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Financial vs. Economic Appraisal: Lessons from Real Case Studies

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  Financial vs. Economic Appraisal: Lessons from Real Case Studies Dr. Engr. Md. Abdur Rashid Director (Research & Publication) NAPD, Dhaka, Bangladesh When we talk about project appraisal, we’re really asking one big question: Should this project go ahead? It’s the final checkpoint before financing, where feasibility, costs, and broader impacts are weighed. But here’s the catch — a project that looks profitable on paper may not always deliver real value to society. That’s where the distinction between financial appraisal and economic appraisal becomes crucial. Economic Appraisal: The Society’s Lens Economic appraisal goes beyond financial returns, focusing on societal impact : Social cost-benefit analysis Shadow pricing to adjust for market distortions Effects on income distribution, savings, and investments Contribution to national goals: self-sufficiency, employment, and social order Financial Appraisal: The Investor’s Lens Financial apprais...
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SmartPLS Statistical Research Software

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  SmartPLS   Statistical Research Software SmartPLS 4 is a powerful statistical software designed for structural equation modeling (SEM), combining ease of use with advanced analytical capabilities. It supports both Partial Least Squares SEM (PLS-SEM) and Covariance-Based SEM (CB-SEM), along with regression, factor analysis, and more. Key Features of SmartPLS 4 PLS-SEM + CB-SEM : Offers both variance-based and covariance-based SEM approaches. Regression & Factor Analysis : Beyond SEM, it supports traditional statistical techniques. User-Friendly Interface : Designed to simplify complex modeling tasks with intuitive workflows. Visualization Tools : Clear graphical outputs for path models and latent constructs. Structural Equation Modeling (SEM) is a statistical technique that combines factor analysis and regression to study complex relationships among variables, including both direct and indirect effects. It’s widely used in social sc...
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Logistic Regression Model Application in Research

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  Logistic Regression Model for Bank loan Default Selection A logit model (or logistic regression model) is a statistical method used to predict the probability of a binary outcome (like yes/no, success/failure) based on one or more independent variables. It transforms a linear combination of predictors into probabilities between 0 and 1 using the logistic function. Example Imagine a bank wants to predict whether a loan applicant will default: Dependent variable: Default (1 = yes, 0 = no) Independent variables: Income, credit score, age The logit model estimates how these factors influence the probability of default. Let’s build a logit model example for predicting bank loan default using the three independent variables we mentioned: Income, Credit Score, and Age . Example: Bank Default Prediction Model Setup Dependent variable (Y): Default (1 = default, 0 = no default) Independent variables (X): (X_1): Income (in $10...
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