Governor Dikko Radda has urged stakeholders to find lasting solutions to the slow pace of development at the sub-national level.
He made the call in Katsina on Wednesday during the opening of the two-day bi-annual meeting of the National Consultative Committee on Statistics.
The governor called for an improved method for compiling the state’s gross domestic product.
Mr Radda assured the audience that future S-GDP compilations in the state would be based on a sound methodology that reflects economic diversity.
He stated that for GDP to measure well-being and living standards, it must capture the total market value of all final goods and services.
Mr Radda stressed the need for an inclusive methodology recognising contributions from micro, meso, medium, and large-scale enterprises within Kaduna.
According to the Katsina governor, since small businesses comprise over 70 per cent of the economy, omitting their input makes S-GDP a purely academic exercise.
He encouraged the participants to apply their expertise to achieve the meeting’s goals and expected outcomes.
“I hope meaningful resolutions are reached that benefit federal, state, and local governments,” the governor added.
Mr Radda said his government would use S-GDP for macroeconomic planning, medium-term expenditure framework, and the fiscal strategy paper. These, he explained, would spur development and boost investor confidence in Katsina.
He noted that the administration values statistical data for evidence-based governance and efficient resource management.
Delivering his keynote, statistician general of the federation, Adeyemi Adeniran, urged participants to focus on progress, especially concerning the S-GDP exercise and the Integrated System of Administrative Statistics.
Adeniran said, “It was logical for NBS to partner with states to produce S-GDP figures for all 36 states and the FCT.
“Despite progress on S-GDP, more effort is needed to finalise, validate, and prepare results for dissemination. If state authorities do not embrace this effort, the usefulness of the data could be significantly undermined.”
