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Hindustan Times
2 days ago
- Politics
- Hindustan Times
Over 10,000 unrecognised schools in Bihar, Jharkhand: Education Ministry
New Delhi: Jharkhand and Bihar together have over 10,000 unrecognised schools enrolling over 1.6 million students and having more than 88,000 teachers, according to the Union education ministry. While Jharkhand has the 'highest' 5,879 unrecognised schools in the country with an enrolment of 8,37,897 students and 46,421 teachers, Bihar has 4,915 such schools with an enrolment of 7,75,704 students and 42,377 teachers. During Project Approval Board (PAB) meetings for the approval of budget and plans under Samagra Shiksha scheme for 2025-26 with the state officials between March and April 2025, the ministry stated that unrecognised schools are violating section 19 of the Right To Education (RTE) Act 2009 which requires pre-existing schools to meet prescribed norms within three years of the Act's commencement. 'The Act also mandates that if such schools fail to fulfil the norms, the recognition shall be withdrawn, and the school shall cease to function,' said the minutes of the meetings uploaded on the ministry's website. The ministry has asked both the states to 'take further course of action and issue suitable instructions to the authorities concerned to recognise these unrecognised schools or to take appropriate action as deemed fit at the earliest.' The ministry has quoted data for unrecognised schools in Bihar and Jharkhand from Unified District Information System for Education (UDISE)+ 2023-24 report. However, the data on unrecognised schools is not publicly available in the said report released in January this year. The education ministry officials did not respond to HT's queries on clarification. 'These [Unrecognised] schools started functioning before the implementation of RTE Act 2009. The state government has already issued directions for recognition of such schools. We have formed district-level recognition committees for recognition of such schools,' Sachidanand Diyendu Tigga, administrative officer at Jharkhand education project council told HT. According to the minutes of the PAB meetings, the ministry has also flagged 'large variation' in reporting of data about out-of-school children (OoSC) by Bihar and Jharkhand on the education ministry's Project Appraisal, Budgeting, Achievements and Data Handling System (PRABANDH) portal and the National Sample Survey Office (NSSO) survey. The Centre describes an OoSC as a child aged six to 14 years, who has never been enrolled in an elementary school or has remained absent from school after enrolment without prior intimation for 45 days. OoSC, therefore, include both never enrolled in schools and the dropouts. Data for OoSC is uploaded by states on PRABANDH portal, the online system used to monitor the implementation and progress of Samagra Shiksha, a shared scheme between the Centre and states supporting public schools with a funding ratio of 60:40. According to the NSSO, 'never enrolled' children are those who have never attended any school or formal educational institution. In Jharkhand, the PRABANDH portal recorded 37,409 Out-of-School Children (aged 6 to 19 years) for 2023–24. In contrast, the NSSO survey for 2022–23 reported 1,07,639 'never enrolled' children in the 6 to 14 age group. In Bihar, PRABANDH data for 2023–24 showed 33,285 OoSCs, while the NSSO reported a significantly higher figure of 6,27,763 'never enrolled' children for 2022–23. The ministry advised both the states to 'monitor the data uploaded on the portal by a responsible officer under the supervision of the State Project Director (SPD).' The ministry also directed both the states to initiate a special enrolment drive with full involvement of school management committees to ensure identification and admission of all OoSC. Tigga said, 'We will look into discrepancies in the number of OoSCs. We are running the campaign 'back to school' to enroll those students who are not going to the schools.'
The Print
4 days ago
- Politics
- The Print
Out-of-school children: Centre flags huge mismatch in Bihar, Jharkhand data & national survey
The discrepancies surfaced during meetings between state officials and the Project Approval Board (PAB) for the Samagra Shiksha Scheme, the largest school education scheme, held between March and April 2025. The minutes of the meetings were released last week. With the mismatch raising concerns about the reliability of state data, the ministry has asked these states to closely monitor the data uploaded to the portal. New Delhi: The Union Ministry of Education has flagged significant discrepancies between the number of out-of-school children reported by Bihar and Jharkhand on the Centre's online portal and the National Sample Survey Office (NSSO) survey, ThePrint has learnt. According to the minutes reviewed by ThePrint, the ministry identified a 'large variation' in the number of Out-of-School Children (OoSC)—defined as those aged 6 to 14 years who are not enrolled in or attending any educational institution—on the PRABANDH portal, the online system used to monitor the implementation of the Samagra Shiksha scheme. In Bihar, state data for 2023-24 showed 33,285 OoSCs on PRABANDH while the number of 'never enrolled' children recorded by the NSSO survey (2022-23) was 6,27,763. Similarly, in Jharkhand, PRABANDH listed 37,409 OoSC in 2023-24 compared with the NSSO's (2022-23) 1,07,639. NSSO defines 'never enrolled' children as students not attending school or any formal education institution at that point of time 'The state was advised to monitor the data uploaded on the portal by a responsible officer under the supervision of the state project directorate,' the minutes stated. The ministry has advised other states and Union territories (UTs) to ensure effective data collection of out-of-school children and timely updates on the PRABANDH portal on bringing them to school. Shashi Ranjan, State Project Director, Jharkhand Education Project Council, told ThePrint the state was investigating the reason behind the data mismatch. 'We are trying to find out how this gap has been reported because we conduct household surveys. We will also coordinate with the NSSO to understand their methodology. However, our process is very meticulous,' he said. He said the education department in Jharkhand conducts door-to-door surveys annually to identify the number of out-of-school children, and the data is updated on the PRABANDH portal. ThePrint reached Bihar Education Department Secretary Ajay Yadav via multiple calls or messages. This report will be updated if and when a response is received. Meanwhile, a Bihar Education Department official told ThePrint on condition of anonymity that they are also looking to ascertain the cause of the discrepancy. Also Read: 'Everything at stake' for Indian students as US pauses visa interviews amid social media vetting plan Teaching posts lying vacant across various states According to the minutes, the ministry also flagged widespread vacancies in teaching posts across various states. Bihar reported 'significant' teacher vacancies, with 208,784 at the elementary level, 36,035 at the secondary level and 33,035 at the senior secondary level. According to the minutes, the state indicated that recruitment is underway, with around 80,000 posts to be filled through the state public service commission and 'plans for further recruitment to follow'. In Haryana, the ministry flagged 7,626 teacher vacancies in elementary schools, 4,070 in secondary schools and 3,847 in senior secondary schools. Madhya Pradesh had 47,122 teacher vacancies in elementary schools, 2,877 in secondary schools and 2,020 in senior secondary schools. Similarly, Punjab had 6,423 vacant teaching posts across the state, including 1,546 at the elementary level, 961 at the secondary level and 3,916 at the senior secondary level. According to the minutes, the states were advised to fill the teacher vacancies by December 2025. Besides, in Maharashtra, the ministry flagged 8,254 vacancies in elementary school teachers, 660 in secondary school teachers and 65 in senior secondary school teachers. 'The state has informed that it has started the recruitment process and has advertised the vacancies. lt will fill the vacant positions within a period of three months,' the minutes stated. Kerala came in for praise by the ministry for 'filling up all sanctioned posts of teachers in all schools'. (Edited by Sugita Katyal) Also Read: To use or not, is no longer the question. From IITs to DU, universities are fighting unethical AI use
The Hindu
22-05-2025
- Business
- The Hindu
Analysing poverty levels in India by comparing various surveys
Himanshu et al, 'Poverty Decline in India after 2011–12', The Economic and Political Weekly, Vol 60, Issue No: 15, April 12, 2025 A recent paper has estimated that poverty reduction in India slowed down significantly after 2011-12. While poverty levels of 37% in 2004-05 fell to 22% by 2011-12, it has since fallen only by 18% in 2022-23, the paper finds based on its own calculations. The paper, titled 'Poverty Decline in India after 2011–12: Bigger Picture Evidence', authored by Himanshu of Jawaharlal Nehru University, and Peter Lanjouw and Philipp Schirmer of the Vrije University in Amsterdam, noted that India hasn't had an official poverty estimate since 2011-12. In the absence of an official estimate, a number of unofficial and often contradictory estimates have been made, of which this one is the latest. Three methodologies The paper notes that the various contradictory estimates can essentially be clubbed into three broad buckets based on their methodology. The most common approach, it noted, has been to use alternative socio-economic surveys of the National Sample Survey Office (NSSO), since there are significant comparability issues between the Household Consumption Expenditure Survey (HCES) of 2022-23 and 2011-12. There are no intervening surveys, either. The HCES for 2017-18 was scrapped by the government, citing 'methodological issues'. In the NSSO's 71st round, which covered the January-June 2014 period, the government introduced a consumption expenditure measure that was derived from a single question in the survey called the Usual Monthly Per Capita Consumption Expenditure (UMPCE). This UMPCE was used for all subsequent rounds of the NSSO surveys as well as in the Periodic Labour Force Surveys (PLFS). However, as the authors correctly note in their paper, this measure can't be compared to earlier estimates of consumption because it is based on a single question 'with no clear definition of what it comprises'. According to this method, poverty estimates range between 26-30% for 2019-20. The second approach has been used by the economist Surjit Bhalla and his colleagues in 2022 in a paper in which they used Private Final Consumption Expenditure (PFCE) estimates from the government's National Accounts Statistics (NAS) to derive consumption aggregates after 2011-12. This method basically scaled the consumption expenditure data from the HCES 2011–12 based on the implicit growth rate of PFCE after 2011-12. The third broad approach — and the one used by the authors themselves — is to use survey-to-survey imputation methods. This basically means data gaps in one survey can be filled using information from a related base survey. This method, the authors note, has occasionally been used by World Bank researchers to update the World Bank's Poverty and Inequality Platform (PIP) database. Looking at different surveys This approach is significantly prone to somewhat divergent results, based on the different surveys used to complement each other, but are useful in revealing trends in data. For example, the paper notes that one estimate by David Locke Newhouse and Pallavi Vyas used the 2011-12 HCES and the 2014-15 survey on Consumption of Services and Durables to estimate that poverty in India declined from 22% in 2011-12 to 15% in 2014-15. Similarly, Ifeanyi Nzegwu Edochie and their colleagues in 2022, used the 2017-18 survey on Social Consumption on Health to estimate poverty at 10% for 2017–18, which confirmed the trend that poverty had reduced since 2011-12. In 2025, Sutirtha Sinha Roy and Roy van der Weide used a radical approach to apply the survey-to-survey imputation using a private sector survey. They used the Consumer Pyramid Household Survey (CPHS) for 2019 by the Centre for Monitoring Indian Economy (CMIE) along with the 2011-12 Consumer Expenditure Survey (CES). Their estimate was that poverty was around 10% in 2019. Himanshu et al also use this survey-to-survey imputation method. However, the authors note that their strategy differs from previous attempts in three aspects. First, they have used the Tendulkar Committee's poverty lines as opposed to the World Bank's poverty lines. Second, they have used the employment surveys of the NSSO for imputation. The Employment-Unemployment Survey (EUS) is a companion survey to the 2011-12 CES, and is based on similar sampling design and survey implementation procedures. Further, the PLFS, which replaced the EUS in 2017-18, is modelled on the EUS, the authors note. What this essentially means is that the two surveys Himanshu and his colleagues used to impute data are similar in their methodology and parameters, yielding a more accurate fit in the data. Third, the authors note that, unlike the World Bank studies, their own imputation models are estimated at the State level or include State-fixed effects when estimated at the sector level. Their methodology shows that while poverty based on the Tendulkar Committee poverty lines fell sharply between 2004-05 and 2011-12 — from 37% to 22% — it subsequently has fallen only to around 18% by 2022. Based on these estimates, the authors add, the number of poor persons in India fell only slightly since 2011-12, from 250 million persons to about 225 million in 2022–23. Different trends across States State-level trends derived from their methodology suggest differing trends across States over this period. Notably, the authors find that Uttar Pradesh, India's most populous State, seems to have markedly reduced its poverty rate. 'However, in other historically poor States, such as Jharkhand and Bihar, progress was much slower,' they added. 'It is noteworthy that in several of the large central and southern States, such as Maharashtra and Andhra Pradesh, poverty reduction appears to have stagnated.' Importantly, the authors do acknowledge that 'a full resolution of the present debate' on poverty is unlikely to be forthcoming without new government data that can be compared with previous years' data. However, they also try to back up their findings using other data sources that point to the same conclusions. For example, they noted that the growth of India's Gross Domestic Product (GDP), which averaged 6.9% per annum between 2004-05 and 2011-12, slowed to 5.7% between 2011-12 and 2022-23. This, they said, is consistent with a slower decline in poverty reduction after 2011-12. Similarly, they point out that the Wage Rates in Rural India (WRRI) data compiled by the Labour Bureau on real wages points to a slowdown in wage rates. It shows that the annual growth rate of wages fell from 4.13% per year between 2004-05 and 2011-12 to 2.3% per year between 2011-12 and 2022-23. Thirdly, the authors point out that while the absolute number of workers in agriculture declined by 33 million between 2004-05 and 2011-12, and by a further 33 million by 2017-18, this trend has reversed since then with 68 million workers being added to the agriculture sector since 2017–18. One consequence of the rising workforce in agriculture, the authors point out, has been the decline in the growth of agricultural productivity in recent years. Lower productivity leads to lower wages, which leads to higher poverty levels. This paper is hardly going to be the last word on poverty estimates, with many more sure to follow. However, as the authors themselves conclude, there's more than enough evidence to show that poverty reduction efforts need to be accelerated.
Indian Express
19-05-2025
- Politics
- Indian Express
Many challenges of Jal Jeevan Mission: Decentralisation is the only way ahead
Written by Amit Kumar Srivastwa Jal Jeevan Mission (JJM), a flagship scheme of the central government, is now facing major structural and functional challenges. The JJM was started in 2019 to provide a 100 per cent Functional Household Tap Connections (FHTC) by 2024. The recent NSSO data suggests that the government has made significant progress, as almost 90 per cent of rural households have access to a tap connection. The government is expected to cover the remaining households by 2028. However, there is a wide gap between tap water access and use, as only 39 per cent of the rural households can use taps as their primary source (NSS 79th Round, 2022-23). Moreover, tap water use is widely different across regions. States like Uttar Pradesh, Jharkhand, Bihar, West Bengal and Odisha have very low tap use, ranging from six per cent to 30 per cent. The gap between tap water access and use indicates that the JJM faces severe challenges at the functionality level. There are also structural challenges. Reports by this newspaper reveal a reduction in financial assistance by the central government, bureaucratic irregularities at the state level, and scams in constructing JJM's infrastructure, which has slowed down JJM's progress. Increasing financial burdens, coupled with incomplete infrastructure and a lack of transparency in the provision of the tender, leave JJM with an uncertain future. While structural problems require deliberation from both central, state, and local governments, it is important to understand how tap water infrastructures are operated and managed in everyday life, and what considerations are helpful in reducing the gap between tap access and use. There are some major technical-bureaucratic concerns that require immediate attention. For the longest time in post-colonial India, the drinking water supply was driven by a centralised technical order. This means that the design, operation, standards, procedures, manuals, norms, and guidelines for managing drinking water supply were made by central-level institutions and were followed by regional and local-level institutions. As a result of global deliberations and constitutional amendments, different governments have tried to create a decentralised and community-driven drinking water supply. However, we are yet to achieve a decentralised mechanism. The local institutions and actors who manage the tap water supply have limited financial and technical autonomy. It is essential to address what social and spatial challenges these institutions face while installing taps and whether they are technically equipped to manage water quality and quantity, and provide a timely supply. There is an urgent need to strengthen the grievance redressal channels and mechanisms. No data is available on how many grievances are received and solved by the local-level actors. Another significant concern is that administrative-level data does not reflect the reality on the ground. The JJM dashboard shows a 100 per cent tap water access for many villages and regions. However, it used outdated census data to elucidate the tap water access. The percentage of rural households has increased over the last 14 years, and with no official account, it is hard to make sense of whether households have tap access. The JJM also requires material consideration of water, infrastructure, and households. Water has its own agency, and the flow and availability of water cannot be totally regulated. The concern of unpredictable climate, untimely precipitation, and surface and groundwater depletion has affected the water quality, quantity, and a timely and adequate drinking water supply. It is important to consider how an unpredictable variable like water could be managed efficiently in a closed drinking water system. Similarly, different parts of the tap infrastructure, for example, pipes, taps, treatment plants, and water tanks, have a lifespan, and they degrade over time. The concerns of leakage, breakage, disruption, breakdown, and suspension are as important as other factors. An efficient tap water supply requires continuous repair and maintenance activities. While material degradation in the larger infrastructure is easily visible and can be repaired, attention to household-level repair and maintenance activities is required. One of the main reasons behind low tap water use is that rural households have marginal living conditions and low infrastructure management capabilities. Finally, technical and material aspects of JJM must function in coherence with the social order. The JJM is designed to serve rural households at the ward level. Carefully considering how different caste groups are located at the ward and village levels is necessary for an efficient tap water installation and supply design. The location of the water tank and the location and distance of households from the tank play a crucial role in receiving adequate pressure. Moreover, including different caste groups in the local-level institutions (e.g., in the roles of plumber, pump operator, and engineers) is crucial for an efficient tap water supply and repair and maintenance activities. Water sources like handpumps and open wells are already degrading and producing unsafe water quality. This again impacts people's health and livelihood conditions. With the increasing population and limited safe infrastructures, rural populations are once again stuck in water source precarity, dependency, and economic burden. The future of the Jal Jeevan Mission must entail these factors for an efficient supply system. The writer is a scholar at Ambedkar University Delhi and works on Jal Jeevan Mission
Indian Express
17-05-2025
- Business
- Indian Express
Knowledge Nugget: What are key highlights of Periodic Labour Force Survey (PLFS) and why is it UPSC essential
Take a look at the essential concepts, terms, quotes, or phenomena every day and brush up your knowledge. Here's your knowledge nugget for today. (Relevance: UPSC has asked questions on labour productivity and unemployment. Understanding the associated terms with PLFS becomes important for your Prelims and Mains examination.) Given the growing demand for more frequent data about the labour market and enhancing the scope, relevance, and coverage of the surveys, the Ministry of Statistics and Programme Implementation (MoSPI) has come up with the first monthly bulletin of the Periodic Labour Force Survey (PLFS). 1. The National Sample Survey Office (NSSO) under MoSPI had launched PLFS in April 2017. Quarterly bulletins provide details of labour force indicators such as Labour Force Participation Rate (LFPR), Worker Population Ratio (WPR), and Unemployment Rate (UR). In 2019, NSSO was merged with the Central Statistical Office (CSO) to form the NSO. 2. Earlier, the MoSPI released rural PLFS data on an annual basis and urban PLFS data on a quarterly basis, along with an annual report that combines data for both urban and rural on an annual basis. 3. The first monthly estimates, published by the National Statistical Office (NSO), are based on the current weekly status (CWS) approach, which measures the activity status of persons surveyed based on the reference period of the last seven days preceding the date of survey. 4. Basically, PLFS collects data in two ways — Usual Status (US) and Current Weekly Status (CWS). Broadly speaking, within the usual status, the survey respondent has to recall their employment details from the last one year, while in the CWS, the respondent has to recall the details over the past one week. 5. According to the CWS approach, the estimate of the labour force is derived by considering those who worked for at least 1 hour or was seeking/ available for work for at least 1 hour on any day during the 7 days preceding the date of survey. 1. India's unemployment rate stood at 5.1 per cent in April for persons aged 15 years and above, with the rate for males at 5.2 per cent and for females at 5.0 per cent. In urban areas, the unemployment rate stood at 6.5 per cent, while in rural areas, the unemployment rate for persons aged 15 years and above was recorded to be 4.5 per cent. 2. Females saw a higher unemployment rate at 8.7 per cent than 5.8 per cent for males in urban areas. In rural areas, however, the unemployment rate for females was lower at 3.9 per cent than 4.9 per cent for males in April. 3. The Labour Force Participation Rate for persons aged 15 years and above in the country stood at 55.6 per cent in April, with the rate for urban areas at 50.7 per cent and for rural areas at 58.0 per cent. The gender-wise split showed the low labour force participation rate of females at 34.2 per cent as against 77.7 per cent for males. 4. The female LFPR was lower for urban areas at 25.7 per cent than 38.2 per cent in rural areas for the age group 15 years and above. In comparison, the male LFPR stood at 75.3 per cent in urban areas and 79.0 per cent in rural areas. 5. The Worker Population Ratio (WPR), which indicates the employment rate, was recorded at 52.8 per cent for persons of age 15 years and above in April. The WPR for persons aged 15 years and above stood at 47.4 per cent in urban areas and 55.4 per cent in rural areas. 6. As per the gender-wise breakup, WPR for females was 23.5 per cent in urban areas and 36.8 per cent in rural areas. For males, the WPR stood at 71 per cent in urban areas and 75.1 per cent in rural areas. (Note: These data are not for you to memorize it but to create a broader understanding. For example, the difference between the unemployment rate in rural and urban areas indicates the job creation pressures in the urban areas. The higher unemployment for females in urban areas could be due to (a) higher female preferences for education in urban areas than rural areas and (b) lower employment opportunities in urban areas than rural areas for females.) 📍The PLFS also tells the sectoral distribution of workers in the economy — what percentage is involved in agriculture, for instance. It also records the type of work people do — for instance, how many are engaged in casual labour, how many work for themselves, and how many have regular salaried jobs. 1. Employed: According to MoSPI, following the usual status approach (with a reference period of 365 days) adopted by NSSO a person in the labour force is considered as working or employed if he/she is engaged relatively for a longer time, during the reference period of last 365 days in any one or more of the work activities. 2. Unemployment Rate (UR): Unemployment refers to the condition where individuals capable of working are actively seeking employment but are unable to secure suitable jobs. An unemployed person, then, is someone who is part of the labor force, possesses the requisite skills, but currently lacks gainful employment. The unemployment in the country is commonly calculated using the formula: Unemployment rate = [Number of Unemployed Workers / Total Labour Force] x 100. Here, the 'total labour force' includes the employed and the unemployed. Those who are neither employed nor unemployed — students, for example— are not considered a part of the labour force. 3. Labour Force Participation Rate (LFPR): Labour force participation rate refers to the part of the population that supplies or offers to supply labour for pursuing economic activities for the production of goods and services and therefore, includes both 'employed' and 'unemployed' persons. Under the CWS approach, labour force refers to the number of persons either employed or unemployed on an average in a week before the date of the survey. LFPR is defined as the number of persons/ person-days in the labour force per 1000 persons /person-days. 4. Worker Population Rate (WPR): Under the CWS, the WPR refers to the number of persons who worked for at least one hour on any day during the days preceding the date of the survey. According to MoSPI, it is defined as the number of persons/person-days employed per 1000 persons/person-days. 5. Work Force: According to MoSPI, 'Persons engaged in any gainful activity are considered 'workers' (or employed). They are the persons assigned any one or more of the nine activity categories under the first broad activity category i.e. 'working or employed'.' Consider the following statements about the PLFS: 1. The NSO has published the first monthly PLFS following the Usual Status (US) approach. 2. The MoSPI had launched the first PLFS in April 2017. 3. Under the CWS, the WPR refers to the number of persons who worked for at least one hour on any day during the days preceding the date of survey. Which of the following statements is/are correct? (a) 1 and 3 only (b) 2 and 3 only (c) 1 and 2 only (d) 1, 2 and 3 Subscribe to our UPSC newsletter. Stay updated with the latest UPSC articles by joining our Telegram channel – IndianExpress UPSC Hub, and follow us on Instagram and X. 🚨 Click Here to read the UPSC Essentials magazine for May 2025. Share your views and suggestions in the comment box or at
