609.3736283337812,0.0,0.0,0.0,2740.0,6346.0,4373.0,0.05583941605839416,0.0797352663094863,0.1781385776354905,0.12883211678832115,0.5836747557516546,0.5270981019894809,0.0,0.0,0.0
\ No newline at end of file
+2,2021.0,1,1.4934951450976033,265.0620009465604,-53.14547793099314,-633.8595195966084,-720.1475014734575,-1107.800225291798,-1342.87225502104,-1403.8462154806814,-0.08438479644779334,-0.08619879087136928,-0.0986032721785602,-0.007173345157579947,0.2515249372084679,0.8663942156554543,0.07706464228203358,0.6663078579117331,0.8797547940899089,0.40073613986657464,0.07534983853606028,0.014303678088651368,0.005291005291005291,1551.2742223751316,2046.3754408190068,1444.9860750462487,3.517761033369214,3.3399874253379442,2.9560616517138256,0.0,0.0,0.0,0.0,0.0,0.0,481.8454121860605,635.2873062086645,614.2412127393665,0.0,0.0,0.0,2787.0,6362.0,4347.0,0.05776820954431288,0.07843445457403332,0.17092247527030136,0.16361679224973089,0.6079849104055328,0.5500345065562456,0.0,0.0,0.0
+2,2022.0,1,4.2008915875651525,251.30086845038204,-43.09792569649835,-623.3441521979,-720.1926982899406,-1107.8637468673705,-1342.9489312044848,-1403.8637477566897,-0.09236213649635039,-0.09155577022067019,-0.09833616463914277,-0.021909977726960905,0.2200729927007299,0.8328080680743776,0.0750057168991539,0.6704379562043795,0.883075953356445,0.4180196661330894,0.0791970802919708,0.010715411282697762,0.002744111593871484,1537.5130898789532,2056.4229930535016,1455.501442444957,3.4262773722627737,3.2477150961235424,2.844729018980105,0.0,0.0,0.0,0.0,0.0,0.0,479.43287964555896,641.238570820656,609.4134967285311,0.0,0.0,0.0,2740.0,6346.0,4373.0,0.05583941605839416,0.0797352663094863,0.1781385776354905,0.12883211678832115,0.5836747557516546,0.5275554539217928,0.0,0.0,0.0
\ No newline at end of file
diff --git a/src/test/java/simpaths/integrationtest/expected/Statistics31.csv b/src/test/java/simpaths/integrationtest/expected/Statistics31.csv
index a47130457..0dec5d5fe 100644
--- a/src/test/java/simpaths/integrationtest/expected/Statistics31.csv
+++ b/src/test/java/simpaths/integrationtest/expected/Statistics31.csv
@@ -2,8 +2,8 @@ run,time,id_Statistics31,fertilityAdjustmentFactor,fertilityRateSimulated,fertil
 1,2019.0,1,-0.279462622342386,0.06028368794326241,0.06092462518117112,-0.613869665517935,0.5485124631466095,0.639752445827121,0.0,0.0,0.0,0.0
 1,2020.0,1,-0.2659197918000082,0.03155701101518309,0.059320659210062375,-0.5988444469664678,0.5111007400493366,0.6494882472052341,0.0,0.0,0.0,0.0
 1,2021.0,1,-0.25543875103684177,0.028126870137642132,0.0570746110314559,-0.5821259283872153,0.48081399215269005,0.6444373367775469,0.0,0.0,0.0,0.0
-1,2022.0,1,-0.24930347300809638,0.02042042042042042,0.05798520015481514,-0.584760268520426,0.45429289588569916,0.6442540414107724,0.0,0.0,0.0,0.0
+1,2022.0,1,-0.24930347300809638,0.02042042042042042,0.05798520015481514,-0.584760268520426,0.4544251885169996,0.6442540414107724,0.0,0.0,0.0,0.0
 2,2019.0,1,-0.279462622342386,0.06028368794326241,0.06092462518117112,-0.613869665517935,0.5485124631466095,0.639752445827121,0.0,0.0,0.0,0.0
 2,2020.0,1,-0.2659197918000082,0.03155701101518309,0.059320659210062375,-0.5988444469664678,0.5111007400493366,0.6494882472052341,0.0,0.0,0.0,0.0
 2,2021.0,1,-0.25543875103684177,0.028126870137642132,0.0570746110314559,-0.5821259283872153,0.48081399215269005,0.6444373367775469,0.0,0.0,0.0,0.0
-2,2022.0,1,-0.24930347300809638,0.02042042042042042,0.05798520015481514,-0.584760268520426,0.45429289588569916,0.6442540414107724,0.0,0.0,0.0,0.0
\ No newline at end of file
+2,2022.0,1,-0.24930347300809638,0.02042042042042042,0.05798520015481514,-0.584760268520426,0.4544251885169996,0.6442540414107724,0.0,0.0,0.0,0.0
\ No newline at end of file

From bf034b5f52cc3a21cc4a1c3be3869671b15f7755 Mon Sep 17 00:00:00 2001
From: justin-ven <43171764+justin-ven@users.noreply.github.com>
Date: Wed, 21 Jan 2026 11:15:31 +0100
Subject: [PATCH 7/7] update of validation statistics

---
 input/DatabaseCountryYear.xlsx                | Bin 6530 -> 6530 bytes
 .../expected/HealthStatistics1.csv            |  36 +++++++++---------
 .../integrationtest/expected/Statistics1.csv  |   8 ++--
 .../integrationtest/expected/Statistics21.csv |   8 ++--
 .../integrationtest/expected/Statistics31.csv |   4 +-
 5 files changed, 28 insertions(+), 28 deletions(-)

diff --git a/input/DatabaseCountryYear.xlsx b/input/DatabaseCountryYear.xlsx
index c962e78608e56adb68d5ad554bffc64ea3ee952c..cddc317cfee52ad0febce16bfe8e63b630935cc0 100644
GIT binary patch
delta 379
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z<xA}^q`LMpR~{BS@VakVPwvWTrvwfLa=tt2oPTMr<=3Xm`n@%*FPDYIE}OJTW#+A&
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Wn9i4gXxbnF5q~S;3Kln&^aTLv#f+l>

delta 379
zcmZoNZZhT#@MdNaVc_84U}*0%*~r_*$PA=6Ph*sXFg7u*fG}cNvcZhWr&$f_oyul!
z`R6^0uSsEDX#vmMt86|&#laW1o8NNOkbPgym%d|B#Z2qUCik~FewSN<nYI;e-L`$s
zywitsoVhLMavX8cx_(o@yZh{%N#|a<v;UsAZOReZZp&P!jPjnus||rOc8KNptUD(y
z`sigoPc;9kV{8gDtWR6S8!q0mG-$yzr4!Egm(+8MElG%f^^@ro|JxcVuVa@>GL9E5
z*($d&=g2nYZGWV@>nF+wc(Zfde;1pzk%581m63q~#jBh5vF&35`?a3)C>xmJE|AC!
zW~>(0fOz_vh!R-M<lka;AeqTF;$~pFRNNX&Zx*-V0E)+eteN~>+zKpcB4Gih^Ccjf
QHb_9k-%7ZG#Z4uB0d-B3r2qf`

diff --git a/src/test/java/simpaths/integrationtest/expected/HealthStatistics1.csv b/src/test/java/simpaths/integrationtest/expected/HealthStatistics1.csv
index 46691c91d..86d6f5ce6 100644
--- a/src/test/java/simpaths/integrationtest/expected/HealthStatistics1.csv
+++ b/src/test/java/simpaths/integrationtest/expected/HealthStatistics1.csv
@@ -2,24 +2,24 @@ run,time,id_HealthStatistics1,demLifeSatScore1to7Avg,demLifeSatScore1to7P10,demL
 1,2019.0,1,5.082882273342355,3.0,4.0,6.0,6.0,6.0,Total,9845.10078575798,94442.85714285714,47.46016576454639,32.93,41.6125,49.375,54.79,58.05,11824,51.582412043302156,37.85,48.135000000000005,54.8,57.57,60.09,11.508203653585927,6.0,8.0,10.0,13.0,19.0
 1,2019.0,1,5.0900653145410795,3.0,4.0,6.0,6.0,6.0,Male,4948.7929222607045,46536.28571428571,48.49371433482308,34.439,43.38,51.0,55.48,58.3,5818,52.14620831900955,40.0,49.39,55.0,57.49,59.72,10.95496734272946,6.0,7.0,10.0,12.0,18.0
 1,2019.0,1,5.075924075924076,3.0,4.0,6.0,6.0,6.0,Female,4896.3078634974345,47906.57142857143,46.45896936396954,31.587,40.71,48.63,54.2,57.33,6006,51.036263736263685,35.59700000000001,47.09,54.67,57.76,60.31,12.044122544122544,6.0,8.0,11.0,14.0,20.0
-1,2020.0,1,5.099288979177247,4.0,5.0,5.0,6.0,6.0,Total,9864.048960583908,94667.57142857142,46.03536584899494,37.00027304971955,42.53516227262855,47.41324393572812,50.67571266574632,52.87033032902951,11814,52.17845740495774,42.25576516632326,49.677349533238996,54.66456027247861,57.06611303909153,58.42843343123053,12.495412159281079,5.5370349843287165,8.676314535669135,12.194970363344012,15.985849526701086,19.69307403459438
-1,2020.0,1,5.108303249097473,4.0,5.0,5.0,6.0,6.0,Male,4962.421819593146,46695.0,47.383985779611436,39.20901973589,44.42667107212651,48.781514052392104,51.54767776573669,53.620359592812875,5817,52.57359667289635,43.91550465962647,50.380985534570584,54.736891829213405,56.97290572048745,58.25220335273802,11.763524481048512,5.013416816498344,8.127335774175966,11.540471364017257,15.163044055478055,18.81147009168079
-1,2020.0,1,5.0905452726363185,4.0,5.0,5.0,6.0,6.0,Female,4901.627140990736,47972.57142857143,44.72722475571583,35.43880994011158,41.22648509740569,45.9451213393288,49.55256663286745,51.917303123670905,5997,51.79517824511138,40.834709760259706,48.833003378983946,54.574158862383214,57.2126921034085,58.609003838500385,13.205332223359573,6.154900388119579,9.276728941370386,12.986934939435692,16.74804430772302,20.414013185439998
-1,2021.0,1,5.043783875338754,4.0,5.0,5.0,5.0,6.0,Total,9783.483778356105,93589.57142857142,45.47178414289231,39.12251021802796,43.03862987261727,46.323684910610545,48.86552999262566,50.59570927824662,11808,52.00169368112773,43.9958188012933,49.91924841602812,53.876879929457836,56.19234654092858,57.55511691219131,12.826579637999787,5.911989417423701,9.120512607494732,12.695229611980281,16.416999440444396,19.90148659469959
-1,2021.0,1,5.055889939810834,4.0,5.0,5.0,5.0,6.0,Male,4921.03969021223,46200.0,46.86752854904576,41.27820467461532,44.81433643179958,47.696642086712195,49.79172538647023,51.449056692963836,5815,52.32599715943045,45.276560183427875,50.48189542288343,53.96630026363008,56.16155474789131,57.422410995122235,12.07620331893435,5.292660852560196,8.435989352910461,12.010606124475975,15.554957198437629,18.93314748044037
-1,2021.0,1,5.032037376939763,4.0,5.0,5.0,5.0,6.0,Female,4862.444088143861,47389.57142857143,44.11749518547859,37.481048772368915,41.747349386998465,44.98408129902768,47.59076862388092,49.43245344407739,5993,51.687022443628834,42.73863310658021,49.314389943995394,53.759416268970895,56.225530582829634,57.717169079738696,13.554668791239507,6.605371367377746,9.710521107580309,13.343607293318746,17.273142902963873,20.655868728161835
-1,2022.0,1,4.994914392269877,4.0,5.0,5.0,5.0,6.0,Total,9663.78016220828,92604.28571428571,45.22967162154171,40.027973300194546,43.220278850570146,45.85021013600231,48.091233151116604,49.65810054240459,11798,51.47648448826025,44.73712915555342,49.720603366480894,52.98676256487179,55.25019642319984,56.60872859796221,12.805737075518161,5.903928458268275,9.049417981122938,12.652345279526287,16.40545555259082,19.87530565546674
-1,2022.0,1,5.006193015654567,4.0,5.0,5.0,5.0,6.0,Male,4867.432518591544,45730.142857142855,46.515280123234966,41.97114948375056,44.848515543831944,47.05675547393735,48.968794613144084,50.50160564385881,5813,51.89661898875793,45.868746603694305,50.18270812470012,53.25119769671678,55.37664117689313,56.6861714610893,12.132217763125537,5.411276202588293,8.466678875479332,12.01251943385119,15.678109590462586,19.02323346230758
-1,2022.0,1,4.983959899749373,4.0,5.0,5.0,5.0,6.0,Female,4796.347643616746,46874.142857142855,43.98100959642188,38.70893405094572,42.08289824411705,44.59182710933337,46.89009796232341,48.52823094560267,5985,51.068424028545216,43.365437928715124,49.161972492000956,52.7139916454248,55.095176524091585,56.550309289777964,13.459900444430277,6.497088524606091,9.67887017040486,13.273767774461579,17.04518846275956,20.72045217297736
+1,2020.0,1,5.1041137633316405,4.0,5.0,5.0,6.0,6.0,Total,9867.629028620691,94757.14285714286,46.06161454306233,37.05800729137269,42.587491101460316,47.432004480092814,50.68859873971846,52.87456665243567,11814,52.18030101407709,42.25576516632326,49.680919587888695,54.665894128667496,57.068693441002054,58.429207300762144,12.477151978299252,5.534893179347836,8.665287181108546,12.188744213920447,15.967226489842192,19.678480450828733
+1,2020.0,1,5.111913357400722,4.0,5.0,5.0,6.0,6.0,Male,4964.0316314330385,46728.0,47.406965541146334,39.249952833151376,44.449420566721116,48.79874828592617,51.55431716630187,53.63085567014427,5817,52.576394035910695,43.91550465962647,50.380985534570584,54.736891829213405,56.975619760277816,58.25838852247566,11.74852852524423,5.003288210730821,8.12187068455188,11.522269155861272,15.152909704327817,18.788181346366713
+1,2020.0,1,5.096548274137069,4.0,5.0,5.0,6.0,6.0,Female,4903.597397187632,48029.142857142855,44.75664426528129,35.50282075868708,41.25303679694558,45.97140874282798,49.567357263319764,51.91896318278354,5997,51.79609672726596,40.834709760259706,48.833003378983946,54.574158862383214,57.2146301348631,58.609003838500385,13.183905792943397,6.153944825084665,9.267597531236369,12.977001345457008,16.71467741966822,20.375702908186494
+1,2021.0,1,5.056487127371274,4.0,5.0,5.0,5.0,6.0,Total,9792.794512293089,93825.28571428571,45.53698910330616,39.28736596985248,43.12104798097639,46.36477346004733,48.889468511237226,50.617988764525194,11808,52.009383090433815,44.042790652756416,49.92889860573529,53.88041332456192,56.201691381397914,57.55961874748001,12.7791923652743,5.886397122941414,9.0825339687628,12.664605546067076,16.37201012423222,19.86214220228487
+1,2021.0,1,5.067239896818573,4.0,5.0,5.0,5.0,6.0,Male,4925.377486670445,46303.71428571428,46.92742305195073,41.40159875119027,44.87523275838807,47.732021438966584,49.8279563681638,51.46856479608388,5815,52.33606601671287,45.2834272075495,50.4981717416713,53.967270766420825,56.172336404468474,57.42661850385404,12.035195139163632,5.272064447569398,8.397260642314148,11.973291551300328,15.498967076850288,18.89999299643993
+1,2021.0,1,5.046053729350909,4.0,5.0,5.0,5.0,6.0,Female,4867.417025622632,47521.57142857143,44.187852875813064,37.66526858343326,41.862561637495475,45.037099440071465,47.63377495546325,49.446385918283475,5993,51.69240307770004,42.74959544099078,49.322708220092466,53.775846655603154,56.229840645013844,57.72624175507087,13.501091893028939,6.533162089878138,9.676477117306487,13.289130815898963,17.173770574514695,20.56876333606342
+1,2022.0,1,5.01398542125784,4.0,5.0,5.0,5.0,6.0,Total,9678.911095704907,92957.85714285714,45.330885675242,40.27396863022321,43.353337370720354,45.956332059413,48.13522835386524,49.696132136029526,11798,51.49339710747833,44.77671019344879,49.737904401112324,52.99920715163458,55.26618096587674,56.619139626071416,12.733576277808323,5.884717768426957,8.990776612223831,12.590363515003684,16.321618670954518,19.78447264624798
+1,2022.0,1,5.025116119043523,4.0,5.0,5.0,5.0,6.0,Male,4874.9555752692095,45903.0,46.61670536172718,42.23914809142157,45.002548062181724,47.124840978677916,49.017683151062755,50.51815597013682,5813,51.91763076988683,45.874383139673576,50.21710500279242,53.2741817975423,55.37949220045634,56.69394084423383,12.062603272014805,5.372843090248836,8.42896883124155,11.917440135003691,15.606935792574298,18.8789921386015
+1,2022.0,1,5.003174603174603,4.0,5.0,5.0,5.0,6.0,Female,4803.955520435719,47054.857142857145,44.08201853446716,38.91756527396111,42.23150893268319,44.67251584512197,46.934168913320605,48.54443392727054,5985,51.081355289670334,43.433537512409515,49.170102629911526,52.733955958548236,55.1022850401056,56.552713849871225,13.385266517186453,6.4063087478722975,9.631762564506932,13.180531379100671,16.927199640950732,20.670750153102137
 2,2019.0,1,5.082882273342355,3.0,4.0,6.0,6.0,6.0,Total,9845.10078575798,94442.85714285714,47.46016576454639,32.93,41.6125,49.375,54.79,58.05,11824,51.582412043302156,37.85,48.135000000000005,54.8,57.57,60.09,11.508203653585927,6.0,8.0,10.0,13.0,19.0
 2,2019.0,1,5.0900653145410795,3.0,4.0,6.0,6.0,6.0,Male,4948.7929222607045,46536.28571428571,48.49371433482308,34.439,43.38,51.0,55.48,58.3,5818,52.14620831900955,40.0,49.39,55.0,57.49,59.72,10.95496734272946,6.0,7.0,10.0,12.0,18.0
 2,2019.0,1,5.075924075924076,3.0,4.0,6.0,6.0,6.0,Female,4896.3078634974345,47906.57142857143,46.45896936396954,31.587,40.71,48.63,54.2,57.33,6006,51.036263736263685,35.59700000000001,47.09,54.67,57.76,60.31,12.044122544122544,6.0,8.0,11.0,14.0,20.0
-2,2020.0,1,5.099288979177247,4.0,5.0,5.0,6.0,6.0,Total,9864.048960583908,94667.57142857142,46.03536584899494,37.00027304971955,42.53516227262855,47.41324393572812,50.67571266574632,52.87033032902951,11814,52.17845740495774,42.25576516632326,49.677349533238996,54.66456027247861,57.06611303909153,58.42843343123053,12.495412159281079,5.5370349843287165,8.676314535669135,12.194970363344012,15.985849526701086,19.69307403459438
-2,2020.0,1,5.108303249097473,4.0,5.0,5.0,6.0,6.0,Male,4962.421819593146,46695.0,47.383985779611436,39.20901973589,44.42667107212651,48.781514052392104,51.54767776573669,53.620359592812875,5817,52.57359667289635,43.91550465962647,50.380985534570584,54.736891829213405,56.97290572048745,58.25220335273802,11.763524481048512,5.013416816498344,8.127335774175966,11.540471364017257,15.163044055478055,18.81147009168079
-2,2020.0,1,5.0905452726363185,4.0,5.0,5.0,6.0,6.0,Female,4901.627140990736,47972.57142857143,44.72722475571583,35.43880994011158,41.22648509740569,45.9451213393288,49.55256663286745,51.917303123670905,5997,51.79517824511138,40.834709760259706,48.833003378983946,54.574158862383214,57.2126921034085,58.609003838500385,13.205332223359573,6.154900388119579,9.276728941370386,12.986934939435692,16.74804430772302,20.414013185439998
-2,2021.0,1,5.043783875338754,4.0,5.0,5.0,5.0,6.0,Total,9783.483778356105,93589.57142857142,45.47178414289231,39.12251021802796,43.03862987261727,46.323684910610545,48.86552999262566,50.59570927824662,11808,52.00169368112773,43.9958188012933,49.91924841602812,53.876879929457836,56.19234654092858,57.55511691219131,12.826579637999789,5.911989417423701,9.120512607494732,12.695229611980281,16.416999440444396,19.90148659469959
-2,2021.0,1,5.055889939810834,4.0,5.0,5.0,5.0,6.0,Male,4921.03969021223,46200.0,46.86752854904576,41.27820467461532,44.81433643179958,47.696642086712195,49.79172538647023,51.449056692963836,5815,52.32599715943045,45.276560183427875,50.48189542288343,53.96630026363008,56.16155474789131,57.422410995122235,12.07620331893435,5.292660852560196,8.435989352910461,12.010606124475975,15.554957198437629,18.93314748044037
-2,2021.0,1,5.032037376939763,4.0,5.0,5.0,5.0,6.0,Female,4862.444088143861,47389.57142857143,44.11749518547859,37.481048772368915,41.747349386998465,44.98408129902768,47.59076862388092,49.43245344407739,5993,51.687022443628834,42.73863310658021,49.314389943995394,53.759416268970895,56.225530582829634,57.717169079738696,13.554668791239507,6.605371367377746,9.710521107580309,13.343607293318746,17.273142902963873,20.655868728161835
-2,2022.0,1,4.994914392269877,4.0,5.0,5.0,5.0,6.0,Total,9663.78016220828,92604.28571428571,45.22967162154171,40.027973300194546,43.220278850570146,45.85021013600231,48.091233151116604,49.65810054240459,11798,51.47648448826023,44.73712915555342,49.720603366480894,52.98676256487179,55.25019642319984,56.60872859796221,12.805737075518161,5.903928458268275,9.049417981122938,12.652345279526287,16.40545555259082,19.87530565546674
-2,2022.0,1,5.006193015654567,4.0,5.0,5.0,5.0,6.0,Male,4867.432518591544,45730.142857142855,46.515280123234966,41.97114948375056,44.848515543831944,47.05675547393735,48.968794613144084,50.50160564385881,5813,51.89661898875793,45.868746603694305,50.18270812470012,53.25119769671678,55.37664117689313,56.6861714610893,12.132217763125537,5.411276202588293,8.466678875479332,12.01251943385119,15.678109590462586,19.02323346230758
-2,2022.0,1,4.983959899749373,4.0,5.0,5.0,5.0,6.0,Female,4796.347643616746,46874.142857142855,43.98100959642188,38.70893405094572,42.08289824411705,44.59182710933337,46.89009796232341,48.52823094560267,5985,51.068424028545216,43.365437928715124,49.161972492000956,52.7139916454248,55.095176524091585,56.550309289777964,13.459900444430277,6.497088524606091,9.67887017040486,13.273767774461579,17.04518846275956,20.72045217297736
+2,2020.0,1,5.1041137633316405,4.0,5.0,5.0,6.0,6.0,Total,9867.629028620691,94757.14285714286,46.06161454306233,37.05800729137269,42.587491101460316,47.432004480092814,50.68859873971846,52.87456665243567,11814,52.18030101407709,42.25576516632326,49.680919587888695,54.665894128667496,57.068693441002054,58.429207300762144,12.477151978299252,5.534893179347836,8.665287181108546,12.188744213920447,15.967226489842192,19.678480450828733
+2,2020.0,1,5.111913357400722,4.0,5.0,5.0,6.0,6.0,Male,4964.0316314330385,46728.0,47.406965541146334,39.249952833151376,44.449420566721116,48.79874828592617,51.55431716630187,53.63085567014427,5817,52.576394035910695,43.91550465962647,50.380985534570584,54.736891829213405,56.975619760277816,58.25838852247566,11.74852852524423,5.003288210730821,8.12187068455188,11.522269155861272,15.152909704327817,18.788181346366713
+2,2020.0,1,5.096548274137069,4.0,5.0,5.0,6.0,6.0,Female,4903.597397187632,48029.142857142855,44.75664426528129,35.50282075868708,41.25303679694558,45.97140874282798,49.567357263319764,51.91896318278354,5997,51.79609672726596,40.834709760259706,48.833003378983946,54.574158862383214,57.2146301348631,58.609003838500385,13.183905792943397,6.153944825084665,9.267597531236369,12.977001345457008,16.71467741966822,20.375702908186494
+2,2021.0,1,5.056487127371274,4.0,5.0,5.0,5.0,6.0,Total,9792.794512293089,93825.28571428571,45.53698910330616,39.28736596985248,43.12104798097639,46.36477346004733,48.889468511237226,50.617988764525194,11808,52.009383090433815,44.042790652756416,49.92889860573529,53.88041332456192,56.201691381397914,57.55961874748001,12.779192365274302,5.886397122941414,9.0825339687628,12.664605546067076,16.37201012423222,19.86214220228487
+2,2021.0,1,5.067239896818573,4.0,5.0,5.0,5.0,6.0,Male,4925.377486670445,46303.71428571428,46.92742305195073,41.40159875119027,44.87523275838807,47.732021438966584,49.8279563681638,51.46856479608388,5815,52.33606601671287,45.2834272075495,50.4981717416713,53.967270766420825,56.172336404468474,57.42661850385404,12.035195139163632,5.272064447569398,8.397260642314148,11.973291551300328,15.498967076850288,18.89999299643993
+2,2021.0,1,5.046053729350909,4.0,5.0,5.0,5.0,6.0,Female,4867.417025622632,47521.57142857143,44.187852875813064,37.66526858343326,41.862561637495475,45.037099440071465,47.63377495546325,49.446385918283475,5993,51.69240307770004,42.74959544099078,49.322708220092466,53.775846655603154,56.229840645013844,57.72624175507087,13.501091893028939,6.533162089878138,9.676477117306487,13.289130815898963,17.173770574514695,20.56876333606342
+2,2022.0,1,5.01398542125784,4.0,5.0,5.0,5.0,6.0,Total,9678.911095704907,92957.85714285714,45.330885675242,40.27396863022321,43.353337370720354,45.956332059413,48.13522835386524,49.696132136029526,11798,51.49339710747833,44.77671019344879,49.737904401112324,52.99920715163458,55.26618096587674,56.619139626071416,12.733576277808323,5.884717768426957,8.990776612223831,12.590363515003684,16.321618670954518,19.78447264624798
+2,2022.0,1,5.025116119043523,4.0,5.0,5.0,5.0,6.0,Male,4874.9555752692095,45903.0,46.61670536172718,42.23914809142157,45.002548062181724,47.124840978677916,49.017683151062755,50.51815597013682,5813,51.91763076988683,45.874383139673576,50.21710500279242,53.2741817975423,55.37949220045634,56.69394084423383,12.062603272014805,5.372843090248836,8.42896883124155,11.917440135003691,15.606935792574298,18.8789921386015
+2,2022.0,1,5.003174603174603,4.0,5.0,5.0,5.0,6.0,Female,4803.955520435719,47054.857142857145,44.08201853446716,38.91756527396111,42.23150893268319,44.67251584512197,46.934168913320605,48.54443392727054,5985,51.081355289670334,43.433537512409515,49.170102629911526,52.733955958548236,55.1022850401056,56.552713849871225,13.385266517186453,6.4063087478722975,9.631762564506932,13.180531379100671,16.927199640950732,20.670750153102137
\ No newline at end of file
diff --git a/src/test/java/simpaths/integrationtest/expected/Statistics1.csv b/src/test/java/simpaths/integrationtest/expected/Statistics1.csv
index 01dfe9a3c..e9edfc266 100644
--- a/src/test/java/simpaths/integrationtest/expected/Statistics1.csv
+++ b/src/test/java/simpaths/integrationtest/expected/Statistics1.csv
@@ -1,9 +1,9 @@
 run,time,id_Statistics1,edi_p50,sIndex_p50,statYHhDispEquivNatGini,statYMktNatGini,yHhDispEquivP50,yHhQuintilesC5P20,yHhQuintilesC5P40,yHhQuintilesC5P60,yHhQuintilesC5P80,yLabP20,yLabP40,yLabP60,yLabP80
 1,2019.0,1,13643.583390133683,NaN,0.0,0.0,13643.667479107155,0.0,6.51833999175728,7.9090821344821665,8.39487669904798,1015.7031862136506,1595.5475473858087,2190.490749493191,3216.4356578295665
 1,2020.0,1,15900.71234301057,NaN,0.0,0.0,15900.71234301057,0.0,7.088076180135259,8.031141956880619,8.506710224623882,1172.9898846787785,1749.5408273403375,2416.9694325845585,3503.635962054756
-1,2021.0,1,15807.886928269101,NaN,0.0,0.0,15807.886928269101,0.0,7.220824954129774,8.09469510117329,8.580964490955218,1255.2279162400785,1872.7710594117166,2549.7755445091757,3637.4464012183344
-1,2022.0,1,16112.837011538813,NaN,0.0,0.0,16111.568220383782,0.0,7.28594478502956,8.108206700240562,8.57947804929593,1273.8431399224642,1888.2673261541092,2555.583259821284,3675.029763548892
+1,2021.0,1,15807.886928269101,NaN,0.0,0.0,15807.886928269101,0.0,7.220824954129774,8.09469510117329,8.580964490955218,1255.2279162400785,1872.7710594117166,2549.7755445091757,3638.0914692035926
+1,2022.0,1,16112.837011538813,NaN,0.0,0.0,16111.568220383782,0.0,7.28597153342024,8.109071579540844,8.57951743271375,1273.8431399224642,1888.3290096639555,2555.749176933975,3676.8576133715255
 2,2019.0,1,13643.583390133683,NaN,0.0,0.0,13643.667479107155,0.0,6.51833999175728,7.9090821344821665,8.39487669904798,1015.7031862136506,1595.5475473858087,2190.490749493191,3216.4356578295665
 2,2020.0,1,15900.71234301057,NaN,0.0,0.0,15900.71234301057,0.0,7.088076180135259,8.031141956880619,8.506710224623882,1172.9898846787785,1749.5408273403375,2416.9694325845585,3503.635962054756
-2,2021.0,1,15807.886928269101,NaN,0.0,0.0,15807.886928269101,0.0,7.220824954129774,8.09469510117329,8.580964490955218,1255.2279162400785,1872.7710594117166,2549.7755445091757,3637.4464012183344
-2,2022.0,1,16112.837011538813,NaN,0.0,0.0,16111.568220383782,0.0,7.28594478502956,8.108206700240562,8.57947804929593,1273.8431399224642,1888.2673261541092,2555.583259821284,3675.029763548892
+2,2021.0,1,15807.886928269101,NaN,0.0,0.0,15807.886928269101,0.0,7.220824954129774,8.09469510117329,8.580964490955218,1255.2279162400785,1872.7710594117166,2549.7755445091757,3638.0914692035926
+2,2022.0,1,16112.837011538813,NaN,0.0,0.0,16111.568220383782,0.0,7.28597153342024,8.109071579540844,8.57951743271375,1273.8431399224642,1888.3290096639555,2555.749176933975,3676.8576133715255
\ No newline at end of file
diff --git a/src/test/java/simpaths/integrationtest/expected/Statistics21.csv b/src/test/java/simpaths/integrationtest/expected/Statistics21.csv
index fe68919af..3fba237f4 100644
--- a/src/test/java/simpaths/integrationtest/expected/Statistics21.csv
+++ b/src/test/java/simpaths/integrationtest/expected/Statistics21.csv
@@ -1,9 +1,9 @@
 run,time,id_Statistics21,demDsbl18to29Share,demDsbl30to54Share,demDsbl55to74Share,demMarried18to29Share,demMarried30to54Share,demMarried55to74Share,demNChild18to29Avg,demNChild30to54Avg,demNChild55to74Avg,demPop18to29N,demPop30to54N,demPop55to74N,healthScore18to29Avg,healthScore30to54Avg,healthScore55to74Avg,labNoWork18to29Share,labNoWork18to54Share,labNoWork30to54Share,labNoWork55to74Share,labWorkFullTime18to29Share,labWorkFullTime30to54Share,labWorkFullTime55to74Share,labWorkPartTime18to29Share,labWorkPartTime30to54Share,labWorkPartTime55to74Share,statInvestLoss18to29Avg,statInvestLoss30to54Avg,statInvestLoss55to74Avg,statYDisp18to29Avg,statYDisp30to54Avg,statYDisp55to74Avg,statYDispGrossOfLosses18to29Avg,statYDispGrossOfLosses30to54Avg,statYDispGrossOfLosses55to75Avg,statYInvest18to29Avg,statYInvest30to54Avg,statYInvest55to74Avg,statYLab18to29Avg,statYLab30to54Avg,statYLab55to74Avg,statYPens18to29Avg,statYPens30to54Avg,statYPens55to74Avg,wealth18to29Avg,wealth30to54Avg,wealth55to74Avg,x18to29Avg,x18to54Avg,x30to54Avg,x55to74Avg,xToLeisureRatio
 1,2019.0,1,0.056818181818181816,0.09244486156733928,0.1300523062291964,0.2703168044077135,0.6707336148912874,0.6131716595339991,0.28546831955922863,0.9438448302831222,0.08083689966714218,2904.0,6393.0,4206.0,3.6797520661157024,3.4509619896762085,3.1238706609605327,-0.03280974462809916,-0.04843401568540323,-0.05500624220240888,0.024626505658583042,0.5953856749311295,0.7888315344908494,0.34141702330004753,0.0946969696969697,0.06162990771155952,0.03281027104136947,0.0,0.0,0.0,42.90138650342101,-235.73251852989438,-757.7467085400431,1329.1136079319922,1863.7884002201056,1321.098886102814,0.0,0.0,0.0,434.0636850782414,594.982264159137,577.842361382952,0.0,0.0,0.0,0.0,0.0,0.0,-720.1491392879234,-1107.7139183419072,-1342.7185177749318,-1403.9198889040383,14.047551522588241
 1,2020.0,1,0.05553602811950791,0.08175113761179978,0.15153631284916202,0.2186291739894552,0.6417699670484858,0.5726256983240223,0.2569420035149385,0.9060097285422878,0.08100558659217877,2845.0,6373.0,4296.0,3.6112478031634447,3.419739526125843,3.038175046554935,-0.0685618978910369,-0.07349366319372502,-0.08183963759610859,0.0019729806331471034,0.6463971880492091,0.8536011297662012,0.38337988826815644,0.07943760984182777,0.023693707829907422,0.013500931098696461,0.0,0.0,0.0,177.74529292695115,-86.8723445196822,-651.3959324665625,1463.9575143555223,2012.6485742303178,1427.4496621762946,0.0,0.0,0.0,452.55804068888983,629.2547269095696,598.9694197981935,0.0,0.0,0.0,0.0,0.0,0.0,-720.1480124876006,-1107.7677439694812,-1342.8141815251993,-1403.841594162565,6.374782073884389
-1,2021.0,1,0.05954088952654232,0.0740449614840434,0.1576773008951113,0.17467718794835008,0.6131111460462192,0.5462474179481295,0.23529411764705882,0.8677880836346487,0.08423226991048886,2788.0,6361.0,4357.0,3.5172166427546627,3.336582298380758,2.9561624971310536,-0.07371705695839315,-0.08367739727528506,-0.09544245760100613,-0.011520310626577879,0.6527977044476327,0.8767489388460934,0.40394767041542345,0.0781922525107604,0.01414871875491275,0.006426440211154464,0.0,0.0,0.0,243.94022081715434,-37.25922833921459,-612.0546826486432,1530.1524422457255,2062.2616904107854,1466.790911994214,0.0,0.0,0.0,481.5680331637875,644.8894704019575,625.5178651791215,0.0,0.0,0.0,0.0,0.0,0.0,-720.1618329444999,-1107.7869670350194,-1342.8409281126894,-1403.824252037514,0.6733706484920436
-1,2022.0,1,0.060087399854333576,0.07533490937746257,0.1726027397260274,0.13619810633648943,0.5910165484633569,0.515296803652968,0.21303714493809178,0.8327817178881008,0.08082191780821918,2746.0,6345.0,4380.0,3.4085943190094685,3.247754137115839,2.860958904109589,-0.10456249694100511,-0.0956735778041719,-0.10005307423167849,-0.024890492237442885,0.6809905316824472,0.8869976359338061,0.42168949771689496,0.0808448652585579,0.00851063829787234,0.002054794520547945,0.0,0.0,0.0,282.68025622681967,-28.80041093353111,-593.5156884789599,1568.8924776553908,2070.720507816469,1485.3299061638972,0.0,0.0,0.0,483.8843787268127,646.8139449685841,624.7138535091243,0.0,0.0,0.0,0.0,0.0,0.0,-720.2122899302651,-1107.8807464261945,-1342.963266292065,-1403.8758961656802,21.525072240888445
+1,2021.0,1,0.05954088952654232,0.0740449614840434,0.1576773008951113,0.17467718794835008,0.6131111460462192,0.5462474179481295,0.23529411764705882,0.8677880836346487,0.08423226991048886,2788.0,6361.0,4357.0,3.5175753228120517,3.3376827542839176,2.9563920128528802,-0.07371705695839315,-0.08367739727528506,-0.09544245760100613,-0.011520310626577879,0.6527977044476327,0.8767489388460934,0.40394767041542345,0.0781922525107604,0.01414871875491275,0.006426440211154464,0.0,0.0,0.0,244.03671500822497,-37.29062072085162,-612.0154685441526,1530.2489364367962,2062.2302980291483,1466.8301260987046,0.0,0.0,0.0,481.5872978554714,644.9106076971538,625.5294693978988,0.0,0.0,0.0,0.0,0.0,0.0,-720.1618329444999,-1107.7869663728059,-1342.8409270257732,-1403.824252037514,0.6733706484920436
+1,2022.0,1,0.060087399854333576,0.07533490937746257,0.1726027397260274,0.13619810633648943,0.5913317572892041,0.515296803652968,0.21303714493809178,0.8332545311268715,0.08082191780821918,2746.0,6345.0,4380.0,3.4107793153678077,3.2512214342001577,2.863013698630137,-0.10456249694100511,-0.0956735778041719,-0.10005307423167849,-0.024890492237442885,0.6809905316824472,0.8869976359338061,0.42168949771689496,0.0808448652585579,0.00851063829787234,0.002054794520547945,0.0,0.0,0.0,282.676114836456,-28.52853207524913,-593.4649228535066,1568.8883362650272,2070.992386674751,1485.3806717893506,0.0,0.0,0.0,483.92594753611274,646.9469360558707,624.7507344788916,0.0,0.0,0.0,0.0,0.0,0.0,-720.2122899302651,-1107.8800003952356,-1342.9620572224005,-1403.8758961656802,21.525072240888445
 2,2019.0,1,0.056818181818181816,0.09244486156733928,0.1300523062291964,0.2703168044077135,0.6707336148912874,0.6131716595339991,0.28546831955922863,0.9438448302831222,0.08083689966714218,2904.0,6393.0,4206.0,3.6797520661157024,3.4509619896762085,3.1238706609605327,-0.03280974462809916,-0.04843401568540323,-0.05500624220240888,0.024626505658583042,0.5953856749311295,0.7888315344908494,0.34141702330004753,0.0946969696969697,0.06162990771155952,0.03281027104136947,0.0,0.0,0.0,42.90138650342101,-235.73251852989438,-757.7467085400431,1329.1136079319922,1863.7884002201056,1321.098886102814,0.0,0.0,0.0,434.0636850782414,594.982264159137,577.842361382952,0.0,0.0,0.0,0.0,0.0,0.0,-720.1491392879234,-1107.7139183419072,-1342.7185177749318,-1403.9198889040383,14.047551522588241
 2,2020.0,1,0.05553602811950791,0.08175113761179978,0.15153631284916202,0.2186291739894552,0.6417699670484858,0.5726256983240223,0.2569420035149385,0.9060097285422878,0.08100558659217877,2845.0,6373.0,4296.0,3.6112478031634447,3.419739526125843,3.038175046554935,-0.0685618978910369,-0.07349366319372502,-0.08183963759610859,0.0019729806331471034,0.6463971880492091,0.8536011297662012,0.38337988826815644,0.07943760984182777,0.023693707829907422,0.013500931098696461,0.0,0.0,0.0,177.74529292695115,-86.8723445196822,-651.3959324665625,1463.9575143555223,2012.6485742303178,1427.4496621762946,0.0,0.0,0.0,452.55804068888983,629.2547269095696,598.9694197981935,0.0,0.0,0.0,0.0,0.0,0.0,-720.1480124876006,-1107.7677439694812,-1342.8141815251993,-1403.841594162565,6.374782073884389
-2,2021.0,1,0.05954088952654232,0.0740449614840434,0.1576773008951113,0.17467718794835008,0.6131111460462192,0.5462474179481295,0.23529411764705882,0.8677880836346487,0.08423226991048886,2788.0,6361.0,4357.0,3.5172166427546627,3.336582298380758,2.9561624971310536,-0.07371705695839315,-0.08367739727528506,-0.09544245760100613,-0.011520310626577879,0.6527977044476327,0.8767489388460934,0.40394767041542345,0.0781922525107604,0.01414871875491275,0.006426440211154464,0.0,0.0,0.0,243.94022081715434,-37.25922833921459,-612.0546826486432,1530.1524422457255,2062.2616904107854,1466.790911994214,0.0,0.0,0.0,481.5680331637875,644.8894704019575,625.5178651791215,0.0,0.0,0.0,0.0,0.0,0.0,-720.1618329444999,-1107.7869670350194,-1342.8409281126894,-1403.824252037514,0.6733706484920436
-2,2022.0,1,0.060087399854333576,0.07533490937746257,0.1726027397260274,0.13619810633648943,0.5910165484633569,0.515296803652968,0.21303714493809178,0.8327817178881008,0.08082191780821918,2746.0,6345.0,4380.0,3.4085943190094685,3.247754137115839,2.860958904109589,-0.10456249694100511,-0.0956735778041719,-0.10005307423167849,-0.024890492237442885,0.6809905316824472,0.8869976359338061,0.42168949771689496,0.0808448652585579,0.00851063829787234,0.002054794520547945,0.0,0.0,0.0,282.68025622681967,-28.80041093353111,-593.5156884789599,1568.8924776553908,2070.720507816469,1485.3299061638972,0.0,0.0,0.0,483.8843787268127,646.8139449685841,624.7138535091243,0.0,0.0,0.0,0.0,0.0,0.0,-720.2122899302651,-1107.8807464261945,-1342.963266292065,-1403.8758961656802,21.525072240888445
+2,2021.0,1,0.05954088952654232,0.0740449614840434,0.1576773008951113,0.17467718794835008,0.6131111460462192,0.5462474179481295,0.23529411764705882,0.8677880836346487,0.08423226991048886,2788.0,6361.0,4357.0,3.5175753228120517,3.3376827542839176,2.9563920128528802,-0.07371705695839315,-0.08367739727528506,-0.09544245760100613,-0.011520310626577879,0.6527977044476327,0.8767489388460934,0.40394767041542345,0.0781922525107604,0.01414871875491275,0.006426440211154464,0.0,0.0,0.0,244.03671500822497,-37.29062072085162,-612.0154685441526,1530.2489364367962,2062.2302980291483,1466.8301260987046,0.0,0.0,0.0,481.5872978554714,644.9106076971538,625.5294693978988,0.0,0.0,0.0,0.0,0.0,0.0,-720.1618329444999,-1107.7869663728059,-1342.8409270257732,-1403.824252037514,0.6733706484920436
+2,2022.0,1,0.060087399854333576,0.07533490937746257,0.1726027397260274,0.13619810633648943,0.5913317572892041,0.515296803652968,0.21303714493809178,0.8332545311268715,0.08082191780821918,2746.0,6345.0,4380.0,3.4107793153678077,3.2512214342001577,2.863013698630137,-0.10456249694100511,-0.0956735778041719,-0.10005307423167849,-0.024890492237442885,0.6809905316824472,0.8869976359338061,0.42168949771689496,0.0808448652585579,0.00851063829787234,0.002054794520547945,0.0,0.0,0.0,282.676114836456,-28.52853207524913,-593.4649228535066,1568.8883362650272,2070.992386674751,1485.3806717893506,0.0,0.0,0.0,483.92594753611274,646.9469360558707,624.7507344788916,0.0,0.0,0.0,0.0,0.0,0.0,-720.2122899302651,-1107.8800003952356,-1342.9620572224005,-1403.8758961656802,21.525072240888445
\ No newline at end of file
diff --git a/src/test/java/simpaths/integrationtest/expected/Statistics31.csv b/src/test/java/simpaths/integrationtest/expected/Statistics31.csv
index ca10c2395..924dec9b9 100644
--- a/src/test/java/simpaths/integrationtest/expected/Statistics31.csv
+++ b/src/test/java/simpaths/integrationtest/expected/Statistics31.csv
@@ -2,8 +2,8 @@ run,time,id_Statistics31,careAdj,demFertAdj,demFertRateSim,demFertRateTarget,dem
 1,2019.0,1,0.0,-0.279462622342386,0.060041407867494824,0.06092462518117112,-0.613869665517935,0.5523988206915036,0.639752445827121,0.0,0.0,0.0
 1,2020.0,1,0.0,-0.2659197918000082,0.027405421507298182,0.059320659210062375,-0.5988444469664678,0.5144874442150137,0.6494882472052341,0.0,0.0,0.0
 1,2021.0,1,0.0,-0.25543875103684177,0.025157232704402517,0.0570746110314559,-0.5821259283872153,0.48475994421940366,0.6444373367775469,0.0,0.0,0.0
-1,2022.0,1,0.0,-0.24930347300809638,0.021039975954313193,0.05798520015481514,-0.584760268520426,0.45628885068112685,0.6442540414107724,0.0,0.0,0.0
+1,2022.0,1,0.0,-0.24930347300809638,0.021039975954313193,0.05798520015481514,-0.584760268520426,0.456553365956884,0.6442540414107724,0.0,0.0,0.0
 2,2019.0,1,0.0,-0.279462622342386,0.060041407867494824,0.06092462518117112,-0.613869665517935,0.5523988206915036,0.639752445827121,0.0,0.0,0.0
 2,2020.0,1,0.0,-0.2659197918000082,0.027405421507298182,0.059320659210062375,-0.5988444469664678,0.5144874442150137,0.6494882472052341,0.0,0.0,0.0
 2,2021.0,1,0.0,-0.25543875103684177,0.025157232704402517,0.0570746110314559,-0.5821259283872153,0.48475994421940366,0.6444373367775469,0.0,0.0,0.0
-2,2022.0,1,0.0,-0.24930347300809638,0.021039975954313193,0.05798520015481514,-0.584760268520426,0.45628885068112685,0.6442540414107724,0.0,0.0,0.0
+2,2022.0,1,0.0,-0.24930347300809638,0.021039975954313193,0.05798520015481514,-0.584760268520426,0.456553365956884,0.6442540414107724,0.0,0.0,0.0
\ No newline at end of file