Independent revenue forecasting challenges

State and local governments are more fiscally responsible when their future revenue and expenditure estimates are substantially accurate and in balance. Accordingly, reliable revenue estimates provide realistic benchmarks for setting credible targets for sector funding. However, the painful reality is that persistently poor revenue forecasts frustrate these goals. State and local government budget implementation is replete with historical negative outturn variance. Sometimes this variation exceeds -50% steadily across several years. For instance, the forecast outturn variance on the 2019 capital expenditure of states such as Bauchi, Oyo, Taraba, Ogun, Imo, Benue, and Cross-River was more than -80%. Cross River and Benue states could only accomplish 2.78% and 5.73% of their forecast capital projects spending. This inability to meet funding requirements for critical sectors is partly due to poor revenue forecasts. The prioritization of spending plans leads to sub-optimal performances, mainly when revenue growth is inconsistent with spending realities.
Understandably, fiscal stress arising from unexpected public needs or price and cost increases beyond a priori planned spending can cause actual revenue from keeping pace with forecasts. Yet, minimizing these challenges (fiscal stress) can be more manageable than imagined with robust forecasting quality. Indeed, budgetary efficiency, such as achieving more with the same or fewer resources and preventing mid-way spending cutbacks, are more effortless with elevated revenue forecast accuracy.
All things being equal, the primary source of unfulfilled promises and trust problems between the state and local governments and the citizens derive essentially from unsatisfactory budget outturns. The higher the outturn, the more convinced the latter is concerning the former’s misleading promises. The citizens expect that with the resources available to the government, they should put in place the best resources for robust revenue projections. But that is not always the case, at least in more than 90% of subnational governments in Nigeria. Bland and inappropriate judgment-based forecasting approaches devoid of the right technical inputs and stakeholder inclusion combine to create extensive forecast biases inconsistent with actuality. But citizens unaware of the sloppy processes leading to the estimates use those parameters to demand that the governments account. As these estimates become increasingly inconsistent with reality, the resulting perception of the public becomes a combination of government irresponsibility, poor economic planning, embezzlement of funds, and chronic failure to keep its promises. These negative perceptions deepen the loss of confidence and the trust gully between the government and the governed.
Garnishing the poor level of budget outturns or unfulfillment of fiscal promises are endless streams of gaping deficits and surpluses. While the former equips politicians with justifications to go borrowing, the latter, when they rarely occur without any accidental largesse, points to low levels of fiscal entrepreneurship among managers of state resources. Poor revenue forecasting among subnational governments in Nigeria also frustrates the ability of their economic managers to track their governments’ performances correctly. In the absence of robust revenue forecasting, it becomes challenging to determine the fiscal capabilities of the considered government precisely. Performance assessments often consider outcomes consistent with actual revenue receipts without contemplating revenue gaps and unoptimized capabilities that ideally inform a higher quantum of deliverables. Therefore, a more accurate benchmark for fiscal performance should include robust revenue forecast estimates.
In the same vein, the prospective impact of new tax changes is difficult to understand in the absence of accurate revenue forecasting. Precise revenue forecasting over sustained periods makes it easier to track the marginal effects of net changes in tax revenue. This revenue impact knowledge vanishes in the absence of good futuristic estimation. Again, aside from tax revenue, reliable forecasting ensures complete identification and specification of appropriate subnational government revenue models. But this opportunity to understand and possibly manage changes in the parameters determining the future of state and local government revenue does not hold. By developing a rich forecasting model, those involved and, by extension, the government, meticulously identify those critical factors determining revenue size and sustainability. Again, subnational governments’ revenue estimates hardly pass the general acceptance test as the forecasting process usually excludes many vital stakeholders. In many instances, a poorly trained or inexperienced technical team leads the forecasting process with minimal or no supervision whatsoever. Even the MDA’s revenue forecasts are rarely reviewed and verified before their inclusion in the submitted comprehensive figures with embarrassing biases.
Unless subnational governments prioritize the reform of the revenue forecasting process, it will be difficult for most of them to exit the miry clay that defines their poor fiscal performance. Our penchant for shortcuts incentivizes finance ministries to abandon the well-known global revenue forecasting standards that deliver the best budgetary performance outcomes. The critical challenge is resolving the who, how and when of subnational government revenue forecasting. Most budget preparation calls involving most state and local government stakeholders concentrate on the projected expenditure programmes. They apportion much time and effort to discuss fiscal resource allocation and the specifics of intended expenditure programmes and projects. Sadly, not as much involvement in stakeholders time and effort goes into reviewing and critiquing the revenue forecasts. Even when they do, the assumption is that the right set of expertise would have informed resulting estimates. Rarely do we have the legislature probe into the appropriateness of the methodology that gives birth to the forecast revenue estimates in a budget.
The ‘when’ question concerns the adequacy of the typical twelve-month time horizon for revenue forecasts and the frequency of reviews of projected estimates. The shortness of the forecasting horizon renders the application of more robust techniques inappropriate. Accordingly, they give ample headroom for the continued deployment of judgment techniques and basic tax elasticity models. Although not entirely out of place, they may not significantly narrow forecasts bias the way more reliable methods would. Longer forecast horizons permit fuller specifications of the revenue model across seasons and cycles. The typical twelve-month period forecasts would otherwise be for forecast review scenarios and consideration of other essential factors specific to that time. There is hardly any state or local government revenue forecasting office that deploys robust time-series or superior and more accurate results yielding modelling approaches. Many state governments’ internal revenue offices hardly use any spreadsheet modelling effectively for fundamental naïve forecasting. Revenue forecasting is never their priority, and therefore there is little effort to provide the technical workforce with the desperately needed skills for best results in that respect. Unofficially, the data processing, spreadsheet modelling, and statistical estimation competency score of the internal revenue service workforce in most state and local governments in Nigeria do not exceed 25%.
The result is the overwhelming deployment of judgment-based methods in a complex economic relationship. But it is convenient, easy to deploy and does not require a comprehensive scale investigation of relationships that ordinarily should be part of a robust model that significantly minimizes forecast bias. The most striking evidence of the challenge created by poor revenue forecasting is the low optimization level of subnational governments income potential. Full specifications of state and local government forecasting models also mean that the forecasters carefully think through all possible sources of revenue and how they will change independently and interactively with other factors over the forecast horizon. But politicians hardly think about these potentials, which should underscore the IGR growth strategy. Otherwise, chief executives of state and local governments and legislators generally would show more interest in the sizes of tax and nontax revenue gaps, the sources of those gaps, and how those gaps should become narrower. They should also be more interested in the granular details of deviations in previous revenue forecasts and use that knowledge in reviewing forecast figures in the extant periods. Unfortunately, most assumptions for revenue estimates revolve around federal governments perceptions of oil prices and supply parameters and perhaps a ‘constant figure’ for growth in PAYE receipts. Beyond this, not much more interest goes into optimized capacities in several other nontax sources.
And until stakeholders begin to ask more profound and critical questions around revenue potential and their forecasting as part of the fiscal programming, massive deviations, particularly deficits, will continue to burgeon. The secondary consequences include providing politicians with the leverage for unwarranted borrowing and helping them to rationalize poor performances where they apply. The deepening of stakeholder concerns around revenue generation and forecasting will make the budgeting process more meaningful. It raises the credibility of spending programmes, and citizens trust that the government will deliver on their promises. It is the absence of this stakeholder interest and naïve approach to the revenue forecasting side of the financial planning process that many Nigerians regard subnational governments’ budgets as mere rituals. Robust revenue forecasting deepens revenue generation planning and enhances the realization of the expenditure plans.
There are at least four principal sources of poor subnational government revenue forecasting. The first is the low technical skills. This competency-poverty gains momentum with the solid aversion to learning them. Other factors include ineffective coordination efforts in data exchanges and forecasting methodologies that are most appropriate for specific MDAs and database inadequacies. Comprehensive and accurate databases are critical to tax and nontax revenue forecasting success. A more comprehensive and precise database informs better tracking of current and future collectable taxes. Sadly, many state and local governments score less than 30% on their taxpayer databases’ quality, adequacy, and accuracy factor. Efforts at revenue forecasting reforms should mainstream taxpayer database adequacy.
While this knowledge helps in reasonably forecasting direct tax revenue, strong technical forecasting skills are necessary for determining how various economic and other factors will shift and interact to deliver future revenue scenarios for most non tax revenue sources. Unfortunately, most governments in Nigeria do not seem to prioritize solid forecasting skills within the revenue service and the finance ministries. Even the legislature and some of the stakeholders that should critique budget proposals ideally should possess such skills to query those submissions effectively. Understandably, tax forecasting is one of the most challenging areas of reform. But it is also a critical requirement for successfully designing IGR improvement strategies and programmes. Therefore, the Internal Revenue Service leadership and the finance ministries in various states and local governments must invest in these skills to optimize their revenue collection potentials. Poor coordination. among revenue-generating ministries, departments and agencies requiring data exchanges and the harmonization of forecasting methodologies also lead to less than desirable outcomes. Different MDAs conduct revenue forecasts using methods that are inconsistent with each other. Some are also reluctant to share their data with other agencies, ministries, and departments that may find them helpful in producing better revenue predictions.
In summary, the mainstreaming of subnational government revenue forecasting on adapting global best practices will naturally result in the best outcomes. Although there is no accepted set of best practices that are superior, the two conditions are crosscutting in delivering robust outcomes. The first is the technical competencies for providing strong revenue forecasts. An essential requirement here is the training and retraining of the IRS team and the ministry of finance members on forecasting state and local government revenue. But beyond the skill set is making strong forecasting skills and competencies an integral part of the behaviour of these organizations. In other words, there should be standard, transparent processes, methodologies, and procedures for determining revenue estimates that go into the budget. The second is the reliance on the best possible economic forecasts. Subnational governments will essentially rely on federally provided official statistics and projections about several economic indicators that will constitute part of their underlying assumptions. Therefore, technical competencies in forecasting mean that forecasters at the subnational government level may either accept, reject, or modify for its use some of the economic forecasts inherited from the official federal statistics. The third is the depth of openness and inclusivity of relevant stakeholders in the revenue forecasting process. Beyond the technical workforce within the ministries of finance, the Internal Revenue Service and other revenue contributing MDA’s, critical stakeholders need to understand and appreciate the deriving of the forecasts. It is no longer sufficient to submit blinded numbers and walk away with some on-the-surface speech about judgment-based forecasting techniques adopted without explaining the method’s weaknesses and reason for excluding others. Legislators, more importantly, need to understand this. This level of transparency and involvement of the critical stakeholders is vital in increasing the efforts to make the forecasts consistent with actuals.