Fact-checked by Grok 2 weeks ago

Centre for Monitoring Indian Economy

The Centre for Monitoring Indian Economy (CMIE) is an independent private limited company founded on 13 April 1976 by economist Dr. Narottam Shah in Mumbai, India, initially as a think tank to provide timely economic research and data services. It has evolved into a leading provider of business information, specializing in primary data collection, databases, and analytics on Indian companies, investments, states, and macroeconomic indicators. Key products include Prowess, a comprehensive database of over 100,000 companies' financial performance, and Economic Outlook, offering projections and analysis of the national economy. CMIE's Consumer Pyramids Household Survey, covering a large panel of households, delivers monthly estimates of employment, unemployment, and consumption patterns, often revealing higher unemployment rates than official government surveys and serving as a critical independent benchmark amid debates over statistical methodologies. While valued for its granularity and frequency, the firm's unemployment metrics have drawn methodological critiques from some experts, including concerns over sampling biases and definitional rigor, though they underscore persistent labor market challenges not fully captured by delayed official data. Under Managing Director Mahesh Vyas since the 1980s, CMIE supports academia, financial markets, media, and policy decisions with empirically grounded insights, maintaining professional independence from government influence.

Overview

Founding and Organizational Profile

The Centre for Monitoring Indian Economy (CMIE) was established on 13 April 1976 by Dr. Narottam Shah, an eminent economist who conceived the idea of a private research organization dedicated to providing efficient economic information services. Initially structured as an independent think tank, CMIE aimed to assist the business community in accessing reliable economic data amid limited official sources during India's post-independence economic planning era. As a privately held entity owned by the founder's family members, CMIE operates without government funding or affiliation, maintaining its independence in data compilation and analysis. Headquartered in , , the organization functions as a business information , spanning the ecosystem from primary fieldwork to secondary aggregation and interpretive research. It employs a team of economists, analysts, and field researchers to build proprietary databases on industries, , and , serving domestic and clients including corporations, policymakers, and institutions. CMIE's foundational commitment to empirical rigor is reflected in its avoidance of ideological influences, prioritizing verifiable metrics over narrative-driven interpretations, which has positioned it as a key non-governmental for India's economic indicators. Following Shah's death in , leadership transitioned to subsequent directors, but the core private, family-influenced governance structure persists, ensuring continuity in its operational focus on data-driven economic monitoring.

Mission and Operational Scope


The Centre for Monitoring Indian Economy (CMIE) functions primarily as an independent and business information company, focused on delivering data-driven insights into the Indian economy through systematic monitoring and analysis. Established to address gaps in availability, CMIE's mission emphasizes the collection, processing, and dissemination of primary to enable informed decision-making by stakeholders including governments, businesses, , financial markets, professionals, and . This objective is pursued via subscription-based services that productize economic research into accessible databases and analytical tools.
CMIE's operational scope encompasses large-scale primary , database development, trend analytics, and , with a core emphasis on tracking corporate finances, behaviors, activities, and . Key activities include maintaining India's largest database on the financial performance of individual companies, covering sheets, and statements, and ratios for thousands of entities. It also conducts the country's most extensive survey, known as the Consumer Pyramids Household Survey, to estimate incomes, expenditures, savings, , and consumption patterns across rural and urban demographics. Beyond corporate and consumer data, CMIE monitors new investment projects, capital expenditures, and sectoral developments to provide an integrated view of economic dynamics, including forecasts on , , and . As a privately owned entity, CMIE maintains operational independence, relying on proprietary survey methodologies and real-time data aggregation to produce unbiased economic intelligence, distinct from government statistics. This scope extends to specialized tools for scenario analysis and policy evaluation, supporting research into areas such as unemployment rates, consumer sentiments, and industry outlooks.

History

Establishment and Early Years (1976–1980s)

The Centre for Monitoring Indian Economy (CMIE) was established on 13 April 1976 by Dr. Narottam Shah, an eminent economist who conceived the idea in the early amid a perceived need for independent, timely economic data in . Shah envisioned a private, subscription-funded research organization dedicated to monitoring the Indian economy, providing superior research and data services to corporate leaders, policymakers, and bankers without reliance on government funding or external influences. As a fully family-owned entity from inception, CMIE operated independently, focusing on high-quality outputs through a small, dedicated team that quickly earned admiration for its reliability in the late 1970s. In its formative years during the late , CMIE functioned primarily as an information organization and , assisting businesses in accessing and analyses that were otherwise scarce in India's developing landscape. The organization's early efforts emphasized compiling and disseminating , industry profiles, and company-specific information to support decision-making, marking one of the first private initiatives to systematically track economic trends beyond official government statistics. This period laid the groundwork for CMIE's reputation as a bridge between raw and practical applications, with services tailored to subscribers' needs for actionable insights. By the 1980s, under Shah's leadership, CMIE anticipated the transformative role of computing technology and invested in developing robust economic databases, which became the foundation for its expansive data repositories. These initiatives involved meticulous and structuring, enabling more sophisticated tracking of economic variables and foreshadowing CMIE's later dominance in . Shah's death in 1984 transitioned leadership, but the decade's focus on technological foresight and database infrastructure solidified CMIE's operational model as a private, data-centric entity resilient to economic policy shifts in .

Database Development and Expansion (1990s–2000s)

During the 1990s, the Centre for Monitoring Indian Economy focused on constructing comprehensive databases to capture the evolving Indian corporate landscape amid post-1991 economic liberalization. The flagship Prowess database emerged as a key initiative, compiling time-series financial statements—including balance sheets, profit and loss accounts, and cash flow data—for listed and unlisted companies starting from fiscal year 1990. This effort addressed gaps in official statistics by aggregating data from company filings with stock exchanges, the Registrar of Companies, and other regulatory sources, enabling analysis of over several thousand firms initially. By providing standardized ratios and segment-wise breakdowns, Prowess supported empirical studies on productivity, investment, and firm growth during the transition to market-driven policies. Parallel to Prowess, CMIE developed the CapEx database to track capital investment announcements and project implementations across industries, with coverage initiating around 1990 to monitor the influx of private sector projects spurred by deregulation. This database recorded details on project costs, capacities, completion timelines, and sectoral allocations, drawing from announcements in financial press, company reports, and government notifications. It proved instrumental for forecasting industrial output and assessing reform impacts, as evidenced by its use in analyses of manufacturing expansions and infrastructure bottlenecks through the decade. Entering the 2000s, CMIE intensified database expansion by augmenting Prowess with enhanced variables such as ownership histories, group affiliations, and market capitalization metrics, while broadening coverage to include smaller unlisted entities and refining data validation protocols. The number of tracked companies grew substantially, reaching tens of thousands by mid-decade, reflecting increased corporate filings and economic formalization. These enhancements facilitated longitudinal research on business groups, foreign investments, and financial performance, with the databases cited in peer-reviewed studies spanning 1989–2003 for robust firm-level panels. Concurrently, CapEx evolved to incorporate implementation delays and cost overruns data, aiding evaluations of investment efficiency in a period of sustained GDP growth averaging 6–8% annually.

Modern Era and Digital Advancements (2010s–Present)

In the 2010s, the Centre for Monitoring Indian Economy (CMIE) advanced its household-level data capabilities by formalizing the Consumer Pyramids Household Survey (CPHS) as a longitudinal panel starting in 2014, encompassing over 174,000 households surveyed three times per year to track incomes, expenditures, employment, and consumption patterns. This expansion enabled the production of monthly estimates beginning in 2016, derived from rigorous sampling across urban and rural areas, providing timely insights into labor market dynamics absent from official periodic surveys. By 2020, the panel had grown to cover more than 236,000 households, incorporating modules on , , and to enhance . Digital enhancements accompanied these survey expansions, with CMIE launching subscription-based online platforms such as Prowess dx and CapEx dx, tailored for streamlined data extraction in text or Excel formats suitable for academic and analytical use. Prowess dx, covering financials of over 50,000 companies including listed entities, supports query builders, API integrations, and ratio analysis for time-series evaluations, while CapEx dx monitors investment announcements, completions, and regional distributions with aggregates like new project values exceeding Rs. 450 billion in government sectors by 2025. These tools replaced earlier CD-ROM deliveries, enabling real-time access to updated datasets on economic indicators, such as GDP projections and sector-specific forecasts. Into the 2020s, CMIE integrated platform economy metrics into CPHS, capturing gig worker participation and earnings as of 2023, reflecting adaptations to digital labor shifts through computer-assisted data collection methods. Economic Outlook, an online service, delivers forward-looking analyses, including five-year projections on variables like petroleum consumption (forecast at 1.1% growth for March 2026) and consumer sentiment indices (116.1 as of recent readings). These advancements underscore CMIE's pivot to scalable, digitally native dissemination, prioritizing empirical tracking over narrative-driven reporting amid debates on data methodologies.

Leadership and Key Personnel

Founders and Initial Leadership

The Centre for Monitoring Indian Economy (CMIE) was founded by Dr. Narottam Shah, an eminent economist, on 13 April 1976. Shah conceived the organization as a private, independent research entity dedicated to delivering efficient economic information services, primarily funded through user subscriptions rather than government or institutional grants, to maintain autonomy in data analysis and dissemination. Under his leadership, CMIE operated with a small, dedicated team focused on superior service delivery, quickly gaining recognition from corporate leaders, policymakers, and bankers for its timely and reliable economic insights. Shah's vision emphasized the pioneering use of for economic monitoring; in the early , he anticipated the role of computers in , directing the development of robust databases that positioned CMIE as a leader in structured economic information. Following Shah's death on 23 March 1984, initial transitioned to Prof. D. T. Lakdawala, Shah's guide and a distinguished who had served as Deputy Chairman of the Commission, ensuring continuity during the organization's formative expansion phase. Lakdawala's tenure marked a period of gradual methodological evolution while preserving Shah's commitment to empirical, subscription-driven economic research. Mahesh Vyas, who joined CMIE in 1980, contributed to early operational efforts before assuming broader responsibilities.

Current Management and Influential Figures

Mahesh Vyas serves as the Managing Director and of the Centre for Monitoring Indian Economy (CMIE), a position he has held since 1996. Vyas joined CMIE in 1980 and has been instrumental in developing its core databases, including those on corporate performance and economic indicators. Under his leadership, CMIE has expanded its focus to include large-scale household surveys, such as the Consumer Pyramids Household Survey, which provides independent estimates of employment and consumption trends in . The board of directors includes Kalpana Sampat, Shobhana Vyas, Ajay Shah, and Sadhana Shah, alongside Mahesh Vyas. Ajay Shah, an economist known for his work on financial markets and policy, remains on the board but has not participated in day-to-day operations since departing the executive team in 1996. Sadhana Shah also oversees CMIE's U.S. subsidiary as its CEO. These figures contribute to strategic oversight, though operational management is centered on Vyas. Mahesh Vyas is a prominent influential figure in economic analysis, frequently cited in and policy discussions for CMIE's on rates and labor market dynamics, such as the rise to double-digit during the in 2021. His public commentary emphasizes empirical tracking over statistics, highlighting discrepancies in participation and job quality. As of 2024, no changes in senior leadership have been reported.

Research Focus and Methodologies

Core Areas of Economic Analysis

The Centre for Monitoring Indian Economy (CMIE) focuses its economic analysis on macroeconomic indicators, sectoral performance, corporate finances, and regional disparities within India. Through its Economic Outlook service, CMIE interprets data on national accounts—including gross domestic product (GDP) estimates based on 2011-12 benchmarks—alongside inflation metrics such as consumer price indices (CPI) and wholesale price indices (WPI), money supply, banking trends, and public finance aggregates for central and state governments. These analyses derive from over 4.1 million time-series data points, emphasizing causal linkages between policy changes, external shocks, and economic outcomes, such as balance of payments fluctuations and foreign trade volumes. Sectoral examinations form a cornerstone, covering agriculture via production volumes, price indices, and irrigation coverage; industry through indices like the Index of Industrial Production (IIP), Purchasing Managers' Index (PMI), and core sector outputs; and infrastructure metrics on railways, ports, energy generation, and project completions. CMIE's Industry Outlook tracks output, capacity utilization, and input costs across manufacturing and services, while agriculture analysis incorporates kharif and rabi crop income trends, with recent data showing year-on-year agricultural income growth of 0.2% in 2024 before a forecasted 2.3% in 2026. This approach prioritizes granular, survey-derived evidence over aggregated official statistics to highlight discrepancies, such as varying state-level irrigation efficiencies impacting yield variances. Corporate and investment analysis centers on firm-level financials, including revenue, profitability, and capital expenditure (CapEx) via databases like Prowess, which compile data on thousands of Indian companies. CMIE monitors new investment projects and enterprise dynamics, integrating forex exposures and global comparisons to assess competitiveness. Regional focus, through the States of India database, dissects state domestic product, banking penetration, public finance liabilities, and energy infrastructure, revealing divergences like Uttar Pradesh's Rs. 450 billion in government project completions as of September 2025. These areas underscore CMIE's emphasis on primary data integration for forecasting, such as projecting merchandise trade deficits and petroproduct consumption at 1.1% year-on-year growth through March 2026.

Data Collection and Survey Approaches

The Centre for Monitoring Indian Economy (CMIE) primarily employs primary data collection through large-scale household surveys, supplemented by compilation of from regulatory and corporate filings for economic and industry analysis. Its flagship survey, the Consumer Pyramids Household Survey (CPHS), utilizes a multi-stage design to capture household-level data on , , and well-being across rural and . Rural sampling begins with the selection of villages from the 2011 within predefined "Homogeneous Regions," followed by systematic selection of households by approaching every nth house (where n ranges from 5 to 15) to target approximately 16 households per village. Urban sampling similarly selects blocks or towns, applying comparable systematic household selection. This framework yields a panel of approximately 174,000 sample households, comprising 111,000 rural and 63,400 units, distributed across most Indian states to ensure regional representation. CPHS operates as a continuous longitudinal survey, with households revisited in four-monthly "waves" to track changes over time, enabling high-frequency updates on economic indicators such as unemployment and consumption. Data collection involves field investigators—numbering around 200—using smartphone applications to record responses on roughly 300 variables per household, including recall data for the preceding four months to minimize memory bias. During disruptions like the COVID-19 lockdowns, telephonic surveys supplemented in-person visits to maintain continuity. The panel design prioritizes repeated sampling of the same households, achieving an average response rate of about 68% across waves, with weights applied to adjust for non-response and ensure national representativeness. For non-survey data, CMIE aggregates and standardizes information from official sources, including company annual reports, stock exchange disclosures, and Ministry of Corporate Affairs filings, to build databases like Prowess. This approach covers over 50,000 companies with more than 3,500 standardized data fields per entity, facilitating time-series and inter-firm comparisons without primary fieldwork. Economic databases draw on these corporate inputs alongside government statistics and CMIE's proprietary surveys to derive macroeconomic estimates, emphasizing timeliness through monthly or quarterly refreshes. Such methods prioritize verifiable secondary sources to complement the granularity of household surveys, though they rely on the accuracy and completeness of public filings.

Key Databases and Products

Corporate and Industry Databases

The Centre for Monitoring Indian Economy (CMIE) maintains comprehensive corporate databases, with Prowess serving as its flagship product for tracking the financial performance of Indian companies. Prowess encompasses data on over 100,000 companies, including all entities listed on the National Stock Exchange and Bombay Stock Exchange, as well as thousands of unlisted public limited companies and select private firms. The database aggregates more than 3,500 standardized data fields per company, drawn primarily from annual reports, quarterly financial statements, and stock exchange feeds, enabling inter-company and temporal comparisons. Variants of Prowess cater to specific users, such as Prowess dx, designed for academic research with features for bulk data downloads, and ProwessIQ, which supports interactive querying and analysis tools like query builders and report viewers. Additionally, CapEx and CapEx dx focus on tracking, providing insights into investment patterns across companies and sectors. For industry-level analysis, CMIE's Industry Outlook database delivers aggregated data, forecasts, and risk assessments across approximately 207 Indian industries, grouped into categories such as food and agro-based products (19 industries), textiles (8 industries), chemicals (21 industries), and consumer goods (13 industries). It incorporates metrics on capacity creation, financial performance projections, price movements, and business cycle trends, sourced from company filings and economic indicators to aid in anticipating sectoral shifts. These products are commercially licensed, emphasizing empirical data standardization over official classifications to facilitate private sector decision-making.

Household and Macroeconomic Surveys

The Consumer Pyramids Household Survey (CPHS), conducted by the Centre for Monitoring Indian Economy (CMIE), serves as the organization's primary household survey, capturing data on consumption, employment, income, and socioeconomic characteristics to assess living standards and economic activity at the household level. This continuous panel survey interviews a stratified sample of approximately 174,000 households across urban and rural India, with fieldwork organized into four-month waves conducted three times annually, enabling high-frequency tracking of changes in household behavior. Data collection employs computer-assisted personal interviewing (CAPI) by trained enumerators, focusing on a core panel while incorporating periodic refreshments to maintain representativeness, though response rates average around 68% across waves. CPHS data is structured into four main modules: People of India (demographic and employment details), Aspirational India (attitudes and aspirations), Income Pyramids (earnings and sources), and Consumption Pyramids (expenditure patterns across goods and services). These modules yield granular insights, such as monthly per capita consumption expenditure, which CMIE aggregates to derive national and regional trends, with rural households spending an average of Rs. 3,773 monthly on food in recent waves compared to Rs. 5,465 in urban areas. The survey's longitudinal design allows for panel analysis, tracking the same households over time to observe shifts, such as a 5-10% year-on-year variation in non-food spending influenced by economic cycles. In the macroeconomic domain, CMIE leverages CPHS outputs to inform broader indicators, including private final consumption expenditure—a major GDP component—by extrapolating household-level consumption to national aggregates using survey weights adjusted for population projections. This approach provides timely estimates that often diverge from official National Sample Survey Office (NSSO) data due to CPHS's higher frequency and larger sample, for instance, revealing consumption growth rates of 4-6% in post-pandemic recovery periods where official figures lagged. CMIE supplements these with compiled macroeconomic time series in databases like Market Beacon, drawing from official sources and CPHS-derived metrics for variables such as inflation proxies via consumption baskets and rural-urban disparity indices, though primary macroeconomic data generation relies more on aggregation than dedicated business or enterprise surveys.

Data Accessibility and Commercial Offerings

The Centre for Monitoring Indian Economy (CMIE) provides access to its databases exclusively through paid subscription models, with no free public availability of detailed datasets or unit-level records. Subscribers, including commercial entities, researchers, educational institutions, and policymakers, obtain online platform access for data extraction, time-series analysis, and custom queries, subject to usage agreements that govern commercial and research applications. This structure ensures data integrity and supports CMIE's operations as a private business information provider. Commercial offerings encompass specialized databases such as Prowess, which compiles financial statements, ownership details, and operational metrics for over 110,000 listed and unlisted Indian companies, representing 76% of corporate tax collections; Consumer Pyramids dx, delivering household-level data from surveys tracking expenditure, employment, and consumption across 236,000 households; and Economic Outlook, aggregating 4.1 million macroeconomic time-series on indicators like inflation, trade, and unemployment. Additional products include CapEx for capital expenditure tracking, Industry Outlook for sector-specific production and financial performance, Commodities for prices and yields across 185 items and 3,291 markets, and States of India for regional economic metrics. These are designed for users such as banks, investors, corporations, and government agencies seeking granular insights beyond official aggregates. Subscription pricing, effective January 1, 2025, differentiates by product, user category (standard single-user, IP-based for institutions), and access type (e.g., data extraction in USD), excluding taxes. Standard annual fees in Indian rupees include Rs. 235,000 for Prowess, Economic Outlook, and States of ; Rs. 295,000 for Industry Outlook; Rs. 2,500,000 for commercial Consumer Pyramids dx; and lower rates like Rs. 125,000 for Commodities. IP-based institutional access commands premiums, such as Rs. 352,500 for Prowess or Rs. 442,500 for Industry Outlook, with hit limits (e.g., 15,000–600,000 queries annually). Data extraction subscriptions for non-educational institutions start at USD 4,350 for Prowess dx and USD 17,500 for Consumer Pyramids dx, enabling bulk downloads and API-like functionality. Educational institutions receive equivalent data extraction rates but may qualify for IP-based volume discounts. Anonymized unit-level data from surveys like the Consumer Pyramids Household Survey is accessible to qualified researchers via these paid channels, facilitating academic studies while maintaining respondent under CMIE's policies. This model prioritizes comprehensive, real-time data delivery over , reflecting CMIE's focus on high-value, economic intelligence since its .

Employment and Unemployment Data

Consumer Pyramids Household Survey (CPHS)

The Consumer Pyramids Household Survey (CPHS) is a continuous, longitudinal panel survey conducted by the Centre for Monitoring Indian Economy (CMIE) since 2014, designed to track household-level economic indicators including consumption expenditures, employment status, and demographic characteristics across India. It comprises interviews with approximately 174,000 households—roughly 111,000 rural and 63,000 urban—drawn from 3,965 villages and 7,920 urban enumeration blocks in 322 towns, enabling repeated observations of the same units to capture temporal changes in well-being. This scale positions CPHS as the world's largest ongoing household panel, filling gaps in official infrequent surveys like India's National Sample Survey by providing quarterly updates on labour market dynamics and consumer behaviour. Data collection follows a three-wave annual cycle, with each wave covering four months and achieving response rates around 68% on average across recent periods, adjusted via weights for non-response and attrition to maintain representativeness. The survey employs stratified multi-stage probability sampling: first, India is divided into 99 urban and 97 rural homogeneous regions based on economic and geographic similarities; rural primary sampling units (villages) are selected with probability proportional to population, followed by systematic random household selection within villages (fixed at 16 households per village minimum); urban areas stratify towns by population size before selecting census enumeration blocks and households. While representative at all-India, state, and regional levels—validated against 2011 Census benchmarks for caste and religion—the design excludes remote areas such as Lakshadweep, Andaman Islands, and select northeastern states (e.g., Arunachal Pradesh, Nagaland), potentially underrepresenting isolated populations. CPHS questionnaires include modules on household demographics, (e.g., , durables, services), and labour force participation, with employment-unemployment derived from a single-question current status recall: individuals report their status—employed, seeking work, or neither—as of the survey date or prior day, without extended reference periods. This yields metrics like force participation rates and unemployment, often higher than official Periodic Labour Force Survey (PLFS) estimates due to the snapshot approach versus PLFS's usual-status principal activity over 365 days, which incorporates secondary activities and . Anonymized microdata is disseminated via Consumer Pyramids dx (CPdx), supporting analyses of trends such as post-2020 employment recovery or rural-urban disparities, though users must account for panel attrition (e.g., via reweighting) for causal inferences. CMIE's Consumer Pyramids Household Survey (CPHS) generates monthly rate estimates for , employing a definition that includes individuals actively seeking and available for work, which often yields higher figures than official Periodic Labour Force Survey (PLFS) data due to broader sampling and real-time tracking. These estimates have tracked significant since CMIE began monthly reporting in 2016, with rates averaging approximately 8.23 percent from 2018 to 2025. The onset of the COVID-19 pandemic marked a sharp peak, with the unemployment rate reaching 20.8 percent in June 2020 amid nationwide lockdowns that disrupted labor markets, particularly in informal sectors. Post-pandemic recovery saw a gradual decline, though rates remained elevated compared to pre-2018 levels, fluctuating between 6 and 10 percent amid seasonal agricultural cycles, urban migration patterns, and economic slowdowns. For example, the rate climbed to 9.2 percent in June 2024, up from 7 percent in May, reflecting renewed pressures from monsoon-related rural disruptions and urban job scarcity. In 2025, trends indicated moderation, with the rate dropping to 6.8 percent in July—the lowest in 34 months—driven by improved rural absorption during peak seasons. By October 2025, the 30-day stabilized at 7.3 percent, underscoring persistent structural challenges such as youth underutilization (rates often exceeding 15 percent for ages 15-29) and urban-rural disparities, where urban unemployment consistently outpaces rural figures by 2-4 percentage points.
PeriodUnemployment Rate (%)Key Context
June 202020.8 lockdown peak
May 20247.0Pre-monsoon baseline
June 20249.2Seasonal rural and urban pressures
July 20256.8Post-monsoon rural recovery low
Oct 2025 (avg)7.3Stabilized
These trends highlight CMIE's emphasis on high-frequency data capturing short-term shocks, contrasting with annual official surveys that smooth cyclical variations.

Methodological Framework

The Centre for Monitoring Indian Economy (CMIE) employs the Consumer Pyramids Household Survey (CPHS) as its primary vehicle for collecting employment and unemployment data, utilizing a longitudinal panel design that surveys a fixed panel of households repeatedly over time to track changes in economic well-being. The survey operates on a high-frequency basis, with households interviewed in three waves annually, each spanning four months, enabling the compilation of monthly estimates of labour market indicators from aggregated responses. This framework covers approximately 174,405 sample households—63,430 rural and 110,975 urban—yielding employment status data for around 522,000 individuals per wave, stratified to ensure representation at national, state, and rural-urban levels. Sampling follows a stratified multi-stage approach, beginning with into Homogeneous Regions (HRs), which are clusters of neighbouring districts sharing similar agro-climatic, economic, and demographic characteristics, followed by selection of primary sampling units (villages in rural areas and Blocks in areas). blocks are further stratified by town size to account for diversity, while rural villages are selected randomly from the 2011 frame; within selected units, households are chosen systematically by selecting every nth house (n ranging from 5 to 15) along main streets or paths. Weights are applied post-collection to adjust for non-response, , and the sample's inherent urban bias, aiming to align the data with population benchmarks for representativeness. Employment and unemployment are assessed via household interviews focusing on individuals' activities on the day preceding the survey, classifying respondents as employed if engaged in any economic activity (including , wage work, or having work available), unemployed if actively seeking or available for work but not engaged, or out of the force otherwise. This current daily status (CDS)-like measure captures one primary occupation per person based on time allocation, with additional details on , , sector, and sources recorded to derive broader indicators such as labour force participation rates. Unemployment rates are then estimated by aggregating weighted responses across waves, with CMIE releasing figures disaggregated by urban/rural residence, , and groups (e.g., 15+ years), though the methodology emphasizes short-term snapshots over long-term usual status. Panels are maintained longitudinally, but households absent for three consecutive waves are replaced, with adjustments to preserve continuity.

Controversies and Criticisms

Disputes with Official Government Data

CMIE's estimates from its Consumer Pyramids Household Survey (CPHS) have consistently shown higher unemployment rates than those reported by the government's Periodic Labour Force Survey (PLFS), conducted by the National Sample Survey Office (NSSO) under the Ministry of Statistics and Programme Implementation (MoSPI). For example, in fiscal year 2023-24, CMIE reported an average unemployment rate of approximately 7-8% in several months, contrasting with PLFS quarterly figures often below 4% for urban areas and around 3% overall. Similarly, for youth aged 20-25, CMIE has estimated rates as high as 44%, while PLFS data places it near 20%. These divergences extend to workforce participation rates, with CMIE capturing lower participation, particularly among women, due to its classification of certain activities as non-employment. Government officials have attributed the discrepancies to methodological flaws in CMIE's approach, including a smaller effective sample size for certain demographics, over-reliance on telephonic surveys during disruptions like the , and failure to fully incorporate in informal sectors, which dominate India's labor market. Prime Minister's economic advisor argued in 2019 that CMIE data undercounts employed individuals in agriculture and casual work, leading to inflated figures that do not align with broader economic indicators like GDP growth. Experts have noted that CMIE's continuous tracking, while timely, uses a stratified random sampling from its proprietary consumer panels, which may introduce urban and higher-income biases compared to PLFS's nationwide household enumeration. In January 2023, the and Employment issued a caution against using private surveys like CMIE's for official assessments, emphasizing PLFS as the benchmark due to its alignment with international standards from the (ILO). CMIE counters that official PLFS data underestimates by relying on infrequent, annual rural surveys and excluding "discouraged workers" who have stopped seeking jobs amid poor prospects, as well as daily wage earners in the unorganized sector. The firm's founder, Mahesh Vyas, has highlighted that PLFS's Usual Status definition of —based on activity over the past year—masks short-term joblessness prevalent in India's volatile labor , whereas CPHS uses a Current Weekly Status for more granular, real-time insights. Independent economists, including those surveyed by in July 2025, have echoed CMIE's view, stating that government figures obscure and structural issues, potentially influenced by delays in data release and past instances of statistical suppression, such as the withholding of 2017-18 NSSO results showing a 45-year high rate of 6.1%. Studies comparing the datasets, such as those from the National Institute of Public Finance and Policy, confirm non-comparability due to definitional variances but suggest CMIE better reflects distress in educated urban youth cohorts. These disputes underscore broader credibility concerns with India's statistical system, where official data faces accusations of political interference—evident in the 2019 sacking of the chief amid the unemployment report leak—while private sources like CMIE, though commercially driven, provide indispensable frequency amid government delays. Academic analyses, including in , recommend triangulating both for policy, noting neither is flawless: PLFS excels in representativeness but lags in timeliness, while CMIE offers speed at the cost of potential sampling inconsistencies. Despite government preference for PLFS, CMIE data has influenced public discourse and assessments, highlighting a tension between state-controlled metrics and independent monitoring in a context where informal exceeds 80% of the .

Methodological and Sampling Challenges

The Consumer Pyramids Household Survey (CPHS), central to the Centre for Monitoring Indian Economy's (CMIE) employment data, utilizes a stratified multi-stage sampling approach targeting approximately 170,000 s across , forming a panel surveyed multiple times annually. However, this design has drawn scrutiny for flaws in primary sampling unit (PSU) and household selection, including the absence of a comprehensive at the second stage, where enumerators select households via systematic random sampling (every nth house, with n ranging from 5 to 15) along main streets or roads in villages and towns. This method systematically excludes an estimated 61.7% of households, particularly those in peripheral, informal, or non-linear settlements, leading to potential under-coverage of rural poor, mobile populations, homeless individuals, and marginalized groups. Critics, including economists Jean Drèze and Pronab Sen, argue that starting selection from central village areas biases the sample toward relatively better-off households, as poorer residences are disproportionately located on outskirts or in less accessible zones. An analysis by researchers at the highlights further implementation challenges, such as reliance on around 200-300 investigators handling up to six households per day across 300 variables, which constrains depth of and contributes to data anomalies like abrupt shifts in occupational categories (e.g., rising urban contradicting broader trends). High attrition rates—exemplified by the complete dropout of certain PSUs like town in 2017—and opaque replacement protocols exacerbate non-response biases, with response rates dropping sharply during events like the 2020 lockdown (e.g., below 50% in some waves). Weighting procedures compound these issues, as CMIE applies equal weights to households despite unequal selection probabilities and coverage gaps, such as exclusions of northeastern border states, certain islands, and security-restricted areas, which are arbitrarily reassigned (e.g., Andaman & Nicobar mapped to ). This results in biased estimators, evidenced by CPHS under-representing women (e.g., 84% of rural women classified as houseworkers versus 67% in official Periodic Labour Force Survey data) and young children, while over-representing educated urban households. Comparisons with National Sample Survey Office () benchmarks reveal persistent discrepancies, including underestimated levels and lower reported female labor force participation, suggesting the panel's evolution toward better-off respondents over time. Sen has noted that such sampling limitations, relying on simple random rather than stratified methods akin to , may inflate unemployment estimates by undercapturing informal sector employment among low-income groups, particularly during economic shocks. CMIE maintains that its methodology yields a nationally representative sample without systematic bias, attributing exclusions to practical constraints like security and logistics absent in government-backed surveys. Independent assessments, however, underscore the need for transparency in attrition handling and probability-based weighting to mitigate these challenges, as unadjusted panels risk amplifying selection effects in high-frequency tracking of volatile indicators like employment.

Responses from CMIE and Independent Assessments

CMIE has consistently defended its Consumer Pyramids Household Survey (CPHS) methodology against criticisms from government officials and economists, emphasizing a stringent definition of that requires individuals to have worked for most of the survey day or the preceding day, rather than minimal activity. In response to Economic Adviser Krishnamurthy Subramanian's application of to question CPHS unemployment figures, CMIE CEO Vyas argued that such "smell tests" undermine rigorous , particularly when government data itself fails similar empirical checks, and highlighted the value of CPHS's daily activity-based metrics for capturing short-term labor market volatility. Vyas has acknowledged survey limitations, such as potential sampling challenges during disruptions like the , but maintained that these do not introduce systematic bias, as evidenced by the survey's consistent application across income groups. Addressing methodological critiques from researchers Jesim Pais and Vikas Rawal, who alleged inadequate documentation, ad hoc field practices, and unequal sampling probabilities leading to underrepresentation of rural and low-income households, CMIE rebutted by detailing its circular systematic random sampling approach, formal enumerator training protocols, and supervisor oversight to prevent arbitrary household exclusions. The organization committed to a dedicated study from September to December 2021 to quantify and correct any sampling biases, with adjustments implemented by early 2022, while attributing discrepancies in female labor force participation—such as lower CPHS estimates compared to official Periodic Labour Force Survey (PLFS) data—to stricter employment criteria in CPHS (e.g., excluding unpaid household work unless principal activity) rather than data quality failures. CMIE has welcomed independent scrutiny, stating that testing enhances the robustness of its relatively new CPHS database, which covers approximately 174,000 households (about 545,000 individuals) through repeated quarterly waves. Independent assessments have yielded mixed findings on CPHS reliability. A 2022 econometric analysis published in the Indian Journal of Labour Economics compared CPHS with and PLFS data, finding high comparability for male (83% prediction accuracy in logistic models and state-level worker-population ratios differing by less than 5 percentage points), but significant divergence for females, where CPHS underpredicted participation by around 40% across types and reference periods, suggesting caution in relying on CMIE data for gender-specific trends due to definitional and probing differences. Other evaluations, such as those questioning potential urban or higher-income bias in simple random sampling versus stratified methods, have prompted CMIE to refine its processes, though proponents note CPHS's advantages in frequency and scale for tracking real-time shifts absent in quinquennial official surveys. These reviews underscore CPHS's utility for aggregate male labor metrics while highlighting needs for improved female capture and cross-validation with administrative data.

Impact and Reception

Influence on Policy and Academia

CMIE's Consumer Pyramids Household Survey (CPHS) and Prowess databases have become staples in academic analyses of India's labor markets, firm dynamics, and macroeconomic shocks, offering high-frequency, panel data that complement official surveys. Researchers have leveraged CPHS for longitudinal studies on employment transitions, revealing volatility in labor force participation amid economic disruptions. For example, peer-reviewed examinations of COVID-19's effects on unemployment and household consumption drew on CMIE data to estimate nationwide job losses peaking at 23% in April 2020, highlighting disparities in urban and informal sectors. Firm-level inquiries into export survival, innovation, and productivity misallocation similarly cite Prowess for its coverage of over 70% of organized sector activity, enabling causal assessments of policy reforms like delicensing. Institutions such as the Indian Institutes of Management and international collaborators, including Duke University's DUPRI, routinely access CMIE datasets for empirical work, underscoring their utility despite ongoing methodological debates. In policy formulation, CMIE data has shaped evaluations and public discourse by providing alternative metrics to government surveys, often referenced in official assessments of economic indicators. The Reserve Bank of India and NITI Aayog have incorporated CMIE insights into reports on investment trends, inflation projections, and MSME competitiveness, such as noting deviations in proprietary sampling from standard methodologies to refine policy benchmarks. Press Information Bureau releases have cited CMIE unemployment rates—ranging from 3.39% to 5.67% between July 2017 and June 2018—to contextualize employability initiatives and youth trends. Historical engagements include CMIE's comprehensive study on industrial competitiveness presented to Commerce Minister Murasoli Maran in 2002, influencing departmental policy reviews. While official adoption remains selective, favoring PLFS for primary statistics, CMIE's granular outputs have prompted independent policy analyses, including green stimulus evaluations that cross-referenced unemployment spikes during lockdowns. This indirect sway extends to economists advising on reforms, where CMIE metrics challenge official narratives and foster evidence-based debates on resource allocation.

Achievements in Economic Monitoring

The Centre for Monitoring Indian Economy (CMIE) has pioneered the creation of extensive, queryable databases that track corporate financial performance across , with the Prowess database encompassing data on over 100,000 companies, including sheets, and statements, and details spanning decades without deliberate survival . This resource has facilitated rigorous on firm-level , deregulation effects, and business group dynamics, as evidenced by its use in studies examining industrial licensing reforms' impact on . By standardizing and updating financial metrics from regulatory filings, Prowess has enabled analysts to monitor trends in income growth (e.g., 7.8% for non-financial companies in FY25) and sectoral investments with unmatched by sporadic official releases. A cornerstone achievement lies in the Consumer Pyramids Household Survey (CPHS), launched as India's largest continuous household panel survey, interrogating over 174,000 households—representing more than 500,000 individuals—across rural and urban areas in quarterly waves to capture real-time shifts in , consumption expenditures, and asset ownership. With a response rate averaging around 68% over multiple waves, CPHS delivers high-frequency indicators such as rates (e.g., monthly moving averages) and consumer sentiment indices (e.g., 116.1 in recent tracking), filling critical voids left by infrequent government surveys like the National Sample Survey Office's periodic quinquennial rounds. This methodology has proven instrumental in quantifying economic shocks, notably post-2016 demonetization when CPHS data first highlighted acute disruptions and declines in informal sectors, prompting wider on policy ramifications. CMIE's monitoring extends to macroeconomic aggregates, including capex tracking across central enterprises (e.g., achieving 90% of FY20 targets despite headwinds) and trade metrics like freight traffic growth (13.5% year-on-year in April-September 2025), derived from integrated datasets that cross-verify official sources with proprietary collections. These efforts have positioned CMIE as a vital independent source for labor market intelligence, with CPHS emerging as a for regular data amid official delays, influencing academic and policy evaluations of trends like urban-rural income disparities and festive consumption patterns. By prioritizing longitudinal panels over cross-sectional snapshots, CMIE's frameworks enhance in economic analysis, such as linking household changes to recovery.

Broader Critiques and Alternative Perspectives

Some economists argue that an over-reliance on CMIE's data risks amplifying discrepancies in India's economic narrative, as its high-frequency estimates often diverge substantially from official surveys like the Periodic Labour Force Survey (PLFS), potentially misleading policymakers without cross-verification. For instance, analyses show no convergence between CMIE's employment rates and PLFS estimates, even when adjusting for reference periods, underscoring fundamental definitional and sampling incompatibilities that broader statistical reforms must address. This perspective emphasizes triangulating private data with public sources to mitigate risks of narrative silos, particularly amid official data delays that have eroded trust in government statistics. Alternative views position CMIE as a vital corrective to infrequent official releases, filling voids in real-time monitoring where public surveys like the National Sample Survey (NSS) lag, though critics note that non-standardized private methodologies can exacerbate public confusion rather than resolve it. Proponents, including some independent assessments, highlight CMIE's utility in capturing dynamic trends like post-lockdown recovery, arguing its rotational panel design offers insights unattainable through static government frameworks, despite acknowledged undercoverage of extreme informal sectors. However, skeptics such as former Chief Statistician Pronab Sen caution that inherent survey limitations, including potential respondent biases in self-reported data, necessitate interpretive caveats when using CMIE for macroeconomic inferences. Broader systemic critiques frame CMIE's prominence as symptomatic of India's fragmented statistical , where entities assume roles without regulatory oversight, raising questions about and in access—CMIE's subscription model limits widespread use compared to official releases. Advocates for propose establishing commissions to standardize high-frequency indicators across sources, integrating CMIE-like innovations with PLFS rigor to foster causal economic over partisan disputes. This approach aligns with calls for methodological , as evidenced by non-comparability findings between CMIE and NSS, which stem from divergent sampling (rotational vs. multistage stratified) rather than isolated errors.

References

  1. [1]
    The Founder - CMIE
    The Centre for Monitoring Indian Economy was set up by the eminent economist, Dr. Narottam Shah on 13 April 1976. The idea of a private research organisation ...
  2. [2]
    About Us - CMIE
    CMIE, or Centre for Monitoring Indian Economy, is a leading business information company. It was established in 1976, primarily as an independent think tank.
  3. [3]
    CMIE
    Centre for Monitoring Indian Economy Pvt. Ltd. Home. Products. Solutions using Company databases. Prowess · Prowess dx · CapEx · CapEx dx. Economic databases, ...About UsUnemployment rate ...Economic OutlookUnemployment Rate (30-DAY ...Cmie Analytics
  4. [4]
    Center for Monitoring Indian Economy Data | DUPRI
    This dataset, collected by Centre for Monitoring Indian Economy (CMIE) , is the world's largest household panel survey; it interviews over 236,000 ...
  5. [5]
    CMIE Survey Limitations May Bias Unemployment Data, Says ...
    Jun 29, 2021 · Former chief statistician of India Pronab Sen said the limitations of the CMIE data could mean that unemployment rates must be interpreted with caution.<|control11|><|separator|>
  6. [6]
    Experts sceptical about CMIE's unemployment data
    May 4, 2022 · Economists on Tuesday are not convinced about the latest monthly unemployment data released by the Centre for Monitoring India Economy (CMIE).Missing: criticism | Show results with:criticism
  7. [7]
    Mahesh Vyas - CMIE
    Mahesh Vyas is Managing Director and CEO of CMIE, joined in 1980, and is the chief architect of CMIE’s databases. He writes on the Indian economy.Missing: founder | Show results with:founder<|separator|>
  8. [8]
    Centre for Monitoring Indian Economy (CMIE) - BYJU'S
    Narottam Shah established CMIE on April 13th, 1976. It started as a think tank, or information organisation, that assisted businesses in gaining access to ...
  9. [9]
    Company Profile: Centre for Monitoring Indian Economy Pvt. Ltd ...
    CMIE is a unique enterprise which productise economic research activities with regular services.It is relied upon the most recent and authentic data on business ...
  10. [10]
    Centre for Monitoring Indian Economy - 2025 Company Profile & Team
    Oct 13, 2025 · The founders of Centre for Monitoring Indian Economy is Narottam Shah. Mahesh Vyas is the CEO of Centre for Monitoring Indian Economy. Here are ...
  11. [11]
    CMIE India - Company Profile and News - Bloomberg Markets
    CMIE India provides a business and economic database and research to organisations in India and overseas. The Company provides information solutions in the ...
  12. [12]
    Centre for Monitoring Indian Economy | Think Tank Wiki - Fandom
    It was founded by Narottam Shah in 1976. When he died in 1984, it was headed by D. T. Lakdawala. Since then it has been headed by Mahesh Vyas. The website is [2].
  13. [13]
    Centre for Monitoring Indian Economy (CMIE) - A Detailed Overview
    The CMIE was founded by Narottam Shah on April 13th, 1976. Initially, it served as an information organization, aiding businesses in accessing economic data and ...
  14. [14]
    [PDF] https://prowessdx.cmie.com
    Oct 9, 2021 · Prowess India delivers data for over 50,000 companies. dx. It provides a me-series of financial and markets data since 1990. The database does ...
  15. [15]
    [PDF] Overview of the Indian Corporate Sector: 1989–2002 - IMF eLibrary
    The data used in this analysis are from a firm-level database on India's corporate sector, compiled by the Centre for Monitoring the Indian Economy, a private ...
  16. [16]
    [PDF] Imported Intermediate Inputs and Domestic Product Growth
    The firm-level data used in the analysis are constructed from the Prowess database which is collected by the Centre for Monitoring the Indian Economy (CMIE).<|separator|>
  17. [17]
    [PDF] Business Group Spillovers - National Bureau of Economic Research
    Our main data source is Prowess, a database maintained by CMIE. This dataset has been used by a number of prior studies on Indian firms, including. Bertrand, ...
  18. [18]
    [PDF] Web Appendix - Amit Khandelwal
    A.1 Data Appendix. We compile a firm-level panel data set that spans the period from 1989 to 2003 based on the Prowess database, collected by the Centre for ...Missing: history | Show results with:history
  19. [19]
    [PDF] ©International Monetary Fund. Not for Redistribution
    1990's. This chapter uses the Prowess database from Centre for Monitoring Indian Economy. (CMIE), a Mumbai-based economic think-tank, which includes detailed ...
  20. [20]
    Consumer Pyramids Dx - CMIE
    Surveyed over. 240,000. households. Consumer Pyramids Household Survey. A continuous survey to measure household well-being in India.How We Do It · Papers · Partnerships · Webinars
  21. [21]
    Prowessdx - CMIE
    This includes listed companies, unlisted public companies and private companies of all sizes and ownership groups. It contains time-series data since 1990.Usage agreement · September 2024 Vintage... · Diagnosis · RegisterMissing: history 2000s
  22. [22]
    Prowess for Interactive Querying - CMIE
    Prowess delivers over a hundred tabulations that present neatly laid out tabulations of the data of the companies. These enable time series analysis, ratio ...Missing: features digital
  23. [23]
    CMIE-CapEx
    Track India's investments cycle · by following the aggregates trends in new announcements · The geograph of birth and blossoming or withering of investments ...
  24. [24]
    Collecting labour market statistics to study the platform economy
    Sep 27, 2023 · Neha Arya describes the efforts taken by the CMIE to collect data on platform workers in the CPHS, and uses this dataset to describe the ...Missing: advancements | Show results with:advancements
  25. [25]
    CMIE Economic Outlook
    Free interpretation of current and prospective trends. A C-suite companion your assistant to decipher the Indian economic environment.Researchers, Faculty, Students · Bankers and Investors · CEOs, CFOs and all CXOs
  26. [26]
    ️Centre for Monitoring Indian Economy Pvt Ltd - CMIE
    CMIE is an independent NGO, an economic think-tank and business information company, providing databases and research reports on the Indian economy.<|control11|><|separator|>
  27. [27]
    Comprehensive Overview of the Centre for Monitoring Indian Economy
    History. CMIE was founded by Narottam Shah on 13 April 1976. It began as an information organisation cum Think Tank that helped the business community gain ...
  28. [28]
    Mahesh Vyas, Centre for Monitoring Indian Economy Pvt/The: Profile ...
    Mahesh Vyas is CEO/Managing Director at Centre for Monitoring Indian Economy Pvt/The. See Mahesh Vyas's compensation, career history, education, & memberships ...
  29. [29]
    Sadhana Shah - CEO at CMIE USA Inc. - LinkedIn
    Experience · CEO. CMIE USA Inc. Nov 2012 - Present 13 years · Centre for Monitoring Indian Economy Graphic. Director. Centre for Monitoring Indian Economy. 1988 - ...
  30. [30]
    Watch CMIE Managing Director & CEO Mahesh Vyas on India's ...
    May 25, 2021 · Centre for Monitoring Indian Economy (CMIE) Managing Director & CEO says that Indian unemployment rate has moved into double-digits in May, ...Missing: initial | Show results with:initial
  31. [31]
    'India's Workforce Not Rising, Quality of Jobs Very Low': CMIE's ...
    May 2, 2023 · In a special interview to The Wire, Mahesh Vyas, managing director and chief executive of CMIE, said, "India's workforce – which is usually ...
  32. [32]
    Mr. Mahesh Vyas, MD & CEO-CMIE at MDAE - Instagram
    Apr 29, 2024 · 8 likes, 0 comments - meghnaddesaiacademy on April 29, 2024: "Mr. Mahesh Vyas, MD & CEO-CMIE at MDAE A decent job is characterised by a ...
  33. [33]
    CMIE's Consumer Pyramids Household Surveys: An Assessment
    Aug 10, 2021 · The CPHS comprises surveys of households living in about 174,000 sample houses (about 111,000 rural and 63,400 urban) spread across most states ...
  34. [34]
    ProwessIQ - CMIE
    There are over 3,500 data-fields per company in the Prowess database. The database is standardised to enable inter-company and temporal comparisons.Missing: methodology | Show results with:methodology
  35. [35]
    Prowess for Interactive Querying - CMIE
    Prowess is a query-able database of over 100,000 Indian companies. The Prowess database includes all companies traded on the National Stock Exchange, ...<|separator|>
  36. [36]
    CMIE Databases - IMT Ghaziabad
    Economic Outlook is the solution to your requirement of reliable data, independent analysis and short to medium-term projections on the Indian economy. Economic ...<|separator|>
  37. [37]
    ProwessIQ - CMIE
    There are over 3,500 data fields per company in the Prowess database. The database is standardised to enable inter-company and temporal comparisons.
  38. [38]
    Prowess dx India - CMIE
    Prowessdx is a delivery of the Prowess database that is specially designed for academia. It facilitates easy download of large amounts of data from the Prowess ...
  39. [39]
    Prowess
    Query Builder, OSC & Worksheets, Report Viewer, Commands, History and Planner, Query Results Today, Analysis, Knowledge Base, Extract Data, API, Documentation
  40. [40]
    Industry Outlook: Summary Overview - CMIE
    Industry Outlook: Summary Overview ; Food & agro-based products, 19, 3,823 ; Textiles, 8, 2,555 ; Chemicals & chemical products, 21, 4,397 ; Consumer goods, 13 ...
  41. [41]
    [PDF] CMIE Industry Outlook - IIM Indore
    Industry Outlook helps you anticipate trends in 207 industries. Expected creation of new capacities, projected financial performance, price movements of ...
  42. [42]
    Industry Outlook - CMIE
    Industry Outlook provides a well-balanced presentation of data, analysis and forecasts on a large number of industries.
  43. [43]
    Industry Outlook - CMIE
    Industry Outlook is your risk management tool. It helps you prepare to better ride the business cycles while anticipating the future.
  44. [44]
    CMIE Industry Outlook
    The service provides detailed historical data on capacity, production, foreign trade, sales, prices, annual and quarterly financial performance of the industry ...CMIE User Registration Site · Usage agreement · DiagnosisMissing: digital advancements
  45. [45]
    Consumer Pyramids Household Survey - CMIE
    The service provides access, for the period of a valid subscription, to data from the first Wave that was conducted during January-April 2016. Data is ...
  46. [46]
    Consumer Pyramids dx - microdata from India's largest household ...
    Dec 10, 2020 · The Consumer Pyramids dx microdata comes from India's largest household survey, CPHS, and includes data on identity, employment, health, ...Missing: methodology | Show results with:methodology
  47. [47]
    [PDF] Understanding CMIE-CPHS Data: A handbook - Dvara Research
    CMIE utilises a multi-stage stratified survey design to create and maintain its sample of households across India. It is conducted as a continuous survey on a ...Missing: approaches | Show results with:approaches
  48. [48]
    [PDF] CMIE's Consumer Pyramids Household Surveys: An Assessment
    Unit-level data from the surveys are provided by CMIE for a fee. The data are divided into four modules that can be subscribed to separately.
  49. [49]
    Centre for Monitoring Indian Economy - Wikipedia
    The Centre for Monitoring Indian Economy (CMIE) is an independent private limited entity that serves both as an economic think-tank as well as a business ...
  50. [50]
    Prices Effective January 1, 2025 - CMIE
    Oct 11, 2024 · IP based Dx, Data extraction, subscription services (for Non-educational research institutions). Prowessdx, USD 4,350, USD 4,350, 0.00, 50,000.
  51. [51]
    Understanding CMIE-CPHS Data: A handbook - Dvara Research
    Sep 9, 2025 · This handbook offers a guide to understanding and using CPHS data, focusing on its stratified multi-stage sampling design, weighting methodology ...
  52. [52]
    Technical Note - Consumer Pyramids Dx - CMIE
    The data of all surveys is made available along with some additional variables to capture unique social, economic, and geographic data on surveyed respondents.
  53. [53]
    How Comparable are India's Labour Market Surveys? - PMC - NIH
    The Centre for Monitoring Indian Economy (CMIE), a private business information organisation, has been collecting data relating to employment and unemployment ...
  54. [54]
    [PDF] Don't jump at 1st unemployment number you see. Different ... - NIPFP
    May 17, 2024 · Govt & pvt sector CMIE surveys, the 2 keenly watched sources of employment data in India, differ in terms of their definitions of households, ...
  55. [55]
  56. [56]
    India Unemployment Rate - Trading Economics
    Unemployment Rate in India increased to 5.20 percent in September from 5.10 percent in August of 2025. Unemployment Rate in India is expected to be 5.30 percent ...
  57. [57]
    Unemployment rate rises to 9.2% in June 2024 - CMIE
    Jul 1, 2024 · Unemployment rate in India rose sharply to 9.2 per cent in June 2024 from 7 per cent in May 2024, according to CMIE's Consumer Pyramids Household Survey.
  58. [58]
    Labour markets data read well - CMIE
    Aug 1, 2025 · India's unemployment rate fell to 6.8 per cent in July 2025. This is the lowest unemployment rate recorded in 34 months. This is also the ...
  59. [59]
    Labour Incomes in India: A Comparison of Two National Household ...
    The CPHS definition of employment differs substantially from the NSO method. An individual is considered employed if they worked or had work on the day of ...Missing: questionnaire | Show results with:questionnaire
  60. [60]
    Unemployment - CMIE
    The Unemployment database contains detailed information on employment and unemployment status about the members residing in all households in the Consumer ...Missing: CPHS methodology
  61. [61]
    Official unemployment data misses the reality - Policy Circle
    Aug 22, 2025 · India's unemployment data undercounts the crisis, masking underemployment and widening the gap between growth and work.
  62. [62]
    India's official unemployment figures miss the mark - Policy Circle
    Mar 26, 2024 · This is why CMIE keeps saying that for 20–25-year-olds, unemployment for youth is 44%. The government says it is nearly 20%.
  63. [63]
    Unemployment debate: PM's economic advisor explains why CMIE ...
    May 1, 2019 · Unemployment debate: PM's economic advisor explains why CMIE data is not reliable. The government must take cognisance of the issue of ...
  64. [64]
    How Modi government's warning about private economic data ends ...
    Jan 24, 2023 · This is not the first time Khattar had criticised the Centre for Monitoring Indian Economy. In July 2021, Khattar had said the survey was ...
  65. [65]
    Official India jobless data is not accurate, say top independent ...
    Jul 23, 2025 · The Indian government's unemployment data is inaccurate and masks the severity of joblessness and underemployment, according to a Reuters ...
  66. [66]
    Govt interference undermining integrity of India's statistics apparatus
    Feb 25, 2019 · As per the NSSO survey, the unemployment rate in India was at a 45-year high of 6.1 per cent in 2017-18, Business Standard had reported in ...Missing: disputes | Show results with:disputes
  67. [67]
    How Reliable Is Labour Market Data in India?
    Dec 25, 2021 · We examine the quality of labour market data from two sources, the Periodic Labour Force Survey (PLFS) and the Centre for Monitoring Indian Economy (CMIE).
  68. [68]
    Gauging the employment scene - The Hindu BusinessLine
    Sep 19, 2024 · Due to coverage and definitional variations, NSSO and CMIE estimates vary. PLFS data is closer to reality.
  69. [69]
    View: There are practical limitations in CMIE's CPHS sampling, but ...
    Jun 23, 2021 · The systematic random sampling exercise of CPHS requires the selection of every nth household in the village, where n is a random number between ...Missing: coverage | Show results with:coverage
  70. [70]
    A Critical Assessment of the Consumer Pyramids Household Survey
    Aug 11, 2021 · ... survey of over 170,000 households, privately executed by the Centre for Monitoring Indian Economy (CMIE) since 2014. Given its breadth and ...
  71. [71]
  72. [72]
    Consumer Pyramids Household Survey: A response and a rejoinder
    Sep 3, 2021 · We thank Mahesh Vyas for his response above of 23 August 2021 to our article on the CMIE's Consumer Pyramids Household Survey (CPHS). It is ...<|separator|>
  73. [73]
    Trajectories of labour market transitions in the Indian economy
    Deshpande & Singh (2021) use twelve periods (four years) of CMIE panel to examine entry and exit into the labour market and find considerable volatility in ...
  74. [74]
    Understanding labour market disruptions and job losses amidst ...
    This paper examines the impact of COVID-19 pandemic-induced lockdown on labour market in India. By using the data of centre for monitoring Indian economy ...
  75. [75]
    Innovative efforts and export market survival - ScienceDirect.com
    We examine the role of innovation on the export market survival of Indian manufacturing firms. To achieve this objective, we source information on 1424 firms ...
  76. [76]
    [PDF] Deregulation, Misallocation, and Size: Evidence from India
    Abstract. This paper examines the impact of the deregulation of compulsory industrial licensing in India on firm size dynamics and reallocation of resources ...
  77. [77]
    A dozen selected academic institutions - CMIE
    Indian institutions of academic research · Indian Institute of Management, Ahmedabad · Indian Institute of Management, Kolkata · Indian Institute of Management, ...
  78. [78]
    [PDF] Enhancing MSMEs Competitiveness in India - NITI Aayog
    The proprietary data collection methodology employed by the Centre for Monitoring the Indian Economy (CMIE) may deviate from national and international ...
  79. [79]
    Improving Employability has been the priority concern of the ... - PIB
    Jul 23, 2018 · According to the Centre for Monitoring Indian Economy Pvt Ltd. (CMIE), unemployment rate is in the range of 3.39% to 5.67% during July, 2017 to ...
  80. [80]
    PLFS data elucidates a burgeoning trend of youth and individuals ...
    Aug 24, 2023 · This article delved into the analysis on employment statistics drawn from the Centre for Monitoring Indian Economy (CMIE). The analysis ...
  81. [81]
    cmie presents findings to maran - Latest Releases
    The study recently concluded by CMIE on behalf of the Ministry of Commerce & Industry (Department of Industrial Policy & Promotion) is the most comprehensive in ...
  82. [82]
    [PDF] TOWARDS A CLEAN ENERGY ECONOMY - NITI Aayog
    “Unemployment Rate in India,” CMIE, accessed 20 May. 2020, https://unemploymentinindia.cmie.com/. 6. “Analysis: India's CO2 emissions fall for the first time.
  83. [83]
    [PDF] Prowessxxxxxxxxxxxxxx - CMIE
    Jun 24, 2024 · Prowess is a query-able database of over 100,000 Indian companies. The Prowess database includes all companies traded on the. National Stock ...Missing: achievements | Show results with:achievements
  84. [84]
    CMIE Consumer Pyramidsdx (CPdx) - Economics - Research Guides
    CMIE CPdx - How to Register for an account and download data. · Every Dartmouth user needs to have their own username and password to access the CPdx website.Missing: platforms | Show results with:platforms
  85. [85]
    CPSEs, DUs managed to achieve 90% capex target in FY20 - CMIE
    Jun 24, 2020 · Among the government agencies, railways was the largest investor in 2019-20 with Rs.1.4 trillion, followed by the National Highways Authority of ...
  86. [86]
    How have household balance sheets changed post the pandemic ...
    Nov 7, 2024 · The CMIE-CPHS dataset covers a vast spread of data points about Indian households such as their income and expenditure patterns, savings and ...
  87. [87]
    How Comparable are India's Labour Market Surveys? - PubMed
    The Centre for Monitoring Indian Economy's (CMIE) Consumer Pyramid Household surveys have emerged as an important source of regular labour market data for India ...
  88. [88]
    India's Statistical System: Past, Present, Future
    Jun 28, 2023 · In 2016, senior government officials asked the Centre for Monitoring Indian Economy (CMIE) to modify data on its project tracking database.
  89. [89]
    With govt data becoming undependable & infrequent, private ...
    Nov 5, 2022 · Absence of basic data, such as census, harms the country's statistical system & policy analysis. In such a scenario, CMIE, CRISIL, Skymet have become reliable ...Missing: criticisms accuracy
  90. [90]
    NSS, CMIE are surveys not comparable. Studies should not relate ...
    Jun 13, 2022 · The huge differences in the headcount ratios between the two studies only add to the confusion in the already complicated measurement issues of ...