This paper analyses the determinants of Rwandan households having savings accounts using Integrated Household Living Conditions Survey (IHLCS) data of 2010/11. After a background discussion and literature review an empirical analysis is presented with different variables adopted and analysed as determinants of household’s head having savings accounts. Poverty level, age, gender, residential area and level education of household head (literate or not) were considered as independent variables of the study. Findings from the estimations of logit models indicate the likelihood of a household having a savings account is positively and significantly related to each of the following: non-poor status of the household,the household residing in an urban area, the household head being male, and the household head being literate. Having the household head be literate tends to be more important for younger household heads and for non-poor households. The proportion of households having money in a savings account more than doubled over the decade between the IHLCS 2000/2001 survey and the IHLCS 2010/2011 survey. Government policies on savings and poverty reduction may explain the trend of increased cash balances in saving accounts. Key Words: Savings, Bank Accounts, Households, Determinants,
This study uses a VAR model to analyse the dynamic relationship between gross domestic product (GDP) and domestic investment (DI) in Rwanda for the period 1970 to 2011. Several selection lag criteria chose a maximum lag of one, and a bivariate VAR(1) model specification in levels was adopted. Unit root tests show that both GDP and DI series are nonstationary in levels but stationary in first differences, implying that both are integrated of order one I(1). Tests of cointegration established that GDP and DI are CI(1,1), suggesting there is a long-run equilibrium relationship between the two series. The error correction model indicates that DI adjusts to GDP with a lag whereby 0.2 percent of the discrepancy between long-term and short-term DI is corrected within the year. Granger causality tests show that there is unidirectional causality where GDP causes DI. The bivariate VAR (1) was unstable when estimated at levels, but was stable in first differences. Finally it was found out that GDP almost perfectly predicts DI in the estimated VAR (1) model. The forecasted value of DI in 2011 was 22.6% of GDP while the actual value was 22.7% of GDP. The small discrepancy may be attributed to the appropriate policy measures the Rwandan government and the private sector federation have thus far taken to facilitate investors in their businesses.
Background Since the outbreak of COVID-19 pandemic in Rwanda, a vast amount of SARS-COV-2/COVID-19-related data have been collected including COVID-19 testing and hospital routine care data. Unfortunately, those data are fragmented in silos with different data structures or formats and cannot be used to improve understanding of the disease, monitor its progress, and generate evidence to guide prevention measures. The objective of this project is to leverage the artificial intelligence (AI) and data science techniques in harmonizing datasets to support Rwandan government needs in monitoring and predicting the COVID-19 burden, including the hospital admissions and overall infection rates. Methods The project will gather the existing data including hospital electronic health records (EHRs), the COVID-19 testing data and will link with longitudinal data from community surveys. The open-source tools from Observational Health Data Sciences and Informatics (OHDSI) will be used to harmonize hospital EHRs through the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). The project will also leverage other OHDSI tools for data analytics and network integration, as well as R Studio and Python. The network will include up to 15 health facilities in Rwanda, whose EHR data will be harmonized to OMOP CDM. Expected results This study will yield a technical infrastructure where the 15 participating hospitals and health centres will have EHR data in OMOP CDM format on a local Mac Mini (“data node”), together with a set of OHDSI open-source tools. A central server, or portal, will contain a data catalogue of participating sites, as well as the OHDSI tools that are used to define and manage distributed studies. The central server will also integrate the information from the national Covid-19 registry, as well as the results of the community surveys. The ultimate project outcome is the dynamic prediction modelling for COVID-19 pandemic in Rwanda. Discussion The project is the first on the African continent leveraging AI and implementation of an OMOP CDM based federated data network for data harmonization. Such infrastructure is scalable for other pandemics monitoring, outcomes predictions, and tailored response planning.
This paper analyses real Gross Domestic Product (GDP), Domestic Investment (DI), Foreign Direct Investment (FDI), Domestic Savings (DS) and Trade (TR) in Rwanda for the period 1970 to 2011. GDP and DI have an upward trend and annual growth of real GDP was around 8% in average for all period. FDI and DS have remained below 2% of GDP each and trade balance of Rwanda is always negative. Augmented Dickey-Fuller (ADF) tests show that GDP, DI and FDI are not stationary at the level but the first differences are stationary. VAR (1) was identified as the appropriate model according to Akaike information criterion, Schwarz information criterion and Hannan-Quinn information criterion. Granger causality tests show that there is bi-directional causality between GDP and TR and TR and DI and unidirectional causality from GDP to DI, from DS to GDP, from DS to DI and from DS to TR. These findings show that GDP can be used to promote Domestic Investment and Trade. Domestic savings have significant effects on GDP, DI and TR. VAR was estimated and the forecasted values of GDP, DI and FDI in 2011 are respectively, 3,843.6233 million, 22.67% and 0.95% while their actual values in 2011 are 3891.9million, 22.7% and 1.66%. There is under-prediction for GDP, DI and FDI. The differences can be explained by the efforts of the Government of Rwanda to promote GDP, Domestic Investment and Foreign Direct Investment.
The aim of this paper is to investigate the impact of trade on economic growth in Rwanda. This paper uses exports and imports for trade and gross domestic product for economic growth. Research questions were formulated as (1) Are exports, imports and economic growth cointegrated? (2) Is there a long or short run relationship between those Variables? (3) Are there any causal relationships between factors (4) what the direction of the causality is it? Annual time series data from World Development Indicators for the period from 1961 to 2018 have been used. The methods of linear regression for estimation of Vector Auto regressions models have been used. Our findings established that VAR was appropriate model, and GDP, Exports were stationary at first differences while Imports was stationary at second difference but not at levels. Hence the two series were integrated of order one and the third one was integrated of order two. Tests of cointegration indicates that the three variables were not cointegrated, implying there was no long run equilibrium relationship between the three series. The causality test indicated that exports and imports influenced GDP. On the other hand, we found that there was a strong evidence of unidirectional causality from exports to economic growth. However, there was bidirectional causality between GDP and imports. These results provide evidence that exports and imports, thus, were seen as the source of economic growth in Rwanda.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.