South African Dataset for MAMS

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1 South African Dataset for MAMS AYODELE ODUSOLA MARNA KEARNEY SAM Used 2005 Quantec SAM as base for MAMS SAM 46 Commodities and activities Government activities disaggregated Trade margins 4 Production factors (3 labour categories) Households, enterprises, government, RoW Factor quantities SAM is widely used by government agencies and academic institutions in SA Various data sources used in the compilation of SAM

2 Changes required for MAMS Developed new macroeconomic SAM to reflect changes required for MAMS Government accounts for education split into primary, secondary and tertiary Government account for water and sanitation split water and sanitation into private and public provision Government infrastructure Education split from personal services in SAM then divided into primary, secondary and tertiary Private health split from medical and other services industry in SAM Savings and investment block added Allocation of GDFI across institutions responsible Interest payment block added Aggregation of commodities and activities in SAM SAM in Pictures

3 SAM in Pictures SAM in Pictures

4 SAM in Pictures SAM in Pictures

5 SAM in Pictures SAM in Pictures Capital Intensive Labour Intensive

6 SAM in Pictures Savings and Investment Block HHD GOV INT-DOM INT-ROW SAV-HHD SAV-GOV SAV-ROWCAP-HHD CAP-GOV CAP-ROWINV-PRV INV-WTSNINV-EDUT INV-EDUS INV-EDUPINV-HLTGINV-OINF INV-OGOVROW TOTAL C HHD GOV 0 INT-DOM INT-ROW SAV-HHD SAV-GOV SAV-ROW CAP-HHD CAP-GOV CAP-ROW INV-PRV INV-WTSN INV-EDUT INV-EDUS INV-EDUP INV-HLTG INV-OINF INV-OGOV DSTK ROW TOTAL General Dataset SAM balanced using entropy GDP growth forecast based on forecast by National Treasury published in February 2010 Population and Labour Growth Forecast based on data and model from Actuarial Society of South Africa Elasticities from existing sources National Treasury CGE model, Thurlow (2003), De Wet (2003), K Gibson (2001) Labour Quantities provided by Quantec with SAM

7 General Dataset in Pictures General Dataset in Pictures

8 General Dataset in Pictures General Dataset in Pictures

9 MDG Dataset Various data sources used Education publications General household surveys 2008 UN datasets Statistics South Africa population time series SARB Child mortality estimation household survey does not cover this MDG Dataset Estimations - mdgeduelas. logit wateraccess Income Prov Popgrp GovspendWaterSanitationpc_ln electricityaccess Iteration 0: log likelihood = Iteration 1: log likelihood = Iteration 2: log likelihood = Iteration 3: log likelihood = Iteration 4: log likelihood = Iteration 5: log likelihood = Iteration 6: log likelihood = Logistic regression Number of obs = LR chi2(5) = Prob > chi2 = Log likelihood = Pseudo R2 = wateraccess Coef. Std. Err. z P> z [95% Conf. Interval] Income e Prov Popgrp GovspendWa~n electricit~s _cons Note: 0 failures and 79 successes completely determined.. margins, dydx(*) Average marginal effects Number of obs = Model VCE : OIM Expression : Pr(wateraccess), predict() dy/dx w.r.t. : Income Prov Popgrp GovspendWaterSanitationpc_ln electricityaccess dy/dx Std. Err. z P> z [95% Conf. Interval] 1.63e e e e-06 Income Prov Popgrp GovspendWa~n electricit~s margins, eyex(*) Average marginal effects Number of obs = Model VCE : OIM Expression : Pr(wateraccess), predict() ey/ex w.r.t. : Income Prov Popgrp GovspendWaterSanitationpc_ln electricityaccess ey/ex Std. Err. z P> z [95% Conf. Interval] Income Prov Popgrp GovspendWa~n electricit~s

10 MDG Dataset Estimations - mdgeduelas. logit sanitationaccess Income Prov Popgrp GovspendWaterSanitationpc_ln electricityaccess Iteration 0: log likelihood = Iteration 1: log likelihood = Iteration 2: log likelihood = Iteration 3: log likelihood = Iteration 4: log likelihood = Iteration 5: log likelihood = Iteration 6: log likelihood = Logistic regression Number of obs = LR chi2(5) = Prob > chi2 = Log likelihood = Pseudo R2 = sanitation~s Coef. Std. Err. z P> z [95% Conf. Interval] Income e Prov Popgrp GovspendWa~n electricit~s _cons Note: 0 failures and 14 successes completely determined.. margins, dydx(*) Average marginal effects Number of obs = Model VCE : OIM Expression : Pr(sanitationaccess), predict() dy/dx w.r.t. : Income Prov Popgrp GovspendWaterSanitationpc_ln electricityaccess dy/dx Std. Err. z P> z [95% Conf. Interval] 7.77e e e e-07 Income Prov Popgrp GovspendWa~n electricit~s margins, eyex(*) Average marginal effects Number of obs = Model VCE : OIM Expression : Pr(sanitationaccess), predict() ey/ex w.r.t. : Income Prov Popgrp GovspendWaterSanitationpc_ln electricityaccess ey/ex Std. Err. z P> z [95% Conf. Interval] Income Prov Popgrp GovspendWa~n electricit~s end of do-file MDG Dataset Estimations - mdgeduelas. svy: logit attendprimaryschool Gender Q128disa Prov Popgrp headattendedschool spouseattendedschool edu_qual pc_income_ln infrastructureaccess wp_b_no (running logit on estimation sample) Survey: Logistic regression Number of strata = 1 Number of obs = Number of PSUs = 2912 Population size = Design df = 2911 F( 10, 2902) = Prob > F = Linearized attendprim~l Coef. Std. Err. t P> t [95% Conf. Interval] Gender Q128disa Prov Popgrp headattend~l spouseatte~l edu_qual pc_income_ln infrastruc~s wp_b_no _cons margins, dydx(*) Average marginal effects Number of obs = Model VCE : Linearized Expression : Pr(attendprimaryschool), predict() dy/dx w.r.t. : Gender Q128disa Prov Popgrp headattendedschool spouseattendedschool edu_qual pc_income_ln infrastructureaccess wp_b_no dy/dx Std. Err. z P> z [95% Conf. Interval] Gender Q128disa Prov Popgrp headattend~l spouseatte~l edu_qual pc_income_ln infrastruc~s wp_b_no margins, eyex(*) Average marginal effects Number of obs = Model VCE : Linearized Expression : Pr(attendprimaryschool), predict() ey/ex w.r.t. : Gender Q128disa Prov Popgrp headattendedschool spouseattendedschool edu_qual pc_income_ln infrastructureaccess wp_b_no ey/ex Std. Err. z P> z [95% Conf. Interval] Gender Q128disa Prov Popgrp headattend~l spouseatte~l edu_qual pc_income_ln infrastruc~s wp_b_no

11 MDG Dataset Estimations - mdgeduelas. svy: logit attendsecondaryschool Gender Q128disa Prov Popgrp headattendedschool spouseattendedschool edu_qual pc_income_ln infrastructureaccess wp_h_b (running logit on estimation sample) Survey: Logistic regression Number of strata = 1 Number of obs = Number of PSUs = 2912 Population size = Design df = 2911 F( 10, 2902) = Prob > F = Linearized attendseco~l Coef. Std. Err. t P> t [95% Conf. Interval] Gender Q128disa Prov Popgrp headattend~l spouseatte~l edu_qual pc_income_ln infrastruc~s wp_h_b _cons margins, dydx(*) Average marginal effects Number of obs = Model VCE : Linearized Expression : Pr(attendsecondaryschool), predict() dy/dx w.r.t. : Gender Q128disa Prov Popgrp headattendedschool spouseattendedschool edu_qual pc_income_ln infrastructureaccess wp_h_b dy/dx Std. Err. z P> z [95% Conf. Interval] Gender Q128disa Prov Popgrp headattend~l spouseatte~l edu_qual pc_income_ln infrastruc~s wp_h_b margins, eyex(*) Average marginal effects Number of obs = Model VCE : Linearized Expression : Pr(attendsecondaryschool), predict() ey/ex w.r.t. : Gender Q128disa Prov Popgrp headattendedschool spouseattendedschool edu_qual pc_income_ln infrastructureaccess wp_h_b ey/ex Std. Err. z P> z [95% Conf. Interval] Gender Q128disa Prov Popgrp headattend~l spouseatte~l edu_qual pc_income_ln infrastruc~s wp_h_b MDG Dataset Estimations - mdgeduelas. svy: logit attendtertiary Gender Q128disa Prov Popgrp headattendedschool spouseattendedschool edu_qual pc_income_ln infrastructureaccess wp_uni_h (running logit on estimation sample) Survey: Logistic regression Number of strata = 1 Number of obs = Number of PSUs = 2912 Population size = Design df = 2911 F( 10, 2902) = Prob > F = Linearized attendtert~y Coef. Std. Err. t P> t [95% Conf. Interval] Gender Q128disa Prov Popgrp headattend~l spouseatte~l edu_qual pc_income_ln infrastruc~s wp_uni_h _cons margins, dydx(*) Average marginal effects Number of obs = Model VCE : Linearized Expression : Pr(attendtertiary), predict() dy/dx w.r.t. : Gender Q128disa Prov Popgrp headattendedschool spouseattendedschool edu_qual pc_income_ln infrastructureaccess wp_uni_h dy/dx Std. Err. z P> z [95% Conf. Interval] Gender Q128disa Prov Popgrp headattend~l spouseatte~l edu_qual pc_income_ln infrastruc~s wp_uni_h margins, eyex(*) Average marginal effects Number of obs = Model VCE : Linearized Expression : Pr(attendtertiary), predict() ey/ex w.r.t. : Gender Q128disa Prov Popgrp headattendedschool spouseattendedschool edu_qual pc_income_ln infrastructureaccess wp_uni_h ey/ex Std. Err. z P> z [95% Conf. Interval] Gender Q128disa Prov Popgrp headattend~l spouseatte~l edu_qual pc_income_ln infrastruc~s wp_uni_h

12 Microsimulation Previous micro simulation models were developed for SA based on 2000 data used 2000 IES combined with 2000 LFS Newer dataset is available IES 2005/2006, but concerns as this survey does not sufficiently cover employment variables and is not linked with LFS Conclusion Need to finalise estimations for mdg dataset Test data in CGE model Data verification with Departments etc. Updated as more /better sources of data becomes available Capacity building

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