At school you learn about GDP, a measure of the total value a country creates. Kuznets reconstructed the historical statistics that lie behind GDP and made economic growth visible in numbers. He showed a consistent pattern: when societies move from farming to industry and services, productivity and living standards rise sharply. Yet he also observed that income inequality often widens temporarily during this transition, a relationship later called the “Kuznets curve.” This finding alerts developing countries to social challenges that may accompany rapid growth. Kuznets further popularised evidence-based policymaking, pushing economics toward an empirical science. Today, government budgets and social-security programs rely on statistics built on the foundations he laid. Consequently, his work influences not only economics but also history and sociology.

Between the 1930s and 1960s Kuznets reconstructed national-income series not only for the United States but also for countries such as the United Kingdom, Germany, Russia and China, creating long-run datasets beginning in 1870. Analysing changing sectoral weights—agriculture, mining, manufacturing, services—he coined the concept of “modern economic growth.” He treated a sustained rise in real income per capita, a slowdown of population growth, higher investment-saving ratios and rapid urbanisation/occupational shifts as an interconnected package. By introducing the Lorenz curve and the Gini coefficient into national accounts, he made income-distribution dynamics quantitatively observable. His empirical findings provided historical context for testing the steady-state predictions of the Solow neoclassical growth model and later informed endogenous-growth theorists. The same statistical approach has been applied to evaluate the effects of infant-industry protection and land reform in developing economies. Widely used structural-change models and comparative economic history are direct descendants of his research agenda. In opening the intersection between economic history and econometrics, Kuznets was also methodologically innovative.

Kuznets, while shaping the prototype of the NIPA, reconciled U.S. Department of Commerce data with IRS tax returns and employed Laspeyres indices to derive constant-price series and deflators. He further least-squares-interpolated regional data to build state- and sector-level GNP and linked them with population censuses to create a panel dataset. This allowed the estimation of sectoral labor-productivity growth and capital utilization for 1870–1960, pioneering subsequent TFP decompositions. He also exploited income quantiles and percentile-ordered Lorenz curves, using log-differences of the Gini coefficient to compute growth-inequality elasticities. In his 1955 paper he proposed the U-shaped inequality hypothesis and tested it via OLS cross-country regressions and time-series ARIMA models. Methodologically, he incorporated mixed-frequency estimation, reconciliation of inconsistent records, and structural-break tests for exogenous shocks—an early precursor to cointegration analysis. His programme effectively defined the field of cliometrics, blending microdata with macro time series and foreshadowing today’s big-data economic history.