Contours of the Public Policy in India in the Amrit Kaal, An Online International Autumn School Programme was organized by IMPRI Centre for the Study of Finance and Economics in September 2023. It was also A One-Month Immersive Online Intermediate Certificate Training Course graced by various experts in the field.
Dr Soumyadip Chattopadhyay commenced his session with the assertion that the future of development and governance hinges on the effective utilization of data. India’s policy-making institutions are embracing this shift, recognizing the exponential growth of data across various sectors and levels. It’s essential to grasp that the formulation and execution of development strategies entail numerous decisions, both small and large, influenced by uncertainties and the ever-changing economic, institutional, and technological landscape.
In the realm of public policy, the significance of understanding data analytics and the necessity for rapid adjustments cannot be overstated. Not too long ago, extensive and labour-intensive surveys were the norm for data collection. However, nowadays, a vast amount of data is effortlessly gathered at no cost, thanks to the migration of activities to the digital sphere, often referred to as the digital footprint. This data is routinely stored on both public and private servers.
Mr. Chattopadhyay highlighted the ongoing “Data Revolution,” which is presenting fresh challenges in data analysis due to the sheer volume, speed, and diversity of unstructured data being generated. Dealing with this data necessitates the development of new theories, methods, and tools for integrating, interpreting, and visualizing it.
He discussed the importance of understanding the 5 V’s of Data. These encompass:
1. Volume: This pertains to the substantial quantity of transactions occurring over a specified period.
2. Velocity: It refers to the speed at which data is continuously streaming in.
3. Variety: This encompasses the various forms of both structured and unstructured data.
4. Veracity: This relates to the reliability and authenticity of the data.
5. Value: Value is derived from considering all the aforementioned aspects and holds a crucial role in the analysis of data.
Quality of Data
We can unanimously agree that merely producing data is insufficient. What truly matters is generating high-quality data to ensure its usefulness in analysis. Key characteristics to consider when evaluating data quality include precision, timeliness, uniformity, and distinctiveness.
– Precision ensures that the data input into the database is accurate and suitable for future analysis.
– Completeness guarantees the comprehensive collection of information.
– Uniformity maintains consistency in data generation and collection, whether it’s structured, semi-structured, or unstructured data.
– Distinctiveness assures that the data captured is relevant and unique.
– Timeliness ensures that the data is current and suitable for real-time reporting.
Adding Value to Public Policy
Public policy encompasses a series of actions that influence the resolution of a policy program and possess the potential to generate value for the public. Defining public value is a complex task, but it can be understood as a combination of the ability to address individual problems, enhance the overall quality of life on a large scale, and exhibit a readiness for change.
The implications for public policy involve the capability to deliver personalized services, engage end-users in the design process (referred to as co-production), and adapt policies while evolving over time. By adhering to these characteristics during data analysis, the quality of public policies can see significant improvement.
Economic Aspects of Data
Economic considerations in data collection encompass both the expenses incurred and the benefits reaped from gathering data. Currently, we are witnessing a decline in the marginal cost of data collection, which can be attributed to several factors. Firstly, there has been a boost in data gathering efficiency owing to recent innovations and the adoption of data-driven technologies. Additionally, data storage costs have risen significantly. Thirdly, there is an increased availability of skills and resources for data processing. Lastly, the cost associated with distributing data after its collection is almost negligible.
Nonetheless, there are direct and indirect expenses linked to data collection. Direct costs include those related to data privacy and security, while indirect costs involve potential data breaches and the ensuing legal and financial consequences.
The marginal benefits of data collection encompass the expansion of evidence-based public policy formulation. Data also enhances accountability in public services, aids in more precise targeting of welfare programs, and reduces potential errors in their implementation. Furthermore, data creates opportunities for experimentation with products and has the potential to integrate markets at both the national and international levels, thereby lowering acquisition and transportation costs.
It is evident that the marginal cost of acquiring data is decreasing, while the marginal benefits of data collection are on the rise. Consequently, there is a pressing need for increased efforts to harness and utilize data. However, this has been hindered by the differing economic incentives faced by private companies engaged in data generation processes compared to those encountered by social planners.
Hypothetical Nationally Integrated Agricultural Market
Establishing a nationally integrated agricultural market would result in societal benefits. However, private firms, in reality, cannot charge the many parties who enjoy these social benefits. As a result, the marginal benefit for private firms is lower than the marginal benefit for society as a whole. This implies that private firms are unable to fully capture the social welfare. In such a scenario, the quantity of data generated by these firms would not reach the level needed for societal optimization. To guarantee the availability of data that aligns with the societal optimum, government intervention becomes necessary.
The government plays a crucial role in ensuring that the data generated aligns with societal optimization. The government manages various types of data, which encompass administrative data (such as birth and death records, pensions, tax records, and marriage records), survey data (including census and sample survey data), transaction data (like United Payments Interface data), and institutional data (such as public school data on students and public hospital data).
These databases are extensive, well-maintained, and the government continually strives to enhance the accuracy and methodologies employed in data collection.
Data Collection Framework in India
Data collection in India is currently highly fragmented, lacking coordination and integration among various ministries. If we could somehow consolidate the scattered pieces of data related to the same individual across different ministries, it would significantly enhance the comprehensiveness of data analysis. Combining diverse datasets is immensely valuable in obtaining the depth of information required for the development and implementation of effective public policies.
Furthermore, it’s essential that the collected data encompasses a substantial portion of individuals and businesses to yield valuable policy insights. Additionally, having a long time-series of data is essential for conducting dynamic analyses. A unified dataset possessing all these characteristics holds much more value than having three separate and disconnected datasets.
Moreover, data shares certain characteristics of public goods, including non-rivalry and the potential for exclusion. This underscores the need for an integrated data system to address these challenges effectively.
Towards an Integrated Data System
The framework for an ideal data system involves several key steps: data collection, data storage, data processing, and data dissemination.
– Data Collection: This encompasses digitizing paper-based records and implementing digital data collection at the source.
– Data Storage: This includes establishing real-time storage for selected data to minimize the delay between data collection and data entry.
– Data Processing: This involves building the analytical capacity of government entities to analyze data and engaging the private sector in generating insights from the data.
– Data Dissemination: This step includes creating scheme dashboards, making district-level dashboards accessible to the public, and sharing data from third-party studies with the public.
It’s also essential to embrace the “three I” concept for data analysis. Firstly, data should be integrated from all available sources. Next, insights should be derived from this integrated data, which can then be used in public policy to identify unmet needs, track metrics, and monitor feedback for impact assessment.
State of Municipal Finance
Successive finance commissions have repeatedly highlighted the absence of reliable information, particularly in three key categories: economic information, financial information, and performance information.
– Economic Information: This category encompasses data related to GDP, employment, migration, and investment, among other economic indicators.
– Financial Information: It involves data concerning budgets, audited annual accounts, and medium-term fiscal plans, among others.
– Performance Information: This category includes data on tenders, contracts, and public disclosures, among other performance-related data.
Urban Local Bodies (ULBs) face several challenges, including the lack of requirements to present revenue capacity estimates, medium-term, and long-term financing needs. Consequently, there are no estimates available at any given time for the required financing, making expansion planning difficult. ULBs also struggle to leverage municipal borrowings, which necessitates standardizing their financial data. Additionally, there’s a notable lack of transparency in the finances and operations of ULBs, with limited opportunities for citizen participation. Recent concerns have included issues such as missing data, underestimation of transfers, inconsistency, and a lack of uniformity in the data.
Optimizing the Property Tax Revenue System
The prevalent use of manual, paper-based systems for establishing and updating property registers, coupled with the absence of comprehensive provisions for regular enumeration in State Acts, requires a reevaluation.
An effective taxation system typically maintains a low tax rate but covers a wide tax base. To broaden the tax base in India, one potential approach is the development of a digital property register based on Geographic Information Systems (GIS). Utilizing data from such a system is straightforward and can be achieved through various means like accessing archived satellite images from the open-source Google platform or employing drone imaging, among others.
In conclusion, the discussions presented by Dr. Soumyadip Chattopadhyay and the various topics related to data utilization, economic aspects, and public policy in India underscore the critical role that data plays in shaping governance and development. The “Data Revolution” has ushered in a new era with its challenges and opportunities, emphasizing the importance of data quality and the need for comprehensive analysis.
Moreover, the challenges faced by entities like Urban Local Bodies (ULBs) in India reveal the need for standardized data, transparency, and citizen participation in the decision-making process. Ensuring accurate and up-to-date data is vital for optimized financial systems, including property tax revenue.
In essence, the evolving landscape of data in India calls for concerted efforts to harness its full potential for the benefit of society. By embracing data-driven technologies, improving data quality, and fostering collaboration among various stakeholders, India can chart a course toward more effective governance, robust economic policies, and improved public services.
Acknowledgement: Aasthaba Jadeja is a research intern at IMPRI.
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