Unpacking Macroeconomic Performance in Pakistan: An Index Construction Approach

Background and Objectives: Pakistan’s macroeconomic trajectory has remained highly volatile since independence, characterized by persistent fiscal deficits, elevated inflation, monetary and external imbalances, rising unemployment, and vulnerability to global shocks. In the early 1970s, post-partition debt pressures and oil shocks contributed to inflation and fiscal deficits hovering around 7–8%. Subsequent decades were marked by recurring cycles of reforms and crises. During the 1980s, relative macroeconomic stability under non-democratic regimes was followed by the liberalization challenges of the 1990s, compounded by nuclear tests and sanctions; inflation reached 12.37% in 1994. The 2008 global financial crisis further exacerbated fiscal deficits and public debt. More recently, IMF-supported programs initiated in 2019 and 2023 sought to address balance-of-payments pressures, yet inflation surged to 30.78% in 2023, unemployment remained in the 4–6% range, and external sector performance remained constrained. Existing research often focuses on one or a few indicators of macroeconomic performance, which can overlook the complex interrelationships among fiscal, monetary, and external variables. Composite indices—such as those developed by the Organisation for Economic Co-operation and Development (OECD) and the International Monetary Fund (IMF)—offer a more comprehensive representation of macroeconomic conditions. Against this backdrop, this study aims to develop a composite macroeconomic performance index (MPI) for Pakistan using annual data for 1972–2024. Methodology: A composite MPI for Pakistan is constructed using two complementary approaches: Principal Components Analysis (PCA) and Sarma’s (2008) distance-based (normative) method. The index integrates eight key indicators of macroeconomic performance: fiscal balance, public debt, inflation rate, trade openness, unemployment rate, broad money supply, interest rate, and the real exchange rate. Using both approaches allows assessment of whether alternative aggregation frameworks yield consistent signals regarding Pakistan’s macroeconomic performance and its evolution over time. For the PCA-based MPI, component weights are derived from eigenvalues, and the first three principal components are retained because they capture the maximum variance. These three components account for 54.6% of the total variation over 1972–2024 and provide the most robust summary of the underlying macroeconomic environment within the PCA framework. In contrast, the distance-based index (UNDP-style) normalizes the same eight indicators using min–max scaling and evaluates each year’s proximity to a predefined “ideal” macroeconomic stance, characterized by low inflation, a moderate increase in broad money supply and interest rates, manageable budget deficits and public debt, a stable exchange rate, and higher trade openness. Together, these methods provide a structured way to synthesize multiple macroeconomic dimensions into a single index while retaining sensitivity to both data-driven variance (PCA) and normative benchmarks (distance-based method). Key Findings: The MPI dynamics reveal substantial shifts in Pakistan’s macroeconomic performance during 1972–2024, with alternating periods of relative stability and instability. Notable instability is observed in the early 1970s, during 2006–2012, and again from 2021 onward, whereas the 1980s, the 1990s, the early 2000s, and 2016 appear relatively more stable. Taken together, the index patterns underscore that macroeconomic performance in Pakistan has not followed a linear improvement path; instead, it has been shaped by repeated shocks and policy adjustment episodes. The use of two index construction methods is intended to provide a more robust depiction of these shifts, by examining whether the broad timing of stable versus unstable periods is consistently reflected across alternative aggregation strategies. Policy Implications: The resul