本文基于印度各行政区（县）与城市的人口规模，对其经济收入（国内生产总值，GDP）开展标度律分析，以探究二者间的依赖关系。标度律分析为识别社会经济组织中的网络效应提供了一种直接方法，而此类效应正是城市与城市化的典型特征。对于印度这一国家层面以下的区域性行政单元——县，我们发现其GDP与人口规模之间近乎呈线性标度关系，该结果显著区别于其他国家城市功能单元中常见的标度模式。通过分析偏离标度律的程度，我们进一步考察了这些区域单元的行为，发现其经济行为存在显著且清晰的地理分布格局。我们对这些格局进行了详细刻画，并将其与关于印度这类多元次大陆国家区域经济发展的既有文献相联系。鉴于印度都市集聚区（Urban Agglomerations）经济数据严重匮乏，我们基于一系列假设，构建了一套以县为单位、面向大型城市的GDP新数据集。该数据集揭示出城市收入与城市规模之间呈现理论预期的超线性标度关系，同时亦展现出与县级经济表现相似的底层经济地理格局。然而，由于缺乏更高精度、直接面向城市层级的经济数据，本研究对印度城市经济表现的分析受到严重制约。我们强调亟需建立印度城市经济规模及其变动的标准化、一致性估算体系，并指出若干可供探索的代理指标。

We undertake an exploration of the economic income (Gross Domestic Product, GDP) of Indian districts and cities based on scaling analyses of the dependence of these quantities on associated population size. Scaling analysis provides a straightforward method for the identification of network effects in socioeconomic organization, which are the tell-tale of cities and urbanization. For districts, a sub-state regional administrative division in India, we find almost linear scaling of GDP with population, a result quite different from urban functional units in other national contexts. Using deviations from scaling, we explore the behavior of these regional units to find strong distinct geographic patterns of economic behavior. We characterize these patterns in detail and connect them to the literature on regional economic development for a diverse subcontinental nation such as India. Given the paucity of economic data for Urban Agglomerations in India, we use a set of assumptions to create a new dataset of GDP based on districts, for large cities. This reveals superlinear scaling of income with city size, as expected from theory, while displaying similar underlying patterns of economic geography observed for district economic performance. This analysis of the economic performance of Indian cities is severely limited by the absence of higher-fidelity, direct city level economic data. We discuss the need for standardized and consistent estimates of the size and change in urban economies in India, and point to a number of proxies that can be explored to develop such indicators.