Who could tell one of the key issues on the reform agenda of the interim government would be statistical data!
Yet, this is indeed the case, as businesses and economists consider this a prerequisite for ensuring the decisions and plans at both public and private levels are not fraught with risks of being faulty. At a discussion meeting titled "Dialogue for democratic reconstruction" organised by the Centre for Governance Studies (CGS) in the capital last week, speakers said the interim government must first reform data, as the previous government not only mismanaged but also manipulated various types of data, making it difficult to form effective policies based on the available information. They said the first priority should be correction of data and finding a way to continue building a sustainable and correct data set.
A lack of reliable data-or even basic data-has long been cited by researchers as a problem critically impeding assessment of various socio-economic situations in the country. While there is no denying this fact, it is also almost impossible to frame policies based on actual ground realities by the government. This rather unpalatable situation can trigger questions about the veracity of many of the macro-level findings on which policies are framed.
A lack of updated data or data mismatch is often alleged to render macro planning and budgetary exercise difficult. Unsurprisingly, a good deal of allocations and projections in the national budget are based on conjectures, and not on valid or near-valid data. No doubt the budget here is a glaring example given its crucial importance to the country's macro planning for the entire fiscal year. While it is the key pointer of all economic aspects on the basis of which plans are to be framed at various levels, absence of data, even provisional data, reflects the speculative nature of our planning at the highest level.
Unreliable and conflicting data are potentially capable of misguiding policy planners. This, experts hold, is because data produced by the government's Planning Division and Statistics Division as well as other key divisions differ significantly. Conflicting data can wreak havoc by way of misdirecting decision making on the part of the government and render research findings grossly erroneous. Experts are of the opinion that a lack of coordination among different agencies of the government is primarily responsible for data anomalies. Besides, there are plenty of fields in the economic, social and industrial arena where extensive efforts are required for creating a comprehensive data base. Data on arable lands, industrial lands, livestock, productivity in various primary and manufacturing sectors as well as on a variety of social and healthcare-related issues constitute a major requirement for pragmatic planning and policy formulation of the government. Equally important, if not more, is the requirement of various development activities undertaken by non-government organisations (NGOs) and foreign donors. As for fiscal data, accuracy has been repeatedly brought under scrutiny as different sources publish different figures for the same indicator and the same source publish different figures for the same indicator.
Given the vital role data can play, there is a growing global awareness to restructure the methods of collecting, assimilating and monitoring data as a key mechanism of policy planning and subsequent monitoring. The concept dubbed at a UN conference some time ago as data revolution implies drawing on existing and new sources of data to fully integrate those into decision-making and promoting open access. At the heart of the matter is an acknowledgement that timely and usable data are critical to informed decision-making, monitoring of progress and evolution of outcomes.
Experts have opined that in the time of the pandemic, there was a substantial change in the poverty levels of low income groups in the country. There has not been enough data collection related to the pandemic situation from government organisations. However, data collected, though not on a comprehensive basis by some think tanks suggested that poverty rate has increased, and that there has been a big crisis in the labour market - including urban to rural migration and increased pressure on the rural job market. In the absence of data, small enterprises in the informal sector suffered most and received the least support during the pandemic. So, addressing the problems job or income loss can hardly be expected to be addressed without reliable information. Not only have the poor become poorer with a drastic rise in poverty rate, there are also groups of people all over the country who can now be termed 'new poor'. The government has no data on the new poor, and hence allocating funds or providing support in kind is likely to be difficult, unless synchronised efforts are in place to enlist these people on an emergency basis.
Public sector apart, decision making on the part of businesses in the private sector is becoming increasingly difficult in the absence of both macro and micro level data.
There are many modern and sophisticated tools to collect and process data in order to render them authentic. Also there are newer methods of data analysis to examine and disseminate sector-specific information. However, the primary requirement is valid and accurate data. This, to start with, calls for streamlining the entire procedure, which ultimately depends on the political will to implement and enforce necessary changes.
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