7.5 Model for assessing the impact of an investment project on socio-economic indicators
7.5.1 Purpose of the model
This Model is designed for: - Assessing the socio-economic effects of implementing investment projects (IP) with and without information about other projects in the city; - Assessing the impact of investment projects on the dynamics of industry investment potentials.
7.5.2 Terms and Definitions
- User Investment Project – an IP created by the user to assess SED effects.
- External Investment Projects – a database of IPs collected from open sources by Digital Twin LLC.
- Inertial SED Scenario – basic development dynamics without external IPs.
- Investment SED Scenario – development dynamics including external IPs.
- Project Impact Assessment – the change in SED indicators resulting from IP implementation.
- GCP – Gross City Product.
- SME – Small and Medium Enterprises.
- Payroll – Payroll funds.
7.5.3 Basic form of the model
The total socio-economic effect of an investment project is expressed in the increase of basic and target SED indicators.
The increase is caused by three types of effects: direct, multiplicative, and system-dynamic.
\[ \overline{E_{j}}=\sum_{i} \overline{E}_{j i} \ \ (1) \]
where: - Direct effect – changes in SED indicators obtained directly from the IP (specified in the IP passport). - Multiplicative effect – changes in economic indicators calculated using the intersectoral multiplier matrix. It reflects the impact on related industries. - System-dynamic effect – changes in SED indicators characterized by indirect correlation dependencies between basic indicators.
7.5.3.1 Direct effect
Corresponds to indicators in the IP Passport: GCP, local budget revenues, net profit, payroll, staff size, residential space, etc.
7.5.3.2 Multiplicative effect
Calculated for economic indicators. Output increase \(\vec{P}\) in related industries:
\[ \vec{P}=\Omega \bullet \overrightarrow{GVA} \ \ (2) \]
where: - \(\overrightarrow{GVA}\) – project’s value-added vector (sum of profit, taxes, depreciation, and payroll). - \(\Omega\) – city-unique matrix multiplier. - \(\vec{P}\) – multiplicative output vector.
Multiplicative effects for payroll (\(S\)), taxes (\(T\)), depreciation (\(A\)), and profit (\(E\)) are calculated based on industry proportions.
7.5.3.3 System-dynamic effect
Calculated using city-unique correlation matrices.
Input indicators for system-dynamic effects include staff size, payroll, taxable income, city budget revenues, fixed assets investment, GVA, revenue, etc.
The impact \(I_{j}\) on an indicator is calculated as:
\[ I_{j}=\sum_{i=1}^{12} V_{j}^{i} * X_{j}^{i} \ \ (7) \]
The effect \(R_{j}\) on an indicator is:
\[ R_{j}=R_{j-1}+R_{j-1} * p \frac{W_{j-1}+W_{j}}{2-p W_{j}}+I_{j} \ \ (8) \]
Two versions of correlation matrices are used: KM1 (without external IPs) and KM2 (including external IPs from the database).
7.5.4 Calculation of IP effects by target indicators
Target indicators include life expectancy, real income growth, population growth, GCP growth rate, productivity, housing provision, employment, etc. Calculations use a set of algebraic formulas linking direct, multiplicative, and system-dynamic effects.
7.5.5 Assessment of IP impact on investment potential dynamics
Changes in investment potential by demand occur due to: - Eliminating product shortages; - Increasing demand for raw material suppliers.
\[ \overrightarrow{\Delta L_{R}^{*}}=\left(\overrightarrow{\Delta R} * E_{R} * T^{*}\right) \ \ (11) \]
7.5.6 Description of model input variables
Input data include IP Passport indicators (investments, revenue, expenses, payroll, staff, residential space, etc.), matrix multipliers, correlation matrices, and inertial forecasts.
7.5.7 Description of model result
- Changes in SED indicators;
- Changes in investment potential;
- Financial and economic indicators (IRR, NPV, PP).
7.5.8 Model calibration
The accuracy of the project impact model depends on the accuracy of its constituent models (demography, system dynamics, intersectoral balance). Regression models are built using a 90% confidence interval.