Search results

Deterioration of integrity is featured in public service across the globe, including Tanzania. Local government authorities (LGAs) are among the areas where such practices have been reported. However, factors compromising integrity in LGAs receives less attention from the literature. Citing 19 LGAs from Tanzania, this study aims to examine contributes to this debate.

A sequential explanatory research design was applied. Data were collected from 54 respondents through survey questionnaires, interviews and a documentary review. The study was guided by Hoekstra theoretical framework for assessing integrity practices in LGAs.

The findings revealed that maintaining integrity in Tanzania's LGAs is still challenging. Poor institutionalization processes, institutional unpreparedness, insufficient integrity policy execution and being less informed of moral development of recruited public servants are said to compromise integrity in Tanzania LGAs. This qualifies to conclude that institutional pathologies and moral history of public servants are the major factors contributing to integrity deterioration in Tanzania LGAs. This has resulted in subpar service delivery and the waste of public funds.

This study confined itself to Tanzania LGAs. More studies could be conducted to LGAs in other countries struggling with the same problem. On the same ground, moral development should be studied more to ensure that the public service receives ethical public servants in the future.

The theoretical framework for assessing integrity systems in LGAs as proposed by Hoekstra (2022) could be applied by other countries struggling with the same challenge.

LGAs must implement an integrity-based self-reflection technique that will allow them to assess their current condition and come up with solutions. Furthermore, institutional policies must be strengthened to govern ethical behavior in LGAs.

This paper was set to develop a model for forecasting maize production in Tanzania using the autoregressive integrated moving average (ARIMA) approach. The aim is to forecast future production of maize for the next 10 years to help identify the population at risk of food insecurity and quantify the anticipated maize shortage.

Annual historical data on maize production (hg/ha) from 1961 to 2021 obtained from the FAOSTAT database were used. The ARIMA method is a robust framework for forecasting time-series data with non-seasonal components. The model was selected based on the Akaike Information Criteria corrected (AICc) minimum values and maximum log-likelihood. Model adequacy was checked using plots of residuals and the Ljung-Box test.

The results suggest that ARIMA (1,1,1) is the most suitable model to forecast maize production in Tanzania. The selected model proved efficient in forecasting maize production in the coming years and is recommended for application.

The study used partially processed secondary data to fit for Time series analysis using ARIMA (1,1,1) and hence reliable and conclusive results.