INTERNAL AUDIT AND QUALITY OF FINANCIAL REPORTING IN THE PUBLIC SECTOR: THE CASE OF UNIVERSITY FOR DEVELOPMENT STUDIES

Many corporate failures have occurred over the years as a result of poor financial reporting practices that have eluded investors and other consumers of financial data. The research used the University for Development Studies (UDS) as a case study. The study focused on three main goals: identifying emerging determinants of quality financial reporting, examining the efficacy and adequacy of UDS's internal control structure, and determining how much Internal Audit contributes to quality financial reporting. The research used a descriptive survey template and a sample size of 70 people who were chosen using purposive and stratified sampling techniques. To achieve objectives one and two, the analysis used binary regression, while to achieve objective three, the Best (2005) index was updated and used. Financial reporting accuracy, a computerized accounting system, and personnel competence were found to be determinants of quality financial reporting in the study. It was discovered that UDS' internal control system is ineffective since two of the five main components that make up an efficient internal control system, namely control environment and information and communication, are not properly implemented. The study found that UDS' internal audit reflects an average level of fraud prevention in terms of the robustness of auditing processes and fraud prevention indicators, with the remaining indicators indicating a high level of fraud prevention. Overall, UDS' internal auditing reveals a high degree of prevention. The University for Development Studies (UDS) should analyze, define, and enforce control setting, information, and communication components of the internal control system that are appropriate for their work processes, as well as enhance the existing components, according to the report. Keywords: Internal Audit, Financial Reporting, Public Sector, Ghana

THE HEADIC ISSUE OF DIGITAL REVOLUTION: AN EMERGENT CHALLENGE FOR THE FUTURE OF JOBS

The article goes through the different stages of human at work and it current practices then focuses on the digital revolution coming on. It explores the effects on jobs in the transport and banking sectors. What are the countries that are well prepared for that revolution? Are Africa and Burkina ready for the digital transformation? The lack of infrastructure is severe in some countries. The main keys are to strengthen the educations systems, to build a solid lifelong system and the use the tools of change management. The findings show that countries have an important role to play but also international organizations like ILO, UNESCO, OCED, EU, and AU. Keywords: Digital revolution, Challenge, Jobs, Future

Validating commonly used drought indicators in Kenya

Drought is a complex natural hazard that can occur in any climate and affect every aspect of society. To better prepare and mitigate the impacts of drought, various indicators can be applied to monitor and forecast its onset, intensity, and severity. Though widely used, little is known about the efficacy of these indicators which restricts their role in important decisions. Here, we provide the first validation of 11 commonly-used drought indicators by comparing them to pasture and browse condition data collected on the ground in Kenya. These ground-based data provide an absolute and relative assessment of the conditions, similar to some of the drought indicators. Focusing on grass and shrublands of the arid and semi-arid lands, we demonstrate there are strong relationships between ground-based pasture and browse conditions, and satellite-based drought indicators. The Soil Adjusted Vegetation Index (SAVI) has the best relationship, achieving a mean r2 score of 0.70 when fitted against absolute pasture condition. Similarly, the 3-month Vegetation Health Index (VHI3M) reached a mean r2 score of 0.62 when fitted against a relative pasture condition. In addition, we investigated the Kenya-wide drought onset threshold for the 3-month average Vegetation Condition Index (VCI3M; VCI3M<35), which is used by the country’s drought early warning system. Our results show large disparities in thresholds across different counties. Understanding these relationships and thresholds are integral to developing effective and efficient drought early warning systems (EWS). Our work offers evidence for the effectiveness of some of these indicators as well as practical thresholds for their use

A dynamic hierarchical Bayesian approach for forecasting vegetation condition

Agricultural drought, which occurs due to a significant reduction in the moisture required for vegetation growth, is the most complex amongst all drought categories. The onset of agriculture drought is slow and can occur over vast areas with varying spatial effects, differing in areas with a particular vegetation land cover or specific agro-ecological sub-regions. These spatial variations imply that monitoring and forecasting agricultural drought require complex models that consider the spatial variations in a given region of interest. Hierarchical Bayesian models are suited for modelling such complex systems. Using partially pooled data with sub-groups that characterise spatial differences, these models can capture the sub-group variation while allowing flexibility and information sharing between these sub-groups. This paper's objective is to improve the accuracy and precision of agricultural drought forecasting in spatially diverse regions with a hierarchical Bayesian model. Results showed that the hierarchical Bayesian model was better at capturing the variability for the different agro-ecological zones and vegetation land covers compared to a regular Bayesian auto-regression distributed lags model. The forecasted vegetation condition and associated drought probabilities were more accurate and precise with the hierarchical Bayesian model at 4- to 10-week lead times. Forecasts from the hierarchical model exhibited higher hit rates with a low probability of false alarms for drought events in semi-arid and arid zones. The hierarchical Bayesian model also showed good transferable forecast skills over counties not included in the training data

Forecasting vegetation condition with a Bayesian auto-regressive distributed lags (BARDL) model

Droughts form a large part of climate- or weather-related disasters reported globally. In Africa, pastoralists living in the arid and semi-arid lands (ASALs) are the worse affected. Prolonged dry spells that cause vegetation stress in these regions have resulted in the loss of income and livelihoods. To curb this, global initiatives like the Paris Agreement and the United Nations recognised the need to establish early warning systems (EWSs) to save lives and livelihoods. Existing EWSs use a combination of satellite earth observation (EO)-based biophysical indicators like the vegetation condition index (VCI) and socio-economic factors to measure and monitor droughts. Most of these EWSs rely on expert knowledge in estimating upcoming drought conditions without using forecast models. Recent research has shown that the use of robust algorithms like auto-regression, Gaussian processes, and artificial neural networks can provide very skilled models for forecasting vegetation condition at short- to medium-range lead times. However, to enable preparedness for early action, forecasts with a longer lead time are needed. In a previous paper, a Gaussian process model and an auto-regression model were used to forecast VCI in pastoral communities in Kenya. The objective of this research was to build on this work by developing an improved model that forecasts vegetation conditions at longer lead times. The premise of this research was that vegetation condition is controlled by factors like precipitation and soil moisture; thus, we used a Bayesian auto-regressive distributed lag (BARDL) modelling approach, which enabled us to include the effects of lagged information from precipitation and soil moisture to improve VCI forecasting. The results showed a ∼2-week gain in the forecast range compared to the univariate auto-regression model used as a baseline. The R2 scores for the Bayesian ARDL model were 0.94, 0.85, and 0.74, compared to the auto-regression model's R2 of 0.88, 0.77, and 0.65 for 6-, 8-, and 10-week lead time, respectively